An Integrated Landscape Management Approach to Sustainable Bioenergy Production
- 399 Downloads
Integrated landscape management has emerged in recent years as a methodology to integrate the environmental impacts of various agricultural practices along with yield and profitability in a variety of cropping systems. In this study, the Landscape Environmental Assessment Framework (LEAF), a decision support toolset for use in integrated landscape management and developed at Idaho National Laboratory, was used to evaluate the profitability of grain-producing subfields, to determine the efficacy of sustainably harvesting residual biomass after grain harvest, and to determine the efficacy of integrating bioenergy crops into grain-producing landscapes to enhance farmer profitability. Three bioenergy crops, sorghum, switchgrass, and miscanthus, were integrated into non-profitable subfields in four US counties. The manuscript describes in detail the material and methods used to define crop rotations, land management units and practices, subfield units and productivity, grain profitability, sustainability criteria, energy crop integration, and feedstock cost estimation. With the integration of bioenergy crops, the overall annual biomass production rates in the four counties could be increased by factors ranging from 0.8 to 21, depending on the energy crop and county, over the annual residue biomass production rates. By modeling the harvesting of residual biomass and energy crops using geo-referenced, precision harvesting equipment and optimal harvesting paths on individual subfields, the average logistics costs including harvesting of both residual biomass and energy crops were observed to fall well below US DOE’s 2017 goals for biomass feedstock price of US$84/ton or US$92.6/dry Mg. Miscanthus, grown in counties in Ohio and Kansas, provided the maximum potential, among the three energy crops considered, for increment in biomass production and also posed maximum threat to the grain production. Considerable variability was observed in the harvesting and total costs because of the size, shape, and productivity of individual subfields. It was shown that variability in the harvesting costs could be used to down-select non-profitable farms with low harvesting costs and high residue and bioenergy crop yields and to reduce the negative impacts of bioenergy crop integration into croplands on grain production. The results of the assessment suggest that (1) the potential to produce biomass is considerably enhanced when non-profitable grain-producing subfields are replaced by bioenergy crops and (2) the subfield-scale integrated landscape assessment provides a defensible methodology to directly address individual farmer’s profitability, sustainability, and environmental stewardship.
KeywordsLandscape Environmental Assessment Framework LEAF Biomass Energy crops Switchgrass Miscanthus Feedstock Herbaceous biomass Logistics cost Bioenergy
This work was supported by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, under DOE Idaho Operations Office Contract DE-AC07-05ID14517. Accordingly, the US Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for US Government purposes. The authors would also like to acknowledge the analytical support provided by AgSolver to this study.
- 1.Sissine F (2007) Energy Independence and Security Act of 2007: a summary of major provisions, DTIC Document.Google Scholar
- 2.Schwab A (2016) Bioenergy Technologies Office Multi-Year Program Plan: March 2016, Bioenergy Technologies Office.Google Scholar
- 9.Vogel KP (2004) Switchgrass. In: Moser LE, Burson BL, Sollenberger LE (eds) Warm-season (C4). Grasses. ASA-CSSA-SSSA, Madison, pp 561–588Google Scholar
- 11.Kiniry JR, Anderson LC, Johnson MV, Behrman KD, Brakie M, Burner DM, Cordsiemon RL, Fay PA, Fritschi FB, Houx JH III, Hawkes C, Juenger T, Kaiser J, Keitt T, Lloyd-Reilley J, Maher S, Raper R, Scott A, Shadow A, West C, Wu Y, Zibilske LM (2013) Perennial biomass grasses and the Mason-Dixon line: comparative productivity across latitudes in the southern Great Plains. BioEnergy Res 6:276–291CrossRefGoogle Scholar
- 12.Shoemaker CE, Bransby DI (2011) The role of sorghum as a bioenergy feedstock. In: R. Braun, D.L. Karlen, and D. Johnson (eds.) Sustainable Alternative Fuel Feedstock Opportunities, Challenges and Roadmaps for Six U.S. Regions. Proceedings of the Sustainable Feedstocks for Advanced Biofuel Workshop. Sept. 27–29, 2010. Atlanta, GA. Soil and Water Conservation Society, Ankeny, IA 50023 149–159.Google Scholar
- 22.Werling BP, Dickson TL, Isaacs R, Gaines H, Gratton C, Gross KL, Liere H, Malmstrom CM, Meehand TD, Ruan L, Robertson BA, Robertson GP, Schmidt TM, Schrotenboer AC, Teal TK, Wilson JK, Landis DA (2014) Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes. Proceedings of National Academy of Sciences (PNAS) 111(4):1652–1657CrossRefGoogle Scholar
- 25.Bourke D, Stanley DA, O’Rourke E, Thompson R, Carnus T, Dauber J, Emmerson M, Whelan P, Hecq F, Flynn E, Dolan L, Stout J (2014) Response of farmland biodiversity to the introduction of bioenergy crops: effects of local-factors and surrounding landscape context. GCB Bioenergy 6:275–289CrossRefGoogle Scholar
- 28.Dale VH, Kling C, Meyer JL, Sanders J, Stallworth H, Armitage T, Wangsness D, Bianchi T, Blumberg A, Boynton W, Conley DJ, Crumpton W, David MB, Gilbert D, Howarth RW, Lowrance R, Mankin K, Opaluch J, Pearl H, Reckhow K, Sharpley AN, Simpson TW, Snyder C, Wright D (2010) Hypoxia in the Northern Gulf of Mexico. Springer Series on Environmental Management. ISBN 0172-6161, ISBN 978-0-387-89685-4, e-ISBN 978-0-387-89686-1, DOI 10.1007/978-0-387-89686-1. Springer New York.
- 30.Min QW, Jiao YL (2002) Effects of agricultural non-point source pollution on eutrophication of water body and its control measure. Acta Ecological Sinica:2002–2003Google Scholar
- 33.Negri MC (2016) Bioenergy Sustainability and the Food, Energy, Land and Water Nexus. Presented at DOE’s Bioenergy Solutions to Gulf Hypoxia Workshop. Aug 30–31, 2016, Washington, DCGoogle Scholar
- 34.Wu M (2016) Modeling water quality in the Mississippi River Basin: Upper Mississippi, Ohio, and Missouri River Basins. Presented at DOE’s Bioenergy Solutions to Gulf Hypoxia Workshop. Aug 30–31, 2016, Washington, DC.Google Scholar
- 35.McIsaac GF (2016) Framing the problem: nutrient source identification, accounting, and attribution. Presented at DOE’s Bioenergy Solutions to Gulf Hypoxia Workshop. Aug 30–31, 2016, Washington, DCGoogle Scholar
- 36.Demissie Y, Yan E, Wu M, Zhang Z (2012) Watershed modeling of potential impacts of biofuel feedstock production in the Upper Mississippi River Basin. Environmental Sciences Division, Argonne National Laboratory Report, ANL/EVS/AGEM/TR-12-07, April 2012.Google Scholar
- 40.Selman, M. (2016) Office of environmental markets: Water quality trading and synergies with BETO. Presented at DOE’s Bioenergy Solutions to Gulf Hypoxia Workshop. Aug 30–31, 2016, Washington, DC.Google Scholar
- 41.Reed D (2016) Supply and demand for ecosystem credits. Presented at DOE’s Bioenergy Solutions to Gulf Hypoxia Workshop. Aug 30–31, 2016, Washington, DCGoogle Scholar
- 42.Fox J (2016) Monetizing ecosystem services and other challenges. Presented at DOE’s Bioenergy Solutions to Gulf Hypoxia Workshop. Aug 30–31, 2016, Washington, DCGoogle Scholar
- 43.Biomass Crop Assistance Program (BCAP). https://www.fsa.usda.gov/programs-and-services/energy-programs/BCAP/index (accessed on December 01, 2016).
- 44.Millennium Ecosystem Assessment (2005) Living beyond our means: natural assets and human well-being. Island Press, WashingtonGoogle Scholar
- 45.Mueller ND, Gerber JS, Johnston M, Deepak KR, Ramankutty M, Foley JA (2012) Closing yield gaps through nutrient and water management. Nature (Published online 29 August 2012) doi: 10.1038/nature 11420.
- 49.Rooney WL (2000) Sorghum. Cellulosic Energy Cropping Systems:109–129Google Scholar
- 51.Revised Universal Soil Loss Equation, Version 2 (RUSLE2), Official NRCS RUSLE2 Program. Official NRCS Database. http://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm.
- 52.Scheffe L, Ferruzzi G, Boetger S, and Woodruff S (2015) RUSLE2 advanced data management. http://fargo.Nserl.Purdue.Edu/RUSLE2_ftp/NRCS_Base_Database/RUSLE2%20Instructional%20Mat erial/training%20Presentations/RUSLE2%20Advanced%20Data%20Management.Pptx.
- 53.Cotton News (2011) County production figures https://www.plainscotton.org/esw/news/cotnewsback/2012/CN120511.htm.
- 54.Jacobson JJ, Roni MS, Lamers P, Cafferty KG (2014) Biomass feedstock and conversion supply system design and analysis. Idaho National Laboratory, Idaho Falls, ID. INL/EXT-14-32377.Google Scholar
- 55.Cafferty KG, Muth DJ, Jacobson JJ, Bryden KM (2013a) Model Based Biomass System Design of Feedstock Supply Systems for Bioenergy Production. In: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 4–7. Portland, OR, USAGoogle Scholar
- 56.University of Georgia’s Extension Engineering Handbook. http://www.caes.uga.edu/departments/bae/extension/handbook/#geninfo.
- 57.Hanna M (2016) Estimating the field capacity of farm machines. Iowa State University, Extension and Outreach, Ames, IA. PM 696. Available from https://www.extension.iastate.edu/agdm/crops/pdf/a3-24.pdf.