Current Perspectives on Achieving Pronounced Enteric Methane Mitigation From Ruminant Production
Bibliographic record
Abstract
Limiting global warming to 1.5°C above pre-industrial levels by 2050 requires achieving net zero emissions of greenhouse gases by 2050 and a strong decrease in methane (CH4) emissions. Our aim was to connect the global need for mitigation of the emissions of greenhouse gases and enteric CH4 from ruminant production to basic research on the biological consequences of inhibiting rumen methanogenesis in order to better design strategies for pronounced mitigation of enteric CH4 production without negative impacts on animal productivity or economic returns. Ruminant production worldwide has the challenge of decreasing its emissions of greenhouse gases while increasing the production of meat and milk to meet consumers demand. Production intensification decreases the emissions of greenhouse gases per unit of product, and in some instances has decreased total emissions, but in other instances has resulted in increased total emissions of greenhouse gases. We propose that decreasing total emission of greenhouse gases from ruminants in the next decades while simultaneously increasing meat and milk production will require strong inhibition of rumen methanogenesis. An aggressive approach to pronounced inhibition of enteric CH4 emissions is technically possible through the use of chemical compounds and/or bromoform-containing algae, but aspects such as safety, availability, government approval, consumer acceptance, and impacts on productivity and economic returns must be satisfactorily addressed. Feeding these additives will increase the cost of ruminant diets, which can discourage their adoption. On the other hand, inhibiting rumen methanogenesis potentially saves energy for the host animal and causes profound changes in rumen fermentation and post-absorptive metabolism. Understanding the biological consequences of methanogenesis inhibition could allow designing strategies to optimize the intervention. We conducted meta-regressions using published studies with at least one treatment with >50% inhibition of CH4 production to elucidate the responses of key rumen metabolites and animal variables to methanogenesis inhibition, and understand possible consequences on post-absorptive metabolism. We propose possible avenues, attainable through the understanding of biological consequences of the methanogenesis inhibition intervention, to increase animal productivity or decrease feed costs when inhibiting methanogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".