Selection for Orchardgrass Seed Yield in Target vs. Nontarget Environments
Bibliographic record
Abstract
Simultaneous improvement of forage traits and seed yield in orchardgrass (Dactylis glomerata L.) has been problematic because of geographic separation of forage and seed production locations. Previous work has shown that a complex multilocation selection program in forage production environments can increase forage yield as well as seed yield in Oregon. The objective of this experiment was to compare target‐environment (TE) and nontarget–environment (NTE) selection approaches for increasing seed yield of orchardgrass in Oregon. Two cycles of recurrent phenotypic selection for panicle seed mass (PSM) and agronomic traits were conducted on four populationsin four eastern USA locations (NTE) and one Oregon location (TE). Seed yield was increased in three of four orchardgrass populations by TE selection, averaging 5.1% cycle−1, but was improved by NTE selection in only one of four populations. Conversely, TE selection for PSM and agronomic traits resulted in no changes to forage yield in the eastern USA and Canada, while NTE selection for PSM and agronomic traits increased forage yield in two of four populations, confirming results of a previous study. It appears that the most efficient system for simultaneously improving forage and seed traits of orchardgrass would be to practice selection for forage traits in forage production environments and seed traits in seed production environments, with sufficiently large populations to allow multitrait selection.
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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 | 0.000 | 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) | 0.001 | 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".