Modeling ecosystem-scale carbon dynamics in soil: The microbial dimension

Soil Biology and Biochemistry

Joshua Schimel

Carbon use efficiency

Microbial

Model

Priming

2023-03-01

10.1016/j.soilbio.2023.108948

https://www.sciencedirect.com/science/article/pii/S003807172300010X

In predicting how soil C fluxes and stocks will change with the environment, models are a critical tool for integrating datasets with theory. Models developed in the 1980's were based on 1st order kinetics of C-pools defined by turnover time. However, new models generally include microbes as decomposers although they vary in the number and nature of microbial pools. They don't, however, integrate modern omics-based datasets because models have coarse resolution and need to function even in the absence of community data—geographically or into the future. There are several issues new models must address to be valuable for large-scale synthesis. First, how to incorporate microbes and their activities—how many pools of organisms? How should they be defined? How should they drive C-cycling? Should their synthesis of degradative enzymes be treated implicitly or explicitly? Second, carbon use efficiency (CUE)—the partitioning of processed C between respiration and re-synthesis into biomass. This term is critical because the size of the biomass influences its rate of organic matter processing. A focus has been on CUE's temperature sensitivity—most studies suggest it declines as temperature rises, which would limit decomposition and organic matter loss. The final novel modeling element I discuss is “priming”—the effect of fresh inputs on decomposition of native organic matter (OM). Priming can either repress or accelerate the breakdown of native OM. But whether, and how, to capture priming effects in soil organic matter models remains an area of exploration.

From the soil plant system to the field