Center for Animal Welfare, Department of Animal Science, University of California, Davis 95616; College of Agriculture, California State University, Chico 95929.
AgResearch Ltd., Hamilton 3240, New Zealand.
J Dairy Sci. 2020 Jun;103(6):5414-5421. doi: 10.3168/jds.2019-16667. Epub 2020 Apr 8.
Body temperature (BT) is widely used to evaluate health and heat load status in cattle. Despite its importance, studies vary in how BT is measured and in the biological interpretation of the results. Costs, practicality, labor, and welfare concerns can affect how BT is measured, including frequency of measurement and the type of device used. Inaccurate BT outcomes may have implications for cattle welfare; for example, animals may only receive treatment when fever is identified. Our objectives were (1) to compare measurement of vaginal temperature (VT) using relatively small, inexpensive, and low-accuracy loggers (±0.5 to ±1°C, iButton range; Embedded Data Systems, Lawrenceburg, KY) to a high-accuracy logger (±0.1°C; StarOddi, Gardabaer, Iceland), and (2) to evaluate how different BT sampling strategies correspond to 24-h VT in lactating dairy cows. To address the first objective, VT data from 54 cows were recorded every 45 min for 12 d/cow, on average, using 2 different types of temperature loggers (StarOddi DST centi-T and iButton DS1921H or DS1922L) attached to a shortened, hormone-free controlled internal drug release insert. Average VT obtained from both loggers were compared using mixed models and regression analyses. In addition, we tested the consistency of the low-accuracy loggers in detecting cows with elevated BT using the kappa coefficient of concordance. To address the second objective, VT data from 20 cows were recorded every min for 9 to 11 d/cow using StarOddi loggers. Using these data, we estimated average VT using 11 sampling strategies (every 5, 10, 15, 30, 45, 60, and 120 min, 1×/d recorded in the morning or afternoon, 2×/d, or 3×/d). Estimates and observed means were compared using linear regression. Compared with StarOddi loggers, the iButtons either underestimated (H model: 38.7 vs. 38.0 ± 0.06°C) or overestimated VT (L model: 38.7 vs. 39.2 ± 0.04°C). When considering elevated or fever VT thresholds, iButtons did not correctly classify animals; kappa coefficients of concordance were ≤0.35. Measuring VT as often as every 120 min resulted in more accurate estimates compared with strategies that recorded it once to thrice per day. These results indicate that the type of device (i.e., data logger) and sampling strategies affect BT outcomes and that these decisions affect the interpretation of BT data.
体温（BT）广泛用于评估牛的健康和热负荷状况。尽管其重要性，但关于 BT 的测量方法和结果的生物学解释在研究中存在差异。成本、实用性、劳动力和福利问题会影响 BT 的测量方式，包括测量频率和使用的设备类型。不准确的 BT 结果可能会对动物福利产生影响；例如，只有在发现发烧时，动物才会接受治疗。我们的目标是：（1）比较使用相对较小、廉价且精度较低的记录仪（±0.5 至±1°C，iButton 范围；Embedded Data Systems，Lawrenceburg，KY）测量阴道温度（VT）与使用高精度记录仪（±0.1°C；StarOddi，Gardabaer，Iceland）的结果，（2）评估不同的 BT 采样策略与泌乳奶牛 24 小时 VT 的对应关系。为了实现第一个目标，我们使用两种不同类型的温度记录仪（StarOddi DST centi-T 和 iButton DS1921H 或 DS1922L），平均每天每头奶牛记录 12 天/头，每 45 分钟记录一次 VT 数据。使用混合模型和回归分析比较两种记录仪的平均 VT。此外，我们使用一致性系数检验了低精度记录仪检测体温升高的奶牛的一致性。为了实现第二个目标，我们使用 StarOddi 记录仪，每天每头奶牛记录 9 到 11 天，每分钟记录一次 VT 数据。使用这些数据，我们使用 11 种采样策略（每 5、10、15、30、45、60 和 120 分钟，每天早上或下午记录 1×/次，每天记录 2×/次，或每天记录 3×/次）来估计平均 VT。使用线性回归比较估计值和观察平均值。与 StarOddi 记录仪相比，iButton 要么低估（H 模型：38.7 比 38.0 ± 0.06°C），要么高估 VT（L 模型：38.7 比 39.2 ± 0.04°C）。当考虑升高或发热的 VT 阈值时，iButton 不能正确分类动物；一致性系数的一致性≤0.35。与每天记录一次到三次相比，每 120 分钟测量一次 VT 可以得到更准确的估计值。这些结果表明，设备类型（即数据记录仪）和采样策略会影响 BT 结果，这些决策会影响 BT 数据的解释。
