The economic impact of Johne’s disease (paratuberculosis) in dairy cattle
Bibliographic record
Abstract
Johne’s disease (JD), or paratuberculosis, is an infectious inflammatory disorder of the intestines primarily associated with domestic and wild ruminants including dairy cattle. The disease, caused by an infection with Mycobacterium avium subspecies paratuberculosis (MAP) bacteria, burdens both animals and producers through reduced milk production, premature culling, and reduced salvage values among MAP-infected animals. The main objectives of this thesis were to estimate the economic impact of MAP infection and potential control practices across a comprehensive selection of dairy-producing regions within a single methodological framework. Additional objectives were to estimate the value of JD control to Canadian dairy producers and to what degree there are economic premiums associated with MAP-negative dairy replacements. Using a combination of Markov Chain Monte Carlo (MCMC) simulation methods, regression analysis, and compensating and equivalent variation analysis, the following results were generated: 1) approximately 1% of gross milk revenue, equivalent to CA$43 (US$33) per cow, is lost annually in MAP-infected dairy herds, with those losses primarily driven by reduced production and being higher in regions characterized by above-average farm-gate milk prices and production per cow; 2) vaccination was the most promising type of JD control practice modelled, with dual-effect vaccines (reducing shedding and providing protective immunity) resulting in BCRs between 1.48 and 2.13 in Canada and a break-even period of between 6.17 and 7.61 years; 3) assuming a within-herd prevalence of 10% and a 50% reduction of that prevalence over 10 years, JD control has an estimated annual value of CA$28 per cow for the average Canadian dairy producer; and 4) MAP-negative replacements are associated with an average benefit of CA$96 per purchase in major dairy-producing regions, equivalent to a premium of 13% of aggregated replacement prices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".