Biplot analysis of trait relations of spinach (Spinacia oleracea L.) landraces
Bibliographic record
Abstract
Interest in growing winter spinach (Spinacia oleracea L.) in Iran is increasing due to its good nutritional potential returns relative to other vegetable crops. The objectives of this research were to investigate the interrelationships among different traits of spinach and to evaluate different Iranian spinach landraces with application of the genotype ? trait (GT) biplot methodology in visualizing research data. 81 spinach landraces were grown during 2-years according to randomized complete block design with four replications. Ranking of the genotypes based on the ideal entry revealed that genotypes G1, G20, G7, G8, G9, G27, G49 G70 and G79 were higher in the measured traits and could be good candidates for improving most of the measured traits. Ranking of traits for the leaf yield showed that petiole diameter, petiole length, leaf numbers at flowering, 1000-seed weight and root dry weight were the most discriminating traits which influence spinach leaf yield at both years. There were 9 winning genotypes and 4 which-won-where patterns at the first year while there were 8 winning genotypes and 4 which-won-where patterns at the second year. As a result, the findings from our study are as follows: (i) traits leaf numbers at flowering, leaf length, leaf width, leaf area, petiole diameter and petiole length could be as selection indices for spinach leaf yield improvement, (ii) genotypes G1, G20, G7, G8, G9, G27, G49 G70, and G79 were the most favorable and is thus recommended for commercial release or incorporating in breeding programs; (iii) the GT biplot method can be used to identify superior genotypes in other crops and in other parts of the world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".