Variability in Estimating Crop Model Genotypic Parameters: The Impact of Different Sampling Methods and Sizes
Xintian Ma,
Xiangyi Wang,
Yingbin He,
Yan Zha,
Huicong Chen,
Shengnan Han
Affiliations
Xintian Ma
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiangyi Wang
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yingbin He
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yan Zha
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Huicong Chen
School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
Shengnan Han
Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
Generic parameter calibration for crop growth models is a very important step in model use. However, studies of the effect of sample size and sampling methods on the calibration and validation of genotypic parameters have seldom been conducted. Scientists commonly apply the hold-out (HO) method, by default, to deal with samples for calibration and validation in the practice of model use. In this paper, we applied the hold-out, cross-validation (CA), and bootstrapping (BS) methods with different sample sizes to analyze the influence of sampling methods and sample size on the final calibration results of genotypic parameters. The results showed that, (1) overall, CA and BS performed better than HO at most observation stations. However, there was great variability in the calibration and validation results obtained from the three methods. (2) Because of data quality differences, we could not conclude that the more samples there were, the greater the validation accuracy of the three methods. (3) The CV of the genotypic parameter values for the three methods and sample sizes varied greatly. Thus, when genotypic parameter calibration is performed, both sampling methods and sample size should be considered.
