Farm practices survey and modelling to estimate monthly NH<sub>3</sub> emissions from swine production in 12 Ecoregions of Canada
Bibliographic record
Abstract
The swine industry in Canada has undergone rapid growth in some areas, but has also been restricted by a variety of environmental issues. Ammonia (NH3) emissions are seldom mentioned among these issues, but emissions do occur and atmospheric NH3 causes a number of impacts including contributing to odour, deposition into sensitive ecosystems and formation of secondary particulate matter, which is a health concern in some regions of North America. This paper describes a new model to estimate NH3 emissions from the swine sector, relying heavily on a recent survey of swine producers to determine the present N and manure management practices. The key hypothesis was that NH3 emission rates vary across Ecoregions and over time in a way that affects the degree of impact. The survey showed many differences across Ecoregions, most importantly related to feed crude protein and landspreading practices. The model estimated that grower pigs (>20 kg to market size) excreted on average about 8.5 kg total ammoniacal N (TAN) per (occupied) pig place per year, and based on the national average farm practices, 25% of this TAN was emitted from barns, 5.3% was emitted from manure storage, and 17% was emitted during landspreading for a total of 4.8 kg NH3 pig-place-1 yr-1. The total loss for grower pigs ranged from 40 to 53% of excreted TAN across the 12 Ecoregions. Nursing sows emitted over twice as much per pig. Regions varied in reliance on homegrown feeds, which affected crude protein feeding and TAN excretion rates. Western regions had relatively low emissions from land application of slurry because of more extensive use of injectors. Emissions from grower pigs in winter were about 0.2 kg NH3 pig-place-1 month-1, mostly from barns, to as high as 0.7 kg NH3 pig-place-1 month-1 in May, mostly from landspreading. Total emission for all of Canada in 2006 was 73 × 106 kg NH3 or about 13% of agricultural emissions in Canada. The results indicate that emission reductions can be achieved with greater use of low-emission application methods and reduced crude protein in feed. The emissions model can be used in future to assess the impacts from changes in feeding regimes and barn designs, and changes in practices such as animal density and increased access to outdoor spaces.Key words: Swine, ammonia, nitrogen, manure, 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".