Variations in the profitability of dairy farms in Victoria, Australia by different levels of engagement in bovine Johne’s disease control
Bibliographic record
Abstract
Paratuberculosis or Johne's disease (JD) prevalence in Australia is low at the cow-level with varying herd-level prevalence. Control strategies incorporating vaccination are limited, suggesting opportunities for changes in regulatory oversight. In order to study this further, we examined the economic benefits of participation in JD control programmes in Australia with and without vaccination as well as knowledge, attitudes, and practices (KAP) relating to JD. We used an online questionnaire to gather information describing demographics and KAP from 71 Australian dairy farms. Data from fully completed questionnaires from 32 farms in Victoria, Australia combined with cost and revenue data averaged from several years of the Dairy Farm Monitor Project were used to then simulate a larger robust dataset. These production data informed the simulation model to establish farm profitability. A partial farm budget was then developed to estimate the benefits of engaging in JD control activities. Respondents who stated they participated in JD control programmes gained an additional $43.80/cow/year net income (profit) compared to non-participants. Respondents also using a JD vaccine gained an additional $35.84/cow/year over non-users; this represents $10.56/cow/year over and above the average producer in the industry. However, we also noted that there clearly exists a barrier between farmers stated intentions to participate and actual participation in JD control activities. These significant differences in net income realized by farms using different approaches to JD control (in this case, adoption of vaccination) offer a starting point from which to explore questions of how much farmers would be willing to pay for control activities, why they are willing to pay, and the likelihood of participating. Communication of the benefits of participation needs to improve to bridge this gap between farmers stated intentions and their actions. Simulation modelling suggests increased profitability from participation in JD control programs and vaccination in Australia. The JD regulatory policies of other countries may benefit from the Australian experience with JD control.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".