Objective The purpose of this study is to develop integrated individual tree biomass equation systems, in which above-ground biomass is compatible with below-ground biomass and stem volume, and stem, bark, branches and foliage biomass are additive to above-ground biomass, for providing a quantitative basis on accurate estimation of forest biomass.

Method Based on the mensuration data of above-and below-ground biomass from 230 and 78 destructive sample trees of Picea spp. in Xinjiang, respectively, one-and two-variable integrated biomass systems with compatibility and additivity, including above-and below-ground biomass, component biomass, and stem volume, were developed using error-in-variable simultaneous equations approach and dummy variable modeling approach, and the impact of region on estimation of biomass and volume was analyzed.

Result The mean prediction errors (mPEs) of above-ground biomass equations in the developed one-and two-variable integrated biomass systems for Picea spp. in Xinjiang were less than 7%, the mPEs of components biomass equations were about 10%, and the mPEs of below-ground biomass equations were less than 15%, which could meet the need of precision requirements from relevant regulation. One-variable equations were better than two-variable equations for estimation of biomass except for stem and bark biomass. Both proportion control and algebraic control methods could ensure the compatibility between above-ground biomass and component biomass, and the difference between estimates of models from the two methods was not significant.

Conclusion Integrating dummy variable into error-in-variable simultaneous equations is a practical approach, which can simultaneously develop a system even though the numbers of above-and below-ground biomass observations are very different, and ensure not only the compatibility between above-and below-ground biomass and stem volume, but also the additivity between above-ground biomass and component biomass. For estimation of above-and below-ground biomass, and stem volume, the dummy variable models are better than population average models.