The effects of water, salt, and nitrogen (N) regulation on changes in the soil water, transport of salt nutrients, and growth indices and yield of cotton (Gossypium hirsutum) were analyzed under drip irrigation covered by film. A two-year three-factor full-combination field experiment was conducted to study the effects of three levels of irrigation (W1: 2700 m³·hm^-2, W2: 3600 m³·hm^-2, and W3: 4500 m³·hm^-2), salinity (S1: 3 dS·m^-1, S2: 6 dS·m^-1, and S3: 9 dS·m^-1), and N (F1: 105 kg·hm^-2, F2: 210 kg·hm^-2, and F3: 315 kg·hm^-2). The effects of different combinations of water-salt-nitrogen on soil moisture, salinity, N distribution, plant growth, and yield were investigated. The results showed the following: the soil moisture was mainly located in the 30-40 cm soil layer, and the water content of different soil depths essentially first increased and then decreased. The average water content of the 0-40 cm soil layer in the bud and boll stages of S3F3W1 and S2F3W1 increased by 1.3%-21.8% compared with that of S1F3W1, and the average water content of the combination of S1F3 increased by 1.39%-13.83% compared with those of S1F2 and S1F1 under the same amount of irrigation. The soil salinity tended to decrease and then increase during the fertile period. The S1 treatment increased and then decreased the soil salinity as N application increased, and the soil salinity in S2 and S3 decreased as the N application increased. The N content was significantly higher with the F3 treatment than F1 or F2. In the W2 and W3 treatments, N slowly accumulated in the 40-60 cm soil layer, and the N content was significantly higher with the S1 treatment than S2 or S3. The soil moisture, salinity, and N content interacted; under appropriate soil moisture and N conditions, lower soil salinity enabled the plants to utilize nutrients more efficiently, which facilitated their growth, and thus enhanced yield. To obtain high economic benefit, the recommended rate of irrigation for low and medium saline soils is 3600 m³·hm^-2 and the rate of N is 210 kg·hm^-2. For highly saline soils, the recommended rate of irrigation is 4500 m³·hm^-2 and the rate of N is 315 kg·hm^-2. This study provides a theoretical basis to explore the mechanism of water and salt nutrient transport and the efficient use of water and fertilizer in cotton farmland ecosystems under multiyear drip irrigation under a membrane in arid areas.

Given the difficulties in the field measurement of soil salinization in Xinjiang and the difficulty in quickly and broadly evaluating the potential hazards of soil salinization, this study considers cotton fields in the Aral Reclamation Area of Xinjiang as the research object, and uses multispectral remote sensing image data from Sentinel-2 SR and Landsat-9 OLI to construct a high-dimensional data set by comprehensively integrating 20 spectral indices and combining spectral indices. The optimal feature subset is screened using the method of exhaustive feature combination and cross-validation, and the inversion accuracy of soil salinization is compared for four machine learning algorithms (i.e., XGBoost, random forest, deep neural network, and K-nearest neighbor) under different feature combinations. Simultaneously, the difference in accuracy between Sentinel-2 SR and Landsat-9 OLI remote sensing images in soil salinization inversion is analyzed. The results show that: (1) The model constructed based on XGBoost algorithm can achieve high-precision prediction of cotton field salinization, with R² higher than 0.74, MSE lower than 0.04, and MAPE lower than 0.13. (2) Under the condition of feature combination 1, Sentinel-2 SR (S3+GBNDVI) and Landsat-9 OLI (SI+NDVI) remote sensing images achieved the highest prediction accuracy using XGBoost algorithm. (3) Sentinel-2 SR image data in cotton field salinization prediction (R²=0.73-0.88) is better than that of Landsat-9 OLI image data. This study realizes the precise monitoring of soil salinization in cotton fields in the Aral Reclamation Area of Xinjiang, which should provide an effective and timely technical reference for soil salinization control and prevention in cotton fields in reclamation areas.

To address the challenges of fresh water shortage and soil quality decline in northern Xinjiang, a field experiment was conducted, investigating the effects of different irrigation water salinity levels and biochar application on the soil hydrothermal conditions, soil salinity, and cotton growth in cotton fields. Four biochar application levels (B0: 0 t·hm^-2, B1: 20 t·hm^-2, B2: 40 t·hm^-2, B3: 60 t·hm^-2) and three irrigation water salinity levels (S1: 1 g·L^-1, S2: 3 g·L^-1, S3: 5 g·L^-1) were established. A two-factor completely randomized combination test was used to analyze the effects of these treatments on soil water and salt temperature distribution, cotton growth index, dry matter accumulation, yield, and water use efficiency. The findings indicated that increased biochar and irrigation water salinity levels raised soil water and salt content. Higher biochar application increased the average soil temperature, while irrigation water salinity notably influenced the average soil temperature (P < 0.01). B2S2 treatment increased the cotton plant height, leaf area index, and aboveground dry matter. Optimal yield and water use efficiency occurred in the B2S2 treatment. In contrast, the B0S3 treatment displayed the lowest values, 18.50% and 26.87% lower in yield and water use efficiency, respectively, compared to the B2S2 treatment. A multiple regression equation, combined with normalization and spatial analysis, was established. The optimal biochar amount and irrigation water salinity range based on cotton yield and water use efficiency were 26-46 t·hm^-2 and 2.45-3.04 g·L^-1, respectively.

Exploring the regional crop water footprint and its spatial and temporal distribution patterns and driving factors can help optimize agricultural production layouts, improve agricultural production and water use efficiency, and achieve sustainable agricultural development. This study quantified and analyzed the water footprint of major crops in 54 counties and districts in the Guanzhong region from 2000 to 2020. Pathway analysis was used to explore the driving factors influencing temporal and spatial changes in crop water footprint. Key findings revealed that: (1) The total water footprint of crops in the Guanzhong region decreased from 2.232 × 10⁸ m³ in 2000 to 2.003 × 10⁸ m³ in 2020. Blue water use was the most dominant, followed by gray water use, with green water use being the lowest, accounting for 37.261%, 36.254%, and 26.485%, respectively. (2) Significant spatial variations existed in the total crop water footprint, showing a high eastern and low western profile. Regions with similar crop water footprints (high-high or low-low) demonstrated an agglomeration distribution. (3) The crops’ green water footprint was primarily influenced by yield per unit area, while the blue water footprint was mainly affected by average wind speed, followed by pesticide use and relative humidity. Additionally, fertilizer application had the greatest impact on the gray water footprint. This finding suggests that agricultural input factors significantly outweigh meteorological factors in influencing the crop water footprint. Consequently, production level and agricultural inputs were primarily responsible for regional water footprint variability. Potential strategies for regulating crop water footprint include: (1) implementing reasonable allocation of precipitation to improve green water utilization to achieve optimal allocation and use of water resources; (2) enhancing irrigation practices by improving irrigation facilities, increasing irrigation efficiency, and reducing irrigation water resource consumption, particularly as irrigation is a significant contributor to the blue water footprint in agricultural water consumption; (3) reducing fertilizer application and pesticide use while ensuring crop yield to minimize water consumption caused by water environment pollution, thereby controlling agricultural water consumption and alleviating pressure on water resources. These study results are beneficial for conserving water resources and improving water use efficiency in the Guanzhong region. They provide crucial support for facilitating sustainable agricultural water management practices.

This study used yield and water and fertilizer usage efficiency as targets to explore a magnetized water fertilization system suitable for tomato processing via drip irrigation under film. Four magnetized water samples with an intensity of 0 Gs (M0), 2000 Gs (M1), 3000 Gs (M2), and 4000 Gs (M3) as well as three nitrogen application levels of 200 kg N·hm^-2 (N1), 250 kg N·hm^-2 (N2), and 300 kg N·hm^-2 (N3) were set up, and a split zone test design was adopted. Field experiments were conducted. By monitoring the soil moisture content, plant height, stem diameter, and above-ground biomass during the growth period of processed tomatoes, combined with the final yield index, the effects of magnetic nitrogen combination on the water and fertilizer usage efficiency of processed tomatoes were explored. The results showed that magnetized water drip irrigation significantly increased soil moisture content and soil water storage. Magnetic nitrogen coupling was also shown to significantly increase the soil moisture content in the 20-40 cm soil layer. When the magnetized water intensity was 2270-3678 Gs and the nitrogen rate was 220-230 kg·hm^-2, the growth of processed tomatoes was promoted. However, when the magnetization intensity was greater than 4000 Gs and the nitrogen rate was more than 250 kg·hm^-2, the growth of processed tomatoes could not be further improved. As magnetization was increased, the yield and water and fertilizer use efficiency of processed tomatoes increased before decreasing. As the nitrogen application rate was increased, the yield and water use efficiency increased, but the partial productivity of nitrogen fertilizer decreased. Among them, the M2N3 treatment had the highest yield and water use efficiency (169.67 t·hm^-2 and 35.61 kg·m^-3), while the M2N1 treatment had the highest nitrogen partial productivity (822.54 kg·kg^-1). Using regression and spatial analyses, the magnetic nitrogen range of yield, water use efficiency, and nitrogen partial productivity was 2270-3678 Gs and 220-230 kg N·hm^-2. This study can provide theoretical support for the scientific application of magnetized water and nitrogen fertilizer in tomato processing in Xinjiang and provide scientific guidance for optimizing the magnetic nitrogen combination configuration to improve the yield of tomato processing.

Chlorophyll content is a crucial indicator for characterizing vegetation growth. In this study, we utilized high-spectral technology to rapidly monitor the chlorophyll contents of cotton leaves. We collected 125 cotton leaf seedling samples from Xinjiang and measured their chlorophyll content and spectral data. To achieve this, we employed various spectral preprocessing techniques and used a combination of vegetation indices. Subsequently, we constructed a whale optimization algorithm/random forest regression (WOA-RFR) quantitative inversion model for cotton leaf chlorophyll content. Finally, we conducted a comparative analysis, contrasting the results of the WOA-RFR model with those obtained from the support vector regression (SVR) and RFR models. The results indicated that the spectral transformation methods (logarithm transformation, fractional order differentiation, and wavelet transformation) effectively improved the correlation between the vegetation indices and the chlorophyll content. We also found that the best inversion performance was achieved with the WOA-RFR model using a fractional order differentiation with a transformation order of 0.9 and the Vogelmann3, RVI, DVI, SR_[675-700], Mndvi₇₀₅, ND, VOG1, NVI, TVI, VOG2 combined vegetation indices. The model exhibited R² values of 0.920 and 0.955 for the training set and validation set, respectively. The corresponding RMSE values were 0.987 and 0.986, while the MRE values were 0.013 and 0.014. Compared to the RFR and SVR models, the WOA-RFR model demonstrated higher predictive accuracy, and the optimization effect of the WOA algorithm was evident. As a result, this study provides valuable decision-making support for accurately quantifying cotton leaf chlorophyll content.