No abstract

To improve the applicability of crop models, this study compared two algorithms for optimizing the single objective parameters of the spring wheat in the dryland grain growth sub-model to identify the more efficient algorithm for application in future model parameter optimization. Based on field experiments from 2015 to 2021 in Gansu Province, this study combined weather data and yearbook yield data from 1984 to 2021 to optimize parameters related to grain growth of spring wheat in dryland based on the next-generation APSIM using two algorithms: the Nelder–Mead simplex algorithm and the DREAM-zs algorithm. The results were as follows: the optimization results of both algorithms were the same, but the DREAM-zs algorithm converged faster; the optimized parameters for the grain growth stage of Dingxi35 spring wheat were: a grain number per gram stem of 25 grains, an initial grain proportion of 0.05, and a maximum grain size of 0.049 g; after optimization, the root mean square error (RMSE) of observed and simulated yield values decreased from 186.84 kg/hm2 to 115.71 kg/hm2, and the normalized root mean square error (NRMSE) decreased from 10.33% to 6.40%. The optimized results were consistent with the growth and development process of wheat and had high applicability.