Construction of compatible and additive individual-tree biomass models forPinustabulaeformisin China

Abstract

Current biomass models for Chinese pine (Pinus tabulaeformis Carr.) fail to accurately estimate biomass in large geographic regions because they were usually based on limited sample trees on local sites, incompatible with stem volume, and not additive among components and total biomass. This study was based on mensuration data of individual-tree biomass from large samples of Chinese pine. The purpose was to construct compatible and additive biomass models using the nonlinear error-in-variable simultaneous equations and dummy variable modeling approach. This approach could ensure compatibility of an aboveground biomass model with a biomass conversion factor (BCF) and a stem volume model and compatibility of a belowground biomass model with a root-to-shoot ratio (RSR) model. Also, stem, branch, and foliage biomass models were additive to the aboveground biomass model. Results showed that mean prediction errors (MPEs) of the developed one- and two-variable aboveground biomass models were less than 4% and MPEs of the three-component (stem, branch, and foliage) and belowground biomass models were less than 10%. Furthermore, the effects of main climate variables on above- and below-ground biomass were analyzed. Aboveground biomass was related to mean annual temperature (MAT), while belowground biomass had no significant relationship with either MAT or mean annual precipitation (MAP). The developed models provide a good basis for estimating biomass of Chinese pine forests.