Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify “heat-sensitive” animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood’s equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd’s chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/days more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.

Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow one to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post-peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large dataset, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

Early predictions of cows’ probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows’ first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle.

In recent years, the scientific community, institutions, and public opinion have shown a growing awareness towards the problem of antimicrobial resistance. Guidelines from the European Union aim at significantly reducing (even avoiding) the antimicrobial use for prophylactic and metaphylactic purposes in veterinary medicine and this represents an important issue for several intensive farming systems, such as the veal industry. This retrospective observational cohort study analyzed net carcass weight data at the slaughter of 618 veal calves belonging to 6 batches of animals fattened in an Italian commercial farm according to the number of individual antimicrobial treatments administered during the fattening cycle. Sixty-eight percent (419 animals) of the overall sample of calves received at least one individual antimicrobial treatment due to a disease event during the fattening, and 280 of these (45 % of the overall sample) had more than one treatment. On average, the net carcass weight of calves individually treated more than once was about 10 kg lower than that of untreated calves or treated only once. Moreover, the carcasses of calves that received more than one treatment were 16 times more at risk of being severely penalized in payment at the slaughterhouse due to a net weight below 110 kg than those of untreated calves. Serum analysis data of a subsample of 105 calves belonging to 3 out of the 6 batches collected within 5 days after their arrival to the veal farm identified 3 persistently infected animals by bovine viral diarrhea (BVD) virus and a prevalence of calves with serum immunoglobulin concentration below 7.5 g/L of 37 %. The presence of specific antibodies against infectious bovine rhinotracheitis (IBR) and bovine respiratory syncytial virus (BRSV) and a serum immunoglobulin level above 7.5 g/L at the onset of the fattening were the most important factors associated with a decreased risk of multiple individual antimicrobial treatments (and thus, disease development) or with a delay in disease onset during the fattening, thus leading to better perfor-mances at the slaughter. The outcomes of this study suggested that a relationship could exist between calf serostatus upon arrival to the veal farm and the possibility to avoid multiple individual antimicrobial treatments during fattening. Further research is needed to deeply study this association, as such knowledge would allow for planning effective preventing strategies to reduce the antimicrobial use in veal calves.