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In dairy farms automatic milking systems and grazing, traffic to the robot is the cornerstone of profitability as higher milking frequency enhances milk yield. In this study, we investigated whether shortening the minimum milking interval (MMI), i.e., the required time between two milkings for an animal to get access to the milking unit, coupled with high concentrate allocation, could increase the daily milking frequency (MF, milking/cow/day) and consequently the milk yield of grazing cows. Two groups of cows ( n = 19 and n = 20) belonging to the same herd were discriminated based on concentrate supply (high vs. low: 4 vs. 2 kg/cow/day) and then further divided on the basis of MMI (4 h vs. 6 h) so that four groups were formed (HC4 h-HC6 h-LC4 h and finally LC6 h). Higher concentrate allocation induced a rise in milk yield (MY, kg/cow/day) and allowed to stabilize it in periods of grass shortage but did not influence milking frequency, while shorter MMI (4 h) was correlated with higher MF without effect on MY. A combination of both strategies (4 h and high concentrate) improved the traffic globally to the robot. This result was linked to a reduction of refused milking and, therefore, the decrease in returns to the robot. This strategy could be advised to maximize the system's efficiency during periods of high milk sales. When the economic conditions do not favour the increase in concentrate supply, short MMI could facilitate the traffic and increase the efficiency of returns.

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

The objective of this paper is to study the influence of physiological factors that affect the energy balance, such as lactation stage and parity, on milk yield and composition, milk and blood fatty acid concentrations, blood metabolites and hormones in healthy early lactation cows. Thirty-two Holstein dairy cows from five Belgian commercial farms were followed. The grass silage-based diets fed to cows fell within normal composition ranges typically offered to dairy cows on commercial dairy farms in the region. Milk and blood were sampled at each official milk recording and used for the determination of milk fat and protein, milk and blood fatty acids, blood metabolites and hormones concentrations. The considered period was 7 to 150 days in milk. As lactation progressed, concentrations of milk 18:0 and 18:1c9, as well as blood non-esterified fatty acids and β-hydroxybutyrate, decreased, and those of milk C4-C14, blood cholesterol, triglycerides, insulin and IGF-I increased, agreeing with the extensive mobilization of body reserves in early lactation. Lower concentrations of milk C4-C14 and 16:0 and concomitant higher concentrations of milk 18:0 and 18:1c9 suggest a larger body reserve mobilization in first parity cows compared with greater than or equal to second parity cows. This study confirms that early lactation stage along with parity significantly influence milk fatty acids, such as 18:1, and blood metabolites and hormones, such as NEFA and insulin.

More dairy farms (up to more than one in four in some countries) are equipped with automatic milking systems (AMS) worldwide. Because of the positive impacts of grazing, e.g., on animal welfare or on production costs, numerous researchers have published papers on the combination of AMS with grazing. However, pasture-based AMS usually causes a reduction in milking frequency (MF) compared to indoors systems. The objectives of this meta-analysis were to review publications on the impacts of pasture-based AMS on MF and mitigation strategies. First, data from 43 selected studies were gathered in a dataset including 14 parameters, and on which a Principal Component Analysis (PCA) was performed, leading to the description of four clusters summarizing different management practices. Multiple pairwise comparisons were performed to determine the relationship between the highlighted parameters of MF on milk yield (MY). From these different analyses, the relationship between MF and MY was confirmed, the systems, i.e., Clusters 1 and 2, that experienced the lowest MF also demonstrated the lowest MY/cow per day. In these clusters, grazed grass was an essential component of the cow's diet and low feeding costs compensated MY reduction. The management options described in Clusters 3 and 4 allowed maintenance of MF and MY by complementing the cows' diets with concentrates or partial mixed ration supplied at the AMS feeding bin or provided at barn. The chosen management options were closely linked to the geographical origin of the papers indicating that other factors (e.g., climatic conditions or available grasslands) could be decisional key points for AMS management strategies., Competing Interests: The authors declare no conflict of interest.

The objective of this work was to observe the effect of three different dry-period diets on blood metabolites ( p = 9) and the production and fatty acid (FA) profile of milk ( p = 19) in the peripartum period. In this study, 32 Holstein dairy cows, during their dry period, were divided in 3 different diet groups, as follows: the CONC diet (n = 11) was based on concentrate meal and straw, the CORN diet (n = 11) was based on corn silage, and the MIXED diet (n = 10) was based on corn and grass silages. According to our results, the variations of C18:2n-6, C18:3n-3, non-esterified fatty acid (NEFA), NEFA/cholesterol ratio, and albumin were significantly ( p < 0.05) different, according to the dry diet. The dry-period diet also had a significant effect on the concentrations of urea and vitamin B 12 in the blood. In early lactation, this work showed that blood metabolites were more sensitive to changes in the dry diet than the production and FA profile of milk.