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.

断奶后的犊牛的集体饲养和计算机化饲喂在奶牛生产者中越来越流行，但是疾病检测仍然是该管理系统的一个挑战。这项研究的目的是调查统计过程控制制图技术在日平均饲喂行为中的应用，以预测和发现疾病，并描述与牛犊人员检测相比，使用该技术查找患病的牛犊的诊断测试特征。这项前瞻性的横断面研究是在明尼苏达州（n = 4）和弗吉尼亚州（n = 6）的10个农场进行的，采用小组饲养和计算机喂养，从2月至2014年10月进行。小牛人员记录了发病率和死亡率事件。每周（MN）或每隔一周（VA）对农场进行一次访问，以收集小牛的入场数据，计算机得出的喂养行为数据以及小牛人员记录的小牛发病率和死亡率。为每只小牛每天的平均摄食行为生成标准化的自启动累积总和（CUSUM）图表，包括饮水速度（mL / min），乳汁消耗量（L / d）和不带乳粉就餐的喂食器（无）。首先使用352个犊牛（176个已治疗，176个健康）的测试子集来查找提供最高诊断测试灵敏度和最佳信号时序的CUSUM图表参数，然后将其应用于所有犊牛（n = 1,052）。使用广义估计方程式估计单个负均值CUSUM图表信号的诊断测试特征，以检测单个进食行为的病牛犊。还研究了进食行为信号的组合。单个信号和包括饮酒速度在内的信号组合提供了最灵敏，最及时的信号，在小牛工作人员发现小牛病之前，平均（±SE）达3.1±8.8 d。但是，考虑到预测值时，对于任何一种进食行为或进食行为组合，使用CUSUM图表比小腿观察都没有明显的优势。这项研究的结果表明，与小牛工作人员相比，对于所监控的进食行为，使用CUSUM控制图不能提供足够的灵敏度或预测值来及时检测患病的小牛。这种检查每日平均进食行为的方法不能代替每天进行仔细的观察。