000			01960nab a2200289 i 4500
003			BR-BrBNA
005			20260417095847.0
008			260417b2019 bl.qr|pooa||| 00| 0 eng |
040			_aBR-BrBNA _beng
072			_aU10 _b3140
072			_aK10
100			_aGuera, Ouorou Ganni Mariel
100			_aSilva, José Antônio Aleixo da
100			_aFerreira, Rinaldo Luiz Caraciolo
100			_aLazo, Daniel Alberto Álvarez
100			_aMedel, Héctor Barrero
245			_aAlternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
500			_aPublicação on-line; 29 ref.; 7 tables; 7 illus.; Summary (En)
520			_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.
650			_aMODELO MATEMÁTICO
650			_aPLANTIO
650			_aPINUS CARIBAEA
773	0		_02929 _9347953 _dRio de Janeiro-RJ Instituto de Florestas - UFRRJ 1994 _o2025-0452 _tFloresta e Ambiente (Brazil) _x1415-0980 / ISSN 2179-8087 0nline _gv. 26(4) p. 1-14; (2019) _wBR2026001227
856			_uhttps://www.scielo.br/j/floram/a/QJWsf9nkLwS5mrtbcrsdRZv/?format=pdf&lang=en
942			_cANA
999			_c341440 _d341440