Trends in Soybean Trait Improvement over Generations of Selective Breeding
Bibliographic record
Abstract
Improvement of agronomic and seed quality traits in soybean [Glycine max (L.) Merr.] occurs in breeding programs as a result of crossing elite soybean genotypes and selection of superior offspring in each cycle. The objective of this study was to evaluate changes in traits over 100 yr of selection in Ontario, Canada, in two soybean breeding programs at the University of Guelph: Guelph Campus (Maturity Group [MG] 0 and I) and Ridgetown Campus (MG II) programs. Fifty‐six Guelph cultivars (released between 1983 and 2016), 46 Ridgetown cultivars (released between 1987 and 2015), and 37 Guelph‐pedigree related historical cultivars (from 1913 to 1985) were grown in field trials over 3 yr (Guelph Campus) and 2 yr (Ridgetown Campus) at two locations per breeding program to measure 13 traits. Yield increased in the Guelph Campus cultivars by 17.1 kg ha−1 yr−1 and by 15.7 kg ha−1 yr−1 in the Ridgetown Campus cultivars, while yield per day of maturity per year of release significantly increased in elite cultivars. Protein in the Ridgetown Campus cultivars increased at 0.76 g kg−1 yr−1, whereas the Guelph Campus cultivars showed no trend. In Ridgetown Campus cultivars 100‐seed weight significantly increased at 0.1 g yr−1. Within the historical cultivars, 100‐seed weight was significantly correlated with both protein (r = 0.50) and oil (r = −0.42). Genotype × trait biplot analyses revealed differential patterns of trait relationships between the elite and historical cultivars. Oil and yield were associated within the Guelph Campus cultivars. The association of 100‐seed weight and protein was only found in the historical cultivars. Yield has increased in both breeding programs while seed traits have been altered over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".