Animal Nutrition and Welfare Service, Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain.
Universitat Pompeu Fabra, Barcelona 08002, Spain.
J Dairy Sci. 2020 Feb;103(2):1874-1883. doi: 10.3168/jds.2019-16887. Epub 2019 Sep 11.
The objective of this study was to analyze whether changes in behavior can be a good early predictor of sickness in calves. Friesian males calves (n = 325; 30 ± 9 d of age; 65 ± 15 kg) were monitored with an activity-monitoring device from 30 to 90 d of life in 4 periods corresponding to 4 seasons. The activity-monitoring device measured number of steps, number of lying bouts, lying time, and frequency and time of visits to the feed bunk. Calf health status was monitored daily and all incidences were recorded. To compare sick and healthy calves, a matched pair design was used to assign calves into the healthy group. Day 0 was defined as the day of sickness diagnosis. For each sick calf, 3 calves with no signs of sickness during the entire period (healthy calves) on the same date, in the same season, and of similar age (±4 d) and weight at entry were identified. A multivariate linear mixed model was used from d -10 to +10 relative to the sickness diagnosis to describe differences between sick and healthy calves. A multivariate logistic regression model was used for predicting sick calves on the days before the diagnosis. Significance was declared at P < 0.05. Daily, healthy calves had 1,476 ± 195 steps, spent 185 ± 32.5 min at the feed bunk, consumed 10 ± 1.1 meals, had 19.5 ± 1.8 lying bouts, and spent an average of 978 ± 30.5 min lying. The difference in behavior between sick (n = 33) and healthy calves (n = 99) began to be evident on d -10. Sick calves had fewer steps and numbers of visits to the feed bunk on d -1 and 0 and spent less time at the feed bunk on d -10 and -1 compared with healthy calves. From d -2 to d 9, sick calves had 15% fewer lying bouts, with no difference in lying time except on d -10, when sick calves spent more time lying. The best prediction model was for d -1 and included season and age at entry as qualifying variables, and frequency of visits to the feed bunk, steps, and lying time as behavior predictors (69% sensitivity, 72% specificity, 72% accuracy, 55% false discovery rate, and 12% false omission rate). However, an earlier prediction would be more useful to reduce the negative effect of sickness on production and welfare. The prediction model for d -10 had 67% sensitivity, 67% specificity, 67% accuracy, 60% false discovery rate, and 14% false omission rate. Results indicate that the occurrence of sickness can be predicted in advance, and an automated alarm system could be used to identify calves at risk of becoming sick and apply a preventive treatment.
本研究旨在分析行为变化是否可以作为犊牛患病的早期预测指标。从 30 日龄到 90 日龄，325 头弗里斯兰公犊（n=325；30±9 日龄；65±15kg）使用活动监测设备进行监测，分为 4 个时期，对应 4 个季节。活动监测设备测量步数、卧姿次数、卧姿时间、以及采食时间和采食次数。每天监测犊牛的健康状况，并记录所有发病情况。为了比较患病和健康的犊牛，使用配对设计将犊牛分配到健康组。第 0 天定义为患病诊断的日期。对于每只患病的犊牛，在同一天、同一季节，选择无患病迹象的 3 只（健康犊牛），并与患病犊牛具有相似的年龄（±4d）和体重（±4d）。在患病诊断前 10 天到后 10 天期间，使用多元线性混合模型描述患病和健康犊牛之间的差异。使用多元逻辑回归模型预测患病前几天的患病犊牛。P<0.05 表示差异有统计学意义。每天，健康犊牛有 1476±195 步，在饲料槽处花费 185±32.5min，采食 10±1.1 次，有 19.5±1.8 次卧姿，平均有 978±30.5min 处于卧姿。患病（n=33）和健康（n=99）犊牛之间的行为差异从第-10 天开始变得明显。患病犊牛在第 1 天和第 0 天的步数和采食次数减少，在第-10 天和第-1 天在饲料槽处的停留时间减少。从第-2 天到第 9 天，患病犊牛的卧姿次数减少了 15%，除了第-10 天，卧姿时间没有差异，患病犊牛的卧姿时间更长。最佳预测模型为第-1 天，包含进入时的季节和年龄作为定性变量，以及采食次数、步数和卧姿时间作为行为预测因子（69%的敏感性、72%的特异性、72%的准确性、55%的假阳性率和 12%的假阴性率）。然而，更早的预测将更有助于减少疾病对生产和福利的负面影响。第-10 天的预测模型的敏感性为 67%，特异性为 67%，准确性为 67%，假阳性率为 60%，假阴性率为 14%。结果表明，疾病的发生可以提前预测，可以使用自动报警系统识别出有患病风险的犊牛，并进行预防性治疗。
