Compared to other lignocellulosic bioenergy crops, switchgrass rates well in its fertilizer, pesticide, and irrigation requirements (Groom et al. 2008). Switchgrass biomass does not appear to be sensitive to phosphorus (P) additions (Muir et al. 2001), and spring nitrogen (N) additions in some cases have had little effect on biomass outcomes when compared to control groups receiving no N at all (Thomason et al. 2005), consistent with switchgrass evolving in low N conditions. Other studies have shown N additions to increase biomass yields, but with diminishing returns such that N additions as low as 56 kg/ha having the highest fertilizer use efficiencies (Lemus et al. 2008). In a study based in Illinois, switchgrass production resulted in minimal N leaching compared to maize (1.4 kg N/ha/yr vs. 40 kg N/ha/ yr), and was slightly better in this capacity (though not statistically different) than Miscanthus x giganteus, a lignocellulosic alternative (Mclsaac et al. 2010). All evidence points towards switchgrass having excellent N-use efficiencies, and the ability to effectively partition nutrients to its roots towards the end of the growing season.

Water-use and water quantity and quality repercussions of switchgrass production are mixed. The amount of water used in the entire ethanol production process is not trivial, and can amount to > 2000 L water to produce 1 L ethanol in some locations (Chiu et al. 2009). This obviously brings forth the notion that areas of the world that rely heavily on irrigation (e. g., western U. S. states) may not be well-suited for future biofuel production. On the agronomic production side alone, evapotranspiration losses for all bioenergy crops are expected to increase in a warming and elevated-CO2 climate (Le et al. 2011). The jury is still out on how switchgrass compares to other bioenergy crop alternatives; some studies have shown that evapotranspiration losses for switchgrass are not as large of a problem as they are for Miscanthus x giganteus (McIsaac et al. 2010), while others essentially equate switchgrass with Miscanthus x giganteus but characterize them both as having higher losses than maize (Le et al. 2011). Subsequent alteration to the water cycle may have large impacts in areas that will experience conversion to bioenergy crop production. The impacts could well be tied to lower water quality via reduced surface runoff under certain land — use changes (Wu et al. 2012); however, models projecting sediment-, N-, and P-associated metrics indicate that perennial grasses, like switchgrass, may be better alternatives than row crops such as maize (Love and Nejadhashemi

2011) . Across large spatial scales, the water-use efficiency of switchgrass is notable; it is generally thought to be better than that exhibited by maize (VanLoocke et al. 2012), and undoubtedly will influence short — and long­term system-wide water quality and quantity issues.

Biomass accumulation ends when switchgrass begins flowering, and the reproduction process (flowering and seed setting) is extremely energy consuming. Therefore, delayed or aborted flowering can extend and promote vegetative growth. For example, lowland ecotypes flower late in high latitude areas and produce higher biomass yields than upland ecotypes (Lemus et al. 2002). However, growing lowland cultivars in high latitudes is challenging because of the winter hardiness and persistent drought stress of the high latitude climate (Lemus et al. 2002). In addition to genotype effects, environmental factors also have a significant impact on switchgrass flowering time, but these factors have not been well studied. Under the same growth conditions, different switchgrass cultivars normally have very similar final leaf numbers; tillers that emerge in the spring season produce a final leaf number ranging from nine to 11, and tillers that emerge in and after the summer season have a final leaf number of less than seven in Texas, USA (Van Esbroeck et al. 1997). In the winter season, it was observed that switchgrass cultivars (both upland and lowland) flower even when they only have 2-3 leaves in the greenhouse (temperature set at 22-28°C, under natural light) at Blacksburg, Virginia, USA (data unpublished). These observations suggest that switchgrass flowering time can be regulated through photoperiod and autonomous pathways.

Flowering pathways have been well studied in the dicot model Arabidopsis. In Arabidopsis, flowering time is mainly controlled by interactions between photoperiod, vernalization, gibberellic acid (GA)-response, and autonomous pathways (Corbesier and Coupland 2006). In monocot plant species, flowering pathways are poorly understood, partially because of the redundancy of gene families involved in flowering time. For example, maize (Zea mays) has more than 1,000 orthologs of Arabidopsis flowering genes (Buckler et al. 2009), where numerous small-effect quantitative trait loci (QTLs) contribute to maize flowering architecture (Buckler et al. 2009). In contrast to the out-crossing plant species (e. g., maize), the flowering pathways in selfing plant species (e. g., Arabidopsis and rice) are mainly controlled by a set of large-effect genetic components (Buckler et al.

2009) . This is possibly because selfing plants can tolerate large changes in flowering time and still produce seeds, while out-crossing species cannot (Maloof 2010).

Despite the difficulty of directly identifying functional orthologs in grasses by BLAST searching with Arabidopsis flowering genes, many known grass flowering genes share common signatures with Arabidopsis flowering genes and are involved in similar signaling pathways. For example, CONSTANS (CO) and FLOWERING LOCUS T (FT) orthologs are key regulatory genes in the photoperiod flowering pathway in both the long-day plant (LDP) Arabidopsis and the short-day plant (SDP) rice (Maloof 2010). One rice FT ortholog, Hd3a, shares key features of Arabidopsis FT, and can complement the Arabidopsis ft mutant (Izawa et al. 2002). In Arabidopsis, stabilized CO protein acts as a positive regulator of flowering by activating the transcription of the FT gene. The FT protein translocates to the apical meristem and induces flowering (Yanovsky and Kay 2002; Turck et al. 2008). However, in rice, CO orthologs [e. g., Heading date 1 (Hd1)] act as a repressor, not an activator, of flowering in the presence of light (Tamaki et al. 2007; Komiya et al. 2009). The photoperiod flowering pathway is also more complex in rice than in Arabidopsis. For example, a grass-specific gene, Early Heading Date 1 (Ehd1), promotes short-day flowering in hd1 mutant rice by inducing the expression of rice FT-like genes (Doi et al. 2004). This suggests an additional grass (or rice)-specific signaling cascade present in the photoperiodic pathway in rice (Doi et al. 2004). In cereal plants (e. g., wheat and barley), flowering time may be largely controlled by perception of cold (vernalization) and photoperiod through regulations of large-effect genes, such as vernalization genes VRN1, VRN2, VRN3, and orthologs of CO (see review by Distelfeld et al. 2009). These large-effect genes differ from, but still share common signatures with, their counterparts in Arabidopsis.

Switchgrass and maize are in the same subfamily of the PACCAD clade (Lawrence and Walbot 2007), and are both out-crossing plant species. Therefore, switchgrass may share some common features in the flowering pathway with maize. Although there are many small-effect QTLs fine-tuning the flowering time in maize, some maize null mutants have dramatically postponed flowering. For example, a homozygous null mutant of maize, Indeterminate1 (Id1), has a late flowering time and prolonged vegetative growth compared to wild type maize (32 leaves in id1 mutant compared to 13 in wild type at flowering time) (Colasanti et al. 1998). A set of microRNAs, miR156 and miR172, have recently been found to be important for regulating plant development and flowering independent of environmental cues (autonomous pathway), and are conserved across Arabidopsis, maize and rice (Xie et al. 2006; Chuck et al. 2007; Wu et al. 2009). As mentioned above, miR156 promotes the juvenile vegetative growth phase by partially repressing the expression of certain SPL genes, which are positive regulators of miR172 and other flowering genes, and the expression level of miR156 declines with the development/age of plants (Xie et al. 2006; Wu et al.

2009) . miR172 acts downstream of miR156 through SPL9 and represses SMZ, which directly represses the transcription of FT (Mathieu et al. 2009). In contrast to miR156, the expression level of miR172 increases with the development/age of plants (Wu et al. 2009). In summary, it is highly possible to isolate homologs of conserved genetic components (such as miR156 and Id1) involved in flowering time of switchgrass. Selecting dominant mutants in flowering pathways, or directly manipulating these genetic components may lead to significantly postponed or aborted flowering, an extended vegetative growth phase, and likely an increase in biomass yield.

Lignocellulosic biomass (such as woods, herbaceous crops, cereal crops, red algae, components of municipal solid waste) consists of lignin, hemicellulose and cellulose that provide the structural framework of plant matter (Chandrakant and Bisaria 1998; Vane 2005). In lignocellulosic biomass, cellulose, hemicelluloses and lignin vary from feedstock to feedstock (see Table 1). Softwood has the highest lignin content compared to any other lignocellulosic biomass.

Various lignocellulosic biomass including switchgrass, sweet sorghum bagasse and loblolly pine, has been explored for biofuel production processes (Jain et al. 2013 and many others). Switchgrass (Panicum virgatum) and other native grasses are perennial warm season herbaceous crops, distributed from North America to Mexico. There are several varieties of switchgrass including the common southern lowland Alamo variety (Panicum virgatum L.). Other native grasses include indiangrass (Sorghastrum nutans), eastern gamagrass (Tripsacum dactyloides), big bluestem (Andropogon gerardii), little bluestem (Schizachyrium scoparium) (en. wikipedia. org/wiki/Panicum_ virgatum). Switchgrass has a wide range of environmental benefits. The ability of switchgrass to tolerate drought, prevent soil erosion and its high yield with low fertilizer input makes it a potential biofuel crop. A typical switchgrass composition contains 33-37% cellulose, 24-40% hemicellulose and 12-19% lignin (Adler et al. 2006; Dien et al. 2006; Schmer et al. 2007).

Sweet sorghum is a warm seasonal crop that matures in a 3-4 month cultivation period. Sweet sorghum syrup has a potential market value similar to sugarcane syrup. There are different varieties of sweet sorghum including Dale, M8IE, Brandes, Theis and Honey, amongst others. A typical sweet sorghum bagasse composition consists of 35-40% cellulose, 20-22%

hemicellulose and 11-20% lignin, depending upon both the plant source and extraction method. Similar to switchgrass, sweet sorghum’s adaptability to sub-humid and semi-arid climates, high yield with low fertilizer input, low water requirements and short cultivation periods are key advantages of using it as a biofuel crop (Lau et al. 2006; Wu et al. 2011).

Loblolly pine (Pinus taeda, also called Arkansas pine, North Carolina pine, and old field pine) grows in humid, warm-temperatures with long, hot summers and mild winters. Loblolly pine is the single most popular species in pulp/paper production. In the US, loblolly pine covers 14 states, from southern New Jersey to central Florida and from Texas to Arkansas (www. na. fs. fed. us/pubs/silvics_manual/Volume_1/pinus/taeda. htm). Loblolly pine is a fast-growing tree and can reach heights ranging from 18-30 m with diameters between 0.3 to 1.5 m, depending on the geographic condition and location. A typical composition of loblolly pine consists of 45-50% cellulose, 25-35% hemicellulose and 25-35% lignin (Sun and Cheng 2002).

Lee (2005) mentioned, "In plant cells, a secondary cell wall consisting of three layers (S1, S2 and S3) surrounded by a thin primary wall composed of cellulose microfibrils. The secondary wall (S1 and S2) is surrounded by lignin. S1 and S3 layers contain non-crystalline or amorphous cellulose and hemicelluloses, whereas S2 layer contains crystalline cellulose. Crystalline region is primarily because of hydrogen bond formation between hydroxide ions of cellulose molecules. Moreover, amorphous cellulose, hemicelluloses and lignin are present between secondary layers."

A wide range of bioproducts could be derived via biological or chemical processes using microorganisms or acids and bases to break down cellulose, hemicelluloses and lignin (Chandrakant and Bisaria 1998; Huber et al. 2006). Bioproducts and fuels derived from lignocellulosic biomass are renewable, low in sulfur content, and as efficient as many fossil fuels (Lau et al. 2006). The conversion of lignocellulosic material to biofuel via fermentation processes is more complicated than the hydrolysis of sugar and starch crops because lignocellulosic materials contain more complex sugar polymers of cellulose and hemicellulose adhered with lignin (Claassen et al. 1999; Lee 2005). In addition, the different components of lignocellulosic biomass have industrial importance in the production of many bioproducts through different processing techniques (see Table 2).

Besides the ALMANAC model, we are not aware of any other mechanistic model that has been used to analyze switchgrass WUE (Kiniry et al. 2008a). The ALMANAC model simulates the water balance by determining plant water use while taking into account soil properties, weather, and plant species cover. ALMANAC calculates the effects of soil water availability on plant growth by calculating the potential evaporation, potential soil water evaporation, and potential plant water transpiration based on the leaf area index (LAI). Potential evaporation is estimated by the Penman-Monteith method (Ritchie 1972). Potential soil evaporation and plant transpiration is estimated by:

EP = E0 (LAI/3) 0 <LAI <3.0 [1]

EP = E0 LAI > 3.0 [2]

ES = minimum of (E0 exp (-0.1BIO), E0-EP) [3]

Where EP and ES are potential plant transpiration and soil evaporation (mm), E0 is potential evaporation (mm), and BIO is the sum of the above­ground biomass and plant residue (Mg ha1). Simulated plant leaf area and biomass is reduced when available soil water is depleted. Water use depends on soil water availability and plant water demand. Water demand is based on potential evapotranspiration and the leaf area index. The water stress factor is estimated and decreases daily leaf area and consequently biomass growth if current available soil water is insufficient to meet demands.

Water Use Efficiency of Four Switchgrass Types

The ALMANAC model has been parameterized and used to simulate WUE for four major switchgrass types: northern upland (NU), northern lowland (NL), southern upland (SU), and southern lowland (SL) in multiple locations across the Great Plains. These locations are representative sites in the region anticipated to be primary production areas for biofuel crops in the U. S. The data for comparing simulated WUE using ALMANAC came from two studies (Table 3). The first study (Kiniry et al. 2008) simulated plant transpiration and biomass for four sites: Stephenville, TX; Mead, NE; Columbia, MO and Ames, IA. The second study (Kiniry et al. 2012) involved 10 sites in the central and southern Great Plains. These ranged from northern MO at Elsberry to subtropical southern TX at Weslaco. The model was used, with appropriate soil parameters for each site and with actual measured weather for the growing season, to calculate EP. For the first study, crop parameters were adjusted to obtain reasonable simulated yields of each switchgrass type, as compared to the mean of the two years of measured yields. For the second study, rainfall was severely limiting due

to a drought throughout much of the region. Therefore, the crop parameters were adjusted to match measured LAI values from early in the growing season in an effort to minimize the impact of severe drought stress on results (Kiniry et al. 2012).

The simulated WUE of all four switchgrass types showed values generally ranging from 3 to 6 mg g-1, with a few exceptions. These values are within the range of empirical data collected. Byrd and May (2000) calculated the WUE of many different potted switchgrass cultivars and reported that WUE ranges from 4.3 to 8.5 mg dry weight per g of water used. In germplasm nurseries in TN and OK, WUE of switchgrass accessions were 3.5 to 6.3 mg CH2O g-1 of water transpired (McLaughlin et al. 2006). In the field in NE, switchgrass WUE values were 1.0 to 5.5 mg g-1 (Eggemeyer et al. 2006), values similar to those demonstrated by switchgrass seedlings in a growth chamber (1.45 to 5.5 mg g-1) (Xu et al. 2006). There are a wide range of WUE values for switchgrass that depend on the environmental conditions and switchgrass ecotype.

The greatest simulated WUE values were most often for the lowland types. The southern lowland types had the highest WUE values in most of TX and the northern lowland types generally had the greatest WUE values at locations further north (Table 3). The northern lowland type had the highest WUE in more than half the cases, being greatest for 8 of the 14 cases. The southern upland’s values for WUE were greatest at 2 sites: Booneville, AR and Mead, NE. The northern upland types only had the greatest value at Stillwater, OK. High WUE of lowland varieties allows for increases in yield in southern locations where water is often limiting.

Based on reactor configuration, gasifiers can be classified into fixed-bed (downdraft and updraft), fluidized-bed, and entrained-bed. Fluidized-bed gasifier can be bubbling or circulating-bed. In fixed-bed gasifiers, biomass is fed from top so the biomass moves downward through the reactor. Ash and residual char pass through and are collected at the bottom grates. The gas flow in the fixed-bed gasifier can be in upward or downward direction. When the gas flow is in upward direction, it is called an "updraft" gasifier; whereas when the flow is in downward direction, it is called "downdraft" gasification. Fixed-bed gasifiers have a relatively simpler design and are best for small-scale applications of up to 1.5 MWe (Bridgwater 2006). Fluidized — bed gasifiers require a bed of fluidizing medium such as silica or olivine, where the biomass and fluidizing agent, such as air are introduced. Due to the bed fluidization, fluidized-bed gasifiers have high heat and mass transfer rates which result in higher efficiency as compared to the fixed-bed gasifier especially as the scale is increased.

Based on the heat source for the gasification reaction, gasifiers can be classified into directly or indirectly heated. In the case of directly heated gasifiers, no external heat is supplied. Oxygen (or any gas containing oxidizing agent) is supplied into the reactor which results in partial oxidation of biomass. Partial oxidation of biomass provides the heat required for the gasification endothermic reactions. In the case of indirectly heated gasifier, heat is provided by external heat source by combustion of fuels, such as char, natural gas or using electricity.

Biological pretreatment may be performed using brown-rot, white-rot and soft-rot fungi. However, based on physiology, enzymology and molecular genetics these fungi degrade the lignocellulosic biomass differently. Brown — rot fungi are effective in the degradation of cellulose, whereas white-rot and soft-rot fungi degrade both lignin and cellulose. White-rot fungi belonging to Basidiomycetes are among the most effective fungi in degrading lignin. Lignin degrading enzymes (such as peroxidases and laccase) produced by white-rot fungi (such as Phanerochate chrysosporium) are regulated by the C/N (carbon/nitrogen) ratio of the lignocellulosic feedstock. The higher C/N ratio of 30:1 for the fungi compared to lower C/N ratio 10:1 for the bacteria, makes the fungi more capable in degrading lignocellulose biomass (Kirk and Farrell 1987; Kerem et al.1992; Kumar et al. 2009). The ability to perform biological pretreatment at low energy levels compared to chemical pretreatment could make this technology a more economically viable pretreatment. However, long processing times ranging from weeks to months and inconsistent utilization of substrate may limit its viability at industrial scales.

The average custom rate charged to windrow, rake, and bale hay and straw into rectangular solid bales as reported by Doye and Sahs (2012) is used to compute the harvest costs reported in Tables 5 and 6. These rates provide a market estimate of the marginal cost of the baling activity for the region. These rates could be expected to be sensitive to the level of timeliness required and to an increase in the demand for custom baling.

One advantage of establishing switchgrass as a bioenergy crop in the U. S. Southern Plains is that it could be harvested once per year anytime between July and February of the following year (Epplin et al. 2007). A harvest season of this length may result in the development of harvest units that include an economically efficient set of machines and workers that can harvest and deliver feedstock in a standardized form. Harvest units could develop in a manner similar to custom grain harvesting firms that harvest a substantial quantity of the grain produced in the U. S. Great Plains. Cost economies are such that a moderate sized grain producer has difficulty justifying combine ownership. For many farms in the region, hiring a custom harvester is more economical than either owning or leasing a combine.

Custom grain harvest firms take advantage of the economies of size associated with ownership and operation of machines used to harvest grain. Kastens and Dhuyvetter (2011) find that a typical custom grain harvest company harvests thousands of hectares per year, with several combines and trucks, and a crew of workers. These harvest companies may begin their season in regions where the crops mature first and migrate as the harvest season progresses. For example, some wheat harvest firms begin harvesting wheat in Texas in May and travel north as the crop matures, eventually into Canada.

Thorsell et al. (2004) introduce the concept of an economically efficient harvest unit for switchgrass. They assume switchgrass harvest and field storage would require machines that could mow, rake, and bale switchgrass biomass and a machine that could collect, transport, and stack bales at a location near an all-weather road. The search was limited to established technology and available agricultural equipment that could travel quickly and legally on country roads and highways. Self-propelled bale transporters that can travel in a field and collect large rectangular solid bales, transport them within and beyond the field, and stack them for field storage are commercially available. Because of differences in weather requirements between mowing and baling, Hwang (2007) modified Thorsell et al.’s (2004) harvest unit concept by separating the mowing unit from the raking-baling — stacking unit.

Table 7 includes a list of harvest machines used to compile a mowing unit and a coordinated raking-baling-stacking unit. The mowing unit consists of a 140 kW self propelled windrower with a 4.9 m rotary header. The coordinated raking-baling-stacking unit includes one bale transporter stacker, three 7.3 m wheel rakes that are powered by three 40 kW tractors, and three balers powered by three 147 kW tractors that produce 1.22 m x 1.22 m x 2.44 m bales. If the material is sufficiently dry when cut, the windrower may be used to mow and place the cut biomass in a windrow

for baling. If the material is not sufficiently dry a rake may be used to turn the material to aid the drying process. The rake may also be used to merge two or more windrows produced by the windrower into a larger windrow for more efficient baling. The raking-baling-stacking unit is coordinated in the sense that the throughput capacity of the three balers is consistent with the collection capacity of one bale transporter stacker.

Primary operating conditions affecting pyrolysis (shown in Fig. 3) are biomass flow rate, flow rate of purge gas, reactor temperature profile, heating rate, and residence time. Biomass properties, such as composition and particle size as well as reactor configuration, also affect the reaction conditions (Fig. 4).

Figure 4. Pyrolysis process variables.

In SSCF, both hydrolysis and fermentation of different sugars (such as glucose and xylose) are performed simultaneously in the same vessel. SSCF is similar to DMC, except SSCF has more freedom in the selection of operating temperature for simultaneous lignocellulosic biomass hydrolysis and ethanol fermentation step. The DMC process is solely dependent on the use of a single microorganism to perform all different steps such as enzyme production, enzyme hydrolysis and ethanol fermentation. The optimum conditions for enzyme hydrolysis are 50-55°C at pH 4.5-5.5, whereas a thermophilic microorganism such as Kluyveromyces sp. IIPE453 MTCC 5314 operates at 40-65°C and at 3.5-5.5 pH conditions (Adihikari et al. 2009). Additionally, SSCF operates at higher temperatures, reducing the risk of contamination and the simultaneous utilization of sugars prevents enzymatic inhibition in the formation of glucose and cellobiose.

Switchgrass is often promoted as a "wildlife friendly" grass. Research on this front suggests that this may not be entirely true. One major conservation concern is the land-use conversion from multi-species mixtures (e. g., in range-, hay-, and pasture-lands) to single-species plantings of bioenergy crops. Indeed, avian and pollinator diversity is correlated with and is driven by plant community diversity (Hatfield and LeBuhn 2007; Bakker and Higgins 2009). Furthermore, areas represented by bioenergy crop monoculture exhibit 60% fewer species than native prairie systems (Bakker and Higgins 2009). The use of bioenergy species mixtures has been proposed to mitigate conservation issues associated with monocultures (Tilman et al. 2006; Wallace and Palmer 2007). However, reliable and consistent ethanol production from species mixtures is currently not standard practice (Merino and Cherry 2007). This, coupled with monocultures producing higher amounts of biomass (Griffith et al. 2011), foreshadows the high likelihood that bioenergy crop monocultures will dominate the landscape.

Insect pollinator and avian responses to biofuel land cover change are based on comparisons among biofuel crop plantings, mixed grass plantings, and row crops, such as maize and soybeans. Studies in these systems have suggested that land conversion to biofuel crops would increase grassland bird and pollinator diversity (Gardiner et al. 2010; Meehan et al. 2010; Fletcher et al. 2011; Robertson et al. 2011). Changes from current non-arable (e. g., "marginal") or rangeland (including range, hay, and pasture) to bioenergy cropland is far less studied, and comparisons of biodiversity in some natural systems compared with those in biofuel crop habitat indicate lower diversity in the latter systems (Fletcher et al. 2011). The resulting structural changes here, which could constitute the majority of landscape changes associated with future widespread planting of switchgrass and other bioenergy crops, may not be as dramatic as with conversion of row crops. This lends some hope to the possibility that certain species of conservation concern (e. g., grassland birds) would not be negatively affected by bioenergy crop production. However, from a compliance standpoint, threats to federally-listed endangered species may provide an additional complication to widespread land conversion to switchgrass bioenergy crop fields.