Estimation of Genetic Parameters for Maturity and Grain Yield in Diallel Crosses of Five Wheat Cultivars Using Two Different Models
Bibliographic record
Abstract
Wheat maturity is important for its adaptability to different environments and geographical regions. In order to obtain genetic information for maturity and grain yield, five wheat cultivars of Cross adl, Marvdasht, Chamran, Shiraz and Darab2 were crossed for producing one-way diallel crosses that were analyzed using two models of Griffing (1956) and Jinks and Hayman (1953). Parents and their F2 progenies were cultivated at two locations of Shiraz and Zarghan, Iran, in 2010-2011. The traits of interest were days to heading (DDH) and maturity (DDM), grain filling duration (GFD), grain yield per plant (GY) and plant height (PH). Genotype × location interaction was significant for PH and GFD but there was no interaction for DDH, DDM and GY. The variances due to general (GCA) and specific (SCA) combining abilities were significant for all the traits. Therefore, both additive and non-additive genetic components were not equally involved in genetic control of the characters studied. The GCA × location interaction was only significant for PH and GFD, an indication for the effects of environment on additive components. The Baker (1978) ratio for DDH (0.90 and 0.91 at Zarghan and Shiraz locations respectively), DDM (0.81 and 0.82) and GY (0.89 and 0.87) under both locations and for PH (0.88) at Shiraz showed the higher importance of additive variances in the genetic control of these traits. The GCA estimates revealed that Chamran for dwarfness, early heading and maturity and GFD, Darab2 for dwarfness, early heading and maturity, Marvdasht for GFD and GY were the best combiners. Graphical analysis and the average degree of dominance (less than 1) showed that gene action for all the traits was of partial dominance type.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".