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.

Freshwater scarcity in arid regions is driving increased use of saline irrigation, yet salinity severely degrades soil structure and suppresses enzymatic function. To address this critical challenge for sustainable soil management, this study systematically evaluated magnetized saline water (MSW) across three salinity levels (1, 3, and 6 g L⁻¹) and four magnetic field strengths (0, 0.2, 0.4, and 0.6 T), confirming the magnetic field intensity (C) × salinity (S) interaction. The comprehensive analysis integrated data on aggregate stability, key ion concentrations (Ca²⁺, Mg²⁺, Cl⁻), and major enzyme activities. Structural Equation Modeling (SEM) was utilized to quantify the underlying mechanisms, demonstrating that structural improvement is primarily driven by strong indirect pathways, mediated by optimized ion dynamics and increased enzyme-mediated organic matter turnover. The moderate-salinity (3 g L⁻¹), moderate-magnetic-field (0.4 T) regime emerged as the optimal balanced strategy for overall soil health. These findings offer a scalable approach, guiding future field-scale research toward long-term agricultural sustainability.

Saline water irrigation is increasingly used in arid regions, but its influence on soil aggregate stability remains uncertain when combined with magnetization pretreatment. A two-year field-pit experiment was conducted during 2024–2025 with four magnetization intensities (0, 0.2, 0.4, and 0.6 T) and three irrigation water salinity levels (≤1, 3, and 6 g L⁻¹). Mechanically stable and water-stable soil aggregates were analyzed in the 0–60 cm soil profile. Magnetization treatments changed aggregate-size distribution, but the dominant response differed between aggregate types. Mechanically stable aggregates mainly showed an increase in the >2 mm fraction and a decrease in the 0.053–0.25 mm fraction, whereas water-stable aggregates mainly showed an increase in the 0.25–1 mm fraction and a decrease in the 0.25 mm (R_0.25), mean weight diameter (MWD), and geometric mean diameter (GMD) varied with soil depth, salinity level, and year. Under S6, C4S6 increased the R_0.25 of mechanically stable aggregates in both years, reaching 73.90% in 2025 compared with 61.60% under C0S6. Correlation and exploratory path analyses suggested that changes in aggregate stability were mainly associated with the redistribution of key aggregate-size fractions. Under the field-pit conditions of this study, some magnetization treatments, particularly 0.4 T, were often associated with higher stability indices than non-magnetized saline irrigation, but this pattern was not consistent across all depths, salinity levels, years, and indicators. Long-term open-field validation is still required.

Amendment studies in saline–alkali cropland often emphasise treatment–control comparisons, whereas rate-dependent responses within the same field system remain less well resolved. We evaluated water-soluble organic fertiliser and Bacillus subtilis rates in a saline–alkali cotton (Gossypium hirsutum L.) field under magnetised–ionised water irrigation. A single-season (2025) randomised complete block experiment with three replicates included CK1 (magnetised–ionised water without amendments), CK2 (conventional water without amendments), and a 3 × 3 factorial of organic fertiliser (300, 600, and 900 kg ha⁻¹) and inoculant (30, 52.5, and 75 kg ha⁻¹) rates. Gravimetric soil water content (GSWC) and electrical conductivity (EC) were measured across five growth stages and seven soil layers; post-harvest bulk density, boll-stage microbial composition, leaf gas exchange, and seed cotton yield were also assessed. GSWC and EC showed stage- and depth-dependent responses. Post-harvest bulk density showed a non-monotonic pattern across organic fertiliser rates. Y2W2 produced the highest observed net photosynthetic rate and seed cotton yield, 14.04% and 10.51% above CK1, respectively, and the lowest observed bulk density at both depths, 2.84–4.00% below CK1. Mantel analysis indicated positive covariation of bacterial assemblage composition with boll-stage GSWC and seed cotton yield, although statistical support was limited after multiple-testing correction. Y2W2 showed favourable responses across several indicators but requires multi-season and multi-site validation.

Introduction: Accurate and stable diagnosis of cotton nitrogen status across growth stages is essential for precision fertilization in drip-irrigated systems. However, the instability of conventional nitrogen-related indicators across different phenological stages often reduces diagnostic performance and limits their broader application. Methods: A field experiment was conducted in Xinjiang, China, under four irrigation levels (60%, 80%, 100%, and 120% ET _c ) and four nitrogen application rates (0, 245, 300, and 350 kg N ha ^-1 ). UAV multispectral imagery was acquired at the squaring, flowering, boll-setting, and boll-opening stages. Based on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed. Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) models were used to evaluate the inversion performance of INDI. In addition, the nitrogen nutrition index (NNI), derived from the critical nitrogen dilution curve, was used to validate the diagnostic stability of INDI. Results: Multispectral vegetation indices were strongly correlated with LAI, LNWupper, and INDI, with red-edge- and near-infrared-based indices showing the highest sensitivity. Among the three models, XGBoost achieved the best inversion accuracy for INDI (R ² = 0.85, RMSE = 0.61). INDI was significantly correlated with NNI across growth stages, with R ² values of 0.58, 0.77, 0.81, and 0.70 at the squaring, flowering, boll-setting, and boll-opening stages, respectively, and the highest accuracy observed at the boll-setting stage. Moreover, the spatial distribution maps of INDI effectively distinguished nitrogen differences under different water–nitrogen treatments and were consistent with NNI-based classifications. Discussion: INDI accurately captured nitrogen dynamics throughout cotton growth, and the INDI–XGBoost framework provided a robust approach for high-precision spatial nitrogen diagnosis. These results support precision fertilization management in drip-irrigated cotton fields in Xinjiang.