Johne’s Disease Control in Beef Cattle: Balancing Test-and-Cull Strategies with Economic and Epidemiological Trade-Offs
Bibliographic record
Abstract
Johne's disease (JD) is a chronic infection of cattle that undermines herd productivity and profitability. While test-and-cull programs are commonly proposed for control, their effectiveness and economic feasibility remain uncertain in beef production systems. This study used an updated agent-based model (ABM) to simulate JD transmission in a representative 300-cow Western Canadian beef herd, coupled with a partial budget model to evaluate net present value (NPV) over a 10-year time horizon. Seven diagnostic test-and-cull strategies were compared, varying in test type (ELISA, individual PCR, and pooled PCR), sampling frequency (6, 12, or 24 mo), and risk-based sampling protocols. Results showed that, under baseline assumptions (6% starting prevalence; 1% prevalence in purchased stock), all strategies reduced JD prevalence relative to no testing, and six of seven yielded higher NPVs. Annual individual PCR testing provided the best balance between prevalence reduction and profitability, whereas semi-annual PCR most effectively reduced prevalence but at greater economic cost. Failure to implement control measures resulted in increasing prevalence and long-term economic losses. Sensitivity analyses demonstrated that strategy performance was consistent across variations in market conditions, cost of production, and replacement female management, although profitability declined substantially when JD prevalence in externally sourced stock was high (i.e., 10%). Collectively, these findings indicate that JD can be controlled economically in beef herds, with long-term application of various test-and-cull strategies offering robust options adaptable to management preferences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".