Allometric above- and belowground biomass models for China's subtropical forests advance pan-tropical carbon assessments

Abstract Accurate allometric models are essential for quantifying forest carbon sinks. China’s extensive subtropical forests lack reliable biomass models, limiting carbon estimation. Here, we developed improved above- and below-ground biomass models using 1,993 harvest-based records from 166 sites. Various biomass models were developed, incorporating tree diameter at breast height (D, in cm), tree height (H, in m), and wood density (ρ, in g cm−3). Model performance was rigorously evaluated against existing approaches using the coefficient of determination (R2), root mean square error (RMSE), mean percent bias (MPB), and mean absolute percent error (MAPE) at individual and plot scales. The optimal above-ground biomass (AGB, in kg) model, AGB = 0.0526 · (ρD2H)0.9891 (R2 = 0.96, RMSE = 62.18 kg, MPB = 2.06% and MAPE = 26.69%), and below-ground biomass (BGB, in kg) model, BGB = 0.0107 · (ρD2H)0.9803 (R2 = 0.90, RMSE = 13.96 kg, MPB = 6.84% and MAPE = 33.16%). Our AGB estimates showed superior accuracy to Chave et al.’s pan-tropical model, with lower systematic bias (MPB = -1.59% vs. 13.88%) and error (MAPE = 27.34% vs. 31.36%), particularly for trees with D < 100 cm (∼97% of total validation data). Root-to-shoot ratios differed between functional types (0.231 ± 0.084 for broadleaf versus 0.194 ± 0.062 for coniferous species) and shifted with spatial scale and soil type; the paired AGB–BGB equations improved belowground estimation relative to a single constant ratio. These models facilitate more robust pan-tropical carbon assessments, thus supporting informed forest management and global climate policy decisions, especially in regions previously understudied.