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.

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.

Using multi-depth continuous observations from nine farmland sites in the Bachu irrigation district, Xinjiang (November 2024–April 2026), this study developed a two-layer conditional prediction model to test whether predicted soil-moisture trajectories improve apparent electrical conductivity (EC) prediction beyond a persistence benchmark. Four prediction windows (0–1, 1–3, 3–7, and 7–15 d) were evaluated using time-forward validation under observed ERA5 forcing, paired ablation, SHAP, and post-hoc meteorological-deficit linkage analysis. For EC₂₀, the R² values were 0.978, 0.923, 0.727, and −1.376, with corresponding Skill values of −0.001, 0.277, 0.540, and 0.635; absolute performance at 7–15 d remained unreliable. EC₄₀ achieved R² of 0.943–0.987, with Skill of 0.029–0.325, and EC₆₀ achieved R² of 0.737–0.974, with Skill of 0.065–0.177. Ablation showed the clearest gain for EC₂₀ at 3–7 d (ΔR² = 0.193; ΔSkill = 0.325), whereas most other combinations showed no consistent improvement. At 0–1 d, SHAP contributions of predicted moisture trajectories to EC₂₀, EC₄₀, and EC₆₀ were 39.2%, 37.6%, and 26.8%, respectively. Meteorological-deficit linkage responses varied by depth and horizon but were generally limited. Overall, trajectory benefits were depth- and horizon-dependent and reflected model dependence rather than causal water–salt mechanisms. The framework is currently applicable mainly to monitored, unfrozen post-irrigation periods without additional wetting recharge.