饲料效率是乳制品生产的重要特征。然而,评估饲料效率受到相关成本和测量单个饲料摄入量困难的限制,尤其是在牧场上。这项研究的目的是调查牧草饲喂奶牛的短期饲料效率性状并筛选潜在的生物标志物(n=238)。衍生的饲料效率性状是基于比率的(即,饲料转化率(FCR)和N使用效率(NUE))或基于残差的(即,残余饲料摄入量(RFI),剩余能量摄入(REI),和残余氮摄入量(RNI))。38头荷斯坦奶牛和16头瑞士Fleckvieh奶牛在泌乳中期和/或后期进行了7天的测量。实验数据(n=100个测量点)涵盖了不同的泌乳和牧草饲喂系统情况:泌乳中期放牧(n=56),泌乳后期放牧(n=28),和后期哺乳谷仓喂养(n=16)。在每个测量期间,使用正构烷烃标记技术估计每头牛的个体牧草摄入量。每一头母牛,代表牛奶成分的生物标志物(n=109),动物特征(n=13),行为,和活动(n=46),呼吸排放(n=3),血液成分(n=35),表面,和直肠温度(n=29),头发皮质醇(n=1),获得了粪便和牛奶(n=2)的近红外(NIR)光谱。用单变量线性回归对生物标志物与效率性状之间的关系进行统计分析,并使用偏最小二乘回归对NIR光谱与饲料效率性状进行统计分析。饲料效率性状相互关联(r:-0.57至-0.86和0.49-0.81)。生物标志物在解释饲料效率性状的变异性方面显示出不同的R2值(FCR:0.00-0.66,NUE:0.00-0.74,RFI:0.00-0.56,REI:0.00-0.69,RNI:0.00-0.89)。总的来说,牛奶和粪便的NIR光谱特征最好地解释了饲料效率性状(R2:0.25-0.89)。生物标志物显示出预测牧草饲喂奶牛饲料效率的潜力。牛奶和粪便的近红外光谱数据分析提供了一种有前途的方法,用于在进一步验证预测模型后估算个体饲料效率。未来的应用将取决于提高生物标志物在更多环境(位置)中预测饲料效率的鲁棒性的能力。管理条件,供料系统,生产强度,和其他方面。
Feed efficiency is an important trait of dairy production. However, assessing feed efficiency is constrained by the associated cost and difficulty in measuring individual feed intake, especially on pastures. The objective of this study was to investigate short-term feed efficiency traits of herbage-fed dairy cows and screening of potential biomarkers (n = 238). Derived feed efficiency traits were ratio-based (i.e., feed conversion ratio (FCR) and N use efficiency (NUE)) or residual-based (i.e., residual feed intake (RFI), residual energy intake (REI), and residual N intake (RNI)). Thirty-eight Holstein and 16 Swiss Fleckvieh dairy cows underwent a 7-d measurement period during mid- and/or late-lactation. The experimental data (n = 100 measurement points) covered different lactational and herbage-fed system situations: mid-lactation grazing (n = 56), late-lactation grazing (n = 28), and late-lactation barn feeding (n = 16). During each measuring period, the individual herbage intake of each cow was estimated using the n-alkane marker technique. For each cow, biomarkers representing milk constituents (n = 109), animal characteristics (n = 13), behaviour, and activity (n = 46), breath emissions (n = 3), blood constituents (n = 35), surface, and rectal temperature (n = 29), hair cortisol (n = 1), and near-infrared (NIR) spectra of faeces and milk (n = 2) were obtained. The relationships between biomarkers and efficiency traits were statistically analysed with univariate linear regression and for NIR spectra using partial least squares regression with feed efficiency traits. The feed efficiency traits were interrelated with each other (r: -0.57 to -0.86 and 0.49-0.81). The biomarkers showed varying R2 values in explaining the variability of feed efficiency traits (FCR: 0.00-0.66, NUE: 0.00-0.74, RFI: 0.00-0.56, REI: 0.00-0.69, RNI: 0.00-0.89). Overall, the feed efficiency traits were best explained by NIR spectral characteristics of milk and faeces (R2: 0.25-0.89). Biomarkers show potential for predicting feed efficiency in herbage-fed dairy cows. NIR spectra data analysis of milk and faeces presents a promising method for estimating individual feed efficiency upon further validation of prediction models. Future applications will depend on the ability to improve the robustness of biomarkers to predict feed efficiency in a greater variety of environments (locations), managing conditions, feeding systems, production intensities, and other aspects.