Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences Vienna, Division of Livestock Sciences, Gregor-Mendel-Strasse 33, 1180 Vienna, Austria.
Federal Agricultural Research and Education Centre Raumberg-Gumpenstein, Altirdning 11, 8952 Irdning-Donnersbachtal, Austria.
J Dairy Sci. 2017 Jul;100(7):5411-5421. doi: 10.3168/jds.2016-12189. Epub 2017 May 17.
The composition of cow milk is strongly affected by the feeding regimen. Because milk components are routinely determined using mid-infrared (MIR) spectrometry, MIR spectra could also be used to estimate an animal's ration composition. The objective of this study was to determine whether and how well amounts of dry matter intake and the proportions of concentrates, hay, grass silage, maize silage, and pasture in the total ration can be estimated using MIR spectra at an individual animal level. A total of 10,200 milk samples and sets of feed intake data were collected from 90 dairy cows at 2 experimental farms of the Agricultural Research and Education Centre in Raumberg-Gumpenstein, Austria. For each run of analysis, the data set was split into a calibration and a validation data set in a 40:60 ratio. Estimated ration compositions were calculated using a partial least squares regression and then compared with the respective observed ration compositions. In separate analyses, the factors milk yield and concentrate intake were included as additional predictors. To evaluate accuracy, the coefficient of determination (R) and ratio to performance deviation were used. The highest R values (for kg of dry matter intake/for % of ration) for the individual feedstuffs were as follows: pasture, 0.63/0.66; grass silage, 0.32/0.43; concentrate intake, 0.39/0.34; maize silage, 0.32/0.33; and hay, 0.15/0.16. Estimation of groups of feedstuffs (forages, energy-dense feedstuffs) mostly resulted in R values >0.50. Including the parameters milk yield or concentrate intake improved R values by up to 0.21, with an average improvement of 0.04. The results of this study indicate that not all ration components may be estimated equally accurately. Even if some estimates are good on average, there may be strong deviations between estimated and observed values in individual data sets, and therefore individual estimates should not be overemphasized. Further research including pooled samples (e.g., bulk milk, farm samples) or variations in ration composition is called for.
牛奶的成分受饲养方案的影响很大。由于牛奶成分通常是通过中红外(MIR)光谱法来确定的,因此 MIR 光谱也可用于估计动物的饲料组成。本研究的目的是确定是否以及如何使用个体动物水平的 MIR 光谱来估计干物质摄入量和精料、干草、青贮草、青贮玉米和放牧在总日粮中的比例。共收集了来自奥地利 Raumberg-Gumpenstein 农业研究和教育中心的 2 个实验农场的 90 头奶牛的 10200 个牛奶样本和饲料摄入量数据。对于每次分析运行,数据集按照 40:60 的比例分为校准数据集和验证数据集。使用偏最小二乘回归计算估计的日粮组成,然后将其与相应的观察日粮组成进行比较。在单独的分析中,将产奶量和精料摄入量作为附加预测因子。为了评估准确性,使用决定系数(R)和相对于性能偏差的比率。对于个别饲料,R 值(kg 干物质摄入量/%日粮)最高的是:牧场,0.63/0.66;青贮草,0.32/0.43;精料摄入量,0.39/0.34;青贮玉米,0.32/0.33;干草,0.15/0.16。对饲料组(粗饲料、能量密集型饲料)的估计大多导致 R 值大于 0.50。包括参数产奶量或精料摄入量可将 R 值提高 0.21,平均提高 0.04。本研究的结果表明,并非所有日粮成分都可以同样准确地估计。即使平均估计值较好,在个别数据集之间也可能存在估计值与观察值之间的较大偏差,因此不应过分强调个别估计值。需要进一步研究包括混合样本(例如,混合奶、农场样本)或日粮组成的变化。