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The study aimed to propose models to predict Asian soybean rust epidemics based on both the occurrence of the disease in the period between seasons and the climate variability index, which is influenced by the El Nino Southern Oscillation (ENSO) phenomenon. The data used to develop these models were obtained from 11 crop seasons, distributed among six regions of Parana and twelve regions of Mato Grosso which was determined by the National Institute for Space Research (INPE). The three-dimensional model was obtained from linear and quadratic polynomial regression analyses, considering the following climatic variables as independent (Y axis): Rainfall (PP), Standardized Precipitation Index (SPI), Southern Oscillation Index (SOI) and Temperature on the sea surface (SST Nino 3.4). The independent variable (X axis) was the number of occurrences of rust in the off-season, and the dependent variable (Z axis) was defined as rust occurrences during the season, which were reported by the Anti-rust Consortium. The best model that explains the epidemic of the disease during the season in Parana state was composed by Rainfall or SST Nino 3.4 variable as the Y axis. The best model for Mato Grosso state used SST Nino 3.4 or SOI variable. The variable number of occurrences in the off-season significantly influenced the model, indicating the potential use of this variable and meteorological variables on a macro scale to predict epidemics even before the start of the season.

journal of agricultural science

Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil