Keeping profitable cows in the herd: A lifetime cost-benefit assessment to support culling decisions
Bibliographic record
Abstract
Increasing the productive lifespan of dairy cows is important to achieve a sustainable dairy industry, but making strategic culling decisions based on cow profitability is challenging for farmers. The objective of this study was to carry out a lifetime cost-benefit analysis based on production and health records and to explore different culling decisions among farmers. The cost-benefit analysis was conducted for 22 747 dairy cows across 114 herds in Quebec, Canada for which feed costs and the occurrence of diseases were reported. Costs and revenues related to productive lifespan were compared among cohorts of cows that left their respective herd at the end of their last completed lactation or stayed for a complete additional lactation. Hierarchical clustering analysis was carried out based on costs and revenues to explore different culling decisions among farmers. Our results showed that the knowledge of lifetime cumulative costs and revenues was of great importance to identify low-profitable cows at an earlier lactation, while only focusing on current lactation costs and revenues can lead to an erroneous assessment of profitability. While culling decisions were mostly based on current lactation costs and revenues and disregarded the occurrence of costly events on previous lactations, there was variation among farmers as we identified three different culling decision clusters. Monitoring cumulative costs and revenues would help farmers to identify low-profitable cows at an earlier lactation and make the decision to increase herd productive lifespan and farm profitability by keeping the most profitable cows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".