Coupling a dynamic epidemiological model into a process-based crop model to simulate climate change effects on soybean target spot disease in Brazil

Abstract

The study of factors affecting soybean development is crucial for informed decision-making and risk analysis, particularly for a crop of such economic significance to Brazil. With the increase in the world population, the demand for soybean by-products is expected to rise, amid a backdrop of climate change that threatens agricultural production by altering the fundamental conditions of plant physiological development, as well as the reducing factors such as pests, diseases, and weeds. In this context, evaluating the new epidemiological conditions of phytopathogens in future scenarios is important for making the best possible decisions. Currently, one of the most significant diseases affecting soybean cultivation in Brazil is target spot. We aimed to assess the changes that will occur with target spot disease in soybean yield, focusing on its severity across three major regions of Brazil. The major challenge of accurately modeling climate change impacts on target spot epidemiology was addressed by modifying a generic epidemiological model for the target spot fungus and dynamically coupling it with the DSSAT/CROPGRO-Soybean model, enabling the simulation of plant-pathogen interactions under various climate scenarios. The results showed a significant increase in soybean yield across all scenarios and future periods in the three major regions. The disease severity also changed over time, increasing until 2039 and then declining until 2100 in all scenarios and regions. The SSP1-RCP2.6 scenario stood out as the most stable, with smaller declines, and relative increases from 1981 to 2019 of 7.9 % (2020–2039), 9.8 % (2040–2069), and 4.8 % (2070–2100) in the North region; 16.35 % (2020–2039), 13.1 % (2040–2069), and 14.45 % (2070–2100) in the Central region; and 3.6 % (2020–2039), 6.3 % (2040–2069), and 4.02 % (2070–2100) in the South region.