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Milk Fatty Acids Estimation by Mid-Infrared Spectroscopy as Proxy for Prediction of Methane Emission in Dairy Cows

  • ANIMAL HUSBANDRY
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Russian Agricultural Sciences Aims and scope

Abstract

Ruminant enteric methane (CH4) emission contributes to global warming. Current enteric CH4 measurement techniques, such as whole-animal chambers and tracer gas techniques require complex instrumentation and, thus, are limited in their use. Thus, a simple, robust and inexpensive measurement technique applicable on a large scale to estimate CH4 emission from dairy cattle would therefore be valuable. Milk fatty acid (MFA) composition has been suggested as a means of predicting enteric CH4 output in lactating dairy cattle because of the common biochemical pathways among CH4 and fatty acids in the rumen. A summary of studies that investigated the predictive power of MFA composition for CH4 emission indicated good potential, with predictive power ranging between 47 and 95%. Until recently, gas chromatography (GC) was the principal method used to determine the MFA profile, but GC is unsuitable for routine analysis. This has led to the application of mid-infrared (MIR) spectroscopy. The major advantages of using MIR spectroscopy to predict CH4 emission include its simplicity and potential practical application at large scale. Disadvantages include the inability to predict important MFA for CH4 prediction, and the moderate predictive power for CH4 emission. It may not be sufficient to predict CH4 emission based on MIR alone. Integration with other factors, like feed intake, nutrient composition of the feed, parity, and lactation stage may improve the prediction of CH4 emission using MIR spectra.

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Authors and Affiliations

  1. Department of Animal Nutrition and Forage Science, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

    M. O. Okpara

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  1. M. O. Okpara
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Correspondence to M. O. Okpara.

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Okpara, M.O. Milk Fatty Acids Estimation by Mid-Infrared Spectroscopy as Proxy for Prediction of Methane Emission in Dairy Cows. Russ. Agricult. Sci. 45, 386–392 (2019). https://doi.org/10.3103/S1068367419040116

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  • DOI: https://doi.org/10.3103/S1068367419040116

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