Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows. (15th December 2016)

• Record Type: Journal Article
• Authors: Castro-Montoya, J. M.; Peiren, N.; Veneman, J.; De Baets, B.; De Campeneere, S.; Fievez, V.
• Is Part Of: Animal. Volume 11:Number 7(2017:Jul.)
• Page Start: 1153
• Page End: 1162
• Publication Date: 2016-12-15
• Subjects: methane, -- milk fatty acids, -- prediction model, -- high emissions, -- low emissions; Animal breeding -- Periodicals; Animal genetics -- Periodicals; Animal nutrition -- Periodicals; Animal physiology -- Periodicals; Environmental sciences -- Periodicals; 636.005
• DOI: 10.1017/S1751731116002627
• Languages: English
• ISSNs: 1751-7311

Abstract

Abstract : 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.