Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional Manancial UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/28182 |
Resumo: | Oat is one of the main winter cereals grown in the world, used in human food and animal feed, ground cover, straw production, and crop rotation in the no-tillage system. In order to enhance the oat production systems, statistical techniques have been used to study the linear relationships between characters, in order to identify characters that directly or indirectly favor the selection of superior genotypes, among these techniques the linear correlation stands out, and path analysis. When performing multivariate analyses such as path analysis, some statistical assumptions must be met to avoid obtaining biased results. Furthermore, when working with this technique, the parameters of the mathematical model referring to the experimental design and treatments are disregarded, using only average observations, without stratifying the possible effects. Therefore, this study was developed with the aim of analyzing the implications of removing the parameters from the mathematical model on the results of Pearson correlation analysis and path analysis, in field trials with the oat crop, cultivated in different years and stratifying agricultural scenarios (with and without the use of fungicide). The experiments were conducted from 2015 to 2019, in the municipality of Augusto Pestana, Rio Grande do Sul, Brazil. The experimental design used was complete randomized blocks, with treatments characterized by oat cultivars and fungicide applications, with three replications. For each year, scenario, and data group, a multicollinearity diagnosis was performed, Pearson's correlation coefficients were calculated, and a path analysis was performed. The occurrence of multicollinearity generates biased path coefficients without biological interpretation, regardless of the environment and data group analyzed. Removing parameters from the mathematical model changes the explanatory capacity of characters in relation to yield variance, for all environments, scenarios, and types of path analysis performed. Removing the effects of model parameters results in changes in direction and magnitude (>50%) in the path coefficients regardless of the environment, scenario, and type of path analysis performed. |
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2023-03-14T13:59:34Z2023-03-14T13:59:34Z2023-02-17http://repositorio.ufsm.br/handle/1/28182Oat is one of the main winter cereals grown in the world, used in human food and animal feed, ground cover, straw production, and crop rotation in the no-tillage system. In order to enhance the oat production systems, statistical techniques have been used to study the linear relationships between characters, in order to identify characters that directly or indirectly favor the selection of superior genotypes, among these techniques the linear correlation stands out, and path analysis. When performing multivariate analyses such as path analysis, some statistical assumptions must be met to avoid obtaining biased results. Furthermore, when working with this technique, the parameters of the mathematical model referring to the experimental design and treatments are disregarded, using only average observations, without stratifying the possible effects. Therefore, this study was developed with the aim of analyzing the implications of removing the parameters from the mathematical model on the results of Pearson correlation analysis and path analysis, in field trials with the oat crop, cultivated in different years and stratifying agricultural scenarios (with and without the use of fungicide). The experiments were conducted from 2015 to 2019, in the municipality of Augusto Pestana, Rio Grande do Sul, Brazil. The experimental design used was complete randomized blocks, with treatments characterized by oat cultivars and fungicide applications, with three replications. For each year, scenario, and data group, a multicollinearity diagnosis was performed, Pearson's correlation coefficients were calculated, and a path analysis was performed. The occurrence of multicollinearity generates biased path coefficients without biological interpretation, regardless of the environment and data group analyzed. Removing parameters from the mathematical model changes the explanatory capacity of characters in relation to yield variance, for all environments, scenarios, and types of path analysis performed. Removing the effects of model parameters results in changes in direction and magnitude (>50%) in the path coefficients regardless of the environment, scenario, and type of path analysis performed.A aveia é um dos principais cereais de inverno cultivados no mundo, utilizada na alimentação humana e animal, cobertura do solo, produção de palhada e rotação de culturas no sistema plantio direto. Com o intuito de potencializar os sistemas de produção de aveia, têm sido empregadas técnicas estatísticas para estudar as relações lineares entre caracteres, a fim de identificar caracteres que direta ou indiretamente favoreçam a seleção de genótipos superiores, entre estas técnicas destacam-se a correlação linear e análise de trilha. Ao proceder análises multivariadas como a análise de trilha, alguns pressupostos estatísticos devem ser atendidos, a fim de evitar a obtenção de resultados viesados. Além disso, ao trabalhar-se com esta técnica, os parâmetros do modelo matemático referentes ao delineamento experimental e tratamentos são desconsiderados, utilizando-se observações médias, sem estratificar os possíveis efeitos. Sendo assim, este estudo foi desenvolvido com o intuito de analisar as implicações da remoção dos parâmetros do modelo matemático sob os resultados de análises de correlação de Pearson e análise de trilha, em ensaios com a cultura da aveia branca, cultivada em diferentes anos e estratificando cenários agrícolas (com e sem o uso de fungicida). Os experimentos foram conduzidos no período de 2015 a 2019, no município de Augusto Pestana, Rio Grande do Sul, Brasil. O delineamento experimental utilizado foi de blocos completos ao acaso, sendo os tratamentos caracterizados por cultivares de aveia e aplicações de fungicida, com três repetições. Para cada ano, cenário e grupo de dados foi realizado diagnóstico de multicolinearidade, calculados os coeficientes de correlação de Pearson e realizada análise de trilha. A ocorrência de multicolinearidade gera a obtenção e coeficientes de trilha viesados e sem intepretação biológica, independentemente do ambiente e grupo de dados analisados. A remoção dos parâmetros do modelo matemático altera a capacidade explicativa dos caracteres em relação a variância na produtividade, para todos os ambientes, cenários e tipos de análises de trilha procedidas. Retirar os efeitos dos parâmetros do modelo, resulta em alterações na direção e magnitude (>50%) nos coeficientes de trilha independentemente do ambiente, cenário e tipo de análise de trilha procedida.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em AgronomiaUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAvena sativaAnálise de trilhaMulticolinearidadeRelações linearesLinear relationshipsMulticollinearityPath analysisCNPQ::CIENCIAS AGRARIAS::AGRONOMIAPressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveiaMultivariate assumptions and effect of mathematical model parameters in multivariate analysis for oats testsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisLúcio, Alessandro Dal'Colhttp://lattes.cnpq.br/0972869223145503Caron, Braulio OtomarFollmann, Diego NicolauSilva, José Antonio Gonzalez daHaesbaer, Fernando Machadohttp://lattes.cnpq.br/0157652076165779Sgarbossa, Jaqueline500100000009600600600600600600600d51ee404-e7f0-41c1-bf30-c5fa02c4e5095ebf982e-bd31-4f86-ad2f-cb01172f1c59a027a05c-4802-4af1-aef0-2e977ec7fbebd0815f49-5308-4e49-812f-a5e3a86be59f612f6370-8f32-4bb4-9fc0-f4600e01c20d5af9321a-3c8e-40b8-94ae-ce1a4cb4eec1reponame:Repositório Institucional Manancial UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGAGRONOMIA_2023_SGARBOSSA_JAQUELINE.pdfTES_PPGAGRONOMIA_2023_SGARBOSSA_JAQUELINE.pdfTese de doutoradoapplication/pdf1916337http://repositorio.ufsm.br/bitstream/1/28182/1/TES_PPGAGRONOMIA_2023_SGARBOSSA_JAQUELINE.pdfbc4f96910d2136b279ecb674f5438c5bMD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.por.fl_str_mv |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
dc.title.alternative.eng.fl_str_mv |
Multivariate assumptions and effect of mathematical model parameters in multivariate analysis for oats tests |
title |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
spellingShingle |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia Sgarbossa, Jaqueline Avena sativa Análise de trilha Multicolinearidade Relações lineares Linear relationships Multicollinearity Path analysis CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
title_full |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
title_fullStr |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
title_full_unstemmed |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
title_sort |
Pressupostos multivariados e efeito dos parâmetros do modelo em análises multivariadas para ensaios com a aveia |
author |
Sgarbossa, Jaqueline |
author_facet |
Sgarbossa, Jaqueline |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Lúcio, Alessandro Dal'Col |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0972869223145503 |
dc.contributor.referee1.fl_str_mv |
Caron, Braulio Otomar |
dc.contributor.referee2.fl_str_mv |
Follmann, Diego Nicolau |
dc.contributor.referee3.fl_str_mv |
Silva, José Antonio Gonzalez da |
dc.contributor.referee4.fl_str_mv |
Haesbaer, Fernando Machado |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0157652076165779 |
dc.contributor.author.fl_str_mv |
Sgarbossa, Jaqueline |
contributor_str_mv |
Lúcio, Alessandro Dal'Col Caron, Braulio Otomar Follmann, Diego Nicolau Silva, José Antonio Gonzalez da Haesbaer, Fernando Machado |
dc.subject.por.fl_str_mv |
Avena sativa Análise de trilha Multicolinearidade Relações lineares |
topic |
Avena sativa Análise de trilha Multicolinearidade Relações lineares Linear relationships Multicollinearity Path analysis CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Linear relationships Multicollinearity Path analysis |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
Oat is one of the main winter cereals grown in the world, used in human food and animal feed, ground cover, straw production, and crop rotation in the no-tillage system. In order to enhance the oat production systems, statistical techniques have been used to study the linear relationships between characters, in order to identify characters that directly or indirectly favor the selection of superior genotypes, among these techniques the linear correlation stands out, and path analysis. When performing multivariate analyses such as path analysis, some statistical assumptions must be met to avoid obtaining biased results. Furthermore, when working with this technique, the parameters of the mathematical model referring to the experimental design and treatments are disregarded, using only average observations, without stratifying the possible effects. Therefore, this study was developed with the aim of analyzing the implications of removing the parameters from the mathematical model on the results of Pearson correlation analysis and path analysis, in field trials with the oat crop, cultivated in different years and stratifying agricultural scenarios (with and without the use of fungicide). The experiments were conducted from 2015 to 2019, in the municipality of Augusto Pestana, Rio Grande do Sul, Brazil. The experimental design used was complete randomized blocks, with treatments characterized by oat cultivars and fungicide applications, with three replications. For each year, scenario, and data group, a multicollinearity diagnosis was performed, Pearson's correlation coefficients were calculated, and a path analysis was performed. The occurrence of multicollinearity generates biased path coefficients without biological interpretation, regardless of the environment and data group analyzed. Removing parameters from the mathematical model changes the explanatory capacity of characters in relation to yield variance, for all environments, scenarios, and types of path analysis performed. Removing the effects of model parameters results in changes in direction and magnitude (>50%) in the path coefficients regardless of the environment, scenario, and type of path analysis performed. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-03-14T13:59:34Z |
dc.date.available.fl_str_mv |
2023-03-14T13:59:34Z |
dc.date.issued.fl_str_mv |
2023-02-17 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
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http://repositorio.ufsm.br/handle/1/28182 |
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http://repositorio.ufsm.br/handle/1/28182 |
dc.language.iso.fl_str_mv |
por |
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por |
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500100000009 |
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600 600 600 600 600 600 600 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Agronomia |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
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