Search Results (3,891)

Red meat sustainability debates encompass consumer expectations and producer management decisions, yet regional evidence considering both perspectives remains limited. We analysed two independently designed Tasmanian surveys: a 2021 non-probability consumer survey (n = 1176) and a 2022 exploratory producer survey (n = 34). Descriptive analyses characterised consumption, sourcing priorities and producers’ reported past and intended adaptation and mitigation practices; a post hoc thematic comparison identified potential correspondences rather than direct agreement. Among consumer respondents, 65% reported consuming all three product categories (red meat, dairy and seafood), and 60% agreed that they ate less red meat than they did five years earlier. Consumer respondents assigned high importance to fair payment to farmers, Tasmanian origin, animal health and welfare, and environmentally responsible production. The practices most frequently reported by producer respondents were adjusting seasonal stocking rates, increasing the extent of deeper-rooted legumes and planting trees, and most respondents intended to continue or adopt each of these. The datasets suggest possible thematic correspondences between these priorities and practices but do not demonstrate shared motivations, producer responsiveness to consumer demand or population-wide patterns. Because the surveys used different instruments and constructs, and because both samples constrain generalisability, the findings should be interpreted as an exploratory regional case study. Aligned surveys with larger, representative samples are needed to test whether these apparent correspondences translate into purchasing or farm-management outcomes. Full article

To better analyze the health and welfare of individual dairy cows, this study proposes BR-Tracker, a multi-object tracking method designed for dense monitoring environments to address missed detections, tracking failures, and frequent identity switches. In the object detection stage, a Receptive Field Attention Downsampling (RFADown) module is introduced into the neck network of YOLOv10s to effectively process the locally visible regions of occluded cows by dynamically adjusting the receptive field. An improved Partial Bi-Level Routing Attention (PBRA) module is incorporated into the backbone network to simultaneously extract global and local features, while a Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) module is adopted to enhance multi-scale feature aggregation. In the object tracking stage, an MPDIoU-based matching algorithm is designed to improve matching accuracy and the reliability of trajectory association. The dataset contains 15,420 images for object detection and 130 independent videos for multi-object tracking, of which 40 videos are selected for the final tracking evaluation. Experimental results show that BR-YOLOv10s achieves a precision, recall, and mean average precision (mAP) of 95.7%, 90.3%, and 95.2%, respectively. Compared with the YOLOv10s-ByteTrack baseline, the proposed method improves HOTA, MOTA, MOTP, and IDF1 by 4.4, 5.7, 3.0, and 6.1 percentage points, respectively, while reducing identity switches by 23.19%. In addition, the tracker achieves a processing speed of 43.2 FPS. These results demonstrate that the proposed method can effectively perform real-time detection and tracking of densely distributed dairy cows in complex intensive farming environments. Full article

This study presents SenseMat, a modular sensing infrastructure based on recycled end-of-life tire (ELT)-derived rubber flooring that integrates continuous body-weight (BW) estimation, environmental monitoring and Internet-of-Things (IoT) connectivity into a single structural livestock infrastructure. Designed as a modular engineering platform, SenseMat provides a structural framework that can be extended with additional sensing modules and adapted to different monitoring applications requiring resilient flooring and distributed sensing. The technical feasibility of the weighing module was evaluated through a 51-day proof-of-concept study conducted under commercial buffalo farming conditions, involving two buffalo calves and generating 4872 BW measurements acquired at 30 min intervals. Following a dedicated preprocessing workflow, continuous BW estimates showed good consistency with weekly reference measurements obtained using a professional livestock scale (R² = 0.975 and 0.948), with mean relative errors of 0.88% and 1.03% and root mean square errors of 2.15 and 3.09 kg for the two animals, respectively. Simultaneously, the integrated environmental module continuously monitored air temperature and relative humidity, suggesting the capability of the platform to provide synchronized environmental information alongside continuous BW acquisition within a unified monitoring framework. These findings demonstrate the technical feasibility of integrating sensing, environmental monitoring and IoT connectivity into recycled ELT-derived livestock flooring, supporting its development as a modular smart platform for continuous monitoring in precision livestock farming. Future validation under larger-scale commercial conditions will further assess its scalability and broader applicability. Full article

Pesticide contamination in fruits and vegetables remains a critical food safety concern in Northern Thailand, posing risks to environmental and human health. Improving knowledge, attitude, and behavior (KAB) in food safety is essential for reducing pesticide exposure, particularly among migrant workers. This cross-sectional study evaluates food safety knowledge, attitude, behavior, and pesticide exposure among Myanmar migrant workers in Chiang Mai Province. Face-to-face interviews were conducted with 137 participants between August and September 2025 in Mueang, Mae Rim, and San Sai districts. Mann–Whitney U test and linear regression were used to explore demographic differences and associated factors of knowledge, attitude, and safety behavior. Most participants demonstrated adequate knowledge (65.7%) and positive attitude (80.3); however, 78.1% reported only moderate safety behavior. Higher knowledge and more favorable attitudes were observed among non-Shan ethnic groups and participants with higher education (p < 0.001). Women and non-farming workers exhibited significantly safer behavior (p < 0.05). Knowledge and attitude were strongly correlated (ρ = 0.556, p < 0.01), but neither was associated with safety behavior. Most participants had cholinesterase activity within the normal reference range (73.0% cellular AChE; 81.0% plasma BChE). Low enzyme activity was observed in 13.9% and 6.6% of participants, respectively. Enzyme activity did not differ significantly by occupation, sex, or ethnicity and was not significantly associated with KAB scores. Linear regression analyses identified ethnicity as significantly associated with knowledge and attitude; whereas, sex and occupation were significantly associated with safety behavior. These findings suggest that individual-level knowledge and attitude may not be sufficient to drive protective behavior among migrant workers, though the cross-sectional study design limits causal inference and generalizability. Full article

Porcine epidemic diarrhea virus (PEDV) variant strains lead to vaccine failures and significant economic losses in swine farming, highlighting the need for new natural small-molecule antiviral candidates. In this study, we screened a library of 45 small molecules using a cytopathic effect (CPE)-based cytoprotective assay. Saikosaponin B2 (SSB2), a triterpenoid saponin derived from Bupleuri Radix, emerged as the most promising candidate, boasting a selectivity index of 19.77, which surpasses that of most natural compounds. Dose–response tests confirmed SSB2’s concentration-dependent anti-PEDV activity, with an inhibitory plateau reached at concentrations above 60 μmol/L (μM). The 50 half-maximal effective concentration (EC₅₀) values differed between CPE reduction and viral titer readouts, a discrepancy commonly due to varying detection sensitivities. Mechanistic assays revealed that SSB2 does not possess direct virucidal properties; instead, its antiviral efficacy depends on sustained treatment to inhibit viral adsorption and internalization. Time-of-addition experiments further demonstrated that SSB2 effectively suppresses both early viral entry and mid-stage intracellular genome replication, without affecting viral release. Notably, post-infection treatment resulted in stronger inhibition compared to continuous administration, possibly due to disrupted cellular proliferation homeostasis from prolonged exposure. Additionally, SSB2 maintained strong inhibitory effects against prevalent G2a (JL-06) and G2b (JL-08) field variants, though its efficacy was reduced for both strains and slightly weaker against JL-08 than JL-06. Overall, this study systematically characterizes SSB2’s dual-stage anti-PEDV profile and its broad-spectrum activity against spike-mutant strains, supporting SSB2 as a promising herbal lead for developing veterinary antivirals targeting variant PEDV. Full article

Ground-based Global Navigation Satellite System (GNSS) stations provide continuous atmospheric remote sensing through electromagnetic propagation delays. Precise point positioning (PPP) yields zenith tropospheric delay (ZTD), and ZTD gradients provide a proxy for off-farm tropospheric structure that is unavailable to supervisory control and data acquisition (SCADA)-only forecasts. We propose a model combining a long short-term memory (LSTM) backbone, GNSS conditioning, and a graph neural network (GNN), denoted LSTM+GNN+GNSS, for 4 h wind-power forecasting. Historical PPP-derived ZTD and quality indicators condition a shared temporal representation; a ZTD-gradient direction proxy, turbine geometry, and observation confidence guide a gated graph-residual correction at 15–90 min. On 666 common Yandun test origins, we compare LSTM, LSTM+GNN, and LSTM+GNN+GNSS. The complete system achieves a normalized mean absolute error (nMAE) of 4.53% (4.527 ± 0.132% across three power-model seeds), reducing nMAE by 7.57% relative to LSTM+GNN and 8.23% relative to LSTM. Paired moving-block 95% confidence intervals support both comparisons, while Bonferroni-adjusted lead-wise tests agree from 30 to 225 min. These results demonstrate the incremental predictive value of the complete GNSS-conditioning pathway under the chronological holdout protocol. Full article

Modern agriculture’s integration of Internet of Things (IoT), Industrial Control Systems (ICSs), and data analytics boosts productivity but introduces significant cybersecurity and data governance challenges. Existing scholarship is divided between policy-focused governance and technical attack analyses, hindering the development of comprehensive, enforceable defenses. This paper introduces an integrated framework that bridges normative data governance in smart farming (SF) with empirical, honeynet-derived threat intelligence. Drawing on the authors’ previous systematic review of SF data governance and a honeynet simulating agricultural IoT/ICS, the study maps governance challenges to quantitative attack indicators from honeynet logs, classifying each pairing as directly supported by telemetry, indirectly supported by telemetry, or not observable using the current methodology. The findings show that the services flagged as governance concerns face sustained attack pressure: the honeynet recorded brute-force attempts against SSH/Telnet on simulated irrigation controllers (149,000 events), connection and login attempts targeting SMB (Server Message Block) and MQTT (Message Queuing Telemetry Transport) on automated machinery (67,156), credential-guessing attempts against management services (14,937), and ICS protocol probes (11,532). Geographic and protocol distributions reveal that legacy industrial protocols and weakly authenticated management interfaces, both highlighted as governance concerns, constitute the primary attack surface. This evidence supports a tiered governance model integrating protocol-level controls, identity governance, and data-sharing policy, demonstrating that effective SF cybersecurity requires empirically calibrated rather than purely policy-driven frameworks. The proposed framework offers actionable guidance for aligning technical defenses with data governance obligations. This work contributes a new methodological protocol (cross-evidentiary mapping), an empirically calibrated tiered framework, and a coherent research agenda at the intersection of governance and measurement, serving as a template for similar analyses in other critical infrastructure sectors. Full article

This study presents a new improved primal–dual interior point method associated with a generation scaling factor (PDIPM–GSF) for congestion-aware optimal power flow (OPF) in power systems with renewable integration in a deregulated electricity market. The proposed framework combines the robustness of the primal–dual interior point method with an adaptive generation scaling strategy that adjusts generator outputs according to transmission line flow sensitivities, enabling early congestion mitigation, improved power flow feasibility, and accurate determination of locational marginal prices (LMPs). The proposed approach is evaluated on the IEEE 30-bus test system and a real 114-bus Algerian power network. The results demonstrate that the proposed framework can reduce generation costs and improve social profit under both single-sided and double-sided market operation through enhanced coordination between generation, demand, and congestion management. The impact of integrating wind energy is analyzed under different levels of wind energy penetration in the first IEEE 30-bus test system, showing that the integration of wind energy can not only improve social welfare and save generation costs but also lower transmission losses. Additionally, the results show that the placement of wind farms can improve the performance of the proposed PDIPM–GSF framework in terms of congestion management and reduction in LMPs at important nodes. For the 114-bus Algerian transmission network, renewable energy integration further demonstrates the capability of the proposed framework to reduce transmission losses and generation costs while improving overall market performance. These results demonstrate that the proposed PDIPM–GSF framework provides an effective smart optimization approach for renewable-integrated power systems by improving congestion management, facilitating large-scale WE integration, enhancing market efficiency, and ensuring reliable LMP computation. The proposed method, therefore, represents a practical and efficient solution for supporting future renewable energy transition and competitive electricity market operation in Algeria and other renewable-rich power systems. Full article

Tropical pumpkin (Cucurbita spp.) is an underutilized, nutrient-dense vegetable crop with potential to enhance food and nutrition security and generate household income among farming communities. In Uganda, farmers primarily obtain pumpkin seed through informal seed systems, including farmer-produced and -managed sources where seed quality is largely undocumented. This study evaluated the quality of pumpkin seeds collected from 16 selected districts in Uganda and provides the first baseline assessment of tropical pumpkin seed quality within farmer-produced and farmer-managed systems. Seed quality in this study was defined as the ability of seeds to germinate, exhibit high vigor, and remain free of fungal growth. Pumpkin seeds were assessed at 0, 3, and 6 months of storage under conditions representative of local seed traders (25 ± 5 °C; 60 ± 5% RH). Seed-associated fungi recovered on PDA containing streptomycin sulfate after 14 days of incubation were identified morphologically. Seed purity ranged widely (43.8–99.5%), indicating substantial variability among seed sources. Seed germination varied among districts, with most seeds showing improved germination after storage, whereas seed vigor declined with storage duration. Five putatively seedborne fungal genera, Fusarium, Aspergillus, Colletotrichum, Alternaria, and Rhizopus spp., were identified. The findings demonstrate substantial heterogeneity in farmer-produced and -managed seed, which is a predominant seed source in Uganda. Full article

Optimizing resource use in smallholder coffee production promotes environmental sustainability. Coffee production in the Sierra Sur of Oaxaca, Mexico, is dominated by small-holder systems characterized by high production variability and limited access to resources. Although organic and conventional systems coexist under similar agroecological conditions, limited evidence exists on how agronomic practices influence efficiency. This study estimated technical efficiency and its determinants using a one-stage stochastic frontier model with data from 290 coffee growers. Mean efficiency was 0.762 for conventional growers and 0.566 for organic growers. In the conventional system, planting density, plantation age, weed control, and shade management significantly reduced inefficiency, whereas off-farm employment increased it. In the organic system, training was the only factor significantly associated with higher efficiency. Despite tending to achieve higher average production and yield, organic growers exhibited larger production gaps and greater heterogeneity in resource use, indicating substantial opportunities to increase output through more efficient input utilization rather than expanding cultivated areas. These findings demonstrate that higher production does not necessarily imply greater technical efficiency, providing evidence to support public policies aimed at strengthening extension services, promoting training, and encouraging agronomic management to improve efficiency and enhance the sustainability of smallholder coffee production systems. Full article

Soil health is central to sustainable agriculture, but remains challenging to assess in diversified agroecosystems such as permaculture. Soil condition reflects the interaction of physical, chemical, and biological properties, but practical assessment commonly relies on a limited set of informative indicators. In this study, a PCA-weighted Soil Health Index (SHI) was constructed from five surface soil indicators: organic carbon, total nitrogen, microbial biomass (PLFA), bulk density, and gravimetric water content. The first principal component explained 75.30% of the total variance. The analysis used 84 observations collected between 2019 and 2021 from permaculture and conventional farming systems across nine locations in Germany and Luxembourg, encompassing different land use types and two soil depths. Permaculture plots showed higher SHI values overall than conventional plots, with the same trend observed across all nine locations, although land use imbalance limited fully matched comparisons. To avoid circular prediction of the PCA-derived target, the five surface variables used directly to construct the SHI were excluded from the predictive feature set. Machine learning models were evaluated using grouped validation in which entire locations were held out from model training. The best-performing full-profile Ridge model achieved an out-of-fold $R^2$ of 0.710, an MAE of 0.159, and an RMSE of 0.214. Out-of-fold SHAP analysis indicated that magnesium, zinc, soil pH, subsoil bulk density, and copper made the largest model-specific contributions to SHI estimation. These findings demonstrate that PCA-based soil health assessment can distinguish systematic differences between studied farming systems and that a leakage-aware, interpretable modeling framework can provide moderate predictive performance across held-out locations. The results should be interpreted as internal evidence from a small multi-location dataset rather than as externally validated or causal estimates of management effects. Full article

Organophosphorus pesticides (OPPs) are widely used and have recently been linked to metabolic syndrome (MS). This study aimed to investigate the probable association between chronic OPP exposure and MS among farm workers in Sharkia Governorate, Egypt, and to assess the potential role of peroxisome proliferator-activated receptor gamma (PPARγ). This comparative cross-sectional study included 140 participants, equally divided into OPP-exposed farm workers and non-exposed subjects. OPP exposure was confirmed by detecting plasma residues and cholinesterase activity. MS was diagnosed by assessing body mass index (BMI), waist circumference (WC), blood pressure, plasma glucose, serum insulin, and lipid parameters. Oxidative and inflammatory markers, including malondialdehyde (MDA), gamma-glutamyl transferase (GGT), ferritin, superoxide dismutase (SOD), tumor necrosis factor-alpha (TNF-α), and high-sensitivity C-reactive protein (hs-CRP), were measured. The mRNA expression of the PPARγ and paraoxonase 1 (PON1) genes was also investigated. In total, 60% of farm workers demonstrated MS, compared with 10% of non-exposed participants. Workers exhibited elevated oxidative and inflammatory indices and reduced PPARγ and PON1 expression. PPARγ positively correlated with high-density lipoprotein (HDL), SOD, and PON1, while negatively correlating with glucose, insulin resistance (IR), low-density lipoprotein (LDL), triglycerides (TGs), MDA, GGT, ferritin, TNF-α, and hs-CRP. The study concluded that chronic OPP exposure was associated with increased oxidative stress and inflammation, reduced PPARγ and PON1 expression, disturbed glucose and lipid metabolism, and increased IR. The observed associations between PPARγ downregulation, metabolic disturbances, and oxidative and inflammatory markers suggest that PPARγ dysregulation may represent a potential mechanistic link between chronic OPP exposure and MS. However, this proposed mechanism requires further validation. Full article

Effective welfare monitoring in laying hens is increasingly challenged by growing flock sizes, declining farm numbers, and the practical limitations of assessor-based protocols under commercial conditions. Precision Livestock Farming (PLF) technologies offer opportunities to support large-scale welfare assessment, yet their application in laying hens remains predominantly limited to small-scale or prototype systems. This study addresses three complementary objectives aimed at informing future computer-vision-based PLF research and development in commercially housed laying hens. First, a systematic literature review was conducted to identify welfare-related categories monitored in laying hens between 2005 and 2025, the methods used to assess them, and the extent to which automated monitoring approaches have been applied. Second, outcomes of a TransRegional Expert Panel (TREP) within the OMELETTE project were synthesised to rank priority welfare challenges, evaluate the feasibility of different monitoring approaches, and compare expert perspectives with trends identified in the literature. Third, two pan-tilt-zoom (PTZ) camera setups implemented in semi-commercial aviary systems were described as proof-of-concept examples illustrating how multiple welfare challenges can be monitored using a single, multipurpose camera system. The literature review revealed a pronounced imbalance in monitoring frequency, with feather pecking dominating the literature while several other welfare challenges, including piling, disturbed sleep, and toe-pecking, remain comparatively underrepresented. TREP outcomes confirmed feather pecking as the highest-priority welfare challenge but also highlighted the importance of integrated monitoring approaches that combine time-intensive assessments, shorter checklists, and automated systems rather than relying on single indicators. Experts further considered the use of digital devices during routine barn inspections to be practically feasible. The PTZ proof-of-concept demonstrates how priority welfare challenges identified through both literature and expert input can be operationalised through automated, scheduled, and location-specific monitoring under commercial conditions. Together, these findings highlight the need for future PLF research to move beyond isolated measurements and small-scale trials towards integrated, cost-effective, and farm-specific welfare-monitoring systems that support adaptive, data-informed management strategies—for example, within a Plan–Do–Check–Act framework—and enable the development of digital standard operating procedures that generate actionable insights under real-world commercial constraints. Full article

Accurate ultra-short-term wind power forecasting at the turbine level is important for grid stability and dispatching. To address the time-varying spatial and temporal correlations among multiple turbines in a single wind farm, we build dynamic spatio-temporal graphs to model dynamic spatial dependencies, propose a parallel multi-scale temporal convolutional encoder to combine short-term and long-term dependencies, and propose a graph fusion layer to achieve weight fusion of different graph sources. Experiments demonstrate that GraphFusionGRU achieves lower overall error in short-term forecasting and achieves competitive average performance relative to other baseline models on longer horizons. The results confirm that the model’s robustness and interpretability are enhanced in complex wind-farm environments. Full article

Agricultural multi-robot systems in narrow and dynamic environments require global coordination, semantic event interpretation, and responsive trajectory execution. This study presents an end–edge–cloud fast–slow semantic planning framework. The cloud maintains a farm topology and generates fleet-level dispatch policies; the edge hosts an asynchronous agentic vision–language planner and a fast trajectory planner; and the robot performs sensing, LiDAR odometry, low-level control, and execution. The fast planner reuses the latest valid semantic condition until an event-triggered update becomes available. The fast branch is pretrained on nuScenes and adapted using the training and validation subsets of a 3780-sample agricultural dataset comprising synchronized front- and rear-view images, robot states, motion histories, and future trajectories, with an independent 630-sample test set reserved for final evaluation. On an edge-side RTX 4080 SUPER, the complete planner achieves an average L₂ error of 0.67 m, a fast-step latency of 96.3 ms, and a throughput of 10.4 Hz. In the four-robot topology experiment, the framework achieves a 100.0% success rate under the representative single-blockage condition and maintains an 86.7% success rate under the dual-blockage condition. During an approximately 30 min operation at a nominal semantic update rate of 2 Hz, the cloud and robot communication round-trip times average 24.43 and 3.85 ms, respectively, with no robot deadline misses, while the mean trigger-to-updated-trajectory latency of the full event-driven pipeline is 2357.37 ms. These results demonstrate the feasibility of assigning global coordination to the cloud, semantic reasoning and trajectory inference to the edge, and sensing and execution to the robot. Full article