Selection of lineages, genetic parameters, and correlations between soybean characters
Bibliographic record
Abstract
Soybean has great economic importance in the world accordingly, this legume has been exploited in breeding programs aiming to provide cultivars with extensive grain yield, tolerant to pests and diseases, and adapted to different soil and climatic conditions. Therefore, the objectives of this work were to evaluate genetic parameters and correlations between soybean traits to select lineages to increase yield and improve agronomic traits. Experiments were carried out on the Capim Branco farm, of the Federal University of Uberlândia, harvest in 2016/2017. Fifteen morph-agronomic traits were assessed on twenty-two genotypes in a randomized complete block design with three replicates. Agronomic traits related to cycle, height, number of nodes and total pods have shown coefficients of genotypic determination higher than 70%. In addition, coefficients of variation of the number of days to the flowering and number of days to maturity were equal to 3.79% and 4.87%, respectively, indicating high homogeneity of data and low random variation. Among evaluated traits, ten have presented the ratios between the coefficient of genetic variation (CVg) and coefficient of environmental variation (CVe) above one, demonstrating high success likelihood in the selection of these traits. Fifteen genotypes have presented grain yield above the national average of the 2016/2017 harvest, which was 2882 kg h-1. Significant phenotypic correlations between traits ranged from -0.49 to 0.89, however genotype correlation was higher than the phenotypic ones, indicating that genetic factors have contributed more than the environmental factors. Traits related to cycle, height, and the number of nodes in the main stem have presented measures of H² and CVg / CVe with extensive magnitudes, evidencing the possibility of selection lineages having superior traits in the Soybean Breeding Program of the Federal University of Uberlândia. To increasing grain yield, the traits Number of pods of three grains and the Total number of pods were identified as appropriated to indirect selection based on the phenotypic and genotypic correlations. The 2lP14, B2P1, B2P28, B1P33 and 2AP11 lineages stand out as superior genotypes to direct selection.
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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) | 0.000 | 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".