Integrating Measurements and Models to Improve Projections of Ecosystem Carbon Balance in Bioenergy Agriculture
Stephanie M. Juice1,2* (firstname.lastname@example.org), Carl J. Bernacchi1,3, Bethany J. Blakely1,3, Melannie D. Hartman1,4, Caitlin E. Moore1,3, William J. Parton1,4, Bryan M. Petersen1,5, Joanna R. Ridgeway1,2, Jacob E. Studt1,5, Andy VanLoocke1,5, Wendy H. Yang1,3, and Edward R. Brzostek1,2
1Center for Advanced Bioenergy and Bioproducts Innovation; 2West Virginia University; 3University of Illinois–Urbana; 4Colorado State University; and 5Iowa State University
The goal of the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) Sustainability Theme research is to design a sustainable bioeconomy (Figure 1). A critical part of meeting this goal is to integrate empirical measurements with models to project future scenarios of bioenergy systems that can inform sustainable management choices that enhance carbon (C) sequestration and nitrogen (N) retention. Here, researchers present multiple case studies demonstrating how observational and experimental measurements have been used to enhance the predictive capabilities of ecosystem models.
Research in the CABBI Sustainability Theme aims to understand the interactions between candidate bioenergy feedstocks, and the environmental conditions and geographic locations under which they are grown in order to inform the understanding of key factors needed for the economically and environmentally sustainable production of bioenergy and bioproducts for fossil fuel displacement. Within this framework, advancing predictive understanding of bioenergy systems through ecological modeling is critical to address how to provide energy, economic, and ecosystem C benefits to help slow the rate of climate change. To meet this challenge, CABBI researchers couple empirical measurements to ecosystem models that simulate plant yields and nutrient dynamics in order to predict future ecosystem C stocks under different environmental and feedstock scenarios. Here, researchers present three case studies illustrating how CABBI researchers integrate data with models to improve projections of bioenergy C balance.
- Researchers developed a new bioenergy ecosystem model, FUN-BioCROP that simulates plant-microbe interactions, microbial physiology, and emerging mechanisms of stable soil C creation and Model parameterization integrated both observational and experimental data. On the observational side, this included long-term measurements of total soil C pools and the form of soil C stabilization under different bioenergy crops. On the experimental side, researchers used a novel laboratory experiment to trace the fate of two bioenergy litters (miscanthus and corn) to improve the parameterization of key microbial traits. When the team ran the improved model forward, researchers found divergent responses of bioenergy feedstocks to environmental change.
- The predictive ability of ecosystem models is primarily constrained by data quality and quantity. Using model sensitivity analyses, researchers identified root and rhizome biomass, the response of plant C allocation to N fertilization and cycling of the litter layer as key data limitations to the predictive understanding of miscanthus by the Agro-IBIS model. To address these limitations, the team designed targeted field campaigns. The team found that N fertilization alters both the magnitude and timing of belowground C allocation and that the litter layer in miscanthus comprises ~1/4 of the total aboveground biomass. Both findings represent key processes and C pools that are priorities for future model revisions.
- DayCent has been at the forefront of models used to predict the C and N consequences of bioenergy production. A common criticism of DayCent has been its assumption that soil decomposition follows first order decay. To address this criticism, researchers integrated the explicit microbial dynamics of FUN-BioCROP into DayCent. The microbial DayCent model better captured the seasonal profile of ecosystem respiration of miscanthus and switchgrass derived from eddy-covariance measurements. Moreover, it also showed an upper limit to soil C accumulation over time, whereby ongoing plant inputs enhanced microbial biomass leading to priming losses of soil C. These results have important implications for estimating soil C accumulation in the emerging bioeconomy.
Across these case studies, the integration of empirical data into models resulted in improved C balance projections that represent the most up to date state of knowledge of the factors creating persistent soil C and allowing for the most sustainable bioenergy crop production.
Figure 1. CABBI Sustainability Theme research integrates measurements with models across scales to identify the combinations of feedstocks, land types, market conditions, and bioproducts that will be able to sustainably displace fossil fuels.
This work was funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-SC0018420). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U.S. Department of Energy.