Crop models can accurately estimate crop growth, biomass yield (BY) and grain yield (GY) with a priori information of the crop, soil properties and water management. Generation of new knowledge through traditional agricultural practices is not possible to meet the requirements for novel agro-technologies and they are generally season specific, expensive and time consuming. Hence, the CERES (Crop Environmental Resource Synthesis) model was calibrated using the data of 2009 and validated with the data of 2010 acquired from the field data of WTC, IARI, India. Irrigation applications comprised rainfed, i.e. no irrigation (I₁), irrigation at 50% of field capacity (FC) (I₂), at 75 % FC (I₃) and 100% FC or full irrigation (I₄). Nitrogen levels were: no nitrogen (N1), 75 kg ha^-1 (N₂) and 150 kg ha^-1 (N₃). Model performance statistics of model efficiency (E), root mean square error (RMSE) and normalized root mean square error (NRMSE) were applied to evaluate the model performance. Model calibration for simulation of GY and BY provided prediction error statistics of 0.78<E<0.84, 0.238<RMSE<0.70 t ha^-1 and 6<NRMSE<7 %, respectively for all irrigation levels. Also, the model was validated for simulation of GY and BY for all treatment levels with the prediction error statistics of 0.86<E<0.88, 0.36<RMSE<0.86 t ha^-1, 0.95<R²<0.98 and 6<NRMSE<8%. Nonetheless, it was observed that the CERES-maize model could be applied to estimate yield and biomass under the regional situations with reasonable accuracy.