Economic evaluation of 4 bovine leukemia virus control strategies for Alberta dairy farms
Bibliographic record
Abstract
Bovine leukemia virus (BLV) is a production-limiting disease common in North American dairy herds. To make evidence-based recommendations to Canadian dairy producers and their consultants regarding cost and financial benefits of BLV on-farm control, an economic model that takes the supply-managed milk quota system into account is necessary. Alberta-specific input variables were used for the presented analysis. A decision tree model program was used to evaluate economic aspects of decreasing a 40% BLV within-herd prevalence on dairy farms by implementing various control strategies over 10 yr. Investigated strategies were (1) all management strategies, including 3 options for colostrum management; (2) some management strategies; (3) test and cull; and (4) test and segregate. Each of these strategies was compared with a no control on-farm approach. The prevalence for this no-control approach was assumed to stay constant over time. Each control strategy incurred specific yearly cost and yielded yearly decreases in prevalence, thereby affecting yearly partial net revenue. Infection with BLV was assumed to decrease milk production, decrease cow longevity, and increase condemnation of carcasses at slaughter from cattle with enzootic bovine leukosis, thereby decreasing net revenue. Cows infected with BLV generated a yearly mean partial net revenue of Can$7,641, whereas noninfected cows generated Can$8,276. Mean cost for the control strategies ranged from Can$193 to Can$847 per animal over 10 yr in a 146-animal herd. Net benefits of controlling BLV on farm, as compared with not controlling BLV, per cow in a 146-animal herd over a 10-yr period for each strategy was: Can$1,315 for all management strategies (freezer); Can$1,243 for all management strategies (pasteurizer); Can$785 for all management strategies (powdered colostrum); Can$1,028 for some management strategies; Can$1,592 for test and cull; and Can$1,594 for test and segregate. Consequently, on-farm BLV control was financially beneficial. Even though negative net benefits were possible and expected for some iterations, our sensitivity analysis highlighted the overall robustness of our model. In summary, this model provided evidence that Canadian dairy farmers should be encouraged to control BLV on their farm.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".