Biofuels from crop residue can reduce soil carbon and increase CO 2 emissions

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1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE2187 Biofuels from crop residue can reduce soil carbon and increase CO 2 emissions Adam J. Liska 1,2*, Haishun Yang 2, Maribeth Milner 2, Steve Goddard 3, Humberto Blanco Canqui 2, Matthew P. Pelton 1, Xiao X. Fang 1, Haitao Zhu 3, Andrew E. Suyker 4 1 Department of Biological Systems Engineering, 2 Department of Agronomy and Horticulture, 3 Department of Computer Science and Engineering, 4 School of Natural Resources, University of Nebraska Lincoln, Nebraska 68583, USA. *e mail: aliska2@unl.edu Supplementary Tables 1 8 Coefficients used in the SOC model; Comparison of measured and modeled SOC change under continuous corn at Mead; Carbon inputs to soil at Mead; Field measurements using tower eddy covariance of respiration and gross primary productivity; Net oxidation of SOC from residue removal compared with no removal; Percent loss of SOC from residue removal compared with no removal; Net contribution of SOC oxidation to the lifecycle of biofuels from crop residue; Net GHG emissions from net SOC oxidation and N 2 O in the life cycle of cellulosic ethanol. Supplementary Figures and Legends 1 4 Modeled oxidation of SOC and crop residue over time; Geospatial modeling of SOC at Mead corn experiment; Percent SOC loss from residue removal; Net contribution of SOC oxidation to the lifecycle of biofuels from residue removal. Supplementary Notes Additional references for SI figures. NATURE CLIMATE CHANGE 1

2 Table S1. Coefficients used in the SOC model. Global data sets 1 were used to calibrate coefficients for soil organic matter (from manure, peat, organic matter, and soils) and plant residues (from wheat, maize, rice, oat, ryegrass, and cover crop data). Parameter Soil Organic Matter Plant Residue k (day (1 S) ) S (unitless 0 S 1) T r (Celsius) Q Residue-C & SOC remaining (%) SOC yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr Days Figure S1. Modeled oxidation of SOC and crop residue over time. Oxidation of SOC and 9 residue components is based on daily average temperatures measured at the experimental site at Mead, NE, and parameters used from Table S1. Table S2. Comparison of measured and modeled SOC change under continuous corn at Mead. All values in Mg C ha 1 30 cm 1, unless noted. Standard Deviation shown. Measured 2001 Measured 2005 Modeled 2005 ΔMeasured ΔModeled Modeled/Measured ± ± % 2

3 a b 4 30cm SOC -2 (Mg ha ) Km SOC (Mg C per hectare) 0-4 R1 R2 R3 R Time (days) Figure S2. Geospatial modeling of SOC at Mead corn experiment. a, Initial SOC map of Mead, Nebraska field site, composed of m x 30 m grid cells (Albers Conic Equal Area projection, NAD83 datum) 3 6 ; the field is roughly a quarter of a square mile (~48 hectares) and is irrigated by a center pivot. b, Removal of 0, 2, 4, 6 Mg ha 1 yr 1 residue (R1 R4 respectively) for the area shown in a, with standard deviation (SD) for no biomass removal; all four simulations have the same SD. For analysis of the area planted in corn or soybean across the US Corn Belt, masks were identified by the 2010 USDA NASS Cropland Data Layer of each state 7. Rainfed county corn grain yield estimates from NASS ( ) 8 were converted to Mg C ha 1 yr 1 using Tiger data and a harvest index (0.53) 9 ; total root C inputs to 0 30 cm soil depth over the growing season were estimated at 29% of aboveground carbon 10, which includes C from root exudates, to not underestimate C added to soil after residue removal. Monthly maximum and minimum average temperatures from the PRISM database ( ) were used 11 ; the original PRISM grid resolution was 30 arcseconds (~4 kilometers). The gssurgo SOC data included non soil data so all zero SOC values were removed 12. 3

4 Table S3. Carbon inputs to soil at Mead. All values are in grams C per square meter (g C m 2 ), except grain yield in Mg ha 1 yr 1 at 15.5% moisture. Crop C is the sum of Grain C, Residue C, and Root C (only 66% of Root C is contained in the top 30 cm of soil 13 ). Input of C to soil in the top 30 cm is C i. For estimation of soil respiration (i.e. CO 2, Table S4), the remaining root biomass C at physiological maturity is estimated as 16% of aboveground shoot biomass (e.g. residue C) 10. Year Grain Yield Grain C Residue C Root C Crop C C i yr

5 Table S4. Field measurements using tower eddy covariance of respiration (Re total, upward flux of CO 2 ) and gross primary productivity (GPP, downward flux of CO 2 ). Irrigation water contains CO 2 which was subtracted from respiration 14,15. The C contained in the crop at physiological maturity (Crop C, Table S3) was subtracted from GPP to estimate crop respiration during growth (Re crop ). To estimate respiration from soil, residue, and dead root (i.e. Re rsoc ), Re crop was 16 subtracted from Re total. Standard deviations associated with these flux measurements were assumed at ±10% of the mean 17,18 (Re rsoc SD). Using the SOC model (Figure S1, & Figure 1 in main text), respiration was calculated based on the parameters above (Re SOCM ). Percent comparisons between the measured and modeled Re values are shown. All values are in grams C per square meter (g C m 2 ), except where noted. The average annual error is 2.5%. The total absolute annual error is 12.4%. Year irrigation Re total GPP Re crop Re rsoc Re rsoc SD Re SOCM % error

6 Table S5. Net oxidation of SOC from removal residue compared with no removal. Removal of 2 (R2), 4 (R3), and 6 (R4) Mg residue ha 1 yr 1 at 5 and 10 year averages, relative no removal (R1). Averages calculated across the Corn Belt (580 million cells). 5 Year Average 10 Year Average R1 R2 R1 R3 R1 R4 R1 R2 R1 R3 R1 R4 Average, Mg C ha 1 yr Standard Deviation Table S6. Percent loss of SOC from removal residue compared with no removal. Averages calculated across the Corn Belt (580 million cells), corresponding to the removal levels in table S5. 5 Year Average 10 Year Average R1 R2 R1 R3 R1 R4 R1 R2 R1 R3 R1 R4 Average, % Standard Deviation Table S7. Net contribution of SOC oxidation to the lifecycle of biofuels from crop residue. Averages calculated across the Corn Belt (580 million cells), corresponding to the removal levels in table S5. 5 Year Average 10 Year Average R1 R2 R1 R3 R1 R4 R1 R2 R1 R3 R1 R4 Average, gco 2 e MJ Standard Deviation

7 a 5 Year % SOC Km b 10 Year % SOC Km c Geospatial cells (millions) yr R1-R2 5yr R1-R3 5yr R1-R4 10yr R1-R2 10yr R1-R3 10yr R1-R Percent (%) SOC loss Figure S3. Percent SOC loss from residue removal. Removal of 6 Mg residue ha 1 yr 1 : a, 5 year average, b, 10 year average, c, percent SOC loss by removal level. 7

8 Table S8. Net GHG emissions from net SOC oxidation and N 2 O in the life cycle of cellulosic ethanol. Removal of 6 Mg ha 1 yr 1 of corn residue. Scenario ΔSOC, Mg C ha 1 yr 1 ΔSOC per Δresidue C, Mg C ha 1 yr 1 Energy, GJ ha 1 SOC adder, gco 2 e MJ 1 N 2 O credit, gco 2 e MJ 1 NET adder, gco 2 e MJ 1 Production, gco 2 e MJ 1 LCA, gco 2 e MJ 1 GHG reduction % Note Mead, NE GIS5+SD GIS5,avg GIS5-SD GIS10+SD GIS10,avg GIS10-SD Net oxidation of SOC from residue removal is from table S5 and Mead (Figure 1). 2. In all scenarios above (R1 R4), change in SOC was divided by 6 Mg biomass ha 1 yr 1, or 2.4 Mg C. 3. Energy yield in cellulosic ethanol from residue was based on 288 liters per Mg 19 and ethanol lower heating value at 21.1 MJ L Emissions from net SOC loss are calculated by multiplying the mass C lost (1) by (44/12) to convert C to CO 2, then dividing by energy yield (3), and multiplying by 1000 to correct for units. The resulting Corn Belt values (table S7) do not correspond exactly to this algorithm, because slightly less biomass was removed on average in the geospatial analysis due to crop yields lower than the desired removal levels; above values are 1 12% higher than direct calculations using mean SOC losses from table S5. 5. Removal of 6 Mg residue ha 1 yr 1 reduces N 2 O emissions by multiplying these factors: biomass N content (0.6%), biomass N converted to N 2 O (1%), mass fraction of N to N 2 O (44/28), and global warming of N 2 O (298 kgco 2 e kg 1, from the IPCC 20, and dividing by energy yield (3). 6. Add (4) plus (5). 7. Near term cellulosic ethanol production intensity Add (6) plus (7). 9. Emissions reduction compared to gasoline baseline, 93.7 gco 2 e MJ Note. The calculations used here (4) for obtaining the net GHG emissions from SOC loss give the same result as calculations using complete LCA models, based on previous analysis 20,22. Note. Biofuel energy yields comparison, based on theoretical conversion yields 23. Cellulosic ethanol: 27 MJ kg 1 x kg kg 1 residue = MJ kg 1 residue, 16 GJ Mg 1 residue. FT Diesel: 42.7 MJ kg 1 x kg kg 1 residue = MJ kg 1 residue, GJ Mg 1 residue 8

9 a 5 Year R1 - R4 gco 2e MJ Km b 10 Year R1 - R4 gco 2e MJ Km Figure S4. Net contribution of SOC oxidation to the lifecycle of biofuels from residue removal. Removal of 6 Mg residue ha 1 yr 1 : a, 5 year average, b, 10 year average. Calculated over the Corn Belt (580 million cells), corresponding to the removal levels in figure 2a, tables S5 & S6, and algorithm in table S7. The negative LCA values occur in Pennington County, SD (a); the 5 year yield values were 1.69, 0.10, 0.50, 0.81 and 0.6, and because the 2006 and 2007 yield values were not reported, the 10 year Pennington County yield was excluded. 9

10 Additional References 1. Yang, H. S. & Janssen, B. H. Relationship between substrate initial reactivity and residues ageing speed in carbon mineralization. Plant Soil 239, (2002). 2. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, (2006) 3. USDA NRCS. Natural Resources Conservation Service. Soil Survey Geographic Database (SSURGO) version 2.1. National Collection. 2010, 1 st quarter ed. (Dec., 2009). 4. Bliss, N. B., Waltman, S. W., & Neale, A. C. Detailed soil information for hydrologic modeling in the conterminous United States. Abstract H43B American Geophysical Union (San Francisco, Calif , Dec. 2010). 5. Waltman, S. W., Olson, C., West, L., Moore, A. & Thompson, J. Preparing a soil organic carbon inventory for the United States using soil surveys and site measurements: Why carbon stocks at depth are important. (19th World Congress of Soil, 2010). 6. Bliss N., Waltman, S. W. & West, L. Detailed mapping of soil organic carbon stocks in the United States using SSURGO. Eos Trans AGU, 90, Fall Meeting Suppl. Abstract B51F (2009). 7. USDA NASS. USDA National Agricultural Statistics Service, Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section. USDA, National Agricultural Statistics Service, 2010 Cropland Data Layers, 2010 Edition. (2010). 8. USDA NASS. Data and Statistics. National Agricultural Statistics Service. Washington, D.C. 10

11 9. Johnson, J. M. F., Allmaras, R. R., & Reicosky, D. C. Estimating source carbon from crop residues, roots and rhizodeposits using the national grain yield database. Agronomy J. 98, (2006) 10. Amos, B. & Walters, D.T. Maize root biomass and net rhizodeposited carbon: An analysis of the literature. Soil Sci. Soc. Amer. J. 70, (2006). 11. PRISM PRISM Climate Group, Oregon State University, created between Feb. 13, 2004 & Apr. 8, Department of Commerce, Census Bureau, Geography Division County & County Equivalent Area, Jan. 1, data/data/tiger line.html 13. Yang, H. S., Dobermann, A., Cassman, K. G., & Walters, D. T. Hybrid Maize: A simulation model for maize growth and yield (University of Nebraska Lincoln, 2006). 14. Verma, S. B. et al. Annual carbon dioxide exchange in irrigated and rainfed maize based agroecosystems. Ag. Forest Met. 131, (2005). 15. Suyker, A. E. & Verma, S. B. Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize soybean cropping systems and water productivity. Ag. Forest Met. 150, (2010). 16. Biscoe, P. V., Scott, R. K., & Monteith, J. L. Barley and its environment. III. Carbon budget of the stand. J. Appl. Eco. 12, (1975). 17. Papale, D. et al. Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: Algorithms and uncertainty estimation. Biogeosciences 3, (2006). 11

12 18. Richardson, A. D. & Hollinger, D. Y. Statistical modeling of ecosystem respiration using eddy covariance data: Maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models. Ag. Forest Met. 131, (2005). 19. Kazi, F. K. et al., Techno economic comparison of process technologies for biochemical ethanol production from corn stover. Fuel 89, S20 S28 (2010). 20. Liska, A. J. et al. Improvements in life cycle energy efficiency and greenhouse gas emissions of corn ethanol. J. Indust. Ecol. 13, (2009). 21. Spatari, S. & MacLean, H. L. Characterizing model uncertainties in the life cycle of lignocellulose based ethanol fuels. Envir. Sci. Tech. 44, (2010). 22. Liska, A.J. in Sustainable Biofuels: An Ecological Assessment of Future Energy (eds Bhardwaj, A. K., Zenone, T. & Chen, J. K.) (Walter De Gruyter, in press). 23. Cherubini, F. & Strømman, A. H. Production of biofuels and biochemicals from lignocellulosic biomass: Estimation of maximum theoretical yields and efficiencies using matrix algebra. Energy Fuel 24, (2010). 12