ScholarWorks@경상국립대학교

초록

Predicting forest productivity is essential to evaluate sustainable forest management or to enhance forest ecosystem services. Ordinary least squares (OLS) and partial least squares (PLS) regression models were used to develop predictive models for forest productivity (site index) from the site characteristics and soil profile, along with soil physical and chemical properties, of 112 Quercus mongolica stands. The adjusted coefficients of determination (adjusted R2) in the regression models were higher for the site characteristics and soil profile of B horizon (R2=0.32) and of A horizon (R2=0.29) than for the soil physical and chemical properties of B horizon (R2=0.21) and A horizon (R2=0.09). The PLS models (R2=0.20-0.32) were better predictors of site index than the OLS models (R2=0.09-0.31). These results suggest that the regression models for Q. mongolica can be applied to predict the forest productivity, but new variables may need to be developed to enhance the explanatory power of regression models.

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