DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES

Detalhes bibliográficos
Autor(a) principal: Filho, Antonio Carlos Ferraz
Data de Publicação: 2015
Outros Autores: Scolforo, José Roberto Soares, Ferreira, Maria Zélia, Maestri, Romualdo, Assis, Adriana Leandra de, Oliveira, Antonio Donizette de, Mello, José Márcio de
Tipo de documento: Artigo
Idioma: por
Título da fonte: Cerne (Online)
Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/65
Resumo: This study investigated the behavior of climatic variables inserted as inclination modifiers of the Chapman-Richards model for estimating dominant height. Thus, 1507 data pairs from a Continuous Forestry Inventory of clonal eucalyptus stands were used. The stands are located in the States of Espírito Santo and southern Bahia. The climatic variables were inserted in the dominant height model because the model is a key variable in the whole prognosis system. The models were adjusted using 1360 data pairs, where the rest of the data was reserved for model validation. The climatic variables were selected by using the Backward model construction method. The climatic variables indicated by the Backward method and inserted in the model were: mean monthly precipitation and solar radiation. The inclusion of climatic variables in the model resulted in a precision gain of 19.8% for dominant height projection values when compared with the conventional model. The advantage of the method used in this study is the actualization of inventory data contemplating climatic history and productivity estimates in areas without prior plantation. 
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spelling DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLESClimatic variabledominant heightprojection modelThis study investigated the behavior of climatic variables inserted as inclination modifiers of the Chapman-Richards model for estimating dominant height. Thus, 1507 data pairs from a Continuous Forestry Inventory of clonal eucalyptus stands were used. The stands are located in the States of Espírito Santo and southern Bahia. The climatic variables were inserted in the dominant height model because the model is a key variable in the whole prognosis system. The models were adjusted using 1360 data pairs, where the rest of the data was reserved for model validation. The climatic variables were selected by using the Backward model construction method. The climatic variables indicated by the Backward method and inserted in the model were: mean monthly precipitation and solar radiation. The inclusion of climatic variables in the model resulted in a precision gain of 19.8% for dominant height projection values when compared with the conventional model. The advantage of the method used in this study is the actualization of inventory data contemplating climatic history and productivity estimates in areas without prior plantation. CERNECERNE2015-05-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/65CERNE; Vol. 17 No. 3 (2011); 427-433CERNE; v. 17 n. 3 (2011); 427-4332317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttps://cerne.ufla.br/site/index.php/CERNE/article/view/65/56Copyright (c) 2015 Antonio Carlos Ferraz Filho, José Roberto Soares Scolforo, Maria Zélia Ferreira, Romualdo Maestri, Adriana Leandra de Assis, Antonio Donizette de Oliveira, José Márcio de Melloinfo:eu-repo/semantics/openAccessFilho, Antonio Carlos FerrazScolforo, José Roberto SoaresFerreira, Maria ZéliaMaestri, RomualdoAssis, Adriana Leandra deOliveira, Antonio Donizette deMello, José Márcio de2015-05-12T11:04:19Zoai:cerne.ufla.br:article/65Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:53:28.991393Cerne (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
title DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
spellingShingle DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
Filho, Antonio Carlos Ferraz
Climatic variable
dominant height
projection model
title_short DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
title_full DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
title_fullStr DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
title_full_unstemmed DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
title_sort DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
author Filho, Antonio Carlos Ferraz
author_facet Filho, Antonio Carlos Ferraz
Scolforo, José Roberto Soares
Ferreira, Maria Zélia
Maestri, Romualdo
Assis, Adriana Leandra de
Oliveira, Antonio Donizette de
Mello, José Márcio de
author_role author
author2 Scolforo, José Roberto Soares
Ferreira, Maria Zélia
Maestri, Romualdo
Assis, Adriana Leandra de
Oliveira, Antonio Donizette de
Mello, José Márcio de
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Filho, Antonio Carlos Ferraz
Scolforo, José Roberto Soares
Ferreira, Maria Zélia
Maestri, Romualdo
Assis, Adriana Leandra de
Oliveira, Antonio Donizette de
Mello, José Márcio de
dc.subject.por.fl_str_mv Climatic variable
dominant height
projection model
topic Climatic variable
dominant height
projection model
description This study investigated the behavior of climatic variables inserted as inclination modifiers of the Chapman-Richards model for estimating dominant height. Thus, 1507 data pairs from a Continuous Forestry Inventory of clonal eucalyptus stands were used. The stands are located in the States of Espírito Santo and southern Bahia. The climatic variables were inserted in the dominant height model because the model is a key variable in the whole prognosis system. The models were adjusted using 1360 data pairs, where the rest of the data was reserved for model validation. The climatic variables were selected by using the Backward model construction method. The climatic variables indicated by the Backward method and inserted in the model were: mean monthly precipitation and solar radiation. The inclusion of climatic variables in the model resulted in a precision gain of 19.8% for dominant height projection values when compared with the conventional model. The advantage of the method used in this study is the actualization of inventory data contemplating climatic history and productivity estimates in areas without prior plantation. 
publishDate 2015
dc.date.none.fl_str_mv 2015-05-12
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://cerne.ufla.br/site/index.php/CERNE/article/view/65
url https://cerne.ufla.br/site/index.php/CERNE/article/view/65
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://cerne.ufla.br/site/index.php/CERNE/article/view/65/56
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 CERNE
CERNE
publisher.none.fl_str_mv CERNE
CERNE
dc.source.none.fl_str_mv CERNE; Vol. 17 No. 3 (2011); 427-433
CERNE; v. 17 n. 3 (2011); 427-433
2317-6342
0104-7760
reponame:Cerne (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Cerne (Online)
collection Cerne (Online)
repository.name.fl_str_mv Cerne (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv cerne@dcf.ufla.br||cerne@dcf.ufla.br
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