Identifying performance benchmarks and determinants for reproductive performance and calf survival using a longitudinal field study of cow-calf herds in western Canada
Bibliographic record
Abstract
The cow-calf industry in North America is in a period of rapid consolidation with corresponding increases in herd sizes and changes in management. The objectives of this study were to examine longitudinal data on reproductive performance in cow-calf herds and identify benchmarks for the most critical measures and important sources of differences among herds. To address these questions, a surveillance network was established in western Canada to collect data between 2013 to 2017 privately owned cow-calf herds during calving (n = 105 herds) and at pregnancy testing (n = 94 herds). Data were summarized for a number of indices of herd performance. However, the values considered to be most reliable and accurate were the percentage of females not pregnant when tested by a veterinarian, the percentage of calves dead within 24 hours of birth, and the percentage of calves dead from 24 hours to weaning. The mean and variation between herds for heifers, measured using standard deviation, was greater than for cows for: non-pregnancy (cows 6.8% (mean)±3.4%(SD), heifers 9.7%±8.2%), calf death from birth to 24 hours (cows 2.1%±1.6%, heifers 3.6%±4.5%), and calf death from 24 hours to weaning (cows 2.5%±2.4%, heifers 2.9%±3.9%). Benchmarks or performance targets derived from the 25th percentiles of these data for both cows and heifers were <5% for non-pregnancy risk and <1% for calf loss within 24 hours of birth. The suggested benchmark for calf loss from 24 hours to weaning was <2% for cows and <1% for heifers. All outcomes consistently displayed greater variation between herds as compared to year to year differences within herds with the exception of calf loss before 24 hours in cows. The timing of the start of breeding season was a consistent source of variation in risks of non-pregnancy and calf losses. Cows bred in April or earlier to start calving in late December or January were at increased risk of low pregnancy percentages (p<0.001) and calf losses at birth (p<0.04), as well as increased calf loss before weaning in both cows and heifers (p<0.02). There was also an increase in the risk of non-pregnancy for cows and heifers (p<0.001) where first exposure to breeding was not until July or August. In contrast, the risks of calf loss within 24 hrs of birth (p<0.001) and from 24 hrs to weaning in cows (p<0.02) first exposed to breeding in July and August were significantly lower than for herds that had earlier breeding seasons.
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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 | 0.002 | 0.003 |
| 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) | 0.000 | 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".