Algorithm for the projection of forest growth and production.
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
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. |
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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 |
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