Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24118 Kiel, Germany.
J Dairy Sci. 2010 Apr;93(4):1702-12. doi: 10.3168/jds.2009-2198.
Selection for milk yield increases the metabolic load of dairy cows. The fat:protein ratio of milk (FPR) could serve as a measure of the energy balance status and might be used as a selection criterion to improve metabolic stability. The fit of different fixed and random regression models describing FPR and daily energy balance was tested to establish appropriate models for further genetic analyses. In addition, the relationship between both traits was evaluated for the best fitting model. Data were collected on a dairy research farm running a bull dam performance test. Energy balance was calculated using information on milk yield, feed intake per day, and live weight. Weekly FPR measurements were available. Three data sets were created containing records of 577 primiparous cows with observations from lactation d 11 to 180 as well as records of 613 primiparous cows and 96 multiparous cows with observations from lactation d 11 to 305. Five well-established parametric functions of days in milk (Ali and Schaeffer, Guo and Swalve, Wilmink, Legendre polynomials of third and fourth degree) were chosen for modeling the lactation curves. Evaluation of goodness of fit was based on the corrected Akaike information criterion, the Bayesian information criterion, correlation between the real observation and the estimated value, and on inspection of the residuals plotted against days in milk. The best model was chosen for estimation of correlations between both traits at different lactation stages. Random regression models were superior compared with the fixed regression models. In general, the Ali and Schaeffer function appeared most suitable for modeling both the fixed and the random regression part of the mixed model. The FPR is greatest in the initial lactation period when energy deficit is most pronounced. Energy balance stabilizes at the same point as the decrease in FPR stops. The inverted patterns indicate a causal relationship between the 2 traits. A common pattern was also observed for repeatabilities of both traits, with repeatabilities being largest at the beginning of lactation. Additionally, correlations between cow effects were closest at the beginning of lactation (r(c)=-0.43). The results support the hypothesis that FPR can serve as a suitable indicator for energy status, at least during the most metabolically stressful stage of lactation.
产奶量的选择会增加奶牛的代谢负担。乳脂率（FPR）可以作为衡量能量平衡状态的指标，并可作为改善代谢稳定性的选择标准。本研究测试了不同固定和随机回归模型对 FPR 和每日能量平衡的拟合程度，以建立适合进一步遗传分析的模型。此外，还评估了两种性状在最佳拟合模型中的关系。数据来自于一个进行公牛母系性能测试的奶牛研究农场。通过记录产奶量、每日采食量和体重，计算奶牛的能量平衡。每周进行 FPR 测量。共创建了 3 个数据集，包含 577 头初产奶牛从泌乳第 11 天到 180 天的记录，以及 613 头初产奶牛和 96 头经产奶牛从泌乳第 11 天到 305 天的记录。选择了 5 种经过验证的、关于泌乳天数（Ali 和 Schaeffer、Guo 和 Swalve、Wilmink、三阶和四阶 Legendre 多项式）的参数函数来模拟泌乳曲线。拟合优度的评估基于已校正的 Akaike 信息准则、贝叶斯信息准则、实际观测值与估计值之间的相关性，以及残差图与泌乳天数的关系。选择最佳模型估计不同泌乳阶段两种性状之间的相关性。随机回归模型优于固定回归模型。一般来说，Ali 和 Schaeffer 函数最适合混合模型的固定和随机回归部分的建模。在能量亏损最明显的初始泌乳期，FPR 最大。当 FPR 下降停止时，能量平衡就稳定下来。相反的模式表明这两种性状之间存在因果关系。两种性状的重复性也呈现出相同的模式，在泌乳初期重复性最大。此外，奶牛效应之间的相关性在泌乳初期最接近（r(c)=-0.43）。结果支持了 FPR 可以作为能量状态的一个合适指标的假设，至少在泌乳期最具代谢应激的阶段是如此。
