Relationships between milk fatty acid profiles and enteric methane production in dairy cattle fed grass- or grass silage-based diets
J. Dijkstra A F , S. van Gastelen A B , E. C. Antunes-Fernandes B C , D. Warner A D , B. Hatew A , G. Klop A , S. C. Podesta A , H. J. van Lingen A B , K. A. Hettinga C and A. Bannink EA Animal Nutrition Group, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands.
B Top Institute Food and Nutrition, PO Box 557, 6700 AN Wageningen, The Netherlands.
C Food Quality and Design Group, Wageningen University, PO Box 17, 6700 AH Wageningen, The Netherlands.
D Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada, J1M 0C8 Sherbrooke, QC, Canada.
E Animal Nutrition, Wageningen UR Livestock Research, PO Box 338, 6700 AH Wageningen, The Netherlands.
F Corresponding author. Email: jan.dijkstra@wur.nl
Animal Production Science 56(3) 541-548 https://doi.org/10.1071/AN15509
Submitted: 30 August 2015 Accepted: 24 November 2015 Published: 9 February 2016
Abstract
We quantified relationships between methane production and milk fatty acid (FA) profile in dairy cattle fed grass- or grass silage-based diets, and determined whether recent prediction equations for methane, based on a wide variety of diets, are applicable to grass- and grass silage-based diets. Data from three studies were used, encompassing four grass herbage and 14 grass silage treatments and 132 individual cow observations. Methane production was measured using respiration chambers and milk fatty acids (FAs) analysed using gas chromatography. The proportion of grass or grass silage (dry matter (DM) basis) was 0.80 ± 0.037. Methane yield averaged 22.3 ± 2.10 g/kg DM intake (DMI) and 14.2 ± 2.90 g/kg fat- and protein-corrected milk (FPCM). Mixed model univariate regression including a random study effect on intercept was applied to predict methane yield, with individual milk FA concentrations (g/100 g FA) as fixed effects. Of the 42 milk FAs identified, no single FA had a strong positive correlation (r; strong correlation defined as |r| ≥ 0.50) with methane yield (g/kg DMI), and cis-12 C18:1 and cis-9,12,15 C18:3 had a strong negative correlation with methane yield (g/kg DMI). C14:0 iso, C15:0, C15:0 iso, C15:0 anteiso, C16:0, C20:0, cis-11,14 C20:2, cis-5,8,11,14 C20:4, C22:0, cis-7,10,13,16,19 C22:5 and C24:0 had a strong positive correlation with methane yield (g/kg FPCM), and trans-15+cis-11 C18:1, cis-9 C18:1, and cis-11 C20:1 had a strong negative correlation with methane yield (g/kg FPCM). Observed methane yield was compared with methane yield predicted by the equations of van Lingen et al. (2014; Journal of Dairy Science 97, 7115–7132). These equations did not accurately predict methane yield as grams per kilogram DMI (concordance correlation coefficient (CCC) = 0.13) or as grams per kilogram FPCM (CCC = 0.22), in particular related to large differences in standard deviation between predicted and observed values. In conclusion, quantitative relationships between milk FA profile and methane yield in cattle fed grass- or grass silage-based diets differ from those determined for other types of diets.
Additional keywords: biomarker, dairy cow, fermentation, milk composition, mitigation, modelling.
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