In arid regions, soil salinization and inefficient water use are major challenges to sustainable agricultural development. Optimizing subsurface drainage system layouts is critical for improving saline soil reclamation efficiency. This study conducted field experiments from 2023 to 2024 to evaluate the effects of varying subsurface drainage configurations—specifically, burial depths (1.0–1.5 m) and pipe spacings (20–40 m)—on drainage and salt removal efficiency in silty loam soils of southern Xinjiang, aiming to develop an optimized scheme balancing water conservation and desalination. Five treatments (A1–A5) were established to measure evaporation, drainage, and salt discharge during both spring and winter irrigation. These variables were analyzed using a water balance model and multifactorial ANOVA to quantify the interactive effects of drainage depth and spacing. The results indicated that treatment A5 (1.5 m depth, 20 m spacing) outperformed all the others in terms of both the drainage-to-irrigation ratio (R_d/i) and the drainage salt efficiency coefficient (DSEC), with a two-year average R_d/i of 32.35% across two spring and two winter irrigation events, and a mean DSEC of 3.28 kg·m⁻³. The 1.5 m burial depth significantly improved salt leaching efficiency by increasing the salt control volume and reducing capillary rise. The main effect of burial depth on both R_d/i and DSEC was highly significant (p < 0.01), whereas the effect of spacing was not statistically significant (p > 0.05). Although the limited experimental duration and the use of a single soil type may affect the generalizability of the findings, the recommended configuration (1.5 m burial depth, 20 m spacing) shows strong potential for broader application in silty loam regions of southern Xinjiang and provides technical support for subsurface drainage projects aimed at reclaiming saline soils in arid regions.

Agricultural production faces critical challenges in arid regions due to global climate change and water scarcity. Exploring optimal water and nitrogen irrigation combinations is essential to enhancing water use efficiency and crop yields. This study employs the logistic growth model to analyze the impact of varying water and nitrogen treatments on summer maize growth in southern Xinjiang. The goal is to identify an optimal irrigation strategy to enhance maize productivity, optimize water use, and ensure precise crop management. Field experiments included three irrigation levels (W1: 80% ETc, W2: 100% ETc, W3: 120% ETc) and four nitrogen rates (N0: 0 kg/ha, N1: 168 kg/ha, N2: 306.5 kg/ha, N3: 444.5 kg/ha). A logistic growth model, incorporating effective accumulated temperature, plant height, and leaf area index (LAI), quantified growth dynamics. Maximum (v_max) and average (v_avg) growth rates were derived, followed by regression analysis to estimate theoretical maxima and corresponding irrigation–nitrogen requirements. The logistic model provided a good approximation of maize growth dynamics. Maximum growth rates for plant height occurred at 106% ETc and 340 kg/hm² nitrogen, with an effective accumulated temperature of 319.30 °C. LAI growth rates peaked at 105% ETc and 334 kg/hm² nitrogen, with 239.75 °C during rapid growth. Optimal water–nitrogen combinations were identified, highlighting a threshold beyond which excess application becomes counterproductive. The W2N2 combination was identified as optimal, achieving a water use efficiency of 3.04 kg/m³. These findings offer practical guidance for optimizing agricultural practices in arid regions.

This study addresses secondary soil salinization driven by shallow groundwater in the Yanqi Basin of southern Xinjiang, focusing on subsurface drainage system (SDS) optimization for salt regulation and oil sunflower productivity improvement in severe saline–alkali soils. Through controlled field experiments conducted (May–October 2024), we evaluated five SDS configurations: control (CK, no drainage) and four drain spacing/depth combinations (20/40 m × 1.2/1.5 m). Comprehensive monitoring revealed distinct spatiotemporal patterns, with surface salt accumulation (0–20 cm: 18.59–32.94 g·kg⁻¹) consistently exceeding subsurface levels (>20–200 cm: 6.79–17.69 g·kg⁻¹). The A3 configuration (20 m spacing, 1.5 m depth) demonstrated optimal root zone desalination (0–60 cm: 14.118 g·kg⁻¹), achieving 39.02% salinity reduction compared to CK (p < 0.01). Multivariate analysis revealed strong depth-dependent inverse correlations between groundwater level and soil salinity (R² = 0.529–0.919), with burial depth exhibiting 1.7-fold greater regulatory influence than spacing parameters (p < 0.01). Crop performance followed salinity gradients (A3 > A1 > A4 > A2 > CK), showing significant yield improvements across all SDS treatments versus CK (p < 0.05). Multi-criteria optimization integrating TOPSIS modeling and genetic algorithms identified A3 as the Pareto-optimal solution. The optimized configuration (20 m spacing, 1.5 m depth) effectively stabilized aquifer dynamics, reduced topsoil salinization (0–60 cm), and enhanced crop adaptability in silt loam soils. This research establishes an engineering framework for sustainable saline–alkali soil remediation in arid basin agroecosystems, providing critical insights for water–soil management in similar ecoregions.

Addressing the challenges of low resource-use efficiency and supply–demand mismatch in Hami melon production, this study investigated the interactive effects of irrigation and fertilization to identify an optimal regime that balances yield, water conservation, and resource-use efficiency (i.e., water use efficiency and fertilizer partial factor productivity). A greenhouse experiment was conducted in Hami, Xinjiang, employing a two-factor design with five irrigation levels (W1–W5: 60–100% of full irrigation) and three fertilization levels (F1–F3: 80–100% of standard rate), replicated three times. Growth parameters, yield, water use efficiency (WUE), and partial factor productivity of fertilizer (PFP) were evaluated and comprehensively analyzed using the entropy-weighted TOPSIS method, regression analysis, and the NSGA-II multi-objective genetic algorithm. Results demonstrated that irrigation volume was the dominant factor influencing growth and yield. The W4F3 treatment (90% irrigation with 100% fertilization) achieved the optimal outcome, yielding 75.74 t ha⁻¹—a 9.71% increase over the control—while simultaneously enhancing WUE and PFP. Both the entropy-weighted TOPSIS evaluation (C = 0.998) and regression analysis (optimal irrigation level at w = 0.79, ~90% of full irrigation) identified W4F3 as superior. NSGA-II optimization further validated this, generating Pareto-optimal solutions highly consistent with the experimental optimum. The model-predicted optimal regime for greenhouse Hami melon in Xinjiang is an irrigation amount of 3276 m³ ha⁻¹ and a fertilizer application rate of 814.8 kg ha⁻¹. This regime facilitates a 10% reduction in irrigation water and a 5% reduction in fertilizer input without compromising yield, alongside significantly improved resource-use efficiencies.