Predicting height growth in bean plants using non-linear and polynomial models

Detalhes bibliográficos
Autor(a) principal: Frühauf, Ariana Campos
Data de Publicação: 2022
Outros Autores: Silva, Edilson Marcelino, Fernandes, Tales Jesus, Muniz, Joel Augusto
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/50674
Resumo: Brazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. This modeling can be performed by linear and non-linear models, but the latter have stood out for providing more information to the researcher, mainly due to the practical interpretation of their parameters. In this sense, in the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations. As criteria to assess the quality of the fit, the adjusted coefficient of determination, the corrected Akaike information criterion and the residual standard deviation were used. The logistic model best fitted the data.
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spelling Predicting height growth in bean plants using non-linear and polynomial modelsPredição do desenvolvimento em altura de plantas de feijoeiro por meio de modelos não lineares e polinomialGrowth curveLogisticsRegressionCurva de crescimentoLogísticoRegressãoBrazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. This modeling can be performed by linear and non-linear models, but the latter have stood out for providing more information to the researcher, mainly due to the practical interpretation of their parameters. In this sense, in the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations. As criteria to assess the quality of the fit, the adjusted coefficient of determination, the corrected Akaike information criterion and the residual standard deviation were used. The logistic model best fitted the data.O Brasil tem se destacado mundialmente como um dos principais produtores e consumidores de feijão, o que faz com que seu cultivo se torne importante para o aspecto econômico e social do país. Como o feijoeiro possui um ciclo curto de crescimento, sua modelagem faz-se essencial para otimização de planos de manejo dessa cultura. Essa modelagem pode ser realizada por modelos lineares e não lineares, porém os modelos não lineares têm se destacado por agregar mais informação ao pesquisador, devido principalmente, ao fato da interpretação prática de seus parâmetros. Neste sentido, foram ajustados por meio do software estatístico R o modelo linear polinomial de terceiro grau e os modelos não lineares Logístico e Gompertz aos dados de altura, em centímetros, em relação ao tempo, em dias após a emergência, totalizando 11 observações. Utilizou-se como critérios para avaliar a qualidade do ajuste do modelo do coeficiente de determinação ajustado do critério de informação de Akaike corrigido e do desvio-padrão residual, sendo o modelo Logístico o que melhor se ajustou aos dados.Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais (IFSULDEMINAS)2022-07-20T22:45:03Z2022-07-20T22:45:03Z2022-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfFRÜHAUF, A. C. et al. Predicting height growth in bean plants using non-linear and polynomial models. Revista Agrogeoambiental, Pouso Alegre, v. 13, n. 3, p. 488-497, 2022. DOI: 10.18406/2316-1817v13n320211625.http://repositorio.ufla.br/jspui/handle/1/50674Revista Agrogeoambientalreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessFrühauf, Ariana CamposSilva, Edilson MarcelinoFernandes, Tales JesusMuniz, Joel Augustoeng2022-07-20T22:45:23Zoai:localhost:1/50674Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2022-07-20T22:45:23Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Predicting height growth in bean plants using non-linear and polynomial models
Predição do desenvolvimento em altura de plantas de feijoeiro por meio de modelos não lineares e polinomial
title Predicting height growth in bean plants using non-linear and polynomial models
spellingShingle Predicting height growth in bean plants using non-linear and polynomial models
Frühauf, Ariana Campos
Growth curve
Logistics
Regression
Curva de crescimento
Logístico
Regressão
title_short Predicting height growth in bean plants using non-linear and polynomial models
title_full Predicting height growth in bean plants using non-linear and polynomial models
title_fullStr Predicting height growth in bean plants using non-linear and polynomial models
title_full_unstemmed Predicting height growth in bean plants using non-linear and polynomial models
title_sort Predicting height growth in bean plants using non-linear and polynomial models
author Frühauf, Ariana Campos
author_facet Frühauf, Ariana Campos
Silva, Edilson Marcelino
Fernandes, Tales Jesus
Muniz, Joel Augusto
author_role author
author2 Silva, Edilson Marcelino
Fernandes, Tales Jesus
Muniz, Joel Augusto
author2_role author
author
author
dc.contributor.author.fl_str_mv Frühauf, Ariana Campos
Silva, Edilson Marcelino
Fernandes, Tales Jesus
Muniz, Joel Augusto
dc.subject.por.fl_str_mv Growth curve
Logistics
Regression
Curva de crescimento
Logístico
Regressão
topic Growth curve
Logistics
Regression
Curva de crescimento
Logístico
Regressão
description Brazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. This modeling can be performed by linear and non-linear models, but the latter have stood out for providing more information to the researcher, mainly due to the practical interpretation of their parameters. In this sense, in the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations. As criteria to assess the quality of the fit, the adjusted coefficient of determination, the corrected Akaike information criterion and the residual standard deviation were used. The logistic model best fitted the data.
publishDate 2022
dc.date.none.fl_str_mv 2022-07-20T22:45:03Z
2022-07-20T22:45:03Z
2022-02
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv FRÜHAUF, A. C. et al. Predicting height growth in bean plants using non-linear and polynomial models. Revista Agrogeoambiental, Pouso Alegre, v. 13, n. 3, p. 488-497, 2022. DOI: 10.18406/2316-1817v13n320211625.
http://repositorio.ufla.br/jspui/handle/1/50674
identifier_str_mv FRÜHAUF, A. C. et al. Predicting height growth in bean plants using non-linear and polynomial models. Revista Agrogeoambiental, Pouso Alegre, v. 13, n. 3, p. 488-497, 2022. DOI: 10.18406/2316-1817v13n320211625.
url http://repositorio.ufla.br/jspui/handle/1/50674
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais (IFSULDEMINAS)
publisher.none.fl_str_mv Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais (IFSULDEMINAS)
dc.source.none.fl_str_mv Revista Agrogeoambiental
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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