INRA, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France.
J Dairy Sci. 2013 Jul;96(7):4197-211. doi: 10.3168/jds.2012-6379. Epub 2013 May 9.
The aim of this study was to predict the fatty acid (FA) composition of bulk milk using data describing farming practices collected via on-farm surveys. The FA composition of 1,248 bulk cow milk samples and the related farming practices were collected from 20 experiments led in 10 different European countries at 44°N to 60°N latitude and sea level to 2,000 m altitude. Farming practice-based FA predictions [coefficient of determination (R(2)) >0.50] were good for C16:0, C17:0, saturated FA, polyunsaturated FA, and odd-chain FA, and very good (R(2) ≥0.60) for trans-11 C18:1, trans-10 + trans-11 C18:1, cis-9,trans-11 conjugated linoleic acid, total trans FA, C18:3n-3, n-6:n-3 ratio, and branched-chain FA. Fatty acids were predicted by cow diet composition and by the altitude at which milk was produced, whereas animal-related factors (i.e., lactation stage, breed, milk yield, and proportion of primiparous cows in the herd) were not significant in any of the models. Proportion of fresh herbage in the cow diet was the main predictor, with the highest effect in almost all FA models. However, models built solely on conserved forage-derived samples gave good predictions for odd-chain FA, branched-chain FA, trans-10 C18:1 and C18:3n-3 (R(2) ≥0.46, 0.54, 0.52, and 0.70, respectively). These prediction models could offer farmers a valuable tool to help improve the nutritional quality of the milk they produce.
本研究旨在利用农场调查中收集的描述养殖实践的数据来预测牛奶的脂肪酸(FA)组成。从 10 个不同的欧洲国家在 44°N 至 60°N 纬度和海平面至 2000 米海拔的 20 个实验中收集了 1248 个批量牛奶样本及其相关养殖实践的数据。基于养殖实践的 FA 预测(决定系数(R(2))>0.50)对于 C16:0、C17:0、饱和 FA、多不饱和 FA 和奇数链 FA 效果良好,对于反式-11 C18:1、反式-10 + 反式-11 C18:1、顺式-9、反式-11 共轭亚油酸、总反式 FA、C18:3n-3、n-6:n-3 比和支链 FA 非常好(R(2)≥0.60)。脂肪酸由奶牛饮食组成和牛奶生产的海拔高度预测,而与动物相关的因素(即泌乳阶段、品种、产奶量和牛群中初产牛的比例)在任何模型中都不显著。奶牛饮食中新鲜草料的比例是主要预测因素,在几乎所有 FA 模型中都有最高的影响。然而,仅基于保守的草料衍生样本建立的模型对奇数链 FA、支链 FA、反式-10 C18:1 和 C18:3n-3 给出了很好的预测(R(2)≥0.46、0.54、0.52 和 0.70)。这些预测模型可以为农民提供一个有价值的工具,帮助他们提高所生产牛奶的营养价值。