Search Results (175)

Globally, Central Asian wild fruit forests are critical repositories of wild fruit germplasm resources and provide essential ecosystem services. However, their habitats are facing escalating degradation risks driven by climate warming, shifting precipitation regimes, and intensifying anthropogenic disturbances. Accurately quantifying climate-driven spatiotemporal variations in habitat suitability for keystone wild fruit tree species is therefore an essential prerequisite for formulating targeted conservation and management strategies in arid and semi-arid landscapes. In this study, we applied the maximum entropy (MaxEnt) model to simulate the current (2000–2020 baseline) and future (2030s, 2050s, 2070s) potential suitable habitats of two dominant wild fruit tree species, Malus sieversii (Ledeb.) M.Roem. and Prunus cerasifera Ehrh., in the Ili Valley, a core distribution area of Central Asian wild fruit forests in northwestern China. We integrated rigorously screened species occurrence records with key environmental predictors and characterized future climate conditions using three Shared Socioeconomic Pathways (SSPs; SSP126, SSP245, and SSP585) spanning low to high radiative forcing levels. The model exhibited excellent predictive performance (AUC > 0.85), confirming the robustness and reliability of our habitat suitability simulations. Elevation and annual precipitation were identified as the dominant environmental variables governing habitat suitability for both species, highlighting the critical role of terrain–hydroclimate interactions in maintaining viable dryland refugia for wild fruit forests. Under the baseline climate scenario, the total area of suitable habitats reached 24.014 × 10³ km² for Malus sieversii and 18.990 × 10³ km² for Prunus cerasifera. Future climate projections revealed a consistent and significant contraction trend in suitable habitats for both species, with the magnitude of habitat loss escalating with increasing radiative forcing and longer projection time horizons. Specifically, under the high-emission SSP585 scenario by the 2070s, the suitable habitat area is projected to decline by 7.579 × 10³ km² for Malus sieversii and 9.883 × 10³ km² for Prunus cerasifera relative to the baseline. Our findings delineate climate-vulnerable hotspots of wild fruit forests and provide a robust spatial scientific basis for prioritizing in situ conservation, targeted habitat restoration, and anthropogenic disturbance regulation to support the long-term persistence of these irreplaceable wild fruit germplasm resources under accelerating global climate change. Full article

Soil heavy metal contamination has emerged as a global environmental and public health challenge. Among them, cadmium (Cd) is of particular concern due to its high mobility and ecotoxicity. To identify the key limiting factors and their nonlinear threshold effects for Cd accumulation in maize grains (Grain-Cd) in heterogeneous soil environments, a coordinated sampling campaign of soil and maize was conducted at the municipal scale in Chongqing, China. A total of 499 paired soil–maize samples were obtained, and the correlations between Grain-Cd concentrations and soil physicochemical properties, as well as soil Cd pollution characteristics, were quantitatively evaluated using the integrated Random Forest (RF) model and SHAP (SHapley Additive exPlanations) algorithm instead of traditional linear statistical methods. The results showed that the average Cd content in the soil of maize-growing areas in Chongqing City was 0.30 mg·kg⁻¹, with a variation coefficient (CV) of 53%, and the spatial heterogeneity was significant. The average Cd content in maize grains was 0.03 mg·kg⁻¹, with an exceedance rate of 9.6% over the Chinese National Standard (0.10 mg·kg⁻¹), indicating a certain food safety risk. The RF model achieved a high predictive accuracy for Grain-Cd (R² = 0.815, RMSE = 0.028 mg·kg⁻¹, MAE = 0.013 mg·kg⁻¹), which was significantly superior to the traditional linear regression model (R² = 0.526, RMSE = 0.0459 mg·kg⁻¹). The available Cd (avlCd) in the soil was identified as the core controlling factor for the Grain-Cd content, while total soil Cd (SCd) only showed its positive contribution at contents higher than 0.5 mg·kg⁻¹. Soil pH, CEC (cation exchange capacity), and total phosphorus (TP) exerted significant influences on the Grain-Cd by regulating soil avlCd. The dependence of Grain-Cd on these soil factors was typically nonlinear, and an obvious turning point (threshold) existed for each factor with its occurring level in soil, determined by SHAP analyses as avlCd: 0.29 mg·kg⁻¹, pH: 6.58, CEC: 18.9 cmol (+)/kg, and TP: 0.5 g·kg⁻¹, respectively. This study clarifies the nonlinear regulatory mechanisms of key soil factors on Cd accumulation in maize grains in Chongqing, and the established RF-SHAP framework and identified soil factor thresholds lay a scientific foundation for the interpretable quantification of the soil–maize Cd system, while providing a scientific basis for the precise, targeted remediation of Cd-contaminated dryland farmland and the assurance of regional maize production safety. Full article

Projected climate change indicates that drought intensity will increase across much of the Global South, intensifying water stress in semi-arid regions. In Botswana, rising temperatures and increasingly variable rainfall are exacerbating drought conditions, particularly for livestock-based systems that depend on reliable grazing and water resources. This study was conducted in the Lake Ngami basin, Botswana, a predominantly pastoral community, to examine the adaptation and resilience of pastoralists to drought and climate change. The study examines the extent to which current coping strategies and institutional frameworks in the Lake Ngami basin contribute to long-term climate resilience among pastoral communities. It also assesses combinations of Climate-Resilient Development Pathways (CRDPs) that are most critical for enabling a transition from reactive coping to proactive and sustainable adaptation. We utilized in-depth community interviews, focus group discussions, and policy content analysis guided by the Climate-Resilient Development Pathways (CRDPs) framework to gather and analyze data using thematic analysis. Key findings indicated that droughts, intensified by factors like El Niño, have negatively affected the community’s livelihood, including grazing systems, access to water, and livestock productivity. The effectiveness of coping strategies was assessed through triangulation of thematic frequency, participant narratives of livelihood recovery, and analysis of policy implementation gaps. Pastoralists employed coping methods such as herd reduction, seasonal migration, and informal alternative livelihoods, but these were largely ineffective in promoting long-term resilience. While seasonal mobility provided short-term relief through access to distant grazing areas, forced livestock sales and herd reduction reduced herd size, weakening households’ long-term recovery capacity and increasing vulnerability. Institutional support programs such as the National Disaster Risk Reduction Strategy and the National Committee on Climate Change were found not adequate to build the necessary long-term pastoralists’ resilience. The study emphasizes that enhancing climate resilience in dryland pastoral systems necessitates combining traditional knowledge with improved infrastructure, climate information, and inclusive governance in comprehensive CRDPs. Full article

Accurate urban expansion mapping in dryland environments is essential for sustainable planning, infrastructure management, and heritage-sensitive development, yet it remains methodologically challenging because built-up surfaces often exhibit strong spectral similarity to bright bare soils. This study comparatively evaluates three widely used urban mapping approaches in Diriyah, Saudi Arabia, a rapidly transforming heritage district of high relevance to Saudi Vision 2030: the Global Human Settlement Layer (GHSL), the Normalized Difference Built-up Index (NDBI), and unsupervised k-means classification. Built-up extent was mapped for 2015, 2020, and 2025, and method performance was assessed using 150 stratified reference points interpreted from high-resolution imagery. The results reveal substantial quantitative differences among methods. GHSL produced the most conservative estimates of urban extent (2.80, 4.94, and 5.31 km²), while NDBI and unsupervised classification generated much larger and less realistic built-up areas due to spectral confusion with bright bare soil. Accuracy assessment confirmed the superiority of GHSL, which achieved the highest overall accuracy (0.88) and Kappa coefficient (0.83), compared with NDBI (0.53; 0.41) and unsupervised classification (0.61; 0.50). To support integrative interpretation, the study also developed a Hybrid Built-up Detection Model (HBDM), which combines the three outputs into a continuous urban intensity layer that helps distinguish persistent urban cores from uncertain transition zones. The findings demonstrate that conservative global built-up products provide a more reliable baseline than index-based or unsupervised methods in bright-soil dryland settings. More broadly, the study offers practical methodological guidance for urban monitoring and sustainable land management in desert cities undergoing rapid transformation under large-scale development agendas such as Saudi Vision 2030. Full article

Global biomass estimates suggest that plants hold 81% of the Earth’s 550 GT C, yet carbon stocks in non-forested and dryland ecosystems remain the largest source of uncertainty in the global carbon budget. Small uncrewed aerial vehicle (UAV) platforms are increasingly used to estimate aboveground biomass at landscape scales. We conducted a systematic review of the remote sensing literature to determine: (1) which plant traits and related remote sensing indicators were used to develop aboveground biomass models; (2) statistical approaches; and (3) the key sources of uncertainty among these methods and models. We found that tundra, dryland, and savanna ecosystems were most underrepresented in the remote sensing literature. Within our systematic review process, we found no consistent UAV sensor combination, platform, or workflow that improved the accuracy and reduced the uncertainty in aboveground biomass estimates. Machine learning and regression models resulted in similar uncertainty levels in shrubland and savanna ecosystems. Expanding allometric equation development in shrublands and savanna ecosystems could reduce uncertainty and improve aboveground biomass estimation. Improved reporting on UAV logistics and workflows would further strengthen comparability. Standardized and validated UAV methods for estimating biomass, carbon stocks, and fuel loads will be essential for producing consistent datasets and enabling robust future meta-analyses. Full article

Global warming profoundly affects hydrological processes and regional aridity. However, the shifts in the arid–humid transition zone and its relationship to divergent surface and subsurface hydrological responses remain not fully understood. This study investigates the spatiotemporal aridity changes in China using hydroclimate datasets (1950–2022) and examines associated hydrological responses via remote sensing (RS) since the early 2000s. The results reveal that: (1) a pronounced ~32-year oscillatory pattern governs both the expansion and contraction of drylands and non-drylands, with China currently in a wetting phase; (2) a distinct climatic transitional zone is identified, and a distinct boundary emerges separating drylands and non-drylands, here referred to as China’s Arid–Humid Divide, reflecting the climatic equilibrium shaped by multiple monsoon systems and local topography; and (3) the nationwide expansion of surface water bodies, following the increase of groundwater storage in partial areas, was detected via recent RS data. These findings provide new insights into the mechanisms driving long-term aridity transitions and support climate adaptation and sustainable land management in China. Full article

Maize/peanut intercropping is increasingly promoted as a climate-smart strategy for enhancing resource use efficiency and reducing environmental impacts in dryland cropping systems. However, its effects on multi-gas greenhouse emissions and yield-scaled climate performance remain insufficiently understood in semi-arid regions with sandy soil. Here, a two-year field experiment was conducted in western Liaoning, Northeast China, to quantify soil CO₂, CH₄, and N₂O fluxes, cumulative emissions, crop yield, global warming potential (GWP), and greenhouse gas intensity (GHGI) under sole maize (SM), sole peanut (SP), and two maize/peanut intercropping systems. SM produced the highest cumulative CO₂ emissions, whereas SP generated the highest CH₄ uptake and the highest N₂O emissions. Compared with peanut monoculture, maize/peanut intercropping significantly reduced soil N₂O emissions, indicating that the introduction of maize in the intercropping system provided an effective regulatory pathway for reducing N₂O emissions. Peanut yields declined by approximately 47.29–49.41%, leading to total land equivalent ratio (LER) values of 0.83–0.99. Although no significant land use advantage was observed for maize/peanut intercropping at the field scale, when crop yields were taken into account for assessment, the global warming potential (GWP) and greenhouse gas emission intensity (GHGI) were lower than those of monoculture uniformity. CO₂, CH₄ and N₂O fluxes were strongly correlated with soil temperature and moisture, underscoring the dominant role of microclimate rather than soil structure in regulating greenhouse gas (GHG) fluxes in monoculture, while in the intercropping system, the microclimate and the soil stucture together regulate the GHG fluxes. Overall, maize/peanut intercropping has the potential of reducing the climate cost per unit of production and represents a promising strategy for enhancing GHG mitigation potential in semi-arid agroecosystems. Full article

Large-scale river modeling has transitioned toward vector-based routing, yet the global fidelity of standalone frameworks like mizuRoute remains poorly characterized due to fragmented observation networks and unquantified systematic biases. This study addresses this gap by establishing a comprehensive global benchmark using a harmonized database of 12,115 in situ gauging stations integrated with multi-dimensional catchment attributes. Simulations utilize the 5 km MERIT-Hydro network driven by ERA5-Land runoff from 1980 to 2024. Our results reveal a robust global median Pearson correlation of 0.53, though simulation efficiency is highly bifurcated with a median Kling–Gupta Efficiency (KGE) of 0.17. High fidelity is concentrated in humid temperate and cold regions, whereas performance collapses in arid zones (median KGE = −0.15) due to the structural omission of channel transmission losses. Attribution analysis identifies the aridity–moisture gradient and vegetation density as primary drivers of model skill, while topographic complexity is well-preserved by the vector framework. Furthermore, anthropogenic regulation significantly degrades accuracy; in basins with high reservoir density, naturalized routing fails to capture regulated flow signatures, leading to a sharp decline in efficiency. This work provides the first global appraisal of the mizuRoute framework and highlights that integrating dryland-specific loss functions and reservoir modules is essential for the next generation of global hydrological reconstructions. Full article

Climate warming and land degradation are reshaping Mediterranean and semi-arid ecosystems, yet their combined effects remain poorly quantified in North Africa. Using four Landsat reference epochs spanning 1984–2024, and four spectral/thermal indices (NDVI, EVI, NDMI, LST), we assessed vegetation dynamics and eco-climatic resilience of Tetraclinis articulata ecosystems in Morocco. Four study sites (Stehat, Merchouch, Tamanar, and Amskroud) distributed along a latitudinal gradient from the northern to southern limits of the species’ Moroccan range were chosen and analyzed. Results reveal a generalized decline in vegetation cover, strongly coupled with increasing land surface temperatures, with threshold-like patterns emerging above 74–75 °C that lead to a rapid reduction in NDVI. The northern site (Stehat) exhibited partial recovery, likely supported by local schist aquifers, whereas the arid southern sites (Tamanar and Amskroud) experienced near-total biomass loss and reduced climate buffering. Moisture indices limited hydrological mediation and suggest that shallow soil water availability constrains T. articulata functioning, amplifying vulnerability under recurrent warming. These findings demonstrate how local edaphic and hydrological conditions modulate the impacts of global change and provide early warning indicators of heightened vulnerability and potential threshold-like behavior in drylands. The study emphasizes the urgent need for targeted management strategies to sustain ecosystem resilience under accelerating climate stress. Full article

This study, conducted in the Lifang Dryland Experimental Area in Jinzhong, Shanxi Province, China, aimed to develop a method to accurately predict maize yield by combining UAV multispectral data with meteorological information. A DJI Mavic 3M UAV was used to capture four-band imagery (red, green, red-edge, and near-infrared), from which 16 vegetation indices were calculated, along with daily meteorological data. Among eight machine learning algorithms tested, ensemble models, Random Forest and Gradient Boosting Trees performed best, with R² values of 0.8696 and 0.8163, respectively. SHAP analysis identified MSR and RVI as the most important features. The prediction accuracy varied across growth stages, with the jointing stage showing the highest performance (R² = 0.7161), followed by the flowering stage (R² = 0.6588). The yield exhibited a strip-like spatial distribution, ranging from 6450 to 9600 kg·ha⁻¹, influenced by field management, soil characteristics, and microtopography. K-means clustering revealed high-yield areas in the central-northern region and low-yield areas in the south, supported by a global Moran’s I index of 0.4290, indicating moderate positive spatial autocorrelation. This study demonstrates that integrating UAV multispectral data, meteorological information, and machine learning can achieve accurate yield prediction (with a relative RMSE of about 2.8%) and provides a quantitative analytical framework for spatial management in drought-prone areas. Full article

Root-knot nematodes (RKN), especially Meloidogyne incognita, threaten global alfalfa crops because of their broad host range and pathogenic nature. Despite its significance, research on resistance is limited. In this study, 24 varieties from China, the US, Canada, Australia, and France were assessed for resistance using the Disease Index (DI) and Egg Mass Index (EMI). Results identified 19 varieties with varying resistance levels and 5 that were susceptible. Chinese Gannong No. 9 was highly resistant (DI: 10) and achieved the highest composite score (91). The US varieties Dryland and Moste were classified as resistant (DI: 14.3% and 12.5%, respectively) and also ranked highly by composite score (65 and 62.5). A moderate correlation between DI and EMI (r = 0.68) led to some inconsistent classifications, including for 2295, Instict, and WL168HQ, highlighting the importance of using multiple complementary metrics for accurate resistance evaluation. Egg mass production was strongly correlated with galling severity (r = 0.70), while root biomass showed no correlation with galling (r = 0.09), indicating root weight is not a reliable resistance indicator. Preliminary infection dynamics showed similar nematode penetration rates at 2 days post-infection across resistant and susceptible varieties. At 7 days post-infection, both resistant and susceptible varieties retained predominantly J2 larvae (78–89%), with no statistically significant differences in developmental stage distributions. These preliminary observations suggest that resistance-associated effects on nematode development, if present, are not strongly expressed at early stages of infection. The mechanistic basis of resistance in alfalfa remains unresolved and warrants further investigation using additional timepoints, histological analyses of feeding-site development, and molecular characterization. Geographically, American varieties displayed broad performance variation, Chinese varieties showed a bimodal distribution, and Canadian varieties exhibited moderate, consistent resistance. These results offer valuable germplasm for breeding and highlight the importance of multiple resistance metrics. Resistant varieties such as Gannong No. 9 provide important genetic resources for developing durable nematode resistance in alfalfa and can guide variety selection in nematode-infested regions. Full article

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article

The simulation accuracy of crop models is highly dependent on the proper calibration of key parameters. To enhance the applicability of the Next-Generation agricultural production systems sIMulator (APSIM NG) in dryland wheat production within the Loess Hilly Region, this study proposes a crop model parameter calibration framework that deeply integrates Morris and DREAM-zs methodologies. Morris was employed to conduct a global sensitivity analysis on parameters related to the APSIM NG dryland wheat grain and leaf growth sub-models. The DREAM-zs algorithm was then utilized for multi-objective collaborative optimization of key parameters. Results indicate that Morris excels at capturing nonlinear and coupled relationships among model parameters. Optimized key parameters include maximum grain size (0.055 g), radiation use efficiency (1.540 g·MJ⁻¹), and extinction coefficient (0.443). Post-optimization, the root mean square error (RMSE) and mean absolute error (MAE) for wheat yield decreased by 24.1% and 23.2%, respectively, while those for LAI decreased by 16.9% and 19.2%. This framework conserves computational resources and accelerates convergence when handling nonlinear internal model parameters and complex coupling relationships, providing technical support for the localized application of APSIM NG in the Loess Hilly Region of Northwest China. Full article

Cover cropping serves as a promising technique with great potential to enhance soil organic carbon (SOC), boost crop productivity, and improve soil quality. The implementation of cover crops as a sustainable agricultural practice has gained popularity worldwide. To further evaluate the role of cover cropping, this systematic review examines empirical evidence from 38 peer-reviewed studies published between 2015 and 2025 to assess the impact of cover cropping on these key outcomes. Studies were selected based on strict inclusion criteria requiring original field data or validated modeling results that evaluated all three outcomes, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data on cropping system, duration, type of cover crop, and outcome metrics were extracted. More than 80% of the literature reported benefits. Multi-species cover crop mixtures that were managed long-term enhanced SOC by 5–30%, with 87% and 55% of studies demonstrating enhanced soil quality and yield, respectively. However, some studies recorded yield reductions in drought-prone regions or when cover crops were terminated at inappropriate times. In some studies, improvements in microbial function and nutrient cycling were observed while several United States (U.S.) studies focused more on physical and biological indicators under dryland conditions. Although outcomes vary by context, cover crops are widely recognized as a viable strategy for climate-smart agriculture and sustainable soil management. To optimize benefits, there is a need for region-specific incentives, targeted agricultural policies, and standardized agronomic guidelines. Cover crops represent a key strategy for climate change mitigation and sustainable soil management. This review reveals that species diversity and long-term adoption are crucial for achieving reliable results. With the integrative focus of this review on the tripartite relationship between SOC, crop yield, and soil quality, as well as its comparative lens on global versus U.S. practices, it is novel because it offers crucial insights for evidence-based policy development and region-specific cover cropping strategies. Full article

A storage-aware water-scarcity risk assessment framework coupling satellite remote sensing, geospatial datasets with the IPCC exposure-hazard-vulnerability (EHV) paradigm was designed to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades. To achieve this, a performance-weighted ensemble machine learning approach was employed to reconstruct long-term terrestrial water storage (TWS) from satellite observations, augmented with glacier-mass calibration to improve reliability in cryosphere-affected regions. Global water withdrawal dataset was generated by integrating remote sensing, geospatial dataset, and machine learning to mitigate the dependency of parameterized land surface hydrological models and enable consistent risk mapping. Satellite-derived results reveal obvious TWS declines in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions. EHV paradigm and big datasets further identified high-water scarcity risk in Asia and Africa, especially in agricultural regions. Water stress has intensified in Africa over the past two decades, while a decreasing trend is observed in parts of Asia. Vulnerability levels in Asia and Africa are approximately eight times higher than those in other global regions. Results reveal a strong connection between water stress and socioeconomic factors in Asia and Africa, reflecting global disparities in water resource availability. Full article