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

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1 ARTICLE NUMBER: DOI: /NENERGY 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 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) Corn (no till) Miscanthus Switchgrass Corn Stover (conventional till) Corn Stover (no till) Energycane N/A Willow N/A Poplar N/A Sugarcane Ethanol (imported) Soybean Biodiesel 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 Predicting Greenhouse Gas Emissions and Soil Carbon from Changing Pasture to Energy Crop. PLOS ONE. DOI: /journal.pone 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. NATURE ENERGY 1

2 DOI: /NENERGY 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 Crop Dryland/ Irrigated CROPLAND BASELINE 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 Crop Dryland/ Stover tillage BASELINE Irrigated Removal (%) 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 2 NATURE ENERGY

3 DOI: /NENERGY SUPPLEMENTARY INFORMATION Supplementary Table 3. GHG emission factors (source: GREET) in kg CO 2 e per unit of input Per kilogram Per liter Per kwh N P K Lime Herbicides Machine Gasoline Fuels Electricity NATURE ENERGY 3

4 DOI: /NENERGY Supplementary Figure 1. DayCent predicted yields for miscanthus, switchgrass, and corn stover in each county averaged for the 15 year climate record. 4 NATURE ENERGY

5 DOI: /NENERGY SUPPLEMENTARY INFORMATION 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. NATURE ENERGY 5

6 DOI: /NENERGY Supplementary Figure 3. Percent difference between NASS reported grain yields and DayCent modeled grain yields. 6 NATURE ENERGY

7 DOI: /NENERGY SUPPLEMENTARY INFORMATION Supplementary Methods Ecosystem Modeling: Daily climate data was downloaded from the Daymet database ( 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 N 2 O were compared with observations in ref. 2. Indirect N 2 O 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 CH 4 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 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 using the climate years 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 NATURE ENERGY 7

8 DOI: /NENERGY cultivation and historical land cover) are available online ( 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 increase 15,16. 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 N 2 O 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 kg N ha-1 yr-1 depending on the location NATURE ENERGY

9 DOI: /NENERGY SUPPLEMENTARY INFORMATION We varied the fertilizer application in the model from 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 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 FASOM 23 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 NATURE ENERGY 9

10 DOI: /NENERGY 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 10 NATURE ENERGY

11 DOI: /NENERGY SUPPLEMENTARY INFORMATION 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 stands 25. 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 NATURE ENERGY 11

12 DOI: /NENERGY cropland pasture to cropland is determined by calibrating the model to replicate observed 5-year land movements between 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. Firstgeneration 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 12 NATURE ENERGY

13 DOI: /NENERGY SUPPLEMENTARY INFORMATION 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 NATURE ENERGY 13

14 DOI: /NENERGY 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 VMT 24,32. 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, Acquired online ( 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 (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, (2000). 14 NATURE ENERGY

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