Macroscale soybean yield estimation using multi-model approach in Beed district of Maharashtra, India

This study focuses on estimating soybean crop yield in Beed district of Maharashtra following the methodology outlined by the government norms in insurance aspects. The research addresses significant weather-induced yield losses in the region and targets Revenue Circle (RC) level assessment using a multi-model approach, incorporating various models for precise yield forecasting. The achieved accuracy, measured with root mean square error (RMSE) below ±30% at the RC level, demonstrates the effectiveness of the ensemble approach. The findings highlight the utility of such models in decision-making for agricultural stakeholders, insurance companies, and government policies, especially in rainfed regions facing soybean productivity challenges under diverse climate change scenarios. Keywords: Remote sensing, GIS, Net primary productivity (NPP), Machine learning, DSSAT, Yield simulation, Revenue circle, Soybean productivity.