PNNL

Abstract

Despite the importance of microbial respiration of soil organic matter (SOM) in regulating carbon flux between soils and atmosphere, soil carbon cycling models remain primarily based on climate and soil properties, leading to large uncertainty in predictions. With data from the 1000 Soils Pilot of the Molecular Observation Network (MONet), we analyzed high resolution water-extractable SOM profiles from standardized soil cores across the United States to address this knowledge gap. Our innovation lies in using machine learning to distill the thousands of SOM formula into tractable units; and it enables integrating data from molecular measurements into soil respiration models. In surface soils, SOM chemistry provided better estimates of potential soil respiration than soil physicochemistry, and using them combined yielded the best prediction. Overall, we identify specific subsets of organic molecules that may improve predictions of global soil respiration and create a strong basis for developing new representations in process-based models.