Energy Inputs for 1 st and 2 nd Generation Ethanol Feedstocks: Modeling Effects of Cultivation Practices and Crop Selection on GHG Emissions

Transcription

1 Energy Inputs for 1 st and 2 nd Generation Ethanol Feedstocks: Modeling Effects of Cultivation Practices and Crop Selection on GHG Emissions Tristram O. West, Laurence M. Eaton, Chad Hellwinckel,* Mark E. Downing, Virginia H. Dale, Keith L. Kline Environmental Sciences Division Oak Ridge National Laboratory * Agriculture Policy Analysis Center, University of Tennessee October, 2009 Argonne, Illinois

2 Outline Data sets, sources, complexity Energy inputs and emissions Direct and off-site (embodied) emissions Soil carbon + other CO 2, CH 4, and N 2 O soil emissions Biomass accumulation/loss from land management Comparison: 1 st and 2 nd generation feedstock Land-use change Uncertainties and research needs 2 Managed by UT-Battelle

3 ORNL Bioenergy Programs Support DOE Energy Efficiency and Renewable Energy Office of Biomass Program Resource and Economic Analysis Environmental Effects Feedstock Logistics and Engineering Modeling International Resource Potential, Environmental Impacts GIS Data management and analysis NEPA support to DOE for the Integrated Biorefineries Intermediate Ethanol blend studies Center for Bioenergy Sustainability - integrates research and communication across multi-disciplinary teams: Many other areas supported e.g. DOE Office of Science - C-SITE (Carbon Sequestration in Terrestrial Ecosystems) focus on carbon flux analysis using multi-site approach: Bioenergy Science Center (BESC) develops viable, clean alternative fuel sources: National Transportation Research Center (NTRC) addresses issues from a systems perspective:

4 References Energy Use and Carbon Dioxide Emissions from Cropland Production in the United States, (2009) in J Environ Qual 38: R.G.Nelson, C.M.Hellwinckel, C.Brandt, T.West, D.De La Torre Ugarte, G.Marland Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems (2007) in Ecol App 17(3). P.R.Adler, S.J.Del Grosso, W.J.Parton Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model (2009) in Global Change Biology S.Ogle, et al. Deriving Comprehensive County-Level Crop Yield and Area Data for US Cropland (2007) in Agronomy Journal 99: E.Lokupitiya et al. In press: Net Ecosystem Exchange and Net Ecosystem Carbon Balance for Croplands in the US: Increasing Geospatial Resolution of Inventory-Based Carbon Accounting T.West et al. 4 Managed by UT-Battelle Data sources include: -USDA NASS -USDA ERS Census -USDA NRCS: NRI -CTIC -CDIAC

5 Data complexity County-level data (3,111 counties; 3,044 with crops) Crops, areas, yields, prices Soils (and historic land use) Weather variables Decomposition rates/factors Gaps and uncertainties in reporting, sampling Agricultural Budget System: Major crops represent 96% of cropland production Over 3,500 management input variables Planting operations, tillage practices Application systems, rates for fertilizers, pesticides, lime Irrigation, new technologies Rotations, regional variations, farming systems 5 Managed by UT-Battelle

6 Fossil-fuel emissions from agricultural production 6 Managed by UT-Battelle

7 Net C-equivalent emissions per county from US cropland production in Managed by UT-Battelle

8 Net C-equivalent emissions per cropland ha, per county from US cropland production, Managed by UT-Battelle

9 Net C-equivalent emissions per total county area from US cropland production in Managed by UT-Battelle

10 County distribution of agricultural carbon fluxes, including NPP and livestock emissions Sink 10 Managed by UT-Battelle

11 US Cropland Carbon Budget in 2004 (in Tg C yr -1 ) NPP 579 Decomposition 317 Food (Human) Feed (Livestock) Processing waste Fiber (Cotton) Fuel (Corn for ethanol) Beginning crop C stocks (11) Beginning nongrain C stocks (0.1) 11 Crop carbon 19 Net soil C change 243 carryover from previous year (2003) Harvested C stock (243) Non-grain C stock (3) Available C Stock for 2004 (248) Exported C Imported C Carryover or Reserve C 23 carryover to following year (2005) 11 Managed by UT-Battelle Approximate United States Agricultural Carbon Budget for 2004

12 Fossil-fuel emissions from agricultural production 12 Managed by UT-Battelle

13 Average annual GHG emissions associated with traditional crop production in the United States C-equivalent emissions (kg Ceq/ha/yr) On-site emissions Off-site emissions CO2 from Lime N2O from N fert Total Emissions Ceq/yr CORN 13 Managed by UT-Battelle SOYBEAN WHEAT COTTON HAY Crop SWG SRWC IDLE LAND CORN RESIDUE (ALL) WHEAT RESIDUE (ALL)

14 Another view of average US emissions Corn Wheat Hay Cotton Corn Residue Wheat Residue SRWC SWG On-site emissions Off-site emissions CO2 from Lime Soybean N2O from N fert 14 Managed by UT-Battelle

15 Average estimated GHG emissions and sources by crop, USA Annual GHG emissions from cropland production (Kg Ceq ha -1 y -1 ) Managed by UT-Battelle 0 CORN SOYBEAN WHEAT COTTON HAY SWG SRWC IDLE LAND Crop N2O from N fert CO2 from Lime Off-site emissions On-site emissions Corn residue Wheat residue

16 Based on prior averages, the annual change in GHG emissions associated with change in land use (Ceq emissions kg ha -1 yr -1 ) 600 C-equivalent emissions (kg Ceq/ha/yr) Change in annual Ceq emissions Change in soil carbon (~20 yr) Change in net Ceq emissions Corn to SWG 16 Managed by UT-Battelle Soy to SWG Wheat to SWG Cotton to SWG Hay to SWG Change in cropping practice Idle to SWG corn to corn residue corn residue to SWG

17 GHG emissions per unit of biomass WHEAT RESIDUE-21% CORN RESIDUE-28% WHEAT RESIDUE (ALL) CORN RESIDUE (ALL) Potential feedstocks SRWC SWG HAY COTTON WHEAT SOYBEAN CORN Managed by UT-Battelle Emissions to Yield Ratio (kg Ceq emissions/mg dry matter harvested - preliminary estimates)

18 Changes in US cropland (NASS planted area) USDA NASS "Area Planted" % change Area Change Change from 2001 to 2009 in: Percent Hectares x 1000 USDA Total for all crops -1.0% -1,321 USDA "Principal Crops" -1.1% -1,500 Losing acreage : 0 Cotton All -42.6% -2,718 Other Coarse Grains (not corn) -28.1% -2,491 Hay All (Dry) -5.3% -1,352 Oilseeds & peanuts exlcuding soy* -28.5% -748 Rice All -9.5% -128 Sugarcane + sugar beets -14.3% -139 Tobacco, potatoes, all others -14.5% -106 Gaining acreage : 0 All corn (grain + silage) 13.2% 4,369 Soybeans 4.9% 1,477 Edible beans, peas, lentils 50.0% 377 Wheat All 0.6% 139

19 Uncertainties Many data gaps still being filled, even for US (Lokupitiya et al.) International data more uncertain, added variables (fire, etc.) Scale matters (temporal, spatial) SOC analysis (Ogle et al. 2009) : median uncertainty from scaling down +/-118% at regional scale (18-500%); +/-740% at site scale Different systems operate on different scales Prior land-use history matters Land-use classifications (tillage intensity; transitions; areas not actively harvested e.g. grasslands. idle etc.) Productivity variables in rotations and LUC scenarios Emission estimates for new feedstocks - scaling up from trials risky. Lack comparable commercial data for cellulosic supply systems 19 Managed by UT-Battelle

20 Future research Build on current data sets to consider effects of changes in US land use in response to biofuels Better data on soils, inputs (detailed land use) and outputs (yields and services) spatially, and over time Characterize management practices associated with bioenergy crop production and effects (yields and soil carbon) Soil carbon (especially below 30cm) and under different biogeochemical conditions To address indirect effects of US policy must improve estimates of direct effects (fossil and renewable) Complex and integrated systems make allocation of GHG emissions among food, feed, fiber and fuel complicated 20 Managed by UT-Battelle

21 Conclusions Small adjustments in major systems (US grains) can have significant impacts on emissions Adoption of reduced tillage 2.4 Tg Ceq reduction (US ) Emissions are products of many interacting factors over time (not simply crop choice, change in cropland area, or no-till in year x) Efficiency is important (caution with uni-dimensional data) Sustainability much broader than GHG emissions. Tools need to measure persistence of productive capacity ability to maintain ecosystem services with decreasing inputs, over time 21 Managed by UT-Battelle

22 Thank you! For more information, contact: Tris O. West Acknowledgement: This research was supported by the U.S. Department of Energy (DOE) under the Office of the Biomass Program. Oak Ridge National Laboratory is managed by the UT-Battelle, LLC, for DOE under contract DE-AC05-00OR The views in this presentation are those of the authors and not necessarily those of ORNL, DOE or other institutions. Extra slide information resources 22 Managed by UT-Battelle

23 Information Resources ORNL Center for Bioenergy Sustainability (final workshop materials to be posted as they become available) DOE Biomass and Biofuels Program - DOE Office of Science, Bioenergy Research Centers Alternative Fuels Data Center - Bioenergy Feedstock Information Network Biomass R&D Initiative EERE INFO CENTER - Perspective on research choice ref. switchgrass (in Southeast)