Review of research on methane and nitrous oxide emissions from manure storage across Canada from 1990 to 2023
Bibliographic record
Abstract
In 2022, Canadian greenhouse gas (GHG) emissions related to manure management were estimated at 7.8 Mt carbon dioxide equivalent, with methane (CH 4 ) emissions primarily from swine and nitrous oxide (N 2 O) emissions mainly from beef production. Because Canada’s agriculture spans diverse climates and livestock types, this estimate carries uncertainty that further research could reduce. This study reviewed 144 Canadian articles published between 1990 and 2023 to compare the current state of research associated with emissions of CH 4 and N 2 O from manure storage across Canada. Among these articles, 140 addressed CH 4 , 72 addressed N 2 O, including 68 for both gases. Research location counts (39 in Alberta, 39 in Quebec, and 34 in Ontario) reflected estimated CH 4 , but not N 2 O emissions. The emissions from liquid manure and solid manure were measured primarily through chamber (46) and incubation (43) methodologies. Dairy was the most studied livestock group, but further research is still needed, as CH 4 emissions continue to increase, along with N 2 O emissions from poultry manure. Mitigation practices for beef manure in Eastern Canada warrant further research, given the region’s humid temperate climate and its relative contribution to GHG emissions. Digesters (55) and composting (22) dominate mitigation studies, implying promise, while other practices remain understudied.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".