Benchmarking calving management practices on western Canada cow–calf operations1
Bibliographic record
Abstract
Abstract: Benchmarking current calving management practices and herd demographics in the western Canadian cow–calf production system helps to fill the gap in knowledge and understanding of how this production system works. Further investigation into the relationships between management decisions and calf health may guide the development of management practices and protocols to improve calf health, especially in compromised calves after a difficult birth. Therefore, the objectives of this cross-sectional study were to describe current calving management practices on western Canadian cow–calf ranches and to investigate the association of herd demographics with herd-level incidence of calving assistance, morbidity, mortality, and use of calving and colostrum management practices. Cow–calf producers were surveyed in January 2017 regarding herd inventory and management practices during the 2016 calving season. Ninety-seven of 110 producers enrolled in the western Canadian Cow-Calf Surveillance Network responded. Average herd-level incidence of assisted calvings was 4.9% (13.5% heifers, 3.2% cows), stillbirths was 2.1% (3.3% heifers, 1.9% cows), preweaning mortality was 4.5%, and preweaning treatment for disease was 9.4% (3.0% neonatal calf diarrhea, 3.8% bovine respiratory disease, 2.6% other diseases). Greater than 90% of producers assisted calvings and would intervene with colostrum consumption if the calf did not appear to have nursed from its dam. Late calving herds (i.e., started calving in March or later) had significantly lower average herd-level incidence of assistance, treatment for disease, and mortality (P < 0.05). In earlier calving herds (i.e., started calving in January or February) producers had shorter intervals between checking on dams for signs of calving or intervening to assist with a calving (P < 0.05). In early calving herds, producers were more likely to perform hands-on colostrum management techniques such as placing the cow and calf together or feeding stored, frozen colostrum (P < 0.05). There were no associations between herd size and herd-level incidences or management techniques (P > 0.05). This study suggests that in western Canada earlier calving herds are more intensively managed, whereas later calving herds are more extensively managed. Herd demographics may be important to consider when investigating factors associated with management strategies, health, and productivity in cow–calf herds.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".