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.

Hemicellulose is a heterogeneous polymer of different polysaccharides mainly present in the secondary cell wall. The most common type of polysaccharide is xylan which co-exists with other polysaccharides such as arabinan, galactan and mannan. Arabinan is the second most abundant polysaccharide present in hemicellulose after xylan in most plants. Other polysaccharides are often present in small amounts. The proportions of these polysaccharides differ significantly depending on their source and method of extraction (Chandrakant and Bisaria 1998).

The crystallinity index of hemicellulose is lower than cellulose, primary because of the highly branched structure and presence of acetyl group within the polymer chain. In addition, the degree of polymerization of hemicellulose is between 150-200 monomeric units (Harmsen et al. 2010).

Although evaluations of switchgrass as feedstock for the bioenergy industry have indicated significant benefits from an energy perspective, it also may adversely impact runoff, sediment and nutrient losses, and associated water quality effects. Field scale monitoring requires long-term measurements that are expensive and cannot be used practically for large-scale monitoring of many watersheds or regions (Harmel et al. 2006). Instead, crop models are gaining favor as they provide a practical alternative for assessing bioenergy cropping effects, due to their versatility and cost-effectiveness when compared to field experimentation.

Soil and Water Assessment Tool (SWAT) has been used at the plot and watershed scale to determine N loss, P loss, and soil erosion from switchgrass fields. Sarker (2009) used SWAT to compare N loss from agricultural systems growing switchgrass and cotton in the southeastern U. S. The plot scale modeling suggested that in the early years of growth there is a significant N loss from switchgrass to streamflow and groundwater, but N loss is significantly reduced as switchgrass matures. Nepple et al. (2002) conducted a similar SWAT watershed scale study in which 50,000 ac of cropland were converted to switchgrass production in the Rathbun Lake watershed in southern IA. Overall, the model indicated that conversion of 15.3% of the watershed area to switchgrass production would significantly reduce soil erosion, N, P, and atrazine loadings into the Rathbun Lake watershed, relative to a baseline of traditional row-crop agriculture (Table 5).

Chamberlin et al. (2011) applied the DAYCENT biogeochemistry model to calculate the nitrate in runoff water when converting land cover from cotton and unmanaged grasslands to a switchgrass system in the southern U. S. Long-term simulations predicted a reduction of nitrate runoff (up to 95%) for conversions from cotton to switchgrass with N application rates of 0-135 kg N ha1. A reduction of nitrate runoff ranging from 50-70% occurs at all levels of fertilization, when converting from unmanaged grasses. The simulated nitrate runoff values from DAYCENT fall within the observed range.

The impact of large-scale biofuel production on water quality is a growing concern. Wu et al. (2012) recently applied the SWAT model to simulate the impact of future biofuel production on water quality and water

cycle dynamics in the Upper Mississippi River basin. Converting pasture lands to switchgrass reduced soil erosion considerably and positively impacts N and P loadings at the projected yield and fertilizer input. In addition, switchgrass increases the water loss associated with evapotranspiration (1% of total precipitation), decreases the base flow (2%), and decreases the surface runoff flow to the basin. Nelson et al. (2006) used the SWAT model to predict reductions in four water quality indicators (sediment yield, surface runoff, nitrate nitrogen (NO3-N) in surface runoff, and edge-of-field erosion) associated with producing switchgrass on cropland in the DE basin in northeast KS. They determined that the production of switchgrass on conventional agricultural cropland had distinct environmental advantages versus traditional (e. g., corn-soybean) cropping rotations.

Field studies that support the findings that switchgrass production will improve surface water quality are slowly becoming available. Lee et al. (1998) compared switchgrass filter strips to cool-season grass filter strips and reported that switchgrass was more effective in removing P and N from runoff. Similarly, Sanderson et al. (2001) noted reductions in P and N runoff from a switchgrass filter strip treated with dairy manure, while Mersie et al. (1999) utilized switchgrass filter strips to reduce the amount of dissolved atrazine and metachlor herbicides by 52% and 59%, respectively. Entry and Watrud (1998) tested the ability of Alamo switchgrass to remediate soil contaminated by the radionuclides cesium-137 and strontium-90. Switchgrass captured 36% of the cesium and 44% of strontium over a five — month period. Overall, all models used predict that converting lands used for pastures or traditional row crop to perennial switchgrass for feedstock production will have a positive long-term environmental impact by reducing sediment loss and nutrient runoff, and improving water quality.

While insects can impede switchgrass establishment and may affect productivity, little information is available in the literature regarding suitable and efficacious insecticides. McKenna and Wolf (1990) reported greater stand density and yield when carbofuran (2,3-dihydro-2,2- dimethyl-7-benzofuranyl methylcarbamate) was applied in the row with seeds at establishment. While variable responses to carbofuran treatment have been reported (Bryan et al. 1984), the compound has been used as a matter of course for establishment in other studies (e. g., Stout and Jung 1995); however, this insecticide is now banned by the US Environmental Protection Agency. Some have suggested that switchgrass seedlings may be particularly vulnerable to injury by herbivory (Hartnett 1989), but others have reported no advantage to using insecticides for establishment (Cassida et al. 2000). This should not be surprising given that insect injury is a function of many factors including climatic conditions and weather, seeding dates and seedling development, and weed competition (Boerner and Harris 1991; Parrish and Fike 2005).

Arbuscular mycorrhizal (AM) fungi are the majority of mycorrhizal fungi and are the focus of this section. AM fungi penetrate the cortical cells of roots and form arbuscules (tree-like structures) and vesicles within host plant cells, and their hyphae penetrate into the soil to aid in absorption of nutrients, extending the area of nutrient acquisition. AM spores can be isolated from soil samples containing roots by the wet sieving method, which is widely used and works well with sandy soil samples (Utobo et al.

2011) . Briefly, after soil samples are collected, they are suspended in water (approximately 15-30 ml/g), and mixed vigorously. If spores form in the interior of roots, soil and root samples are blended and the suspension solution is left to settle for a while, and then the supernatant is decanted through standard sieves, which should capture the spores of interest. The procedure should then be repeated, particularly with soil containing large amounts of clay. Spores isolated can be further purified by sucrose centrifugation particularly if the soil is rich in organic debris because it may be difficult to isolate spores hidden in organic matter (Utobo et al. 2011).

It is well established that switchgrass has a base chromosome number of x= nine (Gould 1975). Diploid (2n=2x=18) was reported once by Nielsen (1944), but not confirmed for its existence in recent extensive investigations (Riley and Vogel 1982; Hopkins et al. 1996; Hultquist et al. 1996; Lu et al. 1998; Costich et al. 2010). Tetraploid (2n=4x=36) is the sole ploidy level in lowland ecotype and also one of the two major ploidy levels (2n=4x=36 and 2n=8x=72) in upland ecotype (Church 1940; Nielsen 1944; McMillan and Weiler 1959; Porter 1966; Riley and Vogel 1982; Hopkins et al. 1996; Hultquist et al. 1996; Lu et al. 1998; Costich et al. 2010). Hexaploid

(2n=6x=54) was reported for upland plants in several studies, but not confirmed in the recent extensive investigation by Costich et al. (2010). This may be interpreted by Martinez-Reyna and Vogel (2002) reporting a post-fertilization incompatibility system that exists in crosses of tetraploid

by octoploid in preventing the production of hexaploid progeny. Octoploid occurs at a higher frequency than tetraploid in upland plants (Church 1940; Nielsen 1944; McMillan and Weiler 1959; Porter 1966; Riley and Vogel 1982; Hopkins et al. 1996; Hultquist et al. 1996; Lu et al. 1998; Costich et al. 2010). Higher ploidy levels (2n=10x=90 and 2n=12x=108) was only reported in an early study (Nielsen 1944), but not confirmed in recent studies (Riley and Vogel 1982; Hopkins et al. 1996; Hultquist et al. 1996; Lu et al. 1998; Costich et al. 2010). Using flow cytometry, chromosome counting and florescent in situ hybridization, Costich et al. (2010) reported extensive aneuploidy chromosome numbers in both upland and lowland switchgrass. They reported observations of aneuploidy at 86% of octoploid chromosome counts, but only 23% for tetraploid counts, suggesting a less stable genome in the octoploid upland switchgrass.

Homologous chromosome pairing and segregation behavior provide critical information with respect to sexual reproduction, chromosome homology and species evolution, and breeding procedures. Chromosome pairing in meiosis of tetraploid plants in both lowland and upland types is regular (Barnett and Carver 1967; Brunken and Estes 1975; Lu et al. 1998). Various studies all reported consistent bivalent pairing in tetraploid switchgrass plants. Martinez-Reyna et al. (2001) reported that chromosome pairing of hybrids between lowland and upland tetraploid parents was primarily bivalent. The regularity of tetraploid chromosomes in meiosis was substantiated by high vigor of pollen grains and good set of seeds (Barnett and Carver 1967; Martinez-Reyna et al. 2001). One important result of Martinez-Reyna et al. (2001) revealed that the genomes of tetraploid upland and tetraploid lowland are highly similar. More recently, two independent groups reported nuclear genome inheritance of lowland tetraploid is disomic (Okada et al. 2010b; Liu and Wu 2012; Liu et al. 2012). Using a population of 279 first-generation selfed progeny of a lowland tetraploid plant genotyped by 12 simple sequence repeat markers, Liu and Wu (2012) demonstrated segregation of the polymorphic codominant markers was consistent with a typical diploid "1:2:1" Mendelian segregation ratio (Fig. 4). More recently experimental results revealed that tetraploid switchgrass is an allotetraploid containing two distinct ("A" and "B") genomes (Young et al. 2010; Liu et al. 2012; Triplett et al. 2012).

Working with octoploids and its aneuploids, and a hexaploid, Barnett and Carver (1967) observed more univalents than with tetraploids. Trivalents and quadrivalents occur at a low number of cells in octoploids and its aneuploids, but were not observed in tetraploid cells (Barnett and Carver 1967). They observed much higher frequencies of abnormal pollen grains in octoploids and aneuploids than in tetraploids and a hexaploid. More studies indicated homologous chromosome pairing of octoploids is primarily of bivalent associations although univalent and multivalents

also occurred at a lower rate (Brunken and Estes 1975; Lu et al. 1998). The prevalence of bivalent associations in meioses of octoploid switchgrass may suggest that some mechanism(s) have evolved to insure the pairing behavior if octoploids are autopolyploid (Lu et al. 1998). Inheritance of octoploid is not known yet.

In addition to the pairing behavior and inheritance information of chromosomes in switchgrass, the mode of inheritance of chloroplast DNA in tetraploids was determined. Using upland x lowland reciprocal hybrids and their DNA samples hybridized with a special chloroplast DNA probe, Martinez-Reyna et al. (2001) indicated the maternal inheritance of chloroplast DNA in tetraploid switchgrass, which is consistent with those of most angiosperm species.

The normal functioning of miRNAs is a prerequisite for plant development. The loss of function of the key genes involved in miRNA biogenesis would cause significant mutant phenotypes in plant growth and development (Jacobsen et al. 1999; Lu et al. 2000; Park et al. 2002; Vaucheret et al. 2004). For example, the loss of function of DCL1, an important gene directly involved in the processing of pri-miRNAs and pre-miRNAs, would impact the maturation of miRNAs causing multiple deficiencies in plant development, such as abnormal leaf shape, delayed flowering and early embryo arrest (Reinhart et al. 2002; Dugas and Bartel 2004; Liu et al. 2005; Nodine and Bartel 2010). The mutants of other miRNA biogenesis-related genes including hyl1, hen1, and hst, all showed developmental deficiencies (Han et al. 2004; Park et al. 2005). These data demonstrated that miRNAs are largely involved in regulating plant development and play vital roles.

In recent years, the impacts of miRNAs on plant development have been extensively studied, especially in the model specie Arabidopsis thaliana. For example, Palatnik et al. (2003) found that miR319/JAW could target some members of TCP family, affecting leaf shape formation. Overexpression of miR319 led to down regulation of several TCP targets, resulting in uneven leaf shape and curvature, cotyledon epinasty, a modest delay in flowering and crinkled fruits phenotypes (Palatnik et al. 2003). Similarly, our study in creeping bentgrass (Agrostis stolonifera) also demonstrated that over expression of a rice miR319 gene causes pleiotropic phenotypes in transgenic plants including increased leaf expansion and stem diameter, which are associated with down regulation of at least four putative target TCP genes (Zhou et al. 2013). MiR165/166 targets some members of Class III HD — Zip and KANADI families, of which PHABULOSA (PHB), PHAVOLUTA (PHV), REVOLUTA (REV), KAN1, KAN2, and KAN3 play important roles in regulating leaf and flower development and vascular polarity (Chen 2005). miR172 regulats floral organ identity, reproductive development through regulating its targets APETALA2 (AP2) and AP2-like genes, such as TOE1, TOE2 and TOE3 (Chen 2004, Zhu et al. 2009).

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.

Enzyme productivity increases with increasing biomass concentration. The biomass concentration of T. reesei is increased by supplementing soluble sugars such as glucose or xylose along with insoluble lignocellulosic material. Mohagheghi et al. (1990) reported that the mixture of xylose and cellulose (30:30 g/l) was effective in reducing the lag phase of cellulolytic enzyme production to 6.25 days and resulted in 122 IFPU/l-h productivity. In addition, the same enzyme productivity was observed with pure cellulose, but over 8 days. At the same time, the enzyme titer was reduced by 27% for a mixture of xylose and cellulose compared to pure cellulose (Mohagheghi et al. 1990).

The modeling exercise is predicated on the following assumptions: (1) An economically competitive technology for converting switchgrass biomass to some type of biofuel (if not cellulosic ethanol, perhaps a drop-in fuel) will be forthcoming; (2) A biorefinery will require a flow of feedstock throughout the year; (3) Switchgrass is the exclusive feedstock; (4) In the U. S. Southern Plains, the switchgrass harvest window extends from July through March; (5) Expected switchgrass biomass yield and fertilizer requirements differ by harvest month; (6) Land use services could be acquired in long term leases; (7) The biorefinery will require material to be delivered in standardized 1.22 m x 1.22 m x 2.44 m bales with no more than 15 percent moisture; (8) Harvest crews may be centrally managed; (9) The biorefinery is assumed to operate 350 days per year and require 3,630 Mg/day of switchgrass biomass. The model is based on an extension of models previously formulated by Tembo et al. (2003), Mapemba et al. (2007), Hwang (2007), Mapemba et al. (2008), Haque (2010), and Haque and Epplin (2012). It is a multi-region, multi-period, monthly time-step, mixed integer mathematical programming model and can be used to determine the cost to deliver a flow of biomass to a biorefinery.

The model was formulated to include all 77 Oklahoma counties as potential production regions and two land classes, cropland and improved pasture land. The model limits switchgrass production to no more than 10 percent of a county’s cropland and no more than 10 percent of a county’s improved pasture land based on data from the Census of Agriculture (USDA 2010). The assumption is made that cropland could be acquired for a long-term lease rate above average U. S. CRP rental rates (Data. gov 2010). The lease rate for cropland for each county was calculated by adding $49/ ha to the average CRP rental rate for that county as determined by Fewell et al. (2011). Long-term lease rates for improved pasture land are derived by adding $76/ha to the 2010 average county pasture rental rate (USDA 2010; Fewell et al. 2011). The modeled rental rates are designed to cover the opportunity costs of alternative production options and to account for increased land-lease rates that may occur in response to an entrant in the market for 10 percent of the county’s land, and to compensate for the lost option value from engaging in long term leases (Song et al. 2011).

Switchgrass biomass yield estimates for each production region were obtained from estimates produced by Oak Ridge National Laboratory (Jager et al. 2010). Yields for cropland and improved pasture land are not differentiated (U. S. Department of Energy 2011). Hwang (2007) used weather data to determine probability distributions for the number of suitable field work days per month for harvesting switchgrass for each Oklahoma county. The 95 percent probability level from the harvest day distributions is selected so that the number of harvest days per month is set to be equal to the number of days that would be suitable for harvest in 19 of 20 years. For most months, the number of mowing days exceeds the number of safe baling days.

The model simultaneously determines how many hectares from which county and which land class are optimal to harvest for each month; how much harvested biomass should be put in field storage each month; how much should be shipped to the biorefinery each month; how much should be put in biorefinery storage each month; and how much should be processed each month. The model accounts for differences in nitrogen and phosphorus fertilizer requirements depending on the month of harvest. An integer variable is included to determine the optimal number of mowing units (windrowers), and another integer variable is included to determine the optimal number of harvest units (rakes, balers, tractors, and stackers) (Table 8, 9). Thus, the model endogenously determines the number of harvest machines. Shipment and processing of biomass can be done in any of the 12 discrete periods (months of the year). In months when biomass is harvested, it may be placed in storage or transported directly from the field to the biorefinery. The harvest season extends from July through March of the following year.

The modified Wang (2009) transportation cost equation used for the budget reported in Table 5 is also used in the modeling exercise. Transportation costs depend on the distance the feedstock will be shipped from the fields to the biorefinery. The distance between any biomass supplying county and any plant location is estimated by the distance from the county’s central point to the plant location. Storage losses at the biorefinery and in the field are assumed to be one percent per month (Hwang 2007). Another assumption is that bales stored in the field would be covered with a plastic tarp. The cost of field storage is estimated to be $1.79/Mg regardless of the number of months the material is in storage (Hwang 2007).