Leaf area estimation of cassava from linear dimensions

ABSTRACT The objective of this study was to determine predictor models of leaf area of cassava from linear leaf measurements. The experiment was carried out in greenhouse in the municipality of Botucatu, São Paulo state, Brazil. The stem cuttings with 5-7 nodes of the cultivar IAC 576-70 were plante...

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Main Authors: SAMARA ZANETTI, LAÍS F.M. PEREIRA, MARIA MÁRCIA P. SARTORI, MARCELO A. SILVA
Format: Article
Language:English
Published: Academia Brasileira de Ciências 2017-08-01
Series:Anais da Academia Brasileira de Ciências
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017005019107&lng=en&tlng=en
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spelling doaj-1d9ab6836f134268bef81e134d6af9fe2020-11-24T22:33:25ZengAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências1678-26902017-08-01010.1590/0001-376520172016-0475S0001-37652017005019107Leaf area estimation of cassava from linear dimensionsSAMARA ZANETTILAÍS F.M. PEREIRAMARIA MÁRCIA P. SARTORIMARCELO A. SILVAABSTRACT The objective of this study was to determine predictor models of leaf area of cassava from linear leaf measurements. The experiment was carried out in greenhouse in the municipality of Botucatu, São Paulo state, Brazil. The stem cuttings with 5-7 nodes of the cultivar IAC 576-70 were planted in boxes filled with about 320 liters of soil, keeping soil moisture at field capacity, monitored by puncturing tensiometers. At 80 days after planting, 140 leaves were randomly collected from the top, middle third and base of cassava plants. We evaluated the length and width of the central lobe of leaves, number of lobes and leaf area. The measurements of leaf areas were correlated with the length and width of the central lobe and the number of lobes of the leaves, and adjusted to polynomial and multiple regression models. The linear function that used the length of the central lobe LA = -69.91114 + 15.06462L and linear multiple functions LA = -69.9188 + 15.5102L + 0.0197726K - 0.0768998J or LA = -69.9346 + 15.0106L + 0.188931K - 0.0264323H are suitable models to estimate leaf area of cassava cultivar IAC 576-70.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017005019107&lng=en&tlng=enManihot esculenta Crantzleaf biometricsstatistical modelsmultiple regression
collection DOAJ
language English
format Article
sources DOAJ
author SAMARA ZANETTI
LAÍS F.M. PEREIRA
MARIA MÁRCIA P. SARTORI
MARCELO A. SILVA
spellingShingle SAMARA ZANETTI
LAÍS F.M. PEREIRA
MARIA MÁRCIA P. SARTORI
MARCELO A. SILVA
Leaf area estimation of cassava from linear dimensions
Anais da Academia Brasileira de Ciências
Manihot esculenta Crantz
leaf biometrics
statistical models
multiple regression
author_facet SAMARA ZANETTI
LAÍS F.M. PEREIRA
MARIA MÁRCIA P. SARTORI
MARCELO A. SILVA
author_sort SAMARA ZANETTI
title Leaf area estimation of cassava from linear dimensions
title_short Leaf area estimation of cassava from linear dimensions
title_full Leaf area estimation of cassava from linear dimensions
title_fullStr Leaf area estimation of cassava from linear dimensions
title_full_unstemmed Leaf area estimation of cassava from linear dimensions
title_sort leaf area estimation of cassava from linear dimensions
publisher Academia Brasileira de Ciências
series Anais da Academia Brasileira de Ciências
issn 1678-2690
publishDate 2017-08-01
description ABSTRACT The objective of this study was to determine predictor models of leaf area of cassava from linear leaf measurements. The experiment was carried out in greenhouse in the municipality of Botucatu, São Paulo state, Brazil. The stem cuttings with 5-7 nodes of the cultivar IAC 576-70 were planted in boxes filled with about 320 liters of soil, keeping soil moisture at field capacity, monitored by puncturing tensiometers. At 80 days after planting, 140 leaves were randomly collected from the top, middle third and base of cassava plants. We evaluated the length and width of the central lobe of leaves, number of lobes and leaf area. The measurements of leaf areas were correlated with the length and width of the central lobe and the number of lobes of the leaves, and adjusted to polynomial and multiple regression models. The linear function that used the length of the central lobe LA = -69.91114 + 15.06462L and linear multiple functions LA = -69.9188 + 15.5102L + 0.0197726K - 0.0768998J or LA = -69.9346 + 15.0106L + 0.188931K - 0.0264323H are suitable models to estimate leaf area of cassava cultivar IAC 576-70.
topic Manihot esculenta Crantz
leaf biometrics
statistical models
multiple regression
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017005019107&lng=en&tlng=en
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AT laisfmpereira leafareaestimationofcassavafromlineardimensions
AT mariamarciapsartori leafareaestimationofcassavafromlineardimensions
AT marceloasilva leafareaestimationofcassavafromlineardimensions
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