Cost-benefit of implementing a participatory extension model for improving on-farm adoption of Johne's disease control recommendations
Bibliographic record
Abstract
The objective of this study was to perform a cost-benefit analysis (CBA) of a participatory extension model, called Ontario Focus Farms (FF), which was designed to facilitate the adoption of on-farm management practices to control Johne's disease (JD) on Ontario (ON) dairy farms. Partial budget models were developed to estimate the annual herd cost of JD on an average 78-cow Ontario dairy herd and the annual herd cost of neonatal calf diarrhea (NCD). With these estimates, a CBA was developed to assess the simulated net benefits of implementing various on-farm management scenarios (i.e., implementing 1, 2, or 3 of the following: calf feeding, maternity pen management, maternity area structure changes), where the benefits represent a reduction in the annual cost of JD and NCD. These models informed the final CBA assessing the net benefits of FF implementation over a 10-yr period. All monetary values are reported in Canadian dollars (Can$; where 1 Can$ = 0.823 US$ at the time of the study). The annual herd cost of JD was estimated to be $3,242 ($41.56/cow), and that of NCD was estimated to be $1,390 ($36/heifer calf). When farms were expected to have both JD and NCD, all scenarios, when implemented over a 10-yr period, yielded positive net benefits ranging from $439 to $2,543 per farm when changes to maternity area structure were combined with calf feeding changes. These effects were sensitive to changes in level of disease (JD and NCD) on the farm, and the costs and effects of making changes. The NPV of making any on-farm change when JD was not present on the farm was negative. Overall, FF implementation yielded positive net benefits of $426,351 or $749,808, depending on whether a veterinarian or non-veterinarian served as the facilitator. The NPV was most sensitive to changes in burden of disease, the cost of implementing changes, and the proportion of FF participants that had JD and NCD on the farm. Benefits of FF implementation are also likely to accrue to veterinarians, as a result of professional facilitator training, and the Ontario dairy industry, as a by-product of improved milk quality and safety; therefore, the true net benefits of FF implementation are likely underestimated. Overall, the FF process should be considered an economically viable program and worthy of investment as part of a JD control strategy, as it demonstrates potential to yield positive net benefits for the Ontario dairy industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".