McParland S, Lewis E, Kennedy E, Moore S G, McCarthy B, O'Donovan M, Butler S T, Pryce J E, Berry D P
Teagasc, Animal & Grassland Research and Innovation Center, Moorepark, Fermoy, Co. Cork, Ireland.
Teagasc, Animal & Grassland Research and Innovation Center, Moorepark, Fermoy, Co. Cork, Ireland.
J Dairy Sci. 2014 Sep;97(9):5863-71. doi: 10.3168/jds.2014-8214. Epub 2014 Jul 3.
Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (REX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (REX=0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (REX=0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions.
无论是出于管理还是动物育种的目的，人们对个体奶牛采食量综合情况的兴趣都在增加。然而，个体奶牛的能量摄入数据并非常规可得。本研究的目的是量化对个体奶牛乳样进行常规中红外（MIR）光谱分析来预测个体奶牛能量摄入和效率的能力。本研究中的饲料效率用剩余采食量（RFI）来描述，即实际能量摄入量与所使用能量（如产奶、维持和身体组织合成代谢）或身体组织动员所提供能量之间的差值。来自爱尔兰一个研究牛群的378头奶牛在535次泌乳期的36个不同测试日，共有1535条能量摄入、RFI和乳MIR光谱数据记录。使用偏最小二乘回归分析将乳MIR光谱数据与能量摄入或效率相关联。在一个外部验证数据集中，预测整个泌乳期RFI的模型相关系数（REX）在0.48至0.60之间；然而，在泌乳早期（产奶<60天）预测能力最强（REX=0.65）。将产奶量作为预测变量可提高预测整个泌乳期能量摄入的准确性（REX=0.70）。整个泌乳期实测RFI与实测能量平衡之间的相关性为0.85，而由MIR光谱预测的RFI与能量平衡之间的相关性为0.65。在整个泌乳期会定期生成个体奶牛的乳MIR光谱数据，因此，本研究中开发的预测方程可立即（以及在存储了MIR光谱数据的情况下进行回顾性）应用于预测能量摄入和效率，以辅助管理和育种决策。
