Application of generalized linear models to estimate height growth
Autor(a) principal: | |
---|---|
Data de Publicação: | 2015 |
Outros Autores: | , , , |
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 |