DataSheet_1_The impact of health disorders on automated sensor measures and feed intake in lactating Holstein dairy cattle.docx

Animal health and feed intake are closely interrelated, with the latter being an important indicator of an animal’s health status. Automated sensors for dairy cattle have been developed to detect changes in indicators of health, such as decreased rumination or activity. Previous studies have identified associations between sensor measurements and feed intake. Thus, the objective of this study was to determine if health disorders impact the associations identified between sensors and dry matter intake (DMI), and to measure the impact of health disorders on DMI. A total of 934 cows with health disorders (lameness, mastitis, and other), of which 57, 94, and 333 cows had observations for a rumen bolus and one of two ear tags, were analyzed to determine how health disorders impact the association of sensors with DMI. Eleven sensor measurements were collected across the three sensors, including total and point-in-time activity, rumination time, inner-ear temperature, rumen pH and rumen temperature. Associations of health disorders and sensor measures with DMI were evaluated when accounting for systematic effects (i.e., contemporary group, parity, and days in milk) and energy sinks accounted for in determination of feed efficiency (e.g., milk production, body weight and composition). In order to determine if inclusion of health disorders or sensor measures improved model fit, model AICs were assessed. Health disorders were significantly associated with all sensor measurements (P< 0.0001), with the direction of association dependent on sensor measure and health disorder. Moreover, DMI decreased with all health disorders, with larger impacts observed in animals in third and higher lactations. Numerous sensor measurements were associated with DMI, including when DMI was adjusted for energy sink variables and health. Inclusion of rumen bolus temperature, rumination or activity with health data reduced model AIC when evaluating DMI as the dependent variable. Some sensor measures, including measurements of activity, temperature and rumination, accounted for additional variation in feed intake when adjusted for health disorders. Results from the study indicate that feed intake and sensor measures are impacted by health disorders. These findings may have implications for use of sensors in genetic evaluations and precision feeding of dairy cattle.

动物健康与饲料摄入密切相关，后者是评估动物健康状况的重要指标。针对乳牛，已开发出自动化传感器以检测健康指标的变化，如反刍减少或活动量降低。先前的研究已确认传感器测量值与饲料摄入之间的相关性。因此，本研究旨在确定健康问题是否会影响传感器与干物质摄入量（DMI）之间的关联，并测量健康问题对DMI的影响。共分析了934头患有健康问题的奶牛（包括跛行、乳腺炎及其他疾病），其中57头、94头和333头奶牛有瘤胃球观察记录以及其中一种耳标的数据，以确定健康问题如何影响传感器与DMI的关联。在三个传感器上收集了11项传感器测量值，包括总活动量和实时活动量、反刍时间、内耳温度、瘤胃pH值和瘤胃温度。在考虑系统效应（即同期组、胎次和产奶天数）以及饲料效率测定中的能量损失（例如，产奶量、体重和组成）时，评估了健康问题与传感器测量值与DMI之间的关联。为了确定包含健康问题或传感器测量值是否改善了模型拟合度，评估了模型AIC值。健康问题与所有传感器测量值均显著相关（P<0.0001），关联的方向取决于传感器测量值和健康问题。此外，DMI在所有健康问题下均有所降低，第三胎及以上胎次的动物影响更大。许多传感器测量值与DMI相关，包括在调整了能量损失变量和健康因素后的DMI。在评估DMI作为因变量时，包含瘤胃球温度、与健康数据相结合的反刍或活动量可降低模型AIC值。一些传感器测量值，包括活动量、温度和反刍量的测量，在调整了健康问题后，可解释饲料摄入量的额外变异。研究结果指出，饲料摄入量和传感器测量值均受健康问题的影响。这些发现可能对传感器在遗传评估和精准饲养乳牛中的应用具有重要意义。