MétaCan
Menu
Back to cohort
Record W2015527152 · doi:10.2527/jas.2013-6583

SPECIAL TOPICS — Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options1

2013· review· en· W2015527152 on OpenAlexaff
A.N. Hristov, J. Oh, J.L. Firkins, J. Dijkstra, E. Kebreab, G. C. Waghorn, H.P.S. Makkar, A.T. Adesogan, Wenzhu Yang, C. Lee, Pierre Gerber, B. Henderson, J.M. Tricárico

Bibliographic record

VenueJournal of Animal Science · 2013
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsForageRumenSilageGreenhouse gasRuminantDry matterAgronomyAnimal feedAnimal scienceEnvironmental scienceMethane emissionsMethaneBiologyPastureFood scienceFermentationEcology

Abstract

fetched live from OpenAlex

The goal of this review was to analyze published data related to mitigation of enteric methane (CH4) emissions from ruminant animals to document the most effective and sustainable strategies. Increasing forage digestibility and digestible forage intake was one of the major recommended CH4 mitigation practices. Although responses vary, CH4 emissions can be reduced when corn silage replaces grass silage in the diet. Feeding legume silages could also lower CH4 emissions compared to grass silage due to their lower fiber concentration. Dietary lipids can be effective in reducing CH4 emissions, but their applicability will depend on effects on feed intake, fiber digestibility, production, and milk composition. Inclusion of concentrate feeds in the diet of ruminants will likely decrease CH4 emission intensity (Ei; CH4 per unit animal product), particularly when inclusion is above 40% of dietary dry matter and rumen function is not impaired. Supplementation of diets containing medium to poor quality forages with small amounts of concentrate feed will typically decrease CH4 Ei. Nitrates show promise as CH4 mitigation agents, but more studies are needed to fully understand their impact on whole-farm greenhouse gas emissions, animal productivity, and animal health. Through their effect on feed efficiency and rumen stoichiometry, ionophores are likely to have a moderate CH4 mitigating effect in ruminants fed high-grain or mixed grain-forage diets. Tannins may also reduce CH4 emissions although in some situations intake and milk production may be compromised. Some direct-fed microbials, such as yeast-based products, might have a moderate CH4-mitigating effect through increasing animal productivity and feed efficiency, but the effect is likely to be inconsistent. Vaccines against rumen archaea may offer mitigation opportunities in the future although the extent of CH4 reduction is likely to be small and adaptation by ruminal microbes and persistence of the effect is unknown. Overall, improving forage quality and the overall efficiency of dietary nutrient use is an effective way of decreasing CH4 Ei. Several feed supplements have a potential to reduce CH4 emission from ruminants although their long-term effect has not been well established and some are toxic or may not be economically feasible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.053
GPT teacher head0.325
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations948
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Animal ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207