Evaluating the Predictive Accuracy of the Weather-Rice-Nutrient Integrated Decision Support System (WeRise) to Improve Rainfed Rice Productivity in Southeast Asia

Abstract

The weather-rice-nutrient integrated decision support system (WeRise) is an information and communications technology (ICT)-based tool developed to improve rainfed rice productivity. It integrates localized seasonal climate prediction based on the statistical downscaling of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) ocean-atmosphere coupled general circulation model and real-time weather data with a crop growth model (ORYZA), to provide advisories on the optimum sowing timing using suitable varieties. Field validations were conducted to determine the applicability of WeRise and SINTEX-F in North Sumatra and West Nusa Tenggara, Indonesia, and Iloilo, Nueva Ecija and Tarlac, Philippines. Results showed that downscaled SINTEX-F outputs were applicable in these target provinces. Hindcast analysis using these outputs also showed a good model performance against locally observed historical weather data for both countries. Moreover, the on-farm experiments showed that higher grain yields were obtained using WeRise advisories on optimum sowing timing compared to the farmers’ sowing timings. Improved fertilizer recovery rates were also observed when WeRise advisories were followed. The results imply that WeRise can improve rainfed rice productivity in Southeast Asia. Further validation is recommended to determine its applicability in more countries of Southeast Asia.

Keywords

• climate crisis
• general circulation model (GCM)
• climate-smart agriculture
• digital agriculture
• information and communications technology (ICT)