Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2116
Title: Site quality evaluation by classification tree: an application to cork quality in Sardinia
Authors: Corona, Piermaria
Dettori, Sandro
Filigheddu, Maria Rosaria
Maetzke, Federico
Scotti, Roberto
Keywords: Quercus suber;Cork quality;Site classification and evaluation;Classification tree;Logistic regression
Issue Date: 2005
Publisher: Springer Verlag
Source: Corona, P. et al. 2005. Site quality evaluation by classification tree: an application to cork quality in Sardinia. "European Journal of Forest Research" 124: 37-46.
Abstract: 
Cork harvesting and stopper production represent
a major forest industry in Sardinia (Italy). The
target of the present investigation was to evaluate the
‘‘classification tree’’ as a tool to discover possible relationships
between microsite characteristics and cork
quality. Seven main cork oak (Quercus suber) producing
areas have been identified in Sardinia, for a total of more
than 122,000 ha. Sixty-three sample trees, distributed
among different geographical locations and microsite
conditions, were selected. A soil profile near each sample
tree was described, soil samples were collected and
analysed. After debarking, cork quality of each sample
tree was graded by an independent panel of experts.
Microsites where trees had more than 50% of the extracted
cork graded in the best quality class, according
to the official quality standard in Italy, were labelled as
prime microsites, the others as nonprime microsites.
Relationships between a binary dummy variable (0 for
nonprime microsites, 1 for prime microsites) and site
factors were investigated using classification tree analysis
to select the relevant variables and to define the
classification scheme. Prime quality microsites for cork
production proved to be characterised by elevation, soil
phosphorus content and sandiness. Results have been
compared with those of the more conventional parametric
approach by logistic regression. The work demonstrates
the advantages of the classification tree
method. The model may be appropriate for classifications
at landscape and stand mapping levels, where it is
possible to sample a number of microsites and to evaluate
distributional characteristics of model output, while
its precision is only indicative when estimating the prime
quality of single microsites.
Description: 
L'articolo è disponibile sul sito dell'editore www.springerlink.com
URI: http://hdl.handle.net/2067/2116
ISSN: 1612-4669
DOI: 10.1007/s10342-004-0047-1
Appears in Collections:DiSAFRi - Archivio della produzione scientifica

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