Department of Veterinary Population Medicine, University of Minnesota, St. Paul 55108.
Department of Veterinary Population Medicine, University of Minnesota, St. Paul 55108.
J Dairy Sci. 2018 Sep;101(9):8135-8145. doi: 10.3168/jds.2017-13947. Epub 2018 Jul 13.
Group housing and computerized feeding of preweaned dairy calves are gaining in popularity among dairy producers, yet disease detection remains a challenge for this management system. The aim of this study was to investigate the application of statistical process control charting techniques to daily average feeding behavior to predict and detect illness and to describe the diagnostic test characteristics of using this technique to find a sick calf compared with detection by calf personnel. This prospective cross-sectional study was conducted on 10 farms in Minnesota (n = 4) and Virginia (n = 6) utilizing group housing and computerized feeding from February until October 2014. Calves were enrolled upon entrance to the group pen. Calf personnel recorded morbidity and mortality events. Farms were visited either every week (MN) or every other week (VA) to collect calf enrollment data, computer-derived feeding behavior data, and calf personnel-recorded calf morbidity and mortality. Standardized self-starting cumulative sum (CUSUM) charts were generated for each calf for each daily average feeding behavior, including drinking speed (mL/min), milk consumption (L/d), and visits to the feeder without a milk meal (no.). A testing subset of 352 calves (176 treated, 176 healthy) was first used to find CUSUM chart parameters that provided the highest diagnostic test sensitivity and best signal timing, which were then applied to all calves (n = 1,052). Generalized estimating equations were used to estimate the diagnostic test characteristics of a single negative mean CUSUM chart signal to detect a sick calf for a single feeding behavior. Combinations of feeding behavior signals were also explored. Single signals and combinations of signals that included drinking speed provided the most sensitive and timely signal, finding a sick calf up to an average (±SE) of 3.1 ± 8.8 d before calf personnel. However, there was no clear advantage to using CUSUM charting over calf observation for any one feeding behavior or combination of feeding behaviors when predictive values were considered. The results of this study suggest that, for the feeding behaviors monitored, the use of CUSUM control charts does not provide sufficient sensitivity or predictive values to detect a sick calf in a timely manner compared with calf personnel. This approach to examining daily average feeding behaviors cannot take the place of careful daily observation.
分组饲养和计算机喂养断奶前奶牛犊牛在奶牛养殖者中越来越受欢迎，但这种管理系统仍然存在疾病检测方面的挑战。本研究的目的是调查统计过程控制图表技术在每日平均喂养行为中的应用，以预测和检测疾病，并描述使用该技术检测患病犊牛的诊断测试特征与犊牛人员检测相比的情况。这项前瞻性的横断面研究于 2014 年 2 月至 10 月在明尼苏达州（n = 4）和弗吉尼亚州（n = 6）的 10 个农场进行，采用分组饲养和计算机喂养。犊牛在进入分组栏时就被纳入研究。犊牛人员记录发病率和死亡率事件。每星期（MN）或隔周（VA）访问农场，收集犊牛登记数据、计算机生成的喂养行为数据以及犊牛人员记录的犊牛发病率和死亡率。为每只犊牛的每个每日平均喂养行为（包括饮水速度[mL/min]、牛奶消耗量[L/d]和无奶餐访问次数[no.])生成标准的自启动累积和（CUSUM)图表。首先使用 352 只犊牛（176 只治疗，176 只健康）的测试子集来找到提供最高诊断测试灵敏度和最佳信号定时的 CUSUM 图表参数，然后将这些参数应用于所有犊牛（n = 1,052)。使用广义估计方程估计单个负均值 CUSUM 图表信号检测单个喂养行为中患病犊牛的诊断测试特征。还探索了喂养行为信号的组合。单一信号和包括饮水速度的信号组合提供了最敏感和及时的信号，在犊牛人员之前平均（±SE)提前 3.1 ± 8.8 天发现患病犊牛。然而，当考虑预测值时，对于任何一种喂养行为或喂养行为组合，使用 CUSUM 图表并不比犊牛人员观察具有明显优势。本研究的结果表明，对于监测的喂养行为，与犊牛人员相比，CUSUM 控制图在及时检测患病犊牛方面的灵敏度或预测值没有足够的优势。这种检查每日平均喂养行为的方法不能代替仔细的日常观察。
