DOMINANT HEIGHT PROJECTION MODEL WITH THE ADDITION OF ENVIRONMENTAL VARIABLES
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , |
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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Cerne (Online) |
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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 |
_version_ |
1799874939151450112 |