Agronomic Changes from 58 Years of Genetic Improvement of Short‐Season Soybean Cultivars in Canada
Bibliographic record
Abstract
Yield progress of short‐season soybean [Glycine max (L.) Merr.] cultivars in Canada has been approximately 0.5% per year since the early 1930s. Our objective was to identify changes in agronomic traits associated with yield increase within a selection of historical cultivars. Where applicable, we measured phenotypic stability of these traits. At Ottawa, ON, we grew 14 cultivars, representing seven decades of breeding and selection (1934–1992), in a randomized complete block design with four replicates, across 6 yr. Data were collected on seed yield, seed weight, plant height, plant population, lodging susceptibility, and foliar disease symptoms. Seed number per plant was calculated from yield, seed weight, and plant population. Seed protein and oil concentration were measured. The increase in seed yield with year of release was associated with a significant increase in the number of seeds produced per plant. There was no relationship between seed yield and seed weight. A significant decrease in seed protein concentration with year of release was offset by a significant increase in seed oil concentration. Newer cultivars were more phenotypically stable for plant height than older cultivars. Modern cultivars were more efficient at establishing, supporting, and filling seeds on a per‐plant basis than older cultivars.
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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 | 0.001 | 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".