3

Projected Supply of Cellulosic Biomass

The consumption mandate for two of the four categories of biofuels listed in the Renewable Fuel Standard as amended by the Energy Independence and Security Act (EISA) of 2007 (RFS2) will likely be met by corn-grain ethanol and biodiesel, as discussed in Chapter 2. The remaining 20 billion gallons per year of mandated consumption is to be met with cellulosic biofuels or advanced fuels, which could include cellulosic biofuels, other types of biofuels derived from sugar or any starch other than corn starch, and imports of ethanol from sugarcane facilities in Brazil and elsewhere. Based on anticipated advances in conversion technologies, earlier studies suggested that over 550 million dry tons per year of nonfood-based resources, including agricultural residues, dedicated bioenergy crops, forest resources, and municipal solid wastes (MSWs), can potentially be produced in the United States (Perlack et al., 2005; NAS-NAE-NRC, 2009). However, the potentially available feedstock that would be supplied to biofuel refineries in the future depends on multiple factors: where the feedstock is grown and collected; expected crop, residue, or forest yields; competition for biomass from other uses (for example, electricity generation versus biofuel production); markets; technology development; public policies; and other unanticipated factors. Potential availability refers to the amount of cellulosic biomass that could be grown and harvested in the United States based on assumptions of recoverable yields from diverse farm and forest landscapes but without specific consideration of the costs of producing, harvesting, and delivering the biomass to a biorefinery. The study Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply1 (Perlack et al., 2005) provided one of the first estimates of potential availability of cellulosic feedstocks in the United States. Supply refers to a schedule of amounts that would be delivered to biorefineries at different costs, taking accessibility of biomass, infrastructure, and other

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1 The report U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry (Perlack and Stokes, 2011) was released while the committee was preparing this report for public release. The committee did not have an opportunity to review the Perlack and Stokes (2011) report.



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3 Projected Supply of Cellulosic Biomass T he consumption mandate for two of the four categories of biofuels listed in the Renewable Fuel Standard as amended by the Energy Independence and Security Act (EISA) of 2007 (RFS2) will likely be met by corn-grain ethanol and biodiesel, as discussed in Chapter 2. The remaining 20 billion gallons per year of mandated consump- tion is to be met with cellulosic biofuels or advanced fuels, which could include cellu- losic biofuels, other types of biofuels derived from sugar or any starch other than corn starch, and imports of ethanol from sugarcane facilities in Brazil and elsewhere. Based on anticipated advances in conversion technologies, earlier studies suggested that over 550 million dry tons per year of nonfood-based resources, including agricultural resi- dues, dedicated bioenergy crops, forest resources, and municipal solid wastes (MSWs), can potentially be produced in the United States (Perlack et al., 2005; NAS-NAE-NRC, 2009). However, the potentially available feedstock that would be supplied to biofuel refineries in the future depends on multiple factors: where the feedstock is grown and collected; expected crop, residue, or forest yields; competition for biomass from other uses (for example, electricity generation versus biofuel production); markets; technol- ogy development; public policies; and other unanticipated factors. Potential availability refers to the amount of cellulosic biomass that could be grown and harvested in the United States based on assumptions of recoverable yields from diverse farm and forest landscapes but without specific consideration of the costs of producing, harvesting, and delivering the biomass to a biorefinery. The study Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply1 (Perlack et al., 2005) provided one of the first estimates of potential availability of cellulosic feedstocks in the United States. Supply refers to a schedule of amounts that would be delivered to biorefineries at different costs, taking accessibility of biomass, infrastructure, and other 1 Thereport U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry (Perlack and Stokes, 2011) was released while the committee was preparing this report for public release. The committee did not have an opportunity to review the Perlack and Stokes (2011) report. 79

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80 RENEWABLE FUEL STANDARD economic conditions into consideration. Taking all those factors into consideration, real- ized supply is likely to be much lower than potential availability. This chapter describes the estimated supply of cellulosic biomass made by different groups, including the researchers at the University of California, Davis, the U.S. Environ- mental Protection Agency (EPA), the Biomass Research and Development Initiative, and researchers at the University of Tennessee. Other factors, such as biotechnology, competi- tion for biomass with other sectors, weather-related losses, and pests and diseases, which are typically not considered in projecting biomass supply, contribute to uncertainties in feedstock supply and are discussed at the end of this chapter. POTENTIAL SUPPLY OF BIOFUEL FEEDSTOCK AND LOCATION OF BIOREFINERIES Several studies attempted to predict the most likely locations for biomass production and corresponding siting of the biofuel biorefineries for regulatory and other planning pur- poses (BRDB, 2008; English et al., 2010; EPA, 2010; Parker et al., 2010b; USDA, 2010). Some studies principally identify the regional availability of bioenergy feedstocks that could be used for biofuel production, while others also identify likely biorefinery locations. The fol- lowing sections describe some of the approaches and assumptions used in the modeling of potential feedstock supply and biorefinery locations and compare projected locations for biorefineries among studies and with some of the proposed locations of cellulosic biofuel refineries. A comparison of the assumptions related to the types and amounts of feedstocks and the conversion rate to energy is provided in Table 3-1. National Biorefinery Siting Model Approach and Assumptions The National Biorefinery Siting Model (NBSM) was developed by researchers at the University of California, Davis (Parker et al., 2010a; Tittmann et al., 2010; Parker, 2011). It integrates geographically explicit biomass resource assessments, engineering and economic models of the conversion technologies, models for multimodal transportation of feedstock and fuels based on existing transportation networks, and a supply chain optimization model that locates and supplies a biorefinery based on inputs from the other models (Parker et al., 2010a). To identify the location of biorefineries, the model first maximizes the profit- ability of the entire national biofuel industry. The profit maximized is the sum of the profits for each individual feedstock supplier and fuel producer. Costs minimized in the model are those associated with feedstock procurement, transportation, conversion to fuel, and fuel transmission to distribution terminals. Fuel production and selling price determine industry revenue. Coproduct revenues are included. NBSM used data from the U.S. Department of Agriculture (USDA) National Agricul- tural Statistics Service (NASS) and Forest Service (USFS) provided by Skog et al. (2006, 2008) to project crop and woody biomass location and abundance and create spatially explicit estimates of biomass availability. NBSM constrained estimates for the supply of corn to be equal to the quantity needed to meet the RFS2 mandate of 15 billion gallons per year for conventional ethanol. Soybean and canola were assumed to be grown and used for biofuels. To limit the proportion of soybean dedicated to fuel production in the model, the use of soybean oil for biodiesel is limited to not more than 38 percent of all soy oil produced.

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81 PROJECTED SUPPLY OF CELLULOSIC BIOMASS TABLE 3-1 Comparison of Assumptions in Biomass Supply Analyses Other Cellulosic Feedstocks Feedstocks Biomass Supply in 2022 Energy Conversion Ratio National Corn stover Greases 500 million dry tons of all Varies by technology and Biorefinery Switchgrass MSW feedstock types feedstock type Siting Model Woody biomass EPA Corn stover MSW 82 million dry tons of corn Over 90 gallons per Switchgrass stover in 2022 dry ton, but varies by Other dedicated 10 million dry tons of feedstock bioenergy crops bagasse 94 gallons per dry ton for Bagasse 44 million dry tons of corn stover in 2022 Sweet sorghum pulp woody biomass Woody biomass USDA Logging residue NA 42.5 million dry tons of 70 gallons per dry ton of Dedicated bioenergy logging residue logging residue grasses Conversion ratios for other Soybean feedstocks not included in Energy cane source Sweet sorghum Canola Corn stover Straw Biomass Corn stover NA 75-79 million dry tons of 80-90 gallons of ethanol Research and Wheat straw dedicated energy crops per dry ton of switchgrass Development Dedicated energy and annual energy crops Conversion ratios for other Initiative crops, including like sweet sorghum in 2022 feedstocks not included in switchgrass 45 million dry tons of source Woody biomass woody biomass in 2022 including hybrid 51-84 million dry tons of poplar and willow corn stover in 2022 Sweet sorghum 20-32 million dry tons of wheat straw in 2022 NOTE: All analyses assumed that the 15-billion gallon mandate for conventional biofuel would be met by corn- grain ethanol. NBSM also constrains cellulosic feedstock acquisition from all sources to an area within a 100-mile radius of the biorefinery site for the most part. Crop residue removal was con- strained to levels that were estimated to prevent erosion. Those levels were estimated using local soil and landscape data and methods to estimate effects on soil quality and erosion (Nelson, 2002; Nelson et al., 2004, 2006). A combination of soil erosion (by wind and water) models were used to estimate the upper limit of crop residue removal. An amount of resi- due lower than the upper limit is considered to be removable without detrimental effects on the environment and resource base. The methods used combine detailed field-scale data on soil type, capability class, and slope from the USDA Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) database (USDA-NRCS, 2008) and an estimation of maximum rate of soil erosion not affecting productivity (the T value calcu- lated using the Universal Soil Loss Equation; Renard et al., 1997). Residue amounts come from crop yields derived from the NASS database cited above. Wind erosion limits are

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82 RENEWABLE FUEL STANDARD also calculated using methods described by Nelson (2002). A limitation of these methods is that they do not account for some aspects of soil management, such as soil organic matter (SOM) maintenance. They do not include estimates of technical limits to stover recovery. Removal rates could be overestimated if the amount of stover left on the field is less than the amount needed to conserve SOM. Switchgrass is modeled based on yields estimated at the Oak Ridge National Labora- tory (Jager et al., 2010) using results from a survey of the most current agronomic literature and predictions from a switchgrass model developed by the Pacific Northwest National Laboratory (Thomson et al., 2009). Harvest costs are estimated using a model from the Idaho National Lab (Hess et al., 2009). Residue and cellulosic yield and cost estimates are resolved to the county level and calculated as an edge of field price. Costs and supply estimates are derived from the Policy Analysis System (POLYSYS) modeling framework (Walsh et al., 2003). POLYSYS estimated switchgrass to be available at $50 to $85 per dry ton, at the farm gate. Estimates of forest biomass to the county level were derived from several sources. Accessible biomass estimates were guided by sustainability principles. However, the sustainability guidelines are site specific or region specific and could vary by owner- ship, with federal rules and guidelines, state guidelines, or professional standards used to guide management and harvest. In all cases, forest biomass generally included is the secondary output of other commercial forestry operations. Therefore, significant vari- ability and uncertainty about resource access exists. Forest biomass available for biofuel production was estimated for the thinning of timberland with high fire hazard, logging residue left behind after anticipated logging operations for conventional products, treat- ment of Pinyon Juniper woodland and rangeland, normal thinning of private timberland, precommercial thinning on National Forest land in western Oregon and Washington, and unused mill residue. EISA excludes credit for wood removed from federal lands, so NBSM provides separate estimates of forest biomass availability with and without federal lands. Forest resources were estimated to be available starting at $20-$30 per dry ton at the roadside, with the ma- jority available at $45-$65 per dry ton, all depending on location, at the time of simulation. Pulpwood is available to biorefineries at suitable locations at up to $100 per dry ton. In ad- dition to USFS data sources already noted, various models were used to estimate amounts available and costs for biomass harvest and removal (Biesecker and Fight, 2006). Biorefineries are sited in or near cities in NBSM. No water constraints on biorefinery operation are assumed in the model for this reason. Water availability could limit the num- ber of new refineries using cellulosic biomass in some regions, and this might be true for some existing corn-grain ethanol refineries as well (NRC, 2008). Corn-grain ethanol pro- duction is modeled using current information on ethanol refinery location, size, and cost. Optimistically, biorefineries are considered to be able to use mixed feedstocks for the most part. Where mixed feedstocks are available, corn-grain ethanol is produced up to the limit imposed by RFS2, and then crop residues, dedicated bioenergy crops, fats, oils and greases, and MSW all contribute to biofuel supply, with the mixtures varying locally. Two conversion technologies are represented in the model: biochemical fermentation of ethanol from grain and cellulosic feedstocks and thermochemical production of biofuels from mostly cellulosic feedstocks. The use and costs of dilute acid hydrolysis followed by ethanol production from fermentation is modeled for lignocellulosic feedstocks. Although several thermochemical pathways could be used to convert cellulosic biomass to fuels (see Chapter 2), gasification followed by Fischer-Tropsch synthesis was used in NBSM to repre- sent a larger class of thermochemical processes, including pyrolysis and other gasification

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83 PROJECTED SUPPLY OF CELLULOSIC BIOMASS technologies, that create biomass-derived diesel. Optimal combinations of feedstocks and technologies vary regionally. However, each simulation provides results of national or industry-wide fuel production at a given fuel price and identifies optimal locations and size of biorefineries and types of biomass resources used at each biorefinery. The selling prices of the product fuels are input parameters that are varied to create a supply curve through multiple model iterations across a range of prices ($1.00-$5.50 per gallon of gaso- line equivalent) (Figure 3-1). Results The model predicted that the RFS2 consumption mandate of 36 billion gallons of bio- fuels by 2022 can be met at $2.90 per gallon of gasoline equivalent (or $1.91 per gallon of ethanol) at the time the most recent simulations were conducted (Figure 3-1). At this price, about 500 million dry tons of different types of biomass (including corn grain, fats, and oil) would be converted to biofuels nationally. Of the 500 million dry tons of biomass, 360 million are cellulosic biomass. The committee cautions that the estimated prices for vari- ous cellulosic feedstocks in NBSM are lower than the more recent estimates presented in FIGURE 3-1 Biofuel supply and fuel pathways estimated from teh National Biorefinery Siting Model. NOTE: About 500-600 million dry tons per year of biomass are considered available and re- coverable at prices needed to meet RFS2 in 2022. Feedstock use reflects availability and price. SOURCE: Jenkins (2010). Reprinted with permission from N.C. Parker, University of Cali- fornia, Davis. Figure 3-1.eps uneditable bitmapped R01935

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84 RENEWABLE FUEL STANDARD FIGURE 3-2 Biomass supply curves estimated by the National Biorefinery Siting Model. SOURCE: Jenkins (2010). Reprinted with permission from N.C. Parker, University of California, Davis. Figure 3-2.eps Chapter 4 of this report. The estimates of feedstock costs used in NBSM are at the farm gate and do not include the opportunity costbitmapped 2 Although delivery costs were uneditable of cropland. not included as part of feedstock costs, NBSM accounts for the cost of feedstock transport R01935 to biorefinery in its analysis. Types and amounts of biomass vary with biofuel cost (Figure 3-2 and Table 3-2). The range and quantities of potential feedstocks increase with higher biofuel costs. As can be expected, agricultural residues are concentrated in the Corn Belt (Figure 3-3A). The areas with the highest yield per acre for perennial herbaceous grasses are not the areas with the largest supply (Figure 3-3B). The area with the highest quantity of forest residue is the Pacific Northwest followed by the Southeast (Figure 3-3C). As discussed in Chapter 2, models projecting yield on the basis of agronomic conditions suggest that Miscan- thus and switchgrass are most productive on existing cropland. Because NBSM maxi- mizes profitability of land use and commodity crops have higher value than perennial 2 Opportunity cost is the net returns forgone by the producer for not using cropland to produce the next-best (or next most profitable) crop or product.

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TABLE 3-2 Biomass Feedstocks for Integrated Biorefineries Projected by the National Biorefinery Siting Model in Selected Regions of the United States, at Prices Sufficient to Meet RFS2 Mandates MSW Feedstock Type (in thousands of dry tons per year) Dedicated Bioenergy Orchard and Region Biorefinery Locations Forest Pulpwood Corn Grain Crop Residues Crops Vineyard Wastes North Central Grayling, MI 338 8.1 17.5 and Northeast Warren, MI 541 123 Port Huron, MI 500 Saginaw, MI 500 636 268 Marquette, WI 194 2.3 Rhinelander, WI 502 Bemidji, MN 289 Syracuse, NY 20.8 268 1,000 30.8 595 60.9 Mid-south Fayetteville, AR 29 301 23.4 13 600 23.6 Poplar Bluff, MO 282 527 Paducah, KY 147 865 Jackson, TN 63.1 1,240 Memphis, TN 12,800 881 479 Morristown, TN 1,120 Murfreesboro, TN 4.8 1,050 Southeast Huntsville, AL 4.8 811 Greenville, MS 1,000 836 306 Vicksburg, MS 540 Columbus, MS 28.2 538 58.3 1.3 Waycross, GA 730 275 42.6 Greenwood, SC 4.1 797 Asheville, NC 52.6 630 29.7 17.2 Fayetteville, NC 600 Lumberton, NC 36.2 646 104 Danville, VA 75.8 496 34.7 22.1 1,300 85 continued

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TABLE 3-2 Continued 86 MSW Feedstock Type (in thousands of dry tons per year) Dedicated Bioenergy Orchard and Region Biorefinery Locations Forest Pulpwood Corn Grain Crop Residues Crops Vineyard Wastes Northern Plains Nebraska City, NE 444 344 Norfolk, NE 2,070 11,100 241 Howard, SD 1,000 526 706 Pierre, SD 80.8 790 Sioux Falls, SD 1,210 1,260 95.8 Watertown, SD 1,000 989 371 Jamestown, SD 1,000 132 379 Southern Plains Garden City, KS 684 18.6 910 Guymon, OK 94.8 770 Keys, OK 1,030 Dumas, TX 1,000 139 1,120 Hereford, TX 115 113 931

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87 PROJECTED SUPPLY OF CELLULOSIC BIOMASS Dry Tons Per County Maximum Dry Tons Dry Tons Per County FIGURE 3-3 Principal amounts and locations of landscape-derived biomass feedstocks for biofuels from the National Biorefinery Siting Model. A. Agricultural residue availability (dry tons per county). B. Energy crop yields (Mg per hectare) and projections of maximum available supplies (maximum dry tons). C. Estimated forest residue on public and private land (dry tons per county). NOTE: Figure 3-3C also includes forest residues from federal forestland that do not qualify as renewable biomass under EISA’s definition. SOURCE: Jenkins (2010). Reprinted with permission from N.C. Parker, University of California, Davis. Figure 3-3.eps herbaceous energy crops, the model estimated bitmapped uneditablethe perennial herbaceous energy crops would be planted on less productive certain overprinting labels except lands. The projected distribution of crop residue, switchgrass, and Miscanthus supplies are consistent with another study that projects R01935 national cellulosic biomass supply using a multimarket equilibrium nonlinear math- ematical programming model called the Biofuel and Environmental Policy Analysis Model (Khanna et al., 2011) and a study that projects regional cellulosic biomass supply in Michigan (Egbendewe-Mondzozo et al., 2010). Most of the supplies of woody biomass are projected to come from the north central and southeastern parts of United States. Biorefinery sites and the associated feedstock sheds identified by the model nationally are presented in Figure 3-4.

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88 RENEWABLE FUEL STANDARD FIGURE 3-4 Biomass deliveries to the biorefineries needed to meet the RFS2 consumption mandate in 2022 projected by the National Biorefinery Siting Model. Figure 3-4.eps SOURCE: Jenkins (2010). Reprinted with permission from N.C. Parker, University of California, Davis. uneditable bitmapped R01935 EPA Approach and Assumptions In its Regulatory Impact Analysis (EPA, 2010) for RFS2, EPA describes a transport tool that estimates the location of cellulosic biorefineries to be built to produce 16 billion gal- lons of cellulosic biofuels by 2022. Biomass data were derived from a number of sources, including NASS for agricultural residues, Elliot Campbell from Stanford University for bioenergy crops, and the U.S. Forest Service for forestry residue. MSW also was included as a potential feedstock for biofuels. For each U.S. county, feedstock availability is estimated from the Forest and Agricul- tural Sector Optimization Model (FASOM, as discussed in Appendix K). FASOM was modi- fied to reflect the current RFS2 program to include updated values for herbaceous energy crop yields, cellulosic ethanol conversions, and modifications to the accounting procedures for rangeland (Beach and McCarl, 2010). Switchgrass yields used in FASOM were derived from Thompson et al. (2009). Crop yields were projected to increase at current rates. The conversion yield from biomass to ethanol was assumed to be 90-94 gallons per dry ton depending on the feedstock (EPA, 2010).

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89 PROJECTED SUPPLY OF CELLULOSIC BIOMASS The EPA siting tool also estimated cost information for each feedstock available. These costs included the roadside cost of production, transportation to move the feedstock to the centroid of its own county or from a neighboring county, and the secondary storage of that feedstock. Each county data point contains a list detailing the total cost of each feed- stock available in that county. This list of feedstock availability and costs is used to choose feedstocks that a centrally located biorefinery would process. A biorefinery is assumed to process multiple feedstocks if they are available in the area. For each county, the cheapest feedstocks are selected for the biorefinery, and the volume of feedstock available at this price was converted to gallons, based on feedstock conversion modeled by FASOM (Tao and Aden, 2008), and added to a running count of the total volume of feedstock processed by that county, up to a maximum processing volume. Feedstock prices and conversion are reported by Beach and McCarl (2010). Biorefineries are assumed to be 100 million gallons per year in capacity, based on assumptions from FASOM and Carolan et al. (2007). Capital costs associated with the increased volume were added to the total cost of the feedstock processing for that county. The model selected feedstock sources using a cost minimization algorithm that selects progressively more expensive feedstocks until the county either reaches a set 90-percent maximum processing volume or if adding another feedstock would produce a more expensive result on a price per gallon basis. At the end of this step, each potential county biorefinery location contained information regarding the cheapest total cost to produce cellulosic ethanol at that location. The most competitive loca- tions were identified by comparing feedstock and capital costs. These locations resulted in a list of estimated least-cost biorefinery locations needed to meet 16 billion gallons per year mandated cellulosic biofuels. (The 4 billion gallons per year of advanced biofuels, which could be met by imports, were not included in this analysis.) The EPA Transport Tool does not take availability of water, permits, or human resources into account. Results The EPA Transport Tool estimated the majority of cellulosic biorefineries to be located in the upper Midwest and the Southeastern states (Figure 3-5). Because the tool projected Bagasse Plant MSW Plant Switchgrass Plant Ag. Residue Plant Forest Residue Plant FIGURE 3-5 Locations of cellulosic facilities projected by the EPA Transport Tool. SOURCE: EPA (2010). Figure 3-5.eps

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94 RENEWABLE FUEL STANDARD Estimates Based on POLYSYS and Related Models POLYSYS, created at the University of Tennessee, has been widely used to estimate di- verse effects of biofuel production from crops, crop residues, and perennial grasses (De La Torre Ugarte and Ray, 2000; Walsh et al., 2003, 2007; BRDB, 2008; Dicks et al., 2009; English et al., 2010; Larson et al., 2010; Perlack and Stokes, 2010). This model is described in Appendix K, but a brief summary focused on land use and crop choice is synthesized here from the studies cited. POLYSYS estimates livestock supply and demand, planted and harvested acres, crop yields, total production, exports, variable costs, demand by type of use, farm prices, cash receipts, government payments, and net realized income and agricultural in- come. Cattle are linked to land use by their consumption of pasture, hay, and grain. Hay and pasture activities can shift in the model in response to prices. Crops included in POLYSYS are the eight major U.S. crops (corn, grain sorghum, oats, barley, wheat, soybeans, cotton, and rice) and the livestock sector (beef, dairy, pork, lamb and mutton, broilers, turkeys, and eggs). Hay and edible oils and meals are not estimated in the model, but values are supplied externally instead. In large parts of the United States, the eight major crops predominate. In other areas, such as parts of the West Coast, Texas, and Florida, crops not included are more important, and the model would estimate any changes in these regions less reliably. POLYSYS incorporates data for 305 agricultural statistical districts (ASDs) based on NASS data and averages soil data from NRCS for dominant soil types within each district (USDA- NRCS, 2006). The State Soil Geographic (STATSGO) database aggregates soil information at a larger scale compared to the SSURGO database, which accounts for soil variation at the field scale. Independent regional linear programming (LP) models are used to model these ASDs, which are assumed to have homogeneous production characteristics (for example, rainfall, soil, crops, and climate characteristics). Proportions of tillage practices used were estimated at the county level (Larson et al., 2010). Data for crops and costs of production were derived from the University of Tennessee’s Agriculture Budget System, which was de- veloped from state agricultural extension budgets starting in 1995 and have been updated repeatedly since then (Larson et al., 2010). POLYSYS includes all cropland, cropland used as pastures, hay land, and permanent pasture. To estimate land use in an ASD or larger aggregated regions, the crop supply model first determines the land area in each ASD available to (1) enter crop production, (2) shift production to a different crop, or (3) move out of crop production. Changes in land use are estimated based on expected crop productivity, cost of production, expected profit, and market conditions (Hellwinckel et al., 2010). However, the model’s developers specified some portion of farmland as committed to crop production to reflect the inelastic nature of agricultural land supply, including the resistance to change that is part of most farming decisions (Walsh et al., 2003). Specific cropping systems are not modeled, but crop choice is constrained based on expert judgment, primarily provided by NRCS scientists. Once the land area that can be shifted is determined, the LP models allocate available acres among competing crops based on maximizing returns above costs. POLYSYS also estimates the production of (nonirrigated) switchgrass, hybrid poplar, and willow. Commercial production of these crops is limited on the farms, so yields and costs are estimated in other ways (English et al., 2010). Switchgrass yield estimates from the Pacific Northwest National Laboratory switchgrass simulation model (Thomson et al., 2009; see also Chapter 2) and other sources of data for perennial grasses and short-rotation tree plantations (Gunderson et al., 2008; Jager et al., 2010; Perlack and Stokes, 2010) have been added to some of the 305 ASDs. Comparative advantage with respect to yields and

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95 PROJECTED SUPPLY OF CELLULOSIC BIOMASS prices for switchgrass produced are predicted for the Corn Belt and Southeastern regions of the United States (Walsh et al., 2007; Dicks et al., 2009). Land shifts are estimated in more than 3,000 counties in the United States. To be in- cluded in the model’s optimum solution, the net present value of bioenergy crops would have to be greater than the regional rental rate for pasture or for conventional crops. If some pastures are converted to bioenergy crop production, the remaining pasture would likely be managed more intensively, and more hay would be fed instead of grazing (English et al., 2010). Limits are placed on the rate (5 percent per year) and total amount of pasture that can be converted (20 percent). The amounts produced are determined by prices sufficient to meet mandated demands. These prices are supplied externally (Hellwinckel et al., 2010). Locations of Department of Energy-Funded and Industry-Funded Advanced Biofuel Projects As discussed in Chapter 2, the U.S. Department of Energy (DOE) funded a number advanced biofuel projects. Those planned facilities that have secured funding for demon- stration of converting agricultural feedstocks or forestry materials to cellulosic or advanced biofuel are listed in Table 2-2 in Chapter 2 and shown in Figure 3-7. FIGURE 3-7 Locations of advanced biofuel projects including pilot-scale, demonstration-scale, and commercial-scale projects funded by DOE (red dots) and proposed by industry (blue dots). DATA SOURCES: Biofuels Digest (2010) and DOE-EERE (2011). Figure 3-7.eps R01935

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96 RENEWABLE FUEL STANDARD A number of companies that have DOE funding have proposed cellulosic biofuel fa- cilities or begun construction of pilot-scale4 or commercial-scale5 facilities (Biofuels Digest, 2010). In each case, a combination of factors has motivated site selection, including feed- stock supply, infrastructure, and federal, state, or local financial support for development at that location. In many instances, lists of proposed facilities or those under construction or in operation do not conform to those locations predicted by the siting models or analy- ses discussed. The site-specific nature of biofuel feedstock production, fuel demand and use, and other factors not easily reducible to general model formulations might influence industry decisions. For example, the models estimated that few biorefineries will be sited in the western and northeastern United States, other than the ones that rely on MSW as feedstock. Innovative businesses may recognize feedstock supplies and other advantages overlooked in larger, aggregated analyses. Comparing Estimated Supplies from Different Studies The different studies described above all concluded that the United States is capable of producing a sufficient quantity of cellulosic biomass to achieve the RFS2 mandate. The USDA and BDRB reports only estimated potential locations of feedstock supply. NBSM and the EPA Transport Tool also project locations of biorefineries on the basis of where feedstocks could be produced or harvested, feedstock costs, and other factors. In all cases, potential locations of feedstock supplies are based on agroecological classification. (See Chapter 2 for agroecological regions suitable for various biomass types.) Although the different approaches were independent efforts to assess future feedstock production and related biofuel supplies, they have some commonalities (Table 3-4). All approaches account for the need to leave some residue in the field to prevent soil erosion, but none of them explicitly include water availability as a constraint. Many studies rely on similar data, on the use of a few critical models, or on the work of the same scientists or models. The federal government is the source of much data used by modelers in estimating feedstock supply and future biorefinery locations. For cropland use and other agricultural data nationwide, the USDA-NASS reports result from hundreds of surveys they conduct annually (USDA-NASS, 2011). Those reports cover most aspects of U.S. agriculture, includ- ing production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm finances, chemical use, and changes in the demographics of U.S. producers. USDA’s Economic Research Service collects the annual Agricultural Resource Management Survey (ARMS). These data are USDA’s primary source of information on the financial condition, production practices, resource use, and economic well-being of American farm households. ARMS provides observations on field-level and livestock man- agement practices, the economics of farm businesses, and the characteristics of American farm households (for example, age, education, occupation, farm and off-farm work, types of employment, and family living expenses). The National Resources Inventory maintained by NRCS has been used to define farm structure (USDA-NRCS, 2010). These surveys form a series from 1982 and provide updated information on the status, condition, and trends of land cover and land use, land capability classes, soil and soil erosion, water and irrigation, 4 A pilot demonstration for biofuel refinery is a facility that has the capacity to process 1-10 dry tons of feedstock per day. 5 A commercial demonstration for biofuel refinery is a facility that has the capacity to process 700 dry tons of feedstock per day.

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TABLE 3-4 Comparison of Models Used to Estimate Biomass Production by Region and Biorefinery Locations University of Tennessee National Biorefinery U.S. Environmental Biomass Research and and Oak Ridge National Source Siting Model Protection Agency (EPA) Development Initiative Laboratory (ORNL) Data or model sources for Crops USDA-NASS (U.S. USDA-NASS USDA-NASS USDA-NASS feedstocks Department of Agriculture USDA-ARMS (U.S. USDA-ARMS USDA-NRI (U.S. National Agricultural Department of Agriculture Department of Agriculture Statistics Service) Agricultural Resource National Research Management Survey) Initiative) FASOM (Forest and Data from ORNL agricultural sector optimization model) Crop residue USDA-NASS USDA-NASS USDA-NASS SURGO (Soil survey SURGO geographic database) RUSLE RUSLE (Revised Universal Soil Loss Equation) Dedicated bioenergy PNNL (Pacific Northwest PNNL POLYSYS (Policy Analysis PNNL crops National Laboratory) CRP (Conservation System Model) Jager et al. (2010) Reserve Program) land CRP land excluded excluded Forest residue FIA (Forest Inventory FIA FIA FIA and Analysis National FASOM USFS (2010) Program) U.S. Department of Agriculture Forest Service (USFS) (2010) Municipal Solid Wastes EPA EPA Arsova et al. (2008) Livestock FASOM USDA-NASS Infrastructure Bureau of Transportation Statistics Models Original optimization FASOM REAP (Rural Energy for POLYSYS model Siting tool America Program) IMPLAN (Impact analysis RUSLE POLYSYS for planning) economic FRCS (Fuel Reduction EPIC (Environment Policy modeling Cost Simulator) Integrated Climate Model) FRCS 97 continued

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TABLE 3-4 Continued 98 University of Tennessee National Biorefinery U.S. Environmental Biomass Research and and Oak Ridge National Source Siting Model Protection Agency (EPA) Development Initiative Laboratory (ORNL) Description/scenarios Minimizes the cost of Minimizes the cost of Uses projected crop Uses projected crop meeting biofuel targets on meeting biofuel targets on yields, land availability, yields, land availability, a national level, subject a national level, subject and targeted biomass land use transformation, to local biomass supplies to local biomass supplies requirements to meet and targeted biomass and infrastructure and local capital costs. biofuel demand. Diverse requirements to meet availability. yield and demand biofuel demand. Diverse price scenarios scenarios modeled. Diverse scenarios modeled. modeled, including different levels of demand for biomass and fuels, yield levels, and carbon storage payments. Environmental restrictions Erosion and maintenance Yes Yes Yes Yes on feedstock production of soil organic matter Nutrients Yes Yes Yes Water Greenhouse gas Yes Yes Yes Biomass power Yes considered Biorefinery capital costs Yes Yes Specific locations of Yes Yes biorefineries References Parker (2011) and Parker EPA (2010) BRDB (2008) De La Torre Ugarte and et al. (2010a) Ray (2000), Walsh et al. (2003, 2007), Dicks et al., (2009), English et al. (2010), Jager et al. (2010)

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99 PROJECTED SUPPLY OF CELLULOSIC BIOMASS and related resources on the nation’s nonfederal lands.6 The USFS data form the basis for many assessments of biomass availability from those sectors (USFS, 2010). Comparisons of projected biorefinery locations and feedstock supplies identified from different studies indicate that there are similarities among studies (Figures 3-4, 3-5, 3-6, and 3-7)—a large amount of crop residues can be derived from the Corn Belt; herbaceous pe- rennial crops will likely be planted in the Southeast; the Pacific Northwest, North Central, and Northeast regions can supply forest residues; and large quantities of MSW, if included, can be supplied from larger urban areas (primarily near urban areas in the Northeast and western states). Both EPA and BRDB estimated that 10 billion gallons of ethanol-equivalent biofuel would be derived from crop residues or dedicated bioenergy crops and 4 billion gallons of ethanol-equivalent biofuels would be derived from forest resources. Some differences in the outcome of the feedstock supply and biorefinery siting studies were observed. NBSM projected more perennial grass crop production along the western edge of the Corn Belt in the southeastern and northern prairie regions than EPA, English et al. (2010), or BRDB (2008). Compared to the estimations of biorefinery locations provided by NBSM and the feedstock locations provided by USDA and BRDB, EPA projected fewer facilities located in the Corn Belt region, more in the Southeastern United States, and more in California (presumably associated with MSW conversion). The influence of capital costs associated with biorefinery permitting and construction was estimated and used in EPA’s model but not in other siting models. These costs are estimated by EPA to be lower in the Southeast and Midwest than elsewhere. EPA’s projected biorefinery locations were sited in locations with the lowest capital costs. In EPA’s analysis, minimizing capital costs was more important than maximizing the yield of perennial grasses or the availability of for- est residues. Nonetheless, many biorefineries are projected to be built in similar regions to those derived from other modeling efforts. UNCERTAINTIES ABOUT CELLULOSIC FEEDSTOCK PRODUCTION AND SUPPLY Although NBSM and other studies estimated that 500-600 million dry tons of biomass could be supplied to biorefineries for fuel production, several factors could alter that sup- ply: competition for biomass, potential for pests and diseases, and yield increase as a result of research. Farmers’ willingness to grow or harvest feedstocks also can affect supply, which is discussed in Chapter 6 in the context of social barriers to achieving RFS2. Cellulosic bioenergy crops can be grown for markets other than biofuels. For example, bioenergy crops can be used for power generation (electricity or combined heat and power) or as forage or bedding for animals. Most states (36 out of 50) have set standards that require the electricity sector to generate a portion of the electricity from renewable or alternative sources. Although NBSM accounted for biomass allocated for electricity generation, com- petition for feedstock between the two sectors could drive up the price of feedstock. The technology for producing fiberboard from sawdust and other residues has improved (Ye et al., 2007; Yousefi, 2009), and crop and wood residues can be used for that purpose and further increase the competition for feedstock. In the case of agricultural residue, it becomes a commodity with value instead of be- ing a residue that incurs an additional cost of its removal when new market opportunities become available. As discussed in Chapter 2, leaving a portion of crop residue can protect 6 Nonfederal lands include privately owned lands, tribal and trust lands, and lands controlled by state and local governments.

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100 RENEWABLE FUEL STANDARD land from soil erosion and maintain soil carbon. The crop residues removed can be used for animal bedding (Tarkalson et al., 2009). The price and supply of bioenergy feedstocks likely to be available to biorefineries de- pend partly on competition with other uses, in addition to other factors including produc- tion, harvesting, and transportation costs. Whether biorefineries can compete for biomass with other sectors will depend on the prices that other sectors are willing to pay for the feedstocks. For example, the likely value of crop residues as bedding to animal producers is the cost of replacing the residues with a substitute. Competition for feedstock could intensify during periods of weather extremes (for ex- ample, drought or flood) if crops are lost to pests and diseases. Fungal diseases that could affect switchgrass have been reported (Gustafson et al., 2003; Crouch et al., 2009). Insect pests could affect the establishment of switchgrass stands; for example, grass seedlings were reported to be susceptible to grasshoppers, crickets, corn flea beetle, and cinch bug (Landis and Werling, 2010). A preliminary study suggested the yellow sugarcane aphid and the corn leaf aphid as potential pests of Miscanthus × giganteus (Crouch et al., 2009). Although severe pest and disease outbreaks have not been observed for herbaceous peren- nial crops outside the tropics (Karp and Shield, 2008), the pest and disease dynamics could change if cultivation of these crops increases and become more intensive. Short-rotation woody crops are susceptible to diseases and pests. Rust diseases can affect poplar and willow severely (Royle and Ostry, 1995). In addition to diseases, insect pests such as cottonwood leaf beetle and defoliators, sap feeders, and stem borers can attack poplar and willow (Landis and Werling, 2010). Cultivar selection, breeding, and genomic approaches can result in bioenergy crops that are resistant to pests and diseases, suitable for their specific agronomic conditions, and have other desirable characteristics as biofuel feedstock (Bouton, 2007; Nelson and Johnsen, 2008). Increase in yield per acre as a result of agronomic and genetic research (Mitchell et al., 2008; Jakob et al., 2009; Wrobel et al., 2009) could alleviate competition for feedstocks among different sectors. CONCLUSION Several studies estimated that the United States has the capability to produce adequate biomass feedstock for production of 16-20 billion gallons of cellulosic biofuels to meet RFS2. Different types of feedstocks predominate in different regions. In the North Central and Northeast regions, forestry residues are most important. In the southeastern United States, forest residues and perennial grasses are most important. In the prairie regions of the United States, crop residues, corn grain, and perennial grasses are predicted to be produced. Some studies constrain the feedstock supply by price with the intent to simulate feedstock supply at a reasonable cost to biorefineries. However, the studies discussed above do not address the gap between the price that farmers are willing to sell their biomass feedstock and the price that biorefineries are willing to pay. The next chapter assesses the economics of feedstock sup- ply in detail. Most studies also constrain the feedstock supply by limiting the amount of crop residues that could be harvested with the intent of minimizing soil erosion. However, soil erosion is only one of many environmental factors that have to be considered in large-scale production of bioenergy feedstock. Chapter 5 discusses various environmental effects to be considered. Knowing feedstock supply and biorefinery locations, local or biorefinery-specific environmental consequences of biofuel production also can be estimated or anticipated. Po- tential harvestable biomass feedstock is unlikely to be the limiting factor in meeting RFS2. At the same time, limits associated with the diverse economic and environmental effects of

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101 PROJECTED SUPPLY OF CELLULOSIC BIOMASS achieving the RFS2 mandate by 2022 could reduce the amount of biomass feedstock projected to be available in the United States for cellulosic biofuels by independent studies. REFERENCES Arsova, L., R. van Haaren, N. Goldstein, S. Kaufman and N. Themelis. 2008. The state of garbage in America. BioCycle 49(12):22. Beach, R.H., and B.A. McCarl. 2010. U.S. Agricultural and Forestry Impacts of the Energy Independence and Secu- rity Act: FASOM Results and Model Description. Final Report. Research Triangle Park, NC: RTI International. Biesecker, R.L., and R.D. Fight. 2006. My Fuel Treatment Planner: A User Guide. Portland, OR: U.S. Department of Agriculture - Forest Service. Biofuels Digest. 2010. Industry Data. Available online at http://biofuelsdigest.com/bdigest/free-industry-data/. Accessed November 12, 2010. Bouton, J.H. 2007. Molecular breeding of switchgrass for use as a biofuel crop. Current Opinion in Genetics & Development 17(6):553-558. BRDB (Biomass Research and Development Board). 2008. Increasing Feedstock Production for Biofuels: Eco - nomic Drivers, Environmental Implication, and the Role of Research. Washington, DC: U.S. Department of Agriculture. BRDB (Biomass Research and Development Board). 2010. BR&D: Biomass Research and Development. Available online at http://www.usbiomassboard.gov/. Accessed November 4, 2010. Carolan, J., S. Joshi, and B. Dale. 2007. Technical and financial feasibility analysis of distributed bioprocessing using regional biomass pre-processing centers. Journal of Agricultural & Food Industrial Organization 5(2):Article 10. Crouch, J.A., L.A. Beirn, L.M. Cortese, S.A. Bonos, and B.B. Clarke. 2009. Anthracnose disease of switchgrass caused by the novel fungal species Colletotrichum navitas. Mycological Research 113:1411-1421. De La Torre Ugarte, D.G., and D.E. Ray. 2000. Biomass and bioenergy applications of the POLYSYS modeling framework. Biomass and Bioenergy 18(4):291-308. Dicks, M.R., J. Campiche, D. De La Torre Ugarte, C. Hellwinckel, H.L. Bryant, and J.W. Richardson. 2009. Land use implications of expanding biofuel demand. Journal of Agricultural and Applied Economics 41(2):435-453. DOE-EERE (U.S. Department of Energy-Energy Efficiency and Renewable Energy). 2011. Integrated biorefineries. Available online at http://www1.eere.energy.gov/biomass/integrated_biorefineries.html. Accessed May 12, 2011. Egbendewe-Mondzozo, A., S.M. Swinton, R.C. Izaurralde, D.H. Manowitz, and X. Zhang 2010. Biomass Supply from Alternative Cellulosic Crops and Crop Residues: A Preliminary Spatial Bioeconomic Modeling Ap- proach. Staff Paper No. 2010-07. East Lansing: Michigan State University. English, B.C., D.G. De La Torre Ugarte, C. Hellwinckel, K.L. Jensen, R.J. Menard, T.O. West, and C.D. Clark. 2010. Implications of Energy and Carbon Policies for the Agriculture and Forestry Sectors. Knoxville: The University of Tennessee. EPA (U.S. Environmental Protection Agency). 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. Washington, DC: U.S. Environmental Protection Agency. Gunderson, C.A., E.B. Davis, H.I. Jager, T.O. West, R.D. Perlack, C.C. Brandt, S.D. Wullschleger, L.M. Baskaran, E.G. Wilkerson, and M.E. Downing. 2008. Exploring Potential U.S. Switchgrass Production for Lignocellu- losic Ethanol. Oak Ridge, TN: Oak Ridge National Laboratory. Gustafson, D.M., A. Boe, and Y. Jin. 2003. Genetic variation for Puccinia emaculata infection in switchgrass. Crop Science 43(3):755-759. Hellwinckel, C.M., T.O. West, D.G.D. Ugarte, and R.D. Perlack. 2010. Evaluating possible cap and trade legislation on cellulosic feedstock availability. Global Change Biology Bioenergy 2(5):278-287. Hess, J.R., K.L. Kenney, L.P. Ovard, E.M. Searcy, and C.T. Wright. 2009. Commodity-Scale Production of an In- frastructure-Compatible Bulk Solid From Herbaceous Lignocellulosic Bioamss. Volume A: Uniform-Format Bioenergy Feedstock Supply Design System. Idaho Falls: Idaho National Laboratory. Jager, H., L.M. Baskaran, C.C. Brandt, E.B. Davis, C.A. Gunderson, and S.D. Wullschleger. 2010. Empirical geo- graphic modeling of switchgrass yields in the United States. Global Change Biology Bioenergy 2(5):248-257. Jakob, K., F.S. Zhou, and A. Paterson. 2009. Genetic improvement of C4 grasses as cellulosic biofuel feedstocks. In Vitro Cellular & Developmental Biology-Plant 45(3):291-305. Jenkins, B. 2010. National Biorefinery Siting Model: Optimizing Bioenergy Development in the U.S. Presentation. Presentation to the Committee on Economic and Environmental Impacts of Increasing Biofuels Production, March 5.

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102 RENEWABLE FUEL STANDARD Johansson, R., M. Peters, and R. House. 2007. Regional Environment and Agriculture Programming Model. Wash- ington, DC: U.S. Department of Agriculture - Economic Research Service. Karp, A., and I. Shield. 2008. Bioenergy from plants and the sustainable yield challenge. New Phytologist 179(1):15-32. Khanna, M., X. Chen, H. Huang, and H. Onal. 2011. Supply of cellulosic biofuel feedstocks and regional production patterns. American Journal of Agricultural Economics 93(2):473-480. Landis, D.A., and B.P. Werling. 2010. Arthropods and biofuel production systems in North America. Insect Science 17(3):220-236. Larson, J.A., B.C. English, D.G.D. Ugarte, R.J. Menard, C.M. Hellwinckel, and T.O. West. 2010. Economic and environmental impacts of the corn grain ethanol industry on the United States agricultural sector. Journal of Soil and Water Conservation 65(5):267-279. Malcolm, S.A., M. Aillery, and M. Weinberg. 2009. Ethanol and a Changing Agricultural Landscape. Washington, DC: U.S. Department of Agriculture. Mitchell, R., K.P. Vogel, and G. Sarath. 2008. Managing and enhancing switchgrass as a bioenergy feedstock. Bio- fuels Bioproducts & Biorefining-Biofpr 2(6):530-539. NAS-NAE-NRC (National Academy of Sciences, National Academy of Engineering, National Research Council). 2009. Liquid Transportation Fuels from Coal and Biomass: Technological Status, Costs, and Environmental Impacts. Washington, DC: National Academies Press. Nelson, C.D., and K.H. Johnsen. 2008. Genomic and physiological approaches to advancing forest tree improve- ment. Tree Physiology 28(7):1135-1143. Nelson, R.G. 2002. Resource assessment and removal analysis for corn stover and wheat straw in the eastern and midwestern United States: Rainfall and wind-induced soil erosion methodology. Biomass & Bioenergy 22:349-363. Nelson, R.G., M. Walsh, J.J. Sheehan, and R. Graham. 2004. Methodology for estimating removable quantities of agricultural residues for bioenergy and bioproduct use. Applied Biochemistry and Biotechnology 113:13-26. Nelson, R.G., J.C. Ascough II, and M.R. Langemeier. 2006. Environmental and economic analysis of switchgrass production for water quality improvement in northeast Kansas. Journal of Environmental Management 79(4):336-347. NRC (National Research Council). 2008. Water Implications of Biofuels Production in the United States; Commit- tee on Water Implications of Biofuels Production in the United States. Washington, DC: National Academies Press. Parker, N., Q. Hart, P. Tittman, M. Murphy, R. Nelson, K. Skog, E. Gray, A. Schmidt, and B. Jenkins. 2010a. Devel- opment of a biorefinery optimized biofuel supply curve for the western United States. Biomass & Bioenergy 34(11):1597-1607. Parker, N., Q. Hart, P. Tittman, M. Murphy, R. Nelson, K. Skog, E. Gray, A. Schmidt, and B. Jenkins. 2010b. Na- tional Biorefinery Siting Model: Spatial Analysis and Supply Curve Development. Washington, DC: Western Governors Association. Parker, N.C. 2011. Modeling Future Biofuel Supply Chains Using Spatially Explicit Infrastructure Optimization. Ph.D. Graduate Group in Transportation Technology and Policy, University of California, Davis. Perlack, R.D., and B.J. Stokes. 2010. Update of the “billion ton” study. Presentation to the Committee on Economic and Environmental Impacts of Increasing Biofuels Production, May 3. Perlack, R.D., and B.J. Stokes (Leads). 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioprod- ucts Industry. Oak Ridge, TN: Oak Ridge National Laboratory. Perlack, R.D., L.L. Wright, A.F. Turhollow, R.L. Graham, B.J. Stokes, and D.C. Erbach. 2005. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply. Oak Ridge, TN: Oak Ridge National Laboratory. Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder. 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Soil Loss Equation (RUSLE). Washington, DC: U.S. Department of Agriculture. Royle, D.J., and M.E. Ostry. 1995. Disease and pest control in the bioenergy crops poplar and willow. Biomass & Bioenergy 9(1-5):69-79. Skog, K.E., R.J. Barbour, K.L. Abt, E.M. Bilek, F.Burch, R.D. Fight, R.J. Hugget, P.D. Miles, E.D. Reinhardt, and W.D. Sheppard. 2006. Evaluation of Silvicultural Treatments and Biomass Use for Reducing Fire Hazard in Western States. Madison, WI: U.S. Department of Agriculture - Forest Service. Skog, K.E., R. Rummer, B. Jenkins, N. Parker, P. Tittmann, Q. Hart, R. Nelson, E. Gray, A. Schmidt, M. Patton- Mallory, and G. Gordon. 2008. A Strategic Assessment of Biofuels Development in the Western States. Pro- ceedings of the Forest Inventory and Analysis (FIA) Symposium.

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