Beef cattle husbandry practices across Ecoregions of Canada in 2011
Bibliographic record
Abstract
Abstract. Sheppard, S. C., Bittman, S., Donohoe, G., Flaten, D., Wittenberg, K. M., Small, J. A., Berthiaume, R., McAllister, T. A., Beauchemin, K. A., McKinnon, J., Amiro, B. D., MacDonald, D., Mattos, F. and Ominski, K. H. 2015. Beef cattle husbandry practices across Ecoregions of Canada in 2011. Can. J. Anim. Sci. 95: 305-321. Beef production in Canada is diverse in many dimensions with numbers of cattle per operation ranging over 10 000-fold, pasture usage from nil to 100%, and types of operations from solely cow-calf to exclusively feedlot finishing. This study summarizes management information obtained from a survey conducted in 2012 (about 2011) on 1009 beef operations in Canada. Many of the results clearly differentiate the practices in the Prairies from those in Ontario and Quebec. Compared to eastern Canada, the Prairies had earlier and shorter calving seasons, higher weaning weights, utilized more winter grazing with a variety of strategies, grew and fed more barley than corn, used more seasonal feeding areas and feedlots (and hence fewer barns), and more commonly spread manure in the fall. Many of the management practices used by cow-calf operations would have low environmental impact, including extensive use of grazing even in winter, low fertilizer inputs and feeding perennial forages with a high content of legumes. Some practices such as not covering forages or manure storage structures were common and could be changed to improve forage quality and reduce manure emissions. Most forage was harvested 3-7 d after full bloom. Earlier harvest has the potential to improve forage quality, which could reduce dependence on arable crops. Finishing operations used more housing, fed more arable-land crops and less perennial forages, and practiced little grazing. Rationale regarding the adoption of many of the management strategies was reported by the producers. For example, winter grazing was adopted primarily to reduce costs and labour, but for some it was also linked to a late calving season. Preferred sources of technical information included their own experience, farm print media, producer organisations and demonstrations at field days. The survey also identified several areas in which the industry may realize improved sustainability.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| 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".