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How much do standard area diagrams improve accuracy of visual estimates of the percentage area diseased? A systematic review and meta-analysis

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Abstract

Plant disease severity, when expressed as percentage area diseased, is commonly estimated visually without or with the aid of standard area diagram sets (SADs). It is generally thought that the use of SADs leads to more accurate and thus more precise estimates, but the degree of improvement has not been characterized in a systematic manner. We built on a previous review and screened 153 SAD studies published from 1990 to 2021. A systematic review resulted in a selection of 72 studies that reported three linear regression statistics for individual raters, which are indicative of the two components of bias (intercept = constant bias; slope = systematic bias) and precision (Pearson’s correlation coefficient, r), to perform a meta-analysis of these accuracy components. The meta-analytic model determined an overall gain of 0.07 (r increased from 0.88 to 0.95) in precision from using SADs. Overall, there was a reduction of 2.65 points in the intercept (from 3.41 to 0.76) indicating a reduction in the constant bias. Slope was less influenced and was reduced slightly (from 1.09 to 0.966), indicating a marginal reduction in systematic bias when using SADs. A multiple correspondence analysis suggested an association of less accurate, unaided estimates with diseases that produce numerous lesions and for which maximum severity of 50% is seldom reached. In contrast, estimates of severity for diseases that cause only a few lesions and those diseases where the lesions coalesce and occupy more than 50% of the organ surface had greater accuracy, which was most pronounced for specimen types other than leaves. By quantitatively exploring how characteristics of the pathosystem and how SADs affect precision and constant and systematic biases, we affirm the value of SADs for reducing bias and imprecision of visual assessments. We have also identified situations where SADs have greater or lesser utility as an assessment aid.

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Data availability

All data and R scripts generated for the analysis of the data and production of the figures are publicly available at: https://osf.io/t2yjw/.

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Funding

Clive H. Bock was funded by the USDA-ARS National Programs through CRIS project 6042-21220-014-00. Sarah J. Pethybridge was supported by the United States Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA) Hatch project NYG-625424. Emerson M. Del Ponte was supported by the National Council for Scientific and Technological Development (CNPq) through a Productivity Research Fellowship (PQ) project 310208/2019-0.

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Authors and Affiliations

  1. Departamento de Fitopatologia, Universidade Federal de Viçosa, Viçosa, MG, 36570-900, Brazil

    Emerson M. Del Ponte, Luis Ignacio Cazón & Kaique S. Alves

  2. Plant Pathology & Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY, 14456, USA

    Sarah J. Pethybridge

  3. United States Department of Agriculture—Agriculture Research Service—Southeastern Fruit and Tree Nut Research Station, Byron, GA, 31008, USA

    Clive H. Bock

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  1. Emerson M. Del Ponte
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  2. Luis Ignacio Cazón
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  4. Sarah J. Pethybridge
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  5. Clive H. Bock
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Contributions

EMD conceptualized the work, performed the analysis, and wrote the manuscript; CHB conducted experiments and wrote the manuscript; LIC and KAS collected the data, conducted experiments, and revised the manuscript; and SJP conducted experiments and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Emerson M. Del Ponte.

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Del Ponte, E.M., Cazón, L.I., Alves, K.S. et al. How much do standard area diagrams improve accuracy of visual estimates of the percentage area diseased? A systematic review and meta-analysis. Trop. plant pathol. 47, 43–57 (2022). https://doi.org/10.1007/s40858-021-00479-5

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