Predicting Body Weight in Pre-Weaned Holstein-Friesian Calves Using Morphometric Measurements.

Regularly weighing calves helps to assess the efficiency of the rearing period and contributes to animal welfare by allowing more precise feeding and medication application in dairy farming, but many farmers do not weigh their calves regularly. Improving the feasibility of this process is, therefore, important. The use of morphometric measurements has been used to estimate the weight of cattle. However, many studies have focused on adult animals or used a wide age range. As calves experience allometric tissue growth, specific models for certain ranges might be more accurate. Therefore, the aim of this work was to develop a weight estimation model specific for pre-weaned Holstein-Friesian calves using morphometric measurements and to compare the model with another equation previously validated for the same breed with young and adult animals. From four dairy farms, 237 measurements of body weight, heart girth, abdominal girth, hip height, withers height, and body length were taken from Holstein-Friesian male and female calves. Linear and non-linear regression analysis was used to test the relationship between body weight and morphometric measurements, with age, sex, and farm as possible explanatory variables. Selected models were compared with goodness of fit and agreement tests. The final model was able to accurately predict body weight (R2 = 0.96) with a mean difference of -1.4 ± 3.24 kg. Differences in the relationship between body weight and morphometric traits were observed between farms, but not between males and females. The genetics of the animal population at farm level may be responsible for this variability and further studies are needed to understand this variability and improve weight prediction models. The developed model was able to perform better in the agreement tests than the previously validated model for Holstein-Friesian animals, suggesting that different equations should be used depending on the growth phase the animal is in. In addition, a web application has been developed to facilitate the use of the developed model by farmers. This avoids the use of calibrated weight bands, which are usually calibrated for a broader age range or for beef cattle.

A Comprehensive Review of Bovine Colostrum Components and Selected Aspects Regarding Their Impact on Neonatal Calf Physiology.

Colostrum contains macro- and micronutrients necessary to meet the nutritional and energy requirements of the neonatal calf, bioactive components that intervene in several physiological aspects, and cells and microorganisms that modulate the calf's immune system and gut microbiome. Colostrum is sometimes mistaken as transition milk, which, although more nutritive than whole milk, has a distinct biochemical composition. Furthermore, most research about colostrum quality and colostrum management focuses on the transfer of maternal IgG to the newborn calf. The remaining components of colostrum and transition milk have not received the same attention, despite their importance to the newborn animal. In this narrative review, a large body of literature on the components of bovine colostrum was reviewed. The variability of these components was summarized, emphasizing specific components that warrant deeper exploration. In addition, the effects of each component present in colostrum and transition milk on several key physiological aspects of the newborn calf are discussed.

Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare.

Precision livestock farming (PLF) research is rapidly increasing and has improved farmers' quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves' research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves' health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves' health, performance, and welfare, if commercial applications are available, which, from the authors' knowledge, are not at the moment.