Diallel Analyses of Agronomic Traits Using Chinese and U.S. Maize Germplasm
Bibliographic record
Abstract
Added genetic diversity among commercial maize (Zea mays L) hybrids may further increase yields and safeguard against vulnerability. Introducing exotic germplasm into breeding programs would increase the genetic base from which elite commercial inbreds are derived. Ten populations of maize, created from Chinese and/or U.S. inbreds or strains, were evaluated by Griffing's diallel analysis for combining ability of grain yield, stalk lodging, ear height, flowering time, and European corn borer (ECB; Ostrinia nubilalis Hübner) resistance to estimate their potential as sources of exotic germplasm for U.S. breeding programs. Grain yield general combining ability was largest for the population Mo17 Syn.(H14)C5, a synthetic improved by half‐sib selection using US13 as a tester. Grain yield specific combining ability was largest in the cross Chinese Mix 2 × Mo17 Syn.(H14)C5. Chinese Mix 2 × Mo17 Syn.(H14)C5 had more stalk lodging than the B73 × Mo17 and Pioneer Brand 3394 checks. Because of the high yield potential and other moderate‐to‐good agronomic traits of the cross combination, Chinese Mix 2 was selected as the best population for selection. Its large specific combining ability effect with Lancaster type material, which is commonly known in breeding programs, shows potential for further improvement. No native ECB resistance in Chinese germplasm was detected (two environments in 1 yr) compared with the resistant check Pioneer Brand 3184.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".