Multi-Objective Co-Optimization of Parameters for Sub-Models of Grain and Leaf Growth in Dryland Wheat via the DREAM-zs Algorithm

The simulation accuracy of crop models is highly dependent on the proper calibration of key parameters. To enhance the applicability of the Next-Generation agricultural production systems sIMulator (APSIM NG) in dryland wheat production within the Loess Hilly Region, this study proposes a crop model parameter calibration framework that deeply integrates Morris and DREAM-zs methodologies. Morris was employed to conduct a global sensitivity analysis on parameters related to the APSIM NG dryland wheat grain and leaf growth sub-models. The DREAM-zs algorithm was then utilized for multi-objective collaborative optimization of key parameters. Results indicate that Morris excels at capturing nonlinear and coupled relationships among model parameters. Optimized key parameters include maximum grain size (0.055 g), radiation use efficiency (1.540 g·MJ⁻¹), and extinction coefficient (0.443). Post-optimization, the root mean square error (RMSE) and mean absolute error (MAE) for wheat yield decreased by 24.1% and 23.2%, respectively, while those for LAI decreased by 16.9% and 19.2%. This framework conserves computational resources and accelerates convergence when handling nonlinear internal model parameters and complex coupling relationships, providing technical support for the localized application of APSIM NG in the Loess Hilly Region of Northwest China.