Short communication: The effect of linseed oil and DGAT1 K232A polymorphism on the methane emission prediction potential of milk fatty acids
Gastelen, S. van; Antunes-Fernandes, E.C. ; Hettinga, K.A. ; Dijkstra, J. - \ 2018
Journal of Dairy Science 101 (2018)6. - ISSN 0022-0302 - p. 5599 - 5604.
DGAT1 K232 polymorphism - enteric methane production - linseed oil - milk fatty acid
Several in vivo CH4 measurement techniques have been developed but are not suitable for precise and accurate large-scale measurements; hence, proxies for CH4 emissions in dairy cattle have been proposed, including the milk fatty acid (MFA) profile. The aim of the present study was to determine whether recently developed MFA-based prediction equations for CH4 emission are applicable to dairy cows with different diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism and fed diets with and without linseed oil. Data from a crossover design experiment were used, encompassing 2 dietary treatments (i.e., a control diet and a linseed oil diet, with a difference in dietary fat content of 22 g/kg of dry matter) and 24 lactating Holstein-Friesian cows (i.e., 12 cows with DGAT1 KK genotype and 12 cows with DGAT1 AA genotype). Enteric CH4 production was measured in climate respiration chambers and the MFA profile was analyzed using gas chromatography. Observed CH4 emissions were compared with CH4 emissions predicted by previously developed MFA-based CH4 prediction equations. The results indicate that different types of diets (i.e., with or without linseed oil), but not the DGAT1 K232A polymorphism, affect the ability of previously derived prediction equations to predict CH4 emission. However, the concordance correlation coefficient was smaller than or equal to 0.30 for both dietary treatments separately, both DGAT1 genotypes separately, and the complete data set. We therefore concluded that previously derived MFA-based CH4 prediction equations can neither accurately nor precisely predict CH4 emissions of dairy cows managed under strategies differing from those under which the original prediction equations were developed.