Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
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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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|| |
_version_ |
1797069082459111424 |