Choice of Beef Herd Adaptation Strategy on Canadian Prairie Mixed Farms Under Extreme Climate Events
Bibliographic record
Abstract
Economic impact of climate extremes on beef operation is expected to be significant due to its direct impact on feed production.Impact of such events on farm management and longer term farm financial situation is relatively unstudied in the Canadian Prairie.This study compared three alternative beef herd management strategies in dealing with climate extreme events under reference climate scenario of 1971-2000 and the future scenario of 2041-2070.The study used farm simulation model that integrated the model of cattle herd simulation, pasture model, crop simulation model combined with models of economic decisions.Purchasing feed and maintaining herd size is preferred option in dealing with drought.Changes in management such as early weaning combined with limit feeding strategies reduce the feed demand and also reduce the financial burden during the years of extreme event, but it has severe negative consequences on amount of slaughter cattle sold.Cull herd strategy not only reduces feed demand but also increases income from sell of herd during the year/s of extreme event, but it severely impacts the farm's long term output supply potential.However, expansion of existing agriculture risk management policy to cover climate risk in beef production is necessary to support farmers in the year/s to extreme events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".