Performance of some Promising Genotypes of Soybean Under Different Planting Dates Using Biplots Analysis
Bibliographic record
Abstract
Soybean yield is affected by planting dates and there are significant efficiency losses when planting are done outward a relatively restricted period. Genotypes and environment are major contributing factor of plant phenotype. Economically important quantitative traits include agronomic characteristics. Four separate experiments are carried out in each season at the experimental farm of Sakha Research Station, Kafr El-Sheikh during 2010 and 2011seasons. Seed yield of six soybeans cultivars i.e. Giza 21, Giza 22, Giza 111, H2L12, H30 and H32 examined at four different sowing dates i.e. 20th April, 5th May, of 20th May and 5th June of their effect on seed yield, and yield components. Highest number of branches/plant, number of pods/plant, 100 seed weight (g), and seed yield. Soybean cultivars showed high difference in seed yield and its component, Giza 21 exhibited maximum number of pods/plant, 100 seed yield, and seed yield. H32 cultivar contributed highest number of branches/plant. Through genotypes and genotypes x environment biplots of regression model analysis results, the performance of a cultivar at different environments was compared, the performance of six cultivars at different environments (planting dates) were compared. The results indicated that sown on 5th May increased seed yield/ha by 19.7% compared with sown on 5th June and increased seed yield by 17.9% compared with sown on 20th April, and increased seed yield by 10.3% compared with sown on 20th May. It could be noticed that Giza 21 cultivar exceeded H32 line by 16.63%, H30 line by 14.6%, Giza 22 cultivar by 13.7%, H2L12 line by 6.5% and Giza 111 by 5.3% in seed yield/ha. Highest yielding cultivars at the different mega environments were identified, and ideal cultivars and test planting date was identified. It could be suggested that soybean genotypes of Giza 21 and Giza 111 are the most promising for planting date 5th May and recorded concentrated seed yield/ha.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".