The APSIM (Agricultural Production Systems Simulator)-Wheat model has been widely used to simulate wheat growth, but the sensitivity characteristics of the model parameters at different soil moisture levels in arid regions are unknown. Based on 2023~2025 winter wheat field data from the Changji Experimental Site, Xinjiang, China, this study conducted a global sensitivity analysis of the APSIM-Wheat model using Morris and EFAST methods. Twenty-one selected parameters were perturbed at ±50% of their baseline values to quantify the sensitivity of the aboveground total dry matter (WAGT) and yield to parameter variations. Parameters exhibiting significant effects on yield were identified. The calibrated APSIM model performance was evaluated against field observations. The results indicated that the order of influential parameters varied slightly across different soil moisture levels. However, the WAGT output was notably sensitive to accumulated temperature from seedling to jointing stage (T1), accumulated temperature from the jointing to the flowering period (T2), accumulated temperature from grain filling to maturity (T4), and crop water demand (E1). Meanwhile, yield output showed greater sensitivity to number of grains per stem (G1), accumulated temperature from flowering to grain filling (T3), potential daily grain filling rate during the grain filling period (P1), extinction coefficient (K), T1, T2, T4, and E1. The sensitivity indices of different soil moisture levels under Morris and EFAST methods showed highly significant consistency. After optimization, the coefficient of determination (R²) was 0.877~0.974, the index of agreement (d-index) was 0.941~0.995, the root mean square error (RMSE) was 319.45~642.69 kg·ha^–1, the mean absolute error (MAE) was 314.69~473.21 kg·ha^–1, the residual standard deviation ratio (RSR) was 0.68~0.93, and the Nash–Sutcliffe efficiency (NSE) was 0.26~0.57, thereby enhancing the performance of the model. This study highlights the need for more careful calibration of these influential parameters to reduce the uncertainty associated with the model.

Under conditions of limited irrigation and excessive fertilizer application in the arid regions of Xinjiang, it is essential to adopt well-coordinated strategies to improve yield and crop water productivity (WP). In this study, a comparative experiment was conducted with three irrigation levels, T1 (100% crop evapotranspiration, ET_c), T2 (75% ET_c), and T3 (50% ET_c), combined with three potassium application rates, K1 (540 kg ha⁻¹), K2 (360 kg ha⁻¹), and K3 (180 kg ha⁻¹). The objective was to investigate their effects on the yield, quality, and economic benefits of jujube trees. Limited irrigation amounts significantly affected the photosynthetic characteristics, growth parameters, and ET_c of jujube trees, whereas potassium fertilizer levels primarily regulated fruit development and yield formation. Compared with full irrigation, mild deficit irrigation caused a moderate yield reduction but significantly enhanced economic returns due to the improved water productivity and fruit quality. In contrast, severe water deficit led to substantial decreases in growth parameters and economic benefits by 12.87–45.70% and 81.69%, respectively. Potassium application demonstrated a significant threshold effect, with the K2 treatment showing greater improvements in fruit quality indices, including reducing sugars, vitamin C, and other key quality parameters, compared to the K3 treatment. Based on hierarchical–grey relational analysis, the combination of 75% ET_c and 300 kg K ha⁻¹ was identified as the optimal water–potassium management strategy. The net profit was 29,199 CNY. The benefit–cost ratio increased to 3.63, and the WP improved by 16.17% compared to full irrigation. Thus, this study provides an important theoretical basis and technical support for water-saving and quality-improving cultivation of jujube trees in arid regions.