Castro-Montoya J M, Peiren N, Veneman J, De Baets B, De Campeneere S, Fievez V
1Institute for Agricultural and Fisheries Research,Animal Sciences Unit,Scheldeweg 68,Melle 9090,Belgium.
3Institute of Biological, Environmental and Rural Sciences,Aberystwyth University,Aberystwyth SY23 3DA,UK.
Animal. 2017 Jul;11(7):1153-1162. doi: 10.1017/S1751731116002627. Epub 2016 Dec 15.
Milk fatty acid (MFA) have already been used to model methane (CH4) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH4 expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R 2 (m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R 2 (v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R 2 (v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH4 emissions, the data set was split into three categories according to CH4 emissions: LOW (lowest 25% CH4 emissions); HIGH (highest 25% CH4 emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH4 emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH4 (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH4 (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH4 emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.
乳脂肪酸（MFA）已被用于模拟奶牛的甲烷（CH₄）排放。然而，用于开发这些模型的数据集所涵盖的饮食条件变化有限，降低了预测的稳健性。在本研究中，一个包含来自9个实验（41头荷斯坦奶牛）的140个观测值的数据集被用于开发预测CH₄排放的模型，CH₄排放以克/天、克/千克干物质摄入量（DMI）和克/千克牛奶表示。该数据集分别分为训练数据集（n = 112）和测试数据集（n = 28），用于模型开发和验证。使用边际R²（m）和赤池信息准则对数据拟合广义线性混合模型以评估模型。所开发的不同模型的验证决定系数（R²（v））在0.18至0.41之间。从与摄入量相关的参数来看，仅纳入总DMI可改善预测（R²（v）= 0.58）。此外，为了进一步探索MFA与CH₄排放之间的关系，根据CH₄排放将数据集分为三类：低排放（CH₄排放最低的25%）；高排放（CH₄排放最高的25%）；以及中等排放（其余50%的观测值）。方差分析显示，与低排放组的观测值相比，高排放组观测值的几种MFA浓度存在差异。此外，基尼系数用于描述每个CH₄排放类别中MFA组的MFA分布。高排放类别中观测值的MFA相对分布，特别是奇数和支链脂肪酸以及单不饱和脂肪酸的相对分布与其他类别不同。最后，为了验证MFA识别高排放或低排放情况的潜力，根据每种单个MFA的比例将观测值重新分类为高排放、中等排放和低排放。记录正确分类的观测值比例。对每种单个MFA和计算出的基尼系数都进行了此操作，发现最多67%的观测值被正确分类为高CH₄排放（反式-12 C18:1），最多58%的观测值被正确分类为低CH₄排放（顺式-9 C17:1）。基尼系数并未改善这种分类。这些结果表明，MFA尚未成为奶牛特定CH₄排放量的可靠预测指标，尽管在区分高排放或低排放情况方面有一定潜力。
