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

  • Nature Energyvolume 3pages211219 (2018)
  • doi:10.1038/s41560-018-0088-1
  • Download Citation
Published online:


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.

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Energy Independence and Security Act of 2007 Public Law 110-140 (US Government, 2007).

  2. 2.

    Sanchez, D. L., Nelson, J. H., Johnston, J., Mileva, A. & Kammen, D. M. Biomass enables the transition to a carbon-negative power system across western North America. Nat. Clim. Change 5, 230–234 (2015).

  3. 3.

    Fuss, S. et al. Betting on negative emissions. Nat. Clim. Change 4, 850–853 (2014).

  4. 4.

    US Department of Energy US Billion-Ton Update: Biomass Supply for a Bioenergy and Biproducts Industry ORNL/TM-2011/224 (eds Perlack, R. D. & Stokes, B. J.) (Oakridge National Laboratory, 2011).

  5. 5.

    Sheehan, J. et al. Energy and environmental aspects of using corn stover for fuel ethanol. J. Ind. Ecol. 7, 117–146 (2003).

  6. 6.

    Robertson, G. P., Hamilton, S. K., Del Grosso, S. J. & Parton, W. J. The biogeochemistry of bioenergy landscapes: carbon, nitrogen, and water considerations. Ecol. Appl. 21, 1055–1067 (2011).

  7. 7.

    Zilberman, D., Hochman, G. & Rajagopal, D. Indirect land use change: a second-best solution to a first-class problem. AgBioForum 13, 382–390 (2010).

  8. 8.

    Paustian, K. et al. Climate-smart soils. Nature 532, 49–57 (2016).

  9. 9.

    Bouwman, A. F., Boumans, L. J. M. & Batjes, N. H. Emissions of N2O and NO from fertilized fields: summary of available measurement data. Global Biogeochem. Cycles 16, 1058 (2002).

  10. 10.

    Davis, S. C. et al. Management swing potential for bioenergy crops. GCB Bioenergy 5, 623–638 (2013).

  11. 11.

    Adler, P. R. et al. in Managing Agricultural Greenhouse Gases (eds Liebig, M. et al.) 203–219 (Elsevier, Oxford, 2012).

  12. 12.

    Aden, A. et al. Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover NREL/TP-510-32438 (National Renewable Energy Laboratory, Golden, CO, 2002).

  13. 13.

    Gelfand, I. et al. Sustainable bioenergy production from marginal lands in the US Midwest. Nature 493, 514–517 (2013).

  14. 14.

    Shield, I. F., Barraclough, T. J. P., Riche, A. B. & Yates, N. E. The yield response of the energy crops switchgrass and reed canary grass to fertiliser applications when grown on a low productivity sandy soil. Biomass Bioenergy 42, 86–96 (2012).

  15. 15.

    Wu, Y., Liu, S. & Li, Z. Identifying potential areas for biofuel production and evaluating the environmental effects: a case study of the James River Basin in the Midwestern United States. GCB Bioenergy 4, 875–888 (2012).

  16. 16.

    Yu, T. E., Wang, Z., English, B. C. & Larson, J. A. Designing a dedicated energy crop supply system in Tennessee: a multiobjective optimization analysis. J. Agric. Appl. Econ. 46, 357–373 (2014).

  17. 17.

    Zhang, X. et al. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems. GCB Bioenergy 2, 258–277 (2010).

  18. 18.

    Gramig, B. M., Reeling, C. J., Cibin, R. & Chaubey, I. Environmental and economic trade-offs in a watershed when using corn stover for Bioenergy. Environ. Sci. Technol. 47, 1784–1791 (2013).

  19. 19.

    Field, J. L., Marx, E., Easter, M., Adler, P. R. & Paustian, K. Ecosystem model parameterization and adaptation for sustainable cellulosic biofuel landscape design. GCB Bioenergy 8, 1106–1123 (2016).

  20. 20.

    Peplow, M. Cellulosic ethanol fights for life. Nature 507, 152–153 (2014).

  21. 21.

    Federal Register Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Final Rule. 75 40 CFR Part 80 4669–15320 (US Environment Protection Agency, 2010).

  22. 22.

    You, F., Tao, L., Graziano, D. J. & Snyder, S. W. Optimal design of sustainable cellulosic biofuel supply chains: multiobjective optimization coupled with life cycle assessment and input–output analysis. AIChE J. 58, 1157–1180 (2012).

  23. 23.

    Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (Interagency Workgroup on Social Cost of Carbon, 2010).

  24. 24.

    Richards, B. K., Stoof, C. R., Cary, I. J. & Woodbury, P. B. Reporting on marginal lands for bioenergy feedstock production: a modest proposal. BioEnergy Res. 7, 1060–1062 (2014).

  25. 25.

    Kang, S. et al. Hierarchical marginal land assessment for land use planning. Land Use Policy 30, 106–113 (2013).

  26. 26.

    Swinton, S. M., Babcock, B. A., James, L. K. & Bandaru, V. Higher US crop prices trigger little area expansion so marginal land for biofuel crops is limited. Energy Policy 39, 5254–5258 (2011).

  27. 27.

    Klingebiel, A. A. & Montgomery, P. H. Land-Capability Classification (Department of Agriculture, Washington DC, 1961).

  28. 28.

    O’Hare, M. et al. Comment on “Indirect land use change for biofuels: Testing predictions and improving analytical methodologies” by Kim and Dale: statistical reliability and the definition of the indirect land use change (iLUC) issue. Biomass Bioenergy 35, 4485–4487 (2011).

  29. 29.

    Wilson, D. M. et al. Establishment and short-term productivity of annual and perennial bioenergy crops across a landscape gradient. Bioenerg. Res. 7, 885–898 (2014).

  30. 30.

    Roncucci, N., o Di Nasso, N. N., Bonari, E. & Ragaglini, G. Influence of soil texture and crop management on the productivity of miscanthus (Miscanthus × giganteus Greef et Deu.) in the Mediterranean. GCB Bioenergy 7, 998–1008 (2015).

  31. 31.

    Fewell, J., Bergtold, J. & Williams, J. Farmers’ willingness to grow switchgrass as a cellulosic bioenergy crop: a stated choice approach. Proc. 2011 Joint Annual Meeting Canadian Agricult. Econ. Soc. Western Agricult. Econ. Assoc. 109776 (2011).

  32. 32.

    Smith, P. & Smith, T. J. F. Transport carbon costs do not negate the benefits of agricultural carbon mitigation options. Ecol. Lett. 3, 379–381 (2000).

  33. 33.

    Larson, E. et al. Co-production of decarbonized synfuels and electricity from coal + biomass with CO2 capture and storage: an Illinois case study. Energy Environ. Sci. 3, 28–42 (2010).

  34. 34.

    Zilberman, D., Hochman, G. & Rajagopal, D. On the inclusion of indirect land use in biofuel regulations. Univ. Illinois Law Rev. 2011, 413–434 (2011).

  35. 35.

    Paustian, K. et al. Counting carbon on the farm: Reaping the benefits of carbon offset programs. J. Soil Water Conserv. 64, 36A–4A (2009).

  36. 36.

    US EPA Summary Lifecycle Analysis Greenhouse Gas Results for the U.S. Renewable Fuels Standard Program (Office of Transportation and Air Quality, 2016).

  37. 37.

    Wickham, J. D. et al. Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sens. Environ. 130, 294–304 (2013).

  38. 38.

    Pervez, M. S. & Brown, J. F. Mapping irrigated lands at 250-m scale by merging MODIS data and national agricultural statistics. Remote Sens. 2, 2388–2412 (2010).

  39. 39.

    Ernstrom, D. J. & Lytle, D. Enhanced soils information systems from advances in computer technology. Geoderma 60, 327–341 (1993).

  40. 40.

    Boryan, C., Yang, Z., Mueller, R. & Craig, M. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto Int. 26, 341–358 (2011).

  41. 41.

    Del Grosso, S. J., Parton, W. J., Keough, C. A. & Reyes-Fox, M. in Advances in Agricultural Systems Modeling (eds Ahuja, L. R. & Ma, L.) 155–176 (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI, 2011).

  42. 42.

    Lee, D. K., Owens, V. N. & Doolittle, J. J. Switchgrass and soil carbon sequestration response to ammonium nitrate, manure, and harvest frequency on Conservation Reserve Program Land. Agron. J. 99, 462 (2007).

  43. 43.

    Framework for Assessing Biogenic CO 2 Emissions from Stationary Sources (US Environmental Protection Agency, 2014).

  44. 44.

    Mesinger, F. et al. North American Regional Reanalysis. Bull. Am. Meteorol. Soc. 87, 343–360 (2006).

  45. 45.

    Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (Institute for Global Environmental Strategies, 2006).

  46. 46.

    Jain, A. K., Khanna, M., Erickson, M. & Huang, H. An integrated biogeochemical and economic analysis of bioenergy crops in the Midwestern United States. GCB Bioenergy 2, 217–234 (2010).

  47. 47.

    Khanna, M., Dhungana, B. & Clifton-Brown, J. Costs of producing miscanthus and switchgrass for bioenergy in Illinois. Biomass Bioenergy 32, 482–493 (2008).

  48. 48.

    James, L. K., Swinton, S. M. & Pennington, D. R. Profitability of Converting to Biofuel Crops Bulletin E-3084 (Michigan State Univ. Extension, 2009).

  49. 49.

    Duffy, M. Estimated Costs for Production, Storage and Transportation of Switchgrass (Iowa State Univ. Extension, 2008).

  50. 50.

    Guideline Switchgrass Establishment And Annual Production Budgets Over Three Year Planning Horizon (Univ. Tennessee Institute of Agriculture, 2009).

  51. 51.

    Dhuyvetter, K. C. & Taylor, M. Kansas Land Prices and Cash Rental Rates (Kansas State Univ., 2014).

  52. 52.

    Conservation Reserve Program—Average Rental Payments by Fiscal Year (US Department of Agriculture, Food Service Agency).

  53. 53.

    Norris, K. UTK Trucking Cost Model (Univ. Tennessee, 2009).

  54. 54.

    Wang, M., Han, J., Dunn, J. B., Cai, H. & Elgowainy, A. Well-to-wheels energy use and greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic biomass for US use. Environ. Res. Lett. 7, 045905 (2012).

  55. 55.

    Gutesa, S., Darr, M. J. & Shah, A. Large Square Bale Biomass Transportation Analysis (Iowa State Univ., 2012).

  56. 56.

    Brander, M., Tipper, R., Hutchison, C. & Davis, G. Consequential and Attributional Approaches to LCA: a Guide to Policy Makers with Specific Reference to Greenhouse Gas LCA of Biofuels Technical Paper TP‐090403‐A (Ecometrica, 2009).

  57. 57.

    Wang, Z., Dunn, Jennifer B., Han, J. & Wang, M. Q. Material and Energy Flows in the Production of Cellulosic Feedstock for Biofuels for the GREET Model (Argonne National Laboratory, 2013).

  58. 58.

    Hanna, M. Fuel Required for Field Operations (Iowa State University Extension, 2005).

  59. 59.

    Dunn, J. B., Eason, J. & Wang, M. Q. Updated Sugarcane and Switchgrass Parameters in the GREET Model (Argonne National Laboratory, 2011).

  60. 60.

    Babcock, B. Measuring unmeasurable land-use changes from biofuels. Iowa Ag. Rev. 15, 4–6 (2009).

  61. 61.

    Warner, E., Zhang, Y., Inman, D. & Heath, G. Challenges in the estimation of greenhouse gas emissions from biofuel-induced global land-use change. Biofuels, Bioprod. Bioref. 8, 114–125 (2013).

  62. 62.

    Zilberman, D. Indirect land use change: much ado about (almost) nothing. GCB Bioenergy 9, 485–488 (2016).

  63. 63.

    Fritsche, U. R., Sims, R. E. H. & Monti, A. Direct and indirect land-use competition issues for energy crops and their sustainable production—an overview. Biofuels Bioprod. Bioref. 4, 692–704 (2010).

  64. 64.

    Wang, M. Q. et al. Energy and greenhouse gas emission effects of corn and cellulosic ethanol with technology improvements and land use changes. Biomass Bioenergy 35, 1885–1896 (2011).

  65. 65.

    Gadberry, S. & Beck, P. Substituting Grain for Hay in Beef Cow Diets (Univ. Arkansas Cooperative Extension Service, 2010).

  66. 66.

    Lardy, G. P. Feeding Corn to Beef Cattle (North Dakota Sate Univ. Extension Service, 2002).

  67. 67.

    Gnansounou, E. & Dauriat, A. Techno-economic analysis of lignocellulosic ethanol: a review. Bioresour. Technol. 101, 4980–4991 (2010).

  68. 68.

    Monthly LCFS Credit Transfer Activity Report for February 2016 (California Air Resources Board, 2016).

  69. 69.

    Cellulosic Waiver Credit Price Calculation for 2016 (US Environmental Protection Agency, 2015).

Download references


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


  1. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA

    • John L. Field
    • , Ernie Marx
    • , Mark Easter
    •  & Keith Paustian
  2. Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA

    • John L. Field
    •  & Bryan Willson
  3. Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, CA, USA

    • Samuel G. Evans
  4. Pasture Systems and Watershed Management Research Unit, Department of Agriculture-Agricultural Research Service, University Park, PA, USA

    • Paul R. Adler
  5. School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA

    • Thai Dinh
  6. Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA

    • Keith Paustian


  1. Search for John L. Field in:

  2. Search for Samuel G. Evans in:

  3. Search for Ernie Marx in:

  4. Search for Mark Easter in:

  5. Search for Paul R. Adler in:

  6. Search for Thai Dinh in:

  7. Search for Bryan Willson in:

  8. Search for Keith Paustian in:


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.

Corresponding author

Correspondence to John L. Field.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–12, Supplementary Tables 1–9, Supplementary References