Although dedicated energy crops will probably be an important feedstock for future cellulosic bioenergy production, it is unknown how they can best be integrated into existing agricultural systems. Here we use the DayCent ecosystem model to simulate various scenarios for growing switchgrass in the heterogeneous landscape that surrounds a commercial-scale cellulosic ethanol biorefinery in southwestern Kansas, and quantify the associated fuel production costs and lifecycle greenhouse gas (GHG) emissions. We show that the GHG footprint of ethanol production can be reduced by up to 22 g of CO2 equivalent per megajoule (CO2e MJ–1) through careful optimization of the soils cultivated and corresponding fertilizer application rates (the US Renewable Fuel Standard requires a 56 gCO2e MJ−1 lifecycle emissions reduction for ‘cellulosic’ biofuels compared with conventional gasoline). This improved climate performance is realizable at modest additional costs, less than the current value of low-carbon fuel incentives. We also demonstrate that existing subsidized switchgrass plantings within this landscape probably achieve suboptimal GHG mitigation, as would landscape designs that strictly minimize the biomass collection radius or target certain marginal lands.
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This work was supported by USDA/NIFA project ‘Decision support tool for integrated biofuel GHG emission footprints’ (grant no. 2011-67009-30083), USDA/NIFA project ‘Sustainable biofuel feedstocks from beetle-killed wood: Bioenergy Alliance Network of the Rockies’ (grant no. 2013-68005-21298), a NSF IGERT fellowship through the Multidisciplinary Approaches to Sustainable Bioenergy programme at Colorado State University and a NSF REU fellowship and graduate Chevron fellowship through the Colorado Center for Biorefining and Biofuels (C2B2). We thank J. Marquez for her assistance in identifying and coding switchgrass field-trial papers for the parameterization and calibration data set, M. Stermer for his assistance in the quality control of this data set, K. Killian and T. Boyak for their advice and assistance in developing the model automation and analysis code, J. Schuler for her contribution to the GIS work, J. Kent for his help with DayCent growth submodel performance visualization and Y. Zhang for his insights on crop model performance in dry climates.
Supplementary Figures 1–12, Supplementary Tables 1–9, Supplementary References
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Nature Energy (2018)