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基于牛群奶罐体细胞计数预测体细胞计数标准违规情况。第一部分:分析变异

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part I: Analyzing variation.

作者信息

Lukas J M, Reneau J K, Kinsel M L

机构信息

Department of Animal Science, University of Minnesota, St. Paul 55108, USA.

出版信息

J Dairy Sci. 2008 Jan;91(1):427-32. doi: 10.3168/jds.2007-0647.

Abstract

The present study examines the relationship between the bulk tank somatic cell count (SCC) mean and sigma (an estimate of variation) and the probability of exceeding a SCC standard. Daily or every other day, bulk tank SCC data were collected for 24 mo from 1,501 herds. Mean and sigma were estimated for each herd monthly and were compared between months and herd production categories using Kruskal-Wallis nonparametric ANOVA. The effect of month on bulk tank SCC mean and sigma was significant, with estimates for all summer months and most of the spring and fall months being significantly greater than estimates of mean and sigma in December 2004. Logistic regression models were developed to examine the relationship between month and herd production and the odds of a herd exceeding a SCC standard. The odds of exceeding a bulk tank SCC standard were significantly greater in the summer months and for smaller herds. A grid was constructed determining the probability of exceeding any of 5 SCC standards (200,000 to 600,000 cells/mL, step 100,000 cells/mL) in the following month, based on the mean and sigma of the past month. The violation probability grid can be used to assess the prospect of meeting quality premium goals and to proactively encourage more consistent performance in all the processes affecting bulk tank SCC.

摘要

本研究考察了奶罐体细胞计数(SCC)均值与西格玛值(变异估计值)之间的关系以及超过SCC标准的概率。从1501个牛群中,每天或每隔一天收集24个月的奶罐SCC数据。每月对每个牛群的均值和西格玛值进行估计,并使用Kruskal-Wallis非参数方差分析在不同月份和牛群生产类别之间进行比较。月份对奶罐SCC均值和西格玛值的影响显著,所有夏季月份以及大部分春季和秋季月份的估计值显著高于2004年12月的均值和西格玛值估计。建立逻辑回归模型以考察月份与牛群生产之间的关系以及牛群超过SCC标准的几率。夏季月份以及较小牛群超过奶罐SCC标准的几率显著更高。根据上月的均值和西格玛值构建了一个网格,以确定下个月超过5个SCC标准(200,000至600,000个细胞/毫升,步长为100,000个细胞/毫升)中任何一个标准的概率。违规概率网格可用于评估实现质量溢价目标的前景,并积极鼓励在影响奶罐SCC的所有过程中保持更一致的表现。

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