Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)

Bibliographic Details
Main Author: Antunes, W. C.
Publication Date: 2008
Other Authors: Pompelli, M. F., Carretero, D. M., DaMatta, F. M.
Format: Article
Language: eng
Source: LOCUS Repositório Institucional da UFV
Download full: https://doi.org/10.1111/j.1744-7348.2008.00235.x
http://www.locus.ufv.br/handle/123456789/22384
Summary: We aimed to evaluate the currently used allometric models, as well as to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W), for estimating the area of leaves of eight field-grown coffee cultivars. For model construction, a total of 1563 leaves were randomly selected from different levels of the tree canopies and encompassed the full spectrum of measurable leaf sizes (0.3–263 cm 2 ) for each genotype. Power models better fit coffee leaf area (LA) than linear models. To validate the model, an independent data set of 388 leaves was used. We demonstrated that the currently used allometric models are biased, underestimating the area of a coffee leaf. We developed a single power model Y= 1⁄4 β◦ x β1 based on two leaf dimensions [LA = 0.6626 (LW) 1.0116 ; standard errors: β◦ = 0.0064, β1 = 0.0019; R2 = 0.996] with high precision and accuracy, random dispersion pattern of residuals and also unbiased, irrespective of cultivar and leaf size and shape. Even when the L (but not width) alone was used as the single leaf dimension, the power model developed still predicted with good accuracy the LA but at the expense of some loss of precision, as particularly found for 8% of the leaves sampled with length-to-width ratios below 2.0 or above 3.0.
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spelling Antunes, W. C.Pompelli, M. F.Carretero, D. M.DaMatta, F. M.2018-10-23T14:53:15Z2018-10-23T14:53:15Z2008-07-290003-4746https://doi.org/10.1111/j.1744-7348.2008.00235.xhttp://www.locus.ufv.br/handle/123456789/22384We aimed to evaluate the currently used allometric models, as well as to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W), for estimating the area of leaves of eight field-grown coffee cultivars. For model construction, a total of 1563 leaves were randomly selected from different levels of the tree canopies and encompassed the full spectrum of measurable leaf sizes (0.3–263 cm 2 ) for each genotype. Power models better fit coffee leaf area (LA) than linear models. To validate the model, an independent data set of 388 leaves was used. We demonstrated that the currently used allometric models are biased, underestimating the area of a coffee leaf. We developed a single power model Y= 1⁄4 β◦ x β1 based on two leaf dimensions [LA = 0.6626 (LW) 1.0116 ; standard errors: β◦ = 0.0064, β1 = 0.0019; R2 = 0.996] with high precision and accuracy, random dispersion pattern of residuals and also unbiased, irrespective of cultivar and leaf size and shape. Even when the L (but not width) alone was used as the single leaf dimension, the power model developed still predicted with good accuracy the LA but at the expense of some loss of precision, as particularly found for 8% of the leaves sampled with length-to-width ratios below 2.0 or above 3.0.engAnnals of Applied BiologyVolume 153, Issue 1, Pages 33-40, August 2008Coffea arabicaCoffea canephoraEstimating modelAllometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdfartigoapplication/pdf799885https://locus.ufv.br//bitstream/123456789/22384/1/artigo.pdfeabeac10a51ce3c64c87a736eb188becMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/22384/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/223842018-10-23 12:02:17.854oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452018-10-23T15:02:17LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
title Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
spellingShingle Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
Antunes, W. C.
Coffea arabica
Coffea canephora
Estimating model
title_short Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
title_full Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
title_fullStr Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
title_full_unstemmed Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
title_sort Allometric models for non‐destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora)
author Antunes, W. C.
author_facet Antunes, W. C.
Pompelli, M. F.
Carretero, D. M.
DaMatta, F. M.
author_role author
author2 Pompelli, M. F.
Carretero, D. M.
DaMatta, F. M.
author2_role author
author
author
dc.contributor.author.fl_str_mv Antunes, W. C.
Pompelli, M. F.
Carretero, D. M.
DaMatta, F. M.
dc.subject.pt-BR.fl_str_mv Coffea arabica
Coffea canephora
Estimating model
topic Coffea arabica
Coffea canephora
Estimating model
description We aimed to evaluate the currently used allometric models, as well as to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W), for estimating the area of leaves of eight field-grown coffee cultivars. For model construction, a total of 1563 leaves were randomly selected from different levels of the tree canopies and encompassed the full spectrum of measurable leaf sizes (0.3–263 cm 2 ) for each genotype. Power models better fit coffee leaf area (LA) than linear models. To validate the model, an independent data set of 388 leaves was used. We demonstrated that the currently used allometric models are biased, underestimating the area of a coffee leaf. We developed a single power model Y= 1⁄4 β◦ x β1 based on two leaf dimensions [LA = 0.6626 (LW) 1.0116 ; standard errors: β◦ = 0.0064, β1 = 0.0019; R2 = 0.996] with high precision and accuracy, random dispersion pattern of residuals and also unbiased, irrespective of cultivar and leaf size and shape. Even when the L (but not width) alone was used as the single leaf dimension, the power model developed still predicted with good accuracy the LA but at the expense of some loss of precision, as particularly found for 8% of the leaves sampled with length-to-width ratios below 2.0 or above 3.0.
publishDate 2008
dc.date.issued.fl_str_mv 2008-07-29
dc.date.accessioned.fl_str_mv 2018-10-23T14:53:15Z
dc.date.available.fl_str_mv 2018-10-23T14:53:15Z
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 https://doi.org/10.1111/j.1744-7348.2008.00235.x
http://www.locus.ufv.br/handle/123456789/22384
dc.identifier.issn.none.fl_str_mv 0003-4746
identifier_str_mv 0003-4746
url https://doi.org/10.1111/j.1744-7348.2008.00235.x
http://www.locus.ufv.br/handle/123456789/22384
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv Volume 153, Issue 1, Pages 33-40, August 2008
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Annals of Applied Biology
publisher.none.fl_str_mv Annals of Applied Biology
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