Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
Bibliographic record
Abstract
Selection indices are good for classification because they consider several evaluated traits simultaneously to identify superior cultivars with a combination of the traits of interest. Adaptability/stability methods enable determining contributions to the genotype-by-environment (G × E) interaction and the risk associated with each cultivar. This study used a univariate and multivariate strategy to identify commercial soybean cultivars that presented both precocity and good productive performance and studied the G × E interaction considering all cultivars both simultaneously and by maturation groups. The experiments were conducted in the agricultural years 2014/15 and 2015/16 in seven distinct environments in southern Minas Gerais State, Brazil, considering a combination of locations and seasons. A randomized complete block design was used, and the treatments included 35 commercial soybean cultivars. In the univariate analysis, were evaluate several traits. Selection indices were calculated considering yield, harvest index, plant height, first pod insertion height and absolute maturation. The selection strategy efficiencies were quantified using the coincidence index. Each cultivar’s contribution to the G × E interaction and associated risk were determined using the ecovalence and confidence index methods, respectively. The results showed that the NS 7000 IPRO and NS 7209 IPRO cultivars were the most productive. The NS 7000 IPRO cultivar, although obtaining a good yield, contributed greatly to the G × E interaction when considering the maturation groups. The low coincidence in ranking the strategies indicates that more than one agronomic trait should be used to classify the superior cultivars.
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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 | 0.001 |
| 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".