Combining ability and gene action analysis in a half-diallel cross of Brassica rapa L.
Bibliographic record
Abstract
Fifteen hybrids along with six parents were tested to identify good combiners through analyzing combining ability and gene action for yield and its attributes. The experiment was conducted in randomized complete block design with three replications during 2010/11 at research farm of Sher-e-Bangla Agricultural University (SAU), Dhaka. Data for days to 50% flowering and 80% maturity, plant height, number of primary and secondary branches, number of pods per plant, number of seeds per pod, pod length, seed yield and 1000-seed weight was recorded for analysis.The results showed that mean squares for parents, hybrids, parent vs hybrid were significantly different (p<0.01 or p<0.05) for most of the traits. Highly significant mean squares due to general combining ability (GCA) were found for all the traits except for length of pod, whereas highly significant mean squares due to specific combining ability (SCA) were also found for days to 50% flowering, number of secondary branches per plant, number of pods per plant, length of pod and seed yield per plant. This indicated that both additive and non-additive gene effects were important but additive gene effects were predominant for the expression of most of the measured traits. Estimates of GCA and SCA effects for yield and its attributes suggested that parent BARI Sarisha-6, BARI Sarisha-15 and Tori-7 was good combiners for different traits. Hayman’s graphical analysis indicated both over- and partial-dominance for growth and yield attributes. As dominant (non-fixable) variation was high for most of the attributes, substantial improvements of these traits may be possible by transferring complementary gene into non-epistatic high-dominance crosses or eliminating duplicate genes from high-dominance crosses. Considering GCA, SCA and per se performance BARI Sarisha-6, BARI Sarisha-15 and Tori-7 might be good parents towards an effective breeding programme.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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".