Predicting height growth in bean plants using non-linear and polynomial models
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
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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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 |
_version_ |
1815439052123406336 |