This study aimed to develop a reliable method for estimating root biomass density in forests using existing data. The authors reviewed and summarized literature on root biomass measurements, testing various statistical relationships between root biomass density (RBD) and root:shoot ratios (R/S) against edaphic and climatic independent variables. Despite examining factors such as aboveground biomass density, latitude, temperature, precipitation, tree type, soil texture, and age, none of these variables showed significant explanatory value for R/S. However, linear regression analysis identified aboveground biomass density, age, and latitudinal category as the most important predictors of RBD, explaining 84% of the variation. The study also found that their method tended to produce RBD estimates that were about 20% higher compared to those based on generalized R/S ratios for forests in the United States. The research highlights the need for better understanding of forest biomass allocation and the factors influencing it, particularly for applications related to forest structure, biogeochemical cycles, and global change.This study aimed to develop a reliable method for estimating root biomass density in forests using existing data. The authors reviewed and summarized literature on root biomass measurements, testing various statistical relationships between root biomass density (RBD) and root:shoot ratios (R/S) against edaphic and climatic independent variables. Despite examining factors such as aboveground biomass density, latitude, temperature, precipitation, tree type, soil texture, and age, none of these variables showed significant explanatory value for R/S. However, linear regression analysis identified aboveground biomass density, age, and latitudinal category as the most important predictors of RBD, explaining 84% of the variation. The study also found that their method tended to produce RBD estimates that were about 20% higher compared to those based on generalized R/S ratios for forests in the United States. The research highlights the need for better understanding of forest biomass allocation and the factors influencing it, particularly for applications related to forest structure, biogeochemical cycles, and global change.