Search Results (1,027)

While continuously promoting agricultural water-saving and efficiency improvement, Ningxia is confronted with problems such as deepening groundwater tables and growing ecological vulnerability. Exploring the trade-off relationships and their evolutionary characteristics between socioeconomic development and water resource carrying capacity, as well as between water use efficiency improvement and groundwater-ecosystem maintenance, is of great significance for coordinated water resource governance in arid irrigation districts. Based on time-series data covering 2000–2024, this paper establishes a DPSIR evaluation model and constructs a progressive quantitative analytical framework coupling the entropy-weight-Tapio decoupling, rate-scissors difference and PLS-SEM models. During the study period, the growth rate of the response (R) dimension (13.76%) was far higher than that of the state (S) dimension (3.23%) from 2011 to 2020, confirming the objective existence of dual trade-offs. The two categories of trade-offs underwent a three-stage evolution of “latent-intensified-remediation”, showing the counter-intuitive feature of “effective total-volume control alongside continuous groundwater table deepening”. Hidden transmission barriers were identified for 2008–2016 (θ₁, θ₂ dropped to 0.46–1.32°): the transfer of water-saving dividends to industry caused groundwater extraction to rise rather than fall to a certain extent. PLS-SEM analysis reveals that structural lock-in acts as the core inhibiting factor for ecological protection. The total effect of socioeconomic development on ecology reaches 0.921, whereas structural lock-in produces a chained negative mediating effect of −0.192 by suppressing water use efficiency. Improvement in water use efficiency presents dual characteristics of overall ecological gain and localized groundwater-recharge loss. It can be concluded that engineering-only water-saving measures cannot balance water-intake reduction and recharge deficits. It is necessary to simultaneously advance low-water-consumption cropping-pattern restructuring, rigid enforcement of the 2.5 m ecological groundwater table threshold, and the substitution mechanism for saved-water volume between industry and agriculture, so as to build a coordinated “water-saving-recharge-ecology” regulation system. Full article

Soil salinization threatens agriculture in arid regions, and remote sensing retrieval still faces challenges of unclear mechanisms and poor generalization. Based on Sentinel-2 data, this study compares the retrieval performance of various machine learning and deep learning models for farmland soil salt content, and introduces an attention mechanism for optimization. The main conclusions are as follows: (1) Random Forest achieved the highest accuracy among classical machine learning models (R² = 0.334), while CNN performed better among deep learning models (R² = 0.70), making it suitable for modeling scenarios with a single dominant salt type, well-defined spatial structures, and samples covering major environmental gradients. (2) In the problem of multispectral salinity retrieval, the complementarity of errors among base models was poor, preventing the ensemble model from fully leveraging its advantages. (3) Specific indices derived from near-infrared, red-edge, and blue–green bands performed well for sulfate-type salinity retrieval. (4) In the seed maize production area of Gansu, approximately 75.47% of farmland is non-saline, with severely saline land accounting for 1.42%; over the past decade, 74.84% of the area experienced a decrease in salt content, among which areas with a significant decline (accounting for 9.31%) corresponded consistently with regions where continuous engineering salt removal and microbial fertilizer management had been implemented for ten years. This demonstrates that, under conditions of limited ground samples, combining Sentinel-2 spectral information with moderate local spatial context can enhance the ability to detect salinity changes in relatively uniform irrigated areas. Full article

Although steel pipelines constitute the primary infrastructure of agricultural irrigation systems, they are highly susceptible to moisture-induced pitting corrosion and severe operational conditions, including internal pressure fluctuations and heavy overburden loads. This study evaluated the structural performance and durability of full-diameter steel pipe specimens (311.5 mm in diameter and 2.0 m long) repaired using CFRP (single-layer) and GFRP (single- and multi-layer) sheet wrapping as well as overlay welding. A 60-mm pinhole defect corresponding to a 6% circumferential damage ratio was introduced to simulate advanced localized corrosion. The repaired pipelines were experimentally assessed under four-point bending, internal hydrostatic pressure, and accelerated salt spray exposure. Under service-level flexural loading, all specimens exhibited similar global load–deflection responses regardless of defect or repair condition. However, localized strain measurements revealed that the unrepaired defect produced tensile strains up to 12 times greater than those of the intact pipe, whereas all repair techniques effectively suppressed the localized strain concentration. The effectiveness of the FRP systems improved with increasing reinforcement thickness. Overlay welding provided the highest structural performance, restoring localized strain behavior to a level comparable to that of the intact pipe. Under an internal pressure of 2.0 MPa, welding and CFRP maintained 100% pressure retention, whereas single-layer GFRP exhibited minor radial bulging, reducing its pressure retention ratio to 73.5%. Increasing the GFRP thickness restored the retention ratio to 93.0%. Accelerated salt spray exposure further demonstrated that GFRP provided effective barrier protection against corrosion by acting as an impermeable dielectric barrier under short-term exposure, whereas welded specimens still exhibited localized corrosion around the heat-affected zone despite epoxy coating. These findings demonstrate that overlay welding offers the greatest immediate structural restoration, whereas adequately dimensioned FRP systems can provide a more balanced solution for multi-hazard durability by simultaneously enhancing structural performance and mitigating electrochemical degradation in aging agricultural steel pipelines. Full article

Agricultural systems face heightened risks from extreme weather events and water insecurity. Producers commonly use irrigation ponds to secure or improve crop yields. The hydrology and storage efficiency of irrigation ponds in the Willamette Valley, Oregon, USA, are not well understood. This study evaluated the hydrological interactions of a tile-drain-fed irrigation pond. A water balance approach was applied over the irrigation season using weather data, evaporation estimates, metered irrigation withdrawals, and bathymetry analysis for pond stage–volume estimates to quantify water budget components. Irrigation withdrawals were the largest output, with 75% of effective pond storage utilized, followed by evaporation (24.5%). Evaporation far exceeded precipitation over the same period. The unaccounted-for proportion of the water balance was negligible, indicating that net drain tile inflows and groundwater exchange had a minimal impact on seasonal irrigation water availability and seepage losses. This study provides an example for measuring water balance components and assessing water input–output relationships of an irrigation pond within a headwaters stream and catchment (<5 ha) of an important agricultural corridor in the Pacific Northwest region in the USA. The study methodology can be replicated in other similar agricultural areas with irrigation ponds worldwide. Findings from this study can be used by farmers, irrigation districts, and other stakeholders to better inform irrigation planning and water management decisions for similar site conditions. Full article

Cyanobacterial blooms are an increasing global concern due to the release of microcystins (MCs), potent hepatotoxins that threaten aquatic ecosystems, livestock, and human health. This study investigated the efficiency of low-cost natural adsorbents—rice straw (R), corn straw (C), and sawdust (S)—compared with commercial activated charcoal (AC) for the removal of microcystins from water sources in Upper Egypt. A total of 72 water samples were collected between June and September 2022 from rivers, irrigation canals, and wastewater channels in Sohag and Assiut governorates. Samples were analyzed for intra- and extracellular MCs using ultra-performance liquid chromatography (UPLC), while adsorbents were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM), pH, moisture content, iodine number, and methylene blue adsorption. Results revealed mean total MC concentrations of 9.39, 3.18, and 11.70 µg/L in river, irrigation, and wastewater samples, respectively—exceeding the World Health Organization (WHO) guideline of 1 µg/L. Adsorption experiments demonstrated that AC exhibited the highest MC removal efficiencies (88.9% in acidified and 92.3% in neutral water), followed by sawdust (84–86.2%), corn straw (74.01–85.2%), and rice straw (75.4–74.8%). Sawdust and corn straw performed particularly well for extracellular MC removal, while AC was most effective for intracellular fractions. This study highlights the potential of agricultural by-products, particularly sawdust and corn straw, as sustainable, low-cost alternatives to activated charcoal for cyanotoxin removal. Their availability and efficiency support their application in water treatment systems without chemical pretreatment. These findings indicate that agricultural residues could serve as practical, low-cost adsorbents for mitigating cyanotoxin contamination in water, especially in resource-limited regions, while also promoting the beneficial reuse of agricultural waste. Full article

Accurate estimation of evapotranspiration (ET) and transpiration (T) is crucial in enhancing irrigation schedules in agricultural ecosystems. A simplified canopy resistance ($r_s^c$) algorithm grounded in the Ball-Berry model was integrated into Penman–Monteith (PM)-based models and assessed in a humid-region kiwifruit orchard. The Shuttleworth–Wallace (SW) and Clumping (CL) models agreed well with eddy covariance ET (ET_EC) throughout the growth season (R² = 0.79 and 0.89, RRMSE = 0.73–0.75 and 0.22, at the sub-daily and daily scales), outperforming the Two-Patch (TP) and topography- and vegetation-based surface energy partitioning (TVET) models. SW and CL also best reproduced sap-flow-based T (T_SF) (R² = 0.72 and 0.71–0.72, RRMSE = 0.85–0.87 and 0.38–0.39), primarily due to their higher accuracy during the mid stage. In this ecosystem, canopy interception evaporation had only a limited influence on the simulation performance of the SW and CL models. ET and T were most sensitive to changes in $r_s^c$ and related environmental factors, including gross primary productivity (GPP), the empirical parameter (a₁), and soil water content (θ). The sensitivity of T to θ was higher during the early stage but lower during the mid and late stages due to seasonal drought. ET was more sensitive to θ than T, due to its direct effect on soil surface resistance ($r_s^s$). T simulated by TP, TVET, and CL models showed greater sensitivity to leaf area index (LAI) than SW, while contrasting T and soil evaporation (E) responses to LAI caused ET to remain relatively insensitive. Both ET and T were highly sensitive to net radiation (R_n), air temperature (T_a), and vapor pressure deficit (VPD), all of which directly affect the energy balance. Overall, integrating the simplified canopy resistance algorithm with PM-based models improves ET and T estimation in humid-region orchards, supporting more efficient water management. Full article

Water scarcity is a critical constraint for agricultural production, particularly in Mediterranean regions, since it is a key stressor directly affecting plant growth, yield, and fruit quality in vegetables. Among others, cherry tomato (Solanum lycopersicum var. cerasiforme) represents a high-value crop with increasing global importance due to its economic, nutritional, and commercial attributes. The objective of this research is to evaluate the seasonal (autumn and spring) effects of reduced irrigation volumes relative to commercial practice on the yield, fruit quality, water-use efficiency (WUE), and physiological responses of cherry tomato plants grown under greenhouse conditions. Plants were irrigated according to growers’ standard practices (100%), as well as 75% and 50% of that water volume, throughout a cultivation cycle of 11 months (August to July). Results indicate strong seasonal variation in quality and physiological parameters (+35.3% single fruit fresh weight and +34.2% flesh firmness, but −68.2% total phenolic compounds content and −38.5% total antioxidant capacity during late autumn assessment). A 50% irrigation level reduction significantly improved water-use efficiency (+82.7%), but reduced single fruit fresh weight (−22.9%) and showed a declining trend in total yield. We conclude that local irrigation practices should be reconsidered in regions highly affected by water scarcity and suggest that the 75% treatment has a practical potential. Full article

Groundwater is a principal irrigation water source worldwide; however, its quality is increasingly diminished by rapid urbanization, improper agricultural practices, and accelerating industrial activities. Groundwater management is especially important in areas where soil salinization and erosion are more probable, such as arid and semi-arid zones. In view of this, the Armavir region of the Republic of Armenia was selected as the study area, being an intensively cultivated agricultural zone. The objective of this study was to assess and map the quality of groundwater for irrigation using advanced methods, taking into account both climatic conditions and anthropogenic influences. A total of 72 groundwater samples were collected during the irrigation season from 41 unconfined and 31 confined aquifer wells. Key hydrochemical parameters (pH, EC, TDS, Cl⁻, HCO₃⁻, CO₃²⁻, Na⁺, K⁺, Ca²⁺ and Mg²⁺), irrigation indices (SAR, Na%, MH, RSC and PI), and graphical methods (Gibbs, USSL and Wilcox diagrams) were applied to assess groundwater quality. An integrated assessment was performed using the Irrigation Water Quality Index (IWQI), and spatial distribution was evaluated through geostatistical analysis and GIS mapping. Although certain individual hydrochemical parameters indicated limitations for irrigation in localized areas, particularly within the unconfined aquifer, the integrated IWQI assessment revealed that groundwater predominantly falls within the good to excellent categories across the study area, with more favorable conditions observed in the confined aquifer. These findings constitute an essential prerequisite for counteracting soil salinization and promoting sustainable agricultural development. Full article

Water deficit represents a major environmental constraint that severely limits the growth and yield of common bean (Phaseolus vulgaris L.). Although inoculation with plant growth-promoting rhizobacteria (PGPR) has emerged as a promising strategy to mitigate drought-induced stress, the efficacy of specific strains, such as Priestia aryabhattai CMAA 1363, remains to be fully elucidated. This study evaluated the morpho-agronomic and biochemical responses of common bean to seed inoculation with P. aryabhattai CMAA 1363 under two contrasting irrigation regimes: 100% (well-watered) and 40% (water-restricted) of available water capacity (AWC) under greenhouse conditions. Water restriction significantly compromised plant performance, reducing plant and pod length, root dry biomass, and yield components (pod and grain counts, and total grain mass). Conversely, bacterial inoculation enhanced vegetative traits, increasing plant length by approximately 15% and root dry biomass by approximately 25% compared to non-inoculated controls. Notably, under severe water deficit (40% AWC), inoculated plants achieved a 20% increase in total grain mass per plant relative to their non-inoculated counterparts. Biochemical profiling indicated that inoculation effectively attenuated oxidative stress, as evidenced by lower malondialdehyde (MDA) accumulation and modulated superoxide dismutase (SOD) activity, while water-stressed plants adapted by accumulating total soluble sugars and increasing peroxidase (POD) activity. Overall, P. aryabhattai CMAA 1363 promotes vegetative development, preserves grain production under drought, and orchestrates antioxidant defense mechanisms, highlighting its potential as a sustainable bioinput to improve common bean resilience in water-limited agricultural systems. Full article

Salicornia is a genus of salt-tolerant plants capable of growing and producing biomass under conditions that severely constrain conventional agriculture. Beyond its ecological niche as a halophyte, Salicornia has emerged as a multifunctional crop for sustainable food, feed, and bio-based production in saline landscapes. The increasing generation of saline wastewaters and brines from aquaculture, municipal treatment, agro-industrial activities, and greenhouse systems intensifies the need for natural solutions that can recover resources while protecting soils and receiving ecosystems. This review synthesizes current understanding of how Salicornia species perform when irrigated with saline wastewaters, with particular attention to their dual role as productive crops and phytoremediation agents. It further examines the biological mechanisms underlying salt tolerance and the influence of wastewater characteristics on biomass production and phytoremediation performance. Overall, the evidence indicates that Salicornia performs particularly well in nutrient-rich, controlled saline effluents, whereas more complex wastewater matrices require careful contaminant management to ensure biomass quality and safe reuse. This synthesis positions Salicornia as a fundamental species for circular and climate-resilient strategies linking saline wastewater reuse with crop production, while emphasizing that standardized reporting, long-term field validation, and contaminant-aware biomass management remain essential to support wider adoption. Full article

The Normalized Difference Vegetation Index (NDVI) is widely used as an indicator of irrigation activity in arid and semi-arid agricultural regions. This study evaluates how NDVI extraction scale and statistical metric influence groundwater level prediction accuracy in the irrigated Tadla Plain, MoroccoA total of 96 Long Short-Term Memory (LSTM) models were developed by combining six NDVI extraction scales, from the well pixel to 20,000 m buffers, and four statistical metrics: mean, median, maximum, and minimum. Results demonstrate that extraction scale is a critical factor controlling model performance. Across the four monitored wells, larger buffers (≥2500 m) generally outperformed smaller ones (≤1000 m), with the optimal scale occurring at 15,000 m for three wells, while one well achieved its best performance at the 1000 m scale. The best models achieved RMSE values between 0.09 and 0.625 m and R² values ranging from 0.94 to 0.997. Maximum NDVI provided the highest predictive accuracy for three wells, whereas minimum NDVI performed best for one well. Statistical analyses further confirmed that extraction scale generally exerts a stronger influence on prediction performance than the choice of NDVI metric. Spatial validation revealed that irrigated areas more than doubled between 2001 and 2017, and model performance improved as NDVI captured this broader irrigation footprint. These findings suggest that groundwater level variations in the Tadla Plain are more strongly associated with NDVI signals extracted at broader spatial scales than with strictly local vegetation conditions around individual wells, highlighting the importance of optimizing both NDVI extraction scale and metric for groundwater forecasting in irrigated semi-arid regions. Full article

This study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops under nitrogen (N) fertilization treatments, irrigation (I), and their interaction (N × I). The U-Net model outperformed all other methods, achieving accuracies for crop nitrogen status of 65–99% in N, 84–100% in I, and 41–82% in N × I treatments, with variation due to different input data. Supervised machine learning also performed well, with Support Vector Machine achieving 53–87, 66–86, and 32–66% respectively, and Random Forest 61–96, 70–81, and 33–65%. Unsupervised K-means yielded the lowest accuracies (47–58, 9–65, and 8–34%), demonstrating necessity of substantial supervision to delineate crop nitrogen and water status. These findings were confirmed by repeated analyses of UAV imagery acquired later in the growing season with consistent results. Comparable classification performance was observed for crop water status and leaf area index at both time points. Despite being demonstrated in a single-field, single-crop framework, the results provide proof of concept for applying deep learning classifiers to detect subtle nitrogen and water stress under field conditions in precision agriculture. Future research could test diverse agroecosystems and growing seasons, alternative deep learning algorithms, and sensor data fusion to improve classification accuracies. Full article

Accurate high-resolution mapping of soil organic carbon (SOC) is essential for agricultural management and carbon pool assessment in arid lakeside oases, a fragile aquatic-terrestrial transition ecosystem. However, targeted high-precision SOC mapping for typical lakeside oases remains insufficient: existing models have poor adaptability to the highly fragmented oasis landscapes, and fine-resolution SOC spatial products for the representative Bosten Lake oasis are lacking. To address this inadequacy, we integrated Sentinel-2 imagery with topographic, bioclimatic, and spectral environmental covariates and developed four machine learning models (Random Forest, XGBoost, SVR with RBF kernel, Cubist) for SOC prediction, based on 153 topsoil samples (0–20 cm) collected via stratified random sampling in the study area. Model performance was validated through 5-fold cross-validation, the optimal model was selected for 10 m resolution SOC mapping, and dominant driving factors were identified via SHAP analysis. The results showed that SOC content in the study area ranged from 2.37 to 20.63 g·kg⁻¹ (mean = 10.59 g·kg⁻¹), with moderate spatial variability (CV = 34.86%). The Cubist model achieved the highest mapping accuracy (R² = 0.8166, RMSE = 1.5812 g·kg⁻¹, MAE = 0.9247 g·kg⁻¹). The generated high-resolution SOC map clearly revealed a spatial pattern of high values in the eastern well-irrigated cropland and low values in bare and salinized areas at the oasis edge. The Bare Soil Index (BSI), surface roughness, and Normalized Difference Red Edge Index 1 (NDRE1) were the dominant factors controlling SOC spatial distribution. This study mitigates the inadequacy of high-precision SOC mapping in typical arid lakeside oases, and the proposed framework is readily applicable to other fragmented arid landscapes worldwide and provides reliable spatial data and a scalable technical framework for precision agriculture and sustainable land management in similar fragile ecosystems. Full article

Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012–2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0–5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40–100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94–96% at filling, then declining to 84–85% at maturity. The two methods agreed well during filling to maturity (differences of 2–10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions. Full article

Groundwater accounting in arid and semi-arid well-irrigated areas is often constrained by difficulties in water-meter installation and maintenance, as well as variability in well–pump operation, thereby limiting refined agricultural water-use management. The electricity-to-water conversion coefficient (Tc) can be used to estimate groundwater abstraction from electricity consumption; however, the applicability of empirical models developed for plain irrigation districts remains uncertain in plateau regions characterized by pronounced topographic relief and complex aquifer conditions. This study examined 56 typical irrigation wells in Chayouzhongqi on the Inner Mongolia Plateau. Based on pumping-test data and using correlation analysis, structural equation modeling, redundancy analysis, and random forest analysis, we investigated the spatial distribution of Tc and its associated mechanisms. Tc ranged from 0.08 to 3.88 m³ kWh⁻¹, with a mean of 1.62 m³ kWh⁻¹, and exhibited a pattern of higher values in the north, lower values in the south, and the lowest values in the western part of the study area. Electricity consumption, rated flow rate, and actual discharge were the principal associated variables, with relative importance values of 43.0%, 21.6%, and 15.7%, respectively. Topographic and aquifer conditions imposed regional constraints on spatial variation in Tc by influencing well–pump operating states. These findings indicate that Tc estimation in plateau well-irrigation districts should not directly adopt empirical relationships developed for plains, but should instead be calibrated according to regional hydrogeological and engineering operating conditions, thereby providing a basis for improved groundwater accounting and water-saving management in arid and semi-arid regions. Full article