Journal Highlight: Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy

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  • Published: Aug 1, 2016
  • Author: spectroscopyNOW
  • Channels: Infrared Spectroscopy
thumbnail image: Journal Highlight: Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy
The suitability of mid-IR spectroscopy for predicting methane emission from lactating cows via their milk fatty acids is reviewed and the advantages and disadvantage are explored.

Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy

Journal of the Science of Food and Agriculture, 2016, 96, 3963-3968
Sanne van Gastelen and Jan Dijkstra

Abstract: Enteric methane (CH4) production is among the main targets of greenhouse gas mitigation practices for the dairy industry. 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 acids (MFA) are related to CH4 production because of the common biochemical pathway between 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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