Wheat head blast is a major disease of wheat in the Brazilian Cerrado. Empirical models for predicting epidemics were developed using data from field trials conducted in Patos de Minas (2013 to 2019) and trials conducted across 10 other sites (2012 to 2020) in Brazil, resulting in 143 epidemics; each being classified as either outbreak (≥ 20% head blast incidence) or non-outbreak. Daily weather variables were collected from the NASA Power website and summarized for each epidemic. Wheat heading date (WHD) served to define four time windows, each comprising two seven-day intervals (before and after WHD), combined with weather-based variables, resulting in 36 predictors (nine weather variables × four windows). Logistic regression models were fitted to binary data, with variable selection using LASSO and sequentially best subset analyses. The models were validated using LOOCV, and their statistical performance was compared. One model was selected, implemented in a 24-year series, and assessed by an experts and literature. Models with two to five predictors showed accuracies between 0.80 and 0.85, sensitivities from 0.80 to 0.91, specificities from 0.72 to 0.86, and AUC from 0.89 to 0.91. The accuracy of LOOCV ranged from 0.76 to 0.81. The model applied to a historical series included temperature and relative humidity in pre-heading date, as well as post-heading precipitation. The model accurately predicted the occurrence of outbreaks, aligning closely with real-world observations, specifically tailored for locations with tropical and subtropical climates.
Keywords: Causal Agent; Crop Type; Epidemiology; Field crops; Fungi; Subject Areas; cereals and grains; climate/weather effects; disease warning systems.