Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland.
Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon P72 X050, Co. Cork, Ireland.
J Dairy Sci. 2021 Jun;104(6):6885-6896. doi: 10.3168/jds.2021-20154. Epub 2021 Mar 25.
Accurate estimates of genetic merit for both live weight and body condition score (BCS) could be useful additions to both national- and herd-breeding programs. Although recording live weight and BCS is not technologically arduous, data available for use in routine genetic evaluations are generally lacking. The objective of the present study was to explore the usefulness of routinely recorded data, namely linear type traits (which also included BCS but only assessed visually) and carcass traits in the pursuit of genetic evaluations for both live weight and BCS in dairy cows. The data consisted of on-farm records of live weight and BCS (assessed using both visual and tactile cues) from 33,242 dairy cows in 201 commercial Irish herds. These data were complemented with information on 6 body-related linear type traits (i.e., stature, angularity, chest width, body depth, BCS, and rump width) and 3 cull cow carcass measures (i.e., carcass weight, conformation, and fat cover) on a selection of these animals plus close relatives. (Co)variance components were estimated using animal linear mixed models. The genetic correlation between the type traits stature, angularity, body depth, chest width, rump width, and visually-assessed BCS with live weight was 0.68, -0.28, 0.43, 0.64, 0.61, and 0.44, respectively. The genetic correlation between angularity and BCS measured on farm (based on both visual and tactile appraisal) was -0.79; the genetic and phenotypic correlation between BCS assessed visually as part of the linear assessment with BCS assessed by producers using both tactile and visual cues was 0.90 and 0.27, respectively. The genetic (phenotypic) correlation between cull cow carcass weight and live weight was 0.81 (0.21), and the genetic (phenotypic) correlation between cull cow carcass fat cover and BCS assessed on live cows was 0.44 (0.12). Estimated breeding values (EBV) for live weight and BCS in a validation population of cows were generated using a multitrait evaluation with observations for just the type traits, just the carcass traits, and both the type traits and carcass traits; the EBV were compared with the respective live weight and BCS phenotypic observations. The regression of phenotypic live weight on its EBV from the multitrait evaluations was 1.00 (i.e., the expectation) when the EBV was generated using just linear type trait data, but less than 1 (0.83) when using just carcass data. However, the regression changed across parities and stages of lactation. The partial correlation (after adjusting for contemporary group, parity by stage of lactation, heterosis, and recombination loss) between phenotypic live weight and EBV for live weight estimated using the 3 different scenarios (i.e., type only, carcass only, type plus carcass) ranged from 0.38 to 0.43. Although the prediction of phenotypic BCS from its respective EBV was relatively good when using just the linear type trait data (regression coefficient of 0.83 with a partial correlation of 0.22), the predictive ability of BCS EBV based on just carcass data was poor and should not be used. Overall, linear type trait data are a useful source of information to predict live weight and BCS with minimal additional predictive value from also including carcass data. Nonetheless, in the absence of linear type trait data, information on carcass traits can be useful in predicting genetic merit for mature cow live weight. Prediction of cow BCS from cow carcass data is not recommended.
准确估计活重和体况评分(BCS)的遗传优势可能对国家和牛群育种计划都有很大的帮助。尽管记录活重和 BCS 在技术上并不困难,但在常规遗传评估中可用的数据通常是缺乏的。本研究的目的是探索常规记录的数据,即线性体型性状(包括 BCS,但仅通过视觉评估)和体躯性状在追求奶牛活重和 BCS 遗传评估中的有用性。数据包括 201 个爱尔兰商业牛群中 33242 头奶牛的农场记录活重和 BCS(使用视觉和触觉线索评估)。这些数据补充了 6 个体型相关的线性体型性状(即体高、角度、胸宽、体深、BCS 和臀部宽度)和 3 个淘汰牛体躯测量值(即胴体重量、体型和脂肪覆盖)的信息,这些信息来自于部分这些动物及其近亲。(协)方差分量使用动物线性混合模型进行估计。体型性状体高、角度、体深、胸宽、臀部宽度和视觉评估的 BCS 与活重之间的遗传相关性分别为 0.68、-0.28、0.43、0.64、0.61 和 0.44。基于视觉和触觉评估的农场测量的角度和 BCS 之间的遗传相关性为-0.79;线性评估中 BCS 与生产者使用触觉和视觉线索评估的 BCS 之间的遗传和表型相关性分别为 0.90 和 0.27。淘汰牛胴体重量与活重之间的遗传(表型)相关性为 0.81(0.21),淘汰牛胴体脂肪覆盖与活牛 BCS 之间的遗传(表型)相关性为 0.44(0.12)。在牛群的验证群体中,使用多性状评估生成了活重和 BCS 的估计育种值(EBV),仅使用体型性状、仅使用体躯性状和同时使用体型性状和体躯性状进行观察;将 EBV 与相应的活重和 BCS 表型观察值进行比较。当 EBV 仅使用线性体型性状数据生成时,表型活重与多性状评估的 EBV 之间的回归为 1.00(即预期),但当仅使用体躯数据时,回归小于 1(0.83)。然而,这种回归在不同胎次和泌乳阶段会发生变化。使用 3 种不同情况(仅体型、仅体躯、体型加体躯)估计的活重 EBV 与表型活重之间的部分相关(调整同期组、胎次-泌乳阶段、杂种优势和重组损失后)在 0.38 到 0.43 之间。尽管使用仅线性体型性状数据预测表型 BCS 相对较好(回归系数为 0.83,部分相关系数为 0.22),但仅基于体躯数据的 BCS EBV 的预测能力较差,不建议使用。总体而言,线性体型性状数据是预测活重和 BCS 的有用信息来源,从包含体躯数据中获得最小的额外预测价值。尽管如此,在没有线性体型性状数据的情况下,体躯性状信息在预测成熟奶牛活重的遗传优势方面仍然有用。不建议使用牛体躯数据来预测牛的 BCS。