In order to determine the effects of different water-saving irrigation techniques on physiological growth, farmland microclimate, and yield of cotton (Gossypium Spp.), a two-year field experiment was carried out in an oasis area of Northwest China, and three irrigation methods were tested, including on-film irrigation (T1), under-film drip irrigation (T2), and automatic irrigation (T3). The results showed that the relative humidity, plant height, leaf area, stem thick, and photosynthetic index with the T3 treatment were significantly higher than those with T2 and T1. The air and soil temperature with T3 (except seedling stage) were considerably lower than those with T2 and T1. According to the fitting and statistical analysis of each index and yield, except for air and soil temperature, the other indices were positively correlated with yield. Based on the analysis of each index, the T3 treatment had the most significant regulatory effect on cotton’s physiological growth and farmland microclimate. Compared with T1, the irrigation amounts of T2 and T3 decreased by 16.43% and 25.90%, but the yield increased by 38.96% and 46.28%, respectively. The automatic irrigation strategy showed significant advantages in water saving and yield increase, which could provide some reference for cotton drip irrigation in similarly arid areas.

In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient (CGC), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover (CC) simulation, while the crop coefficient at full canopy (KCTRX), water productivity (WP), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R² values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R² values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China.

The development of a crop production strategy through the use of a crop model represents a crucial method for the assurance of a stable agricultural yield and the subsequent enhancement thereof. There are currently no studies evaluating the suitability of the AquaCrop model for the drip irrigation of Gossypium barbadense in Southern Xinjiang, which is the primary planting region for Gossypium barbadense in China. In order to investigate the performance of the AquaCrop model in simulating the growth of cotton under mulched drip irrigation, the model was locally calibrated and validated according to different irrigation thresholds during a key growth period of two years. The results of the simulation for total soil water (TSW), crop evapotranspiration (ET_c), canopy coverage (CC), aboveground biomass (Bio), and seed cotton yield demonstrated a high degree of correlation with the observed data, with a root mean square error (RMSE) of <11.58%. The Bio and yield simulations demonstrated a high degree of concordance with the corresponding measured values, with root mean square error (RMSE) values of 1.23 t ha⁻¹ and 0.15 t ha⁻¹, respectively. However, the predicted yield declined in the verification year, though the prediction error remained below 15%. Furthermore, the estimated evapotranspiration (ET_c) value demonstrated a slight degree of overestimation. Generally, the middle and late stages of cotton growth led to an overestimation of the TSW content. However, the prediction error was less than 13.99%. Through the calculation of each performance index of the AquaCrop model, it is found that they are in the acceptable range. In conclusion, the AquaCrop model can be employed as a viable tool for predicting the water response of cotton to drip irrigation under mulched film in Southern Xinjiang. Based on 64 years of historical meteorological data, three years were selected as scenarios for simulation. Principal component analysis (PCA) showed that, in a local wet year in Southern Xinjiang, the irrigation quota was 520 mm, and the irrigation cycle was 6 days/time. In normal years, the irrigation quota was 520 mm, with an irrigation cycle of 6 days/time. In dry years, the irrigation quota was 595 mm, with an irrigation cycle of 10 days/time. This allowed for higher seed cotton yields and irrigation water productivity, as well as the maximization of cotton yields and net revenue in the arid oasis area of Southern Xinjiang.

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.

The low water and fertiliser utilisation efficiency and soil quality degradation caused by high water and fertiliser inputs are the primary challenges facing goji berry cultivation in arid regions. A two-year field experiment was conducted from 2021 to 2022. The experiment included three irrigation rates (I₁, I₂, I₃) of 2160, 2565, and 2970 m³·hm⁻² and three nitrogen application rates (N₁, N₂, N₃) of 165, 225, and 285 kg·hm⁻² to quantify their impacts on soil nutrients, enzyme activity, and goji berry yield in the root zone. Results indicate that the indicators of soil nutrients decrease with increasing soil depth, with depths of 0–20 cm accounting for 24.80–72.48% of total content. With fertility period progression, soil organic matter at depths of 0–80 cm exhibits a “folded-line” trend, while total nitrogen, nitrate nitrogen, and available phosphorus show an “M”-type trend. At depths of 0–40 cm, the proportions of urease, sucrase, and alkaline phosphatase activities all exceeded 70%. At I₁ irrigation rate, enzyme activities gradually increased with rising nitrogen application rates. At I₂ and I₃ irrigation rates, enzyme activities first increased, then decreased with increasing nitrogen application. The highest yields of both fresh and dried fruits were achieved at I₂N₂ treatment, increasing by 14.17% and 14.78%, respectively, compared to conventional management (CK). Analysis of the random forest model indicates that the soil-driven factors influencing yield formation include SA, UA, APA, HPA, SOM, NH₄⁺-N, and TP. Analysis of SQI and yield fitted data indicates that water–nitrogen coupling significantly influences wolfberry yield by regulating soil quality. Partial least squares (PLS-PM) showed that N application and irrigation of soil nutrients did not cause a significant indirect impact on goji berry yield, but a significant positive effect on goji berry yield occurred through enzyme activity.

Saline water irrigation has emerged as a promising approach to mitigate agricultural water shortages; however, its improper use may induce secondary soil salinization. In this study, saline-alkali soil collected from Hami, Xinjiang, was used to conduct a series of indoor one-dimensional vertical soil column experiments. The aim was to systematically investigate the effects of sodium humate and desulfurization gypsum on soil infiltration behavior and the distribution patterns of key cations and anions under different levels of irrigation water salinity. The results showed that sodium humate application markedly improved soil infiltration capacity, while the duration of infiltration decreased with increasing salinity. Under salinity levels of 12 and 16 g/L, the 4 g/kg sodium humate treatment exhibited the most rapid advancement of the wetting front. In contrast, desulfurization gypsum reduced infiltration rates, with the lowest infiltration observed under the 12.5 g/kg treatment at 16 g/L salinity. Under different treatments, the adjusted coefficients of determination (adjusted R²) for the Philip, Kostiakov, and Horton models ranged from 0.8450 to 0.9841, 0.9901 to 0.9989, and 0.9748 to 0.9942, respectively, while the global performance indicator (GPI) ranged from 1.619 × 10⁻³ to 5.103 × 10⁻¹, 4.998 × 10⁻⁹ to 2.166 × 10⁻⁵, and 1.505 × 10⁻⁶ to 2.438 × 10⁻⁴, respectively. These results indicate that the Kostiakov model outperformed the other models in terms of fitting accuracy and overall performance for describing the soil infiltration process. In addition, sodium humate generally increased the sorptivity parameter S in the Philip model and the empirical coefficient K in the Kostiakov model, whereas desulfurization gypsum showed the opposite trend. In terms of salt regulation, sodium humate demonstrated optimal desalination performance at application rates of 6–8 g/kg under low salinity and 4–6 g/kg under high salinity conditions. Conversely, excessive gypsum application tended to exacerbate salt accumulation, although a moderate dosage (5 g/kg) effectively limited the downward migration and accumulation of Na⁺ and Cl⁻. These two ions were identified as the dominant contributors to soil salinization, showing strong positive correlations with soil salt content (SSC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP). In contrast, Ca²⁺, Mg²⁺, and HCO₃⁻ played beneficial roles in alleviating sodicity through ion exchange and buffering mechanisms. Overall, sodium humate enhanced infiltration and facilitated salt leaching in the upper soil layers under saline irrigation conditions. Although desulfurization gypsum reduced infiltration and increased overall salt content, it contributed to mitigating Na⁺ accumulation in deeper soil profiles. These findings highlight the critical importance of selecting appropriate soil amendments and optimizing their application rates to improve saline water use efficiency and promote sustainable management of saline-alkali soils.