Search Results (3,551)

Water scarcity in semi-arid regions makes efficient irrigation scheduling a priority, yet farm-level adoption of soil-moisture monitoring remains limited by the cost, low portability and installation complexity of commercial sensing systems. This study presents the development and field validation of WaziSense, a low-cost, solar-powered Internet-of-Things (IoT) smart tensiometer, developed within the OSIRRIS platform for soil-water monitoring and irrigation scheduling. The device couples a Watermark granular-matrix sensor and a DS18B20 temperature probe to an ATmega328P microcontroller (Arduino Pro-Mini, 3.3 V, 8 MHz) with long-range LoRa communication and a maximum-power-point-tracking (MPPT) solar-charging stage, logging soil matric potential and soil temperature every 15 min. An open-source edge/cloud stack (WaziGate, WaziApp) retrieves weather forecasts from an open API and runs an automated machine learning (AutoML) regression pipeline that forecasts soil-water dynamics and the time to a user-defined threshold, from which irrigation is scheduled and its applied volume verified by a flow meter. The system was deployed at three bioclimatic sites in Tunisia (durum wheat at Cherfech, citrus at Nabeul, apple at Sbeitla), with tensiometers installed at 20 and 40 cm depths, and validated against commercial 10HS capacitive probes coupled to a ZL6 data logger, with which the co-located readings were significantly correlated (r = 0.81). Calibrated readings showed a strong relationship between soil–water content and soil–water potential (R² = 0.99), and the edge forecasting model reproduced soil–water dynamics on unseen data (Sbeitla apple site, 5-day horizon) with R² = 0.73, RMSE = 0.35, MAE = 0.23 and MPE = 12.52%. With a material cost under about 90 EUR per node and fully open-source hardware and software, WaziSense is one to two orders of magnitude cheaper than commercial monitoring stations, offering an affordable, reproducible and scalable tool for data-driven irrigation in water-limited agriculture. Full article

Wind erosion is a major driver of land degradation in drylands worldwide, yet the relative importance of climatic forcing and local surface conditions remains poorly understood. We quantified horizontal mass transport (HMT), characterized the particle-size distribution and organic matter of transported sediments, and identified the main environmental controls on wind erosion across three rangeland sites in the Patagonian steppe of southern Argentina. HMT was monitored over one year using passive sediment collectors (Mendeźs trap), while climatic erosivity, vegetation cover, and topsoil properties were concurrently assessed. Wind erosion exhibited substantial spatial variability, with annual HMT ranging from 0.22 to 2.35 kg m⁻¹ yr⁻¹ among sites. Transported sediments were enriched in both silt and organic matter relative to source soils, indicating the preferential export of fine, carbon-rich fractions during aeolian transport. Multivariate analyses revealed that wind erosion was more strongly associated with local vegetation cover and soil erodibility than with climatic erosivity. These findings suggest that surface conditions can override regional climatic controls on sediment transport in arid rangelands. Our results highlight the critical role of maintaining vegetation cover and soil surface stability to mitigate desertification processes and provide empirical evidence to improve wind erosion assessment and land management strategies in drylands. Full article

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning models across multiple soil and groundwater prediction tasks. This study systematically evaluated the interactions between 10 metaheuristic feature-selection algorithms and 13 predictive models, including random forest (RF), convolutional neural network (CNN), recurrent architectures, CNN–recurrent neural network (RNN) hybrids, squeeze-and-excitation (SE)-enhanced hybrids, and iTransformer-based hybrids, across four prediction tasks involving soil organic carbon (SOC), soil–water extract electrical conductivity (ECe), apparent electrical conductivity (ECa), and groundwater level (GWL) in Xinjiang, China. A total of 149 candidate environmental covariates were considered for ECe, SOC, and ECa, whereas 122 candidate covariates were considered for GWL. The results showed that no single feature-selection method consistently performed best across all four targets; instead, predictive performance depended on the interaction among the feature-selection strategy, predictive architecture, and target variable. CNN–RNN hybrid architectures generally achieved higher predictive performance than standalone models, although their benefits varied among prediction targets. The best-performing combinations yielded coefficient of determination (R²) values of 0.9826, 0.6981, 0.8429, and 0.8085 for GWL, SOC, ECe, and ECa, respectively. These findings indicate that target-specific compatibility, rather than aggressive dimensionality reduction or a universally superior algorithm, is a key determinant of predictive performance in high-dimensional DSM. By demonstrating that feature-selection effectiveness is jointly influenced by model architecture and target characteristics, this study provides a methodological reference for developing target-specific digital soil mapping models in arid regions. Full article

The functional expression of plant growth-promoting (PGP) traits in soil actinobacteria is conditioned by abiotic factors, yet the combined effects of pH and temperature on their metabolic profiles and the stability of these profiles after encapsulated formulation and post-processing stress remain insufficiently characterized. This study aimed to evaluate the physiological plasticity of native actinobacteria and the expression of plant growth-promoting (PGP) traits under different pH and temperature conditions, as well as their stability after encapsulation, dehydration, and exposure to UV irradiation. Strains isolated from a semi-arid agricultural soil were analyzed to determine their ability to produce indole-3-acetic acid (IAA), siderophores, and phosphatases, as well as their ability to fix nitrogen, degrade cellulose, and tolerate salt stress. Temperature and pH significantly affected all evaluated PGP traits (p < 0.001), and their expression was not directly associated with biomass production. Two strains, S1 and S4, exhibited the highest overall PGP indices. Strain S1 maximized IAA and siderophore production under neutral conditions (pH 7.0, 30 °C), whereas strain S4 maintained more stable phosphatase activity across the tested pH and temperature ranges. Cell viability remained above 85% after encapsulation and dehydration. Dehydration enhanced IAA and siderophore production in strain S1, while strain S4 exhibited transient metabolic activation under UV irradiation in non-dehydrated capsules. The encapsulation matrix preserved cell viability more effectively than it preserved the complete PGP functional profile, indicating that viability alone is an insufficient criterion for evaluating the technological success of alginate-based bioinoculant formulations. These findings highlight the importance of integrating environmental preconditioning and functional stability assessments into the development of robust microbial bioinoculants adapted to agricultural systems subjected to fluctuating environmental conditions. Full article

The degradation of Calcisols in the arid regions of Central Asia constrains sustainable agricultural production because of low organic matter content, poor aggregate stability, nutrient limitations, and increasing climatic stress. This study evaluated a rhizosphere engineering approach based on the native plant growth-promoting bacterium Pseudomonas veronii Ps-S/Sh-1503/2022 for the rehabilitation of degraded Calcisols. Four-year field experiments (2022–2025) using Sorghum bicolor assessed plant growth, rhizosphere microbial indicators, physiological responses, pathogen suppression, crop productivity, and implementation feasibility through economic and environmental assessments. Inoculation with P. veronii increased root depth by 45%, improved aboveground biomass, increased the ratio of culturable bacteria to Fusarium spp. from 6.1 to 10.3, and reduced Fusarium abundance by 29.4%. Structural equation modeling suggested that trophic support (42.1%), aggregate stabilization (27.4%), biocontrol (23.3%), and defense-related responses (7.2%) were the principal pathways associated with soil rehabilitation. Economic assessment indicated that the combined inoculation and mineral fertilization treatment provided the highest profitability, while environmental assessment estimated potential reductions in mineral fertilizer use and greenhouse gas emissions. These findings suggest that rhizosphere engineering using a native P. veronii strain represents a promising, economically viable, and climate-smart approach for improving the biological functioning of degraded Calcisols and supporting sustainable agricultural production. Full article

Farmland shelterbelts are important linear vegetation infrastructures in oasis agricultural landscapes. Their accurate extraction is essential for shelterbelt inventory and farmland management, but remains challenging because shelterbelts are narrow, elongated, locally discontinuous, and spectrally similar to croplands, orchards, roadside vegetation, bare soil, and irrigation-related features. This study developed a GF-2-based deep learning workflow for farmland shelterbelt extraction in the 11th Regiment of Alar City, Xinjiang, China. Four representative semantic segmentation models, namely U-Net, U-Net with scSE attention, U-Net++, and DeepLabV3+, were trained and evaluated using four-band GF-2 optical imagery under a unified experimental setting. Model performance was assessed using Precision, Recall, F1-score, Intersection over Union (IoU), overall accuracy, and Kappa coefficient. Patch-level statistical comparison and visual interpretation were further conducted to examine performance differences, shelterbelt continuity, boundary integrity, omission errors, and background confusion. The results showed that U-Net achieved the best overall performance, with a Precision of 94.58%, Recall of 94.77%, F1-score of 94.67%, IoU of 89.88%, overall accuracy of 99.71%, and Kappa coefficient of 0.9452. Compared with U-Net with scSE attention, U-Net++, and DeepLabV3+, U-Net better preserved the continuity and boundary integrity of narrow shelterbelts in regular field-boundary networks. The other models showed varying degrees of omission, boundary fragmentation, or confusion with spectrally similar agricultural objects. The best-performing U-Net model was then applied to the complete study area, and the extracted shelterbelt area was approximately 6.5 km², accounting for about 4.37% of the cultivated land area. These results indicate that GF-2 optical imagery combined with semantic segmentation can support fine-scale farmland shelterbelt mapping in oasis agricultural landscapes. They also show that model evaluation for narrow linear vegetation features should consider not only pixel-level accuracy but also spatial continuity, boundary integrity, and typical error patterns. The proposed workflow provides a practical reference for GF-2-based farmland shelterbelt inventory, high-resolution linear vegetation mapping, and shelterbelt monitoring in arid oasis agricultural landscapes. Full article

As an endemic woody plant resource widely distributed in the arid regions of northwestern China, Hippophae rhamnoides subsp. sinensis Rousi possesses both ecological and medicinal value. Elucidating the effects of climate and soil conditions on the composition, structure, and function of its root-associated bacterial communities is of practical significance for the development and utilization of these indigenous plant resources in this water-limited region. In this study, root samples of H. rhamnoides were collected from 12 sampling sites in the arid region of Northwest China. High-throughput amplicon sequencing was employed to examine bacterial composition, alpha and beta diversity, molecular co-occurrence networks, and PICRUSt-based functional prediction. Mantel tests and redundancy analysis (RDA) were further applied to identify what is associated with shaping bacterial community structure. The main results are summarized as follows: (1) There were significant differences in the number of ASVs among the various sampling sites, with the P8 site having the highest number (435) and the P5 site having the lowest (156). The dominant phyla in the community were Proteobacteria, Actinobacteria, and Cyanobacteria. (2) Both α and β diversity showed significant differentiation among the 12 sampling sites. The Ace and Chao1 indices of P8 sampling sites were the highest, while P5 sampling sites were the lowest. In terms of community aggregation, P7 and P12 sampling sites showed tighter clustering, whereas P4 and P5 sampling sites showed more scattered bacterial assemblages. (3) Functional prediction suggested that metabolism, environmental information processing, and genetic information processing functional potential may all be dominant across 12 sampling sites. The abundance of environmental information predicted that the processing functional potential of the P9 sampling site was higher than that of the other 11 sampling sites, while the genetic information processing functional potential was low. (4) Cluster analysis grouped the 12 sampling sites into two groups: sampling sites P10, P11, and P12 were grouped into Group 1, while the remaining 9 sampling sites were grouped into Group 2. (5) RDA revealed that altitude was associated with shaping the root endophytic bacterial community structure of H. rhamnoides, followed by soil total nitrogen. Taken together, the H. rhamnoides populations investigated and sampled in this study were predominantly distributed in neutral-to-alkaline arid soils. The relevant environmental factors are significantly correlated with the alpha diversity patterns of root endophytic bacteria of this species, and they can modulate the community assembly processes, functional allocation characteristics, and co-occurrence network structures of these root endophytic bacteria. Full article

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Agricultural soils in arid regions require assessment approaches that separate local element enrichment from actual ecological and human health relevance. Here, a site-prioritization framework is applied to potentially toxic elements (PTEs) in agricultural soils from Onaizah, central Saudi Arabia. The approach combines contamination indices, ecological-risk screening, deterministic health-risk estimates, Monte Carlo resampling, and relative ranking of management priorities. A total of 33 surface-soil samples collected from cultivated farms were examined for As, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn. The measured concentration ranges (mg/kg) were 1–5 (As), 1–12 (Co), 10–53 (Cr), 4–38 (Cu), 107–541 (Mn), 6–54 (Ni), 2–23 (Pb), 8–47 (V), and 11–168 (Zn). Based on their mean concentrations, the investigated elements decreased in the following sequence: Mn > Zn > Cr > Ni > V > Cu > Pb > Co > As. The P_N values ranged from 0.127 to 1.339, indicating 27 safe sites, 2 warning-line sites, and 4 slightly polluted sites, mainly controlled by localized Zn enrichment and, in one case, Pb. In contrast, mCd values of 0.099–0.676 indicated nil to very low contamination, while RI values of 2.743–15.701 confirmed low ecological risk across all samples. Non-carcinogenic risk was generally below the threshold of concern, with HI values of 0.204–1.018 for children and 0.024–0.119 for adults. Only one site showed a marginal child HI exceedance, emphasizing localized rather than widespread health concern. Children showed approximately 8.6-fold higher non-carcinogenic risk than adults, with Mn, Cr, As, and V as the main contributors. The total LCR values for As, Cr, and Pb ranged from 7.79 × 10⁻⁶ to 4.07 × 10⁻⁵ for children and from 3.48 × 10⁻⁶ to 1.82 × 10⁻⁵ for adults, within the commonly tolerable range of 1 × 10⁻⁶ to 1 × 10⁻⁴. Chromium was the dominant contributor to LCR. Monte Carlo resampling supported the deterministic risk classification, with only a 3.1% probability of child HI exceeding 1 and no simulated exceedance of the LCR threshold for either children or adults. From the standpoint of sustainable soil management, site 7 should undergo further health-risk assessment, while sites 30 and 33 require source verification and periodic monitoring before any remediation action is considered. Full article

Sustainable agriculture in water-limited regions requires understanding how future climate change may affect crop water use and productivity and whether locally feasible adaptations can offset adverse impacts. This study used an improved SWAT (Soil and Water Assessment Tool) model to simulate multiple future climate scenarios—five single-factor adaptation scenarios, including early or late sowing, long- or short-season cultivars, and crop substitution, and one integrated scenario—in the Palo Duro Watershed of the Texas High Plains. Under future climate, yields declined by 6.7–13.1% for irrigated corn (Zea mays L.) and 11.6–34.1% for irrigated sorghum (Sorghum bicolor L.), but increased by 8.0–32.8% and 8.4–33.7% for dryland and irrigated winter wheat (Triticum aestivum L.), respectively. Early sowing improved yields and water productivity (WP) for irrigated corn, irrigated sorghum, and dryland winter wheat. However, long-season cultivars increased corn and sorghum yields and WP with greater irrigation water use. Crop substitution was generally unfavorable under local conditions. A combined strategy of early sowing and long-season cultivars would increase yields and WP for irrigated corn, irrigated sorghum, and dryland winter wheat, whereas business-as-usual management remained appropriate for irrigated winter wheat. These findings support crop-specific adaptation planning for climate-resilient agriculture and water-resource management in semi-arid systems. Full article

Seasonal freeze–thaw processes affect soil hydrothermal conditions in high-altitude valleys, yet evaluations based only on air temperature or maximum freezing depth may overlook the distinction between surface-connected freezing and delayed thawing within the soil profile. Meteorological conditions, ground surface temperature (GST), ground-temperature profiles, freezing depth, and volumetric water content (VWC) were continuously monitored in an arid valley on the Qinghai–Tibet Plateau. Mean annual GST was 3.23 °C higher than mean annual air temperature, and the freezing and thawing n-factors were 0.72 and 1.54, respectively, indicating weakened cold accumulation and enhanced heat accumulation at the ground surface. The maximum surface-connected freezing depth reached 3.30 m, whereas ground temperatures at 3.5 m and below remained above 0 °C. During spring thawing, a residual frozen layer persisted for 49 days after the shallow layer had thawed, with a maximum thickness of 3.24 m. GST-based freezing degree days represented freezing depth better than air-temperature-based freezing degree days. Soil VWC remained low, and precipitation responses were mainly confined to 0.2 m depth. These findings reveal a thermally dominated freeze–thaw regime with weak deep moisture response and show that distinguishing surface-connected freezing from residual frozen layers improves hydrothermal-state identification in low-water-content sandy gravel deposits. Full article

The spatial variation in acidity, organic matter, nitrate nitrogen, and mobile forms of phosphorus and potassium in the arable horizon (0–20 cm) of southern chernozems was studied in the Fedorovsky, Denisovsky, Altynsarin, and Karasu districts of the Kostanay region. The arable horizons were predominantly alkaline (pH (H₂O) 7.6–8.0) to strongly alkaline (pH (H₂O) above 8.0); organic matter content was low (2–4%); nitrate nitrogen (N-NO₃) and mobile P₂O₅ were very low (less than 10 mg/kg), and mobile K₂O was high (400–700 mg/kg). The spatial distributions of pH (H₂O), organic matter, and K₂O were normal, whereas those of N-NO₃ and P₂O₅ were lognormal. Hierarchical analysis of variance showed that variability within plots of 0.5 km² accounted for a significant proportion of the total variance of the soil properties. Variation between fields (1–3 km²) within farms (10–40 km²) was most pronounced for nitrate nitrogen and mobile phosphorus, whereas for pH (H₂O) and mobile potassium the differences between districts (1000–7500 km²) prevailed. Significant correlations between the soil properties and their dependence on the latitude and longitude of the sampling points were revealed in the moderately arid steppes of the Kostanay region. Full article

Cereal production is increasingly constrained by soil degradation, water scarcity, climate variability, and rising disease and weed pressure. This review synthesizes current knowledge on the role of regenerative agronomic practices in cereal production, with particular emphasis on soil health, plant disease management, water-use efficiency, and yield stability. Available evidence consistently indicates that the greatest benefits arise not from individual practices but from integrated systems combining reduced tillage, crop residue retention, diversified crop rotations including legumes, cover crops, organic fertilization, and biologically based pest management. Such practices can increase soil biological activity and its ability to limit disease by enriching functionally beneficial microbial communities and limiting pathogens through competition for resources and niches, antibiosis, hyperparasitism, and the induction of plant resistance. They can also improve soil structure, water infiltration, water retention, and crop resilience to drought stress and, under certain conditions, reduce erosion, nutrient losses, and yield variability. However, the effects of regenerative practices are strongly dependent on soil type, climate, nitrogen balance, pest pressure, and the extent of adoption of regenerative practices. Risks may arise during the transition period, including yield declines, nitrogen immobilization, weed infestation, and increased disease pressure. Evaluation of these systems should encompass not only yield but also the grain quality and phytosanitary status, soil organic carbon stocks throughout the soil profile, N₂O emissions, and production profitability. The review covers cereal systems from temperate, humid, arid and semi-arid zones, and the results were interpreted considering climate, soil quality, water availability, and agronomic practices, as the same practice can produce different effects in different agroecological zones. Further research should prioritize long-term, multifactorial experiments conducted across diverse agroecological environments that integrate agronomic performance, environmental sustainability, and crop quality. Full article

Groundwater depth is a key control on soil salinization in arid irrigation districts, but seasonal groundwater depth thresholds for different salinization risks remain poorly constrained. Here, we examined the Yichang irrigation area of the Hetao Irrigation District using groundwater depth observations and 0–60 cm soil salinity samples collected before spring irrigation and during the crop-growing season. Indicator Kriging was used to map threshold-based probability zones for groundwater depth and soil salinity in the 0–60 cm root zone; soil salinity thresholds of 2 and 3 g kg⁻¹ represented light-or-higher and moderate-or-higher salinization, respectively, and high-probability matching rates were used to identify the corresponding critical groundwater depths. Groundwater depth showed moderate spatial variability in both seasons, whereas soil salt content showed strong spatial variability. The critical groundwater depths for light and moderate salinization were 2.6 and 2.2 m before spring irrigation and 2.2 and 1.8 m during the growing season, respectively. Soil salt content in the 0–20, 20–40, 40–60 and 0–60 cm layers decreased with increasing groundwater depth, with the 0–60 cm layer well described by an exponential response function. These findings provide a spatially explicit basis for seasonal groundwater regulation, salinization risk zoning and field-scale water–salt management in arid irrigation districts. Full article

Soil salinization reduces crop productivity and threatens agricultural sustainability in arid regions. Reliable estimation of farmland soil salinity is therefore essential for salinization monitoring and land management. During the non-growing season, limited crop cover increases soil surface exposure. Existing studies have mainly relied on optical indices, although SAR features and terrain variables have also been used to improve estimation accuracy. However, climatic and soil texture variables have not been fully considered after the harvest of crops in farmland. In addition, traditional machine learning methods have difficulty effectively learning the complex nonlinear relationship between multi-source variables and soil salinity. Therefore, this research proposed a method to estimate soil salinity by integrating multi-source remote sensing data with a deep learning model. This study focused on farmland in the Wei-Ku Oasis of northwestern China during the non-growing season. Six variable combination scenarios were constructed using Sentinel-1/2 data and environmental covariates, including terrain, land surface temperature (LST), and soil texture. Support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models were developed to estimate soil salinity. The results showed that: (1) integrating optical indices, SAR features, terrain variables, LST, and soil texture achieved the highest estimation accuracy; (2) the CNN showed better overall estimation performance than the traditional machine learning models across 50 random-split experiments (R² = 0.68 and RMSE = 1.24 dS/m); and (3) optical indices contributed most to the SVR and RF models, whereas environmental variables contributed most to the CNN model in this study. This study proposed a soil salinity estimation framework that integrates multi-source remote sensing data with a deep learning model during the non-growing season. It provides new data support and technical support for soil salinity estimation of farmland in arid regions during the non-growing season. Full article