Search Results (10,838)

This study analyzes land use dynamics in Fălticeni Municipality over four decades (1985–2025), examining the influence of major political, socio-economic, and demographic changes on urban development. The transition from a centrally planned economy to a market economy, Romania’s democratic transformation and European integration, together with migration and population dynamics, have driven largely unregulated urban expansion at the expense of agricultural land and natural landscapes. Recent urban growth has also extended into areas with varying geomorphological vulnerability, increasing exposure to landslide hazards, while the city continues to face challenges related to abandoned industrial areas and insufficient forested land and green spaces. By integrating land use change analysis with physical vulnerability indicators, this study highlights the need for risk-informed and sustainable urban planning in medium-sized cities. It proposes a planning framework based on ecological zoning, controlled urban expansion on suitable terrain, brownfield redevelopment, the establishment of peri-urban forests, and the implementation of essential infrastructure supported by comprehensive geomorphological susceptibility assessments. The proposed approach provides practical guidance for enhancing urban resilience and can be replicated in other cities facing similar environmental and development challenges. Full article

Common prosperity constitutes a fundamental goal of Chinese modernization, and digital rural development has emerged as an important pathway for advancing sustainable rural transformation. Based on balanced panel data covering 61 counties in Hubei Province from 2014 to 2023, this study empirically investigates the impact of digital rural development on county-level common prosperity and explores its underlying mechanisms. The entropy weight method is employed to construct the Digital Rural Development Index and the County-Level Common Prosperity Index. A fixed-effects model is adopted for baseline estimation, supplemented by mediation analysis, moderation analysis, and a series of robustness tests. The results reveal that digital rural development significantly promotes county-level common prosperity. Significant heterogeneity exists across county types, with stronger effects observed in Type II counties due to their stronger compatibility between agricultural foundations and digital transformation. Furthermore, financing constraint alleviation serves as an important mediating mechanism, while industrial agglomeration positively moderates this relationship by enhancing counties’ capacity to transform digital resources into inclusive development outcomes. These findings underscore the importance of strengthening rural digital infrastructure, expanding inclusive digital financial services, and implementing differentiated regional strategies to improve the effectiveness of digital rural development and promote county-level common prosperity. Full article

Biochar is a porous carbonaceous material synthesized through the pyrolysis of diverse biomass resources, including agricultural and forestry residues as well as livestock manure. It possesses superior characteristics such as a large specific surface area, adjustable pore architecture, abundant surface functional groups, and favorable electrical conductivity. With the increasingly severe global energy shortage and environmental pollution problems in recent years, biochar has emerged as a green, low-cost functional material with distinct application superiority in multiple key research directions, including energy storage and conversion, chemical catalysis, environmental restoration, and signal sensing and detection. This study comprehensively summarizes the latest research advances of biochar in the aforementioned application fields, focusing on innovative achievements in photocatalytic and electrocatalytic hydrogen generation, supercapacitors and electrochemical energy storage systems, persulfate activation technology, carbon dioxide capture, remediation of heavy metal and organic contaminants, volatile organic compound (VOC) adsorption, as well as electrochemical sensing devices. Existing research results demonstrate that modification strategies including metal and non-metal doping, surface oxidation treatment, and compounding with semiconductors or metal oxide materials can effectively improve the catalytic activity and functional performance of biochar. Furthermore, this paper prospects the future interdisciplinary development trends of biochar, analyzes the existing research gaps in mechanism exploration, structural optimization design, and industrial large-scale preparation, and provides theoretical and practical references for the further popularization and application of biochar in sustainable energy development and environmental governance fields. Full article

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease-monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, the measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): plant–pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): the diverse disease-detection tasks and their corresponding research objectives. (3) Sensor (S): the sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): the environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot, and regional scales. (5) Algorithm (A): the classical and state-of-the-art data-analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): the data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease-detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production. Full article

Land transport infrastructure (LTI) is a core component of sustainable transport systems, shaping mobility, efficiency, and the spatial distribution of development gains. Existing studies of urban–rural income inequality mainly focus on individual transport modes or aggregate infrastructure scale, with limited attention to transport-system composition and its contextual dependence. This study addresses this gap by conceptualizing LTI as a layered system and examining how its internal composition is associated with urban–rural income inequality across different levels of urbanization and economic development. Using a balanced panel of 286 prefecture-level cities from 2013 to 2023, the study constructs ratio-based indicators of compositional shifts within road systems, within rail systems, and between rail and road infrastructure. Two-way fixed-effects models incorporate interactions with urbanization and economic development. Conditional marginal-effect maps are then used to identify how these associations change across development contexts. The results reveal a clear stage-dependent pattern. Urbanization generally attenuates the inequality-widening association of mobility-oriented upgrading, whereas economic development influences whether such upgrading reinforces spatial polarization or supports wider diffusion. When urbanization and development are both sufficiently advanced, the marginal association may shift toward inequality reduction. At earlier stages, accessibility-oriented roads and conventional rail tend to show stronger equalizing associations. Mobility-oriented roads and high-speed rail are more likely to be associated with narrower inequality in more advanced settings. Mechanism-oriented analyses yield evidence consistent with two potential channels: the agricultural–non-agricultural labor-productivity gap and the non-agricultural employment share. The extended analyses and robustness checks broadly support the main findings. These findings indicate that transport infrastructure upgrading should be evaluated not only in terms of efficiency, but also according to whether the resulting infrastructure mix broadens access to opportunities, improves resource allocation, and supports inclusive regional development. Full article

Plant disease forecasting plays an important role in modern crop protection by enabling early disease prediction and supporting optimized management decisions. With the rapid development of digital agriculture, artificial intelligence, and environmental monitoring technologies, forecasting systems have evolved from traditional empirical and mechanistic models to machine learning, deep learning, multi-source data fusion, and hybrid forecasting frameworks. Unlike previous reviews that mainly focused on specific model types, decision support systems, or disease recognition technologies, this review provides a comprehensive synthesis of different forecasting approaches and their practical applications. The strengths and limitations of various models are comparatively analyzed in terms of predictive performance, interpretability, fungicide reduction potential, and practical applicability. In addition, recent advances in climate-driven disease forecasting, precision disease management, and intelligent decision support systems are discussed. Finally, current challenges and future directions, including AI-mechanistic model integration, multi-disease forecasting, IoT and remote sensing data fusion, and climate-adaptive forecasting systems, are highlighted to support the development of sustainable and intelligent crop protection strategies. Full article

Water resources are essential for sustainable agricultural development, and accurate crop water requirement prediction is important for improving irrigation efficiency and optimizing water allocation in irrigation districts. This study focused on the Chushandian Irrigation District in Henan Province, China. Reference evapotranspiration (ET₀) was calculated using the FAO Penman–Monteith equation, and the monthly crop water requirements (ET_C) of wheat, peanut, rapeseed, corn, rice, and vegetables were estimated using crop coefficients (K_c). XGBoost feature importance, Pearson correlation, Mantel, and SHAP analyses were used to examine the meteorological drivers of crop water requirement. Atmospheric pressure showed high nonlinear predictive importance, whereas mean air temperature, relative humidity, and sunshine duration exhibited more consistent physical and statistical relationships with crop water requirement. A process-informed TCN–Transformer framework was then developed for joint and crop-specific prediction. The TCN module extracted local temporal variations, while the Transformer module captured long-term dependencies. In the joint prediction task, the proposed model achieved an R² of 0.9487 and an RMSE of 33.24 mm, outperforming the LSTM, GRU, and CNN–LSTM baselines. The crop-specific results further demonstrated that the model effectively represented seasonal variations and periods of relatively high water requirement across the six crops. The proposed framework can support monthly water-allocation planning and seasonal irrigation scheduling in multi-cropping irrigation districts. Full article

Urban and peri-urban regions increasingly face climate-related pressures, competing land-use demands, and the need to expand renewable-energy infrastructure while maintaining agricultural land and landscape functions. Agrivoltaics, which combines photovoltaic energy generation with agricultural production, represents a potentially multifunctional approach to land use; however, regional GIS assessments often stop at environmental suitability surfaces without translating those results into planning-relevant cadastral inventories. This study develops and applies a planning-oriented Geographic Information System (GIS) framework for preliminary statewide agrivoltaic screening in Connecticut. Annual global solar radiation and terrain slope were integrated through a weighted suitability model, while incompatible land-cover classes were treated as hard exclusions through a binary land-cover mask. The workflow subsequently excluded protected and open-space lands, associated suitable areas with cadastral parcels, normalized and dissolved parcel identifiers using ParcelKey, and a recalculated suitable area from the resulting unique parcel geometries and then applied a minimum requirement of 1 ha of cumulative suitable area per retained parcel. The final baseline inventory contained 3497 normalized unique cadastral parcels encompassing 16,366.49 ha of GIS-identified suitable area, with suitable land representing an average of 42.46% of total parcel area. Peri-urban contexts accounted for the largest share of the final suitable area, containing 2497 parcels and 73.16% of the total, compared with 476 urban and 524 rural parcels. Sensitivity analysis indicated strong stability under alternative weighting schemes, with spatial overlap exceeding 99% relative to the baseline. Reducing the suitability-score threshold from 3.0 to 2.5 produced only minor changes, whereas increasing it to 3.5 reduced the inventory to 3095 parcels and 13,712.89 ha. From a sustainability perspective, the framework provides a spatial decision-support approach for coordinating renewable-energy planning with agricultural land stewardship, conservation constraints, and more efficient use of already fragmented land resources. By making the effects of exclusions, parcel thresholds, and analytical assumptions explicit, the approach supports more transparent and reproducible evaluation of land-use trade-offs relevant to sustainable development. The resulting inventory is intended as a first-stage planning resource rather than a determination of project feasibility or site-level sustainability performance. Full article

While low-impact development and retention strategies are widely studied in urban and agricultural contexts, a distinct knowledge gap remains regarding the quantitative evaluation of dispersed, natural microretention structures in small, ungauged, mountainous forested catchments under complex topographic conditions. To address this limitation, this study provides a novel quantitative assessment of how natural bioretention interventions—specifically arcuate deadwood log barriers, cascading reservoir systems, and strategic afforestation—influence runoff reduction and substrate infiltration dynamics. Focusing on the 4.57 km² basin of the Stankowice Stream in southwestern Poland, the research integrates field geodetic and hydrological measurements with Iszkowski’s empirical flow formulas and high-resolution digital elevation modeling (SCALGO platform). Delineation of 10 key subcatchments revealed that surface runoff potential is heavily concentrated within specific flow pathways rather than determined solely by subbasin area. In unit No. 9, deploying an arcuate arrangement of 19 deadwood logs achieved an 11% reduction in surface runoff (retaining 8662.50 m³), whereas coupling these log structures with a downstream cascading two-dam system significantly enhanced retention performance by establishing 79,065.68 m³ of depression storage and driving 264,066.16 m³ of subsurface infiltration. Furthermore, multi-scenario land use modeling demonstrated that transforming land cover to forest reduced surface runoff by over 70% in topographically steep subcatchments (e.g., unit No. 7). These findings demonstrate that effective flood mitigation in headwater catchments requires a systemic, targeted hybrid strategy combining decentralized bioretention with localized storage nodes, offering a transferable framework for sustainable regional water governance. Full article

Risk assessment is critical for managing health threats at the human–animal–environment interface, yet sector-specific approaches can result in fragmented actions. To address this gap, the Joint Risk Assessment Operational Tool (JRA OT), an operational tool of the Tripartite Zoonoses Guide, was developed by the Food and Agriculture Organization of the United Nations, the World Health Organization, and the World Organization for Animal Health. The JRA OT provides a structured framework for joint qualitative risk assessments that integrate multisectoral expertise to identify risk pathways, assess likelihood and impact, and develop consensus-based risk management and communication options. Surveillance systems play a critical role in this process by providing the multisectoral data needed to inform risk assessments, while the JRA process helps identify information gaps and guide the strengthening of integrated One Health surveillance. Implemented in at least 52 countries, the JRA OT has informed national mandates in Indonesia, Viet Nam, and Tanzania, regional strategies in West Africa, and adaptations in Canada. Cascade training has been used to build subnational capacity to facilitate roll out. Sustained leadership commitment, multisectoral coordination, routine applications, local adaptation, and capacity building remain essential for global implementation. Full article

Children living in agricultural communities are particularly vulnerable to pesticide exposure through multiple environmental pathways, yet community-level public policies specifically designed to protect children remain limited in many low- and middle-income countries. This study aimed to develop a community-based public policy for preventing pesticide exposure among young children in rural Thailand through a participatory action research (PAR) approach. Guided by the Kemmis and McTaggart PAR framework, one complete PAR cycle was conducted between February 2022 and January 2023 in Ban Nangoi Village, Sakon Nakhon Province, Thailand. Purposive sampling recruited 62 stakeholders representing parents, farmers, healthcare personnel, local government representatives, academics, and community members. Data were collected through semi-structured interviews, focus group discussions, participant observations, and community meetings and analyzed using thematic analysis. The PAR process enabled stakeholders to jointly identify local priorities and co-develop a context-specific public policy summarized as the “3 Cs for Farmers, 2 Ps for People, and 1 S for Community” framework. Participants reported greater awareness of pesticide-related health risks, stronger multisectoral collaboration, improved stakeholder communication, and wider adoption of preventive practices. These findings suggest that PAR is a feasible approach for co-creating context-specific public policies while strengthening local environmental health governance. Rather than demonstrating the effectiveness of the policy in reducing pesticide exposure, this study highlights the value of stakeholder participation and policy co-creation in supporting sustainable community action. Future research should incorporate objective pesticide exposure measures, quantitative outcome indicators, and longer-term follow-up to evaluate policy effectiveness across diverse agricultural settings. Full article

Microorganism inoculants are becoming increasingly essential in sustainable agriculture because they improve nutrient availability, promote plant growth, inhibit disease, and increase crop tolerance to environmental stresses. Nonetheless, their field performance is frequently hampered by poor storage survival, low rhizosphere establishment, and susceptibility to harsh climatic conditions. Encapsulation technologies provide an effective solution by encapsulating microbial cells in a biodegradable matrix, extending shelf life, increasing vitality, and allowing for controlled release in the soil. This review focuses on four agriculturally significant microorganisms: Azotobacter chroococcum, Azospirillum brasilense, Pseudomonas brassicacearum, and Trichoderma harzianum. Their modes of action, including nitrogen fixation, phytohormone synthesis, pathogen inhibition, and stimulation of plant defense responses, are reviewed alongside recent advances in encapsulation strategies. Alginate-based formulations and proposed potential multi-species microbial consortia are discussed as promising strategies for improving inoculant performance. However, the successful development of multifunctional potential microbial formulations requires further investigation of microbial compatibility, formulation stability, synchronized release behaviour, and long-term storage performance before broad agricultural implementation can be achieved. Full article

The development of arid-oasis agriculture and territorial spatial restructuring strongly influence regional eco-environmental quality. Clarifying production–living–ecological space (PLES) transitions and the nonlinear mechanisms driving eco-environmental quality is important for sustainable agricultural land use and ecological governance. Northern Xinjiang combines economic agglomeration with ecological sensitivity, making it a representative region for examining changes in eco-environmental quality and their underlying drivers. Land-use data for 1990, 2000, 2010, and 2020 were analyzed using land-use transition matrices, the eco-environmental quality index (EEQI), spatial autocorrelation, and hotspot analysis to characterize spatiotemporal changes under PLES transitions. XGBoost–SHAP was applied to identify the dominant factors, nonlinear responses, and interactions. The results showed that: (1) ecological space remained dominant from 1990 to 2020, but its share declined from 91.72% to 87.36%. Production and living spaces increased from 7.78% and 0.50% to 11.72% and 0.92%, respectively. Agricultural production space expanded most markedly, and spatial restructuring was most intense during 2000–2010. (2) EEQI decreased from 0.3100 to 0.2922 and showed an overall fluctuating decline. Significant spatial clustering was observed, with Moran’s I values consistently above 0.81 (p < 0.001). High-value areas were mainly distributed in the Ili River Valley and along the northern slope of the Tianshan Mountains, whereas low-value areas were concentrated in the Junggar Basin interior and the arid areas along its eastern margin. (3) During 2000–2020, LAI and NPP in agricultural production space increased by 45.7% and 48.2%, respectively. These increases indicated overall improvements in canopy condition and productivity. After 2010, NPP growth slowed markedly while LAI remained relatively stable, indicating a stage-specific divergence. (4) XGBoost–SHAP showed good robustness and spatial generalizability, with R² values of 0.868–0.918 and RMSE values of 0.059–0.075. NDVI remained the primary driver, while Elev contributed consistently. The dominant interaction shifted from NDVI ∩ PopDen in 1990 and 2000 to NDVI ∩ Elev in 2010 and NDVI ∩ GDP in 2020. This shift indicated fundamental natural constraints and stronger joint effects of population and economic activities during the later stages. These findings provide a scientific basis for optimizing PLES, regulating arid-oasis agriculture, and safeguarding regional ecological security. Full article

Agriculture faces the challenge of transitioning toward sustainable practices, driving the use of plant growth-promoting bacteria (PGPB). However, these bacteria suffer critical losses in viability due to environmental stress and drying processes. Although synthetic hydrogels offer protection, their low biodegradability and toxicity pose ecological risks, positioning dextran hydrogels produced by Leuconostoc sp. as a biocompatible biotechnological alternative, despite challenges related to their mechanical stability. The methodology employed consisted of systematic literature mapping in the Scopus database for the period 2010–2026. The search was conducted on 2 May 2026, using a defined search equation, and 447 documents were processed using RStudio (Bibliometrix), VOSviewer, and Plotly Studio to analyze trends and collaboration networks. The results of the systematic mapping reveal an exponentially growing field (R² = 0.998), led by Agricultural Sciences (23.5%) and Biochemistry (16%). China and India dominate scientific output in terms of volume, while Italy and the United States lead in qualitative impact, with researchers such as Cimini, Schiraldi, and Pandey as key references. An evolution is confirmed from the basic characterization of Leuconostoc sp. toward the development of matrices for immobilizing PGPB, reducing viability losses from 6 log to manageable levels of 4 log CFU. Cluster analysis shows a clear trend toward nanotechnology and “smart hydrogels” responsive to multiple stimuli. Finally, strategic gaps were identified in the creation of predictive release models, as well as an urgent need to democratize the technology through low-cost processes, essential aspects for consolidating sustainable precision agriculture. Full article

This study investigates the empirical links between climatic/socio-economic factors and Consumer Price Food Indices (CPFIs) across 47 advanced and emerging economies from 2001 to 2022. Utilizing a balanced panel dataset compiled from the World Bank’s World Development Indicators, FAO databases, and UNDP Human Development Reports, the analysis applies fixed-effects and random-effects panel data models to evaluate the drivers of food price dynamics. The empirical results reveal that socio-economic variables, specifically the Human Development Index (HDI), food price inflation, and agricultural productivity, are consistently associated with food indices, underscoring the critical role of structural development and nominal inflationary pressures. Climatic factors, particularly temperature anomalies, also exert a significant impact: a one-degree Celsius increase in temperature anomalies is associated with a 0.82-unit rise in the food price index level, whereas expansions in forest area and agricultural land are linked to price reductions. These findings indicate that food price dynamics are shaped by the intricate interplay of climate variability and socio-economic conditions. Consequently, policy recommendations emphasize targeted investments in socio-economic development, climate adaptation measures (including support for climate-resilient agriculture), and sustainable land management (forest conservation and optimized land use) to enhance food system resilience and support long-term food security. Full article