Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model ( R 2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.