Search Results (495)

Background: Environmental enrichment is a promising strategy to improve the welfare of dairy goats and sheep. However, studies in this field remain scattered, and its effects on productivity are unclear. Objectives: To evaluate the effects of environmental enrichment on behavioral, physiological, and productive parameters in dairy goats and sheep. Data sources: Scopus and Web of Science were searched for studies published from 2010 to 2025. Study eligibility criteria: Experimental or observational peer-reviewed studies comparing enriched vs. non-enriched housing in dairy goats or sheep, reporting on welfare or productivity outcomes. Methods: This review followed PRISMA 2020 guidelines and the PICO framework. Two independent reviewers screened and extracted data. Risk of bias was assessed with the SYRCLE tool. Results: Thirteen studies were included, mostly with goats. Physical, sensory, and social enrichments showed benefits for behavior (e.g., activity, fewer stereotypies) and stress physiology. However, results varied by social rank, enrichment type, and physiological stage. Only three studies assessed productive parameters (weight gain in kids/lambs); none evaluated milk yield or quality. Limitations: Most studies had small samples and short durations. No meta-analysis was conducted due to heterogeneity. Conclusions: Environmental enrichment can benefit the welfare of dairy goats and sheep. However, evidence on productivity is scarce. Long-term studies are needed to evaluate its cost-effectiveness and potential impacts on milk yield and reproductive performance. Full article

On-farm emergency slaughter (OFES) is employed when cattle are unfit for transport but still suitable for human consumption, thereby ensuring animal welfare and reducing food waste. This study analysed OFES patterns in Northern Italy, where a large cattle population is housed but information on the practice is rarely analysed. A total of 12,052 OFES cases from 2021 to 2023 were analysed. Most involved female cattle (94%) from dairy farms (79%). Locomotor disorders were the leading reason (70%), particularly trauma and fractures, followed by recumbency (13%) and calving-related issues (10%). Post-mortem findings showed limbs and joints as the most frequent condemnation sites (36%), often linked to trauma. A significant reduction in OFES cases occurred over time, mainly due to fewer recumbency and calving issues, likely reflecting stricter eligibility criteria introduced in 2022. Weekly variations, with peaks on Mondays and lows on Saturdays, suggest that logistical constraints may sometimes influence OFES promptness. These findings suggest that on-farm management and animal handling could be improved further to reduce welfare risks and carcass waste. Due to the lack of standardised data collection and regulatory harmonisation, a multi-country investigation could improve our understanding of this topic and inform best practice. Full article

Animal welfare assessments have raised farmers’ awareness of their management practices, contributing to measurable improvements. However, these protocols often highlight critical points without providing clear guidance on the prioritization of corrective actions. To address this gap, qualified advisory support may play a pivotal role in translating assessments into effective and sustainable interventions. This study evaluates the impact of direct and continuous expert support on improving animal welfare in dairy farms. Data were collected from 21 dairy farms in southeastern Tuscany (Italy) using the Classyfarm (CReNBA) protocol. Each farm underwent two assessments at a three-month interval (T0 and T1), during which tailored support was provided to address specific criticalities. At T0, over 60% of the farms obtained only marginally acceptable welfare scores (mean 67.48 ± 4.75), with major deficiencies in farm management practices, particularly regarding hygiene and space management. At T1, all farms showed substantial improvements, with an average increase of 22% in total welfare scores (mean 82.05 ± 5.71) and a mean of nine improved parameters per farm. These findings underscore the effectiveness of structured, continuous consultancy in not only resolving critical issues but also in fostering more informed, proactive, and sustainable farm management. The direct involvement of experts appears to be a key driver in enhancing both animal welfare and operational outcomes in dairy farming. Full article

Staphylococcus aureus frequently causes intramammary infections in dairy cows (bovine mastitis), which impair animal welfare, milk yield, and food safety. This study determined the prevalence and genetic diversity of S. aureus in bulk tank milk (BTM) samples from community-based Alpine dairy pastures in Tyrol, a major milk-producing region in Austria. Throughout the 2023 Alpine season (May–September), 60.3% (94/156) of BTM samples tested positive for S. aureus at least once over the course of up to four samplings. A total of 140 isolates collected from the 94 S. aureus-positive community-based Alpine dairy pastures revealed 33 distinct spa types, with t2953 (n = 33), t529 (n = 12), t267 (n = 11), and t024 (n = 10) being the most common. Selected isolates representing the different spa types were characterised by DNA microarray-based genotyping, multi-locus sequence typing (MLST), and antimicrobial susceptibility testing. Isolates with spa types associated with bovine-adapted CC8 (CC8bov/GTB) were identified as the most common subtype, being detected in BTM samples from 35.3% (55/156) of the pastures. This emphasises the high prevalence of this subtype in dairy herds across European Alpine countries. Other common bovine-associated subtypes were also detected, including CC97, CC151, and CC479. While antimicrobial resistance was rare, enterotoxin-producing genes were detected in all CC8bov-associated spa types. Overall, these findings underscore the importance of rigorous hygiene practices in dairy farming, particularly in community-based Alpine dairy pastures, where the risk of transmission is particularly high. It also emphasises the need for continued surveillance and subtyping to improve animal health, ensure food safety, and promote sustainable milk production. Full article

Infrared thermography (IRT) is explored as a non-invasive method for indirectly measuring parameters related to animal performance and welfare. This study investigates a feature extraction method termed the “thermal signature” (TS), a descriptor vector derived from the temperature matrix of an animal’s body surface, representing the percentage distribution of temperatures within predefined ranges. The TS, combined with environmental data, serves as a predictor attribute for machine learning-based classifier models to assess heat stress levels. The methodology was applied to a dataset collected from two groups of five dairy calves housed in a climate-controlled chamber and exposed to two artificial heat waves over 13 days. Data, including IRT measurements, respiratory rate (RR), rectal temperature (RT), and environmental variables, were collected five times daily (from 6 a.m. to 10 p.m., every four hours). Classifier models were developed using random forest (RF), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) algorithms. The RF models based on RR achieved the highest accuracies, 94.1% for two heat stress levels and 80.3% for three heat stress levels, using TS configurations with six temperature ranges. The integration of TS with machine learning-based models demonstrates promising results for developing or enhancing classifiers of heat stress levels in dairy calves. Full article

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

The European Union’s (EU) Common Agricultural Policy aims to promote sustainable farming practices that ensure the responsible use of natural resources, safeguard biodiversity, and uphold higher animal welfare standards. One pathway to achieving these objectives is through the encouragement of extensive farming. However, the dairy sector in EU countries as well as in Lithuania has shown a clear trend toward intensification. The aim of this study was to assess the technical efficiency (TE) of dairy farms employing extensive and intensive technologies. TE was evaluated using Data Envelopment Analysis (DEA) combined with meta-frontier analysis, which accounts for technological heterogeneity. Prior to the efficiency estimation, farms were grouped into two distinct categories—intensive and extensive—using the k-means clustering algorithm. The empirical results show that extensive dairy farms in Lithuania are smaller in land area and livestock units, rely more on internal resources, and exhibit lower productivity compared to intensive farms. Intensive farms achieved higher technical efficiency, narrower technological gaps, and more optimal scale efficiency, indicating superior resource management. The weaker performance of extensive farms is attributed to both less advanced technologies and production inefficiencies. Full article

This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. Although object detection models, including You Only Look Once (YOLO), EfficientDet, and sequence models, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Long Short-Term Memory (convLSTM), have improved detection and classification, significant challenges remain, including occlusions, annotation bottlenecks, dataset diversity, and limited generalizability. Existing interaction inference methods rely heavily on distance-based approximations (i.e., assuming that proximity implies social interaction), lacking the semantic depth essential for comprehensive SNA. To address this, we propose innovative methodological intersections such as pose-aware SNA frameworks and multi-camera fusion techniques. Moreover, we explicitly discuss ethical challenges and data governance issues, emphasizing data transparency and animal welfare concerns within precision livestock contexts. We clarify how these methodological innovations directly impact practical farming by enhancing monitoring precision, herd management, and welfare outcomes. Ultimately, this synthesis advocates for strategic, empathetic, and ethically responsible precision dairy farming practices, significantly advancing both dairy cow welfare and operational effectiveness. Full article

Cattle breeding has traditionally focused on improving production traits; however, recent interest in positive animal welfare has shifted attention toward selecting for more robust animals that balance productivity with health and well-being. The aim of the current study was to assess whether behavioural responses during milking in dual-purpose cattle are associated with genetic markers, previously linked to temperament traits in dairy and beef breeds. We focused on 185 lactating cows belonging to the Simmental strain (Romanian Spotted, national name), which were evaluated for their milking behaviour. Genotyping was performed using an 88-SNP panel selected based on prior associations with dairy and beef cattle temperament. We identified five SNPs that were significantly associated with milking reactivity in the Romanian Spotted breed, located in genes previously linked to neural development, stress response and behavioural regulation (USH2A, ADAMTS7, TBC1D2B and ZMAT4). Our findings suggest that milking behaviour in dual-purpose Simmental cattle is influenced by genetics, supporting the potential for including behavioural traits in future selection strategies. This study contributes to a better understanding of the genetic mechanisms underlying stress-related behaviours in dual-purpose cattle breeds. Full article

Animal production is a core sector to solve the increasing food demand worldwide, with productivity severely affected by climate change. Experts are predicting huge global productive losses in animal-derived products. Moreover, productive loss affects the economy, and the US dairy industry has reported losses of 1.5 billion dollars annually due to climate change. Beef and dairy production are based on cow reproduction and fertility is a key indicator of productivity. However, under heat stress (HS), several physiological modifications decrease cows’ fertility. Lower levels of estradiol, progesterone, and epidermal growth factor lead to undetectable ovulations, an inability to maintain the embryo and the pregnancy, or increased cortisol levels, inducing immunosuppression and, consequently, puerperal diseases delaying new pregnancies. The welfare of cows under HS, especially those raised on pasture, is a huge concern. Considering the impact of ambient-temperature-induced HS, developing strategies to improve fertility—namely through the selection of thermotolerant breeds allied to environmental management measures—can improve cattle production efficiency and reduce resource use, thereby reducing the carbon footprint. This review focuses on the effects of HS on female fertility, from parturition until the new conception, and on the role of heat shock proteins during this period. Full article

Lameness significantly compromises dairy cattle welfare and productivity. Early detection enables prompt intervention, enhancing both animal health and farm efficiency. Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. This limitation is exacerbated by the distinct kinematic patterns exhibited across lameness severity grades, ultimately reducing detection accuracy. This study presents an integrated computer vision and deep-learning framework for dairy cattle lameness detection and severity classification. The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. Experimental validation utilized 3150 annotated lameness feature maps derived from 175 Holsteins under natural walking conditions, demonstrating robust classification performance. The classification accuracy of the method for varying degrees of lameness was 92.80%, the sensitivity was 89.21%, and the specificity was 94.60%. The detection of healthy and lameness dairy cows’ accuracy was 99.05%, the sensitivity was 100%, and the specificity was 98.57%. The experimental results demonstrate the advantage of implementing lameness severity-adaptive feature weighting through hierarchical network architecture. Full article

Rising rates of antimicrobial-resistant infections have prompted increased scrutiny on antimicrobial use (AMU) in livestock agriculture. Dairy farms primarily use antimicrobials to maintain animal health and welfare by treating and preventing infectious diseases. However, the impact of dairy farm AMU practices on the cattle fecal microbiome remains largely unclear, partly due to difficulties in quantifying AMU. This study leveraged quantitative AMU data from 40 large commercial dairy farms to identify farms with low (n = 4) and high (n = 4) AMU. Using 16S rRNA gene amplicon sequencing, we compared the fecal bacterial communities of dairy calves and cows (healthy, cull, sick) by both AMU designation (high/low) and by individual farm AMU, summarized by animal defined daily dose (DDD) and mg/kg. We found significant differences in beta-diversity between cattle from high- and low-AMU groups using either method and found that Corynebacterium and Clostridium abundances increased with farm AMU. Additionally, we found fecal bacterial communities differed across farms within high- and low-AMU groupings, highlighting the need to account for farm-to-farm variation when assessing AMU impacts. These findings suggest that dairy farm AMU influences the fecal microbiome and identifies specific taxa that warrant further investigation as potential reservoirs for antimicrobial resistance genes. Full article

Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (>98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data. Full article

In the Netherlands, around 52,000 dairy cows die on the primary farm each year due to natural death, euthanasia, or on-farm emergency slaughter (OFES). The decision as to what is the best option is made by the farmer, often after consulting a veterinarian, a livestock trader, or a slaughterhouse operator. To determine which factors play a role in this decision-making process, semi-structured interviews were conducted with dairy farmers, private veterinary practitioners, livestock traders, and slaughterhouse operators in the Netherlands. Dairy cattle culling decisions are influenced and limited by strict enforcement of livestock transport regulations and limited options for on-farm killing methods. Requirements regarding mortality rates imposed by the dairy industry and private quality labels for raw milk also influence culling decisions in the Netherlands. Most participants stated that restrictive conditions regarding OFES and mobile slaughterhouses (MSHs) appear to have (unintended) negative effects on cow welfare and meat salvage in general. Different interests, such as cow welfare, food safety, economic concerns of various stakeholders, the reputational interests of the dairy and beef industries, and sustainability objectives such as meat salvage can be conflictive. The results of this study show that the decision-making process regarding culling or (prolonged) veterinary treatment of dairy cattle is complex because various factors, interests, and uncertainties must be weighed. This weighing can vary between individual dairy farms and individual dairy farmers. Full article