ABSTRACT
This research sought to determine the effect of selected milk protein polymorphisms on the reproduction parameters of 598 black and white Polish Holstein-Friesian cattle. The analyzed genes were kappa-casein (CSN3) and beta-lactoglobulin (BLG). The following reproductive indexes were selected: the age at first calving, the interpregnancy period, the calving interval, and the insemination index. The influence of the identified genotypes on the course of parturition in cows was also analyzed. Source data were collected for each cow from the beginning of the herd life and reproduction to its culling from the herd or the end of its third lactation. Data on the age at first calving, the amount of semen portions for artificial insemination (insemination index), the interpregnancy period, and the calving interval for each cow were also collected. A contingency analysis was performed through contingency tables using a Pearson's chi-squared test for each CSN3 and BLG genotype. The results show that the BB genotype of the kappa-casein gene was associated with the most favorable values of reproductive indicators. In the case of the calving interval, the values were significantly more favorable than those of other genotypes (p ≤ 0.05). No effect of beta-lactoglobulin polymorphism on the analyzed reproductive indices was observed. On the other hand, in the case of the interpregnancy period, significant statistical differences were obtained between the AA and BB genotypes. The analyzed kappa-casein and beta-lactoglobulin genotypes did not significantly influence the course of parturition in cows. To conclude, the genotype polymorphism BB CSN3 is the most favorable for the performance of the cows in the examined herd.
ABSTRACT
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.
ABSTRACT
The authors (Silva, S.R., et al.) unintentionally omitted to cite the article by O'Leary et al. [...].
ABSTRACT
Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.
