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Updated: Aug 5, 2026

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
Published on: September 7, 2015
Short communication: oral microbiome as a potential proxy for methane emissions in grazing tropical composite beef
Chian Teng Ong1, Tony Cavallaro1, Yuhao Li1
1Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Animal Science, The University of Queensland, Brisbane, Queensland 4072, Australia.
None:
Enteric methane emissions from ruminant livestock contribute to global warming, creating an urgent need for effective mitigation strategies that do not compromise animal productivity and welfare. Methanogenic archaea within the rumen microbiome drive enteric methane emissions. However, large-scale rumen-fluid sampling in commercial production systems is impractical, due to its invasive nature and the associated logistical challenges. This study hypothesized that rumination facilitates the capture of rumen microbial signals within the oral cavity, therefore oral microbiome profiles can be a practical alternative for explaining variation in methane emissions commercial production systems. To test the hypothesis, we estimated the oral microbiability, defined as the proportion of phenotypic variance in methane emissions explained by oral microbiome variation. Samples were collected from 209 tropical composite beef cattle across two trials in Queensland, Australia. Oral microbiome samples were obtained from all animals, with paired rumen samples in one trial, and methane emissions were measured using either the sulfur hexafluoride tracer technique or the GreenFeed system. Microbial features were characterized using taxonomic and functional annotations, and microbiability was estimated using mixed linear models incorporating microbiome-based relationship matrices. The oral microbiability reported in this study ranged from 0.27 to 0.63 with standard errors 0.12 to 0.25. Functional microbial profiles explained a numerically greater proportion of methane emission variation than taxonomic profiles in some comparisons, however, the differences were not statistically significant due to large standard errors. These findings demonstrated that oral microbiome sampling provides a practical and scalable proxy method for capturing variation in methane emissions among the individual cattle in grazing systems, where direct methane gas measurements are labor-intensive and difficult to implement. Therefore, further validation of oral microbiability against methane emissions measured in larger animal cohorts is required. Such validation would enable a more robust assessment of the predictive accuracy of oral microbiome-based models while accounting for additional sources of variation, including host genetic, environmental, and management factors, which could not be fully addressed in the present study.
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