To identify the stand attributes that best explain the variability in
wood density, Pinus radiata plantations located in the Chilean coastal sector
were studied and modeled. The study area corresponded to stands located in
sedimentary soil between the zones of Constitucion and Cobquecura. Within
each sampling sector, individual tree variables were recorded and the most
relevant stand parameters were estimated. Fifty trees were sampled in each
sector, obtaining from each one six wood discs from di erent stem heights.
Each disc was weighed in green and then dried to anhydrous weight, and
its basic density was calculated. The pro le identi cation to classify basic
density according to stand characteristics was performed through regression
trees, a technique based in the use of predictor variables to partition
the database using recursive algorithms in regions with similar responses.
The objective of the regression tree method is to obtain highly homogenous
groups (branches), which are identi ed using pruning techniques that successively
eliminate the branches that least contribute to the classi cation of
the variable of interest. The results found that the stand attributes that
contributed signi cantly to basic density classi cation were the basal area,
the number of trees per hectare, and the mean height