The influence of management on the technical efficiency of Ontario cow-calf operations
Bibliographic record
Abstract
Beef cow-calf farmers in Ontario have numerous challenges in the last decade. Faced with such threats to their viability, producers have several options, including exiting, diversifying, adopting technology or improving the production efficiency of operations. It is this last option which is the subject of this study. Efficiency measurement has been extensively developed in the agricultural economics literature. It is common to relate farm efficiency to socioeconomic indicators. Several studies have highlighted the role of management in farm efficiency. This study is the first to investigate the influence of management on the efficiency of cow-calf operations in the Canadian context. Several important conclusions are drawn from this study. The first is that gains from improving efficiency exist, as average farm efficiency is significantly below 100%. A second conclusion is that management decisions affect production efficiency. Efficiency gains can be realized from increasing the size of operations. Similarly, by focusing on biological efficiency, namely the weaning rate, efficiency can be improved. Finally, the maintenance and use of herd performance data is shown to positively affect efficiency.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".