Seeding rate is one of the most important variables determining the success of a new seeding. Seeding rate can be measured as either the weight of seed per unit area, or the number of seeds per unit area. The conversion between these two measures is the specific seed weight (i. e., g seed1), and this conversion varies among species, cultivars, and even seed lots. Seeding rates should be based on the delivery of pure live seed (PLS) per unit area, and thus also needs to account for hard seed, the percent germination of the seed being planted, and the presence of inert materials such as impurities and seed coatings. Switchgrass seed can also have high levels of dormancy, which can further complicate seeding recommendations or practices, and we discuss this in the next section.

Among all crops, recommended seeding rates vary by species, location and intended use of the stand. The recommended rates are usually not a specific value, but a defined range of seed per unit area. Recommended seeding rates have been determined over the years from research and experience in the field and tend to be higher for broadcast than drilled stands to offset poorer seed-soil contact. Rates generally are lower in drier climates due to seedling mortality and because higher seeding rates can decrease stand productivity due to excessive intra-species competition for water. Lower rates are also usually recommended for conservation plantings where forage production is not the primary objective.

Switchgrass seed is relatively expensive; thus, minimizing seed costs will be an important factor for optimizing returns from switchgrass as a commercial bioenergy crop. Proper seeding rate should ensure enough seedling survival so that the optimal 40% stand frequency is achieved for successful switchgrass establishment (Schmer et al. 2006). Current seeding rate recommendations range from 2.2 to 11.2 kg PLS ha-1 (Parrish et al. 2008). Refining seeding rates above which no additional biomass increases are achieved is important for the economic feasibility of switchgrass production. In Tennessee, ‘Alamo’ planted at seeding rates ranging from 2.8 to 14.0 kg PLS ha-1 produced similar DM yield when harvested after frost (Mooney et al. 2009). Foster et al. (2012) reported seeding rates of 2.24 to 4.48 kg PLS ha-1 were optimal for switchgrass emergence and DM yields. The lower the seeding rate for successful stand establishment, the more economical the production of switchgrass for biomass energy purposes (Perrin et al.

2008) .

Fungal endophytes are most commonly found living in aboveground plant tissues and occasionally in roots (Saikkonen et al. 1998). Plants infected with fungal endophytes gain growth promotion, stress tolerance, water use efficiency, and protection against vertebrate herbivores and root nematodes (Schardl et al. 2004; Rodriguez and Redman 2008; Rodriguez et al. 2009). During the interactions, endophytes obtain shelter, nutrition and dissemination through propagules of the host plants (Schardl et al. 2004). Like bacterial endophytes, fungal endophytes also promote host plant growth, such as increased root growth and longer root hairs (Malinowski et al. 1999), which may contribute to enhanced nutrient uptake. For instance, the root and shoot biomass of poplar, maize, tobacco, bacopa, Artemisia, and parsley was doubled compared with their respective controls after four weeks of Piriformospora indica inoculation (Varma et al. 1999).

Fungal endophytes of the genus Neotyphodium (an asexual form of Epichloe spp.) have been well studied for their symbiotic associations with different grass species, especially the family Pooideae, which includes many important species of forage and turf grasses (Clay 1990; Schardl et al. 2004; Sugawara 2011). Through this symbiosis, grasses have exhibited increased growth, tolerance to stress and resistance to herbivores (Schardl et al. 2004; Faeth et al. 2010). For instance, plant growth, biomass yield and tiller number increased when ryegrass (Lolium perenne) was inoculated with N. lolii (Spiering et al. 2006), and Dahurian wild rye (Elymus dahuricus) with Neotyphodium spp. (Zhang and Nan 2007). Endophyte-infected plants showed a higher survival rate, regrowth rate, and more biomass seed production compared to non-infected plants after a year in the field (Iannone et al. 2012).

In switchgrass, NF/GA-993 (a synthetic lowland switchgrass cultivar) inoculated with six strains of Sebacina vermifera fungal endophytes showed increased plant growth, root length, and biomass production (Ghimire et al. 2009). Recently, Sasan and Bidochka (2012) found that the fungal endophyte Metarhizium robertsii was able to endophytically colonize the roots of switchgrass and promoted growth and increased the density of root hairs (Sasan and Bidochka 2012). However, fungal endophytes recently isolated from switchgrass plants had both beneficial and detrimental effects on switchgrass biomass yields in greenhouse conditions. Phaeosphaeria pontiformis, Epicoccum nigrum, Alternaria spp. and Colletotrichum spp. increased total biomass by 25-33%, Stagonospora spp. increased shoot biomass by 22%, and Colletotrichum sp. increased root biomass by 45%, but over 60% of isolates tested reduced switchgrass growth (Kleczewski et al. 2012).

After biomass production, harvesting, and transport, biochemical conversion of biomass to biofuels typically includes a pretreatment to improve accessibility of the biomass, followed by enzymatic digestion to depolymerize the cell wall polysaccharides, and finally fuel synthesis. Here, we briefly discuss pretreatment approaches and then review biochemical conversion platforms, including the goal of consolidating the different steps of biochemical conversion into a single reaction vessel. We then provide an overview of the enzymes and enzyme complexes that digest biomass. In the last section we especially highlight progress in one means of bioconversion consolidation, expressing cell wall digesting enzymes in plants.

Germplasm Characterization by Molecular Markers

Diverse germplasms are fundamental for crop improvement in all plants including switchgrass. Fortunately, switchgrass is genetically highly diverse as revealed by recent molecular marker investigations. The existing germplasm will provide abundant genetic variability to improve bioenergy traits for new cultivars development. As an allogamous species, switchgrass has tremendous genetic diversity among germplasm sources, such as morphological traits, biomass and quality traits, biotic resistance and abiotic stress tolerances. Molecular studies on genetic diversity analysis in switchgrass include RFLP, RAPD, SSRs and high-throughput sequencing (Morris et al. 2011). Gunter et al. (1996) used RAPDs to assess the genetic diversity among and within 14 populations of switchgrass and found markers were useful for population identification. Hultquist et al. (1996) utilized chloroplast DNA RFLP to investigate 18 switchgrass strains, and found polymorphism existed between lowland and upland ecotypes, but not among upland cultivars. Casler et al. (2007) reported that 46 remnant populations and 11 cultivars could be highly unrelated to each other. They further indicated that RAPD markers could not distinguish between cultivars and remnant wild populations. Casler et al. (2007) also used RAPDs to test the plants with the same region and found little differentiation correlated with geography, but part of them was related with hardiness zones and ecotypes. Missaoui et al. (2006) used RFLPs to assess genetic variation between 21 switchgrass genotypes and they found higher diversity between upland and lowland accessions than within each of cultivars. They also used a trnL (UAA) chloroplastic marker and found a polymorphism between upland and lowland ecotypes. The trnL UAA intron region is located on chloroplast genome and is inherited through the maternal parent (Martinez-Reyna et al. 2001). Narasimha-moorthy et al. (2008) used the materials from USDA Germplasm Resources Information Network (GRIN), and found higher variation within populations than among populations. Cortese et al. (2010) used marker and morphological data among 12 populations of switchgrass and indicated that morphological and adaptive traits could be identified by molecular markers. Zalapa et al. (2011) used 55 SSR markers and six chloroplast markers to study diversity within and between 18 switchgrass cultivars. The SSR markers could discriminate ecotypes correctly, but chloroplast markers could not. Zhang et al. (2011a) sampled a total of 384 genotypes from 49 accessions. They identified primary centers of diversity were in the eastern and western Gulf Coast regions. Todd et al. (2011b) utilized amplified fragment length polymorphism (AFLP) procedure to quantify genetic diversity of seven upland and 49 lowland genotypes from throughout the USA. They found upland and lowland accessions clustered according to ecotypes, with one exception (TN104). Morris et al. (2011) sequenced 40.9 billion base pairs of chloroplast genomes from 24 individuals from across the species’ range and 20 individuals from the Indiana Dunes. Analysis of plastome sequence revealed three deeply divergent haplogroups, which correspond to the previously described lowland-upland ecotypic split and a novel upland haplogroup split that dates to the mid-Pleistocene.

Switchgrass breeding programs have aimed to double its biomass yield in the near future (Schubert 2006). Improving the biomass yield of switchgrass under various field or geographic conditions can be achieved by promoting vegetative growth, increasing the photosynthetic sink-source ratio, increasing resistance to biotic/abiotic stress, and improving water and nutrient use efficiency (WUE and NUE). Improving certain biological traits of switchgrass, such as NUE, can also decrease production input. Producing high value additives, such as plastics, enzymes, and secondary metabolic chemicals, can further increase the economics of growing switchgrass (Somleva et al. 2008). Selection and use of plant-growth promoting microbes may also improve grass growth and resistance to stress (Compant et al. 2005). Candidate genetic components, as well as pathways potentially useful for switchgrass improvement, are discussed in the following section with an emphasis on lignin reduction, biomass enhancement, value-added engineering, and stress resistance.

Bio-oil can also be used in furnaces and boilers to produce heat and power after moderate upgrading (Czernik and Bridgwater 2004). Recently, researchers have focused specifically on converting bio-oil to hydrocarbon fuels which are compatible with petroleum fuels. Bio-oil consists of several hundred compounds and separation is very challenging because the concentrations of the chemicals are low and any separation techniques must take into consideration interactions by many other functional groups present in the bio-oil.

Acknowledgements

We appreciate Madhura Sarkar, a graduate student, for helping collect some of the data for this chapter. Financial support is provided, in part, by Oklahoma Agricultural Experiment Station and National Science Foundation under Grant No. EPS-0814361.

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.

Once switchgrass stands reach canopy closure, weed pressure will be minimal with appropriate management. Herbicides such as 2,4-D amine can be used to control broadleaf weeds anytime after switchgrass reaches the 4-leaf stage—which is when most grasses are considered established (Ries and Svejcar 1991). Once established, pendimethalin (Prowl H2O®; Anonymous 2011b) can be applied to winter dormant warm-season grasses

like switchgrass early in the season prior to weed germination to reduce warm-season annual grass weeds like crabgrass in southern USA and foxtails in northern USA.

As new and improved cultivars are developed and marketed, there may be a need or desire to renovate or remove an existing switchgrass stand. Since switchgrass is a bunchgrass, it is susceptible to deep tillage. However, for best results glyphosate should be applied in the autumn, when switchgrass is translocating carbohydrates from the shoots to its roots, prior to tillage. Any survivors can be retreated with glyphosate the following spring.

Mitchell et al. (2005) provided guidelines for converting perennial grasses using minimum tillage and glyphosate-tolerant soybeans. The soybeans can be no-till seeded into the grass stand and managed using standard recommendations for two consecutive growing seasons. This process maintains residue on site, reduces soil erosion and desiccation, and produces income during renovation. Soybeans are preferable to corn because soybeans provide residual nitrogen, produce an excellent seedbed for no-till planting, and leave sufficient residue to protect the soil without interfering with seeding. Additionally, it prepares an excellent weed-free seedbed for grass establishment, and no-till seeding perennial grasses into soybean stubble reduces tillage and weed control costs during establishment.

In order to classify and study functions of endophytes and mycorrhizal fungi, endophytes first need to be isolated from host plants and mycorrhizal fungi from soil samples containing host plant roots. Next, the organisms need to be purified before they are identified and finally characterized using molecular tools.

Endophyte Isolation

In general, for bacterial and fungal endophyte isolation, the samples, including any host plant tissue, are collected and brought to laboratories where they can be stored in plastic bags at 4°C for a few days prior to surface sterilization.

Surface Sterilization

Root samples should first be washed with tap water to remove any soil from the root surface before sterilization. Aboveground plant tissue can be directly washed with 70% ethanol for one minute, sterilized with 20-50% bleach solution for 10-20 min depending on the type of tissue; for tender tissues, a lower concentration of bleach and shorter duration of time should be used. The tissue is finally rinsed with sterile water 3-5 times under aseptic conditions. After sterilization, the tissue surface should be clean and free of microorganisms. To ensure the efficacy of surface-sterilization, 50 gl of the final wash should be plated, and surface-sterilized tissues can be rolled onto culture media and incubated at 27°C for a few days to see if any remaining microorganisms were present (Coombs and Franco 2003).

Lifecycle assessments suggest that production of lignocellulosic biofuels, especially from high-yielding biomass crops such as switchgrass can be achieved with net energy production and substantial greenhouse gas reduction (Farrell et al. 2006). For example, the study of Schmer et al. of 10 multi-hectare fields in the northern midwestern U. S., measured the on-farm energy balance for switchgrass production and then used the literature to estimate the energy balance for conversion to ethanol. The average results were a yield of 2800 L per ha, a net energy yield (energy produced-input) of 80 GJ/ha, and a green house gas displacement of ~80% for use of the bioethanol compared with gasoline (Schmer et al. 2008). This study assumed an ethanol yield of 0.38 L per kg of biomass, which is typical of what is commonly found in other assessments and of laboratory saccharification yields (Fu et al. 2011). Based on the percentage of switchgrass biomass that is sugar (Vogel et al. 2010, Fig. 1), this is ~65% of the theoretical yield of conversion of all polysaccharide to ethanol (0.58 L per kg).

As we have described, substantial progress has been made via a panoply of approaches to improve plant biomass to close the gap between the typical and theoretical saccharification yields. These improvements in yield now await translation from the laboratory into the field. Of course, such efforts require substantial time and money, not to mention regulatory approvals if transgenic methods are employed. Furthermore, we can expect different phenotypes in the field since the range of biotic and abiotic stress conditions under which published studies have been conducted is limited. This is especially important for biofuel crops because they need to be produced on degraded or abandoned crop lands that do not displace substantial food production in order to avoid indirect increases in greenhouse gas production due to land clearing (Fargione et al. 2008; Youngs et al. 2012). No doubt, an even more thorough understanding of cell wall biosynthesis and regulation will be necessary to anticipate and mitigate pleiotropic effects of manipulating the major components of plant biomass. In addition, since the vast majority of studies have not been conducted in switchgrass or other biofuel species, there remains substantial work to be done in testing genes in biofuel species. As thus far only single genes have been examined, when selecting genes for testing in a species such as switchgrass one wonders about whether additive or synergistic effects might be achieved from simultaneously manipulating multiple cell wall synthesis or regulatory pathways. Modeling and informatics studies will certainly facilitate the transfer of information from model species and selection of engineering targets in bioenergy crops (e. g., Ruprecht and Persson 2012).

On the bioprocessing side, the baseline 65% of theoretical ethanol yield is typically achieved with harsh, i. e., expensive, dilute acid pretreatment (0.5% at 180°C for 8 min) and cellulase loading of approximately 15 active units/g biomass (Fu et al. 2011). More efficient catalysts that can function with milder pretreatments at lower concentrations would also facilitate attainment of near maximal yields. Indeed, by combining optimized feedstocks with improved enzymes and bioprocessing methods, we may have already achieved complete and efficient saccharification. Again, the bottleneck seems to be in translating current progress to the industrial scale. As for work with plants, translation to industrial-scale microbiology requires substantial additional understanding and process tuning. Effective scale-up will be facilitated by continued accumulation of additional options in terms of enzymes, strains, and organisms, and understanding at a both detailed biochemical and systems-wide levels. Platforms that reduce the capital requirements of biofuel production will be especially helpful for establishing a second-generation biofuel industry.

Acknowledgements

Thanks to Dr. M. Peck and K. Zhao for helpful comments on the manuscript. This work was supported by the National Science Foundation EPSCoR program under Grant No. EPS-0814361. Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation.