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Record W2981103647 · doi:10.14393/bj-v35n5a2019-42656

Selection of lineages, genetic parameters, and correlations between soybean characters

2019· article· en· W2981103647 on OpenAlexaff
Mariana Silva Vianna, Ana Paula Oliveira Nogueira, Osvaldo Toshiyuki Hamawaki, Larissa Barbosa de Sousa, Géssyca Ferreira Gomes, Raphael Lemes Hamawaki, Carolina Oliveira da Silva

Bibliographic record

VenueBioscience Journal · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsSeneca Polytechnic
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisUniversidade Federal de Uberlândia
KeywordsBiologySelection (genetic algorithm)Coefficient of variationRandomized block designCultivarGenetic variationGrain yieldGenetic variabilityGenotypeAgronomyBreeding programStatisticsMathematicsGeneticsGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.113

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.217
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2019
Admission routes1
Has abstractyes

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