ArticleFrontiers in microbiology2023
Different microbial genera drive methane emissions in beef cattle fed with two extreme diets.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Methane emissions in beef cattle: A review of measurements and mitigation strategies.Veterinary and animal science · 2026Review
- Predicting beef diet nutritional composition and intake from rumen metagenomic profiles.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2026Article
- The role of tannin-based products in mitigating enteric methane emissions in ruminant livestock: A review.Journal of advanced veterinary and animal research · 2026Article
- Dietary modulation of the rumen microbiome drives the expression of metabolic and methanogenic pathways inmSphere · 2025Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ratio of forage to concentrate in cattle feeding has a major influence on the composition of the microbiota in the rumen and on the mass of methane produced. Using methane measurements and microbiota data from 26 cattle we aimed to investigate the relationships between microbial relative abundances and methane emissions, and identify potential biomarkers, in animals fed two extreme diets - a poor quality fresh cut grass diet (GRASS) or a high concentrate total mixed ration (TMR). Direct comparisons of the effects of such extreme diets on the composition of rumen microbiota have rarely been studied. Data were analyzed considering their multivariate and compositional nature. Diet had a relevant effect on methane yield of +10.6 g of methane/kg of dry matter intake for GRASS with respect to TMR, and on the centered log-ratio transformed abundance of 22 microbial genera. When predicting methane yield based on the abundance of 28 and 25 selected microbial genera in GRASS and TMR, respectively, we achieved cross-validation prediction accuracies of 66.5 ± 9% and 85 ± 8%. Only the abundance of
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