Search Results (6,383)

Soybean is an important global crop used for oil, food, and feed production. To increase yield and land-use efficiency, growers often plant soybean at a high density or use intercropping systems. Under these systems, soybeans frequently experience shade stress, which directly affects agronomic traits such as plant height. Although researchers have well documented the genetic basis of plant height under normal conditions, the loci responsible for height variation under shade stress remain largely unexplored. Therefore, we performed a restricted two-stage multi-locus multi-allele genome-wide association study (RTM-GWAS) using SNP linkage disequilibrium block (SNPLDB) markers to identify QTLs associated with soybean plant height under shade stress. We evaluated a natural population of 181 soybean accessions for plant height traits under both normal and shaded conditions across four environments for three years. Using the Soybean40K chip, we derived 11,463 SNPLDB markers and identified 42, 33, and 28 significant SNPLDBs associated with plant height, average internode length, and number of main-stem nodes, respectively. For each SNPLDB, we estimated haplotype (allele) effects and assembled QTL–allele matrices to summarize the population’s genetic composition. Four SNPLDB loci proved stable across multiple environments, exhibiting high −lg(p) values and explaining substantial phenotypic variation. Finally, we projected that 80 candidate genes resided within 180 kb of these stable loci, and we identified four strong candidate genes linked to plant height traits based on combined positional and functional evidence. These results clarify genetic factors that influence soybean height under shading and could aid development of high-yielding soybean varieties. Full article

Disentangling the coupled, nonlinear impacts of climate change and anthropogenic activities on vegetation dynamics is critical yet challenging for global change research. The Qinghai–Tibetan Plateau (QTP), a highly climate-sensitive and ecologically strategic region, serves as a vital arena for examining such complex socio-ecological attributions. Based on multi-source environmental datasets from 2000 to 2020, this study developed an integrated, spatially explicit framework coupling residual trend analysis (RESTREND) and GeoDetector to quantify individual drivers and nonlinear climate–human interactions. The QTP exhibited a significant, widespread greening trend during 2000–2020, featuring prominent spatial clustering with “High–High” clusters in the southeast and “Low–Low” clusters in the northwest. Attribution modeling revealed that vegetation dynamics were governed not by isolated variables, but by multifaceted, nonlinear synergies among precipitation, temperature, topography, vegetation type, and land-use change. Key interactive pairs, particularly elevation–temperature and slope–precipitation, dramatically increased explanatory power over single-factor models. Crucially, climate–human synergies explained substantially more variance than climate variables alone, bounded by a distinct elevational threshold: human activities dominated vegetation dynamics at mid-elevations (2500–3500 m), while climate factors took over as the primary controller at high altitudes (above 3500 m). Quantitatively, human activities induced vegetation improvement across 38.6% of the plateau, maintained stability in 35.8%, and caused degradation in 25.6%. By successfully merging trend decomposition with spatial stratified heterogeneity analysis, this study provides a transferable approach to uncoupling complex environmental interactions. These insights highlight the intensifying human footprint on alpine ecosystems and advocate for zone-specific adaptive management: mitigating human disturbances at mid-elevations and fostering climate adaptation in higher zones to preserve plateau resilience. Full article

The measurement and improvement of urban land use efficiency (ULUE) are crucial for sustainable development in China’s Canal Cities (CCCs). Drawing on the theories of production factors, spatial externalities, and agglomeration economy, this study proposes a framework that explicitly addresses the trade-offs and synergies of sustainable land use. A comprehensive ULUE evaluation index system was established. The super-SBM (Slack-Based Measure) and Global Malmquist–Luenberger (GML) index models were employed to assess the green efficiency of urban land use from 2002 to 2023, while Kernel Density Estimation (KDE) and the optimal parameters-based geographical detector (OPGD) model were used to investigate the spatiotemporal evolution and influencing factors of ULUE. The results reveal a distinctive V-shaped trend in efficiency, marked by significant spatial disequilibrium and predominantly technology-driven sustainable growth. Furthermore, ULUE exhibits a spatial distribution characterized by bipolar and multipolar differentiation, accompanied by concurrent concentration and dispersion, with high-value clusters dominating the spatial clustering type. Government regulation emerges as the dominant factor influencing ULUE, underscoring the pivotal role of policy intervention in guiding the sustainable development of land use. The interactions among pairs of influencing factors strengthened over time; notably, the interaction between government regulation and other factors is the strongest. Four-quadrant analysis profoundly reveals the underlying mechanism, distinguishing a high-quality, sustainable development model driven by technological innovation and a resource-dependent economic growth model. The findings provide valuable insights for promoting green development and formulating sustainable land use policies in CCCs. Full article

Landslides constitute one of the most lethal and costly hydrometeorological hazards at the global scale. There is a growing trend associated with the increase in extreme precipitation and the expansion of urban development on unstable slopes. In the tropical Andes, this problem is intensified under climate change scenarios. The objective of this study is to develop a logistic regression model to analyze socioeconomic risk due to landslides in the Bogotá Savannah (Colombia). An integrated risk model was developed using binary logistic regression and a socioeconomic vulnerability index. A total of 12 physical–biotic variables and SSP climate projections (2021–2040) were used. A GIS-based environment was implemented to generate prospective spatial risk scenarios. The model demonstrated high robustness and predictive capability, with an improvement in statistical goodness-of-fit of 8.2% (AIC: 2574–2367), adequate probabilistic calibration (Pseudo-R²: 0.675; Brier Score: 0.084), and excellent predictive performance (AUC: 0.935; sensitivity: 84.7%; specificity: 90.0%). Simulations estimated maximum risk probabilities close to 0.600 (scale between 0 and 1), concentrated in geomorphologically critical sectors. Simulations under SSP scenarios showed a progressive increase in risk toward 2040 (up to 0.673), associated with precipitation increases between 10 and 30%. Integrated modeling constitutes a reliable technical tool for land-use planning, climate adaptation, and prospective landslide risk management in urbanized Andean regions. Full article

Biodiversity strongly influences key ecosystem processes. However, with ongoing global biodiversity loss, vital ecosystem services are likely to be negatively affected. Studies that monitor biodiversity are thus crucial, especially when performed in relation to environmental conditions. Our study investigated how species diversity and abundance were influenced by soil properties, in association with plant growth forms (i.e., grasses, forbs, and shrubs). The study was conducted across two land-use types: three protected areas (nature reserves) and adjacent unprotected areas. Plant diversity was measured as species richness, and plant abundance was measured as plant density and cover. We used simple and multiple regression analyses, as well as detrended correspondence analysis (DCA) and redundancy analysis (RDA), to elucidate relationships among species richness, plant abundance, growth forms, and soil nutrients. Both species richness and plant abundance were mostly positively associated with soil nutrients across both land-use types. The response was more robust and varied when species were partitioned into growth forms. Growth forms were strong predictors of species richness and abundance across both land-use types, whereas the effects of soil properties were relatively weaker. When growth forms and soil properties were considered jointly, their combined effects strongly predicted species richness and abundance. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Continuous population growth will lead to further expansion and densification of urban environments. In this context, Urban Air Mobility (UAM) has emerged as a new transportation solution through the use of Vertical Take-Off and Landing (VTOL) aircraft, more precisely, configurations such as lift-plus-cruise tilt-rotors. During the conceptual design phase, propeller design methodologies commonly reported in the literature rely on vortex-based approaches or actuator disk theory. However, the accuracy of these methods strongly depends on the inflow angle and operating conditions. This paper introduces an analytical model to predict propeller thrust at a 90° inflow angle (edgewise flight), based on a correction of the thrust under axial flight conditions and the propeller geometry evaluated at 75% span. The approach relies on local velocity and angle of attack estimations derived from classical Blade Element Momentum Theory (BEMT) with an additional correction to account for stall effects at high angles of attack. This capability is particularly relevant for modeling lift-plus-cruise tilt-rotor configurations cruise phase during early design stages while maintaining minimal computational cost. The proposed model is validated against wind tunnel measurements for several propellers tested at different global pitch angles, varying from 0 m/s to 9.1 m/s of windspeed and 1300 to 6200 rpms, demonstrating the applicability of the developed formulation for blades with twist angles up to 16°. Full article

Global forest ecosystems face growing threats from land use change, climate and weather extremes, and insects and diseases. Managing these threats is difficult due to the time, cost, and human error associated with the quality and quantity of data required for research and assessment. While conventional analytical methods are being improved constantly, they are often slow in providing information needed to respond promptly to unprecedented changes driven by both natural and anthropogenic alterations to forest ecosystems. For this reason, potential applications of artificial intelligence (AI) have attracted increasing attention in the field. Here, we examine the benefits and challenges of using AI in near-term forest health monitoring (surveillance, mostly over small scales) and discuss the need for long-term and larger-scale assessment. Abundant evidence shows that existing AI methods already facilitate the rapid collection, compilation, and synthesis of available data from diverse sources. Furthermore, emerging technologies (e.g., agentic AI) are building these capabilities into autonomous systems. However, every AI tool has advantages and limitations. With constant improvements, integrative AI-driven approaches that simultaneously deal with multiple and cross-scale interacting factors are expected to deliver actionable insights about forest health better than any single AI tool. Consequently, they can enhance decision-making processes, reduce monitoring costs, and help mitigate the impacts of forest health threats. As AI continues to evolve, it is essential to circumscribe its role in forest health monitoring. Most importantly, AI should not define what humans value regarding forest health but instead should be applied to help us evaluate data about our chosen value targets. Full article

Agrivoltaics is a promising technique, especially in view of the rapid population growth associated with the expansion of cultivated areas to satisfy the food demands of the population, and the increase in solar power plants, which require considerable space to supply the population with energy. Thus, the transition from agricultural to agrivoltaics systems and the transition from PV power plants to agrivoltaics systems can enable more efficient use of land for energy and agricultural production. However, the configuration of agrivoltaics systems, namely panel elevation, spacing between panels and between rows of panels, and panel size, defines the amount of material used. As a result, configuration can have a major impact on the environment. The aim of this study is to highlight the environmental impact from converting 1 ha of land used entirely for agricultural production to 1 ha of an agrivoltaic system, and from converting 1 ha of land used entirely for solar photovoltaic energy production to 1 ha of an agrivoltaic system through a life cycle assessment. Three different configurations of agrivoltaics systems are considered to assess the environmental potential of agrivoltaics configurations. This analysis is performed with SimaPro 9.4 software, using the ReCiPe Midpoint (H) method and the Eco-invent database. The study determined impacts on global warming, stratospheric ozone depletion, ionizing radiation, ozone formation, mineral resource scarcity, fossil resource scarcity, water consumption, and land use through the determination of the Land Equivalent Ratio (LER). The results show that impacts are highest for PV power plants, followed by the agrivoltaic system with the largest PV panels for all indicators, except for stratospheric ozone depletion, where impacts are highest for agrivoltaics and agricultural use systems. The results of the land evaluation showed that the agrivoltaic system Case 3 gave the best performance, with a Land Equivalent Ratio of 148.7%. Full article

We assessed the current and possible future predicted distributions of the Mexican narrow-mouthed toad, Amphibia, Microhylidae Hypopachus variolosus (Cope, 1866) across its range to evaluate vulnerability under global change. (2) Methods: We integrated 481 validated occurrence records across the species’ distribution range, including 120 records from Mexico, with bioclimatic and land-cover predictors to build ensemble ecological niche models. We additionally incorporated human footprint metrics to evaluate anthropogenic pressure and projected future habitat suitability under climate and land-use change scenarios. (3) Results: Models showed high performance (TSS > 0.80; AUC > 0.90), identifying temperature and precipitation extremes as main drivers. Suitable habitats extended across both coasts and revealed novel areas in central Mexico. The most suitable habitat occurred under low human pressure, although localized impacts were detected. Deforestation in the Yucatán Peninsula reduced tree cover despite high climatic suitability. Future projections for 2050 under RCP 8.5 indicated marked reductions in modeled high-suitability areas, particularly in central Mexico. (4) Conclusions: These findings indicate high vulnerability to climate and land-use change and support updating distribution limits, incorporating new regions into conservation planning, and reassessing threat status to promote long-term persistence. Full article

The White-naped Crane (Antigone vipio), a first-class national protected bird species in China, exhibits a declining global population. To investigate the spatiotemporal patterns and drivers of wintering habitat suitability, data from 71 valid distribution sites were collected from 2015 to 2025 during the wintering period. Using the MaxEnt model, current and future (2050 and 2070) potential suitable habitat distributions were simulated under three climate scenarios: SSP126 (low emissions), SSP245 (medium emissions), and SSP585 (high emissions). The modeling yielded an average AUC value of 0.984, indicating high predictive accuracy. Key environmental variables influencing the wintering distribution of the White-naped Cranes include elevation, distance to major water, precipitation of the driest month, slope, temperature seasonality, and mean temperature of the wettest quarter. The current high-suitable area for the White-naped Cranes spans 5.64 × 10⁴ km² and is primarily distributed in the middle and lower reaches of the Yangtze River and in coastal wetlands along the North China. Among these, Hunan, Hubei, Jiangxi, and Anhui provinces contain relatively concentrated high-suitable areas for the species. Primarily influenced by elevation, distance to major water, precipitation of the driest month, and land-use classification, the suitable wintering habitat of the White-naped Cranes is projected to undergo significant contraction, shifting predominantly to the middle reaches of the Yangtze River. The most severe contraction is projected under the SSP585 scenario by 2070, with a reduction of 4.11 × 10⁵ km². Contraction areas are primarily concentrated along the Bohai and Yellow Sea coasts and in the middle and lower reaches of the Yangtze River, while minimal expansion occurs in Hubei, Anhui, and Zhejiang. The overall southwestward shift in the species’ distribution centroid may be associated with changes in elevation and distance to major water. Finally, habitat conservation strategies for the White-naped Cranes are proposed, providing a scientific basis for population protection and habitat management under future climate change. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

Background/Objectives: The increasing burden of non-communicable diseases, together with accelerating environmental degradation, highlights the urgent need for sustainable dietary patterns that promote both human and planetary health. The Mediterranean diet (MedDiet), traditionally followed in countries bordering the Mediterranean basin, has gained recognition as a model of sustainable nutrition due to its well-documented health benefits and relatively low environmental impact. However, its broader role within sustainable food systems requires comprehensive and interdisciplinary evaluation. The aim of this review is to provide a state-of-the-art synthesis of the evidence on the MedDiet as a sustainable dietary pattern, integrating its health, environmental, economic, and socio-cultural dimensions. Methods: This state-of-the-art narrative review synthesizes evidence from peer-reviewed literature on the MedDiet and sustainability. Relevant studies were identified through major scientific databases, focusing on publications addressing nutritional, environmental, economic, and socio-cultural dimensions. Both observational and interventional studies, as well as modeling and life cycle assessment analyses, were included. Additional sources from international organizations and policy reports were incorporated to contextualize global trends and challenges. Results: High adherence to the MedDiet is consistently associated with a reduced risk of cardiovascular disease, type 2 diabetes, cancer, and all-cause mortality. From an environmental perspective, the MedDiet is associated with lower greenhouse gas emissions, reduced land and water use, and enhanced biodiversity conservation compared with Western dietary patterns. Economically, it may represent a cost-effective dietary model and support local food systems when grounded in traditional practices, although affordability varies across contexts. Socio-culturally, the MedDiet promotes food heritage, culinary skills, and social cohesion. Nevertheless, globalization, urbanization, and the increasing consumption of ultra-processed foods have contributed to declining adherence, posing significant challenges to its sustainability and scalability. Moreover, the sustainability benefits of the MedDiet seem to be context-dependent rather than intrinsic, raising several challenges and limitations for its adoption. Conclusions: The MedDiet should be viewed not as a definitive solution to global food-system challenges but as a valuable reference model that illustrates how dietary practices can contribute simultaneously to human health, environmental sustainability, and cultural continuity. Modern sustainable dietary strategies should build upon the strengths of the MedDiet while recognizing its limitations, embracing contextual adaptation, and addressing the structural determinants that shape food choices. Full article

Hantaviruses are emerging rodent-borne pathogens that pose increasing global public health concerns due to their association with hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS), both of which can result in substantial morbidity and mortality. Environmental change, climate variability, urbanization, and land-use transformation are increasingly recognized as critical drivers of hantavirus emergence and transmission. This review summarizes current evidence regarding hantavirus virology, epidemiology, pathogenesis, clinical manifestations, diagnostics, surveillance systems, prevention strategies, and One Health preparedness approaches. Emphasis is placed on the influence of climate change and ecological disruption on rodent reservoir dynamics and spillover risk, as well as major surveillance and diagnostic gaps in tropical and Caribbean regions where hantavirus circulation may be underrecognized. Advances in molecular diagnostics, genomic surveillance, vaccine development, monoclonal antibody therapies, and climate-based early warning systems are also discussed. Existing evidence highlights the importance of integrated One Health surveillance systems that combine human, animal, and environmental monitoring to improve early detection and outbreak preparedness. Strengthening laboratory capacity, ecological surveillance, regional collaboration, and public health infrastructure will be essential for reducing the global burden of hantavirus infections and improving preparedness for future zoonotic disease threats. Full article

Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R²) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R² of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. Full article