Mortality and health treatment rates of dairy calves in automated milk feeding systems in the Upper Midwest of the United States
Bibliographic record
Abstract
Automated calf feeding systems are increasing in use across the United States, yet information regarding health and mortality outcomes of animals in these systems is limited. The objective of this study was to investigate the relationship between farm management practices, housing, and environmental factors with mortality and health treatment rates of preweaned dairy calves housed in groups with automated feeding systems. Farm records were collected for health treatments and mortality on 26 farms in the Upper Midwest of the United States. Relationships between factors of interest and mortality or treatment rate were calculated using a correlation analysis. Overall median annual mortality rate was 2.6 (interquartile range = 3.6; range = 0.24-13.4%), and 57% of farms reported mortality rates below 3%/yr. Farms that disinfected the navels of newborn calves had lower mortality rate (mean = 3.0%; standard error = 0.8; 78% of farms) than farms that did not disinfect (mean = 7.3%; standard error = 1.6; 22% of farms). Farm size (number of cows on site) was negatively associated [correlation coefficient (r) = -0.53], whereas the age range in calf groups was positively associated (r = 0.58) with mortality rate. Average serum total protein concentration tended to be negatively associated with annual mortality rate (r = -0.39; median = 5.4; range = 5.0-6.4 g/dL). Health treatment rate was positively associated with coliform bacterial count in feeder tube milk samples [r = 0.45; mean ± standard deviation (SD) = 6.45 ± 4.50 ln(cfu/mL)] and the age of calves at grouping (r = 0.50; mean ± SD = 5.1 ± 3.6 d). A positive trend was detected for coliform bacterial count of feeder mixing tank milk samples [r = 0.37; mean ± SD = 3.2 ± 6.4 ln(cfu/mL)] and calf age at weaning (r = 0.37, mean ± SD = 57.4 ± 9.6 d). Seasonal patterns indicated that winter was the season of highest treatment rate. Taken together, these results indicate that, although automated feeding systems can achieve mortality rates below the US average, improvements are needed in fundamental calf care practices, such as colostrum management and preventing bacterial contamination of the liquid diet and the calf environment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".