Though government policies can provide the impetus to promote the production and use of lignocellulosic biomass feedstocks, market interests will undoubtedly increase as demonstrated uses go beyond exclusive ethanol production. Prior to its use as a bioenergy crop, switchgrass was utilized as a forage crop noted for its high forage yields (Parrish and Fike 2005). Before widespread efforts at improving bioenergy-related traits, some of the initial improvement efforts in switchgrass were aimed at forage digestibility by cattle (Vogel 2004). Improvements here, as illustrated in a number of biological and economic metrics, led to the development of cv. Trailblazer (Vogel et al. 1991). Furthermore, switchgrass cv. Shawnee was the result of simultaneous improvements in yields and digestibility (Vogel et al. 1996). Such efforts are germane to those being pursued today relating to higher biomass and ethanol yields, as they are related to strategies for targeted traits of switchgrass and subsequent ethanol processing steps. While the future of switchgrass as a bioenergy crop may well be tied to its ability to remain a forage option, we argue that sustainable approaches along these lines will be complex, and ultimately should stray from switchgrass becoming embedded in a "food vs. fuel" dilemma. Multiple within-year harvests that include an early-season forage harvest may attract farmers concerned with ensuring profits. Early indications point towards subsequent late-season harvests of switchgrass produce admirable amounts of biomass (Mcintosh et al. 2012), though not as much as with a single harvest. How multiple harvests and management for multiple services (see Anderson et al. 2012) will impact N losses, carbon sequestration, and landscape-wide water quality and quantity is unknown.

Promoting other uses for switchgrass will increase its stock in the bioenergy and chemical product arena. Currently, switchgrass is noted primarily for its contribution to biofuels. Advances in technologies that can promote other energy and co-product uses are gaining attention. The co-firing of pelletized switchgrass with coal for electricity (Qin et al. 2006; Aravindhakshan et al. 2010; Khanna et al. 2011) is one area worth further exploration. In addition, high-value commodities that may be produced in or from the ethanol-making process in the future, such as bioplastics, extractives, and other metabolic coproducts, will increase the likelihood of a sustainable bioenergy market that promotes the use of switchgrass (Joyce and Stewart 2012). For bioenergy crops like switchgrass, quick success on these fronts could well dictate its immediate and future prominence.

Acknowledgments

We thank numerous colleagues for their input, and for their valuable contributions to the field of switchgrass biology. These people include, among many others, F. Allen, C. Auer, G. Bates, H. Bhandari, S. Bobzin, V. Dale, R. Efroymson, S. Jackson, Y. Jager, K. Kline, P. Keyser, D. Simberloff, A. Snow. This project was supported by the NIFA Biotechnology Risk Assessments Grant program and funds from the University of Tennessee.

933-948.

Thermochemical technologies employ high temperature, and use of oxidizing agents or catalysts to break down the biomass polymers into liquid or gaseous fuels. These include combustion, gasification, pyrolysis, liquefaction, and hydrogenation. The most noteworthy difference among these processes is the target products that these processes are used to produce. Heat (or power) is the main direct product of combustion. Gaseous fuel (synthesis gas or producer gas) is the direct product of gasification; whereas, the direct product of pyrolysis, liquefaction and hydrogenation is a liquid (bio-oil) or solid (char) depending on the process operating conditions. In some situations, two or more biomass thermochemical processes are applied in series to increase conversion efficiency, obtain desired chemicals, or reduce environmental emissions. For example, biomass gasification followed by combustion of syngas (or producer gas) provides an opportunity to remove contaminants from the gaseous fuels and to use a gas turbine and other gaseous fuel-based technologies.

Steam explosion is a physio-chemical pretreatment, performed in a pressurized vessel at 190-270°C for 1-10 minutes at 200-450 psig pressure with sudden release of pressure to cause an "explosion" within the physical structure of the material. A potential drawback includes the processing of lignocellulosic biomass at elevated temperatures resulting in formation of inhibitory compounds that may further inhibit the fermentation process downstream.

Acid Pretreatment

Diluted- and concentrated-acids are used for acid pretreatment. Sulfuric acid, hydrochloric acid and phosphoric acid are the most common types of acids used. Sulfuric acid pretreatment for lignocellulosic biomass has been a popular method for many years. The use of diluted or concentrated acids is highly dependent on the properties of the lignocellulosic biomass (such as lignin content, crystallinity and available surface area). In addition, the pretreatment processing time is more dependent on the nature of the feedstock. For instance, hardwood and softwood have longer pretreatment times compared to grasses, primarily due to high lignin contents and high crystallinity indexes. The ability of concentrated-acid pretreatment to break lignin at lower temperature makes it a more suitable pretreatment than diluted acid pretreatment. The concentrated sulfuric acid pretreatment (70-77% concentration) is generally performed at 50°C. At higher temperatures, the formation of inhibitory compounds such as furfural and hydroxyl-methyl furfural takes place, inhibiting microorganisms such as

E. coli and S. cerevisiae during ethanol fermentation (Galbe and Zacchi 2002; Drapcho et al. 2008).

Francis M. Epplin,[22]‘* Andrew P. Griffith[23] and
Mohua Haque[24]

Introduction

Prior to investing hundreds of millions of dollars in a switchgrass (Panicum virgatum) biomass biorefinery, due diligence would require a business plan that encompasses the complete chain from feedstock acquisition to the sales of products produced. These issues are only of importance if technology is developed to enable companies to profit from procuring switchgrass biomass, converting it to one or more useful products, and selling these products. Technologies and systems will be required to enable processing of lignocellulosic biomass, including switchgrass biomass, into a product or a

portfolio of products. One or more of these products must either be able to fulfill a unique niche of consumer demand or compete economically with existing products. If these biobased products include substitutes for fossil fuels, the potential market for cellulosic biomass could be very large. The potential market for switchgrass depends on its delivered cost relative to alternative feedstocks such as other dedicated energy crops, crop residues, or other sources of biomass. The purpose of this chapter is to identify practical issues related to the economics of developing switchgrass as a dedicated energy crop and to provide estimates of the price for delivered switchgrass biomass that would be required to compensate for the cost of inputs used to produce and deliver it to a biorefinery.

After the oil embargo of the mid 1970s, Oak Ridge National Laboratory (ORNL) added non-nuclear energy issues, including biofuels, to its research agenda (ORNL was officially incorporated into the newly established U. S. Department of Energy in 1977). At the time that ORNL was seeking an alternative source of energy, others were searching for a solution to the "farm problem". In 1978, more than 10.5 million hectares of U. S. cropland were classified as "idle" (Lubowski et al. 2006). Much of this "idle" land was diverted from crop production as a result of various federal programs including the feed grain, wheat, and cotton commodity programs (Tweeten 1970). The development of switchgrass as an energy crop was envisioned as a way to convert this "idle" land to productive use. At the same time it was seen as a way to reduce the cost of government commodity and conservation programs that were funded to entice land owners to set aside the land from the production of traditional crops. The "billion-ton update" published by the U. S. Department of Energy in 2011 projected that a switchgrass price of $66/dry Mg would be sufficient to entice land owners to convert land from current use and establish switchgrass on 21 million U. S. hectares (U. S. Department of Energy 2011).

In the U. S. the infrastructure for production, harvest, storage, transportation, and price risk management of grain is well-developed. The structure of farms used to produce grain and the infrastructure required to harvest, store, and transport grain in the U. S. has evolved over time. Relative to grain, cellulosic biomass from perennial grasses is bulky and difficult to transport. In the U. S., feedstock acquisition logistics for grains such as wheat and corn are relatively simple. Users may post a competitive price, and grain will be delivered by the existing marketing system. However, the infrastructure required to deliver a steady flow of large quantities of cellulosic biomass from fields where it could be produced and harvested to biorefineries where it would be processed remains to be developed.

One method to be considered is vertical integration. Most large U. S. firms that harvest and process trees (lignocellulosic feedstock) into wood products are vertically integrated. Through either ownership or leases, these firms have acquired the rights to millions of hectares and manage the production, harvest, and delivery of feedstock to their mills. Production characteristics and harvest cost economies could result in a structure for switchgrass production for use as a low-valued dedicated energy crop that more closely resembles the structure of integrated timber production and processing businesses. In some parts of the world, these firms harvest and process timber continuously throughout the year. If the low-cost cellulosic feedstock is a perennial with a long stand life and wide harvest window such as switchgrass, market forces could be expected to drive the structure toward vertical integration. For a mature industry, switchgrass production, harvest, and transportation could be expected to be centrally managed and coordinated, which more closely resembles a vertically integrated timber production, harvesting, and processing business than an atomistic grain system. Whether an atomistic structure such as that for U. S. grain or a vertically integrated structure, such as that for U. S. wood products would be the most economically efficient system for producing, harvesting, and delivering a flow of switchgrass biomass feedstock to biorefineries has yet to be determined.

Based on small plot research, in the years after switchgrass is established, it requires little annual maintenance (Fuentes and Taliaferro 2002). Other than harvest, most stands can be maintained with one pass over the field per year to apply fertilizer. Consequently, the relative share of harvest costs to total production costs is substantially greater for bulky biomass from switchgrass than for more dense grain from corn and wheat. Harvest costs (mowing, raking, baling, field stacking) are estimated to account for 45 to 65 percent of the total farm gate costs (including the cost of establishment, land, and fertilizer) to produce switchgrass biomass (Epplin et al. 2007). In contrast, harvest costs account for less than 15 percent of the total farm gate costs of production for corn grain. The structure is likely to be determined by the most cost efficient harvest, storage, and transportation systems.

Two related but different approaches are used to produce estimates of the switchgrass biomass price that would be required to compensate for the cost of inputs used to produce and deliver biomass. First, we present a listing of operations that may be used to establish, maintain, and harvest switchgrass. Second, this information is followed by a presentation of conventional enterprise budgets. Third, given the importance of harvest costs, assumptions regarding harvest machines are presented. Fourth, results of a mathematical programming model that is designed to estimate the costs to deliver a flow of feedstock throughout the year to a biorefinery is presented.

Most common routes available for conversion of syngas into hydrocarbon fuels are Fischer-Tropsch (FT) process, and syngas to methanol to gasoline (MTG). FT conversion of syngas into hydrocarbons is one of the most recognized technologies with first plant operation in Germany in 1938. Currently three plans use the FT process to produce gasoline, diesel and chemicals from coal and natural gas. FT process uses Fe — or Co-based catalysts for the conversion. The resulting product is a wide range of primarily linear hydrocarbons from C1 compounds to high molecular mass waxes, which need reprocessing to obtain hydrocarbons in the range of diesel or gasoline. Diesel is the most appropriate fuel because the FT product contains mostly linear hydrocarbon which results in diesel with high cetane number (Dry 2004). However, due to the need to additional processing of long chain hydrocarbons (waxes), the capital cost is high (Spath and Dayton 2003). FT process is optimum at syngas H2/CO of 2, which is difficult to achieve in biomass generated syngas without steam reforming. Conversion of syngas to methanol is a well-known process. However, since methanol cannot be used directly because of its toxicity, methanol can be converted into gasoline through methanol to gasoline (MTG) process developed by Mobile Oil Corporation in 1970s. The process uses zeolite-based catalysts (ZSM-5) resulting in higher than 85% selectivity to gasoline-range hydrocarbons (Spath and Dayton 2003).

Cellulose is a long chain of polyglycans linked by P(1^-4)-glycosidic linkages. The synergetic action of cellulase consists of endoglucanase, exoglucanase (cellobiohydrolases) and p-glucosidase (cellobiase) acting together to depolymerize cellulose into glucose molecules with liberated water molecules. The hydrolysis of cellulose involves three different synergetic stages. The following steps are summarized:

• Endoglucanase hydrolyzes inner polyglucan molecules linkages into small polysaccharides chain.

• Exoglucanase (CBHI and CBHII) remove the terminal disaccharide glucan units of cellulose.

• CBHI degrades terminal of cellulose polymer.

• CBHII releases cellobiose units from the endoglucanase degraded

cellulose.

• p-glucosidase hydrolyzes cellobiose units into glucose.

P-glucosidase prevents feedback substrate inhibition of endo — and exgo — glucanases by hydrolyzing cellobiose molecules into glucose. The maximum enzymatic hydrolysis rates using cellulase mixtures are generally carried out at 50 ± 5°C between pH 4.8 and 5.5 (Galbe and Zacchi 2002). Other factors that govern the hydrolysis of cellulose include lignin content, crystallinity, hemicellulose content, particle size and surface area of lignocellulosic biomass (Sun and Cheng 2002; Pan 2008; Zhu et al. 2008; Hendriks and Zeeman 2009; Harmsen et al. 2010).

Charles Kwit, h* Madhugiri Nageswara-Raoha and
C. Neal Stewart Jr. U2

Introduction

Providing food and energy to an ever-increasing world population is arguably the most challenging issue we face today. Exactly how to proceed on the agricultural side of this challenge, in conjunction with worldwide economic growth forecasts, is the subject of intense interest (see Tilman et al. 2011). Indeed, food and energy production comprise top priorities in the U. S. President’s Council of Advisors on Science and Technology’s recent report on agricultural research (Executive Office of the President 2012), and they are primary components of the U. S. Department of Agriculture’s current priority areas and challenges (through Authorization—7 U. S.C. 450i). One seemingly simple way to successfully address this challenge is through increases in agricultural crop biomass. Historically speaking, these challenges, or at least portions of them, are not new. The ‘Green

department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA. “Email: mnrao@utk. edu; mnrbhav@yahoo. com

2BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. Email: nealstewart@utk. edu ^Corresponding author: ckwit@utk. edu

Revolution’ of the 1960s is often heralded for its resultant increases in agricultural biomass and crop yields through traditional and technological improvements in breeding, mechanization, and promotion of irrigation, fertilization, and pest control (see Borlaug et al. 1969; Hesser 2006). At its core, the Green Revolution was primarily focused on food (particularly grain) production, and though its efforts did result in increased yields, biomass for energy production was not emphasized.

The current push for the use of biomass (and other non-biomass renewable energy sources) for energy production is being driven by desires to lessen dependence on foreign oil, and to promote rural development and climate change mitigation. Interest in this area can be found worldwide, as bioenergy crop production comprises a top priority of numerous countries and governments (Sang and Zhu 2011; Nijsen et al. 2012). In the U. S., in the liquid transportation fuel sector alone, recent government mandates put forward in the Energy Policy Act of 2005, the Energy Independence and Security Act of 2007, and the Food, Conservation, and Energy Act of 2008 call for up to 36 billion gallons (> 136 billion liters) of fuel being produced from domestic biomass sources by 2022. This amount would displace 30% of current demands from foreign petroleum sources (U. S. Department of Energy 2011), and may indeed promote rural economic development in certain geographic locations (Leistritz and Hodur 2008). Exactly how to sustainably accomplish such lofty energy-related goals and still provide food and fiber for an ever-increasing human population will indeed prove challenging. Many would agree that one place to start is to ensure that biomass for energy does not compete with biomass for food. This concern is complemented by the need to ensure sustainable practices in bioenergy crop production to reduce carbon emissions, promote environmental stewardship, and conserve biodiversity.

Bio-oil upgrading through catalytic vapor cracking removes oxygen from the bio-oil in the forms of CO2, H2O or CO without using external hydrogen. The reactions take place at temperatures of 330-600°C and atmospheric pressure. Solid acid catalysts such as aluminosilicates and zeolite are used for the bio-oil cracking (Park et al. 2011). Atmospheric pressure reaction without the need of external hydrogen makes this process economically attractive. The higher aromatic content in the product results in better fuel quality as compared to the hydrodeoxygenation process. However, catalysts deactivate at a faster rate and H/C ratio of product is lower as compared to those of hydrodeoxygenation products (Graca et al. 2009; Hew et al. 2010; Mortensen et al. 2011). Reaction of cracking can be summarized in the following equation with respect to carbon of bio-oil (Mortensen et al. 2011).

CH14O04^0.9 CH2 + 0.1 CO2 + 0.2 H2O (2)

Where CH14O04 and CH2 represent bio-oil and hydrocarbon product, respectively.

In addition to continuing to collect empirical data, models of switchgrass biomass production are now being used to address many of these questions. Models of bioenergy crops can be split into two distinct types. Correlational or statistical approaches rely on relationships between environmental variables and empirical biomass estimates. In contrast, mechanistic or process-based approaches simulate the actual processes that govern crop growth. Correlational or statistical models have been used to estimate yields across large spatial extents (Barney and DiTomaso 2010; Evans et al. 2010; Jager et al. 2010; Wullschleger et al. 2010). However, little information is included on soil type, nutrient availability, and management practices, which are known to have large impact of biomass production (Muir et al. 2001; Fike et al. 2006). Instead, processes-based simulations of plant growth include detailed information on climate, soil dynamics, and management (Williams et al. 1989; Kiniry et al. 1992; Kiniry et al. 1996; Arnold et al. 1998; Del Grosso et al. 2005; Di Vittorio et al. 2010; Miguez et al. 2011). These models have been used to not only estimate yields but also to analyze water use efficiency, management practices, long-term effects on soil properties, and the impact of climate change (Kiniry et al. 1996; Kiniry et al. 2008; Brown et al. 2000; Sarker 2009). Due to their wider range of applications, this chapter will focus on process-based simulation models and will highlight recent applications to switchgrass.

Mechanistic models of plant growth that have been used to simulate switchgrass production include Agro-BGC, ALMANAC, BIOCRO, DAYCENT, EPIC, and SWAT (Williams et al. 1989; Kiniry et al. 1992; Kiniry et al. 1996; Arnold et al. 1998; Del Grosso et al. 2005; Di Vittorio et al. 2010; Miguez et al. 2011). These models were created for different purposes (i. e., tracking greenhouse gas (GHG) emissions, water erosion, nutrient cycling, plant growth, etc.). Correspondingly, these models vary in their functions and amount of detail incorporated to simulate growth. Despite these differences, each model shares the following basic functionality. First, they simulate biomass production by specifying light interception, conversion of sunlight to biomass, and partitioning of biomass into structural components (such as below ground roots and above ground shoots). Second, they simulate soil water dynamics, which depends on precipitation, run off, and evapotranspiration. Third, they simulate soil C and N dynamics. Lastly, each model simulates the effect of water stress on plant growth. Models with more complex functions for the effects of environmental stress on plant growth, such as ALMANAC and EPIC, incorporate more stress effects; temperature stress, N and P nutrient stress, salinity, low pH, aluminum toxicity, and soil aeration.

There are several different data types required to parameterize and run each model (Williams et al. 1989; Kiniry et al. 1992; Kiniry et al. 1996; Arnold et al. 1998; Del Grosso et al. 2005; Di Vittorio et al. 2010; Miguez et al. 2011). First, each model requires plant parameters that characterize the developmental stage and rate of biomass accumulation over time. Next, each model requires daily weather values that include daily maximum temperature, minimum temperature, precipitation, and solar radiation. Third, each model requires basic soil properties such as: soil type, nitrogen, texture, moisture availability, or water holding capacity. Lastly, most models incorporate basic crop management practices such as fertilizer application, planting date, harvesting dates, and removal rates.

Hong Luo is a Professor in the Department of Genetics and Biochemistry of Clemson University. He also serves as the Graduate Coordinator for the Clemson Genetics, Biochemistry and Molecular Biology Programs. Dr. Hong Luo received his Ph. D. degree in Molecular Biology at the Catholic University of Louvain in Belgium. His research mainly focuses on plant genomics, gene discovery in grasses and cereal crops, and genetic engineering of perennials and row crops. His work has led to the development of the first genetically engineered, environmentally safe, male-sterile and herbicide-resistant turfgrass plants and the development of a new method for hybrid crop production using site-specific DNA recombination systems. He recently received Clemson University 2013 Godley-Snell Award for Excellence in Agricultural Research. He currently maintains several active research projects for both basic and applied research on genetic modification of crop plants, and collaborates with scientists across continents. He has authored or co-authored numerous research papers, book chapters and review articles, and is the inventor on a number of patents in plant biotechnology issued or in various stages of application. He serves as an ad hoc peer reviewer for more than twenty-five international professional journals and serves as a reviewer or as a panel member for various federal, regional and state funding agencies.

Yanqi Wu is an associate professor and holds the Meibergen Family Professorship in Plant Breeding in Plant and Soil Sciences Department at Oklahoma State University. He earned his Ph. D. degree in Crop Science with an emphasis in Genetics and Breeding of perennial grass species from Oklahoma State University. Dr. Wu’s research is centered on the development of new cultivars, and genetic and genomic research on important agronomic traits in bermudagrass used for turf and forage, and switchgrass for bioenergy. Four new cultivars, ‘Cimarron’ switchgrass, ‘Goodwell’ forage bermudagrass, ‘NorthBridge’ turf bermudagrass, and ‘Latitude 36’ turf bermudagrass have been released from the breeding program in recent five years. Dr. Wu published 31 refereed journal articles, 11 technical publications, 65 abstracts, and 6 book chapters. He received the

2010 Early Career Award of the National Plant Breeders Association. Dr. Wu served as an ad hoc peer reviewer for more than twenty international peer-reviewed journals and as a reviewer or on review panels for federal and regional funding agencies in the United States.