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Growth Curves for Diameter and Height Using Mixed Models: An Application in Eucalyptus Seedling

Growth Curves for Diameter and Height Using Mixed Models: An Application in Eucalyptus Seedling

作     者:Giovana Fumes Clarice Garcia Borges Demé trio Cristian Villegas José Eduardo Corrente Juliane Fumes Bazzo 

作者机构:Department of Exact Sciences ESALQ University of Sã o Paulo Piracicaba Brazil Department of Biostatistics University of Sã o Paulo State Botucatu Brazil Department of Natural Resources-Forest Sciences University of Sã o Paulo State Botucatu Brazil 

出 版 物:《Open Journal of Forestry》 (林学期刊(英文))

年 卷 期:2017年第7卷第4期

页      面:403-415页

学科分类:1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主  题:Box-Cox Normal Distribution Gompertz Model Longitudinal Data 

摘      要:The growth of a tree or a forest settlement is of great value to a forest enterprise, because many decisions are directly dependent of this information, for instance, determining the optimal cutting age. This study aims to apply a new class of models to fit growth curves for diameter and height of Eucalyptus grandis X Eucalyptus urophylla seedling data. Data were collected from a trial conducted in a green house at the Natural Resources Department at School of Agriculture, Botucatu, S?o Paulo, Brazil. The experiment’s design was completely randomized with eight treatments and four replications. In this trial, the growth variables referring to the height and the diameter were evaluated, being measured five and four times, respectively. The methodology was carried in a mixed longitudinal model using a new approach based on Box-Cox Normal (BCN) distribution, and comparisons with this model were made assuming normality of the data. The results revealed that the BCN mixed model provided similar results to the standard model in order to estimate growth curves;however, the BCN model was the best result according to Akaike criterion, considering the slight asymmetry in the data set. This approach is of great interest in case of outliers and robust procedures for parameter estimation.

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