Is the profitability of Canadian tiestall farms associated with their performance on an animal welfare assessment?
Bibliographic record
Abstract
Improving animal welfare on farm can sometimes require substantial financial investments. The Canadian dairy industry recently updated their Code of Practice for the care of dairy animals and created a mandatory on-farm animal care assessment (proAction Animal Care). Motivating dairy farmers to follow the recommendations of the Code of Practice and successfully meet the targets of the on-farm assessment can be enhanced by financial gain associated with improved animal welfare. The aim of the current study was to evaluate the association between meeting or not meeting several criteria from an on-farm animal welfare assessment and the farms' productivity and profitability indicators. Data from 130 freestall farms (20 using automatic milking systems) were used to calculate the results of the animal care assessment. Productivity and profitability indicators, including milk production, somatic cell count, reproduction, and longevity, were retrieved from the regional dairy herd improvement association databases. Economic margins over replacement costs were also calculated. Univariable and multivariable linear regression models were used to evaluate the associations between welfare and productivity and profitability indicators. The proportion of automatic milking system farms that met the proAction criterion for hock lesions was higher compared with parlor farms and lower for the neck lesion criterion. The proAction criterion for lameness prevalence was significantly associated with average corrected milk production per year. Average days in milk (DIM) at first breeding acted as an effect modifier for this association, resulting in a steeper increase of milk production in farms that met the criterion with increasing average DIM at first breeding. The reproduction and longevity indicators studied were not significantly associated with meeting or not meeting the proAction criteria investigated in this study. Meeting the proAction lameness prevalence parameter was associated with an increased profitability margin per cow over replacement cost by $236 compared with farms that did not. These results suggest that associations are present between meeting the lameness prevalence benchmark of the Animal Care proAction Initiative and freestall farms' productivity and profitability. Overall, meeting the animal-based criteria evaluated in this study was not detrimental to freestall farms' productivity and profitability.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".