Open AccessModeling growth of total height using early data from forest inventories in fast growing Eucalyptus spp plantations
Author(s) -
Samuel de Pádua Chaves e Carvalho,
Mariana Peres de Lima Chaves e Carvalho,
Natalino Calegário,
Adriano Ribeiro de Mendonça,
Valdir Carlos Lima de Andrade,
Marcos Felipe Nicoletti,
Carlos Alberto Silva,
Diogo Guido Streck Vendrúscolo
Publication year - 2021
Publication title -
advances in forestry science
Language(s) - English
Resource type - Journals
eISSN - 2359-6570
pISSN - 2357-8181
DOI - 10.34062/afs.v8i3.12250
Subject(s) - logistic function , eucalyptus , growth curve (statistics) , production (economics) , statistics , mathematics , growth function , growth model , function (biology) , stability (learning theory) , econometrics , yield (engineering) , wood production , forestry , forest management , environmental science , agroforestry , computer science , ecology , geography , biology , economics , machine learning , materials science , mathematical economics , evolutionary biology , metallurgy , macroeconomics
This work evaluated the growth trend represented by three biological models used for modeling forest growth and production (Schumacher; Chapman-Richards; Logistic). These curves were chosen because they are widely used by forest science professionals. The functions were adjusted under the hypothesis that there is influence of the initial, 6 and 12 month measurements on the shape of the production curves and, consequently, in the estimate of their parameters. The data that formed the adjustment basis were generated by the continuous monitoring performed at 6, 12, and 24 months and later at each 12 months in order to yield the growth patterns for the evaluated plantations. The results herein presented allow us to conclude that independently of the type of adjustment, the Chapman-Richards function was the one that exhibited the best statistics, with the BIAS values reduced in up to 30% when compared to the others. The Schumacher function presented the worst performance among the proposed criteria in this study. So, given the results obtained, we suggest a broader reflection about the growth and production issue, especially for the use of biometric models applied to forest production forecast, in which stability and adherence of the curves to the data are expected