Cold therapy and pain relief after hot-iron disbudding in dairy calves.

Even when pain control is implemented, calves may experience pain for days after hot-iron disbudding. Whether calves seek pain relief post-disbudding offers a novel approach to assessing pain in these animals. By employing an approach-aversion paradigm, we explored the ability of cold therapy to provide immediate pain relief in disbudded calves. Calves were habituated to the manual placement of a cool or ambient pack on their forehead for a short duration simultaneous to milk reward consumption, prior to disbudding. Calves were then disbudded under local anaesthesia (procaine) and analgesia (meloxicam), and responses to the packs were observed over subsequent days. Individual calves were consistently exposed to either cool or ambient packs in different halves of a two-sided experimental pen, allowing for the testing of approach-aversion and conditioned place preference. We found calves approached milk rewards quicker and maintained contact for longer when receiving cold therapy compared to the ambient control. However, calves did not display any conditioned preference for the pen where they received the cool pack. These results add to the growing evidence of lasting pain following disbudding procedures and suggests that cold therapy provides some form of pain relief post-disbudding. Future studies should seek other ways to use cold therapy post-disbudding to reduce aversiveness and human involvement.

National Greenhouse Gas Emission Reduction Potential from Adopting Anaerobic Digestion on Large-Scale Dairy Farms in the United States.

Waste-to-energy systems can provide a functional demonstration of the economic and environmental benefits of circularity, innovation, and reimagining existing systems. This study offers a robust quantification of the greenhouse gas (GHG) emission reduction potential of the adoption of anaerobic digestion (AD) technology on applicable large-scale dairy farms in the contiguous United States. GHG reduction estimates were developed through a robust life cycle modeling framework paired with sensitivity and uncertainty analyses. Twenty dairy configurations were modeled to capture important differences in housing and manure management practices, applicable AD technologies, regional climates, storage cleanout schedules, and methods of land application. Monte Carlo results for the 90% confidence interval illustrate the potential for AD adoption to reduce GHG emissions from the large-scale dairy industry by 2.45-3.52 MMT of CO2-eq per year considering biogas use only in renewable natural gas programs and as much as 4.53-6.46 MMT of CO2-eq per year with combined heat and power as an additional biogas use case. At the farm level, AD technology may reduce GHG emissions from manure management systems by 58.1-79.8% depending on the region. Discussion focuses on regional differences in GHG emissions from manure management strategies and the challenges and opportunities surrounding AD adoption.

Potential relationships between apelin and metabolic-associated indices in transition dairy cows.

Metabolic disorders pose significant challenges in transition dairy cows. Numerous parameters have been investigated in this context, and apelin has recently emerged as a potential metabolic indicator. Accordingly, this study aimed to assess the associations between this hormone and other metabolic parameters. Twenty-two adult Holstein-Friesian dairy cows, 21 days before their expected calving date, were selected for blood sampling and serum separation at four time points: 21 and 10 days before calving and 10 and 21 days after parturition. Serum concentrations of apelin, leptin, insulin, cortisol, T3, T4, non-esterified fatty acids, glucose, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, triglycerides, cholesterol, high, low and very low-density lipoproteins, total, direct and indirect bilirubin were measured in these samples. Surrogate indices for insulin resistance, body condition score, and milk production were also evaluated. Throughout the transition period, a significant increase in apelin levels was observed. Various models were employed to identify associations between apelin and the studied metabolic parameters. Notably, significant correlations between apelin and Leptin, Insulin, Cortisol, T3, T4, NEFA, Cholesterol, LDL, VLDL, Total Protein, Albumin, Globulin, Total Bilirubin, Direct Bilirubin and Indirect Bilirubin were observed, with some being immediate while others developed over time. These findings indicate a mutual influence between apelin and specific metabolic indices. Changes in any component of the metabolic profile at one stage can lead to alterations in apelin levels in subsequent stages. The correlations uncovered between apelin and other components of the metabolic profile in transitioning dairy cows offer valuable insights, contributing to a better understanding of the potential effects of apelin on the studied indicators and vice versa.

Short communication: Association between prepartum subclinical magnesium imbalance and postpartum diseases in grazing dairy cows in Southern Chile.

This study aimed to evaluate the relationship between prepartum subclinical hypomagnesemia (pre-SHMg) and the occurrence of dystocia, metritis, clinical mastitis, lameness, and subclinical hypomagnesemia postpartum (post-SHMg) in pasture-based dairy cows. Also, the difference in means of prepartum magnesium (Mg) concentration by postpartum health events was evaluated. A total of 890 dairy cows from 32 commercial farms located in southern Chile were enrolled. Cows were examined twice, once between 30 and 3 days before and once between 3 and 30 days after calving. Blood samples were collected on both assessments, and cows were considered as having SHMg if serum total Mg < 0.65 mmol/L. On the postpartum visit, cows were evaluated for metritis and lameness. Information about clinical mastitis and dystocia was collected from on-farm records. Data were analyzed using multivariable mixed linear models and multivariable mixed logistic regression models. The overall prevalence of pre-SHMg was 9.9%, and its presence was associated with the occurrence of post-SHMg (odd ratio [OR] = 5.7; P < 0.0001) and metritis (OR = 3.1; P = 0.04). However, we did not detect an association between pre-SHMg and dystocia, clinical mastitis, or lameness after calving. Prepartum serum Mg concentrations were lower in cows that developed post-SHMg than those that did not (LSM ± SE = 0.75 ± 0.02 mmol/L vs. 0.83 ± 0.02 mmol/L; P < 0.0001). In conclusion, pre-SHMg was associated with a higher risk of post-SHMg and metritis in grazing dairy cows but not other postpartum health events.

Prevalence and risk factors of Toxoplasma gondii infection in dairy cattle from the Western Region of Thailand.

In total, 901 dairy cow sera and data were collected from 51 farms in Nakhon Pathom, Ratchaburi and Kanchanaburi provinces (Western Region of Thailand). Serum samples were processed via the multispecies ELISA method to detect IgG antibodies against Toxoplasma gondii infection. The results demonstrated that the calculated true prevalence was 1.48% (95% CI, 0.64-2.75%) for the individual-level and 29.41% (95% CI, 18.71-43%) for the farm-level. The univariate risk factor analysis showed that the number of total owned cats, the presence of stray cats, and the frequency of cleaning per day were significant factors (p < 0.2). These three factors were subjected to logistic regression analysis, and the results revealed that the frequency of cleaning farms per day was a potential risk factor for T. gondii-seropositive farms (OR = 2.745, 95% CI, 1.15-8.69, p = 0.02). The frequency of cleaning might increase the T. gondii oocyst distribution within the barn area, thus increasing the possibility of infection. Our findings show that T. gondii continues to circulate in the dairy cow population in the western part of Thailand. The presence of cats on farms was not found to be associated with T. gondii infection, but the high frequency of cleaning the floor was, and contributed to the potential risk of infection.

Title: Prévalence et facteurs de risque de l'infection à Toxoplasma gondii chez les bovins laitiers de la région occidentale de la Thaïlande. Abstract: Au total, 901 sérums de vaches laitières et des données ont été collectés dans 51 fermes des provinces de Nakhon Pathom, Ratchaburi et Kanchanaburi (région occidentale de la Thaïlande). Les échantillons de sérum ont été traités via la méthode ELISA multi-espèces pour détecter les anticorps IgG contre l'infection à Toxoplasma gondii. Les résultats ont démontré que la prévalence réelle calculée était de 1,48 % (IC à 95 %, 0,64­2,75 %) au niveau individuel et de 29,41 % (IC à 95 %, 18,71­43 %) au niveau des exploitations. L'analyse factorielle a montré que le nombre total de chats possédés, la présence de chats errants et la fréquence quotidienne de nettoyage étaient des facteurs significatifs (p < 0,2). Ces trois facteurs ont été soumis à une analyse de régression logistique et les résultats ont révélé que la fréquence quotidienne de nettoyage des exploitations était un facteur de risque potentiel pour les exploitations séropositives à T. gondii (OR = 2,745, IC à 95 % = 1,15­8,69, p = 0,02). La fréquence du nettoyage pourrait favoriser la répartition des oocystes de T. gondii dans les étables, augmentant ainsi le risque d'infection. Nos résultats indiquent que T. gondii continue de circuler dans la population de vaches laitières de l'ouest de la Thaïlande. La présence de chats dans les fermes n'a pas été associée à l'infection à T. gondii, mais la fréquence élevée du nettoyage du sol l'était et contribuait au risque potentiel d'infection.

Immune cells phenotype and bioenergetic measures in CD4+ T cells differ between high and low feed efficient dairy cows.

Identifying sources of variance that contribute to residual feed intake (RFI) can aid in improving feed efficiency. The objectives of this study were to investigate immune cells phenotype and bioenergetic measures in CD4+ T cells in low feed efficient (LE) and high feed efficient (HE) dairy cows. Sixty-four Holstein cows were enrolled at 93 ± 22 days in milk (DIM) and monitored for 7 weeks to measure RFI. Cows with the highest RFI (LE; n = 14) or lowest RFI (HE; n = 14) were selected to determine immune cells phenotype using flow cytometry. Blood was sampled in the same LE and HE cows at 234 ± 22 DIM to isolate peripheral blood mononuclear cells, followed by magnetic separation of CD4+ T lymphocytes using bovine specific monoclonal antibodies. The metabolic function of isolated CD4+ T lymphocytes was evaluated under resting and activated states. An increased expression of CD62L+ cells within CD8+ T lymphocytes and CD21+ B lymphocytes was observed in HE cows compared to LE cows. CD4+ T lymphocytes of HE cows exhibited an increased mitochondrial and glycolytic activity in resting and activated states compared to LE cows. These data suggest that immune cells in HE cows exhibit an increased metabolic function, which might influence nutrient partitioning and utilization and serve as a source of variation in feed efficiency that warrants future investigation.

Cow-calf contact: a single-herd observational study of AMS yield during the first 100 days in milk.

An increasing number of dairy farmers plan to implement cow-calf contact (CCC) in their herd which necessitates descriptions of the cows` performance in different systems. The aim of the study was to describe (1) Automatic milking system (AMS) milk yield of cows in a CCC system during the first 100 days in milk (DIM) and (2) AMS milk yield before and after cow-calf separation. In a prospective study at a commercial Norwegian dairy farm, we included all calvings from Norwegian Red cows between January 2019 to April 2020. After calving, cow-calf pairs stayed in an individual calving pen during the first 5-6 d before they were moved to the loose housing unit with the remaining herd. Calves had whole-day (24 h/d) and full physical contact to the cows. Cows were milked in an AMS. From 14 individual cows of which one cow calved twice during the study period, we collected daily AMS yields from 15 different lactations, with parities ranging from 1 (n = 6), 2 (n = 5) and 3 (n = 4). Due to the sample size and structure of the data set, we only calculated descriptive statistics from DIM 7-100. All data is shown separately for primiparous and multiparous cows. Mean (± SD) calf age at (fence-line) separation was 52 d ± 14.8 beyond which suckling was prevented. Our data indicates great individual variation in the AMS milk yield. Prior to separation, primiparous cows` AMS yields ranged from 11.0 to 25.9 kg/d while that of multiparous cows ranged from 4.8 to 28.8 kg/d. Once calves were no longer allowed to suckle, the yield increased gradually. During the week after separation, AMS yields ranged from 17.3 to 30.4 kg/d for primiparous cows and 8.7 to 41.8 kg/d for multiparous cows and these yields increased in DIM 93-100 (26.5 to 34.3 and 20.6 to 38.3 kg/d respectively). This study is limited by a low sample size from a single-herd but may provide useful descriptions of AMS milk yield in a whole-day, full contact CCC system during the first 100 days of lactation.

Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A convolutional neural network (CNN) based model was developed for the identification of Sahiwal and Red Sindhi cows. To increase the classification accuracy, first, cows's pixels were segmented from the background using CNN model. Using this segmented image, a masked image was produced by retaining cows' pixels from the original image while eliminating the background. To improve the classification accuracy, models were trained on four different images of each cow: front view, side view, grayscale front view, and grayscale side view. The masked images of these views were fed to the multi-input CNN model which predicts the class of input images. The segmentation model achieved intersection-over-union (IoU) and F1-score values of 81.75% and 85.26%, respectively with an inference time of 296 ms. For the classification task, multiple variants of MobileNet and EfficientNet models were used as the backbone along with pre-trained weights. The MobileNet model achieved 80.0% accuracy for both breeds, while MobileNetV2 and MobileNetV3 reached 82.0% accuracy. CNN models with EfficientNet as backbones outperformed MobileNet models, with accuracy ranging from 84.0% to 86.0%. The F1-scores for these models were found to be above 83.0%, indicating effective breed classification with fewer false positives and negatives. Thus, the present study demonstrates that deep learning models can be used effectively to identify phenotypically similar-looking cattle breeds. To accurately identify zebu breeds, this study will reduce the dependence of farmers on experts.

Effect of harvesting strategy of second-cut orchardgrass silage on feed intake, digestion, and milk production in dairy cows.

We investigated the effects of regrowth interval and first-cut timing on the dietary characteristics of second-cut orchardgrass silage and feed intake and milk production in dairy cows fed second-cut orchardgrass silage. The second-cut grasses were harvested 7w after the first-cut at the early stage (E7w) or at the heading stage (H7w), or harvested 6w after the first-cut at the early stage (E6w) from orchardgrass sward, and then ensiled. We evaluated the effect of regrowth interval by comparing E7w and E6w, and the effect of first-cut timing by comparing E7w and H7w. Six multiparous Holstein cows were used in a replicated 3 × 3 Latin square design, with three dietary treatments: diets containing E7w, E6w, or H7w silage at 30% dietary dry matter. We observed that feeding E6w silage instead of E7w silage increased fiber digestibility, dry matter intake, and milk production; however, the first-cut timing (E7w vs. H7w) did not affect nutrient content and digestibility, feed intake, or lactation performance. These results show that harvesting at short regrowth intervals for second-cut orchardgrass can be an effective strategy for improving feed utilization and milk yield; however, the first-cut timing for second-cut orchardgrass has little impact.

Can 'cow flu' be eliminated-or is it too late?

Feeble government response and lack of industry cooperation hamper U.S. control efforts.

Awareness and adoption of farm biosecurity practices in commercial dairy, pig and poultry farms of Uttar Pradesh (India).

India's livestock sector has been facing significant losses due to episodes of disease outbreaks since time immemorial. Hence, biosecurity measures are very important to maintain and improve animal health along with prevention of disease outbreak. Keeping these facts into consideration, the study was proposed with an objective to assess the existing biosecurity practices adopted by the commercial dairy, pig and poultry farms. The current study was undertaken in the state of Uttar Pradesh as it is the leading state in milk and meat production. A total of 120 farmers were selected randomly including 40 each practicing commercial dairy, pig and poultry farming. An ex-post facto research methodology was used with face-to-face interview and observation to collect data. The biosecurity practices were assessed under seven dimensions such as, location and design of farm, restricted access, isolation and quarantine, cleaning and disinfection, management of feed and water, disposal of carcass, manure and waste, and health management. Results elicited that about 50% of the farmers had medium level of adoption who adopted 18-34 practices out of 51 practices. The average overall adoption score was 34.17 out of 51 (67%) which makes an overall adoption gap of 33%. Maximum adoption gap was seen in case of restricted access (43%) whereas minimum gap in adoption was seen in case of management of feed and water (27%). Pig and poultry farmers showed significantly higher biosecurity measures than dairy farmers (p < 0.05). The more significant contributors to the adoption of biosecurity measures were the level of knowledge of the farmers (p < 0.01). Other factors such as education, income, herd/flock size, Information and Communication Technology utilization, number of trainings also had a significant contribution (p < 0.05) in actual implementation of biosecurity. Hence, better understanding of these measures among the farmers must be ensured by hands on training along with proper demonstration of various procedures involved in maintaining farm biosecurity is need of the hour.

Evaluating machine learning algorithms to predict lameness in dairy cattle.

Dairy cattle lameness represents one of the common concerns in intensive and commercial dairy farms. Lameness is characterized by gait-related behavioral changes in cows and multiple approaches are being utilized to associate these changes with lameness conditions including data from accelerometers, and other precision technologies. The objective was to evaluate the use of machine learning algorithms for the identification of lameness conditions in dairy cattle. In this study, 310 multiparous Holstein dairy cows from a herd in Northern Colorado were affixed with a leg-based accelerometer (Icerobotics® Inc, Edinburg, Scotland) to obtain the lying time (min/d), daily steps count (n/d), and daily change (n/d). Subsequently, study cows were monitored for 4 months and cows submitted for claw trimming (CT) were differentiated as receiving corrective claw trimming (CCT) or as being diagnosed with a lameness disorder and consequent therapeutic claw trimming (TCT) by a certified hoof trimmer. Cows not submitted to CT were considered healthy controls. A median filter was applied to smoothen the data by reducing inherent variability. Three different machine learning (ML) models were defined to fit each algorithm which included the conventional features (containing daily lying, daily steps, and daily change derived from the accelerometer), slope features (containing features extracted from each variable in Conventional feature), or all features (3 simple features and 3 slope features). Random forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Time series (ROCKET) were used as ML predictive approaches. For the classification of cows requiring CCT and TCT, ROCKET classifier performed better with accuracy (> 90%), ROC-AUC (> 74%), and F1 score (> 0.61) as compared to other algorithms. Slope features derived in this study increased the efficiency of algorithms as the better-performing models included All features explored. However, further classification of diseases into infectious and non-infectious events was not effective because none of the algorithms presented satisfactory model accuracy parameters. For the classification of observed cow locomotion scores into severely lame and moderately lame conditions, the ROCKET classifier demonstrated satisfactory accuracy (> 0.85), ROC-AUC (> 0.68), and F1 scores (> 0.44). We conclude that ML models using accelerometer data are helpful in the identification of lameness in cows but need further research to increase the granularity and accuracy of classification.

Seroprevalence and risk factors for Neospora caninum and Toxoplasma gondii in dairy cattle from São Paulo State, Brazil.

Neospora caninum is a major cause of reproductive loss in cattle worldwide as it leads to abortion and animal repositioning. Although Toxoplasma gondii does not cause a reproductive problem in cattle, consuming raw or uncooked beef poses the risk of transmission. This study aimed to evaluate the occurrence of anti-N. caninum and anti-T. gondii antibodies in dairy cattle in the West and Northwest regions of São Paulo State, Brazil. A total of 653 serum samples from dairy cows were analyzed using an indirect immunofluorescence assay (IFA). Epidemiological data from the farms were associated with the serological results of the animals by logistic regression based on the presence of antibodies. The frequencies of the antibodies against N. caninum and T. gondii were 41.6% (272/653) and 11.5% (75/653), respectively. A statistically significant association was observed between: the serum anti-N. caninum antibodies and breed, history of food supplementation for calves, introduction of outside animals that later presented reproductive problems, and history of reproductive problems by the trimester of gestation. The present study highlights the importance of neosporosis in dairy cattle in the study regions and that the inclusion of this parasite in the investigation of animals with reproductive disorders is important.

Two-year investigation of spore-formers through the production chain at two cheese plants in Norway.

Spore-forming bacteria are the most complex group of microbes to eliminate from the dairy production line due to their ability to withstand heat treatment usually used in dairy processing. These ubiquitous microorganisms have ample opportunity for multiple points of entry into the milk chain, creating issues for food quality and safety. Certain spore-formers, namely bacilli and clostridia, are more problematic to the dairy industry due to their possible pathogenicity, growth, and production of metabolites and spoilage enzymes. This research investigated the spore-forming population from raw milk reception at two Norwegian dairy plants through the cheesemaking stages until ripening. Samples were collected over two years and examined by amplicon sequencing in a culture independent manner and after an anaerobic spore-former enrichment step. In addition, a total of 608 isolates from the enriched samples were identified at the genus or species level using MALDI-TOF analysis. Most spore-forming isolates belong to the genera Bacillus or Clostridium, with the latter dominating the enriched MPN tubes of raw milk and bactofugate. Results showed a great variation among the clostridia and bacilli detected in the enriched MPN tubes. However, B. licheniformis and C. tyrobutyricum were identified in all sample types from both plants throughout the 2-year study. In conclusion, our results shed light on the fate of different spore-formers at different processing stages in the cheese production chain, which could facilitate targeted actions to reduce quality problems.

Microbial trace based on PCR-DGGE to evaluate the ripening stage of minas artisanal cheeses from the Canastra microregion produced by different dairies.

The Minas artisanal cheese from the Serra da Canastra (MAC-CM) microregion is a traditional product due to its production and ripening process. Artisanal chesses manufactured with raw cow's milk and endogenous dairy starters ("also known as pingo") have distinctive flavors and other sensory characteristics because of the unknown microbiota. The aim of this study was to evaluate the microbiota during 30 days of ripening, the physicochemical changes, and their relation in MACs produced in two different microregions located in the Serra da Canastra microregion through culture-dependent and culture-independent methods. The MACs were collected in the cities of Bambuí (MAC-CMB) and Tapiraí (MAC-CMT) in the Canastra microregion (n = 21). Cheeses uniqueness was demonstrated with the multivariate analysis that joined the microbiota and physicochemical characteristics, mainly to the proteolysis process, in which the MAC-CMT showed deeper proteolysis (DI -T0:14.18; T30: 13.95), while the MAC-CMB reached only a primary level (EI -T0:24.23; T30: 31.10). Abiotic factors were responsible for the differences in microbial diversity between the cheese farms. Different microbial groups: the prokaryotes, like Corynebacterium variabile, Lactococcus lactis, and Staphylococcus saprophyticus; and the eukaryotes, like Kluyveromyces lactis and Diutina catenulata dominated ripening over time. The microbial community and proteolysis were responsible for the predominance of volatile groups, with alcohols predominating in MAC-CMB and free fatty acids/acids and esters in MAC-CMT.

Estimation of economic losses due to leptospirosis in dairy cattle.

There are few studies that effectively quantify the economic losses resulting from problems caused by leptospirosis in naturally infected dairy cattle. Given this gap, the objective of this study was to propose and apply a method to quantify the economic losses resulting from productive and reproductive problems in a commercial dairy herd naturally infected by Leptospira spp. For this study, the zootechnical and economic indicators at a property with Jersey cattle were analyzed during the period from 2014 to 2017. The leptospirosis outbreak occurred in 2014, and the therapeutic approach was carried out between 2015 and 2017, with the latter considered the year of control of the outbreak. The adopted integrated control strategy consisted of dividing the herd according to the serological results obtained through the microscopic agglutination test, the treatment of reagents with streptomycin, and vaccination against leptospirosis of non-reagent heifers and cows. The method used to evaluate the economic indicators of the property was the calculation of the gross margin by taking into account the implicit and explicit cost parameters associated with the manifestation of leptospirosis. The prevalence rate of leptospirosis decreased from 49.4 % in 2015 to 21.6 % in 2017. There was a reduction in the abortion rate (from 40.00 % in 2014 to 9.00 % in 2017), in the stillborn rate (from 2.63 % in 2014 to 1.69 % in 2017) and an increase in the calving rate (from 65.00 % in 2014 to 86.00 % in 2017). In addition, there were increases in the number of lactating cows (from 38 in 2014-57 in 2017) and the mean times of lactation duration, which increased from 275 days in 2014-295 days in 2017. As a result, the average annual production of milk increased from 164,655 liters in 2014-248,521 liters in 2017. In 2014, when treatment hadn't yet started, the gross margin per liter of milk sold, considering implicit and explicit costs, was US$0.00. In 2015 and 2016, US$0.27 and US$0.30 were obtained, respectively, for this variable. In 2017, with the disease under control on the property, the gross margin per liter of milk reached US$0.36. The gross margin per liter of milk sold was higher in the period when the disease was controlled, showing losses of up to 84 % of the gross margin during the outbreak. Immediate treatment of positive cows and preventive measures had a significant impact on improving the productive and economic efficiency of the property.

Estimation of maternal and additive effect on reproduction and productive traits in Hardhenu cattle.

The study aimed to assess performance traits in Hardhenu cattle by analysing data from 445 animals born to 59 sires and 227 dams. The investigation focused on estimating (co)variance components and genetic parameters for reproduction and production traits in dairy cattle. Results from least-squares analysis indicated a significant effect (p < .01) of the period of calving (POC) on key production traits, including first lactation milk yield (FLMY), 300-day milk yield (FLMY300), first peak yield (FPY) and total lactation milk yield (TLMY) in studied population. The least squares means for these traits were reported as follows: FLMY (2665.68 ± 45.66 kg), FLMY300 (2425.52 ± 34.41 kg), FLL (312.95 ± 3.83 days), FPY (11.52 ± 0.15 kg) and TLMY (9282.44 ± 167.03 kg) in Hardhenu cattle. In the studied population, only additive genetic variability was found to be present and there was absence of any significant maternal effect with respect to targeted traits in the resource population. Direct heritability estimates (h2) for FLMY, FLMY300, FLL, FPY, TLMY and other traits ranged from 0.03 to 0.41 in Hardhenu cattle. These findings offer valuable insights into the genetic factors influencing performance traits, contributing to the enhancement of breeding and management practices in Hardhenu cattle.

Comparative analysis of computer vision algorithms for the real-time detection of digital dermatitis in dairy cows.

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the planar aspect of the hoof. DD is associated with massive herd outbreaks of lameness and influences cattle welfare and production. Early detection of DD can lead to prompt treatment and decrease lameness. Computer vision (CV) provides a unique opportunity to improve early detection. The study aims to train and compare applications for the real-time detection of DD in dairy cows. Eight CV models were trained for detection and scoring, compared using performance metrics and inference time, and the best model was automated for real-time detection using images and video. Images were collected from commercial dairy farms while facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system with distinct labels for hyperkeratosis (H) and proliferations (P). Two sets of images were compiled: the first dataset (Dataset 1) containing 1,177 M0/M4H and 1,050 M2/M2P images and the second dataset (Dataset 2) containing 240 M0, 17 M2, 51 M2P, 114 M4H, and 108 M4P images. Models were trained to detect and score DD lesions and compared for precision, recall, and mean average precision (mAP) in addition to inference time in frame per second (FPS). Seven of the nine CV models performed well compared to the ground truth of labeled images using Dataset 1. The six models, Faster R-CNN, Cascade R-CNN, YOLOv3, Tiny YOLOv3, YOLOv4, Tiny YOLOv4, and YOLOv5s achieved an mAP between 0.964 and 0.998, whereas the other two models, SSD and SSD Lite, yielded an mAP of 0.371 and 0.387 respectively. Overall, YOLOv4, Tiny YOLOv4, and YOLOv5s outperformed all other models with almost perfect precision, perfect recall, and a higher mAP. Tiny YOLOv4 outperformed all other models with respect to inference time at 333 FPS, followed by YOLOv5s at 133 FPS and YOLOv4 at 65 FPS. YOLOv4 and Tiny YOLOv4 performed better than YOLOv5s compared to the ground truth using Dataset 2. YOLOv4 and Tiny YOLOv4 yielded a similar mAP of 0.896 and 0.895, respectively. However, Tiny YOLOv4 achieved both higher precision and recall compared to YOLOv4. Finally, Tiny YOLOv4 was able to detect DD lesions on a commercial dairy farm with high performance and speed. The proposed CV tool can be used for early detection and prompt treatment of DD in dairy cows. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time DD detection on dairy farms.

Avian Influenza A(H5N1) Virus among Dairy Cattle, Texas, USA.

During March and April 2024, we studied dairy cattle specimens from a single farm in Texas, USA, using multiple molecular, cell culture, and next-generation sequencing pathogen detection techniques. Here, we report evidence that highly pathogenic avian influenza A(H5N1) virus strains of clade 2.3.4.4b were the sole cause of this epizootic.

Invited review: Advances in nutrition and feed additives to mitigate enteric methane emissions.

Methane, both enteric and from manure management, is the most important greenhouse gas from ruminant livestock, and its mitigation can deliver substantial decreases in the carbon footprint of animal products and potentially contribute to climate change mitigation. Although choices may be limited, certain feeding-related practices can substantially decrease livestock enteric CH4 emission. These practices can be generally classified into 2 categories: diet manipulation and feed additives. Within the first category, selection of forages and increasing forage digestibility are likely to decrease enteric CH4 emission, but the size of the effect, relative to current forage practices in the United States dairy industry, is likely to be minimal to moderate. An opportunity also exists to decrease enteric CH4 emissions by increasing dietary starch concentration, but interventions have to be weighed against potential decreases in milk fat yield and farm profitability. A similar conclusion can be made about dietary lipids and oilseeds, which are proven to decrease CH4 emission but can also have a negative effect on rumen fermentation, feed intake, and milk production and composition. Sufficient and robust scientific evidence indicates that some feed additives, specifically the CH4 inhibitor 3-nitrooxypropanol, can substantially reduce CH4 emissions from dairy and beef cattle. However, the long-term effects and external factors affecting the efficacy of the inhibitor need to be further studied. The practicality of mass-application of other mitigation practices with proven short-term efficacy (i.e., macroalgae) is currently unknown. One area that needs more research is how nutritional mitigation practices (both diet manipulation and feed additives) interact with each other and whether there is synergism among feed additives with different mode of action. Further, effects of diet on manure composition and greenhouse gas emissions during storage (e.g., emission trade-offs) have not been adequately studied. Overall, if currently available mitigation practices prove to deliver consistent results and novel, potent, and safe strategies are discovered and are practical, nutrition alone can deliver up to 60% reduction in enteric CH4 emissions from dairy farms in the United States.