Journal of Agricultural Big Data ›› 2021, Vol. 3 ›› Issue (3): 13-22.doi: 10.19788/j.issn.2096-6369.210302

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Parameter Adjustment and Optimization Methods for The WheatSM

Xiafei Jin1(), Xianguan Chen1, Zhihong Gong2, Liping Feng1()   

  1. 1.College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
    2.Tianjin Climate Center, Tianjin 300074, China
  • Received:2021-07-20 Online:2021-09-26 Published:2021-12-22
  • Contact: Liping Feng;


The crop growth model is an effective tool to evaluate crop production, resource utilization, and climate change impact. The WheatSM (Wheat Growth and Development Simulation Model) has been applied to crop production optimization and management and has achieved good achievements. However, because of the large number of model parameters, it’s complicated to debug the model parameters. To determine the parameters of the WheatSM model quickly and accurately, it is necessary to simplify the parameter adjustment work of the model and promote its wide application in the field of agricultural meteorology. In this study, on the basis of automatic adjustment methods of crop model parameters at home and abroad, an automatic adjustment coupling system of WheatSM model parameters is constructed based on the PEST (Parameter Estimation) method. The phenology and yield parameters of the WheatSM model were optimized automatically. Shangzhuang, Beijing was selected as a representative site.This study compared the optimization results with the trial-and-error simulation results, andused the automatic optimization method and trial-and-error method to adjust wheat phenology parameters and yield parameters for wheat growth model WheatSM, based on the meteorological data, soil data of the test sites, and the test data of different sowing dates of winter wheat from 2014 to 2016. The results show that the PEST method has high precision and good simulation effect for automatic adjustment and optimization of model parameters. The error of simulated phenology was less than 7 days, and the error of simulated yield was less than 228.63 kg·hm-2. The PEST method has the advantages of being less time consuming and allowing for the simultaneous batch processing of data. Using this automatic parameter adjustment system can reduce the workload of parameter calibration, save model operation time, simplify work complexity, and obtain higher simulation accuracy. This study provides a convenient method for WheatSM parameters automatic optimization and a theoretical reference and guidance for improving the efficiency and accuracy of crop model parameters calibration.

Key words: wheat, PEST, WheatSM model, parameter optimization, phenology, yield, crop growth model

CLC Number: 

  • S152