01918nab a2200277 i 4500003000900000005001700009008004100026040001800067072001400085072000800099100003200107100003900139100003800178100003600216100002900252245009800281500007000379520083800449650002401287650001201311650001901323773019001342856008101532942000801613999001901621BR-BrBNA20260417095847.0260417b2019    bl.qr|pooa||| 00| 0 eng |  aBR-BrBNAbeng  aU10b3140  aK10  aGuera, Ouorou Ganni Mariel   aSilva, José Antônio Aleixo da   aFerreira, Rinaldo Luiz Caraciolo   aLazo, Daniel Alberto Álvarez   aMedel, Héctor Barrero   aAlternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari  aPublicação on-line; 29 ref.; 7 tables; 7 illus.; Summary (En)  a

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.  aMODELO MATEMÁTICO  aPLANTIO  aPINUS CARIBAEA0 029299347953dRio de Janeiro-RJ Instituto de Florestas - UFRRJ 1994o2025-0452tFloresta e Ambiente (Brazil)x1415-0980  /  ISSN 2179-8087 0nlinegv. 26(4) p. 1-14; (2019)wBR2026001227  uhttps://www.scielo.br/j/floram/a/QJWsf9nkLwS5mrtbcrsdRZv/?format=pdf&lang=en  cANA  c341440d341440