Micro-/nanobubble oxygenation irrigation, as a novel irrigation technique, has been widely utilized to enhance soil phosphorus availability and maize yield. Nevertheless, currently, most of the studies remain unclear about the precise mechanism through which micro-/nanobubble oxygenation improves soil phosphorus availability and maize yield. Therefore, we established two irrigation methods, conventional irrigation (CF) and micro-/nanobubble oxygenation irrigation (MB), to investigate the combined effects on enzyme activity, microbial communities, and soil phosphorus availability in the rhizosphere soil of maize.The results showed that compared to the CF treatment, the MB treatment significantly increased available phosphorus content and alkaline phosphatase activity in maize rhizosphere soil by 21.3% and 15.4%, respectively. Furthermore, MB significantly influenced bacterial diversity in the maize rhizosphere soil but did not considerably affect fungal diversity. Specifically, MB regulated the microbial community structure in the maize rhizosphere by altering the relative abundances of the bacterial phylum Firmicutes and the fungal phyla Mucoromycota, Chytridiomycota, and Basidiomycota. In addition, MB reduced the complexity of the bacterial network while increasing the interaction density among bacterial species. Meanwhile, MB enhanced the complexity of the fungal network. Structural equation modeling indicated that MB primarily promoted soil alkaline phosphatase activity by regulating bacterial community diversity, thereby enhancing soil phosphorus availability. In conclusion, the application of micro-/nanobubble oxygenation irrigation enhances the activity of alkaline phosphatasein the soil by modulating the microbial community within the rhizosphere, thereby facilitating increased phosphorus availability in the rhizosphere of maize.

Soil salinization severely limits Xinjiang cotton production sustainability, urgently requiring efficient, eco-friendly remediation. This study proposes a synergistic strategy of micro-nano bubble aeration + microbial agents (MA-MNBs) to improve saline-alkali soil via “physical aeration + biological regulation”. Field micro-plot experiments (completely randomized block, n=3) on Xinjiang saline-alkali cotton fields set 4 treatments: CK (conventional irrigation), MA (microbial agents), MNBs (micro-nano bubble irrigation), MA-MNBs (combination). Results showed: MA-MNBs significantly reduced soil salt by 26.08% vs CK (P MA > MNBs); increased bacterial Chao1 index, enriched Proteobacteria/Actinobacteriota, and boosted Alternaria/Fusarium abundance; promoted cotton root growth, leaf area index, and dry matter accumulation (P<0.05); seed cotton yield reached 6278.65 kg·hm ^- ² (44.53% higher than CK). Conclusion: MA-MNBs synergistically improves saline-alkali soil productivity by optimizing soil physicochemical and biological functions, providing an innovative solution for arid-region saline soil remediation and agricultural efficiency, as well as a theoretical basis for its mechanism and future application.

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.

Water and nitrogen management are crucial for food security and the efficient use of water and fertilizer, especially in arid regions. Three irrigation levels, namely, 80% crop water requirement (ET_C) (W1), 100% ET_C (W2), and 120% ET_C (W3), and three nitrogen application levels, namely, 0 kg/ha (N1), 207 kg/ha (N2), and 276 kg/ha (N3), were used as the experimental treatments, and a control group, denoted as CK, was created. The results show that the maximum height achieved was 82.16 cm under W3N3. There was a single-peak variation trend throughout the growth stages of SPAD. It peaked at 58.44 under W3N3 and then at 27.9 under W2N2. The net photosynthetic and transpiration rates displayed bimodal peaks and the phenomenon of a “photosynthetic midday depression”. And the prominent peaks in leaf water use efficiency occurred at 14:00 and 18:00, alongside noteworthy enhancements observed under the W3 treatment. Water and nitrogen and their interactions significantly affected the dry matter (DM) of winter wheat, with the spike accounting for the highest percentage. The W2N2 treatment demonstrated superior effectiveness in enhancing winter wheat water use efficiency, offering the potential to decrease irrigation requirements by 20% and nitrogen application by 25%. Moreover, the maximum PFPN attained under W2N2 reached 60.13, representing a noteworthy 35.25% increase compared to the control group (CK), but the HI of the W2N2 treatment only reached 0.56. The highest HI was achieved with W3N2 (0.73), and the nitrogen application of 207 kg/ha was more conducive to obtaining a higher HI. The highest yield was achieved under W3N3 (13.599 t/ha), followed by W2N2 (12.447 t/ha), and the spike proportion exceeded 60% with W2N2, and its production cost and economic benefit ratio of under 0.31 were superior to those for other treatments. Multiple regression analysis revealed that the maximum yield reached 12.944 t/ha with an irrigation amount of 3420.1 m³/ha and a nitrogen application of 251.92 kg/ha. Overall, our study suggests using an optimal water–nitrogen combination, specifically an irrigation level of 2829 m³/ha and a nitrogen application rate of 207 kg/ha, leading to increased winter wheat yields and economic benefits. These research results provide a pragmatic technique for improving winter wheat production in southern Xinjiang.

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.