Heterosis and Combining Ability Analysis for Yield and Related-Yield Traits in Hybrid Rice
Bibliographic record
Abstract
Study of combining ability and heterosis were conducted on 12 F1 hybrids along with seven rice genotypes (threecytoplasmic male sterile lines and four restorer varieties) to know the pattern of inheritance of somemorphological traits for selecting superior genotypes. The experiment was carried out according to line x testermating design, during 2007-08. Analysis of variance revealed significant differences among genotypes, crosses,lines, testers and line x tester interactions for tiller number, plant height, days to 50% flowering, panicle length,number of spikelets per panicle, spikelet fertility and grain yield traits. Variances of SCA were higher than theGCA variances for traits except for plant height which indicated predominance of non-additive gene action in theinheritance of the traits. The highest heterosis (106.60%) was observed in cross IR68899A x Poya followed byother eight crosses for yield and most of its related traits. The proportional contribution of testers was observedto be higher than that of the interactions of line x tester that revealed the higher estimates of GCA variance that isadditive gene action among the testers used. Within CMS parents, IR62829A and among male parents, IR50 andPoya were observed to be good general combiners for most of the characters studied. The cross combinationsIR62829A x Mosa-tarom, IR68899A x Poya, IR58025A x IR50 and IR58025A x Poya were observed to be goodspecific cross combinations for grain yield and most of its related traits due to highly significant SCA andheterotic effects.
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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".