Regression models for estimating stem volume of Aquilaria malaccensis (Lam.) in North East India

Abstract

Aquilaria malaccensis Lam. (Agar), a Critically Endangered tree species native to the tropical rain forests of South and South East Asia, is known for eaglewood and its oil of medicinal and aromatic value. In order to develop stem volume prediction models for agar tree, an exercise was carried out with 441 trees selected in North East India, by non-destructive sampling. The required variables, such as, diameter at breast height (D), tree height (H) and fraction diameters at 1 m interval were physically measured for estimating the volume of each fraction using Smalian formula, and summing up the volumes of fractions to yield the volume of the tree. A comparison of different stem volume models showed that the power function V=aDb and the polynomial of degree-3 V=a+bDH+cD2H+dDH2were the best fitting models for single independent variable D and two independent variables D & H, respectively. The regression models shall be useful for farmers to optimize their income while selling the agar wood. These agar specific regression models will also be helpful to fix the quota for agar wood harvest and trade in the country, while implementing the Convention on International Trade in Endangered Species of Fauna and Flora (CITES).

Keywords

• AIC
• NDF study
• RMSE
• Smalian formula