In theory, the yield of a plant is the product of the solar energy that the plant intercepts, utilizes, expends, and stores in harvestable plant biomass (Heaton et al. 2008). The amount of solar energy that a field of plants can intercept depends on the period and length of vegetative growth, the plant architecture and canopy, and planting density in a field. The solar energy utilization of a plant is largely determined by its net photosynthetic efficiency. The C3 and C4 photosynthetic pathways and theoretical solar energy conversion efficiency, along with their implications on bioenergy grass improvement, were recently reviewed (Heaton et al. 2008; Zhu et al.

2008) . Here, a few phenotypic traits, potential genes, and genetic pathways contributing to these traits are addressed.

Increasing switchgrass photosynthesis efficiency. C4 plants have a greater photosynthetic efficiency than C3 plants, primarily because of the C4 cycle. The CO2 concentrating mechanism results in the avoidance of photorespiration, which increases net CO2 assimilation and leads to a higher water use efficiency (Schmitt and Edwards 1981; Zhu et al. 2008). In most C4 plants, CO2 assimilation is processed in two distinct cell types: Kranz mesophyll cells and bundle sheath cells. The first steps occur in Kranz mesophyll cells. Initially, CO2 reacts with phospoenopyruvate (PEP) and is converted by PEP carboxylase (PEPC) into the C4 acid oxaloacetate (OAA). During C4 photosynthesis, intermediates (e. g., C4 acids) are diffused or transported between mesophyll cells and bundle sheath cells through plasmodesmata. In the bundle sheath cells, these intermediates are decarboxylated to release CO2, which is then used by RuBisCO as a substrate (Sowinski et al. 2008). According to the decarboxylation routes, C4 plants fall into one of three subtypes: 1) a NADP-Malic Enzyme (NADP — ME) subtype, 2) a NAD-Malic Enzyme (NAD-ME) subtype, or 3) a PEP — Carboxykinase (PEP-CK) subtype (Edwards et al. 2004; Weber and von Caemmerer 2010).

Most major C4 crops, such as maize, sorghum, sugar cane, and miscanthus, belong to the NADP-ME subtype, which is more efficient than the other two subtypes (Zhu et al. 2008). However, switchgrass is a NAD-ME subtype species. Notably, it is fairly unique that Panicum species have all photosynthesis types: C3, C3-C4 intermediate species, and all of the three C4 subtypes (Ohsugi and Murata 1986; Ohsugi and Huber 1987). In the same Panicum genus, the NADP-ME subtype species (e. g., P. antidotale) contain about one and a half to two times higher PEPC and RuBisCO activities than the NAD-ME subtype species (e. g., P. coloratum). The NADP-ME subtype species also exhibit about two times higher PEPC activity but one and a half times lower RuBisCO activity than the PEP-CK subtype species (e. g., P maximum) (Ohsugi and Huber 1987). The huge difference in PEPC and RuBisCO activities between species in the Panicum genus indicates a spacious room for improving photosynthesis efficiency of switchgrass by increasing the activities of these two key enzymes.

Recently, a nice review detailed the difference in photosynthetic intermediates between NADP-ME and NAD-ME subtypes (Weber and von Caemmerer 2010). In NADP-ME subtype species, OAA, after it is synthesized in the cytosol, is directly transported back to the chloroplast and converted into malate in mesophyll cells. Then, the malate is diffused or transported into the chloroplasts of bundle sheath cells and decarboxylated by the NADP-malic enzyme to release CO2. However, in NAD-ME subtype species, OAA, once synthesized in the cytosol of mesophyll cells, is not transported back to chloroplast but is instead converted into aspartate in the cytosol. Aspartate is then diffused or transported into the mitochondria of bundle sheath cells and converted into malate. The malate is then decarboxylated by NAD-malic enzymes in the mitochondria and CO2 is released in the bundle sheath cells (Weber and von Caemmerer 2010). Therefore, the photosynthetic pathway between NADP-ME and NAD — ME subtype species branches with the catalysis of OAA into aspartate or malate. The pathway further diverges with differences in their subcellular transportations, possibly because of the presence of selective membrane transporters guarding the chloroplasts and the cytosol. Identifying genetic components behind these differences, and engineering the pathway into switchgrass, will likely convert switchgrass into a "synthetic" NADP-ME subtype species with greater photosynthetic efficiency.

Although a finely constructed genomic map is not yet available for switchgrass and other NAD-ME subtype C4 grasses, high quality genomic sequences of several C4 grasses (maize, sorghum) and C3 grasses (rice, Brachypodium) are publicly available (Goff et al. 2002; Yu et al. 2002; Paterson et al. 2009; Schnable et al. 2009; Vogel et al. 2010). Comparative genomic studies have revealed that certain genetic components contribute to the difference between C3 and C4 photosynthesis. For example, comparison between the genomes of sorghum and rice showed that the "evolution of C4 photosynthesis in the sorghum lineage involved redirection of C3 progenitor genes as well as recruitment and functional divergence of both ancient and recent gene duplicates" (Paterson et al. 2009). The number of genetic components causing differences between C4 subtypes should be less than those between C3 and C4 plants. Moreover, closely related Panicum species comprise a natural pool of photosynthesis types and subtypes. Comparative studies on transcriptomes or genomes between representative Panicum species, as well as functional studies on candidate genes, will assist in elucidating the mystery of photosynthesis types and subtypes. The resultant knowledge can be readily used for genetic improvement of switchgrass and other economic plants.

Improving switchgrass plant architecture. The amount of light intercepted by a field of plants is largely determined by plant architecture (leaf angle and shapes, plant height, and tiller number), planting density, and the vegetative growth period (Heaton et al. 2008; Wang and Li 2008). The vegetative growth period can be prolonged by promoting early emergence of tillers of perennial grasses and by delaying flowering as mentioned above. The planting density of a field is dependent on plant architecture. Grass tiller number is important for field establishment. For grass cultivars with a lower tillering potential, cultivation strategies (e. g., dense planting) can compensate for their disadvantages. Here, we focus on research progress on a few aspects of plant architecture, such as leaf angle, leaf shape and plant height.

Erect leaves (small leaf angle against the stem) enhance light interception in densely planted fields (higher leaf area index), and thereby may increase biomass yield (Sakamoto et al. 2006). Decreasing brassinosteroid (BR) content or sensitivity by selecting BR-deficient mutants, e. g., brassinosteroid — dependent 1 (brd1), ebisu dwarf (d2), dwarf11, osdwarf4-1, or BR-insensitive mutants [dwarf 61 (d61) and leaf and tiller angle increased controller (oslic)] can effectively induce erect leaves in rice by altering lamina joint bending (Yamamuro et al. 2000; Hong et al. 2002; Sakamoto et al. 2006; Morinaka et al. 2006; Wang et al. 2008). Specifically, the rice osdwarf4-1 mutant has erect leaves but no alteration in reproductive development and thereby produces higher grain yields under dense planting conditions without extra fertilizer (Sakamoto et al. 2006). All of these BR-related mutants have erect and dark green (higher chlorophyll content) leaves. However, these mutants are dwarf or semi-dwarf. The dwarf to semi-dwarf stature is important for rice stand and grain yield, as the selection of one semi­dwarf mutant in a GA-biosynthesis gene, OsGA20ox2 (sd1), successfully led to the development of elite rice cultivars in the "Green Revolution" (Sakamoto et al. 2004). However, dwarf stature is not a desirable trait for bioenergy crops where the above-ground vegetative organs account for a large portion of the biomass yield. The semi-dwarf to dwarf phenotype in BR-related mutants is caused by failure of organization and polar elongation in the leaf and stem cells (Yamamuro et al. 2000). On the contrary, GA can positively regulate plant stem elongation (Kende et al. 1998). GA and BR may antagonistically regulate the expression of some downstream genes (Bouquin et al. 2001). Recently, a rice GA-stimulated transcript family gene, OsGSR1, was identified to be involved in the crosstalk between GA and BR (Wang et al. 2009). This study showed that OsGSR1 is a positive regulator of both GA signaling and BR biosynthesis (Wang et al. 2009). However, it has not yet been reported that GA can alter grass leaf angles. Therefore, it is possible to engineer grasses with erect leaves and normal, or increased, plant height by simultaneously manipulating BR and GA-related genes.

Leaf angle and leaf shape are often correlated. In BR mutants, the erect leaves are often short because of failure in elongation of leaf cells (Yamamuro et al. 2000). In several other cases, rolling (typically upward-curling) leaves create more erect leaves in rice (Shi et al. 2007; Zhang et al. 2009; Li et al.

2010) . Rolling leaves may also help prevent water loss by increasing stomatal resistance, decreasing leaf temperature, and reducing light interception per leaf, while simultaneously increasing light transmission rates to lower leaves of the plant (O’Toole and Cruz 1980). Altered expression of a few genes in rice caused rolling leaves, but anatomical reasons for the leaf­curling are different. A rice null mutant of Shallot-like 1 (SLL1, a KANADI family gene) has a broader distribution of mesophyll cells in the region where sclerenchymatous cells distribute in wild type rice (Zhang et al.

2009) . These mutants also have bulliform cells on the abaxial side of the leaf, which thereby induces upward-curling leaves (Zhang et al. 2009). Studies in Arabidopsis showed that a group of YABBY and KANADI family genes regulate abaxial organ identity (Emery et al. 2003; Eshed et al. 2004; Eckardt 2010). A group of HD-ZIP III family genes [e. g., PHABULOSA (PHB) and PHAVOLUTA (PHV)] have been found to promote adaxial organ identity. It could be the antagonism of these genes that coordinates normal leaf polarity and leaf shape (Emery et al. 2003). Similarly, a maize KANADI family gene, Milkweed Pod 1(MWP1), also functions in defining abaxial cell identity (Candela et al. 2008). Recent studies have shown that miRNAs, as well as genes in other families, are also involved in leaf shape formation. For example, overexpression of rice Argonaute 7 (OsAGO7), a gene presumably involved in miRNA metabolism, caused upward-curling leaves in rice (Shi et al. 2007). Overexpression of Abaxially Curled Leaf 1 (ACL1) induced downward rolling (abaxial-curling) leaves by increasing the number and size of bulliform cells on the adaxial side of the leaf (Li et al. 2010).

Xu et al. (2012) obtained erect leaf switchgrass by overexpressing an Arabidopsis NAC domain gene, Long Vegetative Phase 1 (AtLOV1). Interestingly, the transgenic switchgrass plants have a phenotype typical of BR-mutants (dark-green and erect leaf), but are not obviously dwarfed (except one transgenic line with an extreme phenotype). Differential gene expression analysis by microarray did not show significant expression changes of identified BR or GA-related genes in the transgenic plants (unpublished). Overexpression of AtLOV1 in rice induced dark green leaves and a dramatically dwarfed stature, but did not change the leaf angle (unpublished). Although the mechanism controlling the phenotype of transgenic switchgrass and rice is unclear, the results suggest that it is possible to alter leaf angle without causing a dramatic negative effect on other vegetative growth traits.

In summary, several important agronomic traits of switchgrass production and potential genes/genetic pathways underlying these traits are reviewed in this section. Translational and functional genomics studies will allow us to understand the functions of these gene(s) and create a more comprehensive picture of the interactions between physiological pathways. A more thorough understanding of the mechanisms underlying these traits will help improve plant yield and also help to design better strategies for plant genetic improvement (Hammer et al. 2004). We can use transgenic or "cisgenic" strategies to quickly "stack" genes of interests from foreign or native genomic origins. Then, we can use synthetic biological approaches to engineer and move entire essential genetic components of a pathway into switchgrass (Benner and Sismour 2005). For example, certain microbial metabolic pathways can be recruited and integrated into plant systems, essentially making plants bio-factories for desirable products (Somleva et al. 2008). All of these approaches are emerging at an unprecedented speed. We can imagine that many genomics tools will be successfully applied to the genetic improvement of switchgrass in the near future.

Cellulose is a hygroscopic polyglycan. The chemical formula of cellulose is (C6H10O5)n where n=10,000 to 15,000 linked together by P(1^4)-glycosidic linkages between the first and fourth carbon atoms of adjacent glucose units. Cellulose molecules are linear glucans ranging from 300,000 Da to 500,000 Da in molecular size. Cellulose may occur in crystalline or amorphous forms where crystalline structures are highly ordered and poorly depolymerized by cellulase when compared to the amorphous forms. The solubility of cellulose is highly dependent on the degree of polymerization (DP). Crystalline and amorphous cellulose structures are governed by inter and intra-hydroxyl groups. Amorphous cellulose has intra-polymer linkages with hemicellulose through H-bonding that is weaker than the H-bonding found in crystalline cellulose. Cellulose microfibrils are usually 5-50 nm in diameter with a few microns in length (Harmsen et al. 2010; Moon et al. 2011).

Several polymorphs of crystalline cellulose including I, II, III and IV have been studied. Crystalline cellulose I is the most common variety found in lignocellulosic biomass. Cellulose I can be converted to cellulose II by regeneration and mercerization processes. However, cellulose III can be generated from cellulose I or II by aqueous ammonia pretreatment. Cellulose IV is generated by thermal pretreatment of cellulose III (Moon et al. 2011).

Table 2. Primary production of biomass derived bioproducts via chemical (C), fermentation (F), enzymatic (E), and natural (N) processes (Klass 1998). Lignocellulosic Biomass C_ sugars C, sugars Cellulose Lignins Others C F F C E C C C&N Xylitol Lipids* Acetaldehyde Hydroxymethyl furfural Fructose Cellulose esters Vanillin Alkaloids Ethanol Acetic Acid Sorbitol Cellulose nitrates Lignin sulfonates Glycerides Acetone Cellulose ethers Gutta Glycerol Cellulose xanthogenates Phenols n-Butanol Resins n-Butyric acid Rubber Amyl alcohols Saponins Oxalic acid Sterols Lactic acid Tail oils Citric acid Tannins Amino acids Tarpenes Antibiotics Vitamins Ethanol Lipids* Waxes * Reference: Fall et al. 1984

If cellulosic refineries require large-scale switchgrass production, management recommendations to farmers will be aimed at optimizing yearly yields while minimizing input costs. Many empirical studies have focused on the management practices required to enhance yields. However, management is often regionally associated with climate, rainfall, and soil nutrients. Field trials are expensive and time consuming making it virtually impossible for data to be collected for all soil types and climate conditions. Instead, mechanistic models can be used to assess many different management practices in a timely manner to make local recommendations.

ALMANAC, DAYCENT, and EPIC models have been used to simulate the effects of irrigation, fertilization, and harvest frequency on switchgrass yields (Kiniry et al. 1996; Brown et al. 2000; Kiniry et al. 2008b; Thomson et al. 2009; Davis et al. 2011; Lee et al. 2011). The ALMANAC model has been used to determine the optimum management for several locations in the Northern Great Plains and Texas. For the Northern Great Plains, the response of yields to supplemental irrigation and N fertilizer was analyzed. Irrigation does not seem profitable in the Northern Great Plains in ND and SD because supplemental irrigation resulted in at best a small (< 1.5 Mg/ ha) increase (Table 4). Increasing the amount of N fertilizer substantially increased yields by 70% and 48% in NE and SD but had no impact in ND. In Texas, simulating a one — versus two-cut harvest revealed that two-cut does not significantly increase yields.

Other models have revealed similar results. Davis et al. (2011) used the DAYCENT model to estimate the yields of fertilized and unfertilized switchgrass. They determined that yields increased with N addition and consequently C emissions were reduced. Brown at al. (2000) and Lee et al. (2011) similarly found a positive relationship between yield and N fertilizer using the EPIC and DAYCENT models, respectively. Additionally, Lee et al.

(2011) reported higher yields with two or three cuts as opposed to one cut per year. Thomson et al. (2009) only reported increased yields with two-cut for upland ecotypes but not lowland ecotypes.

Primary operating conditions of the gasification reaction (as shown in Fig. 2) are biomass feed rate and composition, equivalence ratio or steam-to-biomass ratio, types and amounts of other oxidizing agents, reactor temperature profile, and supplemental heat in case of an indirectly heated gasifier. These operating conditions affect yield and properties of products (also shown in Fig. 2) such as syngas (or producer gas) flow rate and gas composition, and contents of tars, particulates, NH3, and H2S. Overfeeding biomass can lead to plugging whereas underfeeding can lead to underutilization of reactor volume. Contents of cellulose, hemicellulose and lignin in lignocellulosic biomass also effects the products. However, in general, thermochemical processes can utilize lignin and accept biomass with variable contents of cellulose, hemicellulose and lignin. Equivalence ratio (ER) and/or steam to biomass ratio significantly affect the products. In

Figure 2. Gasification process variables.

air gasification, increase in ER results in an increase in reactor temperature due to increased degree of biomass oxidation. However, with an increase in ER energy efficiencies and concentration of CO and H2 gases initially increase and then decrease after reaching optimum levels. Sharma et al. (2011) found the optimum ER to be 0.32 achieving the maximum hot gas efficiency of 75% for fluidized-bed gasification of switchgrass (Sharma et al.

2011) . Supplying steam into gasifier has shown to increase H2 content and reduce tar content in the syngas due to steam reforming reactions (Narvaez et al. 1996). However, gasification temperatures need to be kept at high level (above 750-800°C) to promote the steam reforming reactions (Lucas et al. 2004; Kumar et al. 2009; Gupta and Cichonski 2007).

Temperature profile of the gasifier is one of the most influential factors affecting the yield and composition of products. Temperature profile in turn depends on amount of oxidizing agents supplied and heat added, if any. Gonzales et al. showed that contents of H2 and CO increased while contents of CH4 and CO2 decreased when the temperature was increased from 700 to 900°C (Gonzalez et al. 2008). CO/CO2 increased linearly from 0.85 at 700°C to 2.7 at 900°C. The trend is possibly due to increase in Boudouard reaction, which becomes predominant at high temperature. Boateng et al. observed that with an increase in gasification temperature from 700 to 800°C, gas yield, gas heating value, energy efficiency, and H2 content increase while CH4, CO and CO content decreased (Boateng et al. 1992). The literature overwhelmingly suggests improvement in gas composition with increase in gasification temperature. However, additional energy penalty, ash agglomeration and need of strong reactor material at high temperature (>900°C) limit its applicability.

For biofuel production processes to be more cost-effective, on-site enzyme production of cellulase and hemicellulase (or xylanase) using lignocellulosic feedstock has been suggested (Singhania et al. 2007). Although lignocellulosic materials have great potential as a feedstock for ethanol production, they could also serve as feedstock for the production of enzymes when pretreated effectively prior to use (Esterbauer et al. 1991; Singhania et al. 2007). Cellulolytic enzymes may be produced by a wide variety of bacteria and fungi. These microorganisms may be cultivated in a wide range of conditions, ranging from aerobic to anaerobic and mesophilic to thermophilic environments. Among bacteria, Cellulomonas fimi and Thermomonospora fusca have been extensively studied for cellulolytic enzyme production. However, anaerobic bacteria such as Clostridium thermocellum and Bacteroides cellulosolvens produce high cellulase activity with low enzyme productivity (Duff and Murray 1996). Under anaerobic conditions, low growth rates limit enzyme productivity (Duff and Murray 1996).

On the other hand, more than 14,000 fungi are known to produce cellulolytic enzymes including Aspergillus niger, Aspergillus awamori, Trichoderma reesei, Phanerochaete chrysosporium and Pleurotus sajor-cajuz. These fungi are a good source of cellulases, but, among these, Trichoderma is one of the best known for a variety of industrial applications such as in the textile, beverages, and biofuels industries. The genetics of T. reesei plays an important role in commercial celluloytic enzyme production. A series of genetic modifications of T. reesei produced T. reesei Rut C-30, resulting in better enzyme productivity. A genetically engineered strain of T. reesei increased both the enzyme production and removal of unwanted enzyme expressions at the same time. For example, the expression of cellulases was suppressed during the production of xylanases by T. reesei.

T. reesei Rut-C-30 is a mesophilic fungus, producing high cellulase activity with considerable amounts of xylanase and p-glucosidase amongst Trichoderma spp. (Tangnu et al. 1981, Table 3). A consortium of different enzymes (consisting of endoglucanase, exoglucanase (cellobiohydrolases) and p-glucosidase (cellobiase)) act synergistically to break down lignocellulosic material (Esterbauer et al. 1991). However, endoglucanase and p-glucosidase respectively comprise approximately 20-36% and 1% of total cellulases produced by Trichoderma spp. (Esterbauer et al. 1991; Xiao et al. 2004).

T. reesei could be cultivated in aerobic fermentation mode using either a solid-state fermentation (SSF) or a submerged culture fermentation (SCF). In commercial cellulolytic enzyme production, SCF is preferred over the SSF process due to easier control of temperature, pH, oxygen, and the moisture

gradient of the substrate. The main aim of SSF is to achieve higher substrate concentration for fermentation, whereas in SCF the substrate concentration is limited to approximately 8% of the solution requiring much greater amounts of water. With SCF processing substrate concentration, nutrient requirement, T. reesei biomass concentration, pH, temperature, aeration and agitation are important parameters that influence the productivity of cellulolytic enzymes.

The agricultural machinery cost software, MACHSEL (Kletke and Sestak 1991) is used to estimate machinery cost. MACHSEL uses the American Society of Agricultural and Biological Engineers standards to calculate ownership costs including depreciation, interest on average investment, insurance, and taxes, and operating costs including fuel, oil, lubricants, and repairs (AAEA 2000; ASABE 2010a, b). Harvest field operations are weather dependent. Hwang et al. (2009) used historical weather data to estimate probability distributions for the number of days per month that mowing operations and baling operations can be conducted. Weather requirements for baling are more stringent than requirements for mowing since baling switchgrass biomass with excessive moisture may result in molding and heating and in some extreme cases spontaneous combustion. The length of time required for mowed switchgrass to dry to levels required for safe baling depends on the moisture content when cut and the weather, and it differs across month and county (Hwang 2007). Estimates of field work days such as those produced by Hwang et al. (2009) may be incorporated into MACHSEL to verify that the selected machinery has the capacity to complete the required field operations during the available time frame.

Cost estimates for the windrower, assuming that it would be used on weather-favorable days from July through March, are included in Table 8. Estimates are provided for three yield levels, 4.48, 8.97, and 13.45 dry Mg/ ha. The estimated cost are $12.28, $24.56, and $36.82/ha for yields of 4.48, 8.97, and 13.45 dry Mg/ha, respectively. By this measure, the estimated cost of the windrowing operation is $2.74/Mg. These estimates follow from the assumption that for different yields, machine speed may be adjusted to maintain an efficient level of biomass throughput. For the enterprise budget reported in Table 5, the estimated cost to hire a custom operator to windrow is $33.56/ha.

Table 9 includes cost and capacity estimates for the raking-baling- stacking unit that consists of three 7.3 m wheel rakes, three 40 kW tractors, three balers, three 147 kW tractors, a bale transporter stacker, and seven laborers. The rakes are available to turn the biomass when warranted to aid drying and to merge two or more windrows, depending on yield, to form windrows that enable efficient baling. One field transporter stacker

has sufficient capacity to collect and stack bales produced by three balers. As shown in Table 9, the machines are budgeted based on throughput capacity. For the relatively low yield of 4.48 Mg/ha, the rakes could be used to merge windrows, and the speed of the balers could be adjusted to meet the designed baler capacity. For higher yielding fields, the rakes may be used, if necessary, to turn the material to enhance drying. The coordinated system is designed to enable the balers to operate near capacity on those days when the biomass is suitable for baling. The throughput capacity of the baler is defined in terms of biomass volume and not hectares.

Given that the system can be managed to maintain a relatively constant throughput capacity, the estimated cost to rake, bale, and stack is approximately $13/Mg for yields ranging from 4.48 to 13.45 Mg/ha (Table 9). The custom rate for baling used in Table 5 that is based on survey data is $17.25/635 kg bale ($27.59/Mg). By this measure, the custom rate for baling as reported by Doye and Sahs (2012) is more than 210 percent of the estimated cost to rake, bale, and stack. This difference is the result of several factors. First, most of the custom rate estimates are for the cost of baling hay for use as a livestock feed, an operation for which timeliness to achieve quality hay is critical. Timeliness is not expected to be as critical for biomass. Second, the window for hay harvest is relatively narrow, which restricts the annual land area over which machine fixed costs can be allocated. Switchgrass harvest in the U. S. Southern Plains is expected to extend from July through March. The harvest machine fixed costs can be spread over substantially more biomass volume when baling switchgrass for biomass

aA raking-baling-stacking harvest unit consists of three 7.3 m wheel rakes, three 40 kW tractors; three balers, three 147 kW tractors; a bale transporter stacker; and seven laborers. bPrice of diesel fuel is budgeted at $0.79/L.

rather than forage for hay. Third, during the nine month harvest window, the machines are budgeted to be operating during all weather favorable days, whereas conventional forage harvesting operations have a much narrower harvest window. By these measures and for these reasons, the cost to harvest switchgrass could be substantially lower than the cost to harvest hay. But, this finding depends critically on the assumption that the switchgrass for biomass harvest window could extend over nine months.

Properties of bio-oil are compared with those of biomass and crude oil in Table 5. In its original form, bio-oil cannot be used directly for fuels, chemicals and power production because of its undesirable properties described below.

*(Mortensen et al. 2011; Sharma et al. 2011.)

High Water Content

Because there is no loss of water during pyrolysis, water content of bio-oil is similar to the water content of the biomass. If existing refining
processes are to be used, bio-oil water content must be reduced to make it compatible to the petroleum feedstock, which has very low moisture content (about 0.1%).

The design of a cellulosic ethanol process for maximum utilization of both pentose and hexose sugars in the most productive way is still an unsolved puzzle. Based on the current technologies, either SHCF or SSCF may result in the best cellulosic ethanol productivity. SSCF may be more economically feasible in setting up a bio-refinery as it requires less number of unit operations. However, for a continuous ethanol production process, the following guidelines are considered in setting up a bio-refinery:

• Reusability of chemicals (for a pretreatment process)

• Recirculation of enzymes for enzyme hydrolysis and cell biomass for ethanol fermentation

• Generation of potential market valued by-products such as lignin

• Maximum utilization of energy generated during the process

• Minimal waste generation

The forecast landscape change incorporating expanses of bioenergy crops planted in monoculture may well create environments conducive to invasive species. Such areas are associated with increased susceptibility to invasion (Hoffman et al. 1995). Weedy and invasive plants will likely be able to take advantage of the disturbed conditions in and adjacent to bioenergy crop fields (Simberloff 2008). Resultant weed management strategies will be critical to establishing and maintaining bioenergy crop production, and to prevent spread of invasive species.

More directly, bioenergy crops, like switchgrass, exhibit a number of characteristics that are correlated with invasiveness. These traits in switchgrass, many of which have been mentioned above, include C4 photosynthesis, a long canopy duration, few known pests and diseases, rapid growth in the early growing season, below-ground partitioning of nutrients in the fall (dormant season), and high water-use efficiency (Raghu et al. 2006). Indeed, many of the traits mentioned here have been championed as the reasons why switchgrass is such a promising biomass feedstock and has attracted U. S. Department of Energy interests (Wright and Turhollow 2010). They are also some of the targets of continued improvement efforts (see Future Prospects below). Bioenergy crops have become invasive in many of the areas they have been planted, even in situations where a particular bioenergy crop species is native to the area (Buddenhagen et al. 2009). Switchgrass itself was recently noted as capable of establishing from seed in disturbed, low-competition areas (Barney et al.

2012) , which could be similar to areas adjacent to agronomic fields.

The prospects of invasiveness of bioenergy crop species are further heightened by the arguments that the only way government benchmarks will be reached is through the use of transgenic crops (Gressel 2008; Sticklen 2009). The inclusion of transgenic energy crops into the landscape matrix in the U. S. carries potential risks related to the transfer of transgenes to related agricultural and wild relatives. This has yet to be thoroughly explored with respect to bioenergy crops, and carries inherent conservation concerns, such as invasive transgenic genotypes via dispersal or introgression, or possibly local extinction of small isolated native populations via genetic swamping of genes that would be selected against (Kwit et al. 2011). There are also regulatory and compliance requirements that would need to be addressed before and after the eventual deregulation and commercial production of transgenic bioenergy crops into the landscape.

Genetic modification of endogenous biochemical and physiological pathways, along with traditional breeding methods, has the ability to improve the lignocellulosic feedstock quantity and quality of switchgrass. In order to decrease reliance on fossil fuels and efficiently utilize the renewable biomass produced by switchgrass, engineering new cultivars with value — added traits must be investigated. Value-added traits include, but are not limited to, enhanced taste, improved nutritional quality, or any features that would provide an additional benefit to consumers. Several value-added traits that are currently being studied include transforming switchgrass to produce bioplastics, as well as introducing cell wall degrading enzymes that will enhance conversion of the lignocellulosic feedstock into bioethanol.

Bioplastics

Bioplastics are currently being considered an alternative choice to petroleum — based polymers (Petrasovits et al. 2012). The most abundant bioplastic is polyhydroxyalanoate (PHA), a polyester that is naturally produced by microbial organisms as a reserve carbon nutrient source (Anderson and Dawes 1990). Polyhydroxybutyrate (PHB) is an extensively studied member of the PHA family that can be thermally altered to produce crotonic acid, a precursor for high demand chemicals such as propylene and butanol (Peterson and Fischer 2010; Coons 2010; Petrasovits et al. 2012).

The first report of PHB expressed in plants was published by Poirier et al. (1992). In this study, acetoacetyl-CoA reductase and PHB synthase, two enzymes from the bacterium Alicaligenes eutrophus, were expressed in Arabidopsis thaliana under the control of the cauliflower mosaic virus 35S promoter (Poirier et al. 1992). These enzymes, along with 3-ketothiolase, are essential in the conversion of acetoacetyl-CoA to PHB (Nawrath et al. 1994). In this experiment, PHB was expressed cytosolically and the plants produced 0.1 percent dry weight of PHB (Poirier et al. 1992). However, the plants displayed stunted growth, suggesting deleterious effects, along with erratic accumulation of PHB in unintended organelles, such as the nucleus and vacuole (Poirier et al. 1992). A couple of years later, Nawrath et al. (1994) expressed all three enzymes necessary for PHB production in Arabidopsis, but included a chloroplast transit peptide to target PHB production to the plastid. These plants were able to accumulate PHB up to 14 percent of their dry weight and displayed normal phenotypes (Nawrath et al. 1994). Collectively, these two studies created a platform for expressing PHB or other bioproducts, such as p-hydroxybenzoate (McQualter et al. 2005) and sorbitol (Chong et al. 2007), in plants.

Since the early 1990s, many studies have focused on engineering PHA-producing pathways into a plethora of crop species including cotton (Maliyakal and Keller 1996), tobacco (Lossl et al. 2003), maize (Poirier and Gruys 2001), sugarcane (Petrasovits et al. 2007), alfalfa (Saruul et al. 2002), and poplar (Dalton et al. 2011). As described earlier, PHB production in switchgrass was also investigated (Somleva et al. 2008).

Despite the occasional negative phenotype, this study has pioneered the way for genetically engineering switchgrass to produce functional multigene pathways. This innovation will ultimately aid in introducing value-added bioproducts in switchgrass that can be manufactured in correlation with biomass production. Further research will be necessary to optimize the expression and output of PHB without compromising plant health and viability. The next series of experiments should focus on optimizing transformation constructs (promoters, cis-acting elements, target peptide signals, etc.) with a target goal of obtaining high levels of PHB synthesis and accumulation in all tissues of the plant.