Calibrating DSSAT CERES-Rice Model to Assess Management Practices And Climate Change Impacts on Rice Yield in Dapoli, Maharashtra.

Rice is a globally significant staple crop, yet its production is increasingly threatened by climate variability and shifting agronomic conditions. This study employs the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Rice model to simulate and assess rice yield dynamics in the Dapoli region of Maharashtra. Calibration and validation were conducted using historical datasets from the 2011 Kharif season and tested against observed yields from 2020 and 2021. Key input parameters, including daily weather records, soil properties, crop management practices, and genetic coefficients, were incorporated to enhance simulation accuracy. Results demonstrate that an optimal planting density of two plants per hill maximized yield potential, while densities exceeding this threshold resulted in yield declines due to intensified resource competition. Furthermore, strategic irrigation interventions substantially improved productivity, particularly for rain-fed Karjat-3 rice varieties. The findings underscore the critical interplay between planting density and irrigation strategies in optimizing rice production under varying climatic conditions. This research provides valuable insights for farmers and policymakers, advocating for precision agriculture and adaptive management practices to bolster climate resilience and sustainable rice cultivation in coastal agro-ecosystems.