Meinert T R, Norman H D
National Dairy Herd Improvement Association, Inc., Columbus, OH 43231-4078, USA.
J Dairy Sci. 1998 Nov;81(11):2951-5. doi: 10.3168/jds.S0022-0302(98)75857-6.
To determine whether the accuracy of the genetic evaluations of sires can be assessed by the presence of extreme daughter records, we studied herd-years with records from first-crop daughters of 217 Holstein bulls that were not sampled by artificial insemination (AI) organizations but that later entered AI. The presence of outliers for standardized milk yield was determined within herd-year. Outliers were defined as records exceeding 1.5 interquartile ranges below the 25th percentile or above the 75th percentile. Herd-years were separated into two groups based on whether or not an outlier daughter record was present for an AI bull that had initially been sampled through non-AI. Herd-years without daughter outliers from those bulls were divided into herd-years with 1) no daughter outliers from any bull, 2) only negative daughter outliers from other bulls, 3) only positive daughter outliers from other bulls, or 4) negative and positive daughter outliers from other bulls. Herd-years with daughter outliers from AI bulls initially sampled through non-AI were divided into herd-years with 1) only negative daughter outliers, 2) only positive daughter outliers, 3) positive daughter outliers from those bulls and negative daughter outliers from other bulls, or 4) both negative and positive daughter outliers. The relationship between the frequency of outlier classes and a change in the Modified Contemporary Comparison genetic evaluations (the difference between the last available second-crop evaluation and the next to the last first-crop evaluation) was examined with logistic regression. For AI bulls that were initially sampled through non-AI and having evaluations that decreased > or = 386 kg, 9% of herd-years had positive first-crop daughter outliers and negative daughter outliers from other bulls; 38% had no outliers. For bulls with evaluations that increased > or = 194 kg, comparable percentages were 2 and 53%.
为了确定能否通过极端女儿记录来评估种公牛遗传评估的准确性，我们研究了一些牛群年份的数据，这些数据来自217头荷斯坦公牛的头胎女儿记录，这些公牛未被人工授精（AI）组织采样，但后来进入了AI体系。在牛群年份内确定标准化产奶量的异常值。异常值被定义为低于第25百分位数或高于第75百分位数超过1.5个四分位距的记录。根据最初通过非AI采样的AI公牛是否存在异常女儿记录，将牛群年份分为两组。那些公牛没有女儿异常值的牛群年份被分为以下几类：1）任何公牛都没有女儿异常值；2）只有其他公牛的负女儿异常值；3）只有其他公牛的正女儿异常值；4）其他公牛的正负女儿异常值。最初通过非AI采样的AI公牛有女儿异常值的牛群年份被分为以下几类：1）只有负女儿异常值；2）只有正女儿异常值；3）那些公牛的正女儿异常值和其他公牛的负女儿异常值；4）正负女儿异常值都有。使用逻辑回归分析了异常类别频率与改良同期比较遗传评估变化（最后一次可用的二胎评估与倒数第二次头胎评估之间的差异）之间的关系。对于最初通过非AI采样且评估下降≥386千克的AI公牛，9%的牛群年份有正的头胎女儿异常值和其他公牛的负女儿异常值；38%没有异常值。对于评估增加≥194千克的公牛，相应的百分比分别为2%和53%。
