Search Results (156)

Livestock production occupies a paradoxical position with respect to climate change: farm animals are highly vulnerable to heat stress, feed and water scarcity, and climate-sensitive disease, while the sector contributes an estimated 14.5% of anthropogenic greenhouse gas emissions, most of which is biogenic methane from enteric fermentation. This review evaluates the evidence for precision livestock farming (PLF)—continuous, automated, real-time monitoring of individual animals’ health, welfare, production and environmental impact—across dairy and beef cattle, small ruminants, pigs, and poultry. For mitigation, precision feeding and additive-dosing strategies have been associated with enteric methane reductions of approximately 10–25%; for adaptation, wearable and non-invasive sensors have been reported to detect heat-stress-related behavioural changes before productivity losses become apparent, and smart climate-control systems have been associated with housing energy-use reductions of roughly 5–10%. Much of this evidence derives from single-farm, small-sample or short-duration studies and should be read as indicative rather than generalisable. Adoption remains constrained by high investment costs, limited interoperability, insufficient technical support, and uneven applicability to extensive and smallholder systems. We conclude that PLF is a valuable enabling technology that, combined with genetic, nutritional, and management strategies, can strengthen the resilience and environmental sustainability of livestock systems under a changing climate. Full article

Fascioliasis, caused by Fasciola hepatica and Fasciola gigantica, is a major veterinary and zoonotic disease affecting cattle production systems worldwide. Despite extensive research, prevalence estimates vary considerably across geographic regions and study methodologies, necessitating a systematic synthesis to inform evidence-based control strategies and public health interventions. A systematic review and meta-analysis were conducted of studies reporting Fasciola prevalence in cattle from 1996 to 2024. A random-effects meta-analysis was performed on 338 studies encompassing approximately 49 million cattle samples. Subgroup analyses were performed to examine variations by continent, diagnostic method, Fasciola species, study period, sample size, and climate zone. Meta-regression identified sources of heterogeneity, and publication bias was assessed using Egger’s test. Certainty of evidence for the principal pooled outcomes was assessed using the GRADE framework. The global pooled prevalence of Fasciola infection in cattle was 26.5% (95% CI: 24.0–29.1), with very high heterogeneity (I² = 99.5%). Significant geographic variation was observed, with Europe showing the highest prevalence (42.1%, 95% CI: 33.1–51.1), followed by Oceania (38.9%, 95% CI: 24.3–53.6), America (31.4%, 95% CI: 24.5–38.4), and Africa (24.3%, 95% CI: 21.1–27.5), and Asia showed the lowest (18.3%, 95% CI: 13.9–22.8). The diagnostic method emerged as the most important source of heterogeneity (R² = 26.3%), with ELISA detecting a substantially higher prevalence (49.0%, 95% CI: 41.8–56.2) than post-mortem examination (19.2%, 95% CI: 16.2–22.3). F. hepatica exhibited a higher prevalence (33.1%, 95% CI: 27.9–38.2) than other species. No temporal trends were detected between the study periods. The climate zone explained 3.1% of the heterogeneity. Publication bias was not significant (Egger’s p = 0.157). Fasciolosis represents a substantial global burden in cattle populations, with its prevalence strongly influenced by diagnostic methodology and geographic location. The critical role of the choice of diagnostic method in prevalence estimation underscores the urgent need for standardized surveillance protocols. These findings have important implications for veterinary disease control programs and zoonotic disease prevention strategies in both endemic and emerging regions worldwide. Full article

Cattle producers must balance animal welfare with productivity, but regular observation of animals in extensively managed operations is often impractical. Artificial intelligence (AI) integrated with computer vision offers an automated alternative, but few studies compare object detection architectures within the same dataset or focus on pastured cattle. Groups (n = 2–7) of heterogeneous beef cattle were recorded on pasture with nine solar trail cameras. Footage (~132 h) was curated in VideoLAN; annotated in Computer Vision Annotation Tool (CVAT) with bounding boxes and behavioral classes; and split 62/21/17% into training (24,508 frames), validation (8231 frames), and testing (6625 frames) sets. Four architectures were trained: Faster R-CNN (ResNet-50 FPN), Single Shot MultiBox Detector (SSD300, VGG-16), RetinaNet (ResNet-50 FPN with focal loss), and YOLOv8 nano (Ultralytics). With validation at 0.50 confidence and 0.50 IoU, Faster R-CNN achieved the highest overall F1 (0.79) and best per-class balance; RetinaNet was intermediate (peak F1 = 0.72); SSD300 saturated at F1 = 0.40; and YOLOv8 nano achieved some minority class recall at lower confidence. Each model detected the classes “grazing” and “hay feeding” accurately but confused cattle with the visually similar “normal” class. Datasets, checkpoints, and analysis scripts are provided to support further refinement of AI-enabled monitoring of extensive cattle systems. Full article

Automated monitoring of cattle behavior through computer vision requires robust detection as a foundational step for tracking, re-identification, and behavior analysis. However, training accurate detection models typically demands extensive manually annotated datasets, creating a significant bottleneck for scaling cattle monitoring systems. In this work, we investigate whether automated annotation using foundation models can reliably replace manual labeling for cattle detection tasks. We introduce EMA (Extensive Mitrovac Annotations), a dataset of barn images with manually annotated cows with oriented bounding boxes including head orientation, posture labels (standing/lying), and visibility status (whole/partially visible). We systematically compare manualy annotated oriented bounding boxes with those generated by the Segment Anything Model 3 (SAM 3), demonstrating high agreement between automated and ground truth annotations. Furthermore, we train YOLO11-OBB detectors on both manual and SAM-generated annotations, showing that models trained on automated annotations achieve comparable performance to those trained on manual labels when evaluated on our ground truth test set. Our analysis reveals that only a small fraction of SAM-annotated data is sufficient to achieve high detection accuracy, proving the feasibility of automated annotation at scale. These findings suggest that foundation models show promise for generating training data in cattle detection pipelines under controlled conditions, potentially reducing annotation costs and supporting scalable deployment of monitoring systems. The EMA dataset and trained models are publicly available to support further research in precision livestock farming. Full article

Biogas plays an increasingly important role in advancing decarbonisation goals, particularly in rural regions where livestock and agricultural waste can be converted into renewable energy. However, predicting annual farm-level biogas electricity output remains challenging due to operational variability, uncertain co-digestion practices, and external constraints such as grid integration and environmental disruptions. Traditional point prediction models often provide limited support for practical decision-making because they do not explicitly represent uncertainty around the estimated output. To address this limitation, this paper proposes a data-driven prediction interval framework for annual farm-level biogas electricity-output estimation. The study does not model future temporal horizons; rather, it estimates annual electricity generation at the farm level and constructs prediction intervals around these estimates. The proposed framework combines Self-Organising Map (SOM)-based clustering with Lower Upper Bound Estimation (LUBE) to account for operational heterogeneity among biogas-producing farms. SOM clustering is performed using pre-prediction operational covariates, specifically cattle population and co-digestion status, while electricity output is used only as the prediction target and for post-hoc interpretation. For each operational cluster, a neural prediction interval model is trained using the LUBE approach. A priority-weighted extension is then incorporated to reflect cluster-level operational or strategic importance in the evaluation of interval performance. Experiments on a real-world dataset from U.S. biogas systems show that the proposed framework can support a trade-off between interval width and coverage performance across standard confidence levels. By combining operational clustering with uncertainty-aware interval estimation, the method improves the interpretability and practical relevance of annual farm-level biogas electricity-output prediction for infrastructure planning. Full article

Dual-purpose cattle systems are among the most important livestock production systems in tropical regions, contributing significantly to food production and rural livelihoods. However, their considerable diversity in management practices, resource endowments, producer-reported climatic stressors, and production intensity remains insufficiently characterized for effective policy design. Therefore, this study aimed to identify and characterize farm typologies by integrating productive, structural, environmental, and management dimensions and to describe differences in farm-system vulnerability among them. Data were collected from 132 commercial farms using structured surveys. Twenty-three variables were analyzed using Principal Component Analysis of which 22 reached the predefined absolute loading threshold for component interpretation. The resulting component scores were subsequently used in the hierarchical cluster analysis. Seven principal components with eigenvalues > 1 were identified, explaining 68.87% of the total variance. Cluster 1 comprised extensive, milk-oriented farms with the lowest milk productivity but the greatest pasture availability and diversity; nevertheless, these farms were particularly affected by drought. Cluster 2 represented the predominant dual-purpose model, with intermediate productive, environmental, and diversification characteristics. Cluster 3 included more intensive and productive farms with limited land availability, the lowest soil conservation and pasture availability scores, high maintenance expenditure, and low investment capacity. Cluster 4 showed the highest milk productivity but also the greatest exposure to excessive rainfall and northerly winds. Accordingly, appropriate strategies should prioritize drought preparedness and forage conservation in Cluster 1, gradual improvements in efficiency and environmental management in Cluster 2, soil and pasture restoration and reduced maintenance dependence in Cluster 3, and climate-resilient infrastructure and risk-management measures in Cluster 4. These findings demonstrate that dual-purpose cattle systems are highly heterogeneous systems structured by multiple productive, environmental, and organizational dimensions. The results demonstrate that typology-specific interventions are more appropriate than uniform recommendations and provide a framework for improving productivity, sustainability, and climate resilience in tropical livestock systems. Full article

Pasture ecosystems are a key component of livestock production in arid and semi-arid regions, where forage availability and nutritive value are often associated with animal performance under grazing conditions. This study aimed to provide an exploratory assessment of pasture productivity, forage nutritive value, and beef cattle productivity across contrasting natural–climatic settings in Kazakhstan. The study was conducted under commercial production conditions on five farms representing different grazing environments during the 2024–2025 grazing seasons. Because each zone was represented by a single farm, the study should be interpreted as an observational assessment of farm-level patterns rather than as a fully replicated experimental comparison. Pasture productivity and forage chemical composition, including crude protein, fiber, and dry matter content, varied among farms and seasons. Average daily gain ranged from 316.7 to 900 g day⁻¹ depending on the study site and year of observation. Exploratory statistical analyses indicated variability among the studied systems; however, pairwise comparisons did not reveal statistically significant differences in animal productivity among farms (p > 0.05). Correlation analyses revealed moderate positive associations between average daily gain, crude protein content, and pasture yield, although these relationships were not statistically significant after adjustment for multiple comparisons. Similarly, linear models incorporating forage nutritive value indicators and study site did not identify statistically significant predictors of animal productivity within the current dataset. Overall, the results describe patterns of variation in pasture characteristics and animal productivity observed under extensive grazing conditions in continental environments. Given the observational design and limited replication at the farm level, the findings should be interpreted cautiously and regarded as preliminary. The study provides baseline information for future investigations of pasture–livestock interactions in arid and semi-arid grazing environments. Full article

Lumpy skin disease (LSD), a vector-borne viral disease of cattle, re-emerged in Italy in June 2025 after six years of absence in Europe, affecting the island of Sardinia, which had previously been disease-free. The insular setting, the predominance of extensive cattle farming systems, and the rapid implementation of control measures provided a unique opportunity to investigate epidemic dynamics and evaluate vaccination effectiveness under field conditions. This study aimed to describe the epidemiological pattern of the first epidemic season (June–October 2025), estimate key transmission parameters, and assess vaccination effectiveness at the farm level. Confirmed outbreaks consistent with local transmission and notified between 20 June and 26 October 2025 were analyzed to characterize epidemic transmission dynamics, while vaccination effectiveness was assessed over an extended follow-up period through 31 December 2025. The between-farm basic reproduction number (R₀) was estimated from the early exponential growth phase using log-linear regression and doubling time calculations. Spatio-temporal clustering was assessed using Kulldorff’s scan statistic under a Poisson model, accounting for the population at risk. Vaccination effectiveness was evaluated using a time-dependent Cox proportional hazards model with a 21-day post-vaccination lag. A total of 79 outbreaks were confirmed, of which 68 were consistent with local transmission. Affected farms included a total of 3443 cattle, with morbidity, mortality, and case fatality rates of 14.4%, 7.0%, and 31.1%, respectively. The exponential growth phase lasted four weeks, with an estimated growth rate of 0.366 per week and a doubling time of 1.89 weeks. The estimated R₀ ranged from 1.55 to 1.92, depending on the assumed generation time, indicating moderate but sustained transmission. The median apparent spatial spread velocity was 4.8 km/day. Spatio-temporal analysis identified a single highly significant cluster in the central-eastern area, accounting for approximately 27% of outbreaks (RR = 58.06; p < 0.001). Vaccination was associated with a substantial reduction in outbreak risk (HR = 0.18; 95% CI: 0.06–0.51; p = 0.001), corresponding to an estimated effectiveness of approximately 82% at the farm level. The 2025 Sardinian epidemic was characterized by moderate transmissibility and strong spatial clustering during the early phase. Rapid implementation of vaccination was associated with a significant reduction in outbreak risk, even under conditions of high infection pressure. The integration of spatio-temporal analyses and time-dependent modeling proved essential to support evidence-based control strategies in newly affected regions. Full article

Animal tuberculosis caused by mycobacteria of the Mycobacterium tuberculosis complex (MTBC) represents a significant challenge to both public and animal health. Although eradication programs focus predominantly on cattle, small ruminants, especially goats, may play a relevant role as reservoirs. This study aimed to characterize tuberculosis cases in small ruminants in Portugal diagnosed at the national reference laboratory for Tuberculosis (Instituto Nacional de Investigação Agrária e Veterinária) over the period from April 2012 to July 2023. In this study, samples from 79 animals suspected of TB (64 goats and 15 sheep) were analyzed by integrating histopathology; bacteriology; and, when applicable, PCR results, thereby allowing the distribution by species to be described, MTBC agents to be identified, and diagnostic agreement to be assessed. Of these samples, 29 positive cases (36.7%) were identified, all of them in goats (45.3% of goats tested), whereas no cases were confirmed in sheep. Mycobacterium caprae was the most frequently identified species (89.7%), followed by Mycobacterium bovis (10.3%). Geographic distribution was concentrated in the Alentejo (48.3%) and Norte (44.8%) regions, where 93.1% of cases occurred. Topographical analysis of lesions revealed respiratory tract involvement (lung and/or thoracic lymph nodes) in 82.7% of positive cases, with gross patterns including caseous/caseocalcified lesions (n = 11), necrosis (n = 9), and nodular granulomatous lesions (n = 10). Agreement between histopathology and bacteriology was 81.5%. Parasitic coinfections were observed in 24.1% of positive cases, complicating histopathological interpretation. The results of this study indicate goats, as opposed to sheep, as non-negligible reservoirs of TB, suggesting species-level differences that warrant targeted investigation. The predominance of respiratory lesions and the detection of extensive cavitary forms indicate potential aerogenous transmission with implications for multi-host systems. Diagnostic discordance and parasitic interference reinforce the need for combined testing methodologies. It is therefore advisable to consider the integration of goats into tuberculosis surveillance and control programs, with greater relevance in Alentejo and Norte. Full article

Phytoestrogens are plant-derived phenolic compounds that structurally resemble endogenous estrogens and can exert both estrogenic and anti-estrogenic effects in animals. In ruminant nutrition, the main classes of phytoestrogens (isoflavones, lignans, stilbenes, coumestans and selected flavonoids) are supplied predominantly by legume forages and soybean-based feeds, in which concentrations can reach several mg/g of dry matter. After ingestion, these compounds are extensively metabolized by the rumen microbiota to derivatives with altered biological potency, such as equol and p-ethyl-phenol, which influence endocrine, immune and metabolic pathways. Experimental and field studies in cattle, sheep and goats indicate that dietary phytoestrogens may improve nitrogen utilization, immune competence, growth performance, antioxidant status and milk yield. However, they can also impair fertility, modify hormone profiles and compromise embryo survival in a compound-, dose-, and species-dependent manner. In this review, we summarize current knowledge on the botanical and nutritional sources, ruminal metabolism and transfer of phytoestrogens in ruminants, and critically examine their effects on blood metabolites, immune responses, growth and carcass traits and lactational performance and reproductive function. A structured literature search based on PRISMA principles was used to identify and appraise experimental and observational studies in both grazing and intensive production systems up to 2025. Remaining knowledge gaps and practical implications for the safe use of phytoestrogen-rich feeds in livestock production are highlighted. Full article

The espinal agroforestry system is a valuable grazing resource for sheep and cattle in the Mediterranean region of central Chile. It is characterized by a woody stratum dominated by Vachellia caven and an herbaceous grassland stratum that together provide important ecological services. Despite its relevance for extensive livestock production, ongoing land-use change threatens the integrity of the espinal agroforestry system, underscoring the need for sustainable management strategies to enhance productivity. This study assessed the long-term impacts of improved management practices in a representative espinal agroforestry system, including annual fertilization, supplementary cereal crop integration, and progressive increases in stocking rate, on plant diversity and soil carbon storage in Cauquenes, Maule Region, Chile (35°58′ S, 72°17′ W), during 2014–2019. A production system was established on 10 ha of espinal grassland, complemented by 1 ha of supplementary crop rotation (oat–purple vetch intercropping and triticale). Due to the scale of the system, a single experimental unit was used; however, multiple sampling areas were evaluated over time to assess the botanical composition, forage yield, and soil carbon. Grasslands were annually fertilized with phosphorus, potassium, and boron. The forage yield in spring ranged from 2 to 4 t dry matter ha⁻¹ year⁻¹ over six years, with strong interannual variability driven by rainfall. The stocking rate increased progressively from 2 to 8 sheep ha⁻¹ and lamb live weight from 80 to 370 kg ha⁻¹ over six-years. The grassland botanical composition shifted markedly, with increased abundance of annual legumes (Trifolium subterraneum, Medicago polymorpha) and Leontodon leysseri. Supplementary crops yielded between 6.0 and 10.5 t DM ha⁻¹, while soil organic carbon increased from 1.6% to 2.2%. These results demonstrate that sustainable intensification of the espinal system can enhance productivity while maintaining environmental sustainability. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article

Madagascar’s Central Highlands have experienced extensive deforestation and soil degradation, limiting the success of reforestation efforts. Poor soil fertility, particularly nitrogen limitation, constrains early seedling growth in degraded landscapes. This study evaluated the field performance of composted Black Soldier Fly frass (CBSFF) as a soil amendment for the native pioneer tree Dodonaea madagascariensis within the Ambohitantely Special Reserve. Four treatments were compared across four sites using a randomized complete block design: unfertilized control, cattle manure (4 g N), CBSFF one-fold (4 g N), and CBSFF two-fold (8 g N). The experiment was conducted on seedlings aged 16 months at the start of the study, and their growth was monitored over a six-month period. Growth responses were analyzed using generalized linear mixed-effects models with site included as a random factor. Seedling survival remained near 100% across all treatments, indicating no phytotoxic effects of composted frass under field conditions. Fertilization significantly enhanced both basal stem diameter and height growth. When standardized by nitrogen input, cattle manure and CBSFF produced comparable growth responses, indicating that nitrogen availability, rather than fertilizer identity, primarily drove early seedling performance. Height growth exhibited a clear dose-dependent response, with the double-dose CBSFF treatment producing the greatest increase. Planting method had a modest effect on height but did not alter the relative performance of fertilizer treatments. These findings demonstrate that composted BSF frass functions as an effective nitrogen source for early tree establishment in degraded tropical soils and performs comparably to traditional manure under field conditions. By validating insect-derived fertilizer within a restoration context, this study supports the integration of circular nutrient systems into sustainable reforestation strategies in biodiversity-rich yet resource-limited landscapes. Full article

Bovine ocular squamous cell carcinoma (BOSCC) is the most common ocular tumour in cattle, with a multifactorial aetiology involving ultraviolet (UV) radiation, genetic factors, pigmentation, and management practices. A detailed epidemiological characterisation of BOSCC in the Azores, Portugal, is provided, with particular emphasis on its spatial distribution and potential risk determinants. Data were obtained through an epidemiological questionnaire completed by field veterinarians between August 2023 and March 2025. A total of 85 BOSCC cases were recorded across 62 farms—45 on Terceira Island and 17 on São Miguel Island. All affected animals were adult Holstein Friesian dairy cows, managed under extensive pasture-based systems. The nictitating membrane was the most frequently affected structure (69.5%), and multiple lesions occurred in 20% of the cases. Farms located at 200–400 m of altitude presented the highest number of cases. Continuous exposure to UV under pasture-based management represents the main environmental risk factor. Although periocular pigmentation may provide partial protection, other environmental and genetic factors can also contribute to tumour development. Artificial insemination is considered a promising preventive tool, enabling genetic selection for protective traits such as periocular pigmentation. This research provides the first regional epidemiological characterization of BOSCC in the Azores, highlighting the interplay among environmental, genetic, and management-related factors in disease occurrence. Full article

Pythiosis is a neglected infectious disease caused by the aquatic oomycete Pythium insidiosum and remains underrecognized in cattle, particularly in tropical regions. Here, we report the first molecularly confirmed outbreak of bovine pythiosis in the Amazon biome, affecting more than 400 animals raised under extensive production systems and areas with prolonged exposure to standing water. Clinically affected cattle presented ulcerative and exudative cutaneous lesions, predominantly involving the distal limbs. Given the diagnostic challenges associated with pythiosis, etiological confirmation was achieved through quantitative PCR (qPCR) targeting the internal transcribed spacer (ITS) region of P. insidiosum, providing rapid and specific molecular detection during the outbreak investigation. Therapeutic interventions were implemented as part of routine field management, including intramuscular triamcinolone combined with topical copper sulfate; this regimen was associated with clinical improvement in a substantial proportion of affected animals, though treatment efficacy was not formally evaluated. The outbreak occurred in flood-prone pastures during the rainy season, highlighting the role of aquatic environments in pathogen transmission. These findings expand the current understanding of bovine pythiosis in tropical ecosystems and underscore the importance of molecular diagnostics, outbreak surveillance, and a One Health approach for the identification and management of water-associated pathogens in livestock. Full article