Data/hora: 01/05/2024 15:23:24

Biblioteca(s): 	Embrapa Milho e Sorgo.	
Data corrente: 	04/12/2023	
Data da última atualização: 	04/12/2023	
Autoria: 	COSTA, T. C. e C. da; RAMOS, L. B.; CAMPANHA, M. M.; GONTIJO NETO, M. M.	
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.	
Título: 	Algorithm for the projection of forest growth and production.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Floresta, v. 53, n. 1, p. 99-109, 2023.	
DOI: 	http://doi.org/10.5380/rf.v53i1.85562	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	Distribuições probabilísticas; Modelagem; Modeling; Probability distributions; Prognose.	
Thesagro: 	Produção Florestal.	
Thesaurus Nal: 	Prognosis.	
Categoria do assunto: 	K Ciência Florestal e Produtos de Origem Vegetal	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159065/1/Algorithm-for-the-projection-of-forest-growth-and-production.pdf	
Registro original: 	Embrapa Milho e Sorgo (CNPMS)	
Exemplares: 	CNPMS - (30226-1) - UPC - AP - DD -  -  -  - DIS