The effects of spring versus summer calving on beef cattle economic performance in western Canada
Bibliographic record
Abstract
Khakbazan, M., Durunna, O. N., Sirski, T. K., Brewin, D. G., Huang, J., Berry, N., Iwaasa, A. D., Scott, S. L., Robins, C. D., Block, H. C. and Lardner, H. A. 2015. The effects of spring versus summer calving on beef cattle economic performance in western Canada. Can. J. Anim. Sci. 95: 475–486. The choice of calving date influences the net revenue of a calving operation as it affects the number of days that calves spend in each feeding phase and when they are subsequently marketed. These two factors determine the costs, revenue, and risk (variance) of each calving system for the calving phase of a beef system. The majority of cow–calf producers in western Canada have adopted early calving (EC) in spring due to management factors. However, late calving (LC) in the summer is an alternative beef calving system associated with higher returns due to lower system costs and higher beef prices. It may offer a better match between cow nutritional requirements and pasture availability. The objective of this study was to compare the revenues and risks for a traditional EC system and an alternative LC system at three sites in western Canada. Biological and economic data from three field experimental sites in western Canada (Brandon Research Centre in Manitoba, Western Beef Development Centre in Lanigan, Saskatchewan and Semi-Arid Prairie Agriculture Research Centre in Swift Current, Saskatchewan) were used to determine the costs and benefits of the alternative beef calving system. The results showed that even though the EC has higher cost than the LC, the EC is slightly better than LC in terms of higher net revenue potential, but it comes at a greater risk due to higher revenue variances. The EC system is usually more preferable for risk-neutral producers, whereas the LC system is more preferable for risk-averse producers.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".