Assessment of Crop Geometry and Nitrogen Management Effects on Rabi Qpm Yield Using Simulation

High production potential and financial benefits of Rabi maize over conventional crops like rice and wheat have made it an essential crop in West Bengal. Accurate crop modeling is essential to assess the effects of climate change and optimize crop management. The Decision Support System for Agro-technology Transfer (DSSAT v-4.6) was used in this work to model the growth of maize. Locally cultivated varieties needed to be calibrated and validated. Based on first-season experimental data, genetic coefficients were computed using GENCALC and the GLUE module in DSSAT. When the model was validated using data from the second season, it produced predictions for phenology, biomass, leaf area index (LAI), and yield that were adequate. The simulated flowering and maturity stages closely matched observed values, with minor deviations falling under statistically significant bounds (p < 0.05). Model validation results showed a strong correlation between observed and simulated yield data (R ² = 0.92, RMSE = 85.4 gm ^-2 ). The range of simulated yields was 787.77-1223.07 gm ^-2 in 2021 and 765.08-1161.67 gm ^-2 in 2022, aligning well with observed data. The findings indicate that increasing plant density and optimizing nitrogen levels (S ₃ and N ₅ treatments) significantly enhanced productivity in Cooch Behar. The calibrated DSSAT model proves to be a reliable tool for evaluating the effects of agronomic management practices and formulating sustainable maize production strategies in diverse agro-climatic settings of West Bengal.