An economic evaluation of management approaches to improve cost effectiveness in Ontario beef enterprises
Bibliographic record
Abstract
This study evaluated two management practices for beef production in the province of Ontario: (1) seasonal calving practice (summer calving versus the traditional winter calving) and (2) fall feeding practice (fall pasture stockpiling versus the traditional fall confinement feeding). Superimposed on each of these two management practices, four marketing alternatives for the saleable progeny were evaluated: (1) Weaned calves (approximately 7 months of age, (2) Yearling calves, (3) Calves at 15 months, and (4) calves at 18 months. Two methods (simple enterprise budget and linear programming model) were used to evaluate these production practices and marketing alternatives. Simple enterprise budgets were designed to compare the sixteen practices (including the traditional and modified practices). The LP model, however, incorporated all the different beef production options and four calf marketing alternatives to seek out the most competitive beef production practice. Thirteen different scenarios were also evaluated to ascertain the competitiveness of production practices and levels of profitability based on changes in key variable costs. In general, summer calving and fall stockpiling were more profitable than traditional winter calving and fall confinement feeding practice. Furthermore, retaining calves to a later marketing stage will bring higher returns to farmers. The combination of summer calving with fall stockpiling management and selling calves at 18 months gave the highest net returns of all the practices considered.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".