Search Results (2,334)

Mammary gland inflammation, clinically expressed as mastitis in dairy ruminants and lactating women, arises from a self-amplifying loop of pathogen sensing, oxidative stress and innate immune activation, and remains a leading driver of antibiotic use, milk-quality loss and morbidity. Mounting pressure to reduce antimicrobial dependence has refocused attention on flavonoids—structurally diverse plant polyphenols with multi-target bioactivity, derived mainly from in vitro and rodent mastitis models. This review integrates contemporary evidence on the six principal flavonoid subclasses within a unifying molecular framework. Across subclasses, flavonoids converge on shared targets: the TLR4–MyD88–NF-κB axis, MAPK cascades, the Keap1–Nrf2–ARE antioxidant pathway, the NLRP3 inflammasome and tight-junction proteins of the blood–milk barrier. Less canonical mechanisms—m⁶A epitranscriptomic regulation, ferroptosis suppression, AhR signalling, anti-virulence binding to bacterial enzymes such as IGPD, and gut-microbiota-driven remodelling of the gut–mammary axis—expand the pharmacological landscape. We additionally appraise the subclasses comparatively, identifying flavanones and the flavone baicalin as carrying the strongest translational evidence, and examine the conflicting findings, model limitations, and delivery, residue and regulatory barriers that currently separate mechanistic promise from on-farm application. We outline structure–activity considerations and translational priorities, and position flavonoids as mechanism-rich, antibiotic-sparing candidates for the prevention and adjunctive management of mammary gland inflammation in dairy ruminants. Full article

Smallholder dairy farms are central to rural livelihoods in Latin America, yet feeding strategies can shift environmental burdens across climate, land, and water dimensions. This study integrated cluster analysis and life cycle assessment to identify methane-related feeding typologies and assess their environmental trade-offs in smallholder dairy farms in the Mexican highlands. Twenty-nine farms from two dairy regions were classified by k-means clustering into two methane-emission-related farm typologies, which were subsequently characterized by their observed feeding profiles: conserved-forage-dominant (Cluster 1, n = 17) and green-forage-dominant (Cluster 2, n = 12). Their environmental performance was assessed with a cradle-to-farm gate life cycle assessment following ISO 14040/14044 and ReCiPe 2016 Midpoint across 18 impact categories. Feed production and ration supply were the main hotspots, contributing 72 to 100% of the total impacts. Global warming potential averaged 2.29 and 2.47 kg CO₂-eq per kg of raw milk in Clusters 1 and 2, respectively. Within the study sample, exploratory comparisons indicated higher marine eutrophication and land use in Cluster 1, whereas Cluster 2 showed higher fine particulate matter formation, terrestrial acidification, terrestrial ecotoxicity, and human carcinogenic toxicity (nominal p < 0.05). These potential trade-offs require validation in larger cohorts and with more geographically representative inventories. These findings show that similar milk outputs can arise from distinct feeding pathways with different environmental trade-offs and that greater reliance on fresh forage does not necessarily imply a more favorable environmental profile. Cluster-based characterization offers a practical basis for improving feed sourcing and ration design in dairy systems facing resource constraints. Full article

Grape pomace (GP) is being explored as a sustainable feed ingredient for ruminants due to its fiber and polyphenol content; however, its systemic effects under farming conditions remain unclear. This study tested whether 5% dried grape pomace (GP5) modulates serum circulating miRNAs (cmiRNAs) in dairy sheep. Twelve Sarda ewes were enrolled in a 30-day trial and assigned to a control (CTRL) or GP5 diet. Small RNA sequencing was initially performed in GP5 animals (paired T30 vs. T0 comparison); after quality filtering, five paired animals were included in the sequencing analyses. The differentially expressed (DE) cmiRNAs were validated by RT-qPCR in GP5 and CTRL groups. RT-qPCR confirmed the NGS-derived miRNA signature, identifying miR-143, miR-382-5p, miR-409-3p, and miR-194 as significantly downregulated (p < 0.05) in GP5 animals. Ingenuity Pathway Analysis (IPA) showed that DE miRNAs target shared hubs, with PPAR signaling among the most enriched pathways (BH-adjusted p = 1.35 × 10⁻⁹), predicted to be inhibited (Z = −2.646). Cholecystokinin/gastrin-mediated and S100 signaling were also enriched (BH-adjusted p = 7.08 × 10⁻¹¹ and 3.21 × 10⁻⁸) and predicted to be activated (Z = 2.828 and 3.162). Overall, GP5 supplementation was associated with a selective cmiRNA signature consistent with targeted post-transcriptional regulation. Full article

Tomato (Solanum lycopersicum L.) is the world’s second-most cultivated vegetable. Production reached 192 million tonnes in 2023; approximately 23% is industrially processed, leaving 4.3 to 10.2 million tonnes of pomace each year. Despite its high content of lycopene, tocopherols, polyphenols, dietary fibre, and seed oil, most of this residue is composted, landfilled, or fed without prior processing. This narrative review examines how the chemistry of tomato by-products maps onto their effects in farm animals, and how green-extraction biorefinery fits within the European Green Deal. Reported pomace lycopene runs from 36.7 to 50.2 mg/100 g DM, with phenolics near 161.8 mg GAE/g. Lycopene is an efficient singlet-oxygen quencher; it activates Keap1–Nrf2–ARE signaling while dampening NF-κB. Supercritical CO₂, ultrasound, microwave, pressurized-liquid, and NADES extractions can now recover up to 91% of peel lycopene using green, food-grade solvents. In broilers, 5 to 10% pomace or 30 to 400 mg/kg purified lycopene improves antioxidant status and lessens heat stress; in dairy ruminants, ensiled pomace at 10 to 40% maintains milk yield while improving milk PUFA. In finishing pigs and rabbits, dietary pomace or lycopene improves tissue oxidative stability, with growth benefits reported in heat-stressed rabbits. Standardized bioactive reporting and clearer inclusion limits remain the main research priorities. Full article

The decoupling of dairy production from cropland can increase environmental burdens and constrain the recycling of nutrients in manure in land-limited, purchased-feed-dependent large-scaled dairy farms. This study evaluated the environmental and marginal economic effects of dairy–cropland recoupling using a cradle-to-farm-gate life cycle assessment and a 10,000-iteration Monte Carlo simulation. The observed farm system was used as the reference, and five partial forage-localization scenarios and one land-unconstrained full-localization benchmark were constructed by replacing off-farm domestic or imported forages with nutritionally equivalent farm-grown alternatives. Baseline global warming potential (GWP), nitrogen footprint (NFP), and phosphorus footprint (PFP) were 0.92 kg CO₂e, 9.72 g N, and 0.43 g P per kg of fat- and protein-corrected milk, respectively. Replacing off-farm domestic alfalfa hay with farm-grown alfalfa hay provided the strongest combined environmental performance per unit of supporting cropland, reducing the GWP and NFP by 1.17 kg CO₂e/m² and 2.76 g N/m², respectively, while also achieving the largest PFP reduction among the partial-localization scenarios. In contrast, replacing imported alfalfa hay generated the greatest marginal economic benefit (USD 0.37/m²) but increased the NFP by 0.12 g N/m², revealing a clear environmental–economic trade-off. Full forage localization reduced system-level GWP by 12.0% but required approximately 3731 ha of supporting cropland, far exceeding the farm’s 133 ha site area. The environmental and marginal economic effects of recoupling depended on the displaced forage supply chain and on scenario-specific differences in transportation, manure nutrient recycling, fertilizer substitution, and crop-production inputs. These findings support the partial and priority-based allocation of supporting cropland as a practical strategy for large-scaled dairy farms with similar purchased-feed dependence and land constraints, rather than complete forage self-sufficiency. Full article

Nutrition, healthcare and management of the rearing environment all interact and have an impact on the performance and feed efficiency of pre-weaned dairy calves. In a survey of 54 dairy farms in Northern Ireland, average daily gain (ADG) of 369 pre-weaned calves was assessed. Dietary and nutritional information detailing intake of milk or calf milk replacer (CMR) and the composition of milk/CMR and solid feed diets was collected. Environmental and hygiene-related parameters within calf rearing facilities were recorded. It was hypothesised that suboptimal environmental management conditions would negatively affect calf performance and cause the observed average daily gain and retained energy to fall below values predicted from nutritional intake alone. Factors that tended to be negatively associated with the observed calf ADG were increased time (>50%) below lower critical temperature (LCT), increased time (>10%) above 0.4 m/s airspeed and reduced straw dry matter (<70%). Potential calf ADG was predicted using the young calf model from the Nutrient Requirements of Dairy Cattle (8th Edition). The predicted and observed calf ADG and retained energy (RE) were compared to assess whether the individual observed calf ADG was greater, equal to or less than that predicted by the model. Environmental factors associated with calf ADG or RE being less than the model prediction included: greater than 30 animals housed in the calf house, increased time (>10%) above 0.4 m/s airspeed and where coliforms were detected in ≤50% of milk samples on that farm. These results demonstrate the relevance of management of the rearing environment in optimising pre-weaned calf performance and feed efficiency. Full article

The environmental sustainability of farms can be improved by the adoption of sound manure management techniques, with anaerobic digestion (AD) being one of the most promising: AD offers the production of renewable fuel, biogas or biomethane, and prevents the emissions of odors and greenhouse gases. While anaerobic digestion (AD) is widely implemented for treating dairy, pig, and poultry manure, limited interest has been directed toward horse farms. A laboratory-scale experiment was performed to assess the biochemical methane potential of horse manure and straw-based horse litter. The test was conducted using a BioReactor Simulator system (six digesters, 2.0 L each) at 38 °C for 40 days, using inoculum from a 1 MWe biogas plant fed with dairy cow manure and silages. Horse manure presented total solids (TS) of 19.4% and volatile solids (VS) of 90.7% TS, while straw-based horse litter showed a TS of 49.8% and VS of 90.0% TS. Methane yields of 48.2 NL/kg, 237.6 NL/kgTS, and 262.1 NL/kgVS were obtained for horse manure, and 82.1 NL/kg, 164.9 NL/kgTS, and 183.2 NL/kgVS for the straw-based horse litter. These results are of practical interest for the management of large horse facilities or for the co-digestion of these feedstocks in existing AD plants. Full article

Background: Cryptosporidium spp. are zoonotic pathogens that threaten animal husbandry and public health. Systematic data on the seasonal distribution of Cryptosporidium in dairy cattle of Inner Mongolia, China, are lacking. This study investigated the effects of seasonal variation on the prevalence and genetic diversity of Cryptosporidium in dairy cattle. During the four seasons of 2024, 505 fecal samples were collected from an intensive dairy farm in Hohhot, Inner Mongolia. Cryptosporidium species were identified using nested PCR targeting the SSU rRNA gene, and C. parvum subtypes were determined by gp60 sequencing. Results: The overall prevalence was 39.21% (198/505), with significant seasonal variation (spring 36.57%, summer 16.22%, autumn 45.38%, winter 55.38%; χ² = 41.37, p < 0.001). No significant differences were observed among age groups or between diarrheic (40.48%) and nondiarrheic (38.58%) cattle. Five species were identified: C. andersoni (46.46%), C. bovis (23.74%), C. ryanae (16.16%), C. parvum (12.12%), and C. suis (1.52%). Two C. parvum subtypes were detected: IIdA20G1 (79.17%) and IIdA19G1 (20.83%). Conclusions: This is the first systematic investigation of the seasonal distribution of Cryptosporidium in cattle in Inner Mongolia and the first detection of C. suis in Chinese cattle. Both C. parvum subtypes detected are zoonotic, highlighting the need for public health surveillance. Full article

Precision livestock farming (PLF) is hindered by high costs, infrastructure demands, and complex deployment. To address these barriers, we developed CABRA, an open-hardware wearable system for real-time behaviour monitoring in pasture-based livestock. The collar-mounted device integrates a 6-axis IMU, a GPS, and a low-power ESP32 microcontroller within a modular architecture, using the routerless ESP-NOW protocol to transmit data directly to a base station—eliminating reliance on network infrastructure or cloud connectivity. The system supports both synchronised data logging for video annotation and real-time embedded behaviour classification via an optimized decision-tree pipeline deployed directly on the microcontroller. Field trials with dairy goats confirmed robust hardware performance, minimal animal disturbance, and reliable communication over 100 m. A two-stage evaluation revealed that while the extracted IMU features are highly discriminative (achieving F1 > 0.99 under window-level validation), cross-animal generalization remains challenging (macro F1 = 0.31 under rigorous animal-level partitioning), primarily due to the “sensor placement effect” and domain shift between individuals. These results honestly quantify the current limitations of uncalibrated wearable livestock sensing while validating the functional feasibility of edge-based inference. All design assets—CAD files, schematics, firmware, and data pipelines—are openly released to ensure full reproducibility and community-driven adaptation for diverse PLF applications. Full article

Intensive dairy farm wastewater (DFW) poses significant environmental challenges due to its high organic loading and complex composition. Anaerobic co-digestion with sewage sludge (SS) offers a promising strategy for simultaneous pollutant removal and bioenergy recovery. However, the optimal inoculation ratio for maximizing both methane production and volatile fatty acid (VFA) accumulation remains unclear for liquid DFW following a solid–liquid separation. This study investigated the effects of three SS addition ratios (0%, 15%, and 45%) on anaerobic co-digestion performance. Daily methane production, cumulative yield, VFA composition, pH, electrical conductivity (EC), ammonium nitrogen (NH₄⁺-N), and chemical oxygen demand (COD) were monitored over 26 days. The 45% SS treatment (S45) achieved the highest cumulative methane yield (2882.60 mL), representing 35.1% and 7.2% increases over S0 and S15. Modified Gompertz modeling confirmed S45 attained the highest methane potential (3056.8 mL) and production rate (569.8 mL/d), with the shortest lag phase (14.47 d). S45 also reached the highest total VFAs peak (2209.02 mg/L) on day 3, advancing acidification by 3 days. Process stability was maintained across all treatments (pH 6.94–8.44), with S45 showing the earliest pH recovery and lowest NH₄⁺-N accumulation. COD removal in S45 exceeded S0 by 25.1% at day 26. These findings indicate that 45% SS addition optimally balances methanogenic performance, acidogenic efficiency, and process stability in DFW anaerobic co-digestion. Full article

Dairy cow management depends on repeated observations of behavior and physical condition to support health, welfare, and operational decisions, but these observations remain labor-intensive. Deep learning (DL)-based computer vision can automate parts of this work, although deployment requirements differ among management operations. This operation-centered structured review retained 97 distinct publications. Five operations were compared: lameness detection, behavior recognition, individual identification and tracking, body condition score estimation, and body weight prediction. The comparison addresses management tasks, inputs, outputs, label requirements, validation designs, and deployment constraints. It identifies shared concerns involving reference-standard reliability, data-capture protocols, multi-animal scenes, identity continuity, cross-farm generalization, and data access. A qualitative three-part research agenda is proposed: candidate foundations in reference standards and identity continuity; transfer and data infrastructure; and conditional extensions involving multimodal and longitudinal evaluation. Full article

Modern dairy farms continuously record individual cow milk yields, yet turning these records into on-demand forecasts requires a multi-step data-to-model pipeline that natural language alone cannot drive. We study large language model (LLM) orchestration over a verified forecasting pipeline, asking not whether it predicts yield but how faithfully it converts requests into correct tool-execution conditions. Against a deterministic Oracle pipeline, we compare a single-LLM function-calling framework and a multi-agent framework that distributes the same workflow across specialized agents. Using Gemini 2.5 Flash, both answer 1085 queries (4340 forecast points) varying in phrasing, horizon, and missingness, drawn from 13 months of commercial-farm records. On execution-matched points (those reproducing the Oracle’s cow, dates, input window, and horizon), the LLM output is statistically equivalent to the Oracle (paired TOST within $\pm 0.5$ kg, $p_{\mathrm{TOST}}<0.001$), so agreement is largely fixed by the deterministic tools. This equivalence is therefore confined to execution-matched points and does not describe overall system reliability. The frameworks differ instead in fidelity: the single-LLM framework reaches only 94.31% completion (zero-filled RMSE 9.70 kg), whereas the multi-agent framework reaches 99.88% at 35% added runtime; the single-LLM losses reflect systematic date-anchoring errors and silent false-accepts, not random noise. In this case study, LLM frameworks can orchestrate verified forecasting tools, and separating responsibilities improves workflow fidelity more than accuracy. Full article

This study evaluated the environmental impact of milk production in Northern Italy using a nutrient life cycle assessment (nLCA) approach with different functional units (FUs) related to the nutritional value of whole milk at the farm gate. Data from 94 dairy farms in the production areas of two protected designations of origin (PDOs) were analyzed. The FUs considered were based on milk mass, protein, calcium, essential amino acids (EAA), portion size, and milk energy content. Climate change (CC) impact was assessed using Intergovernmental Panel on Climate Change (IPCC) guidelines. The assessment estimated environmental impacts of 0.17, 4.78, 0.14, 1.06, 0.21, and 0.33 kg CO₂-eq for the FUs: 100 g of milk, 100 g of protein, 100 mg of calcium, 10 g of EAA, 125 mL portion size, and 100 kcal of milk, respectively. Results showed no significant differences in CC between PDOs and geographical areas (plain vs. mountain) for any selected FU. However, when comparing cow milk with plant-based alternatives, product ranking changed substantially depending on the selected FU: while milk showed the highest impact per 100 g product, its relative performance improved when expressed per 100 g protein. These findings demonstrate that FU choice influences the interpretation of environmental results. Full article

Bovine mastitis, a prevalent and economically detrimental inflammatory disease in dairy cattle, leads to substantial economic losses due to reduced milk yield, impaired milk quality, and increased veterinary costs. Infrared thermography (IRT) provides an efficient, non-invasive approach for early mastitis detection by leveraging computer vision to analyze temperature differences between ocular and udder regions. However, in practical farm environments, arbitrary head movements, particularly horizontal rotation, alter sensor-to-object distance, introducing systematic biases in thermal measurements and degrading diagnostic reliability. To address this limitation, a two-stage detection framework is proposed under a lateral infrared imaging configuration, in which the camera is positioned perpendicular to the walking direction of cows. First, a posture classification module based on pose estimation is employed to identify cows with consistent lateral orientation and minimal head rotation, thereby maintaining stable sensor-to-object distance between the ocular and udder regions. Second, instance segmentation is applied to posture-constrained thermal images to achieve accurate regional temperature extraction and quantitative analysis. In addition, an improved YOLO26 model integrated with a Deformable Attention Transformer (DAT-YOLO26) is developed to enhance feature representation capability and detection performance under variable imaging conditions. On an independent 40-cow blind diagnostic cohort (20 healthy, 20 mastitis-positive), the proposed method achieved an accuracy of 87.5%, an F1-score of 87.18%, a sensitivity of 85%, and a specificity of 90%. These results, while constrained by the size of the test cohort, suggest that enforcing posture consistency improves IRT-based diagnostic performance under the present acquisition conditions. We emphasize, however, that these findings are based on a single-farm, single-season pilot cohort of 40 cows; multi-farm and multi-season validation are required before broader deployment claims can be made. Full article

Teat-end hyperkeratosis (THK) is a welfare concern in dairy cows, associated with pain, discomfort, and increased mastitis risk. Identifying risk factors associated with THK may support targeted preventive management. This cross-sectional observational study aimed to determine the prevalence of THK and identify factors associated with its occurrence. A total of 492 Holstein-Friesian cows were evaluated at the milking parlor of a dairy farm located in Évora, Portugal. All four teats per cow (n = 1968) were scored on a 4-point scale: no lesion (1), smooth ring (2), rough (3), and very rough skin (4). Parity, days in milk, teat length, teat-end shape, udder health, and milking procedures were assessed as potential factors associated with THK. A mixed-effects ordinal logistic regression model was used to evaluate the associations between THK and the selected factors. Overall THK prevalence was 54.4%, with a median score of 2 (range: 1–4; IQR: 1–2). Score distribution was 45.6% (score 1), 43.0% (score 2), 10.3% (score 3), and 1.1% (score 4). Round teat ends were associated with greater odds of severe THK compared with flat teat ends (OR = 6.4; p = 0.016). Longer milking duration (OR = 2.7 per 100 s; p = 0.002) and a history of mastitis (OR = 80.0; p < 0.001) were also positively associated with THK severity. These findings highlight opportunities to mitigate THK through optimized milking management and breeding strategies. Notably, factors such as mastitis and prolonged milking time are also associated with culling, potentially masking the true burden of THK on animal welfare. Full article