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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
04/12/2023 |
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Data da última atualização: |
04/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
COSTA, T. C. e C. da; RAMOS, L. B.; CAMPANHA, M. M.; GONTIJO NETO, M. M. |
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Afiliação: |
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. |
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Título: |
Algorithm for the projection of forest growth and production. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Floresta, v. 53, n. 1, p. 99-109, 2023. |
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DOI: |
http://doi.org/10.5380/rf.v53i1.85562 |
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Idioma: |
Inglês |
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Conteúdo: |
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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Palavras-Chave: |
Distribuições probabilísticas; Modelagem; Modeling; Probability distributions; Prognose. |
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Thesagro: |
Produção Florestal. |
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Thesaurus Nal: |
Prognosis. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159065/1/Algorithm-for-the-projection-of-forest-growth-and-production.pdf
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Marc: |
LEADER 02210naa a2200253 a 4500
001 2159065
005 2023-12-04
008 2023 bl uuuu u00u1 u #d
024 7 $ahttp://doi.org/10.5380/rf.v53i1.85562$2DOI
100 1 $aCOSTA, T. C. e C. da
245 $aAlgorithm for the projection of forest growth and production.$h[electronic resource]
260 $c2023
520 $aThe 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.
650 $aPrognosis
650 $aProdução Florestal
653 $aDistribuições probabilísticas
653 $aModelagem
653 $aModeling
653 $aProbability distributions
653 $aPrognose
700 1 $aRAMOS, L. B.
700 1 $aCAMPANHA, M. M.
700 1 $aGONTIJO NETO, M. M.
773 $tFloresta$gv. 53, n. 1, p. 99-109, 2023.
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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| Registros recuperados : 331 | |
| 17. |  | ALVARENGA, R. C.; BORGHI, E.; GONTIJO NETO, M. M. Integração lavoura-pecuária-floresta. In: PEREIRA FILHO, I. A.; RODRIGUES, J. A. S. (Ed.). Sorgo: o produtor pergunta, a Embrapa responde. Brasília, DF: Embrapa, 2015. cap. 16, p. 267-279. (Coleção 500 perguntas, 500 respostas).| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 18. | | ALVARENGA, R. C.; GONTIJO NETO, M. M.; KLUTHCOUSKI, J. O milho na integração lavoura-pecuária. In: CRUZ, J. C.; MAGALHAES, P. C.; PEREIRA FILHO, I. A.; MOREIRA, J. A. A. (Ed.). Milho: o produtor pergunta, a Embrapa responde. Brasília, DF: Embrapa Informação Tecnológica; Sete Lagoas: Embrapa Milho e Sorgo, 2011. cap. 17, p. 269-278. (Coleção 500 perguntas, 500 respostas).| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Arroz e Feijão; Embrapa Milho e Sorgo. |
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| Registros recuperados : 331 | |
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