Using milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles to predict methane emission of dairy cows

We compared the prediction potential of gas chromatography-based milk fatty acids (MFA) and milk Fourier-transform infrared spectroscopy (FTIR) for methane (CH4) emissions of dairy cows. Data from 9 experiments with lactating Holstein-Friesian cows with a total of 30 dietary treatments and 218 observations were used. Methane emissions were measured in climate respiration chambers. Multivariate MFA-based and FTIR-based CH4 prediction models were developed and, subsequently, evaluated with the concordance correlation coefficient (CCC) analysis. The MFA-based CH4 prediction models estimated CH4 production (g/d), yield (g/kg dry matter intake), and intensity (g/kg fat- and protein-corrected milk) with a CCC of 0.72, 0.59, and 0.77, respectively. The FTIR-based CH4 prediction models estimated CH4 production, yield, and intensity with a CCC of 0.52, 0.40, and 0.72, respectively. These results indicate that for all CH4 emission units, but particularly for CH4 production and yield, the MFA-based prediction models described a greater part of the observed variation in CH4 emission than FTIR-based prediction models.