Sainfoin (<i>Onobrychis viciifolia</i> Scop.): renewed interest as a forage legume for western Canada
Bibliographic record
Abstract
Sainfoin (Onobrychis viciifolia Scop.) is a perennial forage legume that has received renewed interest in western Canada because of its desirable forage characteristics. The objective of this review is to summarize previous studies on the agronomy, forage yield, nutritive value, seed characteristics, and diseases and pests of sainfoin. In addition, the review also focuses on the genetic diversity and registered cultivars of sainfoin and their potential use in temperate grasslands. Past studies have reported that sainfoin has a high nutritive value, and high voluntary intake and palatability to grazing animals. In western Canada, dry matter (DM) yield of sainfoin is 80%–95% that of alfalfa (Medicago sativa L.). Unlike other legumes such as alfalfa, sainfoin does not cause bloat in grazing animals due to the presence of condensed tannins. Sainfoin is suitable for monoculture or binary mixtures with either grasses or alfalfa. Genetic diversity studies revealed that sainfoin germplasm generally has high variation within populations. Genetic variation among plants provides an opportunity to develop improved cultivars with desirable characteristics. However, compared with the other forage legumes, sainfoin is still an underdeveloped forage crop with few cultivars being available in western Canada. New sainfoin cultivars with high DM yield and persistence under regional growing conditions are required.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".