Silva Flávio G, Conceição Cristina, Pereira Alfredo M F, Cerqueira Joaquim L, Silva Severiano R
Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal.
Mediterranean Institute for Agriculture, Environment and Development (MED), Universidade de Évora Pólo da Mitra, Apartado, 94, 7006-554 Évora, Portugal.
Animals (Basel). 2023 Mar 24;13(7):1148. doi: 10.3390/ani13071148.
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.
精准畜牧养殖（PLF）研究正在迅速增加，并改善了农民的生活质量、动物福利和生产效率。奶牛犊的PLF研究相对较新，但在过去几年中有所发展。自动喂奶系统（AMFS）和三维加速度计是奶牛犊中使用最广泛的技术。然而，奶牛犊研究中也出现了其他技术，如红外热成像（IRT）、三维相机、瘤胃丸和声音分析系统，这些技术在科学文献中尚未得到充分验证和综述。因此，通过本次综述，我们旨在分析犊牛技术应用的现状，重点关注奶牛犊。大多数研究集中在检测和预测犊牛健康问题以及监测疼痛指标的技术上。采食和躺卧行为有时与健康和福利水平相关。然而，由于其他因素，如喂奶量，可能会对这些行为产生不同影响，因此目前仍未形成共识。采用多技术方法的研究比仅关注单一技术的研究显示出更好的结果。如果有商业应用（据作者所知目前尚无），将不同技术与机器学习算法集成并自动化，可以提供更多科学知识，并有可能帮助农民改善犊牛的健康、性能和福利。
