Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

Detalhes bibliográficos
Autor(a) principal: Ribeiro, João Everthon da Silva
Data de Publicação: 2021
Outros Autores: Romário Andrade Figueiredo, Francisco, dos Santos Coêlho, Ester, Ferreira Melo, Marlenildo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Bioscience journal (Online)
Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/53711
Resumo: Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.
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spelling Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.Allometric equationsLeaf bladeModelingPerpétua-do-matoRubiaceaeAgricultural SciencesEstimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.EDUFU2021-12-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.ufu.br/index.php/biosciencejournal/article/view/5371110.14393/BJ-v37n0a2021-53711Bioscience Journal ; Vol. 37 (2021): Continuous Publication; e37076Bioscience Journal ; v. 37 (2021): Continuous Publication; e370761981-3163reponame:Bioscience journal (Online)instname:Universidade Federal de Uberlândia (UFU)instacron:UFUenghttps://seer.ufu.br/index.php/biosciencejournal/article/view/53711/32921Brazil; Contemporary Copyright (c) 2021 João Everthon da Silva Ribeiro, Francisco Romário Andrade Figueiredo, Ester dos Santos Coêlho, Marlenildo Ferreira Melohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Ribeiro, João Everthon da SilvaRomário Andrade Figueiredo, Francisco dos Santos Coêlho, Ester Ferreira Melo, Marlenildo2022-05-25T13:27:43Zoai:ojs.www.seer.ufu.br:article/53711Revistahttps://seer.ufu.br/index.php/biosciencejournalPUBhttps://seer.ufu.br/index.php/biosciencejournal/oaibiosciencej@ufu.br||1981-31631516-3725opendoar:2022-05-25T13:27:43Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)false
dc.title.none.fl_str_mv Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
title Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
spellingShingle Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
Ribeiro, João Everthon da Silva
Allometric equations
Leaf blade
Modeling
Perpétua-do-mato
Rubiaceae
Agricultural Sciences
title_short Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
title_full Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
title_fullStr Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
title_full_unstemmed Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
title_sort Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
author Ribeiro, João Everthon da Silva
author_facet Ribeiro, João Everthon da Silva
Romário Andrade Figueiredo, Francisco
dos Santos Coêlho, Ester
Ferreira Melo, Marlenildo
author_role author
author2 Romário Andrade Figueiredo, Francisco
dos Santos Coêlho, Ester
Ferreira Melo, Marlenildo
author2_role author
author
author
dc.contributor.author.fl_str_mv Ribeiro, João Everthon da Silva
Romário Andrade Figueiredo, Francisco
dos Santos Coêlho, Ester
Ferreira Melo, Marlenildo
dc.subject.por.fl_str_mv Allometric equations
Leaf blade
Modeling
Perpétua-do-mato
Rubiaceae
Agricultural Sciences
topic Allometric equations
Leaf blade
Modeling
Perpétua-do-mato
Rubiaceae
Agricultural Sciences
description Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-29
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/53711
10.14393/BJ-v37n0a2021-53711
url https://seer.ufu.br/index.php/biosciencejournal/article/view/53711
identifier_str_mv 10.14393/BJ-v37n0a2021-53711
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/53711/32921
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Brazil; Contemporary
dc.publisher.none.fl_str_mv EDUFU
publisher.none.fl_str_mv EDUFU
dc.source.none.fl_str_mv Bioscience Journal ; Vol. 37 (2021): Continuous Publication; e37076
Bioscience Journal ; v. 37 (2021): Continuous Publication; e37076
1981-3163
reponame:Bioscience journal (Online)
instname:Universidade Federal de Uberlândia (UFU)
instacron:UFU
instname_str Universidade Federal de Uberlândia (UFU)
instacron_str UFU
institution UFU
reponame_str Bioscience journal (Online)
collection Bioscience journal (Online)
repository.name.fl_str_mv Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)
repository.mail.fl_str_mv biosciencej@ufu.br||
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