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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Record number 530130
Title Using milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles to predict methane emission of dairy cows
Author(s) Gastelen, S. van; Mollenhorst, H.; Antunes Fernandes, E.C.; Hettinga, K.A.; Burgsteden, G.G. van; Dijkstra, J.; Rademaker, J.L.W.
Event Methagene final meeting, Caserta, 2017-10-11/2017-10-13
Department(s) LR - Animal Nutrition
Animal Nutrition
LR - Animal Breeding & Genomics
Food Quality and Design
Publication type Other research output
Publication year 2017
Abstract We compared the prediction potential of gas chromatography-based milk fatty acids (MFA) and milk Fourier-transform infrared spectroscopy (FTIR) for methane (CH4) emissions of dairy cows. Data from 9 experiments with lactating Holstein-Friesian cows with a total of 30 dietary treatments and 218 observations were used. Methane emissions were measured in climate respiration chambers. Multivariate MFA-based and FTIR-based CH4 prediction models were developed and, subsequently, evaluated with the concordance correlation coefficient (CCC) analysis. The MFA-based CH4 prediction models estimated CH4 production (g/d), yield (g/kg dry matter intake), and intensity (g/kg fat- and protein-corrected milk) with a CCC of 0.72, 0.59, and 0.77, respectively. The FTIR-based CH4 prediction models estimated CH4 production, yield, and intensity with a CCC of 0.52, 0.40, and 0.72, respectively. These results indicate that for all CH4 emission units, but particularly for CH4 production and yield, the MFA-based prediction models described a greater part of the observed variation in CH4 emission than FTIR-based prediction models.
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