Technology adoption and management practices used in Canadian cow-calf herds
Bibliographic record
Abstract
Canadian cow-calf producers are facing pressure to adopt management practices and technologies that help increase the economic and environmental sustainability, and public perception of beef production. Our aim was to describe technology adoption, management and record keeping practices in Canadian cow-calf herds, assess associations between herd attributes, productivity outcomes and adoption; and identify opportunities for improvement. Surveys from 131 Canadian cow-calf producers recruited through a national surveillance program were analyzed. Individual female records (80%) and feed testing (84%) were commonly reported as currently or occasionally used, followed by on-farm weigh scales (66%). Western herds were likely to utilize feed testing and nutritionists, ionophores, and growth promoting implants, while eastern herds commonly used reproductive technologies. Large herds (>300 cows) were more likely to adopt technologies that aid in data capture (i.e., weigh scales) and follow recommended practices (i.e., feed testing). Paper was the main record keeping format. Production records were commonly utilized for culling and replacement heifer selection. Technology use has increased across the country compared to previous surveys and producers are implementing practices to help increase production efficiency. However, there is an opportunity to increase use of technologies that support individual animal and herd data to help inform ranch decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".