Review of research studies on nitrous oxide emissions from manure-amended soils in Canada from 1990 to 2023
Bibliographic record
Abstract
Nitrous oxide (N 2 O) emissions from manure-amended soils are estimated to be 1525 kt CO 2 e in Canada. The accuracy of this estimate is dependent on emission measurements. However, obtaining accurate measurements is challenging due to the variable distribution of livestock types, climates, soils, and management across Canada. This study compares research studies on the temporal and spatial distribution of N 2 O emissions from land applied manure with emission estimates from the National Inventory Reports to evaluate how research aligns with key factors driving emissions. Overall, 122 articles were identified, including 31 incubation, 57 soil chamber, and 8 micrometeorological studies (the rest were modelling). Although 51 (42%) of the articles were based in Ontario and Quebec, this region still warrants more attention, because its high livestock population and humid climate results in 68.7% of Canada's N 2 O emissions from manure-amended soil. Dairy manure was most common with 55 studies, followed by swine (36) and beef (29). Emissions from beef manure applications are notably lacking in Quebec, while dairy and swine studies were reasonably aligned with provincial emissions. The underutilization of micrometeorological methods creates a significant gap in determining annual emissions. Increasing research focus on year-round and non-growing seasons would improve estimates. Additional studies using solid manure and/or a wider range of soil textures would strengthen the national emission estimate, which currently relies mostly on research involving liquid manure and medium-textured soils. Further research is needed to fill the identified gaps; specifically, high-resolution measurements, considering local livestock industries, and soil textures in humid climates.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".