Abstract: 【Objective】 Analyses of input-parameters sensitivity and yield uncertainty of jujube growth simulation are very significant steps in the hope of providing suggestions for localization and regionalization of jujube growth model to serve as improving the accuracy and efficiency of model simulation prediction. 【Methods】 In this paper, the Extended Fourier Amplitude Sensitivity Test (EFAST) and Monte Carlo (MC) method were used to analyze the input-parameter sensitivity and output-parameter uncertainty of a new jujube-yield prediction Denitrification-Decomposition (DNDC) model, which was generated by the crop-type generator of the DNDC model system, in a modern-agriculture demonstration area located in Kunyu city, Xinjiang Uygur Autonomous Region. 【Results】 The results showed that the jujube-crop parameters including grain fraction of total biomass (Gfra), maximum grain yield (MaxY), thermal degree days (TDD) and water requirement (WaterR), the soil parameters including field capacity (FC) and porosity (POR), and the field management parameters including Irrigation (IrrAm), Fertilizing amount (FerAm) and Manure amount (ManAm) were the most sensitive to modeling output (i.e., jujube yields), respectively.According to the simulation of jujube yields in a typical year of 2018, when the fluctuation ranges of input-parameters were extended from ±5% to ±10%, the correlation consistency coefficients of jujube-yield prediction with the corresponding normal distribution increased, respectively, which showed the stability of the jujube-yield model increasing. 【Conclusion】 Based on the sensitivity and uncertainty analysis of the jujube-yield model parameters, the model parameters were adjusted and optimized, and then, the model was tested and verified to jujube yields from 2015 to 2019.The relative errors of the prediction yields, compared with the in situ data, were controlled within ± 8% (and the minimum error reached -1.99%), which presented a great improvement of the accuracy of the prediction yields and meant that the optimization and adjustment of the model parameters were toward reasonability.