Annual cool season crops for grazing by beef cattle. A Canadian Review
Bibliographic record
Abstract
With the current high feed grain costs and other economic uncertainties in the Canadian beef cattle industry, producers are trying to lower their unit costs of production. Costs can be lowered through extension of the grazing season using perennial pastures and annual crops for grazing. Oat (Avena sativa L.) and fall rye (Secale cereale L.) have traditionally been used to a nominal extent for extending the grazing season. However, there is limited information including a small number of animal grazing trials on the use of other annual cereals and annual ryegrass (Lolium multiflorum Lam.) for low cost grazing systems relative to feeding traditional harvested and stored forages. This review discusses annual cool season crops that show promise for supplementary grazing systems. Systems such as swath grazing of a cereal crop, grazing the regrowth from silage mixtures of spring and winter cereals, or fall grazing annual Italian ryegrass can be used to extend the grazing season. Economic considerations will ultimately determine if there will be an increased role in the future for grazing annual crops on cropping land as a means of extending the grazing season to reduce year-round costs for the beef cow calf operator. Key words: Small grain cereals, annual ryegrass, extended grazing, forage quality
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".