Difficulty in effective removal of syngas tars continues to be one of the biggest barriers to commercialization of gasification-based technologies for power, fuels and chemicals production. Tar is a mixture of condensable organic compound resulted from thermal degradation of biomass and is composed of mostly oxygenated aromatic hydrocarbons (Abu El-Rub, Bramer and Brem 2004). Benzene is generally not considered a tar compound because it is in gaseous form at temperature above 100°C and it does not create clogging problem. Syngas tar content generated from biomass gasification varies from 1 to 100 g/m3 depending on the type of gasifier, biomass properties and gasification conditions (Milne, Evans, and Abatzoglou 1998). Removal of tar from syngas is accomplished through either cracking the tar with high temperature (>600°C) in presence of catalysts (hot gas cleaning) or condensing the tar with solvents such as water, alcohols and oil in a scrubbing unit (cold gas cleaning). Cracking tar results in CO, H2 and other light gases leading to improved syngas composition. However, use of high temperature and catalysts increases the operational cost. Similarly, scrubbing tar with solvents also results in contaminated solvents which need to be treated for recycling. Cost effective and environmental friendly gas technologies are needed for effective removal of syngas tar.

Other contaminants in the syngas include NH3 and H2S. NH3 and H2S are especially problematic if the syngas is to be used for catalytic conversion into fuels and chemicals. Levels of contaminants that can be tolerated by the downstream applications depend on the specific application. For conversion of syngas into fuels and chemicals such as Fischer-Tropsch (FT) hydrocarbon, methanol, and ammonia, the level of sulfur-based contaminants must be below 1 ppm to prevent poisoning of catalysts. NH3 for FT process is acceptable up to 10 ppm Physical and chemical scrubbing systems are commercially available to remove sulfur and nitrogen-based contaminants (Spath and Dayton 2003).

Crystallinity is due to H-bonding between cellulose polymers of lignocellulosic biomass which differs from feedstock to feedstock. The purpose of the pretreatments prior to enzymatic hydrolysis is not only to remove lignin, but also to decrease crystallinity. Reducing the crystallinity of lignocellulosic biomass, sometimes requires the reduction in particle size with associated cost. Since the smaller particle size has more surface area, more effective removal of lignin may occur creating greater H-bond interactions within the pretreatment solvent (Puri 1984). The enzymatic hydrolysis rate is affected by the available surface area. In addition, the complete hydrolysis is a function of surface area per unit of initial pore size volume. The large surface area of a small particle results in better enzymatic sugar yields compared to small surface areas of a large particle. Most of the cellulase enzymes including endoglucanase, exoglucanase and P-glucosidase have molecular weights in the range of 30-170 KDa. Increased pore size of lignocellulosic biomass becomes crucial during the enzymatic hydrolysis (Mooney et al. 1998).

Second-generation bioenergy crops (also known as ‘next generation’ energy crops), often perennial plants as lignocellulosic sources from which biofuel is derived, have been championed as an avenue to avoid the food vs. fuel dilemma (Valentine et al. 2012). The use of lignocellulosic sources from second-generation bioenergy crops for ethanonl production has gained popularity, particularly since recent price increases in grains from first-generation bioenergy crops (e. g., maize) have been attributed, in the popular press and elsewhere (BRDI 2008), to diversions towards biofuels. Indeed, as recently as August 2012, the United Nations has urged the U. S. to reconsider its ethanol mandates. The latest U. S. government figures indicate that approximately 40% of the maize production in the U. S. is dedicated to ethanol production (Wise 2012). The use of lignocellulosic ethanol sources is therefore welcome in this regard, especially if second-generation bioenergy crops can be grown in areas that would not compete with current food and feed crop (e. g., maize, soybean) production (e. g., on "marginal" lands; see Cai et al. 2011), such that the latter could be utilized exclusively for direct and indirect (i. e., as animal forage) human consumption.

Switchgrass, Panicum virgatum L. (Poaceae), is one of a number of second-generation biomass energy crops that can be converted to lignocellulosic ethanol to the transportation sector. Other herbaceous candidates being considered worldwide for agronomic production include, but are not limited to, Miscanthus x giganteus, Miscanthus spp., sorghum, flaccidgrass, Napiergrass, sugarcane bagasse (residue), and maize stover (more commonly referred to as corn stover). Switchgrass is native to North America (Fig. 1), and is a perennial, obligate-outcrossing C4 grass capable

of producing reliable biomass yields in agronomic production fields (Fig. 2) for approximately 10 yr after planting (U. S. Department of Energy 2011). Two ecotypes (also called cytotypes) have been noted in this species: with a few exceptions, ‘lowland’ ecotypes are predominantly tetraploid (2n = 4x = 36) and tend to comprise southeastern and coastal U. S. populations, while the ‘upland’ ecotypes are mostly octaploid (2n = 8x = 72) and tend to be more interior in their U. S. distribution (Zalapa et al. 2011; Zhang et al.

2011) . Switchgrass occasionally reaches "common" status in certain prairies (Howe et al. 2002; Baer et al. 2005; Haught and Myster 2008), marshes (Ford and Grace 1998), conservation reserve program (CRP) settings (Mulkey et al. 2006; Adler et al. 2009), and along roadsides and waste places (Radford et al. 1968). Elsewhere, it is typically not a large component of natural areas (Grelen and Duvall 1966) and hence is often found in much lower densities than those grown in agronomic settings.

Switchgrass is a leading cellulosic biofuel feedstock candidate owing to its high productivity (Sanderson et al. 1996). Even before President George W. Bush’s specific mention of switchgrass in his 2006 State of the Union address, switchgrass had been the target of extensive development as a bioenergy crop by the U. S. Department of Energy (DOE) and other entities (Sanderson et al. 1996), due in part to its high forage yields (Parrish and Fike 2005), which was one of its original utilizations. Switchgrass’ current favored status as a biomass-based renewable energy crop stems from its high yield and seed production under low-input conditions in monoculture at different regional cultivar testing fields in several states in the U. S. (Sanderson et al. 1996). This is complemented by substantial predicted biomass yields, particularly in the Midsouth (approaching 23 Mg/ha; Wulschleger et al. 2010). In terms of biomass and ethanol production, with

Color image of this figure appears in the color plate section at the end of the book.

some exceptions, switchgrass is comparable to other second-generation herbaceous lignocellulosic bioenergy crops and first-generation crops (Table 1). If such yields are feasible in areas where maize and other row crops for human consumption are not being grown, switchgrass cultivation may indeed successfully avoid "food vs. fuel" controversies. However, aspects of associated landscape and land-use change, coupled with concurrent improvements, including multi-use strategies that could lead switchgrass indirectly into the food supply chain (e. g., first-cut for forage), will ultimately dictate the long-term sustainability of switchgrass as a bioenergy crop.

Switchgrass has a reputation for being difficult to establish, and several factors contribute to its status as a challenging crop. For example, small seed size, high seed dormancy, slow germination and poor seedling vigor can cause slow, and often poor stand establishment (Hsu and Nelson 1986b; Aiken and Springer 1995; Hintz et al. 1998; Evers and Parsons 2003; Parrish and Fike 2005). Economically successful production systems will require producers to achieve high yields during the early years after establishment (Schmer et al. 2006; Perrin et al. 2008). Adequate and timely preparation can go a long way to dispel the issues surrounding switchgrass’ reputation of poor establishment. In the following sections we discuss the many important management factors that must be addressed to enhance switchgrass establishment.

Site Selection

The choice of appropriate sites will be an important consideration for achieving economically viable switchgrass yields. A key presumption of many non-agronomically-oriented researchers and policy makers seems to be that marginal lands will produce suitable yields in switchgrass-for — energy cropping systems. Such an idea certainly is appealing in terms of minimizing competition for existing agricultural lands and to tamp down the food vs. fuel debate. It should be telling however, that the USDA NRCS technical field note on switchgrass establishment counsels producers to choose fields "typically used for row crop agriculture" (Douglas et al. 2009, pg. 1) in order to avoid steep slopes, irregular terrain, and wet sites. Thus, it seems that while switchgrass has broad suitability to sites, not all sites may be well-suited to switchgrass for bioenergy production given the issues associated with logistics, production, and sustainability.

In addition to beneficial bacterial endophytes, beneficial fungi also exist with the potential to enhance switchgrass performance. These beneficial fungi represent both mycorrhizae and endophytes. As with the bacterial endophyte interaction, root exudates, as well as CO2 release, play a role in stimulating development of the initial interaction, enhancing fungal spore germination, hyphal growth towards the root and hyphal branching (Giovannetti et al. 1993). The key exudate molecules are the strigolactones (Akiyama et al. 2005; Besserer et al. 2006), which form a concentration gradient helping the fungus to assess closeness of the host root.

As the fungal hyphae grow and then contact the root epidermis, each contacting hypha produces an appressorium (hypophodium), which flattens out and adheres to the epidermal cell surface. Through the production of localized cell wall-degrading enzymes, and the turgor pressure exerted by the contacting hyphal tip, the fungus is able to penetrate the epidermal cell wall (Bonfante and Perotto 1995). Once across the cell wall, the root host cell membranes invaginate to accommodate the fungus, resulting in the development of an apoplastic space between the fungus and plant cell, providing the interface for exchanges between both organisms (Vierheilig

2004) . In order to extend into the inner cortical tissues, a novel structure, the prepenetration apparatus (PPA) develops within the root, which helps direct the course of hyphal movement through the root (Genre et al. 2008). The PPA formation represents a tunnel or bridge through the cortical cells, with microtubules, microfilaments and rich in ER-cisternae. In addition, a reorientation of the plant cell nucleus occurs, with its movement to the site of fungal attachment, and it then leads and serves as a guide for the elongating PPA (Genre et al. 2008), which provides a tunnel for the growing hyphae as they colonize and grow towards the inner root cortical cells (Parniske 2008). The hyphae may grow along and between cells and eventually colonize the internal cortical cells, including longitudinally into adjacent cells, still under the guidance of the PPA (Genre et al. 2008). It is evident that key changes in growth and behavior of both plant and fungal cells take place to allow this process to occur, and a variety of genes/traits from both partners are involved in the success of this process (Gadkar et al. 2001; Genre et al. 2008; Parniske 2008).

Studies with various endophytic fungi have suggested that fungal entry can occur in the leaf through hyphae in wound sites, stomata, or penetration via appressoria (Ernst et al. 2003). Fungal growth tends to be primarily intercellular, having little effect on the surrounding host cells (Ernst et al. 2003; Gao and Mendgen 2006). As some non-clavicipitaceous fungi can be transferred vertically (Rodriguez et al. 2009), fungal growth may extend into inflorescence primordia, and eventually into the ovules with colonization of the scutellum and embryo axis of the seed (Rodriguez et al. 2009). In the case of root-colonizing fungal endophytes, root surface colonization was followed by direct hyphal penetration or through appressorium formation, and subsequent growth through epidermal and cortical cell walls (Gao and Mendgen 2006).

Lignin is a key polymer in vascular plant secondary cell walls that renders the cell wall impenetrable to solutes and enzymes, blocking biochemical conversion of biomass to biofuel. Lignin removal from plant biomass during pretreatment represents a key inefficiency of biochemical conversion. Hence, the enzymes involved in lignin synthesis are clear targets for modification in plants. Several reviews provide comprehensive information on the study of lignin synthesis, polymerization, acylation, and related topics (Hatfield et al. 2009; Penning et al. 2009; Ralph 2010; Vanholme et al. 2010; Harrington et al. 2012; Vanholme et al. 2012). Here, we will highlight results related to understanding and modifying lignin synthesis in switchgrass.

As with cell wall polysaccharide synthesis, our understanding of the pathway for lignin biosynthesis was determined primarily through genetic and biochemical studies of Arabidopsis (Vanholme et al. 2010; Vanholme et al. 2012). This work has been reinforced by the cloning of some of the so-called brown midrib mutants of the grasses maize and sorghum, the altered coloration of which is caused by aberrant accumulation of phenolics in lignified tissues (Harrington et al. 2012). Monolignols, also known as hydroxycinnamyl alcohols, are typically considered to consist of coniferyl alcohol, sinapyl alcohol, and p-coumaryl alcohol (Fig. 3). Their synthesis starts with the general phenylpropanoid pathway and then proceeds to the monoligonol-specific pathway, with deamination of phenylalanine by phenylalanine ammonium lyase (PAL) representing the first committed step of that pathway (Boerjan et al. 2003). Monolignol synthesis then proceeds via a series of phenyl ring hydroxylation and methylation modifications by the following enzymes: cinnamate 4-hydroxylase (C4H), p-coumaroyl shikimate 3′-hydroxylase (C3’H), caffeoyl-CoA methyltransferase (CCoAMT), ferulate 5-hydroxylase (F5H), and caffeic acid methyltransferase (COMT). These reactions are interspersed with a series of modifications leading to the reduction of the carboxylic acid at the end of the 3-carbon monolignol "tail", as catalyzed by the following enzymes: 4-coumaroyl ligase (4CL), hydroxycinnamoyl-CoA:shikimate transferase (HCT), cinnamoyl-CoA reductase (CCR), and cinnamyl alcohol dehydrogenase (CAD) (Boerjan et al. 2003).

Mutant studies have shown that decreasing plant total lignin content by manipulating key enzymes in the lignin biosynthesis pathway is a good way to reduce the recalcitrance of biomass. Forward or reverse genetics, especially in the dicots, Arabidopsis and poplar, showed that the down regulation of the genes that synthesize PAL (Baucher et al. 2003; Chen et al.

2006) , C4H, 4CL, HCT (Besseau et al. 2007), C3H (Abdulrazzak et al. 2006), CCoAOMT, CCR (Leple et al. 2007; Mir Derikvand et al. 2008), and CAD (Sibout et al. 2005) have an obvious effect on total lignin content. However, reduced lignin content is often associated with abnormal plant growth and development (Shadle et al. 2007; Mir Derikvand et al. 2008; Vanholme et al. 2008; Bonawitz et al. 2013). Other work has noted that increasing the S:G ratio by altering expression of lignin biosynthesis enzymes, such as by increasing the expression of F5H, improves processing efficiencies for pulp and biofuel (Stewart et al. 2009; Li et al. 2010).

Progress has also been made recently in unveiling the group of enzymes involved in the acylation of monolignols by p-coumarate (Ralph 2010). A BAHD acyltransferase in rice from the clade identified by Mitchell et al. (2007) as being differentially expressed in grasses compared to dicots was found in vitro to catalyze the acylation of monolignols with p-coumaroyl — CoA (Withers et al. 2012). This study provides a lead for the idea of engineering phenolic pathways to produce modified lignin precursors that contain ester or amide bonds and that are more efficiently processed to biofuels (Weng et al. 2008; Ralph 2010).

Recent publications have extended the study and manipulation of lignin biosynthesis enzymes to switchgrass. This work has been made more complicated by the fact that most of the lignin biosynthesis enzymes exist as large families of closely related proteins in the grasses, while in Arabidopsis there are fewer members (Tobias et al. 2008; Penning et al. 2009; Escamilla-Trevino et al. 2010; Saathoff et al. 2011; Saathoff et al. 2012). Down-regulation of Pv4CL1, one of the genes that encodes a homolog of 4CL in switchgrass, lowered lignin content and G subunits and enhanced saccharification efficiency by as much as 57% (Xu et al. 2011). Furthermore, silencing of a COMT gene decreased lignin content and S:G ratio, and enhanced bioconversion efficiency of lignocelluloses into ethanol by as much as 38% (Fu et al. 2011). Similarly, two groups published the results of silencing switchgrass genes that encodes CAD proteins (Fu et al. 2011; Saathoff et al. 2011). Again, these manipulations reduced the lignin content and improved digestibility by as much as 40% (Fu et al. 2011; Saathoff et al. 2011).

It was shown many bioenergy-related traits, such as biomass yield, seed yield, starch content, digestibility, etc., are quantitatively expressed in plants (Holland 2007; Rae et al. 2009). Heritability is important to plant breeders because it will help to evaluate the role of genetic factors and increase the efficiency of selection. Many studies about heritability were carried out for quantitative traits in switchgrass. Eberhart and Newell (1959) found the mean broad sense heritability for plant yield in two years was 0.78 for all tested strains from an upland population in Nebraska. Later the same research group estimated narrow sense heritability of plant yield ranged from 0.02 to 0.5, depending on different population types (Newell and Eberhart 1961). Talbert et al. (1983) reported the narrow sense heritabilities for plant dry weight were 0.25 and 0.59 based on individual and family means, respectively. But for plant height, average individual and family narrow-sense heritabilities were 0.80 and 0.83, respectively. Godshalk et al. (1986) studied narrow sense heritability for dry mass was 0.20 and 0.52, within and among half-sib families, respectively. Hopkins et al. (1993) reported narrow-sense heritability for forage yield was 0.22 for polycross families from an upland population in seeded rows (76-cm row spacing) in Nebraska. Missaoui et al. (2005a) found heritability of biomass in ‘Alamo’ was 0.6 for individual plants, 0.69 for family means, 0.76 for parent offspring regression in same environment and 0.45 in different environments. Casler (2005) analyzed variance for 49 switchgrass populations in two years at two locations, and showed broad-sense heritability for biomass yield was 0.63, for plant height was 0.90, and for maturity was 0.95. Rose et al. (2007) reported the narrow sense heritabilities for biomass yield grown in different condition were different: it was 0.73 for high yielding environment and 0.65 for low yielding environments when based on phenotypic family mean performance from half-sib populations. Boe and Lee (2007) indicated narrow-sense heritability estimates for biomass production in ‘Summer’ and ‘Sunburst’ switchgrass were 0.6. Bhandari et al. (2010) used 37 half-sib families and reported narrow-sense heritability for biomass yield ranged from 0.13 to 0.29, and stem thickness had low (<0.27) and plant height and tillering ability had low to moderate (0.26-0.48) heritability. Heritabilities were moderate (0.47-0.70) for heading, flowering, and plant spread and relatively high (> 0.82) for spring regrowth (Bhandari et al. 2010). Later Bhandari et al. (2011) used 46 full-sib families and indicated addictive genes were predominant to controlling biomass yield, tillering ability and spring regrowth. Heritability for plant height and stem thickness was smaller when estimated using full-sib families than half-sib families, while the plant spread was reverse. They suggested tillering ability, plant height and stem thickness could be useful indirect selection traits to improve biomass yield in switchgrass. These above-mentioned results suggest QTL analysis will be feasible to locate genomic regions responsible for quantitative traits and to explore their effects and interactions.

Although QTL analysis has been widely used for quantitative trait mapping, gene cloning, and marker assisted selection in major crop species (Kearsey and Farquhar 1998), little information is available in switchgrass due to the fact that it is a recently emerging bioenergy crop. However, significant progress has been made towards QTL mapping of important traits in switchgrass as three linkage mapping investigations have been reported independently: initial one was in a segregating population derived from a cross of one lowland ‘Alamo’ genotype, AP13 and one upland ‘Summer’ genotype, VS16 (Missaoui et al. 2005b), second one was from a population derived from a cross between selected genotypes of ‘Kanlow’ (lowland ecotype) as the female parent and ‘Alamo’ (another lowland ecotype) as the male parent (Okada et al. 2010); and the third population was derived from selfing a lowland genotype, ‘NL94 LYE 16Х13’ (Liu et al.

2012) . Respective QTL analyses with biomass and related traits, including heading date, spring growth vigor, tillering ability, plant height, base size, girth, stem thickness, plant spread, biomass yield, drought resistance, etc., through multiple-year and at multiple-locations are in progress (Y. Q. Wu, Unpublished).

Breeding of switchgrass as a tailor-made lignocellulosic feedstock has four major objectives: 1) increasing biomass yield under various field and geographic conditions, 2) decreasing input of switchgrass field production, 3) improving bioenergy feedstock quality, and 4) developing value-added switchgrass biomass feedstock. Transgenic approaches can substantially contribute to these targets. Switchgrass improvement via genetic transformation has just started. So far, the research has mainly focused on improving its quality as a biofuel feedstock by reducing lignin content and/ or altering lignin composition, biomass yield, and value addition.

Removal of oxygen from bio-oil through the addition of hydrogen is called hydrodeoxygenation (or hydrotreating and hydrogenation). Oxygen is removed in the form of water (H2O). This process takes place at temperatures of 300-400°C and pressures of 80-300 bars. The catalysts used for the dehydrogenation are Co-Mo, Ni-Mo, and their sulfides or oxides or loaded on Al2O3 (Zhang et al. 2006, 2007). This process results in naphtha­like product, which can be used in traditional petroleum refining process. The main advantage of hydrodeoxygenation is that removal of oxygen in the form of H2O retains biomass carbon for formation of hydrocarbon and improves energy content of the product. However, since hydrogen is expensive, high consumption of H2 makes this process economically less attractive. High carbon content (C/H ratio) in bio-oil and resulting products also lead to severe coking of the catalysts. Overall, hydrodeoxygenation reaction can be represented in the following equation with respect to carbon of the bio-oil (Mortensen et al. 2011).

CH14O056 + 0.7 H2^1 CH2 + 0.4 H2O (1)

Where CH1.4O0.56 and CH2 represent bio-oil and hydrocarbon product, respectively.

Modeling Biomass Production

Simulating Plant Growth

Agro-BGC, ALMANAC, BIOCRO, DAYCENT, EPIC, and SWAT are six mechanistic models that have been used to simulate switchgrass productivity (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). Each model keeps track of the number of growing degree-days to specify the developmental rate or phenological stage of switchgrass. The number of growing degree-days is determined by the average of the daily maximum and minimum temperature above the specified baseline temperature (Williams et al. 1984). ALMANAC, EPIC, and SWAT use a function relating radiation use efficiency to biomass based on the leaf area and the amount of light intercepted (Williams et al. 1989). For switchgrass, plant development is initiated when temperatures exceed 12°C (ALMANAC, EPIC, and SWAT). Senescence begins when plants exceed the maximum number of growing degree days (Williams et al. 1989; Kiniry et al. 1992).

Biomass is partitioned into roots and shoots. The DAYCENT model uses a constant energy biomass conversion factor (Parton et al. 1998). BIOCRO was developed from WIMOAC and uses an empirical derivation of the relationship between photosynthesis, stomatal conductance, and biomass production (Humphries and Long 1995; Miguez et al. 2011). Agro-BGC relies on a mechanistic formulation of carbon uptake and assimilation (Di Vittorio et al. 2010).