Dimensionamento amostral para análise de trilha em caracteres de milho
Ano de defesa: | 2014 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
|
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Agronomia
|
Departamento: |
Agronomia
|
País: |
BR
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/3227 |
Resumo: | The objective of this study was to determine the sample size necessary to estimate the average, the coefficient of variation, the Pearson linear correlation coefficient and the direct effects of explanatory variables on grain yield in maize. In 361, 373 and 416 plants, respectively, of the simple, triple and double hybrids of the 2008/09 crop and, in 1,777, 1,693 and 1,720 plants, respectively, of the simple, triple and double hybrids of the 2009/10 crop, were measured eleven explanatory variables: plant height at harvest (AP), ear height (AIE), ear weight (PE), number of grain rows per ear (NF), ear length (CE), ear diameter (DE), cob weight (PS), cob diameter (DS), weight of hundred grains (MCG), number of grains per ear (NGR), grain length (CGR) and the main variable, grain yield (PROD). For each hybrid and crop, descriptives statistics for each variable were calculated and the correlation coefficients and direct effects of explanatory variables on PROD were estimated, in nine scenarios of traditional and ridge path analysis. Then, the sample size necessary to estimate the average, the coefficients of variation and of correlation and the direct effects of each explanatory variable on PROD were determined, for each type of hybrid, crop, scenario and type of path analysis, by resampling with replacement. The sample size necessary to estimate the mean and the coefficients of variation and of correlation ranges among hybrids, crops and variables or pairs of variables. The sample size necessary to estimate the direct effects ranges among hybrids, crops, scenarios, types of path analysis and explanatory variables. Independently of hybrid, crop and variable, 375 plants are enough to estimate the mean and the coefficient of variation with amplitude of the confidence interval of 95% (AIC95%) maximum of 10% and for the estimation of the correlation coefficients with a AIC95% maximum of 0.25. For the estimation of direct effects, with AIC95% maximum of 0.25, are required from 10 to 530 plants, depending of the type of hybrid, crop, scenario, type of path analysis and explanatory variable. The measurement of 120 plants is sufficient to estimate the average with AIC95% maximum of 20%, for the estimation of the coefficient of variation with AIC95% maximum of 15% and for the estimation of correlation coefficients with AIC95% maximum of 0.45, independently of the hybrid, crop and variable. The measurement of 120 plants is also sufficient for the estimation of the direct effects of AIE, CE and DE on PROD in the ninth scenario, with AIC95% maximum of 0.25, and in the ninth scenario, CE and DE have greater direct effects on PROD, independent of the type of hybrid, the crop and the type of path analysis. |
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2017-05-122017-05-122014-05-16TOEBE, Marcos. Sample size for path analysis in traits of maize. 2014. 133 f. Tese (Doutorado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2014.http://repositorio.ufsm.br/handle/1/3227The objective of this study was to determine the sample size necessary to estimate the average, the coefficient of variation, the Pearson linear correlation coefficient and the direct effects of explanatory variables on grain yield in maize. In 361, 373 and 416 plants, respectively, of the simple, triple and double hybrids of the 2008/09 crop and, in 1,777, 1,693 and 1,720 plants, respectively, of the simple, triple and double hybrids of the 2009/10 crop, were measured eleven explanatory variables: plant height at harvest (AP), ear height (AIE), ear weight (PE), number of grain rows per ear (NF), ear length (CE), ear diameter (DE), cob weight (PS), cob diameter (DS), weight of hundred grains (MCG), number of grains per ear (NGR), grain length (CGR) and the main variable, grain yield (PROD). For each hybrid and crop, descriptives statistics for each variable were calculated and the correlation coefficients and direct effects of explanatory variables on PROD were estimated, in nine scenarios of traditional and ridge path analysis. Then, the sample size necessary to estimate the average, the coefficients of variation and of correlation and the direct effects of each explanatory variable on PROD were determined, for each type of hybrid, crop, scenario and type of path analysis, by resampling with replacement. The sample size necessary to estimate the mean and the coefficients of variation and of correlation ranges among hybrids, crops and variables or pairs of variables. The sample size necessary to estimate the direct effects ranges among hybrids, crops, scenarios, types of path analysis and explanatory variables. Independently of hybrid, crop and variable, 375 plants are enough to estimate the mean and the coefficient of variation with amplitude of the confidence interval of 95% (AIC95%) maximum of 10% and for the estimation of the correlation coefficients with a AIC95% maximum of 0.25. For the estimation of direct effects, with AIC95% maximum of 0.25, are required from 10 to 530 plants, depending of the type of hybrid, crop, scenario, type of path analysis and explanatory variable. The measurement of 120 plants is sufficient to estimate the average with AIC95% maximum of 20%, for the estimation of the coefficient of variation with AIC95% maximum of 15% and for the estimation of correlation coefficients with AIC95% maximum of 0.45, independently of the hybrid, crop and variable. The measurement of 120 plants is also sufficient for the estimation of the direct effects of AIE, CE and DE on PROD in the ninth scenario, with AIC95% maximum of 0.25, and in the ninth scenario, CE and DE have greater direct effects on PROD, independent of the type of hybrid, the crop and the type of path analysis.O objetivo deste trabalho foi determinar o tamanho de amostra necessário para a estimação da média, do coeficiente de variação, do coeficiente de correlação linear de Pearson e dos efeitos diretos de variáveis explicativas sobre a produtividade de grãos em milho. Em 361, 373 e 416 plantas, respectivamente, dos híbridos simples, triplo e duplo da safra 2008/09 e, em 1.777, 1.693 e 1.720 plantas, respectivamente, dos híbridos simples, triplo e duplo da safra 2009/10, foram mensuradas onze variáveis explicativas: altura de planta na colheita (AP), altura de inserção de espiga (AIE), peso de espiga (PE), número de fileiras de grãos por espiga (NF), comprimento de espiga (CE), diâmetro de espiga (DE), peso de sabugo (PS), diâmetro de sabugo (DS), massa de cem grãos (MCG), número de grãos por espiga (NGR), comprimento de grãos (CGR) e, a variável principal produtividade de grãos (PROD). A seguir, em cada híbrido e safra, foram calculadas estatísticas descritivas para cada variável e estimados os coeficientes de correlação e os efeitos diretos de variáveis explicativas sobre a PROD, para nove cenários de análises de trilha tradicional e em crista. Após, determinou-se o tamanho de amostra necessário para a estimação da média, dos coeficientes de variação e de correlação e dos efeitos diretos de cada variável explicativa sobre a PROD, em cada tipo de híbrido, safra, cenário e tipo de análise de trilha, por meio de reamostragem com reposição. O tamanho de amostra necessário para a estimação da média e dos coeficientes de variação e de correlação varia entre híbridos, safras e variáveis ou pares de variáveis. O tamanho de amostra necessário para a estimação dos efeitos diretos varia entre híbridos, safras, cenários, tipos de análises de trilha e variáveis explicativas. Independentemente do híbrido, da safra e da variável, 375 plantas são suficientes para a estimação da média e do coeficiente de variação com amplitude do intervalo de confiança de 95% (AIC95%) máxima de 10% e, para a estimação de coeficientes de correlação com AIC95% máxima de 0,25. Para a estimação de efeitos diretos com AIC95% máxima de 0,25, são necessárias de 10 a 530 plantas, dependendo do tipo de híbrido, da safra, do cenário, do tipo de análise de trilha e da variável explicativa. A mensuração de 120 plantas é suficiente para a estimação da média com AIC95% máxima de 20%, para a estimação do coeficiente de variação com AIC95% máxima de 15% e, para a estimação de coeficientes de correlação com AIC95% máxima de 0,45, independentemente do híbrido, da safra e da variável. A mensuração de 120 plantas também é suficiente para a estimação dos efeitos diretos de AIE, CE e DE sobre PROD no nono cenário, com AIC95% máxima de 0,25, sendo que nesse cenário, CE e DE possuem maiores efeitos diretos sobre PROD, independentemente do tipo de híbrido, da safra e do tipo de análise de trilha.Conselho Nacional de Desenvolvimento Científico e Tecnológicoapplication/pdfporUniversidade Federal de Santa MariaPrograma de Pós-Graduação em AgronomiaUFSMBRAgronomiaZea mays L.ReamostragemPlanejamento experimentalRelações linearesSeleção indiretaResamplingExperimental planningLinear relationshipsIndirect selectionCNPQ::CIENCIAS AGRARIAS::AGRONOMIADimensionamento amostral para análise de trilha em caracteres de milhoSample size for path analysis in traits of maizeinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisCargnelutti Filho, Albertohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4790605A0Storck, Lindolfohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788328Y4Lúcio, Alessandro Dal colhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4799931H1Benin, Giovanihttp://lattes.cnpq.br/8634180310157308Lorentz, Leandro Homrichhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4700767T6http://lattes.cnpq.br/1350890583236601Toebe, Marcos500100000009400300300300300300300a106a3de-c40f-40f6-80de-397e8002690ec58279b2-afe8-487f-b596-dc9407fd82091da63f63-00ac-4d75-8280-386c6408637c26cb9b9d-9b21-4c4f-b9f9-a9600f4119980820126b-bdfe-444c-9b78-b87138ce2a06a19a1cc1-3855-48f9-9167-070f4b87f303info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTOEBE, MARCOS.pdfapplication/pdf24237408http://repositorio.ufsm.br/bitstream/1/3227/1/TOEBE%2c%20MARCOS.pdf7ebaba41dbc2433831ca786d5db49045MD51TEXTTOEBE, MARCOS.pdf.txtTOEBE, MARCOS.pdf.txtExtracted texttext/plain331634http://repositorio.ufsm.br/bitstream/1/3227/2/TOEBE%2c%20MARCOS.pdf.txtc463eba97781dd9afa6bed07e993e362MD52THUMBNAILTOEBE, MARCOS.pdf.jpgTOEBE, MARCOS.pdf.jpgIM Thumbnailimage/jpeg4342http://repositorio.ufsm.br/bitstream/1/3227/3/TOEBE%2c%20MARCOS.pdf.jpge16c2db4b19e5d069f13af68ede24c2dMD531/32272017-07-26 22:12:13.01oai:repositorio.ufsm.br:1/3227Repositório Institucionalhttp://repositorio.ufsm.br/PUBhttp://repositorio.ufsm.br/oai/requestopendoar:39132017-07-27T01:12:13Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Dimensionamento amostral para análise de trilha em caracteres de milho |
dc.title.alternative.eng.fl_str_mv |
Sample size for path analysis in traits of maize |
title |
Dimensionamento amostral para análise de trilha em caracteres de milho |
spellingShingle |
Dimensionamento amostral para análise de trilha em caracteres de milho Toebe, Marcos Zea mays L. Reamostragem Planejamento experimental Relações lineares Seleção indireta Resampling Experimental planning Linear relationships Indirect selection CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Dimensionamento amostral para análise de trilha em caracteres de milho |
title_full |
Dimensionamento amostral para análise de trilha em caracteres de milho |
title_fullStr |
Dimensionamento amostral para análise de trilha em caracteres de milho |
title_full_unstemmed |
Dimensionamento amostral para análise de trilha em caracteres de milho |
title_sort |
Dimensionamento amostral para análise de trilha em caracteres de milho |
author |
Toebe, Marcos |
author_facet |
Toebe, Marcos |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Cargnelutti Filho, Alberto |
dc.contributor.advisor1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4790605A0 |
dc.contributor.referee1.fl_str_mv |
Storck, Lindolfo |
dc.contributor.referee1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788328Y4 |
dc.contributor.referee2.fl_str_mv |
Lúcio, Alessandro Dal col |
dc.contributor.referee2Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4799931H1 |
dc.contributor.referee3.fl_str_mv |
Benin, Giovani |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/8634180310157308 |
dc.contributor.referee4.fl_str_mv |
Lorentz, Leandro Homrich |
dc.contributor.referee4Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4700767T6 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1350890583236601 |
dc.contributor.author.fl_str_mv |
Toebe, Marcos |
contributor_str_mv |
Cargnelutti Filho, Alberto Storck, Lindolfo Lúcio, Alessandro Dal col Benin, Giovani Lorentz, Leandro Homrich |
dc.subject.por.fl_str_mv |
Zea mays L. Reamostragem Planejamento experimental Relações lineares Seleção indireta |
topic |
Zea mays L. Reamostragem Planejamento experimental Relações lineares Seleção indireta Resampling Experimental planning Linear relationships Indirect selection CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Resampling Experimental planning Linear relationships Indirect selection |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
The objective of this study was to determine the sample size necessary to estimate the average, the coefficient of variation, the Pearson linear correlation coefficient and the direct effects of explanatory variables on grain yield in maize. In 361, 373 and 416 plants, respectively, of the simple, triple and double hybrids of the 2008/09 crop and, in 1,777, 1,693 and 1,720 plants, respectively, of the simple, triple and double hybrids of the 2009/10 crop, were measured eleven explanatory variables: plant height at harvest (AP), ear height (AIE), ear weight (PE), number of grain rows per ear (NF), ear length (CE), ear diameter (DE), cob weight (PS), cob diameter (DS), weight of hundred grains (MCG), number of grains per ear (NGR), grain length (CGR) and the main variable, grain yield (PROD). For each hybrid and crop, descriptives statistics for each variable were calculated and the correlation coefficients and direct effects of explanatory variables on PROD were estimated, in nine scenarios of traditional and ridge path analysis. Then, the sample size necessary to estimate the average, the coefficients of variation and of correlation and the direct effects of each explanatory variable on PROD were determined, for each type of hybrid, crop, scenario and type of path analysis, by resampling with replacement. The sample size necessary to estimate the mean and the coefficients of variation and of correlation ranges among hybrids, crops and variables or pairs of variables. The sample size necessary to estimate the direct effects ranges among hybrids, crops, scenarios, types of path analysis and explanatory variables. Independently of hybrid, crop and variable, 375 plants are enough to estimate the mean and the coefficient of variation with amplitude of the confidence interval of 95% (AIC95%) maximum of 10% and for the estimation of the correlation coefficients with a AIC95% maximum of 0.25. For the estimation of direct effects, with AIC95% maximum of 0.25, are required from 10 to 530 plants, depending of the type of hybrid, crop, scenario, type of path analysis and explanatory variable. The measurement of 120 plants is sufficient to estimate the average with AIC95% maximum of 20%, for the estimation of the coefficient of variation with AIC95% maximum of 15% and for the estimation of correlation coefficients with AIC95% maximum of 0.45, independently of the hybrid, crop and variable. The measurement of 120 plants is also sufficient for the estimation of the direct effects of AIE, CE and DE on PROD in the ninth scenario, with AIC95% maximum of 0.25, and in the ninth scenario, CE and DE have greater direct effects on PROD, independent of the type of hybrid, the crop and the type of path analysis. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-05-16 |
dc.date.accessioned.fl_str_mv |
2017-05-12 |
dc.date.available.fl_str_mv |
2017-05-12 |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
TOEBE, Marcos. Sample size for path analysis in traits of maize. 2014. 133 f. Tese (Doutorado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2014. |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/3227 |
identifier_str_mv |
TOEBE, Marcos. Sample size for path analysis in traits of maize. 2014. 133 f. Tese (Doutorado em Agronomia) - Universidade Federal de Santa Maria, Santa Maria, 2014. |
url |
http://repositorio.ufsm.br/handle/1/3227 |
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por |
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Universidade Federal de Santa Maria |
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Programa de Pós-Graduação em Agronomia |
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UFSM |
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