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).

Chemicals

Ajay Kumar[15]‘* and Raymond Huhnke[16]

Introduction

There is a critical need to supplement fuels, chemicals and direct power provided by petroleum resources that have been increasingly dependent upon for over a century. There are many approaches to convert biomass renewable resources into fuels, chemicals and power. These approaches can be divided into two primary conversion categories: thermochemical and biochemical. Thermochemical approaches use heat and catalysts to achieve the conversion of biomass; whereas biochemical conversion uses microorganisms and biological catalysts. Advantages in using thermochemical techniques are that all combustible portions of biomass, including lignin, are utilized, products such as producer gas are more compatible with petroleum infrastructure, and the conversion process

is much faster. The major disadvantages are that it is high temperature process, and catalysts for syngas conversion and conditioning and bio-oil upgrading are not yet economical or efficient. The objective of this chapter is to provide an overview of major thermochemical conversion processes for conversion of biomass into fuels, chemicals and power.

Most biofuel processes currently under development require pretreatment followed by hydrolysis to produce monomeric sugars. The goal of any pretreatment is to separate the polysaccharide matrix of cellulose and hemicellulose from lignin and to loosen the structure enabling sites for chemical or enzymatic catalysis. The polysaccharide matrix may be hydrolyzed using chemical routes (such as acid hydrolysis) or by biological enzymatic routes (Huber et al. 2006). The sugars derived from hemicellulose and cellulose are fermented to produce a wide range of biofuels such as ethanol, hydrogen, biodiesel via lipid biosynthesis and butanol (Chandrakant and Bisaria 1998; Kim et al. 2008; Alvira et al. 2010; Panagiotopoulos et al. 2010; Wu et al. 2011a, b).

Many possible methods of chemical pretreatment have been reported, including steam explosion, dilute acid hydrolysis, concentrated acid hydrolysis, supercritical CO2 explosion and extraction, alkaline pretreatment (sodium hydroxide, potassium hydroxide, lime), ionic liquids, soaking in aqueous ammonia (SAA), ammonia recycles percolation (ARP) and ammonia fiber explosion (AFEX) pretreatment (Alizadeh et al. 2005; Mosier et al. 2005; Huber et al. 2006; Kim et al. 2006, 2007, 2008; Isci et al. 2009; Singh et al. 2009; Alvira et al. 2010; Panagiotopoulos et al. 2010; Wu et al. 2011a, b). In many of these pretreatments, the formation of inhibitory compounds at high temperatures (such as furfural and 5-hydroxymethylfurfural (HMF)) are one of the main constraints (Mosier et al. 2005).

Lignocellulosic biomass is highly recalcitrant to fermentation due to natural resistance mechanisms. Moreover, woody biomass such as pinewood has an even greater microbial recalcitrance than herbaceous biomass due to a tightly bound structure with high lignin content (Galbe and Zacchi

2002) . To enhance the release of polysaccharides from the lignocellulosic biomass, upstream processing (including size reduction and pretreatment) is a necessary step in biofuel production. For the production process to be economically feasible, total energy consumption in the size reduction and the pretreatment steps should be minimized as much as possible (Zhu et al. 2010).

Physical pretreatment involves size reduction to increase the available surface area and enhance enzyme hydrolysis of plant polysaccharides. Chemical and biological pretreatment methods are designed to liberate the convertible polysaccharide from the protective lignin casing, as well as to reduce the crystallinity of the cellulose so as to make the polysaccharides available to the hydrolyzing microorganisms (Hendriks and Zeeman 2009; Harmsen et al. 2010; Alvira et al. 2010). Selecting a pretreatment process is dependent on the particle size, moisture content and lignin content of the lignocellulosic biomass.

As users decide on which model is appropriate for their purposes, they will need to consider the level confidence in the inputs required and the appropriate level of model complexity to accomplish their goals. Process — based simulation models are split into those comprised of: 1) detailed leaf photosynthesis components that are integrated up to the leaf canopy level or 2) canopy level models that contain the relationship between plant functions and leaf area index (LAI), light interception, and radiation use efficiency (RUE). There are some concerns with both approaches. Leaf level photosynthetic rates are often not directly related to productivity, as described with RUE or above-ground net primary productivity (ANPP). A good example is a study by Kiniry et al. (1999) reported that sideoats

grama (Bouteloua curtipendula (Michx.) Torr.) had higher photosynthetic rates throughout the range of light levels than Alamo switchgrass, but sideoats gramma is far less productive than switchgrass. Similarly, Aspinwall et al. (2013) looked at several switchgrass ecotypes and concluded that "leaf — level physiological traits are often uncorrelated with genotype ANPP due to confounding of development with physiology, covariation among leaf traits, feedbacks with sink capacity, and increased self-shading". However, they identified "a syndrome of leaf functional traits" which aligned with genotype ANPP revealing that more productive genotypes initiated growth earlier and flowered later.

On the other hand, parameters for whole canopy models such as ALMANAC, EPIC, and SWAT are derived by measuring leaf area and dry matter destructively during the active growing period and fraction of light interception of plants assumed to be grown under nonlimiting water and nutrients conditions. Ideally such models use plant parameters derived at one site, with adequate soil moisture and soil nutrients, which are then applied for simulations under a wide range of environmental conditions. However, problems can arise when applying the model parameters, outside the regions of adaptation of a particular switchgrass ecotype. Latitudinal differences include photoperiod, number of hot days during the growing season, and number of cold days during the winter. Realistic simulation of processes controlling location differences, especially with differences in latitude, requires realistic understanding of the factors affecting such adaptation. These adaptation processes still need to be identified and quantified to more accurately simulate switchgrass ecotypes across a wide range of locations.

Another concern when simulating switchgrass with these process-based plant models is that many of these models were developed for annual crops. The perennial growth process is quite different from that of annuals, and more work is needed to accurately incorporate these differences in models, such as rooting during the establishment year as compared to subsequent years. Especially important for switchgrass modeling is N, P, and carbohydrate storage in roots in autumn and translocation out to above­ground plant parts in the spring. In addition, there has not been sufficient research regarding the possible differences in base temperature, optimum temperature, and root:shoot partitioning for different ecotypes. These will be key to simulating greenup in the spring and growth and development in the hottest part of the growing season.

Production of bioenergy requires the protection of soil and water associated with emerging bioenergy landscapes (Graham et al. 1996). The widespread degradation of the soil resource base and water quality due to past and current agricultural practices is well documented (USEPA 2009). The limited set of sustainability criteria attached to the 2007 Renewable Fuel Standard, which include stipulations about what types of land feedstocks are grown on, and the GHG intensity of biofuel production, were a promising start but may need to be expanded to include additional sustainability dimensions such as soil and water quality.

These mechanistic models will be useful for comparing switchgrass production systems to more conventional agricultural crops. Meki et al. (2011) applied a version of the EPIC model, APEX, to assess the sustainability of corn stover removal from the Upper Mississippi River Basin, based on a set of ‘acceptable planning criteria’ used in the CEAP analysis (USDA-NRCS

2010) , to judge whether or not a farm field needed additional conservation treatment. The ‘acceptable criteria’ included; (a) N in surface runoff < 16.8 kg ha1 y-1 (15.0 lb ac-1 yr-1), (b) N in sub-surface runoff < 28.0 kg ha1 y-1 (25.0 lb ac-1 yr-1), (c) total P losses < 4.5 kg ha-1 y-1 (4.0 lb ac-1 yr-1), (d) Sediment loss < 4.5 Mg ha-1 y-1 (2.0 ton ac-1 yr-1), and (e) SOC with a more ‘stringent’ restriction that the annual rate of change be positive. Given the critical functions of SOC in maintaining soil quality and productivity, biomass removal can only be justified if it does not deplete the SOC pool. These ‘acceptable’ levels represent field-level losses that are feasible to attain using traditional conservation treatment (nutrient management and soil erosion control), are agronomically feasible, and can equally be adapted to switchgrass production systems. Scientific literature on field research and edge-of-field monitoring in the U. S. Midwest, coupled with model simulations of conservation practices effects, provided guidance for identifying these thresholds (USDA-NRCS 2010).

Conclusions

Switchgrass is the "model" bioenergy crop for a potential bioenergy industry throughout the southeastern and south central USA (Wright and Turhollow

2010) . Given the infancy and urgency of the fast-evolving bioenergy industry, crop simulation models can complement and extend the applicability of information collected in field research trials, and when combined with the appropriate climate, soil, crop, and management databases, can be applied effectively to assess the sustainability and long-term impacts of converting land to bioenergy crops in a timely and cost-effective manner.

Acknowledgements

We thank Philip Fay, Daren Harmel, and Lara Reichman for comments on the manuscript. USDA is an equal opportunity provider and employer.

Difficulty in effective removal of syngas tars continues to be one of the biggest barriers to commercialization of gasification-based technologies for power, fuels and chemicals production. Tar is a mixture of condensable organic compound resulted from thermal degradation of biomass and is composed of mostly oxygenated aromatic hydrocarbons (Abu El-Rub, Bramer and Brem 2004). Benzene is generally not considered a tar compound because it is in gaseous form at temperature above 100°C and it does not create clogging problem. Syngas tar content generated from biomass gasification varies from 1 to 100 g/m3 depending on the type of gasifier, biomass properties and gasification conditions (Milne, Evans, and Abatzoglou 1998). Removal of tar from syngas is accomplished through either cracking the tar with high temperature (>600°C) in presence of catalysts (hot gas cleaning) or condensing the tar with solvents such as water, alcohols and oil in a scrubbing unit (cold gas cleaning). Cracking tar results in CO, H2 and other light gases leading to improved syngas composition. However, use of high temperature and catalysts increases the operational cost. Similarly, scrubbing tar with solvents also results in contaminated solvents which need to be treated for recycling. Cost effective and environmental friendly gas technologies are needed for effective removal of syngas tar.

Other contaminants in the syngas include NH3 and H2S. NH3 and H2S are especially problematic if the syngas is to be used for catalytic conversion into fuels and chemicals. Levels of contaminants that can be tolerated by the downstream applications depend on the specific application. For conversion of syngas into fuels and chemicals such as Fischer-Tropsch (FT) hydrocarbon, methanol, and ammonia, the level of sulfur-based contaminants must be below 1 ppm to prevent poisoning of catalysts. NH3 for FT process is acceptable up to 10 ppm Physical and chemical scrubbing systems are commercially available to remove sulfur and nitrogen-based contaminants (Spath and Dayton 2003).

Filamentous growth morphology of T. reesei results in a viscous broth rheology that affects oxygen mass transfer rate and changes the broth from a Newtonian mixture to a non-Newtonian mixture over periods of cellulolytic enzyme production. With increasing viscosity, power input requirements increase to achieve the same level of mixing. The increase in the viscosity increases the bubble size and hence reduces the bubble residence time in the fermenter and decreases the mass transfer coefficient. Enzyme and extracellular protein levels were significantly affected at lower (0.5 vvm) and higher (1.5 vvm) oxygen saturation levels and at lower (130 rpm) and higher (400 rpm) agitation levels (Schaffner and Toledo 1992). The change in morphology of T. reesei affects xylanase production at lower aeration (below 10% oxygen saturation level). In addition, xylanase production is sensitive to shear stress at power agitation (Weber and Agblevor 2005).

The purpose of this chapter is to identify practical issues related to the economics of developing switchgrass as a dedicated energy crop and to provide estimates of the price for delivered switchgrass biomass that would be required to compensate for the cost of inputs used to produce and deliver it to a biorefinery. As noted in the introduction, the potential for switchgrass biomass depends on (a) its production cost relative to alternative sources of feedstock and (b) a system to convert lignocellulosic biomass into economically competitive products.

The estimated breakeven price for switchgrass biomass delivered to a biorefinery ranges from $60 to $120/Mg. For the base estimates obtained from the programming model, 27 percent of the delivered cost of $60/Mg is for transportation from the field to the biorefinery; 26 percent is for harvest (windrowing, raking, baling, stacking) costs; 20 percent is for land rental; 14 percent is for fertilizer; and 13 percent is for establishment. Increasing yield could reduce most but not all the costs on a per unit basis. As modeled, harvest costs per Mg are found to be very similar across a wide range of yields per hectare. Given the rather substantial cost economies associated with harvest machines, and given that a biorefinery is expected to require a continuous flow of feedstock, if switchgrass is established on millions of hectares, a highly coordinated harvest system would be more economical than a haphazard system.

Switchgrass harvest would extend over as many months as permitted by feedstock quality requirements, weather, and policy. Given the quantity of biomass required, and the lack of an existing infrastructure to harvest a continuous flow of massive quantities of biomass, a harvest system would likely develop that exploits the economies of size associated with harvest machines. Whether or not independent companies develop, such as those that exist for grain harvest in the Great Plains, remains to be seen. Alternatively, harvest crews and harvest machines could be managed as wholly owned subsidiaries of biorefineries.

Rational land owners would not enter into switchgrass biomass feedstock production until a market is available. A rational investor would not invest in a biorefinery that did not have a reasonable plan for obtaining a flow of feedstock. One alternative would be for the biorefinery to engage in long-term leases with land owners to acquire the rights to a sufficient quantity of land to produce feedstock to meet its needs.

The U. S. Energy Independence and Security Act of 2007 mandated the production of 61 billion liters of cellulosic biofuels by 2022. But, no commercial sized facilities were operating in 2011. Hence, it seems reasonable to conclude the development of a commercially viable system for production of liquid biofuels has not progressed as rapidly as anticipated. Desirable feedstock properties, the biomass to biofuel conversion rate, and the investment required in plant and equipment differ depending on which one of several competing technologies is used. Determination of the most efficient system will require a holistic field-to-bioproducts model that simultaneously considers land procurement, feedstock production, harvest, storage, transportation, processing, and the value of the final products. Modeling each of the competing conversion systems using a "field to fuel" approach could provide useful information to compare the expected economics of each system and identify unique bottlenecks.

A number of additional issues remain. A system to manage the risk associated with switchgrass yield variability and the risk of fire of standing and stored switchgrass will be required. Knowing how a biorefinery would respond to short crops is not clear. In years of above average yields, not all land would have to be harvested. However, in years of below average yields, the biorefinery may not have sufficient feedstock to operate throughout the year.

If an economically competitive biorefinery technology is developed, entrepreneurs confident of their technology with an enforced government mandate that their produced biofuels be purchased, could contract and convert land from current use to the production of switchgrass or some other dedicated energy crop, in a relatively short period of time. Ambiguities as to what determines feedstock quality and how to provide a flow of feedstock throughout the year are likely to be resolved much more quickly if the annual payment to the land owner is set. Leased land would enable the company to manage a portfolio of switchgrass stands, or a portfolio of energy crops, feedstock quality, and harvest, to optimize the field-to-fuel process. Unwillingness of biorefinery entrepreneurs to engage in long-term lease contracts could be interpreted as a signal that they are unsure of the economics of their conversion technology. The ultimate challenge is to discover, develop, design, and demonstrate an economically competitive biorefinery technology necessary for a profitable business model.

Acknowledgements

Research findings reported in this chapter were produced by projects supported by the USDA NIFA Biomass Research and Development project number 0220352; by USDA NIFA Hatch grant number H-2824; by the Oklahoma Agricultural Experiment Station; by the Jean & Patsy Neustadt Chair; by the Samuel Roberts Noble Foundation; and by a USDA National Needs Graduate Fellowship Competitive Grant no. 2008-38420-04777 from the National Institute of Food and Agriculture. Support does not constitute an endorsement of the views expressed in this paper by the USDA or by the Samuel Roberts Noble Foundation.

Removal of oxygen from bio-oil through the addition of hydrogen is called hydrodeoxygenation (or hydrotreating and hydrogenation). Oxygen is removed in the form of water (H2O). This process takes place at temperatures of 300-400°C and pressures of 80-300 bars. The catalysts used for the dehydrogenation are Co-Mo, Ni-Mo, and their sulfides or oxides or loaded on Al2O3 (Zhang et al. 2006, 2007). This process results in naphtha­like product, which can be used in traditional petroleum refining process. The main advantage of hydrodeoxygenation is that removal of oxygen in the form of H2O retains biomass carbon for formation of hydrocarbon and improves energy content of the product. However, since hydrogen is expensive, high consumption of H2 makes this process economically less attractive. High carbon content (C/H ratio) in bio-oil and resulting products also lead to severe coking of the catalysts. Overall, hydrodeoxygenation reaction can be represented in the following equation with respect to carbon of the bio-oil (Mortensen et al. 2011).

CH14O056 + 0.7 H2^1 CH2 + 0.4 H2O (1)

Where CH1.4O0.56 and CH2 represent bio-oil and hydrocarbon product, respectively.

Switchgrass has substantial variation in many phenotypic and phenological traits that allow it to be adapted to a large portion of the U. S. (Casler et al. 2004; Casler et al. 2007). There are two distinct groups of switchgrass ecotypes: the lowland ecotypes, consisting of solely octaploids, and the upland ecotypes, consisting of both octaploids and tetraploids (Hultquist et al. 1996). Lowland ecotypes are generally adapted to more southern latitudes and upland ecotypes adapted to more northern latitudes (Casler et al. 2004). These ecotypes are also phenotypically distinct with lowland ecotypes often thriving in warmer climates, being taller, and having later anthesis occurrence resulting in longer growth periods (Cornelius and Johnston 1941; McMillan 1959). Whereas, upland ecotypes are generally more capable of surviving harsh late winter freezes (Vogel 2005). Within these two groups of ecotypes there are two subtypes, northern and southern, based on latitude of origin within each region (Casler et al. 2004).

Understanding the adaptation of switchgrass to variable growing conditions is key to determining its potential as a biofuel crop. The growth and development of switchgrass varies with climatic (i. e., temperature and precipitation) and environmental conditions (i. e., soil type, slope, nutrients) (Casler et al. 2004; Casler et al. 2007; Wullschleger et al. 2010). Field trials at relatively small scales (< 5 m2) and large scales (3-9 ha) reveal high biomass production, greater than 12 Mg ha-1 (Schmer et al. 2008; Wullschleger et al. 2010). Northern upland ecotypes flower earlier than southern upland ecotypes, causing reduced biomass production when moved south (Casler et al. 2004). On the other hand, southern lowland ecotypes flower later, which extends the growing season, resulting in increased biomass production when moved northward. However, survival of the lowland type is reduced in the North, which is thought to be due to cold winter temperatures (Casler and Boe 2003). The regional adaptation of switchgrass is complex but thought to be primarily reliant on genetic diversity for heat resistance, cold resistance, photoperiod, and drought tolerance (Casler et al. 2007). There is substantial genetic variation for many key adaptive traits, which may make it possible to enhance biomass production and decrease mortality.

The phenotypic diversity of switchgrass also makes the management practices that optimize yearly biomass yields vary by ecotype and location (Fike et al. 2006). Field trials have focused on identifying management practices (i. e., planting date, fertilizer application, irrigation, seeding rate, harvesting) required for establishment and optimum biomass production (Parrish and Fike 2005). As biofuel production expands globally, it is critical to also understand the environmental consequences of cultivating biofuel crops (Renewable Fuels Agency 2008). Large-scale production of biofuel crops, such as, switchgrass may bring both positive and negative environmental impacts. Some environmental benefits over traditional row crops include reduced water and wind erosion (Paine et al. 1996; McLaughlin and Walsh 1998; Jensen et al. 2007; Blanco-Canqui 2010). Additionally, decreased water runoff reduces the loss of agricultural chemicals, nutrients, and sediment into nearby waters, enhances nutrient cycling and storage, and recharges groundwater supply (Paine et al. 1996; Jensen et al. 2007; Blanco-Canqui 2010). Soils under long-term switchgrass production can also improve over time with increased soil organic matter production and the sequestration of SOC (Paine et al. 1996; McLaughlin and Walsh 1998; Tilman et al. 2006; Jensen et al. 2007; Blanco-Canqui 2010).

The environmental impact of bioenergy feedstock production systems will need to be closely monitored to ensure sustainability. Field scale monitoring is however limited in scope, temporally and spatially, and in most cases fails to fully account for the effects of site-specific conditions of management, land type, soil texture-hydrologic group interactions, slope, and climate. Therefore, many open-ended questions remain about the large — scale production potential of switchgrass and its long-term environmental impacts. For example: (1) Can switchgrass produce enough biomass to support local refineries and what regions of the U. S. will be used for large-scale feedstock production? (2) What are the optimal management practices for all potential field locations? (3) What is the long-term (i. e., 25 or 50 years) effect on soil quality and erosion? (4) How big of an impact will climate change have on switchgrass biomass production? Will lands that currently produce high levels of biomass be able to sustain these levels in the future?