Identification of superior genotypes for leaf architecture traits in Sorghum bicolor through GGE biplot analysis
Bibliographic record
Abstract
Context Well-organised leaf architecture produces compact canopies and allows for greater sunlight penetration, higher photosynthetic rates, and thus greater yields. Breeding for enhanced leaf architecture of sorghum (Sorghum bicolor L.), a key food source in semi-arid regions, benefits its overall production. Aims The study focuses on selecting useful genotypes with excellent leaf architecture for grain sorghum improvement. Methods In total, 185 sorghum genotypes were subjected to multi-environment trials. Leaf flagging-point length, leaf length, leaf width, leaf angle and leaf orientation value (LOV) were characterised under field conditions. Genotype + genotype × environment interaction (GGE) biplot analysis was used to identify the most stable genotypes with the highest LOV. Key results Statistical analysis showed significant effects of genotype × environment interaction (P < 0.001), and high broad-sense heritability for the traits. Correlation analysis demonstrated negative correlations (P < 0.001) between LOV and its components. Singular value decomposition of LOVs in the first two principal components explained 89.19% of the total variation. GGE biplot analysis identified G55 as the ideotype with the highest and most stable LOV. Conclusions Leaf architecture optimisation should be given greater attention. This study has identified a genotype with optimal and stable leaf architecture, laying the foundation for improvement in breeding to increase overall yields of sorghum. Implications Genotype G55 can be utilised as a parent with other parents that display economically important characteristics in breeding programs to produce offspring that can be planted densely to increase population yields. Genotypes identified with loose leaf architecture are useful in dissecting genes controlling leaf architecture by crossing with G55 to construct genetic mapping populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".