A new standard area diagram set for assessment of severity of soybean rust improves accuracy of estimates and optimizes resource use

• Published in: Plant pathology Vol. 69; no. 3; pp. 495 - 505
• Main Authors: Franceschi, Vinicius T., Alves, Kaique S., Mazaro, Sergio M., Godoy, Cláudia V., Duarte, Henrique S. S., Del Ponte, Emerson M.
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01.04.2020
• Subjects: Acceleration; Accuracy; Crop yield; Disease control; Estimates; Evaluation; Experimental research; Fungal diseases; Glycine max; Leaf area; Leaf fall; Phakopsora pachyrhizi; phytopathometry; Resource management; Rust fungi; Soybeans; Test procedures
• ISSN: 0032-0862; 1365-3059
• DOI: 10.1111/ppa.13148

Soybean rust (SBR), caused by Phakopsora pachyrhizi, is the most important yield‐damaging fungal disease of soybean due to severe reduction in healthy leaf area and acceleration of leaf fall. In experimental research, SBR severity is estimated visually aided/trained by a standard area diagram (SAD) developed and validated during the mid‐2000s (Old SAD). In this study, we propose a new SAD set for SBR with six true‐colour diagrams following linear increments (c. 15% increments) amended with four additional diagrams at low (<10%) severities, totalling 10 diagrams (0.2%, 1%, 3%, 5%, 10%, 25%, 40%, 55%, 70%, and 84%). For evaluation, 37 raters were split into two groups. Each assessed severity in a 50‐image sample (0.25%–84%), first unaided and then using either the Old SAD or the New SAD. Accuracy, precision, and reliability of estimates improved significantly relative to unaided estimates only when aided by the New SAD (accuracy >0.95). Low precision (<0.78) and a trend of underestimation with an increase in severity were the main issues with the Old SAD, which did not differ from unaided estimates. Simulation to evaluate the impact of the errors by different methods on hypothesis tests, showed that the new SAD was more powerful for detecting the smallest difference in mean control (e.g., 70% vs. 65% disease reduction) than the Old SAD; the latter required a 2‐fold increase in sample size to achieve the same power. There is a need to improve some SADs, taking advantage of new knowledge and technology to increase accuracy of the estimates, and to optimize both resource use efficiency and management decisions. The new amended linear 10‐diagram set outperformed the existing one with regards accuracy of visual estimates and required a lower sample size to detect differences in treatment efficacy.