Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

• Published in: Journal of Integrative Agriculture 2019-07, Vol.18 (7), p.1547-1561
• Main Authors: LI, Zhen-hai, JIN, Xiu-liang, LIU, Hai-long, XU, Xin-gang, WANG, Ji-hua
• Format: Article
• Language: English
• Subjects: Agricultural sciences; DSSAT; EFAST; global sensitivity analysis; grain protein content; Life Sciences; wheat; yield
• ISSN: 2095-3119; 2352-3425
• DOI: 10.1016/S2095-3119(18)62046-5

A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning. A major challenge of model application is the optimization and calibration of a considerable number of parameters. Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters. In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters. The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables. The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently. Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.