Algorithm for the projection of forest growth and production.

Detalhes bibliográficos
Autor(a) principal: COSTA, T. C. e C. da
Data de Publicação: 2023
Outros Autores: RAMOS, L. B., CAMPANHA, M. M., GONTIJO NETO, M. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1159065
http://doi.org/10.5380/rf.v53i1.85562
Resumo: The modeling of forest growth and production is an essential tool for forestry management because it allows us to perform simulations and project forest biometric variables in the future, thus assisting in stock planning and economic analyses. In this work, a growth and production model by diameter distribution was proposed with the application of the Weibull function based on the recovery of parameters through simplified functions between the forest attributes and the parameters of the Weibull function. The algorithm was developed in Excel’s VBA language. Validation was performed with data from the Continuous Forest Inventory (CFI) in a stand of Khaya grandifoliola and in rows of Eucalyptus spp. in the ILPF system, which were ordinarily organized into seven date combinations, from the most distant from to the closest to the projection date. The results were evaluated by the percentage standard error (SE%) applied to the projected and observed volumes and by the Kolmogorov‒Smirnov test applied to the diameter distributions to verify adherence. It was possible to identify an exact relationship for parameter c of the Weibull function as a function of the percentiles and for parameter b, improving the parameter recovery method. Another methodological improvement was the use of maximum diameter and maximum height for age to adjust the hypsometric function. The algorithm presented results for total volume with errors up to 20% in 85% of the tests.
id EMBR_4a3d5b83af5bdac47c6e221b4413ad13
oai_identifier_str oai:www.alice.cnptia.embrapa.br:doc/1159065
network_acronym_str EMBR
network_name_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository_id_str 2154
spelling Algorithm for the projection of forest growth and production.PrognoseModelagemDistribuições probabilísticasProbability distributionsModelingProdução FlorestalPrognosisThe modeling of forest growth and production is an essential tool for forestry management because it allows us to perform simulations and project forest biometric variables in the future, thus assisting in stock planning and economic analyses. In this work, a growth and production model by diameter distribution was proposed with the application of the Weibull function based on the recovery of parameters through simplified functions between the forest attributes and the parameters of the Weibull function. The algorithm was developed in Excel’s VBA language. Validation was performed with data from the Continuous Forest Inventory (CFI) in a stand of Khaya grandifoliola and in rows of Eucalyptus spp. in the ILPF system, which were ordinarily organized into seven date combinations, from the most distant from to the closest to the projection date. The results were evaluated by the percentage standard error (SE%) applied to the projected and observed volumes and by the Kolmogorov‒Smirnov test applied to the diameter distributions to verify adherence. It was possible to identify an exact relationship for parameter c of the Weibull function as a function of the percentiles and for parameter b, improving the parameter recovery method. Another methodological improvement was the use of maximum diameter and maximum height for age to adjust the hypsometric function. The algorithm presented results for total volume with errors up to 20% in 85% of the tests.THOMAZ CORREA E CASTRO DA COSTA, CNPMS; LUCAS BARBOSA RAMOS, UNIVERSIDADE FEDERAL DE SÃO JOÃO DEL-REI; MONICA MATOSO CAMPANHA, CNPMS; MIGUEL MARQUES GONTIJO NETO, CNPMS.COSTA, T. C. e C. daRAMOS, L. B.CAMPANHA, M. M.GONTIJO NETO, M. M.2023-12-04T10:33:37Z2023-12-04T10:33:37Z2023-12-042023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFloresta, v. 53, n. 1, p. 99-109, 2023.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1159065http://doi.org/10.5380/rf.v53i1.85562enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-12-04T10:33:37Zoai:www.alice.cnptia.embrapa.br:doc/1159065Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-12-04T10:33:37falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-12-04T10:33:37Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Algorithm for the projection of forest growth and production.
title Algorithm for the projection of forest growth and production.
spellingShingle Algorithm for the projection of forest growth and production.
COSTA, T. C. e C. da
Prognose
Modelagem
Distribuições probabilísticas
Probability distributions
Modeling
Produção Florestal
Prognosis
title_short Algorithm for the projection of forest growth and production.
title_full Algorithm for the projection of forest growth and production.
title_fullStr Algorithm for the projection of forest growth and production.
title_full_unstemmed Algorithm for the projection of forest growth and production.
title_sort Algorithm for the projection of forest growth and production.
author COSTA, T. C. e C. da
author_facet COSTA, T. C. e C. da
RAMOS, L. B.
CAMPANHA, M. M.
GONTIJO NETO, M. M.
author_role author
author2 RAMOS, L. B.
CAMPANHA, M. M.
GONTIJO NETO, M. M.
author2_role author
author
author
dc.contributor.none.fl_str_mv THOMAZ CORREA E CASTRO DA COSTA, CNPMS; LUCAS BARBOSA RAMOS, UNIVERSIDADE FEDERAL DE SÃO JOÃO DEL-REI; MONICA MATOSO CAMPANHA, CNPMS; MIGUEL MARQUES GONTIJO NETO, CNPMS.
dc.contributor.author.fl_str_mv COSTA, T. C. e C. da
RAMOS, L. B.
CAMPANHA, M. M.
GONTIJO NETO, M. M.
dc.subject.por.fl_str_mv Prognose
Modelagem
Distribuições probabilísticas
Probability distributions
Modeling
Produção Florestal
Prognosis
topic Prognose
Modelagem
Distribuições probabilísticas
Probability distributions
Modeling
Produção Florestal
Prognosis
description The modeling of forest growth and production is an essential tool for forestry management because it allows us to perform simulations and project forest biometric variables in the future, thus assisting in stock planning and economic analyses. In this work, a growth and production model by diameter distribution was proposed with the application of the Weibull function based on the recovery of parameters through simplified functions between the forest attributes and the parameters of the Weibull function. The algorithm was developed in Excel’s VBA language. Validation was performed with data from the Continuous Forest Inventory (CFI) in a stand of Khaya grandifoliola and in rows of Eucalyptus spp. in the ILPF system, which were ordinarily organized into seven date combinations, from the most distant from to the closest to the projection date. The results were evaluated by the percentage standard error (SE%) applied to the projected and observed volumes and by the Kolmogorov‒Smirnov test applied to the diameter distributions to verify adherence. It was possible to identify an exact relationship for parameter c of the Weibull function as a function of the percentiles and for parameter b, improving the parameter recovery method. Another methodological improvement was the use of maximum diameter and maximum height for age to adjust the hypsometric function. The algorithm presented results for total volume with errors up to 20% in 85% of the tests.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-04T10:33:37Z
2023-12-04T10:33:37Z
2023-12-04
2023
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Floresta, v. 53, n. 1, p. 99-109, 2023.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1159065
http://doi.org/10.5380/rf.v53i1.85562
identifier_str_mv Floresta, v. 53, n. 1, p. 99-109, 2023.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1159065
http://doi.org/10.5380/rf.v53i1.85562
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
_version_ 1794503552658309120