Explore the vast range of titles available.

Oxidative stress (OS) plays a key role in the initiation or progression of numerous diseases, and dairy cows undergo OS at the transition period. However, discrepancies between methodologies make it difficult to make comparisons between studies, and therefore research on this topic may not be implemented in farms. This study aims to test under field conditions the use of an oxidative stress index (OSi) as a combined measurement through a ratio between pro-oxidants and antioxidants throughout the transition period in dairy farms. Serum samples of high-yielding dairy cows were taken, and markers of oxidative damage and antioxidant capacity were measured in four different production stages: (i) late lactation (LL; −2 to −1 months); (ii) prepartum (PrP; −1 month until parturition); (iii) postpartum (PsP; delivery to +1 month); and (iv) peak of lactation (PkL; +1 to +2.5 months). Values were compared between production stages and against a metabolic baseline status (CTR, 4th to 5th month of gestation). To the best of our knowledge, this is the first report in the literature that discusses the values of these oxidative stress biomarkers (and the OS index) for cows with low metabolic demands, as to date most research in this area has focused on the transition period. With the joint evaluation through the OSi, differences were found that were not present with the separate evaluation of pro-oxidants or antioxidants, thus supporting our hypothesis that the OSi indicates more accurately the oxidative status of the animals. It was also confirmed that dairy cows undergo OS after parturition, and that antioxidant supplementation from 1 month before parturition until the peak of lactation may be needed to reduce the risk of OS.

The objectives of this study were to 1) establish cow-level critical thresholds for serum concentrations of nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHBA) to predict periparturient diseases [displaced abomasa (DA), clinical ketosis (CK), metritis and retained placenta, or any of these three], and 2) investigate the magnitude of the metabolites’ association with these diseases within 30 d in milk. In a prospective cohort study of 100 freestall, total mixed ration-fed herds in the northeastern United States, blood samples were collected from approximately 15 prepartum and 15 different postpartum transition animals in each herd, for a total of 2,758 samples. Serum NEFA concentrations were measured in the prepartum group, and both NEFA and BHBA were measured in the postpartum group. The critical thresholds for NEFA or BHBA were evaluated with receiver operator characteristic analysis for all diseases in both cohorts. The risk ratios (RR) of a disease outcome given NEFA or BHBA concentrations and other covariates were modeled with multivariable regression techniques, accounting for clustering of cows within herds. The NEFA critical threshold that predicted any of the 3 diseases in the prepartum cohort was 0.29mEq/L and in the postpartum cohort was 0.57mEq/L. The critical threshold for serum BHBA in the postpartum cohort was 10mg/dL, which predicted any of the 3 diseases. All RR with NEFA as a predictor of disease were >1.8; however, RR were greatest in animals sampled postpartum (e.g., RR for DA=9.7; 95% CI=4.2 to 22.4. All RR with BHBA as the predictor of disease were >2.3 (e.g., RR for DA=6.9; 95% CI=3.7 to 12.9). Although prepartum NEFA and postpartum BHBA were both significantly associated with development of clinical disease, postpartum serum NEFA concentration was most associated with the risk of developing DA, CK, metritis, or retained placenta during the first 30 d in milk.

The effects of β-lactoglobulin (β-LG), β-casein (β-CN), and κ-CN variants and β-κ-CN haplotypes on the relative concentrations of the major milk proteins α-lactalbumin (α-LA), β-LG, αS1-CN, αS2-CN, β-CN, and κ-CN and milk production traits were estimated in the milk of 1,912 Dutch Holstein-Friesian cows. We show that in the Dutch Holstein-Friesian population, the allele frequencies have changed in the past 16 years. In addition, genetic variants and casein haplotypes have a major impact on the protein composition of milk and explain a considerable part of the genetic variation in milk protein composition. The β-LG genotype was associated with the relative concentrations of β-LG (A » B) and of α-LA, αS1-CN, αS2-CN, β-CN, and κ-CN (B>A) but not with any milk production trait. The β-CN genotype was associated with the relative concentrations of β-CN and αS2-CN (A2>A1) and of αS1-CN and κ-CN (A1>A2) and with protein yield (A2>A1). The κ-CN genotype was associated with the relative concentrations of κ-CN (B>E>A), αS2-CN (B>A), α-LA, and αS1-CN (A>B) and with protein percentage (B>A). Comparing the effects of casein haplotypes with the effects of single casein variants can provide better insight into what really underlies the effect of a variant on protein composition. We conclude that selection for both the β-LG genotype B and the β-κ-CN haplotype A2B will result in cows that produce milk that is more suitable for cheese production.

Because of technical limitations, an impact of machine milking on the teat tissue cannot be avoided. The continuance of this impact during and after milking depends on a variety of factors related to the physiological regulation of milk ejection, as well as the different production systems and milking machine settings. Milking machine settings aim to achieve a high milking performance, that is, short machine-on time at a maximum of milk harvest. However, a high milking performance level is often related to an impact on the teat tissue caused by vacuum or liner compression that can lead to pathological dimensions of congestion of the tissue or hyperkeratosis as a long-term effect. Toward the end of milking a decrease of milk flow rate causes a raise of mouthpiece and teat end vacuum levels and hence an increase of the impact on the teat tissue and the risk of tissue damage. The mechanical stress by the milking machine activates a cascade of cellular mechanisms that lead to an excessive keratin growth and thickening of the keratin layer. Consequently, a complete closure of the teat canal is disabled and the risk of bacterial invasion and intramammary infection increases. Another consequence of high vacuum impact is fluid accumulation and congestion in the tissue of teat tip and teat basis because of an obstruction in venous return. The present review paper provides an overview of the available scientific information to describe the interaction between different levels and types of system vacuum, mouthpiece chamber vacuum, teat end (claw) vacuum, liner pressure, and the risk of short-term and long-term impacts on the teat tissue.

Data from 1,010 lactating lactating, predominately component-fed Holstein cattle from 25 predominately tie-stall dairy farms in southwest Ontario were used to identify objective thresholds for defining hyperketonemia in lactating dairy cattle based on negative impacts on cow health, milk production, or both. Serum samples obtained during wk 1 and 2 postpartum and analyzed for β-hydroxybutyrate (BHBA) concentrations that were used in analysis. Data were time-ordered so that the serum samples were obtained at least 1 d before the disease or milk recording events. Serum BHBA cutpoints were constructed at 200μmol/L intervals between 600 and 2,000μmol/L. Critical cutpoints for the health analysis were determined based on the threshold having the greatest sum of sensitivity and specificity for predicting the disease occurrence. For the production outcomes, models for first test day milk yield, milk fat, and milk protein percentage were constructed including covariates of parity, precalving body condition score, season of calving, test day linear score, and the random effect of herd. Each cutpoint was tested in these models to determine the threshold with the greatest impact and least risk of a type 1 error. Serum BHBA concentrations at or above 1,200μmol/L in the first week following calving were associated with increased risks of subsequent displaced abomasum [odds ratio (OR)=2.60] and metritis (OR=3.35), whereas the critical threshold of BHBA in wk 2 postpartum on the risk of abomasal displacement was ≥1,800μmol/L (OR=6.22). The best threshold for predicting subsequent risk of clinical ketosis from serum obtained during wk 1 and wk 2 postpartum was 1,400μmol/L of BHBA (OR=4.25 and 5.98, respectively). There was no association between clinical mastitis and elevated serum BHBA in wk 1 or 2 postpartum, and there was no association between wk 2 BHBA and risk of metritis. Greater serum BHBA measured during the first and second week postcalving were associated with less milk yield, greater milk fat percentage, and less milk protein percentage on the first Dairy Herd Improvement test day of lactation. Impacts on first Dairy Herd Improvement test milk yield began at BHBA ≥1,200μmol/L for wk 1 samples and ≥1,400μmol/L for wk 2 samples. The greatest impact on yield occurred at 1,400μmol/L (−1.88kg/d) and 2,000μmol/L (−3.3kg/d) for sera from the first and second week postcalving, respectively. Hyperketonemia can be defined at 1,400μmol/L of BHBA and in the first 2 wk postpartum increases disease risk and results in substantial loss of milk yield in early lactation.

The objective of this study was to investigate the relationship between temperature–humidity index (THI) and rumination time (RT) in order to possibly exploit it as a useful tool for animal welfare improvement. During summer 2015 (1 June to 31 August), data from an Italian Holstein dairy farm located in the North of Italy were collected along with environmental data (i.e. ambient temperature and relative humidity) recorded with a weather station installed inside the barn. Rumination data were collected through the Heatime® HR system (SCR Engineers Ltd., Hadarim, Netanya, Israel), an automatic system composed of a neck collar with a Tag that records the RT and activity of each cow. A significant negative correlation was observed between RT and THI. Mixed linear models were fitted, including animal and test day as random effects, and parity, milk production level and date of last calving as fixed effects. A statistically significant effect of THI on RT was identified, with RT decreasing as THI increased.

Dairy cow behavior carries important health information. Timely and accurate detection of behaviors such as drinking, feeding, lying, and standing is meaningful for monitoring individual cows and herd management. In this study, a model called Res-DenseYOLO is proposed for accurately detecting the individual behavior of dairy cows living in cowsheds. Specifically, a dense module was integrated into the backbone network of YOLOv5 to strengthen feature extraction for actual cowshed environments. A CoordAtt attention mechanism and SioU loss function were added to enhance feature learning and training convergence. Multi-scale detection heads were designed to improve small target detection. The model was trained and tested on 5516 images collected from monitoring videos of a dairy cowshed. The experimental results showed that the performance of Res-DenseYOLO proposed in this paper is better than that of Fast-RCNN, SSD, YOLOv4, YOLOv7, and other detection models in terms of precision, recall, and mAP metrics. Specifically, Res-DenseYOLO achieved 94.7% precision, 91.2% recall, and 96.3% mAP, outperforming the baseline YOLOv5 model by 0.7%, 4.2%, and 3.7%, respectively. This research developed a useful solution for real-time and accurate detection of dairy cow behaviors with video monitoring only, providing valuable behavioral data for animal welfare and production management.

Lameness poses a considerable problem in modern dairy farming. Several new developments (e.g., herd health plans) strive to help farmers improve the health and welfare of their herd. It was thus our aim to identify lameness risk factors common across regions, breeds, and farming systems for freestall-housed dairy cows. We analyzed data from 103 nonorganic and organic dairy farms in Germany and Austria that kept 24 to 145 Holstein Friesian or Fleckvieh cows in the milking herd (mean=48). Data on housing, management, behavior, and lameness scores for a total of 3,514 cows were collected through direct observations and an interview. Mean lameness prevalence was 34% (range=0–81%). Data were analyzed applying logistic regression with generalized estimating equations in a split-sample design. The final model contained 1 animal-based parameter and 3 risk factors related to lying as well as 1 nutritional animal-based parameter, while correcting for the significant confounders parity and data subset. Risk for lameness increased with decreasing lying comfort, that is, more frequent abnormal lying behavior, mats or mattresses used as a stall base compared with deep-bedded stall bases, the presence of head lunge impediments, or neck rail–curb diagonals that were too short. Cows in the lowest body condition quartile (1.25–2.50 for Holstein Friesian and 2.50–3.50 for Fleckvieh) had the highest risk of being lame. In cross-validation the model correctly classified 71 and 70% of observations in the model-building and validation samples, respectively. Only 2 out of 15 significant odds ratios (including contrasts) changed direction. They pertained to the 2 variables with the highest P-values in the model. In conclusion, lying comfort and nutrition are key risk areas for lameness in freestall-housed dairy cows. Abnormal lying behavior in particular proved to be a good predictor of lameness risk and should thus be included in on-farm protocols. The study is part of the European Commission's Welfare Quality® project.

Our aim was to simultaneously investigate the gut bacteria typical characteristic and conduct rumen metabolites profiling of high production dairy cows when compared to low‐production dairy cows. The bacterial differences in rumen fluid and feces were identified by 16S rDNA gene sequencing. The metabolite differences were identified by metabolomics profiling with liquid chromatography mass spectrometry (LC‐MS). The results indicated that the high‐production dairy cows presented a lower rumen bacterial richness and species evenness when compared to low‐production dairy cows. At the phylum level, the high‐production cows increased the abundance of Proteobacteria and decreased the abundance of Bacteroidetes, SR1, Verrucomicrobia, Euryarchaeota, Planctomycetes, Synergistetes, and Chloroflexi significantly (p < 0.05). At the genus level, the rumen fluid of the high‐production group was significantly enriched for Butyrivibrio, Lachnospira, and Dialister (p < 0.05). Meanwhile, rumen fluid of high‐production group was depleted for Prevotella, Succiniclasticum, Ruminococcu, Coprococcus,YRC22, CF231, 02d06, Anaeroplasma, Selenomonas, and Ruminobacter significantly (p < 0.05). A total of 92 discriminant metabolites were identified between high‐production cows and low‐production cows. Compared to rumen fluid of low‐production dairy cows, 10 differential metabolites were found up‐regulated in rumen fluid of high‐production dairy cows, including 6alpha‐Fluoropregn‐4‐ene‐3,20‐dione, 3‐Octaprenyl‐4‐hydroxybenzoate, disopyramide, compound III(S), 1,2‐Dimyristyl‐sn‐glycerol, 7,10,13,16‐Docosatetraenoic acid, ferrous lactate, 6‐Deoxyerythronolide B, vitamin D2, L‐Olivosyl‐oleandolide. The remaining differential metabolites were found down‐regulated obviously in high‐production cows. Metabolic pathway analyses indicated that most increased abundances of rumen fluid metabolites of high‐yield cows were related to metabolic pathways involving biosynthesis of unsaturated fatty acids, steroid biosynthesis, ubiquinone and other terpenoid‐quinone biosynthesis. Most down‐regulated metabolic pathways were relevant to nucleotide metabolism, energy metabolism, lipid metabolism and biosynthesis of some antibiotics. Our aim was to simultaneously investigated the gut bacteria typical characteristic and conduct rumen metabolites profiling of high‐production dairy cows when compared to low‐production dairy cows. The aim of the study was to explore the typical gut bacteria and rumen typical metabolites matter of high‐production airy cows. After that, the results can be applied in the low‐yield dairy cows to improve their milking performance.

Increased rumination in dairy cattle has been associated with increased saliva production and improved rumen health. Most estimates of rumination are based on direct visual observations. Recently, an electronic system was developed that allows for automated monitoring of rumination in cattle. The objective was to validate the data generated by this electronic (Hi-Tag, SCR Engineers Ltd., Netanya, Israel) rumination monitoring system. Assessments of 2 independent observers were highly correlated (r=0.99, n=23), indicating that direct human observations were suitable as the reference method. Measures from the Hi-Tag electronic system were validated by comparing values with those from a human observer for fifty-one 2-h observation periods from 27 Holstein cows. Rumination times (35.1±3.2min) from the electronic system were highly correlated with those from direct observation (r=0.93, R2=0.87, n=51), indicating that the electronic system was an accurate tool for monitoring this behavior in dairy cows.