On-farm investments into dairy cow health: evidence from 15 case study countries
Bibliographic record
Abstract
Managing investments in dairy cow health at a national and global scale, requires an improved understanding of current on-farm expenses for cow health (e.g., expenditure for medicine and veterinary consultations). The aim of this study was to assess on-farm health investments for typical dairy farms in 15 case study countries, including Argentina, Australia, Bangladesh, Brazil, Canada, India, China, Colombia, Indonesia, Kenya, New Zealand, Uganda, UK, Uruguay, and USA. The study was conducted using a descriptive analysis of a secondary data set that was obtained from the International Farm Comparison Network (IFCN). The results suggest that health expenditures take up a relatively small proportion (<10%) of the annual total production costs per cow across all countries in the sample. The means of production costs (e.g., feed, machinery) can take up to 90% of the total production costs for highly intensive systems, while these costs can be as low as 9% for extensive systems. This study highlights the importance of understanding on-farm animal health investments as a contribution to improved national and global decision making about animal health in the dairy sector.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".