Department of Animal Science, University of Minnesota, St Paul 55108, USA.
J Dairy Sci. 2009 Dec;92(12):5964-76. doi: 10.3168/jds.2009-2066.
This study evaluates the changes in milk production (yield; MY) and milk electrical conductivity (MEC) before and after disease diagnosis and proposes a cow health monitoring scheme based on observing individual daily MY and MEC. All reproductive and health events were recorded on occurrence, and MY and MEC were collected at each milking from January 2004 through November 2006 for 587 cows. The first 24 mo (January 2004 until December 2005) were used to investigate the effects of disease on MY and MEC, model MY and MEC of healthy animals, and develop a health monitoring scheme to detect disease based on changes in a cow's MY or MEC. The remaining 11 mo of data (January to November 2006) were used to compare the performance of the health monitoring schemes developed in this study to the disease detection system currently used on the farm. Mixed model was used to examine the effect of diseases on MY and MEC. Days in milk (DIM), DIM x DIM, and ambient temperature were entered as quantitative variables and number of calves, parity, calving difficulty, day relative to breeding, day of somatotropin treatment, and 25 health event categories were entered as categorical variables. Significant changes in MY and MEC were observed as early as 10 and 9 d before diagnosis. Greatest cumulative effect on MY over the 59-d evaluation period was estimated for miscellaneous digestive disorders (mainly diarrhea) and udder scald, at -304.42 and -304.17 kg, respectively. The greatest average daily effect was estimated for milk fever with a 10.36-kg decrease in MY and 8.3% increase in MEC. Milk yield and MEC was modeled by an autoregressive model using a subset of healthy cow records. Six different self-starting cumulative sum and Shewhart charting schemes were designed using 3 different specificities (98, 99, and 99.5%) and based on MY alone or MY and MEC. Monitoring schemes developed in this study issue alerts earlier relative to the day of diagnosis of udder, reproductive, or metabolic problems, are more sensitive, and give fewer false-positive alerts than the disease detection system currently used on the farm.
本研究评估了疾病诊断前后牛奶产量（产奶量；MY）和牛奶电导率（MEC）的变化，并提出了一种基于观察个体每日 MY 和 MEC 的奶牛健康监测方案。所有生殖和健康事件均在发生时记录，并于 2004 年 1 月至 2006 年 11 月期间，每挤奶一次采集 587 头奶牛的 MY 和 MEC。前 24 个月（2004 年 1 月至 2005 年 12 月）用于研究疾病对 MY 和 MEC 的影响、健康动物的 MY 和 MEC 模型，并开发一种基于奶牛 MY 或 MEC 变化检测疾病的健康监测方案。其余 11 个月的数据（2006 年 1 月至 11 月）用于比较本研究开发的健康监测方案与农场当前使用的疾病检测系统的性能。混合模型用于检查疾病对 MY 和 MEC 的影响。挤奶天数（DIM）、DIM x DIM 和环境温度作为定量变量输入，小牛数量、胎次、产犊困难、与配种的天数、生长激素治疗的天数和 25 种健康事件类别作为分类变量输入。早在诊断前 10 天和 9 天就观察到 MY 和 MEC 的显著变化。在 59 天的评估期内，对 MY 的累积影响最大的是杂类消化障碍（主要是腹泻）和乳房烫伤，分别为-304.42 和-304.17 公斤。对 MY 的平均日影响最大的是产褥热，下降了 10.36 公斤，MEC 增加了 8.3%。使用健康奶牛记录的子集，通过自回归模型对牛奶产量和 MEC 进行建模。使用 3 种不同的特异性（98%、99%和 99.5%）和基于 MY 或 MY 和 MEC，设计了 6 种不同的自启动累积和谢哈特图表绘制方案。与农场当前使用的疾病检测系统相比，本研究开发的监测方案在诊断乳房、生殖或代谢问题的当天之前更早地发出警报，更敏感，并且产生的假阳性警报更少。
