Dairy farmer income, working time, and antimicrobial use under different dry cow therapy protocols
Bibliographic record
Abstract
Mastitis is one of the most common diseases of dairy cattle. It has a high impact on-farm economy, farmers' working time, and antimicrobial usage (AMU). Selective dry cow therapy (SDCT) is an effective means of reducing AMU without negatively affecting udder health. The objective of our study was to evaluate the impact of SDCT implementation on farmer income, working time, and AMU, using a bioeconomic model. A stochastic dairy simulation model (DairyHealthSim) based on a weekly model was used to simulate herd dynamics, reproduction, milk production, culling decisions, health outcomes, and the management of health events. A specific module was developed for the simulation of quarter-level IMI acquisition and elimination during the lactation and dry-off periods, and 25 different farm settings were defined to represent herds with various udder health situations. We then defined 20 scenarios of SDCT by combining both the use of different thresholds of SCC and milk bacteriology for treatment allocation and the use of internal teat sealant (ITS). All SDCT protocols had little effect on farmer income, and we identified some protocols with a positive farm gross margin (up to Can$15.83/dried cow; at time of writing, Can$1 = US$0.72). We also found that adding an ITS to all cows led to greater economic gain. The application of SDCT had little effect on farmers' working time, except when milk bacteriology was used for decision making. Antimicrobial treatment to all cows above 200,000 cells/mL at last control, with the use of ITS on all cows, seems a good choice in most dairy farms. These findings could be used to convince farmers to adopt this strategy at dry-off.
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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 | 0.004 |
| 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 | 0.001 | 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".