Preliminary Investigation of Genetic Variance and Heritability Among Sunflower (Helianthus annuus L.) Genotypes Yield and Yields Components in Botswana
Bibliographic record
Abstract
A study was conducted at the Sebele and Goodhope Research Stations to estimate genetic parameters of sunflower and select the best performing genotypes. Analysis of variance revealed significant difference among the 36 genotypes for all the eight traits. Principal Component Analysis, top 3 components explained 81.6% of total contribution, highlighting major contributors of phenotypic variability. All characters were positively correlated to GYHA. GCV ranged from (5.99-12.64) while PCV ranged from (6.62-19.01) for DF and GYHA for both coefficients respectively. Higher heritability was expressed for most traits, while moderate heritability between (30-60%) was record for GC, FPS, and GYHA. The genetic advance as percentage of mean (GAM) ranged from 9.99% for GC to highest of 25.24% for PH and most of the traits were moderate (10-20%). High heritability coupled with GAM was recorded on PH (85.26% & 25.45%), an indication that this trait is governed by additive gene action. Higher heritability together with moderate GAM, was observed on DF (82.06% & 11.18%), DM (73.69% & 13.29%), LN (70.18% & 17.85%), 1000SW (71.92% & 19.00%). Grain yield ranged from 997 kg/ha for SUN310 to 1862 kg/ha for SUN322 genotypes. The twelve best performing genotypes were SUN322, SUN-MOL4, CH301/105, SUN206, SUN205, SAONA403, RUSSIAN4, RUS4-401, RUS4SH14, JUPWTSH14, JUP2-WTSH14, JUPSH14 with a yield of more than 1500kg/ha. The study revealed higher genetic diversity among the sunflower genotypes; therefore, selection is feasible, based on the traits with higher heritability, genetic advance of mean and positive correlations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".