Water scarcity poses a major challenge to sustainable development in arid regions, particularly where agricultural irrigation dominates water consumption. In glacier-influenced regions, conventional water yield assessments may underestimate water availability by neglecting glacier meltwater contributions. This study developed an InVEST–Glacier framework to quantify water yield service supply and demand in Northwest China Three Water Lines region (TWL region) and identify their dominant driving factors. The results showed that low water-yield areas (0–25 mm) accounted for 60.06%–70.19% of the region, while high water-yield areas were concentrated in mountainous regions. Glacier meltwater contributed more than 70% of water yield in most inland basins of the Tarim Basin and exceeded 90% in some years, but contributed less than 10% in the Yellow River and Datong River. Water demand was highly concentrated in irrigated plains and urbanized areas. The supply–demand relationship exhibited a pattern of “widespread surplus with localized stress”, with areas of ESDR < 0.2 increasing from 67.59% to 79.50%, while areas with ESDR > 0.8 accounted for 14.68%–23.90%. Natural factors dominated the spatial differentiation of ESDR, with elevation, precipitation, and NDVI showing the highest integrated driving scores.These findings highlight the stage-dependent buffering role of glacier meltwater and the need for differentiated water-resource management in glacier-dependent and water-stressed regions.
The daily standardized precipitation evapotranspiration index (DSPEI) aggregates water surplus and deficit series with equal-weight accumulation, which is inconsistent with the physical principle that recent impacts are prominent and distant impacts attenuate during drought accumulation process. In this study, nonlinear attenuation coefficients are constructed using an exponential function. The attenuation intensity is modulated by an attenuation parameter to quantify temporal attenuation of water surplus and deficit; therefore, a modified DSPEI (MDSPEI) incorporating such attenuation characteristics is proposed. China serves as the study area. The correlation coefficients between the water surplus-deficit series and soil moisture series are sequentially calculated under varying attenuation parameters, and the parameter corresponding to the maximum correlation coefficient is defined as the optimal value. The dataset is split into training and test sets for parameter calibration and applicability validation, respectively. Three approaches, namely, comparison with historical drought events, correlation analysis with the standardized soil moisture index (SSMI), and correlation analysis against drought disaster areas, are adopted to systematically verify the advantages of the MDSPEI. The results demonstrate that the MDSPEI presents higher sensitivity and earlier drought identification and can effectively capture intense drought evolution. The MDSPEI outperforms the DSPEI in correlation with the SSMI, suggesting better consistency with the physical nature of drought. Stronger negative correlations between the MDSPEI and drought-affected areas are observed at annual and annually averaged scales, indicating that the MDSPEI identifies and characterizes agricultural drought more realistically. This study contributes to theories of daily SPEI and offers new ideas for drought monitoring.
Dry-direct-seeded rice (DSR), a potential climate-smart alternative to puddled transplanted rice, requires less water and reduces greenhouse gas (GHG) emissions; however, its effects on GHG emissions, global warming potential (GWP), and yield from in situ field measurement in Bangladesh are insufficiently documented. Multi-location and multi-year field experiments were conducted at Rajshahi and Rangpur regions in Bangladesh during two consecutive Boro (dry) and Aus (pre-monsoon) seasons to evaluate the effects of dry DSR, transplanted rice with flooded irrigation (TFI), and transplanted rice with intermittent flooding-drying irrigation (TAD) on rice yield, CH4 and N2O emissions, GWP, and GHG intensity (GHGI). Across various locations and seasons, DSR and TAD decreased overall CH₄ emissions by 13–36% and 4–20%, respectively, compared to TFI. While both DSR and TAD increased N₂O emissions by 28–34% and 12–31%, respectively, compared to TFI, the contribution of N₂O to total GWP was minor, resulting in lower GWP and GHGI under DSR and TAD. Spatial variability was observed, with higher CH4 emissions (10–30%), GWP (10–28%), and GHGI (35%) in Rajshahi than in Rangpur across establishment methods and seasons. DSR reduced yield only in Rajshahi during the Boro season, but no yield penalties were observed in Rangpur or in the Aus season. The sustainability footprint analysis and regional adoption scenarios further demonstrated that expanding DSR coverage notably reduces CH₄ emissions and GWP. Overall, dry DSR and TAD offer substantial potential to mitigate GHG emissions from farmers’ rice fields in Bangladesh; however, successful DSR adoption requires location-specific management to sustain yield.
Climate change is expected to intensify changes in soil freeze–thaw processes, thereby affecting water availability and agricultural productivity. However, the mechanistic links among climate-induced changes in seasonal freeze–thaw conditions, maize productivity, and soil organic carbon (SOC) storage remain poorly understood. Moreover, the combined adaptation potential of residue return, film mulching, and optimized irrigation has not been systematically evaluated. The calibrated Soil-Plant-Atmosphere Continuum System model was applied to investigate the responses of maize production and SOC dynamics to projected climatic conditions during 2021–2060 (near future) and 2061–2100 (far future) under Shared Socioeconomic Pathway 2–4.5 (SSP245) and Shared Socioeconomic Pathway 5–8.5 (SSP585), relative to the baseline period. Fifteen management combinations integrating crop residue return, film mulching, and irrigation regimes were evaluated. Under climate warming, maize yield under local management increased by 11.71% and 15.96% in the near future relative to the baseline period (1981–2020) under the SSP245 and SSP585 scenarios, respectively. By the end of the century, maize production was projected to decline by 17.17% under SSP245 and 20.55% under SSP585 relative to the baseline period. SOC storage exhibited a persistent reduction, decreasing by 11.95%–20.44% in the near future by 20.05%–39.02% in the far future. Among the adaptation strategies, the integrated treatment combining crop residue return, film mulching, and optimized irrigation (CRFMI4) exhibited the greatest potential for climate adaptation. Compared with local management, CRFMI4 increased average maize yield by 27.13% and 19.91% in the near and far future periods, respectively. CRFMI4 mitigated SOC losses in the near future and promoted SOC accumulation in the far future, increasing SOC storage by 16.38%. Future research should investigate the integration of CRFMI4 with climate-adaptive sowing dates and heat-tolerant cultivars to further strengthen the resilience of maize production systems under intensified late-century warming in cold agricultural regions.
Dryland wheat production on the Loess Plateau is constrained by water scarcity and low nitrogen use efficiency. A three-season field experiment (2022–2025) evaluated the combined effects of fallow tillage (subsoiling, SS; no-tillage, NT) and nitrogen rates (0, 90, 135, 180, 225 kg ha⁻¹) in a split-plot design. SS significantly increased soil water storage change during fallow (ΔSWSf) in the 100–200 cm layer by 37.4 mm, leading to a 101.4% increase in post-anthesis deep soil water use. When combined with optimized N (135–180 kg ha−1), SS reduced 0–200 cm nitrate residue compared to NT, whereas excessive N (225 kg ha−1) increased deep leaching risk. SS with optimized N also enhanced post-anthesis flag leaf photosynthesis, nitrogen metabolism enzyme activities, and antioxidant capacity, thereby promoting dry matter and nitrogen accumulation, especially in dry seasons. SS with 180 kg N ha−1 in the wet and normal seasons and 135 kg N ha−1 in the dry season achieved the highest grain yield of 7114, 5699, and 5211 kg ha−1 and water productivity of 13.55, 13.39, and 13.04 kg ha−1 mm−1. SS increased agronomic efficiency, recovery efficiency, and partial factor productivity of N, but these efficiencies declined significantly when N rates exceeded the optimum across all season types. Partial least squares path model identified ΔSWSf as the primary yield driver and flag leaf traits as a key regulator reducing nitrate residue. In conclusion, SS with optimized N rates (135–180 kg ha−1) synergistically improves yield, resource efficiency, and environmental sustainability, but excessive N under wet conditions should be avoided to prevent deep nitrate accumulation.
Thinning can improve the soil water conditions of forested land, decrease potential water stress, and enhance the drought resistance of trees by adjusting the stand density. However, the effect of thinning intensity on water consumption of plantations in semiarid regions remains unclear, interfering with the identification of forest-water relationships under thinning. In this study, we diagnosed the effects of five thinning intensities (0%, 15%, 30%, 45%, and 60%) on tree canopy transpiration (Ec) and growth in a 44-year-old Pinus tabuliformis Carr. plantation on the semiarid Loess Plateau, China. Our findings demonstrated that increasing thinning intensities improved soil water. Stand Ec was reduced with increasing thinning intensity and reached the minimum at 45% thinning. Regarding the environmental controls, relative extractable water (REW) was the dominant factor affecting Ec from 0% to 45% thinning, while vapor pressure deficit (VPD) dominated the variation of Ec under 60% thinning. Increased REW caused by increasing thinning intensity promoted the driving effect of VPD on Ec and reduced the effects of REW. Canopy stomata activity was mainly controlled by VPD, and the stomatal sensitivity to VPD decreased significantly after heavy thinning (60%). Additionally, the mean diameter at breast height of individual trees in the stand under the thinning treatment was significantly larger than the control group (i.e., 0%). Notably, the basal area was the largest, and the proportion of Ec to precipitation after removing canopy interception was the lowest at 45% thinning. These findings suggest that thinning can efficiently improve soil water conditions, and 45% thinning is an appropriate management that promotes tree growth at the stand level. This study elucidates how thinning intensity affects tree water use in Pinus tabuliformis plantations on the semiarid Loess Plateau, providing a reference for the selection of thinning schemes for plantations in similar dryland areas.
Seasonal water deficits can constrain food provision in humid-subtropical agricultural regions, but their spatial patterns are difficult to diagnose when grain statistics are reported by administrative units and drought is represented by a single index. We developed a mass-constrained and interpretable framework to diagnose county-statistics-constrained relative food-provision shortfall (FPL) at 1-km resolution in the Heng–Shao drought corridor, China. County grain production from 2013 to 2024 was allocated using target-year cropland area and a target-year-excluded, cropland-conditioned Landsat Enhanced Vegetation Index (EVI) reference, while FPL was defined relative to a target-year-excluded county-level Theil–Sen statistical reference. Compound drought signals were represented by vapour-pressure-deficit anomaly (Dvpd), soil-moisture anomaly (Dsoil), and climatic-water-deficit anomaly (Dcwd); their associations with FPL were evaluated using spatially blocked Categorical Boosting (CatBoost), cross-fitted SHapley Additive exPlanations (SHAP) and TreeSHAP. FPL was strongly right-skewed and spatially localised, with an uncapped mean of 0.036 and a skewness of 4.047. CatBoost achieved pooled out-of-fold coefficients of determination (R2) of 0.329–0.340 across 10–20 km spatial blocks, and adding the joint hydroclimatic predictor set comprising Dvpd, Dsoil and Dcwd increased R2 by 0.166 beyond the background predictors. Cropland-landscape and hydroclimatic variables formed the two largest SHAP-attributed predictor groups, accounting for 34.67% and 33.72% of total attribution, respectively. The three hydroclimatic indicators also exhibited differentiated nonlinear responses and recurrent joint-attribution structures. These findings show that retaining complementary hydroclimatic dimensions alongside cropland-landscape context provides a spatially explicit diagnostic basis for characterising within-county relative food-provision shortfall.
Global climate change has increased the frequency and intensity of high-temperature droughts, threatening fruit production in the fragile hilly regions of Southwest China. Grapevine (Vitis labrusca×vinifera ‘Shine Muscat’) is widely grown there, but its sap flow dynamics under climate change remain insufficiently explored. This study monitored sap flow and environmental variables from 2023 to 2024 to characterize grapevine sap flow dynamics and their drivers, and evaluated Random Forest (RF) and XGBoost models for daily sap flow prediction. Results showed that: (1) Sap flow exhibited a pronounced seasonal single-peak pattern, with 68.33–88.29% of the annual sap-flow total occurring from April to September; (2) At the daily scale, sap flow generally increased after approximately 6:00 a.m. Under moderate atmospheric demand, a bimodal pattern was commonly observed, with 35.61–57.35% of the daily sap flow total, peaking between 10:00 a.m. and 3:00 p.m. During periods of high vapor pressure deficit (VPD > 2 kPa), the diel pattern shifted to an earlier single peak at approximately 10:00–11:00 a.m.; (3) Path analysis indicated that photosynthetically active radiation was the key factor affecting sap flow. Correlation analysis revealed that the meteorological factors exerted stronger direct effects on sap flow than soil factors (soil temperature, moisture, electrical conductivity, water potential); (4) With both meteorological and soil predictors, RF and XGBoost models exhibited high accuracy in predicting daily grapevine sap flow (test set R²: 0.888 and 0.891 respectively), and the XGBoost model had better generalization ability (RMSE: 0.086, MAE: 0.059). When soil predictors were excluded, the corresponding R² values decreased to 0.804 and 0.808. This study provides a scientific basis for the formulation of orchard irrigation schedules and water resource management of fruit trees under climate change on the hilly slopes of Southwest China.
Hydrological extremes increasingly threaten the stability of rice production, yet irrigation is commonly evaluated by its effects on average yield rather than by its capacity to limit severe losses. Here we examine whether irrigation feasibility buffers production damage once drought or flood shocks occur, using plot-level evidence from rice-based farming systems in Jiangsu, China. We find a clear separation between shock exposure and realized damage. After accounting for local-year conditions, irrigable plots are no less likely to experience hydrological shocks, but they are substantially less likely to suffer severe yield losses when shocks occur. This protective association is concentrated in the lower tail of production outcomes, whereas irrigation feasibility provides little yield advantage under normal conditions. Rice-yield responses further indicate that hydrological penalties are weaker where irrigation is feasible. These findings suggest that the value of mature irrigation systems may lie not primarily in increasing average production or reducing exposure to climatic hazards, but in preventing those hazards from translating into severe production losses. More broadly, distinguishing exposure from conditional damage reveals an important but often overlooked dimension of agricultural infrastructure for climate resilience.
Rabi maize production in north-central Bangladesh depends entirely on groundwater irrigation during a climatically water-stressed period, and progressive aquifer depletion with CMIP6-projected warming poses an escalating threat to its long-term viability. This study presents the first integration of multi-season field experimentation, twice-daily sensor-based soil moisture monitoring (HH2 Moisture Profiler, Delta-T Devices), FAO AquaCrop calibration and validation, and a ten-model CMIP6 ensemble to evaluate raised bed furrow irrigation as a climate adaptation strategy in Mymensingh. Four configurations, conventional flood (CF), narrow (NRB, 25 cm), medium (MRB, 65 cm), and wide (WRB, 110 cm) raised beds, were tested across two seasons (2023–24, 2024–25) under a randomized complete block design. The AquaCrop model achieved R² = 0.997 and NSE = 0.813 for grain yield (GY) at calibration, with near-identical validation performance (R² = 0.997; NSE = 0.829) and PBIAS within ±5.1%. MRB delivered the highest GY (12.65 and 12.56 t/ha), water productivity (4.10 kg/m³), and harvest index (0.48), reducing seasonal water use by 19.1% and 21.4% relative to CF. Sensor profiles confirmed MRB sustained root-zone moisture at 200–400 mm depths comparable to CF despite lower water inputs. Bias-corrected CMIP6 projections, evaluated across the ten-member ensemble and four SSP scenarios, indicated MRB yield losses of 0.71% under near-term SSP1–2.6 (ensemble coefficient of variation, CV = 3.2%, high confidence) to 14.83% under far-term SSP5–8.5 (CV = 16.4%, wider uncertainty); MRB nonetheless retained the highest simulated GY (11.56 t/ha), exceeding WRB (8.86 t/ha) and NRB (10.15 t/ha) under the most severe trajectory, with this relative treatment ranking preserved across the full ensemble uncertainty envelope. These findings establish the 65 cm raised bed as the primary climate-smart adaptation pathway for sustainable rabi maize production in Bangladesh, with high confidence for the near- and mid-term horizon and directional, though less precise, support for the far term.
Climate variability and increasing atmospheric evaporative demand are reducing crop water productivity and yield stability in semi-humid agricultural systems, requiring improved irrigation strategies. This study integrates field experiments, the AquaCrop model, and machine learning to evaluate drip and furrow irrigation systems for tomato production in the Lake Tana Basin, Ethiopia. Field experiments conducted during 2023–2024 were used to calibrate and validate AquaCrop, which showed good agreement with observed tomato yield during calibration (R2 = 0.868 for drip and 0.848 for furrow irrigation) and validation (R2 = 0.833 and 0.811, respectively). Basin-scale yield prediction was performed using 72 spatial samples and environmental predictors, with multiple machine learning algorithms evaluated. XGBoost achieved the highest predictive performance (R2 = 0.86; RMSE = 0.64 t ha−1) and was selected for spatial yield simulation under CMIP6 climate scenarios. Results showed that drip irrigation substantially improved tomato productivity, producing higher mean yields (5.48–6.23 t ha−1) than furrow irrigation (2.10 t ha−1) and increasing water productivity (0.75 vs. 0.21 kg m−3). Under SSP2–4.5 and SSP5–8.5 scenarios, drip irrigation maintained greater yield stability, lower variability (CV = 2.7–2.9%), and higher climate resilience (CRI = 0.97–1.05), whereas furrow irrigation showed greater variability and reduced resilience. The integrated AquaCrop–machine learning framework enables basin-scale assessment of irrigation performance and climate change impacts under heterogeneous conditions. Efficient irrigation management is therefore critical for sustaining tomato productivity, water-use efficiency, and climate resilience under future climate variability.
The transition toward low-carbon cropland systems requires coordinated management of water, energy, food production, and carbon emissions. The North China Plain (NCP), one of China’s most intensively cultivated regions, faces particularly strong trade-offs among these objectives. This study developed a structurally coupled Dynamic Bayesian Network–Bayesian Neural Network (DBN–BNN) framework to assess the water–energy–food–carbon (WEFC) system across 492 counties from 2001 to 2020. The DBN represented temporal dependencies, while an approximate Bayesian neural network based on Monte Carlo dropout captured nonlinear responses and predictive uncertainty. The framework was used to evaluate WEFC coordination, identify dominant coordinating and de-coordinating factors, and derive county-specific management pathways through perturbation analysis. WEFC coordination followed a rise–fall–recovery pattern and showed pronounced spatial heterogeneity, with average scores ranging from 0.75 in Beijing to 1.21 in Shandong on a 0–3 scale. Groundwater conditions, irrigated area, and ecosystem water-use efficiency were the main factors underlying county-level differences. Under the integrated adjustment scenario, cropland carbon emissions decreased by 1.17 × 108 kg CO2-eq (8.1%), while grain production increased by 1.47 × 1010 kg (9.8%) relative to 2020. The identified pathways were strongly location-specific: 110 counties were prioritized for water-use efficiency improvement, 184 showed favorable responses to irrigation restructuring, and 310 required groundwater recovery. In counties facing both irrigation and groundwater constraints, improvements depended on infrastructure rehabilitation, water-saving technologies, and surface-water substitution rather than additional groundwater abstraction. This study contributes to the WEFC nexus literature by introducing a hybrid probabilistic framework that captures nonlinear dynamics and uncertainty propagation, and by providing fine-scale, policy-relevant insights into adaptive water–energy management. The findings offer transferable implications for intensively cultivated regions worldwide facing similar resource–environment trade-offs.
Wheat is the dominant crop in the North China Plain and is strongly affected by precipitation variability. Understanding how wheat yield and water productivity (WP) respond to precipitation anomalies at different growth stages is essential for improving yield stability under increasing climate variability. In this study, stage-specific moisture conditions were characterized using the standardized precipitation index (SPI). A set of precipitation-regulation scenarios was constructed to simulate moisture anomalies and evaluate their effects on wheat yield and WP using the APSIM model. The model performed well in simulating wheat yield and WP, with acceptable RMSE and NRMSE values. Under the historical climate baseline, wheat yield, WP, and wheat-season precipitation were 5279.72 kg ha⁻¹ , 1.839 kg m⁻³ , and 143.91 mm, respectively. Compared with the baseline scenario, the relative change in yield ranged from −15.40–5.86%, whereas WP varied from −13.65–2.94% across precipitation-regulation scenarios. Both crop yield and water productivity exhibited clear deviation under different precipitation anomaly scenarios, and their response intensity varied with moisture background and scenario type. The response per 10% change in wheat-season precipitation ranged from −6.70–8.25% for yield and from −2.69–3.57% for water productivity. Under drought conditions, increased rainfall mainly served to alleviate water limitation, but this was not observed in wet conditions. For water productivity, there were clear trade-off between wheat yield and water productivity under different precipitation scenarios. Besides, precipitation effects were strongly stage dependent, which the second key growth stage was the most sensitive period for the coordinated regulation of yield and water productivity, whereas the first stage had greater effects on stand establishment and yield stability, and the third stage was more vulnerable to excessive moisture. Compared with single-stage perturbations, multi-stage cumulative anomalies produced stronger and more complex impacts on wheat yield and water productivity.These findings highlight the importance of considering stage-specific precipitation anomalies and background moisture conditions when evaluating crop performance under climate variability. Overall, this study provides a scenario-based analytical framework for quantitatively assessing the responses of wheat yield and WP to stage-specific and multi-stage cumulative precipitation anomalies, thereby offering useful insights for agricultural water management and climate adaptation in the North China Plain.
Buried interlayers are an effective measure for ameliorating salt stress in saline-alkali soils under shallow groundwater conditions. Most existing studies have focused on the effects of interlayer materials and emplacement parameters on salt-blocking capacity. However, little attention has been paid to the effective lifespan of this capacity or the structural-hydraulic mechanisms underlying its decline. To address this, we investigated the structural-hydraulic evolution of straw interlayers (SI) and sand interlayers (SaI) under rotary tillage (RT) and no-tillage (NT), elucidated their impacts on water-salt transport, and assessed failure thresholds under progressive capillarization by integrating in situ observations, X-ray Computed Tomography (CT), and numerical modeling. Results revealed a distinct divergence between morphological and functional degradation over the monitoring period. SI exhibited physical collapse (thickness decreased by 87.5%; bulk density increased by 69.9%), but retained its essential capillary barrier capacity. In contrast, SaI showed pore refinement and fine-particle infilling, accompanied under RT by an increase in SSR from 78.8 to 85.2 × 10−4 t ha−1 d−1 and a 23.2% increase in 0–100 cm soil salt storage. Conversely, NT partly mitigated this degradation-induced salt accumulation. Scenario simulations linked increasing capillary pore proportion (CPP) to increased salinization risk and identified treatment-specific CPP thresholds for functional decline. Overall, this study demonstrated that the buried interlayer is a dynamic soil functional layer rather than a static barrier, with SI-NT representing the optimal design for sustaining barrier longevity. These findings highlight that buried interlayers behave as dynamic functional layers and that their service life should be considered alongside their initial salt-blocking capacity.
Irrigation with reclaimed wastewater is key for addressing water scarcity, but managing its environmental and health-related risks requires effective regulatory frameworks. As reuse policies expand globally, risk management approaches remain highly diverse due to differing socio-economic contexts. This review provides a comparative analysis of nine internationally influential agricultural water reuse policies (including European Union Regulation 2020/741, ISO 16075 standards, California Water Recycling Criteria, and Israel Inbar Regulations), examining how their design is shaped by the underlying risk management paradigms. The analysis distinguishes between hazard-based, risk-based, and hybrid regulatory approaches, including the use of fixed prescriptive standards, multiple-barrier strategies, and use restrictions based on water quality. This paper analyzes how risk management approaches dictate treatment requirements, quality thresholds, monitoring intensity, and implementation costs. Results demonstrate that regulatory outcomes are driven primarily by risk management philosophy and socio-economic context, rather than uniform scientific evidence. The review highlights that regulatory effectiveness depends on balancing safety standards with feasibility and practical enforceability. Overly restrictive, end-of-pipe compliance frameworks increase infrastructure costs and, by exceeding local feasibility, may inadvertently drive hazardous, unplanned reuse practices. Conversely, too flexible, fully-risk-based approaches are difficult to enforce and may fail to build public trust. Recent policy developments reveal a convergence toward hybrid regulatory approaches that combine baseline quality benchmarks with flexible, field-level risk mitigation measures such as subsurface drip irrigation and crop restriction. By clarifying how risk management paradigms shape agricultural water reuse regulations, this work provides insights to support the design of realistic and context-appropriate reuse policies.
Intensive winter wheat production in the North China Plain (NCP) is increasingly constrained by excessive nitrogen (N) inputs, groundwater depletion, and environmental burdens. Identifying an optimal water-N management strategy that balances productivity, profitability, energy performance, and environmental sustainability is therefore essential. A four-season split-plot field experiment was conducted under high–low seedbed cultivation (HLSC), with four N application rates (N1: 360, N2: 300, N3: 240, and N4: 180 kg N ha−1) assigned to the main plots and three irrigation quotas (W1: 120, W2: 90, and W3: 60 mm) assigned to the subplots. Economic performance, energy budgets, environmental impacts, and eco-efficiency were jointly assessed to identify an eco-optimal management strategy (EOMS). Across the four growing seasons, N3W2 provided the best overall balance among crop productivity, profitability, energy efficiency, and environmental sustainability. Grain yield under N3W2 was statistically comparable to that under the higher-input treatments, while achieving the highest net income (1370.89 $ ha−1) and benefit–cost ratio (1.61). Reducing N application from N1 to N3 decreased energy input by an average of 18.91% across irrigation levels, while N3W2 maintained a high energy-use efficiency (9.53). Compared with N1W1, N3W2 reduced the total normalized midpoint burden and total endpoint environmental damage by 25.9% and 24.1%, respectively. Fertilizer and irrigation were the dominant contributors to environmental burdens, jointly accounting for 91.3% of the summed normalized midpoint burden. N3W2 also achieved the highest endpoint eco-efficiency (2.20 $ Pt−1), demonstrating that coordinated reductions in water and N inputs can improve economic and environmental performance without a significant yield penalty. Overall, coupling 240 kg N ha−1 with 90 mm irrigation quotas provides a practical EOMS for HLSC-cultivated wheat in the NCP and other regions with comparable conditions.
Accurate prediction of soil moisture is essential for precise agricultural irrigation decision-making and regional water resource optimal scheduling. However, regional soil moisture inversion commonly suffers from three major limitations, including overreliance on single remote sensing data sources, fixed canopy parameters adopted in conventional physical models, and severe cloud contamination in the cloudy mountainous areas of Southwest China. In this study, a synergistic multi-source remote sensing framework integrating Landsat-8/9, Sentinel-2, and Sentinel-1A datasets was established to improve the traditional water cloud model by introducing phenology-driven dynamic canopy parameters. Specifically, canopy coverage and canopy water content of late-maturing citrus across different growth stages were accurately retrieved from Landsat-8/9 and Sentinel-2 imagery to optimize the simulation of canopy backscattering signals. The all-weather C-band radar data effectively mitigate the persistent cloud-cover limitations prevalent in the hilly regions of Southwest China. The results indicated that the vegetation water content in large-scale citrus orchards retrieved by optical remote sensing exhibits significant spatial variability, mainly distributed in the range of 1.65–5.71 kg·m−2 and 1.55–5.78 kg·m−2, respectively. The soil moisture inversion results using multi-temporal Sentinel-1A data in VV polarization outperformed those in VH polarization, with R² ranges of 0.785–0.854 and 0.515–0.759, RMSE ranges of 6.125–8.351% and 6.942–8.412%, respectively. The two polarizations data have the highest inversion accuracy in citrus fruit maturation stage, with R² ranges of 0.811–0.842 and 0.682–0.711, RMSE ranges of 6.125–6.132% and 6.942–7.025%, respectively. This study proposes a novel methodology for high-resolution and precision inversion of surface soil moisture at the regional farmland scale.
Increasing freshwater scarcity and salinity risks in arid regions worldwide necessitate alternative water sources and salt-tolerant crops for sustainable agriculture. However, the effects of such water sources on crop performance and root-zone soil salinity need to be evaluated before advocating the wider use of saline water. This two-year field study used a split plot design to evaluate the effects of saline municipal treated wastewater (TWW) and freshwater (FW, control) (main-plot factor) on the performance of three spring canola (Brassica napus L.) cultivars (CP930RR, CP955RR, and CP9978TF, subplot factor), and root-zone soil salinity (top 0.6 m). Results indicated that canola seed and straw yields did not differ significantly between TWW and FW across three cultivars. Across treatments, seed yield ranged from 2327 kg ha−1 under FW to 2675 kg ha−1 under TWW for CP9978TF, while the straw yield ranged from 5095 kg ha−1 for CP955RR under TWW to 6471 kg ha−1 for CP930RR under TWW. After two growing seasons, average soil salinity (ECe) increased from a baseline level of 2.3 dS m−1 to 3.5 dS m−1 under FW and 4.6 dS m−1 under TWW, while SAR rose from 4.2 to 5.2 and 7.5, respectively. Despite these increases, ECe and SAR remained well below canola thresholds for salinity (9.7 dS m−1) and sodicity (SAR 13). Salinity and sodicity increased with time and depth, particularly under TWW, indicating progressive salt accumulation and redistribution within the soil profile. The findings demonstrated that saline alternative water sources, particularly treated wastewater (TWW), can supplement or partially replace conventional freshwater irrigation and thereby contribute to the sustainability and resilience of irrigated agriculture in water-scarce regions.
Rice-crab co-culture is a representative ecological circular farming system, yet its water and energy requirements and associated emissions remain insufficiently quantified under contrasting irrigation strategies. Here, we combined a three-year field experiment in Heilongjiang Province, Northeast China, with life cycle assessment to quantify partitioned water inputs (rice irrigation and crab-related exchange), energy demand, greenhouse gas emissions, and economic performance under water-saving versus flooded co-culture management. Across the three experimental years, water-saving co-culture reduced rice irrigation by 24.7–26.0% and crab exchange water by 36–37% compared with flooded co-culture. It also reduced total carbon footprint by 32.5% and decreased carbon footprint per unit output value by 35.1%, indicating improved climate efficiency per economic output. Nonrenewable energy consumption was lower under water-saving co-culture, while revenues increased over the three experimental years, indicating an association between water-saving management and improved resource-use and economic performance. Overall, our results provide field-based evidence that optimizing water-level management in habitat-constrained rice-crab systems can simultaneously reduce water and energy inputs and emissions, offering transferable insights for irrigated rice-aquatic production under increasing water and energy constraints.
