Regional Review Workshop on Completed Research Activities
Proceedings of Review Workshop on Completed Research Activities of Crop
Research Directorate held at Adami Tulu Agricultural Research Center, Adami
Tulu, Ethiopia 7-12, October, 2019
Editors
Tesfaye Letta, Tafa Jobie and Girma Mengistu
Oromia Agricultural Research Institute
P.O.Box 81265, Finfinne, Ethiopia
1
© 2020 Oromia Agricultural Research Institute
Correct citation: Tesfaye Letta, Tafa Jobie and Girma Mengistu
(eds.). Proceedings of Review Workshop on Completed Research Activities of Crop
Research Directorate held at Adami Tulu Agricultural Research Center, Adami Tulu, Oromia,
Ethiopia 2020.
2
Table of Contents
Breeding
Evaluation of Black Cumin genotypes for yield and yield related parameters in Bale Mid land,
Southeastern Ethiopia................................................................................................................. 7
The Release of Black Cumin Variety “Qeneni” for mid altitude of Bale, South Eastern Ethiopia
.................................................................................................................................................. 11
Registration of “Gadisa” New Released Coriander Variety ...................................................... 13
Performance Stability for Grain yield and Genotypes by Environment Interaction in Field pea
Genotypes in the highlands of Bale Southeastern Ethiopia ....................................................... 15
AMMI Analysis for Grain yield Stability in Lentil Genotypes Tested in the Highlands of Bale,
Southeastern Ethiopia............................................................................................................... 26
Grain Yield Performance Evaluation of Mung bean (Vigna radiate) in the lowland district of
Bale zone, Southeastern Ethiopia .............................................................................................. 36
The registration of “Moybon and Tosha”, faba bean varieties for the highlands of Bale, South
eastern Ethiopia ........................................................................................................................ 44
The Registration of “Horesoba”, Newly Released Linseed variety for the highlands of Bale,
South eastern Ethiopia .............................................................................................................. 48
Analysis of Bread Wheat Genotypes for Yield Stability Using the GGE Biplots ....................... 50
Stability Analysis of Bread Wheat Genotypes Using the AMMI Stability Model ...................... 61
Analysis of Genotype x Environment Interaction Effect and Stability on Yield of Black Cumin
(Nigella sativa L.) Genotypes. .................................................................................................... 70
Registration of Triticale Variety Named ‘Kombolcha’.............................................................. 83
Release and Registration of ‘Kumsa’ Finger Millet Variety ...................................................... 87
Adaptation Study of Mung Bean (Vigna radiate) Varieties in Western Parts of Oromia, Ethiopia
.................................................................................................................................................. 90
Multi-Location Evaluation of Yield and Yield Related Trait Performance in Bread Wheat
Genotypes at Western Oromia, Ethiopia .................................................................................. 98
Adaptation Study of Released Finger Millet (Eleusinecoracana L.) Varieties in Western Oromia,
Ethiopia .................................................................................................................................. 112
Multi-Location Evaluation of Yield and Yield Related Trait Performance in Sorghum (Sorghum
bicolor L.) Genotypes at Western Oromia, Ethiopia ............................................................... 120
Multi-Location Evaluation of Yield and Yield Related Trait Performance in Sorghum (Sorghum
bicolor L.) Genotypes at Western Oromia, Ethiopia ............................................................... 136
Adaptability Study of Recently Released Small Pod Pepper Variety (Capsicum frutescens L.) at
West and Kellem Wellega Zones ............................................................................................. 151
Evaluation and Selection of Improved Food Barley (Hordeum vulgare L.) Varieties for their
Adaptability in West Hararghe Zone ...................................................................................... 163
Release and Registration of Elemo (ACC. 237261) Sorghum (Sorghum bicolorL.Moench)
Variety .................................................................................................................................... 176
Release and Registration of Milkaye Groundnut (Arachishypogaea L.) Variety for midland of
West Hararghe ....................................................................................................................... 182
Performance evaluation of improved Sesame (Sesamum indicum L.) varieties in West Hararghe
Zone, Oromia, Ethiopia .......................................................................................................... 187
Adaptation Trial of plantain type of Banana Varietiesat Mechara on station ......................... 195
Adaptability study of Chickpea varieties (Cicer arietinum L.) at Bule hora and Abaya, Southern
Oromia ................................................................................................................................... 202
Adaptability Studyof Early Maturing Ground nut Variety in West Guji lowland, Southern
Oromia ................................................................................................................................... 214
Performance evaluation of sesame varieties at Abaya, Southern Oromia ............................... 223
Evaluation and Identification of Adaptable Processing Tomato Varieties with High Yield and
Standard Qualities at Adami Tulu Jido Kombolcha Woreda, East Shoa Zone. ...................... 231
Evaluation of Improved Exotic Head Cabbage (Brassica Oleracea Var Capitata L.) Varieties at
Adola Rede Areas, Southern Oromia, Ethiopia ...................................................................... 243
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Adaptation trial of Market Types Common Bean(Phaseolus Vulgaris L.) Varieties in Eastern
Hararghe Zone, Oromia ......................................................................................................... 251
4
Agronomy
Effect of Cassava Intercropping with Legume Crops Followed by Sorghum on Growth, Yield
and Yield Parameters of Cassava-Based Double Cropping System in Fedis and Babile District,
Eastern Harerghe Zone .......................................................................................................... 258
Effects of Seed Rate, Row Spacing and Phosphorous fertilizer on yield and yield components of
fenugreek (Trigonella foenum-graecum L.) in Bale mid lands, Oromia ................................... 268
Effects of Climate Variability on Wheat Rust (PucciniaSpp.) and Climatic Condition Conducive
for Rust at Highlands of Bale, Southeastern Ethiopia ............................................................. 284
Effect of NPS Rate on Yield and Yield Components of Upland Rice (Oryza sativa L.) In Western
Ethiopia .................................................................................................................................. 303
Effect of Yam Tuber Size Cutting on Its Yield in Western Oromia, Ethiopia ......................... 311
Integrated Management of Barley Shootfly on the Highlands of Guji Zone, Southern Oromia
................................................................................................................................................ 318
Effect of Blended NPS and N Fertilizer Rates on Yield, Yield Components, and Grain Protein
Content of Bread Wheat (Triticum aestivumL.) in Bore District, Guji Zone, Southern Ethiopia
................................................................................................................................................ 327
Response of Common Bean (Phaseolus vulgaris L.) Varieties to Rates of Blended NPS Fertilizer
in Adola District, Southern Ethiopia ....................................................................................... 355
Protection
Evaluation of post-emergence herbicides against major weed species in wheat in Bale highlands,
South- eastern Ethiopia........................................................................................................... 384
Survey of weed flora composition in coffee (Coffea arabica L.) growing areas of East Ethiopia
................................................................................................................................................ 391
Efficacies of Fungicide Application Regimes Against FabaBean Gall (Olpidium species) Disease
................................................................................................................................................ 404
Evaluation of Different Insecticide for Management of Fruit Worm (Helicoverpaarmigera) on
Hot Pepper at Bako, Western Oromia .................................................................................... 414
Integrated Management of Major Faba bean (Vicia faba L.) diseases: Chocolate spot (Botrytis
fabae), Ascochyta blight (Ascochytafabae) and Rust (Uromycesviciae- fabae)) on the Highlands of
Guji, Southern Oromia ........................................................................................................... 421
Integrated Management of Barley Shoot fly on theHighlands of GujiZone,Southern Oromia 433
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Breeding
6
Evaluation of Black Cumin genotypes for yield and yield related parameters in Bale
Mid land, Southeastern Ethiopia
*Getachew Asefa and Mohammed Beriso
Sinana Agricultural Research Center, P.O. Box 208, Bale Robe, Ethiopia
*Corresponding author: Email:-fenetgeach@gmail.com
Abstract
Fourteen black cumin genotypes were evaluated against standard checks for two consecutive
years at Sinana, Goro and Gindhir to identify high yielding and stable black cumin varieties.
The mean total seed yield of genotypes across environment ranged from 24.5 to 16.1Qt/ha.
The highest total seed yield was recorded from genotypes 242826-2 followed by 242826-2
(24.5 and 23.3Qt/ha) while, the lowest total seed yield was obtained from local check. These
two high yielding genotypes had showed a yield advantage of 22.4 and 14.9% over the
standard check variety Derbera. Based on their performance across locations over standard
checks these two genotypes will be promoted as candidate variety for release in the coming
year.
Key words: Black Cumin, genotype, oleoresin content
Introduction
Black Cumin (Nigella sativa L.) is an annual herbaceous plant belonging to the family
Ranunculacea (Hammo, 2008). Its seed constituents have unique chemical properties with
more than one hundred different chemical components (Bardideh et al 2013). The Ethiopian
variety of black cumin seed constitutes up to 50% thymol, amonocyclic phenolic compound
which make valuable source for health care (Merga et al 2018). Black cumin is used
principally to flavor food, either as whole grain in powdered form or as an oleoresin extract
(Black M. et al 2005). Within Ethiopia, its main use is as a spice, which is typically grounded
and mixed with other spices. There is also some use as a traditional medicine (Aminpour and
Karimi 2004). The vast majority of Ethiopia’s black cumin exports go to Arabic countries,
which together with other predominantly Muslim countries (Orgut, 2007). Moreover, the
production and land coverage of black cumin in Bale mid altitude has been increasing while,
the productivity is still less than national average 1.7 ton per hectare (Girma et al, 2015). In
Bale mid altitude, highland seed spices viz. black cumin, fenugreek and coriander are
produced widely. About 42,000 ha of black cumin produced per year both in “Gena” and
“Bona” cropping season in Bale districts (Goro, Ginnir, Golocha and some part of Sawwena
and Sinana. Due to increased demand of black cumin seed for local consumption and other
importance, such as oil and oleoresin for medicinal purposes, its export market, its being
potential crop in crop diversification in the area, income generation and its importance to
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reduce the risk of crop failure and others made the crop as a best alternative crop under Bale
mid altitudes. But the yield of black cumin in these areas is not as much as the potential of the
crop due to many factors among which lack of high yielder and stable varieties are the
majors. Hence, developing an improved variety, after screening of lines/accessions with
desirable traits, is one of the immediate objective of breeding for the mandate areas.
Accordingly, this activity was initiated to evaluate and identify the genotypes of black cumin
that are high yielding and tolerant to major disease in mid altitude of Bale.
Materials and Methods
Twelve black cumin genotypes were evaluated against standard checks viz. Derbera,
Dirshaye and Eden and one local check for two consecutive years (2009-2010 E.C) under
rainfed conditions at Sinana, Goro and Gindhir. The areas possess a bimodal pattern of
rainfall type. This bimodal rainfall pattern has created favorable condition to the produce
crops including black cumin in the areas twice per year.
The trial was laid out in a Randomized Complete Block Design (RCBD) with three
replications. Each variety was planted in four rows at spacing of 30 cm between rows with
the total plot area of 2.4 m2.
Fertilizer application was made as per the national
recommendation made for the crop which is 100 kg ha-1 all applied at planting. Data on
mean seed yield and disease scores of genotypes were computed using genstat 15th edition.
Results and Discussions
The mean total seed yield of genotypes across environment ranged from24.5 to 16.1 Qt/ha.
The highest total seed yield was recorded from genotypes 242826-2 followed by genotype
242826-2 (24.5 and 23.3 Qt/ha respectively) while, the lowest total seed yield was obtained
from local check. These two high yielding genotypes had showed a yield advantage of 22.4
and 14.9% over standard check Derbera. The mean for capsule number per plant, biomass
and primary branch was ranged from 10.7 to 6.9, 63.7 to 39.8 and 4.7 to 3.3 (Table 2). The
highest number of capsule per plant (10.7), biomass (63.9 t/ha) and primary branch (4.67)
were recorded from genotype 242826-2 followed by genotype 205167-2. This implies that
these agronomic parameters were contributed directly or indirectly to total seed yield for
black cumin. Similar findings were also reported previously by Hailemicael et al. (2016) and
Fufa (2016) who indicated black cumin seed yield is positively correlated with plant height,
number of capsules per plant, number of primary branches per plant, and number of seeds per
capsule. Days to maturity and days to flowering were ranged from 144.8 to 128 and 90 to 80
respectively. Genotype 242826-2 has two weeks early maturing genotype as compared to
other test genotypes which this trait is useful for scaping drought stress due to shortage of
rainfall the phenomenon which mostly occurs in the study area. The highest mean of total
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seed yield was recorded from Ginnir (23.6 Qt/ha) followed by Sinana (21.8 Qt/ha). This may
be due to the potential of the these districts for the crop production as compared to other
location (Goro).
Table 1: Means of seed yield (Qt/ha) of 14 black cumin genotypes across location and years
Genotype
Ginnir
2009
2010
Goro
2009
205167-2
26.55
26.60
18.60
207540-2
22.63
22.68
14.68
208688-1
24.99
24.94
17.04
242826-2
28.46
27.13
20.51
242842-1
20.92
22.94
12.97
90510-2
24.41
24.46
16.46
90514-2
21.66
23.57
13.71
90516-2
23.16
23.88
15.21
90575-2
23.45
23.50
15.50
910619-2
17.78
23.28
9.83
Derbera
22.48
22.81
14.53
Dirshaye
19.62
22.00
11.67
Edan
21.83
21.74
13.88
Local
18.16
20.49
10.21
Mean
22.58
23.57
14.63
CV
4.3
9.5
6.7
LSD
1.79
3.75
1.63
Note: DF=Days to Flower, DM=Days to
2010
Sinana
2009
2010
Grand
Means
19.35
24.05
24.80
23.32
15.43
20.13
20.88
19.41
17.69
22.49
23.14
21.72
19.88
25.96
25.33
24.54
15.69
18.42
21.14
18.68
17.21
21.91
22.66
21.18
16.32
19.16
21.77
19.36
16.63
20.66
22.08
20.27
16.25
20.95
21.70
20.23
16.03
15.28
21.48
17.28
16.39
19.98
21.01
19.53
14.75
17.12
20.20
17.56
14.49
19.33
19.94
18.53
13.24
15.66
18.69
16.07
16.38
20.08
21.77
19.83
14.1
4.9
10.3
18.70
3.88
1.63
3.75
2.43
Maturity, PH=Plant Height, PB= Primary
Branches/plant SB=Secondary Branches/plant, CPP=Capsule Per Plant, BMTH= BioMass
Ton per Hectare, and SY= Seed yield in Quintal per hectare.
Table 2. Summary of Mean Yield and other agronomic traits on the two promising Black
cumingenotypes Selected as candidate for release and checks in regional variety trial over the
six environments
Genotypes DF
DM
PH
PB
SB
CPP
BM
SY
205167-2
81.58
135.50
50.90
4.44
2.17
9.22
58.89
23.32
207540-2
88.25
140.17
58.90
3.33
0.61
7.17
51.48
19.41
208688-1
82.58
133.50
51.23
3.72
2.61
7.17
55.14
21.72
242826-2
90.92
144.83
58.90
4.61
3.44
10.72
63.94
24.54
242842-1
80.58
128.50
49.90
4.28
1.44
8.56
39.79
18.68
90510-2
89.58
133.50
55.57
4.61
1.44
8.61
43.06
21.18
90514-2
80.58
129.50
50.23
4.67
1.78
8.56
50.33
19.36
90516-2
81.58
134.50
56.57
3.78
1.50
8.67
48.50
20.27
90575-2
86.58
143.50
57.57
4.56
1.17
8.61
49.28
20.23
910619-2
86.58
138.50
58.90
4.28
0.94
7.28
44.51
17.28
Derbera
81.58
138.50
48.57
4.17
1.72
8.94
43.17
19.53
Dirshaye
83.58
137.50
49.23
3.72
1.50
8.78
42.55
17.56
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Genotypes DF
DM
PH
PB
SB
CPP
BM
SY
Edan
83.58
133.50
50.90
4.22
0.56
6.94
49.94
18.53
Local
84.58
133.50
49.90
4.11
2.11
7.33
43.21
16.07
Mean
84.44
136.07
53.38
4.18
1.64
8.33
48.84
19.83
CV
6.90
4.90
7.60
22.00
35.00
14.00
11.10
18.70
LSD
3.80
4.33
2.70
0.60
0.37
0.80
3.55
2.43
Note: DF=Days to Flower, DM=Days to Maturity, PH=Plant Height, PB= Primary
Branches/plant SB=Secondary Branches/plant, CPP=Capsule Per Plant, BMTH= BioMass
Ton per Hectare, and SY= Seed yield in Quintal per hectare.
Conclusions and Recommendations
Generally considering the yield performance of two black cumin genotypes (242826-2) and
(205167-2) across environment and yield advantage of these two genotypes over standard
checks with the value of 22.4 and 14.9% respectively make them to be a promising black
cumin genotypes for the studied locations. Hence this suggested that, both genotypes were
recommended to be promoted to variety verification trial in the coming year in Bale mid land
and similar agro ecologies.
Acknowledgements
The authors are acknowledged Oromia Agricultural Research Institute and Sinana
Agricultural Research Center for financing and facilitate this research work.
References
Aminpour and Karimi 2004.Underutilized medicinal spices. Spice India. 17: 5-7.
Black M, Bewley D, Halmer 2005. The Encyclopedia of seed science, technology and
uses.wallinoford. CAB P 7. 63
Bardideh K, Kahrizi D, Ghobadi ME (2013) Character association and path analysis of black
cumin (Nigella sativa L.) Genotypes under different irrigation regimes. Not SciBiol 5:
104-108.
Fufa, 2016. Correlation Studies in Yield and Some Yield Components of Black Cumin
(Nigella sativa L.) Landraces Evaluated at Southeastern Ethiopia. Adv Crop Sci Tech 4:
239. doi: 10.4172/2329-8863.1000239
Girma H, Digafie T, Edossa E, Belay YB, Weyessa G (2008). Spices research achievements,
revised edition, Ethiopian Institute of Agricultural Research, Addis Ababa Ethiopia, pp
7-25
GirmaHailemichael, HabtewoldKifelew and Haimanot Mitiku,2016. Spices Research
Achievements, Challenges and Future Prospects in Ethiopia. Academic Research Journal
of Agricultural Science and Research
Hammo YH (2008) Effect of high levels of nitrogen and phosphorous fertilizer, pinching, and
seed rate on growth and yield components of Nigella sativa L. 1-vegetative growth and
seed yield. Mesopotamia J of Agric 36: 1-8. Effect of high levels of nitrogen and
phosphorous fertilizer, pinching, and seed rate on growth and yield components of
Nigella sativa L. 1-vegetative growth and seed yield. Mesopotamia J of Agric 36: 1-8.
MergaJibat, WakjiraGetachewommit a, HabetewoldKifelew, AbukiyaGetu.“Survey and
Identification of Black Cumin (Nigella Sativa L.) Disease in Ethiopia” International
Journal of Research in Agriculture and Forestry, 5(11),pp 31-34
10
The Release of Black Cumin Variety “Qeneni” for mid altitude of Bale, South Eastern
Ethiopia
*GetachewAsefa and Mohammed Beriso
Sinana agricultural Research Center, P.O.Box 208, Bale- Robe, Ethiopia
*Corresponding Author: Email:-fenetgeach@gmail.com
Abstracts
Qeneni a newly released black cumin variety from Oromia Agricultural research institute,
Sinana Agricultural Resarch Center in 2012 E.C. It was released from Arsi Bale landrace
collection after pure line development and rigorous performance evaluation. The variety is
released with the merits of high seed yield (24.54 Qt/ha) and 45.9% of oleoresin content for
Bale mid altitude and similar agro ecologies.
Key words:- Black cumin, oleoresin content, Variety Verification
Introduction
Black cumin (Nigella sativa L.) a member of Rananculaceae (diploid, 2n=12) has an aromatic
odor and bitter taste. It is used principally to flavor food, either as whole grain, in powdered form or as an
oleoresin extract. Within Ethiopia its main use is as a spice, which is typically ground and mixed with other
spices. Black cumin seed is
widely produced in Oromia, Amhara, SPNN and Tigray regional
states among which Oromia takes the lion share. Currently, Ethiopian government has given
due attention to the production and promotion of this crop for its export potential to earn
foreign currency. More recently, a great deal of attention has given to the seed and oils yields
of black cumin. Due to this, their consumption has thus increased and black cumin is the
second seed spice exported next to ginger in Ethiopia (Dessalegn Anshiso and Wubeshet
Teshome, 2018). The major production constraint in the black cumin in the production area is
lack of improved variety that give high seed yield and quality. Sinana agricultural research
center is striving in developing improved black cumin varieties for the farming community in
the major black cumin growing areas of the zone. Hence, the current objective of this project
was to identify high yielding, stable and quality black cumin variety for the farming
community in the major production areas.
Materials and Methods.
The trial was conducted at Sinana Agricultural Research Center from advanced observation
nursery and primary yield trial. From these early stages of genotypes screening, some
promising genotypes were selected and promoted to the regional variety trial. The regional
variety trial was conducted at three locations viz. Sinana on station, Goro and Gindhir on
farm for three three years (2008-2010 E.C) then in 2011 E.C, the variety verification trial was
conducted for verifications of promising genotypes for possible release and the trial was
11
evaluated by the National Variety Releasing Committee (NVRC) and eventually, one
promising genotype was released under the name of Qeneni for Bale mid land and similar
agro ecologies for production/cultivation.
Result and Discussions
Qeneni (Acc. 20750-1) is black cumin varieties developed and released by Oromia
Agricultural Research institute, Sinana Agricultural Research Center from landrace black
cumin germplasms collected from different black cumin growing areas. Originally this
variety was obtained from land race collections through pure line development procedure
following standard black cumin pure line development procedure. Agronomic and
morphological descriptors of variety Qenini are presented in Table 1 as follow:
Table 1.Agronomic and Morphological descriptors for newly released Black cumin variety
Variety Name
Agronomic and Morphological
Characteristics
Adaptation Area
Altitude (masl)
Rain fall (mm)
Seed Rate (kg/ha)
Planting date
Fertilizer rate (kg/ha)
Days to flowering
Days to Maturity
Plant Height (cm)
Growth habit
Seed Color
Flower Color
Yield (Qt/ha)
Research field
Farmer’s field
Oleoresin content (%)
Year of Release
Breeder/Maintainer
Qeneni (Acc. 20750-1)
Sinana, Goro, Ginnir and similar agro ecology
1650 – 2400
550-750
Row planting -10
Broadcasting - 15
End of August to late September (for Bale mid
altitude)
NPS = 100
104
170
70.4
Erect
Black
White
17 – 23.2
12 – 18
45.91
2019
*SARC/IQQO
SARC/IQQO= Sinana agricultural research center of Oromia agricultural research institute
Conclussions and Recommendations
Qeneni black Cumin variety was officially released in September 2012 E.C for Bale mid land
and similar agro ecologies with high seed yield of 23.2Qt/ha as compared to the standard and
local checks and it also has oleoresin content of 45.91%.
12
Acknowledgements
The financial assistance of Oromia Agricultural Research Institute is sincerely acknowledged
and all staff of horticulture and seed spice case team of Sinana Agricultural Research center
is also acknowledged for field management and data collection.
References
Dessalegn Anshiso, WubeshetTeshome. Economic Value of Black Cumin (Nigella sativa L.)
Conservation at Bale Zone of Oromia Region, Ethiopia.American Journal of Business,
Economics and Management. Vol. 6, No. 4, 2018, pp. 104-109.
Registration of “Gadisa” New Released Coriander Variety
*GetachewAsefa and Mohammed Beriso
Sinana agricultural Research Center, P.O.Box 208, Bale- Robe, Ethiopia
*Corresponding Author: Email:-fenetgeach@gmail.com
Abstract
Gadisa (Acc. MAB-030 )is coriander variety developed and released by Oromia Agricultural
Research institute, Sinana Agricultural Research Center. Originally it was obtained from
land race collections through pure line development from landrace populations. Variety
verification trial was conducted during 2011 E.C for verification and evaluation by the
National Variety Releasing Committee and eventually released for Bale mid land and similar
agro ecologies under the local name of Gadisa.
Key Words: Coriander, Gadisa, Variety verification.
Introduction
Coriander (Coriandrum sativum L, 2n=2x=22) is a diploid annual plant, belonging to the
Apiaceae/Umbliferae family (Hedburg and Hedburg, 2013). Due to wide range of climatic,
ecological and topographic conditions, Ethiopia has long been known as a center of origin
and diversity for several plants among which, coriander is the one in which Ethiopia is known
as a center of primary diversity (Jansen, 1981).The existence of sufficient variability for
agronomic and chemical traits for Ethiopian coriander accessions was also reported by
Beemnet and Getinet (2010). Coriander is used as a spice in food, beverage, and in
pharmaceuticals industries (Jansen, 1981). Coriander is also a good melliferous plant and
studies indicated that, one hectare of coriander allows honey bees to collect about 500 kg of
honey (Romanenko et al., 1991). In Ethiopia, mature fruits, which is commonly named as
seeds are commonly used as spice and the fresh green herb also used as a green salad.
Coriander seed is widely used as a spice in diversified societies of the country and its seed is
found in every market. In addition the leaves and the immature fruits are used as an
ingredient for the preparation of ‘‘data’’, a traditional spice eaten as a wot together with meat
(Beemnetetal 2010).
13
Plant breeders usually maintain their own active collections consisting of carefully selected
genotypes, but there is a continuous need for new, specific trait and combinations of trait in
introductions, selection, domestication and improvement programme, allowing new problems
to be solved and new demands to be met. Coriander is the major seed spices produced in Bale
mid altitude, however, its production and productivity is low. Among factors contributing to
low production and productivity of coriander is lack of improved varieties that are high
yielding and resistance/tolerant to disease with wide adaptability which results in low yield.
Hence, it is essential to evaluate and release coriander genotypes that are stable, high yielding
and adaptable for Bale mid altitude and similar agro ecologies.
Materials and Methods
The variety verification trial was done at three locations viz. Sinana, Goro and Gindhir in
2011 E.C under rain feed condition during “bona” cropping season. The trial was sown on
10x10 m2 with non-replicated plots using standard check Derbera and one local check. All
agronomic managements was done as per recommendation for the crop.
Results and Discussions
The verification trial was evaluated by National Variety Releasing Committee (NVRC) and
eventually release for Bale mid midland and similar agro ecologies. The variety has merits of
high yield performance, stability and acceptable quality parameter called Oleoresin content.
Details of yield Morphological and agronomic characters of the variety is presented listed in
Table 1 as follow:
Table 1. Agronomic and Morphological descriptors for newly released Coriander variety
Variety Name
Gadisa (Acc. MAB-030)
Agronomic and Morphological Characteristics
Adaptation Area
Sinana, Goro, Ginnir and similar agro ecology
Altitude(masl)
1650 – 2400
Rain fall(mm)
550-750
Seed Rate(kg/ha)
Row planting -12, Broadcasting - 15
Planting date
End of August to late September
Fertilizer rate(kg/ha)
NPS = 100
Days to flowering
68
Days to Maturity
123
Plant Height(cm)
62
Growth habit
erect
Seed Color
Brown
Flower Color
White
Yield (Qt/ha)
Research field
15 - 33
Farmers field
12 - 21
Oleoresin content (%)
21.36
Year of Release
2019
Breeder/Maintainer
Sinana ARC/IQQO
14
Conclusions and Recommendations
Verification result after being evaluated by National Variety releasing committee, Gadisa
officially released for Bale Mid altitude and similar agro ecologies. Gadisa is stable, high
yielding variety with short days to maturity which make it to produce in both “Gena” and
“Bona” cropping season for bimodal rain fall areas like Bale.
Acknowledgements
Authors are grateful to Oromia Agricultural Research Institute and Sinana Agricultural
Research center for providing financial and facilities to carry out this research work
References
BeemnetMengesha, GetinetAlemaw and BizuayhuTesfaye. 2010. Performance of Ethiopian
Coriander (Coriandrumsativum L.) Accessions in Vegetative, Phenological, Generative
and Chemical Characters.Improving Quality Production of Horticultural Crops for
Sustainable Development Proceedings, Februay 04-05, 2010.Jimma University College
of Agriculture and Veterinary Medicine, Jimma, Ethiopia.
Hedburg, I and Hedburg, O. 2003.Flora of Ethiopia and Eritrea Apiaceae to
Dipsacaceae.Hedeger, I., S. Edwards and SileshiNemomsa (Eds.). Volume 4, Part 1.
Uppsala, Sweden. 352pp.
Jansen, P.C.M. 1981. Spices, condiments and medicinal plants in Ethiopia; their taxonomy
and agricultural significance.Center for Agricultural Publishing and Documentation,
Wageningen, Netherlands. 294pp.
Parthasarathy V, Chempakam B, Zachariah TJ. Chemistry of spices. CAB International.
2008;455.
Performance Stability for Grain yield and Genotypes by Environment Interaction in
Field pea Genotypes in the highlands of Bale Southeastern Ethiopia
Tadele Tadesse*, Gashaw Sefera, Belay Asmare and Amanuel Tekalign
Oromia Agriculture Research Institute, Sinana Agriculture Research Center, Bale-Robe,
Ethiopia
*Corresponding author: tadyeko20@gmail.com or tadeleta20@yahoo.com
Abstract
Thirteen field pea genotypes were evaluated along with two standard checks, Harena and
Tullu shenen, and local cultivar for three consecutive years 2016 to 2018 main cropping
season, bona, in the highlands of Bale, Southeastern Ethiopia. The study was conducted
using randomized complete block design with four replication in order to identify high
yielding, stable field pea genotypes with resistance or tolerant types of reaction for major
diseases in the study areas. Genotypes X environment interaction and grain yield stability
15
were analyzed and estimated using AMMI model analysis. The AMMI model analysis
revealed significant variation for genotypes, environment, genotype x environment
interaction at (P<0.01 %.). The environment accounted for 82.99% of the total variation for
yield whereas the genotypes accounted for 9.54% and the Genotypes x environment
interaction explained for 7.46% of the total variation for grain yield. This indicates that the
tested genotypes responded differently to the environment or the environment differently
discriminate the genotypes. The first two AMMI components also showed significant
variation and totally accounted for 55.45% which indicates at the model fit for this study.
Based on the stability parameters like ASV and GSI used to discriminate the stable
genotypes, G14, G8 G4, G16 and G3 had lower ASV and showed stable performance over
the testing environments. In order to reduce the effect of GE interaction and to make
selection of genotypes more precise and refined, both yield and stability of performance
should be considered simultaneously. Accordingly, genotypes with code, G5, G4 and G14
had lower GSI indicating stable performance. But G5 had almost equal mean grain yield
with the check (G14). Furthermore, this genotype besides its stable performance over the
tested environment, it showed tolerant types of reaction for Powdery mildew, Downey mildew
and Aschochtya blight. Therefore, G4, (ACC32003-2) was identified as candidate genotypes
to be verified in the coming cropping season for possible release for the highlands of Bale
and similar agro-ecologies.
Key words: AMMI, AMMI Stability Value (ASV), Genotypes selection Index (GSI), Grain
yield, Stability
1.
Introduction
Field pea (Pisum sativum L.) is one self-pollinated diploid (2n=14) annual of the most
important annual cool season pulse crop and is valued as high protein food( McKay et al.,
2003). It is widely grown in the cooler temperate zones and in the highlands of tropical
regions of the world. Field pea does well under variety of soil types, but grows best on fertile,
light-textured, well-drained soils; however, the crop is sensitive to salinity and extreme
acidity. The optimum range of soil pH for field pea production is 5.5 to 7.0 (Hartmann et al.,
1988). It grows well with 16 to 39 inches of annual precipitation and it can tolerate
temperature as low as 140F (Elzebroek and Wind, 2008). However, the crop is very sensitive
to heat stress at flowering, which can drastically reduce pod and seed set. Filed pea is
primarily used for human consumption and livestock feed. It contains approximately 21-25
percent protein and high levels of carbohydrates, amino acids, lysine and tryptophan, which
are relatively low in cereals. It is low in fiber and contains 86-87% total digestible nutrients,
16
which makes it an excellent livestock feed. Global field pea production for the period 19992003 was estimated at about 10.5 million tons from an area of 6.2 million hectares (Brink and
Belay, 2006). In Ethiopia this crop is mainly grown for human consumption. During 2007
growing season the total production of field pea was 210,095 tones with an average
productivity of 948kg/ha (Schneider and Anderson, 2010). Understanding the extent and
pattern of G × E interaction effect can also help to effectively design appropriate breeding
strategies, optimize varietal selection vis-à-vis the target production environments, and to
define suitable areas of recommendation domain, where a given cultivar can be better adapted
(Yan and Hunt 2001). In other words, knowledge of the extent and pattern of G × E
interaction can help plant breeders to reduce the cost of genotype evaluation by eliminating
unnecessary spatial and temporal replication of yield trials (Basford and Cooper 1998).
Genotypes respond to changes in environmental conditions such as temperature, rainfall, soil
type, moisture and so on (Robertson, 1959; Cockerham, 1963; Falconer and Mackay, 1995).
Therefore genotypes selected in a breeding program should be tested at various locations for
several years, and analyzed appropriately to determine the extent of the genotype ×
environment (G× E) interaction before being released as cultivars. This technique became
extensively used after the studies of Finlay and Wilkinson (1963) and Eberhart and Russell
(1966). In general genotype by environment (GxE) interaction affects the efficiency of crop
improvement programs that may lead to complicates recommendation of varieties across
divers’ environments. Therefore, information on the structure and nature of GxE interaction
is particularly useful to breeders Yayis et al., 2015). Because of the changing environmental
condition, the performance of field pea genotypes was highly affected in the tested
environment. Therefore, this study was initiated to identify the magnitude of Genotypes x
environment interaction for grain yield variation for the studied field pea genotypes and to
identify high and stable field pea genotypes with tolerant/resistant types of reaction for
majority of field pea diseases for possible releases for the highlands of Bale, Southeastern
Ethiopia and similar agro-ecologies.
2.
Materials and Methods
Thirteen field pea genotypes along with two standard checks, Harena and Tullu shenen, and
local cultivar were used in order to assess the grain yield performance and stability of the
genotypes across the testing environments. The genotypes were evaluated using randomized
complete block design with four replications. The trial was conducted at nine environment
(year by locations), where they are representing field pea production in the highlands of bale
zone southeastern Ethiopia i.e. Sinana, Sinja and Agarfa for three consecutive years from
17
2016 to 2018 cropping season. Recommended seed rate of 75 kg/ha and 100 kg DAP/ha was
used. The plot size sued was 3.2m2 (4 rows at 20cm spacing and 4m long). The field pea
genotypes were firstly brought from Institute of Bio diversity and Conservation (IBC), and
lines were developed at the main research center, Sinana, in the subsequent breeding stge.
2.1.
Statistical analysis
Keeping in view the objectives set out for the study, following statistical tools and methods
have been analyzed. Combined Analysis of Variance (ANOVA) for the grain yield across the
testing environment was analyzed using CropStat7.2 computer program (CropStat., 2009).
Univariate analysis method as suggested by Eberhart and Russell’s (1966) model used to
estimate joining linear regression of the mean of the genotype on the environmental mean as
an independent variable. In this model, it defines stability parameters that may be used to
estimate the performance of a genotype over different environments. Two stability
parameters were calculated based on (a) the regression coefficient, a regression performance
of each genotype in different environments calculating means over all the genotypes, and (b)
mean squares of deviations (S2di ) from linear regression. The performance of each cultivar
in each environment was regressed on the means of all cultivars in each environment.
Cultivars with regression coefficient (bi) of unity and variance of regression deviations (S2di)
equal to zero will be highly stable. Multivariate analysis method: Genotype and Genotype by
Environment interaction AMMI analysis was used to see the GE of the genotypes. For this
purpose the combined analysis was used to create an analysis of variance (ANOVA) table to
determine the presence or absence of GE interactions. The percentage of total variation
attributed to E, G, or GE interaction was calculated using the sums of squares from the
ANOVA table. AMMI Stability Value (ASV) the distance from the coordinate point to the
origin in a two dimensional of IPCA1 scores against IPCA2 scores was calculated by the
method suggested by Purchase et al., 2000. This weight is calculated for each genotypes and
environment according to the relative contribution of IPCA1 to IPCA2 to the interaction SS
as follows,
2
𝑆𝑆𝐼𝑃𝐶𝐴1
(𝐼𝑃𝐶𝐴1𝑠𝑐𝑜𝑟𝑒)⌋ + ⌈𝐼𝑃𝐶𝐴2⌉2
ASV=√⌊
𝑆𝑆𝐼𝑃𝐶𝐴2
Where,
𝑆𝑆𝐼𝑃𝐶𝐴1
𝑆𝑆𝐼𝑃𝐶𝐴2
is the weight given to the IPCA1 value by dividing the IPCA1 sum squares by
the IPCA2 sum of squares.
Genotype Selection Index (GSI): a selection index GSI, was calculated for each genotype
which incorporate both mean grain yield and stability index in a single criteria (GSIi) as
18
GSIi= RYi +RASVi suggested by Farshadfar, 2008. Where RYi is the rank given for the
grainy yield of the genotypes, RASV is the rank given for the ASV of the genotypes.
Table 1 Lists of field pea genotypes used in the study along with and their codes
Genotype code
Genotypes
G1
G2
G3
G4
G5
G6
G7
G8
ACC 32518-1
ACC32021-2
ACC 32197-4
ACC32003-2
ACC 32509-1
ACC 32399-4
ACC 32225-1
ACC32178-4
3.
Genotype code
G9
G10
G11
G12
G13
G14
G15
G16
Genotype
ACC 32512-4
ACC 32487-3
ACC 32180-4
ACC32488-4
ACC 32363-3
Harena
T/Shenene
Local check
Results and Discussions
The combined Analysis of Variance over years and locations revealed highly significant
variation at (P<0.01%) for genotypes, Location and Genotype x Environment Interaction
(GE) (Table 2). This result was in agreement with the findings of Yayis et al. 2014, Girma et
al., 2011, Tamene et al., 2013 who reported that significant variation of genotypes, location
and GE of grain yield by field pea genotypes.
Table 2. Combined Analysis of Variance for field pea genotypes
Source of Variation
YEAR (Y)
Location (L)
Replication
Genotype (G)
YXL
LXG
YXLXG
ESIDUAL
TOTAL
Degree freedom
2
2
3
15
4
30
90
429
575
Sum Squares
212.461
243.989
5.32284
64.1821
11.765
3.6498
6.5581
51.348
829.276
Mean Squares
106.23**
121.995**
1.77428**
4.27881**
25.4413**
0.454994**
0.406201**
0.352793
1.44222
From this study the genotypes which gave maximum grain yield over locations and years as
indicated in (Table 3), were G4 (3.57t/ha), followed by G5 (3.38t/ha), G14 (3.23t/ha)), and
G15 (3.07t/ha) whereas the maximum grain yield was obtained from the environment Sinana
2017 (4.02t/ha), followed by Sinana 2016 (3.75t/ha), Sinana 2018 (3.50t/ha) and Agarfa 2017
(3.39t/ha).
AMMI Analysis: AMMI analysis of variance for grain yield (kg ha-1) of the 16 field pea
genotypes tested in 9 environments showed that the genotypes, environments and G × E
19
interaction effects were significantly different (p<0.01). This result also indicated that the
environments, which accounted for 82.99% of the total yield variation, significantly
influenced the yielding ability of the field pea genotypes. The genotypes accounted for 9.56%
whereas the GE accounted for 7.46% of the total variation for grain yield (Table 4). Similar
result was also reported by Tamen et al., 2013; Yayis et al., 2014 who have indicated highly
significant variation for genotypes, environment and GE for grain yield in field pea genotypes
in their AMMI analysis. A large yield variation explained by environments also indicated the
existence of diverse mega environments, i.e. a group of environments which share the same
cultivar(s) that consistently performed the best with large differences among environmental
means, causing most of the variation in grain yield (Yan and Rajcan 2002). When the
significant GE sum of square value partitioned in to different AMMI components, the first
three IPCA showed significant variation for the grain yield. Accordingly, the sum of square
due to the first AMMI 1 explained about 31.23% where as the second component, AMMI 2
accounted for 24.22% the third AMMI 3 accounted for 19.54% and the fourth AMMI 4
9.97%. The remaining 15.04% of the interaction effect being the residual or noise hence not
interpreted and hence discarded (Gauch, 1993; Purchase et al., 2000). In total the two AMMI
components were responsible for 55.45% of the GE variation with degree freedom of 42
(Table 4). The variation in the contribution of these four IPCAs indicated differential
performance of genotypes for grain yield across environments. However, for the validation of
the variation explained by GEI, the first two multiplicative component axes were adequate
(Gauch, 2006). This is because of notable reduction of dimensionality and graphical
visualization for the adaptation patterns of genotypes (Annicchiarico, 2002).
Table 3. Mean Grain yield of field pea genotypes over the tested environments
Treatment
code
Sinana
2016
(A)
Agarfa
2016
(B)
Sinja
2016
(C)
Sinana
2017
(D)
Agarfa
2017
(E)
Sinja
2017
(F)
Sinana2018
(G)
Sinja
2018
(H)
Agarfa
2018
(I)
TRT
MEANS
4
5
14
15
1
3
6
7
13
2
10
16
11
3.74
3.30
3.48
3.11
2.46
3.13
2.58
2.72
2.26
2.15
2.25
2.65
2.02
1.73
1.25
1.18
0.96
0.79
0.95
0.74
0.83
0.86
0.90
1.00
0.88
0.84
3.13
2.86
3.31
2.62
3.40
2.33
2.27
2.16
3.08
2.76
2.88
1.84
2.25
4.83
4.28
3.98
3.74
4.08
3.65
3.99
4.11
3.79
3.71
3.24
3.15
3.53
3.73
3.84
3.60
3.94
3.56
3.39
3.36
3.27
3.42
3.72
3.36
3.12
3.20
3.54
3.92
3.51
3.94
3.86
3.84
2.64
3.13
3.20
2.47
3.45
3.17
2.61
5.02
4.68
4.45
4.85
3.79
4.23
4.42
4.10
3.80
4.06
3.86
3.37
3.62
4.41
4.31
3.66
2.90
3.56
3.75
4.14
3.53
3.15
3.20
3.21
3.29
3.77
1.99
1.97
1.87
1.53
1.79
1.76
1.59
1.09
1.36
1.58
1.05
1.94
1.27
3.57
3.38
3.23
3.07
3.03
3.00
2.86
2.77
2.77
2.73
2.70
2.60
2.57
20
Treatment
code
Sinana
2016
(A)
Agarfa
2016
(B)
Sinja
2016
(C)
Sinana
2017
(D)
Agarfa
2017
(E)
Sinja
2017
(F)
Sinana2018
(G)
Sinja
2018
(H)
Agarfa
2018
(I)
TRT
MEANS
8
12
9
Mean
LSD 5%
CV%
2.05
2.35
1.77
2.63
0.51
14.0
0.52
0.85
0.49
0.92
0.32
24.0
1.83
1.84
2.19
2.55
0.76
21.0
3.31
3.06
3.51
3.75
0.81
15.0
3.19
2.75
2.75
3.39
0.53
11.0
3.23
3.30
3.53
3.33
1.18
24.0
3.60
3.55
2.93
4.02
0.68
12.0
3.29
2.75
3.12
3.50
0.87
17.0
1.18
1.34
1.20
1.53
0.63
23.0
2.46
2.42
2.39
2.85
0.28
21.0
Table 4. ANOVA for AMMI model
Sources
Genotypes
Environment
GXE
AMMI 1
AMMI 2
AMMI 3
AMMI 4
GXE RESIDUAL
TOTAL
Degree
Freedom
15
8
120
22
20
18
16
44
143
Sum of
Square
16.0455
139.554
12.552
3.91945
3.03998
2.45264
1.25105
1.88885
168.151
Mean Square
1.0697*
17.4442**
0.1046**
0.178157**
0.151999**
0.136258**
0.782
TSS
explained %
9.54
82.99
7.46
31.23
24.22
19.54
9.97
Stability analysis
The three stability parameters suggested by Eberhart and Russel, 1966 i.e. the mean grain
yield, regression coefficient or slop and the deviation from the regression indicates as there
are some genotypes which had stable performance over the tested sites. Accordingly, G4, G3,
G11, G13 and G14 had score of slope value close to unity and the deviation from regression
also close to zero though the mean grain yield performance varied (Table 5). When the ASV
is considered to discriminate the stability of the genotypes, G14, G8, G4, G5 and G7 had
lower ASV value compared to the rest of the genotypes. However, G7 and G8 had mean
grain yield lower than the check (G14). However, since stability in itself should not be the
only parameter for selection, as the most stable genotype wouldn’t necessarily gives the best
yield performance (Mohammadi, 2007), hence, simultaneous consideration of grain yield and
ASV in single non-parametric index is needed or the Genotype Selection Index should be
used to determine the stability of the genotypes by evaluating their mean grain yield and
ASV. Genotype Selection Index (GSI), when the rank of mean grain yield of genotypes
across environments and rank of AMMI Stability Value (ASV) considered to identify the
tested genotypes in relation to stability, G4, G14 and G5 had the lowest GSI values compared
to the other genotypes and showed stable performance over the testing sites. The mean grain
yield difference of G5 compared to the check G14 is almost comparable. Furthermore, G3,
G7 and G15 had the second lower GSI value and indicating moderately stable performance
21
but gave mean grain yield lower than the check. However, the mean grain yield of G11 was
equal to the check used in the study. Therefore, G4 was the stable and high yielder genotypes
across the testing environments.
Table 5. Mean grain yield, stablility parameters, ASV, GSI of field pea genotypes
Trt
code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Genotypes
ACC 32518-1
ACC32021-2
ACC 32197-4
ACC32003-2
ACC 32509-1
ACC 32399-4
ACC 32225-1
ACC32178-4
ACC 32512-4
ACC 32487-3
ACC 32180-4
ACC32488-4
ACC 32363-3
Harena
T/Shenene
Local check
Mean
(t/ha)
3.03
2.73
3.00
3.57
3.38
2.86
2.77
2.47
2.39
2.70
2.57
2.42
2.77
3.23
3.07
2.60
Rank Slope MSDE
S2di)
5
1.01
0.14
10
0.95
0.15
6
1.02
0.07
1
1.05
0.13
2
1.10
0.02
7
1.13
0.15
8
1.13
0.05
14
1.05
0.04
16
0.95
0.15
11
0.96
0.09
13
0.97
0.09
15
0.85
0.06
8
0.97
0.08
3
0.96
0.07
4
1.11
0.20
12
0.78
0.09
IPCA1 IPCA2 ASV
Rank GSI
-0.45
0.26
-0.08
0.54
0.16
0.75
0.31
-0.07
-0.48
-0.35
0.35
-0.30
-0.22
0.00
-0.31
-0.11
15
13
7
3
4
16
5
2
14
8
9
11
12
1
10
6
-0.49
-0.52
0.49
0.11
0.19
-0.05
0.12
0.14
-0.16
-0.25
-0.29
0.41
-0.50
0.00
0.38
0.42
0.760
0.621
0.500
0.277
0.278
0.968
0.418
0.162
0.638
0.513
0.539
0.566
0.569
0.001
0.551
0.445
20
23
13
4
6
23
13
16
30
19
22
26
20
4
14
18
Biplot
Two biplots (AMMI 1 and AMMI 2) were used to demonstrate stability of genotypes for
grain yield. AMMI 1 biplot of main effects are shown along abscissa and the ordinate
represent first principal component (PC1) score. The basic idea of AMMI 1 biplot is to
provide means to select stable high yielding genotypes. AMMI 2 biplot explain the
magnitude of interaction of each genotype and environment. The genotypes and environment
that are farthest from the origin being more responsive fit the worst. The main purpose of
AMMI 2 biplot is to identify genotypes with specific environmental adaptation. In AMMI
biplot 1 showing main effects means on the abscissa and principal component (PC) values as
the ordinates, genotypes (environments) that appear almost on a perpendicular line have
similar means and those that fall on the almost horizontal line have similar interaction
patterns (Chaudhary et al., 2012).
Genotypes that group together have similar adaptation while environments which group
together influences the genotypes in the same way. Genotypes or environment found to the
right of the perpendicular lines gave grain yield higher than the grand mean. In the present
study among the genotypes G3, G1, G15, G14, G5 and G5 whereas from the environments
Env. F, Env. E, Env. H, Env. D and Env. G gave mean grain yield above the grand mean
(2.85t/ha). The rest genotypes and environments gave mean grain yield below the grand mean
22
(Figure 1). Genotypes having zero PC 1 score are less influenced by the environments and
adapted to all environments. Accordingly, G14, G8, G3, G16 and G5 had PCA1 score of zero
and close to zero meaning they were stable genotypes. But all of them were lower in their
grain yield than the check variety, G14. The other genotypes, like G13, G2, G12 and G4
showed PCA1 score higher than zero showing moderately stability over the tested
environments.
Figure 1. Biplot analysis of GEI based on AMMI 1 model for the PCA1 scores
and grain yield
0.8
6
H
4
G
0.42
11
A
IPCA1
0.04 B
8
I
D
7
2
5
14
3
16
E
13
12
-0.34
C 10
9
15
1
-0.72
F
-1.1
0.9
1.54
2.18
2.82
MEANS
3.46
4.1
VARIATE: T/HA DATA FILE: FPRV15CB MODEL FIT: 94.9% OF TABLE SS
AMMI 2 biplot (figure 2) presents the spatial pattern of the first two PC axes of the
interaction effect corresponding to the genotypes and helps in the visual interpretation of the
G X E pattern and identify genotypes or environments that exhibit low, medium, or high level
of interaction effects (Sharma et al., 1998). Genotypes near the origin are non-sensitive to
environmental interactive forces, hence may be considered stable ones and those distant from
origin are sensitive and have large interactions. Accordingly, G14 and G8 which they are
found close to the origin than the rest of the genotypes, showed stable performance over the
testing sites whereas G5, G4 and G7 which they are found some near to the origin showed
moderately stable performance compared to the rest genotypes (Figure 2).
In AMMI 2 biplot, the environment scores are joined to the origin by the site lines.
Environments with short spokes (length of arrow lines) do not exert strong interactive forces.
Those with long spokes (length of arrow lines) exert strong interaction. In the present study,
Agarfa 2016 (B), Agarfa 2918 (I) and Agarfa 2017 (E) having shorter spokes interact less
23
with the genotypes whereas Sinana 2016 (A), Sinja 2016 (C), Sinja 2017 (F) having longer
spokes or length of the arrow line exerts high interaction
4.
Conclusions and recommendations
As yield is affected by complex factors, Genotype x environment interaction was significant
for the grain yield indicating the need to test the genotypes in multiple environments before
effective selection can be made. To make the selection of genotypes more precise and
refined, both yield and stability of performance should be considered simultaneously to
reduce the effect of GE interaction. In the present study it was conclude that genotypes like
G1, G3, G4, G5, G6, G14, and G15 gave grain yield above the grand mean. Furthermore
when different stability indicator like AMMI Stability Value is considered to identify the
stable genotype; G14, G8, G4, G5 and G7 had lower ASV value compared to the rest of the
genotypes. But when GSI is considered to identify the stable and high yielding genotype,
only genotype (G5, G4 and G14) had lower GSI. But G5though it showed stable
performance, it has almost equal mean grain yield with the check (G14) whereas G4, which
showed the second lowest GIS and had mean grain yield greater than the checks, it showed
moderate stability over the testing environments. Furthermore, this genotype showed tolerant
type of reaction for diseases like Powdery mildew, Downey mildew and Aschochyta blight.
Therefore, we recommend this genotype to be used as candidate genotype to be verified in
the study areas for possible release for the highlands of bale, south eastern Ethiopia and
similar agro-ecologies.
5.
Acknowledgement
We would like to thank Oromia Agricultural Research Institute for the financial support,
Sinana Agricultural Research Center for providing all necessary facilities and Pulse and Oil
Crops Research Case team staffs for trial management and data collection.
6.
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Tamene T. Tolessa, Gemechu Keneni, Tadese Sefera, Mussa Jarso and Yeneneh Bekele.
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25
AMMI Analysis for Grain yield Stability in Lentil Genotypes Tested in the Highlands of
Bale, Southeastern Ethiopia
Tadele Tadesse*, Gashaw Sefera, Belay Asmare and Amanuel Tekalign
Oromia Agriculture Research Institute, Sinana Agriculture Research Center, Bale-Robe,
Ethiopia
*Corresponding author: tadyeko20@gmail.com or tadeleta20@yahoo.com
Abstract
Lentil (Lens culinaris Medik.) is an important cool-season food legume and a valuable
source of dietary protein and ranks seventh among grain legumes. Genotype x environment
interaction plays an important role in identifying genotypes for high and stable yield. Sixteen
lentil genotypes along with one local cultivar were evaluated at two locations, Sinana and
Agarfa over three years 2016-2018 in order to identify high yielding genotypes with stable
performance. The genotypes were laid out in randomized complete design with four
replications in each environment. The objective of this study was to identify and recommend
high yielder, stable genotypes for testing sites and similar agro-ecologies using the stability
parameters. The analysis of variance revealed significant variation among the genotypes,
locations and genotypes by location interaction for mean grain yield indication the diversity
of the testing sites and the variation in the performance of the genotypes over the testing
environment. The results of AMMI (additive main effect and multiplicative interaction)
analysis indicated that the first two AMMI (AMMI1-AMMI2) were highly significant
(P<0.01). The partitioning of the total sum of square exhibited that the effect of environment
was a predominant source of variation followed by genotypes and GE interaction effect.
Accordingly, G1, G6, G13, G14, G16 and G17 gave grain yield above the grand mean.
Furthermore from the stability indicator like AMMI Stability Value (ASV) indicated that: G4,
G15 G8, G6 , G10, had lower ASV value and showed stabile performance while G7, G11 and
G1 had relatively lower ASV and showed moderately stable performance over the testing
environments indicating wide adaptation. Furthermore, based on the Genotypes Selection
Index (GSI) the most stable genotypes with high grain yield were G1 and G15. Therefore
these two Genotypes were identified as candidate genotypes to be verified for possible
release in the highlands of bale, Sothern Ethiopia and similar agro-ecologies.
Key words: AMMI Stability Value, GE interaction, Genotype Selection Index, Stability
26
1. Introduction
Lentil is an annual self-pollination diploid (2n = 2x = 14 chromosomes) species and highly
valued food legume grown extensively in many part of the world. Lentil seed is a rich source
of good protein (up to 28%) in human diets in arid and semiarid areas of west Asia (Sarker et
al., 2003). It is the fourth most important legume crop in the world. In most lentil production
areas yield seem to be no more than one-half of potential yields while improved genotypes
contribute to increased lentil production and yields (Erskine, 2009). Selecting genotypes for
high mean yield and yield stability has been a challenge for breeders. The requirement for
stable genotypes that perform well over a wide range of environments becomes increasingly
important as farmers need reliable production quantity (Gauch et al., 2008). Therefore,
identifying most stable genotypes is an important objective in many plant breeding programs
for all crops, including lentil. The performance of a genotype is determined by three factors:
genotypic main effect (G), environmental main effect (E) and their interaction (Yan et al.,
2007). Understanding genotype by environment (GE) interactions is necessary to accurately
determine stability in lentil genotypes and help breeding programs by increasing efficiency of
selection (Sabaghnia et al., 2008). The GE interactions structure is an important aspect of
both plant breeding programs and the introductions of new improved crop cultivars as yield
stability analysis (Neacşu, 2011).
A cultivar or genotype is considered to be more adaptive or stable if it has a high mean yield
but a low degree of fluctuation in yielding ability when grown in diverse environments
(Arshad et al., 2003). The additive main effects and multiplicative interaction model is
frequently used in the analysis of multi-location trials. AMMI analysis has been shown to be
effective because it captures a large portion of the GE sum of squares, it cleanly separates
main and interaction effects that present agricultural researchers with different kinds of
opportunities, and the model often provides agronomically meaningful interpretation of the
data (Gauch, 1992). Additionally, results from AMMI are useful for performing megaenvironment analysis in which a crop’s growing region is subdivided into homogenous subregions that have similar interaction patterns and cultivar rankings, simplifying cultivar
recommendations (Zobel and Gauch, 1988). Therefore due to the lack of stable genotypes
with high grain yield, this study was initiated with the objective to identify lentil genotype
with high mean grain yield with stable performance over the testing sites for the mid and
highland areas of Bale and similar agro-ecologies.
2. Materials and Methods
2.1. Testing Sites al Locations
27
The experiment was carried out at two locations. One of the experiments was conducted at
the research farm of Sinana Agricultural Research Center, Oromia Agriculture Research
Institute, Sinana, and the other two were at a site in the farmer’s field representing for linseed
production. The experiment was conducted at each location on vertisol clay loam soil under
rain fed conditions during the meher season (August-January) of 2016 to 2018 cropping
season. Because of the suitability of the locations for lentil production, it is expected that the
test genotypes would express their genetic potential to a higher extent for the traits under
consideration.
Fifteen lentil genotypes along with one standard check, Asano, and local cultivar were tested
in order to determine their stability across the testing sites during the main cropping season,
Meher, for three consecutive years (2016-2018) at two locations (Sinana, and Agarfa)
representing the highlands of bale zone, south eastern Ethiopia. The experimental layout at
each environment was complete randomized block design with four replications. The plot
size used was 3.2m2 (4 rows at 20cm apart and 4m long). The two central rows were used for
data collection. Combined analysis of variance using balanced ANOVA was computed using
CROPSTAT program. The additive main effect and multiplicative interaction (AMMI)
analysis was performed using the model suggested by Crossa et al. 1991 as:
Yij=µ+gi+ej+∑n=1h λnαni.Ynj+Rij where,
Yij is the yield of the ith genotype in the jth environment, µ is the grand mean, gi is the mean of
the ith genotype minus the grand mean ej is the mean of jth environment minus the grand
mean, λn is the square root of the eigen value of the principal component Analysis (PCA)
axis, αni and Ynj are the principal and the principal component scores for the PCA axis n of
the ith genotype and jth environment, respectively and Rij is the residual. The GE biplot was
projected for the 17 genotypes tested at 6 environments. The regression of yield for each
variety on yield means for each environment was computed and parameters MS-REG, the
contribution of each variety to the regression component of the treatment x location
interaction and MS-TL the contribution of each variety to interaction MS, were estimated
with the CropStat program.
The ASV is the distance from the coordinate point to the origin in a two dimensional of
IPCA1 score against IPCA2 scores in the AMMI model Purchase et al., 2000. Because of the
IPCA1 score contributes more to the GE interaction sum of square, a weighted value is
needed. This weight is calculated for each genotypes and environment according to the
relative contribution of IPCA1 to IPCA2 to the interaction SS as follows,
2
𝑆𝑆𝐼𝑃𝐶𝐴1
(𝐼𝑃𝐶𝐴1𝑠𝑐𝑜𝑟𝑒)⌋ + ⌈𝐼𝑃𝐶𝐴2⌉2
ASV=√⌊
𝑆𝑆𝐼𝑃𝐶𝐴2
28
Where,
𝑆𝑆𝐼𝑃𝐶𝐴1
, the weight given to the IPCA1 value by dividing the IPCA1 sum squares by
𝑆𝑆𝐼𝑃𝐶𝐴2
the IPCA2 sum of squares. The larger the IPCA score, either negative or positive, the more
specifically adapted a genotype is to certain environments. Smaller IPCA score indicate a
more stable genotype across environment.
Genotype Selection Index (GSI)
Based on the rank of mean grain yield of genotypes (RYi), across environments and rank of
AMMI Stability Value (RASVi), a selection index (GSI) was calculated for each genotype
which incorporate both mean grain yield and stability index in a single criteria (Farshadfar,
2008).
GSIi= RYi +RASVi
Table 1. Genotype code and the name of 17 lentil genotypes
No.
Genotype code
Name
1
G1
DZ -2012-LN-0085
2
G2
DZ -2012-LN-0057
3
G3
DZ -2012-LN-0059
4
G4
DZ -2012-LN-00118
5
G5
FLIP-96-49L
6
G6
DZ -2012-LN-0038
7
G7
DZ -2012-LN-00107
8
G8
DZ -2012-LN-0058
9
G9
DZ -2012-LN-0048
10
G10
FLIP-97-33L
11
G11
DZ -2012-LN-0065
12
G12
FLIP-86-38L
13
G13
FLIP-89-19L
14
G14
DZ -2012-LN-0095
15
G15
DZ -2012-LN-0051
16
G16
Asano
17
G17
Local check
3. Result and Discussion
The combined Analysis of Variance (Table 2) revealed that significant variation among
genotypes, locations and GE interaction for mean grain yield of lentil. Similar result was
29
found by Karimizadeh et al., 2010 who sated that significant variation of genotypes and
genotypes by environment interaction. Furthermore, they have explained that the
significances variation among the environments indicate that these locations can be used as
testing stations for different environments while significant differences among genotypes
reveals the differential response of genotypes to different environments. The explained
percentage of sum of square (SS) of grain yield by environment is 29.65%, for genotype it
was 8.11% and for the genotype x environment interaction it was 4.42% (Table-2).
Table 2. The combined ANOVA for grain yield of lentil over locations and years
Source of
Degree
Sum
Mean Squares % explained of TSS
Variation
freedom
Squares
YEAR (Y)
2
42.5821
21.291
15.68
Location (L)
1
80.5456
80.5456
29.65
Replication
3
0.260409 0.0868
0.10
Genotype (G)
16
22.0293
1.37683
8.11
YXL
2
44.0381
22.019
16.21
GXE
16
12.0086
0.750539
4.42
YXLXG
64
31.642
0.494406
11.65
RESIDUAL
303
38.5488
0.127224
14.19
TOTAL
407
271.655
0.667457
Environment significantly explained the largest variation (29.65%) of the total sum of
squares. This largely yield variation, explained by environments, indicated that the
environments were diverse and a major part of variation in grain yield can be resulted from
environmental changes. The same result was reported by Akter et al., 2014 and Karimizadeh
and Mohammadi, 2010.
AMMI Analysis
The combined analysis of variance showed highly significant differences for environment,
genotype and their interactions; combined analysis of variance and AMMI analysis is shown
in Table 3.
Table 3. Analysis of Variance for grain yield of lentil for the AMMI model.
Sources
DF.
SS
MS
TSS explained %
Genotypes
Environment
GXE
AMMI COMPONENT 1
AMMI COMPONENT 2
AMMI COMPONENT 3
AMMI COMPONENT 4
GXE RESIDUAL
TOTAL
16
5
80
20
18
16
14
12
101
5.50732
41.7914
10.9126
5.07988
3.75266
1.3651
0.434769
0.28024
58.2114
0.344207**
8.35829**
0.136408**
0.253994**
0.208481**
0.085319**
0.031055**
9.46
71.79
18.75
46.55
34.39
12.51
3.98
30
It is indicated in Table 3 that 71.79% of the total variation is attributed for environmental
effect whereas 9.46% and 18.75% of the variation was accounted for genotypes and
genotypes by environment interaction, respectively. A large sum of squares for environments
indicated that the environments were diverse, with large differences among environmental
means causing variation in the plant grain yields. The AMMI model demonstrated the
presence of G x E interactions, and this has been partitioned among the first two IPCA
(Interaction Principal Components Axes). Of the total variation observed, AMMI1 explained
46.55% of the interaction sum of squares whereas AMMI2 explained 34.39% of the
interaction sum of squares (Table 3). The first two AMMI components totally accounted for
80.94% of the variation observed. This indicates that the use of AMMI model fit the data well
and justifies the use of AMMI2. According to Crossa et al., 1991, Zobel and Gauch, 1988 the
first two interaction principal component axis best predictive model explains the interaction
sum of squares.
Stability analysis by AMMI model
AMMI Stability Value (ASV): Purchase et al., 2000 indicated ASV as the distance from the
coordinate point to the origin in a two dimensional scatter gram of IPCA1 scores against
IPCA2 score should also seen to decide the stability of a genotypes. The ASV and other
stability parameters values along with the mean yield of the genotype are presented in Table
4. The highest mean grain yield of genotypes averaged over environments was obtained from
DZ -2012-LN-0085 (2.31t ha-1) followed by DZ -2012-LN-0051 (2.06t ha-1) and DZ -2012LN-0095 (1.98t ha-1). The genotypes with low stability value (ASV) is said to be stable and
the breeder chose the stable genotypes, having grain yield above the mean grand yield. In this
study genotype G4 showed lowest ASV followed by G5, G8, G6, and G10 (Table 5)
indicating these genotypes can be suitable for the studied environments.
Table 4. Mean yield, First and second IPCA and various yield-stability statistics investigated
in lentil.
Trt
Mean Rank Slope MS-DEV IPCA1 IPCA2 ASV Rank GSI
Code
Yi
(bi)
(S2di)
ASV
G1
2.31
1
0.86
0.07
0.15
-0.35
0.43
8
9
G2
1.70
11
1.30
0.31
-0.42
0.73
1.01
15
26
G3
1.54
15
1.48
0.12
-0.58
-0.15
0.97
14
29
G4
1.96
4
0.98
0.02
-0.03
0.03
0.05
1
5
G5
1.82
10
1.14
0.06
-0.24
-0.21
0.45
9
19
G6
1.88
6
1.03
0.06
0.03
-0.24
0.24
4
10
G7
1.85
7
1.15
0.10
-0.11
-0.30
0.35
6
13
G8
1.54
15
1.01
0.04
-0.05
-0.19
0.21
3
18
G9
1.85
7
1.36
0.02
-0.39
0.02
0.64
12
19
G10
1.85
7
1.02
0.05
-0.02
0.29
0.29
5
12
31
G11
G12
G13
G14
G15
G16
G17
1.64
1.96
1.59
1.98
2.06
1.59
1.32
12
4
13
3
2
13
17
1.22
1.25
0.68
0.85
1.00
0.39
0.29
0.05
0.11
0.11
0.11
0.02
0.13
0.32
-0.18
-0.27
0.44
0.29
-0.09
0.65
0.82
-0.20
-0.36
0.30
0.18
-0.08
0.47
-0.65
0.36
0.57
0.78
0.51
0.17
1.17
1.50
7
11
13
10
2
16
17
19
15
26
13
4
29
34
However, since stability in itself should not be the only parameter for selection, as the most
stable genotype wouldn’t necessarily gives the best yield performance (Mohammadi et al.,
2007), hence, simultaneous consideration of grain yield and ASV in single non-parametric
index is needed or the Genotype Selection Index should be used to determine the stability of
the genotypes by evaluating their mean grain yield and ASV.
Genotype Selection Index (GSI), when the rank of mean grain yield of genotypes across
environments and rank of AMMI Stability Value considered to identify the tested genotypes
in relation to stability, G4 , G15, G1 and G6 had the lowest GSI values compared to the other
genotypes and showed stable performance over the testing sites. Therefore, G1 and G15 were
the stable and high yielder genotypes across the testing environments.
AMMI 1 biplot
Biplots are graphs where aspects of both genotypes and environments are plotted on the same
axes so that inter relationships can be visualized. There are two basic AMMI biplot, the
AMMI 1 biplot, where the main effects of grain yield (genotype mean and environment
mean) and IPCA1scores for both genotypes and environments are plotted against each other.
On the other hand, the second biplot is AMMI 2 biplot where scores for IPCA1 and IPCA2
are plotted. In the AMMI 1 biplot, the usual interpretation of biplot is that the displacements
along the abscissa indicate differences in main (additive) effects, whereas displacements
along the ordinate indicate differences in interaction effects. Genotypes that group together
have similar adaptation while environments which group together influences the genotypes in
the same way (Kepton, 1984). The graph shows that the genotypes which are in the right side
of perpendicular i.e G9, G7, G5, G10, G6, G4, G12, G4, G14, G15, and G1 gave higher grain
yield than mean value(Figure-1). This indicated that these genotypes are less affected by GxE
inter action.
32
Figure 1. Biplot analysis of GEI based on AMMI 1 model for the PCA1 scores and grain
yield
AMMI 2 biplot
The existence of interaction is displaced by biplot, especially when the interaction is
portioned between two interaction principal component axis. The superiority of the genotypes
determined by first principal components (IPCA1 and IPCA2) and to create a two
dimensional GGE biplot. The genotypes close to ordinate expressed general adaptation,
whereas the farthest genotypes depicted more specific adaptation to environments (Ebdon
and Gauch, 2002). The environmental scores are joined to the origin by side lines. Sites with
short arrow do not exert strong interactive forces. Those with long arrow exert strong
interaction. The genotypes close to ordinate expressed general adaptation, whereas the
farthest genotypes depicted more specific adaptation to environments (Ebdon and Gauch,
2002; Gauch HG, Zobel et al., 1996). According to the present study, G4, G15 and G8 are the
most stable genotypes and showed wider adaptation over the studied environments whereas
G6, G10, G1, and G14 showed moderately stable performance. Therefore when all the
stability parameters and mean grain yield are considered in order to identify best lentil
genotypes, G1 and G15 were the best genotypes. Furthermore these two genotypes have
tolerant types of interaction for the majority of the diseases scores observed during the
growing period (Table 3).
Therefore, we have identified and recommended these two
genotypes to be verified for possible release for the highlands of bale zone and similar agroecologies.
33
Figure 2. Interaction biplot for the AMMI model
Conclusion and recommendation
Crop yield is a complex trait that is influenced by a number of component characters along
with the environment directly or indirectly. AMMI statistical model could be a great tool to
select the most suitable and stable high yielding genotypes for specific as well as for diverse
environments. In the present study, AMMI model has shown that the largest proportion of the
total variation in grain yield was attributed to environments. The genotype, G1 and G15
showed higher grain yield than all other genotypes over all the environments and performed
better at most of the places. Furthermore, these two genotypes showed stable performance
with high mean grain yield over the testing sites. The two genotypes have yield advantage of
45.28% and 29.56% over the standard check, Asano (1.59t/ha). Therefore, we have conclude
and recommended these two varieties to be used as candidate varieties to be verified in the
coming cropping season for possible release in the highlands of bale and similar agroecologies.
Acknowledgements
The authors would like to thank Oromia Agricultural Research Institute for funding for the
execution of the study. Our thanks also goes to Sinana Agriculture Research Center and pulse
and oil crops research case team for full support, data collection and trial management.
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35
Grain Yield Performance Evaluation of Mung bean (Vigna radiate) in the lowland
district of Bale zone, Southeastern Ethiopia
*Belay Asmare,Tadele Tadese,Gashaw Sefera, Amanuel Tekalign
Oromia Agricultural Research Institute. Sinana Agriculture Research Center, Bale- Robe,
Ethiopia
Corresponding Authors: b.asmare12@gmail.com
Abstract
A field experiment was conducted at the sub site, and on farm of Dellomena district
representing the lowland district of Bale zone, Southeastern Ethiopia using seven improved
mung bean varieties under rain fed condition during 2018 and 2019 main cropping season.
The study was envisaged to assess the adaptability of seven improved mung bean varieties at
Dellomena district. The field experiment was laid out in a Randomized Complete Block
Design (RCBD) with two replications for two years (2018 and 2019) on a plot size of 7.2 m
(six rows at 40 cm spacing and 3 m length). The combined analysis f variance revealed that,
there was highly significant difference among the test varieties for mean grain yield. It was
found that variety MH-97-6 (Boreda) (3.70 qt/ha) showed best yield performer followed by
Humera Local (3.35 qt/ha) and Gali (3.20 qt/ha). Besides, variety MH-97-6 has tolerant type
of disease reaction during the entire cropping season. Therefore, based on overall
performance, this variety is recommended for production in the study areas to be used by the
farming community in order to boost their production and productivity till another variety
out yielding this variety is availed for the area.
Key words: Adaptation, Mung bean, Variety
Introduction
Mung bean is one of the most important pulse crops, grown from the tropical to sub-tropical
areas around the world (Khan MA et al., 2012). It is an important wide spreading, herbaceous
and annual legume pulse crop cultivated mostly by traditional famers (Ali MZ et al., 2010).
The crop is characterized by fast growth under warm conditions, low water requirement and
excellent soil fertility enhancement via nitrogen fixation (Yagoob H. et al., 2014).
Fertilization of this crop occurs through self-pollination without requirement of other
pollinators like insects, water and wind (Rashid K.et al., 2013). Among legumes, mung bean
is noted for its protein and lysine-rich grain, which supplements cereal-based diets (Minh
NP.et al., 2014). The crop is utilized in several ways; seeds, sprouts and young pods are all
consumed and provide a rich source of amino acids, vitamins and minerals (Somta P. et al.,
2007). The grain contains 24.2% protein, 1.3% fat and 60.4% carbohydrate (Hussain F. et al.,
2011). It is also known to be very healthy and packed with a variety of nutrients such as
36
vitamin B, vitamin C, protein, manganese and a lot of other essential nutrients required for
effective functioning of the human health. Mung bean has low in calories and rich in fiber
and easily digestible crop without cause flatulence as happens with many other legumes
(Minh NP.et al., 2014).
The optimum temperature range for good production is 27- 30°C (Imrie, 1998). Mung bean is
a quick crop, requiring 75–90 days to mature. It is a useful crop in drier areas and has a good
potential for crop rotation and relay cropping with cereals using residual moisture.
Smallholder farmers in drier marginal environments in Ethiopia grow mung bean. Farmers in
some moisture stress areas have been producing mung bean to supplement their protein needs
and also effectively use scanty rainfall (Asrat A , et al., 2012). There is a need to expand its
production to other potential areas where moisture stress is a challenge for producing long
maturing crops. Even in mung bean producing areas, its farming is based on local cultivars
that are low yielder, late maturing and susceptible to disease. These varieties are challenged
by current climate change. Moreover, there is huge demand for mung bean in the
international market particularly in south-east Asia. However, the improved varieties are not
yet availed to farmers in moisture stress areas particularly in Bale lowlands, including
Dellomana dstrict. Therefore, this activity was carried out to test and select the best
performing mung bean variety for the target areas. Sowing of mung bean mainly occurs
during summer when sufficient rain is available for growth but it is sensitive to water
logging. It is grown in several types of cultivation systems, including sole cropping,
intercropping, multiple cropping and relay cropping, where it is planted after cereals using
residual moisture (Omid, et al,. 2008). Production of mung bean is influenced by biotic and
abiotic limiting factors. Water stress, drought stress, salt stresses are among biotic factors that
influence mung bean growth and development. Negative impacts of the stress can be seen in
the production of leaves, pods and flower parts at key growth stage of mung bean crop. Thus,
depending on the growth stage when the stress occurs, it can significantly reduce final grain
yield (Pandey A, et al,. 2011). As Asfaw et al. (2012) reported, in Ethiopia mung bean is
mostly grown by smallholder farmers under drier marginal environmental condition and the
production capacity is lower than other pulse crops. For resource poor farmers in Ethiopia,
mung bean is mainly used as food, but growing it for income generation can also be
important. The varieties used for income generation may be different than those which can
give a stable yield under harsh environmental conditions. Result from farming practice used
as food, medicine, economic, ecological, socio-cultural, religious and cultural needs. Mung
bean is cultivated for its edible seeds, income generation and fodder for livestocks.
37
Mung bean is a warm season crop requiring 90-120 days of frost-free conditions from
planting to maturity (depending on the variety). The optimum temperature range for growth is
between 27 °C and 30 °C. This means that the crop is usually grown during summer. Seed
can be planted when the minimum temperature is above 15 °C. Mung beans are responsive to
daylight length. Short days result in early flowering, while long days result in late flowering.
However, mung bean varieties differ in their photoperiod response. Mung bean is considered
to be heat and drought tolerant. Mung beans are propagated from seed. A seeding rate that
will ensure a plant population of 200 000 to 350 000 plants/ha under dry-land conditions and
400 000 plants/ha under irrigation, is recommended.
According to ECXA (ECXA, 2014) mung bean is being cultivated as a recently introduced
crop in Ethiopia. As Asfaw et al., 2012 reported, in Ethiopia mung bean is mostly grown by
smallholder farmers under drier marginal environmental condition and the production
capacity is lower than other pulse crops. For resource poor farmers in Ethiopia, mung bean is
mainly used as food, but growing it for income generation can also be important.
Among pulses, mung bean is the most important cash crops in the world (Singh R, et al.,
2011). It is a vital crop in developing countries where it is consumed as dry seeds, fresh green
pods or leaves due to its high protein, vitamin and mineral content. It is also consumed as
forage or green pods and seeds as vegetables (Somta P, et al., 2007). Primarily, the purposes
of this crop are for its protein rich edible seeds and fresh sprout. The seed of mung bean
mainly used for making soups, bread and biscuits (Rashid K, et al., 2013) . Other than food it
is importance to assistance in normal use of land, water resource and enrichment of the soil
through nitrogen fixation. Adaptation to short growth duration, low water requirement, ability
to increase soil fertility and usefulness in crop rotation practices are also another
significances of mung bean (Asrat A, et al., 2012). And also it has the Ability of improving
soil fertility by fixing atmospheric nitrogen into available form with the help of rhizobia
species for plant’s growth and development are characters of mung bean (Jat SL, et al.,
2012).
Mung beans do best on fertile, sandy loam soils with good internal drainage and a pH in the
range of 6.3 and 7.2. Mung beans require slightly acid soil for best growth. If they are grown
in rotation, lime to attain pH of the most acid sensitive crop. Root growth can be restricted on
heavy clays. Mung beans do not tolerate saline soils and can show severe iron chlorosis
symptoms and certain micronutrient deficiencies on more alkaline soils. It is known that Bale
is predominated by cereal crops (particularly Teff, Wheat and maize) production. This
monoculture makes the soil degradation and erosion. Rotation of cereals with legume crops is
not practiced. Although now days some legumes are rarely grown on small scale around
38
lowland (Mung bean, Haricot bean) and highlands (faba bean, field pea, lentil) of Bale zone.
The Community in this area produced Mung bean of unknown source which gives very low
yield. They produced cultivar which is very susceptible to diseases. Since the district has
potential for mung bean production, and farmers produce low yielding cultivar to use it as
cash crop, it is important to bring and evaluate the adaptability and yielding potential of some
improved mung bean varieties to the area. Therefore this study was initiated with the
objective of identifying and recommending best adapted mung bean variety with tolerant or
resistant type of disease reaction to the lowland of Delomena.
Materials and Methods
Description of experimental site: The study was conducted under rain fed conditions at
Dellomena sub-site of Sinana agricultural research center and on farmer field. Dellomena, is
located at the latitude of 5ᵒ 51’-6ᵒ 45’ north and longitude of 39ᵒ 35’-40ᵒ 30’ east, in the
middle and lowland areas and at the altitude of 1314-1508 meters above sea level, with a
prevalence of lowlands. The experimental area is characterized as low land climate. The
mean rainfall is about 986 mm for the last five years. The rainfall has a bimodal distribution
pattern with heavy rains from April to June and September to November. The mean annual
temperatures are 22.5 ᵒC for the last five years.
Experimental materials
Seven mung bean varieties were used as the experiment materials. These planting materials
were collected from Humera agricultural research center. The study was conducted under rain
fed condition for two consecutive years during 2018 and 2019 main cropping season. The
experimental plots were laid out in Randomized Complete Block Design (RCBD) with two
replications for two years in six rows per plot with 2.4 m wide and 3 m long, and with
spacing of 40 cm between rows and 10 cm between plants. Data were taken on days to 50%
flowering, days to maturity, plant height, number of pod per plant, number of seed per pod,
thousand seed weight and grain yield (g/plot).
Data were analyzed using Gen STAT
statistical software package and mean values or Least Significant Differences (LSD) were
compared using the procedures of Duncan’s at the 5% level of significance.
Table 1. Lists of Mung bean varieties used in this study along with their source
Source of Varieties
Humera Agricultural Research Center
Humera Agricultural Research Center
Humera Agricultural Research Center
Humera Agricultural Research Center
Humera Agricultural Research Center
Humera Agricultural Research Center
Varieties
MH-97-6
Arkebe
Shewa Robit
Gali
N-26
N-23
39
Humera Agricultural Research Center
Loca
Results and Discussion
The results of the first year (2018) showed that there was no significant difference on
flowering, maturity, plant height, number of pod per plant and grain yield per hectare. But
significant difference was observed on: number of seed per pod, stand percentage, and
thousand seeds weight. Maximum grain yield of 235 kg/ha was harvested from Humera Local
followed by MH-97-6 (213kg/ha). The result again showed that flowering date, maturity date,
pod per plant and grain yield had showed non significant difference in 2018 whereas number
of seed per pod, thousand seed weight showed significance difference at p<0.05%. The
maximum grain yield was harvested from Humera Local (235kg/ha) followed by Borada (213
kg/ha). The minimum grain yield was harvested from N-23 (103 kg/ha). (Table 2).
Table 2. Mean values of yield and yield components of mung bean varieties during 2018
cropping season
GEN
Gali
Local
MH-97-6
Arkebe
Shewa Robit
N-26
N-23
Mean
5%LSD
CV%
DF
41
41
41
41
42
42
42
41
NS
3.1
DM
83
81
81
82
81
81
82
82
NS
2.8
PLH
60
55
57
52
53
54
52
55
NS
11.0
NPPP
6
6
6
6
6
5
4
5
NS
21.0
NSPP
8
8
7
8
8
9
10
8
2.2
17.9
ST%
89
88
90
88
90
89
84
88
4.9
3.8
TSW
35
26
23
26
26
27
29
27
6.2
15.5
YLD kg/ha
192
235
213
199
160
108
103
173
NS
24.0
NB. DF= days to Flowering, DM=days to maturity, PLH= plant height(cm),NPPP= number
of pods per plant, NSPP= number of seeds per pod, St%= Stand percentage, TSW= Thousand
seed weight (gm), YLD= Yield per hectare
While in the second year, the analysis of variance revealed that non-significant differences
among the varieties for all the traits except thousand seed weight and seed yield (Table 3).
This difference was attributed to the variation in the rainfall pattern which was very erratic
and not uniform during the first year compared to the second year. During 2019 cropping
season the highest yield (4.75 qt/ha) was obtained from variety MH-97-6 (Boreda) while
lowest yield 2.35 and 2.97 qt/ha was obtained from varieties N-23, N-26 respectively while
varieties Gali, Humera Local, and Arkebe gave medium mean seed yield of 4.06, 4.01, 3.44
qt/ha respectively.
Table 3. Mean values of yield and yield components of mung bean varieties during 2019
cropping season at Dellomena
ENTRY
Arkebe
Gali
DF
40.5
39.8
DM
85.3
86.0
PLH
59.8
62.7
PPP
5.8
5.7
SPP
7.3
8.3
St%
72.5
75.0
TSW
36.9
47.5
YLD/ha
344.2
405.8
40
Local
40.0
85.5
63.0
5.7
8.2
70.8
35.8
400.9
MH-97-6
40.3
85.7
66.5
7.2
6.7
75.8
38.2
474.6
N-23
40.7
85.8
57.5
4.5
8.8
72.5
47.1
235.3
N-26
41.0
85.8
62.7
5.0
8.3
71.7
48.4
297.2
Shewa Robi 40.8
86.0
58.2
4.5
8.5
72.5
38.9
321.3
Mean
40.4
85.7
61.5
5.5
8.0
73.0
41.8
354.2
LSD
NS
NS
NS
NS
NS
NS
8.66
50.01
CV
3.7
2.87
14.64
45.7
21.2 6.66
17.65 11.7
NB. DF= days to Flowering, DM=days to maturity, PLH= plant height(cm),NPPP= number
of pods per plant, NSPP= number of seeds per pod, St%= Stand percentage, TSW= Thousand
seed weight (gm), YLD= Yield per hectare
In the combined ANOVA (Table- 4), there was significant difference among the varieties for
all the traits being considered except for traits such as days to follower, days to maturity,
plant height (cm), and stand percentage. The interaction effect of year with variety was
significant for thousand seed weight (TSW) and total seeds per plant (TSPP), which implies
that, the varieties performance for these traits is not variety potential/its actual potential/ but it
is cumulative effect of year and variety. Across season, the highest seed yield (3.70qt/ha) was
obtained from variety MH-97-6 (Boreda) followed by Humera Local (3.35), Gali (3.20qt/ha),
and Arkebe (2.86qt/ha). Significant effect of mung bean genotypes on seed yield had been
reported by different researchers including Omid, 2008; Ahmad et al. (2003) and Khan et al.
(2003).
41
Table 4. Mean values of yield and yield components of mung bean varieties combined over
season.
ENTRY
DF
DM
PLH
NPPP NSPP St%
TSW
YLD/ha
Arkebe
40.5
84.1
56.6
5.7
7.5
78.5
32
286
Gali
40.3
84.9
61.4
5.6
8.0
80.5
42
320
Local
40.4
83.8
59.6
5.8
8.1
77.5
32
335
MH-97-6
40.5
83.8
62.6
6.5
6.8
81.5
32
370
N-23
41.2
84.1
55.2
4.3
9.1
77.0
40
182
N-26
41.2
84.0
59.3
5.1
8.7
78.5
40
222
Shewa Robi 41.2
84.1
56.1
5.1
8.2
79.5
40
257
Mean
40.8
84.1
58.9
5.4
8.1
79.0
36
282
LSD
NS
NS
NS
1.9
1.4
NS
8.8
63.2
CV
3.4
4.1
16.2
38.4
19.8
11.8
27.4
24.7
NB. DF= days to Flowering, DM=days to maturity, PLH= plant height(cm),NPPP= number of
pods per plant, NSPP= number of seeds per pod, St%= Stand percentage, TSW= Thousand seed
weight (gm), YLD= Yield per hectare
Mean performance of the mung bean varieties
Differences in mean performance of the mung bean varieties for the characters studied in two
seasons (2018 and 2019) at Dellomena sub-station is presented in the Tables 2 and 3. The results
indicated that the differences among the means of the mung bean varieties for grain yield was not
significant at 5% probability level for first seasons (2018), but there was significant difference
among the test varieties for the same trait at 5% probability level during 2019 cropping season.
Across season, the highest seed yield (3.70qt/ha) was obtained from variety MH-97-6 (Boreda)
followed Humera Local (3.35), Gali (3.20 qt/ha), Arkebe (2.86 qt/ha). The interaction effect of
year with variety was significant for thousand seed weight (TSW) and total seeds per plant
(NSPP), which implies that, the varieties performance for these traits is not variety potential/its
actual potential/ but it is cumulative effect of year and variety.
Conclusions and Recommendations
Generally, the present study entails the presence of significant variations among mung bean
varieties for grain yield. Results revealed that MH-97-6 (Boreda) (3.70 qt/ha) showed to be best
performing variety in terms of grain yield followed by Humera Local, Gali, Arkebe, Shewa
Robit (3.35, 3.20, 2.86, 2.57 qt/ha) and N-26 (2.22 qt/ha). Varieties were not expressed their full
yield potential due to the moisture stress and sudden pest damage occurred during the cropping
season. Even the variety Boreda (MH-97-6) has the potential of giving about 8.6 qt/ha as
42
previously reported by releasing center while currently gave only (3.70 qt/ha) which is due to the
factors mentioned above. Therefore, we have concluded that, variety MH-97-6 has adapted in the
study area compared to other varieties. Therefore, until other or new varieties introduced to the
areas, variety MH-97-6 is recommended for production to the farming community in the study
areas.
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on the growth and productivity of three mung bean cultivars. Intl. J. Agric. Biol. 5(3): 335338.
Ali MZ, Khan MAA, Rahaman AKMM, Ahmed M, Ahsan AFMS (2010) Study on seed quality
and performance of some mungbean varieties in Bangladesh. Int J Expt Agric 1(2): 10-15.
Asrat A, Fekadu G, Fetsum AYR (2012) Analysis of multi-environment grain yield trials in
mungbean (Vignaradiata L.) Wilczek based on GGE bipot in Southern Ethiopia. Agr Sci
Tech 14(2): 389-398.
Das S, Shekhar UD, Ghosh P (2014) Assessment of molecular genetic diversity in some green
gram cultivars as revealed by ISSR analysis. Advances in Applied Science Research 5(2):
93-97.
ECX 2014. Ethiopia Commodity Exchange Rings Bell for mungbean Addis Ababa.
Hussain F, Malik AU, Haji MA, Malghani AL (2011) Growth and yield response of two
cultivars of mungbean (Vignaradiata L.) to different potassium levels. The Journal of
Animal & Plant Sciences 21(3): 622- 625.
Imrie, B. (1998). The New Rural Industries: A Hand Book for Farmers and Investors: Mung
bean (http://www.rirdc.gov.au/pub /handbook/mung bean.html)(Accessed on November 10,
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Jat SL, Prasad K, Parihar CM (2012) Effect of organic manuring on productivity and economics
of summer mungbean (Vignaradiata Var. radiata). Ann Agric Res New series 33(12): 17-20.
Khan MA, Naveed K, Ali K, Ahmad B, Jan S (2012) Impact of mungbean-maize intercropping
on growth and yield of mungbean. Weed science society of Pakistan department of weed
science. J Weed Sci Res 18(2): 191-200.
Kumari R., Shekhawat K.S., Gupta R. and Khokhar M.K. 2012. Integrated management against
root- rot of mungbean (Vigna radiata (L.) Wilczek) incited by macrophomina phaseolina. J
Plant Pathol Microb 3:5.
Minh NP (2014) Different factors affecting to mungbean (Phaseolus aureus) tofu production.
International Journal of Multidisciplinary Research and Development 1(4):105-110.
mungbean genotypes. Peak Journal of Agricultural Science 2 (3): 30-35.
Omid, S.P. (2008). Effect of withholding irrigation at different growth stages on yield and yield
components of mung bean (Vigna radiate L. Wilczek) varieties
Pandey A, Kumar A, Ramya P (2011) Genetic diversity in green gram accessions as revealed by
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Ranawake A., Dahanayaka N., Amarasingha U., Rodrigo W. and Rodrigo U. 2011.Effect of
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The registration of “Moybon and Tosha”, faba bean varieties for the highlands of Bale,
South eastern Ethiopia
*Tadele Tadesse, Gashaw Sefera, Belay Asmare and Amanuel Tekalign
Sinana Agricultural Research Center, P.O. Box 208, Bale-Robe, Ethiopia
*Corresponding author: tadyeko20@gmail.com/tadeleta20@yahoo.com
Abstract
At different breeding stage, a number of faba bean lines were evaluated in order to identify high
yielder and stable faba bean genotypes. Accordingly eighteen faba bean lines were evaluated at
three locations, Sinana, Agarfa and Sinja for three consecutive years (2016-2018) during main
cropping seasons. Out of eighteen genotypes, EH00021-1/Tosha/ቶሻ/) and EH0110883/Moybon/ሞይቦን/ were found to be superior in their yield, and also showed stable and tolerant
performance to major faba bean diseases. The variety "moybone" is highly characterised with
large seed weight associated with high grain yield (31-43k Qt/ha) with a yield advantage of 35%
over the local and 22% over standard check Gebelcho variety respectively; whereas the other
variety, "Tosha" had more number of pods/plant and gave grain yield of (34-52 Qt/ha) with
yield advantage 51%, and 38% over the local cultivar and the standard check, respectively.
Furthermore, this variety had stable performance across the tested sites, and showed tolerant to
44
chocolate spot (Botrytis fabae Sard.), Rust(Uromyces Vicia-fabae) and aschochyta blight
(Aschocyta fabae Speg.) diseases. Due to these merits varieties “Moybon, and Tosha” were
released in 2019 cropping season for production in the highlands of Bale and similar agroecologies.
Key Words: Moybon, Tosha, Stablity Variety Release,
1. Agronomic and Morphological characteristics
Variety Tosha needs 64 and 138 days for flowering and maturity, respectively. Furthermore,
Tosha is characterized by more numbers of pods/plant i.e. 15 pods/plant compared to the variety
used as standard check, i.e Gebelcho which only had 12 pods/plant. In addition, this variety has
plant height of 121cm with an erect type of growth habit having thousand seed weight of 673.3
gm with cotyledon color of yellow and seed color of light green. Whereas, the other variety
Moybon, flowers in 61 days and gets physiological maturity within 138 days. Thousand seed of
this variety weights 774.5 gm which is higher than the standard check, Gebelcho (766.4 gm). Its
plant height reaches as high as 119 cm with an erect type of growth habit. Moybon variety has
similar cotyledon and seed color with that of variety Tosha. Summary of the agronomic and
morphological characteristics of these released varieties are presented in Appendix I and II.
2. Yield Performance
In the screening stage from 2014 to 2015, these two varieties were evaluated along with other
lines at the main station, Sinana for yield and other agronomic traits and showed better
performance than the checks and promoted to the next breeding stages. During the multi-location
evaluation trials, Tosha gave mean grain yield of 44.3 Qt/ha whereas Moybon gave an average
mean yield of 39.24 Qt/ha on the research field whereas on farmer's field, Tosha gave 36.82
Qt/ha while Moybon gave 33.01 Qt/ha. Tosha has yield advantage of 37.63% whereas Moybon
has 21.78% as compared to the standard check used, Gebelcho (28.83 Qt/ha).
3. Stability Performance
Yield stability analysis was considered for eighteen faba bean lines using AMMI model. Based
on different stability parameters, like the mean grain yield along with their liner regression
coefficient and deviation from regression and AMMI Stability Value, (ASV), and Genotype
Selection Index (GSI) developed by Farshadfar, 2008), all thse stability analysis were considered
to evaluate the stability performance of the varieties. Based on results of these stability
45
parameters, these two faba bean varieties were found to be stable compared to the rest genotypes
across the tested sites.
4. Disease reaction
These two varieties had showed tolerant type of reaction to chocolate spot, Achochyta bilght and
rust diseases which are common yield limiting disease in faba bean crop.
5. Conclusion
These varieties, Tosha and Moybon, are high yielder, stable and showed tolerant types of disease
reaction compared to the standard checks used. Variety Tosha has more number of pods/plant
whereas variety moybon has large seed size which contributes to have more seed yield per unit
area. These varieties were released for the highlands of Bale, south-eastern Ethiopia. Tosha has
got its name from one of the locally named village at Goba district, which is suitable for faba
bean production, whereas moybon has got its name since its performance is above all faba bean
varieties adapted in the highlands of Bale and win them for most of the agronomic parameters.
6. Reference
Farshadfar E. 2008. Incorporation of AMMI stability value and grain yield in a single nonparametric index (GSI) in bread wheat. Pak J Biol Sci, 11(4): 1791-1796
Table 1. Summary of mean grain yield and other agronomic traits for the two varieties
Variety
Days to
Flo Matur
we
ity
r
63
137
64
138
63
138
Plant
ht.
(cm)
Number of
Pods/P S/
lant
Pod
Stan
d%
1000
seed
wt. (g)
Seed yield
(kg/ha)
Disease score (1-9 scale)
Cho.sp Rust ASBLT
Tosha
116
12
3
73
Moybon
123
13
3
74
Gebelcho
119
15
3
73
Local
cultivar
64
137
121
13
3
76
Cho.sp= Chocolate spot, ASBLT= Aschochyta Blight
787.5
775.0
637.1
2795
2951
2940
3
3
5
4
4
5
4
3
5
741.4
2790
5
4
4
Appendix 1. Agronomic and morphological characteristics of Tosha/ቶሻ/ (EH00021-1)
No
1
2
3
4
5
6
Agronomical and Morphological Characteristics
Adaptation area
Sinana, Goba, Agarfa, Gassera, Goro (Meliyu), Adaba,
Dodola and other similar agro-ecologies
Altitude (m.a.s.l.)
2300 – 2600
Rainfall (mm)
750 – 1000
Seed Rate (Kg/ha)
175-225
Planting date
End of July to Early August
Fertilizer Rate (DAP kg/ha)
100
46
No
7
8
9
10
11
12
13
14
15
16
17
18
19
Agronomical and Morphological Characteristics
Days to Flower
64
Days to Maturity
138
Plant Height (cm)
121
Growth habit
Erect
1000 Seed Weight (gm)
673.3
Seed Color
Light green
Cotyledon Color
Yellow
Flower Color
White with black spot
On-station
(Research Field)
On-farm
Disease reaction
Yield advantage over Gebelcho (%)
Year of Release
Breeder and Maintainer
Yield
(Qt/ha)
44.43
36.82
Tolerant to chocolate spot, Rust and Aschochyta blight
37.63
2019
SARC(OARI)
Appendix 1. Agronomic and morphological characteristics of Moybon /ሞይቦን/ (EH011088-3)
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Agronomical and Morphological Characteristics
Adaptation area
Sinana, Goba, Agarfa, Gassera, Goro (Meliyu), Adaba,
Dodola (W. Arsi) and other similar agro-ecologies
Altitude (m.a.s.l.)
2300 – 2600
Rainfall (mm)
750 – 1000
Seed Rate (Kg/ha)
175-225
Planting date
End of July to Early August
Fertilizer Rate (DAP kg/ha)
100
Days to Flower
61
Days to Maturity
138
Plant Height (cm)
119
Growth habit
Erect
1000 Seed Weight (gm)
774.5
Seed Color
Light green
Cotyledon Color
Yellow
Flower Color
White with black spot
On-station
39.24
Yield
(Research Field)
(Qt/ha)
On-farm
33.01
Disease reaction
Tolerant to chocolate spot, Rust and Aschochyta blight
Yield advantage over Gebelcho (%)
21.78
Year of Release
2019
Breeder and Maintainer
SARC(OARI)
47
The Registration of “Horesoba”, Newly Released Linseed variety for the highlands of Bale,
South eastern Ethiopia
*Tadele Tadesse, Gashaw Sefera, Belay Asmare and Amanuel Tekalign
Sinana Agricultural Research Center, P.O. Box 208, Bale-Robe, Ethiopia
*Corresponding author: tadyeko20@gmail.com/tadeleta20@yahoo.com
Abstract
Fourteen linseed lines were evaluated at Sinana and Agarfa for three years (2013-2014) during
main cropping season in order to identify high yielding and stable genotypes with tolerant to
powdery mildew, downey mildew and wilt. Out of these tested lines, Horesoba /CHILALO X
R12-N27G/SPS6/ was found to be superior in its yield, stable and tolerant to the aforementioned
major diseases. Variety Horesoba is characterised with more stand %, higher thousand seed
weight and gave high grain yield (15-21 Qt/ha) with a yield advantage of 37% over the local and
19% over standard check, Jitu, respectively. Because of all these merits and advantage over the
standard and local checks, variety Horesoba was approved to be released for the highlands of
Bale and similar agro-ecologies.
Key Words: Horesoba, linseed, Stability, Variety
1. Agronomic and Morphological characteristics
Variety Horesoba needs 71 and 156 days for flowering and for physiological maturity,
respectively. This variety has plant height of 71cm with an erect types of growth habit . The
variety horesoba also has an average thousand seed weight of 6.2gm with brown seed color. The
variety has good oil content, 38.8% compared to the check, Jitu (34%). Summary of the
agronomic and morphological characteristics of the variety is presented in Appendix I.
2. Yield Performance
In the preceding screening stages of the years 2011 to 2012 cropping seasons, upon evaluation of
the variety along with large number of genotypes in the main station for yield and other
agronomic traits, Horesoba showed superior performance over the checks (both standard and
local) and retained for further evaluation in the subsequent breeding stages. During the multilocation evaluation trials, on average, this variety gave an average grain yield of 16-19 Qt/ha on
research field and 12-15 Qt/ha on farmers field. This variety has yield advantage of 18.97%
compared to the standard check variety Jitu (13.55 Qt/ha) (Table 1).
48
3. Stability Performance
The stability analysis of yield for fourteen linseed genotypes was studied for three consecutive
years across two locations. Based on different stability parameters considered, variety Horesoba
was high yielder and showed stable performance across the tested sites (Tadele et al., 2017).
4. Disease reaction
The variety Horesoba showed tolerant reaction to the major linseed diseases prevailing in the
linseed production areas of Bale highland such as powdery mildew, downey mildew and wilt
diseases.
5. Conclusion
The variety Horesoba is high yielder, stable and showed tolerant types of disease reaction
compared to the standard checks used. Furthermore, variety Horesoba has good seed size with
large oil content compared to the standard check and the local cultivar tested along with it.
Because of these merits, the variety was released for the highlands of Bale, South-eastern parts
of Ethiopia. Horesoba has got its name from one of the locally named village in Dinsho district.
6. Reference
Tadele Tadesse, Amanuel Tekalign, Gashaw Sefera, Behailu Muligeta. AMMI Model for Yield
Stability Analysis of Linseed Genotypes for the Highlands of Bale, Ethiopia. Plant. Vol. 5, No.
6, 2017, pp. 93-98. doi: 10.11648/j.plant.20170506.12
Table 1. Summary of mean grain yield and other agronomic traits for Horesoba variety
Variety
Stand
%
Days to
Flower Maturity
80
71
156
Horesoba
Jitu
79
72
156
DM= Downey mildew, PM= Powdery Mildew
Plant ht.
(cm)
90
88
1000 seed
wt. (g)
6.2
6.0
Seed yield
(kg/ha)
3
4
Disease score (0-5
scale)
DM PM Wilt
3
3
1613
5
3
1355
Appendix 1. Agronomic and morphological characteristics of Horesoba /ሆረሶባ/ Chilalo X R12N27G/SPS6Tosha
No
1
2
3
4
5
6
Agronomical and Morphological Characteristics
Adaptation area
Sinana, Goba, Agarfa, Gassera, Adaba, Dodola) and
other similar agro-ecologies
Altitude (m.a.s.l.)
2300 – 2600
Rainfall (mm)
750 – 1000
Seed Rate (Kg/ha)
25-30 (for row and broadcasting, respectively)
Planting date
End of July
Fertilizer Rate (DAP kg/ha)P2O5/N2 23/23
49
No
7
8
9
10
11
12
13
Agronomical and Morphological Characteristics
Days to Flower
71
Days to Maturity
156
Plant Height (cm)
90
Growth habit
Erect
1000 Seed Weight (gm)
6.2
Seed Color
Brown
Flower Color
Pink
14
Oil content (%)
38.8
15
(Research
Field)Average of three
Yield
years
(Qt/ha)
On-farm
Disease reaction
Yield advantage over Jitu (%)
Year of Release
Breeder and Maintainer
19.33
16
17
18
19
12.93
Tolerant to Powdery Mildew, wilt and pasmo
18.97
2019
SARC(OARI)
Analysis of Bread Wheat Genotypes for Yield Stability Using the GGE Biplots
Tilahun Bayisa1*, Mulatu Abera1 and Tesfaye Letta2
1
Sinana Agricultural Research Center, P.O.Box: 208, Bale Robe, Ethiopia
2
Oromia Agricultural Research Institute, OARI, P.O. Box: 81265, Addis Ababa, Ethiopia
*Corresponding author: tilahunbayisa@gmail.com
Abstract
Evaluation of different genotypes in a multi-environment and years is used to determine high
yielding cultivars that best represent the target environment. However, genotypes grown in
different environments frequently show significant fluctuations in yield performance and these
changes caused by the different environmental conditions. Understanding the cause of GE
interaction is used to identify ideal genotypes and test environments and hence to recommend
genotypes based on areas of optimal genotype adaptation. Thus the objective of this study was to
identify stable and high yielding genotypes using GGE biplots. A total of twenty bread wheat
genotypes including Sanate and (st. check) and Mada walabu (local check) were evaluated for
two consecutive years 2017 and 2018 during main cropping season at three locations: Sinana,
Agarfa and Goba. The experiment was laid out in Randomized Complete Block Design (RCBD)
with three replications. The result of combined analysis of variance showed highly
50
significant differences for Genotype, Environment and Genotype by Environment interaction.
The environmental effect accounted for 67.7%, genotype effect 29.6% and GE interaction only
accounted 2.6% of the total variation. Genotypes near to environmental vector were performed
more than mean yield. Based on this, G13, G11, G20, G17 and G16 were performed than mean
yield of the test genotypes. Eventhough G20 showed higher yield, it is unstable genotype across
tested locations. G11 is best genotype followed by G13 and G17 because they gave high mean
grain yield with stable performance across locations as compared to other test genotypes.
Therefore, these genotypes are recommended as candidate variety for next year to release
as a variety.
Key words: AMMI, Genotype, High yield, IPCA, Stability
Introduction
Wheat is among the major cereal crops that received considerable focus by the agricultural
research system. This is justifiable because of the fact that wheat is among the most important
crops not only in Ethiopia but also worldwide. It has played a significant role in feeding a hungry
world and improving global food security. It contributes about 20% of the total dietary calories
and proteins worldwide (Shiferaw et al., 2013). Wheat produced in Ethiopia is used mainly for
domestic food consumption, seed, and raw material for agro-industries. It accounts for about 1015% of all the calories consumed in the country (Berhane et al., 2011). Moreover, estimated total
wheat consumption (for food, seed and industrial use) is rapidly increasing at the national level
(CSA, 2017).
Ethiopia is the leading wheat producer in Sub Saharan Africa with a total production of 4.6
million tons (FAO, 2018; CSA, 2018). Accordingly Oromia National Regional State contributes
a total production of 2.7 million tons in the country. Among the wheat producing zones of
Oromia, Arsi, West Arsi and Bale zones are considered as the wheat belts of Eastern Africa.
Although the productivity of wheat has increased in the last few years in the country, it is still
low as compared to other wheat producing countries in other parts of the world. The national
average of wheat productivity is estimated to be 2.7 t ha-1 (CSA, 2017), which is below the world
average of 3.0 t ha-1 (Hawkesford et al., 2013).
This low productivity of the crop is attributed to a number of factors including biotic, abiotic,
shortage of high yielding and stable genotypes. High yield stability usually refers to a genotype’s
ability to perform consistently across a wide range of environments (Annicchiarico, 1997). In
51
order to ensure consistent stability and high yields, new genotypes are developed, and tested for
their yield performances in different environments (Mehmet and Telat, 2006). Genotype ×
environment interactions are of major importance, because they provide information about the
effect of different environments on genotype performance and have a key role in assessment of
stability of the breeding materials (Moldovan et al., 2000).
Several statistical models have been proposed for studying the GEI effect and exploiting its
positive part in variety development process. The practical utility of different statistical models
to explain GEI and facilitate variety release decision has been extensively reviewed and
published (Ferreira et al., 2006; Hussein et al., 2000; Zobel et al., 1988). However, not all of
them are always effective enough in analyzing the multi-environment data structure in breeding
program (Navobi et al., 2006; Zobel et al., 1988). The Additive Main Effects and Multiplicative
Interactions (AMMI) and Genotype x Environment Interaction (GGE) biplot models are defined
as powerful tools for effective analysis and interpretation of multi-environment data structure in
breeding programs (Zobel et al., 1988, Ebdon and Gauch, 2002; Samonte et al., 2005). The
objective of this experiment was to apply GGE biplot models to evaluate the magnitude of GEI
effect on bread wheat grain yield and identify the best performing genotype for selection.
Materials and Methods
A total of twenty bread wheat genotypes: Eighteen advanced bread wheat genotypes, including
Sanete (standard. check) and Mada walabu (Local check) were considered in this study as
planting materials. The experiment was evaluated at three locations viz. Sinana, Agarfa and
Goba for two consecutive years during 2017 and 2018 main cropping season. Each year, each
location was considered as a separate environment, making totally six test environments for this
study. The experiment was laid out in Randomized Complete Block Design (RCBD) with three
replications. The plot size was 6 rows of 0.2 m spacing between rows and 2.5 m length (giving a
gross plot area of 3 m2 and net plot area of 2 m2). Seed rate of 150 kg ha-1 and fertilizer rate of
41/46 N/P2O5 kgha-1 respectively was used. The detail descriptions of the twenty test genotypes
included in this study are given in Table 1.
52
Table 1. Lists of bread wheat genotypes used in this study along with their pedigrees/selection
history.
SN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Genotype
code
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
G18
G19
G20
Genotypes
KINDE/4/CMH75A.66//H567.71/5*PVN/3/AERI
Sanate
CHYAK/RL6043/3*GEN C
C80.1/3/BATAVIA//2*WBLL1/3/C80.1/3*QT4522//
BLOUK#1/DANPHE#1BECARD
PASTOR//HXL7573/2*BAU/3/WBLL1/4/1447/PASTOR
WBLL1*2/BRAMBLING/5/BABAX/LR42//BABAX*2/4/
WBLL1*2/BRAMBLING/5/BABAX/LR42//BABAX*2/4/
T.DICOCCON PI254157/AE.SQUARROSA(879)/4/
MOUKA-4/RAYON
FLORKWA2/6/SAKER’S’/5/ANZA/3/KVZ/HYS//YMH/TOB/4/BOW’S’/7/DAJAJKUAZ/PASTOR//FLAG-4
RANA96/SIDS-1
Mada walabu
ETBW7670
ETBW6435
ETBW6861
ETBW8469
ETBW8146
WAXWING//PFAU/WEAVER/3/FRNCLN
Statistical analysis
The grain yield data was subjected to analysis of variance using Genstat software 18 th edition.
Homogeneity variance was tested and combined analysis of variance was done using the Genstat
procedure to partition the total variation into components due to genotype (G), environment (E)
and G × E interaction effects. The following model was used for combined ANOVA:
Yijk = µ + Gi + Ej + GEij + Bk(j) + єijk
Where: Yijk is an observed value of genotype i in block k of environment j; µis a grand
mean; Gi is effect of genotype i; Ej is an environmental effect; GEij is the interaction effect of
genotype i with environment j; B
k(j)
is the effect of block k in environment j; єijk is an error
effect of genotype i in block k of environment j.
Results and Discussions
Genotype performance
Homogeneity variance was tested by using Bartlett’s test and it indicated homogenous error
variance for grain yield in the six environments and hence allowed for a combined analysis
53
across environments. The result of combined analysis of variance for gain yield (t ha-1) of the
twenty bread wheat genotypes tested across six environments showed highly significant
differences (p ≤ 0.01) for genotypes, environment and GE interaction. The environmental effect
accounted for 67.8% of the total yield variation, whereas, genotype and G × E interaction effects
accounted for 29.6% and 2.6% of the total variation, respectively. This shows that grain yield of
bread wheat genotypes were found to be significantly affected by changes in the environment,
followed by genotypic effects and G × E interaction (Table 3). This in turn indicates that, the
effect of environment in the GE interaction, genetic variability and possibility of selection for
stable genotypes (Table 3). Previous reports for bread wheat also indicated that the
environmental effect accounted for the largest part of the total variation (Hintsa et al., 2011;
Mohammadi et al., 2017).
The average environmental mean grain yield across genotypes ranged from the lowest of 0.48 t
ha-1 (G10) at Agarfa 2017 to the highest of 5.91 t ha-1 (G16) at Goba 2017, with a grand mean of
3.55 t ha-1 (Table 2). Thirteen bread genotypes gave higher mean grain yield than the grand
mean. In general, G11, G13 and G20 gave the highest grain yield, while G10 and G11 had
showed lowest mean yield performance across environments.
Table 2. Mean Grain yield (t ha-1) performance of twenty bread wheat genotypes in six
Environments
SN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Genotype Code
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
Year 2017
Sinana Agarfa
4.18
3.28
4.71
3.50
3.81
3.45
5.06
3.51
4.30
3.51
4.56
2.84
1.94
3.46
4.65
2.94
4.64
2.20
1.17
0.48
5.01
3.56
4.07
2.80
4.13
4.54
3.01
0.97
4.52
2.82
4.76
3.26
4.88
3.60
Goba
3.85
5.81
3.84
5.02
5.41
4.13
4.83
4.72
4.62
0.62
5.09
4.15
5.28
1.71
4.78
5.91
5.78
Year 2018
Sinana Agarfa
3.57
2.74
2.01
2.76
2.98
2.91
3.35
3.19
3.43
2.79
3.91
3.29
3.56
3.49
3.68
3.43
2.71
3.42
1.19
1.82
4.15
3.82
2.99
2.60
4.12
3.40
2.35
1.50
3.43
3.03
3.41
2.64
3.31
3.14
Mean
Goba
2.83
4.27
2.81
3.03
3.38
3.93
2.71
3.74
3.58
1.35
3.94
3.58
3.68
2.07
3.33
3.54
3.80
3.41
3.84
3.30
3.86
3.56
3.78
3.83
3.86
3.53
1.10
4.26
3.36
4.18
1.93
3.65
3.92
4.09
54
18
19
20
G18
G19
G20
Mean
CV(%)
3.36
4.50
5.76
3.16
2.67
3.47
4.62
4.60
4.03
3.74
3.46
5.00
2.82
3.48
3.30
3.20
3.55
4.06
3.48
3.71
4.27
5.02
10.7
3.00
22.5
4.44
20.7
3.32
20.6
2.98
20.3
3.32
14.2
3.55
24.6
CV (%): Coefficient of variation
AMMI Analysis
The application of AMMI model for partitioning of GEI (Table 3) revealed the first two terms of
AMMI were significant at (P<0.05) using an approximate F-statistic (Gollob,1968). These two
multiplicative component sums of squares, with their cumulative degrees of freedom of 44, were
captured 66.7% of the G × E interaction sum of squares. In this study, the proportion of the first
interaction Principal Component Axis sum of squares (IPC1 = 43.7%) to the interaction sum of
squares was far greater than that of the second interaction principal component (IPC2 = 23. 0%)
(Table 3). This indicated that the existence of different yield responses among these genotypes
across the testing environments due to the presence of significant G × E interaction effect
(Tamene, 2015). Therefore, in order to identify a genotypes with specific or relatively broader
adaptation, studies on the magnitude and patterns of G × E interaction effect is important in
highlands of Bale.
Table 3. Combined and AMMI analysis of variance and contributions of the principal
components for grain yield of 20 bread wheat genotypes in 6 environments
Source of variation
Degree of freedom
Sum square
Mean square
SS%
Environment
5
119.50
23.89**
67.8
Genotypes
19
198.16
10.43**
29.6
Interactions
95
87.54
0.92**
2.6
AMMI 1
23
42.38
1.84**
43.7
AMMI 2
21
20.46
0.97*
23.0
AMMI 3
19
12.78
0.67ns
15.9
AMMI 4
17
6.93
0.40ns
9.5
AMMI 5
15
4.99
0.33ns
7.8
Residuals
51
150.83
0.63
Error
228
121.80
0.53
Total
359
556.0
1.55
AMMI= additive main effect and multiplicative interaction, ns = not significant, * and **
significant at the 0.05 and 0.01 probability level, respectively.
55
Test for AMMI indicated that, AMMI with only the first two multiplicative component axes
were adequate for cross-validation of the variation explained by the G × E interaction (Zobel
Gauch and 1996; Zobel et al., 1988 and Tamene, 2015). The present investigation has also
revealed that, the first two multiplicative components of the interaction term AMMI 1 and
AMMI 2 were significant at p ≤ 0.001 and p ≤ 0.05, respectively (Table 3). Thus, the interaction
pattern of the twenty bread wheat genotypes with 6 environments was best cross-validated with
the first two multiplicative terms of genotypes and environments that easily visualized with the
aid of a biplot (Figure 1).
2
3
GGE Biplot
Goba201
1
0
10
-1
14
16
17
18
13
12
39
154
197
1 8 11
6
-2
AXIS2 10.85 %
5
2
Agarfa2017
Sinana2018
Sinana2017
-3
20
Agarfa2018
Goba2017
-2
0
2
4
AXIS1 75.82 %
Figure 1: AMMI biplot analysis showing the mega-environments and their respective high
yielding genotypes.
Genotype stability and Environment Evaluation
AMMI biplot where genotypes and environments are depicted as points on a plane is shown on
Figure 1. The abscissa showed the main effects and the ordinate showed the first multiplicative
axis term (PCA1). The horizontal line showed the interaction score of zero and the vertical lines
indicated the grand mean yield (t ha-1). Displacement along the vertical axis indicated interaction
differences between genotypes and between environments, and displacement along the
horizontal axis indicated difference in genotype and environment main effects. The genotypes
56
with PCA1 scores close to zero expressed general adaptation whereas the larger scores depicted
more specific adaptation to environments with PCA1 scores of the same sign (Ebdon and Gauch,
2002). Accordingly, genotypes G3, G12, G18, G9, G15, G19, G7, G8, G11 and G13 with their
relative IPC1 scores close to zero, have less response to the interaction and showed general
adaptation to the test environments. Genotype G20 with larger PCA1 score was better adapted to
Sinana in 2017 with larger and same sign PC1 score (Figure 1) which combination results in a
larger positive interaction. In contrast, G2, G16, G5 and G17 with the larger negative IPC1
scores were adapted to environment Goba 2018 (Figure 1). The best genotype should hold high
yield with stable performance across a range of environments. Based on this, genotypes, G11 and
G13 were had the highest mean yield over test environments (Table 2) with demonstrated low
IPC1 scores (Figure 1) and are hence considered as the most stable genotypes with relatively less
variable yield performance across environments (Figure 1).
Sinana and Agarfa had the relatively smaller variation in the interaction (PC1 score) between the
two years while Goba had the largest interaction value (Figure 1). This indicated that, the relative
ranking of genotypes were stable at Sinana and Agarfa as compared to at Goba. Goba is
described as a location that combined larger main effects with larger interaction effects making it
less predictable location for bread wheat genotypes evaluation.
AMMI 2 biplot was generated using genotypic and environmental scores of the first two AMMI
multiplicative components to cross-validate the interaction pattern of the twenty bread wheat
genotypes within six environments (Figure 2). Connecting vertex genotypes markers in all
direction forms a polygon, such that all genotypes are contained within the polygon and a set of
straight lines that radiate from the biplot origin to intersect each of the polygon sides at right
angles form sectors of genotypes and environments (Hernandez and Crossa, 2000; Yan, 2011).
Based on AMMI2, a biplot with six sections were observed depending upon signs of the
genotypic and environmental IPC scores (Figure 2).
57
Figure 2: AMMI1 biplot showing the mean (main effect) vs. stability (IPC1) view of both
genotypes and environments on grain yield.
The vertex cultivars in each sector are considered best at environments whose markers fall into
the respective sector. Environments within the same sector are assumed to share the same winner
genotypes. Genotype-environment affinity depicted as orthogonal projections of the genotypes
on the environmental vectors to identify the best cultivars with respect to environments. The best
genotype with respect to environment Sinana 2017 and Sinana 2018 was genotype G20.
Genotypes G2, G16 and G17 were better adapted to environments viz. Goba 2018 and Agarfa
2018, respectively. Similarly, G11 and G13 were better adapted to Agarfa 2017 (Figure 3).
58
Conclusions and recommendations
To develop varieties it is very essential for breeders to evaluate their genotypes at multilocation based on years and locations. Environmental variations are important in determining
performance of elite materials. This study also clearly demonstrated the GGE biplot model was
found effective for determining the magnitude and pattern of G × E interaction effect. It
visualized the yielding ability and stability of wheat genotypes of the test environments. Goba
2018 is the most representative environment and it was good environment to evaluate genotypes.
G20 was high yielder but unstable genotype across tested locations. G11 and G13 are high
yielder than the G2 (Sanata st.check) and stable across tested locations. Therefore these
genotypes were recommended as candidate varieties for next year for release as a variety.
Reference
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Stability Analysis of Bread Wheat Genotypes Using the AMMI Stability Model
Tilahun Bayisa1*, Mulatu Abera1, Tesfaye Letta2 and Behayilu Mulugeta1
1
2
Sinana Agricultural Research Center, P.O.Box: 208, Bale Robe, Ethiopia
Oromia Agricultural Research Institute, OARI, P.O. Box: 81265, Addis Ababa, Ethiopia
*Corresponding author: tilahunbayisa@gmail.com
Abstract
Ethiopia is the largest wheat producer in Sub- Saharan Africa. The productivity of wheat has
increased in the last few years in the country, but low as compared to other countries. This low
productivity is attributed to a number of factors including biotic, abiotic, shortage of high
yielding and stable varieties. The objective of the present study is to identify high yielding and
stable bread wheat genotype. A total of twenty bread wheat genotypes including Dambal (st.
check) and Mada walabu (Local check) were evaluated for two cropping season of 2017 and
2018 at four locations viz.Sinana, Agarfa, Goba and Gololcha. The experiment was laid out in
Rondomized Complete Block Design (RCBD) with three replications. The result of combined
analysis of variance showed highly significant differences for genotypes, environment and GE
interaction. The result of AMMI analysis indicated that, 36.3 %, of the total variability was
attributed to environment, 28.6% to genotypes and 34.9% to GE interaction whereas, IPCA 1
and IPCA 2 both captured about 74.2% of the total GE effects. Based on Genotype Selection
Index (GSI), a single criteria for stability and high grain yield, genotypes such as G9, G1,
G14,G10, G15 and G12 had showed the smallest genotype superiority index which means they
were stable and high yielding genotypes. The best genotype with respect to environment
Gololcha 2017 and Gololcha 2018 was genotype G10. Genotypes G3 and G17 were better
adapted to environment of Agarfa 2017. G12 is high yielder stable across tested locations.
Therefore genotype G12 is identified as candidate genotypes to be verified for possible release in
the coming cropping season.
Key words: AMMI, ASV, GSI, IPCA, Stabile Genotype
61
Introduction:
Ethiopia is leading wheat producer in Sub Saharan Africa with a total wheat production of 4.64
million tons during 2018 cropping season (CSA, 2018). Besides, Oromia National Regional State
contributes a total production of 2.67 million tons of wheat production in the country. Among the
wheat producing zones of Oromia, Arsi, West Arsi and Bale are considered as the wheat belts of
Eastern Africa. Although the productivity of wheat has increased in the last few years in the
country, it is still very low as compared to other wheat producing countries in other parts of the
world. The national average of wheat productivity is estimated to be 2.74 t ha-1 (CSA, 2018),
which is below the world average of 3.0 t ha-1 (Hawkesford et al., 2013). This low productivity is
attributed to a number of factors including biotic, abiotic, shortage of high yielding and stable
varieties.
In most of the plant breeding programs, GE interaction effects are of special interest for
identifying the most stable genotypes, mega-environments and other adaptation targets. Various
methods for yield stability analysis are based on different stability concepts and can be classified
accordingly (Flores et al., 1998). Information regarding crop stability is applicable for selection
of genotypes with constant yield across environments. Many of researchers have been reported to
depict the responses of genotypes to the different condition of environments for simultaneous
selection of yield and stability. These techniques use statistical parameters to estimate stability of
genotypes to variation in environments. Linear regression approach is used widely for
identifying of high yielding and stable genotypes (Alberts, 2004).
The additive Main Effect and Multiplicative Interaction (AMMI) method is an approach for
evaluation of genotypes stability under different environments. The AMMI method merges
Principal Components Analysis (PCA) and Analysis of Variance (ANOVA) into an integrated
approach and can be used to analyze the multi-location experiment trias (Zobel et al., 1988). The
AMMI analysis is effective because it provides agronomically meaningful interpretation of data
(Gauch, 1992). The AMMI model can be utilized for three main purposes (Gauch, 1988; Crossa
et al., 1990): (i) useful in the initial stage of statistical analyses of yield experiments, (ii) to
summarize the relationships between genotypes and environments (GE) and (iii) applicable for
understanding the nature of complex genotypes × environment interaction effects. AMMI
analysis has been applied extensively with great success to interpret genotype × environment
interaction in wheat (Petrovic et al., 2010; Mohammadi et al., 2013). In any breeding program,
62
including identifying high yielding and stable wheat varieties in multilocational trials, evaluation
of a number of germplasm is necessary and a prerequisite. Accordingly, Sinana agricultural
research center of wheat breeding program conduct multilocation trials on wheat in different
wheat production areas before releasing wheat variety to see the over location performance of the
candidate genotypes for possible release. Hence, the objective of this study was to select high
yielding and stable bread wheat genotypes/genotype for possible release.
Materials and Methods:
Experimental Design and Methods
The experiment was conducted at four locations during 2017 and 2018 main cropping season viz.
at Sinana, Agarfa, Goba and Gololcha which are the major wheat growing areas in the highlands
of Bale zone. The detailed description of environments is presented in Table 1. A total of twenty
bread wheat genotypes which includes eighteen advanced bread wheat genotypes, one standard
check (Dambal) and one local check (Mada walabu) were tested as test materials. The
experimental materials were laid out in Randomized Complete Block Design (RCBD) with three
replications. A plot size of 6 rows with row spacing of 0.2 meter and row length of 2.5 meter was
used and the four middle rows were used for data collection and analysis. Seed rate 150 kg ha-1
was used and drilled manually to the six rows. Fertilizer rates of 41 kg ha-1 N and 46 kg ha P2 O5
were applied at planting. Other agronomic practices were applied as per the recommendation for
the crop.
Table 1. Descriptions of the Experimental areas
Location
Sinana
Agarfa
Goba
Gololcha
Altitude (m)
2404
2486
2565
1970
Latitude
07 09.49’
07 15.29'
07 03.22'
07 45.04’
Longitude
40 13.77’
39 54.44'
39 59.04'
40 57.29’
Statistical analysis
Mean grain yield data of the experiment were statistically analyzed using AMMI model analysis.
This analysis consists of the sequential fitting of a model of analysis of experiments, initially by
ANOVA (additive fitting of the main effects) and then by analysis of Principal Components
(multiplicative fitting of the effects of interaction). The AMMI model equation is:
Yij=µ+gi+ej+∑n=1hλnαni.Ynj+Rij
63
Where ij Y is the yield of the ith genotype in the jth environment; µ is the grand mean; i g and je
are the genotype and environment deviations from the grand mean, respectively; λn is the square
root of the eigen value of the principal component Analysis (PCA) axis, αni and Ynj are the
principal are the principal component scores for the PCA axis n of the ith genotype and jth
environment, respectively and Rij is the residual. The analysis was done using GEA-R software
(Genotype x Environment analysis with R for windows) version 4.1.
AMMI Stability Value (ASV)
The ASV is the distance from the coordinate point to the origin in a two dimensional of IPCA1
score against IPCA2 scores in the AMMI model (Purchase et al., 2000). Because of the IPCA1
score contributes more to the GE interaction sum of square, a weighted value is needed. This
weight is calculated for each genotypes and environment according to the relative contribution of
IPCA1 to IPCA2 to the interaction SS as follows,
Where, SSIPCA1/SSIPCA2 is the weight given to the IPCA1 value by dividing the IPCA1 sum
squares by the IPCA2 sum of squares. The larger the IPCA score, either negative or positive, the
more specifically adapted a genotype is to certain environments. Smaller IPCA score indicate a
more stable genotype across environment.
Genotype Selection Index (GSI)
Based on the rank of mean grain yield of genotypes (RY) across environments and rank of
AMMI Stability Value (RASV) a selection index GSI was calculated for each genotype which
incorporate both mean grain yield and stability index in a single criterion (GSI) as suggested by
Bose et.al., (2014) and Bavandpori et.al., (2015) as indicated as: GSI = RASV + RY
Results and Discussions
The results of homogeneity tests of variance indicated that, error variance for grain yield in the
eight environments were homogenous and allowed for a combined analysis across environments.
The combined analysis of variance (Table 3) indicated that, the main effects of random
environments and fix genotypes were significant for grain yield that exhibiting the presence of
variability in genotypes and diversity of growing conditions at different environments. The
combined analysis of variance was conducted to determine the effects of environment (location
by year combination), genotype, and their interactions on grain yield of bread wheat genotypes
64
(Table 3). The main effects of environment (E), genotypes (G) and GE interaction were highly
significant at P < 0.01. Environment had the largest effect, explaining about 74.6% of total
variability, while genotypes and GE interaction explained 21.6 and 3.8% of total sum of squares,
respectively (Table 3). A large contribution of the environment indicated that, environments
were diverse, with large difference among environmental means causing most of the variation in
grain yield and higher differential in discriminating the performance of the genotype. The same
result was also reported by Farshadfar, (2008), Jacobsz et al., (2015) and Tadele et al., (2017).
Mean grain yield of genotypes was highest at Gololcha in 2018 main cropping season, and at
Sinana in 2017 main cropping season. Similarly, mean grain yield of genotypes was lowest at
Agarfa in 2018 main cropping season (Table 2). Genotype G5 was the highest yielding genotype
at Goba 2017 and the lowest at Agarfa during 2017main cropping season. Genotypes such as
G12, G19 and G10 gave 13.2, 4.7 and 1.2 tha-1 grain yield advantage over standard check (G5)
respectively, while they gave 86.1, 72.2 and 66.4 tha-1 grain yield advantage over local check
(G11), respectively (Table 2).
Table 2. Mean performance of 20 bread wheat genotypes in 8 Environments
SN Genotype
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
G18
G19
G20
Year 2017
Year 2018
Sinana Agarfa Goba Gololcha Sinana Agarfa Goba Gololcha
4.2
3.4
5.6
4.7
3.6
3.1
4.3
4.0
4.4
3.5
5.1
4.2
3.3
3.1
3.5
4.4
3.6
3.9
4.6
3.6
3.7
3.6
4.4
3.9
4.3
3.4
4.6
4.7
3.6
3.3
3.4
5.1
5.2
3.5
5.8
4.2
3.3
3.5
3.4
5.2
4.3
4.0
4.7
3.4
3.0
3.0
3.5
5.1
4.2
4.2
3.8
4.0
2.6
3.4
3.6
4.8
4.7
3.9
4.9
3.5
2.7
3.0
3.2
4.4
4.7
4.0
4.3
3.9
2.3
3.7
4.0
5.1
4.4
5.2
4.9
4.0
3.0
3.5
4.6
5.0
2.8
0.8
1.8
4.8
2.1
1.4
1.8
5.2
5.5
5.5
5.4
4.4
3.6
3.7
5.1
5.3
5.3
3.6
4.2
3.5
3.0
2.9
3.1
4.7
3.9
3.9
3.9
3.8
2.9
2.7
3.4
4.4
4.7
4.8
4.3
3.9
3.5
2.9
3.8
4.5
4.1
3.2
3.9
3.9
2.7
2.6
4.4
4.3
4.2
4.6
4.1
3.7
3.7
2.9
4.6
4.2
2.6
2.6
1.9
3.8
2.8
2.2
3.5
4.1
5.3
4.9
5.3
3.7
3.9
3.6
4.5
4.4
4.4
3.8
3.0
3.8
3.3
2.2
3.8
4.2
Mean
4.11
3.95
3.91
4.05
4.26
3.89
3.85
3.78
4.00
4.31
2.59
4.82
3.78
3.61
4.06
3.63
3.99
2.97
4.46
3.63
65
AMMI model analysis: in AMMI model, principal component analysis is based on the matrix
of deviation from additivity or residual was analyzed. In this respect, both the results of AMMI
analysis, the genotypes and environment were grouped based on their similar responses (Gauch,
1992; Pourdad and Mohammadi 2008; Tadele et al., 2017). The result of AMMI analysis also
showed that the first principal component axis (IPCA1) accounted for 49.2% over the interaction
SS, IPCA 2, IPCA 3 and IPCA 4 which explained 25.0%, 15.8% and 10.0% of the GE
interaction Sum of Squares respectively. The first two IPCA scores were significant at
(P<0.01%) and cumulatively accounted for 74.2% of the total GE interaction. This indicates that
the use of AMMI model fit the data well and justifies the use of AMMI2.
Table 3. ANOVA for grain yield of Bread wheat genotypes for the AMMI model
Source
d.f.
SS
MSS
SS%
Genotypes
19
108.2
5.69**
21.6
Environments
7
137.5
19.64**
74.6
Block
16
14.6
0.914*
Interactions
133
132.7
0.998**
3.8
IPCA 1
25
61.8
2.47**
49.2
IPCA 2
23
31.4
1.37**
25.0
IPCA 3
21
19.8
0.94**
15.8
IPCA 4
19
12.5
0.66*
10.0
Residuals
85
39.6
0.465
Total
479
538.9
1.125
d.f.=degree freedom, SS= Sum of square, MSS= Mean Sum of square, SS%= Percentage of sum
of square, IPCA 1, 2, 3 and 4= first, second, third and fourth principal component
AMMI Stability Value (ASV): ASV is the distance from zero in a two dimensional scatter gram
of IPCA1 scores against IPCA2 scores. Since the IPCA1 score contributes more to the GE sum
of square, it has to be weighted by the proportional difference between IPCA1 and IPCA2 scores
to compensate for the relative contribution of IPCA1 and IPCA2 to the total GE interaction sum
squares. According to this stability parameter, a genotype with the least ASV score is the most
stable. The high interaction of genotypes with environments was also confirmed by high ASV
value and difference in their rank order, suggesting unstable yield across environments. In
general, the importance of AMMI model is in reduction of noises even if principal components
didnot cover much of the GE Sum of Squares (Gauch, 1992; Gauch and Zobel 1996).
Results of ASV parameter showed, genotypes such as G9, G14, G7 and G16 as the most stable
genotypes, respectively. The most unstable genotypes were G11 and G18 (Table 4). Although,
66
ASV parameter was reported to produce a balanced measurement between the two first IPC’s
(IPC1 and IPC2) scores, however, it seems that, this parameter is useful when the portion of
explained total variation was relatively high (Sabaghnia et al., 2008). In most of the times,
genotypes showed inconsistency in rank of grain yield across different tested environment i.e,
genotype ranked first in one environment may not be first in another tested environment. Hence,
it is advantageous to look for single criteria which help researchers to identify elite genotypes
simultaneously for their high yielding and stable across tested environment. GSI (Genotype
Selection Index) is a single criteria for stability and high grain yield which successfully used by
Bose et.al., (2014) and Bavandpori et.al., (2015) to interpret interaction between genotype
performance and environments. High yielding genotype with better stability has smallest values
of GSI. The smallest Genotype Selection Index (GSI) were exhibited by Genotypes G9 (GSI =9),
G1 (GSI =11), G15 (GSI =14) and G10 (GSI =10). These genotypes were high yielder and
comparatively stable. The Highest GSI (GSI=40) was exhibited by genotype G11 which was
highly unstable and lowest yielding genotype among tested genotypes (Table 4).
Table 4 Mean of 20 genotypes, AMMI stability values, Genotypic selection index and
coefficient of variation
Genotype
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
G18
G19
G20
Mean
4.11
3.95
3.91
4.05
4.26
3.90
3.84
3.78
4.02
4.30
2.59
4.83
3.78
3.61
4.05
3.63
4.00
2.93
4.45
3.58
ASV
0.35
0.43
0.48
0.64
0.91
0.34
0.17
0.62
0.12
0.57
2.35
0.64
0.39
0.12
0.42
0.27
0.74
1.23
0.88
0.56
RASV
6
9
10
14
18
5
3
13
1
12
20
15
7
2
8
4
16
19
17
11
RYI
5
10
11
7
4
12
13
14
8
3
20
1
15
17
6
16
9
19
2
18
GSI
11
19
21
21
22
17
16
27
9
15
40
16
22
19
14
20
25
38
19
29
CV%
19.7
17.7
10.0
17.7
23.3
20.2
17.0
21.4
20.5
18.2
61.2
16.7
22.7
15.8
16.6
19.5
14.4
26.6
15.7
19.2
67
ASV= AMMI stability value, RASV=Rank of AMMI stability value, RYI=Rank of yield index,
GSI=Genotypic selection index and CV%=coefficient of variation in percentage
The vertex cultivars in each sector are considered best at environments whose markers fall into
the respective sector. Environments within the same sector are assumed to share the same winner
cultivars. Genotype-environment affinity depicted as orthogonal projections of the genotypes on
the environmental vectors to identify the best cultivars with respect to environments. The best
genotype with respect to environment Gololcha 2017 and Gololcha 2018 was genotype G10.
Genotypes G3 and G17 were better adapted to environment of Agarfa 2017 (Figure 1).
Figure: AMMI biplot showing the mean (main effect) vs. stability (IPC1) view of both
genotypes and environments on grain yield
Conclusions and Recommendations
To develop varieties, it is essential for breeders to evaluate their genotypes across years and
locations. Both yield and stability performance of the test genotypes should be considered
simultaneously to reduce the effect of GE interaction and to make selection of genotypes
more precise. For such evaluations, a number of stability analysis statistics were developed by
researchers. In this study, different stability parameters were employed to evaluate the yield
68
performance and stability of twenty bread wheat genotypes across different environments.
Based on ASV results, genotype with least ASV scores are the stable and hence, genotypes such
as G9, G14, G7 and G16 were stable genotypes according to their orders. Based on GSI single
criteria for stability and high grain yield genotypes results, genotypes such as G9, G1, G14, G10,
G15 and G12 were found to be stable genotypes. Overall, G12 was found as high yielder and
stable genotype across tested environments. Hence, this genotype (G12) was identified as the
most promising candidate genotype to be verified for possible release in the coming cropping
season for these test locations.
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Vol. 5, No. 6, 2017, pp. 93-98. doi: 10.11648/j.plant.20170506.12
Zobel, R.W., Wright, M.J. & Gauch, H.G. (1988): Statistical analysis of a yield trial. Agron. J.
80, 388–393.
Analysis of Genotype x Environment Interaction Effect and Stability on Yield of Black
Cumin (Nigella sativa L.) Genotypes.
Beshir Hamido1*, Habtamu Zeleke2, Tesfaye Letta3
1
Adami Tullu Agricultural Research Center, P.O.Box 35, Batu, Ethiopia.
2
Haramaya University, College of Agriculture and Environmental Science, P.O. Box 138, Dire
Dawa, Ethiopia
3
Oromia Agricultural Research Institute, P.O. Box 81262, Addis Ababa, Ethiopia
Corresponding Author: Email: beshirhamido@gmail.com, Phone: +251942450807
Abstract
Black cumin is an erect annual herb cultivated for its seed, growing on all kinds of soils. In
Ethiopia black cumin is cultivated as rain fed crop in the highlands from 1500 to 2500 meters
above sea level. Genotype × environment interaction and yield stability analysis are important in
measuring the genotypic yield stability and suitability for cultivation across seasons and
ecological conditions. The objective of this study was to assess the stability of black cumin
genotypes under different agro-ecological conditions of East Shoa and West Arsi zones. Fifteen
black cumin genotypes were evaluated at six locations in Randomized Complete Block Design
(RCBD) with three replications during 2018/19 main cropping season. Analysis of variance for
each location revealed the presence of significant differences among genotypes for seed yield.
The combined analysis of variance over locations showed significant differences amongst
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genotypes. Gammachis variety recorded the highest mean yield (1.35 ton ha-1) followed by
variety Dirshaye (1.26 ton ha-1) while genotype 90575-2 recorded the lowest (0.78 ton ha-1)
mean seed yield. Additive Main Effects and Multiplicative Interaction (AMMI) analysis of
variance had showed that the major part the total variations (41.99%) is attributed to the
environmental effects and the rest were attributed to the genotypic effects (31.96%) and the GEI
(10.96%). AMMI stability analysis identified variety Dirshaye as the most stable genotype
whereas genotype MAB-057 was the most unstable genotype. In general, genotypes (such as
Dirshaye, Gammachis, Soressa and Derbera) could recommended for cultivation at all the test
locations since they perfomed well as compared to the other tested black cumin genotypes.
Keywords: Adaptation trials, Black cumin seed, Nigella sativa, Seed yield performance
1. Introduction
Black cumin (Nigella sativaL.) is a diploid and an erect annual herb cultivated for its seed,
growing on all kinds of soils (Jansen, 1981). It is a medicinal plant belonging to the family
Ranunculaceae grown naturally in Southwest Asia and the Mediterranean region (Toncer and
Kizil, 2004). It originated in Egypt and East Mediterranean countries, but is widely cultivated in
Iran, Japan, China and Turkey (Shewaye, 2011). Nigella sativa is probably indigenous to the
Mediterranean region and the Middle East up to India. Black cumin is cultivated in the
subtropical belt extending from Morocco to Northern India and Bangladesh, East Africa and in
the former Soviet Union. In Europe, North America and South-East Asia, it is cultivated on a
minor scale, mainly for medicinal use (Akhtar and Saha, 1993). It is also cultivated in subSaharan Africa particularly in Niger, and Eastern Africa especially Ethiopia (Iqbal et al., 2010).
In Ethiopia, black cumin is cultivated as rain fed crop in the highlands from 1500 to 2500 meters
above sea level. In Ethiopia, Nigella sativa can be intercropped with barley and wheat (Ahmed
and Haque, 1986). However improved production technology must be available (Ministry of
Agriculture and Rural Development, 2003).
Black Cumin has a long history of uses for food flavors, perfumes and medicinal values. It is
used as an essential ingredient in preparing soup, sausages, cheese, cakes and candies. Studies
have shown that Nigella sativa seeds have high nutritional values: proteins content ranging from
20 to 27%, carbohydrates ranging from 23.5 to 33.2%, moisture content ranging from 5.52 to
7.43% and ash content ranging from 3.77 to 4.92% (Al-Jassir, 1992; Al-Ghamdi, 2001 and
Nergiz and Otles, 1993). It grows on a wide range of soils. Sandy loam soil rich in microbial
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activity is the most suitable for its cultivation. Areas with moderate rainfall and well drained
soils with pH of 7-75 are quite suitable for black cumin production (Orgut, 2007). Some studies
shown that black cumin is able to tolerate moderate water stress and responds well to soil
fertilization (Mozzafari et al., 2000; Bannayanet al., 2008).
In Ethiopia, Amara, Oromia, South Nations, Nationalities and Peoples, (SNNP) and Gambella
regions are the major growing regions of (Atta, 2003 and Takrunet al., 2008).
The productivity of black cumin depends on the genetic potential of varieties and the suitability
of environmental factors across production areas. Identification of high yielding and well
adapted genotypes is achieved through analyzing the effect of genotype × environment
interaction (GEI) and yield stability. Genotype × environment interaction and yield stability
analysis are important in measuring varietal stability and suitability for cultivation across seasons
and ecological conditions (Romagosa and Fox, 1993). Globally, several stability analysis
methods are available, and they have been used in different research efforts to determine the
magnitude of GEI effects for many different crops by different researchers (Akcuraet al., 2005).
Additive Main Effects and Multiplicative Interaction (AMMI) model has found to be more
effective in selection of stable genotypes (Crossaet al., 1991; Haji and Hunt, 1999; Ariyo and
Ayo-Vaughan, 2000; Tayeet al., 2000). It is used to analyze multi-location trials (Gauch and
Zobel, 1988; Zobelet al., 1988; Crossaet al., 1990). AMMI integrates the analysis of variance
and principal component analysis into a unified approach (Bradu and Gabriel, 1978).
The genetic potential contained within the crop, the environmental effects and their interaction
plays a great role in determining the performance and stability of the crop to a given
environment. Therefore, the current experiment was conducted with the objective of evaluating
and identifying the high yielding, stable, and adaptable black cumin genotypes at Eat Shoa and
West Arsi zones.
2. Materials and Methods
2.1. Study Area
The experiment was conducted in East Shoa and West Arsi zones at six locations viz; two sites
in East Shoa zone: Adami Tullu Agricultural Research Center (ATARC) on research station at
Adami Tullu Jiddo Kombolcha district and Bekele Girrisa kebeles at Dugda district and four
sites in West Arsi zone viz. Ali Woyyo, Makko Oda, Bute and Umbure during the 2018/19 main
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cropping season under rain fed condition. The locations are the representative for the diverse
agro-ecologies of spice crops growing environments in East Shoa and West Arsi zones.
Table 1. Description of the test locations used in the study
Locations
ATARC
Altitude
(m.a.s.l)
Average
Annual RF
(mm)
Soil Types
760.9
Sandy, Clay and Silt
(34%, 48% and 18%)
Sandy and Clay
NA
1650
Global Positions
Latitude
Longitude
7o 9' N
38o 7'E
Bekele Girrisa
1600
700-800
7o58' N
38o43' E
Ali Woyyo
1960
900
7o 23' N
38o 43' E
NA
Makko Oda
1980
920
7o 33' N
38o 62' E
o
NA
Bute
980
Sandy
7 23' N
38o 24' E
NA
Umbure
1057
Sandy
7o 12' N
38o 36' E
Key: ‘NA’ stands for not available and ‘ATARC’ for Adami Tullu Agricultural Research Center
2.2. Experimental Materials and Managements
A total of fifteen black cumin genotypes i.e., ten accessions viz; AC-BC-4, AC-BC-9, AC-BC10, AC-BC-19, MAB-042, MAB-057, 90575-2, 20750-1, 242834-1and 244654-1 along with five
standard checks viz; Derbera, Dirshaye, Eden, Gammachis and Soressa that were obtained from
Sinana Agricultural Research Center were used in this study. The materials were evaluated using
Randomized Complete Block Design (RCBD) with three replications at six locations in the main
cropping season of the year 2018/19.
The plot size for each experimental unit was 1.2m × 2m (4 rows, each 2m long).The total area of
a plot was 2.4m2.The spacing between rows, plots and blocks were 0.35m,0.5m and 1m,
respectively. Sowing was done by hand drilling and covered slightly with the soil. Fertilizer rates
of 46 kg Di-Phosphorus pent-oxides (P205) ha-1 and 60kg Nitrogen (N) ha-1 were used to facilitate
and increase root development and increases yield in black cumin (Champawat and Pathak,
1982).
2.3. Data Collection and Analysis
The following data were recorded: days to emergence, days to 50% flowering, days to maturity,
plant height (cm), number of primary branches per plant, number of capsules per plant, seed
yield per hectare (ton ha-1) and thousand seed weight (g). Data were collected from the middle
two harvestable rows for traits estimated from a plot. Data were also collected from ten randomly
selected plants from the central two rows and the average values were calculated.
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All the recorded data were subjected to analysis of variance following the standard procedure for
each location and combined analysis of variance over locations and were computed using the
Gen-Stat 18th Edition Statistical Computer Software Programs. Bartlett’s chi-square test was
used to determine the validity of the combined analysis of variance and homogeneity of error
variances among environments. Then combined analysis of variance was carried out to estimate
the additive effects of environment (E), genotype (G) and their interactions (GEIs).
2.4. Stability Analysis
The additive main effects and multiplicative interaction (AMMI) stability analysis was used to
integrate the analysis of variance and principal component analysis into a unified approach. An
initial analysis of variance was performed for each environment to verify the existence of
differences among the genotypes. Thereafter, the homogeneity between residual variances was
determined, and a joint analysis of variance was used to test the genotype and environment
effects and the magnitude of the GEIs.
Different researchers have been working with AMMI stability analysis as stability measuring
parameter for studying the stability of seed yield and quality of different crop genotypes,
particularly wheat across various environments (Desalegn et al., 2004; Ferney et al., 2006;
Mohammadi and Amri, 2008; Mohammed, 2009; Mut et al., 2010).
The AMMI model used was indicated as follow:
Where;
𝐘𝐢𝐣 = 𝛍 + 𝐆𝐢 + 𝐄𝐣 + ∑ 𝛌𝐤 𝐚𝐢𝐤 𝐘𝐣𝐊 + 𝐞𝐢𝐣
Yij is the yield of the ith genotype in the jth environment,
μ is the grand mean,
Gi and Ej are the genotype and environment deviations from the grand mean respectively.
λk is the eigen value of the interaction principal component axis K;
αik and Yjk are genotype and environment principal component scores for axis K
eij is the error term.
AMMI Stability Value (ASV), IPCA1 and IPCA2 were computed to identify the stable genotype
with consistence yielding performance across the testing environments. The degrees of freedom
for the IPCA axes were also calculated based on the following method (Zobel et al., 1988).
𝐝𝐟 = 𝐆 + 𝐄 − 𝟏 − 𝟐𝐧
74
Where;
df is degree of freedom
G is number of genotypes,
E is the number of environments and
N is the nth axis of IPCA
The AMMI stability value (ASV), was also calculated for each genotype and each environment
as follows according to Purchase et al., 2000:
𝟐
Where,
𝒔𝒔𝟏𝑷𝑪𝑨𝟏
[𝑰𝑷𝑪𝑨𝟏𝒔𝒄𝒐𝒓𝒆 ]] + [𝑰𝑷𝑪𝑨𝟐𝒔𝒄𝒐𝒓𝒆 ]𝟐
𝑨𝑺𝑽 = √[
𝑺𝑺𝟏𝑷𝑪𝑨𝟐
ASV is AMMI stability value
SS is sum of squares and;
IPCA1 and IPCA2 are the first and second interaction principal component axes, respectively
Accordingly, genotypes with the least AMMI stability value (ASV) were considered as the most
stable genotypes, where as those which have the highest ASV were considered as unstable
(Purchase, 1997).
3. Results and Discussions
The analysis of variance of an individual environment revealed that seed yield showed a highly
significant difference (P ≤0.01) at all test environments (Table 2). This indicated that, genotypes
might not express the same seed yield performance at a specified test location’s environmental
conditions; or different genotypes may respond differently to a specified environment.
Accordingly, at location Bute, the variety Soressa ranked 1st in its seed yield performance of 1.20
ton ha-1, while the same variety ranked 5th in its seed yield performance of 1.54 ton ha-1 at
location Makko Oda.
Table 2.Analysis of variance for seed yield of fifteen black cumin genotypes at each of the six
test locations
Sources of df
Sum of Squares
F-ratio
Variations
ATARC Bekele
Ali
Makko
Bute
Umbure
Girrisa
Woyyo
Oda
Replication 2
0.005
0.005
0.012
0.003
0.006
0.001
Genotypes
14 0.137 ** 0.125 ** 0.047 ** 0.22 **
0.07 ** 0.061 **
75
Error
28
0.003
0.006
0.008
0.005
0.003
0.005
2.667
CV (%)
6.0
8.4
7.2
5.4
5.3
7.3
Key:** = highly significant ( P ≤ 0.01 ) at 1% level of significance, df= degree of freedom
The highest and the lowest mean seed yield performance of the tested genotypes across the
testing environments were 1.35 ton ha-1 and 0.78 ton ha-1, which were obtained from genotypes
Gammachis and 90575-2 respectively (Table 3).
Table 3.Mean seed yield of fifteen black cumin genotypes tested at six locations
Genotypes
Testing Environments
ATARC Bekele
Ali
Makko
Girrisa
Woyyo
Oda
bc
cd
abc
Derbera
1.00
1.02
1.35
1.61a
Dirshaye
1.02b
1.22b
1.41ab
1.57ab
Eden
0.94bc
1.01cd
1.34abc
1.56abc
Gammachis
1.39a
1.36a
1.42a
1.55abc
Soressa
0.95bc
1.12bc
1.34abc
1.54abc
AC-BC-4
0.74e
0.84efg
1.28abc
1.54abc
AC-BC-9
0.71ef
0.72gh
1.22cd
1.55abc
AC-BC-10
0.92cd
0.95de
1.22cd
1.39d
AC-BC-19
0.67efg
0.77fgh
1.05ef
0.93fg
MAB-042
0.62fg
0.69gh
1.24bcd
1.43cd
MAB-057
0.97bc
0.93de
1.20cde
1.05f
90575-2
0.58g
0.66h
1.00f
0.89g
244654-1
0.85d
0.90def
1.31abc
1.46bcd
242834-1
0.71ef
0.82efg
1.27abc
1.22e
20750-1
0.63fg
0.70gh
1.08def
0.90g
GM
0.85f
0.91e
1.25b
1.35a
MSE
0.003
0.006
0.008
0.005
SE(d)
0.041
0.063
0.073
0.059
LSD
0.085
0.129
0.149
0.122
CV (%)
6.0
8.4
7.2
5.4
Key: ATARC = Adami Tullu Agricultural Research Center,
EM
Bute
Umbure
1.13abcd 1.12ab
1.204
abc
ab
1.17
1.14
1.256
bcd
bc
1.10
1.05
1.165
ab
a
1.19
1.19
1.350
a
ab
1.20
1.10
1.208
ef
def
0.97
0.91
1.046
cd
cd
1.08
0.97
1.041
fg
def
0.95
0.91
1.057
hi
efg
0.86
0.83
0.849
i
fg
0.81
0.80
0.933
i
g
0.84
0.75
0.954
j
efg
0.72
0.84
0.782
de
cde
1.05
0.93
1.083
ghi
defg
0.88
0.88
0.959
fgh
g
0.95
0.77
0.839
c
d
0.99
0.95
1.048
0.003
0.005
0.01
0.043
0.057
0.06
0.089
0.117
0.12
5.3
7.3
6.60
GM = Genotypic means, EM =
Environmental means, MSE = Mean square of error, SE (d) = Standard error of difference, LSD
= Least Significant Difference. Values with the same letters in a column are not statistically
significantly different.
On the other hand, the combined analysis of variance for seed yield revealed the presence of
highly significant difference among genotypes, environments and GEI. This result is in
agreement with the finding of Fufa (2018) with the result of combined ANOVA showing highly
76
significant variation (P ≤ 0.01) among different genotypes evaluated across location for seed
yield.
Table 4.Combined analysis of variance for mean seed yield of fifteen black cumin genotypes
across locations
Sources of variations
df
Mean Squares
Sum Squares
Total
269
Locations
5
1.812**
9.061
Genotypes
14
0.4935**
6.896
ns
Blocks (within locations)
12
0.014
0.027
G×L
70
0.034**
2.365
Residual
168
0.005
0.864
Key:* and** stand for significant differences at (P ≤ 0.05) and (P ≤ 0.01), respectively; ns for
non-significant difference, df= degree of freedom and G×L = Genotype by location interaction
The mean seed yield values of genotypes averaged across the environments showed that
genotype Gammachis had the highest mean yield (1.35 ton ha-1) followed by genotype Dirshaye
(1.26 ton ha-1) while genotype 90575-2 had the lowest (0.78 ton ha-1) mean seed yield. This
indicates that the test environments were highly variable and showed high contribution in
varying the yield performance of black cumin genotypes. The presence of blocking and/or
replicating within the testing environments could not influence the yield performance of the
tested genotypes. In addition to this, the combined analysis of variance across the locations for
seed yield revealed that genotypes, environments, GEI, error variance and block within
environments contributed 35.89%, 47.16%, 12.31%, 4.50% and 0.14%, respectively (Table 5).
Table 5.Percent contribution of genotypes, environments, GEI and error sum squares over
locations
Traits
G (14)
E (5)
GEI (70)
Blocks (12)
Residual
(168)
SS
SS
SS
SS
SS
SS
SS
SS
SS
SS
(%)
(%)
(%)
(%)
(%)
D50%F 40.719 5.73
539.53
75.88 56.082 7.89 0.985 0.14 73.682 10.36
DM
23.719 0.10 23092.163 99.51 37.615 0.16 0.119 0.00 51.882 0.22
PH
2439.02 18.55 6851.42 52.11 1982.01 15.08 4.05 0.03 1870.2 14.23
NPB
184.756 49.15
85.689
22.80 46.756 12.44 0.6
0.16 58.067 15.45
SYPH
6.896 35.89
9.061
47.16 2.366 12.31 0.027 0.14 0.864 4.50
Key: Values with the same letters have no significant difference and the numbers in the brackets
stand for the degree of freedom. D50%F = Days to 50% flowering, DM = days to maturity, PH
= Plant height, NPB = Number of primary branches and SYPH = Seed yield per hectare
77
AMMI analysis of variance for seed yield of fifteen black cumin genotypes evaluated at six
locations indicated that most of the total sum square of the model (41.99%) was attributed to the
environmental effects and the rest were attributed to the genotypic effects (31.96%) and the GEI
(10.96%) (Table 6).
Table 6.AMMI analysis of variance for seed yield of fifteen black cumin genotypes across
locations
Sources of
% explained % explained
variations
df
Mean Squares
From TSS
From GEI
Total
269
0.0714
Genotype
14
0.4926**
31.96
Locations
5
1.8122**
41.99
Blocks (within locations)
12
0.0055 ns
0.31
GEI
70
0.0338**
10.96
IPCA1
18
0.0824**
6.87
62.67
IPCA2
16
0.0336**
2.49
22.71
Error
168
0.0049 ns
3.82
Key:*, ** represent significant at P ≤ 0.05 and P ≤ 0.01, respectively, ns for non-significance,
df= degree of freedom, MS = Mean Square and TSS = Total Sum Square
The observed large sum of square and highly significant mean of square of location showed that,
the locations were highly diverse, with large differences among the location means causing most
of the variation in seed yield. AMMI stability value (ASV) was calculated for each of the fifteen
black cumin genotypes. Accordingly, the variety Dirshaye was the most stable with an ASV
value of (0.093) followed by genotypes 242834-1 and Soressa with their ASV value of (0.095)
and (0.109), respectively. Genotype MAB-057 was the most unstable with its ASV value of
1.004 followed by genotypes AC-BC-9 and Gammachis with their respective ASV of (0.985)
and (0.913) (Table 7).
Table 7.IPCA1 scores, IPCA2 scores and ASV scores of fifteen black cumin genotypes
Genotypes
GM
IPCA1
IPCA2
ASV
Gammachis
1.350 (1)
-0.307
0.341
0.913 (13)
Dirshaye
1.256 (2)
-0.017
0.08
0.093 (1)
Soressa
1.208 (3)
0.039
-0.01
0.109 (3)
Derbera
1.204 (4)
0.12
0.075
0.340 (7)
Eden
1.165 (5)
0.108
0.064
0.306 (5)
244654-1
1.083 ( 6)
0.12
0.021
0.333 (6)
AC-BC-10
1.057 (7)
-0.018
0.168
0.176 (4)
AC-BC-4
1.046 (8)
0.281
0.046
0.776 (11)
AC-BC-9
1.041 (9)
0.354
-0.113
0.985 (14)
78
Genotypes
GM
IPCA1
IPCA2
ASV
242834-1
0.959 (10)
0.007
-0.094
0.095 (2)
MAB-057
0.954 (11)
-0.359
0.163
1.004 (15)
MAB-042
0.933 (12)
0.31
0.039
0.857 (12)
AC-BC-19
0.849 (13)
-0.24
-0.218
0.698 (10)
20750-1
0.839 (14)
-0.208
-0.331
0.662 (9)
90575-2
0.782 (15)
-0.191
-0.231
0.576 (8)
Key: The number in the parenthesis represent the rank of the values; GM= Grand Mean,
IPCA1= Interaction principal component axis one, IPCA2= Interaction principal component axis
two and ASV = AMMI Stability Values
Regarding AMMI analysis of variance, the location effect was found the most influential factor
in discriminating the seed yield of fifteen black cumin genotypes that were evaluated at six
locations, contributing about 41.99% of the total variation as compared to that of the genotypic
effect and GEI effect with their percent contribution of 31.96% and 10.96%, respectively.
Generally, AMMI stability analysis identified the genotypes Dirshaye, Soressa, AC-BC-10 and
242834-1 as the most stable genotypes. On the other hand, the location Ali Woyyo was identified
as the most favorable black cumin growing environment.
4. Summary and Conclusion
The analysis of variance of an individual environment revealed that seed yield showed highly
significant difference (P ≤ 0.01) at all individual test environments. This pointed out that
genotypes might perform differently at a specified test environment. After the significant
difference of genotype × environment interaction and homogeneous residual variation were
corroborated, combined analysis of variance was computed and showed that there were highly
significant differences among the black cumin genotypes, environments and GEI. The observed
highest variation to the total variations was attributed to the environmental effects. This in turn
shows that the environment had contributed a great influence in varying the seed yield of the the
test genotypes. Accordingly, environment had contributed about 47.16% of the total variations.
The combined mean seed yield values of genotypes averaged across the environments showed
that variety Gammachis had the highest mean yield (1.35 ton ha-1) followed by Dirshaye (1.26
ton ha-1) while genotype 90575-2 had the lowest (0.78 ton ha-1) mean seed yield. Most of the
total sum of squares of the AMMI model (41.99%) was attributed to the environmental effects
and the remaining variation were attributed to the genotypic effects (31.96%) and the GEI
(10.96%).
79
AMMI stability analysis identified the genotypes Dirshaye as the most stable with ASV value of
(0.093) followed by the genotype 242834-1 and Soressa with their ASV value of (0.095) and
(0.109), respectively. On the other hand, the location Ali Woyyo was identified as the most
favorable black cumin growing environment among the test locations. In general, genotypes such
as Dirshaye, Gammachis, Soressa and Derbera could be recommended for cultivation at all the
test locations since they perfomed well in these locations as compared to the other tested black
cumin genotypes.
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82
Registration of Triticale Variety Named ‘Kombolcha’
Geleta Gerema1, Kassa Mamo3, Chemeda Birhanu1, Megersa Debela1, Kebede Dessalegn1,
Girma Chemeda1, Megersa Kebede1, Bodena Gudisa1, Hailu Feyisa1, Girma Mangistu2,
Dagnachew Lule2 and Gudeta Bedada1
1
2
3
Bako Agricultural Research Center, P.O.Box 03, Bako, West Showa, Ethiopia,
Oromia Agricultural Research Institute, Addis Ababa, Ethiopia,
Ambo Agricultural Research Center, West Showa, Ethiopia
*Corresponding author E-mail:geletarabi@gmail.com
Abstract
A Triticale (TriticosecaleWittmack.) variety named ‘Kombolcha’ with the pedigree designation
of 2012MS#51 has been released by Bako Agricultural Research Center, Ethiopia. The variety is
well adapted to altitudes ranging between 2244-2784 meters above sea level in the western
Ethiopia. Kombolcha is characterized by amber seed color, high yielder and has longer panicle.
The grain/seed yield of this variety is 13% heavier than the grain weight of the variety used as
the standard check ’Moti’. Based on stability parameters, Kombolcha showed relatively better
grain yield performance and stable adaptability across locations and across years than the
standard check ‘Moti’.This variety is resistant to the major triticale diseases such as stem rust,
yellow rust and Septoria tritici, and could be cultivated from mid to high altitude areas of
western Ethiopia
Key words: Genotype, Pedigree, Resistance, Triticale, Variety
Introduction
Triticale (TriticosecaleWittmack, 2n = 6x = 42; BBAARR.) is a hybrid cereal of wheat
(Triticum) and rye (Secale) which was developed by using conventional plant breeding followed
by embryo culture(Guedes-Pinto et al., 2001;Chaubey and Khanna, 1986; Nkongolo et al.,1991).
As the maternal plant was used wheat, rye was the paternal plant. Triticale breeding in North
America was formally started in 1954 at the University of Manitoba in Canada, from which the
first commercial variety, Rosner, was released in 1969(Larter et al.,1970). Triticale has high
feeding value and superior adaptation under stress conditions likewise drought, acidic soils,
excess moisture and situations of low fertility where other crops yield less and are poorly
adapted. The grain of triticale is much suitable as feed for ruminants and monogastrics,
especially for silage and swine feed.
83
Varietal Origin and Evaluation
Kombolcha (Acc 2012MS #51) and other genotypes were collected from Ethiopian Institute of
Agriculture Research, Debreziet Research Center. The genotypes were evaluated along with the
standard check, Moti, across two locations (Shambu and Gedo) for three consecutive years
(2015-2017). Based on information of combined data analysis of variance from most of traits
including grain yield, two genotypes “Acc 2012MS #51and Acc.2012 MS #59” were selected as
the most promising candidate varieties and promoted to variety verification trial. Finally,
candidate varieties were evaluated along with one best standard check on 10 m x 10 m plots by
the national variety release technical committee at two locations, each one on-station and two onfarm fields during the 2018/19 cropping season. Ultimately, acc 2012MS #51was recommended
for commercial production and named Kombolcha.
Varietal characteristics
The released variety, Kombolcha (Acc 2012MS #51) is characterized by amber seed color,
average 1000 seeds weight of 49.6 grams and has an average panicle length 10.4 cm (Table 2).
The variety could be resistance to lodging, the ability to withstand high fertility / nitrogen input
and wider adaptation.The detailed agronomic characteristics of the variety are indicated in Table
2 and 3 below.
Yield Performance
The released variety ‘Kombolcha’ is mainly described by high yield over the check and other
candidates, which have 6184.78kgh-1 h of seed yield (Table 1). The grain yield of this newly
released variety has advantages of 13% over the standard check ‘Moti’. Kombolcha(Acc.
2012MS #51) gave seed yield ranging from 39 to 50.1 t h-1 on a farmer’s field and 4.6 to 6.3 t h-1
on research field (Table 2 and 3).
Table 1. The mean of grain yield among two triticale candidates and one standard check across locations and over
years
Grain yield (kgh-1)
2015
2016
2017
Genotypes
Shambu Gedo Mean ShambuGedo Mean ShambuGedo
Mean Over all mean
2012 MS #51 6107.0 8346.7 7226.9 8233.3 5830.0 7031.7 6685.0 1906.7 4295.9 6184.8
2012 MS #59 6587.0 6250.0 6418.5 5966.7 6490.0 6228.4 6040.0 2106.7 4073.4 5573.4
Moti(st.check) 4813.0 6093.3 5453.2 8196.7 5556.7 6876.7 63.0
1730.0 896.5 4408.8
84
Table 2. Mean agronomic traits of two triticale candidates and one standard check during 2015-2017
cropping seasons
Genotypes
Maturity
Plant height
2012 MS #51
2012 MS #59
Moti (st.check)
122.2
123.2
122.8
117.8
103.5
103.7
Panicle
length
10.4
9.7
9.8
1000 grain
weight
49.6
47.0
44.3
Grain yield
(Kg/heck)
6108.9
5573.4
5406.7
Yield
advantage%
13.0
3.1
--
Table 3: Agronomic and Morphological characteristics of Bariso Triticale variety (Acc. 2012 MS
#51).
Adaptation area:
Western Oromia (from
middle to highland ecologies)
Altitude(masl)2244-2784
Rainfall
> 800mm
Seed rate
150 kg/ha
Fertilizer rate
NPS
100gk/ha
UREA
100kg/ha
Days to maturity 121-123.4
1000 seed weight
47.5-51.7
Plant height
113.9-121.7
Panicle length
8.8-11.6
Crop pest reaction
Tolerant to major
wheat diseases
Yield (ton/ha)
Research field
4.6-6.3
Farmers
3.9-5.1
Year of release
2019
Breeder seed maintainer: OARI/BARC
Stability and Adaptability
The variety ‘Kombolcha’ was released for the mid-to-high altitude agro-ecologies of the middle
and western part of the country receiving >800mm average annual rainfall. It is well adapted to
an altitude range of 2244-2784 meters above sea level such Wellega and west Shewa, and similar
agro ecologies. GGE biplot analysis revealed that both candidates showed stable adaptability
across the two locations and across years. Mainly, Acc. 2012MS #51is fall relatively close to the
ideal Environment and in the concentric circle and near to average environment axis, suggested
their potential for wider adaptability with better gain yield performances (Fig 1). Based on most
85
stability parameters, ‘Kombolcha’ showed relatively better performance stability across a range
of environments (Fig 1)
Remark: 4=2012 MS #51, 7=2012 MS #59=7 and 12= standard check(Moti)
Fig 1: GGE biplot analysis depicting the stability of tested genotypes and test environment
Reaction of Major Diseases
Develop resistant triticale varieties to major diseases such stem rust (Puccinia graminis), yellow
rust (Puccinastriiformis f) and Septoria tririciis among the major objectives of the breeding
program. Therefore, Kombolchavariety was showed resistance/moderate disease reaction
particularly to stem (0-5MR) and yellow rust(0-5R) while, The standard check was highly
infected by stem and yellow rusts (Table 4).
Table 4. Diseases reaction of the varieties “candidates and check ‘’
Genotypes
Diseases Reaction
Stem rust
Yellow rust
Septoria tritici
2012 MS #51
0-5MR
0-5R
14.3
2012 MS #59
5-10MR
0-15MR
12.5
Moti (st.check)
10MR-30S
5MR-20S
12.3
Remark: R=Resistance, MR=ModeratelyResistance, S= Susceptible
Conclusion
The Triticale variety ‘Kombolcha’ was high yielder, showed better adaptability and stable
performance than the standard checks. Also, the variety was showed better resistance to rusts and
Septoria tritici. Therefore, it was released and recommended for western and similar agroecology in the country.
Reference
86
Chaubey, N.K., Khanna. V.K .1986. A study of crossability between wheat, triticale and rye.
Curr. Sci. 55: 744–745
Guedes-Pinto, H., Lima-Brito. J., Ribeiro-Carvalho, C.and Gustafson, J.P. 2001. Genetic control
of crossability of triticale with rye. Plant Breed. 120: 27–31
Larter, E., Shebeski, L., McGinnis, R., Evans, L., Kultsikes, P. 1970. Rosner, a hexaploid
triticale cultivar. Can. J. Plant Sci. 50 122–124.
Nkongolo, K.K.C., Stpierre, C.A., Comeau, A .1991. Effect of parental genotypes, cross
direction and temperature on the crossability of bread wheat with triticale and on the
viability of F1 embryos. Ann. Appl. Biol. 118: 161–168 Oettler G, Burger H, Melchinger
AE (2003) Het
Release and Registration of ‘Kumsa’ Finger Millet Variety
Chemeda Birhanu1*, Kebede Dessalegn1, Dagnachew Lule2, Girma Chemeda1, Geleta
Geremew1,
Megersa Debela1, Girma Mengistu2, Gudeta Badada1, Hailu Feyia1 and Bodena Gudisa1
1
Bako Agricultural Research Center, P. O. Box 03, Bako, West Shoa, Ethiopia,
2
Oromia Agricultural Research Institute, Addis Ababa, Ethiopia
*
Corresponding author E-mail:chemeda2012@gmail.com
Abstract
Kumsa finger millet (Eleusinecoracana sub spp. coracana) variety is the brown seeded type
designated by BKFM 0063 (1) pedigree was developed and released by Bako agricultural
research center for western Oromia and similar agro-ecological areas of Ethiopia. Kumsa and
other pipeline finger millet genotypes were evaluated against standard check (Gute) for grain
yield, disease reaction and other agronomic traits across two locations (Bako and Gute) for
three consecutive years (2014-2016) during main cropping season. Additive main effect and
Multiplicative Interaction (AMMI), and Genotype and Genotype by Environment Interaction
(GGI) biplot analysis showed that Kumsa [BKFM 0063 (1)] is stable, disease tolerant and high
yielder (3.17 ton ha-1) with 25.3 % yield advantage over standard check Gute (2.53 ton ha-1),
thus released officially in 2019.
Key words: AMMI, Eleusinecoracana subsp. coracana, GGI, Magnaportheoryzea, stability
Introduction
Finger millet is a climate-resilient and nutritious crop with highly nutritious and antioxidant
properties (Guptaet al, 2017). It is grown mainly by subsistence farmers and serves as a food
security crop because of its high nutritional value, excellent storage qualities and as a low input
grown crop (Dida et al., 2008). Despite its importance, it is one of the neglected and
underutilized crops of Africa because attention directed toward staple cereal crops such as maize,
wheat, rice, and etc.In Ethiopia, finger millet is commonly grown in rural poor farmers at a
87
marginal land with low input and low yield mainly in Amhara and Oromia regions.Lack of stable
and high yielding varieties is one of the major bottlenecks for production and productivity of
finger millets in Ethiopia (Dagnachewet al., 2015). Therefore, developing adaptable, stable, high
yielding and disease resistant variety that withstand the leading climate change is very important.
Varietal Origin and Evaluation
Kumsa [BKFM 0063 (1)] was developed from Bako Agricultural Research Center (BARC)
finger millet landraces collection originally from western Oromia. The variety and other fourteen
finger millet pipeline genotypes were evaluated against standard check (Gute) for three years
(2014-2016) across two locations (Bako and Gute).
Agronomical and Morphological Characteristics
The released variety, Kumsa [BKFM 0063(1)] is characterized by light brown seed color,
average 1000 seeds weight of 3.5 grams and has an average plant height 85 cm. The detailed
agronomic characteristics of the variety are indicated in table 1 below.
Yield Performance
The multi-location blast prone areas and multi-year evaluation data records indicated that Kumsa
[BKFM 0063(1)] is stable and high yielder variety potentially produced 2.5 - 3.2 tons-1 on
research station. On farm yield evaluation recorded from variety verification plots at Bako and
Gute revealed that Kumsa gave an average grain yield ranging from 2.2 - 2.9 tons-1 (Table 1).
Stability and Adaptability Analysis
Eberhart and Russell (1966) model revealed that Kumasa [BKFM 0063 (1)] variety showed
regression coefficient (bi) closer to unity and thus stable and widely adaptable variety than the
remaining genotypes. Both GGE biplot and AMMI analysis also indicated that Kumsa [BKFM
0063 (1)] was stable and high yielding which gave about 25.3% (31.17ton ha-1) yield advantage
over standard check Gute (2.53 ton ha-1). Under variety verification trail, Kumsa gave about
13.5% yield advantage over recently released variety (Bako-09) and therefore, officially released
and recommended for production for testing locations and similar environmental conditions to
boost production and productivity. Accordingly, Kumsa was recommended for western Oromia
(Bako, Gute and Bilo) areas with similar agro ecologies.
88
Comparison biplot (Total - 63.63%)
230103
B16
BKFM0034
Local
BKFM00-63(1)
G 14
214988
214989
G 16
G 15
BKFM0005
BKFM0042
BKFM0007
203360
B14
BKFM0043
G ut e
214997
203353
229738
B15
PC1 - 40.43%
G enot ype scores
Environment scores
AEC
Fig 1: GGE Biplot analysis showing grain yield stability of genotypes and environments
Plot of Gen & Env IPCA 3 scores versus means
BKFM0042
G 14
G 15
20
10
B14
0
BKFM0007
Local
214989
214988
214997
203353
BKFM00-63(1)
BKFM0034
B16
BKFM0005
B15
203360
G ute
-10
BKFM0043
-20
230103
229738
-30
G 16
-40
1000
1500
2000
2500
3000
3500
4000
Genotype & Environment means
Fig 2: AMMI Biplot showing genotypes grain yield stability and preferential adaptation over
environment
Reaction to Major Diseases
Kumsa is moderately tolerant to major diseases particularly blast (Magnaportheoryzea), a
devastating disease that affect all above ground parts of the plant.
Conclusion
Kumsa finger millet variety was released for its high grain yield, showed better adaptability and
stable performance than the standard check. The variety is also tolerant to blast disease in blast
stressed areas. Therefore, it was released and recommended for smallholder farmers and other
finger millet producers at Bako, Gute, Bilo and areas with similar agro-ecology in the
country to boost finger millet productivity.
89
Acknowledgments
The authors would like to acknowledge Oromia AgriculturalResearch Institute for funding the
research work. We also thank Bako Agricultural Research Center and all staff members of the
Cereal Technology Generation Team for implementation of the experiment.
References
Dagnachew, L., Kassahun, T., Awol, A., Masresha, F., Kebede, D., Girma, M., Geleta, G., Hailu, F.,
Kassa, M., Chemeda,B., Girma, C., and Gudeta, B. 2015. Registration of “Addis-01” Finger Millet
Variety, East African J. Sci., 9 (2) 141-142.
Dida, M.M., Wanyera, N., Dunn, M.L.H., Bennetzen, J.L. and Devos, K.M., 2008. Population structure
and diversity in finger millet (Eleusinecoracana) germplasm. Tropical Plant Biology, 1(2), pp.131141.
Eberhart, S. A. and Russell, W. A. 1966. Stability parameters for comparing varieties. Crop Science, 6:
36-40.
Gupta, S.M., Arora, S., Mirza, N., Pande, A., Lata, C., Puranik, S., Kumar, J. and Kumar, A., 2017.
Finger millet: a “certain” crop for an “uncertain” future and a solution to food insecurity and hidden
hunger under stressful environments. Frontiers in plant science, 8, p.643.
Adaptation Study of Mung Bean (Vigna radiate) Varieties in Western Parts of Oromia,
Ethiopia
Solomon Bekele, Meseret Tola and ChalaDebela
Bako Agricultural Research Center, P. O, Box 03, Bako, West Shoa, Ethiopia,
Corresponding Author: slmnbkl092@gmail.com
Abstract
Seven mung bean (Vigna radiate) varieties that released in Ethiopia were evaluated for its
evaluation and adaptability with the objectives of identifying and recommending the adapted
mung bean varieties for Bako and similar agro-ecologies. The study was conducted at three
locations, Bako, B/Boshe and Chewaka during 2017 and 2018 main cropping season in
randomized complete block design (RCBD) with three replications. Days to 50% flowering (DF),
Days to maturity (DM), Plant height (PH), Number of pods per plant (NPPP), Number of seed
per pod (NSPP), hundred seed weight (HSW) and Grain yield (GYLD) data were collected. The
statistical analysis performed on combined data showed that there were significant differences, p
< 0.05 among the tested varieties in terms of yield, days to flowering and number of pods per
plant and highly significant differences, P ≤ 0.001 among mung bean varieties and test
environments for hundred seed weight. The variety x location interaction of NPPP and HSW
showed significant difference among the varieties while the interaction of DF, DM, PH, NSPP
and GYLD not significantly different. The highest pooled mean performance of mung bean grain
yield was 534.4 kgha-1for Chinese and the lowest was 381 kg ha-1for NVL-1 and the grand mean
being 433.2 kg ha-1. The GGE-biplot analysis of Borda(MH-97-6) mung bean variety was more
stable and environment 4 was ideal for the production of mung bean varieties. Grain yield was
correlated positive and highly significant with NPPP (0.45) and negative highly correlation with
DM (-0.62) and it had no relation with DF, PH, NSPP and HSW characters. Further breeding
activities will be required in the future on this mung bean crop due to its economic importance.
Key words: Adaptation, Correlation, GGE-biplot, Location, Mung bean, Stability
90
Introduction
Mung bean (Vigna radiata L. Wilczek) is a self-pollinated diploid legume with short duration
crop (Ketinge, et. al, 2011). Mung bean also known as green gram, maash, moong bean, golden
gram, celera bean. It is one of the legume plant species that belongs to the subgenus Ceratotropis
in the genus Vigna, about 150 mung bean genus vigna are spread in the world (R.M. et a l.,
1985). Among these species, 22 of them indigenous to India, 16 of them also from South East
Asia; but, the majority of this crop species are originated from Africa (R.M. et a l., 1985).
Mungbean germplasm is available as wild (Vigna radiata subsp. sublobata and Vigna radiata
subsp. glabra), cultivated (Vigna radiata subsp. radiata) and weedy populations. It has a
nutritional balance with plenty of vitamins and minerals that has healthy benefit; the seeds of
mung bean contain an average of 26% protein, 62.5% carbohydrates, 1.4% fat, 4.2% fibers, and
vitamins (Mohamed. et. al., 2012).
Mung bean crop is a recently introduced pulse crop in Ethiopia. It is mainly grown in Amhara
region of North Shewa, Oromiya special zone and Southern Wollo, SNNPR (Gofa area), concise
areas in Oromiyaregion, like Hararge and some woredas of Benishangul Gumuz regional state
(EPP, 2004 and ECX, 2014). The average seed yield in kg ha-1for mung bean is as low as 600800 in Ethiopia (EPP, 2004). After five commodities, mung bean is the sixth product that
Ethiopian commodity exchange is trading next to coffee, sesame, white pea beans, Maize and
wheat (ECX, 2014). Before two years, mung bean crop is not known in Bako area and recently,
since it has been a great contributor in national economy, Bako Agricultural Research Center has
tested the adaptability of seven mung bean crop varieties at Bako, BilloBoshe and Chewaka for
the past two consecutive years 2017 and 2018 with the objectives of identifying and
recommending the adapted mung bean varieties for Bako and similar agro-ecologies.
Materials and Methods
Description of the Experimental Area
The study was conducted at Bako Agricultural Research Center and at another two sub sites of
BARC (Billoboshe and Chewaka) in 2017 and 2018 main cropping season.
Experimental Materials
About seven mung bean varieties were tested in randomize complete block design with three
replications at Bako, Billoboshe and Chewaka in 2017 and 2018. The plot size was 3.6m2 (3m
91
length x 0.3m b/n rows x 4 rows). The seven varieties of mung bean and their maintainer center
are listed in table 1.
Table 1. List of Mung bean varieties used in the study and their maintainer center
S.N.
Varieties
Maintainer
1
Chinese
HARC
2
Rasa (N-26)
MARC
3
Shewa Robit
MARC
4
Borda (MH-97-6)
SARI
5
NVL-1
MARC
6
NV
HARC
7
Local Check
Chewaka Area
Where: HARC = Hawasa Agricultural Research Center, MARC = Melkasa Agricultural Research Center,
SARI = Southern Agricultural Research Institute
Results and Discussions
Growth and Phonological Parameters
Days to 50% flowering: From combined analysis, the seven tested mung bean varieties were
affected by days to flowering; which is significantly different at (P<0.05) level of significant.
The maximum days to flowering was recorded for variety Borda (MH-97-6) with 40.2 days and
the minimum days to flowering was for NV-1 variety (38.3 days) and the grand mean being 39.1
days (Table 4).
Days to 90% Maturity: The maximum days to maturity of mung bean tested varieties was 84.5
days for NVL-1 followed by local check (83.83 days) and Chinese (83.7 days) and the minimum
was 82.8 days for Rasa (N-26) with the average of 83.6 days of maturity date (Table 4). The
tested varieties were not affected and not significantly different at (p<0.05) level of significance
for days to maturity.
Plant Height: The effects of varieties on plant height have no significantly different (p<0.05),
Table 4; the highest plant height was recorded for Borda (MH-97-6), 38.67cm. and the minimum
plant height is 34.7cm and the average being 36.2 cm (Table 4).
Table 2. Mean square from analysis of variance for performance of mung bean in Phenology and
Growth Traits
SOV.
Loc.
Year
Rep.
Trt.
Loc x Trt.
Loc x Trt. x Year
Error
CV
DF
2
1
2
6
12
20
82
DF50 %
483.9**
548.9**
6.15ns
8.6*
1.34ns
17.8**
3.4
4.7
DM %
1447.3**
1269.8**
0.72ns
5.2ns
2.0ns
46.0**
3.1
2.1
PH
1552.3**
2.1ns
14.9ns
32.2ns
10.8ns
23.2ns
26.3
14.2
92
Where: ** and *, highly significant at p < 0.01 and significant at P < 0.05 respectively; and ns is
insignificant; SOV: Source of variation, DF: Degree of freedom, CV: Coefficient of variance, DF50%:
Days to 50% flowering, DM, Days to 90% maturity, PH: Plant height (cm).
Yield Components
Number of pods per plant: In legume plants, number of pods per plant is one of the core factors
to decide the performance of the plant yields. The analysis of variance revealed that, mung bean
varieties had influenced by number of pods per plant and significantly different at (p<0.05). The
highest mean average pod number was recorded for Chinese variety; 11.2, the minimum plant
pod number is 7.6 for NV variety and the mean being 9.2 (Table 4). The same results from
Wedajo, 2015 reported that, number of pods per plant is affected by mung bean varieties.
Number of seeds per pod: The analysis of variance showed that, number of seeds per pod had
no significantly different among the tested varieties (Table 4). The mean separation indicated
that, the maximum number of seed per pod = 10.9 recorded for NVL-1 variety followed by NV =
10.7; the minimum one for Borda (MH-97-6) is 9.6 and the mean is 10.3; however, had no
different among the varieties. The result was contrast with the report of Rasul, et al., 2012, who
reported the difference of seed per pod among the varieties are due to the difference of genetic.
Hundred seed weight: The result from analysis of variance for seed weight showed that, there is
highly significant different (P<0.001) among the tested mung bean varieties (Table 4). The
highest hundred seed weight value recorded for variety Chinese = 5.0g followed by Rasa (N-26)
= 4.5g and NVL-1 = 4.1g and the average being 4.2g (Table 4). Wedajo, 2015 also stated that,
the difference of seed weight among mungbean varieties of hundred seeds was because of crop
potential of the yield, growth rate, higher nutrients translocation and hereditary superiority.
Grain yield: The analysis of variance for mung bean tested varieties indicated that, there were
different among the tested varieties at (p<0.05). This means, there were difference among the
tested varieties across locations and years (Table 4). The phenology and growth and yield and
yield components mean performance of tested mung bean varieties are stated in table 4.
Table 3. Mean square from analysis of variance for mung bean in yield and its components
SOV.
Loc.
Year
Rep.
Trt.
Loc x Trt.
Loc x Trt. x Year
Error
CV
DF
2
1
2
6
12
20
82
NPPP
13.6ns
122.4**
1.7ns
22.1*
21.8**
15.1ns
9.2
22
NSPP
7.8*
12.4*
4.0ns
3.7ns
2.6ns
5.0**
2.0
13.9
HSW
0.00037ns
0.22ns
0.39ns
4.57**
1.47*
0.86ns
0.69
20
GYLD
1040464**
2283386**
33230ns
55664*
4847ns
24719ns
27200
18
93
Where: ** and *, highly significant at p < 0.01 and significant at P < 0.05 respectively; and ns =
insignificant; SOV = Source of variation, DF = Degree of freedom, CV = Coefficient of variance, NPPP
= Number of pods per plant, NSPP = Number of seeds per pod, HSW = Hundred seed weight and
GYLD = Grain yield.
Table 4. Pooled mean performance of Mung bean varieties for phenology and growth traits and
yield and yield components
Variety
Chinese
Rasa (N-26)
Shewa Robit
Borda (MH-97-6)
NVL-1
NV
Local Check
LSD
Mean
CV
phenology and growth traits
DF50 %
DM %
PH
39.0
83.7
34.7
38.6
82.8
36.6
39.4
83.4
35.1
40.2
83.1
38.6
38.3
84.5
35.2
38.5
83.7
36.7
39.7
83.8
36.2
1.58
2.14
3.24
39.1
83.6
36.2
4.7
2.1
14.2
NPPP
11.2
9.6
9.0
9.6
8.9
7.6
8.6
2.25
9.3
22
yield and yield components
NSPP
HSW
GYLD
10.3
5.0
534.4
10.6
4.5
396.9
9.9
3.9
436.5
9.6
3.5
474.8
10.9
4.2
381
10.7
4.1
383.5
10.0
3.6
425.2
1.07
0.59
103.17
10.3
4.2
433.2
13.9
20
18
Gge-Biplot Analysis
The Ranking of Genotypes Based on Yield and Stability
The GGE-biplot analysis of Mung bean tested varieties showed that, four varieties: G1 =
Chinese, G4 = Borda (MH-97-6), G3 = Shewa Robit and local check gave better yield mean
performance with 534.4 kg ha-1, 474.8 kg-1, 436.5 kg ha-1 and 425.2 kg ha-1 respectively. Both
PC1 and PC2 were separated based on their scored for seven mung bean tested varieties in the
study area. The designated genotypes code and Environments code were listed in the table
below.
Table 5. Genotypes and environments and their codes for tested mung bean varieties
No Varieties
Genotype code No
Environments
Env. code
1
G1
1
Bako 2017
E1
Chinese
2
G2
2
Bako2018
E2
Rasa (N-26)
3
G3
3
Bilo/boshe 2017 E3
Shewa Robit
4
G4
4
Bilo/boshe 2018 E4
Borda (MH-97-6)
5
G5
5
Chewaka 2017
E5
NVL-1
6
G6
6
Chewaka 2018
E6
NV
7
G7
Local Check
The primarily use of GGE-biplot is grading the tested genotypes for the locations. PC1 indicated
the mean performance of the varieties while PC2 indicated the G x E associated with each
genotype which is the measure of stability or instability (Yan et al., 2000; Yan, 2002). Genotypes
having PC1 > 0 were recognized as high yielding while those genotypes having PC1 score < 0
94
were identified as low yielding, (Kaya et al. 2006). A Borda (MH-97-6) variety is more stable
than other tested mungbean varieties and gives higher yield and environment 4 is the best for the
production of mungbean crop. The stability and GGE- biplot diagrams are sketch below.
Comparison biplot (Total - 92.65%)
G7
G4
G6
G5
G3
G1
PC2 - 28.94%
G2
PC1 - 63.71%
Figure.1. GGE–biplot based on genotype focused scaling for comparison of the genotypes. PC and G for principal
component and genotypes respectively.
Scatter plot (Total - 92.65%)
G7
E6
G4
E4
E2
G6
G5
E3
G3
G1
PC2 - 28.94%
G2
E1
E5
PC1 - 63.71%
Figure 2. The polygon view of the GGE- biplot based on symmetrical scaling for which
-won -where pattern for genotypes and environments. PC, G and E stands for principal
component, genotype and environments, respectively.
Associations of Characters
95
The correlation coefficient is the measures of level of symmetrical association between two traits
and it is used for understanding the nature and degree of association among yield and yield
components. Association between any two traits or among various traits is of very importance to
make desired selection of combination of traits (Ahmad et al., 2003). Pearson correlation was
done for mung bean variety adaptation and the correlation ranged from 0.002 to 0.45 (Table 6).
Grain yield has a strong positive association and negative association with NPPP (0.45**) and
DM (-0.62**) respectively. Inversely, grain yield has no correlation with characters like DF (0.07), PH (0.002), NSPP (0.07) and HSW (0.12). Other characters also highly positively
correlated to each other; like PH with DF (0.23**) and DM (0.39**), and NSPP slightly
correlated with PH (0.22*) and NPPP (0.17*), while NPPP is highly negatively correlated with
DM (-0.27**). This result is in agreement with Kebereet al., (2006), who stated, no correlation
of grain yield with plant height, hundred seed weight and number of seed per pod for common
Table 6. Pearson correlation coefficients between characters of mung bean adaptation
Characters
DF
DM
PH
NPPP
NSPP
HSW
GYLD
DF
1
0.11ns
0.23**
0.01ns
-0.12ns
-0.03ns
-0.07ns
DM
PH
NPPP
NSPP
HSW
GYLD
1
0.39**
-0.27**
0.07ns
0.11ns
-0.62**
1
0.13ns
0.22*
0.02ns
0.002ns
1
0.17*
-0.01ns
0.45**
1
0.15ns
0.07ns
1
0.12ns
1
bean varieties. Some authors result also contradicts this mung bean result as the
positivecorrelation were recorded for grain yield with the number of seeds pod-1 and mean seed
weight in soybean (Karmakar and Bhatnagar, 1996).
Where: DF: Days to 50% flowering, DM, Days to 90% maturity, PH: Plant height (cm), NPPP: Number
of pods per plant, NSPP: Number of seed per pod, HSW: Hundred seed weight and GYLD: Grain yield
kg/ha.
Conclusions
Mung bean adaptation activity was done with seven varieties at Bako, B/boshe and Chewaka in
2017 and 2018 cropping season for its evaluation and adaptability. Chinese (534.4 kg ha-1) and
Borda- MH-97-6 (474.8 kg ha-1) varieties were selected according to their performance and due
to their betterment than local check (425.2 kg ha-1). Both varieties were more stable and gave
higher yield and environment 4 (Bilo/boshe 2018) was ideal environment for the production of
mung bean crop. Grain yield was highly positively and negatively correlated with 0.45** and 0.62** for NPPP and DM respectively. Further research should be undertaken on this particular
96
crop to develop improved varieties, for the immediate use, Chinese and Borda (MH-97-6)
varieties were recommended for Bako and the same agro-ecology areas.
Acknowledgements
The first Acknowledgement goes to Oromia Agricultural Research Institute (OARI) for the
financial support and all staff members of Bako Agricultural Research Center in general and
Pulse and Oil Crops Research Team in particular for the success of the activity.
Reference
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97
Multi-Location Evaluation of Yield and Yield Related Trait Performance in Bread Wheat
Genotypes at Western Oromia, Ethiopia
GeletaNegash*,Biru Alemu and WakgariRaga
Haro Sabu Agricultural Research Center (HSARC), P.0.Box 10, KellemWollega, DembiDollo,
Ethiopia,
*Corresponding author: geleta2017@gmail.com
Abstract
Wheat (Triticum aestivum L) is an important cereal crop, which receives the most attention of
specialists in plant breeding and production worldwide. Knowledge of the interaction between
genotypes and environment with yield and yield components is a principal aspect of effective
selection in crop improvement. This experiment was conducted to evaluate high yielding, insect
pest tolerant genotypes with genotype by environmental interaction on grain yield and yield
stability across environments. The study used 15 bread wheat genotypes against checks at Haro
Sabu Agricultural Research Center (HSARC) sub sites across environments in 2017-2018
cropping season. Ten agronomic traits and four economically important disease reaction data
were evaluated. Analysis of variance detected significant difference among genotypes in both
separated and combined analysis of variance. The combined ANOVA and the additive main
effects and multiplicative interactions (AMMI) analysis for grain yield across environments
exhibited significantly affected by environments,which explained 65.06% of the total variation.
The genotype and genotype environmental interation were significant and accounted for 13.34
and 9.44 % respectively. Pricipal component (PCA1) and 2 accounted for 7.88 and 1.15 % of the
GEI respectively with a total of 9.03 % variation.Generally, G6 and G3 were identified as ideal
genotypes for yielding ability and stability, tolerant to diseases and use as parents in future
breeding programmes.
Key words: AMMI, GGEI, Performance, stability, wheat
Introduction
Worldwide, wheat (Triticum aestivum L.) is an important cereal crop, which receives the most
attention of specialists in plant breeding and production. Yet, its production is limited by the
adverse environmental conditions. Environmental fulactuation and interaction is the major
limitation for wheat production and productivity. Genotype x environment (GE) interaction
reduces genetic progress in plant breeding programmes through minimising the association
between phenotypic and genotypic values (Comstock and Moll,1963).Therefore,multienvironment yield trials are essential in estimation of genotype by environment interactionn
98
(GEI) ,identification of superior and stable genotypes in the final selection cycles (Kaya et al.,
2006; Mitrovic et al., 2012). Phenotypes are a mixture of genotype (G) and environment (E)
components and their interactions (G x E).Genotype by environment interactionn (GEI)
complicate process of selecting genotypes with superior performance.Accordingly, Multienvironment trails (METs) are widely used by plant breeders to evaluate the relative
performance of genotypes for target environments (Delacy et al., 1996). The additive main
effects and multiplicative interaction (AMMI) model have led to more understanding of the
complicated patterns of genotypic responses to the environment (Gauch, 2006).These patterns
have been successfully related to biotic and abiotic factors.Yan et al.(2000), proposed another
methodology known as GGE-biplot for graphical display of GE interaction pattern of MET data
with many advantages. GGE biplot is an effective method based on principal component analysis
(PCA), which fully explores MET data. It allows visual examination of the relationships among
the test environments, genotypes and the GE interactions. The first two principle components
(PC1 and 2) are used to produce a two dimensional graphical display of genotype by
environment interaction (GGE-biplot). If a large portion of the variation is explained by these
components, a rank-two matrix, represented by a GGE- biplot, is appropriate (Yan and Kang,
2003). Using a mixed model analysis may offer superior results when the regression of genotype
by environment interaction on environment effect does not explain all the interaction (Yan and
Rajcan, 2002).
Therefore, the objective of this study was: to identify bread wheat genotypes with high level of
grain yield and yield stability and insect pest tolerant across locations.
Materials and Methods
Study sites: The multi-location yield trial (MLYT) was conducted at three locations in Kellem
and West Wollega zones of Haro-sabu Agricultural Research Center at Belem sub site (altitude
1759 masl, 09° 02' N, 035° 104'E), Mata (altitude 2016 masl, 08° 34' N, 034° 44'E) and
Badesso(altitude 2054 masl, 08° 40' N, 034°47'E) in western Oromia, Ethiopia, during the 20172018 main cropping season.
Breeding materials and experimental design: A total of15 genetically diverse bread wheat
genotypes (Table1) were evaluated against the checks (Liban, Kingbird and one local check).
Arandomized complete block design (RCBD) with three replications were used. Six rows per
plot of 0.2 m spacing between rows and 2.5 m row length, and harvestable plot size was 2 m 2
99
(four harvestable rows per plot) . A seed rate of 150 kgha-1 and fertiliser rate of 100 kg ha-1DAP
and 150 kg ha-1 Urea were used.
Statistical analysis
Analysis of variance is calculated using the model:
Yij = µ + Gi + Ej + GEij
Where Yij is the corresponding variable of the i-th genotype in j-th environment, μ is the total
mean, Gi is the main effect of i-th genotype, Ej is the main effect of j-th environment, GEij is the
effect of genotype x environment interaction.
The AMMI model used was:
Where Yij is the grain yield of the i-th genotype in the j-thenvironment, µ is the grand mean, gi
and ej are the genotype and environment deviation from the grand mean, respectively, ʎk is the
eigenvalue of the principal component analysis (PCA) axis k, Ƴik and δjk are the genotype and
environment principal componentscores for axis k, N is the number of principal components
retained in the model, and Ɛij is the residual term.
Table1. List of bread wheat genotypes evaluated for two years at Western Oromia in Ethiopia
No
Codes
Genotypes
Sources
1
G1
Local check
Farmer
2
G2
ETBW7056
KARC
3
G3
ETBW7104
KARC
4
G4
king bird
KARC
5
G5
ETBW7068
KARC
6
G6
ETBW7076
KARC
7
G7
ETBW7077
KARC
8
G8
ETBW7072
KARC
9
G9
Liban
KARC
10
G10
ETBW7075
KARC
11
G11
ETBW7092
KARC
12
G12
ETBW7069
KARC
13
G13
ETBW7052
KARC
14
G14
ETBW7088
KARC
15
G15
ETBW7071
KARC
G-genotype, ETBW- Ethiopia bread wheat, KARC-Kulumsa Agricultural Reaserch center
GGE-biplot methodology, which is composed of two concepts, the biplot concept (Gabriel,
1971) and the GGE concept (Yan et al., 2000) was used to visually analyse the METs data. This
methodology uses a biplot to show the factors (G and GE) that are important in genotype
100
evaluation and that are also the sources of variation in GEI analysis of METs data (Yan, 2001).
The GGE-biplot shows the first two principal components derived from subjecting environment
centered yield data (yield variation due to GGE) to singular value decomposition (Yan et al.,
2000)
AMMI Stability Value (ASV): ASV is the distance from the coordinate point to the origin in a
two-dimensional plot of IPCA1 scores against IPCA2 scores in the AMMI model (Purchase,
1997). Because the IPCA1 score contributes more to the GxE interaction sum of squares, a
weighted value is needed. This weighted value was calculated for each genotype and each
environment according to the relative contribution of IPCA1 to IPCA2 to the interaction sum of
squares as follows:
Where,SSIPCA1/SSIPCA2 is the weight given to the IPCA1-value by dividing the IPCA1 sum of
squares by the IPCA2 sum of squares. The larger the ASV value, either negative or positive, the
more specifically adapted a genotype is to certain environments. Smaller ASV values indicate
more stable genotypes across environments (Purchase, 1997). Genotype Selection Index (GSI):
Stability is not the only parameter for selection as most stable genotypes would not necessarily
give the best yield performance. Therefore, based on the rank of mean grain yield of genotypes
(RYi) across environments and rank of AMMI stability value RASVi), genotype selection index
(GSI) was calculated for each genotype as:
GSIi = RASVi + RYi
A genotype with the least GSI is considered as the most stable (Farshadfar, 2008). Analysis of
variance was carried out using statistical analysis system (SAS) version 9.2 software (SAS,
2008). Additive Main Effect and Multiplicative Interaction (AMMI) analysis and GGE bi-plot
analysis were performed using Gen Stat 15th edition statistical package (VSN, 2012)
Data collection method: Ten plants were selected randomly before heading from each row (four
harvestable rows) and tagged with thread and plant based data were collected from the sampled
plants.
Plant-based: Plant height, Spike length, and spike weight, spike lets per spike, grain per spike
and grain per spikelet.
101
Plot based: Days to heading, days to maturity, thousand seed weight, grain yield and four
economically important disease reactions like stem rust, leaf rust, septoria and fusarium head
blight.
Results and Discussions
Combined analysis of variance
Mean square of analysis of variance for all genotypes at different environmental conditions for
grain yield and yield related traits are presented in Table2. Highly significant differences were
detected among years (P ≤ 0.01) for all parameters, except for stem rust and septoria. The
combined analysis of variance revealed that year and location effects were significant for all
parameters, except septoria and thousand seed weight. Year*genotypes effects were nonsignificant for all parameters excluding days to heading, days to maturity, fusarium head blight
and spike weight. Year*location *genotypes were significant for some traits such as days to
maturity, spike weight, grain per spike and yieldkgha-1.Genotype by environment interaction
mean square was highly significant (P≤0.01) for days to maturity, days to heading, plant height,
spike length, spikelets per spike and grain yield.
Table 2: Analysis of variance (ANOVA) for grain yield and yield related traits of bread wheat
genotypes evaluated in 2017-2018 main cropping season
Source
DF
Replication
2
Genotype
14
Location
2
Year
1
Geno .*loc.
28
Geno .*yr.
14
Loc.*yr.
2
Geno.*loc.*yr. 28
Table 2: cont
DH
12.68**
265.24**
832.68**
963.33**
12.30**
46.97**
106.68**
12.3
DM
12.86
340.90**
5506.27**
3998.23**
47.71**
122.82**
3232.78**
48.52**
SR
0.22**
0.05*
0.11*
0.00
0.02
0.02
0.11*
0.02
LR
0.03
0.05
0.13*
0.49**
0.03
0.05
0.13*
0.03
SEP
0.02
0.01
0.03
0.07*
0.01
0.01
0.03
0.01
FHB
PH
7.37**
13.08
1.53** 578.88**
1.30* 7274.29**
76.80** 338.08**
0.15
59.27**
1.37**
14.19
1.30* 351.89**
0.15
8.2
Source
DF
SL
SW
STPS
GPS
GPST
TSW
Kg/ha
Replication
2
3.29**
0.41
21.25*
364.69** 2.12**
198.23
42646.84
Genotype
14
11.37**
5.72**
35.06**
597.59** 1.31**
513.13*
1831217.12**
Location
2
28.99**
2.48** 293.70**
433.04** 2.72** 2741.55** 13090998.64**
Year
1
101.14** 27.60**
64.68** 3229.91** 5.18** 4066.07** 89102882.12**
Geno .*loc.
28
0.71**
0.25
11.54**
93.93*
0.41
242.62
376887.84**
Geno .*yr.
14
0.51
0.96**
2.99
72.64
0.31
287.65
206156.52
Loc.*yr.
2
202.90** 11.45** 176.97** 4456.30** 4.96**
261.09
4874484.92**
Geno.*loc.*yr. 28
0.47
0.41*
8.00
95.65*
0.37
352.59
167928.97**
ns * ** non-significant, significant at 5% and 1% respectively, Loc *gen= location by genotype, Yr*Loc*gen =
year by location by genotype, DF -degree of freedom, DH- Days to Heading; DM- Days to Maturity; PH- Plant
Height; SL- spike Length; SW-Spike Weight, STPS-Spikelets per spike; GPS-Grain per spike, GPST-Grain per
spikelets TSW- Thousand Seed Weight, YLD Kg/ha- Yield in kilogram per Hectare.
102
Yield performance across environments
The performance of the tested bread wheat genotypes for grain yield across location and year
presented in Table 3. Some genotypes constantly performed best in a group of environments,
while some are fluctuated across locations. The average grain yield ranged from the lowest
3524.47 kgha-1 at Belem site in 2017 to the highest 5520.17kgha-1 at Bedesso site in 2018, with
grand mean of 4479.47 kgha-1 (Table 3). The grain yield across environments ranged from the
lowest of 3925 kgha-1for local check to the highest of 5069 kgha-1 for genotype ETBW7076
(Table3). This wide variation might be due to their genetic potential of the genotypes. Genotype
ETBW7076 was the topranking pipeline in all environments, except at Belem in 2018.Similarly,
genotype ETBW7104 ranked first at all sites, except at Bedeso in 2017 and 2018 cropping
season. However, genotype ETBW7072 ranked the least in all environmental sites throughout
cropping season (Table3). The difference in yield rank of genotypes across the environments
exhibited the high crossover type of genotypes x environmental interaction (Yan and Hunt, 2001;
Asrat et al., 2009).
Table 3: Mean grain yield (kgha-1) of bread wheat genotypes evaluated at three environments
Grain Yield in Kgha-1
2017
2018
Genotypes
Belem
Bedesso
Mata
Belem
Bedesso
Mata Com.Mean
ETBW7052
3426.5c 3780.6cd
3894.8d
4477.5c-f
5394.6d
4707.4cd
4280ef
ETBW7056
4057.2a
4896a
4744.4ab
3830f
6348.5a
5387.8ab
4877c
ETBW7068
3531.3bc 3689.5cd
4634.2abc
4790.3a-d
5459.4cd
5308.6ab
4568cd
ETBW7069
3278.1cd
3395.1d
3841.1d
4442.3c-f
5132de
4711.1cd
4133fg
ETBW7071
3282.6cd 3735.3cd
4060.6bcd
4466.7c-f
5295.7de 4835.9bcd
4280ef
ETBW7072
3006.9d
3268.7d
3811d
4029.2ef
4889.9e
4636.4d
3940g
ETBW7075
3902.5a
4490ab
4315.8a-d
4353.3c-f
6030.6ab 5056.2a-d
4691c
ETBW7076
4119.9a
4942a
4848.1a
4633.5b-e
6400.2a
5468.4a
5069a
ETBW7077
3228cd 3583.5cd
4436a-d
4477.5c-f
5141.7de 5152.3a-d
4337def
ETBW7088
3320.3cd
3463d
4488.2a-d
5219.3ab
5205.7de 5173.1a-d
4478cde
ETBW7092
3525bc 3668.3cd
4613.9abc
5420a
5429.4cd 5275.7abc
4655bc
ETBW7104
3795.4ab 4125.3bc
4819.6a
5260.8ab
5840.9bc
5430.2a
4880b
kingbird
3295.3cd 3634.5cd
4361.1a-d
4863.7abc
5219.6de
5066a-d
4407de
Liban
3846.8ab 3761.5cd
4663.7ab
4351.8c-f
5834.7bc
5572.4a
4672bc
Local
3251.1cd 3567.4cd
3926.3cd
4107.2def
5179.7de
4721cd
3925fg
Mean
3524.47
3866.71
4363.90
4581.54
5520.17
5100.16
4479.47
R2(%)
82
77
54
67
85
55
88
CV%
5.63
9.24
9.73
9.49
4.46
6.87
8.05
LSD 5%
331.85
597.27
710.29
727.53
411.72
585.82
237.98
F test
**
**
**
**
**
**
**
ETBW–Ethiopia bread wheat, R2, R-squire, CV- coefficient of variation, LSD- least significant different
Agronomic performance
Combined mean grain yield and other agronomic traits are presented in Table 4. High means of spike
length, spike weight, spikelets per spike, grain per spike, grain per spikelets, thousand seed weight and
103
grain yield and medium days to heading and days to maturity were recorded by genotypes ETBW7076.
These offer great flexibility for developing improved varieties suitable for various agro-ecologies with
variable length of growing period and high in grain yield status. However, genotypes ETBW7056,
ETBW7075 and ETBW7088 were with short mean of days to heading and days to physiological maturity,
indicating that early maturing genotypes were desirable when moisture was the limiting factors of
production. Similarly, the local check was recorded high plant height, indicating that the variety might be
susceptible to lodging; but genotypes ETBW7076 and ETBW7104 were with medium plant height
indicated, and the possibility for developing resistant varieties against lodging problems. Moreover,
genotypes ETBW7076, ETBW7104 and ETBW7056 recorded the highest grain yield and had 21.3, 10.9
and 4.4% yield advantages over the best standard check (Liban), respectively (Table 4).
Table 4: Combined mean grain yield and other agronomic traits of bread wheat genotypes
Genotypes
ETBW7052(G13)
ETBW7076(G6)
ETBW7092(G11)
ETBW7069(G12)
ETBW7071(G15)
ETBW7072(G8)
Liban (G9)
Local(G1)
ETBW7104(G3)
ETBW7077(G7)
ETBW7068(G5)
ETBW7056(G2)
ETBW7075(G10)
ETBW7088(G14)
kingbird(G4)
Mean
CV%
R2 %
LSD 5%
F test
DH
DM
PH
SL
80.2a 121.3c 78.1c 9.6bc
77.8b 118.2ef 75.04de 10.4a
76.1c 119.5de 81.9b 7.9hi
74.06de
f 8.5fg
75.9c 117.2fg
75.9cd 118.7e 73.54ef 8.8ef
75de 120.5cd 76.19cd 8.3gh
74.05de
74.8e 121.3c
f 8.4fg
72.6f
123b 93.18a 7.6i
71.6g 113.4h 75.3de 9.2d
71.4g 126.5a 73.47ef 9.1de
70.9g 114.2h 78.18c 9.8b
70.3i 116.3g 76.48cd 9.8b
69.7i 114.1h 71.8f 9.4cd
69.6i 114.5h 68.46g 9.2cd
65.3j 109.6i 72.04f 7.9hi
73.13 117.89 76.12 8.92
1.91
1.85
5.23 6.71
96
97
90
92
0.9
1.43
2.62 0.39
**
**
**
**
SW
STPS
2.14c 16.4bcd
3.7a 19.02a
1.69de 15.01d
1.9cde 15.36cd
1.9cd 16.26cd
1.99cd 15.73cd
2.12c
1.3f
3.02b
2.03c
2.02c
2.06c
1.98cd
1.87cde
1.6ef
2.09
23.36
0.8
0.32
**
GPS GPST
43.09bc 2.64b-f
55.51a 2.97ab
33.82f 2.29fgh
TSWYLD Kgha-1
24.37cd
4280ef
36.54ab
5669a
36.83a
4655bc
37.05def 2.48c-h 25.48bcd
40.48cd 2.46c-h 25.56bcd
37.08def 2.41d-h 25.61a-d
4133fg
4280ef
3940g
-11.5
-8.4
-15.7
16.69bc
45.75b 2.78a-d 22.64cd
12.79e
33.76f 2.67b-e
19.48d
17.85ab 39.75cde 2.21gh 33.13abc
15.90cd
33.59f
2.12h 22.06cd
15.05d
35.44ef 2.39e-h 29.60a-d
16.29cd 41.21bcd 2.52c-g 27.04a-d
15.58cd 37.62def 2.45c-h 28.28a-d
15.52cd 41.63bcd 2.81abc 22.54cd
14.98d 43.90bc
3.06a
21.18d
15.9
39.98
2.55
26.69
14.65
19.06
21.92
63.93
0.69
0.73
0.55
0.43
1.53
5.01
0.37
11.22
**
**
**
**
4672bc
3925fg
5179b
4337def
4568cd
4877c
4691c
4478cde
4407de
4492.83
8.05
88
237.98
**
0
-15.9
10.9
-7.2
-2.2
4.4
0.4
-4.2
-5.7
ETBW-Ethiopia bread wheat, DH-Days to heading, DM-Days to maturity, PH-Plant height, SL-spike length, SW-spike weight, STPSspikelets per spike, GPS-grain per spike, GPST- grain per spikelet, TSW- Thousand seed weight, YLD Kg/ha- Yield in kilogram per
hectare, YAD- yield advantage, CV- Coefficient of variation,R2-R-squere, LSD- least significant.
Table 5: Combined mean of disease reactions (1-5 scale) of bread wheat genotypes evaluated in 20172018 main cropping season
Genotypes
ETBW7052(G13)
ETBW7076(G6)
ETBW7092(G11)
ETBW7069(G12)
ETBW7071(G15)
ETBW7072(G8)
Liban (G9)
Local(G1)
ETBW7104(G3)
ETBW7077(G7)
SR
1c
1.1bc
1c
1.13ab
1c
1.03c
1.03c
1c
1.04bc
1c
YAD
-8.4
21.3
-0.4
LR
1b
1.08ab
1b
1.03b
1b
1.06ab
1b
1b
1b
1.08ab
SEP
1.03ab
1.03ab
1b
1b
1b
1b
1b
1.03ab
1.07a
1.03ab
FHB
1.8b-e
1.9b-e
1.3f
2.4a
2.2ab
1.8b-e
1.9b-e
1.8cde
1.6ef
1.7de
104
ETBW7068(G5)
1.13ab
1.03b
1.03ab
2.1abc
ETBW7056(G2)
1.06bc
1.11ab
1.06ab
2.4a
ETBW7075(G10)
1.17a
1.17ab
1.03ab
2.0bcd
ETBW7088(G14)
1c
1.03b
1b
1.8b-e
kingbird(G4)
1c
1.06ab
1b
1.9b-e
Mean
1.04
1.04
1.02
1.91
CV%
14.14
17.29
9.25
30.15
R2 %
42
41
38
71
LSD 5%
0.097
0.12
0.062
0.38
F test
**
**
**
**
ETBW-Ethiopia bread wheat, CV- Coefficient of variation, LSD- least significant difference, R2-R-Squere, SR-stem rust, LRleaf rust, SEP-septoria, FHB-fusarium head blight.1-5 scale where 1= resistant, 5= susceptible
Major disease reactions
Most genotypes evaluated had significantly low scores for their corresponding economically
important disease reactions (Table5). However, some genotypes (ETBW7075 (G10) and
ETBW7069 (G12)) were less tolerance to stem and leaf rust and septoria. Similarly, genotypes
ETBW7069 (G12) ETBW7071 (G15) ETBW7068 (G5), ETBW7056 (G2) and ETBW7075
(G10) were less tolerance to fusarium head blight (Table 5). On the other hand, genotypes
ETBW7076 (G6) and ETBW7104 (G3) were better tolerance to stem and leaf rust and fusarium
head blight (Table 5).
Additive main effects and multiplicative interaction (AMMI) model
The combined ANOVA and AMMI analysis for grain yield at six environments exhibited by
bread wheat grain yield, was significantly affected by environments. This explained 65.06% of
the total treatment variation, while the G and GEI were significant and accounted for 13.34 and
9.44 %, respectively (Table 6).Similar findings have been reported in previous studies (Kaya et
al., 2006; Farshadfar et al., 2012). A study by Gauch and Zobel (1997) reported in standard
multi-environment trials (METs), environment effect contributes 80% of the total sum of
treatments and 10% effect of genotype and interaction.In additive variance, the portioning of
GEss data matrix using AMMI analysis, indicated the first PCAs were significant (P < 0.01).
PCA 1 and 2 accounted for 7.88 and 1.15 % of the GE interaction, respectively; representing a
total of 9.03 % of the interaction variation (Table 6).Similar results have been reported in earlier
studies (Mohammadi and Amri, 2009).
Large yield variation explained by environments indicated that environments were diverse,with
large differences between environmental means contributing maximum of the variation in grain
yield (Table 7).Grain yield of environments ranged from 3524 kgha-1in E3 to 5520 kg ha-1 in E2.
Genotype mean grain yield varied from 3940 kg ha-1 for ETBW7072 (G8) to 5069 kg ha-1 in
ETBW7076 (G6), with the over all mean of 4493 kg ha-1 (Table 7)
105
Table 6: Additive main effect and multiplicative interaction analysis of variances (AMMI) for
grain yield of 15 bread wheat genotypes evaluated at six environments
Source of variation
Total
Treatments
Genotypes
Environments
Block
GxE
IPCA 1
IPCA 2
Residuals
Error
DF
269
89
14
5
12
70
18
16
36
168
SS
192196683
168813778
25637440
125035456
3230047
18140883
15142869
2201589
796424
20152857
EX.SS%
100
87.83
13.34
65.06
1.68
9.44
7.88
1.15
0.41
MS
714486
1896784**
1831246**
25007091**
269171*
259155**
841271**
137599ns
22123
119957
DF = degree of freedom, SS = sum of squares, MS = mean squares, IPCA = Interaction Principal Component Axis, EX. SS% =
Explained Sum of square ns * ,** non-Significant ,Significant at the 0.5% and 0.1% level of probability respectively
Table 7: Average grain yield (kgha-1) of 15 bread wheat genotypes tested across six
environments in 2017-2018 main cropping seasons
Gen/Env
Local(G1)
ETBW7075(G10)
ETBW7092(G11)
ETBW7069(G12)
ETBW7052(G13)
ETBW7088(G14)
ETBW7071(G15)
ETBW7056(G2)
ETBW7104(G3)
kingbird(G4)
ETBW7068(G5)
ETBW7076(G6)
ETBW7077(G7)
ETBW7072(G8)
Liban (G9)
Mean
E1
3582
4532
3676
3485
3843
3442
3707
4836
4126
3564
3688
4885
3464
3256
3915
3867
E2
5209
6007
5398
5108
5370
5200
5312
6381
5795
5268
5458
6393
5244
4941
5720
5520
E3
3183
3856
3555
3140
3340
3369
3313
4129
3859
3362
3537
4237
3313
2960
3713
3524
E4
4112
4354
5417
4447
4480
5217
4465
3829
5256
4864
4791
4628
4480
4034
4350
4582
E5
3966
4340
4576
3946
3965
4452
4076
4674
4752
4323
4595
4771
4383
3856
4785
4364
E6
4701
5060
5310
4674
4683
5190
4804
5416
5485
5060
5344
5497
5135
4596
5548
5100
Mean
4125
4691
4655
4133
4280
4478
4279
4877
4879
4407
4569
5069
4337
3940
4672
4493
Gen-genotype; Env- environment, E1-BD-2017(Bedesso), E2-BD-2018, E3-BL-2017(Belem), E4-BL-2018, E5-MT2017 (Mata), E6-MT-2018, the number following each location indicates the year, E=environment
The estimation of yield and stability of genotypes were done by using the average coordinates of
the environment (AEC) methods (Yan, 2001; Yan and Hunt, 2001). The average environment is
defined by the average values of PC1 and 2 for the all environments, and it is presented with a
circle. The average ordinate environment (AOE) defines by the line which is perpendicular to the
AEA (average environment axis) line and pass through the origin. This line divides the
genotypes in to those with higher yield than average and in to those lower yield than average. By
projecting the genotypes on AEA axis, the genotypes are ranked by yield;where the yield
increases in the direction of arrow. In this case the highest yield had genotypes G6, G3 and
106
G2,but the lowers had G8, G1 and G12 (Fig1). Stability of the genotypes depends on their
distance from the AE abscissa. Genotypes closer to or around the center of concentric circle
indicated these genotypes are more stable than others. Therfore, the greatest stability in the high
yielding group had genotypes G6, G3 and G2, whereas the most stable and yielder of all was G6
(Fig1)
Figure 1; GGE bi-plot based on genotype-focused scaling for comparison of genotypes for their yield potential and
stability of bread wheat varieties at Western Oromia in Ethiopia
The genotype ranking is shown on the graph of genotype so-called “ideal” genotype (Fig 1). An
ideal genotype is defined as one that is the highest yielding across test environments and it is
completely stable in performance (that ranks the highest in all test environments; such as
genotypes G6, G3 and G2) (Farshadfar et al., 2012; Yan and Kang, 2003). Even though such an
“ideal” genotype may not exist in reality, it could be used as a reference for genotype evaluation
(Mitrovic et al., 2012). A genotype is more appropriate if it is located closer to “ideal” genotype
(Farshadfar et al., 2012; Kaya et al., 2006).So, the closer to the “ideal” genotype in this study
was G6 (Figure1). The ideal test environment should have large PC1 scores (more power to
discriminate genotypes in terms of the genotypic main effect) and small (absolute) PC2 scores
(more representative of the overall environments). Such an ideal environment was represented by
an arrow pointing to it (Figure 2). Actually, such an ideal environment may not exist, but it can
be used as an indication for genotype selection in the METs. An environment is more desirable if
it is located closer to the ideal environment. Therefore, using the ideal environment as the centre,
concentric circles were drawn to help visualise the distance between each environment and the
ideal environment (Yan and Rajcan, 2002). Accordingly, E3 (BL-2017=Belem), which fell into
the centre of concentric circles, was an ideal test environment in terms of being the most
107
representative of the overall environments and the most powerful to discriminate genotypes
(Fig2).
Figure 2: GGE bi-plot based on tested environments-focused comparison for their relationship E1-BD-2017(Bedesso), E2-BD-2018, E3-BL2017(Belem), E4-BL-2018, E5-MT-2017(Mata), E6-MT-2018, the number following each location indicates the year, E=environment.
Figure 3: GGE bi-plot based on tested environments-focused comparison for their relationships. E1-BD (Bedesso),
E2-BD-2018, E3-BL-2017(Belem), E4-BL-2018, E5-MT-2017(Mata), E6-MT-2018, the number following each
location indicates the year, E=environment.
108
Table 8: Correlation coefficients among six test environments
Environment
E2
E3
E4
E5
E6
ns,
E1
0.984**
0.936**
0.197ns
0.571*
0.554*
E2
0.977**
-0.134 ns
0.701**
0.687*
E3
0.066 ns
0.82**
0.806**
E4
E5
0.387 ns
0.378 ns
0.999**
*, ** non- Significant , significant at the 0.05 and 0.01 probability level, respectively.
The correlation coefficients among the six test environments and the vector view of the GGE-biplot delivered a brief summary of the interrelationship between the environments and correlation
coefficients were significant (Fig3) Most environments were positively correlated since the
angles among them were smaller than 90oapart from environment E4 (Belem-2018), which had
negatively correlated with E1 (BD-Bedesso-2017) and E2(BD-Bedesso-2018) since obtuse
angles between them (Figure 3). Similarly, Farshadfar et al. (2012) reported environments ER3
and EI3 which represented rain fed and irrigated conditions in 2011 cropping seasons,
respectively, made an obtuse angle with each other, indicated a negative correlation between the
response of genotypes to rain fed and irrigated conditions. Indirect selection could be functional
in the case where the same character was measured on the same genotypes in different
environments. Where there are no correlations of error effects among environments, the
phenotypic correlation between environments may be used to investigate indirect response to
selection (Cooper and Delacy, 1994). Indirect selection for grain yield can be partial across the
tested environments. This means, for instance, the genotypes adaptable or higher productivity in
E4 may also show similar responses to E5 and E6 as well.
Additive main effects and multiple interactions (AMMI) stability value (ASV)
AMMI stability value (ASV)
Genotypes exhibited significant genotype by environment interaction effects and the additive and
multiplicative interaction effect stability analysis (ASV) implied splitting the interaction effect.
In view of mean grain yield as a first criterion for evaluating, G6 was the highest mean grain
yield (5069kg ha-1), followed by the genotypes G3 and G2 with the mean grain yield of (4879
and 4877kgha-1 respectively). Whereas, genotypes G8, G12 and G1 were with low mean grain
yields across the testing locations (Table 7). The IPCA1 and 2 scores in the AMMI model are
indicators of stability (Purchase, 1997). Considering IPCA1, G6 was the most stable genotype
with IPCA1 value (-16.65), followed by G3 with IPCA1 value of (6.95). Likewise, in IPCA2, G9
was the most stable with interaction principal component value (-18.03). The two principal
109
components have their own extremes, however calculating the AMMI stability value (ASV) is a
balanced measure of stability (Purchase, 1997). Genotypes with lower ASV values are
considered more stable and genotypes with higher ASV are unstable. According to the ASV
ranking in the (Table 7), G8 was the most stable with an ASV value of 1 followed by G1 with
ASV value 2. However, G2 was the most unstable since higher ASV value of 15. The stable
genotype was followed with mean grain yield above the grand mean and this result was in
agreement with (Hintsa and Abay, 2013), who has used ASV as one method of evaluating grain
yield stability of bread wheat varieties in Tigray and similar reports been made by Abay and
Bjørnstad (2009); Sivapalan et al. (2000) in barley in Tigray and bread wheat using AMMI
stability value. A genotype with the least of genotype selection index (GSI) is considered as the
most stable genotype (Farshadfar, 2008). Accordingly, G6 was the most stable genotype since
with the low of genotype selection index (GSI) and the highest mean grain yield of all (Table7).
Table7: AMMI stability value, AMMI rank, yield, yield rank and genotype selection index and
principal component axis
GENOTYPES
G6
G3
G2
G10
G9
G11
G5
G14
G4
G7
G13
G15
G12
G1
G8
ASV
144.3
61.4
245.6
148.8
153.4
145.9
86.4
149.0
82.2
92.9
100.3
44.1
56.6
27.2
23.2
ASV
RANK
10.0
5.0
15.0
12.0
14.0
11.0
7.0
13.0
6.0
8.0
9.0
3.0
4.0
2.0
1.0
YLD
5069
4879
4877
4691
4672
4655
4569
4478
4407
4337
4280
4279
4133
4125
3940
YLD
RANK
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
GSI
11.0
7.0
18.0
16.0
19.0
17.0
14.0
21.0
15.0
18.0
20.0
15.0
17.0
16.0
16.0
IPCAg1
IPCAg2
-16.65
6.95
-28.98
-15.43
-4.16
17.29
6.82
17.97
9.88
5.42
-1.71
0.58
4.02
-3.16
1.16
5.04
2.55
-6.11
9.17
-18.03
3.27
-7.88
-0.03
0.72
-9.80
11.97
5.29
5.51
0.86
-2.54
Conclusion
Based on the two analyses of AMMI and GGE-bi-plot models, G6 and G3 considered by high
yield and more stability, consequently, G6 close to ideal genotype, so this genotype is adaptable
to a wide range of environmental conditions. Therefore, G6 was identified as ideal genotypes in
terms of yielding ability and stability, tolerant to diseases for advancement, release and use as
parents in future breeding programs.
Acknowledgment
110
The authors greatly acknowledged Oromia Agricultural Research Institute (IQQO) for financial
support. Haro-Sebu Agricultural Research Center staff members are warmly acknowledged for
technical and administrative support. Particularly, all cereal research team members thanked for
their technical support and field work and as well Kulumsa agricultural research center is
acknowledged for the provision of test materials.
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UK.” Available: www.genStat.co.uk
Yan, W.2001. GGE bi-plot- a windows application for graphical analysis of multi-environment trial data
and other types of two-way data Journal of Agronomy 93:1111-1118.
Yan, W. and Kang, MS. 2003. GGE bi-plot analysis: a graphical tool for breeders, In Kang MS, ed.
Geneticists, and Agronomist. CRC Press, Boca Raton, FL, pp. 63-88.
Yan, W. and Hunt, LA., Sheng, Q. and Szlavnics, Z. 2000.Cultivar evaluation and mega environment
investigation based on the GGE bi-plot. Crop Science.40:597-605.
Yan, W. and Hunt, LA.2001. Genetic and environmental causes of genotype by environment interaction
for winter wheat yield in Ontario. Crop Science. 41:19-25.
Yan, W. and Rajcan, I. 2002.Bi-plot analysis of test sites and trait relations of soybeaninOntario.Crop
Science. 42:11-20.
Adaptation Study of Released Finger Millet (Eleusinecoracana L.) Varieties in Western
Oromia, Ethiopia
Geleta Negash*,Wakgari Raga and Biru Alemu
Haro Sabu Agricultural Research Center (HSARC), P.0.Box 10, KellemWollega, Dembi Dollo,
Ethiopia,
*Corresponding author: geleta2017@gmail.com
Abstract
The experiment was conducted with eight improved finger millet varieties against local check at
Haro Sabu Agricultural Research Center (HSARC) on station and Chanka research sub site for
two consecutive years (2017-2018) to identify and recommend high yielding, stable and insect
pest tolerantvarieties. The seeds were planted in Randomized Completed Block Design (RCBD)
with three replications in the net plot size of 3m2 using four harvestable rows at the spacing of
30cm. Agronomic traits Viz. Days to heading (DH), Days to maturity (DM), Lodging percentage
(LDG), Grain yield (GY), Plant height (PH), Finger length (FL), Productive tillers (PTR),
Finger per main ear (FPME) Finger weight per plant (FWPP) and Head blast (HB)were
collected and analyzed. Analysis of variance revealed significant difference among varieties for
most observed traits. The combine ANOVA and the AMMIanalysis for grain yield across
112
environments revealed significantly affected by environments, that hold 40.84% of the total
variation.The genotype and genotype by environmental interation were significant and
accounted for 32.67% and 23.44% respectively. Pricipal component 1 and 2 accounted for 17.98
% and 5.09 % of the GEI respectively with a total of 23.07 % variation.In general, Adis-01 and
Boneya varietieswere identified as the best varieties for yielding ability, stability, tolerant to
diseases and recommended in the area and with similar agro-ecologies.
Key words: Adaptability, finger millet (Eleusinecoracana L.) stability varieties
Introduction
Finger millet, (Eleusinecoracana L.) Gaertn. ssp. coracana), is the second most widely grown
millets on the continent of Africa and it is an important crop grown in low input farming systems
by resource poor farmers in eastern and southern Africa (Damar et al., 2016). This is indigenous
to the highlands of Uganda and Ethiopia. Finger millet is widely produced by small scale
landholders and consumed locally (Adugna et al., 2011). It is well adapted to heat, drought and
poor soil stress that succeed in marginal and degraded soils (Okalebo, 1991). It is valued for
nutrition, malt, good storability, income and other uses for animal feeds. In Ethiopia, finger
millet covered 456171.54 hectare of land with the productivity of 22.30 qt/ha (CSA, 2017).
However, low in yielding due to lack of high yielding cultivars, moisture stress, and lodging
effect, diseases and low fertility and poor crop management practices (Degu et al., 2009).
Strengthen the seed production and delivery systems for improved varieties also the most
bottleneck of the crop in the small-scale farmers. Climatic change also directed to reduce the
productivity of many crops around the world. So that a considerable attention should be given to
the effect of genotype x environment interaction in the plant breeding programs, the relative
performance of cultivars for quantitative traits such as yield and the other characters, which
influence yield, vary from an environment to another. Consequently, to develop a variety with
high yielding ability and consistency over locations, high attention should be given to the
importance of stability performance for the genotypes under different environments and their
interactions.
The impacts of phenotypic variation principally based on the environmental situation and the
genetic constitution of the varieties. Such variation is more complicated by the fact that not all
genotypes respond in a similar way to change in the environment and no two environments are
exactly the same. The genotype × environment interaction results in genotype rank changes from
113
one environment to another, a dissimilar in scale among environments, or a combination of these
two situations. It is imperative to detect specific genotypes adapted to or stable in
environment(s), in that way succeeding quick genetic gain through screening of genotypes for
high adaptation and stability under varying environmental conditions prior to release as a variety
(Ariyo, 1989; Flores et al., 1998; Showemimo et al., 2000; Mustapha et al., 2001).While, most
genotypes show fluctuating yields when grown in different environments or agro-climatic zones.
This makes difficulties indicating the superiority of a specific variety. To tackle this challenge,
multi- location yield trials are essential to identify adaptable high yielding cultivars and discover
sites that best represent the target environment (Yan et al., 2000). Adaptability is the result of
genotype, environment and genotype by environment interaction. That means the ability to
perform at an acceptable level in a range of environments, stated to as general adaptability, and
the ability to perform well only in appropriate environments, known as specific adaptability
(Farshadfar and Sutka, 2006).
Combined analysis of variance can quantity G × E interactions and express the main effects
however, does not explain the interaction effect (Yuksel et al., 2002; Worku et al., 2013). The
main reason of additive main effects and multiplicative interactions (AMMI) is appropriate for
agricultural research is that the ANOVA part of AMMI can separate the G and E main effects
and the G × E interaction effects (Gauch et al., 2008). Besides, its greatest advantage is its ability
to take out interaction Principal Component Axis (PCA) along which there is a maximum
variation, thus indicated the number of components necessary to explain the pattern in the
interaction residual (Girma, 1999). Additive Main Effect and Multiplicative Interaction model
and genotype and genotype by environment interaction (GGE) bi-plot analysis are the most
frequently used analytical and statistical tools to determine the pattern of genotypic responses
across environments (Gauch and Zobel, 1996; Yan et al., 2000; Yuksel et al., 2002). AMMI and
GGE bi-plot (Gauch and Zobbel, 1996; Yan et al., 2000; Yuksel et al., 2002) for graphical
display of data and Eberhart and Russell (1966) model are the most commonly used analytical
and statistical tools to identify stable, high yielding and adaptable genotype(s) for wider and/or
specific environments. Therefore, the objective of the study was to evaluate, select and
recommend high yielder, tolerant to diseases, more adapted and stable varieties.
114
Materials and Methods
Description of locations: The experiment was conducted at two different rain fed locations in
Kellem and west Wollega zones of Haro-sebu agricultural research center for two consecutive
year on station and Chanka sub-site in western Oromia, Ethiopia, during the 2017-2018 main
cropping season, that represent the varying agro ecologies of the finger millet growing areas of
the zones.
Experimental materials: Eight finger millet genotypes including improved varieties (Adis-01,
Bareda, Boneya,Diga, Gudetu, Urji and Wama)and local checkwere evaluated.
Experimental design and management: Randomized completed block design (RCBD) with
three replications were used in all locations. Each experimental plot had six rows of 2.5 m long
and 30 cm apart with a plot area of 1.8 m x 2.5 m. Drill planting by hand was used with the same
rate for all locations. Fertilizer was applied at a rate of 150 and 100 kgha-1 Urea and DAP
respectively. All P2O5 and half of N were applied during planting, while the rest half splits were
applied at tillering stages. A seeding rate of 15 kg ha-1 was used. All agronomic management
wascarried out accordingly. The data considered for analysis was from the candidates of the net
plot, thus the four central harvestable rows. The harvested genotypes were sundried before being
tested for moisture content where 12% was the preferred average moisture content using
moisture tester. Grain yield data was then obtained by weighing the dried grain using a digital
scale.
Data collection method: Plants were selected randomly before heading from each row (four
harvestable rows) and tagged with thread and all the necessary plant based data were collected
from these sampled plants. Plot basis: Days to heading (DH), Days to maturity (DM), Lodging
percentage (LDG), Grain yield (GY), and Head blast (HB) was recorded as an economic
important of finger millet diseases. Plant basis: Plant height (PH), Finger length (FL),
Productive tillers (PTR), Finger per main ear (FPME) and Finger weight per plant (FWPP)
Statistical analysis: The collected data were organized and subjected to analyzed using SAS
version 9.2 (SAS, 2008) computer software and additive main effect and multiplicative
interaction (AMMI) analysis and GGE bi-plot analysis were performed using Gen Stat 15th
edition statistical package (VSN International, 2012).
Results and Discussions
Combined analysis of variance
115
The mean square of analysis of variance (ANOVA) is presented in Table 1. Highly significant
differences were detected among the main and the interaction effects (P ≤ 0.01) for most of the
parameters. The combined analysis of variance showed that significant differences were recorded
across location for all parameters except head blast. Year*varieties effects were significant for
most traits. Year*location *varieties were significant for most traits such as days to heading,
days to maturity,finger length,productive tillers, lodging and grain yield.
Table 1: Combined Analysis of variance (ANOVA) for grain yield and yield related traits of
finger millet varieties
Source
Rep
Vrt
Loc
Yr
vrt*loc
vrt*yr
loc*yr
vrt*loc*yr
DF
DH
DM
PH
FL
PTL
FPME
FW
HB LDG
2
7.1**
2.6
63.3
0.36
6.3**
0.6
24.3
0.3
0.1
7
189.6** 70.3** 357.1**
1.49
12.2**
6.1**
83.6** 6.9** 2.2**
1
256.8** 870** 11194** 142**
5.5** 109.4** 1526** 0.0 6.5**
1
2849** 1283** 527.6*
0.29
401.9**
4.4*
9532** 2.3** 6.5**
7
7.5**
11.7**
75.2
2.49**
1.2
2.3*
18.2
0.1
0.5
7
49.4** 36.5**
110.5
6.4**
3.6*
1.0
44.9
0.6* 2.2**
1
25.0** 2214** 585.8* 249.5** 230.5**
1.9
1036** 0.0
0.1
7
9.5**
23.2**
78.2
3.98**
5.1**
0.38
4.1
0.1
0.7*
YLD (kgha1
)
14471.49
5989786.2**
6490671.8**
44471991**
2098575.5**
1470375.4**
1451713**
729102.9**
Key: * **, significant at 5% and 1% respectively, Loc *vrt = location by variety, Yr*Loc*vrt = year by location by variety, DF -degree of
freedom, DH- Days to Heading; DM- Days to Maturity; PTL- productive tillers, Head Blast (HB), (LDG)- lodging, (PH)- Plant Height; Finger
length (FL); Finger Weight per plant (FW),Finger per main ear (FPME) and Yield Kilogram per hectare (YLDkgha-1)
Agronomic performance
Combined mean grain yield and other agronomic traits are presented in Table 2. Adis-01 variety
was recorded medium days to heading, days to maturity, and plant height, productive tillers and
finger per main ear indicated that, the possibility to resist against lodging problems and also it
recorded the highest grain yield. In the other hand, Diga variety was recorded medium days to
maturity, plant height, and finger weight but it recorded the lowest days to heading, and
susceptible to lodging problem.
Table 2: Combined mean grain yield and other agronomic performances of finger millet varieties evaluated.
Varieties
Adis-01
Bareda
Boneya
Diga
Gudetu
Local
Urji
Wama
Mean
R2
CV%
LSD 5%
F-test
DH
78.7d
87.1a
74.8g
74.9g
76.4f
79.7c
77.7e
80.7b
78.74
0.985
1.38
0.89
**
DM
131.8b
127.8c
131.4b
131.6b
128.6c
131.7b
132.5b
135.8a
131.4
0.97
1.254
1.34
**
PH
66.1b
62.4b
76.4a
61.4b
58.9b
65.6b
60.5b
64.9b
64.52
0.757
14.44
7.6
**
FL
4.5ab
4.3abc
4.8a
4.0bc
3.9bc
3.7c
4.1abc
4.0bc
4.174
0.901
22.42
0.76
*
PT
5.6b
7.5a
5.8b
7.4a
5.1b
5.2b
7.3a
5.9b
6.252
0.907
18.44
0.94
**
FPME
5.9b
5.9b
6.0b
5.8bc
6.3b
5.1cd
7.2a
4.9d
5.88
0.781
15.57
0.75
**
FW
14.3ab
10.8b
16.8a
11.1b
14.2ab
12.7b
10.3b
17.1a
13.41
0.892
37.92
4.2
HB
1.5cd
3.0ab
1.7c
3.1a
1.8c
1.2d
2.7b
1.5c
2.055
0.871
17.99
0.3
*
**
LDG
2.1cd
2.5ab
2.0cd
2.7a
1.5e
2.3bc
2.7a
1.8de
2.198
0.778
22.31
0.4
**
YLD (kgha-1)
3424.1a
1553.8e
2991b
2116.5d
2909.5bc
2422.3c
1460.6e
2163.4d
2418
0.97
10.37
204.6
**
116
Key: * **, significant at 5% and 1% respectively, R2- R- square, CV-coefficient of variation, LSD-least significance differences, DH- Days to Heading;
DM- Days to Maturity; PTL- productive tillers, Head Blast (HB), (LDG)- lodging, (PH)- Plant Height; Finger length (FL); Finger Weight per plant
(FW),Finger per main ear (FPME) and Yield Kilogram per hectare (YLDkgha)
Disease reaction with finger millet varieties across environments
Disease reaction: the result revealed that Adis-01, Boneya, Gdetu, Urji and Wama varieties are
better tolerance to economically important head blast disease but Diga and Bareda varieties are
less tolerance to head blast disease (Table3).
Table 3: Disease reactions for yield and yield related traits of the evaluated improved finger millet
varieties
Varieties
Adis-01
Bareda
Boneya
Diga
Gudetu
Local
Urji
Wama
Mean
R-Square (%)
CV%
LSD 5%
F-test
Head Blast
1.5cd
3.0ab
1.7c
3.1a
1.8c
1.2d
2.7b
1.5c
2.055
87.1
17.99
0.3
**
Key: 1-5 scale scoring was used for disease reaction where 1= resistant, 5= susceptible CV =coefficient of variation, LSD =least
significant different
Additive Main Effects and Multiple Interaction (AMMI) model
The mean squares for all varieties evaluated under different environmental condition for grain yield are
presented in Table4. The result indicated that differences among all varieties were significant (P ≤ 0.01).
Variation due to genotypes by environments interaction was significant for the studied traits, indicated
that genotypes differ genetically in their response to different environment. The genotypes by
environments interaction was significant effect on the grain yield, which explained 23.44% of the total
variation whiles the genotypes, contributed 32.67% of the variation. However, large portion (40.84%) of
the total variation was attributed to the environmental effect.
Table 4 Additive main effect and multiplicative interaction analysis of variances (AMMI) for grain yield of eight
finger millet varieties
Source
D.F.
S.S.
EX.SS%
M.S.
Total
95
128353731
100
1351092
Treatments
31
124429241
96.94
4013846**
Genotypes
7
41928507
32.67
5989787**
Environments
3
52414333
40.84
17471444**
Block
8
375731
0.29
46966ns
Interactions (GxE)
21
30086401
23.44
1432686**
IPCA 1
9
23083507
17.98
2564834**
IPCA 2
7
6527931
5.09
932562**
Residuals
5
474964
0.37
94993
Error
56
3548759
63371
117
Key: DF = degree of freedom, SS = sum of squares, MS = mean squares, IPCA = Interaction Principal Component Axis, ** =
highly significant, ns = non-significant, EX. SS%-Explained Sum of square
Significant percentage of genotypes by environments interaction was explained by IPCA-1
(17.98 %) followed by IPCA2 (5.09 %). Accordingly, Gauch and Zobel (1996) recommended
that the most accurate model for AMMI can be predicted by using the first two PCAs. The
genotypes by environments interaction components were smaller
than the genotypic
components related to predictable environment factor (such as geographic areas, major pest
problems,) the breeder searches for a genotypes specific requirements of environment while the
interaction is small and unpredictable (micro climatic or yearly variation in weather and
management practices) the breeder searches for a genotypes that has general adaptability and
unversed performance over the range environments.
Comparison plot for genotypes based on the concentric circle
Based on Figure 1 shows the comparison plot for variety, and an ideal variety is one which is near or at
the center of the concentric circle. Accordingly, the plot reflected that Adis-01 and Boneya are the most
ideal varieties as shown by their position. It also reflects that, these varieties have high mean grain yield
and more stable.
Figure 1: GGE bi-plot based on genotype-focused scaling for comparison of genotypes for their yield potential and
stability
Conclusions and Recommendations
Combined analysis of variance (ANOVA) result revealed significant difference of grain yield
and most of yield contributing traits among evaluated finger millet varieties across locations,
118
years and the interactions. This indicated that, the location and fluctuation of weather condition
over the cropping season had affected performance of varieties. Although the GEI of grain yield
partitioned in to different IPCAs using AMMI model analysis, the first principal component axis
for interaction alone explains most of the interaction sum of squares. The sign and magnitude of
IPCA scores showed the relative contribution of each genotype and environment for the
genotype and environment interactions. This helps to summarize the pattern and magnitude of
GEI and main effects that reveal clear insight into the adaptation of genotypes to environments.
This shows that, Adis-01 and Boneya varieties are fewer contributors to the interaction effect and
have consistent performances across locations. Therefore,Adis-01 and Boneyawere identified as
the best varieties in terms of yielding ability and stability, tolerant to diseases and better
agronomic performance.
Acknowledgment
The authors greatly acknowledged Oromia Agricultural Research Institute (IQQO) for financial
support. Haro-Sebu Agricultural Research Center staff members are warmly acknowledged for
technical and administrative support. Cereal research team significantly thankful for their
technical support in all rounds, Bako agricultural research center is also acknowledged for the
provision of test materials.
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Multi-Location Evaluation of Yield and Yield Related Trait Performance in Sorghum
(Sorghum bicolor L.) Genotypes at Western Oromia, Ethiopia
*
Geleta Negash ,Wakgari Raga, Biru Alemu andDereje Abera
Haro Sabu Agricultural Research Center (HSARC), P.0.Box 10, KellemWollega, Dembi Dollo,
Ethiopia,
*Corresponding author: geleta2017@gmail.com
Abstract
The field experiment was conducted on twelve sorghum genotypes (regional variety trial) against
checks at Haro Sabu Agricultural Research Center (HSARC) sub sites for three consecutive
years (2016-2018) to evaluate high yielding, insect pest tolerant genotypes and to assess
genotype by environmental interaction on grain yield and yield stability across four diverse
environments. The seeds were sown in Randomized Completed Block Design (RCBD) with three
120
replications in the net plot size of 9 m2 using four harvestable rows at the spacing of 0.75m and
0.15m. Eight agronomic traits and three economically important disease reaction were
evaluated. Analysis of variance detected significant difference among genotypes for all observed
traits both separated and combined analysis of variance. All observation attained significant
differences over years except grain yield. Whereas; locations had significantly affected all
observed traits in combined analysis. On the other hand, varieties*location significantly affected
all recorded traits excluding days to heading and thousand seed weight. Similarly, Year*variety
had significantly differences for all recorded trait except days to maturity, head weight and
lodging percentage while varieties*year*location exhibited significant difference for plant height,
head height, lodging percentage and grain yield. A pooled analysis of variance for grain yield
across four different environments, the G × E interaction was significant (P<0.001), and this
justified need for testing for GEI components using the GGE bi-plot analysis to enhance the
understanding the effects of components. The results revealed that four environments were
identifiable, which Hawa Galan had the most discriminating ability and good representativeness
whereby Kombo had a poor discriminating ability as well as least representativeness. GGE biplot analysis revealed that G3, G11 and G12 were identified as ideal genotypes in terms of
yielding ability and stability and were promoted to VVT for advancement, release and use as
parents in future breeding programs.
Key words: Sorghum evaluation, GGE, adaptation and yield stability, discriminating ability,
representativeness.
Introduction
Sorghum bicolor L. (Moench) is an important cereal crop which is ranked 5th in the world based
on its use and production after maize, wheat, rice and pearl millet (cereal statistics). Sorghum is
the most known crop especially in Africa, central America and south Asia and Ethiopia in general
and specifically a major cereal crop in west and KellemWollega zones.Sorghum is the most
known crop in Ethiopia in general and specifically a major cereal crop in west and
KellemWollega zones next to maize(CSA,2017). The national average production of sorghumis
25.25qt/ha (CSA 2017).The area coverage, production and yield (qt/ha) of sorghum in the
Wollegazones were 97,711.83 hectares, 2,989,883.74quintals production and 30.60 qt/ha
respectively (CSA 2017). It used as food, feed, beverage, construction.Sorghum characteristics
such as dense and deep roots, ability to reduce transpiration through leaf rolling and stomatal
closure among others make the crop able to survive dry periods. Hence sorghum has become a
strategic crop in the zones in the face of climate variability. Despite all the crop’s advantages
over other cereals under different condition, the sorghum crop production is still very low and
very low yields are being obtained. Research through, the national breeding programmes has
121
been done for years but with little progress due to limited knowledge on the relationship and
effects of genotype and environment and their interaction on the crop yield performance.
It is important to show the relationship between genotypes and environments for selected traits
graphically by use of a genotype, genotype by environment interaction (GGE) biplot that allows
visual assessment of genotype by environment interaction (GEI) pattern of multi-locational or
multi-environment data (Yan et al., 2000; Yan and Hunt, 2001). GGE is the most recent
approach for analysis of GEI and increasingly being used in GEI studies in plant breeding
research (Butran et al.,2004). The model was proposed by Yan et al. (2000) and has shown
extensive usefulness and a more comprehensive tool in quantitative genetics and plant breeding
(Yan et al., 2001; Yan and Rajcan, 2002). The model covers very critical areas in the study of
stability of multi-locational trials, like the which-won-where pattern, mean performance and
stability of genotypes, discriminating ability and representativeness of environments.
The GGE method emphasizes on two concepts, whereby in the first concept, it clearly points out
that even though the measured yield is a result of combination effect by Genotype (G),
Environment (E) and genotype x environment interaction (GEI), only G and GEI are relevant
and must be considered simultaneously when evaluating genotypes, thus the name GGE. The
second concept is based on the bi-plot technique which is used to estimate and show the GGE of
multi-environmental yield trial (MEYT). The GGE bi-plot is made by the first two principal
components (PC), PC1 and PC2. This is resulting from subjecting the environment centered
yield data (due to GGE) to singular value decomposition. This makes it very easy to identify
which genotype won in which environments. This is facilitated in the form of a polygon to
visualize the interaction patterns between genotypes and environments (Yan and Kang, 2003),
whereby greatestgenotypes are connected from the bi-plot origin such thatall genotypes are
contained in the polygon (Kaya et al., 2006). Some genotypes will be located on the vertices of
the polygon and they are either the best or the poorest in one or more environments (Yan et al.,
2000; Yan and Rajcan, 2002; Yan and Tinker, 2006). The rays are drawn perpendicular to the
sides of the polygon dividing it into sectors, such that the vertex genotypes in each sector is also
the best genotype for sites whose markers fall into respective sector so that sites within the same
sector share the same winning genotype (Yan, 2002; Yan et al., 2000). GGE bi-plot is a visual
display of the G + GE of multi-environmental data where groups of locations with similar
cultivar responses are presented and it identifies the highest yielding varieties for each group.
122
PC1 tend to correlate highly with the genotype means, the ideal cultivar is the one which possess
large scores for PC1, thus indicating high average yield and small PC2 scores indicating less GEI
and greater stability.
The objectives of this study were to identify genotype and environmental components that are
associated with the G × E interaction across the diverse environments and rank locations based
on discriminating ability and representativeness by using the genotype, genotype by environment
interaction (GGE bi-plot analysis) and to evaluate high yielding, insect pest tolerant genotypes.
Materials and Methods
Study sites
The multi-locational yield trial (MLYT) was conducted at four different locations in Kellem and
west Wollega zones of Haro-sebu agricultural research center at Kombo, Haro-sebu,Guliso and
Hawa-Galan research sub-sites (Tablen1)to assess and confirm the effects of genotype,
environment and genotype by environment interaction. The locations have different agroclimatic conditions.Hawa-Galan representing the high-potential area with good rains and soils,
Guliso representing the intermediate potential area with average rainfall, Haro-Sabu and Kombo
representing the low potential area. According to the 2016/7 season weather data collected at
study sites, the low potential areas had an average of 1100 mm annual rainfall and temperature
was 300C, while the high potential areas received an average of 1600 mm and temperature of
220C.The sites also characterized by different soil types, which range from the Light red Sandy
Clay at Guliso, Brown sandy-loam soils at Hawa Galan and black clay loam at Kombo and light
red sandy at Haro Sabu (Table 1).
Table-1. Description of four locations used for evaluation of sorghum genotypes
Locations
code
Harosabu
HS
Kombo
KB
Guliso
GL
Hawa Galan
HG
NI=not identified
Geographical position
Latitude
Longitude
080 19'N
0350 30'E
0
08 92 'N
0350 09'E
NI
NI
080 38' N
0350 50'E
Altitude
(m.a.s.l)
1550m
1440m
1600m
1905m
Average rain
fall(mm)
1100mm
1200mm
1400mm
1600mm
Soil type
Sandy clay
Sandy loam
Sandy Clay
Sandy loam
Breeding materials and experimental design: Twelve genotypes of sorghum including checks
were evaluated for three cropping seasons(2016-2018) at four different locations (Table2). The
trial was planted in randomized completed block design (RCBD) replicated three times. Each
plot consists of six rows (with four harvestable rows), 3m plot length with inter-row and intrarow spacing of 0.75m and 0.15m respectively and 2m spacing between each block was used. A
123
seed rate of 25 kgha-1and a combination of UREA and NPS fertilizer was applied at the
recommend rate of100 kg ha-1(1:1 ratio).NPS fertilizer was applied uniformly for all treatments
equally at the time of sowing and split application was carried out for UREA (half at planting
time and halfafter six weeks from emergency). All other agronomic practices were performed as
per the recommendation for the crop. The trial was raised under rain fed across all the test
locations. The data considered for analysis was from the candidates of the net plot, thus the four
enteral harvestable rows. The harvested panicles were sundried for two days before being tested
for moisture content where 12% was the preferred average moisture content using moisture
tester. Grain yield data was then obtained by weighing the dried grain using a digital scale.
Table 2. Description of sorghum landraces used in the multi-locational trials(pass port data )
NoVariety
Code Region
Zone
Woreda
Village Altitude Soil texture Soil color Source of
collection
/line code
1 SLRC-010
G1 Oromiya K/Wollega
d/Sadi
Laku
1514
sandy Light red
Field
2 SLRC-06
G8Oromiya W/Wollega
Guliso
d/guda
1708 Sandy Clay Light red Back yard
3 SLRC-027
G7Oromiya W/Wollega
Begi
Shelxa
1433 Clay loam
Black
Field
4 SLRC-028
G5 Oromiya W/Wollega
Begi Maganxaya
1584 Sandy loam
Brown
Field
5 SLRC-037
G4Oromiya K/Wollega
Gidam Alchayajilo
1698 Sandy loam
Brown
Field
6 SLRC-043
G3 Oromiya K/Wollega
Seyo
Minko
1690 Sandy loam
Brown
Field
7 SLRC-046
G12 Oromiya K/Wollega
Arbigaba
Masarata
1482 Sandy loam
Brown
Field
8 SLRC-048
G6 Oromiya K/Wollega
Hawawalal
Odamoti
1369 clay loam
Black
Field
9 SLRC-058
G11 Oromiya K/Wollega Yamalogiwalel Hora maka
1429 Clay loam
Black
Field
10 local check
G9Oromiya
11 Gamadi
G2 Oromiya
12 Lalo
G10 Oromiya
source: HSARC 2013/4 Landrace collection. G-genotype, K/Wollega-KellemWollega,W/Wollega-West wollega
Statistical analysis:
Multivariate method, Additive Main Effects and Multiplicative Interaction (AMMI) model was
used to assess genotype by environment interaction (GEI) pattern. The AMMI model equation is:
Yger =μ+αg+βe+Σnλnγgnδen+ εger+ρge; where, Yger is the observed yield of genotype (g) in
environment (e) for replication (r);
Additive parameters: μ is the grand mean; αg is the deviation of genotype g from the grand
mean, βe is the deviation of the environment e;
Multiplicative parameters: λn is the singular value for IPCA, γgn is the genotype eigenvector
for axis n, and δen is the environment eigenvector; εger is error term and ρge is PCA residual.
Accordingly, genotypes with low magnitude regardless of the sign of interaction principal
component analysis scores have general or wider adaptability while genotypes with high
magnitude of IPCA scores have specific adaptability (Gauch, 1992; Umma et al.,2014).
124
AMMI stability value of the ith genotype (ASV) was calculated for each genotype and each
environment according to the relative contribution of IPCA1 to IPCA2 to the interaction SS as
follows (Purchase et al.,2000):
Where, SSIPCA1/SSIPCA2 is the weight given to the IPCA1 value by dividing the IPCA1 sum
of squares by the IPCA2 sum of squares. Based on the rank of mean grain yield of genotypes
(RYi) across environments and rank of AMMI stability value (RASVi) a selection index called
Genotype Selection Index (GSI) was calculated for each genotype, which incorporates both
mean grain yield (RYi) and stability index in single criteria (GSIi) as suggested by Farshadfar,
2008. GSIi = RASVi + RYi
Environmental index (Ii) was obtained by the difference among the mean of each environment
and the general mean. Analysis of variance was carried using statistical analysis system (SAS)
version 9.2 software (SAS, 2008). Additive Main Effect and Multiplicative Interaction (AMMI)
analysis and GGE bi-plot analysis were performed using Gen Stat 15th edition statistical package
(VSN International, 2012). The best genotypes were also selected for the angle between the
genotype and environment is less than 90° (genotype performed above average on that particular
environment), and angle above 90° (below average performance) while that with equal to 90°
(near average performance).
Data collection method: Five plants were selected randomly before heading from each row
(four harvestable rows) and tagged with thread and all the necessary plant based data were
collected from these sampled plants.
Plant-based: Plant height, head height and head weight.
Plot based:Days to heading, days to physiological maturity, lodging percentage, thousand seed
weight, grain yield and three economically important insect pest and disease reaction like stalk
borer, anthracnose and leaf blight.
Results and Discussions
Combined Analysis of Variance
Mean square of analysis of variance for all genotypes at different environmental conditions for
grain yield and yield related traits are presented in Table3. Highly significant differences were
detected among years (P ≤ 0.01) for all parameters except grain yield. The combined analysis of
variance showed that year and location effects were significant for all parameters except head
125
weight and grain yield. Year*variety effects were significant for all parameters excluding days to
maturity, head weight and lodging percentage. Year*location*varieties were significant for most
studied traits such as plant height, head height, lodging percent and grain yield. Genotype by
environment interaction mean square was highly significant (P≤0.01) for all parameters except
days to 50% heading and thousand seed weight. This indicated the tested genotypes responded
differently across environments for those traits.
Table3: Analysis of variance (ANOVA) for grain yield and yield related traits of sorghum genotypes evaluated in
2016-2018 main cropping season
S. of variations
Year
Location
Replication
Varieties
Year*location
Year*variety
loc*vrt
Yr*loc*vrt
DF
2
3
2
11
2
22
33
22
DH
DM
653.0**
22.5**
3472.5** 4859.5**
27.6**
10.3ns
484.7** 1005.8**
60.4** 226.6**
125.6**
3.4ns
ns
4.3
9.6**
4.0ns
4.9ns
PH
HH
HW
62798.1**
55.6* 115810.6**
62051.1** 157.0** 19144.1**
358.2ns
18.2ns
670.3*
22369.7** 180.0**
510.5**
49396.4** 100.7**
4.5ns
6633.8**
20.1*
90.8ns
3571.0**
20.8*
240.2**
1561.6*
21.0*
4.5ns
LGD
3274836.1**
43123.8**
2363.7ns
4067.6ns
15401.4*
4226.2ns
7460.6**
9455.1**
TSW
YLD qt/ha
413.7**
56482.9ns
136.6** 6121673.4**
61.8**
170615.8*
29.4** 20386776**
99.0**
89092.3ns
27.3**
96777.2*
13.4ns 334338.4**
10.3ns 115338.2**
Key ns * ** non –significant, significant at 5% and 1% respectively, Loc *Vrt= location by varieties, Yr*Loc*Vrt = year by location by varieties,
DF -degree of freedom, DH- Days to Heading; DM- Days to Maturity; PH- Plant Height; HH- Head Height; HW-Head Weight, LGD- Lodging
percentage; TSW- Thousand Seed Weight, YLDqt/ha- Yield in quintals per Hectare.
Yield Performance of sorghum genotypes Across Environments
The mean performance of the tested sorghum genotypes for grain yield across location and year
presented in Table 4. It indicated some genotypes constantly performed best in a group of
environments and some are fluctuating across location (Tamene et al.,2013). The average grain
yield ranged from the lowest of 30.45qt ha-1 at Kombo (KM-08A) site to the highest of 40.87
qtha-1at Harosebu (HS-10A) site with grand mean of 37.71qt ha-1 (Table 4). The average grain
yield across environments ranged from the lowest of 24.15 qtha-1for local check to the highest of
50.18 qtha-1 for genotype SLRC-046 (Table4). This variation might be due to their genetic
potential of the genotypes. Genotype SLRC-043 was the top ranking pipeline at all environments
except at Guliso (GU-09A and GU-10A) and Hawa Galan (HG-10A); Genotypes SLRC-058 was
ranked first at HS-09A,HG-09A, HS-10A and HG-10A. Similarly, genotype SLRC-046 ranked
first at all sites except at KM-08A and HS-08A(Table4). The difference in yield rank of
genotypes across the environments exhibited the high crossover type of GxE interaction (Yan
and Hunt, 2001; Asrat et al.,2009).
Table 4: Mean grain yield (qt/ha) of sorghum genotypes evaluated at four environments
Genotypes
SLRC-010
Gamadi
SLRC -043
SLRC-037
KM-08A
22.27fg
28.88d
45.16a
25.75e
2016
HS-08A
33.88cd
43.55a-c
49.19a
28.29de
HS-09A
39.90c
42.99b
51.83a
43.43b
Grain Yield in qt/ha
2017
GU-09A
HG-09A
HS-10A
35.61ef
39.61de
37.57d
38.19d
41.21cd
43.99c
48.21b
52.80a
53.32a
43.95c
43.46bc
47.73b
2018
GU-10A
38.25e
41.18d
48.98b
46.93bc
HG-10A
40.05bc
42.94bc
48.79ab
44.58bc
Comb. Mean
35.89d
40.37c
49.79a
40.52c
126
SLRC -028
33.95c
31.31d
36.82d
36.67ed
38.58de
38.30d
38.63e
38.08c
36.54d
SLRC -048
31.68d
45.03b
43.45c
46.12b
46.26bc 45.31c
48.09ab
43.86b
44.91a
SLRC -027
31.12cd
36.59b-d
35.53d
33.61f
36.87e
33.62e
34.61f
23.60d
33.19e
SLRC -06
20.86g
12.76f
28.86e
26.15g
24.75g
27.81f
25.77g
28.64d
24.45f
Local. Check
17.51h
16.87f
25.75f
26.36g
28.18f
25.86f
25.38g
27.28d
24.15f
Lalo
23.84ef
17.93ef
25.01f
23.88g
25.03g
26.44f
26.02g
27.77d
24.49f
SLRC -058
38.57b
38.17b-d
50.08b
49.23b
53.32a
53.29a
55.64a
54.78a
49.14a
SLRC-046
32.61c
44.59ab
54.63a
54.78a
53.77a
53.86a
52.54a
54.62a
50.18a
Mean
30.45
32.07
40.26
38.41
40.31
40.87
39.40
39.94
37.71
CV%
5.75
19.54
4.3
3.86
4.21
4.13
3.72
13.05
8.52
LSD (5%)
29.53
10.56
29.20
24.97
28.62
28.47
24.69
87.84
18.29
F test
**
**
**
**
**
**
**
**
**
Key: SLRC- Sorghum Land Race Collection, KM – Kombo, HS-Harosebu, GU -Guliso, HG-Hawa Galan. The number following each location
indicates the year (08A = 2016, 09A = 2017, 10A = 2018),CV- Coefficient of variation, LSD- least significant difference
Agronomic performance
Combined mean grain yield and other agronomic traits are presented in Table5. High mean of
days to heading and days to physiological maturity were recorded by genotypes SLRC-058 and
SLRC-046. These offer great flexibility for developing improved varieties suitable for various
agro-ecologies with variable length of growing period.However, genotypes SLRC-028 and
SLRC-048were with short mean of days to heading and days to physiological maturity indicating
that early maturing genotypes were desirable when moisture is the limiting factors for sorghum
production. Similarly,genotypes SLRC-010, SLRC-037, SLRC-06, Local check and standard
check(Lalo) were recorded high plant height indicating that, these genotypes might be
susceptible to root and/or stem lodging butgenotypes like SLRC-043, SLRC-058 and SLRC-046
were with medium plant heightindicating that,the possibility to develop resistant variety against
lodging problems. Moreover, genotypes, SLRC-043, SLRC-058 and SLRC-046 were recorded
the highest grain yield and they had 23.33%, 21.72% and 24.3% yield advantage over the best
standard check (Gamadi) (Table 5).
Table 5: Combined Mean Grain yield and other Agronomic traits of Sorghum genotypes
Genotypes
SLRC-010
Gamadi
SLRC -043
SLRC-037
SLRC -028
SLRC -048
SLRC -027
SLRC -06
Local. Check
Lalo
SLRC -058
SLRC-046
Mean
CV%
LSD (5%)
F test
DH
DM
LDG
PH
HH
HW
127.67d
122.60f
130.37bc
124.02e
124.42e
122.71f
129.83c
120.02g
127.75d
1216.44h
132.58a
131.04b
125.78
1.68
120
**
172.83d
172.92d
174.00d
165.62f
169.17e
169.17e
175.83c
163.08g
166.17f
163.08g
181.88b
183.42a
171.44
1.2
1.17
**
2.5b
2.25cd
1.04h
2.08d
2.62b
1.7ef
1.83e
2.29c
1.60f
2.88a
1.29g
1.10h
1.93
15.83
0.17
**
420.70a
327.12ef
349.80d
407.95ab
388.83c
353.3d
344.05de
407.08ab
394.66bc
403.34bc
326.33f
344.03de
372.26
8.1
17.19
**
32.87a
26.28de
33.07a
31.66a
31.83a
29.60b
28.81bc
27.24cd
33.52a
27.09c-e
25.24e
29.39b
29.71
11.1
1.89
**
99.82c
101.50c
114.75ab
106.35bc
118.88a
99.32c
103.96bc
114.03ab
110.00a-c
110.22c
106.56a-c
105.36bc
106.81
20.5
12.47
**
TSW YLD qt/ha
24.76e
32.79ab
32.58ab
26.56c-e
25.36e
25.69de
27.47c-e
25.36e
29.80a-c
29.40b-d
33.45a
32.48ab
28.83
22.85
3.75
**
35.89d
40.37c
49.79a
40.52c
36.54d
43.86b
33.19e
24.45f
24.15f
24.49f
49.14a
50.18a
37.71
8.52
18.29
**
YAD (%) against best
check (Gamadi)
-11.09%
0%
23.33%
0.37%
-9.47%
8.64%
-17.77%
-39.43%
-40.18%
-39.33%
21.72%
24.3%
Key: SLRC=Sorghum Landrace Collection, DH=Days to heading, DM=Days to maturity, PH= Plant height, HH= Head height, LDG- Lodging percentage, HW-head
weight, TSW- Thousand seed weight, YLD qt/ha- Yield in quintals per hectare, YAD- yield advantage, CV- Coefficient of variation, LSD- least significant difference.
Major disease reaction across environments
Most genotypes evaluated had significantly low scores with their corresponding economically
important insect pest and disease reactions. However, some genotypes Gamadi (G2) andLalo
(G10) were less tolerance to stalk borer but genotypes SLRC-043(G3), SLR-058 (G11) andSLR127
046 (G12) were better tolerance to stalk borer (Table 6). In this study, maximum anthracnose
disease reaction was recorded by genotypes Gamadi (G2) and SLRC-048(G6). Likewise,
maximum Leaf blight disease reaction observed by Gamadi (G2) and Lalo (G10). On the other
hand, genotypes SLRC-043(G3), SLRC-058 (G11) and SLRC-046 (G12) were better tolerance to
stalk borer, anthracnose and leaf blight (Table6).
Table 6. Combined mean of disease and insect pest reactions of sorghum genotypes evaluated in 20162018 main cropping season.
Genotypes
Stalk borer
Anthracnose
Leaf blight
SLRC-010 (G1)
1.00e
1.36d
2.04e
Gamadi (G2)
1.169a
2.5a
2.88a
SLRC -043(G3)
1.027de
1.4d
2.04e
SLRC-037 (G4)
1.022de
2.29b
2.04e
SLRC-028 (G5)
1.00e
2.29b
2.54b
SLRC -048(G6)
1.078bc
2.417a
2.38c
SLRC -027(G7)
1.00e
1.44d
1.88f
SLRC -06 (G8)
1.11b
1.56c
2.21d
L.Check (G9)
1.056cd
1.08e
2.04e
Lalo (G10)
1.167a
2.33b
2.88a
SLR-058 (G11)
1.00e
1.63c
1.57g
SLR-046 (G12)
1.083bc
1.37d
1.29h
Mean
1.06
1.72
2.15
CV%
4.79
8.61
1.37
LSD(5%)
0.03
0.09
0.02
F test
**
**
**
Key: SLRC=Sorghum Landrace Collection, CV- Coefficient of variation, LSD- least significant difference. 1-5 scale where 1= resistant, 5=
susceptible
Additive main effects and multiple interaction (AMMI) models
Combined analysis of variance revealed highly significant (P≤0.01) variations among
environments, genotype x environment interaction, IPCA-1 and IPCA-2 (Table7). This result
indicated there was a differential yield performance among sorghum genotypes across testing
locations and strong GEI.Similar result was reported on wheat (Sial et al., 2000) and rice
(Panwar et al.,2008).The GEI significant effect on the grain yield of sorghum genotypes, which
explained 7.0% of the total variation whereas the genotypes contributed 80.1% of the variation.
However, merely 9.4% of the total variation is credited to the environmental effect (Table7).
This also indicated the existence of large amount of reverent response among the genotypes to
changes in growing environments and the differential discriminating ability of the test
environments. Considerable percentage of GxE interaction was explained by IPCA-1 (4.8%)
followed by IPCA2 (1.3%) and therefore used to create a 2-dimensional GGE bi-plot. Gauch and
Zobel (1996) suggested that the most accurate model for AMMI can be predicted by using the
first two IPCAs. Moreover, several authors took the first and second IPCA for GGE bi-plot
analysis and greater proportion of GEI were explained by the first IPCA for maize (Abera and
Labuschagne, 2005), bread wheat (Yuksel et al., 2002; Farshadfar, 2008; Worku et al., 2013),
common bean (Temesgen et al., 2008) and field pea (Mengistu et al., 2011).
128
Table 7: Partitioning of the Explained Sum of square (SS) and Mean of square (MS) from AMMI analysis
for grain yield of 12 sorghum genotypes evaluated at four environments
Source of variation
Total
Treatments
Genotypes
Environments
Block
Interactions
IPCA 1
IPCA 2
Residuals
Error
D.F
287
95
11
7
16
77
17
15
45
176
S.S
31574
30462
2961
25281
120
2220
1528
407
285
992
EX.SS%
100.0
96.5
9.4
80.1
0.4
7.0
4.8
1.3
0.9
M.S
110
320.7**
269.2**
3611.6**
7.5ns
28.8**
89.9**
27.1**
6.3 ns
5.6
Key: df = degree of freedom, SS = sum of squares, MS = mean squares, IPCA = Interaction Principal Component Axis, ** =
highly significant, ns = non-significant, EX. SS%-Explained Sum of square
Yield Performance per location and AMMI
Genotype SLRC-043 (G3), SLRC-058 (G11) and SLRC-046 (G12) were produced the best
average grain yield (49.79 qtha-1), (49.13 qtha-1) and (50.18 qtha-1) respectively and attained an
IPCA-I value relatively close to zero (-0.83) (0.02) and (0.34) respectively. These indicated
genotypes were stable and widely adaptable advanced line (Table 8, Fig 1). Genotypic stability
was an important in addition to grain yield (Naroui et al., 2013). Genotype SLRC-06 (G8) and
(G9) achieved low IPCA-I score (-0.52) and (-0.09) respectively and recorded low grain yield
(24.45 qtha-1) and (24.15 qtha-1) respectively (Table8, Fig 1). G1, G4, G5 and G6 were
recorded medium grain yield (35.89 qtha-1, 40.51 qtha-1, 36.54 qtha-1, and 43.86 qtha-1)
respectively. However, they recorded the highest IPCA-I score (1.33, 1.90, -1.07 and -1.57)
respectively implying that, these genotypes were unadaptable and unstable genotypes (Table8,
Fig 1).
Fig.1 Matrix plot of environment and genotypes mean grain yield (qtha -1) versus Interaction Principal Component Axis (IPCA-I) score. The
reference line on the x-axis is the average grain yield (39 qtha-1) and on the y-axis is the IPCA-I value indicating genotype stability (IPCA-
I=0)
129
The result indicated most of the tested environments revealed fluctuating mean grain yields and
IPCA scores (Table8, Fig 1). For example, the overall mean grain yield at Harosebu during the
2016 growing season was 32.07qtha-1 while the mean grain yield at the same location during the
2017 cropping season was 40.26qtha-1(Table8, Fig1). This variation might be due to weather
conditions, experimental plots and other soil factors at the tested environment. However, Hawa
Galan is exhibited consistent mean grain yields than the rest test environments
Table8. Mean grain yield (qtha-1) per location and AMMI
Mean grain yield over locations (qtha-1)
KM-08A HS-08A HS-09A GU-09A HG-09A HS-10A GU-10A HG-10A
SLRC-010 (G1)
22.27
33.88
39.9
35.6
39.61
37.57
38.25
40.05
Gamadi (G2)
28.88
43.55
42.99
38.18
41.21
43.99
41.18
42.94
SLRC-043 (G3)
45.16
49.19
51.83
48.21
52.8
53.32
48.98
48.79
SLRC-037 (G4)
25.76
28.29
43.43
43.95
43.46
47.73
46.92
44.57
SLRC-028 (G5)
33.95
31.31
36.82
36.66
38.58
38.3
38.63
38.08
SLRC-048 (G6)
44.91
31.68
45.03
43.45
46.12
46.25
45.31
48.09
SLRC-027 (G7)
31.12
36.59
35.53
33.6
36.86
33.62
34.6
23.6
SLRC-06 (G8)
20.86
12.76
28.86
26.15
24.75
27.81
25.77
28.64
L. Check (G9)
17.51
16.87
25.75
26.36
28.18
25.86
25.38
27.27
Lalo (G10)
23.84
17.93
25.01
23.88
25.03
26.44
26.02
27.77
SLRC-058 (G11)
38.57
38.17
53.32
50.08
53.29
55.63
49.23
54.78
SLRC-046 (G12)
32.61
44.59
54.63
54.78
53.77
53.86
52.54
54.62
Mean
30.45
32.07
40.26
38.41
40.31
40.87
39.4
39.93
Key: KM=kombo, HS=harosebu,GU= guliso, HG=hawagalan
Genotypes
Mean
35.89
40.37
49.79
40.51
36.54
43.86
33.19
24.45
24.15
24.49
49.13
50.18
37.71
IPCA-1 IPCA-2
1.33
0.00
0.73
-0.30
-0.83
0.36
1.90
0.50
-1.07
-0.29
-1.57
-1.15
-1.88
2.72
-0.52
-0.74
-0.09
-0.10
-1.36
-1.05
0.02
-0.48
0.34
0.53
Relationship among test environments
The similarity between two environments is determined by both the length of their vectors and
the cosine of the angle between them (Figure2). Harosebu and Hawa Galan had good
discriminating ability as shown by a long environmental vector, followed by Guliso site.
However, Kombo had poor discriminating ability, as was indicated by its short environmental
vector. The study shows Harosebu and Hawa Galan were the most discriminating locations
which means such sites gave more information on the performance of the genotypes, while
Kombo was the least discriminating environment which means less information about the
performance of the genotypes. This means if the study is carried out for several seasons and
same site continue to be non-discriminating (less informative); such locations can be dropped
and not to be used as test locations. Information on relationships among the test environments
was also given (Figure2) as is indicated by the cosine of the angles; acute angle indicates a
positive correlation, right angle and obtuse angles indicate no correlation and negative
correlation, respectively. Angles between any of the two environments; Hawa Galan (HG-09A)
and Harosebu (HS-09A); Kombo (KB-08A) and Harosebu (HS-08A);Guliso (GL-10A) and
Hawa Galan(HG-10A) were acute and hence showed positive correlations. Kombo (KB-08A)
130
and Hawa Galan (HG-10A); Harosebu (HS-08A) and Hawa Galan (HG-10A) were obtuse and
exhibited negative correlations. The close associations among test environments suggested that
the same information in terms of performance can be obtained from fewer test locations and
some may be dropped without losing any information about the cultivars under test, thus
reducing experimental costs (Yan and Tinker, 2005). The results from the study indicated
genotypes G7, G8, G9 and G10 performedbelow average in the four environments.However,G2,
G4 and G6 performed above average in Kombo (KB-08A), Harosebu (HS-08A), Guliso (GL09A), Guliso (GL-10A) and Hawa Galan (HG-10A) locations whereas G3, G11 and G12 were
performed above average in all environmental condition(Table8, Figure 2)
Scatter plot (Total - 94.69%)
4
HG -10A
8
9
1
6
10
11
12
G U-09A
HS-10A
G U-10A
HS-09A
HG -09A
5
2
KM-08A
7
3
HS-08A
PC1 - 89.22%
G enotype scores
Environment scores
Vectors
Figure 2: GGE bi-plot based on tested environments-focused comparison for their relationships
An essential feature of the GGE bi-plot (which-won- where) was also anticipated. In
environment identification process, furthest genotypes are connected together to form a polygon,
and perpendicular lines are drawn to form sectors which will make it easy to visualize
environments. Environment concept requires multi-year data, but in this study, environment
study was carried out and the results (Figure3) indicated four environments thus four
environments, Kombo (KB), Harosebu (HS), Guliso (GL) and Hawa Galan (HG). The winning
genotypes for each sector are those placed at the vertex. Therefore, G3 is the winner at Kombo
(KB-08A), Hawa Galan (HG-09A), Harosebu (HS-09A) and Guliso (GL) environment, while
G12 is at Hawa Galan (HG-09A), Harosebu (HS-09A) and Guliso (GL) locations and G11 as
well as G4 are the winner at Hawa Galan (HG-10A) location (Figure3). The equality line
131
between G12 and G4 shows that the G12 was better than G4 in all locations. On the line that
connects the two is G11 which means the three can be ranked G12, G11 and G4 in all the
environments (Figure3).
Scatter plot (Total - 94.69%)
4
HG -10A
8
9
1
6
10
11
12
G U-09A
HS-10A
G U-10A
HS-09A
HG -09A
5
2
KM-08A
7
3
HS-08A
PC1 - 89.22%
G enot ype scores
Environment scores
Convex hull
Sect ors of convex hull
Figure 3. The which-won-where view of the GGE bi-plot to show which genotypes performed best in which
environment
Discriminating ability of the test environment and genotype stability
The concentric circles on the bi-plot help to visualize the length of the environment vectors,
which are comparative to the standard deviation within the particular environments and are a
measure of the discriminating ability of the environments (Worku et al., 2013). Environments as
well as genotypes that fall in the central (concentric) circle are considered as an ideal
environments and stable genotypes, respectively (Yan and Rajcan, 2002).An environment is
more desirable and discriminating when located closer to the central circle (Naroui et al., 2013).
As a result, in the presentstudy, Hawa Galan (HG) wasmore representative and discriminating
environments but Kombo (KB) was non-discriminating and less representative site (Fig.2and4)
Similarly, Odewale et al. (2013) reported that only one environment was stable, representative
and discriminating among the nine environments for the performance of five coconut genotypes.
Ranking based on the genotype-focused scaling assumed that stability and mean grain yield were
equally important (Yan and Rajcan, 2002). The best candidate genotypes were expected to have
high mean grain yield with stable performance across all the tested locations. Consequently, high
yielding and comparativelymore stable genotypes can be considered as base line for genotype
evaluation (Yan and Tinker, 2006). Both environments-focused bi-plot and genotype-focused
comparison of genotypes shown that genotype SLRC-043 (G3) fell in the central circle
indicating its high yield potential and comparatively stableto the other genotypes (Figure 5). As
132
well, genotypes such as SLRC-058 (G11) and SLRC-046 (G12) were fell close to the ideal
genotype or around the center of concentric circle indicated these genotypes possessed specific
adaptability with best grain yield potential. Therefore, genotypes SLRC-043 (G3),SLRC-058
(G11) and SLRC-046 (G12)were the best performing pipeline cultivars.
Comparison biplot (Total - 94.69%)
4
HG -10A
8
11
12
G U-09A
HS-10A
G U-10A
HS-09A
HG -09A
9
1
10
6
5
2
KM-08A
3
7
HS-08A
PC1 - 89.22%
G enot ype scores
Environment scores
AEC
Figure 4 Ranking environments comparatively to ideal environment
Comparison biplot (Total - 94.69%)
4 -10A
HG
8
9
10
1
5
7
G
U-09A
HS-10A
G
U-10A
HS-09A
HG -09A 6
2
KM-08A
11 12
3
HS-08A
PC1 - 89.22%
G enotype scores
Environment scores
AEC
Figure 5: GGE bi-plot based on genotype-focused scaling for comparison of genotypes for their yield potential and
stability.
Additive main effects and multiple interaction (AMMI) stability value (ASV)
AMMI Stability Value helps selection of relatively stable high yielding genotypes. The best
genotype should have high mean grain yield and small ASV value. In view of that, genotype (G9
and G5), showed the lowest ASV (4.29 and 13.11, respectively) but recorded the lowest grain
yield (24.15and 36.54 qtha-1), respectively. Moreover, G3, G11 and G12 were the highest yielder
genotypes (49.78, 49.14and 50.17 qtha-1) with relatively moderate ASV (18.38, 20.20 and 28.26,
133
respectively) (Table 9). These genotypes revealed reasonably better stability compared to the
other genotypes. However, stability needed to be considered in combination with grain yield
(Farshadfar, 2008). Similarly, Odewale et al. (2013) evaluated five coconut varieties across nine
environments and found two most stable varieties. Farshadfar (2008) evaluated twenty bread
wheat genotypes for four years across two locations and found that two genotypes were
consistently stable as revealed by AMMI stability value and genotype selection index.
Table 9AMMI Stability Value, AMMI rank, Yield, yield rank and Genotype Selection Index
Genotype
G12
G3
G11
G6
G4
G2
G5
G1
G7
G10
G8
G9
ASV
28.26
18.38
20.20
24.18
30.03
20.43
13.11
16.78
40.22
16.80
16.66
4.29
ASV rank
10.00
6.00
7.00
9.00
11.00
8.00
2.00
4.00
12.00
5.00
3.00
1.00
YLD qt/ha
50.17
49.78
49.14
43.85
40.52
40.37
36.54
35.89
33.19
24.49
24.45
24.15
YLD rank
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
GSI
11.00
8.00
10.00
13.00
16.00
14.00
9.00
12.00
21.00
15.00
14.00
13.00
G-genotype
Conclusions and Recommendations
The results revealed grain yield performance for the 12 genotypes were significantly influenced
by environment, genotype and their interaction. A further analysis on the adaptability and
stability across the four environments were conducted. Therefore, in view of these, G3, G11 and
G12 presented both high yielding and stable across the test environments. These have been
identified as possible candidates for advancement, for release and for use as parents in future
breeding programmes. From the test environments, Hawa Galan was the most discriminating
location which means it gave more information on the performance of the genotypes. It was
exhibited good discriminating ability and representativeness, making it the most ideal
environment in this multi-locational yield trials.
Acknowledgment
The authors greatly acknowledged Oromia Agricultural Research Institute (IQQO) for financial
support. Haro-Sabu Agricultural Research Center staff members are warmly acknowledged for
technical and administrative support. Cereal research team greatly thanked for their technical
support.
134
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Multi-Location Evaluation of Yield and Yield Related Trait Performance in Sorghum (Sorghum
bicolor L.) Genotypes at Western Oromia, Ethiopia
Geleta Negash*,Wakgari Raga and Biru Alemu
Haro Sabu Agricultural Research Center (HSARC), P.0.Box 10, KellemWollega, Dembi Dollo, Ethiopia,
*Corresponding author: geleta2017@gmail.com
Abstract
The experiment was conducted on twelve sorghum genotypes against checks at Haro Sabu
Agricultural Research Center sub sites for two years (2017-2018) to evaluate high yielding,
insect pest tolerant genotypes and to assess genotype by environmental interaction on grain yield
and yield stability. The seeds were sown in Randomized Completed Block Design with three
replications in the net plot size of 9 m2 using four harvestable rows at the spacing of 0.75m and
0.15m. Nine agronomic traits and three economically important disease reaction were
evaluated.Analysis of variance revealed significant difference among genotypes for all observed
traits. All observation attained non-significant differences over years except days to heading,
136
days to maturity, thousand seed weight and grain yield. Similarly, varieties*location was
significantly affected allrecorded traits except root lodging, while varieties*year*location
exhibited significant difference for all traits except plant height, head weight and thousand seed
weight. The results revealed that, Sayo sub site was the most discriminating ability and good
representativeness site. The combined analysis of variances and AMMIanalysis for grain yield
across environments exhibited significantly affected by environments, explained 62.59 % of the
total variation.The genotype and genotype x environmental interation were significant and
accounted for 29.39 % and 6.03 % respectively. Pricipal component one and two accounted for
4.14 % and 1.30 % of the genotype x environmental interation respectively with a total of 5.44
% variation. Generally, G3, G5 and G9 were identified as promising genotypes for yielding
ability and stability, tolerant to diseases and use as parents in future breeding programs.
Key words: Sorghum evaluation, GEI, yield stability, discriminating ability, representativeness.
Introduction
Sorghum bicolor L. (Moench) is an important cereal crop which is ranked 5th in the world based
on its use and production after maize, wheat, rice and pearl millet (cereal statistics). Sorghum is
the most known crop especially in Africa, Central America and south Asia and in Ethiopia a
major cereal crop (CSA, 2017). In Ethiopia, the national average production of sorghum is
25.25qt/ha (CSA, 2017).Sorghum is used as food, feed, beverage, construction. Sorghum
characteristics such as dense and deep roots, ability to reduce transpiration through leaf rolling
and stomatal closure among others make the crop able to survive dry periods. Hence sorghum
has become a strategic crop in the face of climate variability. Despite all the crop’s advantages
over other cereals under different condition, the sorghum crop production is still very low and
very low yields are being obtained. Research through, the national breeding programmes has
been done for years but with little progress due to limited knowledge on the relationship and
effects of genotype and environment and their interaction on the crop yield performance. It is
important to show the relationship between genotypes and environments for selected traits
graphically by use of a genotype, genotype by environment interaction (GGE) biplot that allows
visual assessment of genotype by environment interaction (GEI) pattern of multi-locational or
multi-environment data (Yanet al.,2000; Yan and Hunt,2001). GGE is the most recent approach
for analysis of GEI and increasingly being used in GEI studies in plant breeding research
(Butranet al., 2004). The model was proposed by Yan and Hunt (2001) and has shown extensive
137
usefulness and a more comprehensive tool in quantitative genetics and plant breeding (Yanet al.,
2000; Yan and Hunt, 2002). The model covers very critical areas in the study of stability of
multi-locational trials, like the which-won-where pattern, mean performance and stability of
genotypes, discriminating ability and representativeness of environments. The GGE method
emphasizes on two concepts, whereby in the first concept, it clearly points out that even though
the measured yield is a result of combination effect by Genotype (G), Environment (E) and
genotype x environment interaction (GEI), only G and GEI are relevant and must be considered
simultaneously when evaluating genotypes, thus the name GGE. The second concept is based on
the bi-plot technique which is used to estimate and show the GGE of multi-environmental yield
trial (MEYT). The GGE bi-plot is made by the first two principal components (PC), PC1 and
PC2. This is resulting from subjecting the environment centered yield data (due to GGE) to
singular value decomposition. This makes it very easy to identify which genotype won in which
environments. This is facilitated in the form of a polygon to visualize the interaction patterns
between genotypes and environments (Yan and Kang, 2003), whereby greatest genotypes are
connected from the bi-plot origin such thatall genotypes are contained in the polygon (Kaya et
al., 2006). Some genotypes will be located on the vertices of the polygon and they are either the
best or the poorest in one or more environments (Yanet al., 2000; Yan and Hunt, 2002; Yan and
Tinker, 2006). The rays are drawn perpendicular to the sides of the polygon dividing it into
sectors, such that the vertex genotypes in each sector is also the best genotype for sites whose
markers fall into respective sector so that sites within the same sector share the same winning
genotype (Yanet al., 2000; Yan and Hunt, 2002). GGE bi-plot is a visual display of the G + GE
of multi-environmental data where groups of locations with similar cultivar responses are
presented and it identifies the highest yielding varieties for each group. PC1 tend to correlate
highly with the genotype means, the ideal cultivar is the one which possess large scores for PC1,
thus indicating high average yield and small PC2 scores indicating less GEI and greater stability.
Therefore, the objective of this study was: to identify stability of nine sorghum genotypes across
locations with high level of grain yield and yield stability and insect pest tolerant.
Materials and Methods
Study sites: The multi-locational yield trial (MLYT) was conducted at three different locations
in Kellem and west Wollega zones of Harosebu agricultural research center on main station and
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sub sites ( Haro-sebu on station, altitude 1550 masl, 080 19'N, 0350 30'E) (Guliso, altitude 1600
masl) and Sayo research sub-sites FTC.
Breeding materials and experimental design: Twelve genotypes of sorghum including checks
were evaluated sequentially for two cropping seasons(2017-2018) at three different locations
(Table 1). The trial was planted in randomized completed block design (RCBD) replicated three
times. Each plot consists of six rows (with four harvestable rows), 3m plot length with inter-row
and intra-row spacing of 0.75m and 0.15m respectively and 2m spacing between each block was
used. A seed rate of 25 kg ha-1 and a combination of UREA and NPS fertilizer was applied at the
recommend rate of 100 kg ha-1(1:1 ratio). NPS fertilizer was applied uniformly for all treatments
equally at the time of sowing and split application was carried out for UREA (half at planting
time and knee stage). All other agronomic practices were performed as per the recommendation
for the crop. The trial was raised under rain fed across all the test locations. The data considered
for analysis was from the candidates of the net plot (four harvestable rows). The harvested
panicles were sundried for two days before being tested for moisture content where 12 % was the
preferred average moisture content using moisture tester. Grain yield data was then obtained by
weighing the dried grain using a digital scale.
Table 1: List of sorghum genotypes evaluated at multi-location trials in 2017-2018 main cropping season
No
Codes
Genotypes
Sources
1
G1
Gemedi
BARC
2
G2
SA-07MW6054
MARC
3
G3
SA-06AN6083
MARC
4
G5
SA-07MW6064
MARC
5
G9
SA-07MW6002
MARC
6
G11
Chemeda
BARC
7
G12
SA-06AN7013
MARC
8
G13
SA-02BK7072
MARC
9
G16
SA-07MW6073
MARC
10
G17
Local
Farmer
11
G18
SA-06AN7010
MARC
12
G24
SA-201433
MARC
Source: BARC-Bako Agricultural Research Center, MARC- Malkassa Agricultural Research Center, Ggenotype.
Statistical analysis: Multivariate method, Additive Main Effects and Multiplicative Interaction
(AMMI) model was used to assess genotype by environment interaction (GEI) pattern. The
AMMI model equation is: Yger =μ+αg+βe+Σnλnγgnδen+ εger+ρge; where, Yger is the observed
yield of genotype (g) in environment (e) for replication (r);
Additive parameters: μ is the grand mean; αg is the deviation of genotype g from the grand
mean, βe is the deviation of the environment e;
139
Multiplicative parameters: λn is the singular value for IPCA, γgn is the genotype eigenvector
for axis n, and δen is the environment eigenvector; εger is error term and ρge is PCA residual.
Accordingly, genotypes with low magnitude regardless of the sign of interaction principal
component analysis scores have general or wider adaptability while genotypes with high
magnitude of IPCA scores have specific adaptability (Gauch, 1992; Ummaet al., 2014).
AMMI stability value of the ith genotype (ASV) was calculated for each genotype and each
environment according to the relative contribution of IPCA1 to IPCA2 to the interaction SS as
follows (Purchase et al., 2000)
Where, SSIPCA1/SSIPCA2 is the weight given to the IPCA1 value by dividing the IPCA1 sum
of squares by the IPCA2 sum of squares. Based on the rank of mean grain yield of genotypes
(RYi) across environments and rank of AMMI stability value (RASVi) a selection index called
Genotype Selection Index (GSI) was calculated for each genotype, which incorporates both
mean grain yield (RYi) and stability index in single criteria (GSIi) (Farshadfar ,2008).
GSIi = RASVi + RYi
Environmental index (Ii) was obtained by the difference among the mean of each environment
and the general mean. Analysis of variance was carried using statistical analysis system (SAS)
version 9.2 software (SAS Institute Inc., 2008). Additive Main Effect and Multiplicative
Interaction (AMMI) analysis and GGE bi-plot analysis were performed using GenStat 15th
edition statistical package (VSN International, 2012). The best genotypes were also selected for
the angle between the genotype and environment is less than 90° (genotype performed above
average on that particular environment), and angle above 90° (below average performance) while
that with equal to 90° (near average performance).
Data collection method: Five plants were selected randomly before heading from each row
(four harvestable rows) and tagged with thread and all the necessary plant based data were
collected from these sampled plants.
Plant-based: Plant height, head height and head weight. Plot based: Days to heading, days to
physiological maturity, Root lodging, Stem Lodging, thousand seed weight, grain yield and three
economically important insect pest and disease reaction like stalk borer, anthracnose and leaf
blight
140
Results and Discussions
Combined analysis of variance
Mean square of analysis of variance for all genotypes at different environmental conditions for
grain yield and yield related traits are presented in Table 2. Highly significant differences were
detected among years (P ≤ 0.01) for days to heading, days to physiological maturity and grain
yield. The combined analysis of variance showed that year and location effects were significant
for all parameters except head weight and stalk borer. Year*location *varieties were significant
for most studied traits except plant height, head height, head weight and thousand seed weight.
Table2: Combined Analysis of variance (ANOVA) for grain yield and yield related traits of sorghum
promising genotypes in 2017-2018 main cropping season
Source . V
rep
vrt
loc
yr
vrt*loc
vrt*yr
loc*yr
DF
2
11
2
1
22
11
2
DH
38.74**
3769.23**
1850.30**
26733.38**
54.22**
852.95**
1237.06**
DM
7.42
729.34**
954.73**
362.96**
31.59**
372.19**
661.73**
PH
243.35
122481.39**
126370.74**
1794.24
7472.67**
909.06
5179.25*
HH
4.58
382.28**
18.00
25.11
14.99
2.88
87.23**
LB
0.11
0.26**
0.00
0.00
0.22**
0.11**
1.25**
Ant
0.25
1.06**
0.00
0.01
0.74**
0.17*
1.40**
SB
0.01
0.30**
0.06
0.00
0.07**
0.00
0.04
vrt*loc*yr
22
25.87**
31.59**
1118.43
9.44
0.35**
1.02**
0.15**
Table 2 Cont…..
Source .V
rep
vrt
loc
yr
vrt*loc
vrt*yr
loc*yr
DF
2
11
2
1
22
11
2
HW
2540.26*
6212.22**
48857.68**
314.29
4428.63**
0.76
0.42
SL
0.03
0.10**
0.79**
0.02
0.09**
0.26**
4.40**
RL
0.27
0.88**
1.40**
0.23
0.13
1.48**
7.09**
TSW
167.76*
216.30**
4479.16**
349.86*
139.62**
13.06
425.21**
KGHA
106057.40*
11805995.20**
7625887.80**
34291603.10**
211385.30**
603589.40**
5718636.20**
vrt*loc*yr
22
0.25
0.06*
0.18*
9.83
55527.30*
Key : * **significant, significant at 5% and 1% respectively, Loc *Vrt= location by varieties, vrt*yr- variety by year, loc*yr- location by year,
Yr*Loc*Vrt = year by location by varieties, DF -degree of freedom, DH- Days to Heading; DM- Days to Maturity; PH- Plant Height; HH- Head
Height; HW-Head Weight, SL- Stem lodging, RL-Root lodging; TSW- Thousand Seed Weight, YLD Kg/ha- Yield in kilogram per Hectare.
Yield Performance of sorghum genotypes across locations
Mean performance of the tested sorghum genotypes presented in Table 4. It revealed that some
genotypes continually performed best in a group of environments and some are fluctuating
across location (Tameneet al., 2013). The average grain yield ranged from the lowest of 2418.77
kgha-1 at Guliso sub site in 2018 to the highest of 3726.66 kgha-1at Harosebu on station in 2017
with grand mean of 3233.53kgha-1. The average grain yield across environments ranged from the
lowest of 2123.6 kgha-1for G13 to the highest of 4384.9 kgha-1 for G5. This large variation might
be due to the genetic potential of the genotypes. G3 and G5 was the topranking pipeline
throughout the environments; However, G18 was the lowest yield potential throughout the test
locations. The difference in yield rank of genotypes across the locations exhibited the high
crossover type of GxE interaction (Yan and Hunt, 2001; Asratet al., 2009).
141
Table 4: Over year and across location mean performance grain yield (kg/ha) of sorghum genotype
Grain Yield in kg/ha
Genotypes
Chemeda
G12
G13
G16
G18
G2
G24
G3
G5
G9
Gemedi
Local
Mean
R2
CV %
LSD 5%
F test
Guliso
2235.9ef
3702c
2104.5f
4217.2b
2361.5ef
4972.5a
2520.9e
4696.8a
4802.7a
4108.5b
3277.6d
3022.3d
3501.88
97
6.04
358.09
**
2017
Haro-sabu
2660f
4115c
2532.6f
3582de
2823.4f
4298.6bc
2818.9f
5043.2a
5217.2a
4510.2b
3741.8d
3377e
3726.66
97
4.95
312.25
**
Sayo
2427.6ef
3923.5c
2260.6f
4425.3b
2576.1ef
4552.4b
2744.1e
4994.2a
4978a
4290.3b
3610.7c
3225.7d
3667.37
98
5.15
320.11
**
Guliso
1564.3ef
2553.9c
1475.5f
2901.6b
1649.1ef
3411.4a
1756.6e
3225.3a
3296.8a
2828.3b
2267.4d
2095.1a
2418.77
97
5.9
241.71
**
2018
Haro-sabu
1817.5f
2799.7c
1731.5f
3114.8b
1927.8f
2598.6cd
1924.8f
3426.2a
3543.6a
3066.4b
2547.7d
2301.5e
2566.67
98
4.85
210.77
**
Comb.mean
Sayo
2749.6gh
3759.4cd
2636.9h
4098.1b
2849.9gh
3383.9ef
2963.3g
4482.1a
4471.1a
4007bc
3548.2de
3288.4a
3519.81
97
4.17
248.59
**
2242.5h
3475.6d
2123.6i
3723.2c
2364.6g
3869.6b
2454.8g
4311.3a
4384.9a
3801.8bc
3165.6e
2885.0f
3233.53
98
5.13
109.33
**
Key: G-genotypes, R2-R-squre, CV-coefficient of variation, LSD-least significant difference, Comb.mean- combined mean.
Agronomic performance
Combined mean grain yield and other agronomic traits are presented in Table 5. Medium mean
of days to heading and days to physiological maturity were recorded by genotypes G5 and G9.
These offer great flexibility for developing improved varieties suitable for short to medium
moisture stress area. However, G13 was recorded with short mean of days to heading and days to
physiological maturity indicating that early maturing genotypes were desirable when moisture is
the limiting factors for sorghum production. Similarly, G18, G24, Local check and standard
checks ( Chemeda and Gemedi ) were recorded high plant height indicating that, these genotypes
might be susceptible to root and/or stem lodging but G9 and G13 were with short to medium
plant height indicating that, the possibility to develop resistant variety against lodging problems.
Moreover, G3, G5 and G9 were recorded the highest grain yield and hold 36.19 %, 38.52 % and
20.1 % yield advantage over the best standard check (Gemedi) respectively.
Reaction to the major disease and insect pest across environments
Almost all genotypes evaluated had significantly low scores with their corresponding
economically important insect pest and disease reactions (Table 6). However, some genotypes
such as G13 and G16 were less tolerance to leaf blight but were better tolerance to stalk bore.
The result revealed that maximum anthracnose disease reaction was recorded by G16.On the
142
other hand, genotypes G3, G5, and G9 were better tolerance to stalk borer, Anthracnose and Leaf
blight.
Table 5: Combined mean grain yield and other agronomic traits of sorghum genotypes
Genotypes
DH
DM
PH
HH
HW
RL
SL
TSW
KGHA YLDAVA
137.1b
177.3b
382.0b
27.0c 91.1cde
1.3a
1.6bc
29.2cd
2242.5h
-29.16
Chemeda
134.7c
174.0d 344.1def
21.1e 122.6ab
1.3ab
1.7b
33.9ab
3475.6d
9.79
G12
99.0i
158.4i
171.2i
32.5ab
76.0e
1.1cd 1.3cde 29.9bcd
2123.6i
-32.92
G13
109.8g
169.7ef
230.0h
30.3b
83.0de 1.2a-d
1.3e
24.2e
3723.2c
17.61
G16
126.9f
175.9bc
414.3a
34.9a
118.1b 1.2bcd
2.0a
29.1cd
2364.6g
-25.3
G18
131.5e
168.8f
335.9ef
23.4de
87.4de
1.1d
1.3de
35.6a
3869.6b
22.24
G2
133.6cd
170.6e 372.9bc
33.8a 106.7bc 1.3abc
1.7b
33.9ab
2454.8g
-22.45
G24
134.5cd
174.2d
325.8f
26.3c
138.2a 1.2abc 1.5bcd
34.2ab
4311.3a
G3
36.19
105.4h
164.1h
262.6g
33.7a 89.4cde
1.3ab 1.4cde 28.6cde
4384.9a
G5
38.52
108.4g
166.6g
167.3i
25.3cd 90.0cde
1.2cd
1.3e
27.2de
3801.8bc
G9
20.1
140.1a
175.7c 351.7cde
33.6a
94.5cd
1.3ab
1.7b
27.8de
3165.6e
0
Gemedi
132.9de
181.5a 362.2bcd
30.6b
88.9de 1.3abc
1.6b 32.4abc
2885.0f
-8.86
Local
Mean ± SEM 124.5±1.36 171.4±0.62 310.0±6.57 29.4±0.39 98.8±2.81 1.2±0.02 1.5±0.04 30.5±0.703233.5±66.84
2
1.26
11.89
13.29
26.72
13.95
21.64
22.42
5.13
CV%
99
96
90
70
73
81
76
71
98
R2 %
1.64
1.42
24.3
2.57
17.4
0.11
0.22
4.51
109.33
LSD 5%
**
**
**
**
**
**
**
**
**
F test
Key : G-genotype, DH=Days to heading, DM=Days to maturity, PH= Plant height, HH= Head height, HW-head weight, RL- root lodging, ST- stem lodging,
TSW- Thousand seed weight, YLD Kg/ha- Yield in kilogram per hectare, YAD- yield advantage, SEM-standard error of mean, CV- Coefficient of variation,
R2-R-squre, LSD- least significant difference.
Table 6. Combined mean of disease and insect pest reactions of sorghum genotypes evaluated in 2017-2018 main
cropping season.
Genotypes
Chemeda
G12
G13
G16
G18
G2
G24
G3
G5
G9
Gemedi
Local
Mean ± SEM
CV%
R2 %
LSD 5%
F test
Leaf blight
1f
1.1c-e
1.3ab
1.3ab
1.2bc
1.0f
1.2bcd
1.2bc
1.0f
1.1cde
1.1def
1.0ef
1.13±0.02
17.04
78
0.13
**
Anthracnose
0.83f
1.28cd
1.06e
1.72a
1.50b
1.00ef
1.14de
1.11de
1.17de
1.00ef
1.36bc
1.14de
1.19±0.04
24.72
82
0.19
**
Stalk borer
1.0b
1.1b
1.1b
1.0b
1.1b
1.0b
1.0b
1.0b
0.7c
1.0b
1.0b
1.3a
1.01±0.02
14.36
74
0.096
**
Key: G-genotype, SEM-standard error of mean, CV- Coefficient of variation, R2-R-squre, LSD- least significant difference, 1-5 scale where 1=
resistant, 5= susceptible
Additive main effects and multiple interaction (AMMI) models
Combined analysis of variance revealed highly significant (P≤0.01) variations among
environments, genotype x environment interaction, IPCA-1 and IPCA-2 (Table7). This result
indicated there was a differential yield performance among sorghum genotypes across testing
locations and strong GEI. Similar result was reported on wheat (Sialet al., 2000) and rice
(Panwar et al., 2008). The GEI significant effect on the grain yield of sorghum genotypes, which
account 6.03% of the total variation whereas the genotypes contributed 29.39 % of the variation
143
However, the large portion, which means 62.59 % of the total variation is credited to the
environmental effect. This also indicated the existence of large amount of deferential response
among the genotypes to changes in growing environments and the differential discriminating
ability of the test environments. Considerable percentage of GxE interaction was explained by
IPCA-1 (4.14%) followed by IPCA2 (1.3%) and therefore used to create a 2-dimensional GGE
bi-plot. (Gauch and Zobel, 1996) suggested that the most accurate model for AMMI can be
predicted by using the first two PCAs. Moreover, several authors took the first and second IPCA
for GGE bi-plot analysis and greater proportion of GEI were explained by the first IPCA for
maize (Abera and Labuschagne, 2005), bread wheat (Yukselet al., 2002; Farshadfar, 2008;
Workuet al., 2013), common bean (Temesgenet al., 2008) and field pea (Mengistu et al., 2011).
Table 7: Additive main effect and multiplicative interaction analysis of variances (AMMI) for grain yield
of 12 sorghum genotypes tested.
Source
Total
Treatments
Genotypes
Environments
Block
Interactions (GxE )
IPCA 1
IPCA 2
Residuals
Error
DF
215
71
11
5
12
55
15
13
27
132
SS
207479183
203357879
60980550
129866058
339941
12511270
8594144
2690075
1227051
3781363
EX.SS%
100
98.01
29.39
62.59
0.16
6.03
4.14
1.3
0.59
MS
965019.5
2864195.5**
5543686.4**
25973211.6**
28328.4
227477.6**
572942.9**
206928.8**
45446.3
28646.7
Key: DF = degree of freedom, SS = sum of squares, MS = mean squares, IPCA = Interaction Principal Component
Axis, ** = highly significant, ns = non-significant, EX. SS%-Explained Sum of square
Yield performance of sorghum genotypes per location and AMMI
Yield performance of the evaluated sorghum genotypes in respect to its environments are
presented in table 8. Accordingly, G3, G5 and G9 were recorded the best average grain yield
(4311 kg ha-1), (4385kg ha-1) and (3802kg ha-1) and attained an IPCA-I value relatively small
(8.17) (8.7) and (3.23), respectively indicating stable and widely adaptable genotypes. Genotypic
stability was an important in addition to grain yield (10). Similarly, Chemeda, G13 and G18 were
achieved low IPCA-I score (-11.86), (-12.91) and (-11.11) but recorded low grain yield (2242 kg
ha-1), (2124 kg ha-1) and (2365kg ha-1), respectively.
The result indicated, most of the tested environments revealed fluctuating in mean grain yield
and IPCA scores. For instance, the overall mean grain yield at Guliso sub site during the 2017
growing season was 3502 kg ha-1 while it was 2419kg ha-1 in the same location during the 2018
cropping season. This variation might be due to weather conditions, experimental plots and other
144
soil factors at the tested environment. However, Sayo sub site was exhibited consistent mean
grain yield than the rest environments.
Table 8: Mean grain yield (kgha-1) per location and year from the AMMI additive GE model
Genotype
Chemeda
G12
G13
G16
G18
G2
G24
G3
G5
G9
Gemedi
Local
MEAN
GL
2240
3699
2101
4183
2369
4993
2536
4705
4776
4091
3305
3024
3502
GL2
1481
2567
1374
3196
1576
3226
1712
3325
3363
2856
2280
2069
2419
HS
2608
4122
2465
3754
2778
4189
2800
5113
5246
4520
3761
3363
3727
HS2
1819
2792
1723
3066
1921
2623
1976
3449
3505
3045
2561
2319
2567
SY
2510
3916
2376
4167
2645
4716
2762
4870
4954
4290
3552
3248
3667
SY2
2796
3759
2701
3972
2900
3469
2942
4405
4465
4008
3534
3286
3520
Mean
2242
3476
2124
3723
2365
3870
2455
4311
4385
3802
3166
2885
3234
IPCAg1
-11.86
-0.11
-12.91
2.49
-11.11
31.34
-8.86
8.17
8.7
3.23
-3.9
-5.18
IPCAg2
3.59
-6.04
4.4
18.49
1.8
10.62
4.74
-11.16
-13.46
-8.47
-4.51
-0.01
G- Genotype, GL-guliso 2017, GL2-guliso 2018, HS-harosabu 2017, HS2-haro sabu 2018, SY-sayo 2017,SY2-sayo 2018,
IPCAg = Interaction Principal Component Axis genotype
Relationship among test environments
The similarity between two environments is determined by both the length of their vectors and
the cosine of the angle between them (Fig1). Sayo and Guliso in 2017 had good discriminating
ability as shown by a long environmental vector, followed by Haro-sebu 2017. However, Sayo
and Haro-sebu in 2018 cropping season had poor discriminating ability, as was indicated by its
short environmental vector. Therefore, the study shows Sayo and Guliso in 2017 cropping season
were the most discriminating locations that gave more information on the performance of the
genotypes, whereas Sayo and Haro-sebu in 2018 cropping season were the least discriminating
environment that gave less information about the performance of the genotypes. This means if
the study is carried out for several seasons and same site continue to be non-discriminating (less
informative); such locations can be dropped and not to be used as test locations.
The cosine of the angles; acute angle indicates a positive correlation, right angle and obtuse
angles indicate no correlation and negative correlation, respectively. Angles between any of the
two environments; Guliso and Sayo in 2017, Harosebu and Sayo in 2018 cropping season were
acute and hence showed positive correlations. However, Guliso 2017 and Sayo 2018 were obtuse
and exhibited negative correlations. The close associations among test environments suggested
that the same information in terms of performance can be obtained from fewer test locations and
some may be dropped without losing any information about the cultivars under test, thus
reducing experimental costs (Yan and Tinker, 2006).
145
Fig.1: GGE bi-plot based on tested environments-focused comparison for their relationships
An indispensable feature of the GGE bi-plot (which-won- where) was also anticipated. In
environment identification process, furthest genotypes are connected together to form a polygon,
and perpendicular lines are drawn to form sectors which will make it easy to visualize
environments. Environment concept requires multi-year data, but in this study, three
environments were carried out such as Guliso (GL), Harosebu (HS), and Sayo (SY). The
winning genotypes for each sector are those placed at the vertex. Therefore, G2 is the winner at
Guliso (GL), while G3, G5 and G9 are at Harosebu (HS) and Sayo (SY) locations in 2017
cropping seasons (Fig2).
Figure 2. The which-won-where view of the GGE bi-plot to show which genotypes performed best in which environment
146
Discriminating ability of the test environment and genotype stability
The concentric circles on the bi-plot help to visualize the length of the environment vectors,
which are comparative to the standard deviation within the particular environments and are a
measure of the discriminating ability of the environments (Workuet al., 2013) Environments as
well as genotypes that fall in the central (concentric) circle are considered as an ideal
environments and stable genotypes, respectively (Yan and Hunt, 2002).An environment is more
desirable and discriminating when located closer to the central circle (Narouiet al., 2013). As a
result, in this study, Sayo (SY) was more representative and discriminating environments (Fig.3)
Similar study by Odewaleet al. (2013) reported that only one environment was stable,
representative and discriminating among the nine environments for the performance of five
coconut genotypes.
Ranking based on the genotype-focused scaling assumed that stability and mean grain yield were
equally important (Yan and Hunt, 2002). The best candidate genotypes were expected to have
high mean grain yield with stable performance across all the tested locations. Consequently, high
yielding and comparatively more stable genotypes can be considered as base line for genotype
evaluation (Yan and Tinker, 2006). Both environments-focused bi-plot and genotype-focused
comparison of genotypes shown that G3 and G5 fell in the central circle indicating its high yield
potential and comparatively stable to the other genotypes (Fig. 4). As well, G9 was fall close to
the ideal genotype or around the center of concentric circle indicated this genotype possessed
specific adaptability with best grain yield potential. Therefore, G3, G5 and G9 were the best
performing candidates.
Fig. 3 Ranking environments comparatively to ideal environment
147
Fig.4: GGE bi-plot based on genotype-focused scaling for comparison of genotypes for their yield potential and stability.
Additive main effects and multiplicative interaction (AMMI) stability value (ASV)
AMMI Stability Value helps selection of relatively stable high yielding genotypes. The best
genotype should have high mean grain yield and small ASV value. In view of that, local check
and standard check (Gemedi) were recorded small ASV (one and two) but low in grain yield
(2885 and 3166 kg ha-1 respectively). Moreover, G3, G5 and G9 were more yielder genotypes
(4311, 4385 and 3802 kg ha-1) with relatively moderate ASV (9, 10 and 4 respectively). A
genotype with the least of genotype selection index (GSI) is considered as the most stable and
the stability needed to be considered in combination with grain yield (Farshadfar, 2008).
Accordingly, G3 and G5 were the most stable genotype with low genotype selection index (GSI)
and higher mean grain yield Table 9). Similarly, Odewaleet al. (2013) evaluated five coconut
varieties across nine environments and found two most stable varieties. Farshadfar (2008)
evaluated twenty bread wheat genotypes for four years across two locations and found that two
genotypes were consistently stable as revealed by AMMI stability value and genotype selection
index.
Table 9 AMMI Stability Value, AMMI rank, Yield, yield rank and Genotype Selection Index
GENOTYPES
G5
G3
G2
G9
G16
G12
Gemedi
Local
G24
G18
Chemeda
G13
ASV
28.62
24.70
59.10
16.19
33.32
10.79
10.65
9.25
17.95
20.10
22.13
24.36
ASV RANK
10.00
9.00
12.00
4.00
11.00
3.00
2.00
1.00
5.00
6.00
7.00
8.00
YLD
4385.00
4311.00
3870.00
3802.00
3723.00
3476.00
3166.00
2885.00
2455.00
2365.00
2242.00
2124.00
YLD RANK
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
GSI
11.00
11.00
15.00
8.00
16.00
9.00
9.00
9.00
14.00
16.00
18.00
20.00
G-genotype
148
Conclusions and Recommendations
The result revealed the performance of grain yield and yield related components for the nine
genotypes were significantly influenced by environment, genotype and their interaction. A
further analysis on the adaptability and stability across the three environments were conducted so
far. Consequently, G3, G5 and G9 presented both high yielding and stable across the test
environments. Therefore, these genotypes have been identified as possible candidates for
advancement, for release and for use as parents in future breeding programmes.
Acknowledgment
The authors greatly thanked Oromia Agricultural Research Institute (IQQO) for financial
support. Haro-Sabu Agricultural Research Center staff members are warmly acknowledged for
technical and administrative support. Cereal research team acknowledged for their technical
support and as well Malkasa agricultural research center is acknowledged for the provision of
test materials.
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150
Adaptability Study of Recently Released Small Pod Pepper Variety (Capsicum frutescens
L.) at West and Kellem Wellega Zones
KibiruKena*, ZewduTegenu and AshenafiDebela
Haro Sabu Agricultural Research Center (HSARC), P.0.Box 10, KellemWollega, Dembi Dollo,
Ethiopia,
*
Corresponding author email: kibiruk12@gmail.com
Abstract
Small pod hot Pepper (chili pepper) is a seasonal plant of the family Solanaceae. It is grown as
an annual crop and produced for its fruits. It is one of the most important vegetable crops for
fresh consumption (as chilies), for processing and as a spice (for making stew). A field
experiment was conducted at Harosabu on station and Meti sub site of KellemWollega zone,
Western Ethiopia, during the 2017/2018 and 2018/2019 main cropping. A total of five small pod
hot pepper varieties collected from Melkasa and Bako Agricultural Research and one local
check variety were used as planting materials. The combined analysis of variance (ANOVA) for
total yield and other agronomic traits of six small pod hot pepper varieties grown at three
location locations in 2017/2018 and 2018/2019on number of primary branches per plant,
number of fruit(pods, fruit diameter, fruit length and fruit weight revealed significant varietal
difference. Likewise, there was highly significant difference of variety on fruit rot and
phoshporia blight. The interaction effect of variety and location revealedsignificant effect on
50%days to flowering, 90% days to maturity, marketable yields, unmarketable yield and total
yield. In the present experiment, Melka Oli, MelkaDera and Dinsire varieties were found
superior in terms of economic yield (marketable yield), tolerant to major disease and other
important parameters. Thus, they are recommended for popularization and wider production in
test locations and similar agro-ecologies in the Western Oromia in particular and hot pepper
producing regions of Ethiopia under main rain fed.
Keywords: chili, dinsire, melkadera, melkaoli
Introduction
Hot pepper is a seasonal plant of the family Solanaceae. It is grown as an annual crop and
produced for its fruits. It is one of the most important vegetable crops for fresh consumption (as
chilies), for processing and as a spice (for making stew). It is also a very important crop for spice
extraction since it has a lot of oleoresin for dying of food items. Dried peppers may be
reconstituted whole, or processed into flakes or powders. Chili or C. frutescens (known as
barbaré) is important to the national cuisine of Ethiopia, at least as early as the 19 th century, "that
it was cultivated extensively in the warmer areas wherever the soil was suitable." In Ethiopia,
pepper grows under warm and humid weather conditions and the best fruit is obtained in a
temperature 21-27oC during the daytime and 15-20oC at night IAR, (1996). It is extensively
grown in most parts of the country, with the major production areas concentrated at altitude of
151
1100 to 1800 masl. MoARD, (2009). It is one of the major vegetable crops produced in Ethiopia
and the country is one of a few developing countries that have been producing paprika and
capsicum oleoresins for export market. Because of its wide use in Ethiopian diet, the hot pepper
is an important traditional crop mainly valued for its pungency and color. The crop is also one of
the important spices that serve as the source of income particularly for smallholder producers in
many parts of rural.
The present situation indicates that in study area there is no improved small pod hot pepper
varieties; however hot pepper producer used local cultivar with production per unit area as
compared to national average yield. As a result, varietal information for the improvement of the
crop for high fruit yield and quality in the existing agro ecology is insufficient. There has also
been no research on evaluation of hot pepper which enables the growers to select the best
performing varieties in the study area. Evaluation of selected varieties are therefore one of the
considerations to ease the existing problems of obtaining the desired varieties for which the
output of this study was likely to assist and sensitize hot pepper growers and processors,
furthermore the increasing demand for hot pepper to feed the growing human population and
supply the ever-expanding pepper industries at national and international level has created a need
for the expansion of pepper cultivation in to areas where it has not ever been extensively grown
Beyene and David (2007). Better adaptable and well performing variety (varieties) with
improved cultural practices could be a possibility to boost quality and marketable production of
the crop, so that the farmers benefited by cultivating those adaptable improved verities in the
study area. Therefore, present study was initiated with the objective of investigating the
performance and adaptation of different varieties of hot pepper for growth and yield of small pod
hot pepper varieties for the study area. The diverse climatic soil conditions of Ethiopia allow
cultivation of a wide range of fruit and vegetable crops including small pod and large pod hot
pepper, which is largely grown in the eastern and central parts of the mid- to low-land areas of
the country. However, local production of hot pepper in West and KelemWellega zones is not
able to meet the domestic demand due to lack of improved variety, diseases and another new
technological packages for hot pepper. Therefore, it is important to evaluate different small pod
hot pepper varieties to recommend high fruit yielding and disease tolerant variety/ies for the
study area. Thus, the objective of this study was to evaluate the performance of small pod hot
152
pepper varieties and recommend the best performed variety for production in the studied areas
and similar agrological zones.
Materials and Methods
Experimental Sites, Designs and Experimental Materials
A field experiment was conducted at Haro Sabu on Station and Meti sub site of KellemWollega
zone in Western Ethiopia, during the 2017/2018 and 2018/2019 main cropping season. A total of
five small pod hot pepper varieties viz., Kume, Dinsire, Dame, MelkaDera And Melka Oli
collected from Bako and Melkasa Agricultural research centers with one local cultivar were used
in this study. The experiment was laid out in a randomized complete block design with three
replications and with plot size of 3.5 m length and 3 m width. All other crop management
practices and recommendations were used uniformly to all varieties as recommended for the
crop. The recommended spacing 70 cm between rows and 30 cm between plants were used.
Data collection and statistical analysis
Data were collected in plot and plant basis. Data taken were days to 50% flowering, days to 90%
maturity, plant height, number of primary branches per plant, number of fruits(pods) per plant,
fruit diameter, fruit length, fruit weight, marketable yield, unmarketable yield and total yield.
Besides these parameters disease parts fruit rot and phosphoria blight were taken. All the
collected data were subjected to analysis of variance using GenStat computer software (Gen Stat,
2016) and Least Significant Differences (LSD) was used to compare the varieties using the
procedures of Fishers protected at the 5% level of significance.
Results and Discussion
The combined analysis of variance (ANOVA) for marketable yield, total yield and other
agronomic traits of six small pod varieties grown at three locations in 2017/2018 and 2018
revealed significant varietal difference for all considered traits on varieties and their interaction
with location. The main effect of variety revealed a significant effect on fruit diameter, fruit
length, and number of primary branches per plant, number of pod per plant, fruit rot and
phosphoria blight. This might be due to varietal effect since genetic factor can influence yield
related parameters.
Days to flowering
The analysis of variance showed that there was a highly significant effect (p<0.01) on days to
flowering due to main factors of variety, location and year; and the interaction effect of location
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and year (Appendix Table 1). The highest (89.17) and the lowest (56.56) days to flowering was
record from in year and year two respectively, at Harosabu on station (Table 1). Earliness or
lateness in the days to 50% flowering might be to the inherited characters, early acclimatization
to the growing area and environmental conditions such as temperature, moisture and soil fertility
which enhance growth and developments plants. This result was in agreement with the finding of
Seleshiet al. (2014) who reported that days to flowering and maturity of hot pepper which could
be due to the temperature of the growing area and due to the transplanting disturbance since it is
subjected to loss of feeder roots during uplifting, and consumed their energy to repair damaged
organs and thus the process demanded them more time to resume shoot growth. Earliness to
flowering may be due to inherent characters, different response of varieties to growing
environments (e.g. temperature, rainfall, altitude, pests and diseases, etc.), and acclimatization to
the growing area and/or due to transplanting disturbance (Sam-Aggrey and Bereke-Tsehai,
2005).
Table 1. Interaction effect of location and year on days to flowering
Location
Harosabu
Meti
LSD (0.05)
CV (%)
Year
1
89.17a
88.16a
2.1
4
2
56.56c
82.17b
Means in columns and rows followed by the same letter(s) are not significantly different at
5% level of significant; LSD (0.05) = Least Significant Difference at 5% level; CV=
Coefficient of variation.
Days to Maturity
Analysis of variance showed all the main effects and interaction effects were highly significant
(p<0.01 on days tomaturity (Appendix Table 1). The highest (185.7) days to maturity were
recorded from Melkaoli in year one at Harosabu on station and lowest (149.0) days to maturity
was recorded from variety Dinsire in year two at the same location (Table 2) This variation
ascribed to the differences in the growing environment climatic conditions and genetic make-up
of the varieties. This agrees with the report of Seleshiet al. (2014). Moreover, this finding was in
agreement with Haileslassieet al. (2015) who reported that days to maturity were significantly
affected by pepper varieties.
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Table 2. Interaction effect of variety, location and year on days to maturity
Variety
Year
MelkaDera
Location
Harosabu
185.7a
154.7fgh
170.3b
151.3hi
164.3c
153.7ghi
158.7de
149i
163cd
154.7fgh
180.7a
155.3fgh
Meti
184.6a
176.7b
173.3b
164.3cd
168.3c
159.3ef
162.7de
153.7ghi
166cd
157fg
183.7a
174.7b
1
2
Melka Oli
1
2
Kume
1
2
Dinsire
1
2
Dame
1
2
Local check
1
2
LSD(5%)
4.8
CV(%)
1.8
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level
of significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
Number of primary branches per plant
Analysis of variance showed that there was a significant (P ≤ 0.05) effect on number of primary
branches per plant due to varieties and year. Location and all interactions was no significant
(Appendix. Table 1) .The highest (4.46) and the lowest (2.60) number of pod (fruit) per plant
were recorded from MelkaDera and Dame varieties, respectively (Table 3). This might be due to
different plant canopy among varieties of the same crop. This result was inline with Seleshiet al
(2014) who reported different branch number per plant of hot pepper varieties. Generally, the
differences observed in branching of pepper plants might have been due to genetic variations
existed between varieties and or due to favorable influence of organic and inorganic nutrients
present in the soils or the growing environment which goes in line with the findings of (ElTohamyet al., 2006), that stated the presence of adequate amount of organic nutrients in the soil
improves growth of pepper plants.
Number of pod (Fruit) per Plant
Analysis of variance revealed there was a significant (P ≤ 0.05) difference on fruit number per
plant of on the effect of varieties. Effects location and year, and all the interactions were nonsignificant (Appendix Table 1). The highest (85.77) and the lowest (29.69) number of pod (fruit)
per plant were recorded from Melka Oli and Dame varieties, respectively (Table 3). This might
be due to the highest number of primary branches of Melka Oli variety and genetic character
which influence number of fruits per plant. The highest fruit number in Melka Oli variety was
most likely due to the fruit bearing capacity of the variety and more branch formation nature
which leads to contain high number of fruits per plant. In line with this result, Amare et al.
155
(2013) found different fruit number per plant due to variety differences. Furthermore, Seleshietal
(2014) reported that number of fruits per plant was highly significantly affected by the
interaction of variety by location. These authors also stated that fruit number difference might be
due to the associated traits like canopy diameter that could limit the number of branches, the
temperature stress of the growing environment and the capability of each varieties to with stand
the stress especially on the reproductive development, which is more sensitive to high
temperature stress (day and night temperature) than vegetative development.
Plant Height (cm)
Plant height significantly (P<0.05) influence due to varieties, location and interaction effect of
location and year (Appendix Table 1). The longest (63.19cm) and the shortest (38.62) plant
height was recorded from Melkaoli and Dame Varieties, respectively (Table 3).The significant
different of varieties on plant height might be due genetic makeup. This result was in agreement
with the finding of MARC (2005), which reported different plant height for different varieties.
Similarly, the longest (58.8cm) and the shortest (48.7) plant height was recorded from Harosabu
and Metilocations, respectively (Table 3). This might be due to climatic condition such as sun
light which might influence vertical growth of plant parts.
Table 3. Main effect of variety and Location on plant height of hot pepper varieties
Variey
M/Oli
M/Dara
L/Check
Dinsire
Kume
Dame
LSD(0.05)
Harosabu
Meti
LSD(0.05)
CV (%)
Plant height
63.19a
62.48a
59.4ab
52.88bc
46.09cd
38.62d
7.52
58.8
48.7
4.34
17
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level
of significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
Fruit Diameter
The main effect of variety, location and year as well as the interaction effect of location and year
showed significant (P ≤ 0.05) effect on fruit diameter (Appendix Table 2). The highest (4.13cm)
and the lowest (3.38 cm) fruit diameter were recorded from in year two and year one at Harosabu
on station (Table 4). This different might be due environmental conditions like humidity and
edaphic factors since they influence the thickness of fruits. This result was related with work of
Haileslassieet al, (2015) found that fruit diameter was significantly affected due to varietal
156
effect. Similarly, this was conformed to the finding of Tibebu and Bizuayehu (2014) which
showed different hot pepper variety have different fruit diameter.
Table 4.Interaction effect of location and year on fruit diameter of small pod hot pepper varieties
Year
Location
1
2
Harosabu
3.376b
4.129a
Meti
3.376b
3.389b
LSD(0.05)
0.67
CV (%)
19.9
Means in columns and rows followed by the same letter(s) are not significantly different at
5% level of significant; LSD (0.05) = Least Significant Difference at 5% level; CV=
Coefficient of variation.
Fruit Length
The analyzed result revealed that there was highly significant P ≤ 0.01) of variety on fruit length
whereas other main effects and interactions were non-significant(Appendix Table 2) the
highest(6.4cm) and lowest (3.46cm) fruit length of small pod pepper variety was observed from
MelkaDera and Kume varieties respectively (Table 5). The significant difference in fruit length
among the hot pepper varieties attributed to the inherited traits and adaptability to the
environmental condition of the study area. This current result was supported by the findings of
Haileslassieet al. (2015) and Seleshiet al. (2014) who reported significant fruit length for
different hot pepper varieties. Further, Setiamihardja and Knavel (1982) indicated that fruit
length and fruit diameterwere quantitatively inherited and governed by additive gene action in
crosses of Capsicumannuum Moreover, this finding was supported by the work of Tibebu and
Bizuayehu (2014).
Table 5.Main effects of variety on number of primary branches per plant, number of pods per plant, plant
height(PH),fruit length, fruit rot and phosphoria blight
PB
Variety
NPB
NPPP
PH
FL
FR
MelkaDera
4.458a
67.6ab
62.48a
6.395a
1.083c
MelkaOli
3.917ab
85.77a
63.19a
5.68ab
1.5b
Dinsire
3.836ab
45.56cd
52.88bc
5.228bc
1.708ab
Dame
2.603c
29.69d
38.62d
4.681c
1.875a
Local check
4.027ab
56.3bc
59.4ab
4.492c
1c
Kume
3.45b
44.58cd
46.09cd
3.459d
1c
LSD(.05)
0.68
38.82
7.52
0.81
0.4870
CV (%)
22.1
43
17
19.9
30.8
Means in columns and rows followed by the same letter(s) are not significantly different at
significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
1c
1.333b
2a
1.333b
1.333b
1c
0.2798
18.1
5% level of
Fruit (pod) Weight
The main effect of variety, location and year as well as the interaction effect of variety and year,
location and year revealed significant (P ≤ 0.05) effect on the average dry pod weight of hot
157
pepper (Appendix Table 2. Accordingly, the highest (0.83gram) and the lowest (0.37 gram) fruit
weight were obtained from Local check in year and Melkaoli in year two, respectively(Table
6).On the other hand the highest(0.67gram) and the lowest(0.36gram)fruit weight was recorded
in year two at Harosabu on station and Meti substation respectively(Table 7). The significant of
variety on fruit weight might be due to genetic,makeup of the variety since characteristics, such
fruit length, fruit diameter and fruit weight are mostly influenced by genetic factors and
environmental factors such as sunlight and moisture.
Table 6. Interaction effect of variety and year on fruit weight of small pod hot pepper varieties
Year
Variety
1
2
Local check
0.8333a
0.5283cde
MelkaDera
0.72ab
0.495cde
Melka Oli
0.6667abc
0.4393de
Dame
0.5267cde
0.6533abc
Dinsire
0.5533bcde
0.602bcd
Kume
0.52cde
0.3707e
LSD(.05)
0.184
CV (%)
27.4
Means in columns and rows followed by the same letter(s) are not significantly different at
5% level of significant; LSD (0.05) = Least Significant Difference at 5% level; CV=
Coefficient of variation.
Table 7. Interaction effect of location and year on fruit weight of small pod hot pepper varieties
Year
Location
1
2
Harosabu
0.64a
0.67a
Meti
0.65a
0.36b
LSD(.05)
0.106
CV(%)
27.4
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level
of significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
Disease reaction
Analysis of variance showed there was a significant ((P ≤ 0.05) difference on the major disease
among varieties of small pod hot pepper varieties and there was no significant effect due to
location, year and their interaction (Appendix Table 2). This might be due to genetic characters
which makes individual varieties tolerant to major diseases. Among the major diseases fruit rot
and phosphoria blight were recorded at 1-5 disease scoring scale. From the result above (Table
5) variety MelkaDera, Melka Oli, Kume and Local Check varieties had lower disease reaction.
Whereas Dame and Dinsire varieties had a higher disease reaction which showed they are the
most sensitive to the major hot pepper diseases.
Marketable Yield (Kg/ha)
158
Analysis of variance revealed that there were highly significant (P<0.05) on main effects of
varieties, location and year as well as all their interactions locations on marketable yields of
small pod hot pepper varieties (Appendix Table 2). The highest (4017.5 kg/ha) dry marketable
yield was recorded from Melka Oli variety in year two at Haro Sabu on station and) and lowest
(96.1 kg/ha) was recorded from Dame Variety in year one at both station (Table 8). The variation
of marketable yield of these varieties could be due to difference in genetic characteristics and
agro ecological adaptability nature which is in line with the findings of Fekaduet al. (2008) and
heritability is necessary in systematic improvement of hot pepper for fruit yield and related traits.
Table 8. Interaction effect of variety, location and year on marketable yield of small pod
hot pepper varieties
Variety
Year
Melka Oli
1
2
1
2
1
2
1
2
1
2
1
2
586.86
23
MelkaDera
Dinsire
Local check
Dame
Kume
LSD(0.05)
CV (%)
Harosabu
553.7efghi
4017.5a
744.6efg
1919.8bc
146.4hi
2074.3b
542.1efghi
2003.2bc
91.6i
1438.7cd
224.1ghi
496.2efghi
Location
Meti
544.7efghi
887.8def
754.4efg
1063.2de
154.5hi
958.7def
533.2efghi
454.5fghi
102.3i
729.1efgh
251.3ghi
654.5efghi
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level of
significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
Unmarketable Yield (kg/ha)
Analysis of variance revealed that there were highly significant (P<0.05) effect of variety,
location and year as well as the interaction effect of variety & year, and location and year
showed significant effect on unmarketable yields (Appendix Table 2). The highest (287.6 kg/ha)
unmarketable yield was obtained from variety Dinsire in year one and the lowest (33.8 kg/ha)
was recorded from the Kume variety in year two (Table 9). Similarly, the highest (181.1 kg/ha)
unmarketable yield was obtained from Haro Sabu on station in year one and the lowest (55.3
kg/ha) unmarketable yield was recorded from Meti substation in year two (Table 10). The
variation of unmarketable yield of these varieties could be due to difference in genetic
characteristics and agro ecological adaptability nature which is in line with the findings of
Fekaduet al. (2008) and heritability is necessary in systematic improvement of hot pepper for
fruit yield and related traits.
159
Table 9. Interaction effect of variety and year on unmarketable yield (kg/ha)
Variety
1
287.6a
273.2a
142.4bc
141.4bcd
133.1bcd
108.9bcd
108.549
33.4
Dinsire
Melka Oli
Local check
Kume
Dame
MelkaDera
LSD(0.05)
CV(%)
Year
2
115.3bcd
114.3bcd
206ab
33.8d
130.7bcd
64.9cd
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level of
significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
Table 10. Interaction effect of location and year on unmarketable yield (kg/ha)
Year
1
181.1a
161.6a
62.67
33.4
Location
Harosabu
Meti
LSD(0.05)
CV (%)
2
166.4a
55.3b
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level
of significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
Total Dry Fruit Yield (Qt/ha)
Analysis of variance revealed all the main factors and interactions that there were highly
significant (P<0.01) effect on total yield of small pod hot pepper varieties (Appendix Table 2).
The highest (4177 kg/ha) dry fruit yield was recorded from Melka Oli variety in year two at Haro
Sabu on station and lowest (225 kg/ha) was recorded from Dame Variety in year one at Meti
substation (Table 11). The significance difference among varieties on total yield might be due to
yield related parameters such as number of pods per plant and branch number per plants.
Table 11. Interaction effect of variety, location and year total yield (kg/ha)
Variety
Melka Oli
Year
Harosabu
2089bc
4177a
1008defg
2016bc
1125def
2248b
1204de
2406b
784efghij
1566cd
268ij
535fghij
Location
Meti
827efghij
957defgh
863efghi
1097def
442ghij
1015defg
685efghij
463ghij
225j
863efghi
393hij
683efghij
1
2
MelkaDera
1
2
Dinsire
1
2
Local check
1
2
Dame
1
2
Kume
1
2
LSD(0.05)
614.94
CV(%)
32.1
Means in columns and rows followed by the same letter(s) are not significantly different at 5% level of
significant; LSD (0.05) = Least Significant Difference at 5% level; CV= Coefficient of variation.
160
Comparison biplot (Total - 97.99%)
L/ Check
Kume
Dame
sabu2
HaroHaro
sabu1
Met
i1
Dinsire
M/ Dara
M/ Oli
Met i2
PC1 - 95.27%
G enot ype scores
Environment scores
AEC
Conclusions and Recommendations
The significant difference was shown different yield related traits among varieties. Dinsire and
Dame were the earliest varieties in to reach 50% days to flowering and days to physiological
maturity whereas Melka Oli and MelkaDera were the latest. Generally significant differences for
a number of traits among the tested varieties were observed. Evaluation of varieties for
adaptation is a fast truck strategic approach to develop and promote agricultural technology. In
the present experiment, Melka Oli and MelkaDera varieties were found superior in terms of
economic yield (marketable yield) and other yield related parameters. These varieties also stable
than others. Thus, they are recommended for popularization and wider production in test
locations and similar agro-ecologies in Western Oromia under supplemental irrigation.
Acknowledgements
The authors appreciate Oromia Agricultural Research Institute for funding the research.
Horticulture Department of Haro Sabu Agricultural Research Center is acknowledged for
facilitating the field trials. Finally, thank to Bako and Melkasa Agricultural Research Centers for
providing the seeds of the varieties.
References
AmareTesfawNigussie, Dechasa and Kebede W/Sadik(2013). Performance of hot pepper
(Cupsicum annuum) varieties as influenced by nitrogen and phosphorus fertilizers at
Bure, Upper Watershed of the Blue Nile in Northwestern Ethiopia. Int. J. Agric. Sci.,
3(8): 599-608.
El-Tohamy, W.A., Ghoname, A.A. and Abou-Hussein, S.D. (2006). Improvement of pepper
growth and productivity in sandy soil by different fertilization treatments under protected
cultivation. J. Appl. Sci. Res., 2: 8-12.
FekaduMarame, Lemma Desalegne, Harjit-Singh, Chemeda, Fininsa and Roland Sigvald,
2008. Genetic Components and Heritability of Yield and Yield Related Traits in Hot
Pepper. Research Journal of Agriculture and Biological Sciences, 4(6): 803-809
Gen Stat. 2016. Gen Stat Procedure Library Release. 18th Edition. VSN International Ltd.
161
Haileslassie, G., Haile, A., Wakuma, B. and Kedir, J. (2015). Performance evaluation of hot
pepper (Capsicum annum L.) varieties for productivity under irrigation at Raya Valley,
Northern Ethiopia. Basic Res. J. Agric. Sci. Rev., 4(7): 211-216, July 2015.
IAR (Institute of Agricultural Research). (1996). Progress Report Addis Ababa. Institute of
Agricultural Research, Addis Ababa, Ethiopia.
Melkassa Agricultural Research Center. (2005). Progress Report on Completed Activities. 2005.
pp: 1-7.
MoARD (Ministry of Agriculture and Rural Development). (2009). Variety Register. Issue
No. 9. June 2006. Addis Ababa, Ethiopia.
Sam-Aggrey. W.G. and Bereke-Teshai Tuku. 2005. Proceeding of the1st Horticultural
Workshop. 20-22 February. IAR. Addis Ababa. 212p.
SeleshiDelelegn, DerebewBelew, Ali Mohammed and Yehenew Getachew, 2014. Evaluation
of elite hot pepper varieties (Capsicum spp.) for growth, dry pod yield and quality under
Jimma Condition, South West Ethiopia. International journal of agricultural research, 9,
pp.364-374.
Setiamihardja, R. and Knavel, D., 1982, January. Inheritance of certain fruit characteristics in
capsicum-annuum-l relative to fruit detachment force. In hortscience (vol. 17, no. 3, pp.
477-477). 701 north saint asaph street, alexandria, va 22314-1998: amer soc horticultural
science.
Tibebu Simon and Bizuayehu Tesfaye (2014). Growth and productivity of hot pepper
(Capsicum annuumL.) as affected by variety, nitrogen and phosphorous at Jinka,
Southern Ethiopia. Res. J. Agric. Environ. Manage., 3(9): 427-433.
Appendices
Appendix Table 1. Mean squares of ANOVA for days to 50% flowering (DFL), days to 90% physiological
maturity(DM),number of primary branches per plant(NPB), plant height(PH) and number of pods per plant(NPPP)
of small pod hot pepper varieties
Source of variation
DF
2
5
1
1
5
5
1
5
46
Replication
Variety
Location
Year
Variety. Location
Variety. Year
Location. Year
Variety.Location.Year
Error
CV (%)
DFL
223.347
220.647**
2951.681**
7060.681**
16.714
16.714
2951.681**
16.714
9.898
4
DM
13.722
678.581**
561.125**
3828.125**
50.692**
50.692**
561.125**
50.692**
8.519**
1.8
Mean squares
NPB
PH
3.8306
691.84
4.8307**
1164.99**
0.7476
1828.73**
7.668*
295.5
0.5776
51.95
0.3458
198.57
0.7476
1828.73**
0.5776
51.95
0.6764
83.74
22.1
17
NPPP
5453
4669**
80.8
533.9
468.2
1048.8
80.8
468.2
557.9
27.4
DF= degree of freedom; * and **significant at 5% and 1% level of significance, respectively
Appendix Table 2. Mean squares of ANOVA for Fruit diameter (FD), Fruit length (FL), Fruit weight (FW)
marketable yield(MY) and unmarketable yield(UMY) and total yield(TY) of small pod hot pepper varieties
Source of variation
Replication
Variety
Location
Year
Variety.Location
Variety. Year
DF
2
5
1
1
5
5
FD
0.701
0.6387*
2.4679*
2.6412*
0.0673
0.3707
FL
1.2879
12.4682**
0.1587
0.1991
0.1734
2.0606
Mean squares
FW
MY
0.10907
1006047
0.07148*
1804360**
0.4462**
6483096**
0.26742*
18006872**
0.02005
908341**
0.08814*
938000**
UMY
57001
31802*
55538*
88866*
16054
25710*
TY
1212169
3129467**
14884840**
8231826**
1915272**
248091
162
Location. Year
1
2.4679*
0.1587
0.4462**
6483096**
Variety.Location.Year
5
0.0673
0.1734
0.02005
908341**
Error
46
0.233
0.9814
0.02494
127500
CV (%)
13.5
19.9
27.4
23.0
DF= degree of freedom; * and **significant at 5% and 1% level of significance, respectively
55538*
16054
8724
33.4
2909163**
435183*
139996
19.3
Evaluation and Selection of Improved Food Barley (Hordeum vulgare L.) Varieties for their
Adaptability in West Hararghe Zone
Gebeyehu Chala, Abubeker Terbush and DassuAssegid
Oromia Agricultural Research Institute (OARI), Mechara Agricultural Research Center
(McARC), P.O. Box: 19,Mechara, Ethiopia.
Corresponding author email: gebeyehuchal@gmail.com
ABSTRACT
An experiment was conducted in three districts of West Hararghe Zone at Gemechis
(Qunisegeria FTC), Chiro (Arba Rakate FTC) and Tullo (Gara qufa FTC) in 2018 cropping
season in order to identify and promote well adapted improved barley variety/s. The experiment
was laid out in Randomized Complete Block Design with three replications. Ten improved barley
varieties including local check were used as experimental materials. The most important data of
the trial like days to50% flowering, Days to maturity, plant height, spike length, diseases, and
yield Qt/ha were collected analyzed using Genstat statistical software and means were separated
using least significance difference (LSD). Combined analysis of data revealed that, varieties
varied significantly at (P<0.05) for all traits. HB1307 and Bentu were the two varieties showed
relatively better yield with a value of 46.55 Qtha-1and 44.07 Qtha-1, respectively. HB1965, Shage
and Abdane varieties were the least performing varieties in terms of grain yield having a value
of 37.43, 36.83 and 38.63Qtha-1respectively. Generally, HB1307and Bentu were the two
varieties showed better performance with their mean yield and other measured traits. Therefore,
these two varieties were recommended to be demonstrated under farmers’ field for further
scaling up.
Key Words: Adaptation, Barley Variety, Grain yield, Selection
1. INTRODUCTION
Barley (Hordeum vulgare L.) is a major cereal crop in Ethiopia and accounts for 20% of the total
cereal production (Wosene et al., 2015). It is grown in a wide range of agro climatic regions
under several production systems. Barley grows best on well drained soils and can tolerate
higher levels of soil salinity than most other crops. Food barley is commonly cultivated in
163
stressed areas where soil erosion, occasional drought or frost limits the ability to grow other
crops (Berhanu et al., 2005). Barley has persisted as a major cereal crop through many centuries
and it is the world’s fourth important cereal crop after wheat, maize and rice (Martin et al.,
2006). The area devoted to barley production in Ethiopia over the past 25 years has fluctuated. It
was around 0.8 million hectares in the late 1970s, and rose to more than 1 million hectare in the
late 1980s. It then declined and remained between 0.8 and 0.9 million hectare until the beginning
of the third millennium. The production of barley, by-and-large, has been below 1 million tons
per year for most of the past 25 years, except during the years when the area under barley
increased above 1 million hectare. Productivity, however, has never increased above 1.3 t/ha,
which is about half the world average. Barley has a long history of cultivation in Ethiopia as one
of the major cereal crops and it is reported to have coincided with the beginning of plow culture
(Mulatu and Grando, 2011). It is the most important crop with total area coverage of
951,993.15hectares and total annual production of about 21.57 Qt/ha in Ethiopia,451,279.26
hectares with 24.12Qt/ha in Oromia,and 6,737.49 ha in West Hararghe,respectively (CSA,
2018).In the highlands of the country barley is grown in Oromia, Amhara, Tigray and part of the
Southern Nations, Nationalities and Peoples’ Regional State (SNNPR) in the altitude ranges of
1500 and 3500m, but it is predominantly cultivated between 2000 and 3000 masl (MoA, 1998).
In Ethiopia, barely is a dependable source of food in the highlands as it is produced during the
main and short rainy seasons as well as under residual moisture (Melleet al., 2015). Barley types
are predominantly categorized as food and malting barley based on their uses, while in Ethiopia
the highest proportion of barley production area is allocated for food barley. Food barley is
principally cultivated in the highland areas of Ethiopia where the highest consumption is in the
form of various traditional foods and local beverages from different barley types (Zemede,
2000). Barley grain accounts for over 60% of food for the highlands of Ethiopia, for which it is
the main source of calories (Ceccarelliet al., 1999). According to (Berhanuet al., 2005), barley is
used in diversity of recipes and deep rooted in the culture of people’s diets. Besides its grain
value, barley straw is an indispensable component of animal feed especially during the dry
season in the highlands where feed shortage is prevalent (Girma et al., 1996). Barley straw is
also used in the construction of traditional huts and grain stores as thatching or as a mud plaster,
as well as for use as bedding in the rural areas (Zemede, 2000).
164
Barley is an important crop in Ethiopian cereal production and in food security (Berhanu et al.,
2005). It is currently the fifth most important cereal crop, covering over one million hectares of
land. It is grown both in Meher (June–October) and Belg (February–May) seasons. Meher
production in the country is categorized into early, intermediate and late production systems. The
contribution of the early production system is estimated to be 25% of total barley production.
Although barley is considered a highland crop, it is also among the major cereal crops grown in
the low rainfall areas of the country, which are part of the early production system. In such areas,
the availability and distribution of rainfall during crop growing seasons is the major factor
limiting yield. Early ear emergence is the most important feature of barley adaptation to the low
moisture areas and is common in Ethiopian landraces from these areas. Thus, the farmers in
drought-prone areas grow their own landraces that are well adapted to their environments, but
with poor yielding ability. Hence, it was considered essential that barley productivity in low
moisture areas be improved to increase the contribution of this system to overall barley grain
production. Although Ethiopia is a centre of diversity for barley, most of the farmers in the
country still obtain very low yields due to a combination of genetic, environmental and
socioeconomic constraints. Research has been on-going since 1955 to address these constraints
and improve the livelihoods of farmers by increasing the production and productivity of barley
(Mulatu and Grando, 2011).
West Hararghe zone is among some of the places in the region where food barley is grown as
one of the major cereal food crops of highland and midland agro ecology. Most farmers of the
zone produce food barley on hectares of land (CSA, 2016). However, their average productivity
is low per hectare because the existing cultivation is not supported with new and better
technologies such as high yielding and adaptive varieties with improved cultivation practices. A
critical shortage of improved barley varieties adapted to low-moisture stress conditions is a
major problem and hence farmers are forced to grow low yielding genotypes. Drought is one of
the major production constraints that reduce crop yields in water-limited areas, where many of
the farmers live. This is a serious problem in places where total precipitation is high but
unevenly distributed throughout the growing season. As the population continues to grow and
water resources for crop production decline, development of drought-tolerant cultivars and water
use-efficient crops is a global concern. In the low-rainfall areas (<250 mm annual precipitation)
and in most rain fall limiting areas, barley is the dominant crop. Before the 1980s, drought was
165
most protracted in the northern and eastern regions of Ethiopia. However, the number of
drought-affected areas has dramatically increased and now includes the most productive regions
in the east. Not only is this, but also due to shortage of land in the study area, double cropping
system of barley is commonly practiced to increase their income generation per unit area.
Therefore, this study was initiated with the objective of the following:
Objective:-To select the best adaptive food barley varieties with high yield and good agronomic
trait to the area.
2. MATERIALS AND METHODS
2.1. Description of the study area
The experiment was conducted at Tullo (Gara Qufa FTC), Gemechis (Qunisegeria FTC) and
Chiro (Arbarakate FTC) during 2018 main cropping season. Tullo district is found in West
Hararghe Zone of Oromia National Regional State, Eastern part of Ethiopia. The district is
located about 375 km Southeast of Addis Ababa and 47 km from Chiro town, the capital of West
Hararghe Zone (DOA, 2012). Hirna is found within 1758 m above sea level (m.a.s.l). It receives
an average annual rainfall of 868mm. The average temperature is 22°C. The black, vertisols and
red soils are the three dominant soil types. Gemechis district is found in West Hararghe Zone of
Oromia National Regional State, Eastern part of Ethiopia. The district is located about 343 km
southeast of Addis Ababa and 17 km from Chiro town, the capital of West Hararghe Zone
(DOA, 2012). The district is found within 1300 to 2400 above sea level (m.a.s.l). It receives an
average annual rainfall of 850 mm. The district has bimodal distribution in nature with small
rains starting from March/April to May and the main rainy season extending from June to
September/October. The average temperature is 20°C. The black, brown and red soils are the
three dominant soil types constitute 55, 25 and 20%, respectively (DOA, 2012). Chiro district is
located in West Hararghe Zone of the Oromia National Regional state at about 324 km East of
Addis Ababa, the capital city of Oromia regional national state. The capital town of the district is
Chiro, which is also the capital town of the Zone. The district is founded at an average altitude of
1800 m.a.s.l. It has a maximum and minimum temperature of 23oc and 12oc respectively and the
maximum and minimum rainfall of 1800 mm and 900 mm respectively (2003 E.C data from
Office of Agriculture of the district). The district is mainly dominated with sandy soil, clay soil
(black soil) and loamy soil types covering 25.5%, 32%, and 42.5% respectively according to
2003 E.C data from Office of Agriculture and Rural Development. The soil types vary with the
166
topography mainly black soils are observed in the highland and midlands while one can see red
soil inthe lowland areas.
2.2. Experimental Treatments and Design
Nine recently released food barley varieties were brought from Sinana Agricultural Research
Center and one local check of the respective sub-testing locations were evaluated as
experimental materials. These varieties include HB1965, HB1966, Gobe, Robera, Abdane,
Bentu, HB1307, Shage, EH1493 and Local check. These materials were randomly assigned to
the experimental block and the experiment was laid out in a Randomized Complete Block
Design (RCBD) with three replications. The spacing between blocks and plots was 1.5m and 1m,
respectively. The gross size of each plot was 3m2 (1.2m x 2.5m) having six rows with a row-torow spacing of 20cm.The total area of the experimental field will be 270m 2 (41m X
6.6m).Planting was done by drilling seeds in rows with a seed rate of 100kg ha-1 (30g per plot).
NPS fertilizer was applied at the rate of 100kg ha-1 (30g per plot) at the time of planting; and
Urea was also applied at vegetative stage before booting at the rate of 50 kg ha-1 (15g per plot).
2.3. Data collected
Data was collected from five plants of six rows of each plot and randomly tagged and the
relevant data was recorded. The followings are the major parameters recorded: Days to 50%
emergence (days), grain yield (Qt/ha), days to 50% heading (days), disease data (scale), days to
75% physiological maturity (days), plant height (cm), spike length (cm), and plant aspect.
2.4. Data Analyses
GenStat 16th Edition was used to analyze all the collected data from individual locations and the
combined data over locations.Various statistical models such as analysis of variance (ANOVA),
principal component analysis (PCA) and the additive main effects and multiplicative interaction
(AMMI) model. In this model, the additive and multiplicative components of data were
integrated into a powerful least square analysis. GGE Biplotwas used. Mean separations was
carried out using least significant difference (LSD) at 5% probability level.
167
3. RESULTS AND DISCUSSIONS
Table.1 Mean values of Barley varieties on grain yield and yield components in each districts of western Hararghe Zone in 2018 cropping season
Gemechis (Qunisegeria FTC)
Varieties
DF
Chiro(Arbarakate FTC)
Hirna (Gara Qufa FTC)
DM
PH(cm)
SL(cm)
Yld(Qt
DF
DM
PH
SL
Yld (Qtha
DF
DM
PH(cm)
SL
Yld(Qtha-1
HB1965
81.0ab
114.7ab
76.47b-e
9.47ab
37.85b
54.0c
94.67b
74.73
6.73b
36.02b
65.0bc
98.0c
71.47f
7.8a
38.41a
HB1966
71.3a-d
118.7a
82.13a-d
8.60a-c
37.74b
69.3ab
96.33ab
71.00
7.80ab
50.96a
70.6a-c
101.0ab
80.60cd
7.6ab
33.95ab
Gobe
70.3a-d
100.7ab
71.67e
8.73a-c
46.96ab
54.0c
96.33ab
72.67
7.53ab
42.52ab
73.3ab
98.7c
72.07ef
7.6ab
41.18a
Robera
64.0b-d
101.0ab
73.80c-e
8.00cd
53.52ab
56.0c
96.00ab
67.20
7.13b
44.40ab
65.0bc
99.0bc
71.33f
6.5c
31.01a-c
Abdane
54.0d
98.0b
82.13a-d
8.46bc
54.07ab
55.0c
97.33ab
73.53
8.06ab
37.07b
55.0c
99.7bc
77.80de
8.1a
24.74bc
Bentu
64.0b-d
98.0b
72.87de
8.13cd
67.19a
54.0c
98.67a
74.60
8.73a
34.52b
65.0bc
99.7bc
62.67g
7.0bc
30.51a-c
HB1307
79.7ab
110.0ab
83.00a-c
7.93cd
48.96ab
65.3b
97.67ab
72.93
7.66ab
50.29a
75.3ab
101.0ab
84.17ac
7.6ab
40.40a
Shage
84.7a
110.7ab
91.13a
9.60a
38.41b
74.6a
96.67b
68.73
7.53ab
40.30a
b
86.6a
103.0a
86.57ab
7.8a
31.79a-c
EH1493
72.7a-c
112.7ab
81.40b-d
9.0a-c
58.85ab
74.6a
96.33ab
67.73
6.86b
31.84b
86.0a
102.3a
81.33bd
8.2a
39.11a
Local
check
58.0
cd
108.7ab
83.80ab
7.20d
45.52ab
57.6c
95.33ab
68.80
7.86ab
43.96a
b
55.0c
98.0c
88.00a
6.6c
28.74cd
GM
69.9
107.30
79.84
8.51
48.91
61.5
96.53
71.2
7.59
41.19
69.7
100.0
77.6
7.5
33.98
CV%
14.9
10.9
6.8
7.7
26.1
5.4
2.3
13.4
11.3
18.0
13.7
1.2
4.4
6.1
17.8
LSD(0.05
)
17.9
19.9
9.29
1.13
21.87
5.7
3.86
NS
1.46
12.74
16.3
2.04
5.89
0.79
10.35
168
Table 2. Combined Mean effect of locations by varieties on yield related components at Quni,
Arbarakate and Gara Qufa FTC in 2018 cropping season
Varieties
DF
DM
PH
SL
Dis
PAS
HB1965
HB1966
Gobe
Robera
Abdane
Bentu
HB1307
Shage
EH1493
Local check
GM
CV%
LSD(0.05)
66.67c-e
70.44b-d
65.89c-e
61.67d-f
54.67f
61.00d-f
73.44a-c
82.00a
77.78ab
56.89ef
67.04
15.6
9.83
102.4
105.3
98.6
98.7
98.3
98.8
102.9
103.4
103.8
100.7
101.29
8.5
NS
74.22b-d
77.91a-c
72.13cd
70.78cd
77.82a-c
70.04d
80.03ab
82.14a
76.82a-d
80.20ab
76.21
10.7
7.62
8.00ab
8.00ab
7.97ab
7.22b
8.22a
7.95ab
7.75ab
8.31a
8.04ab
7.24b
7.87
12.2
0.89
1.44c
1.88a-c
1.88a-c
1.88a-c
1.88a-c
2.55a
1.77bc
1.44c
1.33c
2.33ab
1.84
40.4
0.69
1.55
1.77
1.77
1.44
1.44
2.33
1.33
4.66
1.77
1.88
Table 3. Mean grain yield (Qt/ha) of 10 barley varieties at individual environment
Varieties
Quni
FTC
Arbarakate FTC
Gara qufa FTC Combined Mean
Yld Advantage
HB1965
37.85b
36.02b
38.41a
37.43
-
HB1966
37.74b
50.96a
33.95ab
40.88
3.76
Gobe
46.96ab
42.52ab
41.18a
43.55
10.53
Robera
53.52ab
44.40ab
31.01a-c
42.98
9.09
Abdane
54.07ab
37.07b
24.74bc
38.63
-
Bentu
67.19a
34.52b
30.51a-c
44.07
11.85
HB1307
48.96ab
50.29a
40.40a
46.55
18.15
Shage
38.41b
40.30ab
31.79a-c
36.83
-
EH1493
58.85ab
31.84b
39.11a
43.27
9.82
Local check
45.52ab
43.96ab
28.74bc
39.40
-
GM
48.91
41.19
33.98
CV%
26.1
18.0
17.8
LSD(0.05)
21.87
12.74
10.35
Days to 50% flowering:-Statistical analysis of variance for days flowering were showed
significant difference at P<0.05 at all testing individual location (table.1). The performance of
varieties for days of flowering in combined analysis among varieties and within location were
169
also showed a significance difference at (P<0.05) in table 2. Among the tested barley varieties
evaluated, Shage (82 days) late flowering and the shortest day was recorded by Abdane (54.67
days). Therefore, Abdane was considered as earliest flowering variety as compared to other
varieties tested together.
Maturity date: Analysis of variance shows that the individual location data analysis for days of
maturity showed significant difference for all varieties at all tested locations (Table 1), but the
combined mean effects of varieties showed non-significant difference for all varieties (table 2).
The longest days of maturity in combined mean effect of varieties were recorded by HB1966
variety which is (105.3 days) and shorter by Abdane which is (98.3 days) to attain its full
physiological maturity.
Plant height: Analysis of variance shows a significant variation except at Arbarakate
FTCobserved non-significance difference. The combined mean effect of plant height within
variety and location showed a significant difference at P<0.05 (Table 2). Generally, Shage
variety was recorded the highest plant height of (82.14 cm) and Bentuwas recorded the lowest
(70.04 cm).
Spike length:-Barley varieties were showed a significance difference for spike length at each
location as well as combined mean effect of varietiesfor this trait. A combined analysis of
variance showed the highest spike length of (8.31cm) for Shage and the lowest spike length of
(7.22cm) for Robera table 2.
Grain yield: Both the individual and combined analysis of the data showed a significant
difference at P<0.05.From the individual analysis, the highest yield was recorded by Bentu
(61.19 qt/ha) and the lowest for HB1966 (37.74 Qt/ha) at Qunisegeria, The highest for HB1966
(50.96 Qt/ha) and the lowest for Bentu (34.52 Qt/ha) at Arbarakate and The highest for Gobe
(41.18 Qt/ha) and the lowest for Abdane (24.74 Qt/ha) was recorded. Combined analysis
variance for treatment means effect of location interaction was showed significance difference on
grain yield Qt/ha Table 2. The highest yield (46.55 Qt/ha) was recorded for variety HB1307
followed by Bentu (44.07 Qt/ha) and EH1493 (43.27Qt/ha)whilethe lowest for HB1965 (37.34
Qt/ha).
170
Table 4.AMMI analysis of variance for grain yield (Qt/ha) of ten barley varieties tested at three locations
during 2018 main cropping season.
Source
DF
SS
MS
SS%
F cal.
F pr
Total
89
13289
149.3
Trt(at each loc)
29
7857
270.9**
3.20
<0.001
Genotypes
Environments
Block
9
2
6
836
3342
858
92.8*
1671.0**
143.0
6.3
25.15
1.10
11.68
1.69
0.0412
<0.001
0.1416
Interactions
IPCA 1
18
10
3680
2887
204.4*
288.7
27.69
78.45
2.41
3.41
0.0066
0.0016
IPCA 2
Residuals
8
0
792
0
99.0
21.52
1.17
0.3345
Error
54
4573
84.7
DF=Degree of freedom, SS= Sum of squares, MS= Mean of squares, SS%= percentage of sum
of squares
The ANOVA indicated very highly significant differences (P<0.001) for treatments and
environments. The total variation explained (%) was 59.12% for treatment and the remaining %
for error. The greater contribution of the treatment than the error indicates the reliability of this
multi-location experiment. The treatment variation was largely due to GEI variation (27.69%),
genotype that accounted 6.3% and 25.15% for the environment variation, respectively. As
discussed earlier, the high percentage of GEI is an indication that the major factor that
influence yield performance of barley is the interaction effect of GE. In the AMMI ANOVA
the GEI was further partitioned by PCA. The number of PCA axis to be retained is determined
by testing the mean square of each axis with the estimate of residual through the F-statistics. The
result of ANOVA showed that the first IPCA is very highly significant at P<0.001 probability
level and this result suggests the inclusion of the first interactions PCA in the model (Table 3).
In particular, the first IPCA captured 78.45% of the total interaction sum of squares while the
second IPCA explained 21.52% of the interaction sum of squares.
Genotype Performance per Environment (GGE biplot Analysis)
Test locations which are closer to concentric circles like Quni is important under circumstances
when selecting genotypes that are widely adapted which is an ideal environment. An ideal
environment is the one which is on the intrinsic circle (Figure 1). Thus, Quni is found on the
closer proximity or on the edge of the intrinsic circle followed by Hirna. However, Chiro cannot
171
be ideal test location for selecting cultivars which can be adaptable for the whole region, but can
be selected as specific adapted location (Figure 1).
Key: G1= HB1965, G2= HB1966, G3=Gobe, G4= Robera, G5= Abdane, G6= Bentu, G7=
HB1307, G8= Shage, G9= EH1493, G10= Local
Figure 1.GGE biplot analysis showing the stability of genotypes and test environments.
Genotypic stability is quite crucial in addition to genotype yield mean; G9, G3 and G7 were
more stable as well as having appropriate yield. The ideal genotype might have the highest mean
performance and be absolutely stable which is represented by the dot with an arrow pointing to it
(Fig 2). Such an ideal genotype is defined by having the greatest vector length of the high
yielding genotypes. Concentric circles were drawn to visualize the distance between each
genotypes and the ideal genotypes; which is more desirable if it is located closer to the ideal
genotype, so that G9, G3 and G7 falls near to the centre of the concentric circles, which were
ideal in terms of higher yielding ability and stability (Figure 2).
172
Key: G1= HB1965, G2= HB1966, G3=Gobe, G4= Robera, G5= Abdane, G6= Bentu, G7=
HB1307, G8= Shage, G9= EH1493, G10= Local check
Figure 2. The average genotypes coordination (AGC) views to rank genotypes relative to the
center of concentric circles.
The polygon view of the GGE-biplot analysis helps one to detect cross-over and non-crossover
genotype-by-environment interaction and possible mega environments in multi-location yield
trials (Yan et al., 2007). HB1965 (G1), HB1966 (G2), Abdane (G5), Bentu (G6), EH1493 (G9),
and Local (G10), were vertex genotypes (Figure 3). They are best in the environment lying
within their respective sector in the polygon view of the GGE-biplot and thus these genotypes
are considered specifically adapted. Accordingly, G2 was specifically adapted to Chiro, G1 and
G3 were adapted to Hirna and G6 was adapted to Quni.
One of the most attractive features of a GGE biplot is its ability to show the mega environment
pattern of a genotype by environment data set (Yan and Tinker, 2006). Many researchers find the
use of a biplot analysis, as it graphically addresses important concepts such as cross-over GE,
mega environment differentiation, specific adaptation, etc as discussed in Yan and Tinker
(2006). The polygon is formed by connecting the markers of the genotypes that are far away
from the biplot origin such that all other genotypes are contained in the polygon. Genotypes
located on the vertices of the polygon performed either the best or the poorest in one or more
173
locations since they had the longest distance from the origin of biplot. The perpendicular lines
are equality lines between adjacent genotypes on the polygon, which facilitate visual comparison
of them. Those genotypes found in the polygon are widely adapted genotypes. For example, G4,
G7 and G8 were widely adapted genotypes (figure 3).
Figure 3. The GGE biplot to show which genotypes performed best in which environment.
174
4. Conclusions and Recommendations
Studying varietal response to different environment is crucial for plant breeding programmes
where there is a diverse natural, environmental, climatic and soil variability is existing. In line
with this, a total of 10 barley varieties were studied at three locations (Gemechis (Qunisegeria
FTC), Chiro(Arbarakate FTC) and Hirna(Hirna Gara Qufa FTC) during 2018 main cropping
season with the objective to select the best adaptive food barley varieties with high yield and
good agronomic trait to the area. The result of the experiment showed that barley varieties were
showed a significant difference both at individual location and combined mean effects. Varieties
were highly affected by environments and their interaction which show the selective adaptation
to specific location and wider adaptability that favoring their production. Generally, HB1307 and
Bentu were the best varieties thatshowed the stability of these varieties as well as higher yield
advantage over the local check .Therefore; these two varieties are recommended as improved
varieties and demonstrated on farmers’ field for further scaling up.
ACKNOWLEDGEMENTs
Authors would like to thank Oromia Agricultural Institute (IQQO), Mechara Agricultural
Research Center for financial support.
5. References
BerhanuLakew, Hailu Gebre and Fekadu Alemayehu. 1996. Barley production and research. Pp
1-8. In: Hailu Gebre and Joob Van Luer (Eds.). Barley Research in Ethiopia: Past work and
future prospects. Proceedings of the first barley research review workshop 16-19, October.
1993. Addis Ababa, IAR/ICARDA. Addis Ababa, Ethiopia.
Berhanu Bekele, Fekadu Alemayehu and BerhaneLakew. 2005. Food barley in Ethiopia. Pp 5382. In: Grando, Stefania and Helena Gomez Macpherson (Eds.). Food Barley: Importance,
uses and local knowledge. Proceedings of the international workshop on food barley
improvement, 14-17.January 2002, Hammamet, Tunisia.ICARDA.
Ceccarelli, S.S., Grando, V., Shevostove, Vivar, H., Yahyaoui, A., El-Bhoussini, M. and Baun,
M. 1999. ICARDA Strategy for global barley improvement. RACHIS. 18: 3-12.
CSA (Central Statistical Agency), 2016. Agricultural Sample Survey Report on Area and
Production of Major Crops. (Private Peasant Holdings, Meher Season). Statistical Bulletin.
Addis Ababa, Ethiopia.
CSA (Central Statistical Agency), 2018. Agricultural Sample Survey Report on Area and
Production of Major Crops. (Private Peasant Holdings, Meher Season). Statistical Bulletin.
Addis Ababa, Ethiopia.
Girma Getachew, Abate Tedila,SeyumBediye and AmahaSebsibe. 1996. Improvement and
utilization of barley straw. Pp 171-181. In: Hailu Gebre and Joob Van Luer (Eds.). Barley
Research in Ethiopia: Past work and future prospects. Proceedings of the 1st barley research
review workshop 16-19 October. 1993. Addis Ababa, IAR/ICARDA.
175
Martin, J.H Walden, R.P and Stamp, D.L. 2006.Principle of field crop production. Pearson
Education, Inc. USA.
MelleTilahun, Asfaw Azanaw and Getachew Tilahun. 2015. Participatory evaluation and
promotion of improved food barley varieties in the highlands of north western Ethiopia.
Wudpecker Journal of Agricultural Research. Vol. 4(3), pp. 050 – 053.
MoA (Ministry of Agriculture). 1998. National Livestock Development Project (NLDP)
Working Paper 1-4. Addis Ababa, Ethiopia.
Mulatu, B. and Grando, S. (Eds.). 2011. Barley Research and Development in Ethiopia.
Proceedings of the 2nd National Barley Research and Development Review Workshop.28-30
November 2006, HARC, Holetta, Ethiopia.ICARDA, P. O Box 5466, Aleppo, Syria.pp xiv
+ 391.
Wosene, G., BerhaneLakew, Bettina I. G., Haussmann and Karl and Schmid, J. 2015. Ethiopian
barley landraces show higher yield stability and comparable yield to improved varieties in
multi-environment field trials. Journals of Plant Breeding and Crop Science, Vol. 7(8), pp.
1-17.
Zemede Asfaw, 2000. The Barley of Ethiopia. Pp 77-108. In: Stephen B. Brush (Eds.). Genes in
the Field: On farm Conservation of Crop Diversity. IDRC/IPG
Release and Registration of Elemo (ACC. 237261) Sorghum (Sorghum bicolorL.Moench)
Variety
*Gebeyehu Chala Abubaker Terbush, Desu Asegid, Abdela Usmael and Kindie Lemmessa
Oromia Agricultural Research Institute, Mechara Agricultural Research Center, P.O. Box 19,
Mechara, West Hararghe, Ethiopia.
*Corresponding author email: gebeyehuchal@gmail.com
Abstract
Elemo (ACC. 237261 ) is the name for this Sorghum (Sorghum bicolor LMoench.) variety with a
pedigree designation number of Acc.237261. The variety has been developed and released by
Mechara Agricultural Research Center for Mid lands of West Hararghe and similar agroecologies of Ethiopia fromsorghum landrace collection through pure line developing selection
method. It has been tested at Hirna and Mechara on station during 2013-2016 main cropping
seasons and showed consistent better performances in grain yield over standard check (Chiro)
variety during Regional variety trial and Fandisha during Variety verification trial. Thus, the
variety has shown high mean grain yield and consistently stable across locations and years. It
also showed comparable responses to grain mold, head smut, loose smut diseases as compared
to local check. On the other hand, as observed during evaluation it possesses resistance or
tolerance to long smut disease
as compared to Fandisha variety. The early maturing
characteristics of the variety suits to the different cropping systems in the area and gives better
176
adoption potential by the local farmers. The result of Genotype and genotype by environment
(GGE) interaction analysis demonstrated that, this variety was more stable and high yielder than
the check and it is released as a new sorghum variety for the area as approved by National
Variety Releasing Committee (NVRC).
Keywords: Sorghum bicolor, Stability,Yield Performance
1. Introduction
Sorghumis the fifth most important cereal crop worldwide after wheat (Triticum aestivum L.),
rice (Oryza sativaL.), maize (Zea mays L.) and barley (Hordeum vulgare L.). Ethiopia is the
primary center of origin and center of diversity for sorghum. Sorghum is now widely found in
the dry areas of Africa, Asia, Americas and Australia (Dickon et al., 2006). In lowland areas of
Ethiopia, where moisture is the limiting factor, sorghum is one of the most important cereal
crops planted as food insurance, especially in the lowlands of Eastern Ethiopia and in the North
and North-eastern parts of the country where the climate is characterized by unpredictable
drought and erratic rainfall (Degu et al., 2009).
Sorghum is also one of the most important cereal crops of the tropics grown extensively over
wider areas with altitude ranging from 400 to 3000 meters above sea level (m.a.s.l) due to its
ability to adapt to a adverse environmental conditions. This has made sorghum a popular crop in
world wide. It is the major source of energy and protein for millions of people living in arid and
semi-arid region of the world. It occupied third position in terms of production in Africa after
wheat and maize and fifth position in the world after wheat, maize, rice and barley (FAO,
2017).The crop is the major food cereal after maize and tef in terms of area coverage and the
third after maize and wheat in terms of grain production in the country (FAOSTAT, 2017).The
total sorghum production in Oromia was 735, 263.79 ha which produces annual production of
20,810,667.34 quintals (28.30 qt ha-1). From Oromia region, Eastern and Western Hararghe,
most part of West Shoa and East Wolega are among the major sorghum producers, that covers
145, 776.64, 158,230.17, 72,176.19, and 36,605.32 hectares of land; and 2,974,092.51,
3,847,701.06, 2,208,879.10 and 1,112,536.22 quintals of sorghum production, respectively
(CSA, 2018).
Elemo (ACC. 237261) sorghum variety is released on May 30/2019 by Oromia Agricultural
Research Institute, Mechara Agricultural Research Center (McARC). The material has been
177
evaluated together with other genotypes in different breeding nurseries from 2011-2012 and then
advanced to variety trial to see its varietal performance across locations and years in sorghum
producing areas of West Hararghe mid lands. The variety was officially released as a new variety
in West Hararghe mid lands after its approval by of the Ethiopian National Variety Release
Committee in accordance with the guidelines of the national variety release system and variety
registration of the country. Breeder seed and foundation seed of the variety is maintained by
McARC.
2. Varietal Origin and Evaluation
Elemo (ACC. 237261) was introduced from Institute of Ethiopian Biodiversity Conservation
(IBC) which was originally collected from West Hararghe Zone.It was originally developed from
Institute of Biodiversity landrace collections and selected by pure line selection methods at
Mechara Agricultural Research Center (McARC) to develop a variety with high yielding
potential and other better agronomic traits. It was tested together with eleven sorghum genotypes
including checks in regional variety trial at two environments for three years in major sorghum
producing areas of West Hararghe zone for three consecutive years (2013-2016) during the main
cropping seasons. Itwas evaluated along with Chiro variety in regional variety trial and with
Fandisha as standard check including local check in variety verification trial at altitudinal
ranging from 1750-1768 meter above sea level at Hirna and Mechara on station locations in each
year. The variety was consistently gave higher yield and stable performance both across years
and locations in all parameters.
3. Varietal Characteristics
Even though the variety is long; it is early maturing variety with erect growth habit. The variety
matures with an average of 164 days which is more than two weeks earlier in maturity than the
standard variety, Chiro, so that it can utilize moisture more efficiently. This short maturing habit
is preferred by the local community as it can be produced efficiently with the existing short rain
fall season (May-August). It is also characterized by better resistance/tolerance to main
biological insect pest (stem borer) than the standard variety (chiro) (visual observation). The
variety is tall with average height of 332 cm which is preferred by local community for animal
feed. Therefore, it is selected for dual purpose (food and feed) at the tested locations. The
average days to heading and maturity are 104 and 164 days, respectively. On the other hand,
seed color is white and has average head weight of 0.17 g (Table 1).
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4. Yield and Stability Performance
The results of the evaluation indicated important information regarding variety performance and
stability. Thus, grain yield performance of the released sorghum variety and check is described
below in Table (1). During evaluation seasons, the overall location mean grain yield of this
variety was consistently better than all genotypes means both across locations and years. Beside
this, Elemo variety was higher in mean grain yield over check variety, with a yield advantage of
41% over Chiro (standard variety). On research field, variety Elemo gave grain yield ranging
from 41 to 48.43 Qtha-1, whereas on farmers’ field, it gave an average of 26.8 Qtha-1. In addition,
stability analysis was carried out on grain yield using three years (2013-2016) data. In this
regard, Elemo variety is relatively stable variety with high mean grain yield, and stable across
locations and years. Therefore, it has shown stable yield performance across locations of
evaluation as well as higher mean grain yield over check variety (Chiro).
Table 1. Combined Mean values of yield and yield related sorghum genotypes across location
Genotypes DF(days) DM(days) PH(cm)
HW(g)
DIS
PAS
YldQt ha-1
241226
115.1ab
174.0ab
316.1a
0.14a-c
2.06cd
2.78a-c 32.71bc
239240
94.5e
161.7de
169.3e
0.09c
2.89ab
2.78a-c 30.74bc
237260
101.3de
161.5e
271.6c
0.12bc
2.61a-c
2.78a-c 31.85bc
237262
101.2de
162.9de
208.9d
0.12bc
2.94ab
2.33cd 34.93bc
241228
107.8b-d
173.2a-c
310.9ab
0.16ab
2.17cd
2.61b-d 36.27bc
242048
104.2d
166.4a-e
275.3bc
0.14a-c
3.11a
2.89ab 29.52bc
239184
107.9b-d
166.1b-e
297.6a-c
0.16ab
2.22cd
2.22d
40.03ab
M-3
105.6cd
169.8a-e
262.4c
0.11c
2.50a-c
3.28a
28.18c
239179
112.5a-c
171.6a-d
320.0a
0.16ab
2.00cd
2.72b-d 36.88bc
237261
104.3d
164.0c-e
332.1a
0.17a
1.72d
2.56b-d 48.43a
(Elemo)
Chiro
117.9a
176.4a
309.9ab
0.16ab
2.33b-d
2.61b-d 34.13bc
GM
106.6
168
279.6
0.14
2.41
2.69
34.9
LSD(0.05) 7.39
9.95
36.3
0.05
0.66
0.51
10.81
CV%
10.5
9
19.8
48
41
25.7
25.7
DF= days to flowering, DM= days to maturity, PH= plant height, HW= head weight, DIS= disease, PAS=
plant aspect, Yld= Grain yield, GM= grand mean, LSD= Least significant difference, CV= Coefficient of
variation.
5. Disease Reaction
Data recording was done for all genotypes including this released variety for major sorghum
insect pest such as stem borer and for major diseases such as Anthracnoses (Colletotrichum
graminicola), leaf blights (Exserhilum turcicum), and covered and loose smut which are among
179
the major bottleneck for sorghum production at two locations (Table 1). Providentially, this
variety revealed resistance to the above mentioned insect and diseases throughout the study
periods.
6. Farmers Evaluation of the Variety
To evaluate the perception and preferences of the local farmers, sorghum variety verification trial
and selection was conducted at five representative sites in West Hararghe mid lands during 2018
main cropping season. The national variety releasing committee has made farmers selection and
evaluation individually and in group. In this evaluation, Fandisha recently released variety and
local variety were included together with Elemo (ACC. 237261). Among these, the candidate
variety was almost selected or ranked as first variety preferred by the local farmers mainly due to
its yield performance, early maturity, tolerant to grain mold, long smut, loose smut and relatively
disease free than both standard and local check varieties included in the trial.
7. Adaptation
Elemo variety is recommended for production in the mid lands of West Hararghe with annual
rainfall amount of about 600 mm to 900 mm. Nevertheless, the variety can be adapted to other
regions or areas with similar agro-ecologies through adaptation.
Table 2. Agronomic/morphological characteristics of sorghum variety, Acc.237261
Characteristics
Adaptation area
Altitude(m.a.s.l)
Rainfall(mm)
Fertilizer rate
Nitrogen(kg N ha-1)
NPS(kg P2O5 ha-1)
Fertilizer application time
Fertilizer application method
Planting or seeding
Planting date
Seed rate(kg ha-1)
Row spacing(cm)
Plant spacing(cm)
Weeding frequency
Days to flowering (days)
Days to Maturity (days)
Plant height(cm)
Elemo (ACC. 237261)
Mechara, Habro and similar agro-ecologies
1700-1800
900-1236
42
47
Nitrogen applied in split: first split which is 1/2 of the total dose at
planting stage and the second split, which is 1/2 of the total dose at
35-40 days after planting, whereas, the whole dose Phosphorous was
applied at planting
Drilled in rows and mixed with soil to avoid direct contact with seed
The seed drilled in rows and thinned to adjust plant population
Early May to Mid of May
12-13
75
20-25
3-4 depending on weed infestation
105
164
332
180
Inflorescence compactness
Seed color
Crop pest reaction(1-5 scale)
Leaf blight
Stem borer
Grain mold
Yield(Qt ha-1)
Research field
Farmers’ field
Year of release
Breeder seed maintainer
loose
white
2
2
1
41-48.43
26.8
2019
Mechara ARC/OARI
masl=meter above sea level.
8. Conclusion
Elemo (ACC. 237261) sorghum variety was released for Western Hararghe (Mechara and
Habro) areas and similar agro-ecologies based on their higher grain yield, having white grain
color, well preferred by local community. In addition, thisvariety was found to be tolerant to
grain mold and could be stayed white for longer time even if there is rainfall present than other
sorghum varieties.
9. Acknowledgments
The authors want to acknowledge Oromia Agricultural Research Institute for funding the
research. The authors also express their gratitude to all staff members of the Cereal Technology
Generation Team of the Mechara Agricultural Research Center for the execution of the
experiment.
10. References
CSA (Central Statistical Agency). 2018. Agricultural sample survey 2017/2018: report on area
and production of crops (private peasant holdings, main season), vol. 1. Addis Ababa:
Federal Democratic Republic of Ethiopia, Central Statistical Agency.
Degu, E., Debello, A. and KetemaBelete. 2009. Combining ability study for grain yield and yield
related traits of grain sorghum (Sorghum bicolor (L.) Moench) in Ethiopia.Agricultural
Crop Science, 57(2): 175–184.
Dicko, M.H., Gruppen, H., Traore, A.S., Alphons, G.J., Voragen, A.G.J. and Berkel, W.J.H.
2006. Sorghum grain as human food in Africa: Relevance of content of starch and amylase
activities. African Journal of Biotechnology, 5: 384-395.
FAO (Food and Agriculture Organization). 2017.Sorghum and millet production and nutrition,
Chapter 2, www.fao.org/docrep.
FAOSTAT. 2017. Food and Agriculture Organization of the United Nations Data base of
AgriculturalProduction.FAOStatisticalDatabases.Availableathttp://faostat.fao.org/site/339/d
efault. aspx.
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Yan, W. and Kang, M.S. 2003. GGE Biplot analysis: A graphical Tool for Geneticist, Breeders
and Agronomists.CRC press, Boca Raton, FL., U.S.A.
Release and Registration of Milkaye Groundnut (Arachishypogaea L.) Variety for midland
of West Hararghe
Shanene Haile, Fantahun Adugna and Wondimu Bekele
Oromia Agricultural Research Institute, Mechara Agricultural Research Center, P.O. Box 19,
Mechara, West Hararghe, Ethiopia.
ABSTRACT
Milkayeis groundnut (Arachis hypogaea L.) variety with breeding designation number of PI158850 is one newly released ground nut variety released by Mechara agricultural research
center of Oromia agricultural research institute in 2019. In variety evaluation trials, the
performances of seven groundnut genotypes were evaluated for yield and yield components at six
(6) environments as Regional Variety Trail (RVT) in western Hararge. Finally, the variety
Milkaye (PI-158850) was approved for release in 2019 by the National Variety Release
Committee. Milkaye varietyis has an erect type and medium seeded morphology. Milkaye (PI158850) variety gave 2.20 ton ha-1 of dry pod yield (DPY) and has 13.47% yield advantage over
the Werer-962 standard check. Milkaye variety has moderately resistant to leaf spot, bacterial
blight and wilt diseases and also showed drought tolerance as compared to standard check
(Werer-962).Therefore, based on all these mentioned merits, Milkaye variety is recommended for
production in areas with elevation of 1332 to 1750 meters above sea level in the West Hararghe
and similar agroecologies.The breeder seed of Milkaye variety is maintained by the Mechara
Agricultural Research Center.
Keywords: Milkaye; Arachis hypogaea; Variety Registration;Variety
1. Introduction
In Ethiopia, groundnut (Arachis hypogaea L.) is the second most important lowland oilseed
crops of warm climate next to sesame. Groundnut was first introduced to Eritrea and then to
Hararghe in early 1920s by Italian explorers (Yebio, 1984). Nowadays, groundnut is well
disseminated in the warm lowlands of the country. The crop is produced mainly by smallholder
farmers and plays a significant role in Ethiopian economy. It provides raw material for the food
182
oil factory; it has high energy content; and it is also the main source of cash income.Groundnut
seeds has high source of protein, oil, fatty acid, carbohydrates, vitamins and minerals contents. It
contains 45-55% oil, 20-25% protein, 16-18% carbohydrate and 5% minerals (Gulluoglu, 2011;
Gulluoglu et al., 2016a).The Eastern lowland areas of Ethiopia have considerable potential for
increased oil crop production including groundnut. Particularly areas such as Daro Labu, Babile
and Gursum are the major producers of groundnuts for local and commercial consumption
(Chala et al., 2012).
Groundnut yield in the smallholder farmers is low, 1.79 tons (t) per hectare (ha) (CSA, 2018).
The production of the crop is constrained by several biotic and abiotic factors, which include
critical moisture stress especially during flowering, lack of improved varieties and appropriate
production and post-harvest practices, and diseases affecting both above-and underground parts
of the plant (Fredu et al., 2015).Therefore, the Pulse and Oilseed Crops Research Team of the
Mechara Agricultural Research Center has been striving to develop varieties with high yield,
disease resistance, high seed oil content and other desirable agronomic traits to increase the
production and productivity of the crop in the study areas. Several groundnut genotypes which
were brought from Werer Agricultural Research center were evaluated for yield and other
desirable agronomic traits aiming to identify genotypes that have better yield than the existing
varieties and cultivars cultivated in the country including the study areas.
Yield trials were conducted using 7 genotypes at three locations (Mechara, Milkaye and Mieso)
for two years in Western Hararghe; Eastern Ethiopia.The results showed that, genotype with
designation number PI-158850 was superior to the standard check variety Werer-962. The
standard check variety Werer- 962 (ICGV-86928) was released in 2004 by Werer Agriculture
Research Center (MoANR, 2018).Thus, this new variety was verified and approved by National
Variety Release Committee of the country as new variety with local name Milkaye, to be
cultivated in lowlands of Ethiopia particularly for lowlands of Western Hararghe.
2. Agronomic and Morphological Characteristics
Milkaye variety has light green leaves with distinct in its agronomic characteristics. It is an erect
type, with medium seed size and light red in its testa color. It is preferred by farmers mainly
because of its seed size, number of seed per pod and medium maturing variety. Leaf spot and
wilt diseases are the major threats in the groundnut production in the areas. Milkaye variety
showed moderate resistance to the aforementioned diseases throughout the study periods in the
183
study areas.The new variety (Milkaye) is recommended for production in Ethiopia with the areas
having an altitude ranging from 1332 to 1750 meters above sea level. The variety was evaluated
without any application of fertilizers. The description of the varieties is presented in Table 1 as it
was registered in variety registry book (MoANR, 2019).
Table 1. Agronomic and morphological characters of the new groundnut variety.
Adaptation areas
Altitude (meters above sea level)
Rainfall (mm)
Planting date
Seeding rate (kg/ha)
Spacing (cm):
Fertilizer rate( kg/ha):
NPS
N
Days to flowering (days)
Days to maturity(days)
Growth habit
100 seed weight
Seed color
Flower color
Crop pest reaction
Oil content (%)
Protein content (%)
Seed yield (t/ha):
Research field
Farmers’ field
Year of release
Breeder/ Maintainer
Mid and Low land of west Hararghe (Daro Lebu,and
Mieso) and similar agro-ecology
1332-1750
500 - 1236
Late April-Early June
75-100
10 between plants and
60 between rows
No
No
30-38
118-130
Erect type
44.68
light red
Yellow
Moderately resistant to major disease(Wilt, bacterial
blight, Early and late leaf spot)
47.1
35.15
2.20
1.57-1.87
2019
McARC, OARI
3. Origin and Pedigree
Milkaye (PI-158850) variety was imported from the International Crops Research Institute for
the Semi-Arid Tropics (ICRISAT), India; through Werer Agricultural Research Center.
4. Varietal Evaluation
The combinations of the locations (Mechara, Milkaye and Mieso) and years (2015, and 2016)
were treated as 6 environments (Mechara 2015, Mechara 2016, Milkaye 2015, Milkaye 2016,
Mieso 2015 and Mieso 2016). Werer-962, the best adapted variety in the tested sites, was used as
the standard check for comparison. The experiment was arranged in Randomized Complete
184
Block Design (RCBD) with three replications. The spacing between rows and between plants
was 0.60 and 0.10 meter, respectively. Starter fertilizer was not applied into the soil during the
experiment. The verification trial was also carried out in multi locations in 2018 and evaluated
by the National Variety Release Committee (NVRC). The committee approved this variety for
release in 2019.
5. Yield Performance and Stability
The mean dry pod yield performance of the Milkaye variety was found to be superior over the
standard check variety Werer-962. On average, Milkaye has 35 pods per plant, 3 seeds per pod
and a plant height of 30 cm. The results of the evaluation trials indicated important information
regarding variety performance and stability. Accordingly, grain yield performance of this
released groundnut variety and checks is described below in Tables (2 and 3). During evaluation
seasons, the overall location mean grain yield of this variety was consistently higher than all test
genotypes means both across locations and years. Beside, Milkaye was higher in mean grain
yield over check variety, having a yield advantage of 13.47% over Werer-962 (standard variety).
On research field, Milkaye variety gave yield of 2.20 t ha-1, whereas on farmers’ field, it ranges
from 1.57 to 1.87 t ha-1. In this regard, Milkayeis variety showed stable yield performance with
high mean grain yield, and its stability in yield performance is across locations and years.
Therefore, this variety was approved for release and production for the study areas and similar
agroecologies due to its over all merits mentioned above.
Table 2. Agronomic descriptions of PI-158850 and Werer-962.
Variety
Days to
Days to
Number of pods Number of
flowering
maturity
per plant
seeds per pods
Milkaye
30-38
118-130
35
3
Werer-962 35
141
30.86
2
Table 3. Mean dry pod yield of groundnut variety over six environments.
Variety
Milkaye
Werer962
Mean dry pod yield (t ha-1)
2015
2016
Mechara Milkaye Mieso Mechara Milkaye
1.98
1.08
1.23
2.45
5.85
2.15
1.17
1.28
2.79
3.61
Mieso
0.58
0.63
Hundred seed
weight (g)
43.43
34.83
Mean
t ha-1
yield
Adv. Werer962 (%)
Seed oil
contents
(%)
2.20
1.93
13.47
-
47.1
-
6. Reaction to Major Diseases
Bacteria leaf blight,Wilt and Leaf spot diseases are among the major groundnut diseases in
eastern Ethiopia. On 1 to 5 diseases rating scale, Milkaye scored 1.7, 1 and 1.9 for bacteria leaf
185
blight, Wilt and leaf spot, respectively. Accordingly, Milkaye has showed moderate disease
reaction to bacteria leaf blight and leaf spot diseases in the tested environments.
7. Quality Attributes
The seed oil and protein content of the Milkaye variety was 47.1 and 35.15 %, respectively.
Milkayeis preferred for roasted grain (kolo) because of its medium seed size, and it is
confectionery type of groundnut variety.
8. Conclusions and recommendation
The groundnut variety Milkaye gave 2.20 t ha-1 of dry pod yield. The variety showed stable
performances across environments and hence recommended for production for the areas with
altitude ranging from 1332 to 1750 meters above sea level. The result has also revealed that, the
variety is relatively stable over locations and seasons, and has additional desirable merits such as
resistance to leaf spot and wilt disease. The high seed production potential of the variety implies
that increased production and productivity of the crop by smallholder farmers in the country at
large. In conclusion, the newly released variety Milkaye could be cultivated profitably and
sustainably in the mid and lowlands of Ethiopia, leading to enhance income of smallholder
farmers. The breeder seed of Milkaye variety is maintained by Mechara Agricultural Research
Center.
9. Acknowledgements
The authors gratefully thank Mrs. Wolansa Mokonon and Mr. Ahamziyad Abubakar for their
assistance in data collection and field trial management. The authors are also indebted to Pulse
and Oilseed Crops Research Team and Mechara Agricultural Research Center in general for their
contribution in the development of this improved groundnut variety. They are also very grateful
to the Oromia Agricultural Research Institute for financial support.
REFERENCES
Chala A, Mohammed A, Ayalew A, Skinnes H (2012). Natural occurrence of aflatoxins in
groundnut (Arachis hypogaea L.) from eastern Ethiopia.
CSA (Central Statistical Agency). 2018. Agricultural sample survey 2017/18. Report on area and
production of crops (private peasant holdings, Meher Season). The FDRE statistical bulletin,
Addis Ababa, Ethiopia.
Fredu, N., Kai, M., KPC, R. and Gizachew, L. 2015. Scoping Study on Current Situation and
Future Market Outlook of Groundnut in Ethiopia.International Crops Research Institute for
the Semi-Arid Tropics (lCRISAT), pp.40.
Gulluoglu, L. 2011. Effects of Regulator Applications on Pod Yield and Some Agronomic
Characters of Peanut in Mediterranean Region. Turk J Field Crops. 16(2):210-214.
186
Gulluoglu, L., H. Bakal, B. Onat, C. Kurt, and H. Arioglu. 2016a. The Effect of Harvesting
Dates on Yield and Some Agronomic and Quality Characteristics of Peanut Grown in
Mediterranean Region (Turkey) Turk J Field Crops. 21(2): 224-232 (DOI: 10.17557/
tifc.20186).
MoANR (Ministry of Agriculture and Natural Resources). 2018. Crop Variety Registration Issue
No. 21. Addis Ababa, Ethiopia, pp. 166-171.
Yebio W/Mariam. 1984. Groundnut and Sesame in Ethiopia: History, Research and
Improvement Prospects. Pp. 75-82. In: Proceedings of the First Oil Workshop, Sept. 3-5,
Cairo, Egypt.
Performance evaluation of improved Sesame (Sesamum indicum L.) varieties in West
Hararghe Zone, Oromia, Ethiopia
* Fantahun Adugna and Shanene Haile
Oromia Agricultural Research Institute, Mechara agricultural research center, P.O.Box.19,
Mechara Ethiopia
*Corresponding author: firaoladugna12@gmail.com
ABSTRACT
Sesame (Sesamum indicum L.) is an annual crop and one of the important oil crops of the world.
The experiment was conducted at Daro lebu district (on Milqaye FTC), and Mieso district
(Melkasa sub site) in 2018 main cropping season. The objective of the study was to evaluate and
select well adapted sesame varieties with high yield and resistant to major pest and disease in
west Hararghe Zone. The treatments include six released sesame varieties and one standard
check (Adi) were used as planting materials for this study. The experiment was laid out in a
Randomized Complete Block Design (RCBD) with 3 replications and the plot size was 2.4m X
3m areas which contain six rows of sesame in spacing of 40cm X 10cm between rows and plants
respectively. Each variety was sown at seed rate of 5 kg ha–1 by row planting without any
fertilizer application. The result from combined mean analysis of variance revealed significant
(P<0.05) difference among varieties for days to flowering, days to maturity, disease score ,
number of seed per capsule, thousand seed weight and grain yield across location. However,
statistically non-significance difference among varieties was observed for plant height, pest
score, and number of capsule per plant. The grain yields of tested varieties were ranged from
3.92 qt ha–1 (Abasena) to 5.91qt ha–1 (Bha Necho). The variety Bha Necho was gave superior
grain yield 5.91 qt/ha followed by variety Bha Zeyit 5.70 qt/ha among tested varieties. The
combined mean grain yield of Bha Necho and Bha Zeyit varieties showed 48.7% & 41.8% yield
187
advantage over standard check (Adi), respectively. Therefore, based on overall performance,
these two varieties were selected and recommended for further demonstration for the study areas
and similar agro ecologies.
Keywords: Adaptation, Sesame, yield
1.
INTRODUCTION
Sesame (Sesamum indicum L) belongs to the genus Sesamum, order Tubiflorae and family
pedaliaceae and is a diploid species with 2n = 2x = 26 chromosomes. It is an annual selfpollinating plant with an erect, pubescent, branching stem, and 0.60 to 1.20 m tall. The leaves are
ovate to lanceolate or oblong while the lower leaves are trilobed and sometimes ternate and the
upper leaves are undivided, irregularly serrate and pointed (Felter and Lloyd, 1898: cited in
Morris, 2002). The fruit is an oblong, mucronate, pubescent capsule containing numerous small,
oval, and yellow, white, red, brown, or black seeds (Morris 2002; Geremew et al., 2012).
It was one of the first oil seeds from which oil was extracted by the ancient Hindus, whichwas
used for certain ritual purposes (Arnon, 1972). Seegeler (1983) reported that it is an ancient
oilseed, first recorded as a crop in Babylon and Assyria before 2050 BC. Among the other
oilseed field crops, sesame is known as one of the most important crops in the world for edible
oil production. It is produced mainly in India, Myanmar, China, Sudan, Ethiopia, Uganda,
Nigeria, Paraguay, Niger, Tanzania, Thailand, Pakistan, and Turkey (Anonymous, 2010).
Sesame has an important role in human nutrition. Most of the sesame seeds are used for oil
extraction and the rest are used for edible purpose. It is grown primarily for its oil-rich seeds.
The sesame seed is rich in good quality edible oil (up to 60%) and protein (up to 25%) (Brar and
Ahuja1979).The oil is in demand in the food industry because of its excellent cooking quality,
flavor, and stability. The world production is estimated at 3.66 million tones with Asia and
Africa producing 2.55 million tons (Anon, 2008).
Oil crops are the second largest source of foreign exchange earnings nextto coffee (Fiseha et al.,
2019) and sesame is the main oilseed crop in terms of production value. In 2010, Ethiopia was
considered as the second main exporter of sesame seeds in the world, behind India (FAOSTAT,
2012). In Ethiopia, sesame is grown chiefly for export (more than 95%) and direct consumption
(5%) (Annonymous, 2015). In Ethiopia it grows almost in all regions of the country with an
altitude of less than 2000 meter above sea level (Yebiyo, 1985; Adefris et al., 2011) and is a
well-established crop in Amhara, Tigray, Afar and Oromia regions. Reports on peasant holdings
188
in sesame showed that, 89.95% (2466503.09 tons) of the Ethiopian sesame produce comes from
Amhara (48.84%), Tigray (24.52%) and Oromia (16.59%) regions (CSA, 2015). The total
sesame production area and production in Oromia is about 337,926.82 ha (2,678,665.46 qt) and
2,170.25 ha (12,996.62 qt) over last year post harvest estimate respectively (CSA, 2016). The
national average productivity is about 7.93 qt/ha while that of Oromia is about 5.99 qt/ha (CSA,
2016). In Ethiopia, sesame grows well in the lowlands either as sole crop or intercropped with
millet or sorghum (Haile, et al., 2004). Sesame oil and seed are put to great variety of uses. The
oil, besides as a cooking medium, is also used for anointing the body. The oil cake which is rich
in calcium is used as feed. The seed is used in the preparation of different foods (stew called wet,
a souce for porridge, snacks, flavoring, sweets and beverages) (Adefris et al., 2011). It is used as
a source of food; eaten as raw, either roasted or parched, or as blended oil in the form of different
sweets (Weiss, 1971).The seeds are rich source of oil, protein, calcium, phosphorus and oxalic
acid (Caliskanet al., 2004). Low yield had been attributed to cultivation of low yielding
dehiscent varieties with low harvest index values, significant yield loss during threshing and lack
of agricultural inputs such as improved varieties, fertilizers and other agro-chemicals (Ashri,
1994, 1998; Weiss, 2000; Uzun and Cagirgam, 2006).
In western Hararghe, about 8,336.38 kuntals was produced by 76,672.00 household during 2016
cropping season (CSA, 2016). According to zonal agricultural office, sesame production is
largely produced by Anchar, Doba , Mi'eso, Hawi-gudina, low land of Darolebu and Oda bultum
districts. Despite the area is suitable for sesame production, lack improved varieties, biotic and
abiotic factors are among the major production constraits that attributed for low production and
productivity. Among these constraints, lack of improved variety is very serious question of
sesame producers in the area. Therefore, this activity was initiated to evaluate the performance of
recently released varieties of sesame in terms of high grain yield and tolerant to disease in the
study area for subsequent recommendation.
2. MATERIALS AND METHODS
2.1. Description of the study sites
The field experiment was conducted at Daro lebuand and Mieso districts in West Hararghe Zone
during 2018 main cropping season.Milkaye FTC from Daro Labu district and Melkasa sub site
from Mieso district were selected and Adaptability studies of improved Sesame varieties were
conducted. Daro Lebu lies to the east of Finfinne on 446 km and South of Chiro town, the capital
189
of the zone, at a distance of 115 km. The area has bimodal type of rain fall distribution of short
rainy season ‘Belg’ lasts from mid- February to April whereas the long rainy season ‘kiremt’ is
from June to September with annual rainfall ranging from 900-1300 mm (average annual rainfall
of 1094 mm) and ambient temperature of the district varies from 14 to 26oC with an average of
20oC (Climate data obtained from Mechara Metrological Station). The nature of rain fall is very
erratic and unpredictable causing tremendous erosion some times. The major soil type of the area
is sandy clay loam which is reddish in color (Report on farming system of Daro Lebu districts,
Mechara Agricultural Research Center, unpublished data).
Mieso is located at 304 km to East of Finfinne and 25 km to West of Chiro. It is bordered by
Doba district in East direction, Afar Region in West, Chiro district in South and Somali Region
in North. The district has an area of 257,344 ha. It is located at the latitude of 9o13'59.99" and
longitude of 40°45'0". The altitude of the district on average is 1332 m.a.s.l. with maximum and
minimum temperature of 370C and 250C, respectively. The annual rainfall of the district ranges
from 500 mm to 700 mm (Jima D and Birhanu A., 2017)
2.2. Treatments and Experimental Design
Six released sesame varieties namely: Bha Necho, Bha Zeyit, Dicho, Chalesa, Obsa, and Abasena varieties and one standard check (Adi) were used for this study. These varieties were
selected based on average yield performance and agro ecological adaptation. The varieties were
obtained from Bako Agricultural Research center and Haramaya University. The experiment was
laid out in RCBD with three replications and the plot size was 2.4 m X 3 m areas which contain
six rows of sesame in spacing of 40cm X 10cm between rows and plants respectively. The
spacing between plots and blocks was0.5m and 1m respectively. Each variety was sown at seed
rate of 5 kg ha–1 by row plantingwithout any fertilizer application. All other trial management
activity was carried out as necessary.
2.3. Data collection
Phenological Parameters
Phenological parameters such as days to flowering (days), days to maturity (days) and plant
height (cm) were recorded. Days to flowering was recorded by counting the number of days after
flowering when 50% of the plants per plot had the first open flower. Days to maturity was
recorded when 90% of capsules were physiologically matured per plot. Plant height at maturity
190
(PH) (cm): this growth parameter is the stature of the plants in centimeter (cm) from the ground
up to the top of the plants.
Grain Yield and Yield Components
Four central rows were harvested for determination of grain yield. Five plants were randomly
selected from the four central rows to determine yield and yield components, which consisted of
number of capsule per plant and number of seeds per capsule. Capsule number per plant was
determined by counting capsules of the five randomly selected plants while number of seeds per
capsule was recorded by counting the total number of seeds in a capsule from randomly sampled
capsules taken from the five randomly selected plants. Thousand seed weight (gram) (TSW): the
average weight of 1000 seeds randomly collected from the harvested grain yield in grams and
Grain yield (kg/ha): the total grain yield (kg/ha) harvested from the net plot area.
2.4. Statistical Analysis
All the agronomic data were recorded and being subjected to analysis using the R Computer
software. Mean separation was carried out using Least Significant Difference (LSD) test at 5%
probability level.
3. Results and Discussions
The analysis of variance showed that, there were significant (P<0.05) difference among varieties
in days to maturity, plant height, disease (bacterial blight), number of seed per capsule, thousand
seed weight and grain yield. However, statistically non-significance difference was observed for
days to flowering, pest score and number of capsule per plant among tested varieties. The grain
yields of tested varieties were ranged from 4.57 qt ha–1 (Aba-sena) to 7.25 qt ha–1 (Bha Zeyit).
Bha Zeyit variety (7.25 qt ha–1) gave superior grain yield followed by variety Bha Necho (7.07qt
ha–1) among tested varieties. On the other hand, lowest grain yield was obtained from Aba sena
variety (4.57 qt ha–1) at Milqaye FTC (Table-1)
At Mieso sub site, there was significant (P<0.05) difference among the tested varieties for days
to flowering, days to maturity, plant height, thousand seed weight and grain yield but nonsignificant difference was observed for disease (bacterial blight), pest score, number of capsule
per plant, and number of seed per capsule. The grain yields of tested varieties were ranged from
3.05 qt ha–1 (Obsa) to 4.67qt ha–1 (Bha Necho). Bha Necho (4.67 qt ha–1) was gave superior grain
yield followed by variety Bha Zeyit (4.16qt ha–1). On the other hand, lowest grain yield was
obtained from variety Obsa (3.05 qt ha–1) (Table-2)
191
The result from combined mean analysis of variance revealed significant (P<0.05) difference
among the tested varieties for days to flowering, days to maturity, disease (bacterial blight),
number of seed per capsule,thousand seed weight and grain yield across locations. However,
statistically non-significance difference was observed among the test varieties for traits such as
plant height, pest score, and number of capsule per plant (Table 3).
The grain yields of tested varieties were ranged from 3.92 qt ha–1 (Abasena) to 5.91qt ha–1 (Bha
Necho). Bha Necho gave superior grain yield 5.91 qt/ha followed by variety Bha Zeyit 5.70 qt/ha
among tested varieties. On the other hand, lowest grain yield was obtained from Aba-sena (3.92
qt ha–1). The combined mean data across locations indicated that, Bha Necho and Bha Zeyit
varieties performed better than the standard check (Adi) and other tested varieties (Table-3). This
result is in agreement with the reports of Fiseha et al., (2019), who reported that the highest grain
yield was obtained from the variety which is well adapted to the growing environment.
The combined mean grain yield of Bha Necho and Bha Zeyit varieties were 48.7% & 41.8%
yield advantage over standard check (Adi) respectively (Table-3). These varieties were well
performed across all locations. However all varieties were shown grain yield reduction at Mieso
sub site as compared toMilkaye (Table 1 and 2). This yield reduction might be occurred due to
environmental factor not due to genetic factor, i.e, the nature of rain fall at this area is very
erratic and unpredictable causing tremendous erosion during this growing season.Variety Bha
Zeyit and Bha Necho had the highest mean number of capsule per plant 67 and 63 respectively
and thousand seed weight 4.81and 5.21 respectively while Chalesa and Adi showed the lowest
mean number of capsule per plant 60.5 and 57.33 respectively.
Table -1: Mean grain yield and agronomic traits of sesame varieties on Milkaye FTC in2018
Variety
DF
DM
PH
BB
PS
NCPP
NSPC
THSW
GYD
GYD AD%
BhaZeyit
BhaNecho
Chalesa
Decho
Obsa
Adi
Aba-sena
Mean
LSD
CV%
54.66
55.33
55.33
54.33
55
55.6
56
55.19
3.22ns
3.31
99.6b
98b
95.6b
97.6b
96.3b
121.66a
1130a
103.14
9.01***
4.95
99.3c
116ab
106.6bc
104.6c
105.6
120.3a
107bc
108.5
10.28*
5.37
1.33ab
1.00b
1.33ab
1.33ab
1.33ab
2.00ab
2.33a
1.52
1.12*
42.03
1
1
1
1
1
1
1
1
2.06ns
1.16
100.66
84.66
78.33
83.33
95.33
76.66
88.66
86.8
27.1ns
17.7
69.33b
70.66a
63.66ab
60.66
62ab
54.66b
56b
62.42
11.63*
10.56
4.80a
5.83a
4.36ab
4.06ab
4.03ab
3.13b
5.50a
4.53
2.12*
26.56
7.25a
7.07a
5.63b
5.63b
5.12b
4.85b
4.57b
5.68
1.29**
12.9
49.50%
45.80%
16.08%
16.08%
5.56%
DF=days to flowering, DM=Days to maturity, PH= plant
height (cm), BB=Bacterial
blight,PS=Pest Score (1-5), NCPP= Number of capsule Per Plant, NSPC=Number of Seed Per
capsule, THSW=Thousand Seed Weight (g) and GY= Grain Yield (qt/ha).
192
Table -2: Mean grain yield and agronomic traits of sesame varieties on Mieso sub site FTC in 2018
Variety
DF
DM
PH(cm)
BB
PS
NCPP
NSPC
THSW(g)
GY(qt/ha)
BhaNecho
BhaZeyit
Decho
Chalesa
Aba-sena
Adi
Obsa
51.33b
51.66b
50.00b
53.00ab
55.66a
50.33b
52.66ab
99.33b
100.66b
98.66b
102.00b
109.33a
107.66a
98.33b
84.33abc
92.00a
89.66ab
87.00abc
79.00bc
78.66c
77.00c
1
1.66
1
1
1.33
1.66
1
1
1
1
1
1
1
1
42.33
33.33
38
42.66
33.66
38
30.66
63
73.66
68
75.33
61
63
66.66
4.60ab
4.86a
2.86b
3.26ab
4.06ab
3.53ab
3.53ab
4.67a
4.16b
3.74bc
3.28cd
3.28cd
3.19d
3.05d
Mean
52.09
102.28
83.95
1.23
0.67
ns
1
2.1
ns
1.1
6
36.95
24.96n
s
67.23
3.81
3.62
LSD
3.46*
4.78***
10.69*
14.82ns
1.85*
0.46***
CV%
3.77
2.65
7.22
38.3
12.49
27.49
7.21
31.1
YD
AD%
46.4
30.4
17.24
2.82
DF=days to flowering, DM=Days to maturity, PH= plant height (cm), BB=Bacterial blight,PS=Pest
Score (1-5), NCPP= Number of capsule Per Plant, NSPC=Number of Seed Per capsule,
THSW=Thousand Seed Weight (g) and GY= Grain Yield (qu/ha)
Table-3: The combined mean source of sesame varieties on grain yield and yield component over
two locations (Milkaye FTC and Mieso sub site) in 2018
Variety
DF
DM
PH(c
m)
BB
PS
NCPP
NSPC
THSW
(g)
GY(qt/
ha)
YLD
AD%
BhaNecho
BhaZeyit
Decho
53.33b
53.16b
52.16b
98.66b
100.16b
98.16b
100.2
95.66
97.16
1.00b
1.50ab
1.16b
1
1
1
63
67
60.66
66.83ab
71.50a
64.33ab
5.21a
4.83ab
3.46c
5.98a
5.70a
4.52b
48.75
41.8
12.4
Chalesa
54.16ab
98.83b
96.83
1.16b
1
60.5
69.50a
3.81bc
4.45b
10.7
Obsa
Adi
Aba-sena
53.83ab
53.00b
55.83a
97.33b
114.66a
111.16a
91.33
99.5
93
1.16b
1.83a
1.83a
1
1
1
63
57.33
61.16
64.33ab
55.83b
58.50b
3.78c
3.33c
4.78ab
4.08bb
4.02b
3.92b
1.24
Mean
53.64
102.71
96.23
1.38
1
61.88
64.83
4.17
4.67
LSD
2.03**
6.29**
1.25*
3.2
5.17
16.78n
s
22.9
8.85*
CV%
4.08
ns
3.44
11.52
25.31
10.15
ns
8.9
0.64*
39.56
0.73**
*
13
DF=days to flowering, DM=Days to maturity, PH= plant height (cm), BB=Bacterial blight, PS=Pest
Score (1-5), NCPP= Number of capsule Per Plant, NSPC=Number of Seed Per capsule,
THSW=Thousand Seed Weight (g) and GY= Grain Yield (qt/ha)
4. CONCLUSIONS AND RECOMMENDATIONS
Evaluation of different varieties under different environment is crucial to determine their
responses. In line with this, seven sesame varieties were evaluated at two locations representing
Low-land agro-ecologies of West Hararghe zone in 2018 main cropping season with the
objective to evaluate and select adaptable, high yielding, early maturing, and diseases
resistant/tolerant varieties foe eventual recommendation for production in the study areas. The
193
result of the experiment showed that a significant difference for both individual and combined
mean effects for most studied traits. Grain yield was an important character to be considered for
variety selection to address the objective of the conducted activity.For this reason, two improved
varieties i.e. Bha Necho, and Bha Zeyit were showed better performance for most of the studied
characters including grain yield as well as showed higher yield advantage over the standard
check used. Therefore, based on overall performance, these two varieties were selected and
recommended to be demonstrated on farmers’ field for further scaling up in the study areas.
ACKNOWLEDGEMENTS
The authors gratefully thank Mrs. Wolansa Mokonon and Mr. Ahamziyad Abubakar for their
assistance in data collection and field trial management. The authors are indebted to Mechara
Agricultural Research Center in general for facilitating the working conditions throughout the
research Period. They are also very grateful to the Oromia Agricultural Research Institute for
financial support.
5. REFERENCES
Anonymous. 2010. Sesame production data. http://faostat.fao.org/.
Anonymous. 2015. Sesame production data. http://faostat.fao.org/.
Arnon, I. (1972). Crop production in dry regions. Vol. II: Systematic treatment of the principal
crops (pp. 683). Leonard Hill, London. Retrieved: http://www.worldcat.org/title/cropproduction-in-dry regions/oclc/417518.
Ashri A. 1998. Sesame breeding. Plant Breeding Rev. 16:179-228.
Brar, G.S., and K.L. Ahuja, 1979. Sesame: its culture, genetics, breeding and biochemistry, pp.
245-313. Annu. Rev. of Plant Sci. Kalyani Publishers, New Delhi.
Caliskan, S., M. Arslan, H. Arioglu and N. Isler, 2004. Effect of planting method and plant population on growth and yield of sesame (Sesamum indicum L.) in a Mediterranean type of
environment. Asian J. Plant Sci. 3 (5) 610-613
CSA (Central Statistic Authority). 2015. Ethiopian agricultural sample enumeration : Report on
the primary results of area, production and yield of temporary crops of private peasant
holdings in meher season, Addis Ababa, Ethiopia.
CSA (Central Statistical Authority). 2016. Report On Area and Crop Production Forecast for
Major Crops. Addis Ababa, Ethiopia.
FAO (http://www.fao.org/agriculture/seed/cropcalendar/cropcalendar.do)
Felter, H.W., and Lloyd, J.U. (1898). King’s American dispensatory.www.ibiblio.org/herbmed/
eclectic/kings/sesamum.html
FisehaBaraki, YemaneTsehayeandFetienAbay (2016). Analysis of genotype x environment
interaction and seed yield stability of sesame in Northern Ethiopia.
FisehaBaraki and MuezBerhe, (2019).Evaluating Performance of Sesame (Sesamum indicum L.)
Genotypes in Different Growing Seasons in Northern Ethiopia. International Journal of
Agronomy, Volume 2019, Article ID 7804621, 7 pages.
194
GeremewTerefe, AdugnaWakjira, MuezBerhe, and HagosTadesse .2012. Sesame Production
Manual.Ethiopian Institute of Agricultural Research Embassy of the Kingdom of the
Netherlands, EIAR, Ethiopia, 49 p.
Haile, M., Tesfaye, M., Tesfaye, A., & Mulat, E. (2004). Export type sesame and groundnuts
production and marketing.
Jima Degaga and Birhanu Angasu, (2017).Assessment of Indigenous Knowledge of Smallholder
Farmers on Intercropping Practices in West Hararghe Zone; Oromia National Regional
State, Ethiopia. Journal of Agricultural Economics and Rural Development, 3(3): 251-258.
Morris, J.B. 2002. Food, Industrial, nutriceutical and pharmaceutical uses of sesame genetic
resources, 153-156 pp. In: Janick, J. Whipkey, A. (Eds.), Trends in Crops and New Uses,
ASHS, Alexandria, VA.
Raikwar, R. S., &Srivastva, P. (2013). Productivity enhancement of sesame (Sesamum indicum
L.) through improved production technologies. African Journal of Agricultural Research,
8(47), 6073–6078.
Seegeler, C. J. P. (1983). Oil plants in Ethiopia, their taxonomy and agricultural significance.
[sn].Centre for Agricultural Publishing and Documentation, Wageningen.
Uzun B, Cagirgan MI. 2006. Comparison of determinate and indeterminate lines of sesame for
agronomic traits. Field Crops Res. 96:13-18.
Weiss, E.A., 2000. Oilseed Crops. 2nd ed. Blackwell Science, Inc., Malden, MA.
Adaptation Trial of plantain type of Banana Varietiesat Mechara on station
*Sintayehu Girma, Dereje Deressa and Gezahang Assefa
Oromia Agricultural Research Institute, Mechara agricultural research center, P.O.Box.19,
Mechara Ethiopia
*Corresponding author: girmasintayehu@gmail.com
ABSTRACT
Fruit crops are widely grown in west Hararghe by small household farmers and plays significant
role for income generation and nutrition. Plantains are cooking type banana producing fruits
that remain starchy at maturity and need processing before consumption. Even though the
environment is suitable for the production of fruit, the productivity of the crop is highly
constrained by low yielding variety and low moisture stress. In view of this, this trail was
conducted to evaluate different plantain type banana varieties for high yield, drought and
disease resistant/tolerant at Mechara on station. Four plantain varieties were brought from
Melkasa Agricultural Research Center and evaluated for agronomic, yield and yield related
traits in Completely Randomized Block Design (RCBD) in three replications. The Analysis of
variance results revealed significant variation among plantain varieties for all traits over both
195
harvesting cycles except for fruit diameter (cm), number of fruit per bunch and unmarketable
yield. The highest bunch weight, number of hands per bunch, number of fruits per bunch,
marketable yield and total yield was obtained Nijiru variety followed by kardaba. Nijiru variety
was resistance to banana diseases (sigatoka and panama diseases) as compared to the other
varieties. Whereas, the lowest bunch weight, number of fruits per bunch, marketable yield,and
total yield was obtained from Matokke variety. The Pearson correlation coefficient showed that,
the average bunch weight, fruit diameter, number of finger per hand and Marketable yield were
positively correlated with total yield. It is, therefore, concluded that, Nijiru variety was well
performed and can be recommended for the growers at Mechara and similar agro ecology of the
area.
Key words: Adaptation, plantain varieties
1. INTRODUCTION
Bananas and plantains (Musa spps.) are considered as the world’s most important fruit and the
fourth most important staple food crop (Swennen and Vuylsteke, 2001). They provide a starch
staple across some of the poorest parts of the world in Africa and Asia. The all year round
fruiting habit of banana and plantains puts the crop in a superior position in bridging the hunger
gap’ between crop harvests. Nearly all edible plantain cultivar are derived from two wild species,
M. acuminate and M.balbisiana (Robinson, 1996). These wild species are classified on the basis
of the proportion of the genetic constitution contributed by each parental source (Robinson,
1996). Plantains are always cooked before consumption and are higher in starch than bananas.
These are known as plantains and are plants producing fruits that remain starchy at maturity
(Marriot and Lancaster, 1983, Robinson, 1996) and need processing before consumption.
Banana and plantain is contributed significantly to food and income security of people engaged
in production and trade, particularly in developing countries. The plantain fruit is nutritious and
contains high levels of calories, potassium, vitamin C, magnesium and vitamin B6 (Samson,
1986; Robinson, 1996).There are two types of bananas: the sweet dessert and the cooking banana
(including plantains) (Jones, 2000). The dessert banana is left to ripen and then eaten raw, while
the cooking banana is peeled and cooked into a dish (Robinson, 1996). Plantain are usually
cooked and not eaten raw unless they are very ripe. It is similar to unripe dessert bananas in
exterior appearance, although often larger; the main differences in the former being that their
flesh is starchy rather than sweet, and they are used unripe and require cooking (Valmayoret al.,
196
2006). Plantain is drought and disease tolerant fruits than desert banana (M. balbisiana).The
plantain cultivars containing the B-genome that has been reported to exhibit higher tolerance to a
biotic stresses (Hu et al., 2015).The cultivars grown vary with altitude. For instance, at lower
elevations below 1,200 meters above sea level (masl) plantains are mainly cultivated (Dheda et
al., 2011; Ocimati et al., 2013).
Fruit crops are widely grown in Ethiopia from low to highland agro ecologies.The dessert banana
is the major fruit crop grown in different parts of the country and leading both in area and
production among the fruit crops. About 104,421.81 hectares of land is under fruit crops in
Ethiopia; Bananas contributed about 56.79% of the fruit crop area. More than 7,774,306.92
quintals of fruits was produced in the country; Bananas, took up 63.49% of the fruit production
(CSA, 2018). Like other agricultural commodities, banana and plantain production faces several
biotic and abiotic constraints and poor provision of production technologies. In resource poor
production system, productive varieties that are resistant to pests, diseases and drought are highly
suitable for increasing productivity.
Varieties often interact with the environment in an unpredictable manner and as a result
evaluating varieties that are tested across locations and/or years to study their adaptation and
stability of performance before recommendation is very crucial. Therefore, breeding programs
should focus on evaluating and selecting varieties that are high yielding, disease resistant, abiotic
stress resistant and altered agronomic performance for target areas. In this study, four plantain
varieties were evaluated at Mechara Agricultural Research Center on station for four consecutive
cropping seasons. Therefore, this experiment was conducted with the objective to evaluate high
yielding, disease resistant/tolerant plantain varieties to the area.
2. MATERIALS AND METHODS
2.1. Description of the study sites
The experiment was conducted at Mechara Agricultural Research Center on station during the
main cropping season, in 2016 to 2019. Mechara Agricultural Research Center is situated in the
Eastern part of the country at about 434 km away from Addis Ababa, the capital city of Ethiopia
and it is located in Eastern part of country lying between 8.34 N latitude and 40.20’ E longitude.
The altitude of the area is about 1760 m.a.s.l. It has a warm climate with annual mean maximum
and minimum temperature of 31.8oc and 14oc respectively. The area receives mean annual
197
rainfall of about 1100 mm. The major soil of the area is well-drained slightly acidic Nitosol
(McARC, 2010).
2.2. Experimental Treatments and Design
Four plantain varieties of suckers; Matoke, Nijiru, Cardaba and Kitawira were collected from
Melkasa Agricultural Research Center and used as experimental materials. The trail was laid out
in Completely Randomized Block Design (RCBD) with three replications. Six plantain suckers
were planted in a single plot with the spacing between plots 3.5m and between row and plant 2.5
m was used for the trail. Recommended agronomic practice was applied uniformly for all
treatments.
2.3. Data Collection
Data on the growth, yield and yield components were collected from the two consecutive
growing years. These Data were collected for the following characters: Stand count, Number of
hands per bunch, average single bunch weight (kg), number of fingers per bunch, number of
fingers per hand, finger weight per hand, fruit diameter (cm), fruit length (cm), average weight of
single fruit (kg), marketable fruit number and weight in ton ha-1,unmarketable fruit number and
weight in ton ha-1.
2.4. Data Analyses
Analysis of variance was conducted using Genstat statistical software package (16th edition). The
mean separation for any significant effect of the varieties was done using Least Significant
Difference test (LSD) at 5% of probability level. Correlation Coefficients among the traits were
carried out using procedure of SAS software Version 9.0.
3. Results and Discussion
3.1. Mean performance of plantain varieties
The results of analysis of variance (ANOVA) showed the presence of significant difference
among the test varieties for all traits in the first and second harvesting cycle except for
unmarketable yield (Tables 1 and 2). All the parameters were significantly increased with the
harvesting cycle/crop cycle of plantain varieties. This result is in agreement with Tenkouano and
Baiyeri (2007) who reported that both genotypes and cropping cycle significantly influence the
yield and other growth trait of the banana cultivars.
198
Table1. Mean yield and yield components of plantain varieties at Mechara on station,
1stharvesting cycles in 2017/18.
Variety
FD
ABW
NFH
NHB
MY
UMY
TTY
Nijiru
3.4ab
5.7a
53a
5.67a
19.37a
0.66
20.02a
Cardaba
3.2b
4.5ab
47b
3.67b
13.79c
0.48
14.26c
b
b
c
b
c
Matoke
3.23
3.4
34.33
4
12.59
0.38
12.97c
a
b
a
b
Kitawira
3.63
3.2
50.67
4
16.49b
0.8
17.29b
3.4
4.1
47
4.3
15.56
0.58
16.14
Mean
0.25
1.4
3.35
0.67
1.54
ns
1.43
LSD
3.7
16.8
3.6
7.7
15
50.4
4.4
CV%
Table2. Mean yield and yield components of plantain varieties at Mechara on station,
2ndharvesting cycles in 2018/19.
Variety
FD
ABW
NFH
NFB
UMY
MY
TTY
DR
Nijiru
3.7ab
6.5a
55a
5a
3.8
32.4a
36.2a
1c
Cardaba
4.2a
5.1bc
31.7c
4b
4.2
27.1ab 31.3ab 5a
Matoke
3.2b
4.34c
47ab
4.3ab
3.3
20.3b
23.6b
1c
Kitawira
4ab
5.83
35bc
3.67b
2.4
20.1b
22.5b
2b
3.8
5.4
42.7
4.25
3.4
26
29.5
2
Mean
0.8
1.4
14.5
0.9
1.9ns
11.3
10.8
0.6
LSD
10.3
16.8
17.4
11.1
28.3
21.6
18.4
15.1
CV%
-1
-1
Note: TTY=Total Yield (tonha ), MY=marketable yield(tonha ), UM=unmarketable
yield(tonha-1), FD=fruit diameter(cm), ABW=average bunch weight(kg),NFH=Number fruit per
bunches,NHB=Number of hands per bunches
The result of combined mean data analysis showed significance difference among the varieties
for most of the traits except fruit diameter (cm), number of fruit per bunch and unmarketable
yield in ton ha-1 (Table 3).
Table3. Mean yield and yield components of plantain varieties over two harvesting cycles at
Mechara ARC 2017-2019.
Variety
FD
ABW NFH NFB
MY
UMY
TTY
DR
Nijiru
3.5
6.1a
54.2
8.4a
25.6a
2.3
27.9a
1c
Cardaba
3.8
4.78ab 39.5
6.2b
20.3ab 2.4
22.7ab
5a
Kitawira
3.7
3.8b
42.7
7ab
18ab
1.6
19.6b
2b
Matoke
3.4
4.5b
40.8
7.8ab
16.4b
1.8
18.2b
1c
3.56
4.8
45.8
7.4
20
2
22
2.3
Mean
0.5ns 1.49
26.9ns 1.9
7.8
0.9ns
7.7
0.6
LSD
10.6
28.5
38.7
21.8
32
37.3
28.9
24.3
CV%
-1
-1
TTY=Total Yield(tonha ),MY=marketableyield(tonha ),UM=unmarketable yield (tonha-1),
FD=fruit diameter (cm), BW=bunch weight(kg), NFH=Number fruit per bunches,NHB=
Number of hands per bunches.
199
Average bunch weight: Varieties showed significant difference on average bunch weight. The
highest bunch weight was shown on Nijiru (6.1kg) followed bykardaba (4.78kg) variety, while
the lowest bunch weight had recorded for Kitawira (3.8kg) variety.
Number of finger per hand and number of finger per bunch: the results of analysis of
variances showed that the presence significant difference among varieties for number finger per
bunch while non-significant difference for number of finger per hand. Varietal difference causes
significant difference in number of finger per bunch. The Nijiru variety produced more number
of finger per hand (54.2) and number of finger per bunch (8.4) and was statistically superior to
the other varieties. Nevertheless, Cardaba variety produced the less number of finger per hand
(39.5) and finger per bunch (6.2).The highest number of finger per hand in Nijiru variety was
most likely due to the fruit bearing capacity of the variety and more fruit per bunch nature which
leads to contain high number of finger per hand. These results are in agreement with the reports
by other researcher who indicated average number of fingers per bunch ranges of from 27 to 80
(Tekle et al., 2014).
Marketable yield and Total yield (tonha-1): There was significant difference (P<0.05) among
plantain varieties for marketable yield and total yield. Nijiru variety had the highest mean values
for marketable yield(25.6 ton ha-1) and total yield (27.9 ton ha-1) followed by Cardaba variety;
20.3 tonha-1 for marketable yield and 22.7 tonha-1 for total yield. The lowest mean value of
marketable yield (16.4 ton ha-1) and total yield (18.2 ton ha-1) was obtained from Matoke variety.
The significant variation in marketable yield and total yield among the plantain varieties could be
due to their difference in genetic characteristics and adaptability to the environmental condition
of the study area. This result was supported by the findings of Tekle et al., (2014) who reported
average yield ranging from 45.333 ton ha-1 to 18.533 ton ha-1.There was no disease incidence on
Nijiru variety which was resistance to panama and sigatoka banana diseases, whereas Cardaba
variety was susceptible to these diseases. Generally, Nijiru variety had significantly the highest
average bunch weight, number of fruit per bunch, number of fruit per hand, marketable yield,
and total yield than the other test varieties. While the lowest total yield was recorded from
Matoke variety.
3.2. Correlation analysis
Phenotypic and genotypic correlation analysis showed that, most of the traits have shown
significant correlation with total yield and among themselves except unmarketable yield (Table
200
3). Average bunch weight, Fruit diameter, number of finger per hand and marketable yield were
showed positive and significant correlation both at phenotypic and genotypic levels. These
results are in agreement with finding of Baiyeri et al., (2000) reported that most of fruit
characters were related to the yield but with varied magnitude and the correlation was affected
by the genomic group and environment; these effects might explain the consistent and significant
low magnitude correlation of plant height with other growth characters and yield. Average bunch
weight, Fruit diameter, number of finger per hand and marketable yield had significant
correlation among themselves. Average bunch weight showed significant and positive
correlation with number of fingers per bunch both at genotypic and phenotypic correlations
(Table 3).
Table 3. Phenotypic (above diagonal) and genotypic (below diagonal) correlation coefficients
among different characters of Plantain varieties
Traits
ABW
NFB
NFH
FD
MY
UY
TY
ABW
1
0.750*
0.295*
-0.007ns
0.998*
0.659ns
0.995*
NFB
0.064*
1
0.853*
-0.614*
0.782*
0.244*
0.754**
NFH
-0.075*
-0.3224*
1
-0.875*
0.354*
-0.178* 0.306*
FD
-0.185*
-0.266*
0.465*
1
-0.009*
0.089*
0.020*
MY
0.3134*
-0.381*
0.638*
0.379*
1
0.536ns
0.998*
UY
-0.123*
-0.561ns
0.709*
0.537ns
0.612ns
1
0.588ns
TY
0.263*
-0.432*
0.708*
0.4398*
0.992**
0.705ns 1
TTY=Total Yield (ton/ha), MY=marketable yield(ton/ha), UM=unmarketable yield(ton/ha),
FD=fruit diameter (cm), BW=bunch weight(kg), NFB=Number fruit per bunches, NHB=
Number of hands per bunches
4. Conclusions and Recommendations
The results of analysis of variance showed that, all the yield and yield related parameters were
significantly affected by varieties except fruit diameter, number of fruit per hand and
unmarketable yield. Nijiru Variety was superior over all varieties for average bunch weight (6.1
kg), number of finger per bunch (54.2), marketable yield (25.6) and total yield (27.9ton ha-1).
Moreover, Nijiru variety gave higher yield over the other varieties in both harvesting cycle,
indicating that, the Nijiru variety is stable and can provide reasonable amount of yield regularly.
Therefore, it can be concluded that Nijiru variety is recommended for further demonstration in
Daro Lebu and similar agro- ecologies.
201
ACKNOWLEDGEMENTS
The authors gratefully thank to Oromia Agricultural Research Institute for financial support and
Mechara Agricultural Research Center for facilitating the working conditions throughout the
research Period.
5. REFERENCE
Baiyeri KP, Tenkouanoa B, Mbahb BN, Mbagwub JSC (2000). Ploidy and genomic group
effects on yield components interaction in bananas and plantains across four environments
in Nigeria. ScientiaHorticult. 85:51-62.
CSA (Central Statistical Agency), 2018. Agricultural Sample Survey Report on Area and
Production of Major Crops. (Private Peasant Holdings, Meher Season). Statistical Bulletin.
Addis Ababa, Ethiopia.
Dheda DB, Nzawele BD, Roux N, Ngezahayo F, Vigheri N, De Langhe E, Karamura D, Picq C,
Mobambo P, Swennen R, Blomme G (2011). Musa collection and characterization in central
and eastern DR Congo: a chronological overview. ActaHortic. 897:87-94.
Ocimati W, Karamura D, Rutikanga A, Sivirihauma C, Ndungo V, Adheka J, Dhed'a B,
Muhindo H, Ntamwira J, Hakizimana S, Ngezahayo F, Ragama P, Lepoint P, Kanyaruguru
JP, De Langhe, Gaidashova SV, Nsabimana A, Murekezi C, Blomme G (2013). Musa
germplasm diversity status across a wide range of agro-ecological zones in Rwanda,
Burundi and eastern Democratic Republic of Congo. In: Blomme, G. et al. (Eds).
Robinson, J.C., 1996. Bananas and Plantains. University Press, Cambridge.
Tenkouano A, Baiyeri KP (2007). Adaptation pattern and yield stability of banana and plantain
genotypes grown in contrasting agro-ecologies in Nigeria: African Crop Science Conference
Proceedings. 8:377-384.
Adaptability study of Chickpea varieties (Cicer arietinum L.) at Bule hora and Abaya,
Southern Oromia
Ejigu Ejara1*, Kemal Kitaba1, Zinash Misgana1 and Genene Tesema1
1Yabello
Pastoral and Dryland Agriculture Research Centre, Oromia Agricultural Research
Institute, Yabello, Ethiopia.
*Corresponding author. E-mail: ehordofa@gmail.com
Abstract
Chickpea is among the major pulse crops grown in southern Ethiopia including Borana and
West Guji zone. The area has a potential for production of chickpea for food and nutrition
security as well as export purpose. However, scarcity of varieties that fit to the environment is
one of the major constraints of chickpea production. Therefore, this experiment was conducted
to evaluate nine chickpea varieties with aim to select adaptable varieties for yield and
202
agronomic traits. The field experiment was conducted in 2017 and 2018 cropping season at two
locations (Abaya and Bule hora) and varieties were planted in Randomized Complete Block
Design (RCBD). Data were collected on yield and important agronomic traits. Analysis of
variance computed for individual location and combined analysis over locations revealed
significant variations among varieties. Moreover, varieties showed a grain yield as high as
1087.5 kg/ha and 873.79 kg/ha at Bule hora and Abaya respectively. Minjar variety gave
significantly high yield at both locations with yield advantage of 26.13% and 52.07% over
variety mean at Bule hora and Abaya respectively and therefore recommended for both locations
and locations with similar agro ecologies.
Introduction
Chickpea (Cicer arietinum L.) is a diploid species with 2n=16 chromosomes. It is a selfpollinated crop, with natural cross-pollination of up to one per cent (Singh, 1987). Chickpea is
among the oldest crops, being domesticated in the Fertile Crescent 10,000 years ago (Redden and
Berger, 2007) and named as Bengal gram (Indian), Chickpea (English), Garbanzo (Latin
America), Hommes, Hamaz (Arab world), Nohud, Lablabi (Turkey), Shimbra (Ethiopia). It is the
lone domesticated species among the 44 species comprising 33 perennial and eight annual wild
species and highly preferred pulse for human consumption within the genus Cicer (Vander
Maesen, 1987), family Fabaceae, tribe Cicerae. Chickpea is grown in tropical, sub-tropical and
temperate regions. It is a valued crop and provides nutritious food for an expanding world
population and will become increasingly important with climate change (Bulti and Jema, 2019).
Chickpea contains nutritive seeds with high protein content, 25.3-28.9 %, after dehulling (Hulse,
1991), 38-59% carbohydrate, 3% fiber, 4.8-5.5% oil, 3% ash, 0.2% calcium, and 0.3%
phosphorus. Digestibility of protein varies from 76-78% and its carbohydrate from 57-60%
(Hulse, 1991). Chickpea seeds are eaten fresh as green vegetables, parched, fried, roasted, and
boiled; as snack food, sweet and condiments; seeds are ground and the flour can be used as soup,
dhal, and to make bread; prepared with pepper, salt and lemon it is served as a side dish (Saxena,
1990). Chickpea is beneficial to a healthy diet. For example a half-cup serving provides 7 g of
protein (10% of our daily requirement) and 6 g of fiber (20% of our daily requirement) (USDA,
2015). It plays a significant role in improving soil fertility by fixing the atmospheric nitrogen.It
can fix up to 140 kg N ha-1 from air and meet most of its nitrogen requirement (Sheleme et al,
2015).
203
According to CSA report (2016/17) in Ethiopia, Pulse crops production ranks second in terms of
production area. Pulses grown in Ethiopia covered 12.33% (1,549,911.86 hectares) of the grain
crop area and 9.69% (about 28,146,331.73 quintals) of the grain production. In Ethiopia,
chickpea is mainly grown in the central, northern and eastern highland areas of the country at an
altitude of 1400-2300 m.a.s.l., where annual rainfall ranges between 700 and 2000 mm (Bejiga
1994; Anbessa and Bejiga 2002). %. It is best adapted to the areas having Vertisols (Sheleme et
al, 2015). Chickpea production has increased from 60085 tons (1993) to 473570 tons (2017).
The production areas are also increased from 109750 hectare (1993) to 473570 hectare (2017)
(FAOSTAT, 2019)
2017, 473570
500000
400000
y = 18066x + 17612
R² = 0.9153
300000
200000
100000
1993, 60085
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
0
1993
Area (ha) and Production (t)
Chart 1: Chickpea Area and production trends in Ethiopia (1993-2017)
600000
Year
Area(ha)
Yield (ton)
Linear (Yield (ton))
Source: FAOSTAT, 2019
In the study areas, shortage of chickpea varieties that adapt to the prevailing environments are
the top chickpea production constraints. Therefore, this study was incited with the objective to
test the adaptability of chickpea varieties for yield and yield related traits in the study areas.
MATERIALS AND METHODS
Description of experimental sites
The experiment was conducted at Bule hora and Abaya during 2017 and 2018 cropping season.
The experimental areas are located in the Southern part of the country in the Oromia Regional
State. Bule hora and Abaya are located at 465 and 365 km far from Addis Ababa city,
respectively.
Experimental Materials
For this study, nine released chickpea varieties were obtained from Debrezayit Agriculture
Research Centre (DZARC) and evaluated for adaptability of the varieties at these locations.
204
Table 1: Released Chickpea varieties use in the experiment
S.No
1
2
3
4
5
6
7
8
9
Variety
Dhera
Arerti
Hora
Ejeri
Habru
Natoli
Minjar
Dalota
Dimtu
Year of release
2016
1999
2016
2005
2004
2007
2010
2013
2016
Breeding center
DZARC
DZARC
DZARC
DZARC
DZARC
DZARC
DZARC
DZARC
DZARC
Experimental Design and Managements
The experiment was laid out in Randomized Complete Block Design. Each entry was planted in
a plot having 6 rows of 3 meter length. Four rows were harvested and two border rows were left
to exclude border effect. The row and plant spacing was kept at 40 cm and 10 cm, respectively.
Individual plot size was 2.4 m x 3 m=7.2 m2 and 1m and 1.5m between plot and block,
respectively. 60kg NPS/ha Fertilizer was applied at the time of planting. All other agronomic
managements were applied uniformly in all experimental plots as per national recommendation
for the crop.
Data Collection
The following data were collected during the experimentation time both from the whole plot, net
plot and sampled plants by random selection method from the middle of four rows of each plot.
Data recorded on plant basis
Plant height at harvest (cm): Height of five randomly taken plants during harvest period from
each experimental plot was measured in centimeter from the ground level to top of the plants and
the average height was recorded.
Number of primary branches: Number of productive branches extending from the main stem
was recorded from five randomly selected plants and average branch number was taken.
Pod length (cm): The length of five randomly selected pods from each of the five randomly
selected plants was measured at harvesting and the average was used.
Number of pods per plant: this was recorded as average total number of pods of five randomly
selected plants from each experimental plot at harvest.
205
Number of seeds per pod: This was recorded as average total number of seeds of five randomly
selected plants from each experimental plot divided by total number of pod of the same plants at
harvest.
Seeds per plant: Average number of seeds counted from five randomly selected plants.
Data collection on plot basis
Days to Flowering: The numbers of days from the date of emergence to the date on which
about 50% of the plants in each plot produce flowers.
Days to maturity: The number of days from planting to the date when 90% of the
morphological observation of the plant turned to yellow straw colour.
Stand count at harvest: This was recorded by counting the total number of plants from the four
middle rows of each plot at harvest.
Grain yield (g/plot): Grain yield in grams obtained from the central four harvestable rows of
each plot was harvested, threshed and weighted using sensitive balance.
Grain yield (ton/ha): Grain yield obtained from each plot was used to estimate grain yield
(tons) per hectare.
Data Analysis
Analysis of variance
Analysis of variance (ANOVA) was computed for grain yield and other traits as per the methods
described by Gomez and Gomez (1984) using SAS computer software (Version 9) for
Randomized Complete Block Design. Comparison of treatment means was made using Duncan
Multiple Range test (DMRT) at 5% level of significance. Location wise analyses were performed
and error variances were subjected to F-test for homogeneity test of variances. Variables with
homogeneous error variances were directly used for combined analyses, while those with
heterogeneous error variances were analyzed in individual locations. The combined analysis was
based on mixed model (fixed genotype and random environment).
Individual locations and combined ANOVA were computed using the following mathematical
model:
Individual locations ANOVA model
𝑋𝑖𝑗𝑘𝑙 = µ + 𝐺𝑖 + 𝐵𝑗𝑘 + 𝑌 + 𝐺𝑌𝑖 + 𝐸𝑖𝑗𝑘
Where, 𝑋𝑖𝑗𝑘𝑙 = Observed value,
µ = general mean,
206
𝐺𝑖 = effect of variety,
𝐵𝑗𝑘 = effect of replication (block),
𝑌= effect of year,
𝐺𝑌𝑖 = variety x Year,
𝐸𝑖𝑗𝑘 = residual effects or experimental error. Additionally, g, r, y are numbers of geneotypes,
replications, locations and years, respectively
Combined ANOVA model
𝑋𝑖𝑗𝑘𝑙 = µ + 𝐺𝑖 + 𝐵𝑗𝑘𝑙 + 𝐿𝑘 + 𝑌𝑙 + 𝐺𝐿𝑖𝑘 + 𝐺𝑌𝑖𝑙 + 𝐿𝑌𝑘𝑙 + 𝐺𝐿𝑌𝑖𝑘𝑙 + 𝐸𝑖𝑗𝑘𝑙
Where, 𝑋𝑖𝑗𝑘𝑙 = Observed value,
µ = general mean,
𝐺𝑖 = effect of genotype,
𝐵𝑗𝑘𝑙 = effect of replication (block),
𝐿𝑘 = effect of location,
𝑌𝑙 = effect of year,
𝐺𝐿𝑖𝑘 + 𝐺𝑌𝑖𝑙 + 𝐿𝑌𝑘𝑙 + 𝐺𝐿𝑌𝑖𝑘𝑙 = effects of Genotype x Location, Genotype x Year, Location x
Year, and Genotype x Location x Year interactions, respectively.
Eijkl = residual effects or experimental error. Additionally, 𝑔, 𝑟, 𝑙, 𝑦 are numbers of genotypes,
replications, locations and years, respectively.
RESULTS AND DISCUSSION
Analysis of Variance
The experiment was conducted at two locations viz. Bule hora and Abaya. Homogeneity of
variance was computed for each location before the combined analysis of variance computed.
The analysis of variance were computed for days to flowering, days to maturity, plant height,
number of primary branches, pods per plant, seeds per pod, seeds per plant and grain yield per
hectare. The individual location and the combined analysis of variance results are presented in
subsequent sections.
Individual location analysis of variance
Analysis of variance computed for each location revealed that variation among varieties were
highly significant (P<0.01) for all traits at both locations except seeds per pods are significant
(P<0.05) at Bule hora and not significant at Abaya (Table 2 and Table 4). The presence of
variations among varieties under experiment for all the traits studied indicated the presence of
207
sufficient variability among Chickpea varieties that would be exploited through selection. The
year effect was highly significant (P<0.01) at both location, indicated that the performance of
varieties are different in different locations. Ercan et al (2013) also reported different
performance of Chickpea genotypes in different year and location. In Ethiopia, Getachew et al
(2015) reported the presence of highly significant variation among 17 Kabuli type Chickpea
genotypes conducted in five environments. He also reported the existence of significant variation
for days to flowering, days to maturity, plant height, pods per plant, seed per pod, 100 seed
weight and Grain yield. Ercan et al (2013), Rozinaet al (2015), Dan (2016)and Desai et al (2016)
also reported highly significant variation for plant height, pods per plant, seeds per plant,
hundred seed weight and grain yieldin Chickpea which is in line with this finding.
Table 2: Mean squares from combined analyses of variance over two years for 8 traits of
Chickpea varieties grown at Bule hora in 2010 and 2011 E.C
Source of
variation
Year (Y)
Variety
Reps.withn
(Y)
Y* V
Pooled
Error
CV (%)
df
GY (kg/ha)
FD
MD
PH (cm)
NPB
PPP
SPPnt
Spp
1
8
4
9144171.9***
221172.1***
12870.1
200.3***
52.0***
12.1
852.0***
94.6***
28.8**
121.5**
138.0***
17.5
12.9***
2.3**
4.788***
4911.6***
238.3**
793.8*
4907.8***
483.9***
740.1***
0.042
0.055
0.023
8
32
75879.8***
5575.5
6.4
6.5
5.8
5.5
13.6
11.2
2.610**
0.6
44.297
69.1
99.891
80.9
0.008
0.031
9.3
4.1
2.1
7.8
18.4
26.0
29.9
19.14
ns,* ,**&***,non-significant, significant at P<0.05, P<0.01 and P<0.001, respectively. DF= degree of
freedom, FD= days to flowering, GY (kg/ha) = Grain yield in kilogram per hectare, MD= days to
maturity, PH (cm) = plant height in centimeter, NPB= number of primary branch, PPP= pods per plant,
Table 3: Mean squares from combined analyses of variance over two years for 8 traits of
Chickpea varieties grown at Abaya in 2010 and 2011 E.C
S.V
Year (Y)
Variety
Reps.
withn(Y)
Y* V
Pooled
Error
CV (%)
Df
1
8
4
GY (t/ha)
660731.1***
275176.2***
4170.1
FD
168.9***
68.9***
9.5
MD
665.0***
183.7***
21.2
PH (cm)
1026.2***
172.2***
23.1
NPB
66.2***
2.6**
1.9*
PPP
156.4**
162.7***
12.9
SPPnt
109.8*
260.1***
2.1
Spp
0.0
0.1*
0.0
8
32
35941.6***
2068.3
3.2
10.3
63.5***
8.8
14.3
9.5
1.3
0.7
6.8
14.7
20.6
20.4
0.0
0.1
10.9
5.8
3.0
8.2
17.6
24.6
31.5
21.1
ns,* ,**&***,non-significant, significant at P<0.05, P<0.01 and P<0.001, respectively. DF= degree of
freedom, FD= days to flowering, GY (kg/ha) = Grain yield in kilogram per hectare, MD= days to
maturity, PH (cm) = plant height in centimetre, NPB= number of primary branch, PPP= pod per plant
208
Combined analysis of variance over locations
Location wise analyses were performed and error variances were subjected to F-test for
homogeneity of variance. Variables with homogeneous error variances were subjected to
combined analysis, and as well as evaluation of varieties performance were conducted using the
pooled mean values over locations. Whereas, for those traits with heterogeneous error variances,
evaluation of varieties were conducted using each location mean values. Accordingly, pods per
plant, seeds per plant and grain yield exhibited heterogeneous error variances and the mean
squares for locations were also significant indicating the performance of the genotypes cannot be
evaluated on the basis of pooled mean values over locations. However, the homogeneity of error
variances for flowering date, maturity date, plant height, number of primary branches andseeds
per pods were homogeneous that allowed evaluation of the genotypes on the basis of combined
mean values over locations.
The ANOVA results of combined analysis over locations are presented in table 4. The result of
combined analysis ofvariance revealed the presence of highly significant (P<0.01) difference
among locations, varieties and varieties by environment interaction for traits suggested
differences in environments and the presence of sufficient genetic variability for these trait that
can be exploited in breeding programs. Highly significant variation for grain yield other yield
related traits in chickpeawere also reported by various authors (Desalegn and Pichiah 2019;
Desai et al 2016; Getacho et al 2015; Singh et al 1990, Ercan et al 2013). The significant
differences were observed between locations for all traits. This indicates that the two locations
were significantly different for the performance of varieties for these traits. The significant
differences between locations were reported in chickpea by Desalegn and Pichiah 2019, Desai et
al 2016 and Getachewet al 2015. The presence of significant verities x location interaction
(Table 4) suggested that varieties had differential performance at the two locations for these
traits. The differential performance of varieties across environment varies significantly and the
performance of plants depends directly on the environmental conditions (Fox et al., 1990). Other
authors also reported the significant influence of genotype by location interaction on the
performance of chickpea (Getachew et al, 2015, Desalegn and Pichiah 2019 and Desai et al
2016).
209
Mean performance of varieties
Crop phenology
Flowering duration of nine varieties of chickpea ranges from 59.75-69.25 and 49.67-60.00 days
at Bule hora and Abaya respectively while the maturity duration of varieties ranges from 109.00120.67 and 90.5-106.08 days at Bule hora and Abaya respectively. The mean performances of
for these traits are presented in Tables 5 and 6. The varieties showed early flowering and
maturity at Abaya than Bule hora. This might be due to the altitude and temperature differences
of the two locations, where by Abaya is located at an altitude of 1442 m. a. s. l. with mean
minimum and maximum temperature of 12.6-29.9 °C while Bule hora is located at an altitude of
2322 m. a. s. l. with mean minimum and maximum temperature of 15-30 °C. The pooled mean
over location and year (Table 7) for flowering and maturity date ranges from 54.71-64.63 and
99.75-113.38 days respectively. The earliest maturing varieties was Dimtu (99.75 days) followed
by Dalota (101.29 days) and Minjar (102.29 days) while the late maturing variety was Dhera
(113.375 days) followed by Hora (110.58) and Areri (109.67) (Table 7). Four varieties exhibit
lower number of days to maturity than over all mean.
Table 4: Pooled Mean squares from combined analyses of variance over two locations and two
years for four traits of Chickpea varieties grown at B/Hora and Abaya in 2010 and 2011E.C
Source of variation
DF FD
MD
Pht
NPB
Locations (L)
1
2498.891*** 5896.333***
736.333***
3.067*
Replications (L)
4
21.356*
28.01**
22.638
4.803***
Years (Y)
1
368.521***
5.787
926.935***
68.800***
L*Y
1
0.669
1511.259***
220.735***
10.329***
Varieties ( V)
8
116.214***
262.318***
283.613***
2.065**
L*V
8
4.787
15.974
26.647*
2.825***
Y*V
8
7.219
50.459***
13.026
0.945
L *V*Y
8
2.315
18.796*
14.843
3.011***
Pooled Error
68
8.143
8.024
10.806
0.720
CV
4.767
2.66
8.12
18.976
Mean
59.85
106.47
40.49
4.47
ns,* ,**&***,non-significant, significant at P<0.05, P<0.01 and P<0.001, respectively. DF=
degree of freedom, FD= days to flowering, L =locations, MD= days to maturity, PH (cm) = plant
height in centimeter, NPB= number of primary branch, Rep= Replications, V= Variety, Y= year
Growth traits
Mean performances of genotypes for plant height at Abaya ranged from 33.3 cm to 50.06cm
with location mean of 37.88 cm; whereas mean performance of varieties for plant height ranged
from 37.0 cm to 53.8 cm with location mean of 43.10 cm at Bule hora (table 5 and 6). The mean
210
values of chickpea for plant height ranged from 36.18 to 51.93 with over all mean values of
40.49. Similar result for mean and range for plant height in Chick pea varieties were also
reported previously by Getacho et al 2015, Dan et al 2016 and Ercan et al 2013. Genotypes
attained higher plant height at Bule hora than at Abaya. Varieties showed considerable variations
for number of primary branches that ranged from 3.23 for Ejare to 5.27 for Dalota at Bule hora
(table 5); and 3.57 for Dimtu to 5.37 for 5.37 for Dhera at Abaya (Table 6). The mean
performance of varieties for number of primary branches were 4.60 at Abaya and 4.30 at Bule
hora with pooled mean of 4.47. Six varieties recorded superior number of primary branches than
the mean performance of varieties (Table 7). Existence of significant variations among Chickpea
varieties for number of primary branches was also reported by (Dan et al 2016).
Yield and yield components
The variation of varieties for pods number per plant and seeds number per plant ranged from
23.57 to 44.97; and 21.6 to 52.83, respectively at Bule hora. The variation of these two traits
ranged from 9.67 to 27.91 and 7.93 to 30.4, respectively at Abaya. Minjar had significantly
higher pods, seeds number per plant and seed per pod at both locations (Table 5 and 6). The
existence of considerable variations for pods number, seeds number per plant and seed per pod
was also reported by other authors in Chickpea (Getacho et al 2015, Dan et al 2016 and Ercan et
al 2013). The mean grain yield of varieties ranged from 571.7 kg to 1087.5kg; 226.57kg to
873.79kg at Bule hora and Abaya, respectively (table 5 and 6). At Bule hora, significantly
highest mean grain yield was measured from Minjar (1087.5kg/ha) followed by Natoli
(1030.94kg/ha) and the lowest mean grain yield was obtained from Hora (571.7 kg /ha) followed
by Dhera (600.35kg/ha). At Abaya the highest grain yield was obtained from variety Minjar
(873.79kg/ha) followed by dalota (583.16kg/ha) and the lowest grain yield was measured from
Dhera (160.42kg/ha) followed by Hora (226.57 kg/ha). Four varieties gave grain yields greater
than mean grain yield of varieties at Bule hora and four varieties had grain yield greater than
mean yield of varieties at Abaya as well. In all cases, Minjar is significantly well performing
variety at both locations (table 5 and 6).
Table 5: Mean value of yield and yield related traits of 9 Varieties of Chickpea tested at Bule
hora in 2010 and 2011 E.C cropping season
Variety
Dhera
Areri
Hora
FD
69.250a
65.417bc
67.333ab
MD
120.667a
115.417b
116.583b
PH(cm)
53.800a
37.000e
42.133b-d
NPB
PPP
5.00ab
34.63b
4.367a-c 28.07bc
4.067b-d 33.33bc
SPPnt
28.20b
24.967b
29.36b
Spp
0.850b
0.867b
0.850b
GY(kg/ha)
600.35fg
689.06de
571.70g
211
Variety
Ejere
Habru
Natoli
Minjar
Dalota
Dimtu
FD
65.00bc
59.750
66.083a-c
63.417cd
64.667bc
61.083de
MD
115.500b
116.333b
111.500c
109.583c
110.167c
109.00c
PH(cm)
40.667c-e
45.000bc
39.867de
45.900b
41.733b-d
NPB
3.233d
4.400a-c
3.867cd
4.567a-c
5.267a
3.967b-d
PPP
23.57c
31.47bc
25.767bc
44.967a
35.40ab
30.10bc
SPPnt
21.60b
27.367b
26.07b
52.833a
26.067b
29.167b
Spp
0.900b
0.900b
0.967ab
1.150a
0.867b
0.967ab
41.800b-d
Mean
64.67
113.86
43.10
4.30
31.92
30.07
0.92
GY(kg/ha)
856.15c
661.29ef
1030.94ab
1087.50a
759.38d
975.18b
803.50
Range
59.75109.0037.0-53.8 3.2323.5721.60.85571.769.25
120.67
5.27
44.97
52.83
1.15
1087.5
Means with the same letters in the same columns are not significantly differentFD= days to flowering,
GY (kg/ha) = Grain yield in kilogram per hectare, MD= days to maturity, PH (cm) = plant height in
centimetre, NPB= number of primary branch, PPP= pod per plant, SPPnt= seed per plant, Spp = seed per
pod.
Table 6: Mean value of yield and yield related traits of 9 Varieties of Chickpea tested at Abaya in
2010 and 2011 E.C cropping season
Variety
Dhera
Areri
Hora
Ejere
Habru
Natoli
Minjar
Dalota
Dimtu
FD
60.00a
57.25ab
56.750ab
53.750bc
49.667d
58.75a
53.917bc
53.833bc
51.50cd
MD
106.08a
103.92abc
104.58ab
100.42cd
101.08bcd
97.75de
95.00ef
92.42fg
90.50g
PH(cm)
50.06a
35.37cd
37.93c
37.80c
42.53b
33.30d
35.17cd
34.10cd
34.63cd
NPB
5.37a
5.00ab
5.23a
5.30a
4.83a-c
4.13b-d
4.46a-d
3.87cd
3.57d
PPP
9.67c
13.37bc
11.47bc
13.20bc
16.57b
15.30b
27.92a
16.53b
16.10b
SPPnt
9.73cd
10.50b-d
7.93d
11.77b-d
14.50bc
13.23b-d
30.40a
14.67bc
16.30b
Spp
1.01ab
0.78bc
0.72c
0.90a-c
0.88a-c
0.83a-c
1.07a
0.88a-c
1.03ab
GY(kg/ha)
160.42g
278.13f
226.57f
353.94e
388.72de
420.66d
873.79a
583.16b
484.03c
Mean
55.046
99.08
37.88
4.64
15.568
14.337
0.90
418.82
Range
49.66760.00
90.5106.08
33.350.06
5.373.57
27.919.67
30.4-7.93
1.070.72
873.79226.57
Means with the same letters in the same columns are not significantly different, FD= days to flowering, GY (kg/ha)
= Grain yield in kilogram per hectare, MD= days to maturity, PH (cm) = plant height in centimetre, NPB= number
of primary branch, SPPnt= seed per plant, Spp = seeds per pod, PPP= pods per plant
Table 7: Pooled Mean values of yield and yield related traits of 9 Varieties of Chickpea tested at Abaya
and B/ hora in 2010E.C and 2011 cropping season
Variety
FD
MD
Pht
NPB
Dhera
64.63a
113.375a
51.933a
5.183a
Areri
61.33bc
109.667bc
36.183e
4.683ab
Hora
62.04b
110.583b
40.033c
4.650ab
Ejere
59.38cd
107.958c
39.233cd
4.267bc
Habru
54.71e
108.708bc
43.767b
4.617ab
Natoli
62.42ab
104.625d
36.583de
4.00bc
Minjar
58.67d
102.292e
40.533c
4.517a-c
Dalota
59.25cd
101.292ef
37.917c-e
4.567ab
Dimtu
56.29e
99.750f
38.217c-e
3.767c
Means
59.85
106.47
40.49
4.47
Means with the same letters in the same columns are not significantly different FD= flowering date, MD= Maturity
date, PH= plant height, NPB= number of primary branch.
212
CONCLUSIONS AND RECOMMENDATIONS
The results of this investigation showed significant variation among varieties for all traits as well
as significant effect of varieties by location interaction for grain yield and most yield related
traits, which indicated the differential performance of varieties across environments. The highest
mean grain yield was exhibited by Minjar (1087.5kg ha-1) and Natoli (1030.94kg ha-1) at Bule
hora and Minjar had significantly highest mean grain yield (873.79kg ha-1) at Abaya with About
four varieties gave mean grain yield greater than grand mean at Bule hora and and Abaya. Minjar
variety is significantly high yielding variety at both locations with yield advantage of 26.13%
and 52.07% over variety mean at Bule hora and Abaya respectively. The prominent chickpea
varieties Minjar and Natoli are promising varieties due to their relatively higher yield and some
considerable traits at Bule hora and similar agro-ecologies while Minjar is promising variety at
Abaya. Therefore, farmers and chickpea producers around study areas and similar agro ecologies
can use those varieties for chick pea production.
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Adaptability Studyof Early Maturing Ground nut Variety in West Guji lowland, Southern
Oromia
Ejigu Ejara1*, Kemal Kitaba1Zinash Misganaa1, Mulatu Gabisa1 and Genene Tesema1
1Yabello Pastoral and Dryland Agriculture Research Centre, Oromia Agricultural Research
Institute, Yabello, Ethiopia.
*Corresponding author. E-mail: ehordofa@gmail.com
Abstract
Groundnut is an important oil seed crop, grown throughout the tropics and sub tropics
worldwide. It is one of the three economically important oilseed crops grown in Ethiopia.
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Groundnut is commonly produced by small scale farmers as food and cash crops in the study
area. The area has potential for the production of ground nut for food and nutrition security as
well as export commodity. However, lack of improved varieties that are suitable for the areas is
one of the major ground nut production constraints in the area. Therefore, this experiment was
initiated to evaluate five ground nut varieties and to select early maturing varieties with
considerable yield and agronomic traits. The field experiment was conducted in 2017 and 2018
cropping season at Abaya and varieties were planted in Randomized Complete Block Design
(RCBD)in three replications. Data were collected on yield and important agronomic traits. The
analysis of variance revealed significant variations among varieties for days to maturity, number
of primary branches, number of pegs per plants, grain yield and shelling percentage.The result
of pooled over years grain yield mean of varieties indicated ,Tole- 1 variety ( Check) is high
yielding with mean grain yield of 4174.7kg followed by variety Sedi (3552.5kg/ha) and Babile
local (3550.4kg/ha). Variety Sedi has special merit in terms of earliness
and therefore
recommended for moisture stress areas of Abaya and location with similar agro ecologies while
Tole -1 (Standard check) is high yielding varieties and should be used in production until new
varieties is developed through selection/breeding program.
Key words: Early maturity, Grain yield, related traits.
INTRODUCTION
The word A. hypogaea has been derived from two Greek words Arachis meaning a legume and
hypogaea meaning below ground (referring to the formation of pods in the soil). It is an annual
legume which is also known as peanuts, earthnut, monkey-nut and goobers. It is the 13th most
important food crop and 4th most important oil seed crop of the world. Groundnut seeds (kernels)
contain 48-50% oil, 26-28 % protein and are a rich source of dietary fibre, minerals and
vitamins. Groundnut kernels are consumed directly as raw, roasted or boiled kernels while the oil
extracted from the kernel is used as culinary oil. It is also used as animal feed and industrial raw
material (Garko et al, 2016). Groundnut or peanut is an important oil seed crop, grown
throughout the tropics between 40° South and 40° North of the equator where the annual rainfall
ranges between 500 to 1200 mm and with average daily temperature higher than 20°C. The crop
is grown in tropical and subtropical regions of the world. It is grown in six continents, but mainly
in Asia, Africa and America in over 100 countries with a world production of 37.10 million
metric tons from an area of 23.11 million hectares. Groundnut is one of the three economically
215
important oilseed crops including noug, and sesame in Ethiopia and is largely produced in the
eastern part of the country (Mastewal et al 2017). The annual world groundnut production was
around 38.2 million tons from 26.4 million ha of production area. Developing countries
constitute 97% of the global area and 94% of the global production of this crop. The average
national yield of groundnut is about 1.1 ton ha-1 (CSA, 2015), which is significantly lower than
the World’s average of about 1.49 t ha-1 (FAOSTAT, 2010). The major groundnut producer
region in Ethiopia is Oromia region (41,089 ha), followed by Benshangul- Gumuz (14,759 ha)
and Amhara (3,161 ha) regional states (Musaet al.,2016). Groundnut is planted both during the
“Belg” season (March) and also during the main season (June), in some parts of western
Ethiopia. With regard to final utilization, groundnut varieties are categorized into two major
groups: oil types and confectionery ones. Confectionery groundnut varieties are those with large
seeds and are mostly used for various food types (roasted seeds, peanut butter, candies, cookies
and other snacks). A great amount of the groundnut produce in Ethiopia is consumed locally for
confectionery purposes (Amele work et al., 2007). Therefore, this study was undertaken with the
objective of selecting early mature and high yielder Ground nut Variety for the study area.
MATERIAL AND METHODS
Description of the experimental site
The experiment was conducted at Abaya during 2017 and 2018 main cropping season. The
experimental area is located in the Southern part of the country in the Oromia Regional State.
Abaya is the sub-site of Yabello Pastoral and Dryland Agriculture Research Center and located
at 365 km far from Addis Ababa cite. The detail description of the study area is presented in the
Table 1.
Table 12: Description of the study area
Variables
Soil type
Altitude (m.a.s.l.)
Latitude
Longitude
Annual Temperature 0C
Minimum
Maximum
Annual rainfall (mm)
Minimum
Maximum
Sandy clay loam
1442
06o43’520"N
038o25’425"E
12.6
29.9
500
1100
216
Experimental Materials
For this study, four released ground nut varieties were obtained from Haramaya University and
evaluated along with one standard check for adaptability study.
Experimental Design and Managements
The experiment was laid out in Randomized Complete Block Design. Each entry was planted in
a plot having 6 rows of 3 meter length. Four rows were harvested and two border rows were left
to exclude border effect. The row and plant spacing was kept at 40 cm and 10 cm, respectively.
Individual plot size was 2.4 m x 3m=7.2 m2 and 1.5m between each block. All other agronomic
managements were applied uniformly in all experimental plots as per national recommendation
for the crop.
Data Collection: The following data were collected during the experimentation period both from
the net plot and sampled plants by random selection method from the middle of the four rows of
each plot.
Data recorded on plant basis
Plant height at harvest (cm): Height of five randomly taken plants during harvest period from
each experimental plot was measured in centimeter from the ground level to top of the plants and
the average height was recorded.
Number of primary branches: Number of productive branches extending from the main stem
was recorded from five randomly selected plants and average branch number was taken.
Number of pods per plant: this was recorded as average total number of pods of five randomly
selected plants from each experimental plot at harvest.
Number of seeds per pod: This was recorded as average total number of seeds of five randomly
selected plants from each experimental plot divided by total number of pod of the same plants at
harvest.
Seeds per plant: Average number of seeds counted from five randomly selected plants.
Data recorded on plot basis
Days to Flowering: The numbers of days from the date of emergence to the date on which
about 50% of the plants in each plot produce flowers.
Days to maturity: The number of days from planting to maturity period
217
Stand count at harvest: This was recorded by counting the total number of plants from the four
middle rows of each plot at harvest.
Grain yield (g/plot): Grain yield in grams obtained from the central four harvestable rows of
each plot was harvested, threshed and weighted using sensitive balance
Grain yield (kg/ha): Grain yield obtained from each plot was used to estimate grain yield (kg)
per hectare.
Data Analysis
Analysis of variance
Analysis of variance (ANOVA) was computed for grain yield and other traits as per the methods
described by Gomez and Gomez (1984) using SAS computer software (Version 9) for
randomized complete block design. Comparison of treatment means was made using Least
Significant Difference (LSD) at 5% level of significance test. ANOVA was computed using the
following mathematical model:
𝑌𝑖𝑗 = µ + 𝑟𝑗 + 𝑔𝑖 + 𝜀𝑖𝑗𝑙
Where: 𝑌𝑖𝑗 =the observed value of the trait Y for the 𝑖 th variety in 𝑗 𝑡ℎ replication, µ= the general
mean of trait Y, 𝑟𝑗 = the effect of 𝑗 𝑡ℎ replication, 𝑔𝑖= the effect of 𝑖 th variety and 𝜀𝑖𝑗= the
experimental error
Results and Discussions
Analysis of variance
Analysis of variance computed for each location revealed that variation among varieties were
highly significant (P<0.01) for all traits except for number of primary branches which showed
significant (P<0.05) differences while number of pods per plants are not significant (Table 2).
The presence of variations among varieties under the experiment for traits studied indicated the
presence of sufficient variability among ground nut varieties that would be exploited. Similar
results were also reported by (Chavadhari et al., 2017 and Izge et al., 2007) in ground nuts. The
year effect was highly significant (P<0.01) for maturity dates, pods per plants and grain yield
indicated that the performance of varieties are different in different year for these traits.
In Ethiopia, Biru and Dereje (2014) reported the presence of highly significant variation among
twelve ground nut varieties evaluated in two environments. They also reported the existence of
significant variation for days to flowering, days to maturity, plant height, hundred seed weight
and grain yield. Chavadhari et al., 2017 and Izge et al., 2007) also reported highly significant
variation for plant height, pods per plant, seeds per plant, hundred seed weight and grain yield in
ground nut which is in line with this current finding.
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Mean performance of varieties
Crop phenology: Flowering duration of five varieties of ground nut ranges from 45.17-50.5
while the maturity duration of varieties ranges from 145.33-157.67. The mean performances of
these traits are presented in Tables 3. The earliest flowering and maturing varieties was Sedi
(145.33 days) while the late maturing variety was Baha gidu (157.67 days) followed by Babile
local (156.33 days) (Table 3). Similar result was reported by Biru and Daraje 2014.
Growth traits, Yield and yield components: Mean performances of varieties for plant height
ranged from 39.33 cm to 45.13cm. Baha guddo is significantly shorter than the other varieties
while Sedi was significantly taller. Varieties showed considerable variations for number of
primary branches that ranged from 8.33 for Sedi to 13.87 for Tole 1 (table 3). Variation for plant
height and branches in Ground nut is also reported by other author (Izgeet al., 2007;
Chavadhariet al., 2017; Biru and Daraje 2014). Shelling percentage was calculated by dividing
shelled yield weight to total pods weight (unshelled pod) and multiplying by hundred
(Emmanuel et al., 2017). In this experiment, the mean of shelling percentage ranged from
54.015% to 78.077%. The Highest shelling present was recorded by variety Tole- 1 (78.077%)
followed by Sedi (67.105%). According to Jeyaramraja and Fantahun 2014, higher shelling
percent indicates less seed case (pod) weight and more seed weight and so, it is preferable in
ground nut. Other authors also reported similar results in shelling percentage of ground nuts
(Mulatuet al., 2017; Jeyaramraja and Fantahun 2014; Chavadhariet al., 2017).
The variation of varieties for pods number per plant ranged from 24.83 (Baha giddu) to
29.0(Tole-1). The mean shelled grain yield of varieties ranged from 2878.0 kg to 4174.7kg;
(table 3). Significantly highest mean grain yield was recorded from Tole-1 (4174.7kg/ha)
followed by Sedi (3552.5kg/ha) and Bablile local (3550.4kg/ha). The high yielding capacity of
these three varieties may be due to high pods per plant, number of primary branches in Varity
Tole-1 while short maturity periods and relatively higher shelling percentage in Sedi varieties. A
wide range of variation in ground nuts varieties for grain yield was also reported by (Jeyaramraja
and Fantahun 2014; WedajoGebre and WondewosenShiferaw, 2017) which is in line with this
finding.
219
Table 2: Mean squares from combined analyses of variance over two years for 8 traits of Ground nut varieties grown at Abaya in 2017
and 2018 E.C
Sources
of DF FD
MD
PH (cm) NPB
PPP
GY (kg /ha) GY(kg/ ha)
SP (%)
(shelled)
variation
(with shell)
Year
1
4.03
448.53**
2.7
25.03
1068.03** 6373048.66*
3400797.949* 59.36
variety
4
24.12**
144.78**
23.95**
28.62* 17.30
448765.16
1959362.38*
594.269*
Rep within year
2
21.23
6.43
3.43
16.78
98.23
1713251.1
31295.10
293.33
Rep*variety
8
1.225
35.43
1.23
3.24
23.53
593381.88
313206.98
146.22
Year*variety
4
0.95
102.78*
1.12
13.99
163.53
1515385.29
1365636.03
254.47
Error
10 17.27
33.43
0.76
9.9
46.13
919478.68
526073.55
169.971
CV (%)
8.65
3.74
1.78
28.31
25.69
17.75
21.42
20.69
LSD
5.05
7.438
0.318
4.05
8.737
1233.5
933.05
16.76
ns, *, **&***, non-significant, significant, highly significant and very highly significant at P<0.05, P<0.01 and P<0.001, respectively.
DF= degrees of freedom, FD= Flowering date, MD= days to maturity, PH=Plant Height, NPB= number of primary branch, PPP= pods
per plant, SP (%) =Shelling Percentage, GY (Kg/ha) = Grain yield in Kilogram per hectare.
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221
Table 3: Pooled Mean value of yield and yield related traits of 5 Varieties of Ground nut tested at
Abaya for two consecutive years (2017 and 2018)
Varieties
FD
MD
PH
NPB
PPP
*GY(kg SP (%)
GY(kg/ ha)
(shelled)
(cm)
/ha)
Tole 1
50.5a 155.67a 42.93c 13.87a 29.a
5398.1a
78.077a 4174.7a
Baha gidu 49.0b 157.67a 43.07b 11.63ab 24.83a 5108.4a
54.788b 2776.6b
Sedi
45.17d 145.33b 45.13a 8.33b
26.33a 5307.9a
67.105ab 3552.5ab
Baha
48.0bc 155.67a 39.33e 12.17ab 27.0a 5339.7a
54.015b 2878.0b
guddo
Babile
47.17c 156.33a 40.27d 9.57ab 25.00a 5849.5a
61.035ab 3550.4ab
local
CV (%)
8.65
3.74
0.59
28.31
25.7
17.75
20.68
21.42
LSD
5.05
7.438
0.20
2.55
ns
ns
16.764
933.05
CV = Coefficient of variations,FD= Flowering date, MD= days to maturity, PH=Plant Height,
NPB= number of primary branch, PPP= pod per plant, SP (%) =Shelling Percentage, ShGY
(Kg/ha) = Grain yield in Kilogram per hectare, LSD= Least significant diffirence. *= grain yield
with shell.
Conclusion and Recommendations
The results of experiment conducted at Abaya exhibited significant variation among varieties for
all traits except pods per plants and unshelled grain yields. Significant variations among varieties
for phenological traits also point out that the possibility of selecting early maturing varieties for
the study areas. Regardless of this, Sedi variety was significantly early maturing variety in the
study area. The mean of shelling percentage in this experiment ranged from 54.015% to
78.077%. The highest shelling percentage was recorded by variety Tole- 1 (78.077%) followed
by Sedi (67.105%). The highest mean grain yield was exhibited by Tole-1 (4174.7kg ha-1)
followed by Sedi (3552.5kg ha-1) and Babile local (3550.4 ha-1). The high yielding capacity of
these three varieties may be due to presence of high pods per plant, number of primary branches
in Tole-1 Varity while short maturity periods and relatively higher shelling percentage in Sedi
varieties. In this experiment, Tole-1 variety is identified as high yielding variety while Sedi
variety is recommended as early maturing varieties. Therefore, farmers and ground nut producers
around the study area and similar agro ecologies can use those varieties.
References
AmeleworkBeyene, Temesgen Alene, AleminewTagele and AemiroBezabih. Groundnut variety
evaluation in the lowlands of Abergelle, Wag-himra. 2007. Proceedings of the 2nd annual
regional conference on completed crop research activities
Sekota Dry land Agricultural
Research centre, Sekota
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Biru Alemu1 and DarajeAbera. 2014. Adaptation Study Of Improved groundnut (Arachishypogaea
L) Varieties At KellemWollega Zone, HaroSabu, Ethiopia. Journal of Biology, Agriculture and
Healthcare. Vol.4 (23)
Chavadhari R. M., V. H. Kachhadia, J. H. Vachhani* and M. B. Virani.2017. Genetic variability
studies in groundnut (ArachishypogaeaL.).Electronic Journal of Plant Breeding. vol8(4): 12881292
CSA (central statistical authority), 2015. Area and production of crops volume 1 statistical bulletins.
Central Statistical Authority. Addis Ababa, Ethiopia. pp. 446.
Emmanuel Zuza Jnr, AmadeMuitia, Manuel I.V. Amane, Rick L. Brandenburg and Ana M.
Mondjana. 2017. Effect of harvesting time on groundnut yield and yield components in
Northern Mozambique. Journal of Postharvest Technology. Vol. 05(2): 55-63
FAOSTAT. 2010. Available at http://faostat.fao.org/. Accessed date october 2, 2019.
Garko M.S., I.B Mohammed, A.I. Yakubu, Z. Y. Muhammad. 2016. Performance of Groundnut
[ArachisHypogaea (L.)] Varieties As Influenced By Weed Control Treatments. International
Journal of Scientific & Technology Research. Volume 5( 03)
Gomez KA, Gomez AA (1984). Statistical Procedures for Agricultural Research. 2nd edition. John
Willey & Sons Ltd., New York, USA.680p.
Izge A. U, Z. H. Mohammed and A. Goni.2007 Levels of variability in groundnut (Arachishypogaea
L.) to cercospora leaf spot disease – implication for selection. African Journal of Agricultural
Research. Vol. 2 (4), pp. 182-186.
Jeyaramraja P R and FantahunWoldesenbet. 2014. Characterization of yield components in certain
groundnut (Arachishypogaea L.) Varieties of ethiopia. Journal of Experimental Biology and
Agricultural Sciences. Vol. 2(6).Pp 593-596.
MastewalAlehegn, Sakhuja PK and Mashilla D. 2017. Evaluation of Released and Local
Groundnut Varieties against Groundnut Rust (PucciniaArachidis) at Babile, Eastern Ethiopia.
Journal of Agriculture Research. 2017. Vol 2(1): 000123.
Mulatu Gabisa, Tamado Tana and Elias Urage 2017. Effect of planting density on yield components
and yield of Groundnut (Arachishypogaea L.) varieties at Abeya, Borena Zone. International
Journal of Scientific Engineering and Applied Science (IJSEAS). Vol. 3 (3).
Musa H. Ahmed, Hiwot M. Mesfin ,SelteneAbady , WendmagegnMesfin and Amare Kebede. 2016.
Adoption of improved groundnut seed and its impact on rural households’ welfare in Eastern
Ethiopia. Cogent Economics & Finance. Vol 4: pp 1-13
Wedajo Gebre and Wondewosen Shiferaw. 2017. Performance Evaluation of Ground Nut Varieties
in Lowland Areas of South Omo, Southern Ethiopia. International Journal of Research Studies
in Science, Engineering and Technology.Vol 4 (2) .PP 6-8
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Performance evaluation of sesame varieties at Abaya, Southern Oromia
Ejigu Ejara1*, Zinash Misgana1, Kemal Kitaba1, Mulatu Gabisa1 and Genene Tesema1
1Yabello
Pastoral and Dryland Agriculture Research Centre, Oromia Agricultural Research
Institute, Yabello, Ethiopia.
*Corresponding author. E-mail: ehordofa@gmail.com
Abstract: Sesame (Sesamum indicum L.) is an ancient oil crop that has been referred to as the
‘Queen of oilseeds’ by virtue of its high quality oil. It is an important oil seed crop grown
throughout the tropics and sub tropics worldwide. In southern part of Oromia, where agriculture
investors are engaged, the production of sesame is still low. Moreover, there is a need for
selecting high yielding and adaptable sesame varieties for the study areas. Therefore, this
experiment was conducted to evaluate thirteen sesame varieties and select adaptable varieties
with considerable yield and agronomic traits. The field experiment was conducted in 2017 and
2018 at Abaya and varieties were planted in Randomized Complete Block Design (RCBD). Data
were collected on yield and important agronomic traits and analyzed using SAS software. The
analysis of variance revealed significant variations among varieties for days to flowering, plant
height, number of primary branches and Grain yield. The pooled over years mean of varieties
showed Dicho variety is identified as the high yielding variety with mean grain yield of
542.81kg/ha followed by variety Obsa (527.71kg/ha) and Chalasa (515.38 kg/ha) and therefore
recommended for moisture stress areas of Abaya and location with similar agro ecologies.
Key words: Adaptability, Grain yield, Sesame,
1. INTRODUCTION
Sesame (Sesamum indicum L.), a conventional oilseed crop from Pedaliaceae family (Zeb et al.
2017) and Tubeflorae order (Nayar, 1976), is grown well in tropical and subtropical areas of the
world (Gandhi, 2009). It is widely cultivated in the tropical parts of Africa and Asia and about 36
species are said to be existent (Saydut et al., 2008). It is the most ancient oil seed known and
used by man (Kafiriti and Deckers, 2001). Sesame has a small, oval and flat seed with diverse
colors (black, white, grey, yellow, brown and red) depending on the cultivar (Nagendra Prasad et
al. 2012). Due to its very valuable phytochemical content, it is one of the most resistant
vegetable oil to oxidative rancidity. Therefore, it is known as queen of the oilseed crops (Zeb et
al. 2017). It is also stable due to the natural anti-oxidants sesamol and sesamolinol that reduce
the rate of oxidation (Terefe et al., 2012). The chemical composition of sesame seed shows that
223
224
the seed is a good source of carbohydrate (13.5%), protein (18-25), ash (5%) (Borchani et al.,
2010) and about 50% high quality oil (Roy et al., 2009).
Sesame is important oil crop grown in Ethiopia and occurs both as cultivated and wild species
(Zerihun, 2012). It is thought to have originated in Africa, and there is a great weight of evidence
indicating that Ethiopian lowland area is the origin of cultivated sesame (Mahajan et al 2007). It
is the major oil seed in terms of exports, accounting for over 90% of the values of oil seeds
exports of Ethiopia. It is the second largest source of foreign exchange earnings after coffee
(Abadi, 2018).
Sesame (Sesamum indicum) is grown in areas with annual rainfall of 625-1100mm and
temperature less than 27C0. The crop is tolerant to drought, but not to water logging and
excessive rainfall. Sesame is well adapted to a wide range of soils, but requires deep, welldrained, fertile sandy loams (Geremew et al 2012). In Ethiopia, sesame grows well in the
semiarid areas of Amhara, Tigray, Benshangul Gumuz, and Somali Regions. Lowlands of
Oromiya and Southern Nations nationalities and Peoples Regions also grow a significant
amount. Though variations in climatic and edaphic conditions affect sesame yields and
performance (Muhamman and Gungula 2008), the major constraints identified in growing
sesame in most countries are instability in yield, lack of wider adaptability, drought, nonsynchronous maturity, poor stand establishment, lack of response to fertilizer application,
profuse branching, lack of seed retention, low harvest index and susceptibility to insect pests and
pathogens (Rajani Bisen et al., 2014).Ethiopian Sesame has good demand in the world market
and known for its top quality and therefore used as a reference for grading in the international
market. There is an enormous potential to expand sesame seed production in Ethiopia through
cultivation of additional new land (EPOSPEA, 2019). In southern part of Oromia, where
agriculture investors are highly engaged, the production of Sesame is still very low. Moreover,
there is a need for selecting high yielding and adaptable varieties and capacitating farmers and
agricultural investors in the study areas. This experiment was therefore conducted by Yabello
Pastoral and Dryland Agriculture Research Center with the following objective to select and
recommend adaptable sesame varieties for the study area.
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225
MATERIALS AND METHODS
Descriptions of the study area
The experiment was conducted at Abaya during 2017 and 2018 main cropping season. The
experimental area is located in the Southern part of the country in the Oromia Regional State.
Abaya is sub-site of the Yabello Pastoral and Dryland Agriculture Research Center and located
at 365 km far from Addis Ababa city. The detail description of the study area is presented in the
table 1
Table 13: Description of the study area
Variables
Soil type
Sandy clay loam
Altitude (m.a.s.l.)
1442
Latitude
06o43’520"N
Longitude
038o25’425"E
Annual Temperature 0C
Minimum
12.6
Maximum
29.9
Annual rainfall (mm)
Minimum
500
Maximum
1100
Experimental Materials
A total of thirteen sesame varieties were collected from Melka Werar and Bako Agriculture
research Centers and evaluated at Abaya for two consecutive years (2017 and 2018).
Table 2: List of Sesame varieties used in this experiment
S.No
1
2
3
4
5
6
7
8
9
10
11
12
13
Variety
Serkamo
E
Dicho
Argane
Tate
Mehado-80
S
T-85
Kalifo-74
Abasena
Chalasa-EW023 (2)
Obsa
Adi
Year of release
1993
1978
2010
1993
1989
1989
1978
1976
1976
1990
2013
2010
1993
Breeder/ Maintainer
WARC/EIAR
WARC/EIAR
BARC/OARI
WARC/EIAR
WARC/EIAR
WARC/EIAR
WARC/EIAR
WARC/EIAR
WARC/EIAR
WARC/EIAR
BARC/OARI
BARC/OARI
WARC/EIAR
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226
Experimental Design and Managements
The experiment was laid out in Randomized Complete Block Design. Each entry was planted in
a plot having 6 rows of 3 meter length. Four rows were harvested and two border rows were left
to exclude border effect. National seed rate recommendation (5kg/ha) was calculated for each
plot and drilled uniformly to plot. Individual plot size was 2.4 m x 3m=7.2 m2 and 1.5m between
each block. All other agronomic managements were applied uniformly in all experimental plots
as per national recommendation for the crop.
Data Collection
Data recorded on plant basis
Plant height at harvest (cm): Height of five randomly taken plants during harvest period from
each experimental plot was measured in centimeter from the ground level to top of the plants and
the average height was recorded.
Number of primary branches: Number of productive branches extending from the main stem
was recorded from five randomly selected plants and average branch number was taken.
Data recorded on plot basis
Days to Flowering: The numbers of days from the date of emergence to the date on which
about 50% of the plants in each plot produce flowers.
Days to maturity: The number of days from planting to maturity period
Stand count at harvest: This was recorded by counting the total number of plants from the four
middle rows of each plot at harvest.
Grain yield (g/plot): Grain yield in grams obtained from the central four harvestable rows of
each plot was harvested, threshed and weighted by using sensitive balance
Grain yield (kg/ha): Grain yield obtained from each plot was used to estimate grain yield (kg)
per hectare.
Data Analysis
Analysis of variance
Analysis of variance (ANOVA) was computed for grain yield and other traits as per the methods
described by Gomez and Gomez (1984) using SAS computer software (Version 9) for
Randomized complete block design. Comparison of treatment means was made by using Least
Significant Difference (LSD) at 5% level of significance test. Analyses of variance (ANOVA)
was computed using the following mathematical model:
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227
𝑌𝑖𝑗𝑘 = μ + Gi + yj + Bk + Gyij + εijk
Where: 𝑌𝑖𝑗𝑘 = is the observed mean of the ith variety (Gi) in the jth year (yj), in the kth block (Bk)
µ= General mean of trait Y
𝐺𝑖 = Effect of the ith variety
𝑦𝑗= Effect of the jth year
Bk =Block effect of the ith variety in the jth year, Gyij =The interaction effects of the ith variety
and the jth year, and εijk= The error term
Results and Discussions
Analysis of variance
The combined over two years Analysis of variance (ANOVA) computed shown that variation
among varieties were highly significant (P<0.01) for all traits except dates to maturity (Table 3).
The presence of variations among varieties under experiment for traits studied indicated the
presence of sufficient variability among Sesame varieties. The presence of highly significant
variation in sesame varieties for grain yield, Number of primery branch, plant height, days to
flowering and days to maturity was also reported by Fiseha et al., 2016 and Bharathi et al., 2014.
Highly significant variation of year effect (P<0.01) for flowering and maturity dates were
observed indicatingthe presence of variability in both year. The interaction effect of variety by
year was not significant indicating similar performance of varieties in different year for these
traits. Okello-Anyanga et al., 2016 also reported similar findings in sesame.
Table 3: Mean squares from combined analyses of variance over two years for 5 traits of Sesame
varieties grown at Abaya in 2017 and 2018
Sources of variation
DF FD
MD
PH (cm)
NPB
GY (kg/ha)
Year
1
184.615**
1041.346**
250.743
0.461
275011.34*
Rep(Year)
Variety
4
12
3.564
22.510**
14.717
27.517
214.672
1.641
1081.493** 2.467*
43301.332
200871.199**
Year* Varieties
12
0.532
55.679
117.708
1.211
40642.835
Error
48
8.05
22.829
114.069
0.849
27182.255
4.295%
3.559%
12.095%
25.311
69.712%
CV (%)
ns,* ,**&***,non-significant, significant and highlysignificant at P<0.05, P<0.01 and P<0.001,
respectively. DF= degree of freedom, FD= days to flowering, MD= days to maturity, PH (cm) = plant
height in centimeter, NPB= number of primary branch, GY (kg/ha) = Grain yield in kilogram per
hectare.
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Mean performance of varieties
Crop phenology: Flowering duration of 13 sesame varieties ranges from 62.5-70.0 while the
maturity duration of varieties ranges from 129.833-138.167. The mean performances of these
traits are presented in Tables 4. The earliest flowering variety was Dicho (62.5days) while the
late flowering variety were Mahado-80 (70.0 days). Varieties also showed variation in maturity
date which ranged from 129.833days to 138.167days.
Growth traits, Yield and yield components: Considerable variation was shown in 13 sesame
varieties for plant height. Mean performances of varieties for plant height ranged from 71.867
cm to 111.10cm. About 53.85% of varieties were taller than the grand mean. Adi variety was
relatively taller followed by Dicho, chalasa and Obsa wile Variety Kalifo-74, S, E and Mahado80 were relatively short. Varieties showed considerable variations for number of primary
branches that ranged from 2.833 to 4.833 (table 4). Daniel et al., 2017 also reported a presence of
wide range of variability for plant height (54.2 to 163.9cm), days to maturity (82 to 113days),
days to flowering (29 to 66 days) and number of primary branches (1 to 8.3) in sesame varieties.
Table 4: Mean value of yield and yield related traits of 13 Varieties of Sesame tested at Abaya in
2017 and 2018 cropping season
Variety
Serkamo
E
Dicho
Argane
Tate
Mehado-80
S
T-85
Kalifo-74
Abasena
Chalasa
Obsa
Adi
Mean
Range
FD
65.167bcd
MD
133.833a
PH (cm)
94.333bc
NPB
4abcd
Gy/ha (kg)
96.69c
66.167bcd
66.000bcd
66.833abc
66.667abc
70.000a
66.667abc
68.833ab
66.000bcd
62.500d
64.000cd
64.667cd
65.167bcd
66.12508
62.5-70
136.333a
134.000a
134.000a
138.167a
135.500a
133.000a
135.833a
134.167a
132.500a
129.833a
135.833a
132.167ab
134.27
129.833-138.167
72.767d
101.500ab
81.567cd
88.667bc
73.367d
72.633d
82.008cd
71.867d
99.033ab
99.733ab
99.333ab
111.100a
87.798
71.867-111.1
2.833d
4.667ab
3.50bcd
3.833abcd
3.333cd
3.333cd
3.00d
3.167cd
3.167cd
4.333abc
4.833a
3.333cd
3.611
2.833-4.833
81.51c
542.81a
114.85bc
242.04bc
82.98c
69.80c
111.58bc
125.44bc
239.86bc
515.38a
527.71a
323.88b
248.153
69.8-542.81
Means with the same letters in the same columns are not significantly different
FD= flowering date, MD= Maturity date, PH= plant height in centimetre, NPB= number of primary
branch, GY= Grain yield per hectare
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Significantly highest mean grain yield was recorded from Dicho (542.81kg/ha) followed by Obsa
(527.71kg/ha) and Chalasa (515.38kg/ha). The high yielding capacity of these three varieties
may be due to inherent characteristics of those varieties in bearing high number of primary
branches and possessing relatively higher plant height
CONCLUSIONS AND RECOMMENDATIONS
The experiment was conducted at Abaya during 2017 and 2018 cropping season with the
objective to select and recommend adaptable sesame varieties for the study areas. The results of
experiment exhibited significant variation among varieties for all traits except Maturity dates.
Significant variation among varieties for those traits indicated that the possibility of selecting
varieties for the study areas. The mean of flowering and maturity date in this experiment ranged
from 62 to 70 days and 129 to 138 days respectively. The early maturing varieties was Chalasa
with 129.83 days to mature while the late maturing varieties are Tate with 138.167 days to
mature. The mean seed yield ranged from 69.80 kg to 542. 81kg. The highest mean grain yield
were exhibited by Dicho (542.81kg ha-1) followed by Obsa (527.71 kg ha-1) and Chalasa
(515.38kg ha-1). The high yielding capacity of these three varieties may be due to presence of
high number of primary branches and plant height. Therefore, farmers and Sesame producers
around the study area and similar agro ecologies can use those varieties
REFERENCES
Abadi Berhanu, 2018. Sesame Production, Challenges and Opportunities in Ethiopia. Agri Res &
Tech: Open Access Journal. Vol. 15 (5)
Bharathi D., V.Thirumala Rao*, Y.Chandra Mohan, D.Bhadru and V.Venkanna 2014. Genetic
variability studies in sesame (Sesamum indicum L.). International Journal of Applied Biology
and Pharmaceutical Technology.Vol. 5(4), pp. 31-33
Borchani, C., S. Besbes, C.H Blecker and H. Attia. 2010. Chemical characteristics and oxidative
stability of sesame seed, sesame paste and olive oils. J. Agri. Sci. Technol. 12: 585-596.
Daniel Endale 2017. Sesame (Sesamum indicum L.)Breeding in Ethiopia. International Journal of
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Gandhi, A.P. 2009. Simplified process for the production of sesame seed (Sesamum indicum L.)
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Geremew Terefe, Adugna Wakjira Muez Berhe and Hagos Tadesse. 2012.Sesame Production
Manual. ISBN: 978-99944-53-80-8
Gomez KA, Gomez AA (1984). Statistical Procedures for Agricultural Research. 2nd edition. John
Willey & Sons Ltd., New York, USA.680p.
Kafiriti E, Deckers J (2001) Sesame (Sesamum indicum L.). In: RH Raemaekers, Crop Production in
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Muhamman, M. A. and Gungula, D. T. 2008. Growth parameters of sesame (Sesamum indicum L.)
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Nagendra Prasad MN, Sanjay KR, Prasad DS, Vijay N, Kothari R, Nanjunda Swamy S (2012). A
review on nutritional and nutraceuticals properties of sesame. Journal of Nutrition and Food
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Nayar, N.M. 1976. In: Sesame: Evolution of crop plants, Simmonds, N.W. (Ed.). Longman, London
and New York, pp. 231-233.
Okello-Anyanga Walter, Patrick Rubaihayo , Paul Gibson and Patrick Okori. 2016. Genotype by
environment interaction in sesame (Sesamum indicum L.) cultivars in Uganda. African Journal
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Rajani Bisen, Anjay Tripathi, Ravindra P. Ahirwal, Seema Paroha, Roshni Sahu and A. R. G.
Ranganatha. 2014. Study on genetic divergence in sesame (sesamumindicum L.) germplasm
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sciences. Vol. 8(4). Pg 1387-1391.
Roy, N., S.M. Abdullah and M.S. Jahan. 2009. Yield performance of sesame (Sesamum indicumL.).
varieties at varying levels of row spacing. Res. J. Agri. Biol. Sci 5: 823-827.
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indicum L.) seed oil as a biodiesel fuel. Bioresour Technol 99: 6656-6660
Terefe G, Wakjira A, Berhe M, Tadesse H (2012) Sesame Production Manual. EIAR. 6. Monitor
Group (2012) The Business Case for Investing in a Sesame Hulling Plant in Ethiopia.
Zeb A, Muhammad B, Ullah F (2017). Characterization of sesame (Sesamum indicum L.) seed oil
from Pakistan for phenolic composition, quality characteristics and potential beneficial
properties. Journal of Food Measurement and Characterization. Vol. 11(3):1362–1369
Zerihun J. Sesame Sesame indicum L. Crop Production in Ethiopia: Trends, Challenges and Future
Prospects. Sci. Technol. 2012; 1(3):01-07.
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Evaluation and Identification of Adaptable Processing Tomato Varieties with High Yield
and Standard Qualities at Adami Tulu Jido Kombolcha Woreda, East Shoa Zone.
Urgaya Balcha* and Temesgen Dinsa
Oromia Agricultural Research Institute, Adami Tulu Agricultural Research Center
* Corresponding author: urgayab@gmail.com, Batu, Ethiopia
Abstract
Experiment on evaluation and identification of adaptable processing tomato varieties with high
yield and standard qualities at Adami Tulu Jido Kombolcha Woreda of east Shoa zone was
initiated with objective to identify adaptable and most preferable processing tomato varieties
with high yield and required qualities. Nine processing tomato varieties viz., Melkasalsa,
Gelilema, Melkashola, Chali, Cochoro, Sire, Gelila, Venus F1 and Roma Vf were evaluated on
field at two locations (ATARC and Abosa) for two consecutive years in 2018 and 2019 under
irrigation in Randomized Complete Block Design with three replications. Among these varieties,
there was highly significant variations for days to flowering, plant height at fruit harvest, cluster
per plant, floret per clusters, fruit per clusters, primary branch, secondary branch, total soluble
solids and titrable acids. Besides, significant variation was also observed among test varieties
for marketable fruit yield while non significant variation was observed for traits such as
unmarketable fruit yield, total fruit yield and potential acidity. The combined mean performances
of phenological, growth, yield related and quality traits of nine processing tomato varieties was
also analyzed for yield and quality standard. Based on overall performance for these above
mentioned traits across year, three varieties namely Gelila, Gelilema and Melkasalsa were
recommended for Adami Tulu Jido Kombolcha Woreda and similar agro ecologies.
Key Words: Evaluation, Processing type, Quality, Tomato, Yield
INTRODUCTION
Tomato is one of the most important and widely grown vegetables in the World. It is
important in a variety of dishes as raw, cooked or processed products more than any other
vegetables (Lemma Desalegne, 2002). In Ethiopia, it is an important cash crop widely produced
by smallholder farmers and commercial growers under irrigated conditions. Processing types of
tomato are mainly produced in large-scale commercial horticultural farms. It is an important
cash-earning crop to small-scale farmers and provides employment in the production and
processing industries. The processed products such as tomato paste, tomato juice, tomato
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ketchup and whole peel-tomato are produced for local market and export. It is extensively
produced in the Rift Valley and lakes region both for fresh market and processing industries
(Selamawit and Lemma, 2008). It is one of the most popular & important vegetables for fresh
consumption as well as for processing.
Currently, tomato is one of the major regional export vegetables of the country. In Ethiopia, the
crop is produced in the altitude ranging from 700 to 2200 meter above sea level, with about 700
to over 1400 mm annual rain fall, in different areas and seasons, in different soils, under different
weather conditions, but also at different levels of technology (e.g. with furrow, drip or spate
irrigation) and yields (Birhanu and Ketema, 2010). The plant requires a warm and dry climate.
The optimum mean temperature for growth of tomato lies between 210c and 260C.Tomato should
be cultivated at an altitude below 2000 m. Soils for tomato cultivation are loamy sand to silty
loam. Soils with medium organic matter content have better yield than soils with a low organic
content. Good soil drainage is important. Optimum pH ranges from 5.5 to 7.0. The first fruits are
produced 80-100 days from transplanting. Lack of processing type of tomato varieties with high
yield, stable performance and acceptable qualities for small scale farmers in the study area is the
major bottle neck for production and productivity of tomato. Currently, the opening of Bulbula
agro-processing industry is one of the potential market for the farmers in the area who are
engaging on production of these tomato varieties. Hence, the current experiment was initiated
with objective to identify adaptable and most preferable processing tomato varieties with high
yield and required quality for the study area.
MATERIALS AND METHODS
This chapter introduces the description of the study area, plant materials and experimental
design, method of data collection and data analysis.
Description of the Study Area
The experiment was conducted at Adami Tulu Agricultural Research Center (ATARC) and
Abosa under irrigation (from February to June) of 2018 and 2019. Both Abosa and ATARC are
located in Adami Tulu Jido Kombolcha District, East Shoa Zone of Oromia, and it is located in
the mid Rift Valley of Ethiopia.
Experimental Materials, Design and Management
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In this experiment nine nationally released and registered processing tomato varieties were used.
Of these nine varieties, six varieties viz. Melkasalsa, Gelilema, Melkashola, Chali and Cochoro
were collected from Melkassa Agricultural Research center, while one variety, Sire was obtained
from Bako Agricultural Research center. The three remaining test varieties viz. Gelila, Venus F1
and Roma Vf were collected from private farms who introduced from abroad and actually
registered in the country for production. The planting materials were laid out in Randomized
Complete Block Design (RCBD) with three replications. These varieties were grown under
irrigation during the months of February to June of the years 2018 and 2019. The gross plot
size was 16 m2 (4m x 4 m) arranged in 4 rows of 100 cm spacing between rows and 30
cm between plants. The net plot size was 2 m * 3 m (6 m2) . A spacing o f 1.5 m and 1
m between blocks and plots was maintained, respectively. Seedlings which failed to establish
were replaced by replanting within a week of transplanting to maintain the appropriate plant
population. Inorganic fertilizer, NPS-150 Kg/ha at planting while UREA-200 Kg/ha was
applied in 10 cm band from root collar in two splits viz., ½ at transplanting and ½ at 1 month
after transplanting (Lemma Deselegne, 2002). All cultural practices were done according to the
tomato production technique developed and recommended by Melkassa Agricultural Research
Centre (MARC) for Mid Rift Valley region of Ethiopia (Lemma Desalegne, 2002).
Table 1: Description of processing tomato varieties used in an experiment
No
1
2
3
4
5
6
Variety
Melkasalsa
Gelilema
Melkashola
Chali
Cochoro
Sire
Year of registration
1997/98
2015
1997/98
2007
2007
2015
Responsible company
MARC/EIAR
MARC/EIAR
MARC/EIAR
MARC/EIAR
MARC/EIAR
Bako ARC/OARI
7
Gelila
2011
Axum Green life
8
Venus F1
2015
MARKOS PLC
9
Roma Vf
Data Collection and Measurement
Crop phenological, growth, yield and yield components, and quality parameters were
considered in this study. All parameters considered in this study are listed below with their detail
descriptions.
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2.3.1. Phenological data
Data on days to 50% flowering and days to 50% maturity were recorded on a plot basis, while
others i.e. number of flowers per cluster, number of fruits per cluster and fruit set were collected
and recorded from 10 randomly selected plants of the two middle rows of each plot.
Days to 50% flowering: were recorded as the number of days from transplanting to the time
when 50% of plants in each plot flowered.
Days to maturity: were recorded as the numbers of days from the date of transplanting to the
date when 50% of the plants in each plot reached physiologically maturity of fruits for the first
time. In other words, days to maturity were recorded when approximately 50% of plants per plot
attained their first crop harvest.
2.3.2. Growth parameters
Plant height: height of the plants was measured from the ground level to the tip of upper
most part of 10 randomly selected plants at first harvest.
Number of clusters per plant: this was recorded by counting the total number of clusters per
plant from 10 randomly selected plants at full maturity.
Number of flowers per cluster (FlC):this was recorded by counting the total number of
flowers per cluster from 10 randomly selected clusters at bloom.
Number of primary branches: Number of branches extended from the main stem were
counted and recorded on 10 randomly selected plants in harvestable rows at flowering stage.
Number of secondary branches: Number of branches extended from the primary brancheswas
recorded on 10 randomly selected plants in harvestable rows at flowering stage.
2.3.3. Yield and yield related parameters
Two inner rows leaving one plant from the boarder at each side were used to asses
yieldrelated traits. All parameters to be considered were listed below with descriptions.
Marketable fruit yield (qtl ha-1): was recorded by weighing all harvests of marketable
fruitsfrom the three inner rows of each plot and calculated in quintals per hectare.
Unmarketable fruit yield (qtl ha-1): was recorded by weighing all harvests of
unmarketablefruits from the three inner rows of each plot and calculated in quintals per hectare.
Total fruit yield (TFY) (qtl ha-1): was recorded as the sum of the weight of marketable
andunmarketable fruit yields and converted to quintal per hectare.
2.3.4. Chemical quality attributes
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Total soluble solids (TSS) (0Brix): The total soluble solid were determined following
theprocedure
described
by Acedo et
al.
(2008). Aliquot of juice were extracted using
HighPerformance Commercial Blender. A Palette digital refractometer ATAGO®PR-32α with
arange ofoBrix 0 to 32% was used to determine the TSS by placing two drops of clear juice
onthe prism. Between samples, the prism of the refractometerwas washed with distilled water
and dried with tissue paper before it is used for another reading. The refractometer
wascalibrated against distilled water at 0 percent TSS.
pH: Aliquot of clear juice filtered with cheesecloth was used for pH measurement and the
pHvalue of each plot tomato juice was measured by a pH meter with a model of
AD1020 pH/mv/ISE and Tometer calibrated with standard pH buffer 4 and 7.
Titratable acidity: Extracted tomato juice was filtered through cheesecloth and decants clear
juice were used for titration. Ten ml of the tomato juice sample were titrated gradually with 0.1N
NaOH using burette to pink end point (persisted for 15 seconds).
2.4. Data Analysis: Data were subjected to ANOVA by using the GLM Procedure of
SAS software (SAS Institute, 2002). Mean separation was performed at (P ≤ 0.05)
using the Least Significant Difference (LSD).
RESULT AND DISCUSSIONS
Analysis of Variance: Analysis of variance by General Linear Model for 14 characters (2
phenological, 6 growth, 3 yield related and 3 quality parameters) are presented in Table 2.
The result showed the existence of highly significant (P≤ 0.01) variation among the years for
days to flowering, days to maturity, plant height at fruit harvest, cluster per plant, floret per
plant, fruit per cluster, unmarketable fruit yield, pH and titrable acidity. Across the locations
there was highly significant variation for days to maturity, plant height at fruit harvest,
cluster per plant, floret per clusters, fruit per clusters, marketable fruit yield, unmarketable
fruit yield, total fruit yield, total soluble solid and titrable acidity. Among each varieties there
was highly significant variations among days to flowering, plant height at fruit harvest,
cluster per plant, floret per clusters, fruit per clusters, primary branch, secondary branch, total
soluble solids and titrable acids where as significant difference for marketable fruit yield and
no significant variation for unmarketable fruit yield, total fruit yield and pH.The presence of
appreciable differences among varieties for most of the characters studied makes the
possibility to carry out demonstration and scaling up.
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Mean Performance of Varieties
Phenological Parameters
The combined mean values of phenological traits of 9 processing tomato varieties evaluated at
Adami Tulu Jido Kombolcha were shown in table 3. The variation with respect to days to
heading and days to maturity ranged from 35 to 42.5 and 95.33 to 102.33 with mean values of
38.26 and 97.55, and the coefficient of variation of 8.51 and 7.06, respectively (Table 3).This
result is in close agreement with the finding of Tesfa et al., 2016. Among the varieties the variety
Venus F1 was early flowering which took 35 days followed by Sire(35.08), Chali (35.75) and
Gelila (38.58). Among the varieties the variety Melkasalsa was late maturing with 102.33 days
followed by Chali(98.42) and Roma Vf(98.25).This indicates genotypes with shorter days to
flowering and days to maturity showed shorter fruit setting than those with longer days to
flowering and days to maturity.
Growth parameters
The combined mean values of growth characters of 9 processing tomato varieties evaluated at
Adami TuluJido Kombolcha were shown in table 4. The variation with respect to plant height at
fruit harvest, clusters per plant, floret per clusters, fruits per cluster, primary branch and
secondary branches were ranged from 66.02 to 88.33, 15.97 to 24.57, 4.36 to 5.07, 3.53 to 4.43,
2.67 to 3.83 and 15.71 to 19.40, with mean values of 82.51, 19.37, 4.54, 3.94, 3.37 and 17.01,
with coefficient of variation of 13.72, 11.74, 13.85, 8.42, 16.70 and 10.11 respectively.
The maximum plant height at fruit harvests were recorded in varieties Roma Vf(88.33cm), Venus
F1 (87.22cm) and Melkashola (86.92cm), while varieties Chali (66.02cm), Cochoro (77.45cm)
and Gelilema (80.73cm) were recorded minimum plant height at fruit harvest.The result is in
close agreement with the finding of Fiseha (2014)who reported the plant height at fruit harvest
from 96.8 to 106.8. The the maximum number of clusters per plant were recorded in varieties
Melkashola (24.57) and Roma Vf(24.17), while varieties Venus F1(15.97) and sire (16.33) were
recorded minimum number of clusters per plant. The maximum number of florets per cluster
were recorded in Roma Vf (5.07) and Melkasalsa (4.90), while Cochoro (3.73) and sire (4.36)
recorded minimum number of florets per cluster. The maximum number of fruit per cluster was
recorded in Roma Vf(4.43) and Melkasalsa(4.20),while Cochoro(3.53) and Venus F1(3.73) were
recorded the lowest number of fruit per clusters. According to Tesfa et al., 2016, the number of
fruits per cluster ranged from 3.1 to 7.3. The maximum primary branches were recorded in Roma
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Vf(3.83) and Melkasalsa(3.50),while Chali(2.67) and Venus F1(3.30) were recorded lowest
primary branches per plant. The highest secondary branches were recorded in Melkasalsa (19.40)
and Roma Vf(18.57), while Chali(15.71) and Gelilema(15.84) were recorded the minimum
number of secondary branches per plant. Over all the variety Roma Vf recorded the highest mean
values of all the studied growth parameters.
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Table 2: Combined analyses (ANOVA) of phenological, growth, yield and quality parameters of processing tomato varieties tested
over two locations (ATARC & Abosa) for two years (2018 and 2019)
Mean squares
Source of
Phonological parameters
variation
Df
DF
Year
1
Location
Growth parameters
Yield parameters
Quality Parameters
DM
PHFrHa CP
MFY
UnMFY TFY
pH
133.33***
1401.12**
2342.67*** 243.00*** 1.08ns 1.08*** 1.08ns 1.68ns
10576.17ns
22012.76*** 2072.65ns
2.072** 0.107ns 33.745***
1
0.037 ns
51.91***
2930.72** 54.04*** 1.82** 0.85*** 0.01ns 7.84ns
585208.33*** 27293.14*** 865263.63*** 0.560ns 22.595*** 6.283*
Replication
2
18.89 ns
134.23ns
27.57ns
Variety
8
80.09***
2552.08ns
Loc*variety
8
2.53ns
9.77ns
Year*loc
1
56.33*
2455.78*** 6332.67*** 27.00*
0.00ns 0.00ns
0.00ns 0.067ns 362413.55** 1467.62*
410006.54*** 10.09*** 0.03ns
Year*variety
8
26.95**
36.84ns
117.83*
0.00ns
0.00ns 0.00ns
0.00ns 0.00ns
12643.02*
501.57ns
9328.60ns
Year*loc*variety
8
5.54ns
9.34ns
79.55ns
0.00ns
0.00ns 0.00ns
0.00ns 0.00ns
9608.99ns
519.50503ns 10901.7253ns 0.05ns 0.28*** 7.31***
22.09*
FlCl FrPc PBr SBr
0.96ns 0.10ns
TA
441.13ns
24169.50ns
0.140ns 0.140ns 0.184ns
604.47*** 128.67*** 2.13** 0.84*** 1.12** 15.69*** 13612.55*
960.46ns
11089.36ns
0.106ns 0.4513*** 11.305**
58.73ns
231.58ns
7134.64ns
0.069** 0.34*** 1.79**
23.95*** 0.76ns 0.34*
0.36ns 20.34*** 24735.26ns
TSS
1.083* 7.20**
6933.79ns
0.29**
0.15ns 0.59*** 3.64***
Where : Df: Degree of freedom, DF: Days to 50% flowering and DM: Days to maturity, PHFrHa: Plant height at first harvest (cm).CP: Number of cluster per
plant, FlCl: Number of flower per cluster; FrPc : Number of fruit per cluster;PBr: number of primary branch , SBr: Number of Secondary branch, MFY =
Marketable fruit yield (qtls/ha); UnMFY= Unmarketable fruit yield (qtls/ha) , TFY = Total fruit yield (qtls/ha), pH: potential acidity (power of Hydrogen),TSS:
0
Total soluble solid ( Brix), and TA: Titrable acidity
238
Table 3: Combined mean of Phenological parameters of processing tomato varieties
tested over
two locations (ATARC & Abosa) for two years (2018 and 2019)
S.N
Variety
1
Melkasalsa
Phenological parameters
DF
DM
b
102.33a
39.00
Gelilema
39.50
Melkashola
42.50
Chali
35.75
Cochoro
38.17
Sire
35.08
Gelila
38.58
Venus F1
35.00
Roma Vf
40.75
38.26
8.51
2.64
b
2
b
95.67
a
3
ab
97.17
cd
4
ab
98.42
bc
5
b
96.50
d
6
ab
97.58
b
7
b
95.33
d
8
b
96.67
ab
9
Mean
CV (%)
LSD (5%)
b
98.25
97.55
7.06
5.58
Where: Df: Degree of freedom, DF: Days to 50% flowering and DM: Days to maturity
Table 4: Combined mean of growth parameters of processing tomato varieties tested over
two locations (ATARC & Abosa) for two years (2018 and 2019)
S.N
Variety
PHFrHa
1
b
Growth parameters
FlCl
FrPc
CP
b
Melkashola
86.92
Chali
66.02
Cochoro
77.45
Sire
86.13
Gelila
83.83
Venus F1
87.22
Roma Vf
Mean
CV (%)
88.33
82.51
13.72
24.17
19.37
11.74
5.07
4.54
13.85
4.43
3.94
8.42
3.83
3.37
16.70
18.58a
17.01
10.11
LSD (5%)
9.1731
1.8435
0.5094
0.2688
0.4564
1.394
3
4
5
6
7
8
9
a
c
b
ab
ab
a
a
a
24.57
de
17.13
bc
19.50
de
16.33
de
17.23
e
15.97
a
bc
4.43
abc
4.87
bc
4.50
d
3.73
c
4.36
bc
4.46
bc
4.50
a
bcd
3.96
bc
4.03
cd
3.90
e
3.53
cde
3.80
cd
3.83
de
3.73
a
3.37
3.43
2.67
3.43
3.33
3.46
3.30
b
ab
c
ab
b
ab
b
a
19.40
a
80.73
cd
3.50
SBr
ab
Gelilema
18.13
4.20
PBr
85.95
ab
4.90
b
Melkasalsa
2
21.33
a
15.84
17.24
15.71
15.91
16.71
17.51
16.17
e
bcd
e
de
cde
bc
cde
b
240
Yield and yield related parameters
The combined mean values of growth parameters of 9 processing tomato varieties
evaluated at Adami TuluJido Kombolcha were shown in table 5. The variation with
respect to marketable fruit yield, unmarketable fruit yield and total fruit yields were
ranged from 319.79 to 477.6, 43.7 to 64.84 and 373.96 to 533.07 quintals, with mean
values of 400.46, 53.92 and 454.38 quintals, coefficient of variation of 25.80, 34.25 and
24 respectively.
The maximum marketable yield in quintals were recorded in varieties Gelila(477.60) and
Gelilema(441.15), whileSire (319.79) and Venus Vf1 (369.79) were recorded minimum
marketable fruit yield. The highest total fruit yields in quintals were recorded in
Gelila(533.07), Gelilema(484.90) and Melkasalsa (474.22), while the varieties Sire
(373.96), Venus F1 (427.34) and Melkashola(446.72) were recorded the minimum total
fruit yield in quintals per hectare.This result is in close agreement with the finding of
Tesfa et al, 2016 who reported that mean total yield from 46.8 to 87.1 ton per ha for
different hybrid tomato varieties.
Table 5: Combined mean of Yield parameters of processing tomato varieties tested over
two locations (ATARC & Abosa) for two years (2018 and 2019)
S.N
1
2
3
4
5
6
7
8
9
Mean
CV (%)
LSD (5%)
Variety
MFY
UnMFY
ab
Melkasalsa
409.38
Gelilema
441.15
Melkashola
389.32
Chali
418.23
Cochoro
407.81
Sire
319.79
Gelila
477.60
Venus F1
369.79
Roma Vf
371.09
400.46
25.80
83.742
b
bc
ab
ab
c
a
bc
bc
64.84
43.7
a
b
57.40
53.91
46.87
54.17
55.47
56.25
TFY
ab
474.22
ab
484.90
ab
ab
b
ab
ab
ab
ab
52.60
53.92
34.25
14.969
abc
446.72
ab
472.14
abc
454.69
c
373.96
a
533.07
bc
427.34
bc
422.40
454.38
24.00
88.389
240
241
Chemical quality attributes
The combined mean values of quality attributes of processing tomato varieties tested at
Adami TuluJido Kombolcha were shown in table 6. The variation with respect to pH
(power of hydrogen), total soluble solids and titrable acids were ranged from 4.1 to 4.39,
3.62 to 4.21 and 5.45 to 8.5 with the mean values of 4.22, 3.82 and 6.77, coefficient of
variation of 9.23, 10.36 and 15.86 respectively. The maximum pH was recorded in
varieties Melkasalsa (4.39) and Gelilema (4.31), while the varieties Gelila(4.10) and
Venus F1(4.13) recorded the minimum pH values. The average pH range for most tomato
fruit lies between 4.3 and 4.4 (Jones, 2008). However there was no significant difference
among the varieties by pH. The maximum total soluble solids were recorded in varieties
Sire (4.21), Gelila(4.20) and Melkasalsa(4.02), while Venus Vf1(3.62), Gelilema(3.65)
and Chali(3.72) were recorded the lower total soluble solids. It is in line with the finding
of Fiseha (2014), who reported the total soluble solid ranges from 4.10 to 4.36. Similarly
Tesfa et al., 2016 reported the total soluble solids with the range from 3 to 4 for different
hybrid tomato varieties. The maximum titrable acidity was recorded in varieties
Melkasalsa (8.50), Gelila(7.82) and Sire (7.75), while the lowest titrable acidity was
recorded in the varieties Gelilema (5.45), Chali (6.01) and Melkashola (6.25). Over all
the varieties Gelila, Melkasalsa and Sire fulfills the quality standards for processing,
however the yield and fruit size of Sire was very low as compared to all the others.
Table 6: Combined mean of Quality parameters of processing tomato varieties tested over
two locations (ATARC & Abosa) for two years (2018 and 2019)
S.N
1
2
3
4
5
6
7
8
9
Variety
Chemical quality parameters
pH
TSS
a
Melkasalsa
4.39
Gelilema
4.31
Melkashola
4.25
Chali
4.23
Cochoro
4.12
Sire
4.20
Gelila
4.10
Venus F1
4.13
Roma Vf
4.22
a
a
a
a
a
a
a
a
ab
4.02
c
TA
a
8.50
f
3.65
5.45
bc
3.75
c
def
6.25
def
3.72
6.01
bc
3.90
a
cde
6.68
b
4.21
7.75
a
ab
4.20
7.82
c
bc
3.62
7.12
c
3.82b
cd
6.77
241
242
Mean
CV (%)
4.22
9.23
3.82
10.36
LSD (5%)
Ns
0.2874
6.71
15.86
0.8622
0
Where: pH: potential acidity (power of Hydrogen), TSS: Total soluble solid ( Brix), and
TA: Titrable acidity
4. CONCLUSIONS AND RECOMMENDATIONS
Adami Tulu Jido Kombolcha is one of the districts in East Shoa zone of mid rift valley of
Oromia where tomato is widely produced with limited knowledge on fresh market type
and processing type tomato varieties. The current study was conducted to identify
adaptable and most preferable processing tomato varieties with high yield and required
qualities of 9 processing type tomato varieties. Data recorded for 14 characters were
subjected to analysis of variance and the results showed the presence of significant
differences (P≤0.01/0.05) among the tested varieties for almost all traits indicating the
presence of variations among the tested 9processing tomato varieties.The mean
performance of each varieties for different traits were obtained, most of the traits studied
showed a wide range of variations in days to flowering (35-42.5), days to maturity
(95.67-102.33), plant height at fruit harvest in centimeters (66.02-88.33), number of
cluster per plants (15.97-24,17), Number of florets per clusters 3.73-5.07), number of
fruits per clusters (3.53-4.43), number of primary branches (2.67-3.83), number of
secondary branches (15.71-19.40), marketable fruit yield in quintals(319.79-477.60),
unmarketable fruit yield in quintals (46.87-64.84), total fruit yield in quintals (373.96533.07), pH 4.10-4.39), total soluble solids (3.62-4.21) and titrable acids (5.45-8.50).
The present study showed the presence of considerable variations among the evaluated
varieties. Processing tomato varieties Gelila, Gelilema and Melkasalsa have given higher
combined mean yield and quality over the other varieties across years and locations
studied. Therefore, Gelila, Gelilema and Melkasalsa were identified and selected as
the best for different merits to be demonstrated and popularized in the studied
areas. Furthermore, production packages of these varieties should be studied so as to
increase the production and productivity of processing tomato in mid rift valley thereby
to justify food security of farming community and to supply for the tomato processing
agro-industry in the industrial zone.
Acknowledgements
242
243
The authors would like to thank Oromia Agricultural Research Institute, Adami Tulu
Agricultural Research Center (ATARC) and Ethio-Italy (ISVCDO) Project for the
financial support.
5. REFERENCES
Birhanu K, Ketema T .2010. Fruit yield and quality of drip-irrigated tomato under deficit
irrigation. Afr. J. Food, Agric, Nutr. Dev.
Fiseha Tadesse, 2014. Growth, Yield, and Quality Response of Tomato (Lycopersicon
Esculentum Mill.) Varieties to Nitrogen fertilizer at Adami Tulu, Central Rift Valley,
Ethiopia. An MSc Thesis Presented to the School of Graduate Studies of Haramaya
University, Ethiopia.
Jones, J. B. 2008. Tomato plant culture: in the field, greenhouse, and home garden
(2nded.). CRC Press Tylor and Francis Group.
Lemma D. 2002. Tomatoes research experiences and production prospects. Research
Report No. 43. Ethiopian Agricultural Research Organization, Addis Ababa,
Ethiopia. Pp. 811.
Selamawit K. and Lemma D. 2008. Evaluation of tomato Genotypes for salt tolerance at
seed germination. In: Ethiopian Horticulture Science Society (EHSS). 2008. Volume
I. Proceedings of the first conference 23-24 March 2006, Addis Ababa, Ethiopia. pp.
99-102.
Tesfa B., Yosef A., Jibicho G. Gebeyehu W and Melkamu H.2016. Performance of
Introduced Hybrid Tomato (Solanumlycopersicum Mill.) Cultivars in the Rift Valley,
Ethiopia.PP 25-28
Evaluation of Improved Exotic Head Cabbage (Brassica Oleracea Var Capitata L.)
Varieties at Adola Rede Areas, Southern Oromia, Ethiopia
Solomon Teshome1,*, Tekile Bobo2
1,2
Oromia Agriculture Research Institute (IQQO)
Bore Agricultural Research Center (BOARC)
*Corresponding author: Solomon Teshome (solomtesh41@gmail.com)
Abstract
Evaluation of exotic varieties of head cabbage was carried out to select the most
adaptable and high yielding improved cultivars suitable for the study area. Field
experiments were conducted during the 2017 and 2018 short rainy season at three
locations with supplemental irrigation. Randomized Complete Block Design (RCBD) with
three replications was used. Four improved exotic cabbage varieties: Olsen, Royal,
243
244
Monarch and DSA Copenhagen market were used for the study. A widely cultivated
variety (Gloria) was included as check. Results revealed that for each seasons and
locations days to head initiation, days to 80% maturity, plant height, number of expanded
true leaves, diameter of head, untrimmed head mass, trimmed head mass, head yield with
wrapper, head yield without wrapper and total head yield had significant differences (P
< 0.05) among the varieties. But there was non-significance difference (P > 0.05) for
mean of head height of the varieties. The maximum day to head initiation (72 days) was
recorded for variety Gloria whereas lower (63.83, 64.25, 65.08 and 65.91 days) duration
was observed for Olsen, Royal, DSA and Monarch varieties, respectively. The maximum
days (100.5 days) to maturity was recorded for variety Gloria and the minimum days (93
days) were recorded for variety Olsen. The highest plant height (30.74 cm) was attained
by Monarch variety and the minimum (21.93 cm) was recorded for Gloria variety. As
combined analysis of improved varieties over locations and years revealed that the
highest number of expanded true leaves (17) were obtained from Monarch variety
whereas lower number of true leaves (8, 9, and 10) were recorded from Royal, Olsen and
DSA varieties. Similarly the highest diameter of head (21.16 cm) was recorded for
variety Royal. There was non-significant difference among varieties with respect to
height of head. The maximum untrimmed head mass (4735 g) was recorded from variety
Royal whereas the minimum (2180 g) was recorded from DSA Copenhagen variety.
Regarding the trimmed economic head mass the maximum (3960 g) was recorded by
Royal variety and the minimum (1310.3 g) was by DSA Copenhagen variety. The highest
yield without wrapper (78.69 t ha-1) was recorded from Royal while the lowest yield
without wrapper (53.39 t ha-1) was recorded from DSA Copenhagen variety. Similarly,
the highest total yield (164.14 t ha-1) was attained from Royal variety and the lowest
(129.49 t ha-1) was from DSA Copenhagen variety. Generally, as a conclusion and
recommendation, for head cabbage growers in Adola Rede and similar agro ecologies
improved varieties of Royal and Monarch were selected and recommended for better
early maturing, maximum head yield, head shape, head size, and low incidence of loose
heads.
Key words: Improved cabbage varieties, growth, head yield, yield components
244
Introduction
Cabbage (Brassica oleracea L. var. capitata) is a member of the Brassicaceae (Mustard)
family. This family includes broccoli, Brussels sprouts, cauliflower, kale, mustard
(greens), and collards. Collectively, these crops are referred to as cole crops or crucifers.
Cabbage and many of the cole crops are cultivated throughout the world for use fresh and
in processed products. Nutritionally, one cup of raw cabbage contains 93 percent water
and is a good source of dietary fiber as well as vitamins A and C. Worldwide, China is
the leading producer and consumer of cabbage. In the United States, 80,000 acres of
cabbage valued at almost $280 million was harvested in 1997 [1].
Cabbage (Brassica oleraceae var. capitata) is one of the most important leafy vegetables
worldwide [6]. It originated in Northern Europe, the Baltic Sea coast [3] and the
Mediterranean region [6], where it has been grown for more than 3000 years and is
adapted to cool moist conditions [7, 8]. Cabbage is cultivated for its head, which consists
of water (92.8%), protein (1 .4 mg), calcium (55.0 mg) and iron (0.8 mg); the leaves are
eaten raw in salads or cooked. The optimum mean temperature for growth and quality
head development is 1 5 - 1 8°C, with a minimum temperature of 4°C and a maximum of
24°C. Cabbage grows well on a range of soils with adequate moisture and fertility. It
tolerates a soil pH range of 5.5 - 6.8 and it is a heavy feeder.
The importance of head cabbage in tropical and subtropical regions has increased
considerably in recent decades. Recent estimates indicate Africa has about 100,000 ha
planted to head cabbage [9]. Based on sales of commercial seed, at least 40,000 ha of
white-headed cabbage is grown in Kenya, Uganda and Tanzania; 10,000 ha in Malawi,
Zambia and Zimbabwe; 4000 ha in Ethiopia; and 3000 ha in Cameroon. Vegetables can
be planted throughout the year provided there is reliable soil moisture.
Prior to cultivation and use as food, cabbage was mainly used for medicinal purposes. In
addition to the fresh market, cabbage is now processed into Kraut, egg rolls and cole
slaws and there is the potential for other specialty markets for the various types including
red, savoy and mini cabbage. Cabbage is an excellent source of Vitamin C. In addition to
containing some B vitamins, cabbage supplies some potassium and calcium to the diet.
250 mL of raw cabbage contains 21 kilocalories and cooked 58 [3].
Most farmers in the area (Adola and Shakiso) especially those having land along the river
valleys grow cabbages. The importance of head cabbage in Guji areas has increased
considerably in recent years. The cultivars grown so far have less acceptability by the
consumers due different factors. Choice of inappropriate varieties has led to low yields
due to diseases and related constraints. The area offers high potential for the production
of such vegetables. Most of the farmers produce head cabbage widely using unknown
variety and source. As a result the production is characterized by low yield. Therefore
variety development and promoting is important in order to help farmers attain better
yield and markets.
Materials and Methods
Description of the Study Sites
The field trials were conducted during 2017 and 2018 main cropping season at Shakiso
Boke, Dole and Odabuta areas of Adola Rede district. The average climatic condition of
the area is sub humid and moist condition, with relatively short growing season. Adola
district falls in the agro-ecological classification of hot to warm sub-moist mid-lands. The
experimental area is situated at an altitude of 1768 meters above sea level and is located
469 km south of Addis Ababa along the Hawassa road. The district was characterized by
three agro-climatic zones, namely high land, mid land and low land with different
coverage. The main rainy season is from May to October. The mean annual rain fall and
temperature of the district are about 978 mm and 12-34 0c, respectively.
Treatments and Experimental Design
Field experiments were conducted in April main cropping season having relatively short
rainy season. A randomized complete block design with three replications was used; each
plot consisted of five rows and eight plants per row having spacing of 40cm*50cm on a
plot size of 2.5 m* 2.8 m. Cabbage seedlings was raised on flat bed for four weeks and
then transplanted to the sides of the ridges with 0.50 m space between plants. Cabbage
varieties commercially available on the market Olsen, Royal, Monarch and DSA
Copenhagen market were used for the study. A widely cultivated variety (Gloria) was
included as check. Weeding was carried out manually and frequently to maintain weed
free plots. Fertilizer NPS was applied during transplanting and also after transplanting.
Urea was applied at the rate of 138 kg ha-1 in a split application at transplanting and 30
days after transplanting.
246
Data Management and Statistical Analysis
Measurements of plant height, number of expanded true leaves (leaves with a clearly
visible petiole before head initiation), average head mass as untrimmed head mass and
trimmed head mass, diameter of head, head height, days to 50% head initiation, days to
90 % maturity and total fresh marketable yield were recorded from samples of each
treatments. At harvest, total mass (with and without wrapper leaves) was recorded. The
diameter and height of the head was obtained by cutting the head longitudinally.
Statistical Analysis
Analysis of variance procedures were used on every measured parameter to determine the
significance of differences between means of treatments using the SAS software for each
parameters and separated using Least Significant Difference (LSD).Yield and yield
related data were statistically analyzed using the Proc Glm function of SAS and means
were compared using LSD at a probability level of 5 % [5].
Results and Discussions
Days to 80% head initiation, maturity, plant height, head diameter, head height, trimmed
and untrimmed head mass, yield with wrapper and without wrapper and total yield of the
plot were measured and converted to hectare basis.
Phenological and growth variables of cabbage
Results of combined ANOVA over locations and seasons indicated that different
cultivars had significantly varying (P < 0.05) days to head initiation, days to 80%
maturity, plant height and number of expanded true leaves (Table 1). The maximum days
to head initiation (72) was attained from variety Gloria whereas lower durations for head
initiation (63.83, 64.25, 65.08 and 65.91) were recorded from Olsen, Royal, DSA
Copenhagen and Monarch varieties, respectively (Table 1). Similarly the maximum
duration to maturity (100.5) was recorded from Gloria variety while the minimum (93)
was recorded from Olsen variety. The highest plant height (30.74 cm) was observed for
variety Monarch and the least (21.93 cm) was recorded from variety Gloria (Figure 1).
Yield component and yield variables of head cabbage
Results of combined ANOVA over locations and seasons also indicated that different
cultivars had significantly (P < 0.05) varying diameter of head, untrimmed and trimmed
head mass and yield of head cabbage (Table 2). On the other hand, the varieties showed
247
non-significant (p > 0.05) variations for head height (Table 2). The longest head diameter
(21.16 cm) was recorded from variety Royal while the rest of the varieties had lower head
diameter (Table 2). These results suggest wide genetic variability among head cabbage
cultivars and that environmental variables also influence the expression of crop growth
parameters. This however, did not hold true for the height of the head. There was a highly
significant variation (p < 0.01) among the cultivars for untrimmed and trimmed head
mass. The maximum untrimmed head mass (4735 g) was recorded from variety Royal
followed by Monarch (3309.2 g) variety whereas the lowest untrimmed head mass (2180
g) was recorded from DSA Copenhagen variety (Table 2). The highest trimmed
economic head mass (3960 g) was likewise recorded from variety Royal and the least
trimmed head mass was (1310.3 g) was recorded from DSA Copenhagen variety (Figure
2). Combined ANOVA also showed that the highest yield with wrapper (86.52 t ha-1)
was attained from variety Monarch whereas the lowest (63.71 t ha-1) was recorded from
variety DSA Copenhagen (Table 2). Similarly the maximum yield without wrapper
(78.69 t ha-1) was recorded from Royal variety followed by variety Monarch (68.84 t ha-1)
whereas the least (53.39 t ha-1) was recorded from DSA Copenhagen variety (Figure 3).
The highest total yield (164.14 t ha-1) was recorded from variety Royal whereas the
lowest (129.49 t ha-1) was recorded from DSA Copenhagen variety. However Gloria and
DSA Copenhagen varieties showed yield reduction, indicating their unsuitability for
cultivation during the short rainy season. This is because the area is characterized by
inconsistent rainfall and high temperatures that often reach 34 °C. The adaptation of
Royal and Monarch to the short rainy season was evident in the head yields (Table 3).
Table 1. Mean value of varieties for different variables across locations and years
Treatments
Olsen
Royal
Monarch
DSA
Gloria
Mean
Lsd
CV (%)
DHI
63.83b
64.25b
65.91b
65.08b
72.25a
66.26
4.08
7.52
DM
93.58b
98.50ab
96.75ab
96.58ab
100.50a
97.18
6.86
8.63
ETLV
9.41b
8.58b
17.08a
10b
9.41b
10.90
1.63
18.33
PH
25.44b
22.10cd
30.74a
23.88bc
21.93d
24.82
1.93
9.52
Means within the same column followed by the same letter (s) are not significantly different at 5% level of
significance; LSD = Least Significant difference; NS= Not significant; CV= Coefficient of Variation;
DHI=days to head initiation, DM=days to maturity, ETLV=average expanded leaves, PH=plant height
248
Response of varieties to maturity
120
100
80
60
DM
40
20
0
Olsen
Royal
Monarch
DSA
Gloria
Figure 1. Response of varieties to days to maturity
The study showed that cabbage production in Adola areas and the cultivation of Royal
and Monarch varieties evaluated during the short rainy season with supplemental
irrigation could provide considerable maximum head yield.
Head mass (gm)
5000
4000
3000
THM (gm)
2000
1000
0
Olsen
Royal
Monarch
DSA
Gloria
Figure 2. Response of varieties to average head weight
Table 2. Mean value of diameter of head, and height of head, untrimmed head mass,
trimmed head mass.
Treatments
DH
HH
UTHM
THM
Olsen
18.91b
17.20
2746.7bc
2540b
Royal
21.16a
18.54
4735a
3960a
b
b
Monarch
18.88
17.65
3309.2
2007.4c
DSA
18.72b
17.63
2180c
1310.3d
b
bc
Gloria
17.60
16.38
2717.5
1658.1cd
Mean
19.06
17.48
3137.66
2295.16
Lsd
1.96
NS
594.88
483.95
CV (%)
12.57
15.90
23.17
25.77
Means within the same column followed by the same letter (s) are not significantly different at 5% level of
significance; LSD = Least Significant difference; NS= Not significant; CV= Coefficient of Variation;
DH=diameter of head, HH=height of head, UTHM=untrimmed head mass, THM=trimmed head mass.
249
Marketable yield (t/ha)
100
80
60
YWOR
40
20
0
Olsen
Royal
Monarch
DSA
Gloria
Figure 3. Response of varieties to yield without wrapper
Table 3. Mean value of yield with wrapper, yield without wrapper and total yield
Treatments
Olsen
Royal
Monarch
DSA
Gloria
Mean
Lsd
CV (%)
YWR
73.94abc
85.44ab
86.52a
63.71c
71.36bc
76.19
14.56
23.36
YWOR
67.36ab
78.69a
68.84ab
53.39c
58.13bc
65.28
11.55
21.62
TYLD
141.31bc
164.14a
155.37ab
117.11d
129.49cd
141.48
20.86
25.50
Means within the same column followed by the same letter (s) are not significantly different at 5% level of
significance; LSD = Least Significant difference; NS= Not significant; CV= Coefficient of Variation;
YWR=yield with wrapper, YWOR=yield without wrapper, TYLD=total yield.
Summary and Conclusion
The importance of head cabbage in tropical and subtropical regions has increased
considerably in recent decades. Lack of improved varieties and management
recommendations call for introduction and adaptation studies of high yielding varieties
with all agronomic management practices.
Generally results of the study showed that head cabbage varieties Royal and Monarch
were found to be better adaptable than the rest of the varieties. Therefore as a
recommendation, head cabbage growers at Adola Rede and similar agro-ecologies can
grow head cabbage varieties of Royal and Monarch for early maturity, better head yield,
good head shape, firmness, marketable head size and low incidence of loose heads.
References
[1] Alabama and Auburn University (1999). Guide to commercial cabbage production.
Access at www.aces.edu.
250
[2] Haque KMF. 2006. Yield and nutritional quality of cabbage as affected by nitrogen
and phosphorus fertilization. Bangladesh J Sci Ind Res.41:41-46.
[3] Monteiro A, Lunn T. (1998). Trends and perspectives of vegetable brassica breeding.
World Conference on Horticultural Research. 17-20 June 1998. Rome, Italy.
[4] Mwasha A.M. (2000). Status of vegetable production in Tanzania In: Chada ML,
Nono-Womdim R, Swai I, eds. Proceedings of the Second National Vegetable
Research and Development Planning Workshop held at HORTI-Tengeru, Arusha,
Tanzania, and 25-26 June 1998. AVRDC. pp. 22-27.
[5] Statistical Analytical System, (2003). SAS/STAT users Guide for Personal Computers
Version 9.1.3: SAS-Institute. Cary, North Carolina.
[6] Talekar N.S. (2000). Chinese cabbage. Proceedings of the 1st International
Symposium on Chinese Cabbages. AVRDC, Shanhua, Tainan, Taiwan. pp. 67-69.
[7] Thompson J.K. (2002). Yield evaluation of cabbage varieties. J. Agric. Technol.,
5:15-19.
[8] Tindall H.D. (1993). Vegetables in the Tropics Macmillan International College. 3rd
Edition, London, UK. pp. 354-356.
[9] Van der Vossen HAM, Seif A A. (2004). Brassica oleracea L. (headed cabbage) In:
Grubben GJH, Denton OA, eds. PROTA 2: Vegetables/Légumes. [CD-Rom].
PROTA, Wageningen, Netherlands.
[10] Vural H, Esiyok D, Duman I. (2000). The culture of vegetables (Vegetable
growing). Izmir, Turkey. PP: 440.
Adaptation trial of Market Types Common Bean(Phaseolus Vulgaris L.) Varieties in
Eastern Hararghe Zone, Oromia
MotumaDalasa, Habte Berhanu and Adugna Hunduma
Fadis Agricultural Research Center
Oromia Agricultural Research Institute
Abstract
Common bean has tremendous importance in the country’s economy interms of home
consumption,export and soil fertility restoration. However,in Ethiopia.its improvement is
highly hampered by diseases, insect pests, and prolonged drought. This calls for
searching varieties that can withstand these stresses. Therefore, the experiment was
conducted to identify high yielding, biotic and abiotic stress-resistant or tolerant
varietiesthat are also high yielder and early maturing.The study was conducted at Fedis
for two consecutive years of 2017 and 2018 during the rainy season. Analysis of variance
revealed the presence of significant (P ≤ 0.05) differences in seed yield and podsperplant
among the cultivars. The maximum and minimum number of podsper plant of (20.22) and
(11.33) were recorded for varieties Awash-2 and Awash Melkasa, respectively. The
251
highest grain yield of 1711kg/ha was recorded for variety Awash-2 and the least grain
yield of (1410kg/ha) was for the SER125. Generally, Awash-2, KATB1, SAB736 and
Awash-1which gave higher yield than the standard check interims of number of pods per
plant and grain yield. Therefore, Awash-2, KATB1, SAB736 and Awash-1 were
recommended for production in Eastern Hararghe and other areas with similar agroecology.
Key words: Common bean, early maturing, stress-resistance/tolerance
Introduction
Common bean (Phaseolus vulgaris L.) is the most important pulse crops grown in central
southern, eastern and Western lowland and mid altitudes of Ethiopia. It is grown
predominantly in low land areasof altitudinal range 300-1100masl and some mid
highland areas of altitudinal range 1400-2000masl. Besides, its use as a readily available
source of protein for smallholders, it is also an important cash crop and export
commodity that generatessignificant amount of foreign exchange for the country. It is
predominantly grown for cash in the central rift valley, but in other parts, it is a major
staple food supplementing the protein source for the poor farmers who cannot afford to
buy other sources of protein such as animal products.
Common bean is mainly grown in Eastern, Southern, South Western and the Rift valley
areas of Ethiopia (Habte E. et al., 2014).Nationally, area under common bean production
is estimated at about 300-500 thousand hectares (IAR, 1995; EARO, 2001). However,
according to the official statistical data of the country, common bean was grown on
about 166 thousand hectares of land in 1999/2000 and ranked third next to horse bean
and chick peas and the average common bean productivity was about 8 quintals per
hectare(CSA, 2000). However, the experience from experimental plots indicates that
yield level of up to 25-30 quintal per hectare can be attained (EARO, 2001). It is one of
the major food and cash crops in Ethiopia and it has considerable national economic
significance and also traditionally ensures food security in Ethiopia (PABRA, 2014). It
ranks third as an export commodity in Ethiopia, contributing about 9.5% of total export
value from agriculture. It is often grown as cash crop by small scale farmers. The
majority of common bean producers in Ethiopia are small scale farmers, and it is used as
252
a major food legume in many parts of the country where it is consumed in different types
of traditional dishes (Habtu A., et al., 1996).
Common bean seeds contain 20-25% proteins, much of which is made up of the storage
protein phaseolin (Ma Y., and Bliss F.A, 1978). Phaseolin is a major determinant of both
quantity and nutritional quality of proteins in bean seeds (Gepts P, 1984). In addition to
this, it is also very important in providing fodder for livestock and it contributes to soil
fertility improvement through atmospheric nitrogen fixation during the cropping season
(Asfaw A, 2014). Common bean adds not only diversity to production systems on
resource poor farmers’ fields, but also it contributes to the stability of farming systems in
Ethiopia (Asfaw A, 2014). Pulses covered 10.38% (about 2,671,843.040 tons) of the
grain production. Out of this, common beans (red), and common beans (white) were
planted to, 1.95% (about 244,049.94 ha) and 0.91% (about 113,249.95 ha) of the grain
crop area, respectively. The production obtained from common bean (red) and common
bean (white) were 1.43% (380,499.453 tons) and 0.60% (159,739.484 tons) of the grain
production, respectively. Therefore the total area devoted for common bean crop
production and the yield obtained in Ethiopia are 357,299.89 ha and 540,238.94 tons,
respectively (CSA, 2016).
Even though the crop has tremendous importance in country’s economy, such as for
home consumption, soil fertility improvement etc., its improvement is highly challenged
by diseases, insect pests, and prolonged drought in Ethiopia. In spite of this challenge, the
crop is crucial primarily for home consumption, for foreign exchange earnings, soil
fertility improvement by changing unavailable atmospheric nitrogen into available form
and it has high protein content. The current study was initiated with the objective to
identify high yielding, early maturing and stress (biotic and abiotic) resistant/tolerant
common bean variety/ies that are adaptable to Eastern Hararghe areas among the varieties
domestically released for different parts of the country.
Materials and Methods
The study was conducted at Fedis for two consecutive years of 2017 and 2018 during the
rainy season. Important data like plant height, pods per plant, seed per pod, number of
branches per plant and yielded were collected.Six common bean varieties with one
253
standard check were evaluated. The collected data were subjected to statical analysis
using softwares( SAS 2009 and GenStat 18th edition).
Results and discussion
Analysis of variance revealed the presence of significant (P ≤ 0.05) difference in grain
yield, and pods/plant among common bean varieties tested at Fedis. This indicated the
presence of performance variation among the tested varieties for yield, which is
supported by the earlier works of Negash(2006)Kefelegn (2012) and Rezeneet al(2011)
who noticed a large variation in yield performance among different bean varieties.The
maximum and minimum number of pods/ plant of (20.22) and (11.33) were recorded
from varieties Awash-2 and Awash melkasa, respectively (Table 1). In this study, days to
maturity, and number of seeds/ pod was not significantly affected due to varieties (Table
1). The maximum and minimum number of seeds /pod of (4.88) and (3.50) were noted
for the varieties Awash-2 and Awash melkasa, respectively (Table 1). The findings
revealed that the maximum number of pods per plant and the highest number of seeds per
pod resulted in the maximum grain yield of (1711kg/ ha) for the common bean variety
Awash-2 which agree with the finding of Misgana M and Tadesse (2017)that stated the
maximumnumber of pods per plant and the highest number of seeds per pod resulted in
the maximum grain yield of (2.1478t/ha) for common bean variety Dinkinesh. In this
experiment, grain yield of common bean was significantly different at (P < 0.05) (Table1)
and affected by the tested varieties. This finding agrees with the previous findings
reported by Fekadu (2013). The highest grain yield of 1711kg/ha was recorded for the
variety Awash-2 and the least grain yield of (1410kg/ha) was noted for SER125.
Table-2. Combined mean of grain yield and yield related parameters over two year
at Fedis station
Treatment
Days to maturity
Podsperplant
Seedsperpod
Grain yield(kg/ha)
Awash-1
Awash-2
Awash-Melkasa
KATB1
SAB632
SAB736
SER125
LSD (5%)
CV (%)
84.67
85.50
92.50
84.17
82.67
87.17
84.67
NS
18.6
13.66b
20.22a
11.33b
17.89a
16.00ab
18.22a
13.17b
4.50
16.9
3.39
4.88
3.50
4.22
4.23
4.87
4.22
NS
25.1
1505ab
1711a
1455b
1682a
1485b
1602ab
1410b
2.035
17.9
254
Recommendation
Analysis of variance showed that significant variations were recorded for Awash-2,
KATB1, SAB736 and Awash-1which gave high yield than the standard check interims of
number of pods per plant and grain yield. Therefore, Awash-2, KATB1, SAB736 and
Awash-1were recommended for production under the agro-clamitic conditions of East
Hararghe and other areaswith similar agro-ecologies.
References
Abuhay, T. and A. Teshale, 1995. Review of Tef Research in the Marginal Rainfall Areas
of Ethiopia: Past and future Prospects. In: Proceeding of the 25th Anniversary of
Nazareth Agricultural Research Center: 25 years of experience in lowland crops
research, 20-23 September. Nazareth, Ethiopia.
Ma Y, Bliss FA (1978). Seed proteins of common bean. Crop Sci 17: 431–437.
Gepts P, Bliss FA (1984) Enhanced available methionine concentration associated with
higher phaseolin levels in common bean seeds. Нeor Appl Genet 69: 47–53.
Asfaw A, Blair MW (2014) Quantification of drought tolerance in Ethiopian common
bean varieties. Agricultural Sciences 5: 124-139.
CSA (Central Statistics Agency of Ethiopia) (2016) Report on area and crop production
of major crops for 2016 Meher season, 1: 125.
Habte E, Gebeyehu S, Tumsa S, Negash K (2014) Decentralized common bean seed
production and delivery system. Melkassa agricultural research center, Ethiopian
institute of agricultural research, Ethiopia.
Misgana M, Tadesse M. Performance Evaluation of Common Bean (Phaseolus vulgaris
(L.)) Varieties at Benatsemay Woreda of South Omo Zone, SNNPR, Ethiopia. Agri
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Open
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555846.
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10.19080/ARTOAJ.2017.12.555846
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Middle Rift Valley of Ethiopia. Procs. of National Workshop held in Addis Ababa,
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AsnakewWoldeab, 1991. Soil Fertility and Management in the Dry land. pp.70-81, In:
Procs. of the First National Workshop on Dry land Farming System in Ethiopia. 2628 November. Nazareth, Ethiopia. Adv. Environ. Biol., 3(3): 302-307, 2009 307.
Central Agricultural Census Commission (CACC), 2002. Ethiopian Agricultural Sample
Enumeration, 2001/2002: Report on the primary results of area, production and yield
of temporary crops (Meherseason private peasant holding) part I. Addis Ababa.
Fekadu G (2013) Assessment of Farmers’ Criteria for Common Bean Variety Selection:
The case of Umbullo Watershed in Sidama Zone of the Southern Region of Ethiopia.
Ethiopian journal for research and innovation foresight 5(2): 4-13.
Habtu A, Sache I, Zadoks JC (1996) A survey of cropping practices and foliar diseases of
common bean in ethiopia. Crop Protection 15: 179-186.
Kefelegn N (2012) Genotype x environment interaction of released common bean
(Phaseolus vulgaris l.) varieties, in eastern Amhara region, Ethiopia. An MSc Нesis
Presented to the School of Graduate Studies of Haramaya University.
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Kidane Georgis, 1987. A Review of Haricot Bean Agronomy Research in the Semi-Arid
Regions of Ethiopia. Procs. of National work shop held in Addis Ababa, l-3.
Negash K (2006) Studies on genetic divergence in common bean (Phaseolus vulgaris L.)
introduction of Ethiopia. An MSc Нesis presented to the School of Graduate Studies
of Addis Ababa University.
PABRA (Pan Africa Bean Research Alliance) (2014) Phase report and partnership in
research for impact: case of common beans in Ethiopia. Nairobi, Kenya.
Rezene Y, Gebeyehu S, Zelleke H (2011) Genetic variation for drought resistance in
small red seeded common bean genotypes. African Crop Science Journal 19: 303311
Tadele, G.S. and GermewEticha, 1977. Haricot Bean Fertilizer Trial. pp: 87-88, In : IAR,
Progress Report for 1981/82.
TamirieHawando, 1986. Increasing Agricultural Production in Ethiopia through
improved soil water and crop management practices. 243-275.
TilahunMulatu and Teshome Regassa, 1987. Nazareth Mixed Farming Zone Diagnostic
Survey Report, Shoa Region. Research Report No. 2. Institute of Agricultural
Research, Department of Agricultural Economics, Addis Ababa.
Tolessa, D., B. Tesfa, N. Wakene, w. Tenaw, L. Minale, M. Tewodros, M. Burtukan and
M. Waga, 2001. A Review of Fertilizer Management Research on Maize in Ethiopia.
Enhancing the Contribution of Maize to Food Security in Ethiopia. In Proceedings of
the Second National Maize Workshop of Ethiopia, 12-16, Addis
256
Agronomy
257
Effect of Cassava Intercropping with Legume Crops Followed by Sorghum on Growth,
Yield and Yield Parameters of Cassava-Based Double Cropping System in Fedis and
Babile District, Eastern Harerghe Zone
*Gebisa Benti, Gezu Degafa and Mohammed Jafar,
Fadis Agricultural Research Center of Oromia Agricultural Research Institute
Corresponding author e-mail: bantiig@gmail.com
Abstract
Some farmers of eastern Harerghe in the lowland area survive with food aid from government
and different NGOs due to shortage of rainfall and prevalence of recurrent drought. In
Harerghe, intercropping is a popular farming practice where most of farmers intercrop Chat
with sorghum and groundnut in a growing season. The experiment was initated and conducted
under rainfed conditions at Fedis Agricultural Research Center of Oromia Agricultural
Research Institute (OARI), at Boko sub-site to determine the compatibility of crops in double
cropping system and efficient land uses for the component crops. The experiment was conducted
in two phases: cassava with legumes (soybean, haricot bean and cowpea) and cassava based
sorghum varieties (Hormat, Gedo and Birhan) in one cropping season. The treatments were
arranged as intercropping and sole cropping. A total of 7 treatments for preceding and 7
treatments for cassava-based succeeding treatments were arranged in Completely Randomized
Block Design with three replications. Variety Kello was used for the experiment. The results
revealed that there were significant (P < 0.05) differences for cassava average root weight,
number of roots per plant, root diameter and root yield due to cassava-legumes intercropping.
Soybean-cassava intercropping increased average root weight, root numbers and root
enlargement of cassava by 39, 33.6 and 27.7 % as compared to cassava- cowpea intercropping.
Cowpea intercropping in cassava significantly affected cassava root yield as compared to other
legumes intercropping. Cassava-soybean intercropping was found to increase root yield by 41.7
and 21.3 % as compared to cassava-cowpea and cassava-haricot bean, respectively. Cassavasoybean intercropping improved land use efficiency by 16.4 and 19.3 % as compared to cassavacowpea and cassava-haricot bean intercropping, respectively. Accordingly, sole stands could
require 74, 40 and 46 % more land i.e. the mixture cropping gives 74, 40 and 46 % yield
advantage, for soybean, haricot bean and cowpea, respectively as intercropped in cassava than
the pure stand. Following the harvest of legumes, sorghum was sown as double crop for
258
additional yield advantage. Therefore, from this result, cassava-soybean intercropping following
cassava-based early maturing sorghum was recommended for the study area and similar agro
ecologies.
Key words: Cassava, Cowpea, Haricot bean, Intercropping, Sorghum, Soybean
Introduction
Cassava (Mahinot esculenta Cratzy) is a perennial crop native to tropical America with its center
of origin in north-eastern and central Brazil (Allem, 2002). It is one of the most important energy
sources in many tropical countries (Cock, 1985). It is cultivated mainly for its enlarged starchy
roots and one of the most important food staples in the tropics, where it is the fourth most
important energy source (Alves, 2002). Its roots are the main source of calories to approximately
600 million people in Africa, Asia, Latin America and Oceania. Globally it ranks the sixth most
important source of calories in the human diet (FAO, 1999). Given the crop’s tolerance to poor
soil and harsh climatic conditions, it is generally cultivated by small-scale farmers as a
subsistence crop in a diverse range of agricultural and food systems. Roots can be left in the
ground without harvesting for a long period of time, making it a useful crop as security against
famine.
Cropping system aims at making efficient use of growth resources so that high and /or stable
productivity can be achieved (Papendick et al., 1976; Okigbo, 1982). Multiple cropping is the
most common traditional cropping system in tropical Africa. It provides the farmer with a variety
of returns from the land, often increases the efficiency of resource utilization by combining
variety of crops and reduces the risk of dependence on a single crop which may suffer from
environmental or economic fluctuations. It also gives scope for increased labour use efficiency
and provides early income (Prabhakar and Pillai, 1984).
Cropping system involving cassava is the most common throughout the humid and sub-humid
regions of Africa. Cassava is well suited to intercropping with short duration crops because of its
initial slow growth as well as its length of stay in the field (12 to 18 months). In some countrys
of Africa, it is commonly grown in association with crops like maize which exploits the microenvironment early in the growing season and melon a low canopy crop that serves a dual purpose
of protecting the soil against erosion and for weed control. The crops are selected on the basis of
differences in growth habits and can be combined in either simple or complex mixtures.
Complex mixtures consisting of three or more crop species are known to give higher financial
259
and caloric returns (IITA, 1990). Cassava is often left scattered in the field to mature after the
other crops have been harvested (Edje, 1982). However, it has been observed that the fields
become very weedy and, while a few farmers carry out weeding after harvesting the early season
crops, some plant a few stands of okra and other vegetables in the cassava farm in the late season
(Isola. 1998). Cropping could possibly be intensified with appropriate plant arrangement on the
field and by modifying cassava canopy in order to introduce a late season crop like cowpea and
beans. This will not only increase the productivity of the land, but will also prevent weed from
taking over. Results from Nyabyenda (1983) and Neuman (1984) showed that higher cassava
yield was attained when intercropped with soybean and other beans than as a sole crop. Other
reports, however, disagree with this finding (Mason et al., 1986; Balasubramanian and
Sekayange, 1990; Keating et al., 1982). However, legume crops as a source of rich protein are
particularly important if incorporated into the diets of cassava-consuming populations.
Limited availability of additional land for crop production, decreased soil fertility and declining
yield for major food crops have been cited as the major concerns for agriculture’s ability to
provide nourishment for the increasing population (Sinclair and Gardner, 1998). An advantage
commonly claimed for intercropping systems is that, they offer greater yield stability than sole
cropping (Mead and Willey, 1980). The system of intercropping is to a great extent practiced in
various ways based on the extent of spatial arrangement of the crops on the field (Oguzor, 2007).
For subsistence farmers, greater stability in the production of food crops in inter-cropping
systems is particularly meaningful since this characteristic of the production system tends to
better insure their sustainability and substantially reduces the risk of total crop loss.
In Harerghe, intercropping is well practiced and most farmers intercrop Chat with sorghum and
groundnut, but single production per year. Some lowlands of eastern part of Harerghe survive
with some grain support from government and different NGOs due to shortage of rainfall and
prevalence of recurrent drought. To such areas it is important to adapt some technologies that can
tolerate the agro-ecology and increase production per unit land, especially through intercropping
and double cropping using early maturing crops by adjusting with the agro-ecology of the area.
Therefore, intercropping of cassava with legumes crops following early maturing sorghum is an
important method in increasing production per unit land area.
The limitations of these agricultural inputs and rising pressure on the supply of arable land of the
Harerghe regions may lead to more intensive mono cropping of sorghum. Currently, farmers are
260
developing different farming systems. The only way to increase agricultural production in the
small or marginal units of farming is to increase the productivity per unit time and area.
Cropping system and practices in turn could help combat pests. Understanding the association of
disease intensity with cropping systems, crop combinations and management practices will help
to identify the most important variables and focus efforts in developing an integrated and
sustainable management packages. Therefore, this study was aimed to determine the
compatibility of crops in double cropping system and efficient land use for the crops.
Materials and Method
Description of the Experimental Site
The study was conducted under rainfed conditions at Fedis Agricultural Research Center of
Oromia Agricultural Research Institute (OARI) at Boko sub-site, which is located at the latitude
of 9o 07’ north and longitude of 42o 04’ east, in the middle and lowland areas and at altitude of
1702 meter above sea level. The area is situated at the distance of about 24 km from Harar town
in the southern direction.
The soil of the experimental site is black with surface soil texture of sand clay loam that contains
8.20% organic matter; 0.13 % total nitrogen, available phosphorus of 4.99 ppm, soil
exchangeable potassium of 1.68 cmol(+)/kg and a pH value of 8.26 (Table 1). The experimental
area is characterized as lowland climate. The mean rainfall is about 859.8 mm for the last ten
years and has a bimodal distribution pattern with heavy rains received often from April to June
and long and erratic rains from August to October. The mean maximum and minimum annual
temperature are 27.7 and 11.3oC, respectively for the last five years (Fedis Agriculture Research
Center Metrological Station).
The total rainfall distribution during the cropping seasons were 883.8, 1022.2 and 728.7mm in
the years 2016, 2017 and 2018, respectively (fig 1). The first rain set is from March to May and
the second is from August to September. The preceding crops (Cassava + legumes) were planted
in the first week of April during the onset of rainfall and cassava based intercropped legumes
were harvested in the last week of July in the first two years. The succeeding crops (cassava +
sorghum), sorghum was planted with the shower of rainfall, after one week of legumes harvested
in the beginning of August in the first two years.
261
Rainfall Distribution
(mm)
400
2016
2017
2018
300
200
100
0
Figure 1. Rainfall distribution during the three years of cropping seasons
Experimental Treatments and Design
The experiment had two phases: intercropping cassava with legumes and Cassava-based
intercropping of early maturing and striga tolerant sorghum varieties (Gedo, Hormat and Birhan).
Field experiment was conducted using seven treatments for each phase and laid out in
Randomized Complete Block Design in three replications. Cassava cuttings were planted at 1 m
and 1.2 m between plants and rows, respectively. Two rows of legume crops were planted at 40
cm apart from cassava plant rows. Seeds of legumes (haricot bean, soy bean and cow pea) were
planted at 10, 5 and 10 cm, respectively. Both crops were planted at a time during the first
shower of rainfall. In the second phase after legumes were harvested, sorghum varieties were
sawn between cassava in two rows as in the case of legumes. Sorghum was planted 30 cm apart
from the two rows of cassava plants and 40 cm spacing between the two rows of sorghum.
The experiment had two cropping cycles.
First cropping cycle: Cassava + Legumes Second cropping cycle: Cassava + sorghum
1.
Cassava +Haricot bean
1.
Cassava + Gedo
2.
Cassava + Soybean
2.
Cassava + Hormat
3.
Cassava + Cowpea
3.
Cassava + Birhan
4.
Sole Cassava
4.
Sole Cassava
5.
Sole Haricot bean
5.
Sole Gedo
6.
Sole Soybean
6.
Sole Hormat
7.
Sole Cowpea
7.
Sole Birhan
Data Management and Statistical Analysis
Data of each crop were taken randomly from taged plants per experimental unit (plots). The
following data were collected for each crop.
Cassava data: Field stand count, plant height, number of branches, canopy diameter, root length,
root diameter, number of root per plant, average root weight and root yield.
262
Legumes data: seed per pod, pod per plant, hundred seed weight and yield
Sorghum data: field stand count, plant height, panicle length, thousand seed weight, grain yield
Root yield of cassava was weighed using digital balance after harvest, and grain yield of haricot
bean and sorghum were also weighed using ordinary balance. The collected data were subjected
to ANOVA using GenSTAT Software version 15th edition.
Land use efficiency was determined by calculating Land Equivalent Ratio (LER) using (Mead
and Willey 1980) method. Land equivalent ratio of cassava is calculated as intercrop yield of
cassava/sole stand yield of cassava and that of haricot bean and sorghum is calculated as
intercrop yield of haricot bean and/or sorghum/sole stand yield of haricot bean and/or sorghum.
The competitive value was determined by calculating the ratio of the individual LER’s of the
three crops.
Results and Discussions
Cassava-Legumes Intercropping on Root Yield Parameters
The experiment was conducted to evaluate cassava-based double cropping of different
component crops. The results revealed that all growth and yield parameters of cassava were
significantly (P ≤ 0.05) affected due to intercropping except number of branches and root length.
Parameters like average root weight, number of roots per plant and root diameter were
statistically paired for the treatments, except for cassava-cowpea intercropping that was the
lowest value for the parameters (Table 1). Cassava-cowpea intercropping significantly affected
average root weight, root numbers and root diameter. Soybean-cassava intercropping increased
average root weight, root numbers and root enlargement of cassava by 39, 33.6 and 27.7 % as
compared to cassava-cowpea intercropping. Cassava-soybean intercropping increased root yield
by 41.7% and 21.3% as compared to cassava-cowpea and cassava-haricot bean, respectively
(Table 2).
Cassava-cowpea intercropping was significantly decreased cassava root yield as compared to
other legumes intercrop as cowpea had greater leaf canopy that the other legumes and better
competitor for resources as compared to other legumes. This study was in line with Polthanee, et
al., (2007) who reported that cassava inter-cropped with cowpea decreased root yield by 11 to
17%.
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Table 1. Effect of cassava based-legumes-sorghum double cropping on root yield parameters of
cassava.
Treatments
Cassava + Soybean
Cassava + Haricot bean
Cassava + Cowpea
Sole Cassava
LSD(0.05)
CV (%)
ARW (g)
914.70a
816.50ab
557.80b
862.70ab
294.700
18.7
NBPP
3.13
2.60
3.20
2.53
NS
23.5
NRPP
9.80a
9.40a
6.51b
8.47a
1.935
11.3
RD (cm)
5.91a
5.32ab
4.27b
5.61a
1.190
11.3
RL(cm)
46.43
51.93
50.40
49.10
NS
14.9
ARW=Average Root Weight, NBPP=Number of Branches Per Plant, NRPP=Number of Roots Per Plant, RD=Root
Diameter, RL=Root Length.
Cassava - Legumes Intercropping
The result indicated that pure stand of haricot bean was significantly different in grain yield from
intercropping of the same crop. Pure stand of haricot bean provided grain yield of 33.5% over the
intercrop of the same crop. However, the grain yield of legumes intercropped with cassava was
additional benefit for the cassava production land. Intercropping of soybean and cowpea with
cassava did not significantly affected grain yield of same crops as compared to pure stand.
Table 2. Effect of cassava-based legumes and sorghum double intercropping on root (tons ha-1)
and grain yields (kg ha-1) of component crops over the two years.
Treatments
Cassava + Soybean + Hormat
Cassava + Cowpea + Birhan
Cassava + Haricot bean + Gedo
Sole Cassava
Sole Haricot bean
Sole Soybean
Sole Cowpea
Sole Gedo
Sole Hormat
Sole Birhan
LSD (0.05)
CV(%)
Cassava Root
yield
51.36a
29.94c
40.40b
54.28a
------9.5
17.8
Preceding crop
Legumes Grain yield
1618ab
1446b
1406b
-2114a
2018ab
1589ab
---453
26.5
Succeeding crop
Sorghum grain
yield
804b
882b
820b
----1292a
1059ab
1034ab
254.1
25.7
Cassava based-Sorghum Intercropping
In cassava based double cropping, sorghum varieties were followed by legumes and significant
differences were observed between pure stand and intercrops. Pure stand of Gedo sorghum
variety was significantly different from intercrop of the same crop for grain yield. However, the
yield of sorghum varieties intercropped with cassava was low; it might be the competition of
cassava with sorghum for moisture and or soil nutrients because of the shortage of rainfall
264
distribution (241.8mm and 279.7mm, total rainfall for four months of sorghum growing life) in
2016 and 2017 cropping season, respectively. Sorghum yield was declined due to shortage of
rainfall after September in both years in 2016 and 2017. However, the intercrops were
significantly efficient in land use economy. In other way, aboveground sorghum stalk was also
used for cattle feed as farmers' of Harerghe need different forage crops for fattening.
Land Equivalent Ratio (LER)
The land area and yield advantage obtained due to mixed cropping was calculated as land
equivalent ratio (LER). This study showed that intercropping legumes with cassava recorded
land equivalent ratio of more than 1 and was beneficial in land productivity as compared to pure
stand. Accordingly, pure stands could required 74, 40 and 46 % more land i.e. the mixture
cropping gives 76, 51 and 15% yield advantage for soybean, haricot bean and cowpea,
respectively intercropped in cassava than pure stand of these crops. Cassava-soybean
intercropping improved land use efficiency by 16.4 and 19.3 % as compared to cassava-cowpea
and cassava-haricot bean intercropping, respectively. Intercropping led to greater LER compared
with sole cropping. Despite individual yields of component crops being lower under
intercropping compared with sole cropping, the overall land productivity was greater under
intercropping. Similar results have been reported across diverse environments and cropping
systems (Dapaah et al., 2003; Okonji et al., 2007; Ennin and Dapaah, 2008).
Sorghum was cassava-based double cropped following legume crops. Intercropping cassava
based double cropping was advantageous than pure stand of cassava. Sorghum grain yield was
also additional benefit as it was intercropped in cassava following the legume crops. The result
showed that sorghum intercropping in cassava following legume crops was advisable. Because
sorghum benefited additional income from the bare space in cassava and even used as forage.
Table 3. Land equivalent ratio of legume crops and cassava intercropped in the first cropping
cycle
LER
Legumes
Sole crop
Intercrop
Partial LERL
Partial LERC
Soybean
2018
1618
0.802
0.946
1.748
Haricot bean
2114
1406
0.665
0.744
1.409
Cowpea
1589
1446
0.910
0.551
1.461
LERL= Land Equivalent Ratio of Legumes, LERC = Land Equivalent Ratio of Cassava
Table 4. Land equivalent ratio of sorghum varieties and cassava intercropped in the second cropping cycle
Sorghum
Sole crop
Intercrop
Partial LERS
Partial LERC
LER
Hormat
1058.5
803.7
0.759
1.705
0.946
Gedo
1292.3
820
0.634
1.378
0.744
Birhan
1033.8
881.5
0.852
1.403
0.551
LERS= Land Equivalent Ratio of Sorghum, LERC = Land Equivalent Ratio of Cassava
265
Soil Fertility Improvement
Mixture cropping lead to the competition of moisture and nutrients in the soil among the crops.
However, cropping of non-nitrogen fixing crops with nitrogen fixing legume crops can improve
soil fertility. The highest Organic matter and total nitrogen was recorded for the pure stand plot
of cassava followed by cassava-soybean intercropping. Accordingly, the soil of these two plots
had good structural conditions and high structural stability (Emerson, 1991) that might increased
root yield of cassava. According to this study the highest competitor for nutrients was cowpea
intercropped in cassava following haricot bean. This result was in line with the study of Ogola et
al (2013) who reported that cassava-cowpea intercropping was better competitor for resources
compared as to other legumes. The phosphorous was very low across all plots according to the
range of Holford and Cullis (1985) and high exchangeable potassium (Abbott, 1989).
Table 5. Plots based soil chemical analysis
S/N
1
2
3
4
5
6
7
Sampling plots
Cassava + Soybean
Cassava + Haricot bean
Cassava + Cowpea
Sole Cassava
Sole Soybean
Sole Haricot bean
Sole Cowpea
EC
0.17
0.14
0.19
0.17
0.14
0.15
0.15
OM
4.01
3.01
2.53
4.37
4.18
3.82
2.73
pH
7.10
8.30
8.15
8.18
8.30
8.10
7.88
TN
0.24
0.15
0.13
0.25
0.16
0.17
0.13
Avail. P
2.72
4.52
9.00
5.52
1.08
1.28
1.72
Exch. K
129.00
125.50
123.00
129.50
125.00
127.00
127.00
pH (soil to water ratio 1:25) by pH meter, EC (soil to water ratio 1:25) by electro conductivity meter, OM(Organic
Matter by %), Exch. K (cmol (+) kg-1 soil), Avail. P (mg kg-1 soil), TN (Total Nitrogen by %).
Conclusion and Recommendation
Incorporation of grain legumes into the cassava-based cropping systems could enhance overall
productivity of the systems in this dry environment of east Harerghe zone. In this study, soybean
intercropped with cassava did not affect the root yield of cassava. Intercropping soybean in
cassava advanced about 74% yield advantage, that means the pure stand could required 74%
more land as compared to the mixture. Cassava also did not significantly affected grain yield of
soybean intercropped in cassava as compared to pure stand of soybean. Because of the long
duration of cassava roots maturity, drought and disease problems, intercropping grain and
legumes in cassava should be developed. Producing cassava for dual-purpose as root yields and
hay offers a good source of fodder for dairy cows. Cassava tuber is very low in protein content
and inclusion of a pulse crop is quite significant from the point of view of balanced nutrition in
Harerghe. Therefore, from this result, cassava-soybean intercropping following early maturing
sorghum was recommended for the study area and similar agro- ecologies.
266
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Effects of Seed Rate, Row Spacing and Phosphorous fertilizer on yield and yield
components of fenugreek (Trigonella foenum-graecum L.) in Bale mid lands, Oromia
1
1
Chala Gutema
Oromia Agricultural Research Institute, Sinana Agricultural Center; PO.Box:208;Bale-Robe,
Ethiopia
*Corresponding Author:chalagutema@gmail.com
ABSTRACT
Soil nutrient depletion and poor agronomic practices are the major production constraints of
fenugreek in mid-land of Bale Zone in Oromia. Therefore, a field experiment was conducted with
the objectives of assessing the effect of phosphorous fertilizer rates, seed rates and row spacing
on yield components and seed yield of fenugreek. The treatments consisted of factorial
combinations of four levels of P2O5 fertilizer application (75, 90, 105 and 120 kg P2O5 ha-1),
three seed rates (25, 30, and 35 kg ha-1) and three row spacing (20, 25, and 30 cm) in
Randomized Complete Block Design (RCBD) with three replications. The main effects of
phosphorus application significantly affected days to 50% flowering, days to 90% physiological
maturity, number of pod per plant, above ground dry biomass and harvest index, while plant
height was affected by seed rate and row spacing. The shortest (54.43) days to 50% flowering
and days to 90% physiological maturity (117.7) were recorded from phosphorus application at
120 kg P2O5 ha-1rate. The highest number of pod per plant (14.49) and harvest index (26.82%)
were obtained from (120, 105 and 25, 35) kg P2O5 ha-1and kg ha-1 seed rate, respectively. The
268
tallest plant height (44.18 cm) was recorded from 25 kg ha-1 seed rate and 30 cm row spacing.
The highest biomass yields (7550, 6661 and 6561 kg ha-1) were obtained from 120 kg P2O5 ha-1,
25 kg ha-1 seed rate and 25 cm row spacing, respectively. The interaction of P2O5 fertilizer, seed
rate and row spacing significantly affect number of primary branches and seed yield while days
to 90% physiological maturity and number seed per pod did not affected by P2O5 fertilizer, seed
rate and row spacing nor by their interaction. The highest number of primary branches per plant
(3.95) was recorded from 90 kg ha-1 P2O5 fertilizer, 25 kg ha-1 seed rate and 25 cm row spacing
while the highest seed yield (1966 kg ha-1) was obtained from 120 kg P2O5 fertilizer ha-1, 25 kg
ha-1 seed rate and 25 cm row spacing. The partial budget analysis also revealed that the highest
net return (39164.17 Birr ha-1) with MRR 10971.43% was obtained from application of 120 kg
P2O5 ha-1 treated under 25 cm row spacing and 25 kg ha-1 seed rate. Based on the result, it can
be tentatively concluded that 120 kg P2O5 ha-1coupled with 25 kg ha-1 seed rate and 25 cm row
spacing to be appropriate for fenugreek production in study areas.
Key words: fenugreek, seed rate, row spacing, seed yield, Phosphorus
INTRODUCTION
Fenugreek (Trigonella foenum-graecum L.) belongs to the genus Trigonella and family
Fabaceae. The genus consists of approximately 70 species that are annuals and native to
Southern Europe (Engles et al., 1991). In Ethiopia, fenugreek is consumed by nursing mothers,
who consume large quantities of pulses to maintain the supply of breast milk (Smart, 1976).
There is a large genetic diversity of the fenugreek in Ethiopia (Feysal, 2006). Asfaw et al. (1981)
reported that, fenugreek is found in nearly every market in Ethiopia and has been cultivated in
Ethiopia since ancient times for use as a food, spices and medicinal purpose. In Ethiopia,
fenugreek-growing regions are the high plateaus (1800-2300 m a.s.l.) characterized by
subtropical climate of wet and dry seasons (Westphal, 1974). Fenugreek is grown on wide range
of soils but flourishes on well drained loams or sandy loam.
Plant density varies according to the cultivar, yield capacity of the soil, irrigation condition and
cultivation objectives. Optimum row spacing and seed rate play an important role in contributing
to the high yield, because, thick plant population do not get proper light for photosynthesis and
can easily be attacked by diseases and other pests on the other hand, very small population will
also reduce the yield. Fenugreek is very important spice crop but the productivity of this crop is
very low. Not only for maximizing the productivity but also for securing the highest net returns
from a unit area, maintaining optimum plant population per unit area, which depends on best
possible fertilizer, seed rate and spacing, is considered very essential (Miahet al., 1990).
However, very little research work has been done on P2O5 fertilizer, seed rate and spacing of
269
fenugreek. Hence, there is a considerable scope of increasing the productivity of this commercial
crop by adopting the improved management practices along with optimum seed rate and spacing.
Therefore, the objectives of this study were:
i) To assess the effects of seed rate, row spacing and P fertilizer rates on seed yield and yield
components and of fenugreek; and
ii) To identify economically feasible seed rate, row spacing and P fertilizer for fenugreek
production.
MATERIALS AND METHODS
2.1. Description of Study area: the experiment was conducted at Ginir and Goro during ‘Bona’
main cropping season under rain fed condition for three years (2015-2017). Ginir is located 519
km away from Addis Ababa to South eastern, 86 km away from the zonal capital town, Robe. It
is located at 07o 15′ N latitude and 40° 66′ E longitude at 1972 m above sea level (Wubishet et
al., 2016). Goro is located in Bale zone at 708’N and 40011’ E, at 2396 meter above sea level
(m.a.sl) and 473 km Southeast of Addis Ababa. It receives an annual average rain fall of 325.78
mm during the Bona cropping season. Both areas have bimodal rainfall patterns. Based on this,
there are two separate crop growing seasons locally called bona and gana. The main season bona
extends from September to November and gana from March to May. The soil type for both area
is Vertisols. The major crops grown widely in the area are cereals (wheat, barley, maize and tef)
pulses (chickpea, field pea, faba bean, and lentil), seed spices (black cumin, coriander and
fenugreek) and vegetables (onion, garlic, potato and tomato) under rain fed and irrigation.
2.2. Treatments and Experimental Design
The treatments consisted of factorial combination of four P2O5 fertilizer rates (75, 90, 105 and
120 kg ha-1), three seed rates (25, 30 and 35 kg ha-1) and three row spacing (20, 25 and 30 cm) in
factorial combinations. The experiment was laid out in a randomized complete block design in
three replications. The fenugreek variety Ebisa which was released by Sinana Agricultural
Research Centre (SARC) in 2006 was used as planting material while TSP is used as the source
of fertilizer.
2.3. Experimental Procedure and Field Management
The experimental field was ploughed and disked by tractor and pulverized to a fine by hand
digging. Blocking and the required number of rows were marked in each plot according to the
spacing proposed and rows were made to plant the seeds. The gross plot size of 1.2 m × 2 m (2.4
270
m2) which contains four rows and the seeds were seeded at required spacing. The two middle
rows were used for data collection. Weeding and other agronomic practices were applied as
required by the crop.
2.4. Data Collected and Measurement
2.4.1. Crop phenology and growth parameters
Days to 50% of flowering (DF): it was recorded as the number of days from date of emergence
to the appearance of first flower in 50% of the plants based on visual observation.
Days to 90% maturity (DM): days to maturity was recorded by counting days from emergence
to days on which about 90% of the plant on plot attained physiological maturity (leaves and
capsules turned to yellowish-green colour) based on visual observation.
Plant height (PH): Height of five randomly taken plants during physiological maturity period
from each net plot was measured from ground to top of the plant by centimeters (cm).
Number of primary branches per plants: the number of primary branches in five randomly
selected pre tagged plants was recorded at physiological maturity and their average was
expressed as number of primary branches per plant.
2.4.2. Yield components and yield
Number of pod per plant: Number of pods was counted on five randomly taken plants from
each of the net plot at harvest and the mean was expressed as number of pod per plant.
Number of seeds per pod: Total number of pods from five randomly taken plants was threshed
and number of seeds was counted and total number of seeds was divided by total number of pods
to compute average number of seeds per pod.
Aboveground dry biomass yield (kg ha-1): At physiological maturity, plants from the central
two rows of net plot size 0.6 x 2 m (1.2 m2) were manually harvested close to the ground surface.
The harvested plants were sun-dried in an open air, weighed to determine above ground plant
biomass yield.
Seed yield: The central two rows were harvested and threshed to determine seed yield and the
yield was adjusted to moisture level of 10% and yield per plot was converted to per hectare.
Harvest index: Harvest index was recorded as the ratio of dry seed yield to the aboveground
biomass yield per plot.
271
2.5. Statistical Data Analysis
All crop data collected were subjected to analysis of variance (ANOVA) procedure using
GenStat 16th edition software (Gen Stat, 2013). Comparisons among treatment means with
significant difference for measured characters were done by using Fisher’s protected Least
Significant Difference (LSD) test at 5% level of significance.
2.6. Economic Analysis
Yield from experimental plots was adjusted downward by 10% for management difference, to
reflect the difference between the experimental yield and the yield that farmers could expect
from the same treatment. Accordingly, the mean seed yields for the treatments were subjected to
a discrete economic analysis using the procedure recommended by CIMMYT (1988). Total
variable cost (TVC) (ETB ha-1) was calculated by summing up the costs that vary, including the
cost of TSP, seed rate, row planting and the application costs of TSP. Based on partial budget
procedure described by CIMMYT (1998), the variable costs including the TSP fertilizer price
(39 ETB kg-1), fenugreek seed (25 ETB kg-1) and Labor cost involved for application of TSP
fertilizer (4 persons ha-1 for 75 and 90 kg P2O5 ha-1, 5 persons ha-1 for 105 and 120 kg P2O5 ha-1
each 35 ETB day-1), for row planting (4 persons ha-1 for 20 x 10 cm), (3 persons ha-1 for 25 x
10 cm) and (2 persons ha-1 for 30 x 10 cm) and for seeding (3 persons ha-1 for 25 kg), (4 persons
ha-1 for 30 kg) and (5 persons ha-1 for 35 kg) each 35 ETB day-1) for each treatment was recorded
and used also for this analysis. The costs of other inputs and production practices such as labor
cost for land preparation, planting, weeding, harvesting and threshing were considered the same
for all treatments or plots.
RESULTS AND DISCUSSION
3.1. Crop phenology and Growth Parameters
3.1.1. Days to 50% flowering
Number of days to 50% flowering was highly significantly (p<0.01) affected by the main effects
of P205 fertilizer, while, the main effects of seed rate, row spacing and the interaction of P2O5
fertilizer, row spacing and seed rate were not influenced this parameter. Increasing the rate of
P2O5 fertilizer from nil to 120 kg ha-1 significantly decreased the number of days required to
reach 50% flowering rate from 55.57 days to 54.43 days (Table 1).The decrease in days to
flowering at the highest P2O5 fertilizer might be due to the fact that phosphorus enhances
reproductive phase through fastened flowering. In line with this result, Gifole et al. (2011)
272
reported that phosphorus application to haricot bean significantly reduced days to flowering.
Similarly, Acharya et al. (2007) reported that P is important for flowering and seed formation
and fastening crop maturity.
3.1.2. Days to 90% physiological maturity
The analysis of variance showed that main effect of P2O5 fertilizer was highly significantly (p<
0.01) influenced the number of days required to reach physiological maturity. However,
significant variation was not observed due to the seed rate, row spacing, two and three way
interactions. Increasing the rate of P2O5 fertilizer significantly decreased the duration required to
reach physiological maturity. Thus, plants with low application of the P2O5 fertilizer required the
longest number of days (117.7 days) to reach physiological maturity, whereas those treated with
the highest rate of P2O5 fertilizer (120 kg ha-1) required the lowest days (112.1 days) to reach
physiological maturity (Table 1). The decreased number of days required to reach physiological
maturity in response to increased rates of P2O5 fertilizer may be attributed to the enhanced
availability of the nutrient in the soil and its increased uptake by the fenugreek plants, which
might have resulted in a more luxurious vegetative growth that resulted in delayed maturity. This
result is in line with that of Abera (2015), who reported that days to 90% physiological maturity
of chickpea was highly significantly (p<0.01) affected by application of P fertilizer rate, where,
the longest time to maturity (114 days) was recorded for the application of 20 kg P 2O5 ha-1.
Similar effects were also reported earlier where inoculation and P application delayed maturity
time of common bean (Buttery et al., 1987) and chickpea (Gan et al., 2009).
3.1.3. Plant height
The analysis of variance showed that the main effect of row spacing was highly significant
(p<0.01) on plant height. Similarly, significant variation (p<0.05) was observed due to seed rate.
However, the interaction between P2O5 fertilizer, row spacing and seed rate did not significantly
affect this parameter (Table 1). The highest plant height (44.18cm) and (43.65 cm) were
recorded from row spacing of 30 cm and seed rate of 25 kg ha-1, respectively while the lowest
plant height (41.41 cm) and (42.17 cm) were recorded from 20 cm row spacing and 35 kg ha-1
seed rate, respectively. Plant height increased with increasing row spacing. On the other hand,
plant height decreased assed rate increasing. This might be due to intra-specific competition for
the sunlight resulting in shorter plants. This trend explains that as the number of plants increased
in a given area, the competition among the plants for nutrients uptake and sunlight interception
273
also increases. Similarly, Baswana and Pandita (1989) reported that plant height decreased with
increased row spacing in fenugreek. Singh et al. (2005) reported higher plant heights in 22.5 cm row
spacing while Halesh et al. (2000) and Gowda et al. (2006) obtained the highest plant heights
from the 30 cm row spacing for fenugreek. The sparsely sown crop spreads more than the closely
spaced which tends to grow in up right direction (Singh et al., 2012).Moniruzzaman et al. (2013)
reported the plant height and number of leaves/plant was found to be the highest in lower seed
rate 30 kg ha-1 (19.34 cm) and lower in the maximum seed rate 50 kg ha-1 (19.16 cm) in
coriander.
Table 1. Main effects of P2O5fertilizer, seed rate and row spacingon days to 50% flowering, days
to 90% maturity, and plant height of fenugreek
Means followed by the same letter(s) in the table are not significantly different at 5% level of
Treatment
Days to 50%
Days to 90% maturity
Plant Height
flowering
Rate of P2O5(kg ha-1)
75
55.57a
117.7 a
42.95
90
55.13 ab
116.1 b
43.44
105
54.52 bc
113.9 c
43.38
120
54.43 c
112.1 d
42.67
LSD
0.6
0.4
NS
Seed Rate (kg ha-1)
25
54.75
115.1
43.65a
30
55.10
114.9
43.51a
35
54.89
114.9
42.17b
LSD
NS
NS
4.31
Row spacing (cm)
20
55.07
114.8
41.41b
25
54.83
115.0
43.73a
30
54.83
115.0
44.18a
LSD
NS
NS
2.15
CV (%)
3.0
0.8
8.8
significance; LSD=Least significance difference at 5% probability level; CV=Coefficient of variation.
3.1.4. Number of primary branches per plant
The main effect of seed rate was highly significant (p<0.01) and the interaction of P2O5, seed rate
and row spacing were significant (p<0.05) on the number of primary branches produced per
plant. However, the main effect of row spacing and P205 fertilizer didn’t influenced this
parameter. The highest number of primary branches per plant (3.95) was recorded from 90 kg ha1
P2O5 fertilizer,25 kg ha-1seed rate and 25 cm row spacing while the lowest number of primary
branches per plant (2.82) was recorded from 75 kg ha-1 of P2O5 fertilizer, 35 kg ha-1 seed rate and
30 cm row spacing (Table 2).The above result can be attributed to reduced competition among
plants for growth factors due to wider spacing between plants and medium seed rate. These
274
results were confirmed with the findings of Brar et al. (1993a) who also registered the highest
number of branches per plant with a seed rate of 15kg/ha. Similarly, Brar et al. (2005) and Singh
et al. (2005) recorded that sowing of fenugreek seed at a row spacing of 22.5 cm gave
significantly higher number of branches per plant. Low competition among plants for growth
factors such as moisture, nutrients, and light, coupled with genetic potential of fenugreek plants
produced more branches. According to Khan et al. (2017) higher number of branch plant-1 might
have also been possible due to vigorousity and strength of the plants that were attained as a result
of better photosynthetic activities with sufficient availability of growth factors due to reduced
competition. Similar, results were reported by Mohamed (1990), Halesh et al. (2000) and Gowda
et al. (2006) for the number of branches in fenugreek.
Table 2. The interaction effect of P2O5 fertilizer rate, row spacing and seed rate on number of
primary branch of fenugreek
P2O5 rate (kg ha-1) Row spacing (cm) Seed rate (kg ha-1)
20
75
25
30
20
90
25
30
20
105
25
30
20
120
25
30
LSD0.05
P x RS x SR= 0.41CV (%) = 10.0
Means followed by the same letter(s) in the table are not
significance; P= P2O5 fertilizer rate; LSD=Least significance
CV=Coefficient of variation.
25
3.5 abcde
3.65 abcde
3.73 abcde
3.62 abcde
3.95 a
3.32 de
3.68 abcde
3.58 abcde
3.62 abcde
3.82 abcd
3.75 abcde
3.67 abcde
30
3.57 abcde
3.68 abcde
3.73 abcde
3.88 ab
3.90 ab
3.85 abc
3.40 bcde
3.52 abcde
3.58 abcde
3.53 abcde
3.77 abcde
3.53 abcde
35
3.60 abcde
3.52 abcde
2.82 f
3.42 bcde
3.35 cde
3.47 abcde
3.35 cde
3.33 de
3.62 abcde
3.32 de
3.60 abcde
3.28 e
significantly different at 5% level of
difference at 5% probability level and
3.2. Yield Components and Seed Yield
3.2.1. Number of pods per plant
The analysis of variance showed the main effect of P2O5fertilizer and seed rate were highly
significant (p<0.01) affect the number of pods per plant while the main effect of row spacing and
the interaction among P2O5fertilizer, seed rate and row spacing did not show significant effect.
The highest number of pods per plant (14.49) was obtained from 120 kg P2O5 ha-1, however; it
was statistically at par with P2O5 rates of 105 and 95 kg ha-1 while the lowest number of pod
number per plant (12.80) was obtained from 75kg ha-1 of P2O5fertilizer (Table 3). This might be
275
due to adequate availability of N and P which might have facilitated the production of more
primary and secondary branches and plant height, which might, in turn, have contributed for the
production of higher number of total pods. This result is in line with the findings Ali et al. (2004)
who reported that increased number of pods per plant of chickpea by seed inoculation and P
fertilization. Zafar et al. (2003) have also reported that phosphorus fertilization showed
significant increase in number of pods per plant of lentil due to the cumulative effect of
phosphorus in the processes of cell division and balanced nutrition. On the other hand, the
highest (14.99) number of pod per plant was due to seed rate of 25 kg ha-1, while the lowest
(12.56) was from 35 kg ha-1(Table 3).The number of pod plant-1 produced under low seed rate
was significantly higher than that grown under high seed rate. Low seed rate allows wider
spacing to produce a large number of pods per plant due to enough access of plants to nutrients,
sunlight, water and other growth requirements. Brar et al. (1993a) received significantly higher
number of pod per plant (31.47) with a seed rate of 15 kg ha-1, which was statistically at par with
a seed rate of 20 (28.67) and 25 kg ha-1 (26.22).
3.2.2. Number of seeds per pod
The main effects of P2O5fertilizer, seed rate and row spacing and their interaction effects werenot
significant on number of seeds per pod (Table 3)
3.2.3. Above ground biomass
The analysis of variance showed that the main effect of P2O5 fertilizer was highly significant
(p<0.01) on aboveground biomass. Similarly, significant variation (p<0.05) were observed due to
seed rate and row spacing. However, the interaction among P2O5 fertilizer, seed rate and row
spacing did not significantly affect this parameter. The highest biomass yields (7550, 6661 and
6561 kg ha-1) were obtained from 120 kg P2O5 ha-1, 25 kg ha-1 seed rate and 25 cm row spacing,
respectively (Table 3). The increase in biomass yield at maximum rate of P2O5 fertilizer may
indicate that these nutrients play synergistic role in metabolism, chlorophyll formation, and
photosynthesis of the plant which in turn increases the biological yield (Fageria, 2009). This
result is in agreement with that of Alemu (2009) who reported that highest biomass yield (6508.9
kg ha-1) of fenugreek was obtained from 26 kg P ha-1. Similar effects of seed rate on biological
yield of fenugreek observed by Taneja et al. (1985). Fenugreek crop was sown at a spacing of
30.0 x 10.0 cm increased stover yield by 6.4% over crop sown at a spacing of 22.5 x 13.3 cm
observed by Chaudhary (2006).
276
Table 3. Main effects of P2O5fertilizer, seed rate and row spacingon number of pod per plant,
number of seeds per pod and above ground biomass of fenugreek
Treatments
Number of
pod plant-1
number of seeds
per pod
Above ground biomass (kg ha-1)
Rate of P2O5(kg ha-1)
75
12.80 b
15.86
5103 d
90
13.95 a
16.42
5946 c
105
14.14 a
16.15
6888 b
120
14.49 a
15.72
7550 a
LSD
0.80
NS
526.0
Seed Rate (kg ha-1)
25
14.99 a
16.29
6661 a
30
13.98 b
13.98
6393 ab
35
12.56 c
12.56
6060 b
LSD
0.70
NS
455.5
Row spacing (cm)
20
13.43
16.04
6525 a
25
14.24
16.21
6561 a
30
13.87
15.86
6028 b
LSD
NS
NS
455.5
CV (%)
15.3
10.3
21.7
Means followed by the same letter(s) in the table are not significantly different at 5% level of
significance; P= P2O5 fertilizer rate; LSD=Least significance difference at 5% probability level and
CV=Coefficient of variation.
3.2.4. Seed yield
The analysis of variance showed that the main effect of P2O5fertilizer and row spacing were
highly significant (p<0.01) and the interaction among P2O5fertilizer, seed rate and row spacing
significantly (p<0.05) influenced the seed yield. However, there was no significant variation
among the seed rate of P2O5 fertilizeron the seed yield. The highest seed yield (1966 kg ha-1) was
recorded from 120 kg P2O5fertilizer ha-1, 25kg ha-1 seed rate and 25 cm row spacing while the
lowest seed yield (1092 kg ha-1) was recorded from 90 kg P2O5fertilizer ha-1, 35 kg ha-1 seed rate
and 30 cm row spacing (Table 4). The yield increase with increased rate of P2O5fertilizerrate
might be due to cumulative effect of more grain filling percentage and more number of seeds per
pod due to the increased nutrient uptake by the plants might have stimulated the rate of various
physiological processes like growth and assimilation of nutrients. In line with this result, Tolanur
and Badnur (2003) reported the highest seed yield (2379 kg ha-1) in chick pea by application of
mineral and organic fertilization. Chaudhary (2006) reported that the maximum seed yield was
recorded with a seed rate of 25 kg ha-1in fenugreek. The increase in seed yield due to population
277
might be related to contribution of P to profound branching, better fruiting, increased number of
seeds pod-1 and heavier grains that contributed to increased seed yield (Ahmadet al.,
2015).Sharma (2000) reported that the highest seed yield in fenugreek was obtained when seed is
sown at spacing of 30 x 7.5 cm.
Table 4. The interaction effect of P2O5 fertilizer rate, row spacing and seed rate on seed yield (kg
ha-1) of fenugreek
P2O5 rate (kg
ha-1)
75
Row spacing (cm)
Seed rate (kg
ha-1)
25
30
35
1186 n
1389 ijk
1206 lmn
1494ghij
1520 fghij
1092 n
1443 hij
1596 efgh
1494ghij
1836 ab
1767 bcd
1686 bcdef
20
1094 n
1199 mn
25
1105 n
1359 jklm
30
1253 klmn
1250 klmn
20
1551fghi
1574 fgh
90
25
1366 jkl
1461 hij
30
1400 ijk
1189 n
20
1598
efgh
1428 hij
105
25
1639 defg
1653 cdefg
30
1586 efgh
1507 ghij
20
1810 abc
1850 ab
120
25
1966 a
1820 abc
30
1794 bcd
1750 bcde
LSD0.05
P x RS x SR= 145.94 CV (%) = 8.6
Means followed by the same letter(s) in the table are not significantly different at 5% level of
significance; P= P2O5 fertilizer rate; LSD=Least significance difference at 5% probability level and
CV=Coefficient of variation.
3.2.5. Harvest index
The analysis of variance showed that the interaction of P2O5fertilizer and seed rate were
significantly (p<0.05) affect the harvest index while the main effect of P2O5fertilizer, seed rate
and row spacing did not influence this parameter. The highest harvest index (26.82%) was
observed from 105 kg P2O5 ha-1fertilizer and 35 kg ha-1seed rate while the lowest harvest index
(21.84%) was observed from 75 kg P2O5 ha-1fertilizer and 25 kg ha-1 seed rate(Table 5).The
increased HI of fenugreek at application of 105 kg P2O5 ha-1 with 35 kg ha-1seed rate might be
due to increased seed yield, number of branch per plant, number of pod per plant and thousand
seed weight applied with these treatment combinations that improved fenugreek production. In
line with this result Zafar et al. (2003) found that calculated values of harvest index showed an
increasing trend in the harvest index values with application of P on lentil and minimum harvest
index from the control plot. Similarly, Mavai et al. (2000) noticed that harvest index is
278
significantly affected by the seed rate, which was found maximum with a seed rate of 20 kg ha-1
in fenugreek.
Table 5. The interaction effect of P2O5 fertilizer and Seed rate on harvest index of fenugreek
P2O5 rate (kg ha-1)
105
25.87 ab
26.68 a
26.82 a
-1
Seed rate (kg ha )
75
90
120
25
22.30 b
26.11 ab
24.19 ab
30
22.76 ab
23.69 ab
26.12 ab
35
21.84 b
23.91 ab
24.76 ab
LSD0.05
P2O5 x SR =3.640CV (%) = 22.5
Means followed by the same letter(s) in the table are not significantly different at 5% level of
significance; P= P2O5 fertilizer rate; LSD=Least significance difference at 5% probability level and
CV=Coefficient of variation.
3.3. Economic Evaluation
Partial budget analysis of the net benefits, total costs that vary and marginal rate of returns are
presented in Table 6. The partial budget analysis showed that the highest net benefit (39164.17
ETB ha-1) was recorded from the application of 120 kg P2O5 ha-1 treated under 25 cm row
spacing and 25 kg ha-1 seed rate followed by (35324.17 ETB ha-1) due to same P2O5 and seed rate
and 30 row spacing. The lowest net returns (27262.92 ETB ha-1) was obtained from 75 kg P2O5
ha-1, 25row spacing and 30 kg ha-1 seed rate. The results in this study indicated that the use of
higher dose phosphorus fertilizer resulted in higher net benefits than the lower dose phosphorus
fertilizer (Table 6). According to CIMMYT (1988) suggestion, the minimum acceptable
marginal rate of return should be more than 100%. In this study, the combination of 120 kg P 2O5
ha-1 with 25 cm row spacing and 25 kg ha-1seed rate had marginal rate of return (MRR) of
10971.43% which is above the acceptable minimum MRR of 100% and suggests for fenugreek
production. Therefore, on economic grounds, applications of 120 kg P2O5 ha-1coupled with 25kg
ha-1 seed rate and 25 cm row spacing would be best and economical for production of fenugreek
in the study area and other areas with similar agro-ecological conditions.
Table 6. Summary of economic analysis of the effects of phosphorus application, seed rate and
row spacing.
Treatments
P2O5
(kg ha-1)
75
75
75
75
75
Seed
rate
25
25
30
25
30
USY
(kg ha-1)
Row
spacing
30
25
30
20
25
1253.24
1104.63
1250.46
1093.52
1358.80
ASY
(kg ha-1)
GFB
(ETB ha-1)
TVC
(ETB ha-1)
NB
(ETB ha-1)
1127.92
994.17
1125.42
984.17
1222.92
28197.92
24854.17
28135.42
24604.17
30572.92
3240
3275
3275
3310
3310
24957.92
21579.17
24860.42
21294.17
27262.92
MRR
(%)
D
D
D
3292.86
279
Treatments
USY
ASY
GFB
TVC
NB
MRR
-1
-1
-1
-1
-1
(kg ha ) (kg ha ) (ETB ha ) (ETB ha ) (ETB ha )
(%)
75
35
30
1205.56
1085.00 27125
3310
23815
D
75
30
20
1198.61
1078.75 26968.75
3345
23623.75
D
75
35
25
1396.30
1256.67 31416.67
3345
28071.67
2310.21
1185.65
75
35
20
1067.08 26677.08
3380
23297.08
D
90
25
30
1400.00
1260.00 31500
3825
27675
D
90
25
25
1338.43
1204.58 30114.58
3860
26254.58
D
90
30
30
1188.89
1070.00 26750
3860
22890
D
90
25
20
1551.39
1396.25 34906.25
3895
31011.25
534.47
90
30
25
1460.65
1314.58 32864.58
3895
28969.58
D
90
35
30
1114.35
1002.92 25072.92
3895
21177.92
D
90
30
20
1573.61
1416.25 35406.25
3930
31476.25
1328.57
90
35
25
1519.91
1367.92 34197.92
3930
30267.92
D
90
35
20
1493.52
1344.17 33604.17
3965
29639.17
D
105
25
30
1585.65
1427.08 35677.08
4445
31232.08
D
105
25
25
1638.89
1475.00 36875
4480
32395
167.05
105
30
30
1506.94
1356.25 33906.25
4480
29426.25
D
105
25
20
1597.69
1437.92 35947.92
4515
31432.92
D
105
30
25
1652.78
1487.50 37187.5
4515
32672.5
792.86
105
35
30
1494.44
1345.00 33625
4515
29110
D
105
30
20
1470.83
1323.75 33093.75
4550
28543.75
D
105
35
25
1595.83
1436.25 35906.25
4550
31356.25
D
105
35
20
1442.59
1298.33 32458.33
4585
27873.33
D
120
25
30
1793.52
1614.17 40354.17
5030
35324.17
514.89
120
25
25
1965.74
1769.17 44229.17
5065
39164.17
10971.43
120
30
30
1750.46
1575.42 39385.42
5065
34320.42
D
120
20
20
1810.19
1629.17 40729.17
5100
35629.17
D
120
30
25
1819.91
1637.92 40947.92
5100
35847.92
D
120
35
30
1685.65
1517.08 37927.08
5100
32827.08
D
120
30
20
1850.00
1665.00 41625
5135
36490
D
120
35
25
1766.67
1590.00 39750
5135
34615
D
120
35
20
1836.11
1652.50 41312.5
5170
36142.5
D
-1
Where, P=Phosphorus (P2O5) rate (kg ha ); USY = Unadjusted seed yield; ASY = adjusted seed yield;
GFB = gross field benefit; NB = net benefit; MRR = marginal rate of return; D = dominated treatments;
Cost of P2O5 3900.00 Birr 100 kg-1; cost for row sale price of fenugreek seed 2500 Birr per 100 kg during
harvest on farm.
SUMMARY AND CONCLUSION
The productivity of fenugreek is low because of several constraints. Among the production
constraints, imbalanced and inadequate soil nutrition and poor agronomic practices is considered
to be the important limiting factors. One of the alternatives to address such problem is supplying
well-balanced nutrients and adequate agronomic practices to meet the crop nutrient requirements.
Therefore, an experiment was conducted with the objectives of assessing the effect of P2O5
fertilizer rates, seed rates and row spacing on yield components and seed yield of fenugreek. The
280
treatments consisted of factorial combinations of fourP2O5 fertilizer application rates (75,90, 105
and 120 kg P2O5ha-1), three seed rates (25, 30, and 35 kg ha-1) and three row spacing (20, 25, and
30 cm) in Randomized complete block design with three replications. Data was collected on days
to 50% flowering, days to 90% physiological maturity, plant height, number of primary branches
per plant, number of pods per plant, number of seeds per pod, seed yield, above ground biomass
yield and harvest index. The main effects of phosphorus application significantly affected on
days to 50% flowering, days to 90% physiological maturity, number of pod per plant, above
ground dry biomass and harvest index while plant height was affected by seed rate and row
spacing. On the other hand, the interaction of P2O5 fertilizer, seed rate and row spacing
significantly affect number of primary branches and seed yield while days to 90% physiological
maturity and number seed per pod did not affected by P2O5 fertilizer, seed rate and row spacing
nor by their interaction. The shortest (54.43) and longest (55.57) days to 50% flowering were
recorded from phosphorus application at 120 kg P2O5 ha-1 and 75 kg P2O5 ha-1 rate, respectively.
The highest number of pod per plant and harvest index was obtained from (102, 105 and 25, 35)
kg P2O5 ha-1rate and kg ha-1seed rate, respectively. The tallest plant height (44.18 cm) was
recorded from 25 kg ha-1 seed rate and 30 cm row spacing. The highest biomass yields (7550,
6661 and 6561 kg ha-1) were obtained from 120 kg P2O5 ha-1, 25 kg ha-1 seed rate and 25 cm row
spacing, respectively. The highest number of primary branches per plant (3.95) was recorded
from 90 kg ha-1 P2O5 fertilizer, 25 kg ha-1 seed rate and 25 cm row spacing while the lowest
number of primary branches per plant (2.82) was recorded from 75 kg ha-1 of P2O5 fertilizer, 35
kg ha-1 seed rate and 30 cm row spacing. The highest seed yield (1966 kg ha-1) was obtained
from 120 kg P2O5 fertilizer ha-1, 25 kg ha-1 seed rate and 25 cm row spacing while the lowest
seed yield (1092 kg ha-1) was recorded from 90 kg P2O5 fertilizer ha-1, 35 kg ha-1 seed rate and 30
cm row spacing.
The economic analysis also indicated that the highest net benefit/return (39164.17 ETB ha-1) was
recorded from combined application of 120 kg P2O5 ha-1treated under 25 cm row spacing and 25
kg ha-1 seed rate with marginal rate of return (MRR) of 10971.43%. This is above the acceptable
minimum MRR of 100% while the lowest net returns (27262.92 ETB ha-1) was recorded from 75
kg P2O5 ha-1 with 30 kg ha-1 seed rate and 25 cm row spacing. Hence, it can be concluded that
application of 120 kg P2O5 ha-1coupled with 25 kg ha-1 seed rate and 25 cm row spacing is the
281
most appropriate combination for fenugreek production in the study area and similar agro
ecology.
ACKNOWLEDGMENT
Author thanks Oromia Agricultural Research Institute for funding this work and the horticulture
and seed spices technology generating team at Sinana Agricultural Research case team for them
technical and material support.
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Effects of Climate Variability on Wheat Rust (PucciniaSpp.) and Climatic Condition
Conducive for Rust at Highlands of Bale, Southeastern Ethiopia
Zerihun Dibaba*, Bikila Mengistu, Wondmagegn Bekele, Hirpa Aregay and Fikru Ameyu
Oromia Agricultural Research Institute, Sinana Agricultural Research Center, Bale Robe,
Ethiopia
*Corresponding author:zerihun.dibaba@gmail.com
ABSTRACT
Rainfall, temperature and relative humidity are the most important climatic parameters for
agricultural practices and more conducive for disease and insect developments. In this study,
effects of climate variability on wheat rust diseases and climatic condition conducive for rust
were analyzed. Cropping season weather data obtained from nearby stations were used to
analysis impacts on rust disease occurrence. The result of historical data analysis suggested
that, annual rainfall amount was increased by 9.1 mm/yr and 2.8 mm/yr at Sinana and Robe
respectively. On the other hand, the seasonal Kiremt rain was increased by 6.1 mm/yr at Sinana
station and 2.9 mm/yr at Robe station. The study results revealed that climate variability has
played a great role in agricultural practices, which in turn influences crop diseases occurrence.
In particular, it has induced wheat rust diseases over the study areas that significantly affect the
quality and quantity of the yield. The correlation between monthly rainfalls and disease severity
about -0.86, while for relative humidity and diseases severity reached 0.74 at (p= 0.05). This
condition was also true for maximum and minimum temperature with rust diseases, the
correlation analysis indicated 0.61 and 0.79 respectively (p=0.05). From weekly analysis during
284
cropping season, the climatic condition conducive for rust diseases occurrence were identified.
Therefore, the development and spread of rust is highly enhanced with maximum temperature
and minimum temperature ranges 20.8 oC to 28 oC and 8.2 oC to 11.7 oC, while relative humidity
was more than 70 % across the highland regions. In view of this condition, early warning can be
well practiced by acquiring appropriate lead-time climate-based forecasting of on the possible
occurrence of both climates and diseases on varieties of wheat crops across the Bale highlands.
Key words: Climate variability, disease, wheat rust, variety.
Back ground and justification
Ethiopia located between 30N-150N and 330E-480E within the tropical region of horn Africa.
The annual rainfall distribution in the western part of the country has one maximum during July
or August. In Ethiopia there are some regions which experiences three seasons with two rainfall
peak (one peak is more dominant than the other), while some regions have four seasons with two
distinct rainfall peaks (Bimodal type), there are still some regions with two seasons having single
rainfall peak (mono modal type). The area has bimodal rainfall pattern with the first rainy season
starting in March and taper off in July; it is locally named “Ganna” while the second rains falls
between August and December, locally known as “Bona” (Olkeba, 2011). According to Degefu
(1987), 85 to 95% of the food crop of the Ethiopia is produced during June to September period.
Kiremt rain, that falls during June–September months (JJAS) accounts for 50%–80% (Sisay,
2009). Thus, the most severe droughts are usually related to a failure of the JJAS rainfall to meet
Ethiopia’s agricultural and water resources needs, (Korecha and Barnston, 2007).
Agriculture is the most vulnerable and sensitive sector that is seriously affected by the impact of
climate variability and change (Gizachew, 2012). Due to climate variability, most of Ethiopian
economies varied from year-to-year (Sisay, 2009). The impacts of climate change on crop yields
occurring more in developing countries, compared to developed countries (World Bank
2012).The impacts of increased temperature and changes in rainfall patterns resulting to reduce
agricultural production (Valizadehet al., 2013).Weather are one of the key components that
control agricultural production. In some cases, it has been stated that as much as 80% of the
variability of agricultural production is due to the variability in weather conditions, especially for
rain fed production systems (Petr, 1991; Fageria, 1992). Weather has a major impact on plants as
well as pests and diseases. In Ethiopia, wheat has been among the major cereals of choice
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dominating the food habit and known to be a major source of energy and protein for the highland
population (Dereje and Chemeda, 2007). According to Geletuet al., (2012) Ethiopia is the second
largest wheat producer in Sub-Saharan Africa and has good wheat growing conditions. However,
In addition to moisture stress, heat stress, frost, and salinity, wheat production is hindered by
three different types of rust diseases (EATA Group, 2012). The stem rust reduce the wheat yield
up to 100%.Yellow rust can losses the wheat yield by 50-100%.whereas Leaf rust reduce <10%
(ICARDA,2011). Wheat diseases not only reduce yield but also affect the qualities of grains
(Dereje and Chemeda 2007).
Using information on the effect of weather and climatic factors on agricultural productivity can
not only reduce the damage but can also make it possible to enhance agricultural productivity
(Gizachew,2012).The most important climatic parameters influencing agriculture are: Seasonal
rainfall (onset, end date…), temperature ,relative humidity and sunshine. But, apart suffering
from climatic variability, there is no attention and/or efforts to solve the rising problems due to
climate variability particularly in our study area. However, in this paper I was analyzed
meteorological data for Bale highlands to quantify climatic conditions that influence the
development and spreading of rusts on wheat varieties. Therefore this study is initiated to fill the
knowledge gap between problems of climatic variability and professionals so that attention could
be given to alleviate this problem.
Objectives:
To analysis climate variability of study areas
To evaluate and identify the most climatic conditions suitable for wheat rust
MATERIALS AND METHODS
Description of the Study Area
Experiment was conducted at Sinana Agricultural Research Center (SARC) on-station Adaba,
Robe area and Agarfa sub-site at Bona season in 2013/14 – 2015/16. SARC is located at 07o 06’
12’’ to 07o 07’ 29’’ N and 40o 12’ 40’’ to 40o 13’ 52’’ E with altitude 2400 masl. The area
receives annual rainfall of 750 to 1100 mm. The monthly average values of maximum and
minimum temperatures are 21ºC and 9 ºC respectively. Whereas, Agarfa is located 07o 26’N and
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39o 87’E with altitude 2514 meter asl. Its annual rainfall ranges from 1000 to 1100mm.The
monthly average values of maximum and minimum temperatures are 22.8 ºC and 7.3ºC,
respectively.Adaba is located 07o 01’N and 39o 24’ E with altitude 2365 meter asl. The mean
annual rain fall range from 600mm to 750mm and it has a mean monthly temperature varying
from 4.5 to 9.6 oC for the min temperature and 22.6 oC to 26.4 oC for the maximum temperature.
The average annual maximum and minimum temperature is 25.9 oC and -1.21 oC respectively.
Robe is located 7o 06’44’’N and 40o 01’33’’E with altitude 2464 masl.
Data Collection
Meteorological data : Daily, Monthly and Decadal Meteorological data such as rainfall in (mm),
Relative humidity in (%) and temperature (max, min and mean oC), which were obtained from
nearby station.
Observed rainfall: Thirty three years of rainfall data were used to characterize the seasonal and
annual rainfall variability and trends using time series data for which stations had long term data.
Mann-Kendall’s test: Mann- Kendall trend test was employed to detect the trend of climate
variability. The Mann-Kendall’s test statistic was given as:
𝑁−1
𝑠= ∑∑
𝑖=1
𝑁
𝑗=1+𝑖
𝑠𝑔𝑛(𝑥𝑗 − 𝑥𝑖)
Where S is the Mann-Kendal’s test statistics; xi and xj are the sequential data values of the time
series in the years i and j ( j> i ) and N is the length of the time series. A positive S value
indicates an increasing trend and a negative value indicates a decreasing trend in the data series.
The sign function is given as:
+1 𝑖𝑓(𝑥𝑗 − 𝑥ᵢ) > 01
𝑠𝑔𝑛(𝑥𝑗 − 𝑥𝑖) = { 0 𝑖𝑓(𝑥𝑗 − 𝑥ᵢ) = 0
−1 𝑖𝑓(𝑥𝑗 − 𝑥ᵢ) < 0
For n larger than 10, ZMK approximates the standard normal distribution was computed as
follows:
287
𝑆−1
𝑖𝑓 𝑆 > 0
√𝑉𝑎𝑟(𝑆)
0 𝑖𝑓 𝑆 = 0
𝑍𝑀𝐾 =
𝑆+1
𝑖𝑓 𝑆 < 0
{ √𝑉𝑎𝑟(𝑆)
Where, S is variance and the presence of a statistically significant trend is evaluated using the
ZMK value.
The Sen’s estimator of slope: This test was applied when the trend supposes to be linear,
describing the quantification of changes per unit time. The slope (change per unit time) was
estimated above procedure of Sen (1968).
The coefficient of variation of seasonal rainfall variability was analyzed by:
𝑆𝐷
𝑥𝑖
𝐶𝑉 = ( ̃ )*100, Where𝑥̅ = ∑ 𝑁 and 𝑆𝐷 = √
𝑋
(𝑥𝑖− 𝑥̅ )2
𝑁−1
And seasonal rainfall anomaly during kiremt season can be analyzed by:
𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙 𝐴𝑛𝑜𝑚𝑎𝑙𝑙𝑦 𝐼𝑛𝑑𝑒𝑥(𝑅𝐴𝐼) =
𝑥𝑖 − 𝑥̅
𝑆𝐷
Where 𝑥̅ is long year mean and N is total number of year during observations were held for
specific site, SD is standard deviation, Xi is rainfall of each month and RAI is rainfall anomaly
of each month. If RAI is more than 0.5, between -0.5 and 0.5, less than -0.5 is meteorologically
the month is wet, normal and dry respectively.
Treatments and Experimental Design
Six bread wheat varieties (MaddaWalabu, Digalu, Sofumar, Kubsa, Danda’a and Tussie) and two
bread wheat differential cultivars (Morocco and PBW343) were planted in RCBD with three
replications. The plot size was plots of 1.2m x 2m having total experimental area of 16.6 m x 9 m
with between row, plot and block spacing of 0.2 m, 1 m and 1.5 m, respectively. All cultural
practices were done as per the agronomic recommendation for the crop.
Disease data: Disease data collected at different times from SARC wheat experiments and which
were documented as progress report was used. Relationships of each weather factor with three
rust diseases were determined through correlation and regression.
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Agronomic data: Data on crop parameters (all necessary agronomic data) were collected
throughout the cropping season. At crop maturity, the four middle rows were harvested for the
determination of grain yield.
Data Management and Statistical Analysis: All collected data were subjected to the analysis
ofStata andInstat software respectively. A simple correlation and regression analysis was
employed to test the relationship between weather observations and rust diseases occurrence.
RESULTS AND DISCUSSIONS
Characterization of climate variability under Bale highlands
The analysis of temperature and precipitation revealed changes in extreme values during 1984–
2016 in the study area. The analysis of past rainfall variability and trend was conducted after the
quality control was done, missed data was filled.
Start of rainy Seasons
Start of rainy season analysis of long term 1984 to 2016 rainfall data showed that the rainy
season started in minimum on average during the February forBelg season at Sinana and Robe.
The start of the Kiremt season also depicted that the rain begun raining at least in the 2nd decade
of July at Sinana while 2nd decade of June at Robe. Earlier planting before 3rddecades of July
were possible in Sinana and Robe in one out of four years for Kiremt season. Also, planting
before 1st decades of August at Sinana and 3rd decades of July at Robe were possible in three out
of four years’ time of the Kiremt. The start of the Kiremt season CV was 5.3 % for Sinana and
10.2 % for Robe area. From this point of view, the start of Kiremt season was less predictable at
Robe, thus, decisions pertaining to crop sowing activities would be with risk. The standard
deviation for mean start of the season showed high deviations about 11.2 days and 20.2 days at
Sinana and Robe respectively.
End of Seasons
The end of Belg season could be extended to a maximum of 1st decades of May for Sinana and
2nd decades of May for Robe, while for theKiremt season ranged between 1st decade of October
to 1st decade of November at Sinana and 1st decades of October to 2nd decades of October at
Robe. There was 75% probability to end before 1st decades of November at Sinana while there
289
was 75% probability that the end of season before 2nd decades of October at Robe. The end of
the season CV was 5.9% for Sinana area while end of the season CV was 4.6 % at Robe area.
Length of Growing Seasons (LGS)
The length of growing season in Belg season ranged from 2nd decades of February to 1st decades
of May for Sinana and 3rd decade of February to 2nd decades of May for Robe whereas the length
of growing season for Kiremt season ranged from 40 days to 132 days at Sinana and 29 days to
131days at Robe area. The probability that the area could be supported a variety with LGS
greater than 73 days was 25% at Sinana and 58 days was 25% at Robe while the probability that
the area had recorded LGS less than 99 days was 75% at Sinana and 84 days was 75% at Robe
for Kiremt season. Hence crops that require LGS of up to 132 days and 131 days could be
produced with less risk of water shortage in Sinana and Robe areas respectively in Kiremt
season. The issue of LGS requires further due attention in that one needs to know the type and
level of risks of yield loss associated with cultivars of different maturity categories, requiring
different amounts of water during a sequence of growth stages. The LGS of the season CV was
25.1 % for Sinana and 28.9 % for Robe areas. Therefore, the length of growing season in the area
had high annual variability at Robe (Table 1).
Number of Rainy Days
The average numbers of Kiremt rainy days were 136 days and 114 days with CV value of 18.4 %
and 10.6 % at Sinana and Robe stations respectively. The standard deviation of rainy days was
25.1 days and 12.1 days at Sinana and Robe stations respectively. The study also depicted that
the number of rainy days was less than 119 days and 109 days once in four years, less than 156
days and 121 days for three times in four years, it was less than 127 days and 115 days twice in
four years at Sinana and Robe station respectively. The minimum and maximum number of rainy
days was 90 days and 189 days for Sinana station and 81 days and 135 days for Robe station.
This indicates that the amount of rainfall achieved depend on the number of rainy days that could
be available to plants which in turn depends on the rainy season’s onset, length, temporal
distribution and cessation and can indirectly indicate the climatic suitability of the crop and its
success or failure in a season (Ngetichet al.,2008).
Table 1. Descriptive statistics of Kiremt season rainfall characteristics from 1984-2016 on
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SARC station
Rainfall
1stQuartile
Median
3rd Quartile
Ma
StdDev
CV
features
Min
(25%)
(50%)
(75%)
x
Mean
(+)
(%)
SOS(DOY)
195
199
208
219
235
210
11.2
5.3
EOS(DOY)
275
276
287
305
334
293
17.26
5.9
LGS(Days)
40
72.5
79
99
132
83.3
20.9
25.1
NRD(days)
90
119
127
156
189
136
25.06
18.4
TKRF(mm)
194.9
284.3
355
488.2
819
402.5
158.4
39.4
2. Robe station
SOS(DOY)
167
197
201
206
246
196.5
20.1
10.2
EOS(DOY)
275
275
281
289
321
285.3
13
4.6
LGS(Days)
29
58
73
84
131
74
21.4
28.9
NRD(days)
81
109
115
121
135
113.7
12.1
10.6
TKRF(mm)
214. 296.6
377.9
422.9
538.2
367.3
83.2
22.7
Note: SOS, start of season; EOS, end of season; LGS, length of growing Season; NRD, number
of rainy days; TKRF, Total Kiremt season rainfall; SD, standard deviation; CV, coefficient of
variation.
Additionally, the Mann–Kendall trend test showed a decreasing trend of start of season, end of
season and annual number of rainy days at Sinana and end of season and length of growing
season at Robe stations; however, it is not statistically significant (Table 2).
Table 2.Trend analysis of rainfall features for Sinana and Robe areas (1984-2016)
Station SOS
Sinana
EOS
ZMK
p.
S
-0.43
0.67 -0.1
LGS
NRD
ZMK p.
S
ZMK
p.
S
ZMK
p.
-0.35
-0.1
0.78
0.44
0.1
-0.15
0.88 -0.2
0.73
S
291
Robe
0.75
0.45 0.3
-0.21
0.84
-0.2
-1.43
0.15
-0.5 0.76
0.44 0.3
ZMK, Mann–Kendall trend test, S: Sen‟s slope, P: p-value
Probability of maximum dry spells length
Probability of dry spells exceeding 5,7,10 and 15 days length at two stations in the Sinana district
during 1984-2016 was depicted in Figure 3. The graphs in Figure 3 also demonstrate how the
probability of 15 days of dry spell curves stays at their maximum value of near to 80% during the
earlier and later months relative to the growing seasons. When looked in to the probability of dry
spell occurrence of 5 days length, it was at more than 95% over Sinana and 80% over Robe areas
for Kiremt season.
The probability of occurrence of dry spells greater than 5 and 7 days length were observed being
near to 0% at Robe and Sinana for Kiremt season ( Figure 1).There was no chance for the
occurrence of dry spell at greater than 7, 10 and 15 days lengths during the peak months of
Kiremt season for Sinana and Robe areas. As the length of dry spell thresholdbecomes short, the
probability of dry spells occurrence increases and conversely, as the dry spells threshold
becomes longer, the probability of dry spells occurrence decreases with-in the growing seasons
in both locations.
Figure 1.Probabilities of maximum dry spells exceeding 5, 7, 10 and 15 days length within 30
days after starting date at two stations in Sinana district during 1984-2016
Generally, at both locations, curves of probability of dry spells attain minimum during months of
peak rainfall periods and turn upward again from the 2nd decades of November, signaling end of
292
the growing season. This suggests that as the probability of dry spell increased, the standing
crops faced with risk of water shortage.
Trends and Relationship between Rainfall Features
Total annual and Kiremt seasonal rainfall data for the period 1984 to 2016 in Sinana district was
presented in figure2. Total annual and seasonal time series for Sinana and Robe stations revealed
increasing trends after the year of 2006 while more increment was shown at Sinana in near
decade figures 2. There was an observed slightly variability trends of seasons before a decade at
Sinana and Robe stations. Total annual rainfall had shown upward slope which indicated
increasing for historical in trend at Sinana and Robe stations. The result of historical data
analysis suggested that, annual rainfall amount was increased by 9.1 mm/yr and 2.8 mm/yr at
Sinana and Robe respectively. On the other hand, the seasonal kiremt rain was increased by 6.1
mm/yr at Sinana station and 2.9 mm/yr at Robe station.From this point of view, it was possible to
confidentially advise the farmers of the area in a way that they could have information like when
Annual RF at Sinana
y = 9.0456x - 17198
R² = 0.1
1800
1600
1400
1200
1000
800
600
400
200
0
Kiremt RF at Robe
y = 2.9339x - 5500.6
R² = 0.1162
Annual RF at Robe
y = 2.8137x - 4815
R² = 0.0413
2016
2014
2012
2010
2008
2006
2004
2002
2000
Sinana Total Annual Rainfall(mm)
Robe Total Annual Rainfall(mm)
1998
1996
1994
1992
1990
1988
1986
Kiremt RF at Sinana
y = 6.0454x - 11688
R² = 0.1362
1984
Total Annual Rainfall(mm)
to plant and the variety to be used in agriculture practices.
Sinana Kiremt Season Rainfall (mm)
Robe KiremtSeason Rainfall (mm)
Figure 2.Time series of total annual rainfall and Kiremt season total raifall based on the data of
1984-2016 at Sinana and Robe
Annual and seasonal rainfall totals
293
Trends of annual and seasonal rainfall amount at Sinana and Robe are presented in Table 3. The
result indicated that rainfall total of the rainy and dry seasons as well as the annual totals
increased slightly, but trends were not statistically significant for the period 1984-2016. During
the study period, the area received considerable amount of annual rainfall that ranged from 538
mm to 1586 mm at Sinana and 506.2 mm to 1101 mm at Robe (Table 3).
The Kiremtseason contributed more annual rainfall totals compared to another seasons for both
stations. The coefficient of variation also showed that more variable at Sinana and less variable
at Robe when compared all seasonal rainfall (Table 3). Furthermore, the results of seasonal
climate data analysis revealed that the mean of the main rainy (Kiremt season) season was 367.3
mm with CV value of 22.7 % which was the least as compared to the CV values of small rainy
season (Belg season) 36.1 % at Sinana and 34.1 % at Robe while dry period (Bega season) 41.2
% at Sinana and 43.7 % at Robe. This indicates that Belgrain was less variable than
KiremtandBegarains at Sinana while Kiremt rain was less variable than BelgandBegarains at
Robe.
Table 3. Descriptive statistics of rainfall of annual and seasonal rainfall totals of Sinana and
Robe areas during the period of 1984-2016
Sinana station
Descriptive statistics
Trends
Parameters
Mean
Min
Max
Media
SDE
CV (%)
ZMK
p-values
Slope
Annual RF
905.1
537.9
1586.3
863.9
277.6
30.67
0.92
0.356
9.05
Kiremt RF
402.5
194.9
819.0
355.0
158.4
39.35
1.36
0.169
6.05
Belg RF
339.0
160.0
716.2
337.4
122.5
36.14
-0.20
0.843
1.10
Bega RF
163.7
51.1
383.8
156.6
67.7
41.36
1.47
0.140
2.0
Robe station
Annual RF
812.4
506.2
1101
830.5
133.8
16.47
0.60
0.549
2.8
Kiremt RF
367.3
214.3
538.2
377.9
83.22
22.66
1.91
0.056
2.9
Belg RF
259.1
140.6
545.1
232.7
88.38
34.11
-1.04
0.298
-1.03
Bega RF
186.0
36.4
454.4
199.6
81.34
43.73
0.72
0.470
0.91
ZMK Mann–Kendall trend test, Slope: Sen‟s slope
294
Annual and seasonal rainfall anomalies
There was high seasonal and annual rainfall variability in the study area over 1984-2016. There
were wet (0 to +0.5) and dry (0 to -0.5) periods over the study area for both annual and seasonal
rainfall. Similarly, annual rainfall anomalies wet (above +0.5) and dry periods (above -0.5) were
observed in certain years. The years of 2007, 2008, 2010, 2011, 2012 and 2013 experienced
extreme wet condition, while the years 2000, 2002, 2003, 2004, 2005and 2006 were extreme dry
at Sinana station. Similarly, extreme wet condition showed in the years of 1998, 2006, 2010 and
2013 while the years of 1984, 1985, 1991, 2002, 2011 and2014 extreme dry period at Robe
station. The main rainy(Kiremt) season for the years 2007, 2008, 2010, 2011 and 2012 at
Sinana station and the years 1988, 2012 and 2013 at Robe station were experienced extreme wet
condition and the other years 1993, 2002and 2005 at Sinana and the years 1984, 1985, 2002 and
2005 were extreme dry period at Robe. This result implies that the production of crops could be
affected severely in these periods either due to deficit or excess of rainfall required for
2.5
Sinana station
Robe station
1.5
0.5
-2.5
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
-1.5
1986
-0.5
1984
Annual Rainfall Anomaly
agricultural activities at Sinana and Robe areas (Figures 3-4).
Years
Figure 3.Annual rainfall anomalies for Sinana and Robe areas for the period (1984-2016)
295
2016
2014
2012
2010
2008
2006
2004
Years
2002
2000
1998
Robe station
1996
1994
1992
1990
1986
1984
1988
Sinana station
Seasonal
RainfallAnomaly
2.3
1.8
1.3
0.8
0.3
-0.2
-0.7
-1.2
-1.7
-2.2
Figure 4.Kiremt season rainfall anomaly for Sinana area for the period 1984-2016
Trend analysis of annual maximum and minimum temperature
The temporal variability of average maximum and minimum temperatures had been examined at
inter annual time scale for the period 1984-2016 at Sinana and Robe. The result indicated that
annual maximum temperature had decreased by -0.15oC while annual minimum temperature has
increased 1.5oC at Sinana respectively in the last three decades. Similarly, annual maximum and
minimum temperature had increased trend 0.28 0C and 0.41 0C respectively in the last three
decades at Robe. This was due to high variability of climate aspects in the southeastern highland
of the country. The probability of occurrence of dry spell caused due to high heat stress the study
area was increased the impact of temperature variability on agricultural activities would be with
high risk.Mean annual trends of maximum and minimum temperatures at Sinana and Robe
stations for the historical was indicated in (Table 4). The result showed that annual maximum
and minimum temperature at Robe and minimum temperature at Sinanawas increased and
statistically significant in the last three decades for (p<0.01). On the other hand, decreasing
annual maximum temperature at Sinana was not statistically significant (p<0.05).
Table 14.Trends of annual Max.andMin.temperature in Sinana and Robe areas for 1984-2016
Stations Minimum Temperature
Maximum Temperature
Mean
ZMK
P-value
S(oC/yr)
Mean
ZMK
P-value
S(oC/yr)
Sinana
9.6
5.11**
0.000
0.15
20.2
-1.82
0.069
-0.02
Robe
8.1
4.21**
0.000
0.04
21.6
3.54**
0.000
0.03
Note: ZMK: Mann–Kendall trend test, S: Slope (Sen‟s slope) is the change (0C/year) **
indicates significant trend at less than 1% p-values
296
Mean monthly temperature clearly showed that the area experience the highest mean recorded in
March, while the month of November showed that the lowest air temperature was observed
which could reach up to 16.2 0C and 14.5 0C respectively showed the mean monthly maximum
and minimum temperature at Sinana area for the period 1984-2016 (Figure 5).Similarly, the
highest mean monthly temperature was recorded in May and July up to 15.8 0C, while the month
of November showed that the lowest air temperature was observed which could reach up to 13.4
0
C at Robe station. As the result showed that, the highest mean maximum temperature was
recorded in March at Sinana station and in May and July at Robe station, while in the month of
30.0
Sinana station
20.0
10.0
Dec
Nov
Oct
Sep
Aug
Jul
Jun
Mean min. Temp.
Mean. Temp
Robe station
25.0
20.0
15.0
10.0
5.0
Dec
Nov
Oct
Sep
Jul
Jun
Mean min. Temp.
Aug
Mean max. Temp.
May
Apr
Mar
Feb
0.0
Jan
Air Temperature (oC)
Mean max. Temp.
May
Apr
Mar
Feb
0.0
Jan
Air Temperature (oc)
November mean minimum temperature was received for both areas.
Mean. Temp
Figure 5.Mean maximum, mean minimum and mean monthly air temperature for the period
1984-2016 at Sinana and Robe station
Wheat rust severity and average yield loss values for 6-wheat cultivar’s and 2-local check
Rust diseaseseverity data was collected from all succeed sitesand patch into Excel in the order of
recorded time and its reaction with all varieties trials. Diseases severity and host response data
are combined into a single value called coefficient of infection. The coefficient of infection is
297
calculated by multiplying the severity times a constant for host response (i.e. R=0.2, MR =0.4,
MS =0.8 and S =1). The yield loss due to diseases severity calculated by Cobb scale (i.e. 1%
terminal severity is equivalent to a 0.54 % loss in yield). Some of the cultivars such as Maddawalabu, Digalu and Danda’a were resistance for yellow and leaf rust, while moderately
susceptible to stem rust. Kubsa was moderately susceptible for leaf and susceptible to yellow and
stem rust disease. Tussie and Sofumer were susceptible for this climatic condition during the
study periods.
Table.5 Reaction of Bale highland wheat crop to yellow, leaf and stem rust diseases basedon
modified Cobb scale (Peterson et al. 1948)
Degree of infection (reaction) to
Yield loss (%) due to
Variety
yellow rust
stem rust
leaf rust
yellow rust
stem rust
leaf rust
PBW343
S
S
S
31.6
21.6
21.5
Maddawalabu
R
MS
R
1.7
2.9
2.2
Morocco
S
S
S
32.1
26.1
27.9
Kubsa
S
S
MS
25.0
20.7
16.6
Sofumer
S
MS
R
11.0
8.7
8.8
Digalu
R
S
R
15.0
25.9
10.2
Tussie
S
S
MR
12.8
10.8
8.2
Danda'a
R
MS
R
10.4
16.6
9.4
S= Susceptible, MS= Moderately Susceptible, MR= moderately resistant and R= Resistance.
Wheat rust disease over study area in relation to some climate parameters
During study years, wheat rust disease requiresoptimum climate variability for its development
and infection. However, at Bale highlands the minimumthe favoring temperature was 4.5οC to
15.6οC while the maximum temperaturewas ranged from 20οC to 28οC. The results have shown
that the correlation value between monthlyrainfall withleaf rust, yellow rust and stem rust were
indicated -0.76, -0.94 and -0.57 while positively correlated with relative humidity and
temperature, which is statistically good and so facilitates its development and infection on main
cropping season. Therefore, yellow rust diseases has decreased and disfavored by high rainfall
298
with a correlation value coefficient -0.94(Table 6). This is because the condition is not favorable
for the spreading of yellow rust over wheat fields during the cropping season. The correlation
between minimum temperatureand stem rust diseases was indicated 0.89, which is statistically
significant. In fact, the correlation values between maximum temperature and yellow rust is 0.59,
which is not good.This study has shown that whenever the climatic conditions are favorable,
wheat crop is highly affected by climate related disease. The stem rust diseases mostly affectat
vegetative and flowering stages. From the analysis, the correlation between minimum
temperature and stem rust is 0.89 and statistically significant.
The correlation values existed between relative humidity and leaf rust diseases development in
the main croppingseason over thewheat fieldsrevealed 0.88, which was extremely high. The
series of annual diseases severity in the four locations of Bale highlands showed stronginterannual fluctuation, without visually apparent association with climate variability as shown in
table 6. This analysis showed that there was a climate impact onwheat rustdiseases
occurrence.This indicated that the development of rust diseases mostly depends on
temperatures,rainfall and relative humidity for development and spreading.
Table.6. Correlation between climatic parameters with stem rust, yellow and leaf rust intensity
over Bale highlands.
Parameter
Correlation coefficient (r)
Leaf Rust
Yellow Rust
Stem Rust
Seasonal rainfall
-0.76*
-0.94**
-0.57*
Relative Humidity
0.88*
0.72*
0.63*
Minimum temperature
0.66*
0.84*
0.89*
Maximumtemperature
0.69*
0.59*
0.57*
Note **= highlysignificant,*=significant and ns=non-significant’, (p =0.05)
Severity of rust disease versus mean local climate patterns over Bale highlands
The computed correlation values for cropping season rainfall and diseases severity showed
strong associations among the parameters. For instance, the correlation between monthly
rainfalls and disease severity about -0.86, while for relative humidity and diseases severity
reached 0.74. This condition was also true for maximum and minimum temperature with rust
299
diseases, the correlation analysis indicated 0.61 and 0.79 respectively.The results hence revealed
that the rust diseases severity is highly favored by cropping season rainfall than the relative
humidity as shown in table 7. It is found that cropping season rainfall and wheat rust diseases
had indirect relation, while maximum temperature, minimum temperature and relative humidity
had direct relation withwheat rust diseases as table 7. Generally, wheat rust development and
severity follows climate parameters such as rainfall, relative humidity and temperature.
Table 7.Correlation between climatic parameters and wheat rust wheat rust intensity
Parameters
Correlation coefficient (r)
Seasonal rainfall
-0.86
Relative humidity
0.74
Maximum temperature
0.61
Minimum temperature
0.79
Generally, wheat rust development and severity follows local climate variability, since the
relationship between the rust, relative humidity and temperature are directly related to the rust
diseases as shown in table 7. From weekly analysis during cropping season, theclimatic
condition conducive for rust diseases occurrence were identified.Therefore, the development and
spread of rust is highly enhanced with maximum temperature and minimum temperature ranges
20.8 oC to 28oC and 8.2 oC to 11.7 oC, while relative humidity was more than 70 % across the
highland regions. In the warm and humid climate the wheat rust development and infection was
severe and requires long period dew point (RH) at least 6 to 8 hrs. Rust attains maximum
infection at 8 to 12hrs of dew at 18οC. Moreover, the impact of local climatic parameters on rust
diseases over Bale highlands during development and spreading periods summarized in the table
8.
Table 8.The impact of local climatic on wheat rust diseases based on Pearson’s correlation value
Met-parameters
Impacts on rust diseases of wheat
Rainfall
Atincreasing rainfall, decreases spore development by cleaning the spores
from the leaf and stem. In cropping season, if rainfall amount increases, the
infectiondecreases by scrubbing the spores from the plant and increases RH.
300
Maximum
An average ranges of temperature from 20.8 oC to 28 oC increases rust
temperature
spreading during cropping season and favors for spore development.
An average ranges between 8.2 oC to 11.7 oC minimum temperature has
Minimum
positive effect on spore development and below 8.2 oC eliminates rust
temperature
injuries.
RH (moisture)
A function of moisture in the cropping season used to spread wheat rust
onfield, initiate germination within 1 to 3hrs of contact with moisture, health
of the spores rapidly increases at moisture contents more than 70%.
Conclusions and Recommendations
Climate variability was believed to cause the most damaging impacts on agricultural practices in
developing countries like Ethiopia. The study tried to investigate the impact of climate variability
on wheat rust disease development. The results of this study showed that wheat yield variability
over Bale highland determined by fluctuation of rainfall, temperature and relative humidity for
developing and spreading of rust diseases mainly during main season. Results from the analysis
of average wheat yield loss showed that the current levels of 6-cultivers and two check of wheat
yield widely responding to yellow, leaf and stem rust diseases. The studies made on the
relationship between short-term meteorological variations and diseases development and their
spatial spread across the Bale highland has not yet well assessed. Such studies could be used for
predicting wheat rust diseases development and infection during the main seasons. The potential
impacts of local climate variability on the rust diseases and wheat production were also
investigated using various statistical techniques. Moreover, the average relative humidity,
rainfall and temperature observed during cropping season created conducive conditions for the
rust infection on wheat crop across the Bale highlands. Being rust diseases are one of the most
natural factors that affect wheat crops; they reach maximum infection stage in main season.
Generally, rust disease directly relied on the level of climatic conducive prevailing during main
season to temperature, relative humidity and rainfall. The overall results as generated based on
local climatic factors and rust diseases as well as wheat yields can be utilized in the provision of
optimizing climatic information for monitoring wheat crop performances over Bale highlands
and demonstrate these techniques over regions having similar climatic conditions.
301
As stated above, we identified some climatic factors as precursor indicators for the severity of
rust diseases and wheat yield losses over the highland of Bale. This is due to the fact that farmers
are always operate under uncertainty by avoiding some external inputs such as weather forecasts
for their early planning. Therefore, there is a need to establish a system that enables to use
available climatic information towards the optimization of wheat productivity across the Bale
highlands. This in fact requires coordinated efforts among the meteorological institutes,
agricultural research institutes and farmers training centers. Finally, there is a need to design and
expand multi-sectoral researches particularly focusing on microclimates and rust diseases, frosts,
and insect’s pests that are responsible for year-toyearcrop yield variations.
Acknowledgements
The author express his gratitude to the Oromia Agricultural Research Institute for its financial
support and staff of Agroforestry and cereal research teams of Sinana Agricultural Research
Center for their follow-upand data collection timelyduring the trial on the field.
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Effect of NPS Rate on Yield and Yield Components of Upland Rice (Oryza sativa L.) In
Western Ethiopia
Bodena Guddisa*, Fufa Anbassa, Geleta Gerema, Gudeta Bedada and Fikiru Merga
Bako Agricultural Research Center, P. O. Box 03, Bako, West Shoa, Ethiopia
*
Corresponding author E-mail: bodenagud@gmail.com
Abstract
The key elements that contributed to low rice productivity is such biotic, abiotic factors and
inappropriate crop management practices. Moreover, application of balanced fertilizers is the
basis to produce more crop yield from existing land under cultivation and nutrient needs of
crops is according to their physiological requirements and expected yields. Thus, a field
experiment was conducted in 2016-2018 main cropping season from the end of May to end
November at western Oromiya on Bako and Chewaka locations to improve soil fertility and
increase yield of Rice in East Wollega and West Showa and to determine optimum level of NPS
fertilizer for growth, economically feasible rates that maximize the yield of Rice in the area. The
treatments were factorial combination of seven rates of fertilizer (0, 25, 50, 75, 100, 125 kg/ha
NPS) and one previous recommendation (100 kg/ha DAP) with two Rice varieties (Chewaka and
Nerika-4) and with uniform application 23 N kg/ha in randomized complete block design and
replicated three times. The pre soil analysis indicates that the soil of experimental area is acidic
(pH = 5.4) and medium in available Phosphorus (13 ppm). The main effect of plant height,
panicle length and number of effective tillers were not influenced by NPS rate but significantly
(P<0.01) different due to variety both at Bako and Chewaka locations. But the main effect of
above ground biomass and grain yield were significantly different at Chewaka site. Though the
other parameters were non-significantly affected, grain yield of Chewaka variety was
significantly influenced due to the interaction effect of NPS rates and varieties at Bako. Thus,
economic analysis revealed that 125 kg/ha NPS (47.5 P2O5, 23.75 N and 8.75 kg/ha S) rate on
Chewaka variety gave grain yield (6454.8kg/ha) with the net benefit (61160.5 birr/ha) and the
highest Marginal Rate of Return (MRR) (787.69%) were economically feasible alternative to the
other treatments. Therefore, it is recommended to use 125kg/ha NPS rate on Chewaka variety
since it is economically feasible.
Keyword: Economic analysis, biotic and a biotic factors,NPS rates, yield and yield components
303
Introduction
Rice (Oryza sativa L.) is widely grown in tropical and subtropical regions (Bijay and Singh,
2017). In Ethiopia field crops cover the largest cultivated land area from which cereals covered
nearly 80.7% and rice covered 0.51% from cereal land coverage and accounting 2844kg/ha grain
yield (CSA, 2018). A latest report of Central Statistical Authority (CSA) indicates that area
covered by major cereals namely, Tef, Maize, Sorghum and Wheat were 23%, 17%, 16% and
13% respectively, in main crop season of 2010. During the same season, national yield averages
obtained from the same cereals were 1.26, 2.25, 2.00 and 1.86 t/ha, respectively. Rice is also one
of cereal crops which is cultivated in most regions of the country even if specific to a few
locations of the regions. It is said a newcrop to the country. This crop also faces different
challenges like other cereal crops in the country (Helufand Mulugeta, 2006). Despitethe fact that
numbers of farmers’ as well as area coverage are increasing from time to time, crop yields are
generally found in declining trends (CSA, 2009).
Although, low yields of these crops were attributes of several biotic and a biotic factors,
inappropriate crop management practices that mainly include; sowing periods, seeding methods,
weeding practice, and lack of farmers awareness on uses of cropping systems and different soil
fertilization methods are found the key elements that contributed to low crop productivity in the
country. Rice is the high yielding crop in different countries of the world and stable food in some
countries (Riaz et al., 2007). In Ethiopia the productivity is very low then the attainable yield and
not Exide 35 kuntal per hectare due to the challenges mentioned above. Improvement of its
production has not been possible due to low soil fertility and inadequate nutrient management
among other factors (Helufand Mulugeta, 2006). Therefore, to alleviate the aforementioned
persistent problems of crop production, there has been a growing interest to increase the
productivity through improved agronomic practices. Consequently, some crop management
research activities with objectives to identify agronomical optimum and economical maximum
crop management practices are proposed to conduct on various crops in different agro-ecologies
of the country. But the growth and yield of rice is influenced by different nutrients management
and other factors during their production in a field (Riaz et al., 2007). Despite its importance and
increased production, crop productivity, in many parts of the world, is low due to genetic and
environmental factors affecting its yield and yield related traits (Nonnecke, 1989). In many crop
304
producing areas lack of available nutrients is frequently the limiting factor next to the soil water
as their uptake and liberation of N, P and S from soil organic matter depends upon availability of
water (FAO, 2003). Research work has been done on the base of NP in different soil types and in
various climatic conditions, but very limited work has been reported on various sources of
fertilizers for a certain nutrient. Application of only N and P containing fertilizers causes
reduction of the quantity of K and S in most of the soils as there is also evidence of fixation of
potassium and leaching of sulphur in different types of soils in addition to mining by different
crops as result of continues cultivation of land (Murashkinaet al., 2006).Under P deficient
conditions, rice does not respond to application of N, K, and other nutrients (Bijay and Singh,
2017). Therefore, the application of K and S and other micronutrients to soils having even fair
amounts of K and S contents may still show its effect on plants.In rice, number of panicles and
panicle length may be adversely affected by S deficiency (Fageriaet.al.2003).Phosphorus
management must focus on the buildup and maintenance of adequate available P levels in the
soil to ensure that P supply does not limit crop growth and N-use efficiency (Fairhurst
et.al.,2007). The recently cultivated crop(upland rice) in the country also full of these challenges
such as, soil fertility problem, pests(weed, disease, insects) & a biotic factors .Therefore,
improving the productivity of the crop, soil fertility improvement will be the mandatory & this
can be improved by different mechanisms. Thus, the research aimed to investigate different
optimum and economically feasible rate of fertilizers to the rice crop. Therefore, the experiment
is initiated with the objective of determining optimum level of NPS fertilizer for growth,
economically feasible and high productivity of upland rice in the research area.
Materials and Methods
The trial was conducted at Bako Agricultural research center on station and sub- station of
Chewaka site during 2016-2018 main cropping seasons. The treatments were consisted of
different level of NPS compound fertilizer and control treatment without fertilizer application.
The recommended fertilizer rate 46 P2O5 (100kg kg/ha DAP) was used as check in comparison
with different levels of fertilizers. Uniform application of 23 kg/ha Nitrogen (50kg/ha Urea) was
used in split at sowing and tillering. The constituent of Nitrogen, phosphorus and sulfur in 100
kg/ha NPS is 38 kg P2O5, 19 kg N and 7 kg S respectively. Two upland rice varieties (Chewaka
and Nerica-4) were used as tested crop in the trial. Thus, fourteen treatment combinations
consisting seven rates (0, 25, 50, 75, 100 & 125 kg/ha NPS with one recommended rate of 46
305
kg/ha P2O5) fertilizer and two rice varieties (Chewaka and Nerica-4) combined factorially were
arranged in a randomized complete block design in three replications.
Data Collection and Measurements
Growth, Yield and Yield Component
Plant height was measured at physiological maturity from the ground level to the tip of panicle
from five randomly selected plants in each plot and the average was taken. Panicle length was
measured from the node where the first panicle branches emerged to the tip of the panicle from
an average of five selected plants per plot. Number of effective tillers was determined by
counting the number of tillers from five plants from the harvestable rows and the average was
considered. Biomass yield was harvested at maturity at ground level from the whole plant parts,
including leaves, stems, and seed from the net plot area and weight of biomass was taken after
sun drying for a week. Finally the total grain yield was measured by harvesting the crop from the
net middle plot area of 5m x 0.8 m (4 m2).
Results and Discussions
Soil Physico-Chemical Properties of Experimental Site
The soil textural classes consisted proportion of 38% sand, 50% clay and 12% silt indicating
sandy clay at Bako which is ideal for rice production. pH of the soil was 5.4 categorized as
acidic according to rating described by Landon (1991). According to Tekalign (1991) rating, the
organic carbon of the soil showed medium at Bako (2.88%). Total N of the soil (0.23%) was
medium; as rated by Havlin et al., (1999) who rated total N between 0.15 to 0.25% as medium.
Available phosphorus indicated that there was medium (11 mg/kg) phosphorus content of the
soil at Bako site which was in line with (Jones, 2003).
Growth, Yield and Yield Components
From the analysis of variance plant height, panicle length, number of effective tiller, number of
filled grain, Above ground biomass and harvest index were not influenced by main effect of
NPS rate (p>0.05) but highly influenced (p<0.01) due to variety at Bako locations and Chewaka
except non-significance difference on number of filled grain and harvest index at Chewaka.
However, none of their interaction effects were significantly different at both locations during
the main growing season of 2016-2018 cropping calendar (appendix Table 1). In all parameters
the highest values were recorded at Chewaka variety than Nerika-4 (Table 1). The lower values
306
of Nerika-4 when compared with Chewaka variety were probably associated with the severity of
head blast disease to Nerika-4 especially at Bako location.
Table 1. The main effect of rates of NPS on Plant height, Panicle length, Number effective tiller, Number
of filled grains, Above ground biomass and Harvest Index of rice at Bako
NPS rates (kg/ha)
0
25
50
75
100
125
100 DAP
LSD
Variety
Chewaka
Nerika-4
LSD
Cv
PH
100.51
104.58
101.36
103.67
103.39
103.43
101.69
NS
PL
20.78
21.13
20.73
21.03
20.82
21.43
20.94
NS
NET
12.83
13.72
13.14
12.54
12.4
12.22
12.48
NS
NFG
85.1
93.7
83.3
82.8
84.7
84.7
81.1
NS
AGBM
12838
13927
13154
13065
12672
13867
12536
NS
HI
34.58
30.5
31.02
31.71
31.69
33.43
30.37
NS
118.1
87.22
3.81
10.5
21.37
20.59
0.42
5.7
14.13
11.39
0.9
19.8
90.4
79.7
6.84
22.7
16030
10273
1030.4
22.1
38.36
25.44
2.37
21.0
LSD (0.05) = Least significance difference at 5% probably level, CV = Coefficient of variation, NS = non-significant
at 5% probability level. PH=Plant height, PL=Panicle length, NET= Number effective tiller, NFG=Number of filled
grains AGBM=Above ground biomass and HI=Harvest Index.
On the other hand, even if the other traits except panicle length and number of effective tiller
showed non-significant difference due to the main effect of NPS, Plant height, Grain yield and
Above ground biomass were highly (P>0.01) influenced due to the main effect of NPS rates and
rice varieties but all of the parameters were not significantly affected (P>0.05) due to their
interactions at Chewaka location (Appendix Table 2). This result was in line with Increase in the
magnitude of yield attributes is associated with better root growth and increased uptake of
nutrients favoring better growth of the crop (Helufand Mulugeta, 2006). Except number of filled
grain and harvest index all parameters were significantly different due to the main effect of
Variety at Chewaka location and the highest values were also observed at Chewaka variety when
treated with Nerika-4 on the site (Table 2).
Table 2. The main effect of rates of NPS on Plant height, Panicle length, Number effective tiller, Number
of filled grain, Grain yield, Above ground biomass and Harvest Index of rice at Chewaka
NPS rates
(Kg/ha)
0
25
50
75
100
125
100 DAP
LSD(0.05)
Varieties
Traits
GY(kg/ha)
2517c
2939b
3243ab
3291a
3263ab
3318a
3108ab
344.16
AGBM(kg/ha)
5645c
6784bc
7988a
7096ab
7373ab
7677ab
7276ab
1194.9
HI
45.492
43.528
46.368
49.291
46.263
44.77
45.787
NS
NET
6.056
6.489
6.233
6.422
6.467
6.322
5.9
NS
NFG
54.95
57.68
54.53
61.8
56.02
56.89
58.89
NS
PH (cm)
91.17b
95.28b
92.72b
95.56ab
96.5ab
96.5a
95.78ab
5.66
PL (cm)
20.422
20.267
20.922
20.889
20.789
21.58
21.544
NS
307
Chewaka
Nerika -4
LSD
CV
4032a
2199b
183.96
16.7
9528a
4712b
638.69
25.3
46.87
44.98
NS
12.8
6.91a
5.63b
0.51
22.8
58.49
56.01
NS
18.0
109.6a
81.25b
3.03
8.9
21.61a
20.22b
0.56
7.5
LSD (0.05) = Least significance difference at 5% probably level, CV = Coefficient of variation, NS = non-significant at 5%
probability level. PH=Plant height, PL=Panicle length, NET= Number effective tiller, NFG=Number of filled grains
AGBM=Above ground biomass GY= grain yield and HI=Harvest Index.
From analysis variance showed non-significant difference, except grain yield was significantly
affected due the main effect of NPS rate and variety as well as their interactions at Bako location
(Appendix Table 1). The highest grain yield (7172 kg/ha) was recorded from 125 kg/ha NPS
(47.5 P2O5, 23.75 N and 8.75 kg/ha S) on Chewaka variety at Bako location (Table 3). This was
in line with Fageriaet.al (2003) suggesting the above-ground P uptake by high-yielding rice
varieties commonly ranges from 25 to 50 kg P/ ha with 60–75% of the total plant P contained in
the panicles at maturity. Comparative result was also statedas application of phosphorus fertilizer
had significantly increased the grain yield of rice up to the applied level of 46 kg P 2O5 /ha on
baby trial (Getahunet.al., 2017). Generally, the grain yield obtained from Nerika-4 variety was
by far smaller than Chewaka variety on Bako station as well as Chewaka location over years due
to increased number of unfilled grains that had positive correlation for lower total grain yield in
Nerika-4 which might be connected with rice head blast.
308
Table 3. Interaction effects of rates of NPS and Variety on Grain yield (Kg/ha) of rice varieties (Chewaka
and Nerika-4) at Bako
NPS (kg/ha)
0
25
50
75
100
125
100 DAP
LCD
CV
Chewaka
6048bc
6202bc
6314bc
6523b
6192bc
7172a
5742c
611.8
14.9
Nerika-4
2929d
2635de
2211ef
2307ef
2393def
2606de
1896f
LSD (0.05) = Least significance difference at 5% probably level, CV = Coefficient of variation, NS = non-significant at 5%
probability level.
Economic Analysis
The partial budget analysis was done on the basis of total variable cost considering the costs of
different NPS rates, variety, and transport as well as application costs.The economic analysis was
done on the basis of adjusting 10% yield downward for that fact it closest to the farmer yield.The
result of partial budget analysis showed that five NPS rates were non-dominated with an
associated MRR greater than 100% (Table 4). An additional income of 7.87 Ethiopian Birr per
unit Birr invested was obtained from 125 kg/ha NPS rate on Chewaka variety compared to the
other treatments. This analysis revealed that 125 kg/ha NPS rate on Chewaka variety gave
(6454.8kg/ha) with the net benefit (61160.5 birr/ha) and the highest marginal rate of return
(787.69%) are economically feasible alternative to the other treatments (Table 4). Therefore, it is
advisable to use 125kg/ha NPS rate on Chewaka variety since economically feasible to the
farmers.
Table 4. Results of partial budget analysis for NPS fertilizer rates and Rice varieties (Chewka
and Nerika-4).
NPS (kg/ha)
Variety
Gross yield
0
0
25
25
50
50
75
75
100DAP
100
100DAP
100
125
125
Nerika-4
Chewaka
Nerika-4
Chewaka
Nerika-4
Chewaka
Nerika-4
Chewaka
Nerika-4
Nerika-4
Chewaka
Chewaka
Nerika-4
Chewaka
5229
6048
2635
6202
2211
6314
2307
6523
1896
2393
5742
6192
2606
7172
Adjusted
yield (10%)
4706.1
5443.2
2371.5
5581.8
1989.9
5682.6
2076.3
5870.7
1706.4
2153.7
5167.8
5572.8
2345.4
6454.8
Gross
benefit
53061
54432
23715
55818
19899
56826
20763
58707
17064
21537
51678
55728
23454
64548
TVC
NB
1667.5
1767.5
1986.5
2086.5
2310.5
2410.5
2629.5
2729.5
2868.5
2968.5
2968.5
3068.5
3287.5
3387.5
52400.5
52664.5
21728.5
53731.5
17588.5
54415.5
18133.5
55977.5
14195.5
18568.5
48709.5
52659.5
20166.5
61160.5
Dominance MC MB MRR (%)
0
0
100 264
264.00
D
319 1067 334.48
D
324 684
211.11
D
319 1562 489.66
D
D
D
D
D
658 5183 787.69
GB= gross benefit, TVC= total variable cost, NB= net benefit, D=dominance, MC= marginal cost, MB= marginal benefit and
MRR= marginal rate of return
309
Conclusion
Even though the experiment was conducted at Chewaka and Bako locations over years the yield
obtained at Chewaka was relatively lower than Bako location which might be connected with
severity of rice head blasts. From different NPS rate and rice varieties(Chewaka and Nerika-4)
tested, economic analysis showed that 125 kg/ha NPS rate on Chewaka variety gave grain yield
(6454.8kg/ha) with the net benefit (61160.5 birr/ha) with the highest marginal rate of return
(787.69%) are economically feasible alternative to the other treatments. Therefore, it is advisable
to use 125kg/ha NPS rate on Chewaka variety since economically feasible to the farmers. There
would be the need of further NPS rate investigation beyond the highest rate of this
recommendation (125kg/ha NPS rate) to assess the maximum potential of rice and reach its
turning (peak points) to give general conclusion.
References
Bijay-Singh and V.K. Singh, 2017. Fertilizer Management in Rice. Pp. 232-233, In: B.S.
Chauhan et al. (eds.) Rice Production Worldwide. Indian Agricultural Research Institute,
New Delhi,India
CSA (Central Statistical Authority), 20018. Agricultural sample survey, 2018 (2010 E.C) report
on area and production for major crops (private peasant holdings, main season), statistical
bulletin 586, Addis Ababa, Ethiopia.
Fageria NK, Slaton NA, Baligar VC, 2003. Nutrient management for improving lowland rice
productivity and sustainability. Adv Agron 80:63–152.
Fairhurst TH, Dobermann A, Quijano-Guerta C, Balasubramanian V, 2007. Mineral deficiencies
and toxicities. In: Fairhurst TH, Witt C, Buresh RJ, Dobermann A, editors. Rice: a practical
guide to nutrient management, 2nd ed. International Rice Research Institute, Los Banos;
International Plant Nutrition Institute, Norcross; International Potash Institute, Berne, pp.46–
86.
Food and Agriculture Organization (FAO), 2003. World agriculture: towards 2015/2030. An
FAO perspective, edited by J. Bruinsma.Rome, FAO and London, Earthscan.
Getahun D, BogaleW, Assefa G, Solomon H, Hagos A, et al., 2017. Participatory Evaluation
and Determination of N and P Fertilizer Application Rate on Yield and Yield Components
of Upland Rice (NERICA-4) at Bambasi District, Benishangul-Gumuz Regional State. Adv
Crop Sci Tech 5: 303. doi:10.4172/2329-8863.1000303.
He Havlin, J.L., Beaton, J.D., Tisdale, S.L and Nelson, W.L.(1999). Functions and forms of N in
plants. In Soil Fertility and Fertilizers. (6th ed) Prentice Hall, New Jersey. DOI:
http://dx.doi. org/10.1097/00010694-195704000-00019.
HelufGebrekidan and MulugetaSeyoum, 2006. Effects of Mineral N and P Fertilizers on Yield
and Yield Components of Flooded Lowland Rice on Vertisols of Fogera Plain, Ethiopia,
Journal of Agriculture and Rural Development in the Tropics and Subtropics , 107(2) : 161–
176.
Jones, J.B., 2003. Agronomic handbook: Management of crops, soils, and their fertility. CRC
Press LLC, Boca Raton, FL, USA. 450p.
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Landon, J. R., 1991. Tropical soil manual: a handbook for soil survey and agricultural land
evaluation in the tropics and sub tropics. Longman Scientific and Technical, Longman
Group, UK Ltd
Murashkina, M., Southard R.J., Pettygrove, G.S. (2006). Potassium Fixation in Silt, Sand and
Clay Fractions of Soils Derived from Granitic Alluvium of the San Joaquin Valley,
California. The 18th World Congress of Soil Science (July 9-15, 2006) at Philadelphia,
Pennsylvania, USA.
Tekalign, T., (1991). Soil, plant, water, fertilizer, animal manure and compost analysis. Working
Document NO 13. International Livestock Research Center for Africa (ILCA), Addis Ababa
Effect of Yam Tuber Size Cutting on Its Yield in Western Oromia, Ethiopia
ShelemeKaba* and Abraham Negara
Bako Agricultural Research Center, P.O.Box 03, Bako, Ethiopia
*
Corresponding author: Shelemakaba@gmail.com
Abstract
Yams (Dioscorea spp.) are an annual tuber and monocotyledonous crop. The plant Genus
comprises of over 600 species with only 10 species producing edible tuber. A field experiment
was conducted at Western Oromia to recommend appropriate tuber weight of yam cutting for the
farming and its economic feasibility for communities in western Oromia for two consecutive
years during the main cropping season of 2017 and 2018 at Bako Agricultural Research Center
(BARC) on station and subsite of Gute. Yam variety Bulcha, which is adapted to the agroecology of the area, was used for the study appropriate tuber weight of yam cutting. Planting
was done on April and yam tubers cutting at different gram were planted at a spacing of 80 cm
between rows and 30 cm between plants. After cutting up the tuber into mini-setts, takes ash and
rub ash onto the freshly cut fresh that are exposed. Nitrogen fertilizer was used 100kg ha-1in two
splits and 100kg ha-1NPS which were used.NPS 100 kg ha-1and half Nitrogen fertilizer applied
after emergence and the remaining half a month after first application along the rows of each
plot to ensure that N is evenly distributed. The treatment consist of factorial combination of one
variety (Bulch) with six treatment ((50-100g/ha), (101-150g/ha), (151-200g/ha), (201-250g/ha),
(2501-300g/ha) and (whole size as control)) yam tuber cutting different weight size.
The
treatments were arranged in randomized complete block design (RCBD) with three replications.
Each plot will be 4.8m long and 3m wide (6 rows). Finally, yam tuber plant in the central net
plot area (9.6 m2) was harvested at normal physiological maturity. Therefore, the result of the
economic analysis (partial budget) show that best promising yam tuber cutting size weight 201311
250g (78874.3 birr) followed by 151-200g (73096.3birr) and 251-300g (70936.3 birr) and tuber
yield tone hectare (57.95 tone/ha, 56.42 tone/ha and 54.45 tone/ha respectively were
recommended for farmer or end user in western Oromia of yam production area and similar
agroecology.
Key words: Yam tuber cutting, mini-setts
Introduction
Yams (Dioscoreaspp.) are an annual tuber and monocotyledonous crop. The plant Genus
comprises of over 600 species with only 10 species producing edible tuber. Six of these edible
species are cultivated in Africa, West Indies, Asia, South and Central America (Amusa,2000;
Tamirou et al., 2008; Bousalem et al., 2010; Elsie, 2011; Petro et al, 2011; Ibitoye et al., 2013)
and only three (3) of them are available in Gabon. The primary species cultivated are the white
yam (Dioscorearotundata), yellow yam (Dioscoreacayenensis) and water yam (Diosoreaalata),
D. rotundataand D. cayenensis may have been first domesticated in the forest-savannah ecotone
of West Africa (Hamon et al., 1995; Tostain et al., 2003).Yam tubers are important in different
domains. Nutritionally, yams are a major source of nourishment to many populations in the
world (Craufurd et al., 2006). Pharmaceutically, some species of Dioscorea, particularly
Dioscoreazingiberensis, produces high concentration of diosgenin, a chemical used for the
commercial synthesis of sex hormones and corticosteroids (Chen et al., 2003; Yuan et al., 2005;
Islam et al., 2008). Agriculturally, yams tubers are used as planting material (Odjugo, 2008;
Zannou, 2009). Yam also plays vital roles in traditional culture, rituals and religion as well as
local commerce of African people (Izekor and Olumese, 2010). The conventional multiplication
of Dioscoreaspecies is by tuber seeds, a tuber fragment that grows and develops into a new
tuber. The farmers of the area unknowingly cut and use of yam planting materials without
recommendation that causes rotting and desiccation for the majority of planting materials which
leads to yield reduction. Then it is very essential to recommend appropriate yam tuber size
cutting for optimum yield production in the farming communities.
Objective: To recommend appropriate tuber weight of yam cutting for the farming and its
Economic feasibility for communities in western Oromia
Materials and method
A field experiment was conducted at Western Oromia to recommend appropriate tuber weight of
yam cutting for the farming and its economic feasibility for communities in western Oromia for
312
two consecutive years during the main cropping season of 2017 and 2018 at Bako Agricultural
Research Center (BARC) on station and subsite of Gute.
Plant materials: Yam variety Bulcha, which is adapted to the agro-ecology of the area, was
used for the study appropriate tuber weight of yam cutting. Variety Bulcha is the most successful
variety released by Bako Agricultural Research Centre in 2012. Bulcha variety performed tuber
yields of 66.45 t/ha
Experimental Design and plot management
The experimental field was ploughed and harrowed by a tractor to get a fine seedbed and leveled
manually before the field layout was made. Planting was done on April and yam tuber cutting at
different gram was planted at a spacing of 80 cm between rows and 30 cm between plants. After
cutting up the tuber into mini-setts, takes ash and rub ash onto the freshly cut fresh that are
exposed. Allow the mini-setts to dry out for one day in cool dry place before placing them in a
nursery. This prevents the flesh from being infected whilst in the nursery. The animal manure (in
order to conserve moisture inside tuber cutting) was applied along the rows before planting and
mixed with soil then placement yam tubers at recommended spacing was done in hole prepared
in the field. Nitrogen fertilizer was used 100kg/ha in two splits and 100kg/ha NPS which were
used.100kg/ha NPS and half Nitrogen fertilizer appliedafter emergence and the remaining half a
month after first application along the rows of each plot to ensure that N is evenly distributed.
The treatment consist of factorial combination of one variety (Bulcha) with six treatment ((50100g/ha), (101-150g/ha), (151-200g/ha), (201-250g/ha), (2501-300g/ha) and (whole size as
control)) yam tuber cutting different weight size. The treatments were arranged in randomized
complete block design (RCBD) with three replications. Each plot was 4.8m long and 3m wide (6
rows). The inside four rows were set aside for data collection to eliminate any border effects. All
the rest agronomic management of the crop were applied according to the recommended
methods. Finally, yam tuber plant in the central net plot area (9.6 m2) was harvested at normal
physiological maturity. The yam tubers were harvested manually using by hoeing and hand
picking.
Procedure of yam tuber cutting
Five
Yam
tuber
sample
randomly
taken
and
measured
their
average
weight
(500g+1000g+1500g+ 2000g+ 4000g =9000g/5=1800, 9kg/5=1.8kg) of tuber used to calculated
amount of cutting for each treatment and about 50,000 cutting required for a hectare (Table1).
313
Table.1. Yam tuber cutting in different size in gram and the amount of cutting per hectare.
1
2
3
4
5
6
Treatment
50-100g
101-150g
151-200g
201-250g
251-300g
whole size/control
Number Cutting
24
14
10
9
7
1.8kg
1kg/cutting
13.33
9.44
5.56
5
3.89
1.8kg
Kg/ha
3751
5297
8993
10000
12853
25000
Quintal/ha
37.51
52.97
89.93
100
128.53
250
Figure 1. Yam tuber cutting, their measurement and placement of tuber on appropriate space
Economics analysis
Net return (NR ha–1) and benefit: cost ratio (B: C) was calculated by considering the sale prices
of yam tuber (1kg = 6 birr) and labor for all field activities done. Thus, the economic gains of the
different treatments was calculated to estimate the net returns and the cost of cultivation, after
considering the cost of fertilizer N, NPS, and the income from marketable yam tubers for
economic analysis. Hence, following the CIMMYT partial budget analysis methodology, total
variable costs (TVC), gross benefits (GB) and net benefits (NB) will be calculated (CIMMYT,
1988).
Collected Data: Plant height/Vine length (cm), Tuber number per plant, Tuber length per plant
(cm), Tuber diameter per plant (cm), Tuber weight per plant in gram, Tuber weight per plot in
kilo gram and Tuber yield per ha in tone were collected.
Results and Discussions
Yam tuber tons per hectare, tuber weight per plant in kg and tuber weight per plot in kg
The combination analysis main effect yam tuber tone per hectare, tuber weight kilo gram per
plant and tuber weight per plot in kilo gram were showing significant (P<0.05) different between
yam cutting tuber of Bulch of variety.
Similarly, the interaction effect of tuber cutting size
weight of yield parameter tuber kilo gram per plant, tuber weight per plot in kilo gram and
tuber tone per hectare were significant influenced by location and years. The maximum yam
314
tuber yield was obtained by whole tuber size/ control (66.12 tons per hectare) and followed by
251-300g (57.95 tons per hectare), 201-250g (56.42 tons per hectare) and 1051-200g (54.4 tons
per hectare) (Table.2.).
Table 2. Main effect of yield component parameter of yam tuber
Treatment
50-100g
101-150g
151-200g
201-250g
251-300g
whole size
Mean
CV%
Year
2017
2018
Location
Bako
Gute
TRT
Yr
TRT*yr
TRT*Loc
yr*Loc
TWPkg
2.42d
2.74cd
3.21bc
3.15bcd
3.54b
5.44a
3.41
27.84
TWplkg
39.88d
46.56c
52.28bc
54.16b
55.63b
73.08a
53.6
13.25
TTONha
41.54d
48.5c
54.45bc
56.42b
57.95b
66.12a
55.83
13.25
2.96b
3.87a
72.27a
34.92b
75.28a
36.37b
2.68b
4.14a
**
**
**
**
**
52.28a
54.91a
**
**
*
**
**
54.46a
57.2a
**
**
*
**
**
Clue: TRT= treatment, g = gram, CV%= coefficient of variation in percentage, Yr = year, Loc = location,
TTONha= Tuber tone per hectare, TWpkg = tuber weight kilo gram per plant and TWPlkg = tuber weight
per plot in kilo gram
Figure.2. Yam tuber harvesting and their storage
Yam growth parameter
The combination analysis main effect yam growth parameter such as plant height in cm, vine
number per plant, tuber number per plant, tuber length per plant were significantly influenced by
yam cutting size weight ( P <0.05) but tuber diameter per plant not significantly affected by yam
tuber cutting size weight. The vigorously of yam vegetative were increasing as tuber cutting size
weight in gram increases (figare.3) and the maximum plant height, vine number per plant, tuber
315
Table.3. Main effect of yam growth parameter
Treatment
50-100g
101-150g
151-200g
201-250g
251-300g
whole size
Mean
CV%
Year
2017
2018
Location
Bako
Gute
TRT
Yr
Loc
Rep
TRT*yr
TRT*Loc
yr*Loc
TRT*yr*Loc
PHcm
2.69 c
2.74 c
3.17 a
2.99 c
3.06 b
3.39 a
3.01
12.48
VNP
3.05d
3.13cd
3.87a
3.8ab
3.27bcd
3.68abc
3.47
19.45
TNP
2.98b
3.63ab
3.32ab
4.03a
3.63ab
4.05a
3.61
25.77
TLPcm
18.03c
20.04bc
18.95bc
19.41bc
21.12b
20.63a
20.2
14.61
TDPcm
8.27ab
8.41ab
8.07ab
7.79b
8.33ab
8.85a
8.28
15.04
3.22a
2.79b
3.05b
3.88a
3.33b
3.89a
25.42a
14.97b
9.47a
7.1b
3.21a
2.80b
**
**
**
Ns
Ns
Ns
**
Ns
3.69a
3.24
*
**
**
Ns
*
*
ns
ns
4.12a
3.1b
*
*
**
Ns
Ns
Ns
*
Ns
20.83a
19.56a
**
**
ns
ns
ns
ns
ns
ns
8.2a
8.37a
Ns
**
Ns
Ns
Ns
Ns
Ns
Ns
Clue: TRT= treatment, g = gram, CV%= coefficient of variation in percentage, Yr = year, Loc = location, phcm= plant height in
cm, VNP= vine number per plant, TNP= Tuber number per plant, tlpcm= Tuber length per plant in cm, tdpcm= tuber diameter
per plant
number per plant, tuber length per plant and tuber diameter per plant were obtained by yam
whole size/ control and followed by 251-300g (Table.3)
Figure .2. Yam vegetative performance
Economic analysis
Table 4. Partial budget analysis
Return/Bir
r
Treatment Tuber yield Gross return Cost ofProduction
Net benefit birr/ha or Net return
Investment Net return
toneha-1
(Birr ha-1)
(Birr ha-1) or Total Cost (GR – PC)
Benefit:cost ratio(GR/PC (NR/PC) (Eth. Birr
ETB
vary birr ha-1
Eth. Birr
ha-1)
(Birr ha-1)
50-100g
41.54
249240
219605
29635
1.134947 0.134947
101-150g
48.5
291000
231427.7
59572.3
1.257412 0.257412
151-200g
54.45
326700
253603.7
73096.3
1.28823 0.28823 73096.3
201-250g
56.42
338520
259645.7
78874.3
1.303777 0.303777 78874.3
251-300g
57.95
347700
276763.7
70936.3
1.256306 0.256306 70936.3
whole size
66.12
396720
341543.8
55176.2
1.161549 0.161549
316
The result of the economic analysis (partial budget) for different yam tuber cutting size
presented on (Table. 4.) indicated that the treatment of (50-100g, 101-150g, 151-200g, 201-250g,
251-300g and whole size/control) the best ideal yam tuber cutting size were obtained highest net
return (Ethiopia Birr) per hectare from 201-250g (78874.3 birr)
followed by 151-200g
(73096.3birr) and 251-300g (70936.3 birr). The result of the economic analysis (partial budget)
for different yam tuber cutting size presented indicated that the treatment of the best ideal yam
tuber cutting size were obtained highest net return (Ethiopia Birr) per hectare from 201-250g
(78874.3 birr) followed by 151-200g (73096.3birr) and 251-300g (70936.3 birr). Therefore, the
best promising yam tuber cutting size weight 201-250g (78874.3 birr) followed by 151-200g
(73096.3birr) and 251-300g (70936.3 birr) and tuber yield tone hectare (57.95 tone/ha, 56.42
tone/ha and 54.45 tone/ha respectively were recommended for farmer or end user in western
Oromia of yam production area and similar agroecology.
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and yield of yellow yam (Dioscoreacayenensis) in Midwestern Nigeria. Afr. J. Biotechnol.
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Integrated Management of Barley Shootfly on the Highlands of Guji Zone, Southern
Oromia
Yared Tesfaye*1, Seyoum Alemu1, Kabna Asafa1, Girma Teshome1, Obsa Chimdesa1
1,
Oromia Agriculture Research Institute (IQQO), Bore Agricultural Research Center
*Corresponding author: Yared Tesfaye (yaredtesfa1990@gmail.com )
ABSTRACT
This study was initiated to assess the effect of Integrated Management of Barley Shoot flyon yield
and yield componenst of Barley (Hordeum vulgare L.). Afield experiment was conducted during
the 2017-18 main cropping season at Bore Agricultural Research Center to evaluate the effect of
integrated barley shoot fly management on yield and yield components of barely and to
determine an economically feasible optionshoot fly management for barley production. The
treatments consisted of five levels of insecticides (Apron star, Dynamic, Procid plus, Joint and
Torpido and four levels of planting dates. The experiment was laid out in Randomized Complete
Block Design (RCBD) in a factorial arrangement with three replications. Analysis of variance
revealed that interaction of the two factors (chemicals and planting dates) significantly affect
most parameters except thousand kernels weight, number of tillers per plant and number of
productive tillers per plant. Generally, all parameters recorded over all treated plots were
significantly higher than untreated/control plots. Thus using insecticides and adjusting planting
date can help to improve yield andyield components byreducing the degree of barley shoot fly
infestation. The highest grain yield (4403 kg h-1) and lower shoot flyinfestation wereachieved
from combined application of Torpido + first planting date. The partial budget analysis,
however, revealed that combined applications of Torpido insecticide and planting in the last
week of Julygave the best economic benefit 26941.78 Birr ha-1. Therefore, based on this study it
can be concluded that the use of Torpido insecticide and planting in the late Julycan be
recommended for production of barley in the study area and other areas with similar agroecological conditions.
Key words: Insecticides, interaction effect, main effect, shoot fly, sowing date
318
319
Introduction
In Ethiopia, cereal crops are majorly produced for several purposes where they are greatly
contributing towards sustaining food security. Farmers in different parts of the country are
growing different types of cereal crops based on their agro-ecological suitability to address their
family food demand. Particularly, farmers in high land parts of the country are producing barley
for home consumption and income generation. As a result, it's commonly called as a poor man’s
crop that is able to give yield in marginal environments that is unsuitable to other crops at higher
elevation (Zerihunet al., 2007). It ranks 5th in terms of area (993,918.89 ha) and production
(19,533,847.83) next to wheat and followed by finger millet (CSA, 2016).
The crop grows well at altitudes ranging between 1500–3500 masl but is predominantly grown at
altitudes ranging between 2000–3000 masl (MoA, 1998). The highlands of Guji Zone are also
found within most suited agro-ecological adaptation for barley crop production. Farmers in the
area are usually producing barley as major crop for home consumption as well as for cash
generation. It ranks second next to maize both in area (17,969.07 ha) and production
(315,115.09). However, the production and productivity of the crop remains lower (17.54qt/ha)
in relation to the national average (19.65qt/ha) and regional average (22.52qt/ha) productivity
(CSA, 2016). This may be due to several production constraints like in insect pests, diseases low
level of soil fertility, lack of improved varieties and others.
Barley shoot fly is one of the major biotic constraints to barley production on Guji highlands. A
survey of Barley shoot fly incidence and damage level conducted in 2014 and 2015 indicated that
there is high infestation which can cause high yield loss in susceptible varieties.However there is
no known management practices used by farmers so far. Therefore, there is a need to evaluate
and recommend different management options such integrated management which can be
economically and environmentally most viable and sustainable.The objective of the study was to
evaluate integrated approaches in barley shoot fly management and recommend the best option.
Materials and Methods
Description of the study area
The experiment was conducted at two locations of Bore district which represents highland agroecology of Guji Zone.Bore district is located at 385 km from Finfinnee to the South. The climatic
conditions of the district comprise an annual rain fall of 1250mm, mean temperature of 17.5-28
320
Degree Celsius. Bore district was selected for this experiment as it represents the hotspot areas
for barley shoot fly infestation.
Experimental design and treatments
For this experiment five insecticides namely Joint, Torpido, Dynamic, Proced Plus and
Apronstarand four planting datesat seven days interval were used.The experiment was laid out in
RCBD with three replications. Each experimental plot has 2.5 m long and 1.2 m wide, with six
rows 20 cm apart, giving a gross plot area of 3 m2. Spacing for adjacent blocks was 1.5 m and 1
m between plots. Sowing was done by hand drilling and covered lightly with soil. Seed and
fertilizer were applied as per the recommendation ratesfor barley production.All other agronomic
practices were also applied as recommended for barley production.
Data collection
Data were collected from a net plot of four rows and selected plants. Collected data includedays
to heading (DTH), days to 90% maturity (DTM), grain filling period (GFP), plant height (PH),
spike length (SL), total number of tillers/plant, total number of fertile tillers/plant, 1000-kernel
weight (TKW), grain yield/ha (Gy kg/ha) and shoot fly infestation.
Data analysis
The recorded data were subjected to Analysis of Variance (ANOVA) as suggested by Gomez
and Gomez (1984) using GenStat 18thVersion. Mean separation was carried out using Least
Significant Difference (LSD) at 5 percent levels of significance.
Results and Discussion
Days to heading: The Analysis of Variance revealed that the main effect of planting date was
highly significant (P < 0.01) on days to heading of barley while the two-factor interactions of
Chemical × planting datessignificantly (P<0.05) influenced days to 50% heading. However, the
main effect of insecticide did not significantly affect days to 50% heading of the crop. The
highest prolonged duration to reach 50% heading was observed in response to the combination of
planting date one and two across all pesticides. However, the minimum duration to 50% heading
was observed in the application of Apronsarat fourth planting date (Table 1).
Table 15. Interaction effect of chemical and planting date on days to heading of barley
Insecticides
Control
Days to heading
Planting dates
P1
P2
a
83
P3
a
83.33
P4
bc
79.33
Days to maturity
Planting dates
P1
P2
e
75
a
146
P3
c
139.7
P4
d
134.3
e
127.3
321
Insecticides
Apronstar
Dynamic
Proced
Joint
Torpido
Days to heading
Planting dates
P1
P2
a
83
a
a
a
b
e
bcd
cd
e
c
b
d
d
133.3
c
139.7
b
e
127.3
c
b
e
127.3
133
139.3
144
P4
d
133
139
144
e
74.67
P3
c
139.7
144
74.67
79
a
e
bcd
a
b
144
75
79
83
a
d
a
a
f
73.33
78.33
83
83
b
79.67
83
83
P4
a
83
83
P3
Days to maturity
Planting dates
P1
P2
e
126.3
d
133.7
c
d
e
126
e
83
83
78.67
75
144
139.3
133
127.3
LSD(0.05)
0.91
1.55
CV (%)
0.7
0.7
Means with the same letter(s) in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, NS= non - significant, LSD = Least Significant Difference at 5% level
Days to physiological maturity: The main effect of planting dateand interaction of the factors
highly significantly (P < 0.01) influenced days to physiological maturity of barley. But the main
effect ofinsecticidesdid not significantly affect days to physiological maturity.
The longest physiological maturity (168.7 days) was recorded forthe first planting date with
control/untreated whereas the shortest days to physiological maturity (126 days) was recorded
from combination of Joint and the fourth planting date. The increase in days to maturity of
barleyfor the controltreatment might be due to rejuvenation of the crop though the level of
infestation wasthe highest.
Plant height: The two factor interaction and main effect of insecticides significantly (P < 0.05)
influenced plant height. On the other hand, the main effect of planting date had no significant
effect on this same parameter.The result indicated that height of barley plants increased as
infestation was decreased (Table 2). The highest plant height (115.8cm) was recorded for
insecticide Joint coupled with the second planting date while the shortest plant height (101.9cm)
was recordedfor the controlcombined with third planting date of the two factors.
Spike length
The Analysis of Variance revealed significant (P < 0.05) interaction of the two factorsand main
effect of planting date on the spike length whereas the main effect of chemical did not have
significant effect on this parameter. Thus, the longest spikes (9.00 cm) were obtained
fortreatment combination of insecticide Joint and the first planting date whereas the shortest
spikes were produced for the combination of the Procedplus and first planting date(Table 2). The
highest spike length of the treated plotsin relation to the untreated control might have resulted
322
from improved root growth and increased uptake of nutrients and better growth
favouredbyreduced shoot fly infestation.
Table 16. Main effect of chemicals and planting date on plant height and spike length of Barley
Insecticides
Control
Aprstar
Dynamics
Proced
Joint
Torpido
LSD(0.05)
CV (%)
Plant height (cm)
Planting dates
P1
P2
103.6
113.7
111.0
110.2
112.8
107.6
9.33
5.2
cd
ab
a-d
a-d
abc
a-d
102.5
115.1
105.1
109.4
115.8
114.2
P3
d
a
bcd
a-d
a
ab
101.9
110.6
111.2
113.1
109.7
109.1
Spike length (cm)
Planting dates
P1
P2
P4
d
a-d
a-d
ab
a-d
a-d
102.7
102.6
113.8
114.6
107.8
115.1
d
d
ab
a
a-d
a
7.778
7.5 0
def
7.833
7 .00
9.00
c-f
c-f
f
8.500
8.222
8.944
a
7.111
0.98
7.3
8.944
8.667
ef
8.611
P3
ab
abc
a-d
ab
abc
abc
P4
8.944
7.889
8.00
ab
c-f
b-e
8.611
8.167
7.889
abc
a-d
c-f
abc
8.611
c-f
7.944
a-d
8.278
8.5 00
8.111
8.444
abc
a-d
a-d
Yield and Yield Components
Number of tillers per plant
The main effect of chemical and planting date did not significantly (P<0.05) influence the
number of tillers of barley. Similarly the two-factor interaction (chemical × planting date) also
did not significantly affect this parameter. This finding agrees with that of Wakeneet el (2014).
Table 17. Interaction effect of chemical and planting date on number of tillers and number of productive
tiller per plant of barley
Number of tiller/plant
Number of fertile tiller/plant
Planting dates
Planting dates
Insecticides
P1
P2
P3
P4
P1
P2
P3
Control
3.222
3.667
3.722
3.611
2.833
3.167
3.278
Apr
3.667
3.056
3.556
3.389
3.222
2.833
3.056
Dyn
3.50
3.444
3.389
3.278
3.111
3.00
3.00
Pro
3.722
3.50
3.389
3.722
3.167
3.111
3.056
Join
3.389
3.389
3.50
3.389
2.889
2.944
3.056
Torp
3.778
3.278
3.444
3.722
3.222
2.722
3.056
LSD(0.05)
NS
NS
CV (%)
9.9
11.2
Means with the same letter(s) in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, LSD= Least Significant Difference at 5% level
Number of productive tillers
The main effect of insecticide and planting date did not significantly (P<0.05) influence the
number of productive tillers of barley. Similarly the two-factor interaction (insecticide × planting
date) also did not significantly affect this parameter.
323
Thousand kernels weight
The main effect of insecticide and planting date did not significantly (P< 0.05) influence
thousand kernels weight of barley. Similarly the two-factor interactions did not significantly
affect thousand kernels weight.The highest thousand kernels weight (60.42 g) was recorded
forcombined application of Torpidowiththe first planting date whereas the minimum thousand
kernel weight (32.11 g) was observed for application of Torpidocombined with fourth planting
dateeven though there were not statistically significant differences.
Table 18. Interaction effect of chemicals and planting date on number of kernels per spike of barley
Chem.
Control
Apr
Dyn
Pro
Join
Torp
Grain yield (kg/ha)
Planting date
P1
P2
de
2296
2724
ab
3961
a-e
2185
b-e
ab
3894
ab
3853
4403 a
ab
a-e
b-e
37.7
a-e
60.42
39.17
40.54
46.07
ab
44.42
36.27
35.73
38.98
b-e
36.22
42
36.32
50.64
b-e
48.53
36.17
36.19
32.11
2721
a-e
3231
36.82
3962
2880
abc
3727
36.38
3277
3967
3108
42.11
2667
b-e
cde
b-e
2352
2886
2468
P4
48.71
de
a-d
3543
2728
P3
40.41
P4
e
b-e
2719
3331
P3
b-e
TKW (g)
Planting date
P1
P2
36.69
37.47
2877
LSD(0.05)
1327.15
NS
CV (%)
25.6
17.8
Means with the same letter in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, LSD=Least Significant Difference at 5% level
Grain yield
The main effects of insecticide and planting date and their interactions significantly (P< 0.05)
affected grain yield of barley.Late sowing significantly decreased grain yields. Thus, the highest
grain yield (4403 kg ha-1) was obtained from combined application of Torpidoand first planting
date and it was statistically at par with Procedeplus at first planting date and Joint applied for the
first planting date whereas the lowest grain yield (2185 kg ha-1) was recorded from the
combinations of control of third planting date (Table 4). The highest grain yield at the Torpido
and first planting date might have resulted from better growth favouredbydecreased shoot fly
infestation which enhanced yield components and yield.In general, grain yield obtained from the
treated plots exceeded the grain yield from the untreated/control plots by about 33.13%.
Barley shoot fly Infestation
The main effects of insecticide and their interactions significantly (P< 0.0) affected the barley
shoot fly infestation. The highest infestation (62.84) was obtained from combination of control
and third planting date whereas the lowest barley shoot fly infestation recorded from application
324
of Torpido at first planting date (Table 5).This indicated that grain yield is correlated with
infestation level.
Partial Budget Analysis
Analysis of the net benefits, total costs that vary and marginal rate of returns are presented in
Table 5 below. Information on costs and benefits of treatments is a prerequisite for adoption of
technical innovation by farmers. The studies assessed the economic benefits of the treatments to
help develop recommendation from the agronomic data. This enhances selection of the right
combination of resources by farmers in the study area. As indicated in table below, the partial
budget analysis showed that the highest net benefit (Birr 26941.78 ha-1) was recorded at the
combination of Torpido and first planting date and lowest was from control treatment. To use the
marginal rate of return (MRR%) as basis of recommendation, the minimum acceptable rate of
return should be between 50 to 100% (CIMMYT, 1988). In this study application of Torpido at
first planting date gave the maximum economic benefit (26941.78 ha-1). Therefore, on economic
grounds, application of Torpido at 250ml/100kg seed as seed dressing and sowing at late
Julywould be best and recommended for production of barley in the study area and other areas
with similar agro-ecological conditions.
Table 19. Partial budget and marginal rate of return analysis for management of barley shoot fly through
chemical and planting date
Treatments
Insecticidesplanting date
Control
P2
Control
P3
Control
P4
Control
P1
Dynamic
P2
Dynamic
P1
Dynamic
P3
Dynamic
P4
Apron Star
P2
Apron Star
P1
Apron Star
P3
Apron Star
P4
Procideplus
P1
Procideplus
P4
Procideplus
P3
Procideplus
P2
Joint
P2
Joint
P4
Joint
P3
Joint
P1
AGY by 10%
GB (Birr ha-1)
TVC
NR (Birr ha-1)
2451.82
1966.87
2116.47
2066.61
2455.61
2997.69
2597.48
2949.21
2447.40
3565.01
3188.26
2400.05
3504.43
3565.73
3570.14
2221.41
2796.98
2448.54
2592.44
3467.49
17162.76
13768.08
14815.32
14466.24
17189.28
20983.86
18182.34
20644.44
17131.80
24955.08
22317.84
16800.36
24531.00
24960.12
24990.96
15549.84
19578.88
17139.78
18147.06
24272.40
0
0
0
0
475
475
475
475
550
550
550
550
690
690
690
690
800
800
800
800
17162.76
13768.08
14815.32
14466.24
16714.28
20508.86
17707.34
20169.44
16581.80
24405.08
21767.84
16250.36
23841.00
24270.12
24300.96
14859.84
18778.88
16339.78
17347.06
23472.40
325
Torpido
Torpido
Torpido
Torpido
P1
P4
P3
P2
3963.11
2589.70
2908.05
3353.99
27741.78
18127.92
20356.32
23477.94
800
800
800
800
26941.78
17327.92
19556.32
22677.94
AGY:adjusted grain yield, GB:groth benefitTVC:total variable cost, NR: net return
Conclusion
Analysis of the results revealed that interaction of the two factors (insecticides and planting
dates) significantly affected almost all parameters except thousand kernels weight, number of
tiller per plant and number of productive tiller per plant. Generally, all parameters recorded over
all treated plots were significantly higher than untreated/control plot. Thus using of insecticide
and adjusting planting date improved yield and yield components anddecreaseed barley shoot fly
infestation. The highest grain yield (4403 kg h-1) was obtained from combined application of
Torpidoand first planting date whereas the lowest barley shoot fly infestation recorded from
combined application of Torpidoand first planting date. The partial budget analysis revealed that
combined applications of Torpido insecticide and planting in the last week of Julygave the best
economic benefit 26941.78 Birr ha-1. Therefore, based this study it can be concluded that
combined application of this chemical and planting date can be recommended for farmers for
production of barley in the study area and other areas with similar agro-ecological conditions.
References
CIMMYT (Centro Internacional de Mejoramiento de Maiz y Trigo/International Maize and
Wheat Improvement Center). 1988. From Agronomic data to Farmer Recommendations: An
Economic work Book. Mexico, D.F.: CIMMYT.
CSA (Ethiopian Central Statistical Agency). 2016. Agricultural sample survey Report on area
and production for major crops 1: 1-118
MoA (Ministry of Agriculture), 1998. National Livestock Development Project (NLDP)
Working Paper 1-4. AddisAbaba, Ethiopia.
Shegaw Derbew,2017.Adaptations of Exotic Barley Genotypes to Low moistureEnvironments in
Southern Ethiopia.Greener Journal of Agricultural Science. DOI:
http://doi.org/10.15580/GJAS.2017.6.071617088) Vol. 7 (6):126-131
Zerihun J., 2007. Variability and Association of yield and yield-related traits in some barley
(Hordeum Vulgare L.) landraces and Crosses. An M.Sc. Thesis presented to the school of
Graduate Studies of Haramaya University.
326
Effect of Blended NPS and N Fertilizer Rates on Yield, Yield Components, and Grain
Protein Content of Bread Wheat (Triticum aestivumL.) in Bore District, Guji Zone,
Southern Ethiopia
Seyoum Alemu*1, TamadoTana2, Jemal Abdulahi2,
1
Oromia Agriculture Research Institute (IQQO), Bore Agricultural Research Center
2
Department of Plant Science, Haramaya University, Haramaya, Ethiopia
*Corresponding author: Seyoum Alemu (seyoum23@gmail.com )
Abstract
Bread wheat is a major cereal crop in Ethiopia and in the study area, but its yield is limited due
to minimum use of improved varieties, diseases, weeds, low soil fertility and lack of location
specific fertilizer recommendation. Therefore, a field experiment was conducted during the
2017/18 main cropping season at Bore Agricultural Research Center to evaluate the effect of
blended NPS and N fertilizer rates on yield components, yield and grain protein content; and to
determine economically appropriate rates of blended NPS and N fertilizers for bread wheat
production. The treatments consisted of four levels of NPS (50, 100, 150 and 200 kg NPS ha-1)
and four levels of N (23, 46, 69 and 92 kg N ha-1) including one control (0 NPS and 0N). The
experiment was laid out as a RCBD in a factorial arrangement with three replications. Analysis
of the results revealed that interaction of the two fertilizers significantly affect grain yield, above
ground dry biomass, date to heading, number productive tillers, plant height, spike length, straw
yield, harvest index, hectoliter and grain protein content of bread wheat while date to maturity
and thousand kernels weight affected only by main effect of NPS and N.
Generally, all
parameters recorded over all treated plots were significantly higher than unfertilized/control
plot except date to mature, number of tiller per plant and number of kernels per spike. Thus
using of NPS and N fertilizers improve yield components, yield and quality parameters of bread
wheat. The highest grain yield (6.416 t h-1) was obtained from combined application of 150 kg
NPS ha-1 + 46 kg N ha-1whereas the highest grain protein content was recorded from application
of 200 kg NPS ha-1 + 92 kg N ha-1 .The result of economic analysis showed that combined
application of 150 kg NPS and 46 kg N ha-1 gave economic benefit of 93319.68 Birr ha-1 with the
marginal rate of return 32876.5%. Therefore, use of 150 kg NPS and 46 kg N ha-1 can be
recommended for production and productivity of bread wheat in the study area and other areas
with similar agro-ecologies.
327
Key words: Grain yield, interaction effect, main effect, quality, synergetic effect
Introduction
Wheat (Triticum aestivumL.) belongs to the grass family Poaceae and to the tribe Hordeae in
which several-flowered spikelet are sessile and alternate opposite side of the rachis forming a
true spike (Feldman and Sears, 1981). It is one of the most important food grain crops grown in
the world. It ranks first in the world cereal crops accounting for 30% of all cereal food
worldwide and is a staple food for over 10 billion people in as many as 43 countries of the world
(Reddy, 2004). It provides about 20% of the total food calories for the human race (Reddy,
2004). It is cultivated in Ethiopia on about 1.66 million hectares and contributing about 4.22
million tons of grain yields, accounting for 15.81 percent of total grain output in the country
during 2015/16 meher cropping season (CSA, 2016).
Wheat is one of the major staple crops in Ethiopia in terms of both production and consumption.
In terms of caloric intake, it is the second most important food in the country next to maize
(FAO, 2014). Wheat is mainly grown in the highlands of Ethiopia, which lie between 6 - 16° N
and 35 - 42° E, at altitudes ranging from 1500 to 2800 meters above sea level and with mean
minimum temperatures of 6°C to 11°C (Hailu, 1991; MoA, 2012). There are two types of wheat
grown in Ethiopia: durum wheat, accounting for 40 percent of production, and bread wheat,
accounting for the remaining 60 percent (Bergh et al., 2012). Oromia region accounts for over
half of national wheat production (58 percent), followed by Amhara (28 percent); Southern
Nations, Nationalities and Peoples Region (SNNPR) (7.9 percent); and Tigray (4.2 percent)
(CSA, 2016). Of the total wheat production area, about 75 percent is in the Arsi, Bale and Shewa
wheat belts (MoA, 2012). Data from the Central Statistics Agency (CSA) indicated that the
observed increase in wheat production over the last ten years can be attributed both to expansion
of production area and adoption of improved technologies. For example, between 1995/96 and
2014/15 wheat production area increased from 0.8 million ha to 1.66 million ha, and yield
increased from 1.20 t ha-1 to 2.54 t ha-1 (CSA, 2015). The study area (Guji Zone) is also one of
wheat producing Zones of Oromia which covers an area of 4,879.92 ha with production of
11,795.435 t and yield of 2.4 t ha-1 in 2015/16 cropping season (CSA, 2016). Thus, wheat yield
in Ethiopia and the study zone is well below the experimental yield of above 5 t ha-1 (Hailu ,
1991, MoA, 2012).
328
Despite an increase in production and productivity trends, wheat is still the single most important
staple food crop imported from abroad and most of the humanitarian food aid and commercial
import takes in the form of wheat (Demeke and Di Marcantonio, 2013). To feed the growing
human population and fill the yield gaps between wheat consumption and production in Ethiopia,
increasing production of wheat is of paramount importance. Increasing wheat production in
Ethiopia can be achieved by increasing productivity of smallholder producers in the mid and
highlands areas and by bringing more area into wheat production in the lowlands. On the other
hands, in the mid and highlands, wheat production is constrained by both biotic and abiotic
factors such as diseases and pests, poor management practices, poor soil fertility and moisture
stresses. Poor agronomic and soil management, inadequate level of technology generation and
adoption are the most significant constraints to increase wheat production in the highlands and
mid highlands of Ethiopia (Hailu et al., 1990;Demeke and Di Marcantonio, 2013).Thus, addition
of nutrients such N, P and S to low fertile soil is important to increase wheat yield, yield
components and quality of wheat whether it is for consumption or industrial purpose.
Most Ethiopian soils are deficit in nutrients, especially nitrogen and phosphorus and fertilizer
application has significantly increased yields of crops (Tekalignet al., 2001). The causes for
severe deficiency of most of the major nutrients (nitrogen and phosphorus) in Ethiopian
highlands and midlands are the huge loss of soil from agricultural land, which is estimated to be
137 t ha-1 per year; approximately an annual loss of 10 mm soil depth (Zelekeet al., 2010).
Annual nutrient deficit also estimated to be - 41 kg N, -6 kg P and -26 kg K ha-1 (Fassil and
Charles, 2009). A range of environmental factors, such as low soil nitrogen and phosphorus
levels, and acidic soil conditions are important constraints for wheat production in most areas
where the crop is grown. Several researchers in Ethiopia have reported the role of N and P in
wheat production in the highlands indicating that substantial increases in yield and yield
components have been obtained with the application of N fertilizer (SchulthesIset al., 1997;
Amanuelet al., 2000; Tilahunet al., 2000; Muluneh and Nebyou, 2016; Lelagoet al., 2016).
According to Ethiosis (2013 and 14) and Tegbaru (2014). In addition to N and P, S is found to be
low in the major Ethiopian soils. However, Ethiopian farmers used to apply only chemical
fertilizers di-ammonium phosphate (DAP) and urea to increase crop yields for about five decades
and this did not consider soil fertility status and crop requirements. For instance, in southern
Ethiopia (study area), farmers apply 100/50 kg ha-1 DAP/Urea for wheat irrespective of the
329
heterogeneity of the farm areas. In contrast to this, Tegbaru (2014); Fanuel (2015); and Okubayet
al. (2015) reported that agricultural fields are not homogenous and soil macro nutrient status is
highly variable. In addition to this, DAP and urea supply only P and N but not other nutrients
such as K and S. The omission of these nutrients from the fertilizer package was due to the fact
that when the fertilizer was tested (45 years ago) at the national fertilizer demonstration by
Ministry of Agriculture and the Food and Agriculture Organization of the United Nations, no
consistent trend was observed. In addition, a soil fertility survey conducted by Murphy (1968),
found no deficiency of these nutrients in Ethiopian soils. However, in more recent studies,
Abiyeet al. (2004), and Wassie& Shiferaw et al. (2011) reported the deficiency of these nutrients
in some Ethiopian soils. Moreover, the soil fertility mapping project in Ethiopia reported the
deficiency of K, S, Zn, B and Cu in addition to N and P in major Ethiopian soils and thus
recommend application of customized and balanced fertilizers (Ethiosis, 2014).
The farmers in most parts of the country in general and Bore district in particular have limited
information on the impact of different types and rates of fertilizers except blanket
recommendation of nitrogen (41 kg N ha-1) and phosphorus (46 kg P2O5 ha-1) i.e. 50 kg Urea and
100 kg DAP per ha-1 while according to the soil fertility map made over 150 districts, most of the
Ethiopian soils lack about seven nutrients (N, P, K, S, Cu, Zn and B) (EthioSIS, 2013). Muluneh
and Nebiyou (2016) reported the maximum yield (6.4 t ha-1) at 50/150 kg NP ha-1. Malik et al.
(2003) also reported an increment of 25% protein content from a plot fertilized at a combination
of 50/75 kg NP ha-1 as compared to other combinations of NP (25/50 kg ha-1). Similarly, Yasir et
al. (2015) reported the maximum grain yield of wheat (4463.5 kg ha-1) at 140 kg N ha-1 and 20
kg S ha-1 at sowing and at anthesis respectively.
Except the blanket recommendation of nitrogen and phosphorus, the effect of other fertilizers on
yield components, yield, and overall performance and quality of bread wheat are also unknown,
even though new blended fertilizers such as NPS (19% N, 38% P2O5 and 7% S) are currently
being used by the farmers in Ethiopia, including the study area. In addition to this, the amount of
N in the blended NPS is small as compared to the requirement of wheat. Thus, there is a need to
supplement with nitrogenous fertilizer in the form of urea. Moreover, the response of wheat plant
to application of fertilizer varies with varieties (Fageriaet al., 2008), rainfall (Scharf et al., 1993),
soils (Wissumet al., 2009) agronomic practices (Haile, 2011) etc. Thus, there is a need to
develop location specific recommendation on the fertilizer rates to increase production and
330
productivity as well as quality of wheat. Therefore, this study was undertaken with the following
objectives: To evaluate the effect of rates of blended NPS and N fertilizers on yield components,
yield and protein content of bread wheat and to determine economically appropriate rates of
blended NPS and N fertilizers for bread wheat production.
3. Materials and Methods
3.1 Description of the study area
The experiment was conducted at Bore Agricultural Research Center (BoARC), Oromia
Regional State in southern Ethiopia under rain-fed conditions for two cropping season. The site
is located about 8 km North of the town in SongoBericha ‘Kebele’ just on the side of the main
road from Bore to Hawassa city. It is about 378 km far from capital Addis Ababa to south
o
direction. Geographically, the experimental site is situated at latitude of 6 26' 52” North and
o
longitude of 38 56' 21” East at an altitude of 2736 meters above sea level (masl). The research
site represents highlands of Guji Zone, receiving high rainfall and characterized by a bimodal
rainfall distribution. The first/major rainy season is from April up to October and the second
season starts in late November and ends at the beginning of March. The major soil types of Bore
are Nitosols (red basaltic soils) and Orthic Aerosols (Wakeneet al., 2014). The soil is clay- loam
in texture and strongly acidic with pH value of around 5.15 (Table 2).
3.2. Experimental Materials
3.2.1. Plant material
Bread wheat variety “Huluka (ETBW5496)” was used as planting material. The variety was
released by Kulumsa Agricultural Research Center (KARC) in 2011/12 cropping season and
hashas a maturity period of 133 days with the yielding potential of 3.8 – 7.0 t ha-1 (MoA, 2012).
Variety Hulukawas selected on the basis of its adaptation and better agronomic performance in
the study area.
3.2.2. Fertilizer materials
Blended NPS (19% N, 38% P2O5 and 7% S) and Urea (46% N) was used as the sources of
fertilizers.
3.3. Treatments and Experimental Design: The treatments consisted of factorial combination of
four N levels (23, 46, 69, 92 kg ha-1) and four levels of blended NPS (50,100, 150, 200 kg ha-1)
fertilizer with one control (0 NPS and 0 N). The experiment was laid out in a randomized
complete block design (RCBD) with three replications in factorial arrangement of 4 × 4 = 16
331
treatment combinations together with the one control treatment, making a total of 17 treatments.
The gross size of each plot was 2 m × 3 m (6 m2) consisting of ten rows and the distance between
adjacent plots and blocks were 0.5 m and 1 m apart, respectively. The net plot area was 1.6 m ×
2.6 m (4.16 m2) and consisted of eight rows of 2.6 m length. The outermost row on both sides of
each plot and 20 cm on both sides of each rows were considered as border plants and were not
used for data collection to avoid border effects. The details of the treatment combinations and
their nutrient contents are shown in Table 1.
3.4. Soil Sampling and Analysis
Soil samples were randomly taken from the experimental site following a zigzag pattern before
planting at a depth of 0-30 cm across the experimental field from 15 spots using auger before
planting and composited. Then, the collected samples were air-dried at room temperature under
shade and submitted to laboratory where they were ground to pass through a 2 mm sieve whereas
for organic carbon (OC) and nitrogen (N) determination, the soil was ground to pass through a 1
mm sieve. Working samples (1 kg) were obtained from prepared sample/composite and analysed
for selected physico-chemical properties mainly for soil texture, soil pH, cation exchangeable
capacity (CEC), organic carbon, total N, available P and S using standard laboratory procedures
at Horticoop Ethiopia soil and water analysis laboratory.
Organic carbon was determined by the Walkley and Black oxidation method (Walkley and
Black, 1934) while total nitrogen was analysed by the Kjeldhal method (Dewis and Freitas,
1970). The pH of the soil was determined at 1:2.5 (weight/ volume) soil to water dilution ratio
using a glass electrode attached to digital pH meter (Page, 1982). Cation exchange capacity was
measured after saturating the soil with 1N ammonium acetate (NH4OAC) and displacing it with
1N NaOAC (Chapman, 1965) and available phosphorus was determined using the Bray method
(Bray and Kurtz, 1945). Available S was determined using turbid metric method (Chesnin and
Yien, 1951).
3.5. Experimental Procedures and Field Management
The experimental field was ploughed with tractor and oxen to a fine tilth four times and the plots
were levelled manually. According to the design, a field layout was made and each treatment
was assigned randomly to the experimental units within a block. Bread wheat seeds were sown at
the recommended seed rate of 150 kg ha-1 in rows of 20 cm spacing manually by drilling on 27
July 2016. The whole of NPS and ½ N fertilizers was applied at sowing while the remaining ½ N
332
was applied at mid-tillering stage as top dressing. Weeding was done as needed and harvesting
and threshing were done manually.
3.6. Data Collection and Measurement
3.6.1. Crop phenology and growth parameters
Days to 50% heading (DTH): days to spike heading was determined as the number of days
taken from the date of sowing to the date of 50% heading of the plants from each plot by visual
observation.
Days to 90% physiological maturity (DTM): days to physiological maturity was determined as
the number of days from sowing to the date when 90% of the peduncle turned to yellow straw
colour.
Plant height (cm): plant height was measured from the soil surface to the tip of the spike (awns
excluded) of 10 randomly tagged plants from the net plot area at physiological maturity.
Spike length (cm): It was measured from the bottom of the spike to the tip of the spike
excluding the awns from 10 randomly tagged spikes from the net plot.
Lodging percent: The degree of lodging was assessed just before the time of harvest by visual
observation based on the scales of 1-5 where 1(0-15o) indicates no lodging, 2(15-30o) indicate
25% lodging, 3(30-45o) indicate 50% lodging, 4(45-60o) indicate 75% lodging and 5(60-90o)
indicate 100% lodging (Donald, 2004). The scales were determined by measuring the angle of
inclination of the main stem from the vertical line to the base of the stem by visual observation.
However, none of the plots indicated lodging and hence no data were recorded and reported
3.6.2. Yield components and yield
Number of tillers per plant: number of tillers per plant was determined from 10 tagged plants
per net plot at physiological maturity by counting the number of tillers after removing soils
surrounded the tillers.
Number of productive tillers: number of productive tillers was determined at maturity by
counting all spikes bearing tillers from two rows of 0.5 m length per plot at physiological
maturity.
Number of kernels per spike: the mean number of kernels per spike was computed as an
average of 10 randomly taken spikes from the net plot area.
Thousand kernels weight (g): thousand kernels weight was determined based on the weight of
1000 kernels sampled from the grain yield of each net plot by counting using electronic seed
333
counter and weighed with electronic sensitive balance. Then the weight was adjusted to 12.5%
moisture content.
Above ground dry biomass (t ha-1): the above ground dry biomass was determined from plants
harvested from the net plot area after sun drying to a constant weight and converted to tons per
hectare.
Grain yield (t ha-1): grain yield was taken by harvesting and threshing the seed yield from net
plot area. The yield was adjusted to 12.5% moisture content as:
Adjusted grain yield = (100 - MC) × unadjusted grain yield
100 - 12.5
Where MC- is the moisture content of bread wheat seeds at the time of measurement and 12.5 is
the standard moisture content of bread wheat in percent. Finally, yield per plot was converted to
per hectare basis and the yield was reported in t ha-1.
Straw yield (kg ha-1): Straw yield was obtained as the difference of the total above ground dry
biomass and grain yield.
Harvest index (HI): harvest index was calculated as ratio of grain yield per plot to total above
ground dry biomass yield per plot expressed as percent.
3.6.3. Grain quality parameters
Hectolitre weight: It is the weight of flour density produced in a hectoliter of the seed and it was
measured using a standard laboratory hectoliter weight apparatus.
Grain Protein Content (GPC): Grain protein content was determined by using “MININFRA
SMART GRAIN ANALYSER” equipment at Sinana Agricultural Research Center. After
calibrating the equipment for bread wheat, cleaned and prepared sample of 300 g seeds were
added to the equipment and waited for one minute. Then the equipment read grain protein near
infrared and displayed on screen as well as printing on paper.
3.7. Statistical Data Analysis
All data collected were subjected to analysis of variance (ANOVA) procedure using GenStat
(15th edition) software (GenStat, 2012). Comparisons among treatment means with significant
difference for measured characters were done by using Fisher’s protected Least Significant
Difference (LSD) test at 5% level of significance.
3.8. Partial Budget Analysis
334
The economic analysis was carried out by using the methodology described in CIMMYT (1988)
in which prevailing market prices for inputs at planting and for outputs at harvesting were used.
All costs and benefits were calculated on ha basis in Birr. The concepts used in the partial budget
analysis were the mean grain yield of each treatment, the gross benefit (GB) ha-1 (the mean yield
for each treatment) and the field price of fertilizers (the costs of NPS and Urea and the
application costs).The benefit of straw yield was not included in the calculation of the benefit
since the farmers in the area do not use it. Marginal rate of return, which refers to net income
obtained by incurring a unit cost of fertilizer and its application, was calculated by dividing the
net increase in yield of bread wheat due to the application of each fertilizers rate. The net benefit
(NB) was calculated as the difference between the gross benefit and the total cost that vary
NB= (GY x P) – TCV
(TCV) using the formula
Where GY x P = Gross Field Benefit (GFB), GY = Adjusted Grain yield per hectare and P =
Field price per unit of the crop.
Actual yield was adjusted downward by 10% to reflect the difference between the experimental
yield and the yield farmers could expect from the same treatment.
The dominance analysis procedure as described in CIMMYT (1988) was used to select
potentially profitable treatments from the range that was tested. The discarded and selected
treatments using this technique were referred to as dominated and undominated treatments,
respectively. For each pair of ranked treatments, % marginal rate of return (MRR) was calculated
using the formula MRR (%) =
Change in NB (NBb−NBa)
Change in TCV (TCVb−TCVa)
× 100
Where NBa = NB with the immediate lower TCV, NBb = NB with the next higher TCV, TCVa =
the immediate lower TCV and TCVb = the next highest TCV.
The % MRR between any pair of undominated treatments was the return per unit of investment
in fertilizer. To obtain an estimate of these returns, the % MRR was calculated as changes in NB
(raised benefit) divided by changes in cost (raised cost). Thus, a MRR of 100% implied a return
of one Birr on every Birr spent on the given variable input.
The fertilizer cost was calculated for the cost of each fertilizer of NPS (Birr 16 kg-1) and
N/UREA (Birr 14 kg-1) during sowing time. The cost of NPS and Urea application (Birr 525 ha1
) and the average open price of bread wheat at Bore market was Birr 7 kg-1 in January 2017
during harvesting time.
4. RESULTS AND DISCUSSIONS
335
4.1. Soil Physico-Chemical Properties of the Experimental Site
The laboratory results of the analysis of the selected physico-chemical properties of the soil
before sowing is presented in Table 1. The analytical results of the experimental soil indicated
that the soil textural class is clay loam with a particle size distribution of 38% clay, 30% silt and
31% sand. Thus, the soil of the experimental site is suitable for wheat cropping. The pH of the
soil was 4.99, which is strongly acidic according to the rating of Tekalign(1991). FAO (2000)
reported that the preferable pH ranges for most crops and productive soils are 4 to 8. Mengel and
Kirkby (1996) reported optimum pH range of 4.1 to 7.4 for wheat production. Thus, the pH of
the experimental soil was within the range for productive soils.
Organic carbon content of the experimental site was 2.8% which is considered to be
moderateaccording to Tekalign (1991). The analysis further indicated that the soil has medium
total nitrogen (0.25%) according to the rating of Tekalign (1991). The results of the analysis also
indicated that the soil has low available phosphorus content (9.03 mg/kg) according to the rating
of Cottenie (1980). The analysis for available sulfur also indicated that the experimental soil had
values of 18.22 mg/kg which is low according to Ethiosis (2014).
The CEC value of the soil sample is high (30.71 [Cmol (+) kg-1 soil] according to the rating of
Landon (1991) which indicates that the soil has high capacity to hold exchangeable cations.
Table 20.Selected physico-chemical properties of the soil of the experimental site before planting
Parameter
Result
Rating
Reference
Soil texture
Clay (%)
38
Sand (%)
31
Silt (%)
30
Textural Class
Clay loam
pH (1 : 2.5 H2O)
4.99
Strongly acidic
Tekalign (1991)
Total N (%)
0.25
Medium
Tekalign (1991)
Organic Carbon (%)
2.80
Moderate
Tekalign (1991)
Cation Exchange Capacity
[Cmol(+)kg-1 soil]
30.71
Landon (1991)
High
Available Phosphorus (mg/kg)
9.03
Low
Cottenie (1980)
Available Sulfur (mg/kg)
18.22
Low
Ethiosis (2014)
4.2. Phenological and Growth Parameters
4.2.1. Days to 50% heading
The analysis of variance revealed that the main effect of NPS is highly significant (P < 0.01) on
days to 50% heading of wheat while the two-factor interactions of NPS × N significantly
336
(P<0.05) influenced days to 50% heading. However, the supplemented N rate did not
significantly affect days to 50% heading of the crop. The longestperiod to reach 50% heading
was observed in response to the combined application of the highest rates of the two fertilizers
(200 kg NPS + 92 kg N ha-1) whereas the shortest duration to 50% heading was observed in the
50 kg NPS + 23 kg N ha-1 but it was not statistically different from 100 kg NPS + 23 kg N ha-1,
150 kg NPS + 23 kg N ha-1 (Table 3).
The relatively delayed days to heading at the highest rates of NPS and N may be attributed to the
synergic effects of the two fertilizers in promoting cell growth and prolonging vegetative growth.
On the other hand, the number of days to heading did not show a consistent increasing trend with
increasing NPS and N rates. Lack of trend could be attributed to the counteracting effects of P
nutrition on N nutrition because N tends to increase vegetative growth while P hastens it. But
mean of days to 50% heading recorded at all treated plots were not significantly different from
untreated/control plots. This result is in line with the findings of Getachew (2004) who reported
that time to heading was significantly delayed at the highest (120 kg ha-1) N fertilizer rate
compared to the lowest rate on wheat. Similarly, Manna et al. (2005) reported that combined
application of NP and organic fertilizers promoted vegetative growth, leading to prolonged days
to heading. Wakeneet al. (2014) also reported 95.25 days to heading for barley at combined
application of 120 kg N + 0 kg P ha-1. In contrast to these results, Sewnet (2005) reported early
flowering with an increase in the rate of N application in rice.
Table 21. Interaction effect of NPS and N fertilizers on days to 50% heading of bread wheat
-1
-1
NPS rate (kg ha )
50
100
150
200
Treated mean
Control
LSD (0.05)
CV (%)
23
90 d
90 d
90 d
90 d
NPS × N
0.48
0.3
46
90 d
90 d
90 d
90 d
N rates (kg ha )
69
90 d
90 d
90 d
91 c
90.4
89.0
Treated vs Control
NS
0.2
92
90.67 c
90 d
92 b
93 a
Means with the same letter(s) in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, NS= non-significant, LSD = Least Significant Difference at 5% level
4.2.2. Days to 90% physiological maturity
337
The main effect of NPS significantly (P < 0.01) influenced days to 90% physiological maturity
of wheat but the main effect of N and the two-factor interactions of NPS × N did not
significantly affect days to 90% physiological maturity. The results showed that increasing NPS
rates increased days to physiological maturity of wheat. The longest duration to physiological
maturity (168.7 days) was recorded at the highest rate of NPS (200 kg ha-1) whereas the shortest
duration to physiological maturity (167.00 days) was obtained from the control. But days to
physiological maturity of fertilizer rates of 150 and 200 kg NPS kg ha-1 were not significantly
different to each other. Generally, the number of days to maturity recorded at the highest rate of
NPS was significantly higher than that of unfertilized plot. The increase in days to maturity of
wheat at the highest rate of NPS might be due to the three nutrients interaction and their
synergetic effect, especially N and S. According to Fazliet al. (2008), lack of S limits the
efficiency of added N therefore; S addition becomes necessary to achieve maximumefficiency of
applied nitrogenous fertilizer.
Table 22. Main effect of NPS and N fertilizers on days to maturity, spike length and plant height
of bread wheat
Treatment
Days to
maturity
Spike length (cm)
50
166.6 b
37.91
100
167.7 ab
38.53
150
168.5 a
44.13
200
LSD (0.05)
N rate (kg ha-1)
23
46
69
92
LSD (0.05)
CV (%)
168.7 a
1.32
43.09
8.13
167.9
167.7
167.6
168.2
NS
1.4
39.87
40.43
39.93
43.43
NS
11.9
TKW (g)
NPS rate (kg ha-1)
Treated vs control
Treated mean
Control
LSD (0.05)
CV (%)
169.56
167.00
NS
0.3
40.92
34.73
2.69
2.0
b
b
a
a
37.91
38.53
44.13
43.09
8.13
b
b
a
a
39.87
40.43
39.93
43.43
NS
11.9
A
B
40.92
34.73
2.69
2.0
A
B
Means with the same letter (s) in the column are not significantly different at 5% level of significance, CV (%) =
Coefficient of variation, NS= non-significant, LSD= Least Significant Difference at 5% level, TKW=thousand
kernels weight
4.2.3. Plant height
338
The two factor interaction and main effect of N significantly (P < 0.05) influenced plant height.
On the other hand, the main effect of NPS had no significant effect on this parameter.
The result indicated that height of wheat plants increased as NPS and N rates increased (Table
3). The tallest plant (93.59 cm) was recorded at 200 kg NPS and 69 kg N ha-1 rate while the
shortest plant (78.07 cm) was obtained at the lowest rates of the two fertilizers (50 kg NPS and
23 kg N ha-1). This is because of the vital role of N and S fertilizersfor vegetative growth and
resulted for significant influence on plant height. In general, mean plant height of fertilized plots
exceeded control plots by around 21.11%.The result of this study agrees with that of Firehiwot
(2014).
4.2.4. Spike length
The analysis of variance revealed significant (P < 0.05) interaction and main effect of NPS on
the spike length whereas the main effect of N did not have significant influence on this
parameter. The result showed that increasing NPS and N rates increased spike length. Thus, the
longest spikes (8.95 cm) were obtained at the rate of 200 kg NPS and 92 kg N ha -1 and this was
statically at par with 200 kg NPS and 46 kg N ha-1 whereas the shortest spikes were produced at
the combination of the lowest rate of the two fertilizers(Table 4). The increase in spike length at
the highest NPS and N rates might have resulted from improved root growth and increased
uptake of nutrients and better growth favouredby interaction/synergetic effect of the three
nutrients at the highest rates. This result agrees with the findings of Muluneh and Nebyou (2016)
who reported the highest spike length (7.7cm) for wheat at the rate of 50/150 kg N/P 2O5 ha-1.
Firehiwot (2014) also reported the maximum spike length (8.29 cm) at combined application of
64 kg P2O5 + 46 kg N ha-1. Similarly, Iqbal et al. (2002) reported longer spikes in response to
increased application of phosphorus. Generally, spike length recorded over all the treated plots
was significantly higher than the unfertilized plot/control.
Table 23. Main effect of NPS and N fertilizers on plant height and spike length of bread wheat
Plant height (cm)
NPS rate
N rates (kg ha )
46
69
-1
(kg ha )
Spike length (cm)
-1
23
50
78.07
100
84.88
150
86.62
200
Treated
85.33
d
a-d
a-d
a-d
81.03
86.67
83.7
cd
a-d
bcd
93.59
a
87.06
89.36
88.67
92.32
87.66A
abc
abc
abc
ab
-1
92
82.86
86.82
85.71
93.07
N rates (kg ha )
46
69
23
cd
a-d
a-d
a
7.178
7.919
8.394
8.667
c
bc
ab
a
8.461
8.772
8.333
8.728
ab
a
ab
a
8.278
8.522
8.433
8.433
8.76
ab
ab
ab
ab
92
8.456
8.561
8.567
8.956
ab
ab
ab
a
339
mean
Control
LSD (0.05)
CV (%)
NPS × N
8.75
8.8
72.38B
Treated vs Control
12.85
4.6
NPS × N
0.74
7.7
7.77
Treated vs Control
NS
4.9
4.3. Yield Components and Yield
4.3.1. Number of tillers per plant
The main effect of NPS and N did not significantly (P<0.05) influence the number of tillers per
plant. Similarly the two-factor interaction (NPS × N) did not significantly affect this parameter.
This might be due to the counter act of the three nutrients and the finding agrees with that of
Wakeneet el (2014).
Table 24. Interaction effect of NPS and N fertilizers on number of tillers and number of
productive tiller per plant of bread wheat
Number of fertile tiller/plant
NPS rate (kg
-1
Number of tiller/plant
-1
N rates (kg ha )
N rates (kg ha )
-1
ha )
23
46
69
92
23
46
69
92
50
1.417 b
2.711 a
2.611 a
2.478 a
3.194
3.183
2.99
3.65
100
2.522 a
2.628 a
2.433 a
2.783 a
3.022
2.856
2.95
3.011
150
2.633 a
2.367 a
2.7 0a
2.233 a
3.122
2.967
3.05
2.689
200
2.322 a
2.639 a
2.45 a
2.444 a
2.817
3.078
2.878
3.494
Treated mean
2.5
3.2
Control
1.98
2.5
NPS × N
Treated vs Control
NPS × N
Treated vs Control
LSD (0.05)
0.55
NS
NS
NS
CV (%)
19.7
23
25
7.2
Means with the same letter(s) in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, LSD= Least Significant Difference at 5% level
4.3.2. Number of productive tillers
The main effect of NPS was significant (P < 0.05) on thenumber ofproductive tillers produced
per plant whereas the main effect of N was highly significant (P< 0.01) on this parameter. The
two-factor interactions of NPS × N also highly significantly (P< 0.01) affected the number of
productive tillers produced per plant.
The number of productive tillers per plant was increased significantly as the rates of the two
fertilizers increased (Table 5). The NPS × N interaction effect significantly influenced tiller
production of wheat. The maximum number of tillers per plant (2.78) was produced by plants
treated with the combined application of the highest rates of NPS and N (100 kg NPS ha-1 + 92
kg N ha-1) whereas the minimum number of tillers per plant (1.41) was produced at the lower
rates (50 kg NPS + 23 kg N ha-1). The highest number of tillers at the highest rates of NPS and N
340
might be due to the rapid conversion of synthesized carbohydrates into protein and consequently
the increase in number and size of growing cells, ultimately resulting in increased number of
tillers. In other words, it might be due to the fact that plants such as wheat can increase root
proliferation in high-P regions to enhance P uptake, especially during early growth stages. The
improvement in the total number of tillers with NPS application might be due to the role of P
found in NPS in emerging radical and seminal roots during seedling establishment in wheat
(Cook and Veseth, 1991). Generally, number of tillers per plant recorded over all the treated
plots was significantly higher than the unfertilized plot/control
In agreement with this result, Tilahunet al. (2017) reported the maximum number of wheat tillers
per plant (1.97) recorded at N rate of 92 kg ha-1. Firehiwot (2014) also reported higher tillers per
plant (5.58) at combined application of 32 kg N and 46 kg P2O5 ha-1 in bread wheat. Similarly,
Daniel et al. (1998) also reported enhanced number of tillers in wheat with increased rate of P
application.
4.3.3. Thousand kernels weight
The main effect of NPS significantly (P< 0.05) influenced thousand kernels weight of wheat.
However, the main effect of N and the two-factor interactions did not significantly affect
thousand kernels weight of bread wheat.
Increased rate of NPS increased thousand kernels weight of bread wheat even though there was
no significant difference between 200 and 150 kg NPS ha-1 (Table 3). The highest thousand
kernels weight (44.13 g) was recorded at application of 150 kg NPS ha-1 followed by 200 kg ha-1.
On the other hand, the minimum thousand kernel weight (37.91 g) was observed at application of
50 kg N ha-1. Thousand kernels weight obtained from the overall fertilized plots was
significantly higher than thousand seed weight from the unfertilized plot/control. This might be
due to the improvement of seed quality and size due to synergic effect of the three fertilizers (N,
P and S).In agreement with this result, Nasser (2009) reported interaction of N and P on thousand
kernels for bread wheat.
4.3.4. Number of kernels per spike
The analysis of variance showed that the main effects of NPS and the two factors interaction
were significant (P < 0.05) on the number of kernels per spike. The two fertilizers interacted
341
significantly to influence the number of kernels per spike of bread wheat (Table 6). In general,
increasing the rates of both NPS and N increased the number of kernels produced per spike even
though it was not consistent. Generally, the maximum numbers of kernels per spike (50.91) was
produced at the combination of highest rate of NPS fertilizers (200 kg NPS ha-1) and N rates of
23 92 kg ha-1 whereas the minimum number of kernels per spike (38.63) was produced at the
lowest rates of 50 kg NPS ha-1 + 23 kg N ha-1 of the two fertilizers. These also showed the
synergistic effect of the two fertilizers resulting in increased kernel number per spike and grain
production.
This result also agreed with that of Tilahunet al. (2017) who reported higher number of kernels
per spike for durum wheat (28.39) at the highest rate of N (92 kg N ha-1). Dawit et al. (2015) also
found that increasing N rates increased the number of kernels per spike. They also stated that
increasing P rate from 46 to 138 kg ha-1 increased the number of kernels per spike by about
7.7%. Similarly, Daniel et al. (1998) reported readily availability of P during early season gave
plants from early stresses and its higher uptake at higher levels resulted into enhanced number of
grains per spike and 1000-grain weight due to its involvement in grain formation and
development. Similarly, Nasser (2009) also reported the highest number of kernels per spike of
69.85 at 90/45 kg N/P2O5 ha-1 for wheat. Yasir et al. (2015) also reported the maximum numbers
of wheat kernels per spike (56.4) at 140 kg N ha-1 and 20 kg S ha-1 at sowing and at anthesis
respectively. In general, number of kernels per spike obtained from the fertilized plots exceeded
the grain yield from the unfertilized/control plots by about 34.9%.
Table 25. Interaction effect of NPS and N fertilizers on number of kernels per spike of bread
wheat
N rate (kg ha-1)
NPS (kg ha-1)
50
23
38.63 d
46
43.01 bcd
69
40.79 cd
92
45.55 abc
100
47.03 ab
48.56 ab
45.14 abc
44.94 bc
150
200
47.88 ab
45.64 abc
45.68 abc
44.64 bc
47.63 ab
50.91 a
Treated mean
46.25 abc
46.79 ab
45.61A
Control
33.80B
LSD (0.05)
NPS × N
5.94
Treated Vs Control
4.45
11.4
3.2
CV (%)
Means with the same letter in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, LSD=Least Significant Difference at 5% level
4.3.5. Above ground dry biomass
342
The above ground dry biomass was significantly (P < 0.05) affected by the main effects of NPS
and N rates as well as by the interaction of the two factors.The highest above ground dry biomass
(17.15 t ha-1) was obtained at the combined application of 200 kg NPS + 92 kg N ha -1 whereas
the lowest above ground dry biomass (8.47 t ha-1) was produced under application of 50 kg NPS
and 23 kg N ha-1 (Table 7). The increase in above ground dry biomass at the highest rates of NPS
and N might have resulted from improved root growth and increased uptake of
nutrients,favouring better growth and delayed senescence of leaves of the crop due to synergetic
effect of the three nutrients (NPS).
The result is consistent with that of Teng et al. (1994) who found that combined application of
nitrogen and phosphorus increased biological yields of wheat by up to 362% as compared to
control, revealing the benefit realized by exploiting interactions. Similarly, Bekalu and Mamo
(2016) also reported that increasing N rates from 23 to 69 kg ha-1 increased above ground dry
biomass of wheat by about 22.6%. Dawit et al. (2015) also reported that increasing N from 0 to
184 kg ha-1 and P from 0 to 138 kg ha-1 increased the above ground dry biomass by about 70.1%
and 40.6%, respectively. Similarly, Yasir et al. (2015) reported the maximum above ground dry
biomass of wheat (14734.5 kg ha-1) at 140 kg N ha-1 at sowing and 20 kg S ha-1 at anthesis.
In general, the biomass yield obtained from the fertilized plots exceeded the biomass yield from
the unfertilized plot/control by about 20.64%.
Table 26. Interaction effect of NPS and N fertilizers on above ground dry biomass (AGDBM) (t
ha-1) and grain yield (t ha-1) of bread wheat
Grain yield (t/ha)
NPS rate
-1
(kg ha )
50
100
150
200
Treated
mean
Control
Above ground dry biomass (t/ha)
-1
-1
N rates (kg ha )
23
2.464 e
5.447 a-d
4.858 a-d
5.202 a-d
4.96A
1.96B
NPS × N
46
4.724 abcd
5.304 a-d
6.416 a
4.852 a-d
69
4.038 cde
4.268 b-e
6.017 ab
5.975 abc
N rates (kg ha )
92
3.622 de
5.322 a-d
4.259 b-e
4.89 a-d
Treated vs Control
23
8.47 d
13.26 a-d
13.46 a-d
13.71 abc
46
69
14.9 abc
11.5 bcd
11.84 bcd
11.95 bcd
12.28 a-d
10.61 cd
16.01 ab
15.81 ab
13.27A
NPS × N
5.15
92
11.51 bcd
12.3 a-d
10.3 cd
17.15 a
10.53B
Treated vs Control
LSD
(0.05)
1.73
1.31
1.25
CV (%)
21.4
10.8
24.1
3.00
Means in columns and rows followed by the same letters are not significantly different at 5% level of Significance;
LSD (0.05) = Least Significant Difference at 5% level; CV = Coefficient of variation
4.3.6. Grain yield
343
The main effects of NPS and N and their interactions significantly (P< 0.05) affected the grain
yield of bread wheat.Increasing the rates of the two fertilizers (NPS and N) significantly
increased grain yield. Thus, the highest grain yield (6.43 t ha-1) was obtained at combined rates
of 150 kg NPS ha-1 + 46 kg N ha-1 and it was statistically at par with 150 kg NPS ha-1 + 69 kg N
ha-1 with grain yield of 6.02 t ha-1 whereas the lowest grain yield (2.46 t ha-1) was recorded at the
combinations of 50 kg NPS + 23 kg N ha-1 (Table 7). The highest grain yield at the highest NPS
and N rates might have resulted from improved root growth and increased uptake of nutrients
and better growth favouredby interaction (synergetic) effect of the three nutrients which
enhanced yield components and yield.
In general, grain yield obtained from the fertilized plots exceeded the grain yield from the
unfertilized/control plots by about 60.4%.In line with the result of this study, Bekalu and Mamo
(2016) reported that increasing N rate increased grain yield of bread wheat where the application
of 69 kg N ha-1 had 65.5% more grain yield than control. Similarly, Haile et al. (2012) found that
increasing N rate up to 120 kg N ha-1increased grain yield of bread wheat. Bereket et al. (2014)
also reported that increasing P rate from 46 to 69 kg P2O5 ha-1 increased grain yield of bread
wheat by about 6.8%. Kaleem et al. (2009) also recorded maximum yield of 3557 kg ha-1 by the
application of 128-128 kg ha-1 (NP) ratio 1:1 which indicated the importance of phosphorus at its
highest dose in achieving maximum wheat productivity. Erekulet al. (2012) also reported high
grain yield (4813 kg ha-1) of wheat at combined application of 210 kg N and 40 kg S ha- 1.
Likewise, Jarvanet al. (2009) reported that the addition of 100 kg N ha-1 with 10 kg S ha-1 to
winter wheat gave yield of 5.88 t ha-1 while it gave 5.73 t ha-1 when 100 kg N ha-1 with 6 kg ha-1
S was added with increasing grain protein content. This clearly indicates the synergic effect of
the three nutrients in increasing yield and quality of wheat. Similarly, Yasir et al. (2015) reported
the maximum grain yield of wheat (4463.5 kg ha-1) at 140 kg N ha-1 and 20 kg S ha-1 at sowing
and at anthesis, respectively.
4.3.7. Straw yield
Analysis of variance showed that the straw yield of wheat was significantly (P < 0.05) affected
by the main effects of NPS and N. Similarly, the interaction of NPS and N was significant (P <
0.05) on straw yield.Thus, the maximum straw yields (1.26 t ha-1) was obtained at the combined
application of the highest rates of the two fertilizers (200 kg NPS + 92 kg N ha-1) whereas the
lowest straw yield (4.59 t ha-1) was recorded in response to the application of 150 kg NPS + 69
344
kg N ha-1 (Table 8). The significant increase in straw yield in response to the highest rate of
combined application of NPS and N might be attributed to the synergic roles of the two
fertilizers played in enhancing growth and development of the crop as suggested above for grain
yield. In general, straw yield obtained from the fertilized/treated plots exceeded the straw yield
from the unfertilized/untreated plots by about 16.34% (Table 8). The result is consistent with that
of Nasser (2009) who reported increased straw yield of wheat with increase in NP fertilizers
rates of up to 90/45 kg ha-1. Similarly, Bereket et al. (2014) reported highest straw yield of bread
wheat (6827 kg ha-1) at phosphorus rate of 92 kg P ha-1 and nitrogen rate of 138 kg N ha1
;Tilahunet al. (2017) reported straw yield of 8 t ha-1 at 92 kg N ha-1 for durum wheat.
Table 27. Interaction effect of NPS and N fertilizer rates on straw yield and harvest index of
bread wheat.
Straw yield (t/ha)
Harvest index
-1
NPS rate (kg
-1
23
N rates (kg ha )
46
69
92
50
9.042 a-d
10.176 abc
7.46 b-e
100
7.814 b-e
6.539 cde
150
8.598 a-e
200
8.504 a-e
7.59A
-1
ha )
Treated mean
Control
23
N rates (kg ha )
46
69
92
4.851 e
0.214f
0.32cdef
0.35 cde
0.43bcd
7.684 b-e
6.973 cde
0.41 cde
0.45abc
0.36cde
0.44 a-d
5.867 de
4.592 e
6.038 de
0.36 cde
0.57 a
0.56ab
0.41 cde
11.153 ab
9.838 a-d
12.262 a
0.38cde
0.31def
0.38cde
0.29ef
6.3B
NPS × N
4.06
Treated vs Control
1.17
0.46
0.39
NPS × N
0.14
Treated vs Control
NS
LSD (0.05)
CV (%)
20
4.8
21.0
18
Means with the same letter in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, NS= non-significant, LSD= Least Significant Difference at 5% level
4.3.8. Harvest index
The main effects of NPS and N, as well as the two factor interaction (NPS × N) were highly
significant (P < 0.01) on harvest index.The maximum harvest index (0.57) was obtained at the
combined application of 150 kg NPS and 46 kg N ha-1 whereas the lowest harvest index
(0.0.214) was recorded at combined application of 50 kg NPS and 23 kg N ha-1 (Table 8). The
increment in harvest index at medium rate of N combined with NPS might be attributed to
greater photo assimilate production and its ultimate partitioning into grains compared to
partitioning in to straw, i.e. proportionally higher grain yield than vegetative biomass yield. The
result agreed with the findings of Sharer et al. (2003) who reported higher harvest index (0.39)
under higher level of nitrogen and phosphorus (180/130 kg N/P ha-1) than application of lower
345
levels of the fertilizers for maize which could be due to the increase in grain yield more than the
increase in biomass. On the other hand, Dawit et al. (2015) reported no significant effect of N
and P rates on harvest index of bread wheat.
4.4. Grain Quality Parameters
4.4.1. Hectoliter weight
Based on analysis of variance, hectolitr weight was significantly (P< 0.05) affected by the main
effects of NPS and N as well as the two-factor interactions. Increasing the rate of NPS and N
increased hectoliter weight. Thus, maximum hectoliter weight of 81.8 kg hl-1was recorded at
combined rate of 200 kg NPS and 92 kg N ha-1 even though no significant difference 200 kg
NPS and 69 kg N ha-1 while the minimum hectoliter weight (76.6 kg hl-1) was recorded at lowest
rate of the two fertilizers (50 kg NPS and 23 kg N ha-1) (Table 9). The highest hectoliter weight
at the highest NPS and N rates might be due to the role of N and S on quality of wheat such as
flour yield and protein content as N and S increases the plumpness and protein content of the
cereal grains (Foth and Ellis, 1988). This result was in line with that of Gooding and Davies
(1997) who reported slight increase in hectoliter weight in response to N application under more
favourable growing conditions. Similarly, Haile (2011) also reported 72.69 kg hl-1 hectoliter
weight at N rate of 120 kg N ha-1. On the other hand, Dawit et al. (2015) reported non-significant
effect of N rates on hectoliter weight of bread wheat.
4.4.2. Grain protein content (GPC)
The main effect of NPS and N significantly (P< 0.05) affected grain protein content while twofactor interaction highly significantly (P<0.01) affected grain protein content. Grain protein
content under different NPS and N application rates ranged from 11.23 to 13.47% (Table 9). The
highest grain protein (13.47%) was obtained at the highest NPS rate at 200 kg NPS and 92 kg N
ha-1 whereas the lowest grain protein (11.23%) was obtained at the lowest rate of NPS and N
application (50 kg NPS and 23 kg N ha-1). Grain protein was generally found to increase with
increasing NPS and N rates.
Table 28. Main effect of NPS and N fertilizers rates on grain protein content (GPC) and
hectoliter weight (HLW) of bread wheat
-1
Grain protein content(%)
NPS rate
50
100
Hectoliter weight(kg hl )
-1
N rates (kg ha )
23
46
11.23 c
13.13 ab
12.93 ab
13.13 ab
-1
69
13 ab
13.13 ab
92
12.97 ab
12.73 ab
N rates (kg ha )
23
46
76.6 c
81.07a
81.73 a
80.47a
69
79.6ab
80.53a
92
77.67bc
80.73a
346
150
13.23 ab
13.03 ab
13 ab
12.47 b
81.47 a
79.6 ab
79.6ab
79.87ab
200
Treated mean
Control
12.67 ab
12.93 ab
12.6 ab
13.47 a
80.8 a
80.47a
81.8a
81.00a
13.0A
82.18A
10.8B
78.20B
NPS × N
Treated vs Control
NPS × N
Treated vs Control
LSD (0.05)
0.93
1.33
2.43
1.89
CV (%)
4.4
3.2
1.8
0.7
Means with the same letter in the column are not significantly different at 5% level of significance; CV (%) =
Coefficient of variation, NS= non-significant, LSD = Least Significant Difference at 5% level
However, there were no significant differences in grain protein content at NPS and N rates of
150 + 46, 100 + 69 kg ha-1. Thus, increased grain protein content with increased NPS and N rates
might be due to the synergetic effect of the three nutrients (N, P and S) found in NPS fertilizers,
especially N and S, which have synergic effect on yield and quality (MalleJarvanet al. 2012;
Erekulet al., 2012). In similar studies,Njira and Nabwami (2015) reported that S has great role in
protein synthesis as it used as an essential component of amino acids and also the balanced
fertilization that lead to the general high performance of the crop including synthesis of all N
containing compounds such as proteins, chlorophyll and nucleic acids. It is also a building block
of protein and a key ingredient in the formation of chlorophyll (Duke and Reisenaue, 1986).
Without adequate S, crops cannot reach their full potential in terms of yield or protein content
(Zhao et al., 1999). Similarly, Havlinet al. (2005)reported that phosphorus increased protein
content and sugar content in crops such as wheat and maize. In agreement with the above result,
Erekulet al. (2012) reported increased grain (16.1%) and flour (15.0%) protein content when N
and S rates increased to 210/40 N/S kg ha-1. Malik et al. (2003) also reported a maximum protein
content from a plot fertilized at a combination of 50-75 kg NP ha-1 as compared to other
combinations of N (0, 25 and 50 kg ha-1) and P (0, 50, 75 and 100 kg ha-1).
Similar to NPS, N rates also significantly affected grain protein content. Grain protein was
generally found to increase with increasing N rates. Compared with grain protein content
obtained for the control treatment, the mean value of grain protein content obtained at 92 kg N
ha-1 was higher by about 14.3% (Table 10). Generally, grain protein content recorded over all the
treated plots was significantly higher than unfertilized plot. The increase in protein content with
the increase in nitrogen rates might be due to the fact that nitrogen is the building block of
protein in which N increases the plumpness of the cereal grains and protein content of both seeds
and foliage (Foth and Ellis, 1988). The result is in agreement with the findings of Haile (2012)
347
who reported that increasing N rates from 30 to 120 kg ha-1 increased grain protein of wheat by
5.3%. Similarly, Tilahunet al. (2017) also recorded the maximum grain crud protein content
(11.52%) for durum wheat at the highest N rate (92 kg N ha-1). Garrido-Lestacheet al. (2004) and
Brian et al. (2007) also similarly reported that increased N levels consistently increased grain
protein content.
4.5. Partial Budget Analysis
Analysis of the net benefits, total costs that vary and marginal rate of returns are presented in
Table 10. Information on costs and benefits of treatments is a prerequisite for adoption of
technical innovation by farmers. The studies assessed the economic benefits of the treatments to
help develop recommendation from the agronomic data. This enhances selection of the right
combination of resources by farmers in the study area. The results in this study indicated that the
combined application of NPS and N fertilizer resulted in higher net benefits than the
unfertilized/control treatments (Table 10). As indicated in Table 10, the partial budget analysis
showed that the highest net benefit (Birr 42272.5 ha-1) was recorded at the rate of combined
application of 100 kg NPS + 92 kg N ha-1 followed by 100 kg NPS + 69 kg N ha-1 (40618.4 Birr
ha-1), and the lowest was from the control treatment. To use the marginal rate of return (MRR%)
as basis of fertilizer recommendation, the minimum acceptable rate of return should be between
-1
50 to 100% (CIMMYT, 1988). In this study application of 100 kg NPS ha and 92 kg N ha-1
gave the maximum economic benefit (42272.5 ha-) with marginal rate of return (1728.3%).
-1
Therefore, on economic grounds, combined application of 100 kg NPS ha and 92 kg N ha-1
would be best and economical, and tentatively recommended for production of bread wheat in
the study area and other areas with similar agro-ecological conditions. In line with this result,
Bekalu and Mamo (2016) reported that N application at 69 kg ha-1 is effective in attaining higher
grain yield and economic benefit of wheat in southern part of Ethiopia. Dawit et al. (2015) also
recommended 92 kg N ha-1 and 46 kg P2O5 ha-1 for production of wheat for moist and humid
midland vertosols areas of Arsi zone. Similarly, Bereket et al. (2014) recommended 46 kg N ha-1
and 46 kg P2O5 ha-1 for production of bread wheat on sandy soil of Hawzen district.
Table 29. Partial budget and marginal rate of return
fertilizers
Treatments
NPS (kg
AGY by 10%
ha-1))
N (kg ha-1)
(kg ha-1)
Control
Control
1762.20
analysis for response of bread wheat to NPS and N
GB (Birr
ha-1)
29957.40
TVC (Birr
ha-1)
0.00
NR (Birr
ha-1)
29957.40
MRR
(%)
0
348
Treatments
NPS (kg
ha-1))
N (kg ha-1)
50
23
50
46
100
23
50
69
100
46
150
23
50
92
100
69
150
46
200
23
100
92
150
69
200
46
150
92
200
69
200
92
AGY by 10%
(kg ha-1)
2217.61
4251.73
4902.64
3634.55
4773.53
4372.07
3260.11
3840.77
5774.69
4682.12
4790.17
5415.72
4366.90
3832.68
5377.90
4400.91
-1
GB (Birr
ha-1)
37699.44
72279.47
83344.93
61787.41
81150.04
74325.14
55421.87
65293.17
98169.68
79596.08
81432.92
92067.26
74237.37
65155.58
91424.37
74815.46
TVC (Birr
ha-1)
2550.00
3250.00
3350.00
3950.00
4050.00
4150.00
4650.00
4750.00
4850.00
4950.00
5450.00
5550.00
5650.00
6250.00
6350.00
7050.00
NR (Birr
ha-1)
35149.44
69029.47
79994.93
57837.41
77100.04
70175.14
50771.87
60543.17
93319.68
74646.08
75982.92
86517.26
68587.37
58905.58
85074.37
67765.46
MRR
(%)
3.04
49.4
110.66
D
193.63
D
D
98.71
328.77
D
3.67
106.34
D
D
262.69
D
-
Where, NPS cost = 16 Birr kg-1, UREA cost = 14 Birr kg of N, NPS and UREA application cost= 525 Birr ha 1, bread wheat
-1
grain = 17 Birr kg , MRR (%) = Marginal rate of return, D= Dominated treatment, Control = unfertilized
5. Conclusion and Recommendation
Analysis of the results revealed that interaction of the two fertilizers significantly affected grain
yield, above ground dry biomass, date to heading, number productive tillers, plant height, spike
length, straw yield, harvest index, hectoliter weight and grain protein content of bread wheat
while date to mature and thousand kernels weight were affected only by main effect of NPS and
N. Generally, all parameters recorded over all treated plots were significantly higher than
unfertilized/control plot except date to mature, number of tiller per plant and number of kernels
per spike. Thus using of NPS and N fertilizers improve yield components, yield and quality
parameters of bread wheat. The highest grain yield (6.416 t h-1) was obtained from combined
application of 150 kg NPS ha-1 + 46 kg N ha-1whereas the highest grain protein content was
recorded from application of 200 kg NPS ha-1 + 92 kg N ha-1. The partial budget analysis
revealed that combined applications of 150 kg NPS and 46 kg N ha-1 gave the best economic
benefit 93319.68 Birr ha-1 with MRR of 328.765%. Therefore, based on the results of this study
it can be concluded that combined application of these rates can be recommended for farmers for
production of wheat in the study area and other areas with similar agro-ecological conditions.
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349
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Response of Common Bean (Phaseolus vulgaris L.) Varieties to Rates of Blended NPS
Fertilizer in Adola District, Southern Ethiopia
Deresa Shumi*1, Demissie Alemayehu1, Belachew Debelo1
1,
Oromia Agriculture Research Institute (IQQO), Bore Agricultural Research Center
*Corresponding author: Deresa Shumi (deresashumi1990@gmail.com )
Abstract
Common bean is one of the most economically important pulse crops cultivated in Ethiopia.
However, the national average yield is below the potential yield that could be attained. This is
partly due to low soil fertility management, lack of agronomic recommendations and diseases
and pest problems. Hence, this experiment was conducted to investigate the effect of blended
NPS rates on growth, yield and yield components of common bean varieties and to identify
economically feasible rates of blended NPS at Guji Zone Southern Ethiopia.The experiment was
conducted in Adola sub-site of Bore Agricultural Research Center during 2016-2017 main
cropping seasons. The factors studied were six rates of blended NPS (0, 50, 100, 150, 200 and
250 kg ha-1) and three varieties of common bean (Angar, Ibado and Nasir). These were laid out
in a factorial arrangement in Randomized Complete Block Design with three replications. Data
on phonological, growth yield and yield related parameters were collected and analyzed using
SAS software. The result showed that thehighest number of primary branches per plant (2.77)
and the highest number of total pods (18.52) were recorded at the highest rate of 250 kg NPS ha1
whereas the highest number of total nodules (80.47) and effective nodules per plant (35.54)
were obtained from the application of 200 kg NPS ha-1. Among the varieties, Angar gave
significantly the highest number of primary branches per plant (2.55) and number of pods per
plant (15.3). The interaction of variety and blended NPS had significant effect on almost all
parameters except the number of total and effective nodules per plant, number of primary
branches per plant and number of pods per plant. Variety Nasir gave the highest plant height
(99.72 cm) with application of 150 kg NPS ha-1 while Ibado with application rate of 200 kg
blended NPS ha-1 had the highest hundred seed weight (54.33 g). The highest grain yield (3260
kg ha-1) was recorded for variety Angar when 250 kg NPS ha-1was applied. However, the highest
net benefit (29,825 Birr ha-1) was obtained from combination of variety Ibado with application
200 kg ha-1 of blended NPS. Thus, it can be concluded thatapplication of 200 kg ha-1 of blended
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NPS with variety Ibado offers aneconomically feasible package of common bean production for
the farmers.
Key words: Blended fertilizer, Nitrogen, phosphorus, Sulphur
INTRODUCTION
Common bean (Phaseolus vulgaris L.), is herbaceous annual plant domesticated independently
in ancient Mesoamerica and in the Andes, and now is grown worldwide for both dry seeds or as
a green bean. Thousands of legume species exist but common bean in any form is the most
consumed by human beings compared to any other legumes (Broughton et al., 2003). When
common bean is used for its unripe fruit, it is termed as green bean or snap bean. About 23.9
million tons of dry bean, 20.7 million tons of green bean, and 1.9 million tons of string or
common bean were produced worldwide in 2012 (FAOSTAT, 2014). It is estimated that the crop
meets more than 50% of dietary protein requirements of households in Sub-Saharan Africa. The
annual per capita consumption of common bean is higher among low-income people who cannot
afford to buy nutritious food stuff, such as meat and fish (Broughton et al., 2003).
Common bean is highly preferred by Ethiopian farmers because of its short maturing
characteristics that enable households to get cash income required to purchase food and other
household needs when other crops have not yet matured (Legesse et al., 2006). It is also an
important food and cash crop in Guji zone with an area of 15,850.82 ha and average productivity
of 1.52 tons per hectare. Similarly, it contributesabout 39.49% for household consumption,
13.33% for seed, 44.1% for sale, 0.58% animal for feed and 2.05 other uses in the study zone
(CSA, 2016).
Improved common bean production encompasses proper use of different agronomic practices
which include improved variety, seed rate, spacing, fertilizer rate and pesticide application as per
recommendations. However, the current national average yield of common bean (about 1.48
tons) is far less than the attainable yield (2500-3000 kg ha-1) under good management conditions
for most improved varieties. This low yield of common bean in Ethiopia is attributed to several
production constraints, which include lack of improved varieties for the different agro-ecological
zones, poor agronomic practices such as low soil fertility management, untimely and
inappropriate field operations (Alemitu, 2011). A range of environmental factors, such as low
soil nitrogen and phosphorus levels, and acidic soil conditions are important constraints for bean
production in most areas where the crop is grown (Girma, 2009). Wortmann (2006) also reported
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that low soil fertility status, especially low level of N and P to be the major constraints of
common bean production responsible for the loss of grain yield of nearly 1.2 million tons in
Africa. In general, an increase in grain yield and other agronomic parameters of common bean
were observed as the rate of nitrogen and phosphorus increased till 27 kg N ha-1 and 69 kg P2O5
ha-1 (150 kg DAP ha-1) (Girma, 2009). This fertilizer rate also gave yield advantages of 39% over
the control. Among the nutrients, nitrogen is the critical limiting element for growth of most
plants including common beans due to its unavailability and poor fixation (Vance, 2001).
Deficiency in N causes reduced growth, leaf yellowing, reduced branching and small trifoliate
leaves in beans (CIAT, 1986). Previous surveys estimated that over 60% of the bean production
areas in Central, Southern, and Eastern Africa were affected by N deficiency. This caused yield
losses of up to 40% compared to the N-fertilized areas (Singh, 1999). Besides, common bean is
considered to be a poor fixer of atmospheric N when compared with other legumes and generally
responds poorly to inoculation of rhizobia in the field. As a result, common bean is being
generally considered as more responsive than other legumes to N fertilization (Graham, 1981).
Bean N fertilizer requirement depends on soil fertility levels; for low soil nitrogen levels (below
34 kg N ha-1) N fertilizer is generally recommended in order for deficiency symptoms not to
manifest and for full development up to production. Moreover, up to 60 kg N ha-1 also promotes
increased nodule number, mass and size, giving highest yields (Dwivediet al., 1994). However,
nitrogenous activity declines with applied nitrogen (Davis and Brick, 2009), decreasing the sink
strength, and hence, reduce the quantity of photo-assimilate partitioned to nodules and grain.
Early application may also result in excessive vegetative growth leading to delayed flowering,
reduced pod set, lower seed yield and a greater risk of disease infection (Setegne and Leggese,
2003)
The application of inorganic phosphorus fertilizer has positive effect on the yield and yield
components of common bean. Rana and Singh (1998) revealed that grain weight per plant
exhibited a pronounced response to phosphorus application, mean values of grain weight per
plant records of 13.0, 17.4 and 20.7 g due to phosphorus fertilization of zero, 50 and 100 kg P2O5
ha-1, respectively. Veeresh (2003) observed significant increase in grain weight per plant (8.65 g)
due to increased P application of up to 75 kg P 2O5 ha-1. Dwivedi et al. (1994) also reported
linear increase in number of grains per pod of common bean due to increase in phosphorus
fertilization from 50 to 150 kg P2O5 ha-1 but the differences were not significant beyond 100 kg
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P2O5 ha-1. Saxena and Verma (1994) reported that the mean number of grains per pod linearly
increased from 5.53 to 7.50 due to increased phosphorus fertilization from zero to 120 kg P2O5
ha-1.
Sulfur (S) is one of the essential nutrients for plant growth and it accumulates 0.2 to 0.5% in
plant tissue on dry matter basis. It is required in similar amount as that of phosphorus (Ali etal.,
2008). Sulphur plays a vital role in improving vegetative structure for nutrient absorption, strong
sink strength through development of reproductive structure and production of assimilates to fill
economically important sink. Sulphur nutrition of bean and other plants is important since its
application not only increases growth rate but also improves the quality of the seed (Clarkson et
al., 1989). Total number of nodules and active nodules significantly increased with application of
S up to 20 kg S ha-1 (Ganeshamurthy and Readly, 2000). Formation of nodules was increased
due to sulphur application in blackgram (Phaseolus mungo) and is involved in the formation of
nitrogenase enzyme known to promote nitrogen fixation in legumes (Scherer et al., 2006).
Soil fertility mapping project in Ethiopia recently reported the deficiency of K, S, Zn, B and Cu
in addition to N and P in major Ethiopian soils and thus recommend application of customized
and balanced fertilizers (EthioSIS, 2013).To address these nutrient deficiencies, farmers in Guji
zone have been using uniform blanket application of 100 kg DAP ha-1 (18 kg N and 46 kg P2O5
ha-1) for all legumes including common beanto increase crop yields for about five decades and
this did not consider soil fertility status and crop requirement. This emphasizes the importance of
developing an alternative means to meet the demand of nutrient in plants by using blended NPS
that contains S in addition to the commonly used N and P fertilizers. However, no study has been
done on response of common bean (Phaseolus vulgaris L.) varieties to the rates of blended NPS
fertilizer in Adola District, Southern Ethiopia.Thus, the objectives of this study were to
investigate the effect of blended NPS rates on growth, yield and yield components of common
bean varieties and to identify economically feasible rates of blended NPS at Guji Zone, Southern
Ethiopia.
Materials and Methods
Description of the Study Area
The experiment was conducted at Adola sub-site of Bore Agricultural Research Center
(BOARC), Guji Zone, Oromia Regional State in southern Ethiopia under rain-fed conditions
during the 2016 cropping season. The site is located in Adola town in Dufa ‘Kebele’ just on the
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West side of the main road to Negelle town. It is located at about 463 km south from Addis
o
Ababa, capital city of the country. Geographically, the experimental site is situated at 55 36'31”
o
North and longitude of 38 58'91”East at an altitude of 1721 masl. The climatic condition of the
area is a humid moist condition, with a relatively shorter growing season. The area receives
annual rainfall of 1084 mm with a bimodal pattern extending from April to November. The mean
annual minimum and maximum temperature is 15.93 ℃ and 9.89 ℃, respectively. The type of
the soil is red basaltic (Nitisols) and Orthic Aerosols ((Yazachew and Kasahun, 2011). The soil is
clay in texture and moderately acidic with pH of around 5.88 (Table 3).
Experimental Materials
Three common bean varieties, namely: Angar (medium-seeded); Ibado (large-seeded); and Nasir
(medium seeded) were used (Table 1).
Table 30. Description of common bean varieties used for the study
Characteristics
Altitude (masl)
Annual Rainfall (mm)
Planting date
Days of 50 flowering
Days to 95% maturity
Growth habit
Seed colour
Yield in research site (t ha-1)
Year of release
Source: MoARD (2003 & 2005)
Angar
1300-2000
1000-1300
Mid -Late June
41-52
85-96
Bushy
Dark red
2.0 - 3.2
2005
Varieties
Ibado
1400-2250
500-850
Mid-June-Early July
43-58
90-120
Bushy
Red
2-2.9
2003
Nasir
1200-1900
500-800
Mid June-Early July
40-55
86-88
Bushy
Red
2-3.2
2003
Variety Angar was released by Bako Agricultural Research Center in 2005. Ibado was released
by Areka Agricultural Research Center in 2003 and Nasir by Melkasa Agricultural Research
Center in 2003. Blended NPS (19% N, 38% P2O5,7% S) was used as sources of N, P and S,
respectivelyfor the study
Soil Sampling and Analysis
Pre-planting soil samples were taken randomly in a zigzag pattern from the experimental plots at
the depth of 0-30 cm before planting. Twenty soil core samples were taken by an auger from the
whole experimental field and combined to form a composited sample in a bucket. Then, the
collected samples were air-dried at room temperature under shade and ground to pass through a 2
mm sieve for laboratory analysis of soil pH, and available phosphorus. Small quantity of this 2
mm sieved soil material allowed to passed through 0.2 mm sieve for soil organic carbon (OC)
and total nitrogen. The composite soil samples were analyzed for selected physicochemical
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properties mainly textural analysis (sand silt and clay), soil pH, total nitrogen (N), available
sulphur (S), organic carbon (OC), available phosphorus (P), cation exchange capacity (CEC) (c
mol kg-1), exchangeable potassium, magnesium and calcium using an appropriate laboratory
procedures at Horticoop Ethiopia (Horticultural) PLC Soil and Water Analysis Laboratory.
Soil textural class was determined by Boycous Hydrometer Method (Aderson and Ingram 1993).
Organic carbon (OC) was estimated by wet digestion method (Walkey and Black, 1934) and
organic matter was calculated by multiplying the OC% by a factor of 1.724. Total nitrogen was
analyzed by Kjeldhal method (Jackson, 1962). The soil pH was measured potentiometrically in
1:2.5 soil-water suspensions with standard glass electrode pH meter (Van Reeuwijk, 1992).
Cation Exchangeable Capacity (CEC) was determined by leaching the soil with neutral 1N
ammonium acetate (FAO, 2008). Available phosphorus was determined by the Olsen’s method
using a spectrophotometer (Olsen et al., 1954) and available sulfur (S) was measured using
turbidimetric method (EthioSIS, 2014). Exchangeable potassium, magnesium, and calcium were
determined by Melich-3 methods (Mehlich, 1984).
Treatments and Experimental Design
The treatments were factorial combinations of six blended NPS fertilizer rates (0, 50, 100,150,
200 and 250 kg ha-1) (Table 2) and three varieties (Angar, Ibado and Nasir). The experiment was
laid out as Randomized Complete Block Design (RCBD) and replicated three times per
treatment in factorial combination. The gross plot size was 3.0 m × 2.8 m = 8.4 m2. The spacing
between blocks and plots was 1.0 m and 0.6 m, respectively. Each plot had 7 rows spaced 40 cm
apart. One outer most row on each side of a plot and three plants (30 cm) on each end of rows
were considered as border. One row next to the border rows on any side was used for destructive
sampling. Thus, the net pot size was (1.6 m × 2.4 m = 3.84 m2) having four rows each row with
24 plants.
Table 31. Rate of fertilizer and their nutrient content (kg ha-1) treatments for the experiment
No
1
2
3
4
5
6
Blended NPS Fertilizer rate (kg ha-1)
0 kg NPS
50 kg NPS
100 kg NPS
150 kg NPS
200 kg NPS
250 kg NPS
N
0
9.5
19
28.5
38
47.5
P2O5
0
19
38
57
76
95
S
0
3.5
7
10.5
14
17.5
Experimental Procedure and Crop Management
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The experimental field was prepared by using oxen-drawn implements (local plough maresha)
according to farmers’ conventional farming practices. The field was ploughed three times. The
first plough was at the end of May 2016, the second in mid July and the third during the middle
of August before planting the crop to fine tilth. The plots were leveled manually. All the varieties
were sown on 1 October. The dried seeds were planted by hand at a specified spacing (40 cm ×
10 cm) by placing two seeds per hill and later thinned to one plant per hill after emergence. All
the required amount of blended NPS was applied in band during planting. Furthermore, all
necessary cultural and agronomic practices were carried out uniformly for all plots as per the
recommendation for the crop at all stages of growth and development. The crop was harvested
manually using a sickle when 90% of the leaves and pods turned yellow on 12 December, and
dried under the sun for 4 days before threshing. Threshing was done separately for each
treatment manually.
Data Collected
An effect of blended NPS rate was investigated by measuring data on phonology, growth, yield
and yield component parameters. Data on phonological parameters were measured through
visual observation as the number of days from sowing to when 50% of plants in a net plot had
reached flowering and 90% physiological maturity. Data on growth and yield component
parameters were taken in each plot from ten randomly selected plants at physiological maturity
and at harvest time, respectively. For hundred seed weight and grain yield the whole plant from
the net plot area was harvested and the yield per hectare was determined by converting the yield
per plot (kg per plot) into kg per hectare.
Data Analysis
All the measured parameters were subjected to analysis of variance (ANOVA) appropriate to
factorial experiment in RCBD according to SAS software 9.1 versions. Significance Difference
(LSD) test at 5% probability level was used for mean comparison.
Economic Analysis
Economic analysis was performed using partial budget analysis following the procedure
described by CIMMYT (1988) in which prevailing market prices for inputs at planting and for
outputs at harvesting were used. All costs and benefits were calculated on ha basis in Birr. The
concepts used in the partial budget analysis were the mean grain yield of each treatment, the field
price of common bean grain, and the gross field benefit (GFB) ha-1 (the product of field price and
361
the mean yield for each treatment. The net benefit (NB) was calculated as the difference between
the gross benefit and the total cost. The average yield obtained from experimental plot was
reduced by 10% to adjust with the expected farmers’ yield by the same treatment. Prices of grain
(Birr kg-1) were obtained from local market for each variety: Ibado was 12 Birr kg-1 and Angar
and Nasir were 8 Birr kg-1, and total sale from one hectare was computed using adjusted yield.
Other costs such as cost of fertilizer (1400 Birr 100 kg-1 blended NPS) and its application cost
(350 Birr ha-1) were considered as the costs that vary for treatment to treatment.
Results and Discussion
Physico-chemical Properties of the Experimental Site Soil
Soil texture is an important soil physical characteristic as it determines water intake rate
(infiltration), water holding capacity of the soil, the ease of tilling, the amount of aeration, and
also influences soil fertility (Gupta, 2000). It is one of the inherent soil properties less affected by
management and determines nutrient status, organic matter content, air circulation and water
holding capacity of a given soil. According to the soil textural class determination triangle, soil
of the experimental site was found to be clay (Table 3). High clay content might indicate better
water and nutrient holding capacity of the soil. According to the soil analysis test, the soil pH of
the experimental site was 5.88 (Table 3). Thus, according to Landon’s (1991) rating, the
chemical reaction of the experimental site is moderately acidic. The available P level in the
experimental site was 5.61 mg kg-1 (Table 3) which is very low according to the rating of
(EthioSIS, 2014). This low available of phosphorus could be due to fixation in such acidic soils.
Table 32. Physico-chemical properties of the experimental site soil before planting.
Characters
A. Soil texture
Sand (%)
Silt (%)
Clay (%)
Textural Class
B. Chemical analysis
Soil Ph
Organic carbon (%)
Total N (%)
Available P (mg kg-1)
Available S (mg kg-1)
CEC [meq/100g soil)
Value
Rating
30
12
58
Clay
5.88
2.3
0.19
5.61
14.50
14.9
Moderately Acidic
High
Low
Very Low
Low
Low
The result of laboratory analysis showed that the total nitrogen percentage (0.19%) was low as
per the rating of EthioSIS (2014). Cation exchange capacity is the capacity of the soil to hold and
exchange cations. It provides buffering effect to changes in pH, available nutrients, calcium
362
levels and soil structural changes. The result showed the CEC of the experimental soil to be 14.9
meq/100 g, rated as moderate according to the rating of Landon (1991). The total carbon content
in the soil was 2.3% which was rated as high as per the classification of Hazelton and Murphy
(2007). Thus, the OM content of the soil was optimum as rated by EthioSIS (2014). On the other
hand, the available sulphur content in the soils has values of 14.50 mg kg-1 which was rated as
low as per the classification of EthioSIS (2014).
Phenological and Growth Parameters of Common bean
Days to flowering: Days to 50% flowering was significantly (P<0.05) influenced by interaction
of blended NPS rates and varieties but the main effects of variety and blended NPS rate were
found to be highly significant (P <0.01) on days to reach 50% flowering (Table 4). Significantly,
highest number of days (46.67 days) to reach flowering was recorded due to application of 200
kg ha-1 of blended NPS for variety Nasir and for variety Angar at NPS rate of 250 kg ha-1 while
the earliest days to flowering (38.33 days) was recorded due to application of 50 kg ha-1 of
blended NPS for variety Ibado (Table 4). Variety Ibado was found to be early maturing as
compared to the other varieties across all NPS rates. The resultsof the current study revealed that
days to flowering was delayed with increment of application rate of blended NPS fertilizer which
could be due to the delaying effect of nitrogen obtained from blended NPS fertilizer. This result
was in line with the findings of Reta (2015) who reported that increasing the nitrogen rate from
nil to 69 kg N ha-1 significantly prolonged the days to 50% flowering of linseed
(Linumusitatissimum L.). This might be due to the fact that excessive supply of N promotes
luxuriant and succulent vegetative growth, dominating the reproductive phase. This result is
corroborated by that of Ali and Raouf (2011) who reported that the number of days from sowing
to flowering increased significantly with increasing amount of nitrogen from 23 kg N to 46 kg N
ha-1 in chickpea. On the other hand, Tesemma and Alemayehu (2015) reported the interaction of
P with variety to be non-significant on common bean. This result is also in contrast to the finding
of Nebret (2012) who reported non-significant interaction effects of nitrogen and sulphur.
Table 33. Mean number of days to flowering of common bean as affected by the interaction of
variety and blended NPS fertilizer rates at Adola during 2016-2017 main season
Variety
Angar
Nasir
Ibado
Mean
0
45.33abc
45.67abc
41.67d
44.22
50
45.33abc
45.00bc
38.33e
42.89
NPS rate (kg ha-1)
100
150
45.33abc
45.33abc
abc
45.33
45.67abc
e
39.67
39.67e
43.44
43.56
Mean
200
46.33ab
46.67a
42.00d
45.00
250
46.67a
44.67c
42.00d
44.33
45.72
40.50
45.50
363
LSD (0.05)
1.58
CV (%)
2.20
Means followed by the same letters are not significantly different as judged by LSD test at 5%, CV= coefficient of
variation
Days to physiological maturity
Days to physiological maturity was highly significantly (p<0.01) influenced by interaction of
varieties with blended NPS application rates but not significantly influenced by main effect of
variety (Table 5). Physiological maturity of common bean was delayed with increase in blended
NPS rate. The highest number of days required to physiological maturity (99.33 days) was
recorded for the highest rate of blended NPS application rate (250 kg ha-1) for variety Angar
while the shortest days to physiological maturity (91.33 days) was recorded without the NPS
application for variety Ibado (Table 5). The results indicated that days to maturity in most cases
were prolonged in response to the increased levels of blended NPS which can be attributed to the
role of nitrogen in the NPS that promoted vegetative growth. This is in line with the results of
Gupta and Sharma (2000) who reported that nitrogen promoted vegetative and lush growth
thereby delaying plant maturity of onion. This indicates that the nutrients taken up by plant roots
from the soil were used for increased cell division and synthesis of carbohydrate, which will
predominantly be partitioned to the vegetative sink of the plants, resulting in plants with a
luxurious foliage growth (Marschner, 2012).
Table 34. Mean number of days to physiological maturity of common bean as affected by the interaction
of variety and blended NPS fertilizer rates at Adola during 2016-2017 main season
Variety
Angar
Nasir
Ibado
Mean
LSD (0.05)
CV (%)
0
96.00a-d
96.33a-d
91.33e
94.56
50
93.33de
95.67a-d
95.67a-d
94.89
NPS rate (kg ha-1)
100
150
98.00abc
98.67ab
94.00cde
95.33a-d
95.00b-e
97.33a-d
95.67
97.11
3.48
2.2
Mean
200
94.00cde
98.00abc
98.67ab
96.89
250
99.33a
93.33de
98.00abc
96.89
96.56
95.44
96.00
Means followed by the same letters are not significantly different as judged by LSD test at 5%, CV=
coefficient of variation
This result is further corroborated with the finding of Huerta et al. (1997) who reported delayed
physiological maturity due to nitrogen fertilization of up to 80 kg ha -1 in common bean. In
contrast, Nebret (2012) reported that the application of sulphur (0-60 kg ha-1) had no significant
effect on days to maturity on common bean.
Plant height
364
The analysis of variance showed highly significant (P<0.01) effect of varieties, blended NPS
rates and their interaction on plant height at physiological maturity (Table 6). Variety Nasir
showed the highest plant height (99.72 cm) with application of 150 kg NPS ha-1 where as the
shortest plants (31.08 cm) were seen for Ibado without NPS fertilizer (Table 6).
Plant height was significantly increased from 31.08 cm for variety Ibado with 0 kg NPS ha–1 to
99.72 cm for variety Nasir at 150 kg NPS ha–1. The increase in plant height in response to the
increased blended NPS application rate might be due to the maximum vegetative growth of the
plants under higher N, P and S availability. Nitrogen helps in chlorophyll formation, phosphorus
establishes strong root system and sulphur enhanced the formation of chlorophyll and
encouraged vegetative growth (Havlin et al., 2003). In conformity with the current result,
Moniruzzaman et al. (2008) found that plant height was significantly increased up to 160 kg N
ha-1. . Also application of phosphorus at the highest level (120 kg P2O5 ha-1) increased plant
height. The promotion effect of high P level on plant height of maize may be due to better
development of the root system and nutrient absorption (Hussain et al., 2006). The increase in
plant height might also be ascribed in relation to better root formation due to sulphur, which in
turn activated higher absorption of N, P, K and sulphur from soil and improved metabolic
activity inside the plant. Similar results were reported by Jawahar et al. (2017) where sulphur
level of 40 kg ha-1 was found to increase the plant height, LAI, chlorophyll content and number
of branches per plant of blackgram (Vigna mungo). In contrast to this result, Fisseha and Yayis
(2015) reported no significant main and interaction effect of N and P levels on plant height of
common bean. Similarly, Meseret and Amin (2014) also reported that P rate at 0-40 kg ha-1 had
no significant effect on plant height in common bean.
Table 35. Means of plant height (cm) of common bean as affected by the interaction of variety
and blended NPS fertilizer rates at Adola during 2016-2017 main season
Variety
Angar
Nasir
Ibado
Mean
LSD (0.05)
CV (%)
0
56.30ef
63.13de
31.08i
50.17
50
83.44bc
57.17efg
38.57hi
59.73
NPS rate (kg ha-1)
100
150
75.12cd
85.58abc
abc
88.94
99.72a
ghi
43.33
45.6fgh
69.13
77.27
13.69
12.00
Mean
200
89.71abc
90.69abc
48.96fgh
76.45
250
91.97ab
90.52abc
48.55fgh
77.01
80.35
81.69
42.83
Means followed by the same letters are not significantly different as judged by LSD test at 5%, CV=
coefficient of variation
Number of primary branches
365
The analysis of variance showed highly significant (P<0.01) main effect of variety and blended
NPS fertilizer application rates on number of primary branches, while their interaction did not
significantly influence the number of primary branches (Table 7). Variety Angar recorded the
highest number of primary branches per plant (2.55) while the lowest number of primary
branches (2.05) was recorded for variety Ibado. This difference might be due to genetic
differences in production of number of primary branches among the varieties. This difference
might be due to genetic differences in production of number of primary branches among the
varieties. The result was consistent with the finding of Addisu (2013) who reported that number
of primary and secondary branches were highly significantly varied among chickpea varieties at
Debre- Zeit with the desi variety Natoli having significantly higher number of primary (3.21) and
secondary branches (6.73) than the Kabuli variety Acos Dubie (2.26) and (3.49) respectively.
The blended NPS rate had highly significant (P<0.01) effect on number of primary branches per
plant. Increasing rates of blended NPS fertilizer from 0 to 250 kg ha–1 showed progressive
increase in the number of primary branches per plant (Table 7). Thus, the highest number of
primary branches per plant (2.77) was recorded at the highest rate of application of (250 kg NPS
ha-1) and it was statistically at par with NPS rates of 200, 150, and 100 kg NPS ha-1, while the
lowest number of primary branches per plant (1.56) was recorded for the control. The increase in
number of primary branches per plant in response to increased rate of blended NPS application
rates indicates higher vegetative growth of the plants under higher N, P and S availability. In
line with this result, Shubhashree (2007) reported significantly higher number of branches per
plant of common bean with 75 kg P2O5 ha-1 over the control.
The increment in number of branches with increased rate of P might also be due to the
importance of P for cell division, leading to the increase in plant height and number of branches
(Tesfaye et al., 2007). In line with this result, Moniruzzamanet al. (2008) reported that the
number of branches per plant increased significantly with the increase of N up to 120 kg ha-1 on
common bean. The increased primary branches observed under blended fertilizer might be
attributed to readily available form of S that enhanced uptake of nutrients even at the initial stage
of crop growth. The result was also in agreement with the finding of Jawahar et al. (2017) who
reported that application of 40 kg S ha-1 recorded the highest number of branches per plant (7.75)
in blackgram (Vigna mungo).
Total number of nodules
366
The main effect of variety and interaction of variety with blended NPS rate had no significant
effect on total number of nodules, but the main effect of blended NPS rate had highly significant
(P<0.01) effect on total number of nodules (Table 7). Thus, the highest number of total nodules
per plant (80.47) was obtained from the application of blended NPS rate of 200 kg NPS ha-1
while the lowest number of total nodules (40.94) was recorded from nil application of blended
NPS fertilizer. Application of blended NPS fertilizers significantly increased the number of
nodules up to 200 kg ha-1 which might be due to better root development with increasing levels
of these nutrients. But the total nodule number decreased at 250 kg NPS ha-1.The decrease in
number of nodules per plant at highest rates of blended NPS might be due to increasing nitrogen
application rates and thereby attributed to the negative effect of fertilizer-N on nodule formation
and growth at the high rates. This result is in line with that of Chen et al. (1992) and Starling et
al. (1998) who reported that high rate of nitrogen (56.58 kg N ha-1), resulted in reduction of
nodule number and nodule weight in soya bean. The increase in number of total nodules at 200
kg NPS ha-1 might also be due to phosphorus which is needed in relatively large amounts by
legumes for growth and to promote leaf area, biomass, yield, nodule number and nodule mass in
different legumes. Consistent with this result, Amare et al. (2014) reported that nodule number
was significantly increased with increasing levels of phosphorus with the lowest (12.89) and the
highest (31.85) numbers in common bean obtained from the control treatment and application of
20 kg P2O5 ha-1, respectively. Yadav (2011) reported the synergistic effect of phosphorus and
sulphur on number and weight of nodules per plant with the maximum number of nodules per
plant recorded at the highest level of phosphorus (40 kg P2O5 ha-1) along with sulphur (20 kg S
ha-1) on clusterbean(Cyamopsis tetrogonoloba).
Number of effective nodules
Blended NPS fertilizer application had significant (P<0.05) effect on number of effective
nodules per plant, but main effect of variety and interaction of variety with blended NPS had no
significant effect (Table 7). Number of effective nodules per plant increased with increasing rate
of blended NPS application rates. Increasing of blended NPS fertilizer from 0 to 200 kg ha–1
enhanced the number of effective nodules per plant (Table 7). The highest number of effective
nodules per plant (35.54) was recorded at the rate of 200 kg NPS ha-1 while the lowest number of
effective nodules per plant (27.43) was recorded at the rate of 0 kg NPS ha-1. The increased
number of effective nodules with the increase in NPS application up to 200 kg NPS ha-1 might be
367
due to the vital role of phosphorus in increasing the number and size of nodule and the amount of
nitrogen assimilated per unit of nodules. In agreement with this result, Bashir etal. (2011)
reported that phosphorus plays a vital role in increasing plant tip and root growth, decreasing the
time needed for developing nodules to become active (effective) for the benefit to the host
legume. Similarly, Tsai et al. (1993) reported that application of nitrogen in the range of 22 to 33
kg ha-1 enhanced both nodulation and seed yield of French bean (Phaseolus vulgaris).
Table 36. Mean numbers of primary branches, total and effective nodules per plant of common
bean as influenced by the main effects of variety and blended NPS fertilizer rates at
Adola during 2016-2017 main cropping season
Treatments
Variety
Angar
Ibado
Nasir
LSD (0.05)
NSP rate (kg ha-1)
0
50
100
150
200
250
LSD (0.05)
CV (%)
Number
of
primary
branches per plant
Number
of
total
nodules per plant
Number of effective nodule
per plant
2.55a
2.28ab
2.05b
0.28
63.01
68.09
61.83
NS
32.88
32.56
30.38
NS
1.56d
2.05c
2.25ab
2.55a
2.58a
2.77a
0.38
17.7
40.94c
61.16b
58.52b
60.36b
80.47a
64.41b
12.0
20.5
27.43c
30.87bc
31.87b
32.51ab
35.54ab
33.41ab
4.07
14.2
Means in the same column and treatment category followed by the same letters are not significantly
different as judged by LSD at 5% level of significance. NS = non- significant
Increased number of effective nodules with the application of NPS over the control might also be
from increased sulphur application which might be due to the high dose of sulphur and
increasing its availability along with other major nutrients. This result is in line with the finding
of Ganeshamurthy and Reddy (2000) who reported significant increase in the number of active
nodules of soybean with the application of sulphur up to 20 kg ha-1, at which point nodule
production reached a plateau and did not increase further. Scherer et al. (2006) also reported that
formation of nodule in blackgram was increased in response to sulphur application which is
involved in the formation of nitrogenous enzyme known to promote nitrogen fixation in legumes.
Yield and Yield Components
Stand count at harvest
The main effect of NPS and the interaction of varieties and blended NPS rates had highly
significant (P<0.01) effect on stand count at harvest. But varieties had no significant effect on
368
stand count at harvest (Table 8). The highest stand count per plot at harvest (92.67) was obtained
at applied blended NPS rate of 50 kg ha-1 for variety Angar, whereas the lowest stand count at
harvest (72.33) was recorded for variety Angar at highest rate of fertilizer application (250 kg
NPS ha-1). The reduction in final crop stand count at the highest NPS rate could be due to
sufficient supply of nutrients which in turn favored vigorous vegetative growth, thereby resulting
in higher intra-plant competition and crowding out of weaker plants by the vigorous ones.
Table 37. Mean stand count per plot at harvest of common bean as influenced by interaction of
variety and blended NPS fertilizer rates at Adola during 2016-2017 main cropping
season
Variety
Angar
Ibado
Nasir
Mean
LSD (0.05)
CV (%)
0
88ab
77.33efg
81.33cde
82.22
50
92.67a
89.33ab
88.67ab
90.22
NPS rate (kg ha-1)
100
150
79.33cdef
79.00c-f
84.00bc
73.33fg
bcde
83.00
83.67bcd
82.11
78.67
5.62
4.10
Mean
200
84.00bc
83.67bcd
80.67cde
80.67
250
72.33g
76.67efg
83.67bcd
83.67
82.56
80.72
83.50
Means in rows and columns followed by the same letter are not significantly different judged by LSD test
at 5% level of significance, CV= coefficient of variation
Number of pods per plant
Highly significant (P<0.01) effects of blended NPS fertilizer application rate and varieties were
observed on the number of total pods per plant while the interaction effect did not significantly
influence the number of total pods (Table 9). The highest number of total pods per plant (18.52)
was recorded at application rate of 250 kg NPS ha-1 whereas the lowest number of total pods
(8.7) was obtained from the unfertilized plot (Table 9). The increase in number of pods per plant
with the increased NPS rates might possibly be due to adequate availability of N, P and S which
might have facilitated the production of primary branches and plant height which might in turn
have contributed for the production of higher number of total pods. In conformity with this
result, Moniruzzaman et al. (2008) reported significant effect of N fertilizers on pod production
per plant of French bean with the maximum number of pods per plant (25.49) obtained at 120120-60-20-4-1 kg of N-P2O5-K2O-S-Zn-B. The increment of number of pods per plant due to
application of P fertilizer confirms the fact that P fertilizer promotes the formation of nodes and
pods in legumes (Buttery, 1969). In agreement with this result, Dereje et al. (2015) also found
that the number of pods per plant of common bean significantly increased in response to
increasing rate of phosphorus up-to the highest rate (92 kg P2O5 ha-1). On the other hand,
369
Jawahar et al. (2017) reported that application of 40 kg S ha-1 recorded the highest number of
seeds per pod of blackgram. This could be due to the increasing levels of sulphur application that
enhanced its availability to the crop and increase photosynthetic activity of crop
In this study, varieties also exhibited highly significant (P<0.01) difference in the number of
pods per plant. Variety Angar produced the highest number of pods per plant (15.3) while the
lowest number of pods per plant (10.24) was recorded for variety Ibado (Table 9). The variation
in the number of pods per plant among the varieties might be related to the genotypic variation of
the cultivars in producing pods. In accord with the results of the present study, different authors
reported significant variations in the number of pods per plant for common bean varieties
(Fageria et al., 2010; Mourice and Tryphone, 2012).
Number of seeds per pod
The interaction effect of variety and blended NPS application rates and main effects of blended
NPS application rates were not significant, but the main effects of varieties had highly significant
(P<0.01) effect on the number of seeds per pod. (Table 9). The highest number of seeds per pod
(5.35) was recorded for variety Nasir followed by Angar (5.33) whereas the least number of
seeds per pod (3.18) was recorded for variety Ibado (Table 9). This indicates that the trait is
mainly controlled by genetic factors than the management. Consistent with the results of this
study, Mourice and Tryphonne (2012) observed significant variations in number of seeds per pod
among common bean genotypes. The variation in number of seeds per pod could be attributed to
the variation in the size of seeds of the cultivars where variety Ibado with highest seed size
produced lower number of seeds per pod. In agreement with this result, Fageria and Santos
(2008) also reported that the number of seeds per pod of different common bean genotypes
varied in the range of 3.1 to 6 and attributed the difference due to the genetic variation of
cultivars. However, the result of the present study was in contrast with the findings of
Shubhashree (2007) who reported that the number of seeds per pod of French bean increased
significantly with the levels of phosphorus added.
Hundred seed weight was highly significantly (p<0.01) influenced by varieties, blended NPS rate
and their interactions (Table 10). Variety Ibado with application of 200 kg blended NPS ha-1
fertilizer scored significantly the highest hundred seed weight (54.33 g) while the lowest hundred
370
seed weight (20 g) was for variety Nasir with 100 kg blended NPS ha-1 application rate (Table
10).
371
Table 38. Mean number of pods per plant and seeds per pod of common bean as influenced by
varieties and blended NPS fertilizer rates at Adola during 2016-2017 main season
Treatments
Variety
Angar
Ibado
Nasir
LSD (0.05)
NSP rate (kg ha-1)
0
50
100
150
200
250
LSD (0.05)
CV (%)
Number of pods
per plant
Number of seeds per pod
15.30a
10.24c
12.63ab
2.21
5.35a
5.33a
3.18b
0.22
8.70c
11.82bc
12.6ab
12.51ab
14.91ab
18.52a
3.14
25.1
4.40
4.54
4.73
4.54
4.76
4.75
NS
6.2
Means in columns and rows followed by the same letter are not significantly different judged by LSD test
at 5% level of significance; ns = non significant, CV= coefficient of variation
Hundred seed weight
This might be because nutrient use efficiency by crop was enhanced at optimum level of N, P
and S since grain weight indicates the amount of resource utilized during critical growth periods.
The increase in 100 seed weight with fertilizer application is in agreement with the finding of
Shamim and Naimat (1987) who related the increment in 100-seed weight to the influence of cell
division, phosphorus content in the seeds as well as the formation of fat and albumin. The
increase in hundred seed weight as a result of increased P application might be attributed to
important roles the nutrient plays in regenerative growth of the crop (Zafar et al., 2013), leading
to increased seed size (Fageria, 2009), which in turn may improve hundred seed weight.
Similarly, Amare et al. (2014) observed significant increase in thousand seed weights of
common bean as a result of phosphorus application up to 40 kg ha-1. In contrast to the results of
this study, Fisseha and Yayis (2015) reported that the different levels of phosphorus (46, 69 and
92 kg P2O5 ha-1) fertilizer used had not resulted in significant difference in 100 seed weight of
common bean. Variation in hundred seed weight might have occurred due to the presence of
differences in seed size among the common bean varieties as hundred seed weight increases with
increase in the seed size. In line with this result, Tanaka and Fujita (1979) stated that the number
of seeds per pod and weights of hundred seeds were strongly controlled genetically in field bean
(Pisum sativim). The higher 100 seed weight for variety Ibado associated with the size of the
372
seed is in accordance with Hawtin et al. (1980) who explained that the larger the seed, the higher
its seed weight.
Table 39. Means of hundred seed weight (g) of common bean as influenced by interaction of
variety and blended NPS fertilizer rates at Adola during 2016-2017 main season
Variety
Angar
Ibado
Nasir
Mean
LSD (0.05)
0
23.33e
38.33c
21.67e
27.78
50
23.33e
40.00c
20.00e
27.78
CV (%)
NPS rate (kg ha-1)
100
150
38.33c
20.00e
38.33c
38.33c
e
20.00
20.00e
32.22
26.11
5.58
Mean
200
21.67e
54.33a
31.67d
35.89
250
42.33bc
46.67b
30.00d
39.67
28.17
42.67
23.89
10.6
Means in columns and rows followed by the same letters are not significantly different as judged by LSD
test at 5% level of significance. CV=coefficient of variation.
Above-ground dry biomass yield
The above-ground dry biomass yield was significantly (P<0.01) affected by the NPS fertilizer
application and the interactions of fertilizer application with variety. However, the main effect of
varieties had no significant effect on biomass yield (Table 11). The result generally showed an
increase in biomass production with increase in the rate of blended NPS among the bean
varieties. The highest above-ground dry biomass yield (10278 kg ha -1) was recorded due to the
application of highest rate of NPS fertilizer (250 kg NPS ha-1) for variety Angar followed by
variety Angar at 100 kg NPS ha-1,whereas the lowest (4045 kg ha -1) biomass yield was obtained
for variety Nasir under the control NPS rate (Table 11).The increased in biomass yield of
cultivars across blended NPS rates could be attributed to the fact that the enhanced availability of
N significantly increased plant height, number of pods per plant and to the overall vegetative
growth of the plants that contributed to higher aboveground dry biomass yield. This result was in
line with that of Veeresh (2003) who reported that total dry matter production per plant increased
significantly from 12.0 to 16.03 g due to increased nitrogen application from 40 to 120 kg N ha-1
on French bean (Phaseolus vulgaris). The increment in dry matter yield with application of
blended NPS fertilizer might also be due to the adequate supply of P from the NPS that could be
attributed to an increase in number of branches per plant, which increased photosynthetic area
and the number of pods per plant. The significant increase in the aboveground dry biomass yield
in response to increasing rate of phosphorus application proves that the soil of the study area is in
fact deficient in available soil P and requires external P fertilizer application for enhancing
373
cropyield. This result was in conformity with the findings of Getachew and Angaw (2006) who
reported a significant linear response of above-ground dry biomass yield to phosphorus
application in faba bean on acidic Nitisols. In contrast with this result, Nebret (2012) reported
that application of sulphur up to 60 kg S ha-1 and interaction of nitrogen with sulphur did not
result in significant effect on above-ground dry biomass of common bean.
Table 40. Means of above-ground dry biomass yield (kg ha-1) of common bean as influenced by
interaction of variety and blended NPS fertilizer rates at Adola during 2016-2017 main
season
Variety
Angar
Ibado
Nasir
Mean
LSD (0.05)
CV (%)
0
5794def
4129f
4045f
4656
50
5178ef
4936ef
6443b-f
5519
NPS rate (kg ha-1)
100
150
9135ab
5798def
5724def
6527b-f
b-f
6640
5782def
7166.33
6035.67
2421.3
21.9
Mean
200
6191c-f
8802abc
8073a-d
7688.67
250
10278a
7329b-e
9073ab
8893.33
7062.33
6241.17
6676
Means in columns and rows followed by the same letters are not significantly different as judged by LSD
test at 5% level of significance. CV= coefficient of variation
Seed yield
Seed yield was significantly (P<0.05) affected by the main effect of variety, and highly
significantly (P<0.01) affected due to main effects of blended NPS fertilizer rates and the
interaction of varieties with fertilizer combination (Table 12). The highest grain yield was
recorded for variety Angar (3260 kg ha-1) at 250 kg NPS ha-1 which was followed by Nasir (3079
kg ha-1) at similar rate of blended NPS level while the lowest yield (1700 kg ha-1) was observed
for variety Ibado at control fertilizer treatment (Table 12). Differences in seed yield among the
common bean varieties might be related to the genotypic variations in P use efficiency. Hence,
the cultivars which produced higher grain yield might have either better ability to absorb the
applied P from the soil solution or translocate and use the absorbed P for grain formation than
the low yielding cultivar. In agreement with the results of this study, Gobeze and Legese (2015)
and Mourice and Tryphone (2012) observed significant variations in grain yield for common
bean due to genotypic variations for P use efficiency which may arise from variation in P
acquisition and translocation and use of absorbed P for grain formation in common bean. The
result might be attributed to the fact that applying NPS fertilizer increases crop growth and yield
on soils which are naturally low in NPS and in soils that have been depleted (Mullins, 2001).
Similar results were reported by Gebre- Egziabher et al. (2014) that P application at the rate of
374
46 kg P2O5 ha-1 gave higher number of pods per plant and yield as compared to unfertilized plots
in common bean. In line with this result, application of S with or without P recorded significantly
higher seed yield up to 40 kg S ha-1 on chickpea (Shivakumar, 2001); and on blackgram
(Jawahar et al., 2017). It might also be due to increased levels of S, its availability along with
major nutrients and higher uptake of crop and influencing growth and yield components of the
crop, which ultimately lead to effective, assimilate partitioning of photosynthates from source to
sink in post-flowering stage and resulted in highest seed yield.
Differences in seed yield among the common bean cultivars might also be related to their
response to applied N. In conformity to this result, Dwivedi et al. (1994) found increased yield of
common bean due to increasing levels of nitrogen up to 100 kg ha -1 with the difference between
80 and 100 kg N ha-1 being not significant. Boroomanndan et al. (2009) also reported that seed
yield of soybean increased significantly at 40 kg N ha-1 compared to the control treatment.
However, application of 80 kg N ha-1 decreased seed yield, indicating that there is a limit to the
maximum level of nitrogen to be supplied to avoid its detrimental effect on the plant.
Table 41. Means of seed yield (kg ha-1) of common bean as influenced by interaction of variety
and blended NPS fertilizer rates at Adola during 2016-2017 main season
Variety
Angar
Ibado
Nasir
Mean
LSD (0.05)
CV (%)
0
2485cde
1700g
1763fg
1983
50
2360e
2249ef
2500b-e
2370
NPS rate (kg ha-1)
100
150
2582b-e
3044abc
2389de
2521b-e
a-e
2747
2250ef
2573
2605
497.4
11.7
Mean
200
2558b-e
3053abc
2956a-d
2856
250
3260a
3079ab
2505b-e
2948
2715
2499
2453
Means within columns and rows followed by the same letter are not significantly different as judged by
LSD test at 5% level of significance. CV= Coefficient of Variation.
Harvest Index
Harvest index was highly significantly (P < 0.01) affected by the interaction of variety with
blended NPS rate (Table 13). The highest harvest index (0.53) and lowest harvest index (0.28)
were recorded for variety Angar with application of blended NPS at 150 kg ha-1 and for Nasir at
250 kg ha-1, respectively (Table 13). This might be due to the fact that the higher NPS fertilizers
rate had higher influence on vegetative growth than nutrient translocation from plant biomass to
seed. In line with this result, Singh and Kumar (1996) reported the highest harvest index of lentil
was obtained when 45 kg P ha-1 and 30 kg S ha-1 were applied. The increment in harvest index
375
with rates of fertilizer is in agreement with the findings of Dhanjal et al. (2001) who also
reported improvement in harvest index values of 31.60%, 31.99% and 33.86% due to increasing
N level zero to 60 and 120 kg N ha-1 respectively. However, Gifole et al. (2011) found no
significant response of harvest index of common bean to P application.
Table 42. Means harvest index of common bean as influenced by interaction of variety and
blended NPS fertilizer rates at Adola during 2016-2017 main season
Variety
Angar
Ibado
Nasir
Mean
LSD (0.05)
CV (%)
0
0.43ab
0.41ab
0.42ab
0.42
50
0.46ab
0.46ab
0.39abc
0.44
NPS rate (g ha-1)
100
150
d
0.28
0.53a
0.42ab
0.39abc
ab
0.41
0.39abc
0.37
0.44
0.05
7.30
Mean
200
0.41ab
0.35cd
0.37cd
0.38
250
0.32d
0.41ab
0.28d
0.34
0.41
0.41
0.38
Means within columns and rows followed by the same letter are not significantly different as judged by
LSD at 5% level of significance. CV= Coefficient of Variation
Economic Analysis
The agronomic data upon which the recommendations are based must be relevant to the farmers'
own agro-ecological conditions, and the evaluation of those data must be consistent with the
farmers' goals and socio-economic circumstances (CIMMYT, 1988). The net benefit was
computed due to common bean varieties, application of blended NPS fertilizer and interaction of
varieties with application of blended NPS fertilizer. The economic analysis revealed that the
highest net benefit (29825 Birr ha-1) was obtained from combination of variety Ibado with
application of 200 kg NPS ha-1 while the lowest net benefit (12692 Birr ha-1) was obtained from
variety Nasir with no application of fertilizer (Table 14). Therefore, production of Ibado variety
with the application of 200 kg NPS ha-1 was most productive variety for economical production
compared to Angar and Nasir varietiesand can be recommended for the study area. Dereje et al.
(2015) reported that planting of the cultivar Nasir produced the highest net benefit (15903.1 Birr
ha-1) with acceptable marginal rate of return (3040%) compared to other cultivars at Areka.
Fisseha and Yayis (2015) also reported net benefit of 21, 070 ETB ha-1 with marginal rate of
return of 80% by the application of 69 kg P2O5 ha-1 at Areka.
376
Table 43. Result of economic analysis for response of common bean varieties to rates of blended
NPS fertilizer rates at Adola 2016-2017 main season
Treatments
Adjusted
NPS cost
NPS
Total Cost
yield
(Birr ha-1)
application
(Birr ha-1)
-1
-1
(kg ha )
cost (Birr ha )
Angar+0
2235.4
0
0
0
Ibado+0
1529.8
0
0
0
Nasir+0
1586.5
0
0
0
Angar+50
2123.6
700
350
1050
Ibado+50
2024.4
700
350
1050
Nasir+50
2250.1
700
350
1050
Angar+100
2324.0
1400
350
1750
Ibado+100
2150.3
1400
350
1750
Nasir+100
2471.9
1400
350
1750
Angar+150
2739.8
2100
350
2450
Ibado+150
2268.8
2100
350
2450
Nasir+150
2024.8
2100
350
2450
Angar+200
2301.9
2800
350
3150
Ibado+200
2747.9
2800
350
3150
Nasir+200
2660.3
2800
350
3150
Angar+250
2934.2
3500
350
3850
Ibado+250
2771.5
3500
350
3850
Nasir+250
2254.9
3500
350
3850
Where, NPS cost=1400 Birr/100 kg, NPS application cost=350 Birr ha -1, common
Nasir = 8, Ibado=12 Birr kg-1
Total Revenue Net Benefit (Bir
ha-1)
(Birr ha-1)
17883
17883
18358
18358
12692
12692
16989
15939
24293
23243
18001
16951
18592
16842
25804
24054
19775
18025
21918
19468
27226
24776
16198
13748
18415
15265
32975
29825
21282
18132
23474
19624
33258
29408
18039
14189
bean grain price of Angar and
CONCLUSIONS AND RECOMMENDATIONS
Response of common bean (Phaseolus vulgaris L.) varieties to rates of Blended NPS Fertilizer
were investigated on Nitisols and Orthic Aerosols soils of Guji Zone, Southern Ethiopia. It was
conducted during the main 2016-2017 cropping season with the objective toinvestigate the effect
of blended NPS rates on growth, yield and yield components of common bean varieties and to
identify economically feasible rates of blended NPS at Guji Zone Southern Ethiopia.
The result showed that the main effects of NPS rate, variety and their interaction had a
significant effect on some of growth and yield component parameters. The highest level of NPS
rate (200-250 kg ha-1) resulted in higher values of number of primary branches per plant, number
of total nodules, number of effective nodules and total number of pods, number of total pods per
plant, highest number of total nodules and effective nodules. Varieties exhibited variation on the
number of pods per plant, number of primary branches and number of seeds per pod. Variety
Angar gave the highest number of primary branches per plant and number of pods per plant
whereas the highest number of seeds per pod was recorded for variety Nasir. However, the
interaction of variety and blended NPS had significant effect on almost all parameters except on
the number of total and effective nodules per plant, number of primary branches per plant and
377
number of pods per plant.The highest number of days to flowering and days to physiological
maturity were recorded due to application of 200 kg ha-1 and 250 kg ha-1 of blended NPS,
respectively for variety Nasir. Variety Nasir gave the highest plant height with application of 150
kg NPS ha-1 whereas variety Ibado with application rate of 200 kg blended NPS ha-1 had the
highest hundred seed weight. The highest above-ground dry biomass yield was recorded due to
the application of highest rate of fertilizer for variety Angar. The highest grain yield was
recorded for variety Angar at 250 kg NPS ha-1 whereas the highest harvest index was recorded
by variety Angar with application of blended NPS of 150 kg ha-1.
Based on the partial budget analysis, the highest net benefit (29825 Birr ha-1) was obtained from
combination of variety Ibado with application of 200 kg NPS ha-1 whereas lowest was from
variety Nasir (12692 Birr ha-1) with no fertilizer application.Thus, it can be concluded and
recommended that application of 200 kg ha-1 with variety Ibado was found to be superior and can
be used for common bean production in mid-land of Adola district, Southern Oromia.
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382
Protection
383
Evaluation of post-emergence herbicides against major weed species in wheat in Bale
highlands, South- eastern Ethiopia.
Mengistu Bogale*,Girma Fana and Firehiwot Getachew
Oromia Agricultural Research Institute/Sinana Agricultural Research Center Bale-Robe,
Ethiopia
*Corresponding author: mbalemu@gmail.com
Abstract
A field experiment on weed control in wheat was conducted at Sinana agricultural research
center and Robe area during the main bona cropping season of 2016. Different post-emergence
herbicides were evaluated together with the hand weeding and weedy check for weed control and
yield and yield components of wheat. The experiment was laid out in Randomized Complete
Block Design (RCBD) with three replications. Improved bread wheat variety ‘‘Mada walabu’’
with a seed rate of 150 kg ha-1 and different recommended agronomic practices was used in the
experiment. The treatments consist of three post-emergence herbicides: Atlantis OD 37.5, Pallas
45 OD, and Topic plus and two times hand weeding and weedy check were used for comparison.
The analysis result of the two locations showed that the yield and yield components of wheat
were significantly responded to the applied treatments, except plant height, Biomass and HLW at
Sinana on-station and plant height and spike length at Robe area. Maximum wheat grain yield
was recorded (4622 kg ha-1) at Sinana on-station and (4645 kg ha-1) at Robe area in plots
treated with Atlantis OD 37.5.The second maximum grain yield was recorded at both locations
Sinana on-station and Robe area, 4311 kg ha-1 and 4289 kg ha-1 respectively, in plots treated
with Pallas 45 OD. The third maximum grain yield at both locations was recorded in plots
weeded twice. The lowest grain yield was recorded in weedy check plots at both locations,
Sinana on-station and Robe area, 2867 kg ha-1 and 2911 kg ha-1 respectively. The highest weed
control efficacy at both locations for both grass and broad leaved weeds was recorded for
Atlantis OD 37.5 followed by Pallas 45 OD. The economic analysis also revealed that Atlantis
OD 37.5gave the highest net benefit. From one year result it is possible to conclude that if
economically affordable and available, Atlantis OD 37.5 is the best weed management option in
wheat in Bale highlands.
Key words: Broad leaved weed, Herbicide Grass weed and Wheat (Triticum aestivum L.)
384
Introduction
Wheat (Triticum aestivum L.) is one of the most important cereal crops worldwide in gross
production and utility (Evans, 1998). Ethiopia is the major wheat producer in the sub-Saharan
Africa and the southeastern highlands of Arsi and Bale are well recognized as bread basket of the
country. Arsi and Bale highlands form the major wheat belt of Ethiopia accounting for the
country’s 30.5% of wheat production (CSA, 2008). However, the national average yield of wheat
is quite low due to biotic and abiotic factors. Rusts, Insect pests and Weeds are the major biotic
constraints .Weeds are one of the major constraint in wheat production as they reduce
productivity due to competition, allelopathy, by providing habitats for pathogens as well as
serving as alternate host for various insects and fungi and increase harvest cost. Studies indicated
that crop losses due to weed competition throughout the world as a whole, are greater than those
resulting from combined effect of insect pests and diseases. It causes yield reduction in wheat
from 10- 65% (Genene G. and Habtamu S., 2001).
Physical methods are laborious, tiresome and expensive due to increasing cost of labor, draft
animals and implements and weeds cannot effectively be managed merely due to crop mimicry
especially grass weeds in cereal crops. Therefore, the use of chemical weed control has become
necessary (Marwat K. B. et al., 2008). However, the choice of most appropriate herbicide,
proper time of application and proper dose is an important consideration for lucrative returns (
Awan, I.U. et al., 1990). Application of herbicides decreased dry weight of weeds significantly
compared to dry weight in non-treated plots and increased yield components and grain yield
(Ashrafi Z.Y. et al., 2009). Therefore seeking and evaluation of herbicides is excellent option for
efficient weed control.
Materials and methods
The experiment was conducted at two locations (Sinana on- station, and Robe area) for one year
(2016 main cropping season) to evaluate the efficacy of herbicides on weeds and yield and yield
components of wheat. The experimental design in both locations was randomized complete
block design and replicated three times. The treatments were consist of two grass and
broadleaved weed herbicides (Atlantis OD 37.5, and Pallas 45 OD) and One grass weeds
herbicide (Topic plus) applied at 2-4 leaf stage at the rate of 1, 0.5 and 1 lit ha-1 respectively,
two times hand wedding and weedy check (control) were used as a treatments. The test bread
wheat variety (Mada walabu) was sown at the recommended seeding rate of 150 kg ha-1. Seeds
385
were sown into rows of 0.2 m apart. The size of each plot was 5 m x 5 m (25 m2) and the
distance between the plots and blocks were kept at 1 m and 1.5m apart respectively. Weed
density count of the individual species were taken from each plot before and four weeks after
application of herbicides by using 0.25m2 quadrate, and the density from treated plots compared
with the untreated plots. The efficacy of herbicides was calculated by the following formula
(Auskalnis and Kadzys, 2006).
𝑊𝐶𝐸 (%) =
(𝑁𝑊𝐶−𝑁𝑊𝑇)
𝑁𝑊𝐶
𝑥 100
Where, WCE = weed control efficacy; NWT = number of weed species in the treated plot;
NWC = number of weed species in the control plot
Statistical data analysis
Analysis of variance (ANOVA) was done using Gen Stat 15th edition and means comparisons for
the significantly different variables were made among treatments using least significant
differences (LSD) test at (0.05) level of significance.
Economic analysis
The economic analysis was done based on the procedures by CIMMYT (CIMMYT, 1988).
Partial budget and net benefit analysis were performed for weed management options for
selecting the profitable treatments.
Results and discussions
Weed flora
The weed community observed in the experimental fields comprised of both broad leaved and
grass weeds. Out of the total weeds observed in the experimental fields, 62.5% were broad
leaved while 37.5% grass weed species. Among broad leave weed species Amaranthus hybridus,
Chenopodium spp, Galensoga parviflora, Commelina benghlensis, Guizotia scabra were the
most dominant. Whereas Avena fatua,Bromus pectinatus and Cyprus spp were the most
dominant grass weed species observed in the experimental plot at the time of treatment
application.
Wheat yield and yield related traits as influenced by different weed management options
The analysis result of the two locations (Sinana on-station and Robe area) showed that the yield
and yield components of wheat were significantly responded to the applied weed management
options. At both locations kernels per spike, Grain yield, Harvest index and TKW were
significantly influenced by applied weed management options (Table 1and3).
386
Kernels per spike
Kernel per spike was significantly affected by weed management methods. There was
statistically significant difference (P<0.05) between post emergence herbicides and hand
weeding and weedy check. But there was no significant difference among post emergence
herbicides. The highest kernel per spike was recorded from Atlantis OD 37.5 treated plots (45
and 38.2 kernels spik-1) at Sinana on station and Robe area respectively. On the other hand the
lowest kernel per spike (36.3 and32.5) was obtained from weedy check plot at Sinana on-station
and Robe area, respectively (Table 1 and 3).
Thousand kernel weight
Analysis of variance indicated that there was significant variation among weed management
methods regarding to thousand kernel weight at both locations Sinana on-station and Robe area.
The maximum thousand kernel weight (44.7g and 36g) was recorded from plots treated with
Atlantis OD 37.5 at Sinana on station and Robe area, respectively (Table 1and 3). The minimum
thousand kernel weight (38.7g and 26.7g) was obtained from weedy check at both respective
locations. This low thousand kernel weights may be attributed to resource competition of wheat
by weed in un-treated plot. This result is supported by the findings of Ahmad et al. (1991) and
Mason et al. (2006).
Grain yield
Wheat grain yield was significantly affected by weed management options at both Sinana onstation and Robe area (Table 1and 3). Result of the experiment revealed that the herbicide
treatment had a noticeable effect on the grain yield of wheat. The highest grain yield (4622 kg
ha-1 and 4645kg ha-1) was recorded from plots treated with Atlantis OD 37.5 followed by Pallas
45 OD (4311 kg ha-1 and 4289 kg ha-1) at Sinana on-station and Robe area, respectively (Table
1 and 3). Increased grain yield of treated crop may be attributed to availability of more nutrients,
light, moisture and space resulting in crop growth. This finding is in agreement with the work of
Arif et al. (2004) who reported that the applications of herbicides in fact does affect grain yield
of wheat. The lowest grain yield was obtained from weedy check (2867 kg ha-1 and 2911 kg ha-1)
at both respective locations. Such a yield reduction could be due to maximum infestation of
weeds in un-treated plot. This result is similar to the findings of Chaudhary et al. (2008) and
Dalley et al. (2006) who reported that high weeds intensity and more competition time with crop
plants causes more yield reduction in crop yield.
387
Economic analysis: The economic analysis was done based on the procedures by CIMMYT
(CIMMYT, 1988). Partial budget and net benefit analysis were performed for weed management
options for selecting the profitable treatments (Table5). The net benefit analysis indicated that
Atlantis OD 37.5 resulted in the highest net benefit (55516 Birr/ha). The next highest net benefit
was recorded by Pallas 45 OD (51738 Birr/ha).There fore, according to the economic analysis
the best weed management options in wheat in bale highlands was Atlantis OD 37.5 and the
second was Pallas 45 OD.
Conclusions and Recommendations
The analysis result of the two locations (Sinana on-station and Robe area) showed that the yield
and yield components of wheat were significantly responded to the applied weed management
options, except plant height, Biomass and HLW at Sinana on-station and plant height and spike
length at Robe area. At both locations Atlantis OD 37.5 gave the highest seeds per spike, Grain
yield, Harvest index and TKW (Table 1and3). From analysis result and visual observation if
properly used Atlantis OD 37.5 efficiently can control broad and grass weeds in wheat. The next
efficient herbicide was Pallas 45 OD. Efficacy test also indicated that Atlantis OD 37.5 controls
major Grass and Broad leave weed species with better control efficacy than the rest weed control
methods followed by Pallas 45 OD (Table 2 and 4). The economic analysis also revealed that
the highest net benefit was recorded by Atlantis OD 37.5.Therefore, From one year result
possible to conclude that if economically affordable and available Atlantis OD 37.5 is the best
weed management option in wheat in Bale highlands.
ACKNOWLEDGEMENTS
The authors are grateful to all staff of the Sinana Agricultural Research Centre especially those
in the Cereal Crops Technology Generating Team for valuable contributions in data collection.
Oromia Agricultural Research Institute is acknowledged for financing the experiment.
Table1. Effect of weed control methods on yield and yield components of Bread wheat (On-station)
Treatments
PH(cm) SL(cm) KPS
BM(kg) GY(kg) HI
TKW(gm) HLW(kghl1
(%)
)
Atlantis
OD 87.3
8.7b
45a
7968
4622a
0.58a 44. 7a
82
37.5
Pallas 45 OD
87.7
8.8b
40.7ab
7185
4311b
O.60a 41.7ab
81.5
Topic plus
85
9.4a
39.7ab
7613
3045d
0.40b 40. 7ab
81.3
2 times HW
86.7
9.13ab 39ab
6838
3556c
0.52a 40. 7ab
82
Weedy ckeck
86.3
9.1ab
36.3b
7162
2867d
0.40b 38. 7b
81.4
LSD(P<0.05)
ns
0.46
5.87
ns
307
0.08
5.12
ns
CV (%)
3.8
2.7
7.8
13
4.4
9.5
6.7
4.5
388
PH = Plant height; SL = Spike length; KPS = Kernels per spike; HI = Harvest index; BM=Bio-mass yield;
TKW=Thousand kernel weight; HLW=Hectoliter weight; ns = non-significant; LSD = Least
significant difference at P < 0.05; CV (%) = Coefficient of variation (%)
Table 2. Efficacy (%) of weed control options against major weed species in wheat in Bale
highlands (On-station)
Trts
Atlantis OD
37.5
Pallas 45
Topic plus
2 times HW
Weedy
ckeck
Amaranthus
hybridus
88.2
Chenopodium
spp
93.3
Galensoga
parviflora
88.2
Commelina
benghalensis
85
Guizotia
Scabra
100
Bromus
pectinatus
84.6
Avena
spp
92.3
Cyprus
spp
44.4
82.6
-10
83.3
-24
80
-12
81.8
-45
85.7
0
75
-50
75
0
66.7
-23.1
100
0
66.7
-60
63.6
0
30
-50
66.7
76.9
0
0
0
0
25
-33.3
Table3. Effect of weed control methods on yield and yield components of Bread wheat
(Robe area)
PH(cm)
SL(cm)
KPS
BM(kg)
GY(kg)
HI
TKW(gm)
HLW(kghl-1)
82.9
8.5
38.2a
8000
4645a
0.53a
36a
81.1a
Pallas 45 OD
81.3
7.7
32.9c
7222
4289b
0.48a
29.3bc
79.7a
Topic plus
85.7
8.5
36.5ab
7556
3089d
0.40b
30.7bc
77.1b
2 times HW
82.5
7.9
34.5bc
7000
3600c
0.40b
33.3bc
79.4a
Weedy ckeck
83.7
8.4
32.5c
7111
2911d
0.29c
26.7c
76.5b
LSD(P<0.05)
ns
ns
3.18
ns
267
0.05
6.5
1.7
CV (%)
3.6
9.1
4.8
13
3.8
7.2
11.3
1.2
Treatments
Atlantis
OD
37.5
PH = Plant height; SL = Spike length; KPS = Kernels per spike; HI = Harvest index; BM=Bio-mass
yield; TKW=Thousand kernel weight; HLW=Hectoliter weight; ns = non-significant; LSD = Least
significant difference at P < 0.05; CV (%) = Coefficient of variation (%)
Table 4. Efficacy (%) of weed control options against major weed species in wheat in Bale
highlands (Robe area)
Trts
Amaranthus Chenopodium Galensoga Guizotia Bromus
Avena
spp
parviflora Scabra
pectinatus spp
spp
Atlantis OD 37.5 83.3
100
100
83.3
100
100
Pallas 45 OD
75
84.6
100
66.7
75
100
Topic plus
-17.6
0
0
0
0
80
2 times HW
75
70
86.7
80
33.3
44.4
Weedy ckeck
0
0
0
-25
0
0
389
Table 5: Partial budget analysis result for evaluation of post-emergence herbicides against
major weed species in wheat
Treatments
(Weed
management
options)
Atlantis OD Pallas 45 Topic
37.5
OD
plus
Average yield (kg/ha)
4622
4311
3045
Adjusted yield (kg/ha)
4160
3880
2741
Gross field benefits (Birr/ha)
62400
58200
41115
Cost of herbicide(Birr/ha)
1800
1700
450
Cost of labour to apply herbicide 300
300
300
(Birr/ha)
Harvesting,
packing
and 4784
4462
3152
transportation (Birr/ha)
Total costs that vary(Birr/ha)
6884
6462
3902
Net benefits (Birr/ha)
55516
51738
37213
2 times Hand
weeding
3556
3200
48000
6000
0
Weedy
check
2867
2598
38970
0
0
3680
2988
9680
38320
2988
35982
Cost of Atlantis OD 37.5 1800 Birr /Liter; Cost of Pallas 45 OD 1700 Birr /0.5Liter; Cost of
Topic plus 450 Birr /Liter; 2 times hand weeding 60 person @ 100 Birr/ person/day; Herbicide
application 3 person@ 100 Birr/ person/day; harvesting, packing and transportation 115 Birr per
100 kg; sale price of wheat grain 1500 Birr per 100 kg.
References
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weedicides for the control of broad leaf weeds in wheat. Sarhad J. Agri.7(1):1-9.
Arif,M., L. Awan and H.U.Khan, 2004. Weed management strategies in wheat (Triticum
aestivam L.).Pak. J.Weed Sci.Res.10(1-2):11-16.
Ashrafi, ZY et.al.,2009.Analogy potential effects of planting methods and tank mixed herbicides
on wheat yield and weed population. Journal of Agricultural Technology,5(2):391-403.
Awan, I.U., M. Iqbal and H.K. Ahmad,1990. Screening of different herbicides for the control of
weeds in wheat crop. Gomal Univ.J.Res.10(2):77-83.
Chaudhary, S.U., M. Hussain,M.A. Ali and J.Iqbal,2008. Effect of weed competition period on
yield and yield component of wheat. J.agric. Res.46(1):47-54.
CSA (Central Statistical Agency) 2008. Agricultural sample survey report on area and
production of major crops. Statistical bulletin, Vol.1, Addis Ababa.
Dally,C.D., M.L.Bernards and JJ Kells,2006. Effect of weed removal timing and spacing on soil
moisture in corn (Zea mays). Weed technol. 20(2):399-409.
Evans, L.T.1998. Feeding the ten billions: Plants and population growth. Cambridge University
press.
Genene Gezu and Habtamu Seboka,2001. Agronomic research recommendations and seed
production and maintenance techniques. Training manual for development agents. 24-30
July 2001, Bale-Goba.
Manson, H.E. and D.Spaner, 2006. Competitive ability of wheat in conventional and organic
management systems: A review of a literature. Canad. J. Plant sci.86 (2):333-343.
390
Survey of weed flora composition in coffee (Coffea arabica L.) growing areas of East
Ethiopia
Hika Bersisa, Addisu Wagari, Mohammed Jundi and Iyob Alemayehu
Mechara Agricultural Research Center, Mechara, P.O. Box 19, Mechara, Ethiopia
Corresponding Author: hikbersisa@gmail.com, Tel: +251 (0) 917794867
Abstract
Coffee is one of the most important cash crops and the first most traded produce in Ethiopia.
Ethiopia's coffee is exclusively of the arabica type (Coffea arabica L.) which belongs to the
genus Coffea and family Rubiaceae. Arabica coffee (C. arabica L.) has been threatened by
various coffee weed species. Survey was conducted to assess and rank weed species for further
management study in selected areas. It was done in coffee growing areas of East Ethiopia
including Bedeno, Boke, Daro Labu and Habro districts. Assessment was done via counting
weed species, interviewing growers for important points related to weed plants. Quadrate with
size of 0.5×0.5m was used. The collected data were analyzed via quantitative measures like weed
frequency, field uniformity, mean field density, dominance and relative abundance. Accordingly,
a total of 46 different weed species including 31 annuals and 12 perennials grasses which
comprised of 35 broadleaved weeds 7 grasses and 1sedge (Representing by 81.3%, 16.3% and
2.3% respectively, by habitat) were identified in East coffee growing areas of Ethiopia. The
highest frequency value (85.34%) was recorded with Gallant soldier (Galinsoga parviflora),
field uniformity was by African coach grass (Digitaria abyssinica) (68.52%), mean field density
was by Salvia tiliifolia Vahl (3184.94 plants/m2), dominance was by Salvia tiliifolia Vahl
(17.33%), while relative abundance was by Salvia Tiliifolia Vahl. The most abundant weed
species were ranked and prioritized using relative abundance, because, it is summation values of
all quantitative measures of a single weed species. Accordingly, top ten most abundant coffee
weed species in East Ethiopia were Salvia Tiliifolia Vahl (31.96), Gallant soldier (21.67), Witch
weed (20.24), African coach grass (17.57), Brown top millet (15.80), Black jack (15.80), White
wort (15.34), Nut grass (12.55), Congress weed (11.53), and Wandering jaw (10.83). Therefore
any coffee growers should use sound and sustainable weed management practice including
cultural, chemical and integrated weed management approach based on the nature of these
identified weed species and growth habit and further weed management study should be
conducted.
Keywords: Coffea arabica L., frequency, relative abundance, weed species
1. INTRODUCTION
Coffee is one of the most important cash crops and the first most traded produce in Ethiopia.
Ethiopia's coffee is exclusively of the arabica type (Coffea arabica L.) which belongs to the
genus Coffea and family Rubiaceae. The significance of coffee in the Ethiopian economy is
enormous in that it accounts for about 29% of the total export earnings of the nation, where 4.7
391
million small-holders directly involved in producing coffee and about 25 million people directly
or indirectly depends on coffee sector for their livelihoods (CSA, 2015). However, the
production of Arabica coffee in Ethiopia is to a great extent limited by several factors.
Among these, coffee diseases such as coffee berry disease, coffee wilt disease and Coffee leaf
rust, Coffee insect pests, mainly Antestia, leaf miners and coffee berry borer, Perennial grasses
and sedges cause severe crop losses (Demelash, 2017). Research experience has shown that
weeds can be serious competitors (Tadesse and Tesfu, 2015 and Demelash, 2017). Perennial
grasses, sedges, and annual weeds with their fast and vigorous growth can easily smother
coffee, and result in extremely low yields and affect the quality of the crop (Tadesse and Tesfu,
2015).
Excluding environmental variables, yield losses in coffee are caused mainly by competition with
weeds. Weed interference is a severe problem in coffee, especially in the early time of the
growing years, due to slow early growth, narrow canopy and wide row spacing. Weeds compete
with the coffee plants for resources such as light, nutrients, space, and moisture that influence the
morphology and phenology of the crop. Furthermore, high weed infestation increases the cost of
cultivation, lowers value of land, and reduces the returns of coffee growers. These factors vary
across regions and influence the composition and number of predominant weeds of economic
importance to coffee production (Tadesse and Tesfu, 2015). Increased cost of production has
been a principle item in coffee production caused by weed species dominant and prevalent in
areas where they are common, otherwise where they were previously efficiently and effectively
managed.
Information on presence, composition, importance and abundance of weed species is needed to
formulate appropriate weed management strategies. The distribution and nature of the weeds in
coffee growing area could be different due to the different agronomic practices employed and the
altitudes across the main coffee growing areas. Specific sound knowledge on the nature and
extent of infestation of weed flora in the coffee growing area through weed surveys is essential
for planning of their control and an indication to formulate recommendations on the standard
practices as well as appropriate herbicides doses under ideal management. However, detailed
information on the presence, composition, importance and abundance of weed species especially
in main coffee growing areas in East Ethiopia is lacking. Therefore, the present study was
undertaken to assess and rank weed species in their abundance for further management.
392
2. Materials and Methods
This experiment was conducted in four different Hararghe coffee growing districts namely Daro
Labu, Habro, Boke and Bedeno. Four potential PAs per district and 5 to 7 fields were selected
and assessed. Zigzag sampling method was used during implementation of the survey. Quadrate
with 50 cm×50 cm size was used with forward throughing method. Four to five quadrates were
taken depending on farm size. Each and every weed species were counted manually and
recorded. A questionnaire was also used to collect information from every coffee growers.
Answer and question were made with coffee growers on developed questionnaire related to
coffee weed species, usual practice carried out to manage coffee weeds, coffee cropping system,
about already existing weed species and newly emerged weed plants between respondents and
investigators.
Data Computation and Analysis
Collected data were summarized according to the following quantitative measures as described
by Thomas (1985).
Weed frequency (F)
Weed frequency was determined as the percentage of the total number of fields surveyed in
which a species occurred in at least one quadrate in the following formulae;
Fk =
∑𝑛𝑖 Yi
× 100
𝑛
Where; Fk = frequency value for species k; Yi = presence (1) or absence (0) of species k in field
i and n being the number of fields surveyed.
Field uniformity (FU)
The field uniformity was calculated as the percentage of the total number of quadrates sampled
in which a species occurred, as below;
FUk =
∑𝑛𝑖 ∑5−10
Yij
𝑖
× 100
5 − 10(𝑛)
Where; FUk = field uniformity value for species k, Yij = presence (1) or absence (0) of species k
in quadrate j in field i and n being the number of fields surveyed.
Field density (D)
The field density of each species in the field was calculated by summing the number of plants in
all the 20 quadrates per site and dividing by their area.
393
∑5−10
i Zi
Dki =
× 100
Ai
Where; Dki = density (in numbers m2) value of species k in field i, Zi = number of plants of a
species in quadrate j and Ai being the area in m2 of 5 to 10 quadrates in field i.
Mean field density (MFD)
This value was obtained by totaling each field density (D) and dividing by the total number of
fields. MFD is the mean number of plants per m2 for each species averaged over all fields
sampled and it was determined as below;
∑ni DKi
MFDk =
n
Where MFDk = mean field density of species k, Dki = density (in numbers m-2) of species k in
field i and n being the number of fields surveyed.
Dominance (D) is the measure of mean field density of species k (MFDk) expressed as a
percentage of the total mean field density of all weed species (MFDl) and was established as;
D = (MFDk) / Σ MFDl) × 100
Relative abundance (RA)
This value was used to rank the weed species in the survey and it was assumed that the
frequency, field uniformity, and mean field density measures were of equal importance in
describing the relative importance of a weed species. This value has no units but the value for
one species in comparison to another indicates the relative abundance of the species (Thomas
and Wise, 1987). Relative abundance values quantify the predominance of a given weed species
in an environment by calculating the frequency, field uniformity, and density of a particular
weed species relative to all other species observed. This value is an index that is calculated using
a combination of frequency, field uniformity, and field density for each species, as described by
Thomas (1985). Relative abundance allows for comparison of the overall abundance of one weed
species versus another. The relative frequency (RF), relative field uniformity (RFU), and relative
mean field density (RMFD) shall be calculated by dividing the given parameter by the sum of the
values for that parameter for all species and multiplying by 100 as illustrated below.
The relative frequency for species k (RFk) as;
RFk =
Frequency value of species
× 100
Sum of frequency values for all species
Relative field uniformity for species k (RFUk)as;
394
RFUk =
Field uniformity value for species K
× 100
Sum of field uniformity values for all species
Relative mean field density for species k (RFUk)as;
RMFDk =
Mean field density value for species K
× 100
Sum of mean field density values for all species
The relative abundance of species k (RAk) was calculated as the sum of relative frequency,
relative field uniformity, and relative mean field density for that species as;
RAk = RFk+ RFUk+ RMFDk
3. Results and Discussions
3.1. Weed Species Taxonomy
A total of 46 different weed species including 31 annuals and 12 perennials which comprised of
35 broadleaved weeds 7 grasses and 1 sedge (Representing by 81.3%, 16.3% and 2.3%
respectively, by habitat) were identified in Eastern coffee growing areas of Ethiopia (Table 1).
The annual species were greater in number than perennial species and overall annual
broadleaved species were more prevalent than perennial broadleaved species, grasses and sedges.
The same result was obtained by G.G. MIGWI et al. (2017) at KIAMBU country.
The weed species represented 21 families in the surveyed area where Asteraceae family had the
highest number of weed species (8), followed by Poaceae (7), Solanaceae (3), Papavaraceae (3),
Amaranthaceae (2), Lamiaceae (2), Portulacaceae (2), Rubiaceae (2), Oxalidaceae (2) (Table 1).
The rest of the 12 families were represented by one species each. Asteraceae, Poaceae and
Papavaraceae families accounted together for 48.8% of the species established. Family
Amaranthaceae, Lamiaceae, Portulacaceae, Rubiaceae and Oxalidaceae were records together
about 23.3% of the species established in surveyed areas. While 1% of weed species were
established by the remaining families including Acanthaceae, Commelinaceae, Convolvulaceae,
Cyperaceae,
Euphorbiaceae,
Fabaceae,
Orobanchaceae,
Plantaginaceae,
Polygonaceae,
Primulaceae, Tiliaceae and Verbenaceae.
Table 44. Coffee weed species taxonomy surveyed around Hararghe coffee growing belts
Family
Acanthaceae
Amaranthaceae
Asteraceae
Common name
Prostrate
wild
petunia
Slender amaranth
Devil's horsewhip
Wild lettuce
Scientific name
Ruellia prostrata Poir
Life Cycle
P
Amaranthus viridis Hook. F. A
Achyranthes aspera L.
P
Lactuca capensis Thunb
A
Morphology
Broad leaf
Broad leaf
Broad leaf
Broad leaf
395
Family
Common name
Goat weed
Gallant soldier
Guizotia scabra
Congress weed
Scientific name
Ageratum conyzoides L.
Galinsoqa parviflora
Life Cycle
A
A
A
hysterophorus A
Parthenium
L.
Bristly star bur
Acanthospermum hispidum
DC.
False daisy
Eclipta alba L.
Black jack
Bidens pilosa L.
Commelinaceae Wandering jaw
Commelina benghalensis L.
Convolvulaceae Ivy leaf morning Ipomoea hederacea (L.)
glory
Jacq
Cyperaceae
Nut grass
Cyperus rotundus L.
Euphorbiaceae Wild poinsettia
Euphorbia geniculata Orteg.
Fabaceae
Heart leaf indig
Indigofera cordifolia Heyne.
ex Roth.
Lamiaceae
White wort
Leucas martinicensis R. Br.
Tiliifolia
Salvia tiliifolia Vahl
Orobanchaceae Witch weed
Striga asiatica L.
Oxalidaceae
Creeping wood
Oxalis corniculata L.
Clover
Trifolium rueppellianum
Papavaraceae
Mexican poppy
Argemone mexicana L.
Mexican
Tagetes minuta L
marigold
Pimpefnil
Anaqallis arvensis
Plantaginaceae Buckhorn
Plantago lanceolata
Plantain
Poaceae
Brown top millet Brachiaria ramosa (L.) Stapf
African
coach Digitaria abyssinica
grass
Crowfoot grass
Dactyloctenium aegyptium
L.
Bermuda grass
Cynodon dactylon (L.) Pers.
Half grass.
Desmostachya
bipinnata
Stapf
Love grass
Setaria verticillata
Star grass
Cynodon dactylon (L.) Pers.
Polygonaceae
Double thorn
Oxygonum sinuatum
Portulacaceae
Purslane
Portulaca oleracea L.
Morphology
Broad leaf
Broad leaf
Broad leaf
Broad leaf
P
Broad leaf
A
A
P
A
Broad leaf
Broad leaf
Broad leaf
Broad leaf
P
A
A
Sedge
Broad leaf
Broad leaf
A
A
A
P
A
A
A
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
A
A
Broad leaf
Broad leaf
A
P
Grass
Grass
A
Grass
P
P
Grass
Grass
A
P
A
A
Grass
Grass
Broad leaf
Broad leaf
396
Family
Common name
Chicken weed
Scarlet pimpernel
Sticky willy
Snowdenia
Black nightshade
Thorn apple
Chinese lantern
Burbush
Scientific name
Life Cycle
Portulaca quadrifida L.
A
Primulaceae
Anagallis arvensis L.
A
Rubiaceae
Galium aparine L.
A
Snowdenia polystachya
A
Solanaceae
Solanum nigrum L.
A
Datura metel L.
A
Nicandra physalodes
A
Tiliaceae
Triumfetta
rhomboidea P
Jacq.
Verbenaceae
Wild sage
Lantana camara L.
P
3.2. Occurrence and Distribution of Coffee Weed Species in Eastern Ethiopia
Morphology
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Broad leaf
Occurrence of weed species is vary from area to area. Among identified species 42% of them
were found across surveyed areas, whereas 28% were found at one area followed by 21% which
were found at three areas and 9% of them were found at two areas (Table 2). Some weed species
were occurred at all agro-ecology (Boke low land, D/Labu and Habro mid land and Bedeno high
land). This indicates that weed plants have wide adaptability than another plants/crops species.
For example among 46 weed species 18 of them were occurred and grown well across 4
surveyed coffee growing areas/districts. All coffee weed species including broad leaves, grasses
and sedges were recorded from all surveyed areas.
Table 45: Weed species with their family observed and recorded in coffee farm in Hararghe
districts
Weeds Species
Bedeno
Prostrate wild petunia
Slender amaranth
Devil's horsewhip
Wild lettuce
Goat weed
Gallant soldier
Guizotia scabra
Congress weed
Bristly star bur
False daisy
Black jack
Wandering jaw
Ivy leaf morning glory
Nut grass
*
*
*
*
*
*
*
*
*
*
*
*
Boke
D/Labu
Habro
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
397
Weeds Species
Bedeno Boke
D/Labu Habro
Wild poinsettia
*
*
*
*
Heart leaf indig
*
*
*
White wort
*
*
*
*
Salvia Tiliifolia Vahl
*
*
*
*
Witch weed
*
*
*
Creeping wood sorrel
*
*
*
*
Clover
*
Mexican poppy
*
Mexican marigold
*
Pimpefnil
*
Buckhorn Plantain
*
*
*
*
Brown top millet
*
*
*
African coach grass
*
*
*
*
Crowfoot grass
*
*
*
*
Bermuda grass
*
*
Half grass.
*
*
*
Love grass
*
Star grass
*
Double thorn
*
*
*
*
Purslane
*
*
*
Chicken weed
*
Scarlet pimpernel
*
*
Sticky willy
*
*
Snowdenia
*
Black nightshade
*
*
*
*
Thorn apple
*
*
Chinese lantern
*
*
*
Burbush
*
*
*
*
Wild sage
*
*
*
*
Note: * Occurrence of weed species at one area *at two areas *at three areas *at four areas
3.3. Weed Species Frequency (F)
Coffee weed species have recorded with different frequency value from place to place. Weed
species frequency value in an average recorded ranges between 85.34% and 12.50% which
recorded with Gallant soldier (Galinsoqa parviflora) and Spiney pigweed (Amaranthus
spinosus), respectively (Table 3). The ten superior weed frequency across surveyed area an
averagely were Gallant soldier (85.34%), African coach grass (81.03%), Tiliifolia (76.19%),
Wandering jew (73.48%), Nut grass (68.05%), Chicken weed (66.67%), Prostrate wild petunia
398
(66.67%) Black jack (57.40%) and Congress weed (57.19%). Among top ten weed species 40%
and 60% were perennial and annuals, respectively, while 20% of them were grasses and
remaining 80% were broad leaves weed species. Perennials lives throughout the year (twelve
months) with coffee plants in the coffee farm. Unless they controlled timely poor coffee
production and productivity encourage parallel to increasing year. This means finally it result in
yield loss, poor quality, low price and genetic erosion of coffee crop. Similar founding reported
by Begum et al. (2008) and Begum (2006) was revealed that different frequencies of different
weed species including broad leaves, grasses and sedges. Most of common weeds in all surveyed
areas were found in annual nature followed by perennials. Singh et al. (2008) suggested that
seeds of annual weeds survive in unfavorable conditions and they have able to complete their life
cycle from seed to seed in a season.
3.4. Field uniformity (FU) of Weed Species in Surveyed Areas
Among 43 identified weed species high value of field uniformity was appeared with African
coach grass (Digitaria abyssinica) (68.52%), while the lowest was recorded with Spiney big
weed (Amaranthus spinosus) (0.48%). The top ten weed species with first-class field uniformity
are African coach grass (68.52%), Gallant soldier (49.72%), Brown top millet (43.97%), Wild
lettuce (42.58%), White wort (41.84%), Salvia tiliifolia Vahl (32.95%), Congress weed
(24.78%), Nut grass (24.48), Black night shade (24.25%) and Wandering jew (23.93%) (Table
3). Whereas the remaining 33 species were recorded with field uniformity value ranges between
22.22% (Chicken weed, Portulaca quadrifida L.) and 0.48% (Spiney big weed, Amaranthus
spinosus). It was vary from district to districts, even from kebele to kebele. This dissimilar may
occur due to edaphic (including soil pH, soil moisture, etc ) and biological (dominated by
another weed species, seed dormancy, eaten by insects and micro-organisms and etc ) factors
(Hakim, et al, 2010). Similar result was reported by Hakim et al. (2013) in rice field.
3.5. Mean Field Density (MFD) of Weed Species per Surveyed Areas
Salvia tiliifolia Vahl weed species belongs to Lamiaceae was recorded with high field density
value (3184.94 plants/m2) among the identified weed species from surveyed areas, while
Snowdenia was recorded with low field density value (18.18 plants/m2) (Table 3). The superior
ten species under different weed families recorded with field density were Salvia tiliifolia Vahl
(3184.94), Gallant soldier (1184.20), Brown top millet (1139.69), Goat weed (714.92), Black
jack (645.67), African coach grass (644.41), Chicken weed (638.10), Congress weed (379.31),
399
Nut grass (324.35) and Creeping wood sorrel (288.90 plants/m2). This field density was varies
from district to districts and even from kebele to kebele, farm to farm. Unlike field density has
developed due to some factors like weed managements practiced by growers including cultural,
mechanical, chemical and so on, biological including eaten by wild animals, birds, insects, over
dominated by anther weed species and etc, physically like poor germination, soil pH, soil
moisture stress and etc.
3.6. Dominance (D) of Weed Species in Surveyed Areas
Across the surveyed areas the weed dominance relied between 17.33% and 0.02% which was
recorded by Salvia tiliifolia Vahl and Snowdenia polystachya, respectively. Following Salvia
tiliifolia Vahl the dominance values and in a descending order the top ten weed species were
Gallant soldier (6.72), Black jack (6.13), Goat weed (4.99), African coach grass (4.37), Brown
top millet (3.74), Common cocklebur (3.64), Mexican poppy (3.58), Mexican marigold (3.08),
Purslane (2.85) and Nut grass (2.78) (Table 3).
3.7. Relative abundance (RA)
Value for the relative abundance of all weed species was vary from 31.96 to 1.34 which recorded
by Salvia Tiliifolia Vahl and Snowdenia, respectively (Table 3). In this Salvia Tiliifolia Vahl was
significantly outstanding among the forty-six weed species identified in the surveyed areas. It
topped both as a broad leaf weed species as well as in the overall top eleven (11) weeds species
that were established to have a relative abundance (RA) value ≥ 9.75%. In descending order,
Salvia tiliifolia Vahl was followed by Gallant soldier (21.67), Witch weed (20.24), African coach
grass (17.57), Brown top millet (15.80), Black jack (15.80), White wort (15.34), Nut grass
(12.55), Congress weed (11.53), Wandering jew (10.83) and Chicken weed (9.75%). Among ten
most abundant species seven of them are broad leave while 2 are grasses and remain one is
sedge.
Table 46. Quantitative measures of coffee weed species across surveyed areas of East Ethiopia
during 2018
S/No
1
2
3
4
5
6
7
8
9
Weed Species
African coach grass
Asthma herb
Bermuda grass
Black jack
Black night shade
Bristly star bur
Brown top millet
Buckhorn Plantain
Burbush
F%
81.03
45.00
37.90
57.40
46.02
33.33
50.80
24.21
41.89
FU%
68.52
4.69
7.46
8.99
24.25
3.70
43.97
1.98
11.60
MFD, plant/m2
644.41
110.48
118.18
645.67
36.63
19.05
1139.69
43.00
101.11
D%
264.65
53.39
54.51
237.36
35.63
18.69
411.49
23.06
51.53
RA
17.57
6.44
6.58
15.80
6.99
2.73
15.80
6.80
5.38
400
S/No
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
Weed Species
Chicken weed
Chinese lantern
Common cocklebur
Congress weed
Creeping wood sorrel
Crowfoot grass
Double thorn
False daisy
Gallant soldier
Goat weed
Guizotia scabra
Half grass
Heart leaf indig
Humera weed
Ivy leaf morning glory
Love grass
Meskel flower
Mexican marigold
Mexican poppy
Nut grass
polygonum aviculma
Prostrate wild petunia
Purslane
Salvia tiliifolia Vahl
Scarlet pimpernel
Slender amaranth
Snowdenia
Spiney pigweed
Sticky willy
Thorn apple
Wandering jaw
White jute
White wort
Wild lettuce
Wild poinsettia
Wild sage
Witch weed
F%
66.67
25.99
25.00
57.19
35.00
56.11
31.39
26.49
85.34
32.72
25.33
27.18
31.05
33.33
30.93
23.81
12.50
33.88
33.33
68.05
38.39
66.67
26.19
76.19
25.00
40.36
12.50
12.50
25.00
19.64
73.48
14.29
72.23
38.49
30.73
39.24
49.55
FU%
22.22
2.38
1.67
24.78
5.79
15.88
9.86
3.70
49.74
13.74
2.21
8.89
9.21
20.00
6.76
2.56
3.89
11.64
6.67
24.48
11.96
15.58
8.46
32.95
8.33
10.19
0.48
0.48
2.08
1.37
23.93
0.65
41.84
42.58
8.13
10.01
14.26
MFD, plant/m2
638.10
53.51
152.38
379.31
288.90
71.99
23.23
19.05
1184.20
714.92
126.88
18.76
101.30
57.14
68.66
114.29
168.61
236.77
152.38
324.35
142.42
114.29
258.20
3184.94
76.19
39.59
18.18
141.82
38.10
18.61
159.09
38.10
198.02
44.99
145.60
141.45
112.12
D%
242.33
27.29
59.68
153.76
109.90
47.99
21.49
16.41
439.76
253.79
51.47
18.28
47.19
36.83
35.45
46.89
61.67
94.10
64.13
138.96
64.26
65.51
97.62
1098.03
36.51
30.04
10.39
51.60
21.73
13.21
85.50
17.68
104.03
42.02
61.49
63.56
58.65
RA
9.75
3.69
5.67
11.53
4.80
8.12
4.10
1.95
21.67
9.34
4.90
3.77
4.27
7.07
3.92
4.85
5.09
7.83
6.83
12.55
5.00
6.45
5.93
3.15
5.64
1.34
3.93
2.01
1.73
31.96
10.83
2.01
15.34
7.13
5.10
5.09
20.24
401
3.8. Interaction of Important Factors and Both Weed Composition and Field Density
Under Coffee Fields
3.8.1. Impact of coffee age on emergence and growth of weed species
Coffee age has play a great role on weed emergence and its composition. Different weed species
compete more coffee plants at young stage rather than at elder stage. Coffee plant become shade
to weed and suppresses its emergence and growth when aged. As coffee become aged, its canopy
also expanded at the same time and block sun radiation from the emerged weeds underneath of
it. Additionally, the shed/dropped coffee leaves cover ground and prevent weed emergence.
However, coffee plants at young stage are invaded by weed plants unless they are got an
adequate management. In present study the result was revealed that high weed field density
(2483.72 plants /m2) was recorded from young coffee than the aged one (Figure 1). Weed plants
grow freely without any limitation in young coffee's farms rather than in oldest coffee's farm
unless coffee farms well managed.
Age
w
e
e
d
FD
2483.72
d
e
n
s
i
t
y
1200.00
7
5
1
600.00 533.33 500.00 472.73
400.00
25
20
15
12
10
2
3
4
Age of coffee
5
6
7
3.8.2. Weed composition and field density in shaded and open sun coffee farm
High (12 weed kinds) weed composition was recorded under open sun coffee farm, while low (5
weed kinds) weed composition was recorded under shaded coffee farm. Similarly high field
density was recorded from open sun coffee farm, whereas low field density was recorded from
shaded coffee farm. Under shaded coffee farm weed grow has restricted where the shade trees'
canopy always block sun radiation penetration to the weed plants which finally leads to weed
suppression. In the same way leaves shed from the shade trees cover the ground and serve as
mulching which result in blocking of weed seed emergence.
402
3.8.3. Management practice versus weed composition
Under surveyed areas coffee growers were practiced some different agronomic management in
order to manage weed species in their own coffee farm. However, all growers have handled their
farm equally as well as some of them were didn't. During survey different weed field density
were recorded from different coffee farm and areas as well. Accordingly high (12 weed kinds)
field density (2483.72 plants /m2) was recorded from coffee farm remain with weed plant for
long time. Among management practice handled by Hararghe coffee growers hoeing followed by
soil mulching has critically weed field density in coffee farm.
3.8.4. Effect of coffee intercropping with annual crops on weed composition
As usual Hararghe farmers has been practiced intercropping and alley cropping system in order
to win land shortage. Mainly they have been intercropped coffee with maize, sorghum, haricot
bean, ground nut, barely and etc. Accordingly significant weed density was recorded from
different coffee farms intercropped with different crops. For example low field density and small
number of weed kinds were recorded from coffee farm intercropped with legumes (ground nut
and haricot bean) and barely crops which are cover the ground and compete weed on space.
According to their morphological structure crops suppresses weed plants at different degree.
4. Conclusions and Recommendations
In present study, a total of forty-six coffee weed species belongs to twenty one families were
assessed and identified. Among thirty-four annuals and twelve perennials which comprised of 38
broad leaves weeds seven grasses and one sedge. Annual broad leaves weeds were over
dominated and abundant than perennial broad leaves, annual and perennial grasses and sedges
across surveyed areas. During previous survey relatively abundant top ten weed species in
Hararghe coffee growing areas were Salvia Tiliifolia Vahl (31.96), Gallant soldier (21.67), Witch
weed (20.24), African coach grass (17.57), Brown top millet (15.80), Black jack (15.80), White
wort (15.34), Nut grass (12.55), Congress weed (11.53), Wandering jew (10.83). Therefore any
coffee growers should be used a sound and sustainable weed management practice including
cultural, chemical and integrated weed management approach and further weed management
study should be conducted.
5. References
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University Putra Malaysia, Serdang Darul Ehsan, Malaysia.
403
Begum, M. Juraimi, A. S Azmi, M., Syed Omar S. R. and Rajan A. 2008. Weed flora of different
farm blocks in block-1 of muda rice granary inpeninsular Malaysia. J. Biosci. 19: 33–43.
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Survey Report on Area and Production of Major Crops, 2014/2015, Vol. 1, Statistical
Bulletin 578, May 2015, Addis Ababa, 121p.
Demelash Teferi, 2017. Coffee weed management review in South West Ethiopia.
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G. MIGWI, E.S. ARIGA and R.W. MICHIEKA. 2017. A Survey on Weed Diversity in Coffee
Estates With Prolonged Use of Glyphosate In Kiambu County, Kenya. International Journal
of Scientific Research and Innovative Technology; 4(2) : 82-94.
Hakim, M. A., Juraimi, A. S., Razi Ismail, M. Hanafi, M. and Selamat A. 2013. survey on weed
diversity in coastal rice fields of sebarang perak in peninsular malaysia. Journal of Animal &
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Hakim, M. A., A. S Juraimi, M. R. Ismail, M. M. Hanafi and A. Selamat. 2010. Distribution of
weed population in the coastal rice growing area of Kehah in Peninsular Malaysia. Journal
of Agronomy, 9: 9-16.
Singh, A., Sharma, G. P. and Raghubanshi, A. S. 2008. Dynamics of the functional groups in the
weed flora of dryland and irrigated agro ecosystems in the Gangetic plains of India. Weed
Biolological. Manage, 8: 250–259.
Thomas, A. G. 1985. Weed survey system used in Saskatchewan for cereal and oilseed crops.
Weed Science, 33: 34-43.
Thomas, A. G. and Wise , R. F. 1987. Weed survey of Saskatchewan for cereal and oilseed
crops. Weed surveys series. Pub.87-1. Agri. Can. Regina, Saskatchewan. pp. 251.
Tadesse Eshetu and Tesfu Kebede. 2015. Effect of weed management methods on yield and
physical quality of coffee at Gera, Jimma zone, South West Ethiopia. Journal of resources
development and management,11: 82-89.
Efficacies of Fungicide Application Regimes Against FabaBean Gall (Olpidium species)
Disease
Chala Debela*, Abay Guta and GetuAbera
Bako Agricultural Research Center, P. O. Box 03, Bako, West Shoa, Ethiopia,
*
Corresponding author: chalakeneni22@gmail.com
Abstract
Faba bean (Viciafaba L.) is an important pulse crop due to its high nutritive value both in terms of
energy and protein contents. Ethiopia is considered as the secondary center of diversity faba
bean for widely grown in the mid-altitude and highland areas. Average yield of faba bean is
quite low in Ethiopia and the productivity of the crop is far below the potential because of several limiting
404
biotic and abiotic
constraints. In recent years, the crop has become threaten under a new emerged
gall forming faba bean disease. The experiment was conducted to evaluate Matico fungicide
under field conditions for the management of faba bean gall disease and also to assess the yield
losses. Only significant difference disease incidence recorded on at early (vegetative, flowering
and pod setting) fungicide sprayed from other treatments on 4th and 5th assessments and not
significant difference disease severity between fungicides sprayed and unsprayed plots of the
treatments and Area under Disease Progressive Curve (AUDPC) among treatments. On the final
date of disease assessment, at early vegetative and early flowering fungicide, sprayed was
recorded the lowest disease severity (28.3%) whereas the highest disease severity of (35%) was
recorded on faba bean gall on control plot but not significant different. Not significant difference
hundred seed weight(HSW) and yield between fungicides sprayed and unsprayed plots of the
treatments. Using Ridomil fungicide for the management faba bean gall not an effective.The
results of the present study revealed that the novel possibility of using other management system
to decreasefaba bean gall disease symptoms in HoroGuduruShambu area.
Keywords: Faba bean, gall disease, Fungicide, and Yield.
Introduction
Faba bean (Viciafaba L.) is an important diploid (2n = 12 chromosomes) Fabaceous pulse crop
with common names including broad bean, horse bean, tic bean and field bean. It is one of the
earliest domesticated food legumes in the world, probably in the late Neolithic period (Metayer,
2004; Dagneet al., 2016). Faba bean is one of the most important food legumes due to its high
nutritive value both in terms of energy and protein contents (24-30%) and also is an excellent
nitrogen fixer. According to the United Nations Food and Agriculture Organization’s (FAO) the
world area of faba bean production is 2.5 million ha, while common bean is 29 million, chickpea
was 13.5 million ha and dry peas 6.4 million ha (FAOSTAT, 2014). However, faba bean exceeds
both common bean and chickpea in terms of productivity. For instance, the world faba bean
productivity in 2013 was 1.6 t ha-1 while that of dry bean was 0.8 t ha-1 and chickpea 0.9 t ha-1.
Faba bean production in the world is concentrated in nine major agro-ecological regions, namely;
northern Europe, Mediterranean, the Nile valley, Ethiopia, Central Asia, East Asia, Oceana,
Latin America, and North America (Bond et al., 1985). It is cultivated in temperate and
subtropical regions of the world (Torres et al., 2006). China has been the main fababean
405
producing country, followed by Ethiopia, Egypt, Italy, and Morocco (Salunkhe and Kadam,
1989).
Ethiopia is considered as the secondary center of diversity faba bean for widely grown in the
mid-altitude and highland areas (1800-3000 masl)and serves as a multi-purpose crop leading the
pulse category in area and production and it is a source of cash to the farmers and foreign
currency to the country. It is the first among pulse crops cultivated in Ethiopia and leading
protein source for the rural people and used to make various traditional dishes. The average yield
of this crop under small-holder farmer below 1.8t ha-1 (CSA, 2014), while world average grain
yield of faba bean is around 1.8 t ha
-1
(ICARDA, 2008). Faba bean is grown on 443,087.9
hectares in Ethiopia with an annual production of about 838,943.9 tons (CSA, 2014). The crop
takes the largest share of the area under pulses production in Ethiopia.The growing importance
of faba bean as an export crop in Ethiopia has led to a renewed interest by farmers to increase the
area under production (Samuel et al., 2008).
The crop is grown in several regions of the country and production obtained from faba bean was
3.94% of the grain production (CSA, 2014). Amhara and Oromia are the two major fababean
producing regions in Ethiopia. The Oromia region has the largest faba bean area (43.0%) and
contributes to the highest production (48.27%) in the country followed by Amhara region that
has 39.06%of the area and contributes 36.34% to national production (CSA, 2014). It is mainly
produced in Tigray, Gondar, Gojjam, Wollo, Wollega, Shoa and Gamo-Gofa regions of Ethiopia.
Ethiopia’s faba bean export has moved north world since the year 2000 and the major
destinations are Sudan, South Africa, Djibouti, Yemen, Russia and USA, though its share in the
countries pulse export is small (Amanuelet al., 1993; Lupwayiet al., 2011).
Despite its wide cultivation, the average yield of faba bean is quite low in Ethiopia and the
productivity of the crop is far below the potential because of several limiting biotic and abiotic
constraints (Amanuelet al., 2008; EIAR, 2011). According to Samuel et al (2008), diseases are
the most important biotic factors limiting the production of faba bean in Ethiopia. Faba bean is
attacked by more than 100 pathogens. More than 17 diseases causing pathogens are reported in
Ethiopia (Dereje and Tesfay, 1995). Many diseases are affecting faba bean production and
productivity, but only a few of them have economic significance. Among these, fungi are the
largest and perhaps the most important groups affecting all parts of the plant at all growth stages
(Negussieet al., 2008). Diseases such as chocolate spot (Botrytis fabae), rust (Uromycesfabae),
406
black root rot (Fusarium solani), and foot rot (Fusarium avenaceum) are among the fungal
groups that contribute to the low productivity of the crop (Berhanuet al., 2003, Negussieet al.,
2008). In recent years, the crop has become threaten under a new gall forming disease (Olpidium
species) (Hailu et al., 2014).
Apparently, the newly emerged disease “Qormid” faba bean was first recorded in North Shoa,
central Ethiopia, sometime in the early 2010s (Beyene, 2015; Beyene and Wulita, 2012; Dereje,
2012). The disease has spread to the highland faba bean-growing areas of Amhara, Tigray, and
Oromia regions (Endaleet al., 2014). These three regions cover about 89.36% of the total faba
bean production of the country (CSA, 2014). This shows that the spread of the disease has been
very fast and expanding from year to year in all faba bean growing areas of the country. The faba
bean gall incited by the pathogen OlpidiumviciaeKusano infection leads to complete crop failure
over wide areas within short period of time and aggravates the diminution of yield to maximum
nationwide. Moreover, the crops threatened by this disease showed the symptom of green and
sunken on the upper side of the leaf and bulged to the back side of the leaf, and finally develops
light brownish color lesion, chlorotic galls, and progressively broaden to become circular or
elliptical uneven spots (Dereje et al; 2012).
The newly emerged faba bean gall disease is rapidly expand in West Shewa and
HoroGuduruWollega Zones. However, the management of faba bean gall disease through the
effect of fungicide has not been studied so far in the area. Therefore, this study was carried out to
evaluate the fungicide for management of faba bean gall disease under field conditions and also
to assess the economic benefit of fungicide.
Materials and Methods
Description of the study area
The field experiment was conducted in Bako Agricultural Research Center (BARC) Shambu
Sub-site in Horo District, Western Oromia, Ethiopia during the main cropping season of 2016
and 2018. Horo District is located at 302.4 km West of Addis Ababa and its geographic location
is 9°34'0.01N latitude and 37°06’0.00 E longitude with an elevation of 2503 masl. The annual
rain fall distribution is 1800-2000mm and the annual minimum and maximum temperature is 17210C. And have clay loam to loam soil types.
Experimental materials, treatments and applications
407
Local faba bean cultivar was used in this experimental study. Matico (Metalaxyl 80g/kg +
Mencozeb 640g/kg) fungicide was obtained from local market and used in different frequency.
Total of 7 treatments were arranged in a randomized complete block design with three
replications and unsprayed control. Plot size was consisted of 3m x 3.2m and an inter-row and
intra-row spacing of 40 cm and 10 cm, respectively, which having 8 rows with six rows per plot
harvested. The fungicides were applied as per recommendation of the manufacturers using a
manually-pumped knapsack sprayer of 15liter capacity (Table 1). Agronomic practices were
carried out in all the field plots as per recommendations.
Table 1: Treatment combination and frequency of fungicide application
Fungicides application
At early vegetative
AT early flowering
At early vegetative and early flowering
At early vegetative and early pod setting
At early (vegetative, flowering and pod setting)
Control (no application)
Frequency
Once
Once
Twice
Twice
Three time
Zero
Disease assessment
Disease incidence and severity
Disease incidence were made on all rows of each plot starting from the onset of the disease and
continued every ten days till crop maturity. Both diseased and healthy plants were counted from
the all plants in the plots and the percentage of disease incidence (PDI) was calculated according
to the formula used by Wheeler (1969):
PDI(%)=
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑖𝑠𝑒𝑎𝑠𝑒𝑑 𝑝𝑙𝑎𝑛𝑡𝑠
𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑡𝑠 𝑖𝑛𝑠𝑝𝑒𝑐𝑡𝑒𝑑
𝑥 100
Disease severity was assessed as the percentage of the total leaf surface covered with gall spot
lesions on each expanded leaflet separately at regular intervals using a 0–9 scale (Table 2) (Ding
et al., 1993). The severity grades were converted into percentage severity index (PSI) according
to the formula by Wheeler (1969).
PSI (%)=
∑𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑛𝑢𝑚𝑒𝑟𝑖𝑐𝑎𝑙 𝑟𝑎𝑡𝑖𝑛𝑔
(𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑝𝑙𝑎𝑛𝑡𝑠 𝑎𝑠𝑠𝑒𝑠𝑠𝑒𝑑 𝑋 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑠𝑐𝑜𝑟𝑖𝑛𝑔 𝑖𝑛 𝑡ℎ𝑒 𝑠𝑐𝑎𝑙𝑒
𝑋 100
Table 2: percent of infection and scale of faba bean gall
Scale Description
0 no visible infection on leaves
1 a few dot-like accounting for less than 5% of total leaf area
3 discrete spots less than 2 mm in diameter (6–25% of leaf area)
5 numerous scattered spots with a few linkages, diameter 3–5 mm (26–50% of leaf area) with a little
408
7
9
defoliation
confluent spot lesions (51–75% of leaf area), mild sporulation, half the leaves dead or defoliated
complete destruction of the larger leaves (covering more than 76% of leaf area), abundant
sporulation, heavy defoliation and plants darkened and dead
Area under Disease Progress Curve (AUDPC)
The progress of faba bean gall was plotted over time using to mean percentage severity index
for each treatment at each plot, and the DSI values were also used calculate apparent infection
rate (r). The AUDPC values (%-day) were calculated for each variety according to the mid-point
rule formula (Campbell and Madden, 1990).
Where Xi is the disease severity of faba bean gall at I th assessment date, Ti is the time of the I
th assessment in days from the first assessment date and n is the total number of disease
assessments. Because severity was in percentage and time in days, AUDPC was express in
proportion days.
Growth parameters
A. Days 50% emergence: Days from planting to the emergence of 50% plants per plot were
recorded.
B. Days 50% flowering: Days to flowering were recorded for each plot when 50% of the plants
in a plot flowered.
C. Days 90% maturity: Days to 90 % maturity of the crop when 90% of the pods in the plot
reached physiological maturity.
D. plant height: The height of plants from the ground to the tip of the plants was measured five
randomly selected plants per plot at maturity.
Yield and yield components
Number of pods per plant: The number of pods per plant was counted on eight randomly taken
plants from 8 tagged plants from six central rows and the means were recorded as number of
pods/plant.
Number of seeds per pod: number of seeds per pod was threshed and number of seeds were
counted and the total number of seeds was divided by total number of pods to compute average
number of seeds per pod. The grain yield per plot from the three central rows was recorded.
Adjusted yield per plot = (Fw (100-Amc) x)/RDW
Where: Fw = Field weight; Amc = Actual moisture content; RDW = Recommended dry weight
409
The grain yield in gram per plot was then calculated per hectare basis. The weight of 100
randomly taken seeds from the yield of each plot was recorded.
Data Analysis
The analysis of variance (ANOVA) was performed for the disease parameters (incidence,
severity, AUDPC) and yields parameters (seed yield per pod/plant and yield loss) using GenStat
software. Least significant difference (LSD) values were used to separate treatment means
(P<0.05) among the treatments. Correlation coefficient (r) between yield and severity as well as
were determined through yield components correlation analysis using GenStat 18th edition
software, following analysis using the standard procedure (Gomez and Gomez, 1984).
Results and Discussions
Disease incidence
Gall disease of faba bean was first observed at on experimental field at 64 days after sowing
(DAS), around the mid of September in both years (2016 and 2018) and it was recorded on the
leaf of faba bean in all treatments. There was a not significant difference(P<0.05) disease
incidence between treatments forfaba bean gall disease of the treatments between the fungicide
sprayed and unsprayed control form the first assessment (74DAS) and up to third assessment
(94DAS) and only significant difference disease incidence recorded on at early (vegetative,
flowering and pod setting) fungicide sprayed from other treatments on 4th and 5th assessments (table 3)
but, there were no other significant differences(P<0.05) between the fungicide sprayed and
unsprayed control form the first assessment (74DAS) and up to last assessment (114DAS).
Table 3: Disease incidence of faba bean gall treated with fungicide against gall disease
Frequency of Fungicides application
At early vegetative
At early flowering
At early vegetative and early flowering
At early vegetative and early pod setting
At early (vegetative, flowering and pod
setting)
Control (no application)
Mean
LSD(P<0.05)
CV %
Percentage diseases incidence % (10 interval)
74DAS
84DAS
94DAS 104DAS
114DAS
9.0
29.3
40.3
630.
74.0
11.3
23.0
40.3
58.7
81.3
8.0
21.3
29.3
47.3
47.7
6.7
32.0
33.0
55.7
59.3
11.0
18.3
24.0
27.7
44.0
9.0
9.2
ns
57.2
29.3
25.6
ns
22.5
34.3
33.6
11.04
18.1
64.0
52.7
21.23
22.1
84.0
65.1
15.04
12.7
DAS=day after sowing, LSD=least significant different, ns= non-significant, CV=coefficient variation
Disease severity
There was a not significant difference disease severity index between treatments forfaba bean gall disease
of the treatments and also no significance difference disease severity between fungicides sprayed and
410
unsprayed plots of the treatments (table 4).The analysis of variance showed that there were nonsignificant differences (P<0.05) on disease percentage severity index among the fungicide sprayed plots
and control one form the first assessment (74DAS) and up to last assessment (114DAS).
Figure 1: Faba bean gall disease symptom on leaf and stem of faba bean plants.
Table 4: Percentage of diseases severity index of gall disease treated with fungicide
Percentage diseases severity index (10 interval) Other diseases(1-9)
74DAS 84DAS 94DAS 104DAS 114DAS Ch.st
As.bt
At early vegetative
3.67
4.3
14.0
28.3
33.0
3.3
2.3
At early flowering
3.0
6.0
15.0
30.0
33.0
3.3
3.3
At early vegetative and early flowering
3.7
4.7
14.7
24.0
28.3
2.3
3.0
At early vegetative and early pod setting
3.3
5.7
12.7
24.3
29.3
3.3
3.3
At early (vegetative, flowering and pod setting) 5.3
5.7
13.7
24.3
33.0
3.0
3.2
Control (no application)
3.3
4.7
15.7
29.3
35.0
3.3
3.3
Mean
3.7
5.17
14.28
26.7
31.9
3.11
3.08
LSD(P<0.05)
ns
ns
ns
ns
ns
ns
ns
CV %
26.7
25.5
19.8
16.1
9.7
17.3
21.1
DAS=day after sowing, LSD=least significant different, ns= non-significant, CV=coefficient variation
Frequency of Fungicides application
Yield and yield components
Data on yield parameters showed non-significant differences (P<0.01) among treatments in the
number of pods per plant, seeds per pod and seed yield, as well as, no significant differences
were observed in 100 seeds weight. Plots treated withMaticofoliar spray fungicide no difference
yield and yield components against faba bean gall (table 5). But some literature indicate some
chemicals can reduce faba bean gall.Bogaleet al. (2017),Bayleton WP25 (Triadimefon 250 g
a.i./kg) at the rate of 300g fungicide per 100 kg of faba bean used as a seed treatment can
reduced faba bean gall pressure, thus minimizing farmer’s losses and also Woulitaet al. (2019),
application of Triadimefon 250 g/l and Metalaxyl 8% + Mancozeb 64% WP lowered “faba bean
gall” disease severity,
411
Table 5: Yield and Yield components of faba bean at different frequency chemical application on faba
bean gall disease
Frequency of Fungicides application
At early vegetative
At early flowering
At early vegetative and early flowering
At early vegetative and early pod setting
At early (vegetative, flowering and pod setting)
Control (no application)
Mean
LSD(P<0.05)
CV %
Yield and Yield component parameters
PH
PPP
SPP
PL
62.07
8.8
3.2
5.533
58.87
6.8
3.07
5.6
64.4
8.2
3.47
5.4
60.53
6.07
2.93
5.47
56.47
7.87
3.13
5.13
54.07
5.4
3.2
5.67
59.4
7.19
3.17
5.467
ns
ns
ns
ns
6.9
17.8
6.1
7.4
HSW
49.67
52.33
52.67
50.67
53.33
51
51.6
Ns
10.3
YLD(kg ha-1)
1405
1781
1839
1519
1813
1494
1642
ns
16.4
PH= Plant height, PPP= pod per plant, SPP= seed per pod, HSW= hundred seed weight, YLD= Yield, kg ha -1= kilo gram per hectare,
ns=non-significant, LSD= least significant difference, CV= coefficient of variations
Conclusions
This study results showed that levels of disease incidence were no significant difference for the
first three data assessments and significant difference on only fourth(104DAS) and fifth
(114DAS) assessments incidence at early (vegetative, flowering and pod setting) respectively.
The Matco fungicide sprayed and control (unsprayed) no difference in disease severity and yield
and yield components. The results of the present study revealed that the novel possibility of
using Matco foliar spray for managements of faba bean gall was not be an effective to control
faba bean gall disease symptoms on faba bean in Shambu area.
Recommendations
The results of the present study revealed Matco foliar spray is not effective to manage faba bean
gall disease in and around Shambu areas. Therefore, it is recommended to test other chemical
fungicides and use other managements systems for control this fast spreading disease.
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Evaluation of Different Insecticide for Management of Fruit Worm (Helicoverpaarmigera)
on Hot Pepper at Bako, Western Oromia
Abraham Negera*, Sheleme Kaba and Eshetu Mogasa
Bako Agricultural Research Center, P. O. Box 03, West Shoa, Ethiopia
*
Corresponding author. E-mail: abrahamgdr@gmail.com
Abstract
The fruit worm is a polyphagous and highly mobile insect, is a pest of economic importance on
many agricultural and horticultural crops. It feeds on flowers, pods and fruits of hot pepper and
it damaged fruits by making hole, ripen prematurely, or become infected with disease. This may
affect the quality as well as quantity of the dry pod yield of pepper. The objectives of this study
were to evaluate resistance/tolerance of different hot pepper varieties under production and to
evaluate different insecticides registered to manage fruit worm pests. The result of this research
indicated that, use of different variety were significantly differ in fruit worm infestation in both
cropping seasons. Percent of pod infestation per plant were higher, 32.36 and 12.62 % on
Marakofana variety in 2017 and 2018 cropping season respectively. Significant variations were
observed between the insecticides used for fruit worm infestation. Application of Deltametrin at
rate of one l ha-1 two times in the growing season reduced pod infestation. Percent of pod
infestation was 16.01 and 8.62 % in 2017 and 2018 cropping season respectively on Deltametrin
applied plots. Likewise, partial budget indicated positive net change in benefit when changing
convectional practice (no treatment) to chemical fruit worm management. Switching from
untreated control to use of Deltametrin for the insecticide management, the highest MRR
(28.12%) was calculated. Therefore, it was recommended that production of Oda Haro and Bako
local was pertinent where fruit worm infestation was high like Bako area and where Marakofana
414
variety production was important for market purpose application of Deltametrin was very
important.
Key words: Fruit worm, Hot pepper, Insectiside,
Background and Justification
Hot pepper (Capsicum annuum var. annuum L.) is a vegetable crop grown and consumed
worldwide. Hot pepper is a crop of growing significance in the economies of sub-Saharan Africa
(SSA). Unfortunately, the rate of production is far from coping with the demand within and
outside the SSA region. The first introduction of hot pepper to Ethiopia was by Portuguese,
probably in the 17th century (Hafnagel, 1961). Nowadays the crop is adapted to different agroecological zones of the country. Hot pepper fruits have a high nutritional value, particularly
considerable amount of vitamin C at green stage and vitamin A at matured dried fruits
(Mohammed et al., 1992). Pepper is consumed as a fresh vegetable or dried, whole or ground
into powder alone or in combination with other flavoring agents or spices. The high nutritive and
culinary value of pepper gives the crop a high demand in the market year-round (Bosland and
Votava, 2003). Pepper is produced in all mid and lowlands of Ethiopia in the ranging from 1000
to 1800 meter above sea level (Hafnagel, 1961). The average national yield per hectare of red
and green pepper is 2.33 and 6.16 t ha-1, respectively. However, the average yields around Bako
area is declining and below the national yield of 1.9-2.0 and 3.5 t ha-1 for red and green pepper,
respectively (CSA, 2015/16).
The poor quality of the produce is largely attributed to biotic and abiotic stresses in the field and
the poor quality cultivars grown by farmers (Tusiime et al., 2010). Attacks by fungal, bacterial or
viral diseases, nematodes, mites and many insect pest infestations can cause significant losses in
pepper production (Ochoa-Alejo and Ramirez-Malagon, 2001). These disease infections and pest
infestations undoubtedly, severely reduce the production and profitability of this crop even
further by reducing the period in which the crop can be harvested.
Aphids, leaf miners, cutworms, fruit fly, false codling moth, Fruit worm (Heliothisarmigera) and
lesser armyworm are among the major insect pests that attack pepper. Infected fruits with fruit
fly often contain several maggots, and usually rot and drop prematurely and substantial losses
can be occurred (Ministry of Agriculture Natural Resource Sector, 2011). The tomato fruit worm
(Helicoverpaarmigera), a polyphagous and highly mobile insect, is a pest of economic
importance on many agricultural and horticultural crops. It has attained the status of major pest
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on a number of crops, including cotton, tobacco, corn, sorghum, sunflower, soybean, Lucerne
and pepper (Torres-Villa et al. 1996). It has been recorded as a damaging pest on 180 cultivated
and wiled plant species in at least 45 families (Venette et al. 2003a). Tomato fruit worm feeds on
flowers, pods and fruits of pepper. Larvae move from one fruit to the next, destroying only small
portions of each fruit. Damaged fruits may drop, ripen prematurely, or become infected with the
pest. The entrance hole near the pedicel develops a dark scar. Monitor closely, looking for the
larvae on plants; older larvae can be found by cutting into fruits. Young larvae are light yellow
and spotted. Mature larvae are brown to gray in color with lengthwise stripes along the body.
Adults are active at night. Maximum egg-laying coincides prior to or during host flower
production (King 1994). Eggs hatch in 2 or 3 days and the larval stage lasts 14 – 21 days. Larvae
move to green fruit soon after hatching, where they bore deeply into the fruit. Tomato fruitworm
pupates in the soil; the adult emerges in 7 to 14 days.
In western Ethiopia fruit worm infestation on hot pepper was higher. It affects yield quantity as
well as dry pod quality of the crop. In addition, hot pepper pod infestation by this insect was not
studied well in the country generally and in Western Ethiopia particularly. Therefore,
management of this pest is crucial to increase pepper yield by quantity and quality.
Objective of this study were to evaluate resistance/tolerance of different hot pepper varieties
under production at Bako area and to screen different insecticides registered for tomato
fruitworm managements.
Materials and Methods
Study Area
The experiment was conducted at Bako Agricultural Research Center in 2017 and 2018 under
main cropping seasons.
Experimental Materials and Procedures
Three of the released hot pepper varieties (Marakofana, Bako local and Oda haro) were used to
evaluate their resistance /tolerance to the pest. In addition, different insecticides registered
(Deltametrin, Chlorpyrifos and Dimethoat) for the control of the pest was evaluated / screened.
Experimental Design
Treatments was arranged in a randomized complete block design (RCBD) with factorial
combination of three varieties, three registered insecticide for tomato fruit worm and untreated
plots for each variety. It was replicated three times. Plot size of each treatment was 3 m x 3 m.
416
Seedlings were transplanted at spacing of 30 cm apart from each other in a row and 70 cm
between rows. Other agronomic practices were applied as recommended in the study area.
Data Collected
Flowering data, physiological maturity, stand count at harvest were collected. Six plants were
pre-tagged from three middle harvestable rows. Data for number of pods per plant, number of
infected pods, and number of non-infected pod per plant were collected from pre-tagged
plants.Marketable and unmarketable pod dry weight were recorded after the pods were dry.
Data analysis
All collected data were subjected to ANOVA using SAS 9.3 software. Yield losses in different
treatments were calculated as percent yield loss by employing the formula developed by Robert
and James (1991):
Relative percent yield loss =
100 × (YCP − YDP)
YCP
Where, YCP: Yield in controlled plot; YDP: Yield in diseased plots of the treatment.
Results and Discussions
Analysis of variance of the two years data in pod infestation by fruit worm indicated that
significantly different by years (Table 1). Therefore, analysis was carried out separately for each
year.
Table 1: mean square and probability level of pod infestation for two years (2017 and 2018)
Source
Variety
Chemicals
Rep
Year
Variety*chemicals
Variety*Year
chemical*Year
DF
2
3
2
1
6
2
3
Mean Square
5.872
7.660
1.982
39.796
2.785
8.579
2.191
P value
0.098
0.032
0.446
0.000
0.347
0.036
0.445
Total pod number per plant was significantly varied by variety in 2017 (Table 2). Total pod per
plant was highest on Oda Haro (20 pods/plant) followed by Bako local (17 pods/plant). This may
be due to the genetic makeup of the variety. However, total pods per plant were not differed by
insecticide applied for management of fruit worm (Table 3). Number of infected pods per plant
was significantly influenced by insecticide applied. Significantly, lower (2.72) infected pods
were recorded from plots treated with Deltametrin. However, higher (4.79) infestation of pod
417
number per plant were observed on dimethoat treated plots which was at par other management
actions applied. Percent of pod infestation were significantly different (P<0.05) between
varieties. In this cropping season, more than 32 % of the pods produced by MarakoFana variety
was infested with fruit worm. However, lower (20.6%) of pod infestation was observed on Oda
Haro variety. In addition, insecticide application significantly different in percent of pod
infestation by fruit worm. Application of Deltametrin reduced (16.36%) percent pod infestation
than the other treatments. However, no significance difference (P<0.05) were observed between
other treatment options in percent pod infestation. Generally, percent of pod infestation was
higher in 2017 cropping season. This may be due to application of insecticides were late after the
insect infested the crop. Even though higher percent of infestation of fruit worm was recorded on
MarakoFana variety, highest marketable dry pod weight (871.56 kg/ha) was recorded on this
variety followed by Oda Haro (825.26 kg/ha).
Table 2: Effect of variety on yield and fruit worm insect infestation parameters in 2017/18 main
cropping season
Treatment Total podper Infested pod %
Marketable Unmarketable Dried wt. of Total Yield
plant
/plant (no.)
ofInfestation pod (kg/ha) pod wt.
Infected pod
Marakofana
14.583 c 4.62 a (2.22)
32.364 a
871.56 a
186.59 a
88.17 a 1058.15 a
Bako local
17.000 b 3.76 a (2.01)
21.648 b
671.43 b
101.32 a
38.49 b
772.75 b
Oda haro
20.000 a 4.12 a (2.11)
20.865 b
825.26 a
79.17 a
44.71 b
904.43 b
Mean
17.194
4.16 (2.11)
25.35
789.418
122.359
57.12
911.777
Cv
10.994
18.27
41.03
19.772
34.650
44.5264
17.863
P
0.0001
Ns
0.019
0.0123
0.2826
0.0001
0.001
Table 3: Effect of insecticides on yield and fruit worm insect infestation parameters in
2017/18 main cropping season
Treatment
Chlorpyrifos
Deltametrin
Dimethoat
Control
mean
Lsd
Total pod No. of infested
% of
Marketable unmarketable
Dried wt. of Total Yield
per plant pod per plant
infestation pod (kg/ha) pod wt.
Infected pod
17.889 a
4.58 a (2.22)
26.93 a
869.14 a
139.33 a
64.9 a
1008.47 a
17.222 a
2.72 b (1.75)
16.01 b
818.17 a
99.82 a
35.63 b
917.99 a
17.444 a
4.79 a (2.27)
28.49 a
749.38 a
124.69 a
63.49 a
874.07 a
16.222 a
4.57 a (2.21)
28.39 a
720.99 a
124.69 a
64.48 a
846.58 a
16.8055 4.164 (2.113)
24.959
789.417
122.359
57.125
911.777
2.286
1.66
10.01
ns
ns
24.867
ns
In 2018 cropping season, number of pods per plant was significantly varied between the varieties
and insecticide applications for management of fruit worm. Variety Oda Haro showed
significantly higher (37.25 pods per plant) pod number per plant followed by Bako Local (26.62
pods per plant) (Table 4).
Similar to the previous year, percent of pod infection was
significantly varied between varieties, which was higher (12.62% of the total pod per plant) for
MarakoFana variety. The result also showed that marketable dry pod yield was significantly
418
different between the varieties. Oda Haro and MarakoFana varieties showed higher Dry
marketable yield. Similar to these result different hot pepper varieties (accessions) showed
different resistance for fruit worm (Abate and Gashawbeza 1997).
Insecticide application also significantly varied in number of pod infestation and percent of pod
infestation by fruit worm. Application of Deltametrin showed lower (1.9 infested pod/plant)
number of pod infestation and it is at par with Chlorpyrifos applied plots. The highest (14.34 %)
percent pod infestation per plant was observed on untreated control plot. Application of
insecticides for management of fruit worm were at par with each other in percent pod infestation
(Table 4). Lower percent of pod infestation were observed on Chlorpyrifos and Deltametrin
treated plots. Even though not significant marketable yield was higher on Chlorpyrifos and
Deltametrin treated plots.
Table 4: Effect of hot pepper fruit worm management options yield and fruit worm insect
infestation parameters in 2018/19 main cropping season
Treatment
Total pod Infested pod
/plant
number
% of
infestation
Infected pod Marketable
weight
yield/ha (kg)
Total yield Unmarketable
(kg/ha)
yield
Variety
Marakofana 18.18 c 2.29 b
12.62 a
17.49 ab
1105.8 a
1218.5 a 112.74a
Bako local
26.62 b 2.11 b
8.46 b
13.142 b
847.0 b
928.9 b
81.90b
Oda haro
37.25 a 3.58 a
9.54 b
21.117 a
1324.2 a
1417.2 a 92.98 ab
Mean
27.35
2.658
10.206
17.250
1092.35
1188.22
95.87
Lsd
4.016
0.713
2.959
7.90
252.35
268.03
26.422
Cv
17.35
31.685
34.245
54.11
26.97
26.40
32.55
P
<.0001
0.0005
0.022
ns
0.002
0.004
Chemicals
Chlorpyrifos 25.36 b 2.100 b
8.149 b
10.856 b
1148.6 a
1231.5 a 82.91 b
Deltametrin
25.47 b 1.911 b
8.623 b
8.689 b
1105.4 a
1286.0 a 80.65 b
Dimethoat
31.04 a 2.933 a
9.708 b
23.200 a
1083.8 a
1189.9 a 106.10 ab
Control
27.53 ab 3.689 a
14.344 a
26.256 a
1031.6 a
1045.4 a 113.89 a
Mean
27.35
2.658
10.206
17.250
1092.35
1188.2
95.87
Lsd
4.64
0.823
3.417
9.125
291.39
309.49
30.509
Cv
17.35
31.685
34.245
54.11
26.97
26.40
32.55
P
ns
0.0006
0.004
0.001
ns
ns
Means in the column accompanied by the same letter (s) are not significantly difference at (P< 0.05%)
Yield Loss and Cost Benefit
Relative yield loss was calculated from two years data and higher relative yield loss of 62.092 %
was calculated from an untreated plot compared to Deltametrin treated plot (Table 5). However,
lower relative yield loss was observed on plots sprayed with Chlorpyrifos and Deltametrin. In
Pakistan yield loss due to Fruit worm reported about 20% (Usman, et al. 2012) and 25% Umeh et
al. (2002) on tomato. Abate and Adhanom (1982) reported that yield loss on hot pepper due to
fruit worm was as high as 27% in Ethiopia. Partial budget analysis was also calculated for
insecticide management options from the mean of two years yield. The highest (ETB 47973.50
ha-1) marginal benefit was obtained from chlorpyrifos treated plot followed by Deltametrin (ETB
419
47659.25 ha-1) treated plot. However, Marginal Rate of Return (MRR) was higher (28.13%) on
plots sprayed with Deltametrin (Table 5).
Table 5: Yield loss and Cost benefit analysis of hot pepper production as influenced by fruit worm
management in 2017-2018 main cropping season.
Treatments Mean Marketable
yield
Control
360.495
Dimethoat 890.490
Deltametrin 950.985
Chlorpyrifos987.270
Relative Yield
loss
62.092
6.361
0.000
-3.816
Gross Return(ETB
ha-1)
18024.75
44524.50
47549.25
49363.50
Marginal Cost
(ETB ha-1)
0.00
890.00
890.00
1390.00
Marginal benefit
(ETB ha-1)
18024.75
43634.50
46659.25
47973.50
MRR
(%)
24.72
28.12
18.95
Conclusions and Recommendations
The result of this study demonstrated that different varieties responded differently for tomato
fruit worm infestation on hot pepper both years. Compared to other two varieties MarakoFana
showed higher percent of pod infestation with fruit worm. Insecticide application also
significantly reduced percent of pod infestation. Chlorpyrifos and Deltametrin application
showed lower number of pod infestation and percent of pod infestation. Marginal benefit
obtained from Chlorpyrifos treated plot was higher, followed by Deltametrin treated plot.
However, switching from untreated control to use of Deltametrin for fruit worm management
showed highest MRR calculated. Therefore, it was recommended that production of Oda haro
and Bako local were pertinent where fruit worm infestation was high like Bako area and. It was
recommended that producer has economic potential to apply insecticide would be spray
Deltametrin to produce MarakoFana which has highmarket price variety.
References
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Pest Management Journal of Ethiopia 1(1 and 2):1-8
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International, England, p.333.
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Ababa, Ethiopia
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24, Kaiser, W.J. and R. Hannan, 1987. Seed-treatment fungicides for control of seed borne
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hot peppers (Capsicum frutescensL.) in the roadside marketing system in Trinidad. Trop.
Agric. 69: 333-340.
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biotechnology. In Vitro Cellular and Developmental Biology-Plant 37:701-729.
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Tusiime, G., Tukamuhabwa, P., Nkalubo, S., Awori, E. and Tumwekwase, S. 2010.
Development of a hot pepper root rot and wilt disease management strategy through
genetic resistance, chemical application and proper choice of rotational crops. Second
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Assessment: false codling moth, Thaumatotibia(=Cryptophlebia) leucotreta(Meyrick)
[Lepidoptera: Tortricidae]. University of Minnesota, Department of Entomology, CAPS
PRA. 1-30. [Available: http://www.aphis.usda.gov/ppq/ep/pest detection / pra
/tleucotretapra.pdf] accessed: 01/05/2009
Integrated Management of Major Faba bean (Vicia faba L.) diseases: Chocolate spot
(Botrytis fabae), Ascochyta blight (Ascochytafabae) and Rust (Uromycesviciae- fabae)) on
the Highlands of Guji, Southern Oromia
Deresa Shumi*1, Demissie Alemayehu1, Tekalign Afeta1, Belachew Debelo1
1,
Oromia Agriculture Research Institute (IQQO), Bore Agricultural Research Center
*Corresponding author: Deresa Shumi (deresashumi1990@gmail.com )
Abstract
Faba bean is one of the most economically important pulse crops cultivated in Ethiopia.
However, its average yield at the national level remains lower than an attainable potential yield.
This is partly due to low soil fertility, inappropriate agronomic packages and diseases and pest
problems. Hence, this experiment was conducted to investigate integrated disease management
methods against major faba bean diseases in Guji Zone, Southern Oromia. The experiment was
conducted in SongoBericha station of Bore Agricultural Research Center during the 2017 and
2018 main cropping seasons. The factors studied were two fungicides (Fungozeb 80% WP at 3
kg/ha and Natura 250 EW at 500 ml/ha), two frequencies and four planting dates at 7 days
interval.The experiment was laid out in a factorial arrangement of Randomized Complete Block
Design with three replications. Data on phonological, growth yield and yield related parameters
were collected and analyzed using SAS software. The result showed that the highest plant height
(140 cm) and 139.2 cm was recorded for spraying fungicide once and Natura alone,
respectively. The interaction of fungicide spray frequency, fungicide type and planting date had
significant effect on almost all parameters except plant height. The highest number of total pods
per plant (21.27) was recorded at planting date two by application of fungicide Natura twice
whereas the highest number of seeds per pod (6.17) was recorded for fungicide fungozeb spray
421
once at mid-planting of faba bean. The highest grain yield (4674 kg ha -1) was recorded due to
spraying of Fungicide Natura twice. The highest chocolate spot severity (48.4 %) was recorded
from faba bean sown late July; the highest severity Ascochyta blight (46.22%) was recorded
from early planting with spraying of fungicide once and the highest severity (19.26%) was
recorded from faba bean sown on 17th July. Thus, it can be concluded that integration of
moderately resistant variety with sowing date can provide better control of the diseases
compared to fungicidal treatments alone.
Key words: Fungozeb, Natura, Sowing date, Walki.
Introduction
Faba bean (Vicia faba L.) is one of the earliest domesticated food legumes in Ethiopia and is now
cultivated on large areas in the highlands parts, between 1800-3000 masl where it requires
chilling temperature and annual rain fall of 700-1000 mm (Gemechuet al., 2003).
On the highlands of Guji, faba bean is fist in terms of acreage and volume of production among
other pulse crops. It is a multi-purpose crop that plays an important role in the socio-economic
life of farming communities. It serves as source of food because it contain about 24% protein,
2% fat, 50% carbohydrate and offers an average of 700 calories per serving (Etemadiet al., 2015)
and also used as cash crops. In addition it makes a significant contribution to soil fertility
restoration as a rotational crop as it fixes atmospheric nitrogen (Samuel et al., 2008).
In spite of its importance, the productivity of faba bean in this area is nearly less than 1 tha -1 now
days, despite the availability of high yielding varieties (> 4 tha-1) (MOA, 2010). Some biotic and
abiotic factors are the main reason of the low productivity of faba bean (Agegnehuet al., 2006).
Among biotic factors, chocolate spot (Botrytis fabae), Ascochyta blight (Ascochytafabae), rust
(Uromycesviciae-fabae), downy mildew (Peornosporaviciae) and foot rots (Fusarium spp.)
diseases quite important (Torres et al., 2006). Chocolate spot is considered to be the most
important and destructivefaba bean disease in Ethiopia causing yield loss of up to 61% on
susceptible cultivars (Dereje and Beniwal, 1987).Similarly, on the highlands of Guji, it is the
most widespread and destructive disease (BOARC Pulse, 2016).
A number of management options have been developed in other countries to minimize the effects
of chocolate spot on faba bean yield. These include use of resistant/tolerant varieties; use of
cultural practices such as crop rotation, crop residue management, adjusting planting dates, and
fungicide application (Hawthorne, 2004). In Ethiopia, growing moderately resistant
422
varieties,application of chlorothalonil or mancozeb and late planting have been recommended
(Sahile, 2008). Similarly, Ermias and Addisu (2013) also reported that integrated use of sowing
date with fungicides also provides better control of this disease on the Bale highlands. However,
the dynamic nature of disease, environmental prevalence especially acidity (i.e. low level of
phosphorus and potassium) and humidity aggravate the disease from location to location. In
addition to this, the efficacy of fungicide depends on multitude interaction including the
environmental conditions. Therefore, this study was conducted to evaluate integrated effect of
fungicides with plating date for the management of chocolate spot in Guji Zone. Hence, the
objective of the study was to evaluate and recommend an economical integrated disease
management method for fababean producing areas of Guji zone and similar agro-ecologies.
Materials and Methods
Description of the Study Area
The experiment was carried out during the 2017 main cropping season at Bore Agricultural
Research Center, Guji Zone of Southern Oromia. Bore Agricultural Research Center is located at
the distance of about 8 km north of the town of Bore in Songo Bericha, Kebele just on the side
of the main road to Addis Ababa - Adola. Geographically, the experimental site is situated at the
latitude of 06o23‟55‟‟ N – 06o24‟15‟‟ N and longitude of 38o34‟45‟‟ E – 38o35‟5‟‟E at an
altitude of 2728 meters above sea level. The research site represents highlands of Guji Zone,
receiving high rainfall and characterized by a bimodal rainfall. The first rainy season is from
April to October and the second season starts in late November and ends at the beginning of
March. The major soil types are Nitosols (red basaltic soils) and Orthic Aerosols (Yazachew and
Kasahun, 2011; Wakene et al., 2014). The soil is clay loam in texture and strongly acidic with
pH value of around 5.13. According to climate data from National Meteorological Agency,
Hawassa Branch Directorate (2004-2015), Bore had an annual mean rainfall of 1015.6 mm. The
mean annual maximum and minimum temperatures during the aforementioned period were 22.7
and 10.36 o C, respectively. The hottest period of the year extends from December to March
whereas the coldest season extends from May to January. According to meteorological
information recorded in the last one decade, the maximum rainfall was recorded in the months of
August (170.14 mm) and September (196.48 mm) in Bore. During the crop growing season, the
total amount of rainfall received was 1538.8 mm out of which 163.7 mm was received in
423
October followed by 149.3 mm in August. Also the annual maximum and minimum air
temperatures of the growing season were 22.39 and 11.66 o C, respectively.
Experimental Materials, Treatments and Experimental Design
Faba bean variety, Walki which is moderately resistant to chocolate spot and having yield
potential of 24-52 qt/ha on research station and 20-42 qt/ha on farmers’ field was used for the
experiment. The variety was registered in 2008 for areas with altitude range of 1900-2800
m.a.s.l.
Treatments consisted of two fungicides (Fungozeb 80% WP at 3 kg/ha and Natura 250 EW at
500 ml/ha) with two frequencies and four planting dates at 7 days interval. Unsprayed treatment
was included as control. The experiment was laid out in Randomized Complete Block Design
(RCBD) and replicated three times in factorial combination. The plot size was 3.0 m × 2.40 m.
The spacing between plots and blocks was 1.0 m and 1.5 m, respectively. The inter- and intrarow spacing was 40 cm and 10 cm, respectively. The outermost row on both sides of each plot
and three plants on both sides of each row were considered as border plants, and used for data
collection to avoid border effects. Thus, the harvestable plot size was 2.4 m × 1.6 m having four
rows each with 24 plants (96 plants). Blended (NPS) fertilizer was applied at the rate of 150 kg
ha-1 and mixed thoroughly with soil to avoid direct contact with the seeds. Fungicide was tank
mixed and applied at recommended ratewhen diseases appeared and then at two weeks interval.
Finally, all the other agronomic practices were followed as per the recommendation for the crop.
Data Collection and Measurements
Phenological parameters
424
Days to flowering: were recorded as the number of days from sowing to when 50% of plants in
a net plot produced flower through visual observation.
Days to physiological maturity: This was recorded as the number of days from sowing to the
time when about 90% of the plants in a plot had mature pods in their upper parts with pods in the
lower parts of the plants turning yellow. The yellowness and drying of leaves were used as
indication of physiological maturity.
Growth parameters
Plant height: Thiswas measured as the height (cm) of ten randomly taken plants from the
ground level to the apex of each plant at the time of physiological maturity from the net plot area
and the means were recorded as plant height.
Number of primary branches per plant: The average number of primary branches emerged
directly from the main shoot was counted from ten randomly taken plants at physiological
maturity and the average number of primary branches was reported as number of primary
branches per plant.
Yield and yield components
Number of pods per plant: Number of pods was counted from ten randomly taken plants from
the net plot area at harvest and the means were recorded as number of total pods per plant.
Number of seeds per pod: Thiswas recorded from ten randomly taken pods from each net plot
at harvest.
Hundred seed weight (g): was determined by taking weight of 100 randomly sampled seeds
from the total harvest from each net plot area and the weight was adjusted to 10% moisture level.
Grain yield (kg ha-1): The four central rows were threshed to determine seed yield and the seed
yield was adjusted to moisture level of 10%. Finally, yield per plot was converted to per hectare
basis and the average yield was reported in kg ha-1.
Statistical Data Analysis
All the measured parameters were subjected to analysis of variance (ANOVA) appropriate to
factorial experiment in RCBD and the General Linear Model (GLM) of Gen Stat 15 th edition
(GenStat, 2012) and the interpretations were made following the procedure described by Gomez
and Gomez (1984). Least Significance Difference (LSD) test at 5% probability level was used
for mean comparison when the ANOVA showed significant differences.
Results and Discussion
425
Phenological and Growth Parameters of Faba bean
Days to flowering
The interaction of fungicide frequency, fungicide type and planting date had significant (P<.01)
effect on days to 50% flowering. Significantly, highest number of days to reach flowering
(63.67 days) was recorded from early sowing with no fungicide application which was
statistically at par with once or twice fungicide application.The shortest time to flowering (50.67
days) was recorded due to onetime spraying of fungicide Natura combined withthe second
planting date (Table 1). Early sowing was led to early flowering as compared to the other
planting dates across all once or twice fungicide spray.
Table1 Means of days to flowering of faba bean as affected by the interaction of fungicide
frequency and fungicide application rates at Songo Bericha during 2017 and 2018 main cropping
season.
Fungicide Frequency
Fungicide Type
Control
Frequency one
Control
Fungozeb
Natura
Fungozeb
Natura
Frequency two
Planting date
July 17
July 24
63.67a
52.33f-i
61.33abc
51.67ghi
ab
62.33
50.67i
abc
60.33
51hi
bcd
59.33
51.67ghi
July 31
53.64f-i
57.67cde
56def
57.67cde
56def
August 7
55.67def
55efg
55.67def
54.67e-h
54e-i
CV(%)=4.2
LSD(0.05)=3.91
Number of primary branch per plant
The interaction of fungicide frequency, fungicide type and planting date had no significant effect
on primary branch, but the interaction of fungicide frequency and type as well as fungicide types
and planting date had significant (P <0.01) effect on the number of primary branches. Two
timesapplication of fungicide Natura resulted in the highest number of primary branches per
plant (0.88) while the lowest number of primary branches (0.392) was recorded from treatment
that received no fungicide application (Table 2, 3).
Table 2: Means of number of primary branch per plant of faba bean as affected by the
interaction of fungicide frequency and application rates at SongoBericha during 2017 and 2018
main cropping season.
Treatments
Fungicide Frequency
Control
Frequency one
Frequency two
CV(%)=57
Fungicide Type
Control
0.392b
fungozeb
Natura
0.625b
0.675ab
0.78a
0.88a
426
LSD (0.05)= 0.31
Table 3: Means of number of primary branch per plant of faba bean as affected by the interaction
ofplanting date and fungicide type at Songo Bericha during 2017 and 2018 main cropping
season.
Fungicide type
Control
Natura
Fungozeb
CV(%)=50
LSD(0.05)=0.55
Planting date
July 17
0.43bcd
0.88ab
0.61abcd
July 24
0.4cd
0.98a
0.92ab
July 31
0.33d
0.65abcd
0.41d
August 7
0.4cd
0.81abc
0.67abcd
Plant height
The analysis of variance showed significant (P < 0.05) differences in plant height due to the main
effects of fungicide type and its frequency. However, there was no interaction effect of fungicide
frequency, fungicide type and planting date and main effect of planting date on plant height
(Table 4). The highest plant height (140 cm) was attained by one time application of fungicide
and it was statistically at par with twice application of fungicide, while the lowest plant height
(129.9cm) was recorded from the control. In this experiment, fungicide type also exhibited
significant (P<0.05) difference on plant height. Application of Natura resulted in the highest
plant height (139.2cm) while the lowest plant height (129.9cm) was recorded fromnil application
of fungicide (Table 4). Plots treated with Natura, showed 7.03% increase in plant height over
untreated plots. Consistent with this result, Khan et al. (2009) reported that maximum plant
height was observed in field pea treated with fungicide Mancozeb
Table 4. Means of plant height (cm) of faba bean as affected by main effect of planting date,
fungicide application frequency and fungicide application type at Songo Bericha during 2017
and 2018 main cropping season
Treatments
July 17
July 24
July 31
August 1
CV(%)=8.1
LSD(0.05)= NS
Fungicide Frequency (P<0.05)
Plant height
137.9
137.4
136.9
133.4
427
129.9b
140a
136.1ab
Control
Frequency one
Frequency Two
CV(%)= 7.6
LSD(0.05)= 8.48
Fungicide Type(P<0.05)
Control
Natura
Fungozeb
CV(%)=7.7
LSD(0.05)= 8.57
129.9b
139.2a
136.9ab
Yield and Yield Components
Number of pods per plant
The analysis of variance showed significant (P < 0.05) differences among treatments in the
number of pods per plant due to interaction effects of fungicide frequency, fungicide type and
planting date. The highest number of total pods per plant (21.27) was recorded from the second
planting date by twice application of fungicide Natura which was statisticallyat par with the
number of pods obtained by application of both fungicides at first to fourth planting date except
at spraying of both natura and fungozed only at once in late sowing (planting date three and four)
whereas the lowest number of total pods (12.9) was obtained from the control (Table 5).
Table 5: Means of number of pods per plant of faba bean as affected by the interaction of of
planting date, fungicide application frequency and fungicide application rates at Songo Bericha
during 2017 and 2018 main cropping season.
Fungicide frequency
Fungicide Type
Planting date
Control
Control
Fungozeb
Natura
Fungozeb
Natura
July 17
16.8a-e
19.9abc
21.17ab
15.93cde
21.23a
Frequency one
Frequency two
CV (%)=18.4
LSD (0.05)=5.11
July 24
16cde
17.5a-e
19.13a-d
17.73a-e
21.27a
July 31
12.9e
14e
14.63de
14.4de
15.8cde
August 7
16.07b-e
15.93cde
14.63de
15.87cde
16cde
Hundred seed weight: The analysis of variance revealed that interactions of fungicide
frequency, fungicide type and planting date had highly significant (P<0.01) effect on hundred
seed weight (Table 6). Spraying of fungicide Natura twice on late sown (August 7) faba bean
428
scored significantly the highest hundred seed weight (74.3 g) while the lowest hundred seed
weight (64.3 g) was recorded fromspraying fungicide fungozeb twice on early plantedfaba bean.
In conformity with this result, Dagne et al., (2017) reported that the highest (59.57g) 100- seed
weight was obtained when faba bean variety Mosisa was sprayed with Mancozeb every seven
days while the lowest 100- seed weight (42.1g) was obtained from the local varieties in
unsprayed plots.
Table 6: Means of hundred seed weight (g) of faba bean as affected by the interaction of
fungicide frequency and fungicide Type at Songo Bericha during 2017 and 2018 main cropping
season
Fungicide Frequency
Unsprayed
Frequency one
Frequency two
Mean
CV (%)
LSD (0.05)
Fungicide Type
Fungozeb
Natura
Fungozeb
Natura
July 17
68.5 b-f
65.6ef
67.7c-f
64.3 f
68.1 c-f
66.84
Planting date
July 24
July 31
c-f
67.8
67.2 def
a-d
70.9
67.4 c-f
b-f
69.0
68.3 c-f
c-f
68.5
71.7 a-d
a-e
70.5
72.2abc
69.34
69.36
mean
August 7
68.3 c-f
68.9 b-f
69 b-f
73.5ab
74.3a
70.76
67.95
68.20
68.50
69.50
71.23
4.92
4.30
Grain yield: The grain yield was significantly (P<0.05) affected by interactions of fungicide
spry frequency, fungicide type and plating date (Table 7). The result generally showed an
increase in grain yieldas fungicide frequency increased. The highest grain yield (6094 kg ha -1)
was recorded due to spraying of fungicide Natura twice forJuly 24 plantingwhereas the lowest
(3334 kg ha -1) grain yield was obtained from late planted control. Similar result was reported by
Dagne et al., (2017) where the highest(5933 kg ha-1) grain yield was obtained from faba bean
variety Mosisa sprayed with Mancozeb every seven days and the lowest (2021 kg ha-1) yield was
Fungicide Frequency
Unsprayed
Frequency one
Frequency two
Mean
CV (%)
LSD (0.05)
Fungicide
Type
Fungozeb
Natura
Fungozeb
Natura
mean
July 17
3975cde
5334ab
5404ab
5003abc
5163abc
4976
Planting date
July 24
July 31
4549bcde
3703de
4474bcde
3561de
abc
5010
4604bcd
4763bcd
4285cde
a
6094
4311bcde
4976
4093
August 7
3334e
3994cde
4009cde
3623de
4396bcde
3871
3890.3
4341
4757
4419
4991
16.9
1249.02
obtained from unsprayed plots. Teshome and Tagegn (2013) also found that the maximum grain
yield was obtained from first sowing date treated with 4 times spray of fungicide. Generally,
429
Grain yield losses were reduced by fungicide spraying intervals as compared to the unsprayed
plot of the respective treatments. Hawthorne (2004) indicated that the application of Mancozeb
as a protective fungicide helps to reduce yield loss due to chocolate spot as it prevents pod
abortion and plant damage.
Table 7: Means of grain yield (kg ha-1) of faba bean as affected by the interaction of fungicide
frequency and fungicide Type at Songo Bericha during 2017 and 2018 main cropping season
Effect of Fungicides Application and Planting Date on Severity of Faba bean Diseases
Severity of chocolate spot of faba bean: The interaction of fungicide spray frequency,
fungicide type and planting date had significant (P<0.05) effect on chocolate spot (Table 8).
Chocolate leaf spot was aggressive during pre-flowering and post-flowering butless aggressive
during flowering and grain filling growth stages. Severity of chocolate spot was very high for
early sownfaba bean and the highest severity (42.8 %) was recorded from early planting with
onetime spraying of fungicide. The lowest severity (29.88%) was recorded from two times
application of fungicide Natura for late sowing of faba bean which reduced the disease by 9.6 %
as compared to untreated plot. In line with this result, Dagne et al., (2017) also reported that the
highest mean final disease severity index (51.45%) was recorded on the unsprayed plots and the
lowest mean final disease severity (11.11%) was observed on the weekly sprayed plots (Table 8).
Table 8: Interaction effect of fungicide spray frequency, fungicide type and planting date
on severity chocolate spot of faba bean at Songo Bericha 2017 and 2018 main cropping
season
Fungicide spray Fungicide Type
Frequency
Unsprayed
Frequency one
Frequency two
Mean
CV (%)
LSD(0.05)
Fungozeb
Natura
Fungozeb
Natura
Planting Date
July 17
41.4ab
39.5abc
41.2ab
36.5 a-e
35.8 a-e
38.88
July 24
38.8abc
42.8a
33.4cde
39.9abc
33.9cde
37.76
July 31
37abcd
37.5abcd
39.4abc
36.4 a-e
34cde
36.86
Mean
August 7
31.1de
35.19abcd
33.4cde
37.7abcd
29.88 e
37.7
37.08
37.43
36.85
37.63
34.74
11.7
7.12
Ascochyta blight Severity of faba bean
In the beginning, the disease was noticeable because of the irregular dark spots that form on the
pods; when these spots mature they become dark brown and damp and picnidia appeared (these
430
symptoms are observed during grain drying before threshing. Thus, the interaction of fungicide
frequency, fungicide type and planting date had significant (P<0.05) effect on control of
ascochyta blight onfaba bean (Table 9). Earlyplanted of faba bean was highly infected by
ascochyta blight where the highest severity (42.8%) was recorded from faba bean sown on 24th
July with onetime spraying of fungicide fungozeb while the lowest severity (31.2%) was
recorded from late plantedfaba bean at early August.
Table 9: Interaction effect of fungicide spray frequency, fungicide type and planting date
on severity Ascochyta blightof faba bean at Songo Bericha 2017 and 2018 main cropping
season
Fungicide
Frequency
spray Fungicide Type
Unsprayed
Frequency one
Frequency two
Mean
CV (%)
LSD (0.05)
Fungozeb
Natura
Fungozeb
Natura
Planting Date
July 17
41.4ab
39.5abc
41.2ab
36.5a-e
35.8b-e
38.88
July 24
38.8abcd
42.8a
33.4cde
39.9abc
33.9cde
37.76
July 31
37a-e
37.5 a-e
39.4abc
36.4 a-e
34 cde
36.86
Mean
August 7
32.1de
31.2e
33.4cde
37.7a-e
31.9e
33.36
37.33
37.75
36.85
37.65
33.90
11.3
6.86
Rust severity of faba bean at Songo Bericha on station: The interaction of fungicide
frequency, fungicide type and planting date had significant (P <0.01) effect on control of faba
bean rust (Table 10). Thus, unsprayed faba bean was severelyinfected by rust where the highest
severity (27.20%) was recorded from faba bean sown on 17th July for the control while the
lowest rust severity (16.5%) was recorded from one or two times Natura application with
planting of faba bean in July. Khan et al (2009) reported that application of fungicides up to
three times showed significant differences among fungicides and field pea (Pisum sativum)
varieties.
Table 10: Interaction effect of fungicide spray frequency, fungicide type and planting
date on severity rust of faba bean at Songo Bericha 2018 and 2019 main cropping season
Fungicide spray Fungicide Type
Planting Date
Mean
Frequency
July 17
July 24
July 31
August 7
Unsprayed
27.2a
25.9ab
23.5bcde 22.2 c-f
24.70
2 c-f
abcd
ab
c-f
Frequency one
Fungozeb
22.
24.7
25.9
22.2
23.75
Natura
22.2 c-f
22.2 c-f
21.2 def
20.53
16.5g
c-f
c-f
c-f
abcd
Frequency two
Fungozeb
22.2
22.2
22.2
24.7
22.83
Natura
19.0fg
20.6ef
19fg
24.9abc
20.88
Mean
22.56
21.98
22.56
23.04
431
CV (%)
LSD (0.05)
9.90
3.69
Conclusions and Recommendation
Integrated Management of major faba bean (Vicia faba L.) diseases was investigated on Nitisols
and Orthic Aerosols soils of Guji Zone, Southern Ethiopia. It was conducted during the main
2017 cropping season with the objective of investigating integrated disease management
methods against major faba bean diseases at Guji Zone, Southern Ethiopia. The result showed
that fungicidal treatment in combination with sowing faba bean in different day intervals have
resulted in significant variation on phonological, growth yield and yield related parameters
except plant height. Utilizing fungicide Natura with different frequencies under field conditions
compared with fungicide Fungozed revealed that Natura resulted in increasedfaba bean yield.
However, integration of moderately resistant variety with sowing date rather than fungicide
treatment, which is offer less environmental safety, is proved to be better management option of
the disease. Thus, it can be recommended thatintegration of moderately resistant variety with
early sowing is a better management option. Further investigation is needed in vitro and in vivo
to compare effectiveness of biological and chemical methods to control disease on faba bean.
References
Dereje Gorfu and Beniwal, S.P.S. 1987. Preliminary survey of faba bean disease in the major
production areas of Ethiopia. In: Results of Research Done on Faba bean in Ethiopia,
ICARDA/IAR/IFAD-Nile valley project during the cropping season. IAR, Addis Ababa,
Ethiopia. pp. 78-84.
Ermias Teshome and Addisu Tagegn. 2013. Integrated management of Chocolate spot (Botrytis
fabae Sard.) of Faba bean (Vicia faba L.) at highlands of Bale, south eastern
Ethiopia.Research Journal of Agricultural and Environmental Management, 2(1): 11-14.
Etemadi, F., Hashemi, M., Mangan, F. and Weis, S. 2015. Fava beans Growers guide in new
England PP 2-24.
GemechuKeneni, MussaJarso, TezeraWolabu. 2003. Faba bean (Viciafaba L.) genetics and
Breeding Research in Ethiopia: A Review. In: Ali Kemal, Kenneni Gemechu, Ahmed Seid,
Malhatra Rajendra, Beniwal Surendra, Makkouk Khaled, Halila MH, eds. Food and Forage
legumes of Ethiopia: Progress and prospects. Proceedings of the workshop on Food and
Forage Legume, 22-26 September 2003. Addis Ababa, Ethiopia.
Hawthorne, W. 2004. Faba bean disease management strategy for southern region.
http://www.sardi.sa.gov.au/pdfserve/fieldcrops/publications/advicefactsheets/brochure.pd
MoA (Ministry of Agriculture). 2010. Animal and plant health regulation directorate. Crop
variety register. Issue No. 14. Addis Ababa, Ethiopia. pp. 71-73.
Sahile, S., Chemeda Fininsa, Sakhuja, P.K. and Seid Ahmed. 2008. Evaluation of Pathogenic
Isolates in Ethiopia for the control of Chocolate Spot in Faba bean. African Crop Science
Journal, 17(4): 187 - 197
432
Tekalign Afeta. 2016. Quantification and Assessment of Severity and Incidence of Major
Economically Important Faba bean and Field pea Disease in Highlands of Guji Zone,
southern Oromia (unpublished data)
Torres, A.M., Roman, B., Avila, C.M., Satovic, Z., Dubiales, D., Sillero, J.C. and Moreno, M.T.
2006. Faba bean breeding for resistance against biotic stresses:Towards application of
marker technology. Euphytica, 147: 67–80 DOI: 10.1007/s10681-006-4057-6
Integrated Management of Barley Shoot fly on theHighlands of GujiZone,Southern
Oromia
ABSTRACT
This study was initiated to assess the effect of Integrated Management of Barley Shoot flyon yield
and yield componenst of Barley (Hordeum vulgare L.). Afield experiment was conducted during
the 2017-18 main cropping season at Bore Agricultural Research Center to evaluate the effect of
integrated barley shoot fly management on yield and yield components of barely and to
determine an economically feasible optionshoot fly management for barley production. The
treatments consisted of five levels of insecticides (Apron star, Dynamic, Procid plus, Joint and
Torpido and four levels of planting dates. The experiment was laid out in Randomized Complete
Block Design (RCBD) in a factorial arrangement with three replications. Analysis of variance
revealed that interaction of the two factors (chemicals and planting dates) significantly affect
most parameters except thousand kernels weight, number of tillers per plant and number of
productive tillers per plant. Generally, all parameters recorded over all treated plots were
significantly higher than untreated/control plots. Thus using insecticides and adjusting planting
date can help to improve yield andyield components byreducing the degree of barley shoot fly
infestation. The highest grain yield (4403 kg h-1) and lower shoot flyinfestation wereachieved
from combined application of Torpido + first planting date. The partial budget analysis,
however, revealed that combined applications of Torpido insecticide and planting in the last
week of Julygave the best economic benefit 26941.78 Birr ha-1. Therefore, based on this study it
can be concluded that the use of Torpido insecticide and planting in the late Julycan be
recommended for production of barley in the study area and other areas with similar agroecological conditions.
Key words: Insecticides, interaction effect, main effect, shoot fly, sowing date
Introduction
433
In Ethiopia, cereal crops are majorly produced for several purposes where they are greatly
contributing towards sustaining food security. Farmers in different parts of the country are
growing different types of cereal crops based on their agro-ecological suitability to address their
family food demand. Particularly, farmers in high land parts of the country are producing barley
for home consumption and income generation. As a result, it's commonly called as a poor man’s
crop that is able to give yield in marginal environments that is unsuitable to other crops at higher
elevation (Zerihunet al., 2007). It ranks 5th in terms of area (993,918.89 ha) and production
(19,533,847.83) next to wheat and followed by finger millet (CSA, 2016).
The crop grows well at altitudes ranging between 1500–3500 maslbut is predominantly grown at
altitudes ranging between 2000–3000 masl (MoA, 1998). The highlands ofGuji Zone is also
found within most suited agro-ecological adaptation for barley crop production. Farmers in the
area are usually producing barley as major crop for home consumption as well as for cash
generation. It ranks second next to maize both in area (17,969.07 ha) and production
(315,115.09). However, the production and productivity of the crop remains lower (17.54qt/ha)
in relation to the national average (19.65qt/ha) and regional average (22.52qt/ha) productivity
(CSA, 2016). This may be due to several production constraints like in insect pests, diseases low
level of soil fertility, lack of improved varieties and others.
Barley shoot fly is one of the major biotic constraints to barley production onGuji highlands. A
survey of Barley shoot fly incidence and damage level conducted in 2014 and 2015 indicated that
there is high infestation which can cause high yield loss in susceptible varieties.However there is
no known management practices used by farmers so far. Therefore, there is a need to evaluate
and recommend different management options such integrated management which can be
economically and environmentally most viable and sustainable.The objective of the study was to
evaluate integrated approaches in barley shoot fly management and recommend the best option.
Materials And Methods
Description of the study area
The experiment was conducted at two locations of Bore district which represents highland agroecology of Guji Zone.Bore district is located at 385 km from Finfinnee to the South. The climatic
conditions of the district comprises an annual rain fall of 1250mm, mean temperature of 17.5-28
Degree Celsius. Bore district was selected for this experiment as it represents the hotspot areas
for barley shoot fly infestation.
434
Experimental design and treatments
For this experiment five insecticides namely Joint, Torpido, Dynamic, Proced Plus and
Apronstarand four planting datesat seven days interval were used.The experiment was laid out in
RCBD with three replications. Each experimental plot has 2.5 m long and 1.2 m wide, with six
rows 20 cm apart, giving a gross plot area of 3 m2. Spacing for adjacent blocks was 1.5 m and 1
m between plots. Sowing was done by hand drilling and covered lightly with soil. Seed and
fertilizer were applied as per the recommendation ratesfor barley production.All other agronomic
practices were also applied as recommended for barley production.
Data collection
Data were collected from a net plot of four rows and selected plants. Collected data includedays
to heading (DTH), days to 90% maturity (DTM), grain filling period (GFP), plant height (PH),
spike length (SL), total number of tillers/plant, total number of fertile tillers/plant, 1000-kernel
weight (TKW), grain yield/ha (Gy kg/ha) and shoot fly infestation.
Data analysis: The recorded data were subjected to Analysis of Variance (ANOVA) as
suggested by Gomez and Gomez (1984) using GenStat 18thVersion. Mean separation was carried
out using Least Significant Difference (LSD) at 5 percent levels of significance.
Results and Discussion
Days to heading
The Analysis of Variance revealed that the main effect of planting date was highly significant (P
< 0.01) on days to heading of barley while the two-factor interactions of Chemical × planting
datessignificantly (P<0.05) influenced days to 50% heading. However, the main effect of
insecticide did not significantly affect days to 50% heading of the crop. The highest prolonged
duration to reach 50% heading was observed in response to the combination of planting date one
and two across all pesticides. However, the minimum duration to 50% heading was observed in
the application of Apronsarat fourth planting date (Table 1).
Table 47. Interaction effect of chemical and planting date on days to heading of barley
Insecticides
Control
Apronstar
Dynamic
Days to heading
Planting dates
P2
P3
P1
a
83
a
83.33
a
83
a
83
79.33
a
83
P1
e
146
b
f
73.33
d
78.33
a
75
79.67
a
83
P4
bc
c
139.7
b
144
e
75
Days to maturity
Planting dates
P2
P3
b
144
134.3
c
139.7
e
127.3
d
133
c
139
P4
d
e
127.3
d
133
e
127.3
435
Proced
Joint
Torpido
a
83
a
83
a
83
a
83
a
bcd
e
79
74.67
bcd
e
79
a
b
74.67
cd
c
144
b
126.3
d
139.7
b
e
133.3
c
144
e
d
139.3
e
133.7
c
126
d
e
83
83
78.67
75
144
139.3
133
127.3
LSD(0.05)
0.91
1.55
CV (%)
0.7
0.7
Means with the same letter(s) in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, NS= non - significant, LSD = Least Significant Difference at 5% level
Days to physiological maturity: The main effect of planting dateand interaction of the factors
highly significantly (P < 0.01) influenced days to physiological maturity of barley. But the main
effect ofinsecticidesdid not significantly affect days to physiological maturity.
The longest physiological maturity (168.7 days) was recorded forthe first planting date with
control/untreated whereas the shortest days to physiological maturity (126 days) was recorded
from combination of Joint and the fourth planting date. The increase in days to maturity of
barleyfor the controltreatment might be due to rejuvenation of the crop though the level of
infestation wasthe highest.
Plant height: The two factor interaction and main effect of insecticides significantly (P < 0.05)
influenced plant height. On the other hand, the main effect of planting date had no significant
effect on this same parameter.The result indicated that height of barley plants increased as
infestation was decreased (Table 2). The highest plant height (115.8cm) was recorded for
insecticide Joint coupled with the second planting date while the shortest plant height (101.9cm)
was recordedfor the controlcombined with third planting date of the two factors.
Spike length: The Analysis of Variance revealed significant (P < 0.05) interaction of the two
factorsand main effect of planting date on the spike length whereas the main effect of chemical
did not have significant effect on this parameter. Thus, the longest spikes (9.00 cm) were
obtained fortreatment combination of insecticide Joint and the first planting date whereas the
shortest spikes were produced for the combination of the Procedplus and first planting
date(Table 2). The highest spike length of the treated plotsin relation to the untreated control
might have resulted from improved root growth and increased uptake of nutrients and better
growth favouredbyreduced shoot fly infestation.
Table 48. Main effect of chemicals and planting date on plant height and spike length of Barley
Insecticides
Control
Plant height (cm)
Planting dates
P1
P2
d
cd
103.6
102.5
P3
101.9
Spike length (cm)
Planting dates
P1
P2
P4
d
102.7
d
7.778
c-f
8.944
P3
ab
8.944
P4
ab
8.611
abc
436
Aprstar
Dynamics
Proced
Joint
Torpido
LSD(0.05)
CV (%)
113.7
111.0
110.2
112.8
107.6
9.33
5.2
ab
a-d
a-d
abc
a-d
115.1
105.1
109.4
115.8
114.2
a
bcd
a-d
a
ab
110.6
111.2
113.1
109.7
109.1
a-d
a-d
ab
a-d
a-d
102.6
113.8
114.6
107.8
115.1
d
ab
a
a-d
a
7.5 0
def
7.833
7 .00
9.00
c-f
f
8.222
8.944
a
7.111
0.98
7.3
8.500
8.667
ef
8.611
abc
a-d
ab
abc
abc
7.889
8.00
c-f
b-e
8.611
8.167
7.889
abc
a-d
c-f
c-f
7.944
a-d
8.278
8.5 00
8.111
8.444
abc
a-d
a-d
Yield and Yield Components
Number of tillers per plant
The main effect of chemical and planting date did not significantly (P<0.05) influence the
number of tillers of barley. Similarly the two-factor interaction (chemical × planting date) also
did not significantly affect this parameter. This finding agrees with that of Wakeneet el (2014).
Table 49. Interaction effect of chemical and planting date on number of tillers and number of
productive tiller per plant of barley
Number of tiller/plant
Number of fertile tiller/plant
Planting dates
Planting dates
P1
P2
P3
P4
P1
P2
P3
Control
3.222
3.667
3.722
3.611
2.833
3.167
3.278
Apr
3.667
3.056
3.556
3.389
3.222
2.833
3.056
Dyn
3.50
3.444
3.389
3.278
3.111
3.00
3.00
Pro
3.722
3.50
3.389
3.722
3.167
3.111
3.056
Join
3.389
3.389
3.50
3.389
2.889
2.944
3.056
Torp
3.778
3.278
3.444
3.722
3.222
2.722
3.056
LSD(0.05)
NS
NS
CV (%)
9.9
11.2
Means with the same letter(s) in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, LSD= Least Significant Difference at 5% level
Insecticides
Number of productive tillers
The main effect of insecticide and planting date did not significantly (P<0.05) influence the
number of productive tillers of barley. Similarly the two-factor interaction (insecticide × planting
date) also did not significantly affect this parameter.
Thousand kernels weight
The main effect of insecticide and planting date did not significantly (P< 0.05) influence
thousand kernels weight of barley. Similarly the two-factor interactions did not significantly
affect thousand kernels weight.The highest thousand kernels weight (60.42 g) was recorded
forcombined application of Torpidowiththe first planting date whereas the minimum thousand
437
kernel weight (32.11 g) was observed for application of Torpidocombined with fourth planting
dateeven though there were not statistically significant differences.
Table 50. Interaction effect of chemicals and planting date on number of kernels per spike of
barley
Chem.
Control
Apr
Dyn
Pro
Join
Torp
LSD(0.05)
CV (%)
Grain yield (kg/ha)
Planting date
P1
P2
2296
3961
3331
3894
3853
de
ab
a-e
ab
ab
4403 a
1327.15
25.6
2724
2719
2728
2468
3108
3727
b-e
b-e
b-e
cde
a-e
abc
P3
2185
3543
2886
3967
2880
3231
P4
e
a-d
b-e
ab
b-e
a-e
2352
2667
3277
3962
2721
2877
de
b-e
a-e
ab
b-e
b-e
TKW (g)
Planting date
P1
P2
P3
P4
36.69
37.47
40.41
48.71
42.11
36.38
36.82
37.7
60.42
39.17
40.54
46.07
44.42
36.27
35.73
38.98
36.22
42
36.32
50.64
48.53
NS
17.8
36.17
36.19
32.11
Means with the same letter in the columns and rows are not significantly different at 5% level of significance, CV
(%) = Coefficient of variation, LSD=Least Significant Difference at 5% level
Grain yield
The main effects of insecticide and planting date and their interactions significantly (P< 0.05)
affected grain yield of barley.Late sowing significantly decreased grain yields. Thus, the highest
grain yield (4403 kg ha-1) was obtained from combined application of Torpidoand first planting
date and it was statistically at par with Procedeplus at first planting date and Joint applied for the
first planting date whereas the lowest grain yield (2185 kg ha-1) was recorded from the
combinations of control of third planting date (Table 4). The highest grain yield at the Torpido
and first planting date might have resulted from better growth favouredbydecreased shoot fly
infestation which enhanced yield components and yield.In general, grain yield obtained from the
treated plots exceeded the grain yield from the untreated/control plots by about 33.13%.
Barley shoot fly Infestation
The main effects of insecticide and their interactions significantly (P< 0.0) affected the barley
shoot fly infestation. The highest infestation (62.84) was obtained from combination of control
and third planting date whereas the lowest barley shoot fly infestation recorded from application
of Torpido at first planting date (Table 5).This indicated that grain yield is correlated with
infestation level.
Partial Budget Analysis
438
Analysis of the net benefits, total costs that vary and marginal rate of returns are presented in
Table 5 below. Information on costs and benefits of treatments is a prerequisite for adoption of
technical innovation by farmers. The studies assessed the economic benefits of the treatments to
help develop recommendation from the agronomic data. This enhances selection of the right
combination of resources by farmers in the study area. As indicated in table below, the partial
budget analysis showed that the highest net benefit (Birr 26941.78 ha-1) was recorded at the
combination of Torpido and first planting date and lowest was from control treatment. To use the
marginal rate of return (MRR%) as basis of recommendation, the minimum acceptable rate of
return should be between 50 to 100% (CIMMYT, 1988). In this study application of Torpido at
first planting date gave the maximum economic benefit (26941.78 ha-1). Therefore, on economic
grounds, application of Torpido at 250ml/100kg seed as seed dressing and sowing at late
Julywould be best and recommended for production of barley in the study area and other areas
with similar agro-ecological conditions.
Table 51. Partial budget and marginal rate of return analysis for management of barley shoot fly
through chemical and planting date
Treatments
Insecticidesplanting date
Control
P2
Control
P3
Control
P4
Control
P1
Dynamic
P2
Dynamic
P1
Dynamic
P3
Dynamic
P4
Apron Star
P2
Apron Star
P1
Apron Star
P3
Apron Star
P4
Procideplus
P1
Procideplus
P4
Procideplus
P3
Procideplus
P2
Joint
P2
Joint
P4
Joint
P3
Joint
P1
Torpido
P1
Torpido
P4
AGY by 10%
(kg ha-1)
GB (Birr ha-1)
2451.82
1966.87
2116.47
2066.61
2455.61
2997.69
2597.48
2949.21
2447.40
3565.01
3188.26
2400.05
3504.43
3565.73
3570.14
2221.41
2796.98
2448.54
2592.44
3467.49
3963.11
2589.70
17162.76
13768.08
14815.32
14466.24
17189.28
20983.86
18182.34
20644.44
17131.80
24955.08
22317.84
16800.36
24531.00
24960.12
24990.96
15549.84
19578.88
17139.78
18147.06
24272.40
27741.78
18127.92
TVC (Birr
ha-1)
0
0
0
0
475
475
475
475
550
550
550
550
690
690
690
690
800
800
800
800
800
800
NR (Birr ha)
1
17162.76
13768.08
14815.32
14466.24
16714.28
20508.86
17707.34
20169.44
16581.80
24405.08
21767.84
16250.36
23841.00
24270.12
24300.96
14859.84
18778.88
16339.78
17347.06
23472.40
26941.78
17327.92
439
Torpido
Torpido
P3
P2
2908.05
3353.99
20356.32
23477.94
800
800
19556.32
22677.94
AGY:adjusted grain yield, GB:groth benefitTVC:total variable cost, NR: net return
Conclusion
Analysis of the results revealed that interaction of the two factors (insecticides and planting
dates) significantly affected almost all parameters except thousand kernels weight, number of
tiller per plant and number of productive tiller per plant. Generally, all parameters recorded over
all treated plots were significantly higher than untreated/control plot. Thus using of insecticide
and adjusting planting date improved yield and yield components anddecreaseed barley shoot
fly infestation. The highest grain yield (4403 kg h-1) was obtained from combined application of
Torpidoand first planting date whereas the lowest barley shoot fly infestation recorded from
combined application of Torpidoand first planting date. The partial budget analysis revealed that
combined applications of Torpido insecticide and planting in the last week of Julygave the best
economic benefit 26941.78 Birr ha-1. Therefore, based this study it can be concluded that
combined application of this chemical and planting date can be recommended for farmers for
production of barley in the study area and other areas with similar agro-ecological conditions.
References
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Wheat Improvement Center). 1988. From Agronomic data to Farmer Recommendations: An
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CSA (Ethiopian Central Statistical Agency). 2016. Agricultural sample survey Report on area
and production for major crops 1: 1-118
MoA (Ministry of Agriculture), 1998. National Livestock Development Project (NLDP)
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440
Zerihun Dibaba