ABSTRACT Alternaria blight caused by Alternaria brassicae is a major constraint to rapeseed‐mustard production in India, with disease development strongly influenced by weather conditions and varietal susceptibility. Disease progression was quantified in three rapeseed‐mustard varieties (Rohini, PBR‐357 and RL‐1359) over two consecutive cropping seasons using field observations, integrated statistical and time‐series modelling approaches. Percent disease index (PDI) increased progressively during the season, reaching peak values up to 68% in susceptible varieties (Rohini and RL‐1359), while the moderately resistant variety PBR‐357 consistently maintained lower severity (< 20%). Disease severity showed strong positive correlations with maximum and minimum temperatures ( r = 0.83–0.92) and moderate negative correlations with relative humidity ( r = −0.57 to −0.69). Cross‐correlation analysis revealed that lagged weather variables (particularly minimum temperature and relative humidity at 1‐week lag) exhibited stronger associations with disease severity than concurrent conditions. Multiple regression models explained a high proportion of variability in disease severity ( R 2 = 0.93–0.97), with minimum temperature emerging as a significant predictor ( p < 0.05). Time‐series forecasting using SARIMAX models incorporating lagged meteorological variables provided robust short‐term predictions, with lower AIC values (1.97) and stable residual diagnostics across all varieties. Genotype × Environment analysis confirmed significant varietal differences, with PBR‐357 exhibiting consistently lower disease severity across weekly environments ( p < 0.001). Overall, the integration of lag‐aware weather predictors, genotype‐specific responses and predictive modelling provides a quantitative framework for forecasting Alternaria blight and supporting a weather‐based disease management in rapeseed‐mustard.
Loona et al. (Fri,) studied this question.