Developing High‐Protein, High‐Yield Soybean Populations and Lines
Bibliographic record
Abstract
Developing high‐yielding, high‐protein soybean [Glycine max (L.) Merr.] lines is difficult because of the inverse relationship between seed yield and seed protein content. The objective of this study was to evaluate single cross and rapid back cross breeding methods to achieve both high seed yield and high protein content. ‘AC Proteus’ was used as the high‐protein source and ‘Maple Glen’ was used as the adapted high‐yielding parent. Six single plants were randomly selected from each of 149 F3 progeny rows to develop 886 single cross derived lines. Reciprocal back cross populations were made using 60 F1 plants from the single cross and the high‐yielding parent, Maple Glen. Ten single plant selections were randomly taken from each of 80 F1‐derived families to develop 800 back cross derived lines. In 1994, all lines were tested in the field. About 20% of lines were retained for testing through this breeding project from 1994 to 1996 although selection intensity differed across populations and years. In 1997, six single cross and nine back cross lines were tested at six locations in Eastern Canada. The seed yield and protein content of the single cross lines were not significantly different from the back cross derived lines. All the selected lines had higher seed protein content than did Maple Glen. Both breeding strategies produced lines with significantly higher seed protein content than Maple Glen. However, none of these lines had significantly higher seed yield than Maple Glen or a recently released high protein cultivar, AC Proteina. These populations exhibited a very low or no association between seed yield and protein (r = −0.06 to −0.21). Therefore, the parents may be useful sources of alleles which do not exhibit the usual pleiotrophic effect of low seed yield and high seed protein. The use of a back cross to the adapted parent following the single cross was not beneficial in the development of high yielding lines.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".