Forecasting system in decision-making on the chemical control of Asian soybean rust

Abstract

Abstract Asian soybean rust (ASR; Phakopsora pachyrhizi) is one of the most important diseases of soybean that can reduce soybean yield. The frequent use of site-specific fungicides has led to the evolution of resistant populations of the pathogen, resulting in reduced effectiveness of chemical control. Thus, to reduce the number of fungicide sprays in the crop, disease forecasting systems are tools that could help in the decision-making process about the time for fungicide application. The aim of this study was to develop and validate a forecasting system for ASR to guide chemical control. Four experiments were carried out in three locations (Cerro Largo, Entre Ijuís and Passo Fundo) in the state of Rio Grande do Sul in southern Brazil. Treatments consisted of intervals of calculated severity values (CSV) between fungicide applications, in addition to control without application. Thousand-grain mass and yield (kg ha-1) were evaluated in addition to ASR severity. The data were subjected to analysis of variance using the F test and the means compared using the Tukey’s test at 5% error probability level. The results showed that the disease forecasting system allowed the yield maintenance in different locations and crop seasons, associated with a decrease in the number of fungicide sprays. Thus, we conclude that the proposed disease forecasting system is a viable tool for ASR management.