EFAST, we analyzed the sensitivity and uncertainty of crop model(APSIM-Wheat) brought by the cultivar, soil and biological parameters in different climate zones and yield levels(i.e. And then using the global sensitivity analysis method i.e. In this study, we collected crop experimental data in the locations of Luancheng, Hebei Province Changwu in the Chinese Loess Plateau, Yanting in the Sichuan Basin and Wulanwusu in Xinjiang Autonomous Region. However, the performance of crop model is variable in different climatic zones due to the heterogeneity of climate and soil characteristics. Previous studies of sensitivity analysis focused on a single site. EFAST is robust and has lower computational cost than the others. Several global sensitivity methods including Morris, variance-based, linear regression, FAST(Fourier amplitude sensitivity test) and EFAST(extended Fourier amplitude sensitivity test) are widely used in the parameter analysis. It can quantify the influence of single parameter and the interactions among different parameters. Global sensitivity analysis is a better method for exploring the entire multi-dimensional parameters simultaneously. It cannot detect the interactions among the parameters and suffers some shortcomings such as a heavy dependence on the input parameters and instability for non-linear models. Local sensitivity analysis is called derivative-based or one-at-a-time method, which changes only one parameter at a time around a basis point while keeping other parameters constant. the local sensitivity and the global sensitivity. There are 2 categories of parameter sensitivity analysis methods, i.e. Furthermore, uncertainty and sensitivity analysis can improve the reliability of model prediction. It is essential to quantify the parameters' sensitivity and the results' uncertainties of crop model for model calibration and application. Uncertainties of crop model are mainly from model structure, parameters' sensitivity and input error.
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