Formulation of allometric equation
Based on the data collected, several equations were developed. Before establishing the allometric equation, scatter plots were used to see whether the relationship between independent and dependent variables was linear. Furthermore, several allometric relationships between independent and dependent variables were tested. The independent variables included DBH, Height, and wood density, whereas the dependent variable was the dry weight of the AGB. Because the data exhibited heteroscedasticity, a power function was an inappropriate model in this study, so the data was transformed for linear regression using logarithmic transformation. The transformation equalized the variance over the entire range of biomass values, which satisfies the prerequisite of linear regression (Sokal and Rohlf, 1995; Sprugel, 1983).
To develop an allometric model for the above-ground biomass, Diameter at breast height ((DBH), Height (H) and Wood density (WD) were taken as explanatory variables. Given that there are p effect variables X1, X2, . . . ,Xp, there are 2p − 1 models that include all or some of these effect variables (Picard et al., 2012). Thus, in this study, the 3 effect variables can result in 23-1 which becomes 7 candidate regression model (Table 1). Table 1. The seven candidate regression model which serve to select the best models