Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
Tipo de material:
ArtigoAssunto(s): Recursos online:
Em: Floresta e Ambiente (Brazil) v. 26(4) p. 1-14; (2019)Sumário:
ABSTRACT
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 m2in 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.
Keywords: plantations, nonlinear regression, artificial neural networks.
| Tipo de material | Biblioteca atual | Coleção | Número de chamada | Informaçaõ do volume | Situação | Devolução em | Código de barras |
|---|---|---|---|---|---|---|---|
Periódicos
|
Biblioteca Nacional de Agricultura - Binagri Agrobase - Periódicos | Periódicos agrícolas | 2019 26(4) | Online | 2025-0452 |
Publicação on-line; 29 ref.; 7 tables; 7 illus.; Summary (En)
ABSTRACT
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 m2in 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.
Keywords: plantations, nonlinear regression, artificial neural networks.
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