Milk Fatty Acid Profiling as a Tool for Estimating Methane Emissions in Conventionally Fed Dairy Cows

Milk fatty acid (FA) synthesis and enteric methanogenesis share common biochemical pathways related to rumen fermentation patterns and microbial volatile FA production. The FA profile of milk is known to correlate with methane (CH4) emissions; thus, FA profiling has been proposed as an indirect method to predict CH4 emissions from dairy cattle. This study aimed to (1) investigate the milk FA profiles of Holstein cows to identify candidate biomarkers for predicting CH4 output (g/d), CH4 yield (g/kg dry matter intake), and CH4 intensity (g/kg energy-corrected milk), and (2) develop and compare regression models predicting CH4 emissions. Forty-eight cows, fed industry standard diets, were enrolled in an exploratory trial. Milk samples and CH4 measurements were collected thrice per day, and intake was recorded daily. Milk lipids were extracted, transesterified, and subsequently analyzed via gas–liquid chromatography. Three penalized regression models were compared for predicting CH4 emission metrics using milk FAs and management variables. Methane emission metrics corelated positively with short- and medium-chain FAs, polyunsaturated FAs, and branched-chain FAs, while monounsaturated FAs correlated negatively. Notably, this study observed novel correlations between 11-cyclohexyl-11:0; and 20:3 c5,c8,c11 and CH4 metrics (|r| = 0.58–0.79). Across all CH4 metrics, the models demonstrated high predictive accuracy (R2 = 0.71–0.87; concordance correlation coefficient = 0.83–0.93). The findings of this study indicate that milk FA profiling may be an effective method to detect CH4 emissions from cows fed industry standard diets and highlight the need for further refinement of prediction models.