Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewedConventional

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

Gemma Miller; Marc Auffret; Rainer Roehe; Holly Nisbet; Marina Martínez-Alvaro

Animal - science proceedings · 2021

Read source ↗ All evidence

Summary

This study investigated whether rumen microbial composition could serve as a predictive biomarker for enteric methane emissions in beef steers fed either high concentrate or forage-based (cut grass) diets. The work suggests that microbial community profiles may enable rapid prediction of individual animal methane output, with potential application to selective breeding or nutritional management for climate mitigation. The comparison across dietary treatments likely explored whether diet-dependent microbial shifts influence the strength of such prediction relationships.

Regional applicability

The study was conducted in the United Kingdom (based on author affiliations and journal scope) and is directly applicable to UK beef production systems. Findings would be relevant to UK livestock producers and policy-makers seeking evidence-based approaches to reduce enteric methane from cattle, though transferability to other temperate regions would depend on similarity of animal genetics, forage types, and management practices.

Key measures

Enteric methane production, rumen microbial composition (likely 16S rRNA sequencing or similar), diet type (high concentrate vs. cut grass)

Outcomes reported

The study examined relationships between rumen microbial community composition and enteric methane production in beef steers under contrasting dietary regimens (high concentrate versus cut grass). Predictive models were developed to estimate methane emissions based on microbial profiles.

Theme
Climate & resilience
Subject
Climate & greenhouse gas mitigation
Study type
Research
Study design
Field trial
Source type
Peer-reviewed study
Status
Published
Geography
United Kingdom
System type
Intensive livestock
DOI
10.1016/j.anscip.2021.03.079
Catalogue ID
NRmr2mgnaa-001

Topic tags

Pulse AI · ask about this record

Dig deeper with Pulse AI.

Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.