Search Results (2,273)

The Ordos section of the Yellow River Basin represents a typical semi-arid zone in northern China. Due to dual pressures from natural drivers and human activities, this region is at the forefront of desertification. Therefore, rapidly and accurately identifying desertification and analyzing its evolutionary trends plays a vital role in desertification control. Using six-phase Landsat imagery (2000–2023) of Ordos City, this study extracted NDVI and Albedo to construct a fitting model, thereby analyzing desertification severity, spatial distribution patterns, and evolutionary dynamics. Through integrated analysis trends in meteorological and anthropogenic data, key driving factors of desertification processes were further investigated. Conclusions: (1) By 2023, the area of extremely severe and severe desertification reduction accounted for 12.67% of the total study area, the proportion of no desertification area increased by 11.27%, and the expansion of desertification was effectively curbed. (2) Desertification intensification cluster near residential zones and grazing lands, while improved areas concentrate in the western and southern of Mu Us Sandy Land vicinity. (3) Spatial autocorrelation analysis revealed statistically significant clustering patterns across the study area, predominantly characterized by distinct low–low and high–high aggregations. (4) Wind speed, temperature, and pastoral activities were major factors contributing to desertification. These research findings provided references for the ecological restoration and sustainable development of semi-arid areas in the Yellow River Basin. Full article

Soil fertility is the essential attribute of soil quality. Large-scale coal mining has led to the continuous deterioration of the fragile ecosystems in arid and semi-arid mining areas. As one of the key indicators for land ecological restoration in these coal mining regions, rapidly and accurately monitoring topsoil fertility and its spatial variation information holds significant importance for ecological restoration evaluation. This study takes Wuhai City in the Inner Mongolia Autonomous Region of China as a case study. It establishes and evaluates various soil indicator inversion models using multi-temporal Landsat8 OLI multispectral imagery and measured soil sample nutrient content data. The research constructs a comprehensive evaluation method for surface soil fertility based on multispectral remote sensing monitoring and achieves spatiotemporal variation analysis of soil fertility characteristics. The results show that: (1) The 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version)-SVM (Support Vector Machine) prediction model for surface soil indicators based on Landsat8 OLI imagery achieved prediction accuracy with R² values above 0.85 for all six soil nutrient contents in the study area, thereby establishing for the first time a rapid assessment method for comprehensive topsoil fertility using multispectral remote sensing monitoring. (2) Long-term spatiotemporal evaluation of soil indicators was achieved: From 2015 to 2025, the spatial distribution of soil indicators showed certain variability, with soil organic matter, total phosphorus, available phosphorus, and available potassium contents demonstrating varying degrees of increase within different ranges, though the increases were generally modest. (3) Long-term spatiotemporal evaluation of comprehensive soil fertility was accomplished: Over the 10 years, Grade IV remained the dominant soil fertility level in the study area, accounting for about 32% of the total area. While the overall soil fertility level showed an increasing trend, the differences in soil fertility levels decreased, indicating a trend toward homogenization. Full article

Addressing the need to optimize human settlement quality in arid and semi-arid regions under rapid urbanization, this study innovatively constructs an evaluation framework integrating greenness, thermal conditions, impervious surfaces, water bodies, and air transparency. Focusing on 12 prefecture-level cities in Inner Mongolia, Northern China, it systematically reveals the spatial differentiation characteristics and driving mechanisms of human settlement quality. Findings indicate the following: (1) Regional human settlement quality exhibits a spindle-shaped structure dominated by the medium grade (Excellent: 18.13%, High: 23.34%, Medium: 46.48%, Low: 12.04%), with Ulanqab City having the highest proportion of Excellent areas (25.26%) and Ordos City the lowest proportion of Low-grade areas (6.20%), reflecting a critical transition period for regional quality enhancement. (2) Spatial patterns show pronounced east-west gradients and functional differentiation: western arid zones display significant blue-green space advantages but face high-temperature stress and rigid water constraints, eastern humid zones benefit from superior ecological foundations with weaker heat island effects, the core Hetao Plain experiences strong heat island effects due to high impervious surface density, while industrial cities confront prominent air pollution pressures. Consequently, implementing differentiated strategies—strengthening ecological protection/restoration in High/Low-grade zones and optimizing regulation to drive upgrades in Medium-grade zones—is essential for achieving three sustainable pathways: compact development, blue-green space optimization, and industrial upgrading, providing vital decision-making support for enhancing human settlement quality and promoting sustainable development in ecologically fragile cities across northern China. Full article

Loess, a typical unsaturated soil, is a Quaternary sedimentary deposit widely distributed across arid and semi-arid regions worldwide. In recent years, global climate change has led to significant temperature fluctuations in Northwest China, impacting loess properties and soil–water characteristic curves (SWCCs). This study investigated typical loess deposits in Mizhi County, Shaanxi Province, systematically analyzing their basic physical properties and microstructure. The SWCCs of the loess were measured at three temperature gradients (15 °C, 20 °C, and 25 °C) using the dynamic dew-point isotherm method to investigate the impact of temperature on SWCC hysteresis. The results showed that with increasing temperature, the SWCC exhibited increasing divergence. The magnitude of the water content change and the corresponding suction forces along the wetting and drying paths increased, leading to an enlargement of the hysteresis loop area. These findings indicate that temperature significantly affects the hysteresis behavior of loess, providing a certain basis and ideas for the study of the soil–water characteristic curves of unsaturated soils such as loess under the influence of temperature. Full article

Addressing limitations in remote sensing irrigation monitoring (insufficient resolution, single-source constraints, poor terrain adaptability), this study developed a high-precision identification framework for Jianping County, China, a semi-arid region. We integrated Sentinel-1 SAR (VV/VH), Sentinel-2 multispectral, and MOD11A1 land surface temperature data. Savitzky–Golay (S-G) filtering reconstructed time-series datasets for NDVI, SAVI, TVDI, and VV/VH backscatter coefficients. Irrigation mapping employed random forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Key results demonstrate the following. (1) RF achieved superior performance with overall accuracies of 91.00% (2022), 88.33% (2023), and 87.78% (2024), and Kappa coefficients of 86.37%, 80.96%, and 80.40%, showing minimal deviation (0.66–3.44%) from statistical data; (2) SAVI and VH exhibited high irrigation sensitivity, with peak differences between irrigated/non-irrigated areas reaching 0.48 units (SAVI, July–August) and 2.78 dB (VH); (3) cropland extraction accuracy showed <3% discrepancy versus governmental statistics. The “Multi-temporal Feature Fusion + S-G Filtering + RF Optimization” framework provides an effective solution for precision irrigation monitoring in complex semi-arid environments. Full article

The present paper deals with the combined application of near-surface geophysical techniques in a sustainable agriculture project. Their application is focused on the identification of any subsurface water in the context of sustainable water management for the selected living hub, located in the semi-arid area of Agios Georgios-Mandra Attiki. The objective of the multidisciplinary geophysical study was to determine the depth of the bedrock and the thickness of the post-Alpine deposits. In addition, the subsurface karstification and the possible aquifer presence were examined. For that reason, the following techniques were implemented: Electrical Resistivity Tomography, Seismic Refraction Tomography, Ground-Penetrating Radar, and Very-Low Frequency electromagnetic technique. The study was also supported by drone LiDAR usage. The investigation revealed several hydrogeological characteristics of the area. The thickness of the post-Alpine sediments is almost 3 m. However, no shallow aquiferous systems have been developed in this formation, as indicated by their relatively high resistivity values (100–1000 Ohm.m). Furthermore, the alpine bedrock exhibits extensive karstification, facilitated by the development of fracture zones. The absence of an underlying impermeable layer prevented the development of aquiferous zones, at least up to a depth of 100 m. Full article

Simulating scenarios under climate change is essential to understanding vegetation dynamics, ensuring the survival of forage species, and minimizing uncertainties in project costs and timelines. This study aimed to simulate historical probabilities and develop a biomass production model using PHYGROW software (Texas A&M University, College Station, TX, USA), combined with Monte Carlo Simulation (MCS) in the @RISK program (Ithaca, NY, USA), to evaluate long-term biomass production in a native pasture area of the Caatinga biome. The results show strong agreement between software estimates and field data. For 2016, PHYGROW estimated 883 kg/ha, while field measurements reached 836.8 kg/ha; for 2017, 1117 kg/ha was estimated, while 992.15 kg/ha was observed. For 2018, the model estimated 1200 kg/ha compared with 1763.5 kg/ha in the field, and for 2019, 1230 kg/ha was estimated versus the 1294.3 kg/ha observed. The Monte Carlo simulations indicated that the Weibull distribution best fitted the synthetic series, with 90% adherence. Biomass production values ranged from 618 to 1427 kg/ha with a 90% probability. Only 5% of the simulations projected values below 600 kg/ha or above 1400 kg/ha. Moreover, there was a 95% risk of production issues if planning was based on biomass values above 1000 kg/ha. These findings highlight PHYGROW’s potential for pasture management under semi-arid conditions for predicting and avoiding degradation scenarios that could even lead to areas of desertification. Full article

Context: To revive the practice of planting legume green manure (GM) in the fallow period in rainfed agricultural areas, it is essential to demonstrate the benefits of this practice on the yields and water use efficiency (WUE) of subsequent crops, especially when integrating with optimized water and fertilizer management. Objectives: We conducted a field experiment to determine the positive effects of planting legume GM in the summer fallow on the yield, WUE, and nitrogen uptake efficiency (NupE) of subsequent winter wheat, which was grown with plastic film mulching and integrated fertilization in the Loess Plateau of China. Methods: A split-plot-designed experiment was arranged with two main treatments, namely (1) wheat planting followed by GM planting in the summer fallow (GM) and (2) conventional wheat monoculture followed by bare land summer fallow (BL), and three sub-treatments: (1) control treatment without any chemical fertilizer (Ct), (2) application of chemical N, P, and K as basal fertilizer (B), and (3) application of basal fertilizer plus wheat straw return (BS). Results: In the initial two years, even in a dry year, GM did not decrease the soil water content and storage (0–200 cm layer) during the subsequent winter wheat season, relative to BL. But in the third and fourth years, GM increased the grain yield of winter wheat by 3.2% and 3.8%, respectively. B and BS increased the grain yield of winter wheat by 14.4% and 22.2%, respectively, during the third experimental year, and by 12.7% and 19.4% during the fourth experimental year, primarily through increasing the population density of winter wheat. The increase in the grain yield contributed to a higher WUE of winter wheat. In the third year, GM increased the water consumption (WC) and WUE of wheat by 2.4% and 1.7%, respectively, though they were far lower than B (8.3% and 5.6%) and BS (10.4% and 10.7%). B and BS resulted in a higher yield and N nutrition than GM alone, but GM combined with B and BS resulted in the highest yield and N nutrition, thus greatly decreasing the NupE and increasing N productivity. Conclusions: Planting legume GM in the fallow can further increase the long-term yield, WUE, and N utilization of winter wheat when integrated with chemical fertilization and wheat straw return in rainfed agriculture. Implications: Our study yields new insights into the agronomic benefits of legume GM application in semi-arid or analogous rainfed agroecosystems and underscores the critical role of water conservation in ensuring dryland agricultural production, particularly in regions undergoing optimization of fertilization. Full article

Finding out the dynamics of soil organic carbon and inorganic carbon is paramount for sustaining terrestrial carbon cycling and climate change mitigation. From the 1980s to 2010s, substantial changes in land use, climate, and agricultural practices have occurred across North China. This study systematically quantified the stratified dynamics of soil carbon stocks (0–100 cm with 20 cm intervals) and their compositional shifts by using the geographically weighted regression kriging model. The model integrated soil sample data from provincial surveys across North China with key environmental covariates (e.g., elevation, precipitation, air temperature, and the vegetation index) to spatially predict and analyze vertical carbon stock changes. The results indicated that soil carbon stocks decreased considerably by 5.86 Gt in the one-meter soil profile from the 1980s to the 2010s. Significant losses in soil inorganic carbon stocks directly contributed to net soil carbon sources. These significant soil inorganic carbon losses of 7.03 Gt, originating primarily from losses of 7.35 Gt in deeper soil layers (20–100 cm), effectively offset increases of 1.17 Gt in soil organic carbon. About two-thirds of regions in North China have been categorized as carbon source regions. These are distributed for the most part in arid and semi-arid areas and the Qinghai–Tibet Plateau. The remaining one-third of regions have been classified as carbon sink regions which are primarily found in the Loess Plateau, the Huang–Huai–Hai Plain, the Middle-lower Yangtze Plain, and the Northeast China Plain. Significant losses in soil inorganic carbon stocks caused by strong carbon sources may undermine global measures aimed at enhancing terrestrial ecosystem carbon sequestration and fixation. Our results highlight the urgent need to account for vulnerable subsurface inorganic carbon pools in regional carbon sequestration strategies and climate models. Full article

The Community-Based Conservation (CBC) approach views local people as interested parties who should actively participate in and control conservation efforts, which contrasts with the conventional 'fortress conservation' common in government-protected areas isolated from human disturbance. The transition from fortress conservation to CBC, however, has not been a smooth journey for many African countries, especially in the sub-Saharan Africa region. This is because, in some cases, local communities do not see themselves as part of the governance structure of these conservancies, which affects the long-term ecological sustainability of the conservancies. Using eight (8) conservancies in the arid and semi-arid counties of Isiolo and Samburu, Kenya, this study used exploratory research to gather data from 24 Focus Group Discussions (FDGs) and forty-eight (48) key informant interviews (KIIs) to assess the influence of communities’ involvement on ecological outcomes of the conservancies. Other secondary sources also supported the primary data sources. Our findings showed that the governance model does influence community support for conservancies, and the benefits that communities receive or expect from the conservancies also have a strong influence on their support for conservation. However, it was established that community conservancies have brought positive changes to the wildlife population trends and habitat health. The study recommends the development of the National Rangelands Resources Management Policy and institutional arrangements to strengthen and safeguard the future of wildlife conservation within those conservancies and to provide clarity on the roles of different stakeholders. The study also recommends further studies on the actual impact of governance on community perception, the value of existing investments in community benefits, and the long-term implications of climate change impacts on conservancy ecosystems. Full article

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

This study, conducted in Southern Italy in 2023, investigated the effects of a dynamic agrivoltaics (AV) system on microclimate, water consumption, plant growth, and essential oil yield in six medicinal species: lavender (Lavandula angustifolia L. ‘Royal purple’), lemmon thyme (Thymus citriodorus (Pers.) Schreb. ar. ‘Aureus’), common thyme (Thymus vulgaris L.), rosemary (Salvia rosmarinus Spenn. ‘Severn seas’), mint (Mentha spicata L. ‘Moroccan’), and sage (Salvia officinalis L. subsp. Officinalis). Due to the rotating solar panels, two distinct ground zones were identified: a consistently shaded area under the panels (UP), and a partially shaded area between the panels (BP). These were compared to an adjacent full-sun control area (T). Microclimate parameters, including solar radiation, air and leaf infrared temperature, and soil temperature, were recorded throughout the cultivation season. Reference evapotranspiration (ET_O) was calculated using Turc’s method, and crop evapotranspiration (ET_C) was estimated with species-specific crop coefficients (K_C). Results showed significantly lower microclimatic values in the UP plot compared to both BP and especially T, resulting in ET_C reductions of 81.1% in UP and 13.1% in BP relative to T, an advantage in water-scarce environments. Growth and yield responses varied among species and treatment plots. Except for mint, all species showed a significant reduction in fresh biomass (40.1% to 48.8%) under the high shading of UP compared to T. However, no biomass reductions were observed in BP. Notably, essential oil yields were higher in both UP and BP plots (0.60–2.63%) compared to the T plot (0.51–1.90%). These findings demonstrate that dynamic AV systems can enhance water use efficiency and essential oil yield, offering promising opportunities for sustainable, high-quality medicinal crop production in arid and semi-arid regions. Full article

This study analyzes spatiotemporal aridity dynamics in Central Kazakhstan (1960–2022) using a monthly Aridity Index (AI = P/PET), where P is precipitation and PET is potential evapotranspiration, Mann–Kendall trend analysis, and climate zone classification. Results reveal a northeast–southwest aridity gradient, with Aridity Index ranging from 0.11 to 0.14 in southern deserts to 0.43 in the Kazakh Uplands. Between 1960–1990 and 1991–2022, southern regions experienced intensified aridity, with Aridity Index declining from 0.12–0.15 to 0.10–0.14, while northern mountainous areas became more humid, where Aridity Index increased from 0.40–0.44 to 0.41–0.46. Seasonal analysis reveals divergent patterns, with winter showing improved moisture conditions (52.4% reduction in arid lands), contrasting sharply with aridification in spring and summer. Summer emerges as the most extreme season, with hyper-arid zones (8%) along with expanding arid territories (69%), while autumn shows intermediate conditions with notable dry sub-humid areas (5%) in northwestern regions. Statistical analysis confirms these observations, with northern areas showing positive Aridity Index trends (+0.007/10 years) against southwestern declines (−0.003/10 years). Key drivers include rising temperatures (with recent degradation) and variable precipitation (long-term drying followed by winter and spring), and PET fluctuations linked to temperature. Since 1991, arid zones have expanded from 40% to 47% of the region, with semi-arid lands transitioning to arid, with a northward shift of the boundary. These changes are strongly seasonal, highlighting the vulnerability of Central Kazakhstan to climate-driven aridification. Full article

As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. However, in recent years, driven by climate change and human activities, grassland degradation has become increasingly serious. In view of the lack of comprehensive evaluation indicators and the inconsistency of grassland evaluation grade standards in remote sensing monitoring of grassland resource degradation, this study takes the current situation of grassland degradation in Ili Prefecture in the past 20 years as the research object and constructs a comprehensive evaluation index system covering three criteria layers of vegetation characteristics, environmental characteristics, and utilization characteristics. Net primary productivity (NPP), vegetation coverage, temperature, precipitation, soil erosion modulus, and grazing intensity were selected as multi-source indicators. Combined with data sources such as remote sensing inversion, sample survey, meteorological data, and farmer survey, the factor weight coefficient was determined by analytic hierarchy process. The Grassland Degeneration Comprehensive Index (GDCI) model was constructed to carry out remote sensing monitoring and evaluation of grassland degradation in Yili Prefecture. With reference to the classification threshold of the national standard for grassland degradation, the GDCI grassland degradation evaluation grade threshold (GDCI reduction rate) was determined by the method of weighted average of coefficients: non-degradation (0–10%), mild degradation (10–20%), moderate degradation (20–37.66%) and severe degradation (more than 37.66%). According to the results, between 2000 and 2022, non-degraded grasslands in Ili Prefecture covered an area of 27,200 km², representing 90.19% of the total grassland area. Slight, moderate, and severe degradation accounted for 4.34%, 3.33%, and 2.15%, respectively. Moderately and severely degraded areas are primarily distributed in agro-pastoral transition zones and economically developed urban regions, respectively. The results revealed the spatial and temporal distribution characteristics of grassland degradation in Yili Prefecture and provided data basis and technical support for regional grassland resource management, degradation prevention and control and ecological restoration. Full article

Arid and semi-arid areas serve a critical regulatory function within the global carbon cycle. Understanding the response mechanisms of vegetation productivity to variations in moisture availability represents a fundamental scientific challenge in elucidating terrestrial carbon dynamics. This study systematically disentangled the respective influences of summer surface soil moisture (RSM) and precipitation (PRE) on gross primary productivity (GPP) across arid and semi-arid regions of China from 2000 to 2022. Utilizing GPP datasets alongside correlation analysis, ridge regression, and data binning techniques, the investigation yielded several key findings: (1) Both GPP and RSM exhibited significant upward trends within the study area, whereas precipitation showed no statistically significant trend; notably, GPP demonstrated the highest rate of increase at 0.455 Cg m⁻² a⁻¹. (2) Decoupling analysis indicated a coupled relationship between RSM and PRE; however, their individual effects on GPP were not merely a consequence of this coupling. Controlling for evapotranspiration and root-zone soil moisture interference, the analysis revealed that under conditions of elevated RSM, the average increase in summer–autumn GPP (SA_GPP) was 0.249, significantly surpassing the increase observed under high-PRE conditions (−0.088). Areas dominated by RSM accounted for 62.13% of the total study region. Furthermore, examination of the aridity gradient demonstrated that the predominance of RSM intensified with increasing aridity, reaching its peak influence in extremely arid zones. This research provides a quantitative assessment of the differential impacts of RSM and PRE on vegetation productivity in China’s arid and semi-arid areas, thereby offering a vital theoretical foundation for improving predictions of terrestrial carbon sink dynamics under future climate change scenarios. Full article