Manure storage practices in Canada: farm survey analysis with implications for GHG emissions
Bibliographic record
Abstract
Manure storage systems enable farmers to apply nutrients at the right time for crop production but are also a source of greenhouse gases. This study analyzed Canadian Farm Management Surveys (conducted in 2017 and 2021) to quantify the prevalence of manure management systems and use of mitigation practices and identify changes over time. The surveys represented the beef, dairy, poultry, and swine sectors fairly well with some exceptions (e.g., underrepresentation of swine in western Canada). Results show a shift towards liquid manure in the dairy sector and dominance of liquid manure in the swine sector and solid manure in beef and poultry sectors. Practices that may reduce emissions include mechanical separation, which gained popularity in the BC dairy sector. A stable minority (10%) of farmers use additives for their manure in some provinces. Anaerobic digestion remains rare (∼1% in the dairy sector and less in other sectors). For solid manure, storage for >6 months (many >1 year) was common. Adoption of solid manure active composting was modest at 5%–10%. These baseline data show there is a high potential for further adoption of beneficial management practices that decrease emissions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".