Mathematical models to estimate cassava leaf area

Detalhes bibliográficos
Autor(a) principal: Guimarães, Miguel Julio Machado
Data de Publicação: 2019
Outros Autores: Coelho Filho, Maurício Antônio, Gomes Junior, Francisco de Assis, Silva, Matheus Almeida Machado, Alves, Carlos Vítor Oliveira, Lopes, Iug
Tipo de documento: Artigo
Idioma: por
Título da fonte: Revista de Ciências Agrárias (Belém. Online)
Texto Completo: https://ajaes.ufra.edu.br/index.php/ajaes/article/view/3015
Resumo: The objective of this study was to determine equations that allow estimating the leaf area of cassava genotypes from biometric measurements of the leaves. Leaves of 17 cassava genotypes were collected and the length and width of the central lobe and the real leaf area were measured in each unit. The genotypes were grouped using the UPGMA multivariate analysis method, using the ratio between the length and width of the central lobe (C/L). After grouping, Pearson’s correlation test was performed between the biometric measurements and the real leaf area. Linear and potential equation models were tested for the groups found through cluster analysis. The biometric variables that showed the greatest correlation with the leaf area were the product of the length and width of the lobe and the length of the central lobe. Four different groups were found, in which the linear equation models were best adjusted when using the product between the length and width of the central lobe and the potentials when using the length of the central lobe.
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spelling Mathematical models to estimate cassava leaf areaModelos matemáticos para a estimativa da área foliar de mandiocaLeaf modelingManihot esculenta CrantzMorphometryAgronomyplant morphologyModelagem foliarManihot esculenta CrantzMorfometriaMorfologia VegetalAgronomiaThe objective of this study was to determine equations that allow estimating the leaf area of cassava genotypes from biometric measurements of the leaves. Leaves of 17 cassava genotypes were collected and the length and width of the central lobe and the real leaf area were measured in each unit. The genotypes were grouped using the UPGMA multivariate analysis method, using the ratio between the length and width of the central lobe (C/L). After grouping, Pearson’s correlation test was performed between the biometric measurements and the real leaf area. Linear and potential equation models were tested for the groups found through cluster analysis. The biometric variables that showed the greatest correlation with the leaf area were the product of the length and width of the lobe and the length of the central lobe. Four different groups were found, in which the linear equation models were best adjusted when using the product between the length and width of the central lobe and the potentials when using the length of the central lobe.Objetivou-se com este trabalho determinar equações que possibilitem estimar a área foliar de genótipos de mandioca a partir de medidas biométricas das folhas. Foram coletadas folhas de 17 genótipos de mandioca e mensurados em cada unidade o comprimento, a largura do lóbulo central e a área foliar real. Os genótipos foram agrupados por meio do método de análise multivariada UPGMA, utilizando-se a razão entre o comprimento do lóbulo central com a largura do mesmo (C / L). Após o agrupamento foi realizado o teste de correlação de Pearson entre as medidas biométricas e a área foliar real. Foram testados modelos de equação linear e potencial para os grupos encontrados na análise de agrupamentos. As variáveis biométricas que apresentaram maior correlação com a área foliar foram o produto do comprimento e a largura do lóbulo, e o comprimento do lóbulo central. Quatro diferentes grupos foram encontrados, nos quais os modelos de equação linear se ajustaram melhor quando se aplica o produto entre o comprimento e a largura do lóbulo central e os potenciais quando se usa o comprimento do lóbulo central.Universidade Federal Rural da Amazônia/UFRA2019-07-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTextoinfo:eu-repo/semantics/otherapplication/pdfhttps://ajaes.ufra.edu.br/index.php/ajaes/article/view/3015Amazonian Journal of Agricultural Sciences Journal of Agricultural and Environmental Sciences; Vol 62 (2019): RCARevista de Ciências Agrárias Amazonian Journal of Agricultural and Environmental Sciences; v. 62 (2019): RCA2177-87601517-591Xreponame:Revista de Ciências Agrárias (Belém. Online)instname:Universidade Federal Rural da Amazônia (UFRA)instacron:UFRAporhttps://ajaes.ufra.edu.br/index.php/ajaes/article/view/3015/1565Copyright (c) 2019 Revista de Ciências Agrárias Amazonian Journal of Agricultural and Environmental Sciencesinfo:eu-repo/semantics/openAccessGuimarães, Miguel Julio MachadoCoelho Filho, Maurício AntônioGomes Junior, Francisco de AssisSilva, Matheus Almeida MachadoAlves, Carlos Vítor OliveiraLopes, Iug2020-01-20T14:14:53Zoai:ojs.www.periodicos.ufra.edu.br:article/3015Revistahttps://ajaes.ufra.edu.br/index.php/ajaes/PUBhttps://ajaes.ufra.edu.br/index.php/ajaes/oaiallan.lobato@ufra.edu.br || ajaes.suporte@gmail.com2177-87601517-591Xopendoar:2020-01-20T14:14:53Revista de Ciências Agrárias (Belém. Online) - Universidade Federal Rural da Amazônia (UFRA)false
dc.title.none.fl_str_mv Mathematical models to estimate cassava leaf area
Modelos matemáticos para a estimativa da área foliar de mandioca
title Mathematical models to estimate cassava leaf area
spellingShingle Mathematical models to estimate cassava leaf area
Guimarães, Miguel Julio Machado
Leaf modeling
Manihot esculenta Crantz
Morphometry
Agronomy
plant morphology
Modelagem foliar
Manihot esculenta Crantz
Morfometria
Morfologia Vegetal
Agronomia
title_short Mathematical models to estimate cassava leaf area
title_full Mathematical models to estimate cassava leaf area
title_fullStr Mathematical models to estimate cassava leaf area
title_full_unstemmed Mathematical models to estimate cassava leaf area
title_sort Mathematical models to estimate cassava leaf area
author Guimarães, Miguel Julio Machado
author_facet Guimarães, Miguel Julio Machado
Coelho Filho, Maurício Antônio
Gomes Junior, Francisco de Assis
Silva, Matheus Almeida Machado
Alves, Carlos Vítor Oliveira
Lopes, Iug
author_role author
author2 Coelho Filho, Maurício Antônio
Gomes Junior, Francisco de Assis
Silva, Matheus Almeida Machado
Alves, Carlos Vítor Oliveira
Lopes, Iug
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Guimarães, Miguel Julio Machado
Coelho Filho, Maurício Antônio
Gomes Junior, Francisco de Assis
Silva, Matheus Almeida Machado
Alves, Carlos Vítor Oliveira
Lopes, Iug
dc.subject.por.fl_str_mv Leaf modeling
Manihot esculenta Crantz
Morphometry
Agronomy
plant morphology
Modelagem foliar
Manihot esculenta Crantz
Morfometria
Morfologia Vegetal
Agronomia
topic Leaf modeling
Manihot esculenta Crantz
Morphometry
Agronomy
plant morphology
Modelagem foliar
Manihot esculenta Crantz
Morfometria
Morfologia Vegetal
Agronomia
description The objective of this study was to determine equations that allow estimating the leaf area of cassava genotypes from biometric measurements of the leaves. Leaves of 17 cassava genotypes were collected and the length and width of the central lobe and the real leaf area were measured in each unit. The genotypes were grouped using the UPGMA multivariate analysis method, using the ratio between the length and width of the central lobe (C/L). After grouping, Pearson’s correlation test was performed between the biometric measurements and the real leaf area. Linear and potential equation models were tested for the groups found through cluster analysis. The biometric variables that showed the greatest correlation with the leaf area were the product of the length and width of the lobe and the length of the central lobe. Four different groups were found, in which the linear equation models were best adjusted when using the product between the length and width of the central lobe and the potentials when using the length of the central lobe.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-22
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Texto
info:eu-repo/semantics/other
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://ajaes.ufra.edu.br/index.php/ajaes/article/view/3015
url https://ajaes.ufra.edu.br/index.php/ajaes/article/view/3015
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://ajaes.ufra.edu.br/index.php/ajaes/article/view/3015/1565
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 da Amazônia/UFRA
publisher.none.fl_str_mv Universidade Federal Rural da Amazônia/UFRA
dc.source.none.fl_str_mv Amazonian Journal of Agricultural Sciences Journal of Agricultural and Environmental Sciences; Vol 62 (2019): RCA
Revista de Ciências Agrárias Amazonian Journal of Agricultural and Environmental Sciences; v. 62 (2019): RCA
2177-8760
1517-591X
reponame:Revista de Ciências Agrárias (Belém. Online)
instname:Universidade Federal Rural da Amazônia (UFRA)
instacron:UFRA
instname_str Universidade Federal Rural da Amazônia (UFRA)
instacron_str UFRA
institution UFRA
reponame_str Revista de Ciências Agrárias (Belém. Online)
collection Revista de Ciências Agrárias (Belém. Online)
repository.name.fl_str_mv Revista de Ciências Agrárias (Belém. Online) - Universidade Federal Rural da Amazônia (UFRA)
repository.mail.fl_str_mv allan.lobato@ufra.edu.br || ajaes.suporte@gmail.com
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