Modeling climate‐resilient maize production: Calibration and evaluation of DSSAT‐CERES‐model under changing climate

Abstract Climate change poses a serious threat to maize ( Zea mays L.) productivity in Ethiopia, where rainfed farming predominates. This study aimed to develop a climate‐resilient maize production system by modeling crop responses under varying environmental and management conditions using the DSSAT‐CERES‐maize model. The model was calibrated with experimental data from two sites during 2020–2021 and validated with data from three sites in 2022. Field experiments evaluated four nitrogen fertilizer levels (69, 138, 207, and 276 kg N ha − 1 ) and four planting densities (55,555, 62,500, 76,923, and 80,000 plants ha − 1 ) using the BH540 maize variety. Model performance showed strong agreement between observed and simulated data, with calibration normalized root mean square error (NRMSE) ranging from 6.43% to 7.66% and validation NRMSE from 5.23% to 6.08%. The model accurately simulated key growth parameters, such as leaf area index and biomass accumulation, confirming its robustness for predictive analysis. Simulations under future climate scenarios (SSP2‐4.5 and SSP5‐8.5) indicated that the highest maize yield (8.8 t ha − 1 ) would occur under high planting density (80,000 plants ha − 1 ) and nitrogen rate (276 kg N ha − 1 ) during the near‐term period of SSP2‐4.5, while the lowest yield (5.65 t ha − 1 ) was projected under the very low planting density and nitrogen rate (D1N1) during the mid‐century of SSP5‐8.5. These findings demonstrate that optimizing planting density and nitrogen fertilizer application can substantially enhance maize productivity and resilience to climate change, providing practical guidance for sustainable, climate‐smart maize management strategies to support food security in Ethiopia.