International Journal of Environment and Climate Change

The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used tool for assessing crop performance under varying conditions. The present study focused on validating the CSM-CROPGRO-Pigeonpea model (DSSAT v4.7.5). This model utilizes inputs of weather, soil, management, and genotype data to simulate crop growth, development, and yield, thereby enabling the evaluation of management practices and climate impacts on pigeonpea productivity. We calibrated and tested the model for four varieties—Vipula, Rajeshwari, BDN 711, and ICPH 2740—using field data from Pune over two growing seasons (2017–18 and 2018–19). Model performance was evaluated for phenology, leaf area index (LAI), grain yield, and stalk yield using RMSE and NRMSE statistics. The results demonstrated a close agreement between simulated and observed values. The model exhibited excellent accuracy in predicting phenology, with NRMSE values for days to flowering initiation, 50% flowering, and physiological maturity ranging from 2.11–4.34%, 1.49–4.42%, and 4.29–7.05%, respectively. For growth and yield parameters, the model's performance was good, with LAI simulations showing moderate agreement (NRMSE: 14.91–19.87%). Predictions for final yield were reliable, with NRMSE values of 8.96–14.22% for grain yield and 12.78–17.77% for stalk yield across the four cultivars. In conclusion, the CSM-CROPGRO-Pigeonpea model effectively simulated growth, development, and yield for the tested pigeonpea varieties, confirming its suitability as a robust tool for agricultural decision-support in the semi-arid conditions of Pune.