Abstract: Process-based crop models use a large number of variety and soil parameters to simulate dynamic changes of crop growth and soil moisture. Many of the parameters are difficult to measure directly for different crop varieties or environments, recalibrations are often needed. Determining the importance of specific parameters to the model outputs is helpful to simplify the crop model calibrations. Sensitivity analysis (SA) can quantify the impact of input parameters on the model outputs and is helpful for model parameterizations. This study aimed to obtain model parameters of DSSAT-CROPGRO-Cotton model for irrigation schedule optimization of cotton in Xinjiang, China through sensitivity and uncertainty analyses. Based on the field cotton experiments in Shihezi Region of Xinjiang Uygur autonomous region, the Morris method and extended Fourier amplitude sensitivity test (EFAST) method were applied to analyze the sensitivity of six outputs of the CROPGRO-Cotton model to the variety and soil parameters at three irrigation levels. The model outputs included days of initial flowering and maturing, seed cotton yield, aboveground dry biomass, maximum leaf area index and evapotranspiration. In addition, the correlation between the two methods was analyzed and the uncertainty analysis was conducted for the model outputs from the EFAST method. Results showed that EFAST method was better than Morris method in sensitivity test. The Spearman rank correlation analysis showed that the correlation coefficient was between 0.87 and 0.93 for the aboveground dry biomass and maximum leaf area index, and between 0.66 and 0.81 for the days of maturing, seed cotton yield and evapotranspiration. The numbers of sensitive parameters was smaller from Morris method than EFAST method, indicating that Morris method may oversimplify sensitivity problem. Sensitivity and uncertainty analyses indicated that irrigation levels had no significant effects on the days of initial flowering and a simplistic parameter sensitivity issue existed for simulation of the days of initial flowering and maximum leaf area index. Soil parameters in different soil layers had different effects on the model outputs. The days of maturing were more sensitive to the soil parameters in soil layer of 40-80 cm, but the aboveground dry biomass and evapotranspiration were more sensitive to the soil parameters in soil layer of 80-120 cm. The maximum leaf area index and evapotranspiration were both overestimated to a certain extent, it was necessary to make an improvement so as to enhance the simulation accuracy before this model could be applied in Xinjiang. The most sensitive parameters for cotton mature days simulation was time between plant emergence and flower appearance (EMFL), time between the first flower and first seed (FLSD) and time between the first seed and physiological maturity (SDPM). The most sensitive parameters for seed cotton yield simulation was maximum fraction of daily growth that was partitioned to seed and shell (XFRT) and the most sensitive parameters for aboveground dry mass are drainage rate (SLDR) and field capacity in soil layer from 80 to 120 cm (SDUL3). The most sensitive parameters for evapotranspiration simulation was SDUL3. The parameter of seed filling duration for pod cohort at standard growth conditions (SFDUR) was not sensitive for all outputs and thus could be set as constant values. Surface albedo (SALB), runoff curve number (SLRO) and saturated hydraulic conductivity (SSKS1) were only sensitive to mature days. The results above would help to improve simulation efficiency and precision of CROPGRO-Cotton model in Xinjiang region.