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URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11845

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    Fuzzy control systems for decision-making in cultivars recommendation
    (Acta Scientiarum. Agronomy, 2018) Carneiro, Vinícius Quintão; Prado, Adalgisa Leles do; Cruz, Cosme Damião; Carneiro, Pedro Crescêncio Souza; Nascimento, Moysés; Carneiro, José Eustáquio de Souza
    The objective of the present study was to propose fuzzy control systems to support the recommendation of cultivars of different agronomic crops. Grain yield data from 23 lines and 2 cultivars of red bean were used to evaluate the applicability of these controllers. Genotypes were evaluated in nine environments in the Zona da Mata region, Minas Gerais State, Brazil. Using the parameters of Eberhart and Russell analysis, fuzzy controllers were developed with the Mamdani and Sugeno inference systems. Analyses of adaptability and stability were carried out by the method of Eberhart and Russell. The parameters obtained for each genotype were submitted to the respective controllers. There were significant genotypes x environments interaction, which justified the necessity of performing an adaptability and stability analysis. For both controllers (Mamdani and Sugeno), seven lines presented general adaptability, while only one presented adaptability to unfavorable environments. It was also found that both inference systems were useful for developing controllers that had the aim of recommending cultivars. Thus, it was noted that fuzzy control systems have the potential to identify the behavior of bean genotypes.
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    Quantile regression for genome-wide association study of flowering time-related traits in common bean
    (Plos One, 2018) Nascimento, Moysés; Nascimento, Ana Carolina Campana; Silva, Fabyano Fonseca e; Barili, Leiri Daiane; Vale, Naine Martins do; Carneiro, José Eustáquio; Cruz, Cosme Damião; Carneiro, Pedro Crescêncio Souza; Serão, Nick Vergara Lopes
    Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest. The objectives of this study were to estimate genetic parameters and to use QR to identify genomic regions for phenological traits (Days to first flower—DFF; Days for flowering—DTF; Days to end of flowering—DEF) in common bean. A total of 80 genotypes of common beans, with 3 replicates were raised at 4 locations and seasons. Plants were genotyped for 384 SNPs. Traditional single-SNP and 9 QR models, ranging from equally spaced quantiles (τ) 0.1 to 0.9, were used to associate SNPs to phenotype. Heritabilities were moderate high, ranging from 0.32 to 0.58. Genetic and phenotypic correlations were all high, averaging 0.66 and 0.98, respectively. Traditional single-SNP GWAS model was not able to find any SNP-trait association. On the other hand, when using QR methodology considering one extreme quantile (τ = 0.1) we found, respectively 1 and 7, significant SNPs associated for DFF and DTF. Significant SNPs were found on Pv01, Pv02, Pv03, Pv07, Pv10 and Pv11 chromosomes. We investigated potential candidate genes in the region around these significant SNPs. Three genes involved in the flowering pathways were identified, including Phvul.001G214500, Phvul.007G229300 and Phvul.010G142900.1 on Pv01, Pv07 and Pv10, respectively. These results indicate that GWAS-based QR was able to enhance the understanding on genetic architecture of phenological traits (DFF and DTF) in common bean.