Combining Ability and Gene Action of Tropical Maize (Zea mays L.) Inbred Lines under Low and High Nitrogen Conditions
Bibliographic record
Abstract
This study was conducted to determine combining ability and gene action in elite maize inbred lines under low and high soil nitrogen conditions for hybrid breeding. Forty two tropical inbred lines (three testers and 39 lines) were crossed using line × tester mating design. The resulting 117 F1 hybrids, along with 4 hybrids used as checks, were evaluated using an 11 × 11 lattice design with two replications for grain yield and yield related traits during the 2012 and 2013 cropping seasons at two sites (Mbalmayo and Nkolbisson). Results revealed predominant additive gene effect under high soil nitrogen (N) conditions. Non-additive gene effect influenced grain yield under low soil and thus could be exploited for hybrid development. Under high N conditions inbred lines CLYN246, J16-1, CLWN201, TL-11-A-1642-5, CLQRCWQ26 and 1368 were good general combiners. Lines CML 343, ATP S6 20-Y-1, CLWN201, 1368, ATP S9 30 Y-1 and CLQRCWQ26 were good general combiners for grain yield under low N. They could be used to develop low N tolerant varieties. Different single cross hybrid combinations were identified for high grain yields under both low and high N conditions. The selected lines and single cross hybrids are a useful source of valuable genetic material for future maize hybrid breeding or direct production under low N.
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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) | 0.001 | 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".