Abstract
Abstract In sire evaluation using the best linear unbiased prediction (BLUP) method a relationship matrix including only the relationships among sires can be used. The aim of the present study was to show properties of this method when models with or without grouping of sires are applied. The examination consisted of Monte-Carlo simulation of special situations occurring in dairy sire evaluation. Up to 20 repetitions were generated for each data set. Sire effects were equal for all repetitions of a particular set, cow and residual effects being sampled afresh. Thus, in addition to the correlation between true and estimated breeding value, the true standard deviation of estimated breeding value could be calculated from the results of repeated sire evaluation. With respect to these parameters, the results show that models which do not contain sire grouping but include relationships were superior to grouping models even when relationships are considered in addition. This superiority increases with decreasing number of daughters per sire. The hypothesis that a model without sire grouping but including a relationship matrix leads to a bias in the estimation of breeding values when dams of bulls are selected, could not be confirmed. On condition that the frequency of sires which are related to other sires is high, a model including a relationship matrix but without grouping can be recommended.
Published Version
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