Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
Floresta e Ambiente
Endereço:
Rodovia BR 465, km 07, Campus da UFRRJ - IF-NIDFLOR - UFRRJ
Seropédica / RJ
23890-000
Site: http://www.floram.org
Telefone: (21) 2681-4986
ISSN: 2179-8087
Editor Chefe: João Vicente de Figueiredo Latorraca
Início Publicação: 31/12/1993
Periodicidade: Trimestral
Área de Estudo: Ciências Agrárias, Área de Estudo: Recursos Florestais e Engenharia Florestal
Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
Ano: 2019 | Volume: 26 | Número: 4
Autores: Ouorou Ganni Mariel Guera; José Antônio Aleixo da Silva; Rinaldo Luiz Caraciolo Ferreira; Daniel Alberto Álvarez Lazo; Héctor Barrero Medel
Autor Correspondente: Ouorou Ganni Mariel Guera | [email protected]
Autor Correspondente: Ouorou Ganni Mariel Guera | [email protected]
Palavras-chave: plantations; nonlinear regression; artificial neural networks
Resumos Cadastrados
Resumo Inglês:
The objective of this study was to obtain regression equations and artificial neural networks (ANNs) for prediction and prognosis of the yield of Pinus caribaea var. caribaea Barrett & Golfari. The data used for modeling comes from measuring the variables diameter at breast height (DBH) and total height (Ht) in 550 temporary plots and 14 circular permanent plots with 500 m2 in Pinus caribaea var. caribaea plantations, aged between 3 and 41 years old. In growth prediction, the results indicated Schumacher model as the best fit to the data. On prognosis, the modified Buckman system was better than Clutter’s. ANNs presented a similar performance to the Buckman model in volume prognosis, however these were superior for basal area prognosis.
