Analyzing the Performance and Application of CERES-Wheat and APSIM in the Guanzhong Plain, China

Highlights APSIM and CERES-Wheat were calibrated and evaluated for winter wheat in the Guanzhong Plain, China. Both models performed well as the result of the calibration based on comprehensive field data. CERES-Wheat showed high sensitivity to field capacity. APSIM showed high sensitivity to nitrate at sowing. A crop model ensemble is better than a single model application, particularly for grain yield. Abstract . Cropping system models are useful tools to estimate the impact of climate and environment on agricultural production and to improve the management of agricultural systems. Numerous crop models are used worldwide to simulate wheat crop growth and predict yield. In this study, two process-based crop models (APSIM and CERES-Wheat) were assessed for simulating wheat crop growth in the Guanzhong Plain, China. The main objectives were to compare the performance of both models and perform sensitivity analysis. The possibility of reducing the model output uncertainty by estimating ensemble means was also explored. Crop models were calibrated and evaluated using local experimental data from three growing seasons (2009 to 2012), which encompassed 27 crop managements based on different levels of irrigation and nitrogen application. APSIM and CERES-Wheat performed well after calibration. The nRMSE for aboveground biomass, grain yield, LAI, canopy nitrogen, cumulative evapotranspiration, water use efficiency, and nitrogen fertilizer productivity was less than 21%. Sensitivity analysis of both models showed that grain yield in CERES-Wheat was more sensitive to field capacity. In APSIM, grain yield was sensitive to nitrogen application rate. Mean ensemble model results showed reduced uncertainty for simulated crop parameters, especially for differences between observed and simulated grain yield, which were reduced by up to 4.1%. Overall, the results suggest that APSIM and CERES-Wheat are both suitable for simulating wheat crop growth, development, and yield in the Guanzhong Plain. Furthermore, by using the ensemble modeling approach, decision-makers can be more confident in their planning, as the information will be based on multiple models’ results. Keywords: Crop model ensemble, Model calibration, Multiple crop models, Sensitivity analysis, Winter wheat.