Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari

Detalhes bibliográficos
Autor(a) principal: Guera,Ouorou Ganni Mariel
Data de Publicação: 2019
Outros Autores: Silva,José Antônio Aleixo da, Ferreira,Rinaldo Luiz Caraciolo, Lazo,Daniel Alberto Álvarez, Medel,Héctor Barrero
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Floresta e Ambiente
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000400135
Resumo: ABSTRACT The objective of this study was to obtain regression equations and artificial neural networks (ANNs) for prediction and prognosis of the yield of Pinus caribaea var. caribaea Barrett & Golfari. The data used for modeling comes from measuring the variables diameter at breast height (DBH) and total height (Ht) in 550 temporary plots and 14 circular permanent plots with 500 m2 in Pinus caribaea var. caribaea plantations, aged between 3 and 41 years old. In growth prediction, the results indicated Schumacher model as the best fit to the data. On prognosis, the modified Buckman system was better than Clutter’s. ANNs presented a similar performance to the Buckman model in volume prognosis, however these were superior for basal area prognosis.
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spelling Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfariplantationsnonlinear regressionartificial neural networksABSTRACT The objective of this study was to obtain regression equations and artificial neural networks (ANNs) for prediction and prognosis of the yield of Pinus caribaea var. caribaea Barrett & Golfari. The data used for modeling comes from measuring the variables diameter at breast height (DBH) and total height (Ht) in 550 temporary plots and 14 circular permanent plots with 500 m2 in Pinus caribaea var. caribaea plantations, aged between 3 and 41 years old. In growth prediction, the results indicated Schumacher model as the best fit to the data. On prognosis, the modified Buckman system was better than Clutter’s. ANNs presented a similar performance to the Buckman model in volume prognosis, however these were superior for basal area prognosis.Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000400135Floresta e Ambiente v.26 n.4 2019reponame:Floresta e Ambienteinstname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJ10.1590/2179-8087.038117info:eu-repo/semantics/openAccessGuera,Ouorou Ganni MarielSilva,José Antônio Aleixo daFerreira,Rinaldo Luiz CaracioloLazo,Daniel Alberto ÁlvarezMedel,Héctor Barreroeng2019-09-09T00:00:00Zoai:scielo:S2179-80872019000400135Revistahttps://www.floram.org/PUBhttps://old.scielo.br/oai/scielo-oai.phpfloramjournal@gmail.com||floram@ufrrj.br||2179-80871415-0980opendoar:2019-09-09T00:00Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
title Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
spellingShingle Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
Guera,Ouorou Ganni Mariel
plantations
nonlinear regression
artificial neural networks
title_short Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
title_full Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
title_fullStr Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
title_full_unstemmed Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
title_sort Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
author Guera,Ouorou Ganni Mariel
author_facet Guera,Ouorou Ganni Mariel
Silva,José Antônio Aleixo da
Ferreira,Rinaldo Luiz Caraciolo
Lazo,Daniel Alberto Álvarez
Medel,Héctor Barrero
author_role author
author2 Silva,José Antônio Aleixo da
Ferreira,Rinaldo Luiz Caraciolo
Lazo,Daniel Alberto Álvarez
Medel,Héctor Barrero
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Guera,Ouorou Ganni Mariel
Silva,José Antônio Aleixo da
Ferreira,Rinaldo Luiz Caraciolo
Lazo,Daniel Alberto Álvarez
Medel,Héctor Barrero
dc.subject.por.fl_str_mv plantations
nonlinear regression
artificial neural networks
topic plantations
nonlinear regression
artificial neural networks
description ABSTRACT The objective of this study was to obtain regression equations and artificial neural networks (ANNs) for prediction and prognosis of the yield of Pinus caribaea var. caribaea Barrett & Golfari. The data used for modeling comes from measuring the variables diameter at breast height (DBH) and total height (Ht) in 550 temporary plots and 14 circular permanent plots with 500 m2 in Pinus caribaea var. caribaea plantations, aged between 3 and 41 years old. In growth prediction, the results indicated Schumacher model as the best fit to the data. On prognosis, the modified Buckman system was better than Clutter’s. ANNs presented a similar performance to the Buckman model in volume prognosis, however these were superior for basal area prognosis.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000400135
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019000400135
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2179-8087.038117
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro
publisher.none.fl_str_mv Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro
dc.source.none.fl_str_mv Floresta e Ambiente v.26 n.4 2019
reponame:Floresta e Ambiente
instname:Universidade Federal do Rio de Janeiro (UFRJ)
instacron:UFRJ
instname_str Universidade Federal do Rio de Janeiro (UFRJ)
instacron_str UFRJ
institution UFRJ
reponame_str Floresta e Ambiente
collection Floresta e Ambiente
repository.name.fl_str_mv Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)
repository.mail.fl_str_mv floramjournal@gmail.com||floram@ufrrj.br||
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