Potential for improving description of bovine udder health status by combined analysis of milk parameters.
Potential for improving description of bovine udder health status by combined analysis of milk parameters.
复制标题
通过对牛奶参数进行组合分析，有可能改善对牛乳房健康状况的描述。
DOI：
10.3168/jds.s0022-0302(03)73706-0
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发表时间：
2003
影响因子：
3.5
通讯作者：
K. Ingvartsen
中科院分区：
文献类型：
--
作者：
K. Sloth;N. Friggens;P. Løvendahl;P. Andersen;J. Jensen;K. Ingvartsen
本研究的目的是评估逐步多变量程序根据八个牛奶参数量化奶牛乳房健康的潜力：产奶量、蛋白质百分比、脂肪百分比、乳糖百分比、柠檬酸盐百分比、体细胞计数 (SCC) 和两个电导率参数。这些数据是在一个研究牛群中收集的，包括 821 头奶牛水平的观察结果。除了牛奶参数外，还包括整个哺乳期每八周四分之一牛奶样本的疾病记录和细菌学数据。将多变量混合模型应用于健康子集的牛奶参数，以调整以下系统因素：总混合日粮（TMR）能量密度、品种系组合、胎次、哺乳阶段和季节。混合模型所解释的方差比例范围为 0.14 至 0.82，具体取决于牛奶参数。将健康子集中估计的调整应用于整个数据集，包括与不健康奶牛有关的观察结果。通过主成分分析对牛奶参数的调整变化进行组合描述。第一个主成分 (Prin1) 描述了 30% 的调整后变异，并被解释为乳腺炎的主要后果。最后，基于 Prin1 的聚类分析将观察结果分为九个聚类，这些聚类与乳房健康密切相关，随着 Prin1 水平的增加，临床和亚临床乳腺炎也会增加。结论是，通过牛奶参数评估乳房健康的多变量方法有可能大大改善对乳房健康的描述。
The objective of this study was to assess the potential of a stepwise multivariate procedure to quantify cow-level udder health based on eight milk parameters: milk yield, protein percentage, fat percentage, lactose percentage, citrate percentage, somatic cell count (SCC), and two electrical conductivity parameters. The data were collected in one research herd and included 821 cow-level observations. In addition to milk parameters, disease recordings and bacteriology on quarter milk samples every eighth week throughout lactation were included. A multivariate mixed model was applied to the milk parameters in a healthy subset to adjust for the following systematic factors: total mixed ration (TMR) energy density, breed-line combination, parity, stage of lactation, and season. The proportion of variance accounted for by the mixed model ranged from 0.14 to 0.82 depending on milk parameter. The adjustments estimated in the healthy subset were applied to the whole dataset, including observations pertaining to nonhealthy cows. Combined description of the adjusted variation in the milk parameters was performed with a principal component analysis. The first principal component (Prin1) described 30% of the adjusted variation and was interpreted as being the main consequences of mastitis. Finally, cluster analysis based on Prin1 separated the observations into nine clusters, which were strongly associated with udder health in terms of increasing clinical and subclinical mastitis with increasing level of Prin1. It was concluded that a multivariate approach to assess udder health from milk parameters has the potential to substantially improve description of udder health.