Search Results (29,463)

Shellfish aquaculture can alter sediment conditions and affect benthic ecosystem functioning, so dredging is widely applied as a management strategy to mitigate sediment deterioration. However, its ecological effectiveness remains uncertain. This study evaluated ecological quality under the disturbance of dredging in shellfish farming areas of Gamak Bay, South Korea, using multiple macrobenthic indices. Macrobenthic samples and environmental data were collected before (May 2025) and after dredging (August 2025). Five macrobenthic indices, including the AZTI Marine Biotic Index (AMBI), BENTIX, Benthic Polychaete/Amphipod ratio (BPA), Benthic Pollution Index (BPI), and Multivariate AMBI (M-AMBI), along with a composite index, were used to assess ecological quality. Temporal changes within groups were tested using Wilcoxon signed-rank tests, and differences between dredged and control stations were examined using Mann–Whitney U tests. Multivariate analyses were used to explore environmental gradients and community responses. Results showed clear seasonal variation in environmental conditions and macrobenthic community structure. Most indices indicated a decline in ecological quality after dredging, with higher AMBI values and lower BENTIX, BPI, and M-AMBI values at dredged stations. However, these changes were not statistically significant (p > 0.05), suggesting limited short-term effects of dredging. The proportion of stations with acceptable ecological status decreased slightly from May to August. Seasonal factors, particularly temperature and salinity, played a dominant role in structuring benthic communities. Overall, the findings indicate that the short-term dredging effects were weaker than seasonal environmental variability. A multi-index approach is recommended for robust ecological assessment, and long-term monitoring is necessary to fully evaluate the effectiveness of dredging in shellfish aquaculture systems. Full article

Arid agricultural systems face increasing sustainability challenges due to water scarcity, climate variability, and structural resource constraints. Although Artificial Intelligence (AI) is widely promoted as a key enabler of sustainable agriculture, empirical evidence on its long-term effects on agriculture-related Sustainable Development Goals (SDGs), particularly in arid regions, remains limited. This study investigates the role of AI in supporting sustainable agricultural development in Saudi Arabia’s Ha’il region. Using annual data from 1995 to 2025, AI adoption—proxied by SDG9 indicators that reflect AI-enabling digital infrastructure and innovation readiness rather than observed on-farm AI deployment—is examined in relation to a composite Sustainable Agricultural Development Goals index (SADGH), which integrates SDG2 (food security), SDG6 (water management), SDG8 (economic performance), SDG12 (responsible production), SDG13 (climate action), and SDG15 (land sustainability). Econometric analysis based on a Vector Error Correction Model (VECM) reveals a stable long-run relationship between AI adoption and agricultural sustainability, with approximately 32% of short-term disequilibrium corrected annually. In the short run, AI adoption is positively associated with food security, economic performance, and land sustainability, while water- and climate-related indicators adjust more gradually. Dynamic analyses suggest that AI-related shocks may generate cumulative effects over time. In addition, deep learning models using Long Short–Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are applied within an exploratory framework to capture potential nonlinear dynamics and generate indicative forecasts. The GRU model shows lower prediction errors; however, results should be interpreted with caution, given the limited sample size. Overall, the findings suggest that AI may contribute to sustainable agricultural development in arid regions, while highlighting the need for further research based on larger datasets. Full article

The transition toward renewable energy sources has positioned wind energy as a critical technology for achieving global carbon neutrality targets. While large-scale wind farms dominate current installations, micro-scale horizontal-axis wind turbines present significant potential for distributed energy generation in remote and rural areas. This study presents a comprehensive methodology for designing micro-scale wind turbine blades through comparative analysis of three computational approaches: classical blade element momentum theory (BEMT), QBlade 2.0.9.6 software, and Computational Fluid Dynamics (CFD) simulations, with the design methodology selected based on a trade-off between accuracy and computational cost. A numerical campaign for airfoil assessment was conducted to identify optimal blade geometries, with performance evaluated based on power coefficient distribution, peak power output, and cut-in wind speed. The investigation reveals that steady CFD simulations predict peak power coefficients 23.34% higher than those predicted by BEMT and 22.46% higher than those predicted by QBlade due to three-dimensional effects, including rotational stall delay. Considering unsteady effects, the CFD simulations show a decrease of 4.08% with respect to steady simulations. The addition of endplates to the optimized blade design demonstrates significant performance improvements. This multi-fidelity approach provides a robust framework for micro-scale wind turbine design, balancing computational efficiency with accuracy requirements, and examines the impact of adding endplates. Full article

Sustainable vegetable production requires strategies that optimize yield while conserving water and minimizing resource inputs. This study, conducted at the Horticulture Research Farm near Absaraka, ND, evaluated the performance of several paste-type tomato (Solanum lycopersicum) cultivars under different irrigation strategies in high-tunnel and open-field production systems to identify cultivar and irrigation combinations that support sustainable production. Across seasons and production environments, cultivar significantly influenced marketable yield, fruit number, fruit size, and the proportion of unmarketable fruit, whereas irrigation treatments had limited effects on total and marketable yield. High-yielding cultivars such as ‘Granadero’, ‘Pozzano’, ‘Cauralina’, and ‘Amish Paste’ consistently produced greater marketable yields in both production systems, although ‘Cauralina’ also exhibited higher levels of fruit cracking and unmarketable yield. In high-tunnel production, deficit irrigation strategies based on soil moisture thresholds (10% and 30% management allowable depletion) maintained yields comparable to time-based irrigation, suggesting that water-efficient irrigation scheduling can sustain productivity. In the open field, cultivar responses varied under different irrigation regimes, highlighting the importance of selecting cultivars adapted to water-limited conditions. Fruit quality attributes, including soluble solids content and titratable acidity, were primarily influenced by cultivar rather than irrigation. Overall, the findings demonstrate that cultivar selection combined with water-efficient irrigation management can maintain tomato productivity while reducing water use and production losses. These results support the development of more sustainable tomato production systems that enhance resource-use efficiency, reduce waste from unmarketable fruit, and maintain fruit quality across diverse production environments. Full article

Accurate estimation of crop development, water use and yield is essential for improving irrigation management in Mediterranean agricultural systems under increasing climate variability. However, many crop models require extensive input data and technical expertise, limiting their operational use by farmers and technicians. This study proposes an integrated agro-modelling framework that combines thermal time modelling, satellite-derived vegetation indices and simplified yield estimation approaches to assess maize phenology, crop water use and productivity under real farming conditions. A key component of the framework is the use of the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series to dynamically identify crop growth stages and derive actual basal crop coefficients (K_{cb act}), enabling the estimation of actual crop transpiration (T_{c act}). These NDVI-based estimates of actual K_cb and T_c were evaluated against simulations from the previously calibrated soil water balance model SIMDualKc. The results showed that the temporal profiles of the NDVI successfully captured the progression of the maize growth stages, although some discrepancies were observed during early stages of development due to the effects of the soil background and the satellite revisit intervals. An empirical relationship between the NDVI and K_cb was developed using multi-year observations and model simulations, improving crop transpiration estimation under field conditions. The NDVI-based approach adequately reproduced daily transpiration dynamics with good agreement with SIMDualKc simulations, yielding RMSE values of 0.11–0.69 mm d⁻¹ and errors generally below 21% of the mean transpiration rate. Seasonal transpiration estimates showed stronger agreement once canopy cover reached its maximum. The integrated AEZ–Stewart modelling framework incorporating NDVI-based transpiration estimations provided accurate yield predictions, with RMSE values of 1.7–2.3 t ha⁻¹ (representing less than 14% of the observed yields). Overall, the proposed framework demonstrates strong potential as a practical and scalable decision-support tool for irrigation management and yield assessment in Mediterranean maize systems. Its novelty lies in the operational integration of NDVI-derived crop development and transpiration estimates within a simplified yield modelling structure, offering a transferable approach applicable to other regions and cropping systems where satellite data are available. Full article

Charcoal rot caused by Macrophomina phaseolina limits mungbean yield around Faisalabad. Fields surveyed during Kharif 2024 showed 43–58% disease incidence. At the research farm, disease severity rose from 8.6% (14 days after sowing) to 62.4% (maturity). Plants with 40–55% infection lost 42% of grain yield (1182 to 684 kg/ha). Soil temperature at a 10 cm depth best predicted disease (r = +0.86). Each 1 °C above 27 °C added 8% more severity. Early sowing in April and resistant varieties were recommended for farmers to cut losses. Full article

Two new species, Aprostocetus eucalyptus Zheng & Yefremova sp. nov. (Hymenoptera: Eulophidae) and Megastigmus bipolaris Zheng & Yefremova sp. nov. (Hymenoptera: Megastigmidae), were discovered on populations of the invasive gall wasp Ophelimus bipolaris (Hymenoptera: Eulophidae) infesting Eucalyptus in Guangxi, China. An integrative taxonomic approach combining morphological characterization and 28S rRNA-based phylogenetic analysis was used for species identification and classification. Detailed morphological descriptions, illustrations, and an identification key for both sexes are provided. Field parasitism data confirm their potential as native natural enemies, supporting their utility for the biological control of this economically important pest. Full article

In the U.S., extreme heat is the leading cause of weather-related fatalities. Farmers, ranchers and other outdoor workers who are exposed to the elements and engaged in strenuous physical activity are disproportionately impacted. This manuscript summarizes the number and severity of heat-related illnesses and injuries collected through the AgInjuryNews.org system, highlights their characteristics, provides recommendations for farmworkers and employers, and calls for future research. Heat-related illness cases from 2016–2024 were analyzed. Fourteen agricultural heat-related incidents covered by U.S. media were identified. Most incidents took place in June and July. A content analysis was conducted to identify news articles that included mention of prevention strategies, laws and regulations related to working conditions, or OSHA. Over half of the cases were from southern states. Eleven of the incidents involved male farmworkers, one involved a male farmer, and two involved first responders (gender unspecified). All of the farmer/farmworker incidents were single-victim fatalities. Seven articles mentioned prevention strategies, ten mentioned laws or regulations, and nine mentioned OSHA, often cursory. These findings suggest that media reports provide a limited and selective image of agricultural heat-related injuries, with coverage emphasizing fatalities and investigation information more often than prevention. Full article

As global food security challenges intensify, edible crickets are recognized as sustainable protein alternatives; however, genomic resources for commercially important species remain limited, restricting evolutionary inference and the development of robust tools for farm management. We sequenced and assembled the complete mitochondrial genomes of Gryllus bimaculatus and provided the first report for Teleogryllus mitratus, both derived from commercial farms in Thailand, using high-throughput Illumina sequencing, achieving high coverage depths of 32,391× and 63,258×, respectively. The circular mitochondrial genomes were 15,955 bp and 16,046 bp and exhibited the typical insect mitochondrial gene complement of 37 genes, with a strong AT bias. Selective pressure analyses indicated pervasive purifying selection across all protein-coding genes (PCGs) (ω < 1), while episodic diversifying selection was detected in cox1, cox3, cytb, and nad5; additionally, atp8 displayed a comparatively elevated ω. Codon usage analyses revealed a strong preference for AT-ending codons, with leucine codons showing the highest bias. Phylogenetic analyses using concatenated protein-coding and ribosomal RNA genes recovered well-supported relationships within Gryllidae. These farm-derived mitogenomes provide practical foundations for molecular species authentication, population monitoring, and comparative analyses relevant to breeding and traceability. Furthermore, they provide candidate loci for future investigations into mitochondrial evolutionary dynamics and the potential development of molecular markers for commercial breeding management. Full article

Bluetongue (BT) is an infectious, non-contagious, arthropod-borne viral disease of ruminants, and has a severe impact on livestock. It is caused by Bluetongue virus (BTV), a double-stranded (ds) RNA virus with a segmented genome (10 segments), belonging to the Seoreoviridae family, Orbivirus genus. Over the last 25 years, Europe has suffered multiple incursions of different BTV serotypes with serious consequences, which have mainly been controlled thanks to vaccination. In the case of Spain, from 2000 to 2023, BTV serotypes 1, 2, 4 and 8 have caused epidemics, and, sporadically, BTV-1 and -4 were detected in the same area and period. In 2024, BTV serotypes 1, 3 and 8 circulated simultaneously in the southwest of the country, causing a severe clinical impact in sheep but also in cattle and a multitude of outbreaks. Additionally, despite vaccination, serotype 4 also circulated that year, especially in areas where the other serotypes were already circulating. Whole-genome sequencing and phylogenetic analyses allowed us to confirm that serotypes 1 and 4 were homologous to viruses circulating in the country since 2000s, while serotypes 3 and 8 were homologous to BTVs recently notified in neighboring countries. In this context, many BTV co-infections of two or more different serotypes were confirmed by serotype-specific RT-PCRs, both in farms and individual animals. An epidemic caused by four serotypes coinciding in space and time had never occurred before in Spain, being a challenge for the diagnosis and control of this disease. Moreover, it could have favored the appearance of reassortant viruses with an unknown virulence, posing an additional risk. The data presented here raise the question of whether the co-circulation of different BTV strains, an exceptional situation, could become the new normal in certain areas of Europe. Full article

Raw milk dairy products, an integral part of Italian food heritage, are the primary products of small-scale farms in mountain regions where pasture is seasonal. While raw milk dairy products offer potential health benefits, their physicochemical properties make them susceptible to foodborne pathogens. Long-term surveillance of these products is essential to safeguard consumer health. Here, we present a fourteen-year microbiological surveillance of raw milk dairy products and intermediate matrices from northeastern Italy’s alpine areas, analyzing coagulase-positive Staphylococci (CPS), β-glucuronidase-positive Escherichia coli, Listeria monocytogenes, and Shiga toxin-producing E. coli (STEC). The most frequently detected pathogens were CPS and β-glucuronidase-positive E. coli, with up to 19.6% and 51.7% of samples exceeding regulatory limits, respectively. Butter, curd, and fresh cream were the most contaminated matrices. Detection rates of staphylococcal enterotoxins, L. monocytogenes, and STEC aligned with European detection averages (6.7%, 2.6%, and 2.1%, respectively). These findings underscore the necessity of Good Hygiene and Management Practices, together with regular microbiological monitoring to mitigate contamination risks, supporting the safety and quality of traditional raw milk dairy products in alpine regions. Full article

Point cloud semantic segmentation is a pivotal technology for realizing non-contact body measurement and refined management of livestock. However, processing sheep point clouds in smart livestock scenarios presents specific challenges, primarily due to non-rigid posture deformations and severe background interference. To address these issues, this paper proposes a novel symmetric encoder–decoder architecture named Local–Global Aggregation Network (LGA-Net), which achieves high-precision parsing of sheep point clouds by constructing a dual-scale feature aggregation mechanism. First, a Dual Attention Aggregation (DAA) module is designed to jointly encode geometric and color features, significantly enhancing the network’s ability to capture fine-grained local boundaries, such as sheep ears and hooves. Second, a Global Semantic Relation (GSR) module is introduced, utilizing spatial occupancy ratios to establish long-range dependencies, thereby effectively resolving semantic ambiguity caused by posture variations. Furthermore, a plug-and-play Dual-domain Feature Enhancement (DFE) module is proposed. By fusing bilinear interactions between explicit 3D space and implicit feature space, the DFE module constructs a high-pass filtering mechanism to suppress low-frequency background noise. Extensive experiments on a self-constructed point cloud dataset involving two semantic classes (Sheep and Fence) demonstrate that LGA-Net achieves a mIoU of 97.3%, an OA of 99.0%, and a mAcc of 97.8%. These results indicate that the proposed method outperforms existing mainstream algorithms in both segmentation accuracy and robustness. This study not only proposes a feasible solution for precise sheep extraction under the tested experimental conditions, but also provides solid technical support for subsequent automated body measurement and behavior analysis. Full article

Salmonella enterica remains a major threat to animal and human health because of its broad host range, increasing antimicrobial resistance (AMR), and capacity to form biofilms. Biofilm formation enhances bacterial persistence in host tissues, farm environments, food-processing systems, and clinical reservoirs, while also contributing to their tolerance against antibiotics, disinfectants, and other stresses. However, biofilm capacity is not uniform across serovars and is influenced by host adaptation, niche specialization, and accessory genome content. This review synthesizes current knowledge on the relationship between biofilm formation, AMR, and serovar-specific adaptation in Salmonella. It examines biofilm-associated traits across various hosts (e.g., gastrointestinal tract and gallbladder, and environmental (e.g., food-production and clinical) niches, and discusses comparative evidence from genomic, transcriptomic, proteomic, and metabolomic studies. Particular attention is given to the emerging concept of comparative biofilmomics, which integrates phenotypic and multi-omics data across diverse serovars and host sources to identify conserved and niche-specific determinants of persistence. This framework may help define high-risk lineages that couple multidrug resistance (MDR) with enhanced biofilm-forming capacity. A better understanding of these linked traits will support the development of more targeted interventions for controlling persistent Salmonella in veterinary, food production, and public health settings. Full article

Given the energy-intensive operations and considerable carbon emissions of offshore oil and gas platforms (OOGPs) in deep-sea regions, adopting floating offshore wind farms (FOWFs) as power sources offers substantial benefits. However, the expenses associated with dynamic submarine cables constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, highlighting the crucial need for optimization in the power collection system design. In this study, we present a mixed-integer quadratic programming (MIQP) model designed to reduce both the costs of investment and power losses associated with dynamic submarine cables, taking into account the influence of the wake effect in local wind conditions. Due to the complexity of this problem, we employ the Benders’ decomposition method to reformulate it into a master problem and a slave problem. Additionally, two valid inequalities are specifically incorporated into the master problem to accelerate the solution process. These constraints are derived from a heuristic combination of various cable connection configurations and a greedy-based spanning tree structure. Through multiple case studies, we first demonstrate the accuracy and rapid convergence of our method. Furthermore, we reveal that as the wind farm grows in size, the influence of the wake effect becomes increasingly pronounced. Full article

Cattle behavior constitutes important phenotypic information reflecting animals’ health status, activity level, and welfare condition, and is therefore of considerable significance for automated monitoring and precision management in smart livestock farming. However, under complex barn conditions, cattle behavior recognition is easily affected by factors such as illumination variation, partial occlusion, background interference, and individual differences, thereby reducing recognition stability and generalization capability. To address these challenges, this study proposes a pose-driven method for cattle behavior recognition in complex barn environments. First, a 16-keypoint annotation scheme suitable for describing bovine posture, termed cow16, was constructed. Based on this scheme, OpenPose was employed to extract heatmaps (HMs) and part affinity fields (PAFs), which were then used to build an intermediate HM/PAF posture representation. Subsequently, this representation was taken as the input to a lightweight convolutional neural network for classifying three behavioral categories: stand, walk, and lying. On this basis, class-imbalance correction during training and a multi-random-seed logits ensemble strategy during inference were further introduced. In addition, knowledge distillation was adopted to transfer knowledge from a high-performance teacher model to a lightweight student model. Experimental results demonstrate that training-stage class-imbalance correction and inference-stage multi-random-seed logits ensembling exhibit strong complementarity; when combined, the AB configuration improves the test-set Macro-F1 by 3.83 percentage points. Moreover, the distilled student model still achieves competitive recognition performance while maintaining 1× inference cost, indicating a favorable trade-off between accuracy and efficiency. This study provides a useful reference for deployment-oriented cattle behavior recognition in smart farming scenarios and offers a lightweight technical basis for subsequent practical applications. Full article