Soil water, salt, and nutrient variability are essential factors that impact crop productivity in agriculture systems. However, effective management of small farms requires access to fine-scale data on soil water, salt, and nutrients. Large-scale assessments of spatial variability using classical statistics and geostatistical methods can help identify nutrient-deficient zones. In Xinjiang, China, inadequate water and nutrient management has resulted in low crop productivity in agriculture systems. To address this issue, this study evaluated the mechanical composition, bulk density, and contents of water, salt, ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3--N), and available phosphorus (A-P) in soil at the farm level in the Xinjiang region. Results showed low variability in soil bulk density, medium variability in soil water content, mechanical composition, NO3--N, and A-P, and high variability in soil salt content and NH4+-N. Mechanical composition and A-P showed a small range of variation across different soil depths, while soil water content and NO3--N in the surface layer varied significantly more than in other soil layers. NH4+-N variability increased with soil depth. Soil properties showed minimal differences over time. Multi-factor deficiencies, particularly in nitrogen, were observed throughout the study area. The generated maps offer a useful tool for farm managers and policymakers. In summary, this study highlights the significance of evaluating the spatial variability of soil properties for identifying zones deficient in water and nutrients, as well as those with salt accumulation. This information can be utilized to develop effective strategies for site-specific nutrient management.

This study aimed to determine the effect of irrigation amount (W), nitrogen (N), potassium (K), and zinc (Zn) on the net photosynthetic rate (Pn) of closely planted apple trees on dwarf rootstocks in arid areas of Xinjiang. Taking the “Royal Gala” apple as the experimental material, a mathematical model for Pn was established using the principle of four-factor five-level quadratic regression with a general rotation combination design. The results show that: (1) The regression equations reached significant levels (F = 37.06 > F0.01(11.11) = 4.54). (2) The effect of W, N, K, Zn on Pn is significant with relative importance W >N>Zn>K. (3) The results of single factor analysis showed that with an increase in W, N, K, and Zn, Pn exhibits an n-shaped parabolic response. (4) The positive coupling between W and N is significant, and the positive coupling between W and Zn is also significant. (5) Analysis of the interaction between sets of three factors revealed that W, N, and Zn could be combined to best effect, with the maximum value reaching 12.77 μmol m−2s−1. Compared with W×K×Zn and W×N×K, the combination of W×N×Zn reduces W by 9.2% and 6.3%, respectively, which indicates its suitability for use in the dry and water deficient planting environment in Xinjiang. (6) Within the 95% confidence level, when W is 258–294.75 mm, N is 33.44–39.51 kg/hm2, K is 53.82–69.39 kg/hm2, and Zn is 6.46–7.84 kg/hm2, the net photosynthetic rate reaches 11 μmol m−2s−1.

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ETO) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ETO, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ETO as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAImax) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CETO) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CETO as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAImax is 6.043 cm2/cm2, the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ETO crop growth models to simulate and predict crop growth and yield.

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