Weather‐Driven Dynamics of Fruit Rot Disease in Arecanut—A Time Series Approach Using <scp>ARIMA</scp> and  <scp>SARIMA</scp> Models

ABSTRACT Arecanut fruit rot disease (FRD) caused by Phytophthora meadii poses a significant threat to arecanut production in Southeast Asia. This study hypothesised that weather parameters significantly influence the temporal progression of FRD and time‐series models could be effectively used for forecasting. To test this, we analysed the relationship between key weather variables—temperature, relative humidity, rainfall and wind speed—and FRD severity across three agroclimatic regions of Karnataka, India (Malnad, Coastal and Maidan), during 2018 and 2019. Correlation and multiple linear regression analyses identified temperature and rainfall as significant positive predictors of FRD severity, while wind speed showed a negative association. The regression models explained a moderate level of variance with R 2 values of 0.145 (2018) and 0.15 (2019). To model and forecast disease progression, we employed time‐series analyses using ARIMA and SARIMA models. The ARIMA model effectively captured short‐term fluctuations, forecasting FRD severity up to 6 weeks in advance, with predicted ranges of 61.8%–78.4% (Malnad), 44.0%–38.4% (Coastal) and 13.4%–18.5% (Maidan). In contrast, SARIMA better captured seasonal trends and provided longer‐term forecasts, predicting severity values of 12.4%, 59.8% and 45.2% in Maidan, Malnad and Coastal regions, respectively. This is the first study to apply both ARIMA and SARIMA models for forecasting arecanut FRD. The findings highlight the significant influence of climatic factors on disease dynamics and advocate for region‐specific disease management strategies that incorporate predictive modelling tools for timely interventions.