Evaluating the performance of alfalfa cultivars in rotationally grazed pastures
Bibliographic record
Abstract
Although alfalfa (Medicago sativa L.) is an important and reliable hay crop in eastern Canada, it is generally not considered suitable for pastures. However, local studies have shown that alfalfa is capable of superior yield in mixtures under rotational grazing. This study evaluates the performance of commercially available, locally adapted, hay-type alfalfa cultivars and new experimental, grazing-type alfalfa synthetics under hay and two rotationally grazed regimes. Ten alfalfa cultivars, including five hay-type and five grazing-type cultivars, were seeded with the timothy (Phleum pratense L.) cultivar, Richmond, in mixtures. The two pasture regimes consisted of grazing to low and high residual heights. Over two dry growing seasons, the pasture management with high residual grazing height was more productive than the low; on average lenient rotational grazing pressure (high residual heights) produced 20% higher yield than low residual heights. Alfalfa cultivars selected specifically for continuous grazing were not, on average, superior to those hay-type cultivars selected for general adaptability to soil and environmental conditions in Atlantic Canada. Differences among cultivars within alfalfa type were significant. Over 2 production years, mixtures containing the hay-type cultivar, Apica, and the grazing type, Alfagraze, produced more dry matter on average than three others in the study, regardless of management regime. Key words: Grazing height, cattle grazing
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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 | 0.000 | 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 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".