Search Results (106)

Water scarcity and soil salinization pose significant challenges to sustainable agricultural development in arid and semi-arid regions globally. This study applies Positive Mathematical Programming (PMP) to analyze agricultural water resource management in the Hetao Irrigation District (HID), China. The research constructs a comprehensive multi-stress-factor integrated PMP model to evaluate the compound impacts of water resource constraints, pricing policies, and environmental stress on agricultural production systems. The model incorporates crop-specific salinity tolerance thresholds and simulates farmer decision-making behaviors under various scenarios including water supply reduction (0–100%), water pricing increases (0.2–1.0 CNY/m³), and soil salinity stress (0–10 dS/m). The results reveal that the agricultural system exhibits significant vulnerability characteristics with critical thresholds concentrated in the 60–70% water resource utilization interval. Water pricing policies show limited effectiveness in low-price ranges, with wheat demonstrating the highest price sensitivity (−23.8% elasticity). Crop salinity tolerance analysis indicates that wheat–sunflower rotation systems maintain an 85% planting proportion even under extreme salinity conditions (10 dS/m), significantly outperforming individual crops. The study proposes a hierarchical water resource quota allocation system based on vulnerability thresholds and recommends promoting salt-tolerant rotation systems to enhance agricultural resilience. These findings provide scientific evidence for sustainable water resource management and agricultural adaptation strategies in water-stressed regions, contributing to both theoretical advancement of the PMP methodology and practical policy formulation for irrigation districts facing similar challenges. Full article

The management of groundwater depth (GWD) in alluvial soils under irrigation in arid climates is critical for soil and water conservation, given its influence on salt dynamics and water availability for crops. GWD is influenced by the interaction of irrigation water supply and drainage system design and operation. Controlling GWD is a significant issue in the Hetao Irrigation District due to continuous irrigation, arid climate, and high risks of soil salinization, which concerns farmers and water management authorities. To address this issue, a study was conducted based on open-air laboratory experimentation to rigorously assess the effects of GWD on soil salt dynamics and capillary rise contribution to maize cultivation under level basin irrigation. Data collected served as the basis for parameterizing and calibrating the HYDRUS-1D model, facilitating simulation of soil water and salt dynamics to enhance understanding of GWD effects ranging from 1.25 m to 2.25 m. It was concluded that during calibration and validation, the model demonstrated strong performance; SWC simulations achieved R² > 0.69, RMSE < 0.03 cm³ cm⁻³, and NSE approaching 1; and EC simulations yielded R² ≥ 0.74 with RMSE < 0.22 S cm⁻¹. Additionally, the simulated bottom boundary moisture flux closely matched the measured values. The most favorable GWD range should be between 1.75 m and 2.0 m, minimizing the negative impacts of irrigation-induced soil salinity while maximizing water use efficiency and crop productivity. A higher GWD causes crop water stress, while a lower value results in a greater risk of soil salinity. This study anticipates future field application in Hetao to assess drainage system effectiveness and variability in salinity and productivity effects. Full article

The ameliorative mechanism of biochar in reducing soil greenhouse gas emissions in arid saline farmland remains unclear. A two-year field study in sorghum farmland in China’s Hetao Irrigation District was conducted to assess the influence of corn straw-derived biochar on GHG emissions and explore the role of soil physicochemical properties in regulating GHG fluxes. Four different biochar application rates were tested: 0 (CK), 15 (C15), 30 (C30), and 45 t hm⁻² (C45). Compared to CK, C15 reduced CH₄ emissions by 15.2% and seasonal CH₄ flux by 77.0%. The N₂O flux followed CK > C45 > C30 > C15 from 2021 to 2022. C15 and C30 significantly decreased GWP, mitigating GHG emission intensity. Biochar application enhanced sorghum grain yield. Soil temperature was the primary determinant of CH₄ flux (total effect = 0.92). In the second year, biochar’s influence on CH₄ emissions increased by 0.76. Multivariate SEM identified soil moisture (total effect = −0.72) and soil temperature (total effect = −0.70) as primary negative regulators of N₂O fluxes. C40 lead to salt accumulation, which increases CH₄ emissions but inhibits N₂O emissions. Averaged over two years, GWP under C15 and C30 decreased by 76.5–106.7% and 5.3–56.1%, respectively, compared to CK. Overall, the application of biochar at a rate of 15 t hm⁻² significantly reduced CH₄ and N₂O emissions and increased sorghum yield. Full article

Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article

The shifting irrigation reduction in the Hetao Irrigation District and the inability to effectively discharge salts from the system have led to significant changes in salt migration patterns. Based on the integration of long-term field observations (2017–2023) with soil hydrodynamics and solute transport models, this study explored the impact of irrigation reduction on water and salt migration in a cropland–wasteland–lake system. The results indicated that before and after the reduction in irrigation and decline in groundwater levels, the migration rates of groundwater from croplands to wastelands and from wastelands to lakes remained relatively stable, averaging 78% and 40%. During the crop growth period, after irrigation reduction and groundwater level decline, the volume of groundwater recharging lakes from wastelands decreased by 80–120 mm, causing a water deficit in the lakes of 679–789 mm. After irrigation reduction and groundwater level decline, during the crop growth period, 1402 kg/ha of salt remained in the wasteland groundwater, and 597–861 kg/ha of salt accumulated in the cropland groundwater, exceeding previous levels, leading to salinization in the cropland and wasteland groundwater. This study provides insights relevant to managing groundwater and soil salinity in irrigation areas. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R² value of 0.7964 and a root mean square error (RMSE) of 0.021 cm³·cm⁻³. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm³·cm⁻³. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm³·cm⁻³, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article

As a critical ecologicalbarrier in the semi-arid to semi-humid transition zone of northern China, the interaction between afforestation and climatic stressors in the Yellow River Basin constitutes a pivotal scientific challenge for regional sustainable development. However, the synthesis effects of afforestation and climate on primary productivity require further investigation. Integrating multi-source remote sensing data (2000–2020), meteorological observations with the Standardized Precipitation Evapotranspiration Index (SPEI) and an improved CASA model, this study systematically investigates spatiotemporal patterns of vegetation net primary productivity (NPP) responses to extreme drought events while quantifying vegetation coverage’s regulatory effects on ecosystem drought sensitivity. Among drought events identified using a three-dimensional clustering algorithm, high-intensity droughts caused an average NPP loss of 23.2 gC·m⁻² across the basin. Notably, artificial irrigation practices in the Hetao irrigation district significantly mitigated NPP reduction to −9.03 gC·m⁻². Large-scale afforestation projects increased the NDVI at a rate of 3.45 × 10⁻⁴ month⁻¹, with a contribution rate of 78%, but soil moisture competition from high-density vegetation reduced carbon-sink benefits. However, mixed forest structural optimization in the Three-North Shelterbelt Forest Program core area achieved local carbon-sink gains, demonstrating that vegetation configuration alleviates water competition pressure. Drought amplified the suppressive effect of afforestation through stomatal conductance-photosynthesis coupling mechanisms, causing additional NPP losses of 7.45–31.00 gC·m⁻², yet the April–July 2008 event exhibited reversed suppression effects due to immature artificial communities during the 2000–2004 baseline period. Our work elucidates nonlinear vegetation-climate interactions affecting carbon sequestration in semi-arid ecosystems, providing critical insights for optimizing ecological restoration strategies and climate-adaptive management in the Yellow River Basin. Full article

This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 10⁷ m² study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors of the groundwater environment in the context of soil salinity–alkalinity improvement, as well as the impact of irrigation on the ionic characteristics of groundwater. Furthermore, based on this analysis, a groundwater numerical model and a prediction model for the study area were developed using Visual MODFLOW Flex 6.1 software to forecast the future groundwater levels in the study area and evaluate the effects of varying irrigation scenarios on these levels. The key findings are as follows: (1) The groundwater depth stabilized at 1.63 ± 0.15 m (0.4 m increase) post-improvement measures, maintaining equilibrium under current irrigation but increasing with reductions in water supply. The groundwater salinity increased by 0.59–1.2 g/L across the crop growth period. (2) Spring irrigation raised the groundwater total dissolved solids by 15.6%, as influenced by rock weathering (38.2%), evaporation (31.5%), and cation exchange (30.3%). (3) Maintaining current irrigation systems and planting structures could stabilize groundwater levels at 1.60–1.65 m over the next decade, confirming the sustainable hydrological effects of soil improvement measures. Reducing irrigation to 80% of the current water supply of the Yellow River enables groundwater level stabilization (2.05 ± 0.12 m burial depth) within 5–7 years. This approach decreases river water dependency by 20% while boosting crop water efficiency by 18.7% and reducing root zone salt stress by 32.4%. Full article

Vegetation productivity, as an essential global carbon sink, directly influences the variety and stability of ecosystems. Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. This model employs TTHHO to effectively navigate the search space and adaptively optimize network node positions, integrates CNN-LSSVM for feature extraction and regression analysis, and incorporates ABKDE for probability density function estimation and outlier detection, resulting in accurate interval probability prediction and enhanced model resilience to interference. Experimental data indicate that the TCLA model improves prediction accuracy by 10.57–26.47% compared to conventional models (Long Short-Term Memory (LSTM), Transformer). In the presence of 5–15% outliers, the fusion of multimodal data results in a substantial drop in RMSE (p < 0.05), with a reduction of 45.18–69.66%, yielding values between 0.079 and 0.137, thereby demonstrating the model’s high robustness and resistance to interference in predicting the next three years. This work introduces a scientific approach for precisely forecasting alterations in regional vegetation productivity using the proposed multimodal TCLA model, significantly enhancing global vegetation resource management and ecological conservation techniques. Full article

The Hetao Plain Irrigation District of Inner Mongolia faces critical agricultural sustainability challenges due to its arid climate, exacerbated by tightening Yellow River water allocations and pervasive water inefficiencies in the current wheat cultivation practices. This study addresses water scarcity by evaluating the impact of regulated deficit irrigation strategies on spring wheat production, with the dual objectives of enhancing water conservation and optimizing yield–quality synergies. Through a two-year field experiment (2020~2021), four irrigation regimes were implemented: rain-fed control (W0), single irrigation at the tillering–jointing stage (W1), dual irrigation at the tillering–jointing and heading–flowering stages (W2), and triple irrigation incorporating the grain-filling stage (W3). A comprehensive analysis revealed that an incremental irrigation frequency progressively enhanced plant morphological traits (height, upper three-leaf area), population dynamics (leaf area index, dry matter accumulation), and physiological performance (flag leaf SPAD, net photosynthetic rate), all peaking under the W2 and W3 treatments. While yield components and total water consumption exhibited linear increases with irrigation inputs, grain yield demonstrated a parabolic response, reaching maxima under W2 (29.3% increase over W0) and W3 (29.1%), whereas water use efficiency (WUE) displayed a distinct inverse trend, with W2 achieving the optimal balance (4.6% reduction vs. W0). The grain quality parameters exhibited divergent responses: the starch content increased proportionally with irrigation, while protein-associated indices (wet gluten, sedimentation value) and dough rheological properties (stability time, extensibility) peaked under W2. Notably, protein content and its subcomponents followed a unimodal pattern, with the W0, W1, and W2 treatments surpassing W3 by 3.4, 11.6, and 11.3%, respectively. Strong correlations emerged between protein composition and processing quality, while regression modeling identified an optimal water consumption threshold (3250~3500 m³ ha⁻¹) that concurrently maximized grain yield, protein output, and WUE. The W2 regime achieved the synchronization of water conservation, yield preservation, and quality enhancement through strategic irrigation timing during critical growth phases. These findings establish a scientifically validated framework for sustainable, intensive wheat production in arid irrigation districts, resolving the tripartite challenge of water scarcity mitigation, food security assurance, and processing quality optimization through precision water management. Full article

Groundwater depth has a significant impact on salinization in irrigated areas. In this study, different groundwater depths were controlled via pit tests and we conducted pit tests with different groundwater depths (DGWs) to investigate the relationship between irrigation water volume and salt migration during the crop growth period, as well as the influence of DGW on maize growth and yield. The aim of this study was to determine an appropriate DGW for maize growth in the Hetao Irrigation District, the largest irrigation area of Asia, under the dual goals of water conservation and salt control. The results showed that the upward replenishment of groundwater was 179.60 mm, 139.17 mm, 119.98 mm, 68.62 mm, and 48.38 mm for each respective DGW, i.e., negatively correlated with DGW during the maize growth period. Soil electrical conductivity (EC) was exponentially related to DGW. For DGWs > 1.75 m, surface soil EC decreased significantly and soil EC exhibited less variation with DGW. Moreover, the desalination rate and depth after irrigation were improved at DGW values of 2.00 m and 2.25 m. Shallow DGW values resulted in increased evapotranspiration and intensified crop stress, which reduced water use efficiency. To reduce resource waste and salt stress on crops, we suggest that a DGW of 2.00~2.25 m is more suitable for maize growth and development. These results provide a reference for determining appropriate DGWs for maize growth in salinized irrigation areas. Full article

Agriculture consumes a large amount of water. Water-saving initiatives can alter the water cycles in irrigation areas, thereby influencing the ecological and environmental processes associated with water circulation. The greater the intensity, scale, and extent of these efforts, the more significant their impact on ecological and environmental systems. Therefore, it is essential for water-saving initiatives to ensure regional ecological well-being. This paper examines the balance between water saving and ecological well-being in northwest China, which is characterized by severe water scarcity, fragile ecological environments, significant water waste in agriculture and ecological usage, and substantial potential for water savings. By integrating deep water-saving controls with ecological protection, this paper proposes an ecological development model for implementing deep water-saving strategies in irrigation areas. This approach is crucial for mitigating water scarcity in the arid regions of northwest China. The Hetao Irrigation District is used as a case study to calculate the maximum water-saving potential while considering ecological conservation. Full article

Seasonal freeze–thaw irrigation areas face challenges of soil salinization and water scarcity, requiring a deep understanding of soil freeze–thaw dynamics under the interaction between irrigation and groundwater. An in situ lysimeter experiment was conducted in the winters of 2020–2021 and 2023–2024 to investigate the effects of autumn irrigation (AI) timing (late AI conducted in late November and icing AI conducted in early December) and quota (0, 35, 135, 270 mm) on soil water, heat, and salt transport under varying groundwater levels in the Hetao Irrigation District, Northwest China. Results showed that AI had a strong short-term effect on the groundwater depth and there was a significant negative correlation between groundwater depth and air temperature on a monthly scale. The quota and air temperature during AI were the key factors in utilizing the “refrigerator effect”—where irrigation water pre-cooled by frozen layer accelerates soil freezing—to regulate soil water and salt transport under freeze–thaw cycles. The drastic reduction in AI water consumption lowered the groundwater level, highlighting air temperature as the dominant driver of soil dynamics. Thus, icing AI with low quota (35 mm) can optimize water use (water saving of 77% compared to the traditional quota of 150 mm) while maintaining soil moisture (an increase of 17.4% in water storage) and salinity control (a decrease of 41.6% in salt storage) in the root zone (0–40 cm) through the “refrigerator effect”, demonstrating its potential for sustainable irrigation in water-scarce cold regions. Full article

In order to explore a model for the deterioration rate law and mechanism of concrete performance in salt lake water or sea water, the mixed sand concrete test of different forms of chloride ion erosion under a dry–wet cycle was simulated in the laboratory. The compressive strength and penetration depth were used to characterize the structural degradation degree of mixed sand concrete. The performance degradation of mixed sand concrete was analyzed through field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), thermogravimetry (TG), and nuclear magnetic resonance (NMR) testing. Experimental investigations have revealed that, at an age of 140 days and under alternating wet–dry conditions, liquid chloride ion erosion results in a 17.47% reduction in the compressive strength of blended sand concrete, accompanied by an erosion depth of 28.077 mm. This erosion progresses from the exterior towards the interior of the material. Conversely, gaseous chloride ion erosion exhibits a bidirectional penetration pattern. When subjected to gaseous chloride ion erosion, the compressive strength of blended sand concrete decreases by 31.36%, with an associated erosion depth of 38.008 mm. This exposure subjects the structure to heightened crystalline pressures, leading to severe deterioration of both the micro-porous structure within the concrete and the dense structure of hydration products. Consequently, the overall extent of structural damage is more pronounced, and the rate of degradation progression is accelerated. Under the action of liquid chloride ion erosion, the degradation of mixed sand concrete structure is caused by dry–wet fatigue, crystallization pressure, chloride salt erosion and calcium ion dissolution. Under the action of spray-born chloride erosion, the degradation of the mixed sand concrete structure is caused by dry–wet fatigue, crystallization pressure, chloride salt erosion, and calcium ion dissolution, among which crystallization degradation plays a major role. In line with the engineering standards for the utilization of vast desert resources in Inner Mongolia and the long-term service of concrete in the Hetao Irrigation District, our approach contributes to the achievement of sustainable development. Full article