Hasil untuk "Cattle"

J.B Russell, J.B Russell, J. D. O'Connor et al.

Cunsheng Zhang, Gang Xiao, Liyu Peng et al.

M. Reichel, M. Alejandra Ayanegui-Alcérreca, L. Gondim et al.

R. Z. Abbas, M. Zaman, D. Colwell et al.

Ubaidullah, Muhammad Abid Hussain, Mohsin Raza Jafri et al.

Lumpy Skin Disease (LSD) is a contagious viral infection that significantly deteriorates livestock health, thereby posing a serious threat to the global economy and food security. Owing to its rapid spread characteristics, early and precise identification is crucial to prevent outbreaks and ensure timely intervention. In this paper, we propose a hybrid deep learning-based approach called LUMPNet for the early detection of LSD. LUMPNet utilizes image data to detect and classify skin nodules -- the primary indicator of LSD. To this end, LUMPNet uses YOLOv11, EfficientNet-based CNN classifier with compound scaling, and a novel adaptive hybrid optimizer. More precisely, LUMPNet detects and localizes LSD skin nodules and lesions on cattle images. It exploits EfficientNet to classify the localized cattle images into LSD-affected or healthy categories. To stabilize and accelerate the training of YOLOv11 and EfficientNet hybrid model, a novel adaptive hybrid optimizer is proposed and utilized. We evaluate LUMPNet at various stages of LSD using a publicly available dataset. Results indicate that the proposed scheme achieves 99% LSD detection training accuracy, and outperforms existing schemes. The model also achieves validation accuracy of 98%. Moreover, for further evaluation, we conduct a case study using an optimized EfficientNet-B0 model trained with the AdamW optimizer, and compare its performance with LUMPNet. The results show that LUMPNet achieves superior performance.

Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby et al.

Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual-stream architecture with weight-shared encoders, modality-specific self-attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual-gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel-level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs--outperforming state-of-the-art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual-task learning is essential for optimal performance. Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at https://github.com/taminulislam/fume.

Rahul Jana, Shubham Dixit, Mrityunjay Sharma et al.

Monitoring cattle health and optimizing yield are key challenges faced by dairy farmers due to difficulties in tracking all animals on the farm. This work aims to showcase modern data-driven farming practices based on explainable machine learning(ML) methods that explain the activity and behaviour of dairy cattle (cows). Continuous data collection of 3-axis accelerometer sensors and usage of robust ML methodologies and algorithms, provide farmers and researchers with actionable information on cattle activity, allowing farmers to make informed decisions and incorporate sustainable practices. This study utilizes Bluetooth-based Internet of Things (IoT) devices and 4G networks for seamless data transmission, immediate analysis, inference generation, and explains the models performance with explainability frameworks. Special emphasis is put on the pre-processing of the accelerometers time series data, including the extraction of statistical characteristics, signal processing techniques, and lag-based features using the sliding window technique. Various hyperparameter-optimized ML models are evaluated across varying window lengths for activity classification. The k-nearest neighbour Classifier achieved the best performance, with AUC of mean 0.98 and standard deviation of 0.0026 on the training set and 0.99 on testing set). In order to ensure transparency, Explainable AI based frameworks such as SHAP is used to interpret feature importance that can be understood and used by practitioners. A detailed comparison of the important features, along with the stability analysis of selected features, supports development of explainable and practical ML models for sustainable livestock management.

Neha Bansal, Katerina Kaouri, Thomas E. Woolley

Epidemiological models help policymakers mitigate disease spread by predicting transmission metrics based on disease dynamics and contact networks. Calibrating these models requires representative network sampling. We investigate the Random Walk (RW) and Metropolis-Hastings Random Walk (MHRW) algorithms for three network types: Erdős-Rényi (ER), Small-world (SW), and Scale-free (SF). Disease transmission is simulated using a stochastic susceptible-infected-recovered (SIR) framework. For ER and SW networks, RW overestimates infected individuals and secondary infections by $25\%$ due to size bias, favouring highly connected nodes. MHRW, though more computationally intensive, reduces size bias and provides more representative samples. For time-to-infection, both algorithms provide representative estimates. However, neither algorithm samples SF networks representatively, exhibiting significant variability. Furthermore, removing duplicate sample nodes reduces MHRW's accuracy across three network types. We apply both algorithms to a cattle movement network of 46,512 farms combining ER, SW, and SF features. RW overestimates infected farms by about $100\%$ and secondary infections by over $900\%$, reflecting significant size bias, while MHRW estimates align within $1\%$ of the cattle network values. RW underestimates time-to-infection by about $40\%$, while MHRW overestimates it by $10\%$. Accuracy, again, deteriorates when duplicates nodes are removed. Our findings guide algorithm selection and intervention strategies based on network structure and disease severity; RW's conservative estimates suit high-mortality, fast-spreading epidemics, while MHRW enables more precise interventions for slower epidemics.

Alexander Weinberg, Franziska Witte, Dana Carina Schubert et al.

Anthropogenic climate change is primarily caused by CO2 and CH4 emissions, with a significant portion originating from agriculture and livestock. Reducing methane emissions in ruminant husbandry has been a longstanding goal. Therefore, in this study, we aimed to influence the fermentation processes in the artificial rumen model (rumen simulation technique, RUSITEC) using five different carbons—one activated carbon (AC) and four biochars (BCs)—and one control without supplement. The carbons were included at 2% of dry matter (DM) of the basal diet, which corresponded to 0.3 g DM of the assigned additive. The treatments were conducted on 12 fermenters with two replications (n = 4/treatment) in a randomized block design. The experimental period consisted of a 7-day adaptation phase and an 8-day data and sample collection phase. Parameters included gas volume, gas composition, disappearance rates, volatile fatty acid (VFA) production, and nutrient digestion. Except for biochar (BC) 3, carbons showed no impact on gas parameters, while BC 3 decreased CO2 production (p = 0.0453), gas volume (p = 0.0255), and the ratio of CO2 (p = 0.0304), CH4 (p = 0.0304), and gas volume (p = 0.0304) to disappeared organic matter (dOM). BC 3 also showed a tendency to decrease in methane production (p = 0.0878). The effects on produced VFA were only found for BC 3, which reduced the daily production of total VFA (p = 0.0226), acetic acid (p = 0.0248), propionic acid (p = 0.0166), i-butyric acid (p = 0.0366), and the ratio of VFA to dry matter loss (p = 0.0172) and to dOM (p = 0.0304), while pH (p = 0.0309) was higher compared to the control. Only BC 3 had decreasing effects on disappearance rates (p = 0.0304). Although BC 3 reduces greenhouse gas emissions, it does so at the expense of fermentation, as indicated by its decreasing impact on digestion rate, VFA production, and the resulting increase in pH. In conclusion, biochar has the potential to affect rumen fermentation in vitro. However, general statements regarding the effects of biochars on fermentation cannot be derived from this experiment; each biochar source needs to be evaluated individually.

Catherine Krus, Ian Zander, Tyler J. Sherman et al.

Bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV) are two viruses belonging to the genus Orbivirus that are transmitted via insect vector, the Culicoides biting midge, causing disease in domestic and wild ruminants. These infections can lead to significant morbidity, mortality, and production losses in livestock, with economic consequences for cattle and sheep industries. Despite their growing impact due to environmental and anthropogenic changes, little is known of the prevalence of these viruses in North American bison (Bison bison). We present the first cross-sectional survey of BTV and EHDV in North American bison, with samples collected from 287 animals across 9 herds in 7 U.S. states from September to November 2023. Using competitive enzyme-linked immunosorbent assays (cELISA), we detected seroprevalence rates of 56.5% for BTV and 57.5% for EHDV. We found higher seroprevalence in North American bison compared to reports in European bison populations, suggesting that bison could potentially serve as incidental hosts of orbiviruses during key transmission periods; however, their role in virus transmission remains uncertain and warrants further investigation, particularly regarding the duration of viremia, potential amplification capacity, and year-to-year variability in PCR positivity. Logistic regression analysis revealed age as a significant predictor for both BTV (OR: 1.15, CI: 1.05–1.26, p: 0.006) and EHDV (OR: 1.16, CI: 1.06–1.28, p: 0.0014) seropositivity. PCR amplification identified circulating BTV serotypes 6, 11, 13, 17. Additionally, age was negatively associated with PCR positivity for both BTV (OR: 0.70, CI: 0.53–0.93, p: 0.014) and EHDV (OR: 0.56, CI: 0.33–0.93, p: 0.024), suggesting a decline in detectable viremia with increasing age. Although complex environmental and epidemiological factors likely play a role, this trend may be due to older animals having experienced more vector seasons, thereby increasing their cumulative exposure and subsequent immunity to these viruses over time. The significant age-associated dynamics reveal the importance of considering life stage in disease surveillance and management. Our study also highlights the importance of integrating bison into future vector-borne disease research and control strategies to mitigate risks to livestock, wildlife, and ecosystem health.

Donagh P. Berry, E. Wall, J. Pryce

Excellent reproductive performance in both males and females is fundamental to profitable dairy and beef production systems. In this review we undertook a meta-analysis of genetic parameters for female reproductive performance across 55 dairy studies or populations and 12 beef studies or populations as well as across 28 different studies or populations for male reproductive performance. A plethora of reproductive phenotypes exist in dairy and beef cattle and a meta-analysis of the literature suggests that most of the female reproductive traits in dairy and beef cattle tend to be lowly heritable (0.02 to 0.04). Reproductive-related phenotypes in male animals (e.g. semen quality) tend to be more heritable than female reproductive phenotypes with mean heritability estimates of between 0.05 and 0.22 for semen-related traits with the exception of scrotal circumference (0.42) and field non-return rate (0.001). The low heritability of reproductive traits, in females in particular, does not however imply that genetic selection cannot alter phenotypic performance as evidenced by the decline until recently in dairy cow reproductive performance attributable in part to aggressive selection for increased milk production. Moreover, the antagonistic genetic correlations among reproductive traits and both milk (dairy cattle) and meat (beef cattle) yield is not unity thereby implying that simultaneous genetic selection for both increased (milk and meat) yield and reproductive performance is indeed possible. The required emphasis on reproductive traits within a breeding goal to halt deterioration will vary based on the underlying assumptions and is discussed using examples for Ireland, the United Kingdom and Australia as well as quantifying the impact on genetic gain for milk production. Advancements in genomic technologies can aid in increasing the accuracy of selection for especially reproductive traits and thus genetic gain. Elucidation of the underlying genomic mechanisms for reproduction could also aid in resolving genetic antagonisms. Past breeding programmes have contributed to the deterioration in reproductive performance of dairy and beef cattle. The tools now exist, however, to reverse the genetic trends in reproductive performance underlying the observed phenotypic trends.

Asheesh Sharma, Lucy Randewich, William Andrew et al.

This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Therefore, real-time cattle identification is essential for the farms and a crucial step towards precision livestock farming. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data from an off-the-shelf 3D camera. The method relies on CNN and MLP backbones that learn well-generalised embedding spaces from the body shape to differentiate individuals -- requiring neither species-specific coat patterns nor close-up muzzle prints for operation. The network embeddings are clustered using a simple algorithm such as $k$-NN for highly accurate identification, thus eliminating the need to retrain the network for enrolling new individuals. We evaluate two backbone architectures, ResNet, as previously used to identify Holstein Friesians using RGB images, and PointNet, which is specialised to operate on 3D point clouds. We also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet architectures, which consume depth maps and point clouds, respectively, led to high accuracy that is on par with the coat pattern-based backbone.

Gavin Buxton

A model of lightning that captures the propagation of lightning channels on an irregular lattice is developed. The irregular lattice allows us to capture large two-dimensional systems (2 km x 2 km), while allowing grid refinement (on the order of cm) near areas of interest. Furthermore, the direction of lightning propagation is not biased in the orthogonal or diagonal directions of regular lattices. The probability of lightning strikes on people in standing positions, people in crouched positions, people near a tree and cattle are estimated.

Abhijin Adiga, Ayush Chopra, Mandy L. Wilson et al.

One Health issues, such as the spread of highly pathogenic avian influenza~(HPAI), present significant challenges at the human-animal-environmental interface. Recent H5N1 outbreaks underscore the need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a synthetic spatiotemporal gridded dataset of livestock production and processing, human population, and wild birds for the contiguous United States, called a \emph{digital similar}. This representation is a result of fusing diverse datasets using statistical and optimization techniques, followed by extensive verification and validation. The livestock component includes farm-level representations of four major livestock types -- cattle, poultry, swine, and sheep -- including further categorization into subtypes such as dairy cows, beef cows, chickens, turkeys, ducks, etc. Weekly abundance data for wild bird species identified in the transmission of avian influenza are included. Gridded distributions of the human population, along with demographic and occupational features, capture the placement of agricultural workers and the general population. We demonstrate how the digital similar can be applied to evaluate spillover risk to dairy cows and poultry from wild bird population, then validate these results using historical H5N1 incidences. The resulting subtype-specific spatiotemporal risk maps identify hotspots of high risk from H5N1 infected wild bird population to dairy cattle and poultry operations, thus guiding surveillance efforts.

Axiu Mao, Meilu Zhu, Zhaojin Guo et al.

Deep learning techniques are dominating automated animal activity recognition (AAR) tasks with wearable sensors due to their high performance on large-scale labelled data. However, current deep learning-based AAR models are trained solely on datasets of individual animal species, constraining their applicability in practice and performing poorly when training data are limited. In this study, we propose a one-for-many framework, dubbed Cross-species Knowledge Sharing and Preserving (CKSP), based on sensor data of diverse animal species. Given the coexistence of generic and species-specific behavioural patterns among different species, we design a Shared-Preserved Convolution (SPConv) module. This module assigns an individual low-rank convolutional layer to each species for extracting species-specific features and employs a shared full-rank convolutional layer to learn generic features, enabling the CKSP framework to learn inter-species complementarity and alleviating data limitations via increasing data diversity. Considering the training conflict arising from discrepancies in data distributions among species, we devise a Species-specific Batch Normalization (SBN) module, that involves multiple BN layers to separately fit the distributions of different species. To validate CKSP's effectiveness, experiments are performed on three public datasets from horses, sheep, and cattle, respectively. The results show that our approach remarkably boosts the classification performance compared to the baseline method (one-for-one framework) solely trained on individual-species data, with increments of 6.04%, 2.06%, and 3.66% in accuracy, and 10.33%, 3.67%, and 7.90% in F1-score for the horse, sheep, and cattle datasets, respectively. This proves the promising capabilities of our method in leveraging multi-species data to augment classification performance.

U. Arshad, J.E.P. Santos

Hepatic lipidosis is a prevalent metabolic disorder, and in vivo models to study intermediary lipid metabolism are needed in dairy cows. Objectives were to apply a method to induce hyperlipidemia and characterize the responses and safety of the intervention in feed-restricted dry Holstein cows at 8 mo of gestation. It was hypothesized that infusion of tyloxapol would induce hyperlipidemia without deleterious effects on health of dairy cows. Pregnant, nonlactating parous Holstein cows (n = 33) at a mean (± standard deviation) of 234 ± 2.2 d of gestation were fed for ad libitum intake on d 1 to 5 and restricted to 41% of the required NEL from d 6 to 13. On d 14, when cows were 247 ± 2.2 d of gestation, cows were kept off feed, and received i.v. a 10% solution of tyloxapol at 120 mg/kg body weight to block hydrolysis of triacylglycerols in very-low-density lipoprotein (VLDL) particles. Blood was sampled for 720 min and analyzed for concentrations of triacylglycerol, VLDL cholesterol, and total cholesterol in serum to reflect hepatic secretion or reduced clearance of such metabolites from blood. Rectal temperature, respiration and heart rates, and clinical signs related to potential anaphylaxis were monitored for the first 30 min relative to tyloxapol infusion, and for any abnormal behavior in the subsequent 24 h. Infusion of tyloxapol progressively increased the concentrations of triacylglycerol, VLDL cholesterol, and total cholesterol in serum. Tyloxapol increased rectal temperature by 0.19°C at 30 min after infusion and increased respiration and heart rates in the first 10 min after infusion by 29% and 40%, respectively. Tyloxapol induced tachycardia (heart rate >80 beats/min) in 66.7% (n = 22), frothy salivation in 39.4% (n = 13), muzzle twitching in 15.2% (n = 5), eyes twitching in 12.1% (n = 4), muscle twitching in 48.5% (n = 16), nystagmus in 6.1% (n = 2), signs of hyperexcitement in 18.2% (n = 6), staggering gait in 18.2% (n = 6), and anaphylaxis in 12.1% (n = 4) of the cows; however, all these signs were transient, and cows returned to normal after 20 min of infusion. No other abnormal behavior was observed past 20 min of tyloxapol infusion. None of the cows aborted and gestation length, calf birth weight, and risk of diseases in the first 21 d postpartum did not differ between cows receiving tyloxapol and a companion group that did not receive tyloxapol. Infusion of tyloxapol induced hyperlipidemia in cows with some animals showing transient reactions to the treatment, but without complications to the cow and the offspring. Application of this model can be useful to study intermediary lipid metabolism in dairy cows.

Christopher J. Banks, Katherine Simpson, Nicholas Hanley et al.

Financial incentives encourage the plantation of new woodland to increase habitat, biodiversity, carbon sequestration, as a contribution to meeting climate change and biodiversity conservation targets. Whilst these are largely positive effects, it is worth considering that this expansion of woodland can lead to increased presence of wildlife species in proximity to agricultural holdings that may pose an enhanced risk of disease transmission between wildlife and livestock. Wildlife and the provision of a reservoir for infectious disease is particularly important in the transmission dynamics of bovine tuberculosis, the case studied here. In this paper we develop an economic model for predicting changes in land use resulting from subsidies for woodland planting. We use this to assess the consequent impact on wild deer populations in the newly created woodland areas, and thus the emergent infectious disease risk arising from the proximity of new and existing wild deer populations and existing cattle holdings. We consider an area in the South-West of Scotland, having existing woodland, deer populations, and extensive and diverse cattle farm holdings. In this area we find that, with a varying level of subsidy and plausible new woodland creation scenarios, the contact risk between areas of wild deer and cattle increases between 26% and 35% over the risk present with a zero subsidy. This provides a foundation for extending to larger regions and for examining potential risk mitigation strategies, for example the targeting of subsidy in low disease risk areas, or provisioning for buffer zones between woodland and agricultural holdings.

Simon Lafontaine, Rémi Labrecque, Patrick Blondin et al.

ABSTRACT: The use of assisted-reproduction technologies such as in vitro fertilization (IVF) is increasing, particularly in dairy cattle. The question of consequences in later life has not yet been directly addressed by studies on large animal populations. Studies on rodents and early data from humans and cattle suggest that in vitro manipulation of gametes and embryos could result in long-term alteration of metabolism, growth, and fertility. Our goal was to better describe these presumed consequences in the population of dairy cows produced by IVF in Québec (Canada) and to compare them to animals conceived by artificial insemination (AI) or multiple ovulation embryo transfer (MOET). To do so, we leveraged a large phenotypic database (2.5 million animals and 4.5 million lactations) from milk records in Québec aggregated by Lactanet (Sainte-Anne-de-Bellevue, QC, Canada) and spanning 2012 to 2019. We identified 304,163, 12,993, and 732 cows conceived by AI, MOET, and IVF, respectively, for a total of 317,888 Holstein animals from which we retrieved information for 576,448, 24,192, and 1,299 lactations (total = 601,939), respectively. Genetic energy-corrected milk yield (GECM) and Lifetime Performance Index (LPI) of the parents of cows were used to normalize for genetic potential across animals. When compared with the general Holstein population, MOET and IVF cows outperformed AI cows. However, when comparing those same MOET and IVF cows with only herdmates and accounting for their higher GECM in the models, we found no statistical difference between the conception methods for milk production across the first 3 lactations. We also found that the rate of Lifetime Performance Index improvement of the IVF population during the 2012 to 2019 period was less than the rate observed in the AI population. Fertility analysis revealed that MOET and IVF cows also scored 1 point lower than their parents on the daughter fertility index and had a longer interval from first service to conception, with an average of 35.52 d compared with 32.45 for MOET and 31.87 for AI animals. These results highlight the challenges of elite genetic improvement while attesting to the progress the industry has made in minimizing epigenetic disturbance during embryo production. Nonetheless, additional work is required to ensure that IVF animals can maintain their performance and fertility potential.

Esma Yuca, Sinan Kopuzlu

This study was carried out on 114 Brown Swiss cattle reared in intensive conditions at Ataturk University Food and Livestock Application and Research Center and at the private cattle farm found in Erzurum province. Genotypic structures were examined in terms of Insulin-like growth factor (IGF)-II gene locus and the distribution of genotypes and allele frequencies of the cattle concerning the genes were determined. The identified Insulin-like growth factor (IGF)-II genotypes were associated with milk yield traits such as actual milk yield, 305-day, and daily milk yield. Insulin-like growth factor (IGF)-II genotypes were determined by using the Polymerase Chain Reaction (PCR)- Restriction fragment length polymorphism (RFLP) method from blood samples taken from the cattle. The CC, CT, and TT genotype frequencies of the Insulin-like growth factor (IGF)-II gene found in the population were 41 (34%), 65 (54%), and 14 (12%), and the frequency of the C allele and the T allele was found to be 0.61 and to be 0.39. The general averages of actual true milk yield, 305-day and daily milk yield were 4317±272.9 kg, 5277±240.7 kg and 18±0.9 kg, respectively, while CC, CT and TT genotypes 4168±515.8, 3756±321.7 and 5382±600.3 kg, respectively. As a result, correctly identified IGF-II genotypes were detected by using the PCR-RFLP method in the blood samples obtained from Brown Swiss cattle. Genotype and allele frequencies determined for IGF-II gene polymorphism can be considered sufficient to demonstrate the genotype diversity of the race. According to the Hardy-Weinberg genetic equilibrium test, the distribution of genotype frequencies of the cattle was observed in equilibrium.

O. Mwai, O. Hanotte, Young-jun Kwon et al.

At least 150 indigenous African cattle breeds have been named, but the majority of African cattle populations remain largely uncharacterized. As cattle breeds and populations in Africa adapted to various local environmental conditions, they acquired unique features. We know now that the history of African cattle was particularly complex and while several of its episodes remain debated, there is no doubt that African cattle population evolved dramatically over time. Today, we find a mosaic of genetically diverse population from the purest Bos taurus to the nearly pure Bos indicus. African cattle are now found all across the continent, with the exception of the Sahara and the river Congo basin. They are found on the rift valley highlands as well as below sea level in the Afar depression. These unique livestock genetic resources are in danger to disappear rapidly following uncontrolled crossbreeding and breed replacements with exotic breeds. Breeding improvement programs of African indigenous livestock remain too few while paradoxically the demand of livestock products is continually increasing. Many African indigenous breeds are endangered now, and their unique adaptive traits may be lost forever. This paper reviews the unique known characteristics of indigenous African cattle populations while describing the opportunities, the necessity and urgency to understand and utilize these resources to respond to the needs of the people of the continent and to the benefit of African farmers.