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.

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.

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.

Numerous publications have highlighted the potential role of grassland in the mitigation of the greenhouse gas (GHG) emissions of the agricultural sector. Yet, the preservation of grassland is included in the main objectives of the greening policies of the European Union (EU). To assess how this greening program has influenced the behaviour and perceptions of the dairy sector, two surveys were foreseen during the project LIFE dairyclim and sent to all the dairy farmers of Wallonia (Belgium) at three-year intervals (2016 – 2019). The answer rates reached 32.6%. Questions concerned four topics: General farm description, grazing practices, perceptions and expectations, and general grassland management. Following the results of these surveys, a large majority of lactating dairy cows grazed in Wallonia (96.5%). The main differences between 2016 and 2019 were the increase in size of the dairy farms in terms of surface (77.2 ha in 2016 to 84.4 ha in 2019) and production, the disappearance of small farms of less than 60 cows, and the progression of the organic sector from 9.3% to 11.7%. Perceptions of farmers on grazing differed following their grazing practices: No-grazing farmers were very sceptical about the advantages of grazing. On the opposite hand, organic farmers were very enthusiastic. Awareness of farmers about the preservation of landscape and the image of the sector for the consumers has increased during the three-year period. The requirements of the dairy industry and authorities were cited as incentives to keep grazing. The expectations of the farmers differed from 2016 to 2019 with a smaller proportion desiring to increase their grazing practices and more, staying stable. These results seem to indicate that the support of farmers and the involvement of authorities and stakeholders could contribute to maintain grazing.

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 B₁₂ 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.