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.

这篇综述综合了有关对健康疾病的行为和生产反应以及随后使用自动（自动）挤奶系统（AMS）对牛群进行疾病检测的一系列研究发现。我们讨论了健康疾病对奶牛行为和生产的影响，特别是那些由AMS和相关技术常规记录的变量。该信息用于告知行为和生产变量的最终使用情况，并总结和批判当前的疾病检测研究。对于常规和AMS牧群，我们分别检查了过去20年的研究，并在农场上自动记录了那些可能对疾病和la行的发展做出反应的变量。确定的主要变量包括牛奶产量，反刍时间，活性和体重，以及成功的频率，拒绝，并在AMS牧群中获取（非自愿）挤奶。无论是在牛内进行比较，还是在病牛和健康牛之间进行比较，都可以观察到活性，反刍时间和产奶量持续下降。然而，me行对产奶量有明显的负面影响，但不一定对反刍时间或活性有负面影响。最后，我们讨论了使用AMS中常规收集的数据来识别me行症和其他健康疾病的检测模型，特别侧重于它们的科学验证以及任何需要进一步研究的研究局限性。在当前致力于疾病检测的研究中，许多数据已被排除或分离以用于孤立的模型（例如，新鲜奶牛，某些泌乳组以及患有多种疾病或中度病例的奶牛）。因此，未来的研究应（1）纳入整个泌乳群，同时考虑每只动物的泌乳阶段和胎次；（2）评估母牛表现出的偏离其自身基线轨迹以及相对于健康的当代人的偏离；（3）将几个变量的使用结合到健康警报中；（4）区分健康障碍的可能类型。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，这需要进一步的研究来弥合技术与畜群健康管理之间的差距。（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，这需要进一步的研究来弥合技术与畜群健康管理之间的差距。（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，这需要进一步的研究来弥合技术与畜群健康管理之间的差距。