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

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

BHARGAV SONAWANE, UPASANA SINGH, PRIYANKA SHAMRAJ* and ASHUTOSH PAWAR

Department of Remote Sensing and GIS, Semantic Technologies and Agritech Services Pvt. Ltd., Pune.

*Corresponding Author: priyanka.shamraj@semantictech.in

ABSTRACT

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.