Objective  Stand-level volume, biomass and carbon stock models or tables are necessary quantitative tools for implementing forest management inventory. Developing stand volume, biomass and carbon stock models for ten major forest types in forest region of northeastern China is not only an exploration of methodology, but also provides reference results for practice.

  Method  Based on the field measurement data of 2 000 sample plots distributed in 10 forest types in northeastern China, i.e. spruce & fir (Picea spp. & Abies spp.), larch (Larix spp.), Mongolian scotch pine (Pinus sylvestris var. mongolica), Korean pine (Pinus koraiensis), oak (Quercus spp.), birch (Betula spp.), poplar (Populus spp.), elm (Ulmus spp.), linden (Tilia spp.), and other three precious broadleaved species (Fraxinus mandshurica, Juglans mandshurica & Phellodendron amurense), the stand-level volume, biomass and carbon stock models were developed through independent nonlinear regression (INR), simultaneous error-in-variable equations (SEIVE), and SEIVE with dummy variable modeling approach.

  Result  The coefficients of determination (R²) of the population-averaged stand-level volume, biomass and carbon stock models based on all sample plots were 0.945, 0.805 and 0.839, respectively; and those of tthe models with type-specific parameters were 0.959, 0.949 and 0.951, respectively. The R² values of stand-level volume, biomass and carbon stock models for 10 forest types were all more than 0.86, the mean prediction errors (MPE) were all less than 3%, and the mean percent standard errors (MPSE) were almost less than 10%. For the volume stock models, the R² values were between 0.876−0.980, MPE were between 0.90%−1.95%, and MPSE were between 5.14%−11.89%; for the biomass stock models, the R² values were between 0.864−0.988, MPE were between 0.66%−2.07%, and MPSE were between 3.61%−11.60%; and for carbon stock models, the R² values were between 0.866−0.988, MPE were between 0.67%−1.96%, and MPSE were between 3.65%−11.57%.

  Conclusion  The volume stock per hectare of different forest types mainly depends upon basal area and mean tree height of forest stands, and the biomass stock mainly relates to volume stock and mean tree height. The SEIVE with dummy variable modeling approach is a feasible method for developing stand-level stock models. The developed volume, biomass and carbon stock models for 10 major forest types in northeastern China in this study meet the need of precision requirements to the regulation on forest management inventory, indicating that the models can be applied in practice.