Soybean rust (SBR) is a disease of significant impact to Brazilian soybean production. Twenty-four locations in a major growing region in southern Brazil, where long-term (30 years) weather information was available, were selected to estimate the risk of SBR epidemics and identify potential predictors derived from El Niño 3.4 region. A rainfall-based model was used to predict SBR severity in an "epidemic development window" (the months of February and March for the studied region) in the time series. Twenty-eight daily simulations for each year-location (n = 720) were performed considering each day after 31 January as a hypothetical detection date (HDD) to estimate a severity index (SBRindex). The mean SBRindex in a single year was defined as the 'growing season severity index' (GSSI) for that year. A probabilistic risk assessment related GSSI and sea surface temperatures (SST) at the El Niño 3.4. region (here categorized as warm, cold or neutral phase) in October-November-December (OND) of the same growing season. Overall, the median GSSI across location-years was 34.5%. The risk of GSSI exceeding 60% was generally low and ranged from 0 to 20 percentage points, with the higher values found in the northern regions of the state when compared to the central-western. During a warm OND-SST phase, the probability of GSSI exceeding its overall mean (locations pooled) increased significantly by around 25 percentage points compared to neutral and cold SST phases, especially over the central western region. This study demonstrates the potential to use El Niño/Southern Oscillation information to anticipate the risk of SBR epidemics up to 1 month in advance at a regional scale.
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