Integrating climate and soil factors enhances biomass estimation for natural white birch (Betula platyphylla Sukaczev)

Introduction Accurate biomass estimation is crucial for quantifying forest carbon storage and guiding sustainable management. In this study, we developed four biomass modeling systems for natural white birch ( Betula platyphylla Sukaczev) in northeastern China using field data from 148 trees. Methods The data included diameter at breast height (DBH), tree height (H), crown dimensions, and biomass components (stem, branch, foliage, and root biomass), as well as soil and climate variables. We employed Seemingly Unrelated Regression (SUR) and mixed-effects models (SURM) to account for component correlations and spatial variability. Results The base model (SUR ba ), using only the DBH variable, explained 89-96% of the biomass variance (RMSE%: 1.34-19.94%). The second model (SUR bio ) incorporated H for stem/branch biomass and crown length (CL) for foliage, improving the predictions of stem, branch, and foliage biomass (R 2 increased by 1.69–4.86%; RMSE% decreased by 10.76-59.04%). Next, the SUR ba-abio and SUR bio-abio models integrated abiotic factors, including soil organic carbon content (SOC), mean annual precipitation (MAP), degree-days above 18°C (DD18), and soil bulk density (BD). Both models showed improvement, with the abiotic factor model SUR ba-abio performing similarly to the biotic factor model SUR bio (ΔR 2 < 4.36%), while the SUR bio-abio model performed the best. Subsequently, random effects were introduced at the sampling point (Forestry Bureau) level, developing seemingly unrelated mixed-effects models (SURM ba , SURM bio , SURM ba-abio , SURM bio-abio ), which improved model fitting and prediction accuracy. The gap between the SURM ba-abio model (with abiotic factors) and the SURM bio-abio model (including both biotic and abiotic factors) was minimal (ΔR 2 < 2.80%). The random effects model stabilized when calibrated with aboveground biomass measurements from four trees. Discussion In conclusion, these models provide an effective approach for estimating the biomass of natural white birch in northeastern China. In the absence of biotic factors, the SUR ba-abio and SURM ba-abio models serve as reliable alternatives, emphasizing the importance of abiotic factors in biomass estimation and offering a practical solution for predicting birch biomass.