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High-resolution techno–ecological modelling of a bioenergy landscape to identify climate mitigation opportunities in cellulosic ethanol production


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.

Author information

J.L.F. co-developed the analysis concept, conducted the DayCent and GREET analyses and optimization, and wrote the manuscript. S.G.E. developed the switchgrass-production budget and cost-assessment methods. E.M. and M.E. performed the GIS work and DayCent input data processing. P.R.A. helped develop the analysis concept and provided key spatial-data inputs. T.D. assisted with the lifecycle-assessment modelling and system optimization. K.P. and B.W. helped develop the analysis concept and supervised the work.

Competing interests

The authors declare no competing financial interests.

Correspondence to John L. Field.

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Further reading

Fig. 1: Soil textures, current land use and land quality in the case study area.
Fig. 2: Optimal soils and management for feedstock production on cropland under increasing carbon prices.
Fig. 3: GHG and cost performance of optimized feedstock production on cropland under increasing carbon prices.
Fig. 4: Cellulosic ethanol cost and GHG performance trade-offs.