Genetic progress from 40 years of orchardgrass breeding in North America measured under management-intensive rotational grazing
Bibliographic record
Abstract
There has been considerable activity in breeding orchardgrass (Dactylis glomerata L.) cultivars in North America during the latter half of the 20th century. The objective of this study was to determine if genetic improvements in the North American orchardgrass germplasm pool have been realized or can be detected under management intensive grazing. Forty-three orchardgrass cultivars, in three maturity groups, representing adapted North American germplasm and potentially unadapted European germplasm, were evaluated under management-intensive rotational grazing at two locations in the northern USA. Cultivar means varied for apparent preference and this variation was largely due to herbage availability at State College, PA, where grazing pressure was relatively high, but to other factors at Arlington, WI, where grazing pressure was relatively low. North American cultivars tended to have higher net herbage accumulation than European cultivars, corresponding well to differences in forage yield of hay plots. Orchardgrass cultivars that were significantly higher in hay yield than their predecessors showed mixed results with respect to net herbage accumulation under grazing: some increases, some decreases, and some with no change. These results indicate that an individual orchardgrass cultivar can be well adapted to both management systems, only one of the two systems, or neither system. Genetic increases in net herbage accumulation may occur serendipitously, but directed selection for specific traits that confer productivity under grazing, or net herbage accumulation per se, will probably ensure a higher probability of success for an orchardgrass breeding program directed to pastures. Key words: Dactylis glomerata, cultivar evaluation, breeding, selection, 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".