Evaluating variations in metabolic profiles during the dry period related to the time of hyperketonemia onset in dairy cows

Hyperketonemia (HYK) in early lactation can have a different impact on health and productivity depending on the timing of HYK onset. While specific metabolites measured during the dry period may serve as biomarkers of HYK, the correlations between metabolites represent a challenge for the use of metabolic profiles dataset, and little has been explored on HYK. This exploratory cohort study aimed a) to characterize the correlations among metabolites measured during the late dry period in dairy cows, and b) to identify biomarkers in the late dry period associated with the onset of HYK at the first (wk1) and second (wk2) week of lactation. Individual blood samples from 440 Holstein dairy cows were collected at 21 ± 3 days before expected parturition. From each sample, 36 different metabolites were measured in serum and plasma. Hyperketonemia was diagnosed in wk1 and wk2 of lactation based on the blood concentration of beta-hydroxybutyrate (BHB > 1.2 mmol/L). Principal component analysis (PCA) was performed to reduce metabolites to a smaller number of uncorrelated components. Multivariable logistic regression models were applied to assess the associations between principal components (PC) and HYK at wk1 only (HYK+ wk1), wk2 only (HYK+ wk2), or both weeks (HYK+ wk1-2). The incidence of HYK was 16.2% in the first week, 13.0% in the second week, and 21.2% within the first two weeks of lactation. The results of PCA highlighted 10 PCs from which two were associated with HYK+ wk1 as compared with cows without HYK during the first two weeks of lactation (non-HYK); the PC a2 led by bilirubin and non-esterified fatty acids (OR = 1.29; 95%CI: 1.02-1.68), and the PC a5 led by alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) (OR = 2.77; 95%CI: 1.61-4.97). There was no evidence of an association between any PC and HYK+ wk2 (vs. non-HYK cows). Cows with elevated PC a5 (led by ALP and GGT) in the dry period were 3.18 times more likely to be HYK+ wk1 than HYK+ wk2 (OR: 3.18, 95%CI: 1.34-8.73; P = 0.013). Overall, the main hypothesis generated by our exploratory study suggests that cows with biomarkers of liver dysfunction (ALP, GGT, bilirubin) assessed by PCA at 3 weeks before calving are more likely to develop HYK during the first week of lactation compared to the second week. In addition, results suggest that cows with HYK in both of the first two weeks of lactation had an overall metabolic disbalance during the onset of the late dry period, which based on PCs, encompass biomarkers related to glucogenic and ketogenic metabolic pathways as well as liver dysfunction and fatty liver. Further research is needed to determine the underlying mechanisms associated with the different adaptations between cows that develop HYK during the first and second week of lactation.

Examination of milk microbiota, fecal microbiota, and blood metabolites of Jersey cows in cool and hot seasons

Microbiota of individual cow milk, bulk tank milk, and feces of Jersey cows were examined. Samples were collected from two farms (F1 and F2) in cool (November, Nov) and hot (July, Jul) seasons. Milk yield and milk composition were similar between the two farms and between the two seasons. Prevalent taxa of the fecal microbiota, i.e. Ruminococcaceae, Bacteroidaceae, Lachnospiraceae, Rikenellaceae, and Clostridiaceae, were unaffected by the farm and season. Relative abundance of milk microbiota for Pseudomonadaceae, Enterobacteriaceae, and Streptococcaceae (F1 > F2) and Lactobacillaceae, Bifidobacteriaceae, and Cellulomonadaceae (F1 < F2) were different between the two farms, and those for Staphylococcaceae, Bacillaceae, Ruminococcaceae, and Veillonellaceae (Nov < Jul) and Methylobacteriaceae and Moraxellaceae (Nov > Jul) were different between the two seasons. The microbiota of bulk tank milk was numerically different from that of individual cow milk. Principal coordinate analysis indicated that the milk microbiota was unrelated to the fecal microbiota. The finding that relative abundance of Pseudomonadaceae and Moraxellaceae appeared greater than those reported for Holstein milk suggested that higher protein and fat content may result in a greater abundance of proteolytic and lipolytic taxa in Jersey cow milk.

Influence of feeding methods on blood biochemical parameters of Japanese Black breeding herd

The aim of this study was to investigate the effect of different feeding methods on blood biochemical parameters by using a total of 32 Japanese Black breeding cows. The diet (silage) was distributed at a rough estimate for 18 days (FN period), and stanchions were not used in this period. After the FN period, the cows were separated in stanchions for feeding and were fed specified amount of silage by using scales on a feeder wagon, which was determined according to the diet formulation and average body weight of cows, for 30 days (FW period). On the last day of the two experiment periods, the body weight of all cows was recorded and blood samples were collected from 10 randomly selected cows. β-hydroxybutyric acid and albumin (Alb) levels were significantly higher after the FW period than after FN period. The variation of glucose, Alb, calcium and lactic acid was significantly smaller after the FW period. Several blood parameters of the FW period were approximately the appropriate range of a Japanese Black breeding herd in the dry period. Our results suggest that the feeding method by use of a stanchion and proper diet formulation affects blood biochemical parameters and improves nutritional conditions for breeding cows.

Invited review: Big Data in precision dairy farming

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V’s) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V’s) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

Differences in rumen fermentation characteristics between low-yield and high-yield dairy cows in early lactation

Relationship between rumen fermentation parameters, blood biochemical profiles and milk production traits in different yielding dairy cows during early lactation was investigated. Twelve dairy cows were divided into two groups based on their milk yield, that is low-yield (LY) and high-yield (HY) groups. Rumen fluid and blood were collected at 3 weeks prepartum and 4, 8 and 12 weeks postpartum. Results showed that proportions of acetate, propionate to total short chain fatty acids and acetate : propionate ratio were changed (P < 0.05) in both groups during the peripartum period, whereas butyrate and acetate : butyrate ratio were only altered in the HY group. Blood cholesterol, beta-hydroxybutyric acid (BHBA) and glutamic oxaloacetic transaminase in the HY group were higher (P < 0.01) than those in the LY group. Principal component analysis revealed that milk yield and milk compositions were differently clustered between groups. These parameters showed similar direction with dry matter intake in the HY group and adverse direction in the LY group. Linear regression analysis indicated that butyrate was positively correlated with BHBA (P < 0.05) in the HY group. This study suggests that cows in the HY group seem to accommodate appropriately to negative energy balance in early lactation through rumen fermentation.