Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFU |
Texto Completo: | https://repositorio.ufu.br/handle/123456789/21630 http://dx.doi.org/10.14393/ufu.di.2018.742 |
Resumo: | The objective of this work was: a) to verify the genotype x environment relationships for the physical and potential characteristics of the methods of Eberhart and Russel (1966), Centroid, as well as the use of artificial neural networks in the adaptability and stability of the cotton genotypes of b) to analyze a genetic difference between cotton genotypes of fiber and power propagation factors by the UPGMA and Tocher methods to identify the parents of potential risk factors and to evaluate the phenotypic and genotypic and indirect correlations on productivity, yield and technological characteristics of colored cotton fiber. The experiment was carried out at the experimental farm of Capim Branco, in Uberlândia-MG, in the crop year 2013/2014, 2014/2015, 2015/2016 and 2016/2017. Twelve cotton fiber genotypes were evaluated. The experimental design was completely randomized blocks with three replicates. The yield of cotton seed, fiber yield and technical characteristics of the fiber were evaluated with the aid of the HVI apparatus (High Volume Instrument), being: Average length of fiber (UHML), Uniformity of length (UI), Index of short fibers (SFI), fiber resistance (STR), fiber elongation (ELG), micronaire (MIC) and fiber maturity (MAT). GxA, which demonstrates the differential behavior of genocysts in the face of environmental oscillations. The interaction was predominantly intentional and adaptive, and a correlation was found between the Eberhart and Russell methods and the RNAs, and the genotypes UFUJP-02 and UFUJP-17 were shown to be responsive to environmental stimuli with high predictability, and to be shown to be the quality and quality of fibers. The RNA's method demonstrated how much adaptability was compared to the Eberhart and Russell and Centroid methods. Through the contribution of Singh, UHML and MAT were the characteristics that contributed most to a divergence. Five divergent groups were formed, one less than Tocher with 6 groups. Commercial applications may be more useful for the generation of segregant residues and with greater genetic variability. Aiming to increase the productive and improved potential of fiber quality, crosses between UFUJP-16 and the most frequent witnesses, the greater chance of success in the breeding program. The MIC, MAT, STR and ELG characteristics were highly comic, and the exterior was negative, that is, an inverse association with productivity. In the analysis of the MIC, MAT and STR tracks, the deleterious effects exceed the magnitude of the residual effect, and the MAT has direct effect in the unfavorable sense, demonstrating absence of cause and effect on productivity. The MIC characteristic, despite the high direct effect, has low genotype determination coefficient, making its use in indirect selection impossible. It was verified that the compromise of fiber and resistance can be used in the selection of direct, as long as a truncated selection is made between the two. |
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Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionaisBreeding colored cotton: Artificial Neural Networks versus conventional methodsGossypium hirsutumGossypium barbadenseInteligência computacionalAdaptabilidade e estabilidadeGossypium hirsutumGossypium barbadenseArtificial intelligenceAdaptability and stabilityAgronomiaAlgodão - Melhoramento genéticoCNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETALThe objective of this work was: a) to verify the genotype x environment relationships for the physical and potential characteristics of the methods of Eberhart and Russel (1966), Centroid, as well as the use of artificial neural networks in the adaptability and stability of the cotton genotypes of b) to analyze a genetic difference between cotton genotypes of fiber and power propagation factors by the UPGMA and Tocher methods to identify the parents of potential risk factors and to evaluate the phenotypic and genotypic and indirect correlations on productivity, yield and technological characteristics of colored cotton fiber. The experiment was carried out at the experimental farm of Capim Branco, in Uberlândia-MG, in the crop year 2013/2014, 2014/2015, 2015/2016 and 2016/2017. Twelve cotton fiber genotypes were evaluated. The experimental design was completely randomized blocks with three replicates. The yield of cotton seed, fiber yield and technical characteristics of the fiber were evaluated with the aid of the HVI apparatus (High Volume Instrument), being: Average length of fiber (UHML), Uniformity of length (UI), Index of short fibers (SFI), fiber resistance (STR), fiber elongation (ELG), micronaire (MIC) and fiber maturity (MAT). GxA, which demonstrates the differential behavior of genocysts in the face of environmental oscillations. The interaction was predominantly intentional and adaptive, and a correlation was found between the Eberhart and Russell methods and the RNAs, and the genotypes UFUJP-02 and UFUJP-17 were shown to be responsive to environmental stimuli with high predictability, and to be shown to be the quality and quality of fibers. The RNA's method demonstrated how much adaptability was compared to the Eberhart and Russell and Centroid methods. Through the contribution of Singh, UHML and MAT were the characteristics that contributed most to a divergence. Five divergent groups were formed, one less than Tocher with 6 groups. Commercial applications may be more useful for the generation of segregant residues and with greater genetic variability. Aiming to increase the productive and improved potential of fiber quality, crosses between UFUJP-16 and the most frequent witnesses, the greater chance of success in the breeding program. The MIC, MAT, STR and ELG characteristics were highly comic, and the exterior was negative, that is, an inverse association with productivity. In the analysis of the MIC, MAT and STR tracks, the deleterious effects exceed the magnitude of the residual effect, and the MAT has direct effect in the unfavorable sense, demonstrating absence of cause and effect on productivity. The MIC characteristic, despite the high direct effect, has low genotype determination coefficient, making its use in indirect selection impossible. It was verified that the compromise of fiber and resistance can be used in the selection of direct, as long as a truncated selection is made between the two.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)O objetivo deste trabalho foi: a) verificar a presença da interação genótipos x ambientes para a adapatabilidade e estabilidade pelos métodos de Eberhart e Russel (1966), Centróide, assim como avaliar o uso das redes neurais artificiais na adaptabilidade e estabilidade dos genótipos de algodoeiro de fibra colorida, para características da fibra e produtividade b) analisar a diversidade genética entre genótipos de algodoeiro de fibra colorida, utilizando características tecnológicas da fibra e produtividade pelos métodos de UPGMA e Tocher, para obtenção de genitores em potencial e c) avaliar as correlações fenotípicas e genotípicas e seus efeitos diretos e indiretos sobre produtividade, rendimento e caracteres tecnológicos da fibra de algodoeiro colorido. O experimento foi realizado na fazenda experimental Capim Branco, em Uberlândia-MG, nas safras 2013/2014, 2014/2015, 2015/2016 e 2016/2017. Foram avaliados 12 genótipos de algodão de fibra colorida. O delineamento experimental foi de blocos completamente casualizados com três repetições. Foram avaliados a produtividade de algodão em caroço, rendimento de fibra e as características tecnológicas da fibra com o auxílio do aparelho HVI (High Volume Instrument), sendo estas: Comprimento médio da fibra (UHML), Uniformidade de comprimento (UI), Índice de fibras curtas (SFI), Resistência de fibras (STR), Elongamento da fibra (ELG), Micronaire (MIC) e Maturidade da fibra (MAT). As características apresentaram interação GxA, que evidencia o comportamento diferencial dos genótipos frente as oscilações ambientais. A interação foi predominantemente do tipo complexa e, ao analisarmos a adaptabilidade e estabilidade, houve concordância entre os métodos de Eberhart e Russell e as RNA’s, sendo que os genótipos UFUJP-02 e UFUJP-17, demonstraram ser responsivos aos estímulos ambientais com alta previsibilidade, além de demonstrarem ser promissores para a característica produtividade e qualidade de fibras. O método de RNA’s demonstrou confiabilidade quanto a adaptabilidade se comparada aos métodos Eberhart e Russell e Centróide. Pela contribuição relativa de Singh UHML e MAT foram as características que mais contribuíram para a divergência. Formaram-se cinco grupos divergentes, um a menos que Tocher com 6 grupos. Verificou-se que possíveis hibridações entre UFUJP-17 e as cultivares comerciais podem ser promissoras para obtenção de populações segregantes com maior variabilidade genética. Visando o aumento do potencial produtivo e melhorias da qualidade de fibra, cruzamentos entre UFUJP-16 e as testemunhas comercias, teriam maior chance de se obter êxito no programa de melhoramento. As características MIC, MAT, STR e ELG tiveram correlação significativa com produtividade, sendo que alongamento obteve correlação negativa, ou seja, uma associação inversa com produtividade. Na análise de trilha MIC, MAT e STR tiveram efeito direto superior a magnitude do efeito residual, sendo que MAT obteve efeito direto em sentido desfavorável, demonstrando ausência de causa e efeito sobre produtividade. A característica MIC, apesar do alto efeito direto, possui baixo coeficiente de determinação genotípico, inviabilizando sua utilização na seleção indireta. Verificou-se que comprimento de fibra e resistência podem ser utilizados na seleção indireta, desde que seja feito, entre ambas, uma seleção truncada.Universidade Federal de UberlândiaBrasilPrograma de Pós-graduação em AgronomiaSousa, Larissa Barbosa deNogueira, Ana Paula OliveiraMaciel, Gabriel MascarenhasBatista, Renata OliveiraCardoso, Daniel Bonifácio Oliveira2018-06-25T14:41:35Z2018-06-25T14:41:35Z2018-02-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfCARDOSO, Daniel Bonifácio Oliveira. Melhoramento genético de algodoeiro colorido: redes neurais artificiais versus métodos convencionais .2018. 99 p il. Dissertação (Mestrado em agronomia) - Universidade Federal de Uberlândia, Uberlândia, 2018.http://dx.doi.org/10.14393/ufu.di.2018.742https://repositorio.ufu.br/handle/123456789/21630http://dx.doi.org/10.14393/ufu.di.2018.742porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFUinstname:Universidade Federal de Uberlândia (UFU)instacron:UFU2021-10-21T18:03:46Zoai:repositorio.ufu.br:123456789/21630Repositório InstitucionalONGhttp://repositorio.ufu.br/oai/requestdiinf@dirbi.ufu.bropendoar:2021-10-21T18:03:46Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU)false |
dc.title.none.fl_str_mv |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais Breeding colored cotton: Artificial Neural Networks versus conventional methods |
title |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais |
spellingShingle |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais Cardoso, Daniel Bonifácio Oliveira Gossypium hirsutum Gossypium barbadense Inteligência computacional Adaptabilidade e estabilidade Gossypium hirsutum Gossypium barbadense Artificial intelligence Adaptability and stability Agronomia Algodão - Melhoramento genético CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL |
title_short |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais |
title_full |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais |
title_fullStr |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais |
title_full_unstemmed |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais |
title_sort |
Melhoramento genético de algodoeiro colorido: Redes Neurais Artificiais versus métodos convencionais |
author |
Cardoso, Daniel Bonifácio Oliveira |
author_facet |
Cardoso, Daniel Bonifácio Oliveira |
author_role |
author |
dc.contributor.none.fl_str_mv |
Sousa, Larissa Barbosa de Nogueira, Ana Paula Oliveira Maciel, Gabriel Mascarenhas Batista, Renata Oliveira |
dc.contributor.author.fl_str_mv |
Cardoso, Daniel Bonifácio Oliveira |
dc.subject.por.fl_str_mv |
Gossypium hirsutum Gossypium barbadense Inteligência computacional Adaptabilidade e estabilidade Gossypium hirsutum Gossypium barbadense Artificial intelligence Adaptability and stability Agronomia Algodão - Melhoramento genético CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL |
topic |
Gossypium hirsutum Gossypium barbadense Inteligência computacional Adaptabilidade e estabilidade Gossypium hirsutum Gossypium barbadense Artificial intelligence Adaptability and stability Agronomia Algodão - Melhoramento genético CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL |
description |
The objective of this work was: a) to verify the genotype x environment relationships for the physical and potential characteristics of the methods of Eberhart and Russel (1966), Centroid, as well as the use of artificial neural networks in the adaptability and stability of the cotton genotypes of b) to analyze a genetic difference between cotton genotypes of fiber and power propagation factors by the UPGMA and Tocher methods to identify the parents of potential risk factors and to evaluate the phenotypic and genotypic and indirect correlations on productivity, yield and technological characteristics of colored cotton fiber. The experiment was carried out at the experimental farm of Capim Branco, in Uberlândia-MG, in the crop year 2013/2014, 2014/2015, 2015/2016 and 2016/2017. Twelve cotton fiber genotypes were evaluated. The experimental design was completely randomized blocks with three replicates. The yield of cotton seed, fiber yield and technical characteristics of the fiber were evaluated with the aid of the HVI apparatus (High Volume Instrument), being: Average length of fiber (UHML), Uniformity of length (UI), Index of short fibers (SFI), fiber resistance (STR), fiber elongation (ELG), micronaire (MIC) and fiber maturity (MAT). GxA, which demonstrates the differential behavior of genocysts in the face of environmental oscillations. The interaction was predominantly intentional and adaptive, and a correlation was found between the Eberhart and Russell methods and the RNAs, and the genotypes UFUJP-02 and UFUJP-17 were shown to be responsive to environmental stimuli with high predictability, and to be shown to be the quality and quality of fibers. The RNA's method demonstrated how much adaptability was compared to the Eberhart and Russell and Centroid methods. Through the contribution of Singh, UHML and MAT were the characteristics that contributed most to a divergence. Five divergent groups were formed, one less than Tocher with 6 groups. Commercial applications may be more useful for the generation of segregant residues and with greater genetic variability. Aiming to increase the productive and improved potential of fiber quality, crosses between UFUJP-16 and the most frequent witnesses, the greater chance of success in the breeding program. The MIC, MAT, STR and ELG characteristics were highly comic, and the exterior was negative, that is, an inverse association with productivity. In the analysis of the MIC, MAT and STR tracks, the deleterious effects exceed the magnitude of the residual effect, and the MAT has direct effect in the unfavorable sense, demonstrating absence of cause and effect on productivity. The MIC characteristic, despite the high direct effect, has low genotype determination coefficient, making its use in indirect selection impossible. It was verified that the compromise of fiber and resistance can be used in the selection of direct, as long as a truncated selection is made between the two. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-25T14:41:35Z 2018-06-25T14:41:35Z 2018-02-02 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
CARDOSO, Daniel Bonifácio Oliveira. Melhoramento genético de algodoeiro colorido: redes neurais artificiais versus métodos convencionais .2018. 99 p il. Dissertação (Mestrado em agronomia) - Universidade Federal de Uberlândia, Uberlândia, 2018.http://dx.doi.org/10.14393/ufu.di.2018.742 https://repositorio.ufu.br/handle/123456789/21630 http://dx.doi.org/10.14393/ufu.di.2018.742 |
identifier_str_mv |
CARDOSO, Daniel Bonifácio Oliveira. Melhoramento genético de algodoeiro colorido: redes neurais artificiais versus métodos convencionais .2018. 99 p il. Dissertação (Mestrado em agronomia) - Universidade Federal de Uberlândia, Uberlândia, 2018.http://dx.doi.org/10.14393/ufu.di.2018.742 |
url |
https://repositorio.ufu.br/handle/123456789/21630 http://dx.doi.org/10.14393/ufu.di.2018.742 |
dc.language.iso.fl_str_mv |
por |
language |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Uberlândia Brasil Programa de Pós-graduação em Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Uberlândia Brasil Programa de Pós-graduação em Agronomia |
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reponame:Repositório Institucional da UFU instname:Universidade Federal de Uberlândia (UFU) instacron:UFU |
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Universidade Federal de Uberlândia (UFU) |
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UFU |
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UFU |
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Repositório Institucional da UFU |
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Repositório Institucional da UFU |
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Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU) |
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diinf@dirbi.ufu.br |
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1813711310179794944 |