Milk Production, Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland.
Animal Genetics, Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland.
J Dairy Sci. 2019 Sep;102(9):7904-7916. doi: 10.3168/jds.2018-15792. Epub 2019 Jul 10.
The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (ΔBW), change in body condition score (ΔBCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30, 0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; all means ± standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW, and days in milk. The model resulted in a cross-validation coefficient of determination (Rcv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (Rcv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 × C14:0, and days in milk (Rcv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (Rcv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.
奶牛饲养目标中包含采食量和效率性状可能会增加代谢应激的风险。因此，需要一种简单且廉价的方法来监测奶牛产后能量状态（ES）。可以通过计算能量摄入和输出的能量平衡来估算奶牛的 ES，并通过体重变化（ΔBW）、体况评分变化（ΔBCS）、乳脂：蛋白比（FPR）或乳脂肪酸（FA）组成等指标来预测。在这项研究中，我们使用血浆非酯化脂肪酸（NEFA）浓度作为 ES 的生物标志物。我们确定了 NEFA 浓度与 ES 指标之间的关联，并评估了仅使用体况和乳性状或同时使用它们来预测奶牛 ES 的有用性。数据来自 2013 年至 2016 年的 2 个研究牛群，包括 137 头北欧红牛奶牛，所有奶牛均处于首次泌乳期，其中 59 头奶牛还处于第二次泌乳期。数据包括每日体重、产奶量和采食量以及每月 BCS。在泌乳第 2 和第 3 周采集两次血浆样本，在第 20 周采集一次。在采血当天采集乳样以分析脂肪、蛋白质、乳糖和 FA 浓度。血浆 NEFA 浓度在泌乳第 2 和第 3 周高于第 20 周（0.56±0.30、0.43±0.22 和 0.13±0.06mmol/L，分别为平均值±标准差）。在个体指标中，C18:1 cis-9 和乳中 C18:1 的总和与 NEFA 的相关性最高（r=0.73）。使用逐步选择法开发了 7 个用于预测 NEFA 的多元线性回归模型。在包含牛奶性状（除牛奶 FA 外）和体况性状的模型中，最佳拟合是使用产奶量、FPR、ΔBW、ΔBCS、FPR×ΔBW 和泌乳天数的模型。该模型的交叉验证决定系数（Rcv）为 0.51，均方根误差（RMSE）为 0.196mmol/L。当仅考虑乳 FA 浓度时，使用夜间奶样比清晨奶样进行 NEFA 预测更准确（Rcv=0.61 比 0.53）。包含 FPR、C10:0、C14:0、C18:1 cis-9、C18:1 cis-9×C14:0 和泌乳天数的含乳性状最佳模型（Rcv=0.62；RMSE=0.177mmol/L）。使用牛奶和体况两种性状的最先进模型比仅使用牛奶性状的模型拟合度略好（Rcv=0.63；RMSE=0.176mmol/L）。我们的研究结果表明，通过常规牛奶测量可以以中等精度监测奶牛早期泌乳期的 ES。
