School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, United Kingdom.
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, United Kingdom.
J Dairy Sci. 2019 Nov;102(11):10471-10482. doi: 10.3168/jds.2019-16442. Epub 2019 Aug 22.
In this study, we assessed for the first time the use of a reticuloruminal temperature bolus and a thresholding method to detect drinking events and investigated different factors that can affect drinking behavior. First, we validated the detection of drinking events using 16 cows that received a reticuloruminal bolus. For this, we collected continuous drinking behavior data for 4 d using video recordings and ambient and water temperature for the same 4 d. After all the data were synchronized, we performed 2 threshold algorithms: a general-fixed threshold and a cow-day specific threshold algorithm. In the general-fixed threshold, a positive test was considered if the temperature of any cow fell below a fixed threshold; in the cow-day specific threshold, a positive test was considered when the temperature of specific cows fell below the threshold value deviations around the mean temperature of the cow for that day. The former was evaluated using a threshold varying between 35.7 and 39.5°C, and the latter using the formula μ-n10σ, where µ = mean of the temperature of each cow for one day, n = 1, 2, …, 20, and σ = standard deviation of the temperature of each cow on that day. The performance of the validation of detection using each of the threshold types was computed using different metrics, including overall accuracy, precision, recall (also known as sensitivity), F-score, positive predictive value, negative predictive value, false discovery rate, false omission rate, and Cohen's kappa statistic. The findings of the first study showed that the cow-day specific threshold of n = 10 performed better (true positives = 466; false positives = 167; false negatives = 165; true negatives = 8,416) than using a general-fixed threshold of 38.1°C (true positives = 449; false positives = 181; false negatives = 182; true negatives = 8,402). With the information gained in this first study, we investigated the different factors associated with temperature drop characteristics per cow: number of drops, mean amplitude of the drop, and mean recovery time. For this, we used data from 54 cows collected for almost 1 yr to build a mixed-effect multilevel model that included days in milk, parity, average monthly milk production, and ambient temperature as explanatory variables. Cow characteristics and ambient temperature had significant effects on drinking events. Our results provide a platform for automated monitoring of drinking behavior, which has potential value in prediction of health and welfare in dairy cattle.
在这项研究中，我们首次评估了使用反刍体温探头和阈值方法来检测饮水事件，并研究了可能影响饮水行为的不同因素。首先，我们使用 16 头接受反刍体温探头的奶牛验证了饮水事件的检测。为此，我们使用视频记录收集了 4 天的连续饮水行为数据，以及相同 4 天的环境和水温数据。在所有数据同步后，我们进行了 2 种阈值算法：一种是通用固定阈值，另一种是奶牛日特定阈值算法。在通用固定阈值中，如果任何奶牛的体温低于固定阈值，则视为阳性测试；在奶牛日特定阈值中，如果特定奶牛的体温低于该日该奶牛体温平均值的阈值偏差值，则视为阳性测试。前者使用 35.7 至 39.5°C 之间变化的阈值进行评估，后者使用公式μ-n10σ，其中μ= 一天中每头奶牛的平均温度，n = 1、2、…、20，σ= 当天每头奶牛的温度标准差。使用不同的指标评估了使用每种阈值类型进行检测的验证性能，包括总体准确性、精度、召回率（也称为灵敏度）、F 分数、阳性预测值、阴性预测值、假发现率、假漏诊率和 Cohen's kappa 统计量。第一项研究的结果表明，n = 10 的奶牛日特定阈值的性能优于 38.1°C 的通用固定阈值（真阳性= 466；假阳性= 167；假阴性= 165；真阴性= 8416）（真阳性= 449；假阳性= 181；假阴性= 182；真阴性= 8402）。利用这项初步研究获得的信息，我们研究了与每头奶牛的温度下降特征相关的不同因素：下降次数、下降幅度的平均值和恢复时间的平均值。为此，我们使用了从 54 头奶牛收集的数据，这些数据采集了将近 1 年，以建立一个包含牛奶天数、胎次、平均每月牛奶产量和环境温度的混合效应多级模型作为解释变量。奶牛特征和环境温度对饮水事件有显著影响。我们的研究结果为自动监测奶牛的饮水行为提供了一个平台，这在预测奶牛的健康和福利方面具有潜在价值。
