Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India

Abstract

Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.