The stand age governs forest root: Shoot ratios across northeast China

10 páginas.- 6 figuras.- referencias.- Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.agrformet.2025.110595

Root: shoot (RS) ratios are widely used to estimate global and regional forest carbon stocks and to model the forest carbon cycle. However, limited knowledge is available regarding factors that determine RS spatial patterns, particularly in high-latitude temperate regions. Therefore, in this study, we compiled 189 measurements of forest RSs across Northeast China to evaluate the main drivers of RS patterns. An optimal machine learning model was selected to upscale the RS data and estimate belowground biomass carbon in Northeast China. The results showed that the stand age had the greatest impact on RS variation (contribution of 17.6 %), exceeding the influence of other predictors and increasing the coefficient of determination of the RS by 41 % in a structural equation model. Regional RS values decreased from 0.22 ± 0.02 to 0.16 ± 0.01 as the stand age increased from less than 20 years to over 60 years. Higher estimated RS values were found in both forests with a stand age of 40–60 years (19.3 %) and over 60 years (22.6 %) when the stand age was not considered. We also found that our RS estimates were lower (mean value = 0.21 ± 0.05) than Earth system models (0.25 ± 0.03) and remote sensing-based estimates (0.5 ± 0.05), resulting in 33.2 % and 62.7 % lower estimates of belowground biomass carbon in Northeast China, respectively. The results of this study highlight the importance of the stand age in forest carbon allocation, representing a factor that should be incorporated when estimating current and future carbon sequestration.

This work was supported by the National Natural Science Foundation of China (grant numbers 42261144688, 32241032, 32471667, 32471683, 32271713, and 42203076); Heilongjiang Touyan Innovation Team Program (Forest Carbon Sink Assessment and Carbon Sequestration Management Innovation Team), the Fundamental Research Funds for the Central Universities (grant number 2572022BA08).

Peer reviewed