Exploring herd-level perinatal calf mortality risk factors in eastern Canadian dairy farms
Bibliographic record
Abstract
This closed cohort study aimed to identify the associations between dairy calf management practices and herd-level perinatal calf mortality risk. From February 2020 to June 2021, predominantly Holstein dairy farms in Québec (n = 1,832) and New Brunswick (n = 52), Canada, that were registered in the dairy herd improvement program were visited once. A questionnaire covering all aspects of precalving, calving, and colostrum management was administered. Data regarding perinatal mortality were retrieved from the dairy herd improvement program database for each farm for 2021. Perinatal mortality was calculated for each farm as the proportion of calves dead at birth or dying within 24 h after birth. A multivariable negative binomial model was used to assess herd-level factors associated with the risk of perinatal mortality. The final model included the lying surface in the calving area, the typical time to first colostrum intake, typical cow-calf contact time, the proportion of males born, the proportion of assisted calvings, and herd size. Herd-level perinatal mortality risk ranged from 0% to 38.1% (mean ± SE = 7.6% ± 0.1%). A greater proportion of males born, a higher proportion of assisted calvings, and delayed colostrum feeding were associated with increased herd-level perinatal mortality. Factors associated with a decreased herd-level perinatal mortality risk were having a typical cow-calf contact time between 7 and 12 h after calving compared with reduced cow-calf contact time, soft lying surfaces in the calving area compared with concrete and mat-lying surfaces, and an increased number of calvings per year. Our results show that although some of the significant risk factors are not well understood (i.e., calving area lying surface, typical cow-calf contact time), Canadian farmers could focus on the factors under their control (i.e., time to first colostrum feeding, proportion of difficult calvings, males born, and calvings per year) to reduce the risk of perinatal mortality. Future work should focus on qualitative research to understand the dairy farmer motivations and limitations to implementing practices identified in this and other studies to reduce perinatal mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".