64 2001 Journal of the ASFMRA | www.asfmra.org An Economic Analysis of Cow-calf Retained Ownership Strategies
Bibliographic record
Abstract
Retained ownership takes place when a producer keeps title of a group of calves beyond the traditional weaning period. The potential benefits and risks to retaining ownership are well-documented (Murra et. al., Joerger, Pierce, Guyer, Mc Kissick & Ikerd, Little et. al., Marshall & Wagner, Lawrence). For purposes of this article, ownership is assumed to be retained via contractual arrangements with custom feeders. Custom feeding means maintaining ownership of calves and the right to make major management decisions, even though the animals are not kept on the cow-calf producer's farm. Retained ownership as a management strategy is not widely practiced by Saskatchewan beef producers. The sale of most calves after weaning in September and October still dominates the industry. Some ownership is retained by backgrounding animals, but it will be shown below that this puts the cow-calf producer in a worse position with respect to both return and risk. Objective The objective of this article is to explore the risk efficiency of various retained ownership strategies for cow-calf producers. Six alternatives are examined in this paper. The first is a cow-calf (CC) operation only, wherein ownership of a calf crop is relinquished after weaning. The second is cow-calf production and custom The article explores the risk efficiency of six strategies for beef producers using data from Saskatchewan, Canada for 1978 to 1997. The results indicate that the risk efficient strategies for the Saskatchewan beef industry are the cattle finishing enterprise and the cow-calf producer retaining ownership through both custom backgrounding and finishing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".