Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    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.
    Relationships between methane emission of Holstein Friesian dairy cows and fatty acids, volatile metabolites and non-volatile metabolites in milk
    Gastelen, S. van; Antunes-Fernandes, E.C. ; Hettinga, K.A. ; Dijkstra, Jan - \ 2017
    Animal 11 (2017)9. - ISSN 1751-7311 - p. 1539 - 1548.
    dairy cow - methane emission - milk fatty acid - milk non-volatile metabolite - milk volatile metabolite
    This study investigated the relationships between methane (CH4) emission and fatty acids, volatile metabolites (V) and non-volatile metabolites (NV) in milk of dairy cows. Data from an experiment with 32 multiparous dairy cows and four diets were used. All diets had a roughage : concentrate ratio of 80 : 20 based on dry matter (DM). Roughage consisted of either 1000 g/kg DM grass silage (GS), 1000 g/kg DM maize silage (MS), or a mixture of both silages (667 g/kg DM GS and 333 g/kg DM MS; 333 g/kg DM GS and 677 g/kg DM MS). Methane emission was measured in climate respiration chambers and expressed as production (g/day), yield (g/kg dry matter intake; DMI) and intensity (g/kg fat- and protein-corrected milk; FPCM). Milk was sampled during the same days and analysed for fatty acids by gas chromatography, for V by gas chromatography–mass spectrometry, and for NV by nuclear magnetic resonance. Several models were obtained using a stepwise selection of (1) milk fatty acids (MFA), V or NV alone, and (2) the combination of MFA, V and NV, based on the minimum Akaike’s information criterion statistic. Dry matter intake was 16.8±1.23 kg/day, FPCM yield was 25.0±3.14 kg/day, CH4 production was 406±37.0 g/day, CH4 yield was 24.1±1.87 g/kg DMI and CH4 intensity was 16.4±1.91 g/kg FPCM. The observed CH4 emissions were compared with the CH4 emissions predicted by the obtained models, based on concordance correlation coefficient (CCC) analysis. The best models with MFA alone predicted CH4 production, yield and intensity with a CCC of 0.80, 0.71 and 0.69, respectively. The best models combining the three types of metabolites included MFA and NV for CH4 production and CH4 yield, whereas for CH4 intensity MFA, NV and V were all included. These models predicted CH4 production, yield and intensity better with a higher CCC of 0.92, 0.78 and 0.93, respectively, and with increased accuracy (C b) and precision (r). The results indicate that MFA alone have moderate to good potential to estimate CH4 emission, and furthermore that including V (CH4 intensity only) and NV increases the CH4 emission prediction potential. This holds particularly for the prediction model for CH4 intensity.
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