Study of genetic divergence for forage yield and biochemical traits in sorghum [Sorghum bicolor L. Moench] germplasm
Bibliographic record
Abstract
The present investigation was carried out with two hundred eighty sorghum germplasm lines in augmented block design at GBPUAT, Pantnagar under normal sown condition during the Kharif season 2018 and 2019. The observations were recorded on different yield contributing traits such as days to flowering, plant height, number of leaves; stem girth etc., quality traits such as protein content, total soluble solids, in vivo dry matter digestibility etc., and biochemical traits like cellulose content, silica content, and hemicelluloses etc. The statistical analysis for genetic diversity was done using hierarchical cluster analysis. The hierarchical cluster analysis revealed that significant amount of genetic diversity was present in sorghum germplasm with respect to different yield related traits, quality traits and biochemical traits. The 280 germplasm lines were grouped into XI distinct non-overlapping clusters. The highest number of genotypes was grouped into cluster-VIII (75) whereas cluster-XI exhibited only single genotype. The maximum intra-cluster distance was exhibited by cluster-II (58.202) whereas minimum intra-cluster distance was exhibited by cluster-XI (0.000). The clusters with high intra-cluster distances suggested that genotypes in these clusters were more genetic diverse than the genotypes in other clusters with low intra-cluster distances. The highest inter-cluster distance was observed between clusters-V and XI (341.437) suggested distant relationship between members of these two clusters and upon crossing the members of these two clusters will give more genetic diversity in segregating generation whereas the lowest inter-cluster distance was observed between clusters-VIII and IX (53.27) suggested a closer relationship between these two clusters and low degree of genetic diversity among the genotypes. Presence of substantial genetic diversity among the genotypes screened in the present study indicated that this material may serve as a good source for selecting the diverse parents for hybridization programme. In order to increase the possibility of isolating good trangressive segregants in the segregating generations it would be logical to attempt crosses between the diverse genotypes belonging to clusters separated by large inter-cluster distances.
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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) | 0.000 | 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".