1 Department of Environment and Primary Industries, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia.
2 Dairy Futures CRC, Agribio, 5 Ring Road, La Trobe University, Bundoora 3083, Australia.
Animal. 2014 Jan;8(1):1-10. doi: 10.1017/S1751731113001687. Epub 2013 Oct 16.
Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.
饲料是与奶牛系统相关的可变成本的主要组成部分，因此是育种目标的重要考虑因素。因此，饲料效率的衡量标准正在成为遗传分析中受欢迎的特征。已经有几个国家通过近似生产牛奶、维持等所需的能量来考虑饲料效率，从而将其纳入其育种目标。然而，实际饲料摄入量的变化目前并没有被纳入奶牛选择目标，尽管通过评估残留饲料摄入量（RFI）等特征可能会有可能，RFI 定义为实际和预测饲料（或能量）摄入量之间的差异。由于在大量奶牛上准确测量饲料摄入量成本高昂，因此从其衍生的表型是基因组选择的明显候选者，前提是：（1）该特征是可遗传的；（2）基因组预测的可靠性对那些使用育种值的人来说是可以接受的；（3）如果为小母牛估计了育种值，而不是奶牛，那么小母牛和奶牛特征需要相关。干物质摄入量（DMI）和 RFI 的基因组预测准确性在牛肉和奶牛研究中估计约为 0.4。有机会提高预测的准确性，例如，从三个研究牛群（澳大利亚和欧洲）汇集数据已被证明可以提高 DMI 的基因组预测准确性，从 0.33 增加到使用三个国家参考群体的 0.35。在将 RFI 作为选择目标之前，需要估计与其他特征的遗传相关性。已经发表了 RFI 与生育率之间的遗传相关性较弱的报道。这可能是因为 RFI 在数学上与能量平衡的计算相似，如果不能正确地考虑到身体储备的动员，那么选择的结果可能类似于选择减少（或负）能量平衡。因此，如果 RFI 要成为选择目标，那么在包括净收益的综合多特征选择指数中包含它是合理的，因为这将允许正确考虑与其他特征的遗传相关性。如果遗传参数准确估计，则 RFI 是一个合乎逻辑的育种目标。如果这些参数存在不确定性，那么 DMI 可能是首选。
