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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.
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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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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.
SOURCE: Jenkins (2010). Reprinted with permission from N.C. Parker, University of California, Davis.
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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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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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)
USDA-NASS USDA-NASS
USDA-NASS
Data or model sources for Crops USDA-NASS (U.S.
USDA-ARMS
feedstocks Department of Agriculture USDA-ARMS (U.S. 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)
USDA-NASS USDA-NASS
Crop residue USDA-NASS
SURGO
SURGO (Soil survey
RUSLE
geographic database)
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 FIA FIA
FIA (Forest Inventory
FASOM USFS (2010)
and Analysis National
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
POLYSYS
FASOM REAP (Rural Energy for
Models Original optimization
IMPLAN (Impact analysis
Siting tool America Program)
model
for planning) economic
POLYSYS
RUSLE
modeling
EPIC (Environment Policy
FRCS (Fuel Reduction
FRCS
Integrated Climate Model)
Cost Simulator)
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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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.
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