Individual Cattle Research Articles

Quantitative TMT-based proteomics revealing host, dietary and microbial proteins in bovine faeces including barley serpin Z4, a prominent component in the head of beer

There has been little information about the proteome of bovine faeces or about the contribution to the faecal proteome of proteins from the host, the feed or the intestinal microbiome. Here, the bovine faecal proteome and the origin of its component proteins was assessed, while also determining the effect of treating barley, the major carbohydrate in the feed, with either ammonia (ATB) or sodium propionate (PTB) preservative. Healthy continental crossbreed steers were allocated to two groups and fed on either of the barley-based diets. Five faecal samples from each group were collected on Day 81 of the trial and analysed by quantitative proteomics using nLC-ESI-MS/MS after tandem mass tag labelling. In total, 281 bovine proteins, 199 barley proteins, 176 bacterial proteins and 190 archaeal proteins were identified in the faeces. Mucosal pentraxin, albumin and digestive enzymes were among bovine proteins identified. Serpin Z4 a protease inhibitor was the most abundant barley protein identified which is also found in barley-based beer, while numerous microbial proteins were identified, many originating bacteria from Clostridium, while Methanobrevibacter was the dominant archaeal genus. Thirty-nine proteins were differentially abundant between groups, the majority being more abundant in the PTB group compared to the ATB group. SignificanceProteomic examination of faeces is becoming a valuable means to assess the health of the gastro-intestinal tract in several species, but knowledge on the proteins present in bovine faeces is limited. This investigation aimed to characterise the proteome of bovine faecal extracts in order to evaluate the potential for investigations of the proteome as a means to assess the health, disease and welfare of cattle in the future. The investigation was able to identify proteins in bovine faeces that had been (i) produced by the individual cattle, (ii) present in the barley-based feed eaten by the cattle or (iii) produced by bacteria and other microbes in the rumen or intestines. Bovine proteins identified included mucosal pentraxin, serum albumin and a variety of digestive enzymes. Barley proteins found in the faeces included serpin Z4, a protease inhibitor that is also found in beer having survived the brewing process. Bacterial and archaeal proteins in the faecal extracts were related to several pathways related to the metabolism of carbohydrates. The recognition of the range of proteins that can be identified in bovine faeces raises the possibility that non-invasive sample collection of this material could provide a novel diagnostic approach to cattle health and welfare.

A novel Jinnan individual cattle recognition approach based on mutual attention learning scheme

The accurate recognition of individual Jinnan cattle is crucial for efficient breeding. However, existing ear tagging and machine learning approaches are inefficient, and deep learning models with a significant number of parameters can be computationally expensive. To address these challenges, this study proposes an innovative mutual-attention learning method for efficiently recognizing individual Jinnan cattle. This method employs two attention-enhanced student networks that collaborate and exchange knowledge on cross-dimensional attention during training, achieving improved recognition performance compared to counterparts without mutual learning. Additionally, smaller student networks can achieve improved recognition performance with much less computational cost, leveraging knowledge from larger networks. Furthermore, the proposed approach integrates attention modules such as block attention module (BAM), convolutional BAM (CBAM), and triplet attention units to capture spatial and channel attention for obtaining robust feature representations, enhancing performance. Our proposed model achieves state-of-the-art classification accuracy of 98.91%, surpassing other models in both accuracy and efficiency. Experiments also demonstrate its superiority by improving accuracy by over 2.97% and reducing loss value by over 50% compared to existing methods. Overall, the proposed approach has great potential to revolutionize the recognition of individual Jinnan cattle.

Research on 3D reconstruction method of cattle face based on image

As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features.

117 Effects of Cattle Removal Date on Sustainability of Cool-Season Annual Grazed Cover Crop Systems

Abstract Grazing of cool-season cover crops has been shown to be a viable tool in extending the grazing season while mitigating environmental risks associated with row crop farming systems. While there is a vast amount of research available on cover crops in relation to soil health, a more novel approach would be to focus on forage production and animal performance when grazing cover crops. Therefore, our objective was to evaluate the effects of cattle removal date on forage yield, forage quality, and steer performance when grazing cool-season cover crops. A cool-season forage mix consisting of ‘Cosaque’ oats (Avena strigose), ‘FL401’ cereal rye (Secale cereal), ‘AU Sunrise’ crimson clover (Trifolium incarnatum), and ‘T-raptor’ brassica (Brassica napus × B. rapa) was established in twelve, 0.62 ha paddocks in Headland, AL. Each paddock was then randomly allocated to one of four cattle removal treatments: 1) no grazing (CON); 2) February removal (FEB); 3) March removal (MAR); or 4) April removal (APR). Three tester steers were randomly allotted, based on body weight (BW), to each of the grazing treatments, excluding CON. The tester steers were weighed every 30 d for calculation of initial BW, final BW, and average daily gain (ADG). At inception and every subsequent two weeks, forage was harvested to analyze average forage yield, neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude protein (CP). All data were analyzed using GLIMMIX procedure of SAS version 9.4 (SAS Inst., Cary, NC). Significant differences in forage yield were detected between CON and FEB (3,913 vs. 2,261 kg/ha; P < 0.001), CON and MAR (3,913 vs. 1,790 kg/ha; P < 0.001), and CON and APR (3,913 vs. 1,810 kg/ha; P < 0.001). There were no differences found for ADG (1.2 kg/day; P = 0.56), NDF (50.94%; P = 0.46), or ADF (30.34%; P = 0.25) among treatments. Results indicate that grazing of cool-season annual cover crops will reduce forage yield, but removal date of cattle will not further affect forage yield, quality, or individual cattle performance. Thus, we can reduce winter supplementation needs for cattle by increasing grazing days; however, under continuous grazing for stocker cattle production, forage availability may inevitably be the limiting factor.

A review of Image Processing Technique for Monitoring The Growth and Health of Cows

In general, monitoring of animal growth and health is done directly by farmers (invasive measurement methods) which can cause cows to be injured or experience stress. To avoid this, several studies have been conducted on non-invasive methods using image processing technology. In this study, we systematically reviewed several works of literature to identify and synthesize published articles on image processing technology and image processing applications related to weight estimation and individual cattle identification. Analysis of image processing technologies used for weight estimation and individual cattle identification is the main objective of this article. Articles were searched through several databases and studies that met the inclusion criteria were analyzed and used in the review. The studies were divided into three main themes: image processing technologies, applications using image processing, and image processing research on cattle growth and health. It can be concluded that deep learning approaches are increasingly being studied, tested and considered as a viable and promising approach to monitor cattle weight and health in several aspects

AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry

In order to improve a livestock breeding environment that considers securing safe cattle resources and improving productivity for the intelligent farm, we propose an animal-friendly and worker-friendly intellectual monitoring system with Artificial Intelligent (AI) technology. In order to secure safe cattle resources and increase productivity for the livestock industry, it is necessary to secure the self-activities of the cattle and predict the estrous state of target cattle as quickly as possible. For the prediction of the estrous state, it is necessary to continuously observe the cattle behavior by workers and quantify the behavior of the target cattle, but that is not easy for workers and needs a long period of continuous observation. We developed the intelligent monitoring system (IMS) with the ARM (Augmented Recognition Model) for the intelligent farm that can predict the estrus of target cattle and get activity data for individual cattle, and then the system was applied to a typical cattle farm for activity monitoring of the Korean cattle (Hanwoo). Therefore, we confirmed the target Hanwoo group with more than 400 activities among the Hanwoo groups using the ARM threshold. Thus, we verified the potential of the proposed system for tracking multiple similar objects.

A Cascaded Individual Cow Identification Method Based on DeepOtsu and EfficientNet

Precision dairy farming technology is widely used to improve the management efficiency and reduce cost in large-scale dairy farms. Machine vision systems are non-contact technologies to obtain individual and behavioral information from animals. However, the accuracy of image-based individual identification of dairy cows is still inadequate, which limits the application of machine vision technologies in large-scale dairy farms. There are three key problems in dairy cattle identification based on images and biometrics: (1) the biometrics of different dairy cattle may be similar; (2) the complex shooting environment leads to the instability of image quality; and (3) for the end-to-end identification method, the identity of each cow corresponds to a pattern, and the increase in the number of cows will lead to a rapid increase in the number of outputs and parameters of the identification model. To solve the above problems, this paper proposes a cascaded dairy individual cow identification method based on DeepOtsu and EfficientNet, which can realize a breakthrough in dairy cow group identification accuracy and speed by binarization and cascaded classification of dairy cow body pattern images. The specific implementation steps of the proposed method are as follows. First, the YOLOX model was used to locate the trunk of the cow in the side-looking walking image to obtain the body pattern image, and then, the DeepOtsu model was used to binarize the body pattern image. After that, primary classification was carried out according to the proportion of black pixels in the binary image; then, for each subcategory obtained by the primary classification, the EfficientNet-B1 model was used for secondary classification to achieve accurate and rapid identification of dairy cows. A total of 11,800 side-looking walking images of 118 cows were used to construct the dataset; and the training set, validation set, and test set were constructed at a ratio of 5:3:2. The test results showed that the binarization segmentation accuracy of the body pattern image is 0.932, and the overall identification accuracy of the individual cow identification method is 0.985. The total processing time of a single image is 0.433 s. The proposed method outperforms the end-to-end dairy individual cow identification method in terms of efficiency and training speed. This study provides a new method for the identification of individual dairy cattle in large-scale dairy farms.

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Mask the Target: Mask R-CNN-Based Approach for Open-Range Individual Cattle Segmentation

Mask the Target: Mask R-CNN-Based Approach for Open-Range Individual Cattle Segmentation

Cattle Facial Matching Recognition Algorithm Based on Multi-View Feature Fusion

In the process of collecting facial images of cattle in the field, some features of the collected images end up going missing due to the changeable posture of the cattle, which makes the recognition accuracy decrease or impossible to recognize. This paper verifies the practical effects of the classical matching algorithms ORB, SURF, and SIFT in bull face matching recognition. The experimental results show that the traditional matching algorithms perform poorly in terms of matching accuracy and matching time. In this paper, a new matching recognition model is constructed. The model inputs the target cattle facial data from different angles into the feature extraction channel, combined with GMS (grid-based motion statistics) algorithm and random sampling consistent algorithm, to achieve accurate recognition of individual cattle, and the recognition process is simple and fast. The recognition accuracy of the model was 85.56% for the Holstein cow face dataset, 82.58% for the Simmental beef cattle, and 80.73% for the mixed Holstein and Simmental beef cattle dataset. The recognition model constructed in the study can achieve individual recognition of cattle in complex environments, has good robustness to matching data, and can effectively reduce the effects of data angle changes and partial features missing in cattle facial recognition.

Comprehensive Analyses of the Bacterial Population in Non-Healing Claw Lesions of Dairy Cattle.

Non-healing claw lesions (NHCLs) are a newly characterized disorder affecting the deep dermis of the hoof in dairy cattle. Although NHCLs are thought to be associated with bovine digital dermatitis (BDD), their precise etiology is not yet understood. To investigate the bacterial populations present in each type of NHCL (toe necrosis: TN, non-healing white line disease: nhWLD, and a non-healing sole ulcer: nhSU), and the newly added entity non-healing verrucous-like lesions (nhVLL), 16S rRNA-based metagenomic analysis with next-generation sequencing (NGS) was employed. Twelve cases of NHCLs (3 TN, 3 nhWLD, 4 nhSU, and 2 nhVLL) were collected from five dairy farms in two prefectures in Japan. Three samples of healthy hoof dermis collected from two farms and a slaughterhouse were used as controls. Furthermore, culture-dependent and -independent approaches were conducted for detecting Treponema species and Fusobacterium necrophorum. As reported in BDD, Treponema species and F. necrophorum were detected frequently from NHCLs by PCR and immunohistochemistry, but NGS showed that these bacterial genera were not predominant in NHCLs. The predominant bacterial genera in NHCLs differed among the lesions examined, suggesting that Treponema species present predominantly in BDD were not predominant in NHCLs and that the bacterial population in NHCLs may vary among individual cattle and/or farms.

Evaluating the effects of antimicrobial drug use on the ecology of antimicrobial resistance and microbial community structure in beef feedlot cattle.

Use of antimicrobial drugs (AMDs) in food producing animals has received increasing scrutiny because of concerns about antimicrobial resistance (AMR) that might affect consumers. Previously, investigations regarding AMR have focused largely on phenotypes of selected pathogens and indicator bacteria, such as Salmonella enterica or Escherichia coli. However, genes conferring AMR are known to be distributed and shared throughout microbial communities. The objectives of this study were to employ target-enriched metagenomic sequencing and 16S rRNA gene amplicon sequencing to investigate the effects of AMD use, in the context of other management and environmental factors, on the resistome and microbiome in beef feedlot cattle. This study leveraged samples collected during a previous longitudinal study of cattle at beef feedlots in Canada. This included fecal samples collected from randomly selected individual cattle, as well as composite-fecal samples from randomly selected pens of cattle. All AMD use was recorded and characterized across different drug classes using animal defined daily dose (ADD) metrics. Overall, fecal resistome composition was dominated by genes conferring resistance to tetracycline and macrolide-lincosamide-streptogramin (MLS) drug classes. The diversity of bacterial phyla was greater early in the feeding period and decreased over time in the feedlot. This decrease in diversity occurred concurrently as the microbiome represented in different individuals and different pens shifted toward a similar composition dominated by Proteobacteria and Firmicutes. Some antimicrobial drug exposures in individuals and groups were associated with explaining a statistically significant proportion of the variance in the resistome, but the amount of variance explained by these important factors was very small (<0.6% variance each), and smaller than associations with other factors measured in this study such as time and feedlot ID. Time in the feedlot was associated with greater changes in the resistome for both individual animals and composite pen-floor samples, although the proportion of the variance associated with this factor was small (2.4% and 1.2%, respectively). Results of this study are consistent with other investigations showing that, compared to other factors, AMD exposures did not have strong effects on antimicrobial resistance or the fecal microbial ecology of beef cattle.

Computer Vision-Based Approach for Automatic Detection of Dairy Cow Breed

Purpose: Identification of individual cow breeds may offer various farming opportunities for disease detection, disease prevention and treatment, fertility and feeding, and welfare monitoring. However, due to the large population of cows with hundreds of breeds and almost identical visible appearance, their exact identification and detection become a tedious task. Therefore, the automatic detection of cow breeds would benefit the dairy industry. This study presents a computer-vision-based approach for identifying the breed of individual cattle. Methods: In this study, eight breeds of cows are considered to verify the classification process: Afrikaner, Brown Swiss, Gyr, Holstein Friesian, Limousin, Marchigiana, White Park, and Simmental cattle. A custom dataset is developed using web-mining techniques, comprising 1835 images grouped into 238, 223, 220, 212, 253, 185, 257, and 247 images for individual breeds. YOLOv4, a deep learning approach, is employed for breed classification and localization. The performance of the YOLOv4 algorithm is evaluated by training the model on different sets of training parameters. Results: Comprehensive analysis of the experimental results reveal that the proposed approach achieves an accuracy of 81.07%, with maximum kappa of 0.78 obtained at an image size of 608 × 608 and an intersection over union (IoU) threshold of 0.75 on the test dataset. Conclusions: The model performed better with YOLOv4 relative to other compared models. This places the proposed model among the top-ranked cow breed detection models. For future recommendations, it would be beneficial to incorporate simple tracking techniques between video frames to check the efficiency of this work.

Attitudes towards Use of High-Importance Antimicrobials-A Cross-Sectional Study of Australian Veterinarians.

The timely implementation of antimicrobial stewardship interventions could delay or prevent the development of higher levels of antimicrobial resistance in the future. In food-producing animals in Australia, high-importance antimicrobials, as rated by the Australian Strategic and Technical Advisory Group (ASTAG), include virginiamycin and third-generation cephalosporins (in individual pigs or cattle). The use of high-importance antimicrobials in companion animals is more widespread and less regulated. There is no national antimicrobial use surveillance system for animals in Australia. Consequently, there is a gap in the knowledge about reasonable use across all sectors of veterinary practice. This study explored attitudes towards the use in veterinary medicine of antimicrobials with high importance to human health, and determined levels of agreement about the introduction of restrictions or other conditions on this use. An online survey was distributed via social media and email from June to December 2020 to veterinarians working in Australia. Of the 278 respondents working in clinical practice, 49% had heard of the ASTAG rating system, and 22% used a traffic light system for antimicrobial importance in their practice. Overall, 61% of participants disagreed that veterinarians should be able to prescribe high-importance antimicrobials without restrictions. If there were to be restrictions, there was most agreement amongst all respondents for only restricting high-importance antimicrobials (73%). There is a need for education, guidance, and practical support for veterinarians for prescribing high-importance antimicrobials alongside any restrictions.

A Vision-Based Cattle Recognition System Using TensorFlow for Livestock Water Intake Monitoring

This letter presents a new method for identifying individual cattle for the purpose of tracking and efficiently measuring their daily water intake using a vision-based machine learning system. In addition to the current solution of using radio frequency identification (RFID), the proposed system uses TensorFlow Object Detection to detect labels on the RFID tag. The proposed system can be integrated into the current water intake monitoring system and alleviates the errors introduced by the unreliable RFID readers. The system allows users to train an object recognition model that can recognize and differentiate the labels on the ear tags of individual farm animals, so the drinking events can be recorded by the water intake monitoring system. The models are trained using custom datasets of manually annotated tag images with pretrained model architectures from TensorFlow 2 Model Zoo. The system is tested using images from event-triggered weather-proof cameras deployed in the grazing site. Experimental results of the system showed an accuracy of around 90%. In comparison to other present methods, this newly proposed system provides scalability and flexibility, making it an attractive vision-based solution for machine learning systems in agriculture.

Cattle Segmentation and Contour Detection Based on Solo for Precision Livestock Husbandry

Segmenting objects such as herd of cattle in natural and cluttered images is among the herculean dense prediction tasks of computer vision application to agriculture. To achieve the segmentation goal, we based the segmentation on the model of single objects by locations (SOLO) which is capable of exploiting the contextual cues and segmenting individual cattle by their locations and sizes. For its simple approach to instance segmentation with the use of instance categories, SOLO outperforms Mask R-CNN which uses detect-then-segment approach to predict a mask for each instance of cattle. The model is trained using synchronized stochastic gradient descent (SGD) over GPU to achieve a mAP of 0.94 making it 0.02 higher than the result recorded by the Mask R-CNN model. By using the focal loss, the proposed approach achieved 32.23 ADE on cattle contour detection making its performance better than the Mask R-CNN’s performance.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Temporal Changes in the Faecal Microbiota of Beef Cattle on Feedlot Placement.

Simple SummaryMicroorganisms inhabiting the gut influence the health and productivity of livestock. Studies in ruminants including beef cattle show that ruminal microorganisms can help convert poor-quality or indigestible feed into nutrients, thereby influencing economically important traits like feed conversion efficiency and meat quality (e.g., marbling), and also interact with receptors on intestinal epithelial cells to regulate a variety of physiological processes including immune responses. Studies have also shown that the proportions of different groups of microorganisms in the gut are influenced by exposure to stress. In beef cattle, given that most cattle are finished on feedlots, induction into feedlots can expose cattle to stress due to transportation, change in diet and change in the handling and management of animals. Therefore, the primary objective of this study was to characterise how gut microorganisms change over time when cattle are inducted into feedlots. The results indicate that there are significant changes in the profiles of gut microorganisms in cattle that are visible soon after feedlot placement. These changes primarily include a reduced diversity of microorganisms within the gut of individual cattle and increased differences in the types of microorganisms that inhabit the gut of different cattle.The microbial communities that inhabit the intestinal tract play an important role in modulating health and productivity. Environmental stressors can impact microbial communities, which can significantly influence host physiology. Cattle are subjected to several environmental stressors when placed on feedlots, such as transportation stress, exposure to feedlot environments and change in diet and management. Exposure to these stressors could influence host gut microbiota, which in turn, could potentially influence host health and performance. The aim of the current study was to characterise the temporal changes that occur in intestinal microbiota as a consequence of feedlot placement by profiling 16s rRNA sequences in rectal faecal samples. When faecal microbiome profiles were compared in terms of relative abundances and alpha diversity metrics, the results showed significant, observable changes in profiles 2 days post-feedlot induction. Furthermore, beta-diversity analysis indicated that the phylogenetic similarity between samples significantly decreased on day 2 (PERMANOVA, p < 0.001). These trends were suggestive of a short-term reduction in microbial diversity coupled with decreased similarity between animals. These changes warrant further investigation and could provide opportunities for improved performance, health and even welfare of feedlot cattle in future.

40. Predictive equations to detect incidence of feedlot bloat in finishing cattle in individual cattle management systems

40. Predictive equations to detect incidence of feedlot bloat in finishing cattle in individual cattle management systems

Intelligent Grazing UAV Based on Airborne Depth Reasoning

The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of “collectivization” and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.