Over the past few years, the Brazilian government has invested on research, development and innovation regarding cellulosic ethanol production, with the creation of new research institutions, such the Brazilian Bioethanol Science and Technology Laboratory (CTBE) — part of the Brazilian Center of Research in Energy and Materials (CNPEM) — and Embrapa Agroenergy. National and state research funding agencies have been providing support for cellulosic ethanol research through programs such as the FAPESP Bioenergy Research Program (Bioen). There has also been substantial financing by BNDES (Brazilian Bank for Economic and Social Development) for technological innovation, creation of infras­tructure (e. g., ethanol pipelines) and financing of new production units in the sugarcane expansion areas, as well as other initiatives such as the PAISS program to foster develop­ment, production and marketing projects of new industrial sugarcane biomass processing technologies.

The rising domestic and external demand for ethanol will lead to increased production of feedstocks in Brazil. Cellulosic ethanol production, especially from crop residues, can supply part of the increased demand, reducing the need for land use change, which can potentially increase greenhouse gas emissions. The trend towards phasing out biomass burn­ing in Brazil, driven by municipal, state and federal legislation, as well as from sugarcane sector stakeholder arrangements will increase the availability of crop residues as feedstock for cellulosic ethanol production. In the industrial phase, improvements in boiler efficiency can make more bagasse available for 2G ethanol production. There are still challenges regarding conversion technologies, but cellulosic ethanol has the potential to be technically and economically feasible in Brazil, especially if integrated with first generation ethanol production from sugarcane.

In a “real-world” setting, it will be very difficult to achieve JIT delivery of any raw material for 24/7 operation. All multibale handling unit concepts, must include some at-plant storage. Even when known quantities of feedstock are stored in a network of SSLs, and a Feedstock Manager is controlling the deliveries, there will always be random delays.

To give a frame of reference, an initial decision was made to include 2.5 days of at-plant storage in this example. This minimum may work in the Southeast United States where ice and snow on the roads is not typically a significant problem for winter operations. In the U. S. Midwest, more days of at-plant storage will be required.

A visualization of at-plant storage for the rack system concept is shown in Figure 13.12. The number of racks shown is not part of the “cost analysis” example given later.

The bale remains in the rack until processed — there is no individual bale handling at the plant. This is a very important aspect of any multibale handling system. This reduction in bale handling not only reduces cost, but also reduces damage to the bales. The integrity of the bales must be maintained. The reader should visualize a multibale lift of large rectangular bales when the strings on one of the bales “pop” and the whole lift comes tumbling down. The same problem exists when the wrap on a round bale fails.

Facility managers typically take a large number of factors into consideration to build an optimal procurement plan to minimize woody biomass cost. In practice, those plans vary in detail from expert opinion and trial-and-error [11] to metaheuristic solvers that are incorporated into a geographic information system (GIS) [12]. While expert opinion is often used to minimize costs for an individual operation, it tends to produce substantial uncertainty when a supply chain is complex and compared to alternative operations occurring across vast landscapes over long periods of time. In that light, forest management, which typically covers large areas, has multiple objectives, delivers raw materials to many destinations, and utilizes long time horizons, often relies on building logistical costs into a GIS that can be used to compare multiple scenarios in a spatial and temporal manner.

In simple terms, a GIS is a collection of software procedures and data that use geometry as a primary relationship among records [13]. GIS data reside in a relational database structure that link records with one another based on primary keys and topological relationships. Within a GIS, real objects such as roads, harvest units and mills are symbolically represented as table records in either vector or raster form. Each record within a table stores descriptive information of each object (attributes) such as size, length, area, and cost along with a collection of coordinates that depict shape and location in the form of points, lines, polygons, or raster cells. Moreover, because an object’s geometry (shape and location) is stored, spatial relationships such as proximity to, touching, adjoining, within, and containing can be used to relate attributes of neighboring objects to one another.

In the context of managing woody biomass supply chains, these objects represent the base components that can be attributed costs. For example, a polygon that symbolizes a 40 hectare harvest unit on a gentle slope located next to a primary road can be allocated costs related to the weight of the biomass collected, a skidder harvesting system, and primary road access. Allocating cost across a landscape within a GIS is straightforward and can be accomplished by defining a clear set of rules that constrain cost to specific locations based on a combination of spatially explicit factors. These rules are typically defined by setting lower and upper bounds (i. e., thresholds) on transportation distance, the types of equipment that can be used, and the amount of material that can be removed from a given location. Thresholds can be based on a wide range of factors including regulations, policy, management objectives, the physical limitation of equipment being used, transportation infrastructure, and the characteristics of the landscape, and should be derived in a manner that represents yes or no outcomes in terms of supply. Records or spatial locations meeting the defined thresholds can then be attributed a designated cost and mapped appropriately.

Commonly, logistics costs are based on rates such as dollars per unit of distance, area, or weight. While rates can be easily attributed to specific objects (e. g., harvest units), it can be helpful to convert rates to an absolute value when aggregating different sources of cost for an activity. For example, plotting total cost against total amount can provide useful supply curves. Again, within a GIS this process is straightforward, as long as there are estimates of distance, area, and weight for each of the different cost types. Common tables developed to store these kinds of estimates include vector and raster data sets that spatially depict woody biomass stocks, topography, road and stream networks, receiving facilities, and treatment units.

One of the most common ways to generate the geometry of objects within these tables is to use “heads up digitizing” and image interpretation where a technician manually converts maps and other imagery into a digital format that can be used in GIS [14]. For larger landscapes, though, this tends to be cost prohibitive. In those situations, remote sensing techniques are often employed to automate the creation of GIS data. Regardless of how an object’s geometry is created, once it is defined it can be attributed with the base information needed to calculate absolute cost and biomass yield.

Cornelia Butler Flora1 and Charles F. Curtiss2

1 Department of Sociology, Iowa State University, U. S.A.

2Distinguished Professor Emeritus of Sociology, Agriculture and Life Sciences Research Professor,

Kansas State University

16.2 Introduction

Social sustainability is the capacity to create personal, social, political and economic envi­ronments that facilitate healthy human existence as part of the entire global ecosystem [1]. As cropping systems are changed to produce cellulosic energy crops, there will often be accompanying changes in land use, personal, group and community opportunities. Moving crops or cropland from food to fuel use means creation of new value chains that have impli­cations not only for the individuals involved but also on the interactions among individuals within the surrounding communities. These changes will likely have differential impacts on human communities depending upon their magnitude and implementation strategy. Inter­actions among individuals, which are crucial for mutual support, will ultimately determine social sustainability and acceptance of these new systems. Incorporating cellulosic energy crops into traditional cropping systems faces many physical, economic, and environmental challenges, as outlined in other chapters. But perhaps an even more important challenge that is often overlooked is how these new systems are organized and operated. Paying attention to these social implications is crucial for increasing the sustainability of healthy social structures in ways that will encourage humans to embrace and act on the goal of having a sustainable, well-functioning planet.

Social sustainability operates on many levels — the individual, the family, the commu­nity, ethnic and racial groups, politics, the economy, and local, national and international

Cellulosic Energy Cropping Systems, First Edition. Edited by Douglas L. Karlen. © 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.

spheres. Social justice concerns play an important political role in the development of sustainability standards used by governments, private firms, and civil society [2-4]. As the market for cellulosic energy is global, standards are increasingly being set and enforced by international bodies, such as the International Standards Organizations, the International Financial Corporation, and private certifiers (e. g. Roundtable on Sustainable Biofuels). In contrast to past endeavors, these standards are generally based on the process of production, not just observable qualities of the product.

Social sustainability does not mean that everything in a community remains the same, but implies balanced change and adaptation. Social resilience is the ability of a system to sustain itself in terms of human communities through adaptation and transformation. By looking at the potential impact of cellulosic energy cropping systems on human communities in terms of community assets and their interactions, the potential impacts can be anticipated. Using the community capitals framework [5], the potential of a variety of cellulosic energy cropping systems is analyzed in terms of their impacts on the stocks, flows and interactions of seven community capitals — natural, cultural, human, social, political, financial and built — and how those impact the community in terms of ecosystem health, economic security and social inclusion (Figure 17.1). This approach has been used in ex-post evaluations of a variety of interventions [6-8]. In this chapter, the impacts are analyzed based on potential interactions, as few studies have been made of social impacts of bioenergy cropping systems to date.

As will be evident in Section 14.4, there are a large number of established and emerging equipment options for harvesting and in-woods preprocessing of woody biomass. The level of preprocessing that occurs and the point at which it occurs in the supply chain have important impacts on supply chain efficiency because transportation costs, whether from stump to landing, landing to depot, or over long distances by rail or barge, are affected by the energy and mass density of the material. In general, supply chains that reduce the parti­cle size, ash content, and moisture content of woody biomass close to the harvest location have the greatest transportation efficiency. This is because more densely packed densified, dried biomass contains the highest energy content per unit volume or mass (BTUs m-3,

BTU ton-1). To address this characteristic of the woody supply chain, a number of special­ized harvesters and forwarders have evolved that process, comminute, and densify biomass in the woods, immediately after harvest, to varying degrees. These include, for example, slash bundler forwarders, self-feeding chipper-forwarders, and even mobile depot units that dry, grind, and densify regional woody biomass supply at tactical scales (e. g., 3-4 years within a draw area) before moving to another location. Some of the more common har­vesting and combined harvesting and processing equipment types currently available are described briefly and generally in Section 14.6.

Most woody biomass currently used or being actively studied in the context of biofuels and bioenergy development is derived from three major source categories: dedicated short — rotation woody crops (SRWCs), thinning materials, and logging residues.

Yield risk is defined here as the risk that the yield of a crop may differ from that which was expected at planting time. Crops are subject to many types of damage. Weather affects yields in many ways. Inadequate supply of water at critical points in the growth of the plant reduces yield and extreme drought can kill the plant. Excessive heat or frost can damage or kill plants. Severe storms can damage or kill plants by hail, wind, or flooding. Fire, whether started by lightning or other cause, poses a risk to crops. Biological factors also affect yields. Disease, fungus, insects, weeds, birds and other animals all can reduce crop yields in terms of quality and quantity.

Yield risk can be quantified in several ways. By recording yields in different fields each season, a frequency distribution can be assembled for each type of crop. Historical fre­quency distributions can be adjusted for trends in average yields to estimate probability distributions for the coming crop season. Crop insurance underwriters may estimate prob­ability distributions for crops to determine what level of yield to insure and what premium to charge for insurance. Note that yield is conditional on many factors. Distinct yield prob­ability distributions may be estimated for the same crop grown under different conditions. Locational factors, such as soil type and climate, affect historical yield frequency distribu­tions as well as estimated probability distributions. Managerial factors, such as seed variety or genetic type, particularly in relation to planting date and days required for crop maturity, can affect the estimated probability distribution. Similarly, irrigated crops can have very different yield probability distributions than dryland crops of the same type.

Figure 15.2 illustrates three yield probability distributions. The horizontal axis indicates yield and the probability of occurrence is indicated on the vertical axis. Each of the curved

lines represents a different yield probability distribution. The middle curve represents a symmetric distribution with similar probabilities of yields above and below the mean or average (point B). The curve with higher probabilities of lower yields and a long tail of declining probability to the right has mean labeled ‘A’ and represents a crop that seldom achieves its potential and often suffers reduced yields. This probability distribution can represent a crop being grown in adverse conditions. Conversely, the curve with a long tail of declining probability to the left and higher probabilities of high yields with mean ‘C’ represents a crop that frequently achieves near maximum yields. This probability distribution may represent a crop that is being grown in favorable conditions and could represent an irrigated crop. A general observation from this example is that crop yield probability distributions may differ both with respect to their mean or expected yield as well as the probability of various degrees of yield loss below the mean.

The economic effects of yield risk are many. Farmers incur routine costs to prevent or reduce risk of yield loss. Herbicides, pesticides, and fungicides are examples of inputs that are not used directly by the plant for growth but that reduce the frequency of losses due to pests. Irrigation and fertilizers provide inputs used by the plant and affect both average yield and the probability of yield loss. Farmers also incur costs of excess input use when yield is reduced or crops fail. For example, fertilizer may be applied at a rate sufficient for the expected yield or for a higher yield that is quite possible. When a lower yield is realized, the value of excess fertilizer is wasted. In other words, had they known the yield would be so low, the farmer could have applied less fertilizer and saved some input cost. Conversely, if a farmer applies only enough fertilizer for the average or expected yield and conditions occur that would have supported a higher yield, the farmer loses the value of foregone yield net of the additional fertilizer cost. The purchase of crop insurance is a common method of mitigating the financial effects of yield risk.

Sam W. Jackson

Genera Energy Inc, U. S.A.

18.1 Overview

In the United States during the mid-1970s, numerous cellulosic bioenergy research and development activities were conducted by U. S. Department of Energy (DOE) and U. S. Department of Agriculture (USDA) scientists and engineers, as well as many private sector companies and investors. Unfortunately, most of those efforts waned because obstacles to commercialization were not overcome. This chapter provides a perspective on devel­opments since that time from the viewpoint of an active biomass conversion facility and examines five major challenges facing the emerging bioenergy industry. These include land availability, crop selection, financing, agronomic challenges, and risk management. Despite these challenges, it appears that public interest, political will, engineering and agronomic advances have now achieved a level where successful commercial operations can be developed.

18.2 Introduction

The processes and technologies required for producing, handling, and converting lignocel — lulosic biomass to energy have been key focus areas for research and development activities throughout the last decade. Universities, federal agencies and laboratories, private compa­nies and foundations have all invested heavily to bring a biomass-based energy sector to reality for the United States. Early on, research focused on processes by which biomass could be converted to energy. Success was achieved utilizing both biochemical and ther­mochemical pathways. The next stage in the development of the industry focused on the

Cellulosic Energy Cropping Systems, First Edition. Edited by Douglas L. Karlen. © 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.

development and production of a variety of biomass sources. From crop residues to dedi­cated energy crops, agricultural and forest-based biomass resources have been identified as the primary source of these materials. Biomass development and production research has focused on utilization of biotechnology and traditional plant breeding to improve existing crops with increased yield and conversion efficiencies, introduction of exotic plant species (e. g., Miscanthus giganteus), and development of appropriate agronomic production prac­tices for each of the differing types of systems. Additional efforts have been directed toward development of logistical systems associated with moving, handling, and storing large volumes of biomass for energy production.

All of these efforts have significantly advanced the developing biofuels industry. Increased biomass yield per acre, decreased cost per ton at the farm gate, and increased gallons of ethanol, butanol or other fuel per ton of biomass have all improved when com­pared to their baseline values [1,2]. As conversion technologies near commercialization, increased emphasis has been placed on commercial scale biomass production. Producing hundreds of thousands of tons of a given crop for a single facility is an industrial sup­ply chain challenge that involves many players, including landowners, farmers, harvesting equipment manufacturers, truckers and logistical firms. All are dealing with a product that is different than any traditional agriculture or forest product, both in format and scale. Therefore, to be successful, commercial scale biomass supply chains must achieve a sus­tainable environmental and economic balance while overcoming several logistic and other obstacles. This chapter highlights five key areas of concern when commercializing dedi­cated energy crop biomass supply chains. Regardless of the scale of the required biomass production, many of these issues remain key.

The Figure 13.1 example introduces a very significant point for the design of the logistics system. The business plan can have equal, or greater, impact on delivered cost of feedstock than the technical issues. The technical issues are defined as the functionality (tons per hour handled) and cost ($/h) of individual pieces of equipment in the logistics chain. The business plan specifies “who will do what”; it assigns the various unit operations to the several business entities.

Ma and Echoff [2] compare a commodity model (current grain industry) with a contract model (plant contracts with farmers to grow feedstock). They state, “Based on commodity pricing, the producers/suppliers who are farther away from the refinery will realize less profit since they have to pay more for transportation; therefore, it is less attractive for them to participate in the project. Even with contract pricing, it is less attractive to sign contracts with more distant producers.” In the presence of this reality, the remaining discussion in this chapter envisions that the load-haul activities will be done by the bioenergy plant (or its contractor). Then, all highway hauling cost to move biomass from SSLs to the bioenergy plant is borne by the plant, and all farmgate contractors get the same opportunity for profit, no matter their distance from the plant. Ma and Eckhoff [2] state that biorefineries can achieve a lower unit production cost for liquid fuel using the contract pricing method rather than the commodity pricing method.

One main reason that the assignment of the business entities in the logistics system is so important is that it establishes the capitalization requirements. Generally, the plant is in a better position to obtain capital than the small business owner (producer). The next key economic factor is how many hours-per-year (and thus tons-per-year) can be handled by the purchased equipment and facilities. This factor has always been a major issue for agriculture. Most field equipment is used a relatively few hours (200-400) per year, thus the ownership cost per operating hour is high.

Suppose a logistics system is designed using the traditional model — the producer must deliver the raw biomass to the plant. The plant needs year-round deliveries, so it schedules the producers to deliver certain days of each week for 47 weeks of annual operation. During the planting and harvesting seasons, the farmers are working dawn to dusk on their field operations, thus they do not have time to make deliveries. The plant will receive no deliveries during those periods, thus it will need a large inventory in at-plant storage.

If each farmer is assigned only a few delivery days each month, the total tons delivered is too low for them to afford to invest in equipment required to make the delivery cost efficient. Consider the delivery of round bales as an example. They will choose to use the equipment they have — gooseneck trailer pulled by a pickup truck or perhaps a flat-bed truck — and the plant must now deal with a situation shown in Figure 13.3. Note the different type of trucks lined up to deliver grain to a buying point. A bioenergy plant would end up with a similar situation. There would be no uniformity in the loads received, and the plant would have to unload what comes through the plant gate. It is a challenge for a receiving facility at a bioenergy plant to operate cost effectively in this manner.

Now consider an option that emulates the cotton model. In this model, the deliveries are done with specialized transport equipment (module haulers). Each load is the same when it arrives at the plant. Now the receiving facility can be organized to receive the maximum loads per unit time. The time a truck waits in the queue to unload is minimized, thus the truck cycle time is minimized, and the truck cost ($/ton) is minimized.

An additional benefit of uniform deliveries is realized by the bioenergy plant. Each unit of equipment in the receiving facility handles more tons per unit of operating time, thus the

unload cost ($/ton) is less. For example, suppose the cost to operate a forklift is $50/h and, on average, it handles 50 ton/h throughout the workday. The cost is then

$50/h 50 ton/h

If this same forklift has to wait for trucks to arrive and thus averages 25 ton/h over the workday, the cost is

$50/h 25 ton/h

This simple example illustrates a very important principle in calculating the cost for a unit operation. Cost, ownership plus operating, for a commercial machine is defined by standard methods [3] used to calculate that cost. (An example calculation for a forklift is given in Appendix 13.A.) When this machine is used in a logistics system in such a manner that tons handled per operating hour is maximized, then cost ($/ton) is minimized.

There are studies in the literature that seek to define an optimum cost for a single unit operation in the logistics chain, or, more typically, a series of unit operations that define one segment of the total logistics chain. There is nothing wrong with this approach; however, this author has observed that if an attempt is made to optimize one unit operation in isolation, then the cost of the unit operation immediately upstream, or downstream, is increased such that the overall solution, in this case, the average delivered cost of feedstock for 24/7 operation, is higher. The reader is admonished to watch for this potential problem as they “design” a logistics system for a specific application.

Most equipment for primary harvesting and extraction in forestry has been designed for handling sawlogs or whole stems, which are single or multiple large, heavy objects. Relative to sawlogs, the material properties of woody biomass are very different, including small particle size and bulkiness. For this reason, use of portable conveyors for in-woods biomass handling applications, such as forwarding, have received some attention. Portable belt conveyors and continuous loop cable systems have important advantages over conventional skidding and forwarding equipment options. The continuous material flow properties of conveyors make it possible for high production rates to be maintained, regardless of turn distance [4, 5]. This differs from the production function for most skidding and forwarding equipment, which tends to decline with increasing turn distance. Set-up costs, or total equipment costs, tend to offset production gains associated with deploying conveyors for primary extraction to a landing or roadside. However, an additional advantage of conveyors is that many are able to handle bulky biomass in a variety of raw or comminuted forms, including, for example, chips, hog fuel, and unprocessed slash and tops. This flexibility makes it possible for portable conveyors to function as part of a variety of different system and equipment configurations.

Start-up of a new crop-to-fuel system poses difficult challenges. Farmers unfamiliar with a crop and uncertain of the longevity of the only market for that crop may be willing to risk very little on such a venture, particularly if the crop is a perennial that generates little revenue in the first two years of cultivation. Processors may be unwilling to invest a lot of money in a new facility if feedstock supply is uncertain. In this case, a collaboration between farmers, the processor, and interested government agencies is likely required. Long-term marketing or production contracts are required to satisfy farmers and processor that their needed market outlet and feedstock supply will exist. Some degree of establishment cost-share may be required to enable farmers to commit land and other resources to the project for years before any revenue is generated. This may be in the form of a loan that could be repaid over the life of the contract if the venture survives. Work to establish scientific support programs for the crop, as well as regulatory programs to permit chemical use and enable crop insurance coverage, must be completed very early in the process. Farmers and the processor should have adequate equity financing to carry them through the first few years of establishment and provide a subsequent sound basis for ongoing production. Product marketing contracts for major co-products are needed to assure market outlets for the processor unless they are selling into a very large market with many buyers and sellers. Even in the latter case, contracts that provide some degree of price assurance would be constructive. Terms for renewing contracts and re-negotiating terms are critical to good contracts.

In the case where the crop is an annual, farmers may be more willing to enter contracts with limited start-up investment. If the crop is an abundant co-product such as corn stover, contracts may not needed or they may be single season contracts with emphasis on timing of delivery, storage, transport, and pricing. In either case, the processor must have realistic expectations of what prices will be necessary in which locations to acquire the necessary supply. Contracts can determine such prices prior to planting.