Biofuels from crop residue can reduce soil carbon and ... · Biofuels from Crop Residue Can Reduce...

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2187

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 11

Biofuels from Crop Residue Can Reduce Soil Carbon and

Increase CO2 Emissions Adam J. Liska1,2*, Haishun Yang2, Maribeth Milner2, Steve Goddard3, Humberto Blanco‐Canqui2,

Matthew P. Pelton1, Xiao X. Fang1, Haitao Zhu3, Andrew E. Suyker4

1Department of Biological Systems Engineering, 2Department of Agronomy and Horticulture,

3Department of Computer Science and Engineering, 4School of Natural Resources, University of

Nebraska‐Lincoln, Nebraska 68583, USA. *e‐mail: [email protected]

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 N2O 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.

Biofuels from crop residue can reduce soil carbon and increase CO2 emissions

© 2014 Macmillan Publishers Limited. All rights reserved.

http://www.nature.com/doifinder/10.1038/nclimate2187

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Table S1. Coefficients used in the SOC model. Global data sets1 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)) 0.0024 0.149

S (unitless 0 ≤ S ≤ 1) 0.462 0.66

Tr (Celsius) 10 10

Q102 2 2

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

2001‐05

ΔModeled

2001‐05 Modeled/Measured

69.38 ± 1.19 66.18 ± 1.24 65.65 3.20 3.73 117%

Days

0 1000 2000 3000

Res

idue

-C &

SO

C r

emai

ning

(%

)

0

20

40

60

80

100

SOC yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr9


3

Figure S2. Geospatial modeling of SOC at Mead corn experiment. a, Initial SOC map of Mead,

Nebraska field site, composed of 531 30 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 state7. Rainfed county corn

grain yield estimates from NASS (2001‐2010)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 carbon10, 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 (2001‐2010) were used11; the original PRISM grid

resolution was 30‐arcseconds (~4 kilometers). The gSSURGO SOC data included non‐soil data so

all zero SOC values were removed12.

0 0.2 0.40.1 Km

30cm SOC(Mg ha )-2

26

55

65

66

Time (days)

0 1000 2000 3000 4000-8

-4

0

4

SO

C (

Mg

C p

er h

ecta

re)

R2

R3

R4

R1

a b


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 soil13). Input of C to soil in the top

30 cm is Ci. For estimation of soil respiration (i.e. CO2, 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 Ci yr‐1

2001‐02 13.51 521 486 78 1085 538

2002‐03 12.97 503 446 71 1020 493

2003‐04 12.12 470 438 70 978 484

2004‐05 12.24 470 382 61 913 422

2005‐06 12.02 447 436 70 953 482

2006‐07 10.46 401 327 52 780 361

2007‐08 12.79 487 416 67 969 460

2008‐09 11.99 447 407 65 919 450

2009‐10 13.35 501 520 83 1104 574


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Table S4. Field measurements using tower eddy covariance of respiration (Retotal, upward flux

of CO2) and gross primary productivity (GPP, downward flux of CO2). Irrigation water contains

CO2 which was subtracted from respiration14,15. The C contained in the crop at physiological

maturity (Crop C, Table S3) was subtracted from GPP to estimate crop respiration during

growth (Recrop). To estimate respiration from soil, residue, and dead root (i.e. RerSOC), Recrop was

subtracted from Retotal16. Standard deviations associated with these flux measurements were

assumed at ±10% of the mean17,18 (RerSOCSD). Using the SOC model (Figure S1, & Figure 1 in main

text), respiration was calculated based on the parameters above (ReSOCM). 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 Retotal GPP Recrop RerSOC RerSOCSD ReSOCM % error

2001‐02 22 1392 1929 844 548 55 735 34

2002‐03 20 1339 1799 779 560 56 537 ‐4

2003‐04 25 1241 1676 698 543 54 538 ‐1

2004‐05 15 1302 1664 751 551 55 495 ‐10

2005‐06 22 1339 1617 664 675 68 524 ‐22

2006‐07 17 1400 1622 842 558 56 443 ‐21

2007‐08 18 1414 1900 931 483 48 467 ‐3

2008‐09 16 1293 1781 862 431 43 475 10

2009‐10 7 1401 1952 848 553 55 523 ‐5


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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‐1 0.23 0.46 0.66 0.16 0.32 0.47

Standard Deviation 0.02 0.03 0.08 0.01 0.02 0.04

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, % 1.23 0.85 0.43 0.87 0.59 0.30


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, gCO2e MJ‐1 70.0 69.7 69.5 48.7 48.6 48.8



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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.

% SOC

0 250 500125 Km 0 1.0 2.0 27.24.0

5 Year

0 250 500125 Km

% SOC

0 1.0 2.0 16.94.0

10 Year

Percent (%) SOC loss

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Geo

spat

ial c

ells

(m

illio

ns)

0

50

100

150

200

5yr R1-R2 5yr R1-R3 5yr R1-R410yr R1-R210yr R1-R310yr R1-R4

a

b

c


8

Table S8. Net GHG emissions from net SOC oxidation and N2O 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, gCO2e MJ‐1

N2O credit, gCO2e

MJ‐1

NET adder, gCO2e

MJ‐1

Production, gCO2e

MJ‐1

LCA, gCO2e

MJ‐1

GHG reduction

%

Note 1 2 3 4 5 6 7 8 9

Mead, NE 0.47 0.20 36.4 47.3 ‐4.6 42.7 30 72.7 22 GIS5+SD 0.74 0.31 36.4 75.9 ‐4.6 71.2 30 101.2 ‐8 GIS5,avg 0.66 0.28 36.4 69.5 ‐4.6 64.9 30 94.9 ‐1 GIS5-SD 0.58 0.24 36.4 64.1 ‐4.6 59.5 30 89.5 4

GIS10+SD 0.51 0.21 36.4 53.1 ‐4.6 48.4 30 78.4 16 GIS10,avg 0.47 0.20 36.4 48.8 ‐4.6 44.2 30 74.2 21 GIS10-SD 0.43 0.18 36.4 44.5 ‐4.6 39.9 30 69.9 25 1. 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 Mg19 and ethanol

lower heating value at 21.1 MJ L‐1 20. 4. Emissions from net SOC loss are calculated by multiplying the mass C lost (1) by 3.667 (44/12) to

convert C to CO2, 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 N2O emissions by multiplying these factors: biomass N content (0.6%), biomass N converted to N2O (1%), mass fraction of N to N2O (44/28), and global warming of N2O (298 kgCO2e kg‐1, from the IPCC20, and dividing by energy yield (3).

6. Add (4) plus (5). 7. Near term cellulosic ethanol production intensity21. 8. Add (6) plus (7). 9. Emissions reduction compared to gasoline baseline, 93.7 gCO2e MJ‐1 21.

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 analysis20,22.

Note. Biofuel energy yields comparison, based on theoretical conversion yields23.

Cellulosic ethanol: 27 MJ kg‐1 x 0.593 kg kg‐1 residue = 16.01 MJ kg‐1 residue, 16 GJ Mg‐1 residue.

FT‐Diesel: 42.7 MJ kg‐1 x 0.369 kg kg‐1 residue = 15.33 MJ kg‐1 residue, 15.33 GJ Mg‐1 residue


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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.

0 250 500125 Km

5 Year

2gCO e MJ-1R1 - R4

40 55 70 85

0 250 500125 Km

2gCO e MJ-1R1 - R4

10 Year

40 55 70 85

a

b


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, 215‐224 (2002).

2. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and

feedbacks to climate change. Nature 440, 165–173 (2006)

3. USDA‐NRCS. Natural Resources Conservation Service. Soil Survey Geographic Database

(SSURGO) version 2.1. National Collection. 2010, 1st 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‐1227. American Geophysical Union (San

Francisco, Calif. 13‐17, 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‐0367.

(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.

http://soildatamart.nrcs.usda.gov (2010).

8. USDA‐NASS. Data and Statistics. National Agricultural Statistics Service.

http://www.nass.usda.gov/Data_and_Statistics/index.asp Washington, D.C.


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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, 622–

636 (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, 1489–1503 (2006).

11. PRISM. 2004‐2011. PRISM Climate Group, Oregon State University,

http://prism.oregonstate.edu, created between Feb. 13, 2004 & Apr. 8, 2011.

12. Department of Commerce, Census Bureau, Geography Division. 2000. County & County

Equivalent Area, Jan. 1, 2000. https://www.census.gov/geo/maps‐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, 77‐96 (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, 553‐563 (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, 269‐293 (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, 571‐

583 (2006).


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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, 191‐

208 (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, 58‐74 (2009).

21. Spatari, S. & MacLean, H. L. Characterizing model uncertainties in the life cycle of

lignocellulose‐based ethanol fuels. Envir. Sci. Tech. 44, 8773‐8780 (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, 2657‐2666 (2010).


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