Host-specific microbiome-rumination interactions shape methane-yield phenotypes in dairy cattle
Bibliographic record
Abstract
ABSTRACT Enteric methane emissions (EMEs) negatively impact both the environment and livestock efficiency. Given the proposed link between CH 4 yield and the rumination time (RT) phenotype, we hypothesize that this connection is mediated by the gut microbiome. This study investigated the RT-microbiome-EME connection using rumination-bolus, fecal, and rumen microbiomes as non-invasive proxies for identifying low-EME cows. High-RT cows ruminated 94 minutes longer per day (20%) and exhibited 26% lower EME than low-RT cows, confirming a strong RT-CH 4 -yield association. Microbial analysis revealed conserved methanogen diversity across the rumen, bolus, and fecal microbiomes, though functional differences were evident. High-RT cows had a greater abundance of Methanosphaera stadtmanae , suggesting an increased potential for methylotrophic methanogenesis, whereas low-RT cows exhibited higher Methanobrevibacter YE315 abundance, indicative of CO 2 -utilizing methanogenesis. Additionally, high-RT cows showed increased alternative hydrogen sinks, supported by upregulated genes encoding fumarate reductase, sulfate reductase, nitrate reductase, and ammonia-forming nitrite reductase, thereby reducing hydrogen availability for methanogenesis. Metabolically, high-RT cows had higher propionate concentrations and were enriched with rapid-fermenting bacteria ( Prevotella , Sharpea , Veillonellaceae , and Succinivibrionaceae ), whereas low-RT cows exhibited higher acetate concentrations with elevated acetate-producing pathways, reflecting differences in energy partitioning mechanisms. This study establishes RT as a microbiome-linked, non-invasive screening tool for identifying low-EME cows. The observed microbial and metabolic shifts in high-RT cows suggest that RT-based selection could enhance methane mitigation, rumen efficiency, and climate-smart livestock production. Leveraging RT-associated microbial profiles offers a scalable and cost-effective approach to reducing EME in cattle. IMPORTANCE Methane emissions from livestock contribute to climate change and reduce animal efficiency. This study reveals that cows with longer rumination times (chewing cud for an extra 94 minutes daily) produce 26% less methane than cows with shorter rumination times. The gut microbiome plays a key role—low-methane cows host microbial communities that produce less methane while efficiently utilizing hydrogen for energy conservation in the rumen. By analyzing rumination sensor data and/or in combination with microbial profiles from rumen or fecal samples, farmers can non-invasively identify and select cows that naturally emit less methane. This scalable, cost-effective strategy offers a practical solution for reducing livestock’s environmental footprint while enhancing efficiency and advancing climate-smart agriculture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".