Search Results (310)

Artificial intelligence (AI) offers new opportunities for evidence retrieval, data interpretation, experimentation, feedback, and evidence-based problem solving in engineering education. This study developed a theoretically grounded “One Core, Two Channels, and Four-Dimensional Drivers” model to align AI, research-informed teaching, and New Agricultural Science within an undergraduate Building Materials course, and examined preliminary between-cohort outcome differences. A 32-teaching-hour nonequivalent comparison-group study involving 123 students was conducted. The intervention integrated research-informed cases, rural and green-construction contexts, purpose-specific AI-supported tasks, laboratory work, staged projects, and multidimensional assessment. The intervention cohort scored higher on the final examination (mean difference 8.00 points; Hedges’ g = 1.19), experimental grade (4.67; g = 1.28), continuous-assessment score (35.56; g = 3.34), and course evaluation (1.63 on a 70-point scale; g = 0.58). Project innovation data were highly sparse. The continuous-assessment difference should not be interpreted as a standalone AI literacy effect because structured AI-learning opportunities differed by condition, and equivalence of non-examination scoring was not independently established. Given nonrandomized allocation, the absence of participant-level baseline measures, and unequal measurement strength across outcomes, the findings represent preliminary between-cohort evidence rather than causal treatment effects. Full article

Satellite altimetry has become an important tool for monitoring inland water dynamics and has been widely used for water-level retrieval, hydrological simulation, and flood–drought risk assessment. To review research progress and development trends over the past three decades, this study combines bibliometric analysis with a traditional review approach. A total of 4764 publications indexed in the Web of Science Core Collection from 1991 to 2025 were analyzed. Using VOSviewer_1.6.20 and CiteSpace_6.4, we constructed knowledge maps of publication trends, disciplinary intersections, author collaboration, keyword clustering, and burst evolution. Representative studies were further synthesized qualitatively. The results show that remote sensing, geology, and imaging science form the core disciplinary framework of this field. Research hotspots have shifted from single water-level observation to multi-parameter retrieval and integration with hydrological models. New missions, including Surface Water and Ocean Topography (SWOT) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), have improved spatial resolution and coverage, accelerating the development of this field. However, agricultural water management and the integration of artificial intelligence with hydrological models remain limited. Key challenges include monitoring small and complex water bodies, multi-source data fusion, uncertainty quantification, physics-informed artificial intelligence, and operational applications. Full article

Northeast China is one of China’s major commodity grain bases and plays a strategic role in national food security. Against the background of rural population outflow and agricultural modernization, new agricultural business entities (NABEs), including family farms, farmers’ cooperatives, and agribusinesses, have become important actors in reshaping agricultural production organization. Based on registration data for 2014, 2018, and 2023, this study uses kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, spatial autocorrelation analysis, ordinary least squares (OLS) regression, and multiscale geographically weighted regression (MGWR) to examine the spatiotemporal patterns and driving factors of NABEs in Northeast China. The results show that: (1) NABEs expanded rapidly from 2014 to 2023 and became increasingly concentrated in agriculturally advantageous plain areas. (2) Family farms showed the fastest expansion, farmers’ cooperatives had the widest spatial coverage, and agribusinesses were mainly concentrated around transport corridors and market nodes. (3) In terms of industrial structure, crop-production entities remained dominant, followed by animal husbandry entities, while forestry, fishery, and agricultural support service entities accounted for relatively small shares; however, their numbers continued to increase. (4) The OLS results showed that the reclamation rate and road network density had relatively stable associations with the spatial distribution of multiple entity types, whereas economic development, science and technology investment, and fiscal support showed differentiated relationships across entity types and regions. (5) The MGWR results further reveal spatial heterogeneity in the effects of driving factors. These findings provide empirical evidence for type-specific cultivation and differentiated policy support for NABEs in major grain-producing areas. Full article

Urban agriculture and farming (UAF) initiatives are recognised for their potential to enhance urban resilience, support local food systems, and deliver ecosystem services. However, current scholarship remains fragmented, treating UAF initiatives as isolated green interventions, rather than integrated components of urban fabric. This study examines how landscape-based approaches (LbAs) and systems thinking (ST) have been applied concurrently to analyse and design these initiatives. We argue that LbA is necessary to provide the spatial logic for physical integration, while ST provides the functional logic for metabolic efficiency. This systematic literature review screened 92 records across Scopus, Web of Science, and Google Scholar, resulting in a refined corpus of 12 peer-reviewed articles published between 2015 and 2025. This reflects the nascent state of an interdisciplinary approach at this intersection. Utilising VOSviewer and Atlas.ti, the study identified four thematic clusters: urban green infrastructure, urban food systems, landscape planning, and socio-ecological systems. A cross-comparative analysis of these clusters and their underlying methodologies led to a new theoretical dual-lens systemic landscape framework to evaluate the sustainability outcomes of UAF. The findings reveal limited integration of spatial analysis with systems thinking across scales. This review contributes a novel multi-scale methodology that emphasises the need for integrated spatial and systemic interdependencies to achieve truly resilient urban food systems. Full article

Accurate quantification of soil organic matter (SOM) and carbon content is critical for understanding climate change, evaluating soil health, supporting agricultural sustainability, and implementing carbon sequestration policies. For decades, classical analytical and statistical approaches have underpinned soil carbon assessment, but the emergence of machine learning (ML) techniques offers new opportunities to improve prediction accuracy, scalability, and efficiency. This review summarises the current knowledge on classical and ML-based approaches for analysing SOM and carbon content. We examine the strengths, limitations, and practical applications of conventional methods, including wet chemistry, dry combustion analysis, and geostatistical techniques, alongside modern ML approaches such as random forests (RFs), gradient boosting machines, neural networks, deep learning, and hybrid ML-geostatistical frameworks. Special emphasis is placed on comparative analysis across dimensions, including prediction accuracy, computational requirements, data availability needs, interpretability, uncertainty quantification, and scalability. Soil carbon stocks and dynamics are tightly regulated by indigenous soil microbial communities and their management-driven alterations, creating substantial biologically driven variation that remains difficult to capture with current modelling approaches. We therefore explore hybrid approaches that integrate classical pedological knowledge with ML capabilities. Finally, we discuss emerging challenges, future research directions, and the complementary role these approaches play in advancing soil carbon science. This review concludes that neither classical nor ML approaches alone are sufficient for accurate carbon assessment across diverse scales and environments. Instead, their strategic integration, combining classical mechanistic grounding alongside machine learning’s scalability, represents the most promising path toward realistic soil carbon evaluation for climate change mitigation and agricultural sustainability. Full article

Phytophthora species are among the most destructive plant pathogens worldwide, causing severe losses in agricultural, forest, and natural ecosystems. In recent years, the management of Phytophthora diseases has increasingly shifted toward eco-sustainable strategies, with growing interest in plant-derived extracts, particularly essential oils, as low-risk alternatives to synthetic fungicides. In this study, a structured review was combined with new in vitro assays to assess the antifungal activity of essential oils from Thymus vulgaris (TV-EO) and T. serpyllum (TS-EO) against P. cinnamomi, P. drechsleri, P. cactorum, P. citrophthora, P. nicotianae, P. palmivora, and P. infestans. Literature searches were conducted in April 2025 using the Web of Science and Scopus databases, following PRISMA guidelines, with the search term “Thymus” or “Thyme” and “Phytophthora”. Twenty studies included in the review demonstrated that the activity of Thymus essential oils against Phytophthora species was highly variable and shaped by chemotype, Thymus species, pathogen, and experimental setup. Additional in vitro assays further confirmed a clear dose-dependent inhibitory effect for both TV-EO and TS-EO. TS-EO consistently exhibited stronger activity than TV-EO, likely reflecting its carvacrol-rich chemotype, while thymol-based TV-EO showed lower but still significant inhibition depending on the pathogen species. Overall, these results highlight the potential of Thymus essential oils as eco-friendly tools for the management of Phytophthora diseases. However, the strong dependence on chemotype, pathogen species, and assay conditions underscores the need for standardized testing, detailed chemical characterization, and in vivo validation. Full article

The accelerated advancement of artificial intelligence has triggered new discussions concerning the link between technological progress and the distribution of income. This study frames China’s National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIIDPZ) policy as a quasi-natural experiment, enabling us to identify the causal effect of AI promotion strategies on the urban–rural income inequality. Drawing on panel data from 257 Chinese cities over the period 2012–2023, we estimate the impacts using a multi-period difference-in-differences (DID) approach. The results demonstrate that the pilot zone policy significantly lowers the urban–rural income inequality index, by roughly 8.41%. The mechanism analysis reveals two primary pathways. First, the policy stimulates innovation in agricultural science and technology, which in turn boosts rural productivity. Second, it deepens the attention that the government directs toward artificial intelligence, contributing to a more balanced allocation of technological dividends between urban and rural areas. Heterogeneity tests further indicate that the inequality-reducing effects are especially notable in eastern regions, as well as in cities characterized by well-developed digital infrastructure and relatively weaker endowments of human capital. By offering empirical insight into how developing countries can reconcile distributional equity with the application of artificial intelligence, this study contributes to advancing the Sustainable Development Goals (SDGs). Full article

The International Conference on Plant Biodiversity and Sustainability is a global forum dedicated to advancing scientific knowledge and collaborative action in plant diversity, conservation, and sustainable development. Bringing together established and early-career researchers as well as students from diverse fields, the conference underscores the urgent need to protect plant resources and foster sustainable solutions. By promoting an open, interdisciplinary environment, the event encourages dialogue among botanists, ecologists, agronomists, biotechnologists, chemists, and related experts, integrating multiple perspectives to address biodiversity challenges comprehensively, especially in the field of medicinal and aromatic plants. Aligned with the United Nations 2030 Agenda for Sustainable Development, the conference covers a wide range of topics, including habitat conservation, ecological restoration, ethnobotany, climate change adaptation, sustainable agriculture, technological and biotechnological innovation, and science-based policy approaches. The scientific program features keynote lectures by internationally recognized experts, thematic oral sessions, hands-on workshops, and collaborative roundtables designed to stimulate discussion and knowledge exchange. Participants present cutting-edge research, innovative methodologies, and case studies highlighting both theoretical advances and practical applications. Panel discussions and networking opportunities further support new partnerships, joint research efforts, and capacity-building initiatives, strengthening the global community committed to biodiversity protection. Beyond sharing scientific results, the conference emphasizes the importance of connecting research with policy and real-world practice. Contributions therefore address decision-making frameworks, community engagement, nature-based solutions, and the use of emerging technologies for monitoring and managing plant ecosystems. This multidimensional approach ensures that the event not only showcases academic excellence but also contributes to concrete strategies that inform governance, education, and sustainable land-use planning, with a particular focus on plant resources. Full article

Brazil holds 13% of the global biodiversity; however, agricultural expansion threatens its biomes. Farmers are pivotal for conservation, as 71% of the country’s territory is rural property. A ‘citizen science’ project, which engaged students and farmers to monitor wildlife in forest remnants using camera traps was carried out in a rural municipality located in the Atlantic Forest biome. The endangered species Puma concolor and the invasive species Sus scrofa, alongside other native fauna, were documented in the area. In addition to securing these new records, the project aimed to open dialogs, fight misinformation, and strengthen local partnerships. It highlighted how community-based science can bridge the gap between biodiversity conservation and agricultural production. Full article

Growers in the grain-producing continental cold desert and cold semiarid regions are interested in the local adaptation of winter malting barley (Hordeum vulgare) as a potential alternative crop to winter wheat (Triticum aestivum). Variety selection for specific environments is a critical first step in producing high yields of winter malting barley at the same production costs. Twenty-two winter malting barley entries were planted under irrigation in randomized complete blocks at New Mexico State University’s Agricultural Science Center at Farmington (cold desert; 3 replicates) and Rex E. Kirksey Agricultural Science Center at Tucumcari (cold semiarid; 4 replicates) in September 2023 and harvested for grain in July 2024. All entries at Tucumcari were heavily grazed by wildlife over winter, which may have influenced grain production of some varieties, although there was no site × cultivar interaction for grain yield, which ranged from 2558 to 4157 kg ha⁻¹. Irrigation and N fertilization differences between sites likely influenced (p < 0.0001) grain yield and grain protein (4421 and 2172 kg grain yield ha⁻¹ at Farmington and Tucumcari, respectively; 109 and 93 g grain protein kg⁻¹ at Farmington and Tucumcari, respectively). Future research in cold desert and semiarid regions should evaluate cultivar differences regarding irrigation and nutrient management. Full article

The field of polysaccharide systems, primarily chitosan and its derivatives, is a constantly evolving area of science. It offers significant promise for many practical applications due to the numerous beneficial properties of these biopolymers. This trend is confirmed by the next special issue of the journal “Molecules”, titled “Chitosan, Chitosan Derivatives, Polysaccharides and Their Applications—Second Edition.” This issue is devoted to the latest advances in polysaccharide research conducted in leading laboratories worldwide, with a particular emphasis on new perspectives and the latest, interesting applications in the food, agricultural, medical, biotechnological, and tissue engineering industries. The manuscripts included in this collection focus on research data regarding the production of these systems, their modification, and the application of highly specialized methods to advance science and, above all, improve health and quality of life. This special issue is organized and thematically divided into several areas related to medical and food applications, the combination of polysaccharides with other substances such as polymers, and theoretical studies. Several of the chapters conclude with a review article or even several. All articles (24 in total) included in this special issue are of high quality and represent the latest trends in research on innovative polysaccharide systems. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

The aquaculture industry is growing rapidly. It is the fastest growing food industry in the world, with production expanding 16-fold between 1985 and 2018, according to the Food and Agriculture Organization FAO. The industry operates in an environment of high uncertainty, as the management of biological and environmental risks is critical. The aim of this research is to identify machine learning (ML) algorithms applied to quantify risks, categorize applications by sector, and evaluate data linkage to the extent that they feed into formal risk management protocols. A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. This search was conducted in Scopus and Science Direct for publications up to January 2026. Initially, 134 records were identified, of which 38 studies were ultimately included in the analysis. The results showed that artificial intelligence (AI) and ML offer new predictive capabilities. Integrating Internet of Things (IoT) sensors, AI methods and ML algorithms improve risk mitigation. However, there is a significant disconnection between algorithmic predictions and operational action. Only 3 of 38 studies demonstrated integration with standardized risk management frameworks (e.g., ISO31000). The study concludes that while AI tools provide predictive efficiency, interdisciplinary frameworks are required to filter predictions through economic and ethical criteria. Strengthening this connection will bring the use of AI as a tool for proactive and standardized risk mitigation. Full article

The issue of land use is currently commonly taken up by researchers in many aspects, e.g., geography, GIS or related sciences. However, the research gap occurs in the historical, partial reconstruction of the old agricultural and natural realities. The main objective of this article is to determine potential and actual places that were most useful for agriculture in the Early Middle Ages and to present human pressure on the natural environment. The results were developed in the form of colorful models that were generated on the basis of the following parameters: slope, river network, settlement, landscape and climate-vegetation belts. As a result, after summing up the above-mentioned maps, a new model was created, which was properly analyzed in terms of geoarchaeology in relation to early-medieval hillforts and the soil map in southern Małopolska. This article illustrates methods that can support broader interdisciplinary research in other regions of Europe (e.g., Italy) and the delimitation of medieval administrative borders. Full article

Human activities in Panama, such as agriculture, industry, and transport, have led to the release of pollutants that affect the health of marine and coastal ecosystems. However, there is a lack of bibliographic compilation studies to understand the current state of research on marine pollution in Panama. In recent years, bibliometric studies have attracted attention due to the development of new analytical and integrative online tools. This study conducts a bibliometric analysis of marine pollution and its environmental effects on Panama’s coastal areas. The results show consistent growth in scientific production, with increased collaboration among researchers. However, the involvement of national institutions is limited, highlighting the need to strengthen local research. Most publications focus on environmental sciences, with a recent shift towards studying a broader range of pollutants. Full article