Predicting enteric methane production from the rumen microbial composition of beef steers fed either a high concentrate or cut grass diet

G A Miller, MD Auffret, R Roehe, H Nisbet, MM Martinez Alvaro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Application: This research shows the potential of the rumen microbiome as a proxy for enteric methane (CH4) yield from cattle fed two contrasting diet types.Introduction: The development, testing and implementation of strategies to reduce or mitigate enteric CH4 emissions would greatly benefit from the development of reliable, large scale methods to measure emissions from individual animals. Genetic sequencing of the rumen microbiome has received increasing attention as a potential proxy that could fulfil this requirement. The objectives of this study were to assess the differences in the rumen microbial composition between cattle on contrasting diets and to assess the diet specific potential of relative abundances of 16S rRNA gene sequenced microbiota as a proxy for enteric CH4 production.
Material and Methods: Thirty-six beef steers (18x Limousin cross and 18x Aberdeen Angus cross) were allocated to either a high concentrate total mixed ration (TMR, 136:864 forage:concentrate) or a fresh cut grass diet (ryegrass and clover mown once per day). Cattle were housed one pen per treatment, bedded on sawdust and fed ad libitum. Animals were allocated to six indirect open-circuit respiration chambers over a 6-week period. 
Concentrations of CH4 in chamber exhaust air was measured by infra-red absorption spectroscopy and daily feed intake was recorded. Rumen fluid samples were collected after exiting the respiration chambers using a naso-gastric tube. DNA was extracted from the rumen samples and subjected to 16S rRNA gene sequencing. Microbial genera with different abundance amongst the two diets were identified by a projection to latent structure regression discriminant analysis (PLS-DA). The sign and magnitude of the differences between diets were tested using a Wilcoxon Rank Sum test and corrected p-values by Bonferroni procedures. A linear PLS analysis was used to identify those microbial genera abundances that best explained CH4 emissions within each diet group.
Results: There was a significant difference (p < 0.001) in CH4 emissions between diets, which was independent of breed. A total of 31 microbial genera were identified in the DA-PLS model as discriminating between diets. The differential abundances between selected genera showed a clear separation between diets. The PLS-DA model was based on a single component and displayed a 94.5% discriminating ability and 93.6% after cross-validation. The Wilcoxon Rank Sum test elucidated significant differential abundances between diets in 16 microbial genera (p < 0.1), of which 15 were also identified in the PLS-DA, corroborating these results. The PLS analysis for the TMR diet identified a total of 29 microbial genera in a model based on a single component explaining 89.0% of the variation in CH4 yield and 70.6% after cross-validation. Following the same approach for the Grass diet, 26 microbial genera explained 69.2% of CH4 yield variation and 42.4% after cross-validation.
Conclusion: A strong divergence in rumen microbial genera was observed between two extreme diets. Prediction of CH4 yield using rumen microbial genera abundances was more accurate for the high concentrate than the grass diet.
Original languageEnglish
Title of host publicationAnimal
Subtitle of host publicationProceedings of the British Society of Animal Science
Pages57
Number of pages1
Volume12
DOIs
Publication statusPrint publication - Apr 2021

Publication series

NameAnimal - science proceedings

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