Abstract:
Growth model provides an efficient way in preparing resource forecasts especially on decision making options and silvicultural alternatives. Diameter increment is one of the common and important tree characteristics used in forest management decision making. In this paper, diameter increment models were developed for individual tree of dipterocarpaceae and non-dipterocarpaceae tree species in semi-evergreen forest in Seam Reap, Cambodia. Regression analysis is the preferred technique used in growth and yield modeling in forestry. The stepwise ordinary least square (OLS) regression technique has been used to fit model parameters. The predictor variables in both models represent tree size attribute, which are diameter at breast height (DBH) and basal area (BA) and also the tree position attribute, which is sum of basal area (m2) in trees with DBHs are larger than subject tree’s DBH (BAL). Each model was then validated and found to be good predictor by the small values of the four lacks of fit statistics. As a result, both of the models give better fit especially with regards to bias and relative bias.
