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

Abstract

Soybean rust (SBR) in Brazil is controlled with fungicides, which have shown variable, eventually declining, efficacy. A Monte Carlo simulation framework was proposed to approximate profitability depending on the fungicide program's efficacy and total cost. 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 determined for tebuconazole (high decline), showed that the program may remain profitable during the first 5 to 7 years of use. Contrasting to cyproconazole, a fungicide that would be profitable during the entire decade. These simulations can be useful to aid in decision-making when planning fungicide programs. This approach 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/.

Modelling quantitative fungicide resistance and breakdown of resistant cultivars: designing integrated disease management strategies for Septoria of winter wheat

“…We have considered full-dose applications of fungicide here; future work could include an analysis of dose choice and optimise for number of applications and dose in each application. A basic economic analysis could be included by accounting for the cost of each fungicide application ( Alves et al, 2021 ; van den Bosch et al, 2020 ). By extending the model to include two fungicides we could address questions on fungicide mixtures and whether mixtures outperform alternations in the case of quantitative resistance.…”

“…something analogous to the so-called ‘Merry Dance’ ( Oliver, 2016 ). There are many other crop diseases which are controlled by azole fungicides, for example powdery mildew, brown rust in wheat crops, citrus fruit mould and soybean rust ( Price et al, 2015 ; Alves et al, 2021 ). There are also pathogens (including Z. tritici ) controlled by other fungicides to which resistance is quantitative, for example demethylation inhibitors ( Blake et al, 2018 ).…”

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative

“…For example, in earlier years acceptable control could be obtained using low doses, and dose only increased once necessary to achieved desired levels of control. An economic analysis accounting for fungicide costs would also be interesting ( Alves et al, 2021 ; van den Bosch et al, 2020 ). This approach would help us understand how to delay resistance development whilst maintaining good crop and economic yields for as long as possible.…”