Session: Biogeochemistry: New Paradigms In Biogeochem Cycling
Data-based model representations of plant-microbe interactions improve predictions of bioenergy sustainability
Wednesday, August 4, 2021
Link To Share This Presentation: https://cdmcd.co/Wkzrq5
Joanna Ridgeway, Christopher A. Walter and Edward R. Brzostek, Department of Biology, West Virginia University, Morgantown, WV, Stephanie Juice, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT
Department of Biology, West Virginia University Morgantown, WV, USA
Background/Question/Methods The viability of bioenergy relies on accurate predictions of how policy and management strategies impact sustainability in a changing world. Enhancing soil carbon (C) storage in bioenergy systems may increase their sustainability by compensating for C emissions from the production and use of biofuels. However, predictions of net C dynamics remain limited by a lack of data to inform model parameters that control how microbial physiology and plant-microbial interactions impact soil C cycling.
To meet this need, we used a coupled model-experiment approach to reduce parameter uncertainty in the FUN-CORPSE (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) which we recently adapted to bioenergy. By tracing the fate of 13C labeled bioenergy litter in a laboratory soil incubation, we were able to constrain key parameters that control soil C dynamics including microbial carbon use efficiency, turnover, and the protection rate of new soil C. We then compared the ability of baseline model runs vs. model runs with empirically-based parameters to capture our experimental results and field-based estimates of soil C dynamics from the University of Illinois Energy Farm. Results/Conclusions Data-driven parameters for maize, energy sorghum, and miscanthus x giganteus (MXG) shifted modeled soil C by up to 25% and altered the distribution between protected and unprotected forms by up to 40%. These model results more accurately reflected measured differences across the bioenergy crops in our lab experiment than simulations with the base model decomposition parameters. When we tested the refined model against field data, FUN-CORPSE captured differences in soil C stocks across all three feedstocks. These results highlight how coupled model-experiment approaches can improve our predictive understanding of soil C dynamics and the sustainability of bioenergy.