Abstract: Global sensitivity analysis generally refers to a sort of numerical approach for the evaluation of the variation in the output of a model, as the input parameter varies. In ecosystem model based on dynamic processes, the parameter verification can be difficult in the model application, due to a complex structure, multiple input parameters, and strong spatial variability. In this case, the parameter sensitivity analysis can effectively identify the factors of main effect, and thereby to improve significantly optimization of parameters and models. Given a fixed value to an insensitive parameter, it is expected to enhance the predict accuracy, while improve computing efficiency of model calibration, verification, and simplification. In this study, a Vegetation Interface Processes (VIP) model was used to simulate the nitrate content of soil in the North China Plain, in order to evaluate the efficiency of different global sensitivity analysis methods in screening sensitive parameters of agro-ecosystem models. Eight sensitivity analysis methods were selected, including six qualitative and two quantitative sensitivity analysis methods, provided by Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE). The results show that: 1) The screening information of sensitivity parameter differed greatly in two method of Spearman's correlation coefficient (SPEA) and the Gaussian Process (GP) from others, when verifying the effectiveness of six qualitative sensitivity analysis methods. The SPEA method cannot effectively identify the decomposition rate of microbial pool, whereas, the GP method cannot identify the decomposition rate of structural litter pool. The similar sensitive parameter screening can be found in the Morris method, multiple adaptive regression spline (MARS), Delta Test (DT), and Sum of Tree (SOT) methods; 2) Sensitive parameters of soil nitrogen cycle that screened by DT, MARS, Morris, and SOT methods were: the potential nitrification rate, urea hydrolysis rate, Michaelis constant, microbial nitrogen-carbon ratio, slow humus nitrogen-carbon ratio, decomposition rate of microbial pool, decomposition rate of structural litter pool, and decomposition rate of metabolic litter pool. In contrast, the SPEA and GP screening showed that the insensitive parameters were the potential nitrification rate, and urea hydrolysis rate, indicating inconsistence with other qualitative sensitivity analysis; 3) When analyzing the efficiency of sensitivity methods, it was found that the sampling methods in the Orthogonal Array (OA) and Orthogonal Array based on Latin Hypercube (OALH) were suitable for Multivariate Adaptive Regression Splines (MARS), indicating that the required sample size was 361. In the DT and SOT methods, the Monte Carlo (MC) was the most suitable for the DT and SOT sensitivity analysis, where the minimum sample sizes were required 425 and 510, respectively. Compared with MARS and DT sensitivity analysis methods, the SOT required larger sample size in the process of screening sensitive parameters. The OALH sampling was the most suitable for the McKay sensitivity analysis methods, while requiring the least sample points. In addition, the minimum sample size can be achieved in the Morris and Sobol' method with 340 and 810, respectively. In summary, the qualitative sensitivity analysis method required less sample size than the quantitative sensitivity method, but it cannot be used to quantitatively describe the sensitivity of parameters during screening. Therefore, a recommendation during this time can be made that, in the complex system models with many parameters, the qualitative sensitivity analysis method can be used first to screen the preliminary sensitivity parameters at a low computational cost, and then the quantitative analysis of selected sensitive parameters by quantitative sensitivity analysis method.