Individual Cattle Research Articles

Patterns of variation and relationships among fat, protein, and milk yield of individual dairy cattle in a Thai multibreed population.

This study systematically examines the patterns of milk yield (MY, kg), fat (FAT, %), and protein (PROT, %) in a diverse population of Thai multibreed dairy cattle, considering the tropical environment's impact on lactating cows. Using a dataset of 47,205 monthly test-day records from 4,440 first-lactation cows across 446 farms, we analyze variations and interrelationships through mathematical averaging and introduce the fat-to-protein ratio (FPR) to assess acidosis (FPR < 1.1) and ketosis (FPR > 1.5) risks during lactation. Pearson correlation analysis elucidated trait associations. The findings, aligned with established lactation norms, indicate peak production at 297 days in milk (DIM) for FAT (4.08%; SD = 0.96%), PROT (3.43%; SD = 0.47%), and 52 DIM for MY (18.09kg; SD = 4.91kg). Nadirs are observed at 72 DIM for FAT (3.27%; SD = 0.74%), 47 DIM for PROT (2.86%; SD = 0.36%), and 299 DIM for MY (9.05kg; SD = 2.95kg). FPR variations highlight acidosis (46.48%), normal (43.66%), and ketosis (9.86%), especially during early lactation (100 DIM). Significant negative correlations emerge between MY, FAT, and PROT (P < 0.05), while a positive correlation is identified between FAT and PROT (P < 0.01), with robust correlations during early lactation. This study contributes to understanding tailored nutritional strategies for dairy cows' holistic health and sustainability in tropical environments, guiding efficient production practices and mitigating health-related productivity impediments.

Seroprevalence and Associated Risk Factors of Infectious Bovine Rhinotracheitis (IBR) and Animal Owners' Knowledge, Attitude and Practice (KAP) Towards the Disease in Selected Districts of East Wollega Zone, Oromia Regional State, Ethiopia.

Infectious bovine rhinotracheitis (IBR) is one of the most important diseases affecting production and productivity. Cross-sectional study was aimed at to determine the seroprevalence of IBR and associated risk factors, and animal owners' knowledge, attitude and practice towards the disease from April 2021 to June 2022. Accordingly, a total of 384 serum samples were collected from both crossbreed (70) and local breed (314) cattle from purposively selected districts of East Wollega zone of Western Ethiopia. Competitive enzyme-linked immunosorbent assay (ELISA) was used for testing glycoprotein antibodies (anti-gB) for bovine herpes virus-1 (BoHV-1) virus in collected serum, and the obtained data were analysed by multiple logistic regressions by using R software 3.62 version. However, questionnaire data were analysed for descriptive statistics by SPSS version 20.0 (IBM. Corp, 2011). The total prevalence of IBR in the study area was found to be 70.54% at herd and 80.47% at individual cattle level. The significant association (p ) was found for breed, age, body condition and herd size but not for district and sex as risk factors. The BoHV-1 virus seropositivity in adult animals increased significantly, with an odds ratio of 1.65 (95% CI 0.705-3.85) compared to young. Local breed cattle were 2.055 times more likely to test positive for IBR with an odds ratio of 0.77 (95% CI 0.23-2.22) compared to crossbreed cattle. The chances of cattle in medium herds testing positive for the BoHV-1 virus with an odds ratio of (1.78 95% CI 1.303-7.50) are greater than the chances of cattle in smaller herds testing positive. The survey results showed that 70% of animal owners identified IBR as a major challenge in animal production, whereas 35% mentioned long calving intervals. However, 92% of the participants were not informed about the level of knowledge and attitude regarding particular diseases such as IBR. This study showed that there is a high prevalence of IBR in cattle in the study area, and that owners have low awareness of the disease. Therefore, it is necessary to develop an immediate control system and conduct additional research on molecular detection to evaluate its effects on reproductive performance.

PSX-22 Boosting techniques for prediction of dry matter intake of cattle in confinement settings and SHAP analysis on the models’ output

Abstract Measurement of individual dry matter intake (DMI) is critical to improving the management of the beef herd and is necessary to determine the feed efficiency of individual cattle. Combining machine learning models and proxies for DMI (e.g., water intake, body weight, climatic variables) has the potential to replace the need for dedicated feed intake equipment. It would also open the possibility of measuring DMI in situations like grazing where quantifying DMI directly is not possible. In this study, we examined the relationship between DMI, animal variables and environmental variables. In this study, we developed and tested machine learning models based boosting techniques to develop approaches to predict DMI in cattle, using the same data we have previously published results from Random Forest techniques in the past. The dataset included both bulls and steers. The developed models include both regular boosting regression models and random effects boosting models. The regular boosting technique models such as gradient boosting regression, light gradient boosting regression, extreme gradient boosting regression. Similarly, random effects boosting machine learning model Gaussian process boosting (GPBoost) was developed to consider the random effects associated with the intake behavior of the animals. The extreme gradient boosting model outperformed other regular boosting regression models with a r2 of 0.81 and 0.48 on training and testing datasets respectively. However, it does not consider the random effects associated with the longitudinal nature of the animal intake and weather variables and prone to overfitting. GPBoost model variants were tested and developed to avoid overfitting and to incorporate a random effects model, which we propose as the overall best performing model. The GPBoost generalized well on the training and testing datasets with r2 scores of 0.55 and 0.54 respectively. To further understand the model’s performance on the unseen dataset, we used SHAP analysis on the GPBoost. The SHAP analysis revealed that water intake, full body weight and age of the animals contributed significantly to predicting unseen data.

188 Prediction of individual grazing beef cattle dry matter intake

Abstract Estimation of individual dry matter intake (DMI) of pastured beef cattle is critical to grassland management, genetic improvement of efficiency, and increasing the sustainability of animal agriculture. Currently, the only way to measure individual DMI of grazing cattle is through expensive, labor intensive, and spatially demanding research protocol. Very few options for assessment of individual DMI of grazing animals exist for cattle producers. We have developed a machine learning approach to predicting individual DMI of pastured beef cattle that requires only body weight (BW), water intake, and open-source climate variables. Training data were collected in Wardensville, WV at the West Virginia University Research, Outreach, and Education Center. Crossbred steers (n = 32) were phenotyped for Residual Feed Intake (RFI) in the barn. Animals were split into four groups of two low RFI and two high RFI. After a grazing acclimation period, animal groups were rotationally grazed concurrently through 0.05 ha plots for 7 d. Daily BW and water intake data were collected throughout the experiment using an RFID-equipped front-end scale and metered waterers. Beginning on d 2, animals were provided a bolus of Cr2O3 (10 g) at 0700 and 1900 h. Beginning on d 5, fecal samples were collected from each animal at 0700 and 1900 h. Samples were analyzed for Cr2O3 content and used in conjunction with forage digestibility from paired clipped samples to generate individual intakes. These data were used as ground truth data to assess model performance. We used our Repeated Measures Random Forest (RMRF) model to predict individual DMI of animals using daily BW, water intake, and climate variables. Intakes estimated by RMRF were compared with intakes estimated by NASEM equations. Our model outperformed NASEM predictions by 70%, indicating that our method achieves better results than the current most efficient approach. Though iterative model improvements will be made (collection of more training data, collection of more diverse training data, and use of first-differenced, moving average, and seasonally adjusted variables), our work demonstrates a novel, efficient method for researchers and producers to more accurately estimate DMI of individual pastured beef cattle.

271 Does social buffering amongst familiar weaned beef calves mitigate castration stressors?

Abstract Co-mingling of beef feeder calves from multiple sources results in biological and behavioral stressors. This study is part of a project exploring social relationships between individual beef cattle and potential benefits of social buffering during stress events. Here, the specific objective was to explore whether feeder calves form preferential relationships which buffer them from surgical castration pain. Within the experiment, two treatment groups were established: Familiar calves were defined as calves who shared a source farm with two other calves in the pen, and Unfamiliar calves were defined as calves who did not share a source with any other pen-mates. It was hypothesized that Familiar calves would exhibit more lying, fewer pain-related behaviors, and fewer agonistic interactions following surgical castration than Unfamiliar pen-mates. Weaned beef bull calves [n = 102, body weight (BW) range ± SD = 112.95 to 370.10 ± 67.8 kg] from 23 source farms were randomly assigned to home pens, each comprised of three Familiar calves and three Unfamiliar calves. Cameras placed above the pens recorded video from which postural (standing and lying), social (agonistic and bunk displacements) and pain-related behaviors (wound checking and hind leg stamping) were quantified. Cattle were handled through a hydraulic squeeze chute once weekly over four consecutive weeks for data collection and standard processing procedures. On d 14 relative to arrival, calves were surgically castrated with multimodal pain mitigation. During all processing events, calves were marked with livestock paint for behavioral observations. Behavior was observed continuously for 15 min at five different time points (0 h, 1 h, 4 h, 24 h, 48 h) relative to when the last calf returned to the home pen after processing, and researchers were blind to treatment allocation. These time periods corresponded to expected periods when pain was absent and present based on duration of local anesthesia and systemic pain relief. Calf was the experimental unit. For the purpose of this abstract, data from the 48 h periods following d 7 and 14 were analyzed using the PROC GLIMMIX procedure of SAS; the model included treatment (Familiar, Unfamiliar) by week interaction. A P-value of ≤ 0.05 was considered significantly different. Regardless of treatment, preliminary analyses identified decreased lying (P &amp;lt; 0.02) and increased wound checking (P &amp;lt; 0.01) 48h following castration (d 14) when compared with the previous week (d 7). There were no observed differences for hindleg stamping (P = 0.2), agonistic behaviors (P &amp;gt; 0.06) or bunk displacement (P = 0.09). Based on preliminary analyses, we did not observe evidence of social buffering effects on castration stress during the immediate post-surgical period.

PSII-7 Development and assessment of a mobility scoring system for beef cattle

Abstract Lameness is a disruption of normal gait due to infection, injury, or abnormal conformation, causing pain and discomfort. Several scoring systems have been developed to determine lameness severity; a key indicator in deciding if an animal is fit to withstand transport. However, most scoring systems were developed for dairy cattle and may not be suited for use in feedlot cattle due to the large genetic variation between beef breeds as well as the highly variable environment in which they are housed. Lack of consistency when scoring lameness and reduced ability to detect early lame cattle can result in poor animal welfare and increased costs. The objectives of this study were 1) to develop a mobility scoring system that aids in transport decisions for beef cattle, and 2) to determine observer repeatability using this scoring system. Over a 7-mo period, video cameras were placed in an alley to record the ambulation of individual cattle exiting the processing barn at 4 commercial feedlots in Southern Alberta. Cameras were set to record whole-body videos of cattle walking, running, or trotting. Five researchers trained to use this mobility scoring system, individually evaluated 174 cattle from the collected videos, for fitness for transport and the presence/absence of gait abnormalities: shortness of stride, stiffness (mild, moderate, or severe), limp (mild, moderate, or severe), hip hike, head dropped, head bob, arched back, non-weight bearing, or reluctance to move. The mobility scoring system used was a modified (NCBA and Zinpro) 4-point scale (0-3) for locomotion: 0) an animal with no gait abnormalities; 1) an animal with mild stiffness and shortness of stride; 2) an animal with difficulty taking some steps and one or more of the following: severe stiffness, limp favoring affected limb, limps with a head bob when walking but still bears weight; and 3) an animal reluctant or unable to move, bearing little or no weight on affected limb, the head of the animal is dropped and back arched with pronounced head bob and severe limp detected, or incoordination. Fitness for transport was scored as fit, compromised, or unfit according to the humane transportation guidelines of the Canadian Food Inspection Agency. A kappa statistic was calculated to assess inter-observer reliability for both mobility and transport fitness scoring systems as well as gait abnormalities. The combined mobility scores had substantial agreement between observers (Κ = 0.63; P &amp;lt; 0.001). Mobility score of 2 had the highest agreement among all variables (Κ = 0.88; P &amp;lt; 0.001), followed by head bob (Κ = 0.85; P &amp;lt; 0.001), and arched back (Κ = 0.76; P &amp;lt; 0.001). Results of this study will aid in the development of a mobility score that improves industry stakeholder agreement regarding lameness severity and transport loading decisions.

Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning

Monitoring animal welfare, disease prevention, vaccination administration, production supply, and ownership management all depend on accurate cattle identification. Ear tag-based cattle identification techniques are frequently used in livestock farm management. These are not used to identify specific cattle on large-scale farms. However, ear tags can come off, which makes it challenging to identify a particular individual. Ear tags are susceptible to fraud, can be copied, and run the risk of being damaged. Long-term animal identification is impossible with lost tags. For this purpose, a data set was created by taking images of cattle in their natural environment. The dataset, which contains 15,000 records from 30 different cattle, was divided into three sections: training, validation, and testing. Deep learning algorithms InceptionResNetV2, MobilenetV2, DenseNet201, Xception, and NasNetLarge were used in this study to identify specific cattle faces. The DenseNet201 algorithm achieved the highest test accuracy of 99.53% with a validation accuracy of 99.83%.

AI-enhanced real-time cattle identification system through tracking across various environments

Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system

Validating statistical properties of resilience indicators derived from simulated longitudinal performance measures of farmed animals

Resilience is commonly defined as the ability of an individual to be minimally affected or to quickly recover from a challenge. Improvement of animals’ resilience is a vital component of sustainable livestock production but has so far been hampered by the lack of established quantitative resilience measures. Several studies proposed that summary statistics of the deviations of an animal’s observed performance from its target performance trajectory (i.e., performance in the absence of challenge) may constitute suitable quantitative resilience indicators. However, these statistical indicators require further validation. The aim of this study was to obtain a better understanding of these resilience indicators in their ability to discriminate between different response types and their dependence on different response characteristics of animals, and data recording features. To this purpose, milk-yield trajectories of individual dairy cattle differing in resilience, without and when exposed to a short-term challenge, were simulated. Individuals were categorised into three broad response types (with individual variation within each type): Fully Resilient animals, which experience no systematic perturbation in milk yield after challenge, Non-Resilient animals whose milk yield permanently deviates from the target trajectory after challenge and Partially Resilient animals that experience temporary perturbations but recover. The following statistical resilience indicators previously suggested in the literature were validated with respect to their ability to discriminate between response types and their sensitivity to various response features and data characteristics: logarithm of mean of squares (LMS), logarithm of variance (LV), skewness (S), lag-1 autocorrelation (AC1), and area under the curve (AUC) of deviations. Furthermore, different methods for estimating unknown target trajectories were evaluated. All of the considered resilience indicators could distinguish between the Fully Resilient response type and either of the other two types when target trajectories were known or estimated using a parametric method. When the comparison was between Partially Resilient and Non-Resilient, only LMS, LV, and AUC could correctly rank the response types, provided that the observation period was at least twice as long as the perturbation period. Skewness was in general the least reliable indicator, although all indicators showed correct dependency on the amplitude and duration of the perturbations. In addition, all resilience indicators except for AC1 were robust to lower frequency of measurements. In general, parametric methods (quantile or repeated regression) combined with three resilience indicators (LMS, LV and AUC) were found the most reliable techniques for ranking animals in terms of their resilience.

Boosting cattle face recognition under uncontrolled scenes by embedding enhancement and optimization

Accurate individual cattle identification is crucial for modern precision cattle farming. However, practical applications encounter challenges due to factors such as shooting distance and angles, cattle movements, weather conditions, and cattle face posture. Existing models struggle with low recognition accuracy for low-recognizable images and poor robustness to facial pose variations, presenting urgent problems that need to be addressed. In this study, a novel cattle face recognition method was proposed, aiming to solve the aforementioned issues by analyzing and optimizing the embedding distribution of cattle faces. Firstly, MobileFaceNet was employed as the feature extraction network to sufficiently extract discriminative representations. Secondly, due to the tendency of low-recognizable samples to cluster in specific areas of the embedding space, an Embedding Enhancement Module (EEM) was proposed. This module drives embedding features away from ineffective embedding spaces, thereby enhancing the model's ability to extract identity features from less recognizable cattle faces. Finally, an Embedding Optimization Module (EOM) was proposed, which utilized a Sub-Center method to alleviate the model's learning difficulty during early training stages and achieved "pose-irrelevant" clusters by merging Sub-Centers, enhancing robustness to facial pose variations. Experimental results confirmed the effectiveness of the proposed method, with it outperforming the baseline by up to 2.12 % in accuracy, achieving a recognition accuracy of 98.69 %. Furthermore, the application of the proposed method in cattle farms further demonstrated the significant potential value of this work.

Automated monitoring of brush use in dairy cattle.

Access to brushes allows for natural scratching behaviors in cattle, especially in confined indoor settings. Cattle are motivated to use brushes, but brush use varies with multiple factors including social hierarchy and health. Brush use might serve an indicator of cow health or welfare, but practical application of these measures requires accurate and automated monitoring tools. This study describes a machine learning approach to monitor brush use by dairy cattle. We aimed to capture the daily brush use by integrating data on the rotation of a mechanical brush with data on cow identify derived from either 1) low-frequency radio frequency identification or 2) a computer vision system using fiducial markers. We found that the computer vision system outperformed the RFID system in accuracy, and that the machine learning algorithms enhanced the precision of the brush use estimates. This study presents the first description of a fiducial marker-based computer vision system for monitoring individual cattle behavior in a group setting; this approach could be applied to develop automated measures of other behaviors with the potential to better assess welfare and improve the care for farm animals.

Quality of human-animal interactions during beef cattle auctions in Panama

This study aimed to determine the quality of human-animal interactions during cattle auctions in Panama and to assess the relationship between cattle reactivity, value paid, and time spent on commercialization. Data were recorded by watching video records of 4531 individual cattle sold in the main Panamanian auctions. Cattle reactivity was assessed by one previously trained observer when the animals entered the box where they were exposed for sale, assigning one of three scores: 1 = calm, 2 = reactive, and 3 = very reactive. Human-animal interactions were assessed using a binary score to assess the occurrence of hitting (1) or not (0) during commercialization. When cattle were hit to stimulate movement, their reactivity was assessed again to determine whether hitting altered their behavior. The value paid (USD/kg of live weight), and the time spent on commercialization (s) of each animal were also recorded in a subsample of 704 animals. Cattle reactivity, genetic group, animal category, auction location and phase of commercialization influenced the occurrence of hits (p<0.05). Hitting altered cattle reactivity (p<0.001); 95 % of the animals scored calm when they entered the auction box, dropped to 85 % after being hit. There was a significant effect of the reactivity score (p<0.05), genetic group (p=0.01), location (p<0.001) and auction phase (p=0.002) on the number of hits received by the cattle when inside the auction box. The median value paid for very reactive cattle tended to be higher than for calm cattle (1.85 vs 1.82 USD/kg; respectively; p=0.06). There were no differences in the time spent on commercialization according to cattle reactivity (p>0.05). In conclusion, cattle reactivity, and auction phase, in addition to other factors such as genetic group, animal category, and auction location, affected the frequency of negative human-animal interactions during commercialization. Cattle reactivity tended to affect the price paid per head of cattle but not the time spent in marketing.

Case-control study to identify management practices associated with morbidity or mortality due to bovine anaplasmosis in Mississippi cow-calf herds

Bovine anaplasmosis (BA) is a costly disease affecting the U.S. beef cattle industry. Chlortetracycline (CTC) in feed or mineral supplements is often used to control clinical signs of BA. The objective of this study was to determine if manage­ment practices, such as feeding CTC, are associated with ill­ness or death from BA in Mississippi cow-calf herds. Case and control herds were solicited from veterinary practices across Mississippi. Cases were herds with clinical BA diagnosed by a veterinarian within the previous calendar year. Controls were herds under the care of the same practice with no clini­cal BA diagnosed in the previous year. Blinded interviewers conducted telephone surveys of case and control herd owners. Management and biosecurity factors were tested for associa­tion with herd status using a logistic regression generalized linear mixed model with veterinary practice as a random vari­able. Twenty-two case and 25 control herds from 6 veterinary practices across Mississippi were interviewed, representing 22 counties. The average herd size was 132 for case herds, and 136 for control herds. Twenty case herds and 13 control herds fed CTC medicated mineral or feed. Providing CTC was associated with case herd status (OR = 9.2, 95% C.I. = 1.7-50.7). The association observed between case herds and feeding CTC might be because: 1) herds that had experienced previous BA morbidity and mor­tality subsequently began feeding CTC, or 2) some individual cattle consume enough CTC to achieve clearance of the per­sistent carrier state, thereby increasing risk of reinfection and clinical BA.

Optimization of a bovine cytokine multiplex assay using a new bovine and cross-reactive equine monoclonal antibodies

Cytokines are important markers for immune activation, regulation, and homeostasis. The lack of monoclonal antibodies (mAbs) and sensitive assays to evaluate cytokine secretion has hindered research of bovine inflammation and immune regulation. We recently developed a fluorescent bead-based multiplex assay (multiplex assay) for bovine IL-10, TNF-α, and IFN-γ. Although the original assay covers a broad concentration range for the 3 targets, analytical sensitivity for IL-10 and IFN-γ could be improved to facilitate detection of these cytokines in their physiological low pg/mL range. To optimize the multiplex assay, we generated a new bovine IL-10 mAb and explored its use for the detection of intracellular and secreted bovine IL-10. The new bovine IL-10 mAb 130 recognized recombinant bovine IL-10 fusion protein and did not react with the fusion protein tag, or the TNF-α and IFN-γ standards in the multiplex assay. For improving IFN-γ detection, we explored cross-reactivity of anti-equine IFN-γ mAbs by intracellular staining of bovine stimulated peripheral blood mononuclear cells (PBMC). Equine IFN-γ mAb 3 showed excellent cross-reactivity with bovine IFN-γ by intracellular detection. Adding IL-10 mAb 130 and IFN-γ mAb 3 to the bovine multiplex assay substantially improved the analytical sensitivity with lower limits of detection in the low pg/mL range for all analytes. The detection ranges for the optimized multiplex assay were determined as 2 – 134,000 pg/mL for IL-10, 8 – 127,000 pg/mL for IFN-γ, and 12 – 193,000 pg/mL for TNF-α. The assay was next used to measure cytokine concentrations in cell culture supernatants from PBMC stimulated in plasma from whole blood stimulation to confirm native IL-10, TNF-α, and IFN-γ recognition and to explore the upper detection limits of the assay. In PBMC stimulation with a mix of phorbol myristate acetate (PMA) and ionomycin resulted in highest cytokine concentrations, while in plasma from whole blood stimulation, highest concentrations were observed in samples stimulated with a mix of lipopolysaccharide (LPS), phytohemagglutinin (PHA), and the TLR-2/6 agonist Pam2Csk4. PBMC and whole blood stimulation protocols showed that the optimized multiplex assay covers a wide linear detection range for measuring cytokine concentrations in bovine samples. For whole blood stimulation, a cocktail of pathogen associated molecular patterns elicited a stronger cytokine response than a mix of PMA and ionomycin, but response varied considerably between individual cattle. In conclusion, optimizing the bovine cytokine assay with new reagents improved the lower detection limits and widened the linear detection ranges while lowering the background of the multiplex assay.

Adaptive Grazing of Native Grasslands Provides Ecosystem Services and Reduces Economic Instability for Livestock Systems in the Flooding Pampa, Argentina

There is a widespread concern about the negative impact of intensive livestock farming on climate change and biodiversity loss. We analyzed the trade-off between meat production and environmental variables related to global warming—energy consumption, use efficiency of energy, greenhouse gas (GHG) emissions, carbon footprint, and GHG balance—of two alternative intensification strategies of livestock farming in the Flooding Pampa: conventional intensification (CI) based on external inputs, and ecological intensification (EI) based on maintaining native grassland in good condition through adaptive multi-paddock grazing (AMPG). We also explored the relationship between meat production and the economic variables gross margin and its year-to-year variation. Energy consumption was positively correlated with meat production (ρ = 0.95, p= 0.0117), and EI farms consumed less fuel energy and showed higher energy use efficiency than CI farms (294 ± 152 vs. 2740 ± 442 MJ ha−1 y−1, 38.4 ± 28.8 vs. 1.23 ± 0.13 MJ kg LW−1 y−1, p &lt; 0.05, respectively). GHG emissions and carbon footprint did not show significant differences between EI and CI strategies. As soil carbon sequestration was significantly higher in EI farms than in CI farms (1676 ± 304 vs. −433 ± 343 kg CO2eq ha−1 y−1, p &lt;0.05), GHG balance resulted almost neutral and higher under the EI strategy (−693 ± 732 vs. −3520 ± 774 kg CO2eq ha−1 y−1, p &lt; 0.05). CI strategy obtained higher meat production but a similar gross margin to the EI strategy and a more unstable economic return, as the coefficient of variation in the gross margin doubled that of the EI strategy (84 + 13.3 vs. 43 + 2.6, respectively, p &lt; 0.05). Ecological intensification of cattle production in the Flooding Pampa demonstrates the potential for a positive relationship between individual cattle farmers’ profits and overall societal benefits, as reflected in improved environmental performance.

Gaushala Safety – Automation of Cattle Livestock

Automation of Cattle Livestock is a ground-breaking initiative aimed at transforming cattle care and management. In a nation where cattle play a pivotal role in rural economies, the absence of accessible veterinary care, delayed vaccinations, and disease outbreaks pose significant threats to cattle health and the livelihoods of dependent communities. This project offers a comprehensive solution by harnessing cutting-edge technology. It includes a robust cattle information management system, utilizing speech recognition for data input to accommodate diverse user literacy levels..The core feature is an intelligent vaccination scheduler, creating personalized vaccination plans based on individual cattle profiles, and sending timely reminders to owners. Additionally, it employs advanced image processing to detect early signs of disease. The project ensures accessibility through voice-enabled interaction and simplifies the registration of new cattle and along with that it includes the cattle tracker and shed cleaning. By enhancing cattle health, increasing accessibility, and promoting sustainable cattle rearing, Automation of Cattle Livestock seeks to empower farmers, protect livestock, and drive rural community development. This project represents a pioneering step toward a brighter future for both cattle and their caretakers in India.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle.

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments.

Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen.

Consistent individual differences in cattle grazing patterns

Cattle grazing patterns can have a significant impact on the economic and environmental sustainability of rangelands and cattle production. Grazing patterns appear to be driven by consistent individual differences in cattle, deemed ‘grazing personalities’. Optimal distribution of cattle on rangelands can be manipulated by alteration of the environment, like adding water, supplement, or fencing to targeted sites, but few studies address how these management tools are impacting grazing patterns of individual cattle. Our study sought to fill this gap in knowledge by investigating consistency of grazing patterns among fifty cattle, fitted with GPS collars, across two years given the addition of a water site at higher elevation prior to year two. We used Bayesian multivariate mixed models to evaluate consistency, evidenced by among-individual correlations across years with confidence intervals that do not cross 0. We found that cattle were consistent in all rangeland use metrics across years after the addition of the new water site: elevation (Estimate = 0.38, CI = [0.07,0.64]), slope (Estimate = 0.59, CI = [0.21,0.86]), distance to water (Estimate = 0.38 CI = [0.03,0.67]), distance to supplement (Estimate = 0.42, CI = [0.04,0.72]), distance to loafing sites (Estimate = 0.68, CI = [0.24,0.95]), except for distance traveled (Estimate = 0.16,CI = [−0.18, 0.48]). Cows that ventured higher on rangeland in year one (Estimate = 0.84 CI = [0.59,0.98]) and year two (Estimate = 0.93 CI = [0.79,0.99]) and further from watering sites in year two (Estimate = 0.72 CI = [0.41,0.92]) also had more within-individual variability in their grazing patterns than those that stayed at lower elevation and closer to water. These results indicate differences in plasticity or variability can be linked to grazing personalities. There was strong evidence that temperature affected grazing distribution such that on hotter days, cows clumped closer to water and loafing sites, were found at lower elevation, and traveled less far, not reaching supplement sites that were placed at the higher elevations. Based on results of this study, ranchers should consider the effectiveness of management tools on individual cattle and conduct cost-benefit analyses of tools implemented for specific behavior types of cattle. Cattle with behavior types demonstrating more within-cow variability and more ‘hill-climbing’ will impact how cattle graze especially patchy landscapes where resources are distributed across elevation, thus ranchers could potentially match cattle types to rangeland characteristics.

Mask YOLOv7-Based Drone Vision System for Automated Cattle Detection and Counting

Conventional method of counting animals is one of the most challenging tasks in livestock management; moreover, counting of animals in drone-acquired imagery, though promising, is more challenging in intelligent livestock management. In this paper, we apply state-of-the-art object detection model, Mask YOLOv7, for detection and counting of cattle in different scenarios such as in controlled (feedlot) environment and uncontrolled (open-range) environment. Mask mechanism was embedded into the backbone of the YOLOv7 algorithm (Mask YOLOv7) for instance segmentation of individual cattle object. We evaluate the performance of the model proposed in this study using Intersection over Union threshold of 0.5, average precision (AP), and mean average precision. The results of the experiment conducted in this study show that the proposed model achieves an accuracy of 93% in counting cattle in controlled environment and 95% in uncontrolled environment. These results affirm the potential of the model, Mask YOLOv7, to perform competitively with any other existing object detection and instance segmentation models in terms of accuracy and AP especially when the speed of object detection matters. Moreover, the research has potential applications in livestock inventory, which helps in tracking, monitoring, and reporting vital information about individual cattle.