Farm practices as they affect NH3 emissions from beef cattle
Bibliographic record
Abstract
Sheppard, S. C. and Bittman, S. 2012. Farm practices as they affect NH 3 emissions from beef cattle. Can. J. Anim. Sci. 92: 525-543. Beef cattle farms in Canada are very diverse, both in size and management. Because the total biomass of beef cattle in Canada is larger than any other livestock sector, beef also has the potential for the largest environmental impact. In this study we estimate NH3 emissions associated with beef cattle production across Canada using data on farm practices obtained from a detailed survey answered by 1380 beef farmers in 11 Ecoregions. The farms were various combinations of cow/calf, backgrounding and finishing operations. The proportion of animals on pasture varied markedly among Ecoregions, especially for cows and calves, and this markedly affected the estimated NH3 emissions. The crop components of feed also varied among Ecoregions, but the resulting crude protein concentrations were quite consistent for both backgrounding and finishing cattle. Manure was stored longer in the west than in the east, and fall spreading of manure was notably more common in the west, especially when spread on tilled land. The estimated NH3 emissions per animal were relatively consistent across Ecoregions for confinement production, but because the proportion of animals on pasture varied with Ecoregion, so did the overall estimated NH3 emissions per animal. Temperature is a key factor causing Ecoregion differences, although husbandry and manure management practices are also important. Hypothetical best management practices had little ability to reduce overall emission estimates, and could not be implemented without detailed cost/benefit analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".