Profitability of fungicide applications for managing soybean rust in scenarios of variable efficacy and costs: A stochastic simulation

Abstract

AbstractSoybean rust (SBR) in Brazil is controlled with fungicides, which have shown variable, sometimes declining, efficacy over time. A Monte Carlo simulation framework was implemented as a decision tool and to estimate the probability for a fungicide programme being profitable depending on efficacy and total cost defined by the user. Probability distributions were fitted to slopes and intercepts of the disease–yield relationship and severity in the untreated plots reported in the literature, as well as historical records of soybean price. Simulations of disease reduction conditioned to predefined control efficacy and total application costs were split into scenarios that combined two categories of severity (high and low) and two attainable yield classes (high and low). These categories were defined based on the median of severity (57.8%) and median of the intercept (yield when severity is zero, 2995.1 kg/ha). Probability matrices were constructed relating fungicide efficacy and costs. A higher frequency of break‐even events occurred in scenarios of high disease pressure and higher yield. Yearly simulations, starting with 79.4% efficacy, assuming two rates of decline previously determined for tebuconazole (high decline rate), showed that the programme may remain profitable during the first 5 to 7 years of use, in contrast to cyproconazole (low decline rate), a fungicide that would be profitable during the entire decade. This approach was shown to be useful and can be adapted to other diseases of soybean and other crops, as long as damage functions are available. An interactive web app was developed to perform the simulations accessible at alvesks.shinyapps.io/rusty‐profits/.