PNNL

Abstract

Biochar application to croplands has been proposed as a potential strategy to decrease losses of soil-reactive nitrogen (N) to the air and water. However, the extent and spatial variability of biochar function at the global level are still unclear. Using Random Forest regression modelling of machine learning based on data compiled from the literature, we mapped the impacts of different biochar types (derived from wood, straw, or manure), and their interactions with biochar application rates, soil properties, and environmental factors, on soil N losses (NH3 volatilization, N2O emissions, and N leaching) and crop productivity. The results show that a suitable distribution of biochar across global croplands (i.e., one application of 30%; and one application of