Genetic Progress from 40 Years of Orchardgrass Breeding in North America Measured under Hay Management
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, but little effort devoted to quantification of breeding progress. The objectives of this study were to quantify changes in mean cultivar performance for that time compared with the progress achieved from one cycle of half‐sib progeny selection within the USDA population of orchardgrass accessions. Forty‐two cultivars (32 North American cultivars and 10 European cultivars) were tested at three locations (Arlington, WI and Rock Springs, PA, and Ottawa, Ontario, Canada) in 1995 through 1997. Cultivars were grouped into three experiments by maturity class: early, medium, and late. North American cultivars averaged 3, 9, and 12% higher in forage yield than European cultivars for early, medium, and late maturity groups, respectively. Between 1955 and 1997, forage yield and ground cover of early‐maturity cultivars increased by 2.5 Mg ha−1 decade−1 and 4.0% decade−1, respectively. Forage nutritional value of medium‐maturity cultivars increased during that time, although this was probably not due to direct selection. Significant gains were made in forage yield and Drechslera spp. leafspot reaction of cultivars derived from two individual breeding programs, although the majority of orchardgrass cultivars lack improvements in forage traits.
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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".