Benefits of mixed grass–legume pastures and pasture rejuvenation using bloat-free legumes in western Canada: a review
Bibliographic record
Abstract
Forage mixtures containing legume out-yield monocultures, fix atmospheric nitrogen, and have lower carbon footprints. However, evidence-based information on creating forage mixtures by direct seeding legumes into existing pastures is limited, and information on bloat-free legumes is nonexistent. Traditionally, pastures requiring improvement in western Canada were fully replaced by breaking up the old stand and reseeding. With new and improved forage cultivars, better seeding equipment, and increased knowledge about pasture management, there is a growing interest among producers in rejuvenating pastures instead of replacing them. Pasture rejuvenation refers to the improvement in biomass productivity and (or) nutritional quality of existing pasture without removing the existing vegetation. This can be done through fertilizer application, which is generally expensive and causes negative environmental impacts. Amelioration of compacted pastureland via mechanical aeration is short-lived and can lead to weed problems. As an alternative, direct seeding of productive, nutritive and bloat-free legume species into existing pasture is an attractive option for pasture rejuvenation. For high performance grazing systems, identification of suitable bloat-free legumes and methods for direct seeding into old grass and legume stands will be essential strategies. This review includes information on the benefits of mixed pastures and seeks possible methods of introducing bloat-free forage legumes into existing pastures in western Canada for rapid improvement in productivity and quality while positively influencing animal, soil, and environmental health.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".