Hasil untuk "Cattle"

H. Daetwyler, A. Capitan, H. Pausch et al.

L. Grobet, L. Martin, D. Poncelet et al.

F. Owens, D. S. Secrist, W. Hill et al.

William Grolleau, Achraf Chaouch, Astrid Sabourin et al.

Animal re-identification (ReID) faces critical challenges due to viewpoint variations, particularly in Aerial-Ground (AG-ReID) settings where models must match individuals across drastic elevation changes. However, existing datasets lack the precise angular annotations required to systematically analyze these geometric variations. To address this, we introduce the Multi-view Oriented Observation (MOO) dataset, a large-scale synthetic AG-ReID dataset of $1,000$ cattle individuals captured from $128$ uniformly sampled viewpoints ($128,000$ annotated images). Using this controlled dataset, we quantify the influence of elevation and identify a critical elevation threshold, above which models generalize significantly better to unseen views. Finally, we validate the transferability to real-world applications in both zero-shot and supervised settings, demonstrating performance gains across four real-world cattle datasets and confirming that synthetic geometric priors effectively bridge the domain gap. Collectively, this dataset and analysis lay the foundation for future model development in cross-view animal ReID. MOO is publicly available at https://github.com/TurtleSmoke/MOO.

Rabin Dulal, Wenfeng Jia, Lihong Zheng et al.

Accurate cattle live weight estimation is vital for livestock management, welfare, and productivity. Traditional methods, such as manual weighing using a walk-over weighing system or proximate measurements using body condition scoring, involve manual handling of stock and can impact productivity from both a stock and economic perspective. To address these issues, this study investigated a cost-effective, non-contact method for live weight calculation in cattle using 3D reconstruction. The proposed pipeline utilized multi-view RGB images with SAM 3D-based agreement-guided fusion, followed by ensemble regression. Our approach generates a single 3D point cloud per animal and compares classical ensemble models with deep learning models under low-data conditions. Results show that SAM 3D with multi-view agreement fusion outperforms other 3D generation methods, while classical ensemble models provide the most consistent performance for practical farm scenarios (R$^2$ = 0.69 $\pm$ 0.10, MAPE = 2.22 $\pm$ 0.56 \%), making this practical for on-farm implementation. These findings demonstrate that improving reconstruction quality is more critical than increasing model complexity for scalable deployment on farms where producing a large volume of 3D data is challenging.

Jin Wang, Angelo De Castro, Yuxi Zhang et al.

Computer vision provides automated, non-invasive, and scalable tools for monitoring dairy cattle, thereby supporting management, health assessment, and phenotypic data collection. Although transfer learning is commonly used for predicting body weight from images, its effectiveness and optimal fine-tuning strategies remain poorly understood in livestock applications, particularly beyond the use of pretrained ImageNet or COCO weights. In addition, while both depth images and three-dimensional point-cloud data have been explored for body weight prediction, direct comparisons of these two modalities in dairy cattle are limited. Therefore, the objectives of this study were to 1) evaluate whether transfer learning from a large farm enhances body weight prediction on a small farm with limited data, and 2) compare the predictive performance of depth-image- and point-cloud-based approaches under three experimental designs. Top-view depth images and point-cloud data were collected from 1,201, 215, and 58 cows at large, medium, and small dairy farms, respectively. Four deep learning models were evaluated: ConvNeXt and MobileViT for depth images, and PointNet and DGCNN for point clouds. Transfer learning markedly improved body weight prediction on the small farm across all four models, outperforming single-source learning and achieving gains comparable to or greater than joint learning. These results indicate that pretrained representations generalize well across farms with differing imaging conditions and dairy cattle populations. No consistent performance difference was observed between depth-image- and point-cloud-based models. Overall, these findings suggest that transfer learning is well suited for small farm prediction scenarios where cross-farm data sharing is limited by privacy, logistical, or policy constraints, as it requires access only to pretrained model weights rather than raw data.

Parul Tiwari, Malavika Smitha, Hammed Olawale Fatoyinbo

Highly pathogenic avian influenza (HPAI) has expanded its host range with recent detections in dairy cattle, raising critical concerns regarding within-herd persistence and cross-species spillover. This study develops a stochastic $SEI_sI_aR-B$ compartmental model to analyse HPAI transmission, explicitly accounting for environmental pathogen reservoirs and noise intensities through Wiener processes. The positivity and boundedness of solutions are established, and the disease-free and endemic equilibria are analytically derived. The basic reproduction number is determined using the next-generation matrix method. Numerical simulations confirm that the model dynamics are consistent with theoretical analysis and illustrate how stochastic fluctuations significantly influence disease persistence. Furthermore, sensitivity analysis using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC) identifies the transmission rate from asymptomatic infectious cattle ($β_a$) as the primary driver of transmission. The model effectively captures the dynamics of environmental variability affecting HPAI spread, suggesting that effective control strategies must prioritise the early detection and isolation of asymptomatic carriers alongside environmental management.

Alex S. C. Maia, John B. Hall, Hugo F. M. Milan et al.

Advances in technology are transforming sustainable cattle farming practices, with electronic feeding systems generating big longitudinal datasets on individual animal feed intake, offering the possibility for autonomous precision livestock systems. However, the literature still lacks a methodology that fully leverages these longitudinal big data to accurately predict feed intake accounting for environmental conditions. To fill this gap, we developed an AI-based framework to accurately predict feed intake of individual animals and pen-level aggregation. Data from 19 experiments (>16.5M samples; 2013-2024) conducted at Nancy M. Cummings Research Extension & Education Center (Carmen, ID) feedlot facility and environmental data from AgriMet Network weather stations were used to develop two novel environmental indices: InComfort-Index, based solely on meteorological variables, showed good predictive capability for thermal comfort but had limited ability to predict feed intake; EASI-Index, a hybrid index integrating environmental variables with feed intake behavior, performed well in predicting feed intake but was less effective for thermal comfort. Together with the environmental indices, machine learning models were trained and the best-performing machine learning model (XGBoost) accuracy was RMSE of 1.38 kg/day for animal-level and only 0.14 kg/(day-animal) at pen-level. This approach provides a robust AI-based framework for predicting feed intake in individual animals and pens, with potential applications in precision management of feedlot cattle, through feed waste reduction, resource optimization, and climate-adaptive livestock management.

Mangesh Vaidya, V. R. Patodkar, Prajakta Kuralkar et al.

Livestock-generated methane, particularly from cattle, was a significant contributor to climate change. Methane emissions from ruminant animals, such as cows and sheep, are primarily caused by the microbial fermentation of food in their digestive systems, a process known as enteric fermentation by making this process a prime source of greenhouse gas emissions in animal production. Considerable knowledge gaps existed in animal agriculture regarding effective strategies for mitigating these emissions while maintaining productivity. A key factor was the uncertainty surrounding methods for estimating emission rates, each having inherent limitations. For example, the suitability of the GreenFeed system varied based on specific experiment objectives. Compared to respiration chambers and the sulfur hexafluoride tracer method, the The GreenFeed system often required more time and a larger number of animals for treatment comparisons due to higher within-day variances. It measured numerous short-term methane emissions from individual animals at various times throughout the day over several days. Recent advancements focused on improving accuracy, ease of use, and cost-effectiveness, essential for better monitoring of greenhouse gases. Traditional methods, such as respiration chambers, while accurate, were costly and impractical for field measurements. The GreenFeed system’s software facilitated control over feed availability timing and CH4 measurement allocation. Therefore, careful planning was necessary to ensure accurate estimates of methane production. This review emphasized the need for effective measurement techniques to mitigate methane emissions from livestock.

Marlyn H. Romero, Sergio A. Gallego-Polania, Jorge A. Sanchez

The Colombian Orinoquia is considered one of the eight most important ecosystems in the world. Savannah ecosystems play an essential role in providing ecosystem services. The objectives were (a) to explore and identify the perceptions of traditional farmers and institutional representatives regarding human well-being, animal welfare and the environment, and (b) to identify environmental challenges and opportunities for improvement based on the “One Welfare” concept. Three focal groups were developed: male and female farmers and institutional representatives <i>(n =</i> 24) from Vichada. A thematic analysis, coding with an inductive approach and the definition of themes and sub-themes were carried out. The concept of being “a good farmer” explains the behavior of the producers, where the perception of human well-being is associated with the value of the family and the sense of pride in their tradition as “llanero”. Animal welfare was considered a symbol of profitability and prestige for cattle farmers, a concept that, for them, mixed traditional management, water supply, shade and feed, good health and the natural behavior of cattle. Regarding the environment, this concept is based on their awareness of the importance of conserving the natural savannah. Among the environmental challenges described are climate change, solid waste management and the use of controlled burns. Regarding opportunities for improvement, they proposed strengthening institutional dynamics, partnerships, environmental governance and education. Our results can provide information on the “One Welfare” approach and the motivation of farmers to care for animals and the environment, a fundamental aspect for developing effective intervention strategies.

Lukas Schulthess, Fabrice Longchamp, Christian Vogt et al.

The advent of the Internet of Things (IoT) is boosting the proliferation of sensors and smart devices in industry and daily life. Continuous monitoring IoT systems are also finding applications in agriculture, particularly in the realm of smart farming. The adoption of wearable sensors to record the activity of livestock has garnered increasing interest. Such a device enables farmers to locate, monitor, and constantly assess the health status of their cattle more efficiently and effectively, even in challenging terrain and remote locations. This work presents a maintenance-free and robust smart sensing system that is capable of tracking cattle in remote locations and collecting activity parameters, such as the individual's grazing- and resting time. To support the paradigm of smart farming, the cattle tracker is capable of monitoring the cow's activity by analyzing data from an accelerometer, magnetometer, temperature sensor, and Global Navigation Satellite System (GNSS) module, providing them over Long Range Wide Area Network (LoRaWAN) to a backend server. By consuming 511.9 J per day with all subsystems enabled and a data transmission every 15 minutes, the custom-designed sensor node achieves a battery lifetime of 4 months. When exploiting the integrated solar energy harvesting subsystem, this can be even increased by 40% to up to 6 months. The final sensing system's robust operation is proven in a trial run with two cows on a pasture for over three days. Evaluations of the experimental results clearly show behavior patterns, which confirms the practicability of the proposed solution.

Baptiste Sorin-Dupont, Antoine Poyard, Sebastien Assie et al.

The overuse of antibiotics has become a major global concern due to its role in diminishing treatment effectiveness and positively selecting antibiotic-resistant bacterial strains. This issue is particularly important in the beef cattle sector, where Bovine Respiratory Diseases (BRD) impose significant economic and welfare burdens. BRD are complex, multifactorial conditions primarily affecting young calves and feedlot cattle, caused by a combination of viral and bacterial pathogens, environmental factors, and stressors. Despite efforts to reduce antimicrobial use (AMU), the cattle production system remains heavily reliant on antibiotics to control BRD, often through the implementation of collective treatments to prevent outbreaks. This study aimed at evaluating the impact of various treatment practices on the spread of BRD, specifically focusing on criteria for implementing collective treatments. Using a mechanistic stochastic model, we simulated the spread of \textit{Mannheimia haemolytica} in a multi-pen fattening operation under sixteen different scenarios, considering pen composition, individual risk levels, and treatment strategies. Our findings suggest that an alternative criterion for collective treatments based on the speed of the disease spread, could reduce BRD incidence and AMU more effectively than conventional methods. This research highlights the importance of responsible treatment practices and the potential benefits of novel criteria for collective treatment strategies in improving animal health. Moreover, it emphasizes the need for transparency on the exposure to risk factors along the production chain.

Damaris Leiva-Tafur, Jesús Rascón, Fernando Corroto de la Fuente et al.

Cattle ranching is a fundamental economic activity in northern Peru, where proper management of water resources is crucial. This study, a pioneer in the region, evaluated water quality and its suitability for human consumption, vegetable irrigation, and livestock production. It is also the first study to document the presence of metals and metalloids in vulnerable areas because they are located at the headwaters of river watersheds. The spatiotemporal evaluation of physicochemical parameters, metals, and metalloids was performed in five micro-watersheds (Cabildo, Timbambo, Pomacochas, Atuen, and Ventilla) from water samples collected in the dry season (October 2017) and wet season (March 2018). The parameters were analyzed using microwave plasma atomic emission spectrometry. The results were contrasted with international and Peruvian quality standards related to dairy cow production. The highest values of pH, total dissolved solids, and electrical conductivity were reported during the dry season, and the highest turbidity during the wet season. Of the metals evaluated, arsenic (As) was omnipresent in all the micro-watersheds, followed by lead (Pb).In contrast to World Health Organization regulations, concentrations of As, cadmium (Cd), Pb, and iron represent a risk; according to Peruvian regulations, As and Pb exceed the concentrations established for use in animal drinking water and vegetable irrigation, and according to water guidelines for dairy cattle, concentrations of As, Pb, Cd, and Al exceed the permitted limits. The high concentrations of these metals in the study area are attributable to a synergy between natural factors, such as Andean geology and livestock activity. The data reported will allow for proper water resource management, pollution prevention, and the design and adoption of mitigation measures.

B. Sharma, Deepika Koundal

Health monitoring of dairy cattle plays a vital role for increasing the dairy products supply worldwide. Nowadays farmers are showing less interest in dairy sector as animals are suffering from various ailing health issues, unpredictable killing diseases, and advanced breeding costs. Therefore, it is necessary for farmers to adopt efficient technical methods for cattle health monitoring to increase the milk production supply. This study documented various wireless sensor network (WSN)-based automatic health monitoring systems for monitoring various diseases of dairy cattle. The main objective of WSN-based intelligent monitoring systems in farm automation is to monitor the health of dairy cattle on regular basis. This monitoring system needs to be installed in local and remote locations of farms that will assist the concerned farmers in monitoring their cattle activities from diverse locations for the whole day. All collected factors from the automated system will be stored in a database. Subsequently, with the help of farm automation, farmers can retrieve information for the execution of correct farm control strategies. Moreover, WSN is low-cost technology which is specific for the identification of diseases in dairy animals. This revolution in advanced technological farm automation will aid in improving the productivity rate with the reduction of human intervention. This review study concludes all cattle monitoring systems along with various issues and challenges.

Santosh Kumar, A. Pandey, K. Satwik et al.

Jaemin Kim, O. Hanotte, O. Mwai et al.

BackgroundThe history of African indigenous cattle and their adaptation to environmental and human selection pressure is at the root of their remarkable diversity. Characterization of this diversity is an essential step towards understanding the genomic basis of productivity and adaptation to survival under African farming systems.ResultsWe analyze patterns of African cattle genetic variation by sequencing 48 genomes from five indigenous populations and comparing them to the genomes of 53 commercial taurine breeds. We find the highest genetic diversity among African zebu and sanga cattle. Our search for genomic regions under selection reveals signatures of selection for environmental adaptive traits. In particular, we identify signatures of selection including genes and/or pathways controlling anemia and feeding behavior in the trypanotolerant N’Dama, coat color and horn development in Ankole, and heat tolerance and tick resistance across African cattle especially in zebu breeds.ConclusionsOur findings unravel at the genome-wide level, the unique adaptive diversity of African cattle while emphasizing the opportunities for sustainable improvement of livestock productivity on the continent.

E. Tóth, B. Deák, O. Valkó et al.

S. Terry, J. Basarab, L. Guan et al.

Abstract: Globally, there are approximately one billion beef cattle, and compared with poultry and swine, beef cattle have the poorest conversion efficiency of feed to meat. However, these metrics fail to consider that beef cattle produce high-quality protein from feeds that are unsuitable for other livestock species. Strategies to improve the efficiency of beef cattle are focusing on operational and breeding management, host genetics, functional efficiency of rumen and respiratory microbiomes, and the structure and composition of feed. These strategies must also consider the health and immunity of the herd as well as the need for beef cattle to thrive in a changing environment. Genotyping can identify hybrid vigor with positive consequences for animal health, productivity, and environmental adaptability. The role of microbiome–host interactions is key in efficient nutrient digestion and host health. Microbial markers and gene expression patterns within the rumen microbiome are being used to identify hosts that are efficient at fibre digestion. Plant breeding and processing are optimizing the feed value of both forages and concentrates. Strategies to improve the efficiency of cattle production are a prerequisite for the sustainable intensification needed to satisfy the future demand for beef.

Kwondo Kim, Taehyung Kwon, T. Dessie et al.

G. N. Kimani, P. Oluwadara, P. Fashingabo et al.

Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require specialized equipment and are susceptible to attacks. Biometric identification using time-immutable dermatoglyphic features such as muzzle prints and iris patterns is a promising solution. This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. Two deep learning classification models are implemented - wide ResNet50 and VGG16\_BN and image compression is done to lower the image quality and adapt the models to work for the African context. From the experiments run, a maximum accuracy of 99.5\% is achieved while using the wide ResNet50 model with a compression retaining 25\% of the original image. From the study, it is noted that the time required by the models to train and converge as well as recognition time are dependent on the machine used to run the model.