Effect of changes in management practices and animal performance on ammonia emissions from Canadian beef production in 1981 as compared with 2011
Bibliographic record
Abstract
The present study compared ammonia (NH3) emissions from Canadian beef production in 1981–2011. Temporal and regional differences in cattle categories, feed types and management systems, average daily gains, carcass weights, and manure handling practices were considered. A scenario-based sensitivity analysis in 2011 estimated the impact of substituting corn dried distillers’ grains with solubles (DDGS) for grain in feedlot diets. On average, 22% of the total nitrogen (N) intake was lost as ammoniacal nitrogen (NH3-N) in both years. Manure emission sources were consistent across years, averaging 12%, 40%, 28%, and 21% for grazing, confinement, storage, and land spreading, respectively. Emissions per animal in 1981 and 2011 were 16.0 and 18.4 kg NH3 animal−1 yr−1, respectively. On an intensity basis, kilogram of NH3 emitted per kilogram of beef decreased 20%, from 0.17 in 1981 to 0.14 in 2011. This reduction was attributed to increases in reproductive efficiency, average daily gain and carcass weight, and improved breeding herd productivity. In 2011, substituting DDGS for grain in feedlot diets increased total NH3 emissions and losses per animal. Although addition of by-products from the bioethanol industry can lower diet costs, it will be at the expense of an increase in NH3 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".