Search Results (573)

Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification. Full article

Furunculosis caused by Aeromonas salmonicida poses a significant challenge to the sustainable production of maraena whitefish (Coregonus maraena). This case report outlines a multi-year disease management strategy at a European whitefish facility with two production departments, each specialising in different life-cycle stages. Recurrent outbreaks of A. salmonicida necessitated the development of effective vaccination protocols. Herd-specific immersion vaccines failed to confer protection, while injectable formulations with plant-based adjuvants caused severe adverse reactions and mortality rates exceeding 30%. In contrast, the bivalent vaccine Alpha Ject 3000, containing inactivated A. salmonicida and Vibrio anguillarum with a mineral oil adjuvant, yielded high tolerability and durable protection in over one million whitefish. Post-vaccination mortality remained low (3.3%), aligning with industry benchmarks, and furunculosis-related losses were fully prevented in both departments. Transcriptomic profiling of immune-relevant tissues revealed distinct gene expression signatures depending on vaccine type and time post-vaccination. Both the herd-specific vaccine and Alpha Ject 3000 induced the expression of immunoglobulin and inflammatory markers in the spleen, contrasted by reduced immunoglobulin transcript levels in the gills and head kidney together with the downregulated expression of B-cell markers. These results demonstrate that an optimised injectable vaccination strategy can significantly improve health outcomes and disease resilience in maraena whitefish aquaculture. Full article

Leptospirosis is a globally significant zoonosis affecting animal health, productivity, and the environment. While typically associated with tropical climates, its persistence in semi-arid regions such as La Laguna, Mexico—characterized by low humidity, high temperatures, and limited water sources—remains poorly understood. Although these adverse environmental conditions theoretically limit the survival of Leptospira, high livestock density and synanthropic reservoirs (e.g., rodents) may compensate, facilitating transmission. In this cross-sectional study, blood sera from 445 dairy cows (28 herds: 12 intensive [MI], 16 semi-intensive [MSI] systems) were analyzed via microscopic agglutination testing (MAT) against 10 pathogenic serovars. Urine samples were cultured for active Leptospira detection. Risk factors were assessed through epidemiological surveys and multivariable analysis. This study revealed an overall apparent seroprevalence of 27.0% (95% CI: 22.8–31.1), with significantly higher rates in MSI (54.1%) versus MI (12.2%) herds (p < 0.001) and an estimated true seroprevalence of 56.3% (95% CI: 50.2–62.1) in MSI and 13.1% (95% CI: 8.5–18.7) in MI herds (p < 0.001). The Sejroe serogroup was isolated from urine in both systems, confirming active circulation. In MI herds, rodent presence (OR: 3.6; 95% CI: 1.6–7.9) was identified as a risk factor for Leptospira seropositivity, while first-trimester abortions (OR:10.1; 95% CI: 4.2–24.2) were significantly associated with infection. In MSI herds, risk factors associated with Leptospira seropositivity included co-occurrence with hens (OR: 2.8; 95% CI: 1.5–5.3) and natural breeding (OR: 2.0; 95% CI: 1.1–3.9), whereas mastitis/agalactiae (OR: 2.8; 95% CI: 1.5–5.2) represented a clinical outcome associated with seropositivity. Despite semi-arid conditions, Leptospira maintains transmission in La Laguna, particularly in semi-intensive systems. The coexistence of adapted (Sejroe) and incidental serogroups underscores the need for targeted interventions, such as rodent control in MI systems and poultry management in MSI systems, to mitigate both zoonotic and economic impacts. Full article

In this study, we analyzed data from 2992 cows to comprehensively evaluate the adult weight (WEI), a key growth and body-size indicator, in West China cattle, aiming to estimate the related phenotypic and genetic parameters. The analysis focused on four weight traits while considering non-genetic factors such as parity, season, year, and birth weight. Data were processed and corrected using a MIXED procedure and a multi-trait animal model. Results showed that these non-genetic factors significantly affected the weight traits (p < 0.05), which had high heritability (0.25–0.39) (p < 0.01). WEI is crucial for improving the genetic traits of cattle in western China and provides innovative approaches for optimizing herd management, enhancing the efficiency of genetic selection, and boosting beef cattle productivity. Full article

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

Our study aimed to identify the overall and cluster-specific characteristics of Czech beef cattle breeding systems. We used data from an online survey to ascertain farmers’ preferences in breeding objectives. Considering various evaluation criteria and clustering approaches in 41 farms, three beef systems were defined according to herd size, management, marketing, breeding strategies and structures, and farmer age. Breeding values and performance were jointly used as the primary information in all three systems. Cow temperament and calf viability, maternal fertility and longevity, and animal health were found to be the most important traits. Cluster 1 represents pure-breeding farms that specialize in producing breeding animals. Farms in clusters 2 and 3 combined pure- and crossbreeding strategies with production, which was partially (cluster 2) and fully (cluster 3) diversified for all beef categories. Farms also prioritized calving performance and calf growth (clusters 1 and 2) and exterior traits (cluster 3). Production type scores significantly (p < 0.05) differed in clusters 3 (4.12) and 2 (3.25). The proportion of production, functional, and exterior trait categories was 12:37:51, with low variability among clusters (±1 to 2 percentage points). The inter-cluster comparison showed that specific characteristics were compatible with certain breeding goal trait preferences. 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

The specific objectives of the current paper were the assessment of potential associations of weather conditions with the presence of Coxiella burnetii in the milk-tank of sheep and goat farms and the investigation for possible interactions between weather conditions and management practices on these farms. The presence of C. burnetii in milk-tank samples collected from 325 sheep flocks and 119 goat herds was assessed by means of a commercially available real-time PCR. Climatic variables present at the location of each farm were downloaded from ‘The POWER Project’. Univariable and multivariable analyses were carried out. Among the climatic variables assessed, only the average wind speed during the 15 days that preceded each visit was found to be a significant predictor for both sheep (p = 0.003) and goat (p = 0.034) farms. The current findings serve to provide information about the epidemiology of C. burnetii infections in small ruminant farms and the possibilities for contamination of the milk produced in these farms, which is important due to the zoonotic nature of the pathogen; these findings thus provide guidance to implement appropriate preventive measures. Full article

Gastrointestinal (GI) parasites not only significantly impact goat health and productivity but can also affect human health due to the zoonotic potential of some species. This study investigates the prevalence of internal parasites within the tropical monsoon ecosystem of southern Thailand, focusing on both phenotypic and molecular characteristics of the parasites and identifying associated risk factors in caprine farming systems. A total of 276 meat goats from Nakhon Si Thammarat province were examined, indicating an overall GI parasite prevalence of 88.8% (245/276), with strongyles and Eimeria spp. identified as the dominant parasites. In addition, mixed parasitic infections were observed in 72.2% of cases, whereas single infections comprised 27.8%. Strongyle-positive fecal samples were cultured and genetically sequenced, revealing the presence of Haemonchus contortus, Trichostrongylus colubriformis, and Oesophagostomum asperum. For associated risk factors, gender and grazing with other herds significantly impacted overall GI parasitic infections, while the gender, breed, and packed cell volume (PCV) affected the strongyle infection. A correlation analysis revealed a substantial relationship between strongyle egg per gram (EPG) counts and clinical parameters, indicating that monitoring animals with low body condition scores (BCS) and high Faffa Malan Chart (FAMACHA) scores could be an effective strategy for controlling strongyle infections. These findings highlight the importance of continued research and effective farm management practices to address strongyle infections in meat goats, improving their health and agricultural productivity in tropical regions. Moreover, the detection of four zoonotic parasites (Giardia spp., H. contortus, T. colubriformis, and Fasciola spp.) indicates the necessity for the routine surveillance and monitoring of zoonotic parasites in goats to mitigate potential human health risks. Full article

Livestock contributes to global warming through greenhouse gas (GHG) emissions. Reducing these emissions is an ongoing challenge for the small ruminant sector. Despite its significant role in national economies, limited studies on the carbon footprint (CF) of dairy small ruminants in Mediterranean countries exist. The study aimed to achieve the following: (a) estimate the GHG emissions of eleven semi-extensive sheep and goat farms in a mountainous region of southern Greece, using the Tier 1 and Tier 2 methodologies; (b) compare the outcomes of both methods; and (c) calculate farms’ CF, as a means of their environmental impact evaluation. All on-farm activities (except machinery or medicine use) related to sheep or goat production were considered to estimate GHG emissions. The results show differences between Tier 1 and Tier 2 estimates, reflecting the simplified computational approach of Tier 1. The average CF values estimated via Tier 1 for goat and sheep farms were 2.12 and 2.87 kg CO₂-eq./kg FPCM, respectively. Using Tier 2, these values increased to 2.73 and 3.99 kg CO₂-eq./kg FPCM. To mitigate environmental impact, farms could enhance productivity by improving herd management and feeding strategies. Full article

Gastrointestinal parasitic diseases pose significant health risks to equine populations. This study investigated the epidemiological patterns of equine gastrointestinal parasites in Xinjiang by analyzing 83 fecal samples collected from Ili (n = 62) and Urumqi (n = 21) between August and November 2024. The modified McMaster technique was employed to quantify fecal egg counts (EPG) and was complemented by morphological identification to assess infection dynamics related to geography, breed specificity, and management practices. The results demonstrated an overall infection prevalence of 66.3% (55/83), with strongyles, Parascaris equorum, and Eimeria oocysts being present. Significant geographical variation was observed, with Ili exhibiting a higher prevalence (74.2%) compared to Urumqi (42.9%). Breed susceptibility analysis revealed that there was a 94.1% prevalence in Yili horses versus 42.9% in Kazakh horses. Pasture-managed herds showed markedly higher infection rates (94.1%) than stable-based systems (50.0%). Parasite community composition was dominated by strongyles (82.1%), followed by Triodontophorus spp. (27.7%) and P. equorum (2.4%). These findings highlight severe parasitic infection risks in Xinjiang’s grazing equids, underscoring the urgency of implementing targeted anthelmintic protocols to mitigate disease transmission. Full article

The automatic recognition of animal vocalizations is a valuable tool for monitoring pigs’ behavior, health, and welfare. This study investigates the feasibility of implementing a convolutional neural network (CNN) model for classifying pig vocalizations using tiny machine learning (TinyML) on a low-cost, resource-constrained embedded system. The dataset was collected in 2011 at the University of Illinois at Urbana-Champaign on an experimental pig farm. In this experiment, 24 piglets were housed in environmentally controlled rooms and exposed to gradual thermal variations. Vocalizations were recorded using directional microphones, processed to reduce background noise, and categorized into “agonistic” and “social” behaviors using a CNN model developed on the Edge Impulse platform. Despite hardware limitations, the proposed approach achieved an accuracy of over 90%, demonstrating the potential of TinyML for real-time behavioral monitoring. These findings underscore the practical benefits of integrating TinyML into swine production systems, enabling early detection of issues that may impact animal welfare, reducing reliance on manual observations, and enhancing overall herd management. Full article

Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions. Full article

The stochastic correlation for Brownian motions is the integrand in the formula of their quadratic covariation. The estimation of this stochastic process becomes available from the temporally localized correlation of latent price driving Brownian motions in stochastic volatility models for asset prices. By analyzing this process for Apple and Microsoft stock prices traded minute-wise, we give statistical evidence for the roughness of its paths. Moment scaling indicates fractal behavior, and both fractal dimensions (approx. 1.95) and Hurst exponent estimates (around 0.05) point to rough paths. We model this rough stochastic correlation by a suitably transformed fractional Ornstein–Uhlenbeck process and simulate artificial stock prices, which allows computing tail dependence and the Herding Behavior Index (HIX) as functions in time. The computed HIX is hardly variable in time (e.g., standard deviation of 0.003–0.006); on the contrary, tail dependence fluctuates more heavily (e.g., standard deviation approx. 0.04). This results in a higher correlation risk, i.e., more frequent sudden coincident appearance of extreme prices than a steady HIX value indicates. 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