Abstract

Rice yield forecasting is a crucial task for management and planning. A rice yield estimation system (RIICE) was developed based on the crop growth model ORYZA and SAR derived key information such as start of season (SOS) and leaf area growth rate. Results from study sites in Sikasso and Segou regions suggest that incorporating remote sensing data, specifically Synthetic aperture radar (SAR), into a process-based crop model improves the spatial distribution of yield estimates. From the findings, it was evident that the RIICE tool adequately predicted rice yield in the rice growing environments in Mali and can be used by the Ministry of Agriculture and private sector to plan investment to achieve rice self-sufficiency. Nevertheless, continued enhancement of the processing chain, with a specific focus on optimizing output delivery, is essential.