Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden.
Natural Resources Institute Finland (Luke), Milk Production, 31600 Jokioinen, Finland.
Animal. 2019 Oct;13(10):2277-2288. doi: 10.1017/S1751731119000247. Epub 2019 Feb 26.
Direct measurement of individual animal dry matter intake (DMI) remains a fundamental challenge to assessing dairy feed efficiency (FE). Digesta marker, is currently the most used indirect technique for estimating DMI in production animals. In this meta-analysis we evaluated the performance of marker-based estimates against direct or observed measurements and developed equations for the prediction of FE (g energy-corrected milk (ECM)/kg DMI). Data were taken from 29 change-over studies consisting of 416 cow-within period observations. Most studies used more than one digesta marker. So, for each observed measurement of DMI, faecal dry matter output (FDMO) and apparent total tract dry matter digestibility (DMD), there was one or more corresponding marker estimate. There were 924, 409 and 846 observations for estimated FDMO (eFDMO), estimated apparent total tract DMD (eDMD) and estimated DMI (eDMI), respectively. The experimental diets were based mainly on grass silage, with soya bean or rapeseed meal as protein supplements and cereal grains or by-products as energy supplements. Across all diets, average forage to concentrate ratio on a dry matter (DM) basis was 59 : 41. Variance component and repeatability estimates of observed and marker estimations were determined using random factors in mixed procedures of SAS. Between-cow CV in observed FDMO, DMD and DMI was, 10.3, 1.69 and 8.04, respectively. Overall, the repeatability estimates of observed variables were greater than their corresponding marker-based estimates of repeatability. Regression of observed measurements on marker-based estimates gave good relationships (R2=0.87, 0.68, 0.74 and 0.74, relative prediction error =10.9%, 6.5%, 15.4% and 18.7%for FDMO, DMD, DMI and FE predictions, respectively). Despite this, the mean and slope biases were statistically significant (P<0.001) for all regressions. More than half of the errors in all regressions were due to mean and slope biases (52.4% 87.4%, 82.9% and 85.8% for FDMO, DMD, DMI and FE, respectively), whereas the contributions of random errors were small. Based on residual variance, the best model for predicting FE developed from the dataset was FE (g ECM/kg DMI)=1179(±54.1) +38.2(±2.05)×ECM(kg/day)-0.64(±0.051)×BW (kg)-75.6(±4.39)×eFDMO (kg/day). Although eDMD was positively related to FE, it only showed a tendency to reduce the residual variance. Despite inaccuracy in marker procedures, eFDMO from external markers provided a reliable determination for FE measurement. However, DMD estimated by internal markers did not improve prediction of FE, probably reflecting small variability.
直接测量个体动物干物质采食量（DMI）仍然是评估奶牛饲料效率（FE）的基本挑战。粪污指示剂目前是生产动物中最常用的间接估计 DMI 的技术。在这项荟萃分析中，我们评估了基于标记的估计值与直接或观察测量值的性能，并为 FE（g 能量校正奶（ECM）/kg DMI）的预测制定了方程。数据取自 29 个转换研究，其中包括 416 个牛内期观察。大多数研究使用了不止一种粪污指示剂。因此，对于每一个观察到的 DMI 测量值，粪便干物质输出（FDMO）和表观全肠道干物质消化率（DMD），都有一个或多个对应的标记估计值。分别有 924、409 和 846 个观察值用于估计 FDMO（eFDMO）、估计表观全肠道 DMD（eDMD）和估计 DMI（eDMI）。实验日粮主要以草青贮料为基础，添加大豆或菜籽粕作为蛋白质补充料，以谷物或副产品作为能量补充料。在所有日粮中，干物质（DM）基础上的粗饲料与精饲料的比例平均为 59：41。使用 SAS 混合程序中的随机因素确定了观察和标记估计的方差分量和可重复性估计。观察到的 FDMO、DMD 和 DMI 的个体间变异系数分别为 10.3%、1.69%和 8.04%。总体而言，观察变量的可重复性估计值大于其相应的基于标记的可重复性估计值。观察测量值与基于标记的估计值的回归给出了良好的关系（FDMO、DMD、DMI 和 FE 预测的 R2 分别为 0.87、0.68、0.74 和 0.74，相对预测误差分别为 10.9%、6.5%、15.4%和 18.7%）。尽管如此，所有回归的均值和斜率偏差均具有统计学意义（P<0.001）。所有回归中，误差的一半以上归因于均值和斜率偏差（FDMO、DMD、DMI 和 FE 的偏差分别为 52.4%、87.4%、82.9%和 85.8%），而随机误差的贡献较小。基于剩余方差，从数据集开发的预测 FE 的最佳模型为 FE（g ECM/kg DMI）=1179（±54.1）+38.2（±2.05）×ECM（kg/天）-0.64（±0.051）×BW（kg）-75.6（±4.39）×eFDMO（kg/天）。尽管 eDMD 与 FE 呈正相关，但它仅表现出降低剩余方差的趋势。尽管标记程序不准确，但来自外部标记的 eFDMO 为 FE 测量提供了可靠的确定。然而，内部标记估计的 DMD 并没有提高 FE 的预测，可能反映了较小的变异性。
