Management of cull dairy cows: Culling decisions, duration of transport, and effect on cow condition
Bibliographic record
Abstract
Many cows culled from dairy farms arrive at livestock markets or abattoirs in compromised condition, but it is unknown whether cows are compromised at the time of culling or deteriorate during the marketing process. This study followed cull cows from farm to abattoir, monitored any deterioration, and tested the effect of providing such information on subsequent culling. For 11 mo, data were collected from 20 dairy farms, livestock markets, and abattoirs. Cows' body condition, lameness, and udder condition (as binary outcomes) were assessed at 4 points: when placed on the culling list, before leaving the farm, at auction, and at the abattoir. Based on the first 5 mo of data (baseline period), information materials were provided to 10 randomly selected farms (Informed treatment), and the other 10 served as a Control. Logistic mixed models were used to test differences in cows' condition at the 4 points of assessment, using the assessment before leaving the farm as a reference value and cow-within-farm as a random effect. The effects of providing information on outcome measures (proportion thin, severely lame, with udder edema, poor fitness for transport, euthanized, and died) were tested with a general linear model. As demand for milk increased during the baseline period, additional linear mixed models were fitted to test whether the same outcome measures were affected by time (baseline or treatment period) and treatment (Informed or Control) using farm as a random effect. While cows were on the culling list (before shipping) the odds of being thin or having poor fitness for transport increased. After leaving the farms, cows spent (mean ± standard deviation) 82 ± 46 h in the marketing system before being slaughtered. Shipping from farm to abattoir increased the odds of being thin, having udder edema, or showing poor fitness for transport [odds ratio (OR) 5.8, 95% confidence interval 4.2 to 8.1; OR 14.7, 10.7 to 20.2; and OR 7.3, 5.7 to 9.5, respectively]. Providing this information to farmers (Informed treatment) did not affect the outcome measures. Compared with the baseline, during the treatment period fewer cows were shipped when thin (4.9 versus 9.0%) and with poor fitness for transport (15.7 versus 25.8%), and fewer cows died (5.4 versus 11.9%) or were euthanized (13.3 versus 20.5%). Increased demand for milk appeared to lead to delayed culling, such that more compromised cows were sent to slaughter. The results of this study show a need for clear and specific guidelines to assess fitness for transport of cull dairy 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 | 0.001 | 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".