Aplicação de análises estatísticas univariada e multivariada na seleção clonal de Eucalyptus spp. para a produção de carvão vegetal

Ciencia Florestal

article

Lucas de Freitas Fialho¹  Angélica de Cássia Oliveira Carneiro¹  Clarissa Gusmão Figueiró²  Letícia Costa Peres¹  Antônio Policarpo Souza Carneiro¹  Paula Gabriella Surdi¹ 

[1] Universidade Federal de Viçosa;Protermo Engenharia Ltda

关键词: Statistical tools;    Scott-Knott;    Principal components;    Hierarchical cluster;    Charcoal quality improvement;   

DOI  :  10.5902/1980509840443

学科分类：农业科学（综合）

来源: Universidade Federal de Santa Maria * Centro de Pesquisas Florestais

【 摘 要 】

The aim of this study was to select superior materials of Eucalyptus spp. using univariate and multivariate statistical analyzes. Twenty-five genetic materials from Eucalyptus spp. collected in Itamarandiba, Minas Gerais were used. The properties of wood and charcoal were determined for all genetic materials, in addition to the gravimetric yield. Data were submitted to Scott-Knott hierarchical clustering algorithm as univariate analysis. For the multivariate approach, a combination between principal component analysis and hierarchical cluster analysis was used. Both analyzes were efficient in the selection of genetic materials for charcoal production. According to the Scott-Knott test, genetic materials 9 and 21 were the most suitable to produce charcoal. By means of the multivariate analyzes the most indicated were 9, 10 and 21. The Scott-Knott test allowed the visualization of the results of each quality parameter independently. On the other hand, the multivariate tools enabled the observation of the relation between the properties of wood and charcoal.

【 授权许可】

CC BY-NC