Seasonal and Monsoon Forecasting for Agricultural Planning: Bridging Climate Prediction and Farm-Level Decision-Making

Accurate seasonal and monsoon forecasting is critical for agricultural planning in monsoon-dependent regions, where the timing, intensity, and distribution of rainfall directly influence sowing schedules, irrigation management, and crop yield outcomes. This study examines the application of seasonal-to-subseasonal (S2S) forecasting techniques, integrating dynamical climate models with statistical and machine learning approaches, to improve the lead-time and reliability of monsoon predictions for agricultural end-users. Key predictors, including El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and regional sea surface temperature anomalies, are analyzed for their teleconnections with monsoon onset, withdrawal, and intraseasonal variability. The forecasting framework is evaluated against historical rainfall and crop yield data to assess its skill in predicting critical agricultural windows, such as optimal sowing dates and periods of dry spell risk. Results indicate that combining large-scale climate indices with localized meteorological data significantly enhances forecast accuracy compared to conventional climatological averages, offering actionable lead times of several weeks to months. The study further discusses the translation of these forecasts into farmer-oriented advisories, highlighting challenges in communication, uncertainty representation, and adoption at the field level. Findings underscore the potential of integrated seasonal forecasting systems to reduce climate-related agricultural risk, support proactive farm management, and improve resilience to monsoon variability. This work contributes to the broader goal of aligning climate science outputs with practical decision-support tools for sustainable agriculture in vulnerable regions.