Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.
J Dairy Sci. 2018 Sep;101(9):8605-8614. doi: 10.3168/jds.2018-14521. Epub 2018 Jun 28.
This review synthesizes a range of research findings regarding behavioral and production responses to health disorders and subsequent illness detection for herds using automatic (robotic) milking systems (AMS). We discuss the effects of health disorders on cow behavior and production, specifically those variables that are routinely recorded by AMS and associated technologies. This information is used to inform the resultant use of behavior and production variables and to summarize and critique current illness detection studies. For conventional and AMS herds separately, we examined research from the past 20 yr and those variables recorded automatically on-farm that may respond to development of illness and lameness. The main variables identified were milk yield, rumination time, activity, and body weight, in addition to frequency of successful, refused, and fetched (involuntary) milkings in AMS herds. Whether making comparisons within cow or between sick and healthy cows, consistent reductions in activity, rumination time, and milk yield are observed. Lameness, however, had obvious negative effects on milk yield but not necessarily on rumination time or activity. Finally, we discuss detection models for identifying lameness and other health disorders using routinely collected data in AMS, specifically focusing on their scientific validation and any study limitations that create a need for further research. Of the current studies that have worked toward disease detection, many data have been excluded or separated for isolated models (i.e., fresh cows, certain lactation groups, and cows with multiple illnesses or moderate cases). Thus, future studies should (1) incorporate the entire lactating herd while accounting for stage of lactation and parity of each animal; (2) evaluate the deviations that cows exhibit from their own baseline trajectories and relative to healthy contemporaries; (3) combine the use of several variables into health alerts; and (4) differentiate the probable type of health disorder. Most importantly, no model or software currently exists to integrate data and directly support decision-making, which requires further research to bridge the gap between technology and herd health management.
本综述综合了一系列关于使用自动（机器人）挤奶系统（AMS）对健康障碍和随后的疾病进行 herd 检测的行为和生产反应的研究结果。我们讨论了健康障碍对奶牛行为和生产的影响，特别是那些经常由 AMS 和相关技术记录的变量。这些信息用于告知行为和生产变量的使用，并总结和评价当前的疾病检测研究。我们分别检查了过去 20 年的常规和 AMS herd 的研究，以及在农场自动记录的可能对疾病和跛行发展做出反应的变量。在 AMS herd 中，主要确定的变量是产奶量、反刍时间、活动量和体重，此外还有成功、拒绝和提取（非自愿）挤奶的频率。无论是在奶牛内部进行比较，还是在患病奶牛和健康奶牛之间进行比较，都观察到活动量、反刍时间和产奶量的持续减少。然而，跛行对产奶量有明显的负面影响，但不一定对反刍时间或活动量有影响。最后，我们讨论了使用 AMS 中常规收集的数据识别跛行和其他健康障碍的检测模型，特别是关注其科学验证以及任何造成进一步研究需求的研究限制。在已经致力于疾病检测的当前研究中，许多数据被排除或分离用于孤立的模型（即，初产牛、特定泌乳组和患有多种疾病或中度疾病的奶牛）。因此，未来的研究应该（1）在考虑每个动物的泌乳阶段和胎次的情况下，纳入整个泌乳 herd；（2）评估奶牛相对于自身基线轨迹和健康同龄牛的偏差；（3）将几种变量结合到健康警报中；（4）区分可能的健康障碍类型。最重要的是，目前没有模型或软件可以集成数据并直接支持决策制定，这需要进一步的研究来弥合技术和 herd 健康管理之间的差距。
