Search Results (24,109)

Global water scarcity constitutes a critical sustainability challenge, particularly in agricultural and rural contexts exposed to climate variability. Beyond technical and infrastructural solutions, collective and community-based water management practices have gained increasing relevance as sustainable alternatives grounded in local and ancestral knowledge. This study presents a systematic qualitative review of collective practices for alternative water management implemented worldwide between 2018 and 2023, following the PRISMA methodology, and based on a screening of the Scopus database, 31 peer-reviewed studies were selected and analysed through thematic synthesis. The systematic review identified five interconnected dimensions: (1) water management and governance, (2) conservation and storage, (3) hydrological restoration, (4) efficient water use, and (5) recognition of local knowledge. The results show that collective water management practices contribute to water security, ecological resilience, and adaptive capacity in rural territories, particularly when aligned with local socio-environmental conditions. The study highlights the importance of integrating scientific and community-based knowledge to advance context-specific and sustainable water management strategies, contributing to ongoing debates on sustainability, rural development, and adaptive water governance. Full article

Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based semantic segmentation models can effectively identify crop types, they often produce coarse or incomplete boundaries. The Segment Anything Model (SAM) can produce high-quality boundaries, but it depends on manual prompts and lacks semantic recognition ability, which limits its use in large-scale automatic mapping. To address this issue, this study proposes a parcel-level crop field identification framework based on Semantic–SAM fusion, enabling automatic semantic recognition and fine boundary extraction without manual prompts. Based on UAV RGB remote sensing imagery, this study developed a two-stage Semantic–SAM framework. Semantic segmentation models, including DeepLabv3+, U-Net, HRNet, and PSPNet, were first used to generate initial results. Then, bounding boxes or internal high-confidence points were extracted from the initial field regions as prompts for SAM to refine the segmentation. The final results preserved crop category information while producing finer boundaries. To evaluate the framework, this study compared four semantic segmentation models and their Semantic–SAM versions on the same-region test set, and further tested their spatial generalization ability on the different-region test set. The results showed that the Semantic–SAM framework provided more consistent gains in boundary quality, with regional recognition accuracy improving in several models and test scenarios. On the same-region test set, the PSPNet-based framework showed clear improvement, with mean Intersection over Union (mIoU) increasing from 78.99% to 83.13% under point-box prompts. The U-Net-based framework achieved the best mIoU of 87.09% with box prompts. On the different-region test set, the DeepLabv3+-based framework showed the largest gain in spatial generalization, with mIoU increasing from 67.22% to 73.45% under point-box prompts. Overall, the PSPNet-based fusion framework showed a better balance in accuracy, boundary quality, and robustness under different-region conditions. These results demonstrate that Semantic–SAM fusion supports automatic multi-class crop field mapping and boundary refinement from UAV RGB imagery without manual prompts or SAM fine-tuning, providing a practical approach for parcel-level crop monitoring and precision agriculture applications. Full article

This research examines how the relationship between cities and rural areas has evolved in light of the profound transformation affecting rural areas of high landscape value, which has been driven by the expansion opportunities granted to the real estate sector by urban planning regulations. The role of the landscape dimension in interpreting the relationship between territorial wealth and landscape value is considered, based on the convergence of two complementary disciplinary perspectives on territory: land planning and valuation science. Against this backdrop, and with a view to containing the progressive contamination of rural and agricultural heritage by the real estate sector, this study proposes a structured observation, valuation, interpretation, and regulatory tool to support the development of territorial planning in areas significantly characterized in terms of rural landscape value. The proposed tool is based on evidence regarding the phenomenon of building expansion in the agricultural territory of a municipality in southeastern Sicily, where favorable conditions for the development of the building sector exist, such as the vastness of the municipal territory and extensive farming as the mainstay of agricultural activity. This wider sub-regional area has also received attention due to the over-tourism phenomenon that has occurred in its cities of art. The evaluation approach experienced is a value-based representation of the evolution of this process over three observation periods: 2000, 2007, and 2012, relating the quantitative observation of the building expansion to the connected qualitative impact on rural landscape. It is the result of coordinating a large set of data in a hierarchical model of indices that converge to construct a synthetic index of rural landscape resilience. This achievement is based on the linguistic progression of “lexicon”, “semantics”, “syntax”, and “pragmatics”, each of which robustly supports “observation”, “valuation”, “interpretation”, and “planning”, respectively. The final stage is based on the convergence of explanatory indices, which are developed by coordinating evidence and assessments (factual and value judgements). This stage enables the proposal of a constraints system that supports a modus vivendi between the interests of the real estate sector and the values of the rural landscape in such a rich and fragile area. Full article

This study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps and technological readiness levels. Results indicate frequently detected emerging contaminants including pesticides, antibiotics and antidepressants in surface water, groundwater and wastewater systems. Advanced analytical methods, particularly liquid chromatography coupled with high-resolution mass spectrometry, stands out as the main detection technique, allowing the identification of trace levels of contaminants. These techniques also support the identification of pollution patterns associated with agriculture, urban and industrial effluents. However, significant asymmetries persist between international and Portuguese research. Particularly evident in systematic monitoring networks and integrated risk assessment approaches. Conventional water/wastewater treatment plants show limited removal efficiency, while advanced oxidation processes, adsorption technologies and microalgae-based systems demonstrate promising but variable performance depending on scale and operational maturity. The findings highlight gaps between scientific advances and regulatory implementation, emphasizing the need for strengthened monitoring frameworks and technology scale-up strategies. They also call for improved integration between science, governance, and sustainability policies to ensure resilient water resource management in line with the Sustainable Development Goals. Full article

Elucidating the mechanisms by which cropland fragmentation impacts production and ecological functions is critical for ensuring food security and ecological sustainability. Using Jilin Province as a case study, this research develops a cropland fragmentation evaluation framework based on landscape pattern indices. A restricted cubic spline model is employed to quantify nonlinear relationships and identify critical thresholds between fragmentation and both production and ecological functions. Furthermore, the PLUS model is utilized to simulate land-use patterns for 2030 under three scenarios: natural development, cropland protection, and ecological protection. The primary findings are as follows: (1) From 2000 to 2023, cropland fragmentation displayed pronounced spatial heterogeneity. Fragmentation was consistently high in the eastern mountainous areas and showed significant spatial clustering; the central region maintained relatively contiguous cropland, while the western region exhibited marked spatial variability. (2) Cropland fragmentation exhibits a nonlinear negative correlation with production functions, wherein the marginal negative impact attenuates beyond a threshold of 0.340. Conversely, its association with ecological functions follows a U-shaped trajectory, with a critical inflection point at 0.363 marking a directional shift in the fragmentation–ecology nexus. (3) Based on these nonlinear thresholds, the study area was delineated into production-ecology synergy zones, dysfunctional sensitive zones, and ecosystem landscape trade-off zones. Specifically, the central agricultural core is characterized by functional synergy; the ecologically fragile western zone resides near the nadir of the U-shaped curve, rendering its balance between production and ecological functions highly vulnerable to shifts in development intensity; and the eastern ecological barrier zone manifests a distinct trade-off prioritizing ecological functions. (4) Multi-scenario simulations reveal that the natural development scenario exacerbates the expansion risk of dysfunctional sensitive zones. While the cropland protection scenario enhances production capacity, it concurrently introduces risks of ecological instability. Conversely, the ecological protection scenario effectively steers sensitive zones toward ecological recovery. Consequently, we propose a differentiated spatial regulation strategy: prioritizing land consolidation in the central region, integrating ecological restoration with capacity enhancement in the west, and sustaining ecological barriers in the east, thereby fostering sustainable regional development. Full article

Maize is a globally important staple crop, and automated monitoring of germination and seedling emergence is essential for precision agriculture, enabling timely reseeding and reducing potential yield loss. To address this need, we propose Seedling-DETR, a transformer-based model for the real-time detection of emerged and missing maize seedlings using multispectral UAV imagery in an end-to-end manner. First, we construct a multispectral UAV dataset and ntroduce a dedicated annotation strategy in which missing seedlings were labeled individually rather than inferred indirectly. Then, we modify the feature fusion module of RT-DETR and develop a hybrid-scale feature fusion module to obtain richer and more expressive feature representations for missing seedling detection and improve the precision of missing seedling detection. Finally, we propose a channel fusion module to incorporate multispectral images into our model without requiring a dedicated multispectral backbone or additional pretraining, thereby improving model adaptability. The results show that, under a random train–test split (8:2), when using RGB images as input, our Seedling-DETR achieves a mean average precision (mAP) of 83.1% at an IoU threshold of 0.5, outperforming YOLOv11x and RT-DETR by 2.5% and 1.1%, respectively. The proposed method achieves an AP of 69.3% at an IoU threshold of 0.5 for missing seedling detection, which increases to 71.7% when multispectral inputs are incorporated. Similar performance trends are observed on an independent validation set collected on a different date. Although the model introduces moderate computational overhead (282 GFLOPs for RGB input and 418 GFLOPs for multispectral configuration, with 84.0 M and 85.1 M parameters, respectively), it can maintain efficient detection performance suitable for actual agricultural field deployment. The method is further validated at the field scale using orthomosaic-based analysis. Overall, this study provides an effective and scalable framework for the detection of emerged and missing maize seedlings under complex field conditions. The proposed framework supports accurate reseeding decisions, and contributes to automated maize production. Full article

Reducing agricultural carbon emissions is an important component of China’s efforts to achieve its carbon peaking and carbon neutrality goals. As an important policy oriented financial instrument, green credit can facilitate lower agricultural carbon intensity by directing resources more efficiently across regions and encouraging low carbon transformation in agriculture. Using panel data for 30 Chinese provinces from 2005 to 2022, this study measures agricultural carbon emission intensity (ACEI) from six sources. It then examines the spatial spillover effects, transmission channels, and nonlinear characteristics associated with green credit by using a spatial Durbin framework, mediation analysis, and panel threshold model. The results indicate that: (1) green credit development is significantly associated with lower ACEI; (2) green credit exhibits significant spatial spillover effect, being associated with lower ACEI both within a province and in neighboring provinces; (3) green credit exhibits marked regional heterogeneity in its impact on ACEI: it shows both direct and spillover effects in the eastern region, only spillover effects in the central region, and only direct effects without effective diffusion in the western region; (4) green credit is associated with lower ACEI through industrial structure upgrading and lowering agricultural energy consumption intensity; (5) green credit has a single threshold effect on ACEI based on its own development level. After crossing the threshold, the emission intensity reduction effect weakens but remains significant. These results offer empirical evidence for refining green credit arrangements and advancing coordinated agricultural emission reduction across regions. Full article

Improving the climate adaptability of farmers is crucial to ensuring agricultural production and achieving the goal of sustainable development in agriculture. Against the background of climate change aggravating agricultural risks, how do farmers’ own risk attitudes affect their adaptive behavior? Based on the micro-survey data of 480 grape growers in the Turpan-Hami Basin in 2025, we used the least squares method (OLS) to explore the impact of risk appetite on the climate adaptation behavior of farmers and its mechanism. The study found that risk appetite significantly promoted the adoption of adaptive behaviors by farmers. For every 1 unit increase in the risk preference score, the number of climate-adaptive behaviors adopted by farmers increased by an average of 0.322. Mechanism testing shows that both formal credit and informal credit play a partial intermediary role. The intermediary effect accounts for 18.3% and 36.3% respectively, and the transmission effect of informal credit is stronger; Institutional trust and interpersonal trust both positively regulate the relationship between risk preference and adaptive behavior at the level of 1%. Research shows that we should take into account risk education and production environment optimization, pay attention to the supplementary role of private lending, and build a multi-level trust promotion system to jointly improve the climate adaptability of farmers. Full article

Agricultural pesticide wastewater represents a significant environmental and public health challenge, highlighting the need for scalable and resource-efficient treatment strategies. This review adopted a PRISMA-based methodology using the Scopus and Web of Science databases, leading to the analysis of 176 peer-reviewed studies published between 2014 and 2025. The selected literature was critically examined to assess pesticide wastewater treatment technologies, including adsorption, membrane filtration (MF), advanced oxidation processes (AOPs), biological treatments, and hybrid configurations. Particular attention was given to their treatment performance, scalability from farm to district level, resource recovery potential, economic feasibility, and life-cycle assessment (LCA) implications. Among the evaluated systems, hybrid configurations combining biological processes with AOPs or MF generally showed higher removal performance, often achieving more than 80% pesticide residue removal, while offering greater adaptability and compatibility with circular biorefinery frameworks. The review identifies key opportunities for resource recovery, including methane and hydrogen production, nutrient recycling, water reuse, and chemical reclamation, thereby supporting circular bioeconomy objectives. Overall, this review proposes an integrated, multiscale circular biorefinery perspective for sustainable pesticide wastewater management and identifies research priorities for developing resilient, safe, and resource-efficient agricultural water treatment systems. Full article

Understanding the spatial organisation of small towns is essential for sustainable spatial planning and regional development. This study examines the spatio-functional pattern of Brzeziny, a small town located within the Łódź Metropolitan Area in Central Poland, selected as a representative case due to its typical Central European small-town morphology shaped by historical continuity, demographic stagnation, and metropolitan influence. The analysis is based on updated cadastral land-use data verified through field surveys and supplemented with topographic datasets (BDOT10k and OpenStreetMap). A modified land-use classification comprising nine categories is applied, and spatial analysis is performed using a regular grid and GIS tools. Dominant land-use structures are identified using the K. Doi method, enabling the delineation of spatio-functional zones. The results reveal a strong dominance of undeveloped land (77% of the total area), particularly agricultural land, alongside a compact central zone characterised by residential and service functions. The study demonstrates how historical development, economic structure, and metropolitan proximity shape the spatial organisation of small towns. The proposed methodology highlights the usefulness of cadastral data combined with grid-based spatial analysis for identifying S-FPs and supporting local planning processes. Full article

(1) Background: Climate change and inequality are topics of major interest in Mediterranean Archaeology. However, comparatively less attention has been dedicated to how these themes are interlinked in the literature. No scoping review has ever addressed this issue. This study aims to identify major research trends on inequality and climate change in the Mediterranean c. 4000 BC–AD 500. It also pinpoints current research gaps on the topic and nascent areas of enquiry. (2) Method: We performed a scoping review on JSTOR, Scopus, Google Scholar and PubMed in December 2025–January 2026. A modified version of the PRISMA-ScR protocol was followed. We sampled journal articles, book chapters, edited volumes and monographs published between 2015 and 2025 which matched the search and inclusion criteria. Additional searches were done on Google Scholar in February 2026 to expand upon emerging research trends relevant to our topic but largely absent from the scoping review. We manually extracted, charted, analysed and synthesised the data. (3) Results: A total of 154 studies were eligible for the scoping review. We identified six research trends prominent in the sampled literature: 1. the rise and fall of world systems, macroscale causal links, and collapse research; 2. inequality, subalternity, and marginality; 3. agriculture, crops, and diet; 4. natural resource management, and water supply; 5. epistemology and methodology; and 6. natural archives and climate proxy datasets. We also recognised the following research gaps or topics that were comparatively less addressed: collapse research applied to the microscale level and marginalised communities; isotope analysis applied to both climate change and inequality in the same study; biomedical approaches applied to both climate change and inequality in the same study; social marginality as a complex construct in human–climate interactions; and the environmental and climate dimensions of the early Roman expansion, especially regarding marginality and the microscale. Finally, we identified artificial intelligence (AI), Big Data, environmental and climate activism, and the perception of climate hazards by subaltern communities as nascent topics of interest that might rise to prominence in the future. (4) Conclusions: We identified major research trends and gaps on climate change and inequality in the ancient Mediterranean in literature published 2015–2025. We also recognised nascent or unexplored topics. The review is intended as a benchmark for developing novel research on the cutting-edge of Mediterranean Archaeology. Full article

The article is devoted to developing methodological approaches to multi-criteria resource optimization of technological solutions in Nature Use Projects, considering the growing shortage of water and energy resources, climate change, and post-war transformation of Ukraine’s agricultural sector. The need to transition from traditional technical and economic optimization models to integrated assessment approaches, which consider ecological, resource, and economic aspects of the project implementation effectiveness, is substantiated. The methodological basis of the study is a combination of Multi-Criteria Decision-Making and the Water-Energy-Food Nexus concept, enabling the necessary adaptive management and formalizing the process of project decision-making under multifactor uncertainty. A set of indicators of resource-ecological and economic efficiency is proposed, including indicators of productivity, weather and climate risk, resource use, environmental reliability, investment attractiveness, etc. A key feature of this approach is the transformation of resource-ecological indicators into a value form, ensuring their integration with economic indicators within a single optimization model. Based on a machine experiment for the conditions of the Kherson region, an assessment of the effectiveness of various irrigation regimes, which differ from the project irrigation regime in terms of watering and irrigation norms, in terms of their level of provision with water and energy resources, was carried out. It was determined that, under the studied conditions, in dry years (p = 70%), the permissible deficit threshold is approximately 30%, achieving a compromise between economic efficiency and environmental acceptability. Adaptive management of irrigation regimes has been shown to reduce the resource intensity of production without a significant loss of productivity. This creates a basis for revising outdated design standards, which focused on 100% satisfaction of water needs, in favor of adaptive models that account for the real resource potential of the territory. This approach transforms irrigation from a resource-intensive industry into a tool for sustainable territorial development, where the priority is the efficiency of each cubic meter of water and kilowatt-hour of energy used, rather than gross collection. It has been proven that the implementation of resource optimization as a basic principle of natural resource project management contributes to increasing the efficiency of natural capital use, minimizing ecological risks, and ensuring the sustainable development of the agricultural sector. The obtained results can be used to substantiate engineering solutions in projects for the restoration and modernization of water management and land reclamation systems in Ukraine. Full article

The digital transformation of agriculture has generated vast heterogeneous datasets from sensors, machinery, and administrative systems; however, interoperability and data sovereignty remain critical challenges. This study presents an IDS-compliant Agricultural Data Space tailored to the Italian context, integrating regulatory frameworks (General Data Protection Regulation, Data Governance Act and Data Act) with the International Data Spaces (IDS) Reference Architecture Model. The study addresses key barriers to data sharing, including technical fragmentation, governance gaps, and economic incentives, by mapping Italian agricultural data flows onto the five-layer IDS model. Policy-based usage control is implemented through machine-enforceable Open Digital Rights Language policies, enabling farmer-centric data sovereignty. Three use cases, namely administrative Common Agricultural Policy (CAP) declarations, machine-generated data portability, and agri-food supply-chain traceability, demonstrate how structured and interoperable data exchange can reduce redundancy, mitigate vendor lock-in, and support sustainable agri-food systems. The findings highlight the feasibility of IDS-driven solutions in real-world agricultural ecosystems, emphasizing the need for sector-specific policy templates and scalable governance mechanisms. This work contributes to the development of the Common European Agricultural Data Space by bridging institutional, technical, and regulatory gaps. Full article

A major challenge in wastewater treatment lies in developing cost-effective and sustainable adsorbent materials for efficient dye removal. In this study, a novel biochar functionalized with MgO nanoparticles derived from palm leaf waste (MgO/PLB nanoparticles) was synthesized and evaluated for the removal of Congo red (CR) from aqueous solutions. FTIR, SEM, BET, and TGA investigations were used to thoroughly analyze the produced nanocomposite’s physicochemical properties. FTIR analysis verified the successful incorporation of MgO nanoparticles, as evidenced by the presence of characteristic Mg–O vibrations and noticeable changes in surface functional groups. SEM analysis revealed a transformation from a compact structure to a rough, particle-decorated morphology, indicating increased surface heterogeneity. BET analysis indicated the development of mesoporous structures, accompanied by a substantial increase in specific surface area from 2 to 178 m²/g. TGA results further confirmed enhanced thermal stability, indicating the formation of a structurally robust adsorbent. Batch adsorption tests showed that CR removal depends on pH, dosage, concentration, and contact time, with maximum efficiency (~99%) achieved at pH 4 using 0.03 g of adsorbent. The adsorption followed pseudo second order kinetics and was best described by the Langmuir isotherm, with a maximum capacity of 23.4 mg/g. The regenerated nanomaterial retained more than 89% of its adsorption capacity after four successive cycles, demonstrating good reusability and stability. The developed MgO/PLB nanoparticles exhibit efficient adsorption performance, combined with low-cost synthesis and the utilization of abundant agricultural waste, making it an affordable and long-lasting adsorbent for applications involving wastewater treatment. Full article

Gene editing technology, a revolutionary tool in molecular biology, enables precise modifications of genomic sequences and gene expression patterns, thereby conferring desired traits to cells or organisms. Since 2014, CRISPR/Cas9 has rapidly become the most widely used gene editing method in agricultural animals due to its high editing efficiency. Subsequently, the development of novel gene editing systems, such as base editors and prime editors, has provided enhanced precision and reduced off-target effects. These advancements have facilitated the transition of gene editing from laboratory research to clinical and agricultural applications. Gene editing has been extensively utilized to enhance production traits, improve disease resistance, facilitate disease detection, and establish disease models. This review outlines the development of gene editing technologies, discusses the advantages and limitations of key gene editing tools, and explores their applications in poultry. Furthermore, it examines the challenges and future prospects of gene editing in animal husbandry, including off-target effects, ethical concerns, and technical complexities. Full article