Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland.
Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland.
J Dairy Sci. 2018 May;101(5):4268-4278. doi: 10.3168/jds.2017-13936. Epub 2018 Feb 22.
The main objective of this study was to assess the genetic differences in metabolizable energy efficiency and efficiency in partitioning metabolizable energy in different pathways: maintenance, milk production, and growth in primiparous dairy cows. Repeatability models for residual energy intake (REI) and metabolizable energy intake (MEI) were compared and the genetic and permanent environmental variations in MEI were partitioned into its energy sinks using random regression models. We proposed 2 new feed efficiency traits: metabolizable energy efficiency (MEE), which is formed by modeling MEI fitting regressions on energy sinks [metabolic body weight (BW), energy-corrected milk, body weight gain, and body weight loss] directly; and partial MEE (pMEE), where the model for MEE is extended with regressions on energy sinks nested within additive genetic and permanent environmental effects. The data used were collected from Luke's experimental farms Rehtijärvi and Minkiö between 1998 and 2014. There were altogether 12,350 weekly MEI records on 495 primiparous Nordic Red dairy cows from wk 2 to 40 of lactation. Heritability estimates for REI and MEE were moderate, 0.33 and 0.26, respectively. The estimate of the residual variance was smaller for MEE than for REI, indicating that analyzing weekly MEI observations simultaneously with energy sinks is preferable. Model validation based on Akaike's information criterion showed that pMEE models fitted the data even better and also resulted in smaller residual variance estimates. However, models that included random regression on BW converged slowly. The resulting genetic standard deviation estimate from the pMEE coefficient for milk production was 0.75 MJ of MEI/kg of energy-corrected milk. The derived partial heritabilities for energy efficiency in maintenance, milk production, and growth were 0.02, 0.06, and 0.04, respectively, indicating that some genetic variation may exist in the efficiency of using metabolizable energy for different pathways in dairy cows.
本研究的主要目的是评估初产奶牛在不同途径（维持、产奶和生长）中代谢能效率和代谢能分配效率的遗传差异。比较了剩余能量摄入量（REI）和代谢能摄入量（MEI）的重复模型，并使用随机回归模型将 MEI 的遗传和永久环境变异分解为其能量汇。我们提出了 2 个新的饲料效率性状：代谢能效率（MEE），通过对代谢能拟合回归到能量汇[代谢体重（BW）、能量校正奶、体重增加和体重损失]直接建模形成；部分 MEE（pMEE），其中 MEE 的模型通过在加性遗传和永久环境效应内嵌套回归来扩展。使用的数据是 1998 年至 2014 年间从 Luke 的实验农场 Rehtijärvi 和 Minkiö 收集的。在泌乳的第 2 至 40 周，有 495 头北欧红牛初产奶牛共 12350 个每周 MEI 记录。REI 和 MEE 的遗传力估计值适中，分别为 0.33 和 0.26。MEE 的剩余方差估计值小于 REI，表明同时分析每周 MEI 观测值和能量汇是可取的。基于赤池信息量准则的模型验证表明，pMEE 模型拟合数据的效果更好，并且也导致剩余方差估计值更小。然而，包括 BW 随机回归的模型收敛速度较慢。从 pMEE 产奶系数中得出的遗传标准偏差估计值为 0.75MJ MEI/kg 能量校正奶。维持、产奶和生长中能量效率的部分遗传力分别为 0.02、0.06 和 0.04，表明奶牛在不同途径中使用代谢能的效率可能存在一定的遗传变异。
