RICE-MAP: A Prototype Decision Support System for Climate-Informed Rice Security in Southeast Asia

Southeast Asia is a cornerstone of global rice security, contributing substantially to regional and international food supply chains. Accurate and timely information on rice yield is essential for effective agricultural planning, trade policy formulation, and food security management. However, conventional approaches to yield estimation, which often rely on historical trends or sparse in situ observations, are insufficient for capturing the complex interplay between climate variability, extreme weather events, and crop dynamics. The increasing frequency and intensity of climate shocks, including droughts, floods, and heatwaves, underscore the need for an advanced rice yield forecasting system. The development of a decision support system--RICE-MAP (Rice Information & Climate Evaluation- Monitoring And Prediction), integrates state-of-the-art climate forecasts with machine learning techniques to provide dynamic, high-resolution predictions of rice yield under current and future climate scenarios. RICE-MAP synthesizes satellite-derived datasets, global climate model outputs, and agricultural statistics to monitor and predict yield variability across rice-growing regions in Southeast Asia. By leveraging spatially and temporally resolved climate variables with machine learning models, the system provides lead-time-specific yield predictions, accompanied by rigorous evaluations of forecast performance using established metrics. The system’s capabilities are accessible through a user-friendly dashboard, designed to facilitate decision-making for policymakers, agricultural planners, and other stakeholders. Case studies in Southeast Asia demonstrate the system’s potential of integrating climate science and artificial intelligence to enhance climate resilience and adaptive capacity in the agricultural.

Keywords: Decision Support System; Rice yield forecast; climate shocks.