Estimating the Implicit Prices of Beef Cattle Attributes: A Case from Alberta
Bibliographic record
Abstract
Pressures on beef producers to provide lean beef of consistent quality have been mounting in recent years. Yet this requires beef breeders to alter and broaden cattle improvement objectives to include carcass merit traits. They need information on heritability and the values associated with genetic traits in order to effectively do this. This study estimates the implicit prices in east‐central Alberta, Canada, for bull attributes using a hedonic pricing model. The results indicate that the most important bull attributes to buyers (breeders) in this region are sale weight, birth weight and scrotal circumference. Also important are ribeye area and average daily gain. Selection of these attributes conforms with expectations because they are moderately to highly heritable and are associated with improved fertility and reproduction, reduced production costs and higher returns. In addition, the results suggest that breeders have been changing selection emphasis away from reproduction traits and toward carcass traits associated with improved production efficiency and consumer demand. Depuis quelques années, les producteurs subissent des pressions grandissantes pour fournir du bœuf maigre de qualité uniforme. Ces pressions contraignent les éleveurs à modifier et àélargir leurs objectifs d'hybridation en y incluant les caractères génétiques qui codent les paramètres de la carcasse. Pour y arriver, les éleveurs ont besoin de renseignements sur l'héritabilité et la valeur des caractères en question. Dans cet article, le prix implicite des attributs des taureaux dans le centre‐est de l'Alberta est estimé selon un modèle hédoniste. Les attributs les plus importants pour les acheteurs (éleveurs) de la région sont le poids à la vente, le poids à la mise bas et la circonférence du scrotum. Comptent aussi pour beaucoup la surface du faux‐filet et le gain quotidien moyen. La sélection de tels attributs est conforme aux prévisions, car il s'agit de caractères très héréditaires qu'on relie à une fertilité accrue et de meilleures aptitudes à la reproduction, done à une réduction des coûts d'élevage et à un rendement plus élevé. Par ailleurs, les résultats de l'analyse laissent croire que les éleveurs ont réorienté leurs programmes de sélection, laissant de côté les aptitudes à la reproduction pour les paramètres de la carcasse associés à un meilleure productivité et à la demande des consommateurs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".