Toward Improved Model Structures for Analyzing Priming Effect


Rising atmospheric carbon dioxide (CO2) concentrations are projected to increase plant inputs to soil, which may stimulate soil carbon decomposition. Many studies attempting to quantify this priming effect use a simple analytical framework that is inappropriate for inferring complex dynamics. Using a multipool soil carbon model, a recent study shows that changes in carbon flows that would be attributed to priming in a one-pool model (using overall respiration and carbon stocks) can be explained without a change in decomposition rate constants of individual pools. Furthermore, a sensitivity analysis demonstrates the potential range of “false priming” responses inferred from simple, first-order models. The researchers argue that, in addition to standard measurements of carbon stocks and CO2 fluxes, quantifying the fate of new plant inputs requires isotopic tracers and microbial measurements. They discuss the pitfalls of using simple model structures to infer complex dynamics and suggest appropriate model structures and necessary observational constraints for projections of carbon feedbacks.


Georgiou, K., C. D. Koven, W. J. Riley, and M. S. Torn. 2015. “Toward Improved Model Structures for Analyzing Priming: Potential Pitfalls of Using Bulk Turnover Time,” Global Change Biology 21(12), 4298–4302. DOI:10.1111/gcb.13039.