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Research Paper | Geoinformatics | India | Volume 13 Issue 4, April 2024
Evaluating Soybean Yield Using Multi - Model Ensembles in Osmanabad District (Kharif - 2023)
Priyanka Shamraj | Ashutosh Pawar [2] | Upasana Singh [2] | Bhargav Sonawane
Abstract: A study was conducted at the Semantic Technologies and Agritech Services, Pvt. Ltd., GIS, and Remote Sensing Team in Pune during the Kharif - 2023 season. The methodology outlined in the YESTECH manual, under the Pradhan Mantri Fasal Bima Yojana (PMFBY), was diligently followed. Osmanabad district has been experiencing significant weather - based yield losses in recent years. This case study aimed to estimate the yield of soybean crops for agricultural stakeholders, insurance companies, and government policies at the Revenue Circle level (RC). A multimodal approach was adopted over a single - model yield estimation approach to ensure the ensemble yield for precise forecasting/estimating of crop yield. The accuracy achieved was within a certain percentage, with Root Mean Square Error (RMSE) measured was less than ?30% at the RC level. Consequently, the overall findings suggest that employing such models for yield estimation represents one of the best approaches for decision - making among insurance stakeholders, particularly in rainfed regions where adverse impacts on soybean productivity have been observed under various climate change scenarios.
Keywords: Remote Sensing, GIS, Net Primary Productivity (NPP), Machine Learning, Decision Support System for Agrotechnology Transfer (DSSAT - 4.8), Soybean, Osmanabad, Yield Simulation, Revenue Circle, Soybean Productivity
Edition: Volume 13 Issue 4, April 2024,
Pages: 248 - 256