Search Results (15,043)

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Red blood cells (RBCs) are sensitive biomarkers of human health, influenced by urbanization and agricultural exposures. Using scanning electron microscopy (SEM) within an exposome framework, we examined RBC morphology in residents of an industrialized area of Thessaloniki, Greece, and in a rural population primarily exposed to agricultural stressors. Blood samples and questionnaires covering demographics, lifestyle, and environmental factors were statistically analyzed. SEM revealed moderate morphological alterations without significant differences between groups. Observed features were associated with longer residence duration and suboptimal nutrition, suggesting subclinical cellular stress. Integrating these findings into exposome research may clarify cumulative industrial and agricultural impacts on RBC morphology. Full article

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, emerging priorities, and evidence gaps. This study maps the structure and evolution of this literature using 219,261 Scopus-indexed documents selected from 290,560 records published between 1990 and 2025. A text-mining workflow combined BERTopic-based semantic modeling with supervised thematic classification into 18 macro-themes, while annual shares, z-scores, and document-level primary–secondary co-framing were used to assess temporal salience and cross-theme coupling. The results show sustained growth in research output, with 53.67% of publications produced between 2016 and 2025, and strong geographical concentration in the United States and China, which together account for 41.98% of the corpus. Hydrology and water management, crop production, impact assessment, and atmospheric processes remain central pillars, while socio-economic vulnerability, food security, sustainability, biotechnology, and greenhouse gas mitigation have gained prominence. The resulting evidence map provides a reproducible overview of the climate–agriculture knowledge landscape and can support research prioritization and policy design for climate-resilient agrifood systems. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

As the Italian hop industry undergoes consolidation, assessing the environmental pressure of diverse cultivation and processing models is essential for sustainable growth. This study characterizes the Product Environmental Footprint (PEF) of Italian hop production through a multi-case analysis of eight representative farms. A primary data collection tool was utilized to quantify resource inputs, including water management, nutritional strategies, and phytosanitary defense. Following a rigorous thermodynamic consistency screening of the field data to eliminate unrepresentative parameters, the life cycle inventory focused on two validated regional anchor cases. The findings reveal a high degree of management heterogeneity, with dry cone yields ranging from 400 to 1673 kg of dry matter per hectare. Two functional units were defined: 1 kg of fresh hop cones (FU1) to assess cultivation impacts, and 1 kg of processed products (FU2) at the brewery gate to evaluate the full supply chain. Integrating deterministic life cycle impact outputs with a probabilistic Monte Carlo uncertainty analysis, the results indicate that the environmental impact varies significantly across commercial formats: Cryogenic Powder (2.33 ± 0.34 mPt/kg) represents the most resource-intensive format, while Raw Bales and T90 Pellets from high-yield models exhibit scores as low as 1.36 and 1.55 mPt/kg, respectively. The study identifies the agricultural phase as the primary environmental hotspot, driven predominantly by water deprivation. To address these burdens, a Sustainable Italian Hop (SIH) integrated scenario was developed. By combining precision irrigation, thermal decarbonization via biomass valorization, and a direct-to-pellet processing flow, this model achieved a 70% total reduction in the environmental footprint score (0.465 ± 0.076 mPt/kg) and an 86% reduction in water use impacts. Finally, the socio-technical and financial barriers to implementing the SIH framework are qualitatively evaluated. These results provide actionable benchmarks for aligning the emerging Italian hop supply chain with European Union climate neutrality objectives. Full article

Background/Objective: Achievement of Sustainable Development Goals SDG 2 (child nutrition) depends upon SDG 6 (water insecurity) and SDG 13 (climate action) in multiple ways. However, the current climate–nutrition literature mostly considers water’s effects on nutrition through agriculture and food production. Here, we identify the climate’s impact on child nutrition through its effect on both household food and water security and on their interaction across varying environmental contexts. Methods: Using nationally representative data from India, we estimate the climate’s direct association with household water access (time spent fetching water), and both direct and indirect association with household food security (women’s dietary diversity), and child’s dietary diversity and nutrition (HAZ score). Data from 42,567 women and 39,667 children (6–23 months) are analyzed using linear regression and structural equation modeling. Results: A unit increase in rainfall is linked to an 18 percent decrease in time to water and an 8.3 percent increase in women’s dietary diversity score. A temperature increase is associated with an increase in time to water and decreased women’s dietary diversity. Time to water mediates the association of temperature and rainfall with women’s dietary diversity, child’s dietary diversity and child’s HAZ score. Households in regions of higher water availability are associated with increased dietary diversity, increased HAZ, and decreased time to water; however, the interaction between climate and regional water availability shows varying effects. Conclusions: Climate is associated with household food and water security, which together mediate its association with nutrition. These findings call for broadening the climate action framework to explicitly recognize the multidimensional linkages between SDG 6 and SDG 2. Full article

The integration of biochar into asphalt binders represents a significant advancement toward global sustainability in pavement engineering. Produced through biomass pyrolysis, biochar enables the valorization of agricultural and industrial waste while reducing dependence on petroleum-derived binder constituents. This review critically synthesizes current research regarding the impact of biochar on the physical, rheological, and aging performance of bitumen. The evidence consistently shows that biochar improves binder stiffness, raises softening points, and strengthens rutting resistance at elevated temperatures, largely due to its porous microstructure and high carbon content. Biochar-modified binders also exhibit enhanced aging resistance through the adsorption of volatile light fractions. These improvements are primarily ascribed to the carbonaceous composition and high porosity of the biochar particles. However, systemic challenges, including phase stability at high concentrations, long-term oxidative aging, and a lack of standardized characterization protocols, hinder widespread implementation. By identifying consistent findings, contradictions, and critical research gaps across the literature, this review provides a consolidated foundation to guide the transition of biochar-modified bitumen from laboratory investigation to large-scale pavement infrastructure applications. Full article

Banana production plays a vital role in food security and livelihood in developing countries, yet scholarly attention has highlighted the growing attention on climate change, CO₂ emissions, and land dynamic impacts on agricultural production; however, empirical evidence on short- and long-term effects of climate change and CO₂ emissions on banana production in China remains limited. This study employed the autoregressive distributed lag error correction model (ARDL-ECM) framework using time-series data from China over three decades spanning 1991–2023, to investigate the long-run, short-run effects of CO₂ emissions, precipitation, temperature, and production inputs of land and labor on banana production. The empirical results indicate that the CO₂ emissions exert a significant and negative long-run effect on banana production. Precipitation exhibited a positive influence on banana production in China. Banana-harvested area presents a positive and significant impact on banana production, underscoring the importance of land management for long-run growth of banana production. Findings demonstrate that greater resilience, supported by advanced technology, a mechanized production system, and stronger institutional capacity, reduces climate impact on banana production. Study findings contribute to the empirical evidence to the climate–agriculture nexus in China and offer actionable policy for enhancing banana resilience in developing countries. Full article

Against the dual objectives of food security and sustainable agriculture, this study examines how agricultural machinery services—China’s primary organized mode of agricultural production—affect agricultural carbon emissions. Using panel data covering 30 provinces in China from 2010 to 2022, this study applies two-way fixed effects, mediation, and moderation models to investigate the effects of these services on carbon emissions as well as the mechanisms involved. The results show: (1) Both carbon emissions and the level of machinery services in China differ by region and over time. Carbon emissions are stabilizing, while machinery services are steadily improving. Both variables cluster in certain areas. (2) Machinery services exhibit a significant inverted U-shaped impact on carbon emissions. As the level of machinery services grows, emissions first rise, then fall. (3) The emission reduction impact of machinery services varies widely. It differs across topographic relief, farmland types, and grain crop types, but the inverted U-shaped relationship remains in most cases. (4) The efficiency of the division of labor and agricultural chemical input intensity partly explain the effect. They help reduce emissions by enabling labor substitution and lower input levels. (5) Large-scale agricultural operations strongly influence how machinery services affect carbon emissions. To accelerate the low-carbon sustainable transformation of Chinese agriculture, efforts should prioritize establishing a differentiated, regionally tailored agricultural machinery socialized service system, improving service efficiency and green development capacity, and optimizing large-scale land management structures. Full article

Hungary is experiencing more frequent and severe droughts due to climate change, with 60% of its arable land in the vulnerable Great Hungarian Plain. Drought events in 2012 and 2022 reduced maize yields by more than 50% in some regions. This review synthesizes studies (2000–2025) on remote sensing capabilities, climate change impacts, and farmer adaptation in Hungarian agriculture. Remote sensing technologies (Sentinel, Landsat, MODIS) and indices (NDVI, VCI, LST, TCI) achieve high accuracy (often >80%) in drought detection under validated conditions, yet technical and financial barriers limit uptake among smallholder farmers. Climate projections indicate that a 2 °C temperature rise by 2050 will expand drought-affected areas. Farmer adaptation varies sharply by farm size: large farms (>100 ha) adopt precision agriculture (65% uptake), while smallholders (<10 ha) rely on crop rotation and drought-resistant varieties. Although substantial support is provided through the EU Common Agricultural Policy, institutional fragmentation and weak extension services—which reach only 32% of farmers—undermine its effectiveness. Bridging this gap requires integrating accessible remote sensing tools with targeted smallholder support and reformed extension services. Full article

The rapidly increasing global demand for high-performance thermal insulation materials necessitates a significant shift towards more sustainable and cost-effective solutions. This study unveils a novel and efficient pathway to synthesize engineered cordierite, a highly coveted magnesium aluminosilicate ceramic, by intelligently harnessing biogenic silica extracted directly from rice husk. Rice husk, an abundant agricultural by-product, represents a readily available and often underutilized resource. The methodology involved a precise precipitation method to successfully yield high-purity silica from rice husk ash. This extracted silica was then meticulously combined with commercial magnesium oxide (MgO) and aluminum oxide (Al₂O₃) through a solid-state reaction to synthesize the desired cordierite. The study systematically investigated the profound impact of various sintering temperatures, ranging from 850 °C to 1100 °C, on both the cordierite yield and its crucial physicochemical properties. Our experiments revealed that a sintering temperature of 1100 °C achieved a remarkable 66.5% cordierite yield. Beyond yield, the material processed at 1100 °C exhibited exceptional mechanical and thermal characteristics: a compressive strength of 65 kN/m², a flexural strength of 44 kN/m², a tensile strength of 17.5 kN/m², and a remarkably low thermal conductivity of just 3.2 W/m·K. These attributes match the mechanical requirements for structural insulation, with a thermal conductivity of 3.2 W/m·K. While higher than some high-porosity commercial cordierites (typically 1.2–2.0 W/m·K), the biogenic version offers a 40% reduction in production energy and utilizes 100% recycled silica, balancing thermal performance with superior sustainability. By utilizing agricultural waste, this method reduces CO₂ emissions associated with mineral extraction and minimizes reliance on non-renewable raw materials, providing a practical pathway for the circular economy. Full article

Reservoir construction and agricultural irrigation have substantially altered the natural hydrological regimes of many Mediterranean watersheds. This study aims to reconstruct the natural flow conditions of the Egirdir Lake Basin (Türkiye) and quantify the impacts of irrigation and reservoir operations on water inflows to Egirdir Lake using the Soil and Water Assessment Tool (SWAT). The SWAT model consisted of 14 subbasins and 274 hydrologic response units (HRUs) and initially calibrated and validated using naturalized flow data provided by the State Hydraulic Works (DSI) for the period from 1990 to 2014. The same model structure and parameters were then applied to simulate a regulated condition representing the combined effects of irrigation and reservoir operation. Results showed a considerable reduction in annual streamflows under the regulated condition. This study demonstrated the significant impact of irrigation water use and reservoir operation on the hydrological dynamics of semi-arid basins. Full article