Associations between on-farm animal welfare indicators and productivity and profitability on Canadian dairies: I. On freestall farms
Bibliographic record
Abstract
Motivating dairy producers to financially invest in the improvement of their animals' comfort and welfare can pose some challenges, especially when financial returns are uncertain. Economic advantages for dairy producers associated with increased animal welfare are likely to come from either a premium paid for the milk or increased productivity. The aim of the current study was to evaluate the associations between measures of herd productivity and farm profitability and animal-, management-, and resource-based indicators of cow welfare and comfort. The cow welfare measures were collected during a cow comfort assessment conducted on 130 Canadian freestall dairy farms, including 20 using an automatic milking system. Herd productivity and farm profitability measures were retrieved or calculated from data collected by the regional dairy herd improvement programs, and included milk production and quality, longevity, and economic margins over replacement costs. Univariable and multivariable linear regression models were used to assess the associations between welfare indicators and productivity and profitability measures. Increased yearly corrected milk production was associated with reduced prevalence of cows with knee lesions [β = 7.40; 95% confidence interval (CI): 2.6, 12.2], dirty flanks (β = 26.9; 95% CI: 7.4, 46.5), and lameness (β = 11.7; 95% CI: 3.3, 20.1). The farms' economic margin per cow, calculated over replacement costs, was associated with the within farm average lying time standard deviation (β = -7.2; 95% CI: -12.7, -1.7), percent of stalls with dry bedding (β = 6.4; 95% CI: 1.4, 11.4), and prevalence of cows with knee lesions (β = -5.1; 95% CI: -8.9, -1.3). Some of the relationships found were complex, including several interactions between the animal-, management-, and resource-based measures. Overall, the results suggest that improved cow comfort and welfare on freestall farms is associated with increased herd productivity and profitability, when the latest is calculated by the margins over the replacement costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".