impacts of a 32-billion-gallon bioenergy landscape on … iluc (g co2e/mj) corn (conventional till)...

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SUPPLEMENTARY INFORMATION ARTICLE NUMBER: 15005 | DOI: 10.1038/NENERGY.2015.5 NATURE ENERGY | www.nature.com/natureenergy 1 Supplementary Table 1. Annual soil carbon sequestration and GHG intensity of ILUC by feedstock. The international ILUC effect of corn stover is negative although the production of corn stover does not require land, it affects relative land prices in the GTAP model in favor of forestry and causes a small amount of reforestation. Feedstock Soil Carbon (kg CO 2 e/ha/Yr) ILUC (g CO2e/MJ) Corn (conventional till) 365 1 1.2 2 Corn (no till) 1016 1 1.2 2 Miscanthus 6485 1 0.8 3 Switchgrass 4550 1 4.4 3 Corn Stover (conventional till) -178 1 -0.9 3 Corn Stover (no till) -429 1 -0.9 3 Energycane 8213 4 N/A Willow 4502 5 N/A Poplar 4898 5 N/A Sugarcane Ethanol (imported) -- 11.8 6 Soybean Biodiesel -- 29.1 6 1 DayCent; 2 BEPAM 3 Taheripour F. and E. Tyner. (2013). Induced Land Use Emissions due to First and Second Generation Biofuels and Uncertainty in Land Use Emission Factors. Economics Research International. 4 DayCent values from Duval, B. D., K.J. Anderson-Teixeira, S.C. Davis, C. Keogh, S. P. Long, W.J. Parton and E. H. Delucia. 2013. Predicting Greenhouse Gas Emissions and Soil Carbon from Changing Pasture to Energy Crop. PLOS ONE. DOI: 10.1371/journal.pone.0072019. 5 Beach, R.H. and B. A. McCarl. (2010)_U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description. Research Triangle Park, NC: RTI International. 6 California Environmental Protection Agency (2014). Staff Report: Initial Statement of Reasons for Proposed Rulemaking. Impacts of a 32-billion-gallon bioenergy landscape on land and fossil fuel use in the US Tara W. Hudiburg, WeiWei Wang, Madhu Khanna, Stephen P. Long, Puneet Dwivedi, William J. Parton, Melannie Hartman and Evan H. DeLucia

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Page 1: Impacts of a 32-billion-gallon bioenergy landscape on … ILUC (g CO2e/MJ) Corn (conventional till) 3651 1.22 Corn (no till) 10161 1.22 Miscanthus 64851 0.83 Switchgrass 4550

SUPPLEMENTARY INFORMATIONARTICLE NUMBER: 15005 | DOI: 10.1038/NENERGY.2015.5

NATURE ENERGY | www.nature.com/natureenergy 1

Supplementary Information

Supplementary Table 1. Annual soil carbon sequestration and GHG intensity of ILUC by feedstock. The international ILUC effect of corn stover is negative although the production of corn stover does not require land, it affects relative land prices in the GTAP model in favor of forestry and causes a small amount of reforestation.

Feedstock Soil Carbon (kg CO2e/ha/Yr)

ILUC (g CO2e/MJ)

Corn (conventional till) 3651 1.22

Corn (no till) 10161 1.22

Miscanthus 64851 0.83 Switchgrass 45501 4.43 Corn Stover (conventional till)

-1781 -0.93

Corn Stover (no till) -4291 -0.93

Energycane 82134 N/A Willow 45025 N/A Poplar 48985 N/A Sugarcane Ethanol (imported)

-- 11.86

Soybean Biodiesel -- 29.16 1DayCent; 2BEPAM 3Taheripour F. and E. Tyner. (2013). Induced Land Use Emissions due to First and Second Generation Biofuels and Uncertainty in Land Use Emission Factors. Economics Research International. 4DayCent values from Duval, B. D., K.J. Anderson-Teixeira, S.C. Davis, C. Keogh, S. P. Long, W.J. Parton and E. H. Delucia. 2013. Predicting Greenhouse Gas Emissions and Soil Carbon from Changing Pasture to Energy Crop. PLOS ONE. DOI: 10.1371/journal.pone.0072019. 5Beach, R.H. and B. A. McCarl. (2010)_U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description. Research Triangle Park, NC: RTI International. 6California Environmental Protection Agency (2014). Staff Report: Initial Statement of Reasons for Proposed Rulemaking.

Impacts of a 32-billion-gallon bioenergy landscape on land and fossil fuel use in the US

Tara W. Hudiburg, WeiWei Wang, Madhu Khanna, Stephen P. Long, Puneet Dwivedi, William J. Parton, Melannie Hartman and Evan H. DeLucia

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2 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

Supplementary Table 2. DayCent simulations for each county in each region. The base corn rotation was irrigated for the following regions: Montana, Wyoming, North Dakota, South Dakota, Nebraska, Colorado, Kansas, New Mexico, Oklahoma, Oregon, Texas, Utah, and Washington.

SIMULATION YEARS 1971 - 2006 2007 - 2035

CROPLAND BASELINE

Crop Dryland/ Irrigated

Stover Removal (%)

tillage

CORN ROTATION corn rotation dryland 0 conventional CORN ROTATION corn rotation dryland 30 conventional CORN ROTATION corn rotation dryland 0 No till CORN ROTATION corn rotation dryland 50 No till CORN ROTATION corn rotation irrigated 0 conventional CORN ROTATION corn rotation irrigated 30 conventional CORN ROTATION corn rotation irrigated 0 No till CORN ROTATION corn rotation irrigated 50 No till CORN ROTATION miscanthus dryland CORN ROTATION switchgrass dryland MARGINAL LAND BASELINE

Crop Dryland/ Irrigated

Stover Removal (%)

tillage

GRAZED PASTURE corn rotation dryland 0 conventional GRAZED PASTURE corn rotation dryland 30 conventional GRAZED PASTURE corn rotation dryland 0 No till GRAZED PASTURE corn rotation dryland 50 No till GRAZED PASTURE corn rotation irrigated 0 conventional GRAZED PASTURE corn rotation irrigated 30 conventional GRAZED PASTURE corn rotation irrigated 0 No till GRAZED PASTURE corn rotation irrigated 50 No till GRAZED PASTURE miscanthus dryland GRAZED PASTURE switchgrass dryland

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NATURE ENERGY | www.nature.com/natureenergy 3

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

Supplementary Table 3. GHG emission factors (source: GREET) in kg CO2e per unit of input

Per kilogram Per liter Per kwh N P K Lime Herbicides Machine Gasoline Fuels Electricity 10.66 0.67 0.65 0.015 21.19 1.58 2.98 3.41 0.608

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4 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

Supplementary Figure 1. DayCent predicted yields for miscanthus, switchgrass, and corn stover in each county averaged for the 15 year climate record.

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NATURE ENERGY | www.nature.com/natureenergy 5

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

Supplementary Figure 2. Model evaluation of (a) soil organic carbon to a depth of 30 cm with NRCS Soil Survey Statistics, (b) miscanthus (solid circles) and switchgrass (open circles) harvested yields. Research sites for miscanthus and switchgrass are located in Nebraska, Illinois, Kentucky, New Jersey, South Dakota, Louisiana, Michigan, Mississippi, Oklahoma, and Georgia.

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6 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

Supplementary Figure 3. Percent difference between NASS reported grain yields and DayCent modeled grain yields.

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NATURE ENERGY | www.nature.com/natureenergy 7

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

Supplementary Methods

Ecosystem Modeling: Daily climate data was downloaded from the Daymet database

(http://daymet.ornl.gov/;1). Historical simulations on cropland followed native vegetation (e.g.

grasslands) with disturbance history (e.g. fire, harvest) followed by ~110 years of agricultural

history. Agricultural history included corn-soy rotations, alfalfa, and wheat. Soil carbon stocks

were simulated to represent the pre-agricultural native vegetation levels with a subsequent

decline as the land was cultivated each year for the annual crops. Model output of yield and soil

carbon were evaluated against data at a variety of scales (Supplementary Figure 2) and further

evaluation of direct N2O were compared with observations in ref. 2. Indirect N2O emissions were

calculated using the IPCC indirect emission factor for leaching/runoff (0.75%) and the IPCC

indirect emission factor for volatilized N (1%). DayCent modeled CH4 emissions (consumption

through oxidation in non-flooded soils) have been evaluated in US cropping systems 3.

Moreover, DayCent output of crop yields and GHG emissions has been evaluated in numerous

studies and at sites all around the world 4-12.

Marginal land (designated as cropland currently used for pasture) historical simulations

included the agricultural history where appropriate. For any given county, the cropland baseline

simulations may use different soils and weather than those used for the marginal land

simulations. This is because our data for crop soils comes from locations in the county where

crops actually occur, and our marginal land soils and weather come from locations in the county

where pasture actually occurs. Following the agricultural or pastureland history, the future

simulations were run from 2008-2022 using the climate years 1984-1998. Corn productivity and

baseline soil carbon were calibrated using NASS agricultural statistics and SSURGO soils carbon

data. For this study, DayCent was parameterized to model soil organic carbon dynamics to a

depth of 30cm. Crop physiological parameterizations, soil files, and schedule files (for

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8 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

cultivation and historical land cover) are available online

(http://www.life.illinois.edu/delucia/Public%20Data%20Archive/Hudiburg_etal_2016_NatureEn

ergy_DayCentFiles_public.zip). Soil texture, bulk density, and pH were parameterized based on

the actual values reported for the pasture land (marginal) and the cropland in each county using

the SSURGO database 13.

For corn, fertilizer applications followed current cultivation practices in each county

based on data from the National Agricultural Statistics Service 14. Corn production (grain and

stover) was simulated with the reference rotation for the county (see Supplementary Table 2;

varied with corn-soy, continuous corn, corn-corn-soy, corn-soy-wheat etc.). Several studies have

examined the relationship between crop yields and crop residue produced and find this

relationship to be non-linear with the ratio of residue to grain declining as crop yields

increase15,16. We use the DayCent model to dynamically allocate NPP to grain or leaf/stem

biomass based on environmental conditions (specifically water stress) and grain allocation

actually ranged from 0.3 to 0.59 in our model output (maximum allocation to grain is set to 0.6

for corn).

Several studies provide estimates of the rate at which crop residue can be collected from

cropland while maintaining soil organic matter and preventing erosion (see review in ref. 16).

While estimates vary across studies, some studies show that this estimate should vary with tillage

practice and that only about 35% of residue should be harvested with conventional tillage and 68-

82% can be harvested with no-till. We assume a removal rate of 30% with conventional tillage

and 50% with no-till corn production. We use DayCent to examine the soil C and N2O dynamics

associated with 0, 30%, and 50% removal based on the reference cropping system and scenario.

Switchgrass yields can increase by up to 30% with fertilization; however the range of

optimum fertilizer application varies from 56-150 kg N ha-1 yr-1 depending on the location 17.

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NATURE ENERGY | www.nature.com/natureenergy 9

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

We varied the fertilizer application in the model from 60-120 kg N ha-1 depending on soil

quality. A number of studies have examined the response of miscanthus yields to fertilization and

show there is no significant response. For example, there was no significant response of

miscanthus yield to nitrogen applications over a three year experiment in Illinois 18 and no

significant response over a two year period of miscanthus yield to nitrogen application in IL, NE,

NJ and KY 19. In Europe, there was no significant response of miscanthus yield to nitrogen

application in the first three years of growth at any of the sites in the EU; except for a 5-12%

increase in yield when very high rates of N (100 kg N ha-1) were applied and/or under irrigated

conditions on otherwise very dry sites 20. In a more recent study, miscanthus yields in some

locations responded to very high application rates (202 kg ha-1) of nitrogen, but not every year

17. The absence of a consistent response to fertilization is consistent with the reported high rates

of N retranslocation from shoots to rhizomes at senescence for miscanthus (up to 90%) 21.

Because there remains some N removed in the harvested biomass, we added 25-30 kg N ha-1 as

replacement N for miscanthus in the model simulations.

We used energy cane soil carbon changes from a prior DayCent modeling study in the

region 22 as look-up table values for BEPAM-F. The study simulated energycane as a biofuel

crop in the southeastern US using observations of production and soil respiration from an

experimental plot in Florida for calibration of expected soil carbon changes. Because willow and

poplar were included as sources of cellulosic ethanol in BEPAM, we included the soil carbon

changes from the FASOM23 model for these two crops.

Economic Modeling: BEPAM-F includes linear demand curves for vehicle kilometers travelled

(VKT) with four types of vehicles, including conventional gasoline, flex fuel, gasoline-hybrid,

and diesel vehicles. These demand curves for VKT with each type of vehicle over time as

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10 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

projected by the Annual Energy Outlook to capture the growth in demand. We include linear

supply curves for domestic gasoline and diesel as well as for gasoline supply and demand in the

rest of the world. Imports of gasoline from the rest of the world to the US are determined

endogenously and displacement of US demand for gasoline by biofuels can affect the world price

of gasoline.

The agricultural sector includes fifteen conventional crops, eight livestock products, three

energy crops (miscanthus, switchgrass and energy cane), two short rotation woody crops (poplar

and willow), crop residues from the production of corn and wheat, various processed

commodities, and co-products from the production of corn ethanol and soybean oil. In the crop

and livestock markets, primary crop and livestock commodities are consumed either domestically

or traded with the rest of the world. Primary crop commodities can also be processed or directly

fed to various animal categories. Domestic and export demands and import supplies are

incorporated by assuming linear price-responsive demand/supply functions.

The agricultural sector is represented by 295 Crop Reporting Districts (CRDs) in 41 US

states that are spatially heterogeneous decision making units. These CRDs differ in their

production costs and yields of individual crop/livestock activities and resource endowments.

Crops can be produced using alternative tillage, rotation, and irrigation practices. Crop yields

increase over time at exogenously given rates based on econometrically estimated trends and

price responsiveness of crop yields in the US. Key assumptions about the elasticity of demand

for VKT, supply elasticities of fuels, elasticities of demand for domestic consumption and

exports of agricultural commodities and elasticity of supply for agricultural imports and methods

for calibrating the demand and supply curves are provided in ref. 24.

The structure of forest sector is similar to that in FASOM and consists of 11 marketing

regions; forestry production occurs in 9 of these regions. Forestland is characterized by two types

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NATURE ENERGY | www.nature.com/natureenergy 11

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

of trees, softwoods and hardwoods that are grown on land privately owned by the forest industry

or land under nonindustrial private ownership. The model uses the Forest Service’s 2010 RAP

Timber Assessment forest inventory, which describes the distribution of trees by age and

timberland acres, downscaled to the CRD level. The forest sector includes forestland and

forestland pasture, which is distinguished by various site productivity classes that determine yield

per unit land. Current and future timber yields are based on the 2000 RAP Timber Assessment

and differ depending on management intensity and age cohorts for stands25. Harvest of a forest

acre results in the simultaneous production of a mix of softwood and hardwood logs in the form

of sawlogs, pulpwood and fuelwood. The product mix varies with the stand age, regions and site

classes. The model also includes the conversion of these intermediate products into 40 major

products including solid wood products and fiber products and milling residues; pulpwood and

forest and milling residues can also be used for bioenergy. Demand for forest products is

represented at the national level by downward sloping demand functions that shift rightward over

time.

The agriculture and forest sectors are linked by competing demands for the private lands,

which can produce either food/feed or forest products or dedicated energy crops as feedstocks to

meet the biofuel demand for VKT. Land can be converted across different uses, cropland,

cropland pasture and pastureland and forestland depending on the net present value of returns to

alternative uses, including the costs of land conversion. Land moves between sectors until the

markets equilibrate and the net present value of returns to land minus the investment cost to

transfer land (land clearing, leveling, seedbed preparation, etc.) and any conversion cost are

equated across uses. Cost of converting land between agriculture and the forest sector is obtained

from the most recent FASOM model which was derived from data from Natural Resource

Inventory by the Natural Resource Conservation Service 13. Additional cost of conversion of

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12 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

cropland pasture to cropland is determined by calibrating the model to replicate observed 5-year

land movements between 2002-2007. The cost of converting pastureland to energy crops is

assumed to be the returns to land from the least profitable crop in each CRD due to the absence

of empirical data 24.

The biofuel sector includes several first- and second- generation biofuels. First-

generation biofuels include domestically produced corn ethanol and imported sugarcane ethanol,

soybean biodiesel, DDGS-derived corn oil and waste grease. Second-generation biofuels

included here are cellulosic ethanol derived from agricultural and forest biomass. Costs of

producing energy crops and crop residues are determined as in 26,27. Technological parameters for

converting feedstock to different types of biofuel and the industrial costs of processing

feedstocks and producing biofuels are described in 24. These costs are assumed to decline due to

learning-by-doing as cumulative production increases using an experience curve approach 28. The

conversion efficiencies (yield of biofuel per bushel or ton of feedstock) are exogenously fixed

and based on the estimates in GREET 1.8c for corn ethanol and in ref. 29 for cellulosic ethanol.

We used energy cane soil carbon changes from a prior DayCent modeling study in the region 22

as look-up table values for BEPAM-F. Because willow and poplar were included as sources of

cellulosic ethanol in BEPAM-F, we included the soil carbon changes from the FASOM model

for these two crops.

GHG and ILUC calculations:

We determine the direct above ground GHG emissions related to feedstock production

(including carbon sequestered in soils), feedstock processing, feedstock transportation, and

conversion of feedstock to ethanol using methods described in ref. 27. This is determined by

using the same input application rates and stages as used for the economic analysis in BEPAM-F

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NATURE ENERGY | www.nature.com/natureenergy 13

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

and estimating the GHG intensity using emissions factors per unit input and for the different

stages of the production process obtained from the GREET model. Estimates of the above- and

belowground emissions associated with biofuel feedstocks are provided in Supplementary

Tables1 and 3. BEPAM-F estimates the cumulative change in emissions between the BAU and

the policy scenario in 5-year intervals. This includes changes not only due to the direct

production of biofuels to meet policy targets but also those due to indirect land use change within

the US as land shifts from one use to another in response to changes in market demand and crop

prices.

We assume that the average GHG intensity of oil is increasing over time due to an

increasing share of unconventional heavy crude oil. Our assumptions about the trend in GHG

intensity of gasoline over time are described in ref. 30. The international component of the ILUC

related emissions intensity of each feedstock is determined by using the estimates provided by

ref. 31 for change in four types of land caused per liter of biofuel produced from corn, corn

stover, miscanthus, and switchgrass in the European Union, Brazil, and other countries. The four

types of land included are forest, pasture, cropland, and cropland pasture. For each of these four

types, an emissions factor based on the Woods Hole data set is used to convert the amount of

land use change to ILUC related emissions per mega-joule internationally. Estimates are reported

in Supplementary Table 1.

Domestic and Global rebound effects:

The domestic and international price of gasoline is endogenously determined by the

domestic demand for gasoline derived from the downward sloping demands for VMT and the

demand for gasoline in the rest of the world and the upward sloping domestic and the rest of the

world supply of gasoline. The increased production of biofuels due to biofuel policies reduces the

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14 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

domestic demand for gasoline and the US demand for imports from the rest of the world. Since

the US is a major importer of gasoline from the rest of the world, this leads to a reduction in the

world price of gasoline and a corresponding reduction in the domestic price of gasoline in the

US. This could lead to a rebound in fossil fuel consumption in the US and the rest of the world,

such that biofuels displace less than the energy equivalent amount of fossil fuels and offset a part

of the GHG savings with biofuels. We estimate the domestic and global rebound effects and their

implications for GHG emissions endogenously. Key assumptions in determining the magnitude

of these effects are the price responsiveness of the rest of the world supply of gasoline and of the

domestic demand for VMT24,32. We estimate the sensitivity of our estimates of the GHG savings

with biofuels to alternative values of the elasticity of the rest of the world supply of gasoline and

the demand for VMT. The estimate of the rebound effect obtained here is based on the

assumption of competitive oil markets in which the price of gasoline is determined by its

marginal cost. This is similar to assumptions about oil markets in other general equilibrium

models. Under other assumptions about oil market structure and strategic behavior by oil

producers that result in their reducing oil production to maintain oil prices in the presence of

biofuels, the GHG savings that could be realized would likely be larger. The results obtained here

should therefore be viewed as a conservative estimate of the GHG savings possible due to the

biofuel policies analyzed here.

Supplementary References

1 Thornton, P., MM Thornton, BW Mayer, N Wilhelmi, Y Wei, RB Cook. Daymet: Daily surface weather on a 1 km grid for North America,1980 - 2008. Acquired online (http://daymet.ornl.gov/) on 20/09/2012 from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

2 Hudiburg, T. W., Davis, S. C., Parton, W. & Delucia, E. H. Bioenergy crop greenhouse gas mitigation potential under a range of management practices. Global Change Biology Bioenergy 7 366-374 (2015).

3 Grosso, S. J. D. et al. General CH4 oxidation model and comparisons of CH4 Oxidation in natural and managed systems. Global Biogeochemical Cycles 14, 999-1019 (2000).

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NATURE ENERGY | www.nature.com/natureenergy 15

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2015.5

4 Stehfest, E. & Müller, C. Simulation of N2O emissions from a urine-affected pasture in New Zealand with the ecosystem model DayCent. Journal of Geophysical Research: Atmospheres 109, D03109 (2004).

5 Del Grosso, S. J. et al. DayCent Model Simulations for Estimating Soil Carbon Dynamics and Greenhouse Gas Fluxes from Agricultural Production Systems. Managing Agricultural Greenhouse Gases: Coordinated Agricultural Research through Gracenet to Address Our Changing Climate, 241-250 (2012).

6 Del Grosso, S. J. et al. Global scale DAYCENT model analysis of greenhouse gas emissions and mitigation strategies for cropped soils. Global and Planetary Change 67, 44-50 (2009).

7 Del Grosso, S. J., Halvorson, A. D. & Parton, W. J. Testing DAYCENT model simulations of corn yields and nitrous oxide emissions in irrigated tillage systems in Colorado. Journal of Environmental Quality 37, 1383-1389 (2008).

8 Del Grosso, S. J., Mosier, A. R., Parton, W. J. & Ojima, D. S. DAYCENT model analysis of past and contemporary soil N(2)O and net greenhouse gas flux for major crops in the USA. Soil & Tillage Research 83, 9-24 (2005).

9 Cheng, K., Ogle, S. M., Parton, W. J. & Pan, G. X. Simulating greenhouse gas mitigation potentials for Chinese Croplands using the DAYCENT ecosystem model. Global Change Biology 20, 948-962 (2014).

10 Cheng, K., Ogle, S. M., Parton, W. J. & Pan, G. X. Predicting methanogenesis from rice paddies using the DAYCENT ecosystem model. Ecological Modelling 261, 19-31 (2013).

11 Chamberlain, J. F., Miller, S. A. & Frederick, J. R. Using DAYCENT to quantify on-farm GHG emissions and N dynamics of land use conversion to N-managed switchgrass in the Southern U.S. Agriculture Ecosystems & Environment 141, 332-341 (2011).

12 Campbell, E. E. et al. Assessing the Soil Carbon, Biomass Production, and Nitrous Oxide Emission Impact of Corn Stover Management for Bioenergy Feedstock Production Using DAYCENT. Bioenergy Research 7, 491-502 (2014).

13 NRCS. Soil Survey Geographic (SSURGO) Database for Eastern. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Available online at http://soildatamart.nrcs.usda.gov (2010).

14 NASS. National Agricultural Statistics Service. Census of Agriculture Quick Stats 2.0 Beta, United States Department of Agriculture. Available online at http://www.nass.usda.gov/Quick_Stats/ (2011).

15 Bentsen, N. S., Felby, C. & Thorsen, B. J. Agricultural residue production and potentials for energy and materials services. Progress in Energy and Combustion Science 40, 59-73 (2014).

16 Scarlat, N., Martinov, M. & Dallemand, J.-F. Assessment of the availability of agricultural crop residues in the European Union: Potential and limitations for bioenergy use. Waste Management 30, 1889-1897 (2010).

17 Arundale, R. A., Dohleman, F. G., Voigt, T. B. & Long, S. P. Nitrogen Fertilization Does Significantly Increase Yields of Stands of Miscanthus x giganteus and Panicum virgatum in Multiyear Trials in Illinois. Bioenergy Research 7, 408-416 (2014).

18 Behnke, G. D., David, M. B. & Voigt, T. B. Greenhouse Gas Emissions, Nitrate Leaching, and Biomass Yields from Production of Miscanthus x giganteus in Illinois, USA. Bioenergy Research 5, 801-813 (2012).

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SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2015.5

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