Abstract: Forest biomass estimation methods of regional scale attract most attention of the resear-chers, with developing stand-level biomass model being a research trend. Based on the biomass data from fix forest types, two additive systems of biomass equations based one- and two-variable were developed. The model error structure (additive vs. multiplicative) of the allometric equation was evaluated using the likelihood analysis. The nonlinear seemingly unrelated regression (NSUR) was used to estimate the parameters in the additive system of stand-level biomass equations. The results showed that the assumption of multiplicative error structure was strongly supported for the stand-level biomass equations of total and components for those forest types. Thus, the additive system of log-transformed biomass equations was developed. The adjusted coefficient of determination of the additive system of biomass equations was 0.78-0.99, the mean relative error was between -2.3%-6.9%, and the mean absolute relative error was between 6.3%-43.3%. Adding mean tree height in the additive systems of biomass equations could significantly improve the model fitting performance and predicting precision for most of the models. The biomass equations of total, aboveground and stem were better than biomass equations of root, branch, foliage and crown. In order to estimate model parameters more effectively, the additivity property of estimating tree total, sub-totals, and component biomass should be taken into account. Overall, the stand-level biomass models established in this study would be suitable for predicting stand-level biomass of six forest types in Daxing’an mountains.