Economic analysis and stochastic simulation of alternative beef calving and feeding systems in western Canada
Bibliographic record
Abstract
Khakbazan, M., Carew, R., Scott, S. L., Chiang, P., Block, H. C., Robins, C., Durunna, O. N. and Huang, J. 2014. Economic analysis and stochastic simulation of alternative beef calving and feeding systems in western Canada. Can. J. Anim. Sci. 94: 299-311. Biological and economic data from three field-experimental sites in Manitoba (Brandon Research Centre) and Saskatchewan (Western Beef Development Centre; Semi-Arid Prairie Agriculture Research Centre) were used to determine the economic and financial benefits of alternative beef calving and feeding systems. Stochastic budgets and Monte Carlo simulation techniques were used to evaluate the profitability and risk levels of four alternative livestock beef management systems: early-calving/rapid-gain post-weaning feeding (ER), late-calving/rapid-gain post-weaning feeding (LR), early-calving/slow-gain post-weaning feeding (ES), and late-calving/slow-gain post-weaning feeding (LS). Economic budgets were constructed to evaluate the viability of different phases of beef production including backgrounding, pasture, swath grazing and finishing. Statistical analysis considered operational costs (feed, yardage, implants, medicine, labor, marketing, and other costs), price variables (fed-steer, feeder cattle, and grid pricing premiums/discounts) and steer performance measures such as average daily gain (ADG). Results from the simulation analysis revealed that late-calving systems provided higher returns for livestock producers, while rapid-feeding systems were more preferable for producers who were more risk-averse.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".