Search Results (5,811)

Mediterranean agroecosystems are highly vulnerable to climate change, soil degradation, and declining soil organic carbon (SOC), threatening long-term agricultural sustainability. Carbon farming and regenerative agriculture have emerged as complementary approaches to restore soil functionality while contributing to climate change mitigation. This review synthesizes peer-reviewed literature published between 2015 and 2025 to assess the agronomic effectiveness of key regenerative and carbon farming practices in Mediterranean systems. A structured bibliographic analysis using Scopus and Web of Science evaluated practices influencing SOC dynamics, erosion control, water regulation, and associated ecosystem services. Evidence indicates that the introduction of cover crops in the crop rotation and reduced or no-tillage are the most consistently effective practices for enhancing SOC stocks, particularly when combined with organic amendments and diversified rotations. Crop diversification, intercropping, and agroforestry further support SOC accumulation and erosion control, especially in perennial systems such as vineyards and olive orchards. Organic inputs stimulate microbial-mediated carbon stabilization, while regenerative grazing contributes to nutrient cycling under context-specific conditions. Across practices, integrated management consistently delivers greater and more stable benefits than single interventions. Regenerative agriculture thus provides a systems-based foundation for carbon farming in Mediterranean agroecosystems. Long-term field experiments and improved monitoring frameworks remain essential to quantify carbon persistence and support policy implementation. Full article

Allergic diseases are increasing globally, particularly among children, who are highly vulnerable due to critical windows of immune development. This review examines climate change as a key environmental determinant driving the rising burden of pediatric allergic diseases, including asthma, allergic rhinitis (AR), atopic dermatitis (AD), and food allergy (FA). Climate change influences disease risk through interconnected pathways, such as increased air pollution, altered aeroallergen patterns, and more frequent extreme weather events. Elevated carbon dioxide (CO₂) levels and rising temperatures prolong pollen seasons and enhance allergenicity, while pollutants such as ozone (O₃) and particulate matter (PM) exacerbate airway inflammation and immune dysregulation. Emerging evidence emphasizes the role of early-life exposure, particularly during prenatal and early postnatal periods, when environmental insults can induce long-term effects via epigenetic modifications and immune reprogramming. These mechanisms may increase susceptibility to allergic sensitization and subsequent disease development. Epidemiological studies consistently link exposure to air pollution, including PM_2.5 (PM with aerodynamic diameter < 2.5 μm) and nitrogen dioxide (NO₂), with increased risk of allergic diseases in children. Additionally, climate change-related events such as wildfires, sand and dust storms, and thunderstorms further elevate exposure to allergens and pollutants, contributing to acute exacerbations and disease progression. Climate change may also contribute to allergic diseases through microbiome dysbiosis, as altered environmental microbial exposures, biodiversity loss, air pollution, and antibiotic-associated microbial disruption may impair immune tolerance and promote allergic sensitization in children. Addressing this growing public health challenge requires integrated mitigation strategies to reduce greenhouse gas (GHG) emissions and improve air quality, alongside adaptive interventions to enhance resilience and reduce exposure. Understanding these mechanisms is essential for developing targeted prevention strategies and protecting child health in a changing climate. Full article

The paper examines the macroeconomic and environmental outcomes associated with green financing across 22 emerging markets and developing economies from 2002 to 2024. Driven by the increased policy focus on climate finance as a two-fold tool of sustainability and development, the analysis assesses whether green financing is an economic growth driver. A multi-model structure is used (fixed effects, non-linear (quadratic), threshold, dynamic (lagged), and first-difference specifications) to achieve strength and eliminate model-specific bias. The findings show that green financing exhibits a weak positive association with economic growth in baseline and regime specifications. Still, this relationship is not robust across dynamic and first-difference models. Moreover, there is no indication of non-linearity or a threshold effect (a Green Laffer Curve). Patterns that indicate a weak positive relationship are cross-sectional and not robust to panel estimation; they are therefore aggregation-biased. Conversely, green financing has a low negative correlation with CO₂ emissions, indicating partial environmental efficiency. The results show that climate finance is limited in scale and inefficiently structured, which limits its macroeconomic impact. In general, the paper concludes that green finance, although environmentally applicable, is not sufficient as it currently stands to spur economic growth in emerging economies. Full article

Climate change poses significant challenges to urban infrastructure, particularly semi-outdoor stadiums, which are highly susceptible to climate-related hazards. The current research community has gradually recognized this issue but lacks systematic insights into the capacity and methods for stadiums to cope with climate change. This review assesses the vulnerability and climate performance of semi-outdoor stadiums and identifies adaptation strategies to enhance resilience. A systematic literature review was conducted using Web of Science and Scopus databases. Key themes included thermal comfort, wind comfort, and rain protection. Thermal comfort and CFD emerged as the most dominant research focus. This review highlighted the importance of long-term climate adaptation strategies, including the use of sustainable materials, improved ventilation, and renewable energy systems. The results also indicate a lack of research on tropical climates and that more comprehensive adaptation strategies are needed. The core contribution is a structured vulnerability framework that transforms scattered evidence into an integrated knowledge structure, identifying not only dominant themes and missing links but also cross-cutting trade-offs. These findings provide actionable insights for urban planners, architects, and policymakers aiming to enhance stadium resilience and contribute to sustainable urban development goals. Full article

The red palm weevil, Rhynchophorus ferrugineus (Olivier, 1791), is among the most destructive pests of date palm (Phoenix dactylifera L.) globally, posing a severe and escalating threat to agricultural productivity across the Arabian Peninsula. Despite its well-documented economic impact, the potential influence of climate change on its future distributional dynamics within this region remains poorly quantified. This study employed Maximum Entropy (MaxEnt) species distribution modelling to assess current and projected habitat suitability for R. ferrugineus across the Arabian Peninsula (~3.2 million km²) under two contrasting Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) for the mid-century (2050) and late-century (2070). The model was calibrated using 52 spatially thinned occurrence records and six non-collinear environmental predictors selected following Variance Inflation Factor (VIF) analysis, with sampling bias corrected through a kernel density-based background weighting approach. Model performance was robust, with mean training and test AUC values of 0.921 ± 0.023 and 0.840 ± 0.052, respectively, and a mean TSS of 0.583 ± 0.046. Precipitation of the coldest quarter (Bio 19) and precipitation seasonality (Bio 15) emerged as the most influential predictors of habitat suitability, followed by elevation. Currently, approximately 727,589.8 km² (26.11%) of the peninsula is classified as suitable habitat, concentrated along the eastern Arabian Gulf coastline and the western Red Sea plain. Under SSP1-2.6, suitable habitat is projected to expand by 16.34% and 31.60% by 2050 and 2070, respectively. Under the high-emission SSP5-8.5 scenario, expansions are considerably more pronounced, reaching 34.11% by 2050 and 60.15% by 2070, with total suitable area approaching 1,158,474.8 km² (41.58%) by late-century. Habitat contraction was negligible across all scenarios, indicating a unidirectional range expansion dynamic. These findings highlight the substantial threat posed by climate-driven habitat expansion of R. ferrugineus and provide spatially explicit projections to inform proactive biosecurity planning and pest management strategies for date palm cultivation across the Arabian Peninsula. Full article

This study analyses the economic performance, sustainability, and resilience of family farming systems in São Tomé and Príncipe, using an approach that combines quantitative and qualitative data. Primary data were collected through a survey of 50 rural families from the seven districts of the country, focus group discussions, and field observations. Quantitative analysis included descriptive statistics and exploratory comparative procedures, complemented by economic evaluation, while thematic analysis examined the qualitative data. The findings reveal diversified agroforestry systems, integrating up to 33 crops and small-scale livestock production. At the individual and aggregate levels, agroforestry shows viable economic performance, with a net profit margin of 57.4%, capable of generating income and marketable surpluses. This improves rural livelihoods, strengthens resilience to climate and market shocks, and supports both subsistence and market-oriented production. Despite these strengths, structural constraints persist, including fragile value chains, limitations in access to credit and markets, low technology adoption, and climate vulnerability. Human capital, particularly education, emerges as a key factor in improving productivity and value creation. Integrated policies on access to resources and education are needed to promote diversification, multi-activity, and market integration as central strategies for increasing sustainability, food security, and risk reduction in family farming. Full article

The transition to a circular economy (CE) in the automotive sector is increasingly critical amid rising resource pressures and climate imperatives. In India, this shift is influenced by regulatory initiatives, corporate sustainability goals, and life-cycle-wide environmental challenges. However, current studies remain fragmented, often neglecting the linkages between policy drivers, material issues, and firm-level responses. This study aims to evaluate how CE strategies are operationalized across the Indian automotive value chain using a Drivers–Materiality–Response (DMR) analytical framework. A multiple-case qualitative analysis was conducted involving six major automotive firms and associated ecosystem actors, with data sourced from corporate reports, national policies, and third-party assessments from 2018 to 2024. Semi-structured interviews with 11 industry experts were incorporated to strengthen triangulation, validate firm-level circular economy claims, and support the reliability of the DMR-based interpretation. Findings reveal strong alignment with national CE policies among leading firms, particularly Tata Motors and Mahindra, with comprehensive integration of electrification, battery reuse, zero-waste goals, and digital mobility solutions. However, challenges remain in end-of-life vehicle (ELV) formalization and circularity in downstream systems. The DMR model effectively bridges gaps in existing frameworks by offering a life-cycle-based lens that links Environmental, Social and Governance (ESG), Life Cycle Assessment (LCA), and policy–firm dynamics. The study contributes a scalable diagnostic tool for assessing CE maturity in emerging economies. While limited by reliance on secondary data, the triangulated approach enhances reliability and provides actionable insights for policymakers and industry leaders. Full article

Soybean is a strategic crop for global protein and vegetable oil supply chains; however, genetic improvement remains constrained by genotype-dependent regeneration, variable transformation efficiency, and regulatory concerns regarding stable transgene integration. This review synthesizes emerging DNA-free and genotype-independent genome-editing frameworks for soybean, where genotype independence is defined as the ability to recover fertile, non-chimeric edited plants across elite germplasm. We critically examine the soybean genome-editing toolbox, including CRISPR-Cas9, Cas12a, multiplex editing systems, base editing, and prime editing, and discuss persistent bottlenecks associated with target selection, off-target assessment, editability, and plant recovery. Particular emphasis is placed on artificial intelligence (AI)-assisted approaches that integrate genomic, epigenomic, chromatin-accessibility, and multi-omics datasets to improve target prioritization, guide RNA design, off-target prediction, and locus- and genotype-specific editability assessment. We further evaluate DNA-free genome-editing technologies, including CRISPR-Cas ribonucleoproteins, transient RNA-based systems, and nanocarrier-mediated delivery platforms, highlighting their potential to generate non-integrative edits while reducing prolonged nuclease exposure. In addition, we discuss regeneration reprogramming strategies based on developmental regulators and morphogenic modules, including BBM-WUS, GRF-GIF, de novo meristem induction, and somatic embryogenesis, as enabling technologies for overcoming cultivar-dependent regeneration barriers. Importantly, this review proposes an integrated AI-to-field framework that connects target discovery, editability prediction, DNA-free editing, regeneration reprogramming, phenotypic validation, and breeding deployment into a unified soybean improvement pipeline. We further highlight emerging opportunities in multi-omics-guided target discovery, genotype-aware prediction models, regeneration-aware editing strategies, and closed-loop machine-learning systems that continuously improve editing decisions through experimental feedback. Collectively, these convergent innovations provide a practical foundation for accelerating the development of climate-resilient, nutritionally enhanced, and industry-ready soybean cultivars. Full article

The rapid pace of urbanization, coupled with the variability in climatic conditions, has led to a marked increase in global food demand. Simultaneously, this phenomenon has resulted in a decline in the overall quality of food, highlighting the need to improve existing agricultural production systems. In this context, zeolite has emerged as a promising soil amendment for optimizing its physical properties and crop yields. However, there is limited information on its effects during tomato cultivation, particularly for the Saladette variety (Solanum lycopersicum L.) in Hidalgo, Mexico. This includes the use of this zeolite variety, the evaluation of its antioxidant properties, and its antioxidant activity at different applied concentrations. This study evaluated the effect of different concentrations of zeolite applied to the soil on tomato growth and fruit quality. After crop establishment, the treatments were monitored monthly. The results showed that the application of zeolite significantly improved crop yield, with Treatment 3 (5 kg zeolite plant⁻¹) showing the best performance without affecting the physical characteristics of the fruit. The tomatoes maintained adequate commercial standards, with weights ranging from 104 a 169 g, sizes from 5.16 to 6.20 cm, and firmness values between 1.19 and 2.27 N; therefore, this treatment was selected for the determination of the antioxidant activity on the fruits. Furthermore, an increase in antioxidant capacity was observed, reaching 5.50 µmol TE/100 g of dry sample in the DPPH antioxidant capacity test. This demonstrates that zeolite application positively influences the quality and antioxidant capacity of tomatoes. This suggests that zeolite could be used in various crops, potentially improving the quality of the final product and offering health benefits to consumers thanks to the antioxidant compounds generated during harvest. However, further studies are needed to determine the optimal application rates and the long-term effects on soil health and crop productivity. Full article

Climate change may intensify the deterioration of river water quality by altering streamflow regimes, precipitation patterns, and organic matter transport pathways. In this study, a Hydrological Simulation Program-FORTRAN (HSPF)-based streamflow and total organic carbon (TOC) water quality model for the Tamjin River Basin, Korea, was developed, and future TOC pollution was evaluated under quantile delta mapping (QDM) bias-corrected Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) climate scenarios. Unlike previous studies that generally applied climate bias correction, watershed modeling, or pollutant-load assessment as separate procedures, this study links QDM-preserved climate change signals, process-based HSPF simulations, and TOC-specific discharge-load, delivered-load, exceedance-frequency, and load-reduction indicators within a single management framework. The model showed acceptable performance, with Nash–Sutcliffe efficiency (NSE) values of 0.67 and 0.68 for streamflow at Jangheung Dam and Gamcheon Bridge, respectively, and a TOC deviation of volume (DV) of 0.6% at Tamjin5. Under the SSP5-8.5 no-action scenario for the 2040s, the mean streamflow decreased by 33.1%, whereas the mean TOC concentration increased by 76.8% relative to the baseline. The number of days exceeding 4 mg/L TOC increased from 41 to 216 days yr⁻¹, and the Korean TOC-based water quality class deteriorated from Ib to III. In contrast, the 20% and 30% load reduction scenarios offset approximately 33.8% and 67.9% of the climate-driven increase in TOC, respectively, with the 30% reduction scenario showing greater effectiveness during low-flow seasons. Elevated TOC levels may have implications for downstream water treatment because organic matter can increase chemical demand and disinfection-byproduct formation potential. However, these treatment-related effects were not directly evaluated in this study. These results suggest that TOC should be considered as a complementary indicator to conventional biochemical oxygen demand (BOD)-based management when developing climate-resilient water-quality strategies for the Tamjin River Basin. Full article

This study examines the development of urban air mobility, which requires the creation of vertiports capable of ensuring the safe operation of electric vertical takeoff and landing (eVTOL) systems. Key operational constraints include unstable power supply, external climatic conditions, and reliance on battery systems. This study aims to develop a risk-based model for vertiport planning those accounts for the stochastic nature of eVTOL operational safety. A continuous-time Markov model incorporating nominal operational characteristics, system constraints, and transitions into emergency and catastrophic flight modes is proposed. State transitions within the model are primarily driven by climatic indicators, power supply reliability, battery parameters, maintenance quality, and diagnostic coverage. To interpret the low probabilities of transitioning to a catastrophic mode, this study introduces a safety index (integrated safety index), which facilitates the comparison of various operational scenarios and regulatory maturity levels. The practical importance of the research lies in applying the proposed model to precisely select vertiport locations; assess energy infrastructure requirements; and organize onboard monitoring, robotic preflight inspection systems, and decision support systems. The results demonstrate that eVTOL operational safety is assessed not only through spatial and infrastructure metrics but also through an integrated indicator encompassing power supply, climate, battery degradation, diagnostics, and hardware–software reliability of the entire vertiport system. Full article

Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

This paper presents innovative research on power grid reliability, which is essential for the overall sustainability of energy systems in the emerging age of electricity. The research primarily analyzes the profound methodological disparities between the fragmented European approach and the exact statistical model employed in the United States (the 2.5 Beta method defined by the IEEE 1366 standard). A novel dimension addressed in this research is the compounding effect of climate hazards and the massive proliferation of artificial intelligence (AI) data centers. Unlike conventional single-layer machine learning models (such as standard Support Vector Machines or regressions) that rely solely on historical weather data, this study proposes the Hierarchical Spatiotemporal Multiplex Networks (HMN-RTS) predictive framework. By dynamically fusing structured environmental data with unstructured social sensor data (Geographic Information Systems—GIS, and social media feeds), the proposed HMN-RTS framework significantly outperforms traditional models in predicting outage risks and their exact durations. Full article

Honey bees (Apis mellifera) are key pollinators in agricultural ecosystems that face increasing pressure from pathogens and environmental change. However, how these environmental factors interact remains incompletely understood. To assess associations between climate, landscape composition, and pathogen occurrence in real agroecosystems, we monitored honey bee colonies across 30 apiaries in southern Italy over two years, in summer and autumn. Molecular screening revealed widespread multi-pathogen exposure, with two viruses, Black Queen Cell Virus (BQCV) and Deformed Wing Virus (DWV), and gut trypanosomatid parasite (Lotmaria passim) being the most frequently detected. In contrast, Nosema ceranae, along with Bee Macula-like Virus (BeeMLV) and Acute Bee Paralysis Virus (ABPV), occurred at lower but still notable frequencies. Infections were generally more frequent in adult foragers than in in-hive bees and larvae, and overall pathogen occurrence tended to be higher in summer than in autumn. Higher humidity was associated with higher overall pathogen occurrence and coinfection levels, whereas higher temperature showed a weaker association with these outcomes. Associations between landscape composition and pathogen occurrence differed across pathogens: a higher proportion of semi-natural habitats was associated with lower viral occurrence, particularly BQCV and DWV; however, N. ceranae was more frequently detected under the same landscape conditions. In contrast, L. passim showed context-dependent responses, with landscape effects emerging only through interactions with humidity and temperature. Pathogen coinfections were more occurrent under warm, humid conditions, although this pattern was partially buffered in landscapes richer in semi-natural habitats. Together, these results indicate that, within the studied apiaries, honey bee pathogen occurrence was associated with climate, season, and land use. These findings suggest that environmental context should be considered when interpreting honey bee health monitoring data in heterogeneous agricultural landscapes, with potential implications for apiary management. Full article