Application of generalized linear models to estimate height growth

Detalhes bibliográficos
Autor(a) principal: Hess, André Felipe
Data de Publicação: 2015
Outros Autores: Cianorschi, Lucas, Silvestre, Raul, Scariot, Rafael, Ricken, Pollyni
Tipo de documento: Artigo
Idioma: por
Título da fonte: Pesquisa Florestal Brasileira (Online)
Texto Completo: https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/604
Resumo: Height growth analysis presents great importance in forestry, as it expresses site production capacity. Its use is associated with lower adjustment error models to generate estimates to inference with precision and reliability. The present study examined generalized linear models in predicting height growth of Pinus taeda L. depending on the age and diameter at 1.30 m height above ground level in stands in the highlands of Santa Catarina State. The data were obtained from complete stem analysis of 25 trees with 8 years old, divided into diameter classes from Lages, SC. Data were processed in original form without variables transformation. The model with gamma distribution and identity link function presented the best fit, with superior performance criteria deviation (1.21), Akaike (255.39) and residuals homogenization, showing potential to generate estimates of the variable.
id EMBRAPA-5_bfcfc53cc8cf992bcb94385d7a555b6d
oai_identifier_str oai:pfb.cnpf.embrapa.br/pfb:article/604
network_acronym_str EMBRAPA-5
network_name_str Pesquisa Florestal Brasileira (Online)
repository_id_str
spelling Application of generalized linear models to estimate height growthAplicação dos modelos lineares generalizados para estimativa do crescimento em alturaAnálise de troncoPredição de estimativasAcurácia do modeloStem analysisPrediction estimatesModel accuracyHeight growth analysis presents great importance in forestry, as it expresses site production capacity. Its use is associated with lower adjustment error models to generate estimates to inference with precision and reliability. The present study examined generalized linear models in predicting height growth of Pinus taeda L. depending on the age and diameter at 1.30 m height above ground level in stands in the highlands of Santa Catarina State. The data were obtained from complete stem analysis of 25 trees with 8 years old, divided into diameter classes from Lages, SC. Data were processed in original form without variables transformation. The model with gamma distribution and identity link function presented the best fit, with superior performance criteria deviation (1.21), Akaike (255.39) and residuals homogenization, showing potential to generate estimates of the variable.A análise do crescimento em altura é de extrema importância na área florestal, pois expressa a capacidade produtiva do local. Seu uso está associado ao ajuste, com menor erro, dos modelos para gerar estimativas que permitam a inferência com precisão e confiabilidade. O presente trabalho analisou o emprego dos modelos lineares generalizados na predição do crescimento em altura de Pinus taeda L. em função da idade e diâmetro a 1,30 m de altura em povoamentos no planalto catarinense. Os dados utilizados foram obtidos de análise de tronco completa de 25 árvores com oito anos, distribuídas em classes de diâmetro provenientes de Lages, SC. Os dados foram trabalhados na sua forma original sem a transformação das variáveis. O modelo com distribuição Gama e função de ligação identidade foi o que apresentou melhor ajuste, com desempenho superior nos critérios desvio (1,21), Akaike (255,39) e homogeneização dos resíduos, mostrando potencial para gerar estimativas da variável.Embrapa Florestas2015-12-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/60410.4336/2015.pfb.35.84.604Pesquisa Florestal Brasileira; v. 35 n. 84 (2015): out./dez.; 427-433Pesquisa Florestal Brasileira; Vol. 35 No. 84 (2015): out./dez.; 427-4331983-26051809-3647reponame:Pesquisa Florestal Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/604/455Hess, André FelipeCianorschi, LucasSilvestre, RaulScariot, RafaelRicken, Pollyniinfo:eu-repo/semantics/openAccess2018-03-20T18:28:22Zoai:pfb.cnpf.embrapa.br/pfb:article/604Revistahttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/PUBhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/oaipfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br1983-26051809-3647opendoar:2018-03-20T18:28:22Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Application of generalized linear models to estimate height growth
Aplicação dos modelos lineares generalizados para estimativa do crescimento em altura
title Application of generalized linear models to estimate height growth
spellingShingle Application of generalized linear models to estimate height growth
Hess, André Felipe
Análise de tronco
Predição de estimativas
Acurácia do modelo
Stem analysis
Prediction estimates
Model accuracy
title_short Application of generalized linear models to estimate height growth
title_full Application of generalized linear models to estimate height growth
title_fullStr Application of generalized linear models to estimate height growth
title_full_unstemmed Application of generalized linear models to estimate height growth
title_sort Application of generalized linear models to estimate height growth
author Hess, André Felipe
author_facet Hess, André Felipe
Cianorschi, Lucas
Silvestre, Raul
Scariot, Rafael
Ricken, Pollyni
author_role author
author2 Cianorschi, Lucas
Silvestre, Raul
Scariot, Rafael
Ricken, Pollyni
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Hess, André Felipe
Cianorschi, Lucas
Silvestre, Raul
Scariot, Rafael
Ricken, Pollyni
dc.subject.por.fl_str_mv Análise de tronco
Predição de estimativas
Acurácia do modelo
Stem analysis
Prediction estimates
Model accuracy
topic Análise de tronco
Predição de estimativas
Acurácia do modelo
Stem analysis
Prediction estimates
Model accuracy
description Height growth analysis presents great importance in forestry, as it expresses site production capacity. Its use is associated with lower adjustment error models to generate estimates to inference with precision and reliability. The present study examined generalized linear models in predicting height growth of Pinus taeda L. depending on the age and diameter at 1.30 m height above ground level in stands in the highlands of Santa Catarina State. The data were obtained from complete stem analysis of 25 trees with 8 years old, divided into diameter classes from Lages, SC. Data were processed in original form without variables transformation. The model with gamma distribution and identity link function presented the best fit, with superior performance criteria deviation (1.21), Akaike (255.39) and residuals homogenization, showing potential to generate estimates of the variable.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-31
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://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/604
10.4336/2015.pfb.35.84.604
url https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/604
identifier_str_mv 10.4336/2015.pfb.35.84.604
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/604/455
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 Embrapa Florestas
publisher.none.fl_str_mv Embrapa Florestas
dc.source.none.fl_str_mv Pesquisa Florestal Brasileira; v. 35 n. 84 (2015): out./dez.; 427-433
Pesquisa Florestal Brasileira; Vol. 35 No. 84 (2015): out./dez.; 427-433
1983-2605
1809-3647
reponame:Pesquisa Florestal Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Florestal Brasileira (Online)
collection Pesquisa Florestal Brasileira (Online)
repository.name.fl_str_mv Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br
_version_ 1783370934293364736

Registros relacionados