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Record W2111989590 · doi:10.4141/a05-010

Methane and nitrous oxide emissions from Canadian animal agriculture: A review

2006· review· en· W2111989590 on OpenAlexaffvenueabout
E. Kebreab, Karen Clark, Claudia Wagner‐Riddle, J. France

Bibliographic record

VenueCanadian Journal of Animal Science · 2006
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGreenhouse gasManureManure managementEnvironmental scienceAgricultureNitrous oxideLivestockMethaneClimate changeAgricultural scienceAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

Considerable evidence of climate change associated with emissions of greenhouse gases (GHG) has resulted in international efforts to reduce GHG emissions. The agriculture sector contributes about 8% of GHG emissions in Canada mostly through methane (CH 4 ) and nitrous oxide (N 2 O). The objective of this paper was to compile an integrative review of CH 4 and N 2 O emissions from livestock by taking a whole cycle approach from enteric fermentation to manure treatment and storage, and field application of manure. Basic microbial processes that result in CH 4 production in the rumen and hindgut of animals were reviewed. An overview of CH 4 and N 2 O production processes in manure, and controlling factors are presented. Most of the studies conducted in relation to enteric fermentation were in dairy and beef cattle. To date, research has focussed on GHG emissions from the stored manures of dairy, beef cattle and swine; therefore, we focus our review on these. Several methods used to measure GHG emissions from livestock and stored manure were reviewed. A comparison of methods showed that there were agreements between most of the techniques but some systematic differences were also observed. Additional studies with comprehensive comparisons of methodologies are needed in order to allow for comparison of results obtained from studies using contrasting methodologies. The need to standardize measurement methods and reporting to facilitate comparison of results and data integration was identified. Prediction equations are often used to calculate GHG emissions. Various types of mathematical approaches, such as statistical models, mechanistic models and estimates calculated from emission factors, and studies that compare various types of models are discussed herein. A lack of process-based models describing GHG emissions from manure during storage was identified. A brief description of mitigation strategies focussing on recent studies is given. Reduction in CH 4 emissions from ruminants through the addition of fats in diets and the use of more starch was achieved and a transient beneficial effect of ionophores was reported. Grazing management and genetic selection also hold promise. Studies focussed on manure treatment options that thave been suggested to reduce gas fluxes from manure storage, composting, anaerobic digestion (AD), diet manipulation, covers and solid-liquid separation, were reviewed. While some of these options have been shown to decrease GHG emissions from stored manure, different studies have obtained conflicting results, and additional research is needed to identify the most promising options. GHG emissions from pasture and croplands after manure application have been the subject of several experimental and modelling studies, but few of these have linked field emissions to diet manipulation or manure treatments. Further work focussing on the entire cycle of GHG formation from feed formulation, animal metabolism, excreta treatment and storage, to field application of manure needs to be conducted. Key words: Greenhouse gases, enteric methane, nitrous oxide, manure management

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)0.002
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 score0.852
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.014
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.042
GPT teacher head0.279
Teacher spread0.237 · 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

Citations248
Published2006
Admission routes3
Has abstractyes

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