{"title":"Bertalanffy-Pütter Models for the Growth of Tropical Trees and Stands","authors":"N. Brunner, M. Kühleitner","doi":"10.4236/ojmsi.2020.84006","DOIUrl":null,"url":null,"abstract":"The \nBertalanffy-Putter (BP) five-parameter growth model provides a versatile \nframework for the modeling of growth. Using data from a growth experiment in \nliterature about the average size-at-age of 24 species of tropical trees over \nten years in the same area, we identified their best-fit BP-model parameters. \nWhile different species had different best-fit exponent-pairs, there was a \nmodel with a good fit to 21 (87.5%) of the data (“Good fit” means a normalized \nroot-mean-squared-error NRMSE \nbelow 2.5%. This threshold was the 95% quantile of the lognormal distribution \nthat was fitted to the NRMSE \nvalues for the best-fit models for the data). In view of the sigmoidal character of this model \ndespite the early stand we discuss whether the setting of the growth experiment may have \nimpeded growth.","PeriodicalId":56990,"journal":{"name":"建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"建模与仿真(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/ojmsi.2020.84006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The
Bertalanffy-Putter (BP) five-parameter growth model provides a versatile
framework for the modeling of growth. Using data from a growth experiment in
literature about the average size-at-age of 24 species of tropical trees over
ten years in the same area, we identified their best-fit BP-model parameters.
While different species had different best-fit exponent-pairs, there was a
model with a good fit to 21 (87.5%) of the data (“Good fit” means a normalized
root-mean-squared-error NRMSE
below 2.5%. This threshold was the 95% quantile of the lognormal distribution
that was fitted to the NRMSE
values for the best-fit models for the data). In view of the sigmoidal character of this model
despite the early stand we discuss whether the setting of the growth experiment may have
impeded growth.