Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

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.

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.

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.

Contexte : L’augmentation régulière depuis plusieurs décennies de la température environnementale s’est accompagnée dans l’espèce bovine, notamment d’une diminution de la fertilité dans différentes régions du monde. Objectifs : Cette revue de littérature passe en revue les divers effets et leurs mécanismes sur les divers aspects de la reproduction du mâle et de la femelle bovine. Méthode : À partir de la base PubMed, cette revue de littérature s’est concentrée dans un premier temps sur les articles de synthèse puis a été complétée par les références des articles identifiés. Résultats : La diminution de la fertilité résulte des effets du stress thermique sur la croissance folliculaire ovarienne, avec une incidence plus marquée sur les follicules pré-antraux que sur les follicules antraux, l’ovocyte et le corps jaune. Ces effets sont la conséquence d’une réduction de la synthèse d’oestradiol, de la progestérone, de l’inhibine et de l’hormone lutéotrope (LH) ainsi que de l’augmentation de celle de l’hormone folliculostimulante (FSH), une modification des gradients de température au niveau du système génital n’étant pas non plus à exclure. Le stress thermique est également responsable d’une augmentation de la fréquence de la mortalité embryonnaire particulièrement durant la première semaine de la gestation. L’exposition des vaches à un stress thermique durant la gestation en raccourcit la durée, affecte le placenta, modifie le métabolisme de la vache lors de sa lactation, altère son immunité et exerce des effets négatifs sur la croissance pondérale, le développement mammaire et folliculaire, la longévité et les performances de reproduction du veau. Chez le mâle, le stress thermique se traduit par une réduction de la motilité et une augmentation des anomalies morphologiques des spermatozoïdes. Conclusions : La reconnaissance croissante des effets du stress thermique sur les performances reproductives de bovins souligne la nécessité pour les responsables de la santé animale de les intégrer dans leurs approches. Il est essentiel de mettre en place des stratégies visant à en atténuer les impacts sur la rentabilité des élevages, d’autant plus que son influence épigénétique sur les performances de la descendance est de mieux en mieux documentée.