Modelagem de percentuais de germinação de sementes de milho em função do tempo

Detalhes bibliográficos
Autor(a) principal: Gazola, Sebastião
Data de Publicação: 2009
Tipo de documento: Tese
Idioma: por
Título da fonte: Repositório Institucional da Universidade Estadual de Maringá (RI-UEM)
Texto Completo: http://repositorio.uem.br:8080/jspui/handle/1/1155
Resumo: In 2008, the national production of grains was about 58.7 million of tonnes for which the seed quality had the most effective contribution. However, the seed quality is reduced during warehouse storage and the prediction of seed lot performance under such conditions has been a challenge for seed technologist. Therefore, research programs must always investigate the seed lots performance in which the percentage of germination during the storage could properly be described by regression models. In this context, the germination performance of aged seeds of maize was estimated by the probit P(Y=y)=C+ (1-C).F(β0+β1.log(x)) as an alternative to the simplified equation in which Vp=Vі‾p.tg(β). Seeds from three seed lots of the hybrid maize OC 705 were aged at 43 °C for 24, 48, 72, 96, 120, 144, 168 and 192 h. The seeds were stored under warehouse conditions and the experiment was replicated three times. Coefficients of determination at most of 0.92 had no goodness of fit to describe the data by the simplified equation. On the other hand, the x² of the Pearson and log-likelihood tests indicated significant goodness of fit for the same data described by the probit model. Next, another analysis was carried out to fit the logistic model y(t)=C/(1+exp(B(t-M))) to the same data. The goodness of fit was evaluated by the parameter and intrinsic curvatures and bias of Box which were important in identifying the seed lot with the best fit. Finally, data from another experiment was analyzed to describe the germination performance of hybrid seeds of maize °C 705 and CD 5501 along sampling days and during two planting times by using the nonlinear model y(t)=A-B.exp(-Ct). First, the identity of the models indicated that in the seeding time 28 Oct. 1996 the performance from both hybrid seeds were described by the same equation. The date 28 Oct. 1996 was the best planting time and 57 d after anthesis was the best harvesting time when the percentage of seed germination was 96.1%. The methodology Bayesiana made possible the study of the germination curves and it allowed to recommend, for the two hybrid, the first harvesting time, as the viable for the sown. Still, for means of credibility intervals, it was possible the comparison of the adjusted equations for the combination of hybrid and harvesting times.
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spelling Modelagem de percentuais de germinação de sementes de milho em função do tempoMilhoGerminação de sementesModelagemRegressão não linearRegressão probitoInferência bayesiana.Nonlinear regressionSeed germinationProbit analysesBayesian inference.Ciências AgráriasAgronomiaIn 2008, the national production of grains was about 58.7 million of tonnes for which the seed quality had the most effective contribution. However, the seed quality is reduced during warehouse storage and the prediction of seed lot performance under such conditions has been a challenge for seed technologist. Therefore, research programs must always investigate the seed lots performance in which the percentage of germination during the storage could properly be described by regression models. In this context, the germination performance of aged seeds of maize was estimated by the probit P(Y=y)=C+ (1-C).F(β0+β1.log(x)) as an alternative to the simplified equation in which Vp=Vі‾p.tg(β). Seeds from three seed lots of the hybrid maize OC 705 were aged at 43 °C for 24, 48, 72, 96, 120, 144, 168 and 192 h. The seeds were stored under warehouse conditions and the experiment was replicated three times. Coefficients of determination at most of 0.92 had no goodness of fit to describe the data by the simplified equation. On the other hand, the x² of the Pearson and log-likelihood tests indicated significant goodness of fit for the same data described by the probit model. Next, another analysis was carried out to fit the logistic model y(t)=C/(1+exp(B(t-M))) to the same data. The goodness of fit was evaluated by the parameter and intrinsic curvatures and bias of Box which were important in identifying the seed lot with the best fit. Finally, data from another experiment was analyzed to describe the germination performance of hybrid seeds of maize °C 705 and CD 5501 along sampling days and during two planting times by using the nonlinear model y(t)=A-B.exp(-Ct). First, the identity of the models indicated that in the seeding time 28 Oct. 1996 the performance from both hybrid seeds were described by the same equation. The date 28 Oct. 1996 was the best planting time and 57 d after anthesis was the best harvesting time when the percentage of seed germination was 96.1%. The methodology Bayesiana made possible the study of the germination curves and it allowed to recommend, for the two hybrid, the first harvesting time, as the viable for the sown. Still, for means of credibility intervals, it was possible the comparison of the adjusted equations for the combination of hybrid and harvesting times.No ano de 2008, a safra brasileira de milho foi de 58,7 milhões de toneladas de grãos. Essa produção dependeu, dentre outros fatores, da qualidade das sementes, que é prioridade nas unidades de beneficiamento, secagem e classificação. Nesse contexto, propõe-se realizar estudos sobre a germinação de sementes por meio de métodos de regressão. Utilizando-se nove conjuntos de sementes de milho híbrido OC 705, submetidas ao teste de envelhecimento acelerado à temperatura de 43ºC a cada 24 horas, estimou-se o percentual de germinação, por meio da análise de regressão probito, cuja equação do probito é dada por P(Y=y)=C+ (1-C).F(β0+β1.log(x)). Os testes do Chi-quadrado de Pearson e do Chi-quadrado da razão de verossimilhança indicaram que a regressão probito proporcionou bom ajuste aos dados, fornecendo valores estimados com boa precisão. Também foi possível descrever, por meio da regressão não-linear, a curva de percentuais germinativos utilizando o modelo de regressão logístico dado pela equação y(t)=C/(1+exp(B(t-M))). Esse modelo ajustou-se adequadamente aos nove conjuntos de dados de percentual de germinação e o cálculo do viés de Box e das curvaturas permitiu a escolha do lote três como o de melhor qualidade. Utilizando-se de dados de percentuais germinativos de sementes de milho híbrido OC 705 e CD 5501, em função dos dias de amostragens, em duas épocas de semeadura, ajustou-se o modelo y(t)=A-B.exp(-Ct). Os testes de comparação de modelos indicaram que a época de semeadura de (28/10/96) pode ter o percentual de germinação dos dois híbridos descritos por uma única equação. Verificou-se, ainda, que a época de (28/10/96) foi a melhor para a semeadura dos dois híbridos, devendo a colheita ser realizada 57 dias após o florescimento feminino, para obter o percentual máximo de 96,1% de germinação. A metodologia Bayesiana possibilitou o estudo das curvas de germinação e permitiu recomendar, para os dois híbridos, a primeira época de semeadura, 28/10/96, como a mais viável. Os resultados indicaram o tempo ideal de colheita, 57 dias após o florescimento feminino, para obter o percentual máximo de 96,1% de germinação. Ainda por meios de intervalos de credibilidade foi possível a comparação das equações ajustadas para a combinação de híbridos e/ou épocas de semeadura.x, 116 fUniversidade Estadual de MaringáBrasilPrograma de Pós-Graduação em AgronomiaUEMMaringá, PRDepartamento de AgronomiaCarlos Alberto ScapimBruno Grimaldo Martinho Churata - UFPRDécio Sperandio - UEMAntonio Carlos Andrade Gonçalves - UEMTerezinha Aparecida Guedes - UEMGazola, Sebastião2018-04-04T17:23:42Z2018-04-04T17:23:42Z2009info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesishttp://repositorio.uem.br:8080/jspui/handle/1/1155porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da Universidade Estadual de Maringá (RI-UEM)instname:Universidade Estadual de Maringá (UEM)instacron:UEM2018-04-20T17:26:57Zoai:localhost:1/1155Repositório InstitucionalPUBhttp://repositorio.uem.br:8080/oai/requestopendoar:2024-04-23T14:54:03.569557Repositório Institucional da Universidade Estadual de Maringá (RI-UEM) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Modelagem de percentuais de germinação de sementes de milho em função do tempo
title Modelagem de percentuais de germinação de sementes de milho em função do tempo
spellingShingle Modelagem de percentuais de germinação de sementes de milho em função do tempo
Gazola, Sebastião
Milho
Germinação de sementes
Modelagem
Regressão não linear
Regressão probito
Inferência bayesiana.
Nonlinear regression
Seed germination
Probit analyses
Bayesian inference.
Ciências Agrárias
Agronomia
title_short Modelagem de percentuais de germinação de sementes de milho em função do tempo
title_full Modelagem de percentuais de germinação de sementes de milho em função do tempo
title_fullStr Modelagem de percentuais de germinação de sementes de milho em função do tempo
title_full_unstemmed Modelagem de percentuais de germinação de sementes de milho em função do tempo
title_sort Modelagem de percentuais de germinação de sementes de milho em função do tempo
author Gazola, Sebastião
author_facet Gazola, Sebastião
author_role author
dc.contributor.none.fl_str_mv Carlos Alberto Scapim
Bruno Grimaldo Martinho Churata - UFPR
Décio Sperandio - UEM
Antonio Carlos Andrade Gonçalves - UEM
Terezinha Aparecida Guedes - UEM
dc.contributor.author.fl_str_mv Gazola, Sebastião
dc.subject.por.fl_str_mv Milho
Germinação de sementes
Modelagem
Regressão não linear
Regressão probito
Inferência bayesiana.
Nonlinear regression
Seed germination
Probit analyses
Bayesian inference.
Ciências Agrárias
Agronomia
topic Milho
Germinação de sementes
Modelagem
Regressão não linear
Regressão probito
Inferência bayesiana.
Nonlinear regression
Seed germination
Probit analyses
Bayesian inference.
Ciências Agrárias
Agronomia
description In 2008, the national production of grains was about 58.7 million of tonnes for which the seed quality had the most effective contribution. However, the seed quality is reduced during warehouse storage and the prediction of seed lot performance under such conditions has been a challenge for seed technologist. Therefore, research programs must always investigate the seed lots performance in which the percentage of germination during the storage could properly be described by regression models. In this context, the germination performance of aged seeds of maize was estimated by the probit P(Y=y)=C+ (1-C).F(β0+β1.log(x)) as an alternative to the simplified equation in which Vp=Vі‾p.tg(β). Seeds from three seed lots of the hybrid maize OC 705 were aged at 43 °C for 24, 48, 72, 96, 120, 144, 168 and 192 h. The seeds were stored under warehouse conditions and the experiment was replicated three times. Coefficients of determination at most of 0.92 had no goodness of fit to describe the data by the simplified equation. On the other hand, the x² of the Pearson and log-likelihood tests indicated significant goodness of fit for the same data described by the probit model. Next, another analysis was carried out to fit the logistic model y(t)=C/(1+exp(B(t-M))) to the same data. The goodness of fit was evaluated by the parameter and intrinsic curvatures and bias of Box which were important in identifying the seed lot with the best fit. Finally, data from another experiment was analyzed to describe the germination performance of hybrid seeds of maize °C 705 and CD 5501 along sampling days and during two planting times by using the nonlinear model y(t)=A-B.exp(-Ct). First, the identity of the models indicated that in the seeding time 28 Oct. 1996 the performance from both hybrid seeds were described by the same equation. The date 28 Oct. 1996 was the best planting time and 57 d after anthesis was the best harvesting time when the percentage of seed germination was 96.1%. The methodology Bayesiana made possible the study of the germination curves and it allowed to recommend, for the two hybrid, the first harvesting time, as the viable for the sown. Still, for means of credibility intervals, it was possible the comparison of the adjusted equations for the combination of hybrid and harvesting times.
publishDate 2009
dc.date.none.fl_str_mv 2009
2018-04-04T17:23:42Z
2018-04-04T17:23:42Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.uem.br:8080/jspui/handle/1/1155
url http://repositorio.uem.br:8080/jspui/handle/1/1155
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Estadual de Maringá
Brasil
Programa de Pós-Graduação em Agronomia
UEM
Maringá, PR
Departamento de Agronomia
publisher.none.fl_str_mv Universidade Estadual de Maringá
Brasil
Programa de Pós-Graduação em Agronomia
UEM
Maringá, PR
Departamento de Agronomia
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Estadual de Maringá (RI-UEM)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Repositório Institucional da Universidade Estadual de Maringá (RI-UEM)
collection Repositório Institucional da Universidade Estadual de Maringá (RI-UEM)
repository.name.fl_str_mv Repositório Institucional da Universidade Estadual de Maringá (RI-UEM) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv
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