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Effects of Clustering Herds with Small-Sized Contemporary Groups in Dairy Cattle Genetic Evaluations

机译:当代小型群体聚集群在奶牛遗传评价中的作用

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Most test-day models used in genetic evaluations of dairy cattle define contemporary groups (CG) as the herd-test-date effect. Fitting this effect as fixed may minimize prediction bias, but requires a minimum number of observations per CG to simultaneously maximize the effective number of observations and minimize the residual error and prediction error variance. Nearly 4 million test-day records from the Portuguese Holstein database of 238,271 cows calving in 1,330 herds from 1994 through 2006 were used to evaluate the effect of clustering CG from small herds based on the similarity of their production environments. Principal component analysis was used to summarize 14 descriptive variables in 5 eigenvectors that explained 88% of the total variation. Based on the distance matrix, 2 different approaches were applied to group the herds. For each approach, 4 data sets were built having at least 3, 5, 10, or 15 observations per CG, respectively. For the data sets of group A, all herds, with or without the required number of observations per CG, were used in the clustering process. For the data sets of group B, only herds without the minimum number of observations were candidates to form clusters. All data sets were analyzed by an autoregressive test-day animal model fitting a fixed herd test date in a multiple-lactation setting, and results were compared with the current clustering procedure used in the Portuguese genetic evaluations. The data set from group B, with a minimum of 3 records per CG, was the one that provided the highest accuracy of prediction and the smaller within-CG variance, revealing a better fit for the data. This procedure also preserved the original herd structure of the database, better maximizing the number of herd groups. Correlations among EBV, rank, prediction error variance, and accuracies of prediction for this datarnset were high (0.97, 0.97, 0.85, and 0.82, respectively), suggesting that no major reranking is to be expected.
机译:奶牛遗传评估中使用的大多数测试日模型都将当代群体(CG)定义为牧群测试日期效应。固定固定此效果可能会最大程度地减少预测偏差,但每个CG需要最少数量的观察值,以同时使有效观察值数量最大化,并使残留误差和预测误差方差最小。从1994年至2006年,来自葡萄牙荷斯坦奶牛数据库的238,271头牛在1,330头牛群中的近400万个测试日记录,用于根据生产环境的相似性评估小群CG的聚类效果。主成分分析用于总结5个特征向量中的14个描述性变量,这些特征变量解释了总变异的88%。基于距离矩阵,采用了两种不同的方法对畜群进行分组。对于每种方法,构建4个数据集,每个CG分别具有至少3、5、10或15个观察值。对于A组的数据集,在聚类过程中使用所有畜群(无论是否具有每个CG所需的观察数)。对于B组的数据集,只有没有最少观察数的牛群才是形成簇的候选者。所有数据集均通过自回归试验日动物模型进行分析,该模型适用于在多个泌乳环境中固定牛群试验日期的试验,并将结果与​​葡萄牙遗传评估中使用的当前聚类程序进行了比较。来自B组的数据集(每个CG最少有3条记录)是提供最高预测准确度和CG范围内较小方差的数据集,显示出对该数据的更好拟合。此过程还保留了数据库的原始畜群结构,从而更好地最大化了畜群数量。该数据集的EBV,等级,预测误差方差和预测准确性之间的相关性很高(分别为0.97、0.97、0.85和0.82),这表明没有重大的重新分类期望。

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