In the context of climate change, estimating forest biomass for large regions is key to national carbon stocks, but few models have been developed at regional level. Based on mensuration data from large samples (4818 and 1626 trees for above-and belowground biomass, respectively) of eight major tree species in China, the author developed oneand two-variable compatible integrated model systems for aboveground and belowground biomass, biomass conversion factor (BCF) and root-to-shoot ratio (RSR),

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... using the error-in-variable simultaneous equations. Furthermore, the differences of aboveground and belowground biomass among various species were analyzed using the dummy variable approach. The results indicated that (1) two-variable models were almost better than one-variable models for aboveground biomass estimation, while the two model systems were not significantly different for belowground biomass estimation; (2) the eight species can be ranked in terms of aboveground biomass from Quercus (largest), Betula, Populus, Pinus massoniana, Picea, Larix, Abies to Cunninghamia lanceolata and in terms of belowground biomass from Quercus (largest), Betula, Larix, Picea, Populus, P. massoniana, C. lanceolata to Abies; (3) mean prediction errors (MPEs) of aboveground biomass models for the species were less than 5%, whereas MPEs of belowground biomass equations were less than 10%, except for Abies.