The composition of cellulolytic enzymes produced by T. reesei Rut C-30 is influenced by changing pH (Juhasz et al. 2004). An increase in p-glucosidase activity has been reported at pH 6 in comparison with a lower pH range of 4-5 (Tangnu et al. 1981; Nagieb et al. 1985). The purpose of supplied chemicals for the pH control is not limited to providing a stable environment for enzyme production but also to fulfill the requirement for the enzyme synthesis. A citric acid and aqueous ammonium hydroxide solution is used for pH control. In addition, citric acid helps by inducing cellulase production while aqueous ammonium hydroxide provides a nitrogen source for enzyme synthesis (Watson and Nelligan 1983; Kadam and Keutzer 1995).

Temperature affects the growth rate of T. reesei and xylanase production. Xylanase production was studied within the temperature range of 17°C to 37°C, and increased as temperatures approached 37°C. However, cellulase production decreases with increasing temperatures within this range. Therefore, a temperature shift could be a useful tool in the selectivity of cellulolytic enzyme production (Xiong 2004).

A summary of estimated costs for supplying a biorefinery with a flow of switchgrass biomass feedstock is provided in Table 11. For the average net yield of 10 Mg/ha, the estimated cost to deliver a flow of switchgrass is $60/Mg (Table 11). This cost is less than the conventional budget estimate reported in Table 5 of $79/Mg based on a yield of 8.97 Mg/ha. If the budgeted yield estimate in Table 5 is set equal to 10 Mg/ha, the budget cost estimate would decline to $75/Mg. The cost estimates between the two methods for establishment, land rental, and fertilizer are very similar. However, the cost estimates differ for harvest and transportation.

Based on the conventional budget, the harvest cost estimates, assuming a yield of 10 Mg/ha would be $32/Mg. This cost is twice as much as the $16/ Mg estimate obtained from the programming model. This cost difference reflects the potential economies that could result from a coordinated harvest system and a nine-month harvest window. The estimated transportation costs are $10.40/Mg for the budget and $16.06/Mg for the model. These costs differ because the average field to biorefinery distance is assumed to be 48 km in the budget, but is estimated to be 76.55 km by the model. The

average transportation distance is sensitive to the size of the biorefinery and biomass requirements and to the imposed restriction to limit switchgrass to no more than 10 percent of a county’s cropland and improved pasture land.

If the price of fuel used to harvest and transport biomass doubled from the base level of $0.79/L to $1.58/L, the cost to deliver switchgrass increases to $73/ Mg (Table 11). The land lease rates are doubled and the model solved to determine sensitivity to land rental rates. The estimated cost to deliver biomass increases by $12/Mg from $60/Mg (base scenario) to $72/Mg when the land rental rate is doubled.

Genetics and Origin of Upland and Lowland Ecotypes

Before one can begin to make appropriate decisions about cultivar selection, it is import to know something of switchgrass ecotypes and their origins. Switchgrass is characterized by a wide degree of genetic diversity, which conveys broad adaptation. The plant’s native range extends from Canada to Mexico and from the Atlantic coast to the Sierra Nevada mountain ranges. In addition to this wide geographic adaptation, switchgrass also divides out into two groups—uplands and lowlands—that describe the species’ typical adaptation relative to position on the landscape.

Essentially all switchgrass is categorized as either upland or lowland "ecotypes", although some indistinct plant types may represent hybrids of these two forms (Zhang et al. 2011a). These two ecotypes may be able to interbreed and produce fertile offspring, and both can be found in switchgrass populations within a habitat (Hultquist et al. 1997). However, the differences between ecotypes promote fitness for survival in unique environments. Upland and lowland ecotypes have also been described as "cytotypes", which reflects small but distinct differences in their chloroplastic DNA sequences (Hultquist et al. 1996).

In terms of ploidy, lowland ecotypes have been thought to be tetraploid (2n = 4x = 36), although recent discoveries suggest there may be octoploid lowlands (Zhang et al. 2011a, b). Upland ecotypes can contain either tetraploid and octoploid genotypes (2n = 8x = 72) (Zalapa et al. 2011). Although there is significant overlap in their regional distribution, lowlands generally predominate in the southern USA while uplands generally originate from the drier, colder northern Great Plains.

McMillan (1959) postulated that all current switchgrasses likely hailed from three regions of North America—safe havens, really—that provided refuge from the glacial conditions of the Ice Ages. More recent research (e. g., Cortese et al. 2010; Zhang et al. 2011a, b) provides evidence of the geographic origins of switchgrass ecotypes. Lowland ecotypes appear to have their origins in the Eastern Gulf Coast and Southern Great Plains while uplands hail geographically from the Central and Northern Great Plains and the Eastern Savannah regions (Zalapa et al. 2011; Zhang et al. 2011a). While such consideration may seem to be an academic exercise, they have very real, practical implications for cultivar selection in the context of bioenergy production systems, which we discuss in the section of the same name below.

The first interaction of soil bacteria with the plant occurs at the rhizoplane, and a sufficient titer of robust bacteria are required in the soil region in close proximity to the root surface (rhizosphere). An experiment demonstrated that sustained, high rhizosphere soil populations of the endophyte Bacillus subtilis GY-IVI were required for efficient endophytic colonization of the root (Zhao et al. 2011). It has also been suggested that these robust and high titer levels of the colonizing bacteria in the soils help bacterial endophyte competition, indicating that these bacteria are highly competent at rhizosphere/rhizoplane colonization (Whipps 2001; Compant et al. 2005a).

A variety of bacterial traits are known to be required for rhizosphere and/ or rhizoplane colonization competence (Compant et al. 2010). Numerous studies have shown that bacterial colonization of the rhizoplane occurs initially with localization across various regions of the root, including root tips, sites of lateral root emergence, and root hair zones (Compant et al. 2008; Prieto and Mercado-Blanco 2008; Zhang et al. 2010). During rhizoplane colonization, single cells have been observed, leading to the development of colonies along the root surface, and to the establishment of biofilms (Hansen et al. 1997; Benizri et al. 2001). However, rhizoplane colonization does not occur uniformly (Compant et al. 2010). For example, Pseudomonas fluorescens PICF7 predominantly colonized the root differentiation zone (Prieto and Mercado-Blanco 2008), and the more mature parts of the root exhibited little colonization by Pantoea agglomerans YS19 (Zhang et al.

2010) . This variation on rhizoplane colonization distribution may be due to differences in root exudate production (Lugtenberg and Dekkers 1999), the protective microenvironment of different regions of the root (Prieto and Mercado-Blanco 2008), and/or the presence of specific or preferential cell surface binding sites for the bacteria (Miao et al. 2008). Regardless of the sites of rhizoplane colonization, it has been reported that the population densities of bacteria in the soil are approximately 2 orders of magnitude higher (107-109 CFU per g of rhizosphere soil) than are found on the root surface (Benizri et al. 2001; Bais et al. 2006).

One promising strategy for improving switchgrass biomass quality, and possibly yield, is to utilize knowledge about the synthesis of cell walls. The last decade has seen an enormous increase in our understanding of the enzymes and regulators of cell wall synthesis. Most of this work has been in the reference dicotyledenous plant and forward and reverse genetics workhorse, Arabidopsis thaliana. However, as described above, the differences between grass and dicot cell walls limit some of the transferability of work between these clades. Indeed, a recent analysis of expression of cell wall synthesis genes in ovary tissues with primary walls revealed a wide divergence between Arabidopsis expression patterns and those of rice and maize (Penning et al. 2009). Fortunately, there has also been a steady increase in the study of cell wall synthesis and regulation in grass species, with work in rice, maize, and the diminutive wheat

relative, Brachypodium distachyon (BGI 2010), paving the way for more facile improvement of bioenergy grasses, such as switchgrass. The genomic colinearity of grasses (Devos 2005), as well as conventional phylogenetic analysis, facilitate transfer of information among grasses with sequenced genomes. Importantly, the effort to reconstruct the switchgrass genome sequence is underway, with the first draft assembly (v0.0) released in early 2012 (http://www. phytozome. org/panicumvirgatum. php) (Casler et al.

2011) . As will be described below, researchers have already characterized in switchgrass a number of lignin biosynthesis genes and a secondary cell wall transcriptional regulator.

How can functional information about cell wall synthesis and regulation be utilized? On a basic level, many mutants in cell wall related genes are in themselves easier to digest. Though many single gene cell wall mutants have reduced stature or disease resistance, for others, at least when grown in greenhouses, vegetative development is apparently unaffected or increased, as is resistance to specific pathogens. Geneticists have already used information about cell wall synthesis to design molecular markers that correlate with altered cell wall content (Truntzler et al. 2010; Wegrzyn et al. 2010). Furthermore, examination of cell wall synthesis variants with increased or reduced amounts of specific polymers permits dissection of the function of various cell wall constituents. From this information in the longer term, researchers may be able to engineer grass cell walls, with widely divergent, but still functional, cell walls that are optimized for conversion into biofuels via specific conversion technologies. In this section, we will discuss the current understanding of the synthesis of the major components of the switchgrass cell wall, namely, cellulose, xylan, and lignin. Table 1 and Table 2 list genes that function in the synthesis of cellulose and xylan, respectively, in Arabidopsis and grass species.

**All rice loci are abbreviated from their full identifier by removing the LOC_ that is part of the normal MSU annotation.

The construction of highly saturated maps is often a time-consuming process, especially if investigators are employing different parental stocks and markers are not easily transferable. Merging maps are attractive since their integration allows for an increase in marker density without the need of additional genotyping, increased marker portability (i. e., polymorphic markers can be used in more than one population), and improved marker alignment precision (i. e., congruent anchor maker position). Using JoinMap, a number of integrated linkage maps have been developed in numerous economically important crop plants including wheat (Triticum aestivum

aF= female; M=male; b RFLP=restriction fragment length polymorphism; SSR=simple sequence repeat; STS= sequence tagged site; c Segregation distortion, percentage of markers showed segregation distortion.

L. Somers et al. 2004), maize (Zea mays L., Falque et al. 2005), red clover (Trifolium pratense L., Isobe et al. 2009), and ryegrass (Lolium ssp. Studer et al.

2010) . So far no similar studies have been reported in switchgrass probably due to limited linkage maps available.

MiRNA-mediated gene repression is a conserved mechanism in plants and animals. Although most of the genes in the genome are not the targets of miRNAs, artificial miRNA (amiRNA) can be designed to repress expression of target genes by replacing new miRNA duplex from a natural miRNA precursor (Schwab et al. 2006; Ossowski et al. 2008; Khraiwesh et al. 2008; Molnar et al. 2008). The amiRNA technology is currently becoming a powerful tool for gene silencing and has been used successfully in many plant species, including Arabidopsis thaliana (Schwab et al. 2006; Ossowski et al. 2008), rice (Warthmann et al. 2008), moss (Physcomitrella patens) (Khraiwesh et al. 2008), and alga (Chlamydomonas reinhardtii) (Molnar et al.

2008) . There are several examples where down-regulating gene expression level or loss of gene function results in improved traits in switchgrass (Fu et al. 2011; Xu et al. 2011). AmiRNAs as a highly specific approach for effective post-transcriptional gene silencing (PTGS) provides a new molecular tool in switchgrass genetic improvement and can significantly contribute to cost-effective and environmentally friendly production and utilization of renewable bioenergy.

Bio-oil contains 30-40% oxygen (shown in the Table 5), which is similar to oxygen content in the original biomass that bio-oil is derived from. In comparison, petroleum crude oil and heavy fuel oil contain less than 1% oxygen. The oxygen in bio-oil is distributed in the form of many functional groups. High oxygen contents results in many of its undesirable properties such as low energy content and high acidity. Removal of oxygen from the bio-oil is one of the biggest barriers of using bio-oil especially for the production of drop-in hydrocarbon fuels.

High Acidity

The pH of bio-oil is 2.5 (shown in the Table 5), which makes it very acidic and highly corrosive. The acidity of bio-oil is due to presence of carboxylic acids such as acidic acid and formic acid (Zhang et al. 2007).

Kathrine D. Behrman, h* Manyowa N. Meki,[19] [20] Yanqi Wu[21]
and James R. Kiniry,’a

Introduction

Switchgrass (Panicum virgatum L.) is a highly productive, warm season, perennial, C4 grass that is native to most of the central and eastern U. S. (Sanderson et al. 1996). It has a high leaf area index (LAI) and rooting depths of more than 2.0 m, which provide access to large amounts of soil moisture and nutrients (Kiniry et al. 1999). Switchgrass is tolerant of poorly and well-drained soils, nutrient-depleted lands, and low pH, thus allowing it to produce reasonable biomass yields on marginal agricultural soils and under drought stress conditions (Moser and Vogel 1995; McLaughlin et al. 2006; Blanco-Canqui 2010). In addition, switchgrass requires fewer chemical

inputs (fertilizers and pesticides) than traditional row crops and miscanthus (Miscanthus x giganteus J. M. Greef & Deuter ex Hodk. & Renvoize) while maintaining relatively high yearly biomass yields (McLaughlin et al. 2006). These qualities make it one of the leading potential biofuel crops for the Southern and Northern Great Plains (Perlack et al. 2005).

The main objective of this chapter is to highlight five applications of process-oriented models of switchgrass growth and show how they can be used to generate a better understanding of large-scale switchgrass biomass production. Model differences are presented to give the reader an idea of the underlying assumptions and an understanding of why there are differences in model output. However, we are not trying to compare all the differences in model assumptions and functionality. Instead, see Surendran Nair et al.

(2012) for a comprehensive review of the differences between the models developed to estimate bioenergy crop production.

This chapter begins by describing the biology of switchgrass as a biofuel crop and introducing several types of crop models. Next, we show how process-oriented crop models have been used to estimate switchgrass biomass production and assess water use efficiency (WUE) of major switchgrass ecotypes in the U. S. Third, we highlight how mechanistic models can be used to determine the impact of different management scenarios on short-term yield production. Fourth, we show how models can be used to determine the long-term effects of biomass production on soil organic carbon (SOC), soil nutrients, erosion, and water quality. Lastly, we highlight studies that have analyzed the potential impacts of climate change on sustainable biomass production.

Improvements in other aspects of the biology of switchgrass to enable it to grow effectively in more places, including current marginal lands, while utilizing even fewer inputs, will be important to meet lignocellulosic biofuel mandates. In many of these cases, the likely candidate traits for improvement are already noteworthy in switchgrass, many of them are correlated with invasiveness (Raghu et al. 2006); attaining improvements while minimizing invasion risk will prove challenging, but should be addressed. In addition, most trait improvements would carry environmental repercussions that have historically been difficult to convert to monetary value (Chamberlain
and Miller 2012). This includes improvements in carbon sequestration and N loss minimization (see Garten 2012), and water-use efficiency, all of which may be critical in "climate proofing" switchgrass for future conditions. How improvement in these traits will affect landscape-scale water quantity and quality, and associated sediment and nutrient runoff (e. g., Wu et al. 2012) is still empirically unknown.

The widespread planting of agronomic fields in monocultures of switchgrass for bioenergy will increase the susceptibility of switchgrass to pests and diseases. While switchgrass’ candidacy as a bioenergy crop is due in part to the absence of historical mention of pest and disease problems (Wright and Turhollow 2010), it is not free from such pressures. In fact, insect, fungal, and viral pests have been documented to have negative effects on switchgrass growth and production (Crouch et al. 2009; Prasifka et al. 2010; Schrotenboer et al. 2011; Burd et al. 2012), and some of the pests may well have negative effects on neighboring row crops (Burd et al. 2012). How to effectively and sustainably control these pests will prove imperative (Thomson and Hoffmann 2011).