Improvement of key agronomical traits in soybean through genomic prediction of superior crosses
Bibliographic record
Abstract
Abstract Maximizing yield is very important when developing new cultivars. However, yield must usually be improved jointly with other key traits, which can prove challenging when they are unfavorably correlated. Genomic predictions can facilitate the selection of promising lines among the progeny of crosses, but it may also help in selecting crosses that are more likely to produce improved lines by predicting progeny performance for the various key traits considered jointly. To assess whether genomic predictions of cross performance could help breeders simultaneously improve multiple traits, yield and maturity were predicted for 60,000 soybean [ Glycine max (L.) Merr.] crosses. These predictions were then compared with the persistence of 101 biparental crosses throughout the selection process measured as the success in advancing progeny lines through to registration and commercialization. All but 2 of the 22 superior crosses retained by breeders had been predicted to display above‐average mean yield within different maturity windows. At the opposite end of the spectrum, 96.2% of all crosses predicted to produce progeny with a below‐average mean yield within a specific maturity window were eliminated during selection. Our results therefore suggest that by making crosses predicted to produce progeny meeting target requirements for multiple key traits, breeders could either achieve the same genetic gains with fewer resources or invest the same resources on a more promising set of crosses and thereby achieve greater gains.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".