Barriers to recording calf health data on dairy farms in Ontario
Bibliographic record
Abstract
Establishing accurate illness and treatment rates in dairy calves is crucial, yet calf health records are often incomplete. Thus, the objective of this study was to investigate barriers for dairy farmers for recording calf illnesses and treatments on dairy farms in Ontario, Canada. An online survey was completed by a convenience sample of 88 Ontario dairy farms in 2022, with 34 questions regarding farm demographics, current practices surrounding record keeping and analysis, and factors that would improve recording compliance. Multivariable models were built to assess associations between explanatory variables and the following outcomes: likelihood of making management or treatment protocol changes based on records analysis, factors that would increase the use of electronic recording methods, and whether all calf illnesses and treatments are recorded. Pearson's chi-squared tests were also used to investigate associations between explanatory variables and whether the respondent agreed or disagreed with a proposed reason for why a calf illness or treatment would not be recorded on their farm. Producers had 3.45 times greater odds of recording all antimicrobial treatments if they used a computer software system compared with those that did not. With respect to anti-inflammatory treatments, producers had 3.11 times greater odds of recording these treatments if records were located in the calf barn than elsewhere. Nonfamily employees had 6.08 times greater odds of recording all supportive therapy treatments than farm owners. When calf health records were kept in the calf barn, respondents were less likely to report that illnesses were not recorded due to time constraints (5% vs. 36% if records were elsewhere) or because calf health records were not analyzed (10% vs. 34% if records were elsewhere). On farms that recorded calf treatments in a paper booklet, respondents were more likely to report that treatments were not recorded because calf health records were not analyzed (44% for paper records vs. 21% for other systems). The most commonly indicated factors that would increase recording of illness were recording with a mobile app (27% of respondents) and for the recording system to be easy to use (31% of respondents). Overall, these data indicate that recording may be improved by keeping calf health records in close proximity to the calves and using a recording method that allows for data analysis. An easy-to-use mobile app may also improve recording if it could be used in the calf barn, provide data analytics, and allow for time-efficient data entry.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".