Performance Evaluation of AquaCrop and DSSAT-CERES for Maize Under Different Irrigation and Manure Application Rates in the Himalayan Region of India

The agricultural modeling sector faces a lot of challenges when it comes to the evaluation of “what if” scenarios due to the immense data requirement of common models specially the crop coefficients which are not usually available for all varieties. The present study assesses the performance of two commonly used crop models: AquaCrop (less data requiring model) and DSSAT-CERES (data-intensive model) for Maize (Zea mays) at higher Himalayan region of India. Model calibration and validation were done based on the combination of different irrigation and farmyard manure application rates. Furthermore, model comparison was done post-validation with field experimental data by comparing grain yield, total biomass yield, harvest index, total soil water content and leaf area index (LAI). Both AquaCrop and DSSAT-CERES underestimated yield by 1.0 and 0.2%, respectively. The corresponding models underestimated total aboveground biomass by 0.15 and 1.7%; however, DSSAT-CERES overestimated LAI by 0.5%, whereas AquaCrop underestimated by 0.1%. Statistical tests for all the output variables indicate insignificant variation among the simulated and observed values. Results suggest that both AquaCrop and DSSAT-CERES are suitable for higher altitudes in the Himalayan region of India. However, AquaCrop being parsimonious with relatively incomparable results to DSSAT-CERES and low data requirement is recommended.