Multivariate analysis for the selection of eucalyptus clones destined for charcoal production

The objective of this work was to evaluate the wood quality of Eucalyptus spp. clones for the production of charcoal, to study the correlations between wood and charcoal properties, and to identify clones with the greatest potential for energy use. Data of clones from 18 settlements, in the Cerrado region of the state of Minas Gerais, Brazil, were subjected to the multivariate analysis of the canonical correlation analysis; principal component analysis; and cluster analysis. Charcoal properties are strongly correlated with the wood ones, mainly charcoal bulk density and gravimetric yield. Older materials with higher wood density have a higher quality for energy use. Principal component analysis is efficient to rank the materials as for wood quality, and the clustering method is able to successfully stratify clones by their wood quality.

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Bibliographic Details
Main Authors: Castro, Ana Flávia Neves Mendes, Castro, Renato Vinícius Oliveira, Carneiro, Angélica de Cássia Oliveira, Lima, João Eustáquio de, Santos, Rosimeire Cavalcante dos, Pereira, Bárbara Luísa Corradi, Alves, Isabel Cristina Nogueira
Format: Digital revista
Language:por
Published: Pesquisa Agropecuaria Brasileira 2013
Online Access:https://seer.sct.embrapa.br/index.php/pab/article/view/13580
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Summary:The objective of this work was to evaluate the wood quality of Eucalyptus spp. clones for the production of charcoal, to study the correlations between wood and charcoal properties, and to identify clones with the greatest potential for energy use. Data of clones from 18 settlements, in the Cerrado region of the state of Minas Gerais, Brazil, were subjected to the multivariate analysis of the canonical correlation analysis; principal component analysis; and cluster analysis. Charcoal properties are strongly correlated with the wood ones, mainly charcoal bulk density and gravimetric yield. Older materials with higher wood density have a higher quality for energy use. Principal component analysis is efficient to rank the materials as for wood quality, and the clustering method is able to successfully stratify clones by their wood quality.