Understanding Adoption of Livestock Health Management Practices: The Case of Bovine Leukosis Virus
Bibliographic record
Abstract
Herd‐level livestock health management decisions have implications for farm profitability and the potential public impact of a livestock disease outbreak. Thus, adoption of health management practices is of interest to government officials concerned with managing the risk of disease outbreak and controlling the spread of infection. This paper uses a fractional logit model to estimate the disease risk reduction for livestock health management practices on farms, and then uses the economic benefits of these risk reductions as explanatory variables in an econometric model of adoption of these practices. We find that the economic damages from disease associated with a particular practice are statistically significant but ultimately of little practical economic importance in adoption decisions. Implications for policy and relation to prior research findings are discussed. Les décisions entourant la gestion sanitaire du troupeau ont des répercussions sur la rentabilité des fermes et sur l'impact qu'une éclosion de maladies animales pourrait avoir sur la population. Par conséquent, l'adoption de pratiques de gestion sanitaire intéresse les représentants du gouvernement soucieux de gérer le risque d'éclosion de maladies et de maîtriser la propagation d'une infection. Dans le présent article, nous avons utilisé un modèle logit fractionnaire pour estimer la diminution du risque de maladies lorsque des pratiques de gestion sanitaire du troupeau sont adoptées à la ferme et nous avons ensuite utilisé les avantages économiques de cette diminution du risque comme variables explicatives dans un modèle économétrique d'adoption de ces pratiques. Les résultats ont montré que les dommages économiques liés aux maladies associées à une pratique en particulier sont statistiquement significatifs, mais qu'ils sont finalement sans importance économique dans les décisions d'adoption. Nous avons examiné les répercussions sur la politique agricole et avons fait le lien avec des résultats de recherche antérieurs.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".