Abstract #594
Section: Production, Management and the Environment
Session: Production, Management, and the Environment II
Format: Oral
Day/Time: Tuesday 2:45 PM–3:00 PM
Location: Panzacola H-1
Session: Production, Management, and the Environment II
Format: Oral
Day/Time: Tuesday 2:45 PM–3:00 PM
Location: Panzacola H-1
# 594
Predicting methane emission of dairy cows using fatty acids and volatile and non-volatile metabolites in milk.
Sanne van Gastelen*1,2, Elsa C. Antunes-Fernandes1,3, Kasper A. Hettinga3, Jan Dijkstra2, 1Top Institute Food and Nutrition, Wageningen, the Netherlands, 2Animal Nutrition Group, Wageningen University, Wageningen, the Netherlands, 3Food Quality Design Group, Wageningen University, Wageningen, the Netherlands.
Key Words: methane emission, milk fatty acids, milk volatile and non-volatile metabolites
Predicting methane emission of dairy cows using fatty acids and volatile and non-volatile metabolites in milk.
Sanne van Gastelen*1,2, Elsa C. Antunes-Fernandes1,3, Kasper A. Hettinga3, Jan Dijkstra2, 1Top Institute Food and Nutrition, Wageningen, the Netherlands, 2Animal Nutrition Group, Wageningen University, Wageningen, the Netherlands, 3Food Quality Design Group, Wageningen University, Wageningen, the Netherlands.
The objective of this study was to develop prediction equations for methane (CH4) emission of dairy cows using fatty acids (FA), volatile metabolites, and non-volatile metabolites in milk. Data from a randomized block design experiment with 32 multiparous Holstein Friesian cows and 4 diets was used. All diets had a roughage:concentrate ratio of 80:20 based on dry matter. Roughage consisted of grass silage only, corn silage only, or mixtures of both silages. Methane emission was measured in climate respiration chambers, and expressed per unit dry matter intake (DMI) and per unit fat- and protein-corrected milk (FPCM). Milk samples were analyzed for FA by gas chromatography, volatile metabolites by gas chromatography-mass spectrometry, and non-volatile metabolites by nuclear magnetic resonance. A multivariate model was developed using a stepwise procedure with selection of FA, volatile, and non-volatile metabolites. Only variables with P < 0.05 entered the model, and variables with P < 0.10 were retained in the final model. Multivariate analysis, using only FA (g/100g total FA), resulted in equations: CH4 (g/kg DMI) = 29.5 (±1.33) – 2.13 (±0.98) × C18:2n-6 – 5.37 (±1.31) × total CLA (adjusted R2 = 0.53), and CH4 (g/kg FPCM) = 11.7 (±2.13) + 42.7 (±7.55) × C15:0iso – 9.88 (±3.23) × C17:0 (adjusted R2 = 0.51). Multivariate analysis, using FA, volatile metabolites (peak area) and non-volatile metabolites (area change), resulted in equations: CH4 (g/kg DMI) = 28.6 (±0.97) – 6.33 (±1.00) × total CLA – 6.21 (±1.65) × N-acetyl sugar + 2.46 (±0.83) × choline (adjusted R2 = 0.69), and CH4 (g/kg FPCM) = 7.2 (±2.32) + 1.23 × 10–6 (5.26 × 10–7) × 3-nonanone + 22.1 (±5.69) × C15:0iso – 205.1 (±66.21) × uridine diphosphate hexose + 62.7 (±28.17) × Unknown (adjusted R2 = 0.68). The potential of milk FA only to predict CH4 emissions was moderate. Including volatile and non-volatile metabolites enhanced the predictive power, suggesting that these metabolites, in combination with milk FA, hold potential to predict CH4 emission of dairy cows.
Key Words: methane emission, milk fatty acids, milk volatile and non-volatile metabolites
