Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRPE |
Texto Completo: | http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8149 |
Resumo: | The objectives of this work were: to propose methods that generate models of growth and decrease applied in the agrarian sciences and to propose models from these methods. This thesis is divided into five chapters. The first chapter consists of a bibliographical review of the themes related to growth curves and a collection of the most used nonlinear models. In the second chapter, the method that generates growth and decay models is presented, and a new non linear model was developed from the method presented for the first article of the thesis. In addition, a new non-linear model is proposed for describing the growth of goats and sheep from the method. For the applications of the new proposed model, the SRD goat database presented in the work of Cavalcante et al. (2013) and data of Santa Inês sheep from the work of Sarmento et al. (2006a). The proposed model was compared, statistically, with the nonlinear models Logistic, Von Bertalanffy, Brody, Gompertz and Richards. The parameter estimation for the models was done by least squares methods and the Levenberg-Marquardt iterative process of the IBM SPSS Statistics 1.0 program. Afterwards, the selection of the best model to describe the growth curves was based on the mean square of the residue (QMR), Akaike information criterion (AIC), Bayesian information criterion (BIC), absolute mean deviation (DMA) and adjusted determination coefficient R2 aj.. For the third chapter, we present a new model-generating method, obtained from combinations of existing models, called constructor methods. In addition, we propose a model to describe the kinetic curve of gas production by the technique vitro of different accessions of ten genotypes of forage peanuts. The gas production readings were two, four, six, eight, ten, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 hours of incubation. The adjusted coefficient of determination (R2 aj.), mean square of residue (QMR), mean absolute deviation (DMA), Akaike information criterion (AIC) and Bayesian Schwarz criterion (BIC ) were used to select the best fit model of the curves. It is understood that this chapter is of fundamental importance for the support of the article presented in the fourth chapter of this thesis. Chapter four consists of a proposal for a new model based on the combination of the Logistic and Von Bertalanffy models, using the constructor method (VII) of chapter three, in order to compare and or identify with the bicompartmental logistic model the one with the highest quality of fit to the cumulative gas production (GWP) kinetics curve of sunflower, corn and mixtures of silages. The parameters were estimated using the least squares method using the Gauss-Newton algorithm implemented in the nls function of the R software. The fit quality of the models was measured by the adjusted coefficient of determination R2 aj., mean square of the residue (QMR), absolute mean deviation (DMA), Akaike information criterion (AIC), Bayesian Schwarz criterion (BIC), and relative efficiency (ER). Chapter 5 presents the final considerations about the work. The results show that the proposed models were superior to the other models commonly used to study animal growth curves and or to describe kinetics of in vitro gas production. |
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MOREIRA, Guilherme RochaBRITO, Cícero Carlos Ramos deSILVA, Frank Sinatra Gomes daBARRETO, Larissa SantanaBRITO, Cícero Carlos Ramos deSILVA, Frank Sinatra Gomes daCUNHA FILHO, Moacyrhttp://lattes.cnpq.br/6284510889652754SANTOS, André Luiz Pinto dos2019-07-17T12:48:28Z2019-06-25SANTOS, André Luiz Pinto dos. Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias. 2019. 92 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8149The objectives of this work were: to propose methods that generate models of growth and decrease applied in the agrarian sciences and to propose models from these methods. This thesis is divided into five chapters. The first chapter consists of a bibliographical review of the themes related to growth curves and a collection of the most used nonlinear models. In the second chapter, the method that generates growth and decay models is presented, and a new non linear model was developed from the method presented for the first article of the thesis. In addition, a new non-linear model is proposed for describing the growth of goats and sheep from the method. For the applications of the new proposed model, the SRD goat database presented in the work of Cavalcante et al. (2013) and data of Santa Inês sheep from the work of Sarmento et al. (2006a). The proposed model was compared, statistically, with the nonlinear models Logistic, Von Bertalanffy, Brody, Gompertz and Richards. The parameter estimation for the models was done by least squares methods and the Levenberg-Marquardt iterative process of the IBM SPSS Statistics 1.0 program. Afterwards, the selection of the best model to describe the growth curves was based on the mean square of the residue (QMR), Akaike information criterion (AIC), Bayesian information criterion (BIC), absolute mean deviation (DMA) and adjusted determination coefficient R2 aj.. For the third chapter, we present a new model-generating method, obtained from combinations of existing models, called constructor methods. In addition, we propose a model to describe the kinetic curve of gas production by the technique vitro of different accessions of ten genotypes of forage peanuts. The gas production readings were two, four, six, eight, ten, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 hours of incubation. The adjusted coefficient of determination (R2 aj.), mean square of residue (QMR), mean absolute deviation (DMA), Akaike information criterion (AIC) and Bayesian Schwarz criterion (BIC ) were used to select the best fit model of the curves. It is understood that this chapter is of fundamental importance for the support of the article presented in the fourth chapter of this thesis. Chapter four consists of a proposal for a new model based on the combination of the Logistic and Von Bertalanffy models, using the constructor method (VII) of chapter three, in order to compare and or identify with the bicompartmental logistic model the one with the highest quality of fit to the cumulative gas production (GWP) kinetics curve of sunflower, corn and mixtures of silages. The parameters were estimated using the least squares method using the Gauss-Newton algorithm implemented in the nls function of the R software. The fit quality of the models was measured by the adjusted coefficient of determination R2 aj., mean square of the residue (QMR), absolute mean deviation (DMA), Akaike information criterion (AIC), Bayesian Schwarz criterion (BIC), and relative efficiency (ER). Chapter 5 presents the final considerations about the work. The results show that the proposed models were superior to the other models commonly used to study animal growth curves and or to describe kinetics of in vitro gas production.Os objetivos deste trabalho foram: propor métodos geradores de modelos de crescimento e decrescimento aplicado nas ciências agrárias e propor modelos a partir destes métodos. A presente tese divide-se em cinco capítulos. O primeiro capítulo consiste numa revisão bibliográfica dos temas relacionados à curvas de crescimento e fazemos um apanhado dos modelos não-lineares mais utilizados. No segundo capítulo, apresenta-se o método gerador de modelos de crescimento e decrescimento e também foi realizado o desenvolvimento de um novo modelo não linear a partir do método apresentado para o primeiro artigo da tese. Além disto, propõe-se um novo modelo não linear para descrição de crescimento de caprinos e ovinos a partir do método. Para as aplicações do novo modelo proposto foi utilizado a base de dados de caprinos SRD apresentado no trabalho de Cavalcante et al. (2013) e dados de ovinos da raça Santa Inês oriundo do trabalho de Sarmento et al. (2006a). O modelo proposto foi comparado, estatisticamente, com os modelos não lineares Logístico, Von Bertalanffy, Brody, Gompertz e Richards. A estimação dos parâmetros para os modelos foi feita pelos métodos de mínimos quadrados e pelo processo iterativo de Levenberg-Marquardt do programa IBM SPSS Statistics 1.0. Posteriormente, a seleção do melhor modelo, para descrever as curvas de crescimento, teve como base o quadrado médio do resíduo (QMR), critério de informação de Akaike (AIC), critério de informação Bayesiano (BIC), desvio médio absoluto (DMA) e coeficiente de determinação ajustado R2 aj.. Já para o terceiro capítulo trazemos um novo método gerador de modelos, obtidos a partir de combinações de modelos existentes, denominados de métodos construtores, além do mais, propomos um modelo para descrever a curva da cinética de produção de gases pela técnica in vitro semiautomática de diferentes acessos de dez genótipos de amendoim forrageiro. As leitura da produção de gases foram de dois, quatro, seis, oito, dez, 12, 14, 17, 20, 24, 28, 32, 48, 72, e 96 horas de incubação. O coeficiente de determinação ajustado (R2 aj.), quadrado médio do resíduo (QMR), desvio médio absoluto (DMA), critério de informação de Akaike (AIC) e critério Bayesiano de Schwarz (BIC) foram utilizados para escolha do modelo de melhor ajuste das curvas. Entende-se que este capítulo é de fundamental importância para a sustentação do artigo apresentado no quarto capítulo desta tese. O capítulo quatro é constituído de uma proposta de novo modelo a partir da combinação dos modelos Logístico e Von Bertalanffy, a partir do método construtor (i) do capítulo três, afim de comparar e/ou identificar com o modelo logístico bicompartimental aquele que apresenta maior qualidade de ajuste à curva de cinética de produção cumulativa de gases (PCG) das silagens de girassol, de milho e de suas misturas. A estimação dos parâmetros foi feita pelo método dos mínimos quadrados utilizando o algoritmo de Gauss-Newton implementado na função nls do software R. A qualidade de ajuste dos modelos foi medida pelo o coeficiente de determinação ajustado (R2 aj.), quadrado médio do resíduo (QMR), desvio médio absoluto (DMA), critério de informação de Akaike (AIC), critério Bayesiano de Schwarz (BIC) e a eficiência relativa (ER). No capítulo cinco são apresentadas as considerações finais sobre o trabalho. Os resultados mostram que os modelos propostos foram superiores aos outros modelos comumente utilizados para estudo das curvas de crescimento animal e/ou para descrever a cinética de produção de gases in vitro.Submitted by Mario BC (mario@bc.ufrpe.br) on 2019-07-17T12:48:28Z No. of bitstreams: 1 Andre Luiz Pinto dos Santos.pdf: 2896303 bytes, checksum: 93d9306b7253feeab7bc0a1267ad4819 (MD5)Made available in DSpace on 2019-07-17T12:48:28Z (GMT). No. of bitstreams: 1 Andre Luiz Pinto dos Santos.pdf: 2896303 bytes, checksum: 93d9306b7253feeab7bc0a1267ad4819 (MD5) Previous issue date: 2019-06-25Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfporUniversidade Federal Rural de PernambucoPrograma de Pós-Graduação em Biometria e Estatística AplicadaUFRPEBrasilDepartamento de Estatística e InformáticaModelo bicompartimentalRegressão não linearModelo matemáticoCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAMétodos geradores de modelos de crescimento e decrescimento aplicados às ciências agráriasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis768382242446187918600600600600-6774555140396120501-58364078281851435172075167498588264571info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRPEinstname:Universidade Federal Rural de Pernambuco (UFRPE)instacron:UFRPEORIGINALAndre Luiz Pinto dos Santos.pdfAndre Luiz Pinto dos Santos.pdfapplication/pdf2896303http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/8149/2/Andre+Luiz+Pinto+dos+Santos.pdf93d9306b7253feeab7bc0a1267ad4819MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/8149/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede2/81492019-07-17 09:48:28.554oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttp://www.tede2.ufrpe.br:8080/tede/PUBhttp://www.tede2.ufrpe.br:8080/oai/requestbdtd@ufrpe.br ||bdtd@ufrpe.bropendoar:2024-05-28T12:36:35.765845Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE)false |
dc.title.por.fl_str_mv |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
title |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
spellingShingle |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias SANTOS, André Luiz Pinto dos Modelo bicompartimental Regressão não linear Modelo matemático CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
title_short |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
title_full |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
title_fullStr |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
title_full_unstemmed |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
title_sort |
Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias |
author |
SANTOS, André Luiz Pinto dos |
author_facet |
SANTOS, André Luiz Pinto dos |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
MOREIRA, Guilherme Rocha |
dc.contributor.advisor-co1.fl_str_mv |
BRITO, Cícero Carlos Ramos de |
dc.contributor.advisor-co2.fl_str_mv |
SILVA, Frank Sinatra Gomes da |
dc.contributor.referee1.fl_str_mv |
BARRETO, Larissa Santana |
dc.contributor.referee2.fl_str_mv |
BRITO, Cícero Carlos Ramos de |
dc.contributor.referee3.fl_str_mv |
SILVA, Frank Sinatra Gomes da |
dc.contributor.referee4.fl_str_mv |
CUNHA FILHO, Moacyr |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6284510889652754 |
dc.contributor.author.fl_str_mv |
SANTOS, André Luiz Pinto dos |
contributor_str_mv |
MOREIRA, Guilherme Rocha BRITO, Cícero Carlos Ramos de SILVA, Frank Sinatra Gomes da BARRETO, Larissa Santana BRITO, Cícero Carlos Ramos de SILVA, Frank Sinatra Gomes da CUNHA FILHO, Moacyr |
dc.subject.por.fl_str_mv |
Modelo bicompartimental Regressão não linear Modelo matemático |
topic |
Modelo bicompartimental Regressão não linear Modelo matemático CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
description |
The objectives of this work were: to propose methods that generate models of growth and decrease applied in the agrarian sciences and to propose models from these methods. This thesis is divided into five chapters. The first chapter consists of a bibliographical review of the themes related to growth curves and a collection of the most used nonlinear models. In the second chapter, the method that generates growth and decay models is presented, and a new non linear model was developed from the method presented for the first article of the thesis. In addition, a new non-linear model is proposed for describing the growth of goats and sheep from the method. For the applications of the new proposed model, the SRD goat database presented in the work of Cavalcante et al. (2013) and data of Santa Inês sheep from the work of Sarmento et al. (2006a). The proposed model was compared, statistically, with the nonlinear models Logistic, Von Bertalanffy, Brody, Gompertz and Richards. The parameter estimation for the models was done by least squares methods and the Levenberg-Marquardt iterative process of the IBM SPSS Statistics 1.0 program. Afterwards, the selection of the best model to describe the growth curves was based on the mean square of the residue (QMR), Akaike information criterion (AIC), Bayesian information criterion (BIC), absolute mean deviation (DMA) and adjusted determination coefficient R2 aj.. For the third chapter, we present a new model-generating method, obtained from combinations of existing models, called constructor methods. In addition, we propose a model to describe the kinetic curve of gas production by the technique vitro of different accessions of ten genotypes of forage peanuts. The gas production readings were two, four, six, eight, ten, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 hours of incubation. The adjusted coefficient of determination (R2 aj.), mean square of residue (QMR), mean absolute deviation (DMA), Akaike information criterion (AIC) and Bayesian Schwarz criterion (BIC ) were used to select the best fit model of the curves. It is understood that this chapter is of fundamental importance for the support of the article presented in the fourth chapter of this thesis. Chapter four consists of a proposal for a new model based on the combination of the Logistic and Von Bertalanffy models, using the constructor method (VII) of chapter three, in order to compare and or identify with the bicompartmental logistic model the one with the highest quality of fit to the cumulative gas production (GWP) kinetics curve of sunflower, corn and mixtures of silages. The parameters were estimated using the least squares method using the Gauss-Newton algorithm implemented in the nls function of the R software. The fit quality of the models was measured by the adjusted coefficient of determination R2 aj., mean square of the residue (QMR), absolute mean deviation (DMA), Akaike information criterion (AIC), Bayesian Schwarz criterion (BIC), and relative efficiency (ER). Chapter 5 presents the final considerations about the work. The results show that the proposed models were superior to the other models commonly used to study animal growth curves and or to describe kinetics of in vitro gas production. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-07-17T12:48:28Z |
dc.date.issued.fl_str_mv |
2019-06-25 |
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.citation.fl_str_mv |
SANTOS, André Luiz Pinto dos. Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias. 2019. 92 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
dc.identifier.uri.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8149 |
identifier_str_mv |
SANTOS, André Luiz Pinto dos. Métodos geradores de modelos de crescimento e decrescimento aplicados às ciências agrárias. 2019. 92 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
url |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8149 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
768382242446187918 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 |
dc.relation.department.fl_str_mv |
-6774555140396120501 |
dc.relation.cnpq.fl_str_mv |
-5836407828185143517 |
dc.relation.sponsorship.fl_str_mv |
2075167498588264571 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal Rural de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Biometria e Estatística Aplicada |
dc.publisher.initials.fl_str_mv |
UFRPE |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Departamento de Estatística e Informática |
publisher.none.fl_str_mv |
Universidade Federal Rural de Pernambuco |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFRPE instname:Universidade Federal Rural de Pernambuco (UFRPE) instacron:UFRPE |
instname_str |
Universidade Federal Rural de Pernambuco (UFRPE) |
instacron_str |
UFRPE |
institution |
UFRPE |
reponame_str |
Biblioteca Digital de Teses e Dissertações da UFRPE |
collection |
Biblioteca Digital de Teses e Dissertações da UFRPE |
bitstream.url.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/8149/2/Andre+Luiz+Pinto+dos+Santos.pdf http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/8149/1/license.txt |
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93d9306b7253feeab7bc0a1267ad4819 bd3efa91386c1718a7f26a329fdcb468 |
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MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE) |
repository.mail.fl_str_mv |
bdtd@ufrpe.br ||bdtd@ufrpe.br |
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1810102259723272192 |