Pasture rejuvenation using sainfoin and cicer milkvetch in western Canada
Bibliographic record
Abstract
Abstract Sod‐seeding of depleted pastures with non‐bloating legumes can be a low‐cost pasture rejuvenation strategy for ranchers in the North American prairies. A study was conducted to determine if sainfoin ( Onobrychis vicifolia subsp. vicifolia ) or cicer milkvetch (CMV) ( Astragalus cicer L.) populations or cultivars (hereinafter populations) can be used to rejuvenate alfalfa ( Medicago sativa L.) or grass pastures. The study included sainfoin populations with increased fitness to grow with alfalfa and CMV cultivars that were selected for early emergence and rapid seedling growth. These populations were seeded using three seeding methods: traditional re‐seeding, drilling seed with Great Plains drill, or Pan drill, using split‐plot randomization at three locations in Alberta in 2015 or 2016. Lethbridge had pre‐existing alfalfa while Ponoka and Red Deer were predominantly grass pastures. The sod‐seeded rejuvenation, as indicated by percentage dry matter (DM) contributed by newly planted species succeeded at Lethbridge (≥15), failed at Ponoka (∼1), and partially succeeded at Red Deer (≥9). Successful establishment of new plants did not increase the total forage mass of the rejuvenated pasture. Sainfoin populations established better and contributed greater forage mass compared to CMV cultivars when sod‐seeded on alfalfa pasture, but the improvement was not consistent on grass pasture. Forage nutritive values were not different among populations within species, but pasture seeded with CMV cultivars had greater crude protein than unseeded plots. Sod‐seeded grass–legume pasture had greater crude protein than unseeded plots at Red Deer. Sod‐seeding alfalfa pasture with sainfoin populations can be an efficient pasture rejuvenation strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".