The Economics of Controlling Infectious Diseases on Dairy Farms
Bibliographic record
Abstract
Cost‐effective disease control on the dairy farm can enhance productivity and subsequently profitability. Previous economic studies on animal disease have focused on production losses and evaluation of disease eradication programs and have provided little guidance on the optimal prevention action. This paper presents a theoretical model on the economics of livestock disease and develops an empirical model to determine the optimal set of control strategies for four production‐limiting cattle diseases: bovine viral diarrhea (BVD), enzootic bovine leukosis (EBL), Johne's Disease (JD) and neosporosis. Control functions indicating the prevalence of infection with each of the four diseases for each of the 10 strategies are estimated. The optimal strategies that minimize total disease cost (direct production losses and control expenditures) are provided for each disease on the basis of farm survey results from the maritime provinces. The results emphasize the importance of introduction checks before new animals enter the herd and adequate vaccination protection as cost‐effective control strategies. Lutter contre la maladie d'une manière rentable dans les élevages de bovins laitiers peut déboucher sur un meilleur rendement et des profits plus élevés. Les études économiques antérieures s'intéressant à cet aspect portaient essentiellement sur les pertes de production et l'évaluation des programmes d'éradication. Elles donnaient peu d'indications sur la solution idéale au niveau de la prévention. Cet article présente un modèle théorique de l'économique des maladies du bétail et aboutit à un modèle empirique permettant d'établir le jeu optimal de moyens pour lutter contre quatre maladies réduisant la production animale : la diarrhée à virus des bovins (DVB), la leucose bovine enzootique (LBE), la paratuberculose et la néosporose. Les auteurs estiment les fonctions qui indiquent la prévalence d'une infection pour chacune des quatre maladies retenues, dans le cadre des dix stratégies examinées. Ensuite, ils présentent les meilleures stratégies, à savoir celles qui minimisent le coût total de la maladie (pertes de production directes et dépenses associées à la lutte contre la maladie), pour chaque maladie en fonction des résultats d'un sondage auprès des éleveurs des provinces de l'Atlantique. Tout indique que les méthodes de lutte les plus rentables sont l'examen de l'animal avant son addition au troupeau et une vaccination qui protègera les bêtes de manière adéquate.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".