Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks

Transcription

1 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks Modelled Supply Chain Logistical Costs Associated with Cellulosic Ethanol Production in Canada Final Report April 29 th 2008 Prepared for: Agriculture and Agri-Food Canada By: 825, rue Raoul-Jobin Québec (QC) G1N 1S6 Telephone: (418) Fax: (418) , rue St-Alexandre, #302 Montréal (QC) H2Z 1P8 Telephone: (514) Fax: (418) and AGRONOVITA INC Meadowlands Drive, Suite 1209 Nepean (ON) K2E 7B4 Telephone: (613) Mobile: (613)

2 Team members Brunelle, Jérôme-Antoine - Agrifoods Analyst - ÉcoRessources Consultants Forest, Jean-François - Director Agrifoods - ÉcoRessources Consultants Girouard, Patrick - President - AgroNovita Inc. Lessard, Catherine - Agrifoods Research Assistant ÉcoRessources Consultants Vaillancourt, Kathleen (Project Manager) - Director Energy - ÉcoRessources Consultants i

3 EXECUTIVE SUMMARY At this moment in time, advances in genetics and farming practices can increase the efficiency of producing biofuel from existing agricultural products, but cannot overshadow Canada s potential for the producing second generation biofuel using biomass and new energy crops. However, before an infrastructure for producing second generation biofuel can be built, feedstock supply chain logistics issues related to harvesting, transporting and storing biomass must be addressed. This project, mandated to ÉcoRessources Consultants and Agronovita Inc. by Agriculture and Agri-Food Canada (AAFC) in the winter of 2008, investigates and analyzes the logistical costs of harvesting, storing and transporting large amounts of biomass from the point of harvest to the gate of a second generation biofuel plant. Its main goal is to provide information related to these costs in a Canadian context. Feedstock potential and availability Not all feedstocks that can be provided by the agricultural sector offer the same potential in terms of biofuel (energy) production. Also, each feedstock source, including crop residues, forage crops and new energy crops not yet largely produced in Canada, holds a different potential in terms of biomass production, availability and supply chain logistical costs. Industry information suggests that the optimal scale of a second generation biofuel plant is linked to an annual production capacity of around 200 million litres. Depending on the type of technologies employed, such a plant requires approximately dry tonnes of feedstock, which must be supplied equally throughout the year. Assessments show that crop residues, including cereal straw and corn stover, are the most abundant feedstock source available in Canada in terms of supplying such a demand. It is important to keep in mind that all the elements related to the second generation biofuel industry (upstream and downstream from production) are constantly evolving. New technologies and new plant varieties for biofuel production are the subject of many research projects across the globe and will therefore continue to emerge and alter feedstock potential, both from a technical and an economical perspective. Most of Canada s cereal straw is produced in the Prairie provinces. As demonstrated in the figure below, second generation biofuel plants which aim to use cereal straw as their main feedstock should be located in Alberta, Saskatchewan and/or Manitoba. However, strong uncertainties exist in straw availability on a regional basis. In fact, the inherent problem with this biomass production is its annual variation, which depends largely on weather conditions. Also, the amount of surface residues required for erosion control varies depending on soil texture and field slope, so not all of the straw produced can be removed (Sokhansanj et al., 2006). Some of the barriers to the economic use of cereal straw are uncertainty in its i

4 availability; straw quality; cost of collection, transport, and storage; and location of the straw. Only a portion of farmers with excess straw are willing to sell it and farmers typically do not commit 100% of their excess straw in a given year. In fact, the amount that a producer is prepared to remove and supply to an industrial user will depend on the producer s perceived value of straw. Hence, in establishing an industrial plant, it is essential to investigate the amount of land in production; the local yields and harvest practices and the general economic, environmental, and social contexts. STRAW AVAILABILITY IN WESTERN CANADA The straw densities presented in this map are based on the total number of square kilometres within each census region. No adjustment was made for forested areas, rivers, lakes, cities, etc. Each census region covers a fairly large area and significant variability in crop production, forested areas, etc., may exist within each region; this is not reflected in the current computation. Areas near Prince Albert (northern Saskatchewan) should have a higher straw density than depicted in this map as there is a significant amount of forested areas within those regions. In Alberta, the area with the largest straw density on this map (Calgary Lethbridge) is outside of the black soil zone and is likely over-estimated as a result of the larger amounts of straw produced per hectare in the Lethbridge region (irrigated land). Ideally, this mapping should be done at the county level and including only areas used for agricultural production. This was outside of the scope of this study. Source: ÉcoRessources Consultants Corn stover represents the most abundant source of feedstock that can potentially supply the biofuel industry in Eastern Canada. The development of biofuel production based on this crop residue should be concentrated in areas where corn production is significant. Therefore, biofuel plants using this feedstock could potentially operate in Ontario and Québec. More specifically, there are two potential corn stover ii

5 production basins where biofuel plants using this feedstock could conceivably be established. As the circles on the figure below indicate, the first basin is depicted by the Ontario Peninsula and the second includes southwestern Québec and eastern Ontario. CORN PRODUCTION IN ONTARIO AND QUÉBEC Source: ÉcoRessource Consultants Although forage crops contain lignocellulosic carbon, most are greatly valued by the livestock sector. Therefore, they are usually not considered to be an economically viable feedstock for the second generation biofuels industry. Plant material collected in untamed meadows and pastures are also considered to be a potentially interesting low-cost feedstock for the second generation biofuels industry. However, these materials are usually located in remote and dispersed areas. Just as any other feedstock, they must be collected, stored and transported, which could considerably increase the price of these feedstock sources. Considering that switchgrass production presents greater potential on marginal lands, which are currently dedicated mainly to hay production, only certain regions can truly be identified as presenting a real potential for this production. On the other hand, regions that produce sufficient amounts of crop residues iii

6 for the biofuel industry are not likely to reallocate a significant amount of hectares to such new energy crops. Other plants such as hemp (Cannabis sativa), prairie cordgrass (Spartina pectinata), giant Chinese silver grass (Miscanthus giganteus) and reed canarygrass (Phalaris arundinacea) present a certain potential for biomass production. Some of these are categorized as prairie grasses and tend to grow naturally in Canada but only on marginal lands in remote areas. These are therefore often identified as low-input highdiversity (LIHD) biomass. Converting this type of biomass into cellulosic ethanol and electricity is estimated to net 17.8 GJ/ha (Tilman and al., 2006), which is substantial. Unfortunately, research activities concerning the cultivation, harvesting and storing of these plants, as well as their overall potential for the production of second generation biofuel, are still at a very preliminary stage. Consequently, these feedstock sources could not be assessed in detail. Feedstock potential can be enlarged significantly when considering other sources of biomass (starchy grains and oil seeds from dedicated crops) than traditional sources. New sources include the following (EERE, 2007): In the short term, and for small scale applications, low- or negative-cost feedstocks such as industrial residues (black liquor from the pulp and paper industry and animal manures) could provide a good proportion of biomass. In the medium term, the addition of forestry and agricultural residues will provide the most important portion of biomass and significantly increase the potential for fuels and power production. In the long term, dedicated cellulosic biomass crops for energy production could offer additional benefits. Developing a feedstock-oriented second generation biofuel industry The second generation biofuel industry s growth relies heavily on the development of technologies capable of (1) converting cellulosic biomass and lignin (rather than grains or starchy/sugar-based feedstocks) into biofuel and other products (chemicals or materials) and (2) gasifying or liquefying biomass for power production or for catalytic conversion to valuable products. For this purpose, two platform technologies exist: the sugar platform and the thermochemical platform. They are called platforms since they are the basic technologies that can be used to produce a large variety of products. Therefore, future biorefineries would convert biomass into a range of biofuels and other products (chemicals, materials, power, etc.). iv

7 The obstacles that still need to be overcome for the widespread use of second and third generation technologies are mainly technical. However, once these obstacles are removed, the development of the second generation ethanol industry could still be limited by biomass location and availability. By definition, biomass refers to all plants or materials and biomass energy refers to all energy originally captured by photosynthesis (EERE, 2007). Biomass is considered to be a renewable source of energy, which can be used either for fuel production or power generation, since the carbon dioxide emissions generated during its combustion balance with the emissions captured during plant growth. Furthermore, biofuels are currently the only alternative for supplementing conventional fuels used for transportation. Because crop residue is a byproduct of grain production, it is currently abundant, underutilized, and low cost. This latter element in particular leads many experts to believe that crop residue biomass is an attractive starting feedstock that shows the best near-term promise for use by the second generation biofuel industry. Unavoidably, the feedstock supply chain of this industry will need to incorporate other sources of biomass; however, not all energy crops are readily adaptable to agricultural practices. In order to diversify feedstock sources and assure long-term supply of biomass to this industry, better adapted varieties of energy crops and production technologies must be developed. Models to estimate the logistical costs of feedstock supply chains Two economic models that can be used to estimate the costs associated with a second generation biofuel plant s overall supply chain have been developed through this research project. These include different database spreadsheets, in Microsoft Excel files. The two models are structured in the same way but their default values and equipment selection are set to address different feedstocks. One model is specific to Western Canada (Alberta, Saskatchewan and Manitoba) and allows the user to estimate the delivered cost of cereal straw and/or perennial grasses such as switchgrass. The other model is specific to Eastern Canada (Québec and Ontario) and allows the user to estimate the delivered cost of corn stover and/or perennial grasses such as switchgrass. While various production, harvesting, storage and transportation schemes were studied. Only those using readily available technologies were modelled. The information gathered during the literature review led to the modelling of a global cost-efficient supply scheme which takes into account the most feasible and the least costly options for establishing a biomass supply chain in the current context of the Canadian biofuel industry. Upon opening the supply-chain logistical costing models, the user is presented with the Scenario sheet. As its name indicates, this sheet allows the user to establish the scenario he or she wishes to analyze. This scenario is set up by attributing values to different variables that are to be considered in the calculation of the total supply chain logistical costs. These different variables are grouped into categories and include v

8 plant capacity, feedstock proportions, basic financial data (e.g., interest rates), harvesting equipment options, etc. Results of different supply chain scenarios Using the developed supply chain logistical costing models, different scenarios for both Western and Eastern Canada were analyzed. Two basic scenarios were established. While the basic scenario for Western Canada was based on a supply chain utilizing 75% cereal straw and 25% switchgrass, the basic scenario for Eastern Canada is based on a 100% corn stover supply chain. As presented in the table below, logistical costs of transporting large amounts of biomass for a commercial sized biofuel plant are higher in the Eastern Canada scenario than in the Western Canada scenario. When compared on a dry basis, it is significantly more costly to store and to transport corn stover than it is to store and transport the baled feedstock options. RESULTS OF BASIC DEFAULT SCENARIOS Model for Western Canada Model for Eastern Canada Total estimated $/dry tonne $67.63 $86.54 feedstock cost $/dry tonne/km $0.34 $0.43 Feedstock options Cereal straw Switchgrass Corn stover Grower s $/tonne $12.50 $15.75 $15.00 payment* $/dry tonne $14.37 $18.53 $34.09 Harvesting costs Storage costs $/tonne $24.03 $27.27 $5.11 $/dry tonne $27.62 $32.08 $11.61 $/tonne $0.10 $0.10 $4.90 $/dry tonne $0.11 $0.12 $11.15 Transportation $/tonne $20.22 $20.22 $13.06 costs ** $/dry tonne $23.24 $23.78 $29.68 *Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modelled parameters As previously mentioned, agricultural residues (corn stover for Eastern Canada and cereal straw for Western Canada) represent the most abundant feedstock source available in Canada to supply the quantities of biomass needed to supply a second generation biofuel plant of optimal scale (production capacity of around 200 million litres). Using the costing models, we therefore simulated supply chains that are exclusively based on using agricultural residues in both regions of Canada. The comparison of the total vi

9 logistical costs of supply chains based exclusively on agricultural residues for both regions of Canada is presented below. RESULTS OF SUPPLY CHAIN SCENARIOS BASED EXCLUSIVELY ON THE USE OF CROP RESIDUES Feedstock options: Western Canada Eastern Canada 100% crop residues Cereal straw Corn stover Total estimated $/dry tonne $65.34 $86.54 feedstock cost $/dry tonne/km $0.33 $0.43 Grower s payment* Harvesting costs Storage costs $/tonne $12.50 $15.00 $/dry tonne $14.37 $34.09 $/tonne $24.03 $5.11 $/dry tonne $27.62 $11.61 $/tonne $0.10 $4.90 $/dry tonne $0.11 $11.15 Transportation $/tonne $20.22 $13.06 costs** $/dry tonne $23.24 $29.68 * Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modelled parameters The total cost of harvesting, storing and transporting corn stover in Eastern Canada is higher than the logistical cost of using cereal straw in Western Canada. On the other hand, we notice that a 100% straw scenario slightly reduces cost, when compared to the basic default scenario that utilizes switchgrass for 25% of total feedstock needs for a biofuel plant in Western Canada. If better-adapted varieties and production technologies are developed in the long term, new energy crops such as switchgrass may be called upon to play a more important role in the development of the second generation biofuel industry as a significant cost-effective feedstock source. The cultivation of these new energy crops is therefore inevitable. Using each model individually, we compared the total cost of a supply chain exclusively based on agricultural residues to a supply chain based exclusively on switchgrass for each region in Canada. The result of this analysis is presented below. vii

10 RESULTS OF DIFFERENT FEEDSTOCK SCENARIOS Feedstock Options Model for Western Canada 100% cereal straw 100% switchgrass Model for Eastern Canada 100% corn stover 100% switchgrass Total estimated $/dry tonne $65.34 $74.50 $86.54 $63.41 feedstock cost $/dry tonne/km $0.33 $0.37 $0.43 $0.32 Grower s $/tonne $12.50 $15.75 $15.00 $15.72 payment* $/dry tonne $14.37 $18.53 $34.09 $18.49 Harvesting $/tonne $24.03 $27.27 $5.11 $19.15 costs $/dry tonne $27.62 $32.08 $11.61 $22.52 Storage costs $/tonne $0.10 $0.10 $4.90 $0.10 $/dry tonne $0.11 $0.12 $11.15 $0.12 Transportation $/tonne $20.22 $20.21 $13.06 $18.94 costs * $/dry tonne $23.24 $23.77 $29.68 $22.28 * Price of feedstock field basis (unharvested) **Includes loading and unloading Although such 100% switchgrass scenarios are not likely to become a reality, this analysis demonstrates that switchgrass is not significantly more costly than cereal straw in Western Canada. Modelled parameters pertaining to yields, moisture content and production costs are at the root of this end result. However, this analysis demonstrates that switchgrass could be a more cost-effective feedstock source in Eastern Canada, especially compared to the logistical costs of a supply chain based exclusively on corn stover as a feedstock source. This of course, provided that switchgrass can be produced at a reasonable price in the same regions as corn for grain. From a general perspective, the results of the different scenarios analyzed are considerably higher than data found in the literature. They clearly demonstrate that a delivered cost of $35/dry tonne for agricultural residues is unlikely to be a reality in the present economic context. For several years, this value has been adopted as a rule of thumb in order for a second generation biofuel plant to be economically viable (Sokhansanj and Turhollow, 2005). The recent, rapid, rise of the price crude oil and steel has had an impact on supply chain logistical costs for the second generation biofuel industry. This new business environment is leading the way to a new price discovery process between farmers and cellulosic ethanol plants. Other scenarios could involve smaller biofuel plants that use switchgrass and alternative energy crops as primary feedstocks. These would only be viable in the long term, as the cost of second generation viii

11 technologies for biofuel production requires that biofuel plants produce large volumes of biofuel in order to benefit from economies of scale. Furthermore, smaller plants established in remote regions would have to transport their end product (biofuel) longer distances in order to market it. This would increase the overall production cost of such plants and would, to a certain degree, impact economic viability. Other costs and benefits to society Economies of scale play an important role in the economics of chemical and oil and gas facilities and a similar trend is expected for the second generation biofuel industry. Beyond technology and access to capital, the capacity to build ever-larger plants will likely be constrained by access to cellulosic raw materials at an economically viable price. Cellulosic raw materials are bulky products and therefore cannot be shipped over long distances without incurring a significant cost premium. Economies of scale will allow plants to pay a premium for feedstocks sourced from a further distance but sourcing feedstocks from a different feedstock basin will likely require new and more efficient feedstock-handling technologies than modelled in this study. Beyond economies of scale realized at the plant level, feedstock supply chains developed for second generation biofuel plants make it possible to reap some economic benefits from the optimal use of the supply chain equipment as opposed to traditional use. The large and steady volume of feedstock required by biofuel plants allows maximization of the annual use of farm balers, collection units and trucks, which in turn spread the capital cost of such equipment over a larger number of working hours and tonnes of product. The economies realized would then allow new custom rate equilibrium prices to be achieved by the industry. Environmental costs and benefits After assessing emission factors for the production of ethanol derived from corn and wheat using first generation technology, results show that the largest sources of greenhouse gas (GHG) emissions in the ethanol lifecycle are land-use changes and cultivation, fuel production, feedstock recovery and fertilizer manufacture. Emission coefficients associated with the well-to-tank portion of the lifecycle are higher for ethanol than for gasoline in most cases. Crop-based ethanol requires large inputs of energy and therefore produce large amounts of emissions for feedstock recovery, land use, cultivation and fertilizer manufacture, which are either very small or non-existent factors for fossil fuels. However, emissions from gas leaks and flares are not a factor for ethanol and in all cases the production of biofuel results in a large amount of displaced emissions. Finally, GHG emissions from fuel distribution, storage and dispensing represent a small proportion of the lifecycle analysis for ethanol. ix

12 It is interesting to note that although cellulosic ethanol derived from switchgrass in fact produces higher emissions associated with fuel production than other types of ethanol, important emission credits are given for switchgrass ethanol in order to take into account co-product benefits (avoided emissions). The model used in these analyses, GHGenius, is extremely thorough and complete in its analysis of the lifecycles of fuels used for transportation. However, its large scope normally considered and advantage, can also be a disadvantage in terms of the details of a particular fuel pathway. Further studies on refining and validating the lifecycle analysis of biofuels produced using second generation technologies that are currently under development as well as those based on experiences in Canada and other countries are required in order to assess environmental costs and benefits more thoroughly. Conclusion Collecting, handling, storing and transporting various types of feedstock in an efficient manner presents a unique challenge for the production of second generation biofuels. In part, this research project seeks to provide information that will help mitigate the risk of encountering logistical problems while developing the second generation biofuel industry in Canada. The elements contained in this final report can indeed help policymakers develop appropriate programs to alleviate the risks associated with harvesting, storing and transporting biomass. This type of policy would not only help the Canadian biofuel industry to flourish, but would also accompany the agricultural sector into a new era of crop marketing. However, many elements that were not within the scope of this study deserve further research in order for this to become a reality. Mechanisms that will ensure the availability of feedstock of a specific grade and quality, in sufficient quantities, need to be developed. One of many issues is the fact that the collection of corn stover and the production of new energy crops such as switchgrass are not yet readily adaptable to current agricultural practices. Better-adapted varieties and practices, as well as more efficient production technologies need to be studied. The further development of the costing models created in this research project could permit alternative feedstock options to be explored and analyzed. Developing the optimal road map to commercialize second generation biofuels is also an issue that needs to be addressed with further research efforts. Economies of scale will allow plants to pay a premium for feedstocks sourced from a greater distance, but sourcing feedstocks from a different feedstock basin will likely require new and more efficient feedstock handling technologies than modelled in this study. A more in-depth discussion of economies of scale and the expansion of the feedstock collection radius would shed more light on this issue. x

13 Table of contents CONTEXT... 1 INTRODUCTION CHARACTERISTICS AND POTENTIAL OF AGRICULTURAL FEEDSTOCKS Crop residues Cereal straw Corn stover Adopted agricultural crops Miscellaneous forage crops New energy crops Switchgrass Hemp Other energy crops Spartina pectinata (sloughgrass, prairie cordgrass, freshwater cordgrass, marshgrass, sloughgrass, rip gut) Miscanthus giganteus (Giant Chinese silver grass or giant miscanthus) Phalaris arundinacea (Reed canarygrass) DEVELOPING A FEEDSTOCK-ORIENTED SECOND GENERATION BIOFUEL INDUSTRY IN CANADA Second generation technologies Sugar platform Thermochemical platform Other platforms Biomass feedstock Logistical strategies for the development of the second generation biofuel industry DESCRIPTION OF MODELS General assumptions and main input cells Types of cells...42 Comment boxes General assumptions...43 Characteristics of biofuel plant Establishing the scenario ( Scenario sheet) Availability of feedstocks plant capacity Feedstock proportions Basic financial data Harvesting scheme Storage scheme Transportation scheme Crop Residue Estimate sheet Results sheet...51 Save in file button Specific assumptions - model for a cellulosic ethanol plant in Western Canada Scenario sheet default scenario Feedstock characteristics Harvesting costs Storage costs Transportation costs...62 Truck Loading Sheet...63 Transportation Sheet Specific assumptions - model for a cellulosic ethanol plant in Eastern Canada Basic scenario Feedstock characteristics...70 i

14 3.3.3 Harvesting costs Storage costs Transportation costs...80 Truck Loading sheet...80 Transportation sheet ANALYSES OF DIFFERENT SCENARIOS AND LIMITS OF THE ECONOMIC MODELS Analyses of different supply chain scenarios Other logistical costs not taken into account Sensitivity analysis of impact of on-farm labour costs on total feedstock price Examples of crop residue estimates and radius of supply PORTRAIT OF BIOMASS PRODUCTION IN CANADA USING POTENTIALLY EXPLOITABLE FEEDSTOCKS Feedstock production basins in Western Canada Feedstock production basins in Eastern Canada DISCUSSION OF OTHER COSTS AND BENEFITS TO SOCIETY Economies of scale Environmental Costs and Benefits Overall greenhouse gas emissions Soil balance CONCLUSION REFERENCES ii

15 List of figures FIGURE 1 FIRST AND NEXT GENERATION TECHNOLOGIES FOR BIOMASS CONVERSION...27 FIGURE 2 MOST PROMIZING PLATFORMS FOR BIOMASS CONVERSION...28 FIGURE 3 THE ENZYMATIC HYDROLYSIS PROCESS...30 FIGURE 4 THE CONCENTRATED ACID HYDROLYSIS PROCESS...32 FIGURE 5 THE DILUTE ACID HYDROLYSIS PROCESS...33 FIGURE 6 R & D PATHS TO THE STORAGE OF UNIFORM BIOMASS FEEDSTOCK...48 FIGURE 7 EXAMPLES OF LOADING EQUIPMENT USED...63 FIGURE 8 EIGHT AXLE B-TRAIN WITH FLAT DECKS FOR STRAW TRANSPORTATION...65 FIGURE 9 CONTAINER-TYPE WAGONS USED TO COLLECT CORN STOVER...75 FIGURE 10 BULK DUMP TRAILER...82 FIGURE 11 CENSUS REGIONS USED TO DEMONSTRATE THE CROP RESIDUE ESTIMATE SHEET...92 FIGURE 12 STRAW AVAILABILITY IN WESTERN CANADA...98 FIGURE 13 CORN PRODUCTION IN ONTARIO AND QUÉBEC FIGURE 14 FUEL LIFECYCLE ANALYSIS FIGURE 15 TOTAL EMISSION COEFFICIENTS FOR GASOLINE AND ETHANOL OVER THE ENTIRE LIFECYCLE (GRAMS OF CO 2 -EQ /LITRE) IN FIGURE 16 EMISSION COEFFICIENTS FOR GASOLINE AND ETHANOL OVER THE WELL-TO-TANK PORTION OF THE LIFECYCLE (GRAMS OF CO 2 -EQ /LITRE) IN List of tables TABLE 1 SUMMARY OF FEEDSTOCK ENERGY POTENTIALS...5 TABLE 2 SUMMARY OF FEEDSTOCK FARM-GATE PRICES...6 TABLE 3 SUMMARY OF FEEDSTOCK CONSTRAINTS...7 TABLE 4 THE THREE MAIN COMPONENTS OF BIOMASS BY FEEDSTOCK TYPE...36 TABLE 5 BIOREFINERIES CURRENTLY UNDER CONSTRUCTION IN THE WORLD...38 TABLE 6 BASIC DEFAULT SCENARIO IN THE MODEL FOR A CELLULOSIC ETHANOL PLANT IN WESTERN CANADA...52 TABLE 7 COSTS ASSOCIATED WITH DIFFERENT TRANSPORTATION NETWORKS...64 TABLE 8 BASIC DEFAULT SCENARIO IN THE MODEL FOR A CELLULOSIC ETHANOL PLANT IN EASTERN CANADA...68 TABLE 9 RESULTS OF BASIC DEFAULT SCENARIOS...86 TABLE 10 RESULTS OF SUPPLY CHAIN SCENARIOS BASED EXCLUSIVELY ON THE USE OF AGRICULTURAL RESIDUES..87 TABLE 11 RESULTS OF A SUPPLY CHAIN SCENARIO EXCLUSIVELY BASED ON THE USE OF CEREAL STRAW IN DIFFERENT TYPES OF BALES...88 TABLE 12 RESULTS OF DIFFERENT FEEDSTOCK SCENARIOS...89 TABLE 13 FARM WAGES SENSITIVITY ANALYSES...91 TABLE 14 PARAMETERS AND RESULTS OF THE CENSUS REGIONS USED TO DEMONSTRATE THE CROP RESIDUE ESTIMATE SHEET...93 TABLE 15 REGIONAL ANALYSES...94 iii

16 TABLE 16 RADIUS OF SUPPLY SENSITIVITY ANALYSES ON REGIONAL SCENARIOS...95 TABLE 17 ESTIMATION OF STRAW CONSUMPTION BY THE LIVESTOCK SECTOR...97 TABLE 18 REGIONS PRELIMINARILY IDENTIFIED AS HAVING POTENTIAL FOR SWITCHGRASS PRODUCTION iv

17 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT CONTEXT Based on recent Canadian government regulations, minimum requirements of 2% biodiesel content in all diesel fuel and heating oil sold in Canada will be obligatory in 2012 and a minimum of 5% renewable fuel content in gasoline will be required by In order to attain these objectives, the Government of Canada is now looking at ways to stimulate growth in the biofuel industry. Given the current incentives, it is anticipated that about 2.74 billion litres of ethanol will be produced annually in Canada by the end of 2010 (Forge, 2007). Based on the average sales of diesel in Canada between 2003 and 2006, a mandatory 2% biodiesel content would have have required the production of 520 million litres of this biofuel for alone. If these levels of production are to be attained, infrastructure growth will be necessary. The biofuel industry in Canada (including production, storage, blending and transportation) is considered to be in its infancy. In 2007, the annual biodiesel production in Canada reached approximately 120 million litres, from 97 million litres in The annual ethanol production will have gone from 190 million litres in 2004 to an estimated 919 million litres for At this moment in time, advances in genetics and farming practices can increase the efficiency of producing biofuel from existing agricultural products, but cannot overshadow Canada s potential for the production of second generation biofuel using biomass and new energy crops. However, before an infrastructure for producing second generation biofuel can be built, feedstock supply chain logistics issues related to harvesting, transporting and storing biomass must be addressed. In other words, efficient networks must be established in order to guarantee a relative uniformity in materials and above all, a regular and steady supply. Subsequently, new levels of efficiency can be achieved by redefining and upgrading the feedstock supply chains of the industry. In order to prepare for the creation of an infrastructure for the second generation biofuel industry, this project seeks to investigate and analyze the logistical and economic implications of moving biomass from the point of harvest to the gate of a biofuel plant, in the present context of the Canadian economy. 1 Part of the Renewable Fuels Bill introduced in Parliament on December 3rd 2007 by the Honorable John Baird Minister of the Environment, on behalf of the Honorable Gerry Ritz, Minister of Agriculture and Agri-Food and Minister for the Canadian Wheat Board. ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 1

18 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT INTRODUCTION This project, mandated to ÉcoRessources Consultants and Agronovita Inc. by Agriculture and Agri-Food Canada (AAFC) in the winter of 2008, seeks to investigate and analyze the logistical costs of harvesting, storing and transporting large amounts of biomass from the point of harvest to the gate of a second generation biofuel plant. Its main goal is to provide information related to these costs in a Canadian context. Section 1 consists of a literature review conducted in order to establish which feedstocks present the most potential for the second generation biofuel industry in Canada. It is based on a number of sources, including government reports, published scientific articles and private sector information. Potential feedstocks are extensively described in terms of the agronomic, technical, logistical, economical and environmental constraints associated with their production, collection, storage and transportation in this section of the report. For this, the feedstocks are grouped into three general categories: 1. Crop residues (including cereal straw and corn stover) 2. Common agricultural crops (mainly miscellaneous forage crops) 3. New energy crops (switchgrass, hemp and others) Section 2 reviews the different technologies that will most likely allow the production of second generation biofuels to emerge as a significant industry in Canada, as well as different logistical strategies for the development of this industry. These elements helped determine which feedstocks are more likely to be exploited initially, as well as those which would be used on a more long-term basis. The findings contained in the two first sections of this report lead to the identification of the feedstocks that are most fit to be at the centre of the development of the Canadian second generation biofuel industry in the coming years. After discussions with the project authority at AAFC, it was agreed that the next steps of this research project, should focus primarily on the logistical costs of using crop residues, more specifically cereal straw and corn stover, as well as certain new energy crops (switchgrass and low-lignin content alfalfa) for ethanol production by commercial-scale biorefineries. Subsequently, the third section describes in detail the assumptions and modelling approach that were considered in the development of two economic models that can be used to estimate the costs associated with a second generation biofuel plant s overall supply chain. The two models are structured in the same way but their default values and equipment selection are set to address different feedstocks. One model is specific to Western Canada (Alberta, Saskatchewan and Manitoba) and allows the user to estimate the delivered cost of cereal straw and/or perennial grasses such as switchgrass. The other model is specific to Eastern Canada (Québec and Ontario) and allows the user to estimate the delivered cost of corn stover ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 2

19 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT and/or perennial grasses such as switchgrass. Assumptions common to both models are presented first, followed by assumptions specific to each model. Section 4 of this report provides the estimated delivered plant gate cost of feedstocks in both Western and Eastern Canada locations based on the most likely supply chain configurations for early commercialization. Section 5 provides an overview of biomass production in the key feedstock basins in Canada. The main attributes of each basin, positive and negative, for second generation biofuels are briefly discussed. The analysis will help in developing the optimum road map to commercialize second generation biofuels in each feedstock basin. Development of those commercialization plans is beyond the scope of this study. Finally, other costs and benefits to society, including potential impacts and issues that could arise from an expected increase in second generation biofuel production and consumption in Canada, are briefly discussed in the sixth section of this report. ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 3

20 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT 1. CHARACTERISTICS AND POTENTIAL OF AGRICULTURAL FEEDSTOCKS Advances in technology for converting biomass into energy will in all likelihood set the stage for the further expansion of the biofuel industry during the next decade. Given its vast amounts of available biomass due mainly to the agricultural and forestry sectors Canada s potential for the production of second generation biofuel is substantial. However, not all feedstocks that can be provided by the agricultural sector offer the same potential in terms of biofuel production. In this section, each feedstock s potential is evaluated and summarized in the form of a table, focusing on biomass production potential of agricultural crop residues and forage crops, as well as new energy crops not yet largely produced in Canada. The key available data and information used to produce these tables were sourced through a review of the most recent literature; however, it is important to keep in mind that the different elements analyzed in this section are themselves constantly evolving. New technologies and new plant varieties for biofuel production are the subject of many research projects across the globe and will therefore continue to emerge and alter feedstock potential, both from a technical and an economical perspective. Tables 1, 2 and 3 serve as a summary of the more complete information detailed in this section. ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 4

21 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT TABLE 1 SUMMARY OF FEEDSTOCK ENERGY POTENTIALS Feedstock Energy potential Estimated availability (J/g) (litres/ hectare) (litres/ tonne)* (EJ/year)** Cereal straw 340 Wheat straw 0.12 Corn stover 340*** Miscellaneous forage crops Same as Other grasses Oats straw Barley Rye Flax Corn stover from grain corn Corn for silage Tamed hay Switchgrass *** Switchgrass n.a. Hemp n.a. n.a. n.a. Hemp n.a. Other grasses (Miscanthus giganteus, Phalaris arundinacea, etc.) or low-input highdiversity (LIHD) biomass Converting this type of biomass into cellulosic ethanol and electricity is estimated to net 17.8 GJ/ha. Other grasses (Miscanthus giganteus, Phalaris arundinacea, etc.) or low-input highdiversity (LIHD) biomass *Cellulosic ethanol **Based on Wood, Susan M. and David B. Layzell (2003). A Canadian Biomass Inventory: Feedstocks for a Bio-based Economy. ***These production levels represent long-term objectives. For corn stover, 255 L/dry ton is the overall ethanol yield from biomass that has been demonstrated up to this day (Sheehan et al., 2004) n.a. ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 5

22 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT TABLE 2 SUMMARY OF FEEDSTOCK FARM-GATE PRICES Feedstock Cereal Straw Estimated farm-gate price For several years, the rule of thumb was that the delivered cost for agricultural residues could not be over $35/dry ton in order for a second generation biofuel plant to be economically viable (Sokhansanj and Turhollow, 2005). The recent rapid rise of the price of crude oil has had a two-fold impact on this assessment. On the one hand, the cost of diesel fuel used by the supply chain has increased, which must be absorbed by the biofuel plant. On the other hand, depending on the structure of the ethanol market and contractual agreements with oil companies, higher crude oil prices translate, to a certain degree, into higher ethanol selling prices. This new business environment is leading the way to a new price discovery process between farmers and cellulosic ethanol plants. Corn Stover Miscellaneous Forage Crops Switchgrass Hemp For Western Canada, straw cost in the windrow usually varies between $10-$15/tonne (field dry basis) and the average delivered cost at the plant should be expected to be in the range of $50/tonne (field dry basis). For Québec and Ontario, due primarily to competition for livestock bedding, straw is retailing in the order of $100/tonne, which is too expensive for transportation fuel production. Experience with large scale collection of corn stover is very limited. The preferred straw supply chain configuration for Québec and Ontario has yet to be determined and tested. The supply chain selected will have to meet the delivered cost criterion of ethanol plants to succeed. As corn stover should produce approximately as much ethanol per tonne as cereal straw, a delivered cost in the order for $50/tonne (field dry basis) is a reasonable initial objective. Corn stover can provide a low-cost feed source for mid-gestation beef cows, but in Québec and Ontario this represents only a very small fraction of the corn stover produced. Corn stover has a fertilizer value (mostly phosphorus and potassium) but soil incorporation of corn stover usually requires additional nitrogen fertilizer application to allow decomposition to take place. Stover removal may also allow for a transition to less intensive soil management practices such as minimum tillage and no-tillage, which results in cost reductions. The net effect should be considered when farmers decide on their asking price for corn stover in the windrow. The farm-gate price of forage crops depends on an large number of variables. Farms specializing in animal husbandry value this feedstock and do not necessarily wish to sell it. On the other hand, hay produced exclusively for the market tends to be of a higher quality and is consequently highly priced. Ideally, poor quality hay and forage would serve as feedstock for the second generation biofuel industry. However, the quantity and pricing of this feedstock is unsure. In the United States, for a yield of 3.6 dry tonnes/acre (4 tons/acre), the total production cost would range from $30.03 to $36.09 per tonne ($33.37 to $40.10 per ton). The total plant-gate price (biomass delivered to a biofuel plant) of switchgrass would therefore include these production costs, the producer s mark-up, and the collection and transportation costs (Kumar and Sokhansanj, 2007). In Canada, switchgrass delivered cost in the range of $60-$70/tonne is likely. However, the opportunity cost of the land selected to grow switchgrass strongly impacts the expected delivered cost. Hemp is unique in terms of the wide range of markets that can be targeted from the different components of its harvest. Moving from small hemp acreage to a hemp crop that is widely grown for the biofuel industry requires the founding of mutually profitable contracting arrangements between hemp producers and biorefineries. In Canada, the supply of hemp fibre for ethanol production is expected to remain marginal for the foreseeable future. ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 6

23 Analysis of the Logistical Costs Associated with Second Generation Biofuel Feedstocks DRAFT FINAL REPORT TABLE 3 SUMMARY OF FEEDSTOCK CONSTRAINTS Feedstock Cereal Straw Associated technical, logistical and economical constraints The amount that a producer is prepared to remove and supply to an industrial user depends on the producer s perceived value of straw. Some of the barriers to the economic use of agricultural crop residue are uncertainty in its availability, its quality, cost of collection, transport, and storage, and its location. j In establishing an industrial plant, it is essential to investigate the local producing areas, yields, and harvest practices and the economic, environmental, and social competition for the straw. Corn Stover Corn stover is not without its limitations. For instance, the current method of collection (a two-pass system) is not attractive in Ontario due to the wet conditions in the fall after grain harvest. A one-pass collection system that collects the grain and stover is expected to make stover removal more attractive. However, these systems are not yet commercialized and therefore their performance as well as their economic and environmental implications is uncertain. Results in the United States indicate that a one-pass harvest of both grain and stover, wet storage and rail transport to the processor could have a delivered cost of $30/dt. Miscellaneous Forage Crops Switchgrass Hemp Although forage crops contain lignocellulosic carbon, most are greatly valued by the livestock sector. Therefore, they are usually not considered to be an economically profitable feedstock for the second generation biofuel industry. Plant material collected in untamed meadows and pastures are a potentially interesting lowcost feedstock for the second generation biofuel industry. Nevertheless, these materials are usually located in remote and dispersed areas. Just as any other feedstock, they need to be collected, stored and transported, which could considerably increase the total cost. In the United States, delivered cost of biomass (without coverage of production costs and a producer s mark-up) to a biorefinery with a capacity of 1814 dry tonnes/day (2000 dry tons/day) for four collection systems (based on different forms of biomass during transport) amounts to approximately $44 $47/dry tonne for baling, $37/dry tonne for loading, $40/dry tonne for chopping and piling and $48/dry tonne for chopping and ensiling. Farmers will need to adapt to different cropping techniques and harvesting strategies in order to meet the requirements of the second generation biofuel industry. The single most important issue to be resolved for biorefineries is the development of mechanisms which will ensure that hemp of a specific grade and quality is made available in sufficient quantities. There are still a great number of risks associated with the production of this industrial crop. The permits and inspections required might discourage certain farmers to grow industrial hemp. ÉcoRessources Consultants and Agronovita Inc. for Agriculture and Agri-Food Canada 7

24 1.1 Crop residues Cereal straw Cereal Straw French name (Nom Français) Paille de céréales Wheat 1 Oats 2 Barley 3 Rye 4 Flax 5 and other mixed cereals French name (Nom français) Blé 1, Avoine 2, Orge 3, Seigle 4, Lin 5 Latin Names Triticum spp. 1, Avena Sativa 2, Hordeum Vulgare 3, Secale Cereale 4, Linum usitatissimum 5 GENERAL DESCRIPTION Consists of the dried yellow stems of crops that are traditionally used as food for animal feed or litter. However, the use of straw as an energy source is increasing rapidly. ON-FARM PRODUCTION Straw makes up about half of the yield of a cereal crop. Agronomic requirements Wheat Oats Barley Rye Flax Sandy loam Sandy loam Loams, clayloams Loams, clay- Medium-to Soil type (texture) and loams and heavy- to heavy clay to heavy clay soil textures. soil textures. silty-clay silty-clay textured soils loams loams Fertilizers Specific recommendations are based on soil samples. General recommendation for Manitoba e Nitrogen (N) 0-30 lb/acre following fallow or legume* lb/acre following grass and grasslegume breaking** lb/acre following stubble*** 0-20 lb/acre* lb/acre** lb/acre*** Phosphate (P 2 O 5 ) lb/acre n.a lb/acre lb/acre lb/acre lb/acre n.a. in a sideband in a sideband Potassium (K 2 O) lb/acre lb/acre broadcast broadcast Sulphur 15 lb/acre when required Not limited n.a. Machinery and equipment Basic farm equipment: plough and/or cultivator, seeder, sprayer, combine, baler, etc. n.a.

25 Weeds, insects and diseases Other environmental considerations OVERALL PRODUCTION (2006) Western Canada (BC and AB) Weeds: cockle, shepherd s purse, flixweed, narrow leaved hawk s beard, cow cockle, ragweed, tartary buckwheat, vetch, wild oats, cereal grains, non-cereal domestic grains such as peas, corn, domestic buckwheat, lentils and others. Cereal crops insects: Grasshoppers, aphids, cutworms, wireworms and armyworms, grass thrips, wheat stem maggots, hessian fly, wheat midge and others. Cereal crops diseases: Smuts, leaf and stem rust, ergot, barley yellos dwarf virus, seed rot, seedling blights, fusarium head blight, tan spot, septora leaf blotch, glume blotch and others. Competitive crops such as barley, rye and wheat can choke out many weeds; herbicides may not be necessary, depending on the relative time of emergence and the weed density Proper crop rotations and cultural strategies control weeds and pathogens. CANADA (provincial averages, 0 values taken out) Prairie Provinces (MB and SK) Central Canada (QC and ON) Atlantic Canada Wheat 2,640,432 ha a 6,610,358 ha a 554,221 ha a 15,095 ha a Average grain yield 2,800 kg/ha c 2,300 kg/ha c 3,850 kg/ha c 3,133 kg/ha c Straw /grain ratio 1.6 b 1.6 b 1.6 b 1.6 b Estimated straw production* 11,829,135 t 24,326,117 t 3,414,001 t 75,668 t Oats 546,441 ha a 1,320,343 ha a 179,150 ha a 17,678 ha a Average grain yield 2,600 kg/ha c 2,700 kg/ha c 2,500 kg/ha c 2,567 kg/ha c Straw /grain ratio 1.16 d 1.16 d 1.16 d 1.16 d Estimated straw production* 1,648,066 t 4,135,314 t 519,535 t 52,640 t Barley 1,681,651 ha a 1,764,538 ha a 195,278 ha a 48,494 ha a Average grain yield 2,950 kg/ha c 2,750 kg/ha c 3,300 kg/ha c 3,000 kg/ha c Straw /grain ratio 1 b 1 b 1 b 1 b Estimated straw production* 4,960,870 t 4,852,480 t 644,417 t 145,482 t Rye 49,618 ha a 132,826 ha a 29,015 ha a 3,726 ha a Average grain yield 1,150 kg/ha c 2,200 kg/ha c 1,050 kg/ha c n.a. Straw /grain ratio 0,7 d 0,7 d 0,7 d 0,7 d Estimated straw production* 39,942 t 204,552 t 21,326 t - Flax 24,468 ha a 9,096 ha a 2,800 ha a 317 ha a Average grain yield 650 kg/ha c 1,200 kg/ha c n.a. n.a. Straw /grain ratio 1.2 b 1.2 b 1.2 b 1.2 b Estimated straw production* 19,085 t 13,098 t - - Estimated production Across Canada: 57,106,283 tonnes

26 Availability Data on the true availability of straw residue is incomplete. One of the inherent problems with straw supply is its annual variation, which depends largely on weather conditions. j The amount of surface residues required for erosion control vary depending on soil texture and field slope, so not all of the straw produced can be removed. j Straw collection losses must also be factored in. For instance, only 50-63% of the straw produced per acre can be turned into bales. Taking these elements into consideration, the net straw available on the prairies is of about 20.9 Mt, not including the use of straw by the livestock sector. j Use of straw by the livestock sector varies in each region. Also, because the livestock industry uses straw for feeding and bedding only during part of the year, the amount of cattle surveyed in a given region and this region s particular climate, will affect the net straw availability. In Western Canada, a 5 kg/day coefficient j is used to calculate feeding and bedding needs of a herd. The total straw consumption by the livestock sector at a local level can then be assessed using this coefficient along with the number of days when feeding and/or bedding needs have to be met and the total number of cattle in a given region. In addition, only a portion of the farmers with excess straw are willing to sell it and farmers typically do not commit 100% of their excess straw in a given year. For the Prairies, research results show that straw is only available reliably in the black soil zone. In the three Prairie provinces, the available amount of cereal straw and chaff was estimated at two million tonnes of material for use as a biomass resource. k In Eastern Canada, a total of 1.6 to 2.4 million tonnes of straw would be available, depending on the year. k However, only a portion of the farmers with excess straw are willing to sell it, and farmers typically do not commit 100% of their excess straw in a given year. Strong uncertainties exist in straw availability on a regional basis. j ODT: Oven-dried tons C: CO 2 EJ: exajoules (equal to 1*10 18 joules) Sustainably removable amount (M ODT/y) CANADA l Amount available (M ODT/y) C content (Mt C/y) Wheat straw Oats straw Barley Rye Flax ENERGY POTENTIAL L/t 340 Potential production and use of co-products Lignin for energy production through combustion Energy potential (EJ/y) AVAILABLE TECHNOLOGY Harvesting Rotary combine: grinding action leaves less baleable straw and may either enhance or worsen further processing Conventional combines: less harvest capacity and increasingly difficult to purchase Storing Bales Transportation Truck or rail

27 ESTIMATED TOTAL PRODUCTION COST AND FARM-GATE PRICE Western Canada (BC and AB) Prairie Provinces (MB and SK) Central Canada (QC and ON) Atlantic Canada Wheat Operating costs - $141.79/acre f $204.85/acre h $205.37/acre i Fixed costs - $63.77/acre f - $96.75/acre i Total costs $125.69/acre g $222.81/acre f $254.60/acre h $302.12/acre i Oats Operating costs - $125.82/acre f $169.45/acre h $169.67/acre i Fixed costs - $63.77/acre f - $96.75/acre i Total costs $107.54/acre g $206.84/acre f $219.20/acre h $266.42/acre i ESTIMATED TOTAL PRODUCTION COST AND FARM-GATE PRICE (Grain) Western Canada (BC and AB) Prairie Provinces (MB and SK) Central Canada (QC and ON) Atlantic Canada Barley Operating costs - $139.07/acre f $186.35/acre h $198.36/acre i Fixed costs - $63.77/acre f - $96.75/acre i Total costs $ $/acre g $220.09/acre f $263.60/acre h $295.11/acre i Rye Operating costs - $95.65/acre f n.a. n.a. Fixed costs - $63.77/acre f n.a. n.a. Total costs $ $/acre g $176.67/acre f n.a. n.a. Flax Operating costs n.a. $123.60/acre f $149.05/acre h n.a. Fixed costs n.a. $63.77/acre f - n.a. Total costs n.a. $206.62/acre f $198.80/acre h n.a. STRAW Expected delivered cost $50/tonne $50/tonne $100/tonne n.a. Estimated farm-gate price ASSOCIATED TECHNICAL, LOGISTICAL AND ECONOMICAL CONSTRAINTS *These are based on a grain moisture content of approximately 13% For several years, the rule of thumb was that the delivered cost for agricultural residues could not be over $35/dry ton in order for a second generation biofuel plant to be economically viable. m The recent rapid rise of the price of crude oil has had a two-fold impact on this assessment. On one hand, the cost of diesel fuel used by the supply chain has increased, which must be absorbed by the biofuel plant. On the other hand, depending on the structure of the ethanol market and contractual agreements with oil companies, higher crude oil prices translate, to a certain degree, into higher ethanol selling prices. This new business environment is leading the way to a new price discovery process between farmers and cellulosic ethanol plants. For Western Canada, straw cost in the windrow usually varies between $10- $15/tonne (field dry basis) and the average delivered cost at the plant should be expected to be in the range of $50/tonne (field dry basis). For Québec and Ontario, due primarily to competition for livestock bedding, straw is retailing in the order of $100/tonne, which is too expensive for transportation fuel production. The amount that a producer is prepared to remove and supply to an industrial user depends on the producer s perceived value of straw. j Barriers to the economic use of agricultural crop residue are uncertainty in its availability, its quality, cost of collection, transport, and storage, and its location. j In establishing an industrial plant, it is essential to investigate the local producing areas, yields, harvest practices and the economic, environmental, and social competition for the straw. j Analyses must be conducted from a plant straw basin perspective.

28 n.a.: Data not available a Statistics Canada (2006). Census of Agriculture, Farm Data and Farm Operator Data, catalogue no XWE. b Second Annual Western Provincial Conference on Rationalization of Water and Soil Research and Management. AND Agriculture Statistics Saskatchewan < c Statistics Canada (2007). Field Crop Reporting Series. Catalogue no XIE, < d James A. Duke (1983). Handbook of Energy Crops. Unpublished, < e Manitoba Agriculture, Food and Rural Initiatives (2007). Cereal Crops. < f Manitoba Agriculture, Food and Rural Initiatives (2008). Guidelines For Estimating Crop Production Costs < g Alberta Agriculture and Food (2008). Crop Enterprise Cost and Return Calculator. < h Ontario Ministry of Agriculture, Food and Rural Affairs (2008) Field Crop Budgets. Publication 60. < i New Brunswick Agriculture Fisheries and Aquaculture (2004). Cereal Crop Production Costs 2004 Guideline. < j Sokhansanj, S., S. Mani, M. Stumborg, R. Samson and J. Fenton (2006). Production and distribution of cereal straw on the Canadian prairies. Canadian Biosystems Engineering, volume 24, n. 3, pp k Watson, PA, PA Bicho and MA Stumborg (1998). Wheat straw: a viable fibre sources for Canada? Pulp & Paper Canada, volume 99, iss. 12, p m Sokhansanj, Shahab and Anthony Turhollow (2005). Biomass Supply Systems & Logistics. DOE/USDA Joint Feedstock Portfolio Review Day 2 March 15 th. <

29 1.1.2 Corn stover Corn Stover French name (Nom français) Paille de maïs/résidus de maïs Latin name Zea mays ssp. mays L. GENERAL DESCRIPTION Corn stover consists of the non-grain, non-root portions of the corn plant (stalks, leaves, corn husks, and cobs) left in a field after harvest. Stover can be incorporated into the soil to enhance its organic matter content or grazed as forage. It can be collected for use as feed but is commonly not utilized in this manner. It can also be used as a fuel for bioenergy or as feedstock for bioproducts. ON-FARM PRODUCTION Field corn produced in Canada is harvested as grain (80%) or silage (20%). Although corn is grown in every province, approximately 96% of the crop is grown in Eastern Canada in the provinces of Ontario (63% or 630,000 hectares) and Québec (33% or hectares). a Corn stover makes up about half of the yield of a grain crop and is similar to straw. Agronomic Requirements Soil type (texture) Fertilizers Nitrogen (N) Silt loam soils are the most productive in terms ofproducing corn with lower requirements for nitrogen. b For heavy clay soils there is an additional requirement of about 30 lbs N/acre. b Lighter sand soils require additional amounts of nitrogen (about 30 lbs N/acre) when compared to silt loam soils. b A soil ph between 6.2 and 6.5 is ideal. a Specific recommendations are based on soil samples. General recommendation for Ontario c Expected grain yield (t/ha) Most profitable nitrogen rate (kg/ha of N) Eastern Ontario Western and central Ontario Southwestern preplant Southwestern side-dressed Phosphate (P 2 O 5 ), Potassium (K 2 O) and other nutrients Fertilizer with seed Fertilizer banded 5 cm to the side and 5 cm below the seed Fertilizer broadcast Machinery and equipment Maximum safe rates of nutrients for corn (all soils) 7 kg (nitrogen + potash) per hectare in 1m rows 55 kg nitrogen or 90 kg (nitrogen + potash) per hectare If urea is the nitrogen source, 200 kg nitrogen or 250 kg (nitrogen + potash) per hectare Basic farm equipment

30 Weeds, insects and diseases Weeds: Weeds serve as alternate hosts for insects and diseases and compete with the crop for moisture, light, nutrients and space. The critical period for weed control in corn is the two- to eight-leaf stage. Weeds include barnyard grass, proso millet, wild oats, green foxtail, yellow foxtail, cleavers, groundsel, lamb's quarters, redroot pigweed, round leaved mallow, velvetleaf, quackgrass, field bindweed, field horsetail, yellow nutsedge and others a Corn insects: C. graminicolca, northern and western corn rootworm, European corn borer, seed corn maggot, corn earworm, wireworms, aphididae, armyworm, black cutworm and others, corn flea beetle, European chafer and other minor pests a Corn diseases: leaf blights, seed rots, root rots, stalk rots, ear rots, kernel rots, stewart s wilt, common rust and common smut. Infection of corn seedlings by fungal pathogens (fusarium, pythium, rhizoctonia, etc.) can occur. Gibberella ear rot (Gibberella zeae) is one of the most economically important corn pathogens in Canada due to its production of mycotoxins a Other environmental Corn is commonly grown in rotation with soybeans in Ontario and Québec. considerations This crop rotation offers several advantages including better weed control options, less acute weed problems, lower disease and insect build-up and reduced nitrogen use. a Corn stover left on the soil surface provides a good protection against wind and rainfall. a CANADA OVERALL PRODUCTION (provincial averages, 0 values taken out) (2006) Western Canada Prairie Provinces Central Canada (BC and AB) (MB and SK) (QC and ON) Atlantic Canada Corn for grain 2,113 ha e 62,593 ha e 1,043,567 ha e 5,712 ha e Average grain yield 2,200 kg/ha f 3,150 kg/ha f 8,750 kg/ha f 7,350 kg/ha f Stover/grain ratio The stover to grain ratio is usually in the order of 1:1 but varies between regions, hybrids, etc. For instance, a corn yield of 140 bu/ac, the approximate yield of the largest corn growing regions in the US, is estimated to produce about 3.3 dry tons/acre (7.5 dry Mg/ha) of corn stover. i This is an average value and it can be lower or higher in certain cases. Also, not all of it can be collected. From a practical standpoint, 2 to 3 tons/acre (4.5 to 6.5 tonnes/hectare) should be collectable using proper collection methods while considering sustainability issues and weather conditions at the time of harvest. Lower collectable yields have been reported in the literature but ethanol plants considering purchasing corn stover will likely stay away from those regions or collection systems. Estimated stover production 4,649 t 197,168 t 9,131,211 t 41,983 t Corn for silage 41,374 ha e 40,030 ha e 187,387 ha e 9,324 ha e Average yield* 45,970 kg/ha f 33,600 kg/ha f 36,650 kg/ha f 19,450 kg/ha f Estimated production of total biomass 1,901,963 t 1,345,008 t 6,867,734 t 181,352 t

31 Availability ODT: Oven-dried tons C: CO 2 EJ: exajoules (equal to 1*10 18 joules) Only stover from grain corn is available for harvest. Only a portion of the stover produced can be collected. The collection rate varies between 33 and 70% according to the literature. f A rule of thumb is 2-3 tons/acre ( tonnes/ha). Assuming a stover collection rate of 5.6 tonnes/ha, the total collectable corn stover production available in Ontario and Quebec is estimated at 5.9 million tonnes. Competing demand for this resource does not really exist except for the requirements to maintaining sustainability. In addition, only a portion of the farmers with stover available may be willing to sell it, and those deciding to sell will not commit 100% of their acres in a given year. Many gaps in research on soil carbon remain practical limitations on stover removal in one-pass harvesting. f From a logistics standpoint, harvesting corn stover under the weather conditions of the fall season is a significant risk element to factor-in. CANADA h Sustainably Removable Amount (M ODT/y) Amount Available (M ODT/y) C Content (Mt C/y) Corn stover from grain corn Corn for silage ENERGY POTENTIAL Energy Potential (EJ/y) L/t 340 L/dry ton of biomass (255 L/t mentioned as a short term goal) n Potential production and use of co-products Lignin for energy production through combustion AVAILABLE TECHNOLOGY Harvesting One pass harvest of both grain and stover, wet storage and rail transport to the processor appear to be advantageous, with a delivered cost of US$30/dt. d One-pass nets the grower $22 to $47/ac depending on the yield. d Baling, nets $16 to $22/ac to the farmer for the same collection area as one-pass harvest. d Storing Bales pressed and stacked in the field Piles of biomass (similar to silage) Transportation Truck (rail under investigation in the US) ESTIMATED TOTAL PRODUCTION COST AND FARM-GATE PRICE (Grain) Western Canada (BC and AB) Prairie Provinces (MB and SK) Central Canada (QC and ON) Atlantic Canada Corn Operating Costs n.a $/acre j $/acre k $/acre l Fixed Costs n.a $/acre j n.a $/acre l Total Costs n.a $/acre j $/acre k $/acre l STOVER Expected delivered cost n.a. n.a. $50/tonne n.a.

32 Estimated farm-gate price ASSOCIATED TECHNICAL, LOGISTICAL AND ECONOMICAL CONSTRAINTS There exists very limited experience with large scale collection of corn stover. The preferred stover supply chain configuration for Quebec and Ontario still has to be determined and tested. The supply chain selected will have to meet the delivered cost criterion of ethanol plants to succeed. As corn stover should produce approximately as much ethanol per tonne as cereal straw, a delivered cost in the order for $50/ tonne (field dry basis) is likely a reasonable initial objective. Corn stover can provide a low cost feed source for mid-gestation beef cows but in Quebec and Ontario, this represents only a very small fraction of the corn stover produced. Corn stover has a fertilizer value (mostly P and K) but soil incorporation of corn stover usually requires additional nitrogen fertilizer application to allow decomposition to take place. Stover removal may also allow for a transition to less intensive soil management practices such as minimum tillage and no-tillage, which results in cost reduction. The net effect has to be looked at when farmers decide what they will be asking in terms of price for corn stover in the windrow. Corn stover is not without its limitations The current method of collection [a two-pass system] is not attractive in Ontario due to the wet conditions in the fall after grain harvest. A one-pass collection system that collects the grain and stover is expected to make stover removal more attractive. However, these systems are not yet commercialized and therefore their performance as well as their economic and environmental implications are uncertain. Results in the United States indicate that stover can be collected, stored, and hauled for about $43.60 to $48.80/dry ton ($ $53.80/dry Mg) using conventional baling equipment for conversion facilities ranging in size from 500 to 2000 dry tons/day ( dry Mg/day). These estimates are inclusive of all costs including farmer payments for the stover. These results also suggest that costs might be significantly reduced with an unprocessed stover pickup system provided more efficient equipment is developed. i n.a.: Data not available *Estimates of production for fodder corn are calculated using a standard percentage moisture content of 70% (Statistics Canada, 2007). a Agriculture and Agri-food Canada (2006). Crop Profile for Field Corn in Canada. Pesticide Risk Reduction Program, Pest Management Centre. < 57p. b Stewart, Greg and Ken Janovicek (2005). Cost Effective Nitrogen Rate Adjustments in Ontario Corn Producer Newsletter. < c Ontario Ministry of Agriculture, Food and Rural Affairs (2002). Agronomy Guide for Field Crops: Chapter 3. < d Atchison, J.E. and J.R. Hettenhaus (2004). Innovative Methods for Corn Stover Collecting, Handling, Storing and Transporting. National Renewable Energy Laboratory, US Department of Energy. < 63 p. e Statistics Canada (2006). Census of Agriculture, Farm Data and Farm Operator Data, catalogue no XWE. f Statistics Canada (2007). Field Crop Reporting Series. Catalogue no XIE, < g Spatari, Sabrina, Yimin Zhang and Heather L. Maclean (2005). Life Cycle Assessment of Switchgrass and Corn Stover Derived Ethanol-Fueled Automobiles. Environmental Science and Technology, volume 29, p

33 h Wood, Susan M. and David B. Layzell (2003). A Canadian Biomass Inventory: Feedstocks for a Bio-based Economy. BIOCAP Canada Foundation. Report prepared for Industry Canada. Contract # i Perlack, Robert D. and Anthony F. Turhollow (2002). Assessment of Options for the Collection, Handling, and Transportation of Corn Stover. Oak Ridge National Laboratory, U.S. Department of Energy. Bioenergy Feedstock Development Program, Environmental Sciences Division. j Manitoba Agriculture, Food and Rural Initiatives (2008). Guidelines For Estimating Crop Production Costs < k Ontario Ministry of Agriculture, Food and Rural Affairs (2008) Field Crop Budgets. Publication 60. < l New Brunswick Agriculture Fisheries and Aquaculture (2004). Cereal Crop Production Costs 2004 Guideline. < m Baag, Joel (2007). Pricing Corn Silage in Ontario Ministry of Agriculture, Food and Rural Affairs. < n Sheehan, John, Andy Aden, Keith Paustian, Kendrick Kilian, John Brenner, Marie Walsh and Richard Nelson (2004). Energy and Environmental Aspects of Using Corn Stover for Fuel Ethanol. Journal of Industrial Ecology, volume 7, number 3-4, p

34 1.2 Adopted agricultural crops Miscellaneous forage crops Miscellaneous Forage Crops French name (Nom français) GENERAL DESCRIPTION ON-FARM PRODUCTION Agronomic requirements Machinery and equipment Cultures fourragères Forage crops constitute biomass (mainly plant leaves) eaten by grazing animals. When these crops are cut, dried, stored and carried to livestock in the form of hay or silage, they are referred to as fodder plants. Forage crops are produced across Canada to feed many different types of livestock. Different types of forage crops can be grown on many different types of soils and in different climates. As a result, forage crops are the most cultivated agricultural crop in Canada. Basic farm equipment. CANADA OVERALL PRODUCTION (2006) Western Canada Prairie Provinces Central Canada (BC and AB) (MB and SK) (QC and ON) Atlantic Canada Alfalfa and alfalfa mixtures 1,794,466 ha a 2,278,501 ha a 958,633 ha a 43,959 ha a Average yield 4,050 kg/ha b 3,390 kg/ha b 4,995 kg/ha b 4,815 kg/ha b Estimated forage production 7,2 Mt 7,7 Mt 4,8 Mt t Other tamed hay 1,023,620 ha a 751,754 ha a 933,290 ha a 184,985 ha a Average yield 4,050 kg/ha b 3,390 kg/ha b 4,995 kg/ha b 4,815 kg/ha b Estimated forage production t 2,5 Mt 4,7 Mt t Availability Hay is a relatively bulky, low-value commodity and availability varies from region to region. Hay markets are therefore highly variable. The main factors affecting these markets include transportation costs and quality as perceived by the end user. CANADA c ODT: Oven-dried tons C: CO 2 EJ: exajoules (equal to 1*10 18 joules) Sustainably removable amount (M ODT/y) Amount available (M ODT/y) C content (Mt C/y) Tamed hay Tame hay which is unused in an average year AVAILABLE TECHNOLOGY Harvesting Storing Transportation ESTIMATED TOTAL PRODUCTION COST AND FARM-GATE PRICE Basic haying equipment Bales or silage Truck or rail Western Canada (BC and AB) Prairie Provinces (MB and SK) Central Canada (QC and ON) Energy potential (EJ/y) Atlantic Canada

35 Alfalfa Timothy Hay Operating costs n.a. $98.41/acre d $227.80/acre e n.a. Fixed costs n.a. $40.79/acre d n.a. n.a. Total costs n.a. $143.99/acre d $296.60/acre e n.a. Estimated farm-gate price The farm-gate price of forage crops depends on a large number of variables. Farms specializing in animal husbandry value this feedstock and are not necessarily interested in selling it. On the other hand, hay produced exclusively for the market tends to be of a higher quality and is consequently highly priced. Ideally, poor quality hay and forage would serve as feedstock for the second generation biofuel industry. However, the quantity and pricing of this feedstock is unsure. ASSOCIATED TECHNICAL, LOGISTICAL AND ECONOMICAL CONSTRAINTS n.a.: Data not available Although forage crops contain lignocellulosic carbon, most are greatly valued by the livestock sector. Therefore, they are usually not an economically viable feedstock for the second generation biofuel industry. Plant materials collected in untamed meadows and pastures are also considered to be a potentially interesting low-cost feedstock for the second generation biofuels industry. However, these materials are usually located in remote and dispersed areas. Just as any other feedstock, they need to be collected, stored and transported, which could considerably increase the price of these feedstocks. a Statistics Canada (2006). Census of Agriculture, Farm Data and Farm Operator Data, catalogue no XWE. b Statistics Canada (2007). Field Crop Reporting Series. Catalogue no XIE, < c Wood, Susan M. and David B. Layzell (2003). A Canadian Biomass Inventory: Feedstocks for a Bio-based Economy. BIOCAP Canada Foundation. Report prepared for Industry Canada. Contract # d Manitoba Agriculture, Food and Rural Initiatives (2008). Guidelines For Estimating Crop Production Costs < e Ontario Ministry of Agriculture, Food and Rural Affairs (2008) Field Crop Budgets. Publication 60. <

36 1.3 New energy crops Switchgrass Switchgrass French name (Nom français) Panic érigé Latin Name Panicum Virgatum L. GENERAL DESCRIPTION ON-FARM PRODUCTION Agronomic requirements Varieties Soil type (texture) Fertilizers Nitrogen (N) Phosphate (P 2 O 5 ) Potassium (K 2 O) A perennial warm season grass, it is one of the three major native grasses found in the North American tall grass prairie prior to settlement. In the United States, particularly in the south, switchgrass is grown in as a mid-summer forage crop and for erosion control purposes through the Conservation Reserve Program (CRP). After several years of research, this crop proves to be promising for energy applications in Canada. The successful production of switchgrass requires different production techniques and harvest schedules than those used for cool season grasses such as timothy and alfalfa, used for hay production. Although it is slow to establish, it often produces a high-yielding stand in subsequent years. Two categories distinguish the different varieties of switchgrass: lowland varieties, which have developed under floodplain conditions, and upland varieties, which grow better under drier conditions. Lowland varieties cannot be grown in Eastern Canada because of their lack of resistance to cold temperatures. However, upland varieties can be grown in eastern Ontario and western Québec. The Cave-in-Rock variety seems to be the most productive variety for this region. In Western Canada, upland varieties would also be necessary due to lack of precipitation. Although switchgrass can be cultivated in all types of soils, the type of land on which it is planted will affect the overall cost of production through the land charge and the opportunity cost. c Switchgrass performs best on well-drained soils. a Optimal yields can be obtained at much lower nitrogen fertilizer application rates than cool season grasses. b Furthermore, to avoid competition from weeds, no nitrogen is applied in the establishment year. During production years, nitrogen fertilizer can be applied at 112 kg/ha (100 lb./acre). c However, 50 to 60 kg/ha (45-53 lbs./acre) is sufficient to sustain production. a During the establishment year, it is assumed that phosphorus and potassium are applied at the rate of 33.6 kg/ha (30 lb./acre) and 44.8 kg/ha (40 lb./acre), respectively. c In following years, switchgrass responds to phosphorus fertilization only on soils with low, or very low, phosphorus levels, and rarely responds to potassium fertilization. b With each tonne of switchgrass harvested, there are 0.42 kg of phosphorus and 9.47 kg of potassium removed (0.83 lb. of phosphorus and lb. of potassium/ton of switchgrass). c

37 Lime Machinery and equipment Weeds, insects and diseases Other environmental considerations Lime requirements will vary by field. It is assumed, however, that at some time over the life of the switchgrass stand, lime will have to be applied. c Pre-harvest machinery operations can be carried out with basic farm equipment used for establishing forage. However, some fields may require additional seed-bed preparation. Grass weeds are the most difficult to control as the crop is slow to establish and only a limited number of herbicides are registered for use on switchgrass. A possible weed control strategy involves clipping the weeds just above the switchgrass canopy. Switchgrass provides an excellent nesting cover for birds. It has a tremendous root mass creating a large underground biomass carbon pool, thereby helping to offset greenhouse gases. OVERALL PRODUCTION IN CANADA IS AT THIS TIME UNMEASURED BY GOVERNMENTAL STATISTICS AGENCIES Yields Up to 14 tonnes of biomass per acre have been recorded in the U.S. 3.2 to 5.3 tonnes per acre in Central Canada 2.5 to 3 tonnes per acre in the Prairies are considered to be reasonable (but conservative) production levels. b Potential production and use of Lignin for heat or energy production through combustion co-products ENERGY POTENTIAL J/g 18,409 L/ha 4,400 L/t 400 b AVAILABLE TECHNOLOGY Harvesting Storing Transportation Standard balers Bales (square or round) Truck ESTIMATED TOTAL PRODUCTION COST AND FARM-GATE PRICE Iowa c Ontario a The perennial nature of switchgrass, along with its low input requirements and high productivity, are the main factors contributing to its low cost of production compared to most traditional crops. Research indicates that at $50/dry tonne, the crop is profitable for eastern Ontario farmers. Across North America, this value is estimated to vary between $45 and $75/dry tonne, depending on yields and the type of land used. Approximately 50% of the costs incurred in growing switchgrass are related to harvesting and transporting the crop to the biorefinery. Effiency gains in these two areas will strongly influence returns to farmers.

38 Estimated farm-gate price ASSOCIATED TECHNICAL, LOGISTICAL AND ECONOMICAL CONSTRAINTS n.a.: Data not available In the U.S., for a yield of 3.6 dry tonnes/acre (4 tons/acre), the total production cost excluding the collection and storage ranges from $30.03 to $36.09 per tonne ($33.37 to $40.10 per ton). The total delivered cost of switchgrass to a biofuel plant would then need to include these production costs along with the collection and transportation cost. d In Canada, switchgrass delivered cost in the range of $60-$70/tonne can be expected. The opportunity cost of the land selected to grow switchgrass strongly impacts the expected delivered cost. In the U.S., delivered cost of biomass (without coverage of production costs and producer s mark-up) to a biorefinery with a capacity of 1814 dry tonnes/day (2000 dry tons/day) for four collection systems (based on different forms of biomass during transport) amount to approximately $44 $47/dry tonne for baling, $37/dry tonne for loafing, $40/dry tonne for chopping and piling and $48/dry tonne for chopping and ensiling. d a Blais,P.A., Girouard, P., Mehdi, B. and R. Samson (1998). Switchgrass in Eastern Ontario: A Management Guide. Resource Efficient Agricultural Production (REAP), Canada. < %20and %20Newsletters/Bioenergy/21 %20Le %20panic.pdf>. b Samson, R. (1991). Switchgrass: a living solar battery for the praires. Resource Efficient Agricultural Production (REAP), Canada. < %2091 %20L.htm>. c Duffy, M., Nanhou, V.Y. (2001). Costs of producing switchgrass for biomass in southern Iowa. Iowa State University Extension publication PM1866 (April 2001), 10 pages. < d Kumar, Amit and Shahab Sokhansanj (2007). Switchgrass (Panicum vigratum, L.) delivery to a biorefinery using integrated biomass supply analysis and logistics (IBSAL) model. Bioresource Technology 98 (2007) pp

39 1.3.2 Hemp Hemp French name (Nom français) Latin name GENERAL DESCRIPTION ON-FARM PRODUCTION Agronomic requirements Soil type (texture) Fertilizers Nitrogen (N) Phosphate (P 2 O 5 ) Potassium (K 2 O) Machinery and equipment Weeds, insects and diseases Chanvre Cannabis sativa Hemp (Cannabis sativa L.) is an annual, herbaceous plant with a slender stem. The stem is more or less branched depending on the crop density. The leaves are of a palmate type and each leaf has 7 to 11 leaflets, with serrated edges. The strong tap-root penetrates deep into the soil. Hemp is cultivated virtually everywhere in the world except for the United States, and its cultivation in western countries is growing steadily. China and other eastern countries have never prohibited its cultivation and use it extensively. Industrial hemp can be grown on a wide variety of soil types. Hemp prefers a sufficiently deep, well-aerated soil, along with good moisture and nutrient holding capacity. Poorly drained soils, however, are not recommended as excess surface water after heavy rains can result in damage to the hemp crop. Hemp is extremely sensitive to flooding and soil compaction. a To achieve an optimum hemp yield, twice as much nutrient must be available to the crop as will finally be removed from the soil at harvest. A hemp field produces a very large bulk of plant material in a short vegetative period. The nitrogen uptake is most intensive in the first six to eight weeks, while potassium and in particular phosphorus are more important during flowering and seed formation. a Industrial hemp requires 80 to 100 lbs/ac (90 to 112 kg/ha) nitrogen a Industrial hemp requires 35 to 50 lbs/ac (39 to 56 kg/ha) phosphate a Industrial hemp requires 52 to 70 lbs/ac (60 to 80 kg/ha) potash a No-till systems can also be used with good results, but may be more vulnerable to erratic emergence depending on the growing season Industrial hemp is an extremely efficient weed suppressor No chemicals are needed for growing this crop There are no registered chemicals for weed control in hemp. A normal stand of 200 to 300 plants per square metre shades out weeds, leaving the fields weed-free at harvest Crops grown with 15 to 20 lbs/acre of seed may be at risk with regards to weed infestation

40 Other environmental considerations Yields Potential production and use of co-products J/g L/ha L/t Harvesting Storing Transportation Hemp can be grown on the same land for several years in succession but rotation with other crops is desirable. Hemp responds well to most preceding crops. Introduction of hemp in a crop rotation may improve soil health. OVERALL PRODUCTION IN CANADA: 10, hectares in 2005 b Based on yield data from 1995, 1996 and 1997, yield expectations are between three to four tons of baled hemp stalks per acre on well drained loamy soils in southwestern Ontario. a Lignin for heat or energy production through combustion ENERGY POTENTIAL n.a. n.a. n.a. AVAILABLE TECHNOLOGY Fibre hemp is normally ready to harvest in 70 to 90 days after seeding. The end use of the product has a significant impact on the harvesting method implemented. Harvesting systems must be compatible with processing technologies. For fibre production the crop will be cut and dew retted in the field. For storage, the moisture content of hemp stalks should not exceed 15%. The bales can be stored for a long time in dry places such as storage sheds, barns or any other covered storage. Truck ESTIMATED TOTAL PRODUCTION COST AND FARM-GATE PRICE Manitoba c (hemp fibre) Ontario d Total Operating Costs $150.80/acre n.a. Total Fixed Costs $60.27/acre n.a. Total Costs $228.32/acre $657.61/acre Estimated farm-gate price ASSOCIATED TECHNICAL, LOGISTICAL AND ECONOMICAL CONSTRAINTS n.a.: Data not available Hemp is unique in terms of the wide range of markets that can be targeted from the different components of its harvest. To move from small hemp acreage to a hemp crop that is widely grown for the biofuels industry will require the establishment of mutually profitable contracting arrangements between hemp producers and biorefineries. e In Canada, the supply of hemp fibre for ethanol production is expected to remain marginal for the foreseeable future. Farmers will need to adapt to different cropping techniques and harvesting strategies in order to meet the requirements of the second generation biofuel industry. e The single most important issue to be resolved for biorefineries is the development of mechanisms to ensure that hemp of a specific grade and quality are made available in sufficient quantities. e There are still a great number of risks associated with the production of this industrial crop. The permits and inspections required might discourage certain farmers from growing industrial hemp.

41 a Dragla, Peter (2007). A Cropping Guide for Farmers: Growing Industrial Hemp for the 21st Century. Cropping guide published on-line by Kenex Ltd. < b Health Canada (2005). Industrial Hemp Licensing and Authorization Summary. Office of Controlled Substances, Healthy Environments and Consumer Safety Branch. < c Manitoba Agriculture, Food and Rural Initiatives (2008). Guidelines For Estimating Crop Production Costs < d Budget provided by Kenex Ltd. ( This number is used as a guideline for potential producers in Ontario. e Girouard, Patrick and Bano Mehdi (1999). Re-experimenting with hemp in Québec. Resource Efficient Agricultural Production (REAP). < Fibres/6%20Re-Experimenting%20with.pdf> Other energy crops The plants described in this section have been identified as presenting a certain potential for biomass production. These tend to grow naturally in Canada but only on marginal lands in remote areas. They are therefore often identified as low-input high-diversity (LIHD) biomass. Converting this type of biomass into cellulosic ethanol and electricity is estimated to net 17.8 GJ/ha (Tilman and al., 2006). Unfortunately, research activities concerning the cultivation, harvesting and storing of these plants, as well as their overall potential for the production of second generation biofuels are still at a very preliminary stage. Consequently, each of these potential feedstocks is briefly described below Spartina pectinata (sloughgrass, prairie cordgrass, freshwater cordgrass, marshgrass, sloughgrass, rip gut) This plant was one of the dominant natural grasses of the tall grass prairie region. It is one of the tallest native grasses but is often shorter under less ideal conditions, particularly in dry years. Although it can be found in southern Canada and over a good portion of the eastern and central U.S., it exists primarily in wet ditches, near sloughs and in pothole prairie remnants. It often grows in dense, nearly pure stands near the edges of wetlands, with the vigorous rhizomes and shade from the tall stems excluding most other species. Although livestock do not readily eat cord grass, it can make good hay if it is cut several times a year to prevent it from becoming coarse. No data was available concerning the energy potential of spartina pectinata. However, a two-year project, initiated in 2007 by researchers at South Dakota State University, is dedicated to examining the chemical

42 composition of spartina pectinata. It also seeks to improve disease resistance and to reduce the amount of lignin in the dry matter (South Dakota State University, 2007). Also, Madakadze and al. (1998) demonstrated that switchgrass and spartina pectinata present the most potential for the production of bulk biomass in Canada, whether it be used for combustion or biofuel production. Evidently, this plant represents another resource eventually exploitable by the second generation biofuels industry Miscanthus giganteus (Giant Chinese silver grass or giant miscanthus) This warm-season grass is used in Europe and the U.S. for energy production, mostly through combustion of dried biomass pellets. It requires full sun, can tolerate a wide range of soil types and is moderately drought-tolerant. Although it can resist to snow, a pilot-project in Québec concluded that this plant is poorly adapted to Canada s colder climate Phalaris arundinacea (Reed canarygrass) This tall, leafy, high-yielding perennial crop is grown across much of southern Canada. The plant grows well in poorly drained soils subject to prolonged flooding and is one of the most drought-tolerant of the cool-season grasses. Although it is well-adapted to extreme weather conditions, this plant is considered to be highly invasive and is therefore not often used in agricultural fields. Nonetheless, Scandinavian countries have performed a substantial amount of research to develop reed canarygrass as an energy crop. The crop requires greater amounts of nutrients than switchgrass, for example, but is well-adapted to growing areas which are too cold for switchgrass production.

43 2. DEVELOPING A FEEDSTOCK-ORIENTED SECOND GENERATION BIOFUEL INDUSTRY IN CANADA This section reviews the different technologies that are most likely to allow the production of second generation biofuels to emerge as a significant industry in Canada. More specifically, the section contains: Second generation technologies likely to be commercialized. Main known operational specifications for each technology. Known biomass feedstock requirements of each technology. Logistical strategies for the development of this industry are then presented. This section will ultimately help determine which feedstocks can be exploited initially and those that will more likely be used on a more long-term basis. 2.1 Second generation technologies Technologies to convert biomass (plant or animal matter, biodegradable wastes or any other organic material) into liquid fuels that themselves will replace petroleum products can all be categorized as second or third generation technologies. Next generation technologies gasify or liquefy solid biomass by heating it with limited or no oxygen. The intermediate product (synthesis gas or pyrolysis oil) can be burned more efficiently and used for biomass conversion into chemicals or materials. Biodiesel production using next generation technologies produce biodiesel with chemical properties that are very similar to those of traditional petrodiesel. For ethanol production, second generation technology involves enzymes capable of hydrolyzing cellulose into sugars that can afterwards be fermented. This biofuel, also called cellulosic ethanol, can seemingly be made from any feedstock containing cellulose such as corn stover (leaves and stalks), straw, grasses (switchgrass, miscanthus giganteus, etc.) wood chips, sawdust, etc. In other words, the second generation biofuel industry s growth relies heavily on the development of technologies capable of (1) converting cellulosic biomass and lignin (rather than grains or starchy/sugarbased feedstocks) into biofuel and other products (chemicals or materials) and (2) gasifying or liquefying biomass for power production or for catalytic conversion to valuable products (Figure 1). FIGURE 1 FIRST AND NEXT GENERATION TECHNOLOGIES FOR BIOMASS CONVERSION

44 Source: Brown, 2007 For this purpose, two platform technologies are considered: the sugar platform and the thermochemical platform (Figure 2). Although these technologies, which are under study by the Office of Energy Efficiency and Renewable Energy's Biomass Program (EERE, 2007), are the most promising, other platforms have a potential for second generation biofuel production. They are called platforms because they are the basic technologies that can be used to produce a large variety of products. Therefore, future biorefineries would convert biomass into a variety of biofuels and other products (chemicals, materials, power, etc.). FIGURE 2 MOST PROMIZING PLATFORMS FOR BIOMASS CONVERSION Source: Bruce, 2007 Many of the drawbacks of first generation technologies are currently under development to create second and third generations. The obstacles that must still be overcome to reach widespread use of next generation technologies are mainly technical (Green Car Congress, 2005). As the next generation

45 technologies develop, the price of cellulosic ethanol will compete more and more with gasoline and start penetrating the market in a substantial proportion. However, the development of the ethanol industry could be then limited by the biomass location and availability Sugar platform This technology breaks cellulose and hemicellulose into their sugar components, which can then be fermented or converted to biofuel and chemicals. Lignin can be burned to provide heat and electricity or can be also converted to fuels and chemicals. Indeed, the lignin portion remains as a residual product after the sugars have been converted to ethanol; the use of this byproduct to generate other products such as power has a significant role in the financial feasibility of a technology. The first step in the process is thermochemical pretreatment, which hydrolyzes hemicellulose, breaking it down into xylose and others sugar components as well as solubilizing the lignin (EERE, 2007). Several hydrolysis technologies, including enzymatic hydrolysis, exist to break down the biomass into its sugar components. In enzymatic hydrolysis technology the cellulose contained in hemicellulose and lignin sheaths is enzymatically hydrolyzed to release its sugars which can then be used to produce ethanol by fermentation and processes such as catalytic conversion, gasification or combustion can be applied to the residual lignin to generate heat and electricity (Figure 3). The first version of this technology replaces the cellulose acid with a cellulase enzyme for the hydrolysis step; this process is called separate hydrolysis and fermentation (SHF) (EERE, 2007). In a second version, simultaneous saccharification and fermentation (SSF) are introduced and cellulase and microbes are mixed. The separate hydrolysis module can be removed and problems of product inhibition associated with enzymes are eliminated. The microorganisms convert sugars in ethanol as they are produced. SSF has been extended to include the co-fermentation of various sugar substrates (SSCF) (EERE, 2007). Enzymatic hydrolysis is the most promising technology for reducing fuel production costs (compared with already well-developed thermochemically based processes), but some challenges need to be resolved for larg- scale applications (EERE, 2007). Technologies for sugar production from wood biomass have been under development for a long time and are already available. However, the understanding of different cellulase enzymes action modes is in its infancy, and these enzymes remain considerably expensive. This is the main reason for current ethanol production technologies being mainly based on acid hydrolysis technologies.

46 Consequently, the availability of cost-effective cellulase enzymes represents the main limitation to make this technology competitive and commercially available for large-scale application. Intensive research is underway in biochemistry and protein engineering in order to reduce the cost of using those enzymes by 10 to 15 times: from the current rate of 30 to 50 cents per gallon to less than five cents per gallon of ethanol produced (EERE, 2007). Cellulase enzymes are already used for stone-washing jeans, but this application is recent, the production scale is very small (compared to what would be needed for large-scale ethanol production) and the process is simpler: while this textile application requires only 1% hydrolysis, the ethanol production required almost complete hydrolysis. Achieving an effective cost reduction for cellulase enzymes, by reducing their production cost and improving their performance, combined with platform improvements, would make this technology surpass thermochemical hydrolysis technologies and make biomass ethanol production competitive with starch ethanol production. The Office of Energy Efficiency and Renewable Energy's Biomass Program is currently working with the two biggest enzyme manufacturers (Genencor International and Novozymes) towards these goals (EERE, 2007). FIGURE 3 THE ENZYMATIC HYDROLYSIS PROCESS Source: Saville, 2007 This technology is used at the Iogen plant in Ottawa. Iogen in association with Petro-Canada and the Government of Canada, has invested in the construction of the first cellulose ethanol plant worldwide using this cellulase enzyme-based technology commercially. The Canadian government has invested a

47 repayable amount of $7.7 million in a research and development project to develop this technology, capable of converting agricultural residues such as straw and corn stalks into sugars which can be then converted to ethanol by fermentation (Industry Canada, 2008). In a different approach, the BC International Company, based in Los Angeles, will soon start using acid hydrolysis technology and will eventually move to enzyme hydrolysis technology when it becomes costeffective (EERE, 2007). Finally, funding from the U.S. Department of Energy could lead to the opening of a new enzyme hydrolysis-based plant in California producing commercial ethanol from rice straw (EERE, 2007). Other hydrolysis technologies have been employed for a long time in some industries. Being older, they are better established, but offer fewer opportunities for cost-efficiency improvements. Nevertheless, these are the technologies that currently support the development of cellulosic ethanol production for commercial uses. In the long term, they are not considered to be the most promising technologies economically viable for the sugar platform. These technologies are of two main types: concentrated acid hydrolysis and dilute acid hydrolysis. The concentrated acid hydrolysis process is based on concentrated acid decrystallization of cellulose followed by dilute acid hydrolysis to sugars at near theoretical yields (EERE, 2007). Afterward, sugars are converted to ethanol by fermentation (Figure 4). This process is used for dissolving and hydrolyzing cellulose in cotton for an extended period of time. After cellulose decrystallization, a gelatin is formed with the acid and the cellulose can be quickly and completely hydrolyzed to glucose with water dilution. For example, in Arkenol's process, about 75% of sulfuric acid is added to dried biomass (10% moisture) for decrystallization, then water is added to dilute the acid to about 25% in order to release the sugars. The fermentation converts both the xylose and the glucose to ethanol at theoretical yields of 85% and 92%, respectively. A triple effect evaporator is required to reconcentrate the acid. Arkenol claims that sugar recovery in the acid/sugar separation column is at least 98%, and acid lost in the sugar stream is not more than 3% (EERE, 2007).

48 FIGURE 4 THE CONCENTRATED ACID HYDROLYSIS PROCESS Source: EERE, 2007 As mentioned, these technologies are already commercially available, but are not necessarily costeffective for cellulosic ethanol production due to the large amounts of acid required. Further improvements in the process are necessary to support large scale applications. In the United States, two companies are currently working with the U.S. Department of Energy on the commercialization of this technology (EERE, 2007): Arkenol is trying to build a commercial plant in California to produce ethanol from rice straw. Because rice straw normally poses disposal problems, it becomes a cheap feedstock for ethanol production and provides a good opportunity to improve costeffectiveness. Masada Resource Group is involved in the construction of an ethanol production plant from the lignocellulosic portion of municipal solid waste in New-York state. The plant is based on the concentrated sulfuric acid process because it is robust enough to treat complex feedstocks such as municipal solid waste. As with rice straw, high tipping fees for waste disposal provides good opportunities for more costeffective ethanol production. In the dilute acid hydrolysis process, hydrolysis occurs in two stages to maximize sugar yields from the hemicellulose and cellulose fractions of biomass. The first stage is operated under milder conditions to

49 hydrolyze hemicellulose, while the second stage is optimized to hydrolyze the more resistant cellulose fraction (EERE, 2007). Resulting liquids at both stages are converted to ethanol by fermentation (Figure 5). The remaining cellulose and lignin can be used for heat or electricity production. Tests with this process resulted in yields of 89% for mannose, 82% for galactose and 50% for glucose (EERE, 2007) and ethanol production by fermentation at 90% of the theoretical yield. FIGURE 5 THE DILUTE ACID HYDROLYSIS PROCESS Source: EERE, 2007 As in the concentrated acid hydrolysis process, those technologies are already commercially available, but they are not necessarily cost-effective for cellulosic ethanol production due to the large amounts of acid required. Two companies are currently working with the U.S. Department of Energy on the commercialization of this technology (EERE, 2007): BC International is producing 20-million gallons ( liters) of ethanol using this process at its Los Angeles plant. Dilute acid hydrolysis is used for conversion of bagasse into sugar. Tembec and Georgia Pacific, of the pulp and paper industry, are using this process to dissolve hemicellulose and lignin from wood in pulp mills and produce cellulose pulp. The sugars are converted to ethanol by fermentation and the lignin is burned to generate steam or converted to other products (additives, soil stabilizer, etc.).

50 2.1.2 Thermochemical platform This technology gasifies or liquefies solid biomass by heating it with limited or no oxygen. The intermediate product (synthesis gas or pyrolysis oil) can be burned more efficiently and used for biomass conversion into chemicals or materials. Gasification: When heating biomass with limited oxygen (one-third of the proportion needed for normal combustion), gasification produces a synthesis gas, or syngas, composed of carbon monoxide and hydrogen. Since gaseous fuels are easier to mix with oxygen than liquid fuels (which in turn are easier to mix than solid fuels), the combustion is more efficient than it would be by using solid biomass directly (EERE, 2007). As a result, the overall efficiency of the plant is improved. This technology is well-suited to treat forest industry residues and black liquor from the pulp and paper industry. Syngas can be burned in gas turbines to produce electricity in a more efficient manner than with fossil fuels. In addition, if the heat can be recuperated and used for different purposes (space or water heating for instance), the efficiency is further increased. Gaseous fuels are easier to mix with chemical catalysts than solid fuels and improve the conversion process into chemicals and materials as well. Several gasification processes exist, including (EERE, 2007): Fisher-Tropsch: converts syngas to liquid fuels Water-gas shift: converts syngas to high concentrated hydrogen for fuel cells Other catalytic processes: convert syngas into chemicals or materials By gasifying biomass prior to making a liquid, these technologies permit the use of a much wider variety of fuel sources, including crop residues or waste. Production costs can be reduced by using lower-quality material, since new processes are able to produce high-quality fuel regardless (Green Car Congress, 2005). Several major players in next generation biodiesel technologies are in Europe, including Choren, which has developed a large-scale pilot plant in Germany, and in collaboration with Shell is developing a prototype commercial plant ( tonnes per year) predicted to be ready by 2009 (Green Car Congress, 2005). Pyrolysis and thermal processing: Biomass can also be liquefied without oxygen, through pyrolysis. It can also be liquefied by hydrothermal liquefaction (or other thermochemical processes). Hydrothermal liquefaction consists in contracting the biomass with water at high temperature and pressure, thereby maintaining the water in liquid form (EERE, 2007). These processes produce pyrolysis oil or other organic liquids which can be used as fuel, as well as various chemicals and materials. This technology has been tested to produce high-quality fuels, but is currently not available at reasonable costs (though currently in

51 a near-commercial phase). Several reactor configurations are possible to reach a 75% yield of fuel based on the initial dry biomass weight (EERE, 2007): bubbling fluid beds, circulating and transported beds, cyclonic reactors, ablative reactors. These processes are used by several companies (EERE, 2007). Ensyn Technologies has built six circulating fluidized bed plants, including one with a large processing capacity of 50 tonnes per day in Wisconsin. In Vancouver, DynaMotive is scaling up a bubbling fluidized bed plant at 100 tonnes per day of capacity. In the Netherlands, BTG is scaling up its plant at 50 tonnes per day, and Fortum operates a 12 tonnes per day plant in Finland. Neste Oil, a Finnish company, has developed the NExBTL process at one of its refineres to use vegetable oil and animal fat in a hydrothermal process. The company has also signed an agreement with Total to evaluate the possibility of building a large-scale plant of one of Total s refineries (Green Car Congress, 2005). Other companies are also investing in next generation biodiesel technologies, such as BP in the UK and Diester Industrie in France. Hydrothermal liquefaction technologies are developed by Changing World Technologies (New-York State), EnerTech Environmental Inc (Georgia State) and Biofuel B.V. (Netherlands) Other platforms Other technologies have potential to support the development of the biomass energy industry. These possibilities include (EERE, 2007): Biogas platform: Biomass is decomposed with microorganisms in anaerobic digesters (closed tanks). This process produces biogas, composed of methane and carbon dioxide, which can be used as fuel (like natural gas) or to produce electricity. Carbon-rich chains platform: Transesterification of vegetable oil or animal fat to produce a fatty acid methyl ester (biodiesel) and glycerin, an important byproduct. The glycerin and the fatty acids could be used as platform chemicals in biorefineries. Plant products platform: Genetic engineering research can lead to the development of plant strains, which would produce larger amounts of feedstocks and convert the products in a biological plant rather than in an industrial plant.

52 2.1.4 Biomass feedstock The cellulose and hemicellulose contained in most types of biomass are polymers of sugars that can be broken down in their different components (Table 4). Afterward, sugars can be fermented for the production of fuel grade ethanol. TABLE 4 THE THREE MAIN COMPONENTS OF BIOMASS BY FEEDSTOCK TYPE Hardwood (eucalyptus) Softwood (pinus) % Lignin % Cellulose % Hemicellulose Gal/ODT* (cellulose) Gal/ODT* (hemicellulose) Total Tons/day** (cellulose) Tons/ day (both) Corn stover Switchgrass Wheat straw Rice straw Sugarcane bagasse *theoretical using 0.51 g ethanol/g sugar **Dry tons required for 100 MM gpy Source: Baum, The analysis of biomass particularities is important in order to understand its potential as an energy source and consequently, to plan its production for the use of different technologies. For example, the biomassto-biofuel conversion process is strongly affected by sugar components. Consequently, the complexity of the conversion technology will depend on the biomass feedstock to be treated (EERE, 2007): Monomeric sugars: Using monomeric sugar biomass such as sugarcane and sugar beets is the simplest way of producing ethanol since these materials can be converted directly to ethanol by fermentation. Starch: Starch contains biopolymer sugars (glucose molecules) that must be chemically treated to obtain simple sugars. Currently, starch contained in corn, for example, is the main type of biomass feedstock used for ethanol production and the enzyme technology used for the conversion is one of the earliest methods created for this purpose.

53 Cellulose: This is the most common form of carbon in biomass. It also contains biopolymer sugars, but the linkages between molecules are more stable and resistant to chemical attack due to the hydrogen bonding in the cellulose chains. Hemicellulose: Another form of polymers which contains five-carbon sugars (usually D-xylose and L-arabinose) and six-carbon sugars (D-galactose, D-glucose and D-mannose) and uronic acid (EERE, 2007). Those four forms of sugars require technologies with different levels of complexity to convert the biomass feedstock into fuels and will also serve to illustrate the technological evolution on which the biofuel industry is based. Since monomeric sugars and starch-containing crops have food and feed applications, their uses for fuel production is limited, in contrast to cellulose and hemicellulose biomass, which are the most dominant forms of carbon in biomass. However, these last two are also the most difficult to use for fuel production. Although the complexity of technology required for the conversion process is increased significantly, numerous advantages are associated with those next generation technologies, including longterm economical benefits as well as important environmental gains, as compared with first generation technologies. Large amounts of biomass feedstock will be needed to supply biorefineries or other conversion technologies and to support the development of a large scale biofuel industry. Important changes in agricultural practices (e.g. for harvesting and collection of feedstocks) and in the biomass distribution system (including transportation and storage) will be necessary to meet these requirements.

54 2.2 Logistical strategies for the development of the second generation biofuel industry Biorefineries are emerging in locations throughout the United States, Canada, Europe and Asia for conversion of many types of biomass into cellulosic biofuel and other products. Table 5 presents existing plants as well as plants that are under construction or currently planned around the world. TABLE 5 BIOREFINERIES CURRENTLY UNDER CONSTRUCTION IN THE WORLD Company Location Feedstock Technology Capacity (gallons/year) Abengoa Kansas corn stover, wheat straw, milo (sorghum) stubble, switchgrass, and others Abengoa Nebraska corn stover, residual starch Abengoa Salamanca Spain wheat straw, cereal ALICO, Inc. Florida yard and citrus wastes BioEthanol Japan wood construction waste Biofuel Energy Corp. BlueFire Ethanol Colusa Biomass China Resources Alcohol Corporation DuPont-BP Biofuel Texas grass and tree trimmings thermochemical and biochemical processing enzymatic hydrolysis (Chrysosporium lucknowense), integrated with dry mill corn production steam pretreatment, enzymatic hydrolysis gasification, fermentation of syngas enzymatic hydrolysis, fermentation (Klebsiella oxytoca and E. coli) Capacity (litres/year) 11.4 million 75.7 million 0.47 million (0.02 million from corn stover) 1.8 million (76 thousand from corn stover) 1.3 million 4.9 million 13.9 million 52.6 million 0.37 million 1.4 million 4 million 15.1 million California green waste acid hydrolysis, fermentation 3.1 million 11.7 million California rice straw and hulls China corn stover Steam pretreatment, enzymatic hydrolysis England sugar beets enzymatic hydrolysis, fermentation to biobutanol acid hydrolysis, fermentation 20 million 75.7 million 1.7 million 6.4 million 9 million 34 million

55 Company Location Feedstock Technology Capacity (gallons/year) Iogen Idaho wheat straw, barley straw, corn stover, switchgrass and rice straw Iogen Ontario wheat, oat and barley straw Lignol British Colombia softwood and hardwood Mascoma New York paper sludge, wood chips, switch grass and corn stover Poet Emmetsburg Iowa corn fibre, corn stover enzymatic hydrolysis (Trichoderma reesei, Saccharomyces) enzymatic hydrolysis (Trichoderma reesei, Saccharomyces) pulping liquor pretreatment, enzymatic hydrolysis enzymatic hydrolysis and fermentation (Thermoanaerobacterium saccharolyticum) enzymatic hydrolysis, integrated with dry mill Capacity (litres/year) 18 million 68.1 million 0.79 million 3 million 1.3 million 4.9 million 0.5 million 1.9 million 30 million million Range Fuels Georgia timber and forest residue Verenium Los Angeles sugarcane bagasse and specially bred energy cane pyrolysis and catalytic conversion enzymatic hydrolysis, fermentation (Klebsiella oxytoca and E. coli) 40 million million 1.4 million 5.3 million Western Biomass Wyoming ponderosa pine wood chips, waste CO 2 pretreatment, enzymatic hydrolysis 1 million 3.8 million Source: Bio, 2007 As mentioned, the development of the second generation biofuel industry could be limited by biomass location and availability. By definition, biomass refers to all plants or materials and biomass energy refers to all energy originally captured by photosynthesis (EERE, 2007). It is considered to be a renewable source of energy and can be used either for fuel production or for power generation, since the carbon dioxide emissions generated during its combustion balance with the emissions captured during plant growth. Furthermore, biofuels are currently the only alternative for large-scale replacement of conventional fuel used for transportation. Biomass potential can be enlarged significantly when considering other non-traditional sources of biomass (starchy grains and oil seeds from dedicated crops). Among the new sources, are the follosing (EERE, 2007):

56 In the short term, and for small-scale applications, low- or negative-cost feedstocks such as industrial residues (black liquor from the pulp and paper industry and animal manures) could provide a good proportion of biomass. In the medium term, the addition of forestry and agricultural residues will provide the most important portion of biomass and significantly increase the potential for fuel and for power production. Examples of lignocellulosic biomass that can be converted to energy are corn stover, straw, or forestry residues. In the long term, dedicated cellulosic biomass crops for energy production could offer additional benefits. Because crop residue is a byproduct of grain production, it is currently abundant, underutilized, and low cost. This last element in particular leads many experts to believe that crop residue biomass is an attractive starting feedstock that shows the most short-term promise for use by the second generation biofuel industry. However, assumptions about soil carbon change due to the use of these residues and conversion of land to the cultivation of energy crops significantly impact the relative attractiveness of different cellulosic ethanol feedstocks and could potentially impact the overall attractiveness of cellulosic ethanol (Spatari et al., 2005). Further research is required in order to identify the specific conditions under which residues can be removed without increasing erosion or reducing soil productivity. Decision-making tools for on-farm application must also be developed. Furthermore, understanding the impact of removing each specific component of residues on erosion, carbon, and nutrient cycling, would create the need to develop innovative harvesting systems. Industry information suggests that the optimal scale of a second generation biofuel plant is related to an annual production capacity of around 200 million litres. Depending on the type of technologies in place, such a plant requires approximately dry tonnes of feedstock, which must be supplied equally throughout the year. Corn stover and cereal straw are the two most abundant feedstock sources available in Canada to supply such quantities. Therefore, the remaining steps of this research project focus primarily on the logistical costs of adopting these biomass sources for ethanol production using the most promising sugar platforms (namely, cellulase enzyme-based technology or other hydrolysis technologies) by commercial-scale biorefineries. The feedstock supply chain of the second generation biofuel industry will be unable to avoid the need to incorporate other sources of biomass. However, as demonstrated in the preceding section, not all energy crops are readily adaptable to agricultural practices. Better-adapted varieties and production technology must be developed. Therefore, this research project focuses on the cultivation of switchgrass and low-

57 lignin alfalfa complementary feedstocks that will be available for the second generation biofuel industry in smaller quantities. More long-term scenarios that consider a more widespread utilization of these new energy crops can nevertheless be established. Determining an accurate cost for feedstocks is difficult because existing biomass markets do not yet exist on a commercial scale. For any given geographic area, the amount and quality of feedstock which is economically available for a biorefinery varies depending on annual growing conditions, the amount that must be left in the field for sustainability and other purposes, the efficiency of the harvest, the transportation infrastructure and post-harvest losses associated with storing and handling. Past research has demonstrated that after factoring in all these elements of variability, the delivered cost of biomass feedstocks at the scale of the biorefinery is estimated at approximately $50 to $55/dry ton, which includes a modest $10/dry ton return to the biomass producer (USDOE, 2003). An agricultural producer s approach to collecting crop residues or to cultivating new energy crops, will naturally consider financial elements, such as harvesting and production costs, as well as opportunity cost and available governmental incentives. Growers and equipment manufacturers want a reliable market and dependable prices before investing in equipment for harvesting large quantities of biomass feedstock. Therefore, cost-effective and sustainable harvest and collection of crop residue biomass is critical to the success of the second generation biofuel industry, along with an efficient storage and transportation scheme. The logistical costs of moving large amounts of biomass in the Canadian context that are analyzed in depth in the following sections of this report provide vital information to accompany this new industry in its initial phases of development.

58 3. DESCRIPTION OF MODELS This section describes in detail the assumptions and the modelling approach considered in the development of two economic models that can be used to estimate the costs associated with a second generation biofuel plant s overall supply chain. The two models are structured in the same way but their default values and equipment selection are set to address different feedstocks. Assumptions common to both models are presented first, in subsection 3.1. Assumptions within the model specific to Western Canada (Alberta, Saskatchewan and Manitoba) are presented in subsection 3.2. Similarly, assumptions within the model specific to Eastern Canada (Québec and Ontario) are presented in subsection General assumptions and main input cells The costs related to a basic feedstock s supply chain, including those associated with production, collection, storage and transportation of various feedstocks identified in the first two sections of this report, have been assessed and modelled into a database spreadsheet using Microsoft Excel. This section seeks to describe in detail the general assumptions and the modelling approach used in designing these spreadsheets. While various production, harvesting, storage and transportation schemes were studied, only those employing technologies that are readily available were modelled. In fact, the information gathered during the literature review led to the modelling of a global cost-efficient supply scheme which takes into account the most feasible and the least costly options for establishing a biomass supply chain in the current context of the Canadian biofuel industry Types of cells As described in the legend present at the top of each sheet in the Excel files, different input cells hold different functions. Blue cells are independent variable cells that can be freely changed by the user, and pink cells represent multiple choices for the user. These cells are those from which the user establishes the supply chain s general scheme. Green cells are independent variable cells whose value is based on data from the literature. Similarly to blue cells, these green cells can be changed by the user but represent variables whose values are not expected to change in the short term. Finally, purple cells contain equations that are based on foundational assumptions and should not be changed.

59 Comment boxes The assumptions that have been considered in the attribution of default values and formulation of equations are explained in detail throughout this report, but are also stated in the commentary boxes of key component cells; the user simply places the curser over these key cells in order to view the related assumption. This function applies mainly to green and purple cells. For cells requiring that the user select a value from multiple choices (pink cells), the comment boxes then serve as a choice menu in which different options are listed General assumptions Characteristics of biofuel plant Assumption: ethanol plant s baseline consumption is established at dry metric tonnes of feedstock per year From a general perspective, industry information suggests that the optimal scale of a second generation biofuel plant should be approximatly 200 million litres of annual production capacity. As a result, the ethanol plant s baseline consumption is established at dry metric tonnes of feedstock per year in the economic models. Nonetheless, smaller plants or larger plants could be considered depending on feedstock availability, conversion technology, etc. In any event, the feedstock supply chains implemented will be larger than any other biomass supply chain currently operating in the world. The ethanol plant s total feedstock needs [cell C14 in the Scenario sheet] can eventually be adapted to reflect a smaller or larger biofuel plant. Assumption: ethanol plant s production level is established at 340 litres of cellulosic ethanol per dry tonne of feedstock According to Sheehan et al. (2004), this production level represents a long-term objective for most feedstock sources. Ethanol yield per tonne of feedstock will vary depending on technologies, stages of development of those technologies, and feedstocks. For instance, the theoretical ethanol yield for switchgrass is identified as approximately of 400 litres per tonne by Samson (1991). The theoretical ethanol yield per tonne for cereal straw and corn stover is also higher than 340 litres per tonne. The plant efficiency factor [cell C16 in the Scenario sheet] can consequently be changed in order to represent the feedstock needs of a more efficient second generation biofuel plant.

60 Assumption: ethanol plant will operate 24 hours/day, 7 days/week for 350 days/year (96%). This schedule is based on industry standards. The three assumptions listed above constitute the variables which are used in the equations that establish the ethanol plant s daily needs in terms dry tonnes of feedstock per day [cell C15 in the Scenario sheet] and its total production capacity in litres of cellulosic ethanol per year [cell C17 in the Scenario sheet] Establishing the scenario ( Scenario sheet) Upon opening the supply-chain logistical costs models, the user is presented with the Scenario sheet. As its name indicates, this sheet allows the user to establish the scenario he or she wishes to analyze. This scenario is set up by attributing values to different variables that are to be considered in the calculation of the total supply-chain logistical costs. These different variables are grouped into categories, which appear in large red cells Availability of feedstocks plant capacity The first category of variables to be confirmed by the user pertains to the availability of feedstocks and the cellulosic ethanol plant s production capacity. These variables are attributed default values which are justified by the three general assumptions described in the previous section Feedstock proportions The second category of variables which help to establish the supply-chain scenario concerns the proportions in which the ethanol plant will utilize the different available feedstocks. These proportions are established by percentages that are entered by the user in the independent variable cells (blue cells) of this category [cells C19 to C23 in the Scenario sheet]. The equation cells (purple cells) then calculate the quantity of each feedstock needed in absolute value (tonnes), based on the plant s capacity established in the previous category of variables. Cells C25 and E25 in the Scenario sheet are condition cells which will indicate an inconsistency in the independent variable cells relating to feedstock proportions by displaying the word ERROR. Such incoherence can occur if feedstock proportions amount to more than a total of 100% or if the calculated amount of feedstock in tonnes is not equal to the plant s capacity [cell C14]. The Alternative Feedstock Option cells represent efforts to integrate a satisfying level of flexibility for future development of these models. These cells can eventually be attributed to feedstocks other than those presently identified as being readily available by the Canadian second generation biofuel industy. As

61 techniques in producing and harvesting new energy crops emerge and their costs are better documented, the integration of these other alternative feedstock options into the present models can be achieved. Thus, values in these cells are for consideration by the user at this time Basic financial data This next category of variables contains a limited number of independent variable cells (blue cells). The first of these cells [cell C27 in the Scenario sheet] determines the annual interest rate that will be used for the calculation of various annuities and loans throughout the supply chain. The default annual interest rate (5.6%) is based on the rate used in the Farm Machinery Custom and Rental Rate Guide , published by Saskatchewan Agriculture and Food (2006), which provides the majority of assumptions considered in the model. The other set of cells [cells in line 29 and columns C to G in the Scenario sheet] are used to set the monetary value of the growers compensations. These values represent the price at which the producer is ready to sell feedstock in the windrow prior to any collection activity. In other words, these compensations are what can persuade producers to adopt new energy crops or to sell agricultural residues for energy purposes. For feedstocks that are not crop residues (switchgrass and low-lignin alfalfa), if no value is entered in these cells, the estimated compensation is based on production costs. These are established in the Feedstock Characteristics sheet, described in more detail in subsequent sections of this report. Ideally, the values entered in this set of cells will be based on contracting terms negotiated between the farmers and the biofuel plant Harvesting scheme As its name indicates, this category of variables is related to harvesting options and financial data. The first two variables express levels of taxation that are applied to the purchase of farm equipment both at the federal and the provincial level [cells C31 to C32 in the Scenario sheet]. In order to leave these models non-specific to a particular province, these cells have been left at a default value of 0. The farmer s ownership equity on harvesting equipment [cell C33 in the Scenario sheet] is of course related to the amount of money the grower is willing to invest in order to partially finance the different harvesting assets. Two input cells are used to reflect the growers margin in the harvesting scheme. The first [cell C34 in the Scenario sheet] is identified as being the growers profit margin on harvesting activities. As a default

62 value, this profit margin is estimated at 15%. The second [cell C35 in the Scenario sheet] leaves the user with an option that can cover unforeseen additional costs. Although it is branded as being an additional margin, this input cell can be used to cover administrative or logistical fees that were initially unexpected during supply contract negotiations with the ethanol plant. The other monetary variables in this category of options that must be established by the user are the wage rate for on-farm workers [cell C36 in the Scenario sheet], twine costs [cell C37 in the Scenario sheet] and the price of diesel fuel used by farm machinery [cell C38 in the Scenario sheet]. While the first is an independent variable cell (blue cell), the others are independent variable cells whose value is based on data from the literature (green cells). Twine cost at a rate of $0.76 /bale by default is based on the Farm Machinery Custom and Rental Rate Guide , published by Saskatchewan Agriculture and Food (2006). The same default value applies to both Western and Eastern Canada. Diesel Prices entered by default are based on price averages between January 3 rd 2006 and February 19 th 2008, using Natural Resources Canada s Fuel Focus Web-based database ( in the mains cities of the relevant regions (Eastern or Western Canada). The farm diesel price entered by default is equal to the average diesel price less federal and provincial taxes. Main options The following set of multiple choice cells (pink cells) define the main elements of the harvesting scheme. As mentioned in section 3.1.1, comment boxes of these option cells [cells in lines 41 to 44 and columns C to G in the Scenario sheet], serve as a selection menu in which different available options are listed. The user thus simply places the curser over these cells to view the related optional choices. The first element shows the type of power equipment that will be used to harvest the different feedstocks. This is measured by the tractors power take-off (PTO) horsepower. In the comment box, the user will notice that the choice is limited to a scale from 100 to 400, in increments of 50. The second element displays the type of harvester used. In all cases, the most commonly used piece of harvesting machinery, the rotary disc cutter, has been considered in the model. The third element in this set of variables involves how the user will choose to package the different feedstocks after initial harvesting. For example, cereal straw and switchgrass can either be collected in large round bales (diameter x height = 6 ft x 5 ft = 141 cubic feet) or in large rectangular bales (length x

63 width x height = 4 ft x 4 ft x 8 ft = 128 cubic feet). These two options represent the most cost-efficient ways to supply the required amount of biomass. The final element in this set of variables describes how biomass will be collected in the field. Feedstocks packaged in bales (round or rectangular) will be collected with Stinger systems 2, which are self-propelled bale transporters and stackers. Considering once again the quantity of biomass needed to operate a cellulosic ethanol plant of commercial scale, the Stinger system represents the fastest and most productive way to collect and stack bales. It can move 220 bales off of a field per hour and/or stack 125 bales per hour with only one machine and one operator. This reduces labour costs and results in the lowest operating and maintenance cost on the market Storage scheme For this part of the supply chain, it has been assumed that on-farm storage facilities will be used to store the required quantities of biomass. This roadsiding storage scenario presumes that bales are stacked at the edge of the field where the feedstock is cut. From there, the bales of biomass are transported directly to the biofuel plant. This storage option is represents what is currently the most common and cost efficient option; it is illustrated as Supply Chain Scheme 1 in Figure 6. As Figure 1 also demonstrates, research efforts are geared toward developing supply chain schemes that include preprocessing facilities. In theory, moving preprocessing forward in the supply chain would create down-stream uniformity and increase system efficiencies (Hess et al., 2008). Similarly, biomass preprocessing centres (BPC) could resolve transportation and storage difficulties, while addressing potential market power issues that can lead to high transaction costs related to contracting between a large number of farmers for biomass supply and a limited number of biofuel plants (Carolan et al., 2007). BPC are conceptualized as flexible processing facilities capable of pretreating and converting biomass into appropriate feedstocks for a variety of final products such as fuels, chemicals, electricity, and animal feeds. Consequently, they would also allow for market diversification. However, this type of facility will most likely become financially feasible only in the long term, once a more diversified biomass industry materializes. Due to the fact that research in this field is still at a premature state and that the cost efficiency of these types of storage options has yet to be demonstrated for commercial scale facilities, the models do not incorporate these options. 2 For more information:

64 FIGURE 6 R & D PATHS TO THE STORAGE OF UNIFORM BIOMASS FEEDSTOCK Source: Hess et al., The independent variables cells (blue cells) in this category of variables [cells in line 48 and columns C to G in the Scenario sheet] further define the scenario in establishing the number of contracted growers who will supply the biofuel plant. These variables inevitably have an impact on transportation costs. Also, assuring maintenance and access to the roadside storage facility will be the grower s responsibility. A yearly compensation for growers, rewarding them for the on-farm storage service they provide, can therefore be considered [cells in line 49 and columns C to G in the Scenario sheet]. The default values here are kept at $0/year for all types of feedstocks due to the fact that this type of compensation is more often than not implicitly included in the grower s established in the Basic Financial Data category of variables (see section ). In the end, the equation cells (purple cells) in this category of variables calculate the average quantity of biomass that will be stored at each storage site. These calculations are based on the overall amount of biomass each feedstock will provide in a given supply chain scenario, which will have been previously determined by the user (see section , on feedstock proportions).

65 Transportation scheme This final category of variables enables the user to complete the scenario he or she wishes to analyze. First, independent variable cells allocate the level of taxation that is to be considered when financing transportation equipment, both at a federal and a provincial level [cells C54 and C55 in the Scenario sheet]. Second, the average radius of supply and the average distance between storage sites in kilometres must be attributed [cells C56 and C57 in the Scenario sheet]. Similarly to the variables in the harvesting scheme, ownership equity on transportation assets [cell C58 in the Scenario sheet], owner s profit margin [cell C59 in the Scenario sheet], an additional margin to cover unforeseen administrative and logistical fees [cell C60 in the Scenario sheet], the wage rate for transportation activities [cell C61 in the Scenario sheet] and the price of diesel applicable to these same activities [cell C62 in the Scenario sheet] are determined by the user. The diesel price entered by default for transportation activities is equal to the average diesel price in the main cities of the relevant regions (Eastern or Western Canada) Crop Residue Estimate sheet This sheet can be used to calculate the amount of crop residue available in a given region. Thus, net availability of crop residues can be calculated for corn stover or cereal straw. This net availability will depend on a number of factors entered by the user. First, the region s total area, in hectares, of all land tenures, the total farm area and the total land area dedicated to crops and summerfallow (excluding Christmas trees) are entered [lines 14 to 16]. Then, the total land area in the concerned region which is dedicated to the crop of interest (corn, wheat or other cereal grains) must also be entered by the user [line 17]. This same land area dedicated to the crop of interest is then reported in terms of the percentage it represents compared to the total land area dedicated to crops and summerfallow (excluding Christmas trees) in the region [line 18]. Second, crop and residue production parameters are analyzed. The user is required to enter the grain yields in the region [line 21] on a dry tonnes per hectare basis. These yields will vary from region to region. Afterwards, the total quantity of grain produced is calculated based on yields and total land area dedicated to the crop of interest [line 22]. Harvestable residue yield (considering losses due to field manipulations) is entered by the user in terms of the percentage that crop residues represent as compared to total grain production. [line 23]. Although this percentage can vary from region to region, it is considered to be an independent variable taken from the

66 literature (green cell). This figure is then used to calculate the local residue yield (harvestable) in terms of dry tonnes per hectare [line 24]. At the same time, the gross amount of crop residue available in the region is also calculated using the local residue yield and total land area dedicated to the crop of interest [line 25]. Third, the farms reporting agricultural production in the region are considered. The total farms reporting production area in crops and summerfallow (excluding Christmas trees) and the total number of farms producing the crop of interest (corn, wheat or other cereal grains) are entered by the user [lines 28 and 29]. Subsequently, the total number of farms producing the crop of interest is reported back in terms of the percentage of the total farms reporting production area in crops and summerfallow (excluding Christmas trees) in the region [line 30]. The average area owned by each farm individually is also calculated in hectares, using the total land area which is dedicated to the crop of interest and the total number of farms producing this same crop of interest in the concerned region. In order to calculate the net total amount of crop residue available in the given region, a certain number of availability parameters must be taken into account. The percentage of farms that are willing to sell their residues is the first of these parameters [line 34]. Based on local characteristics including soil texture, organic matter content in the area, field slope, vulnerability to erosion and other factors, this percentage can fluctuate. Within the group of farms that are willing to harvest and sell their crop residues to a biofuel plant, each of these farms will, in all probability, refrain from selling all of their available residues. Therefore, the percentage of total area sold by farmers willing to harvest and sell their crop residues through a supply contract should be considered [line 35]. Finally, the net total amount of crop residue available in the given region is calculated using the different parameters entered by the user [line 39]. Also, the radius of supply is calculated in kilometres [line 41], using the very first parameter entered by the user in this sheet, i.e. the total area of all land tenures. Using this radius of supply, the average trucking distance is calculated (71% of calculated radius) [line 42]. Afterwards, the net total amount of crop residue available is given on a dry tonnes per kilometre of radius basis [line 43]. The user has the further option of entering another given radius of supply [line 44], in order to test the variability in the calculated trucking distance [line 45] and the net total amount of crop residue available based on this user-defined radius [line 46]. It is important to note that although the calculated values in this sheet of the model can greatly benefit the user in establishing his or her scenario, they are not automatically copied into the Scenario sheet.

67 3.1.5 Results sheet This sheet not only presents the results of the analysis but also a summary of the chosen scenario. While all the elements established by the user in the Scenario sheet are reported in the right half of the Results sheet, the cost components of each segment of the supply chain are reported both on a $/tonne and a $/dry tonne basis for all the different feedstock options in the left half of this sheet. The most important result reported in this sheet is entitled total estimated feedstock cost [cells B12 and B13 in the Results sheet] which is reported both on a $/dry tonne and a $/dry tonne/km basis. In essence, these results represent the total feedstock logistical cost associated with cellulosic ethanol production in the established scenario. Save in file button This button at the top left of the Results sheet is a macro that has been programmed into the spreadsheets. This button enables the user to save the results of a given analysis into a separate Excel file. After establishing a scenario and observing the calculated results, the user can save the entire content of the Results sheet into a separate Excel file for later consultation by clicking on this button. This separate file will be saved into a separate folder entitled results that will be created automatically in the same folder that holds the original spreadsheet model. In this way, the user can establish a different scenario and still conserve the results of the previous one. 3.2 Specific assumptions - model for a cellulosic ethanol plant in Western Canada This section explains the input cells and assumptions considered in the analysis of feedstock supply chain scenarios that apply to Western Canada. In these scenarios, different elements related to feedstock characteristics, harvesting equipment, storage compensation and transportation issues must be understood and correctly entered into the model by the user. This model specific to Western Canada (Alberta, Saskatchewan and Manitoba) allows the user to estimate the delivered cost of cereal straw and/or perennial grasses such as switchgrass. The different sheets of the Excel-based model used in estimating the total feedstock logistical cost associated with cellulosic ethanol production in Western Canada, are discussed individually in each of the following subsections.

68 3.2.1 Scenario sheet default scenario The model does not present a completely blank scenario for analysis. In fact, a basic scenario, based on plausible default values, has been established to portray the modelled supply-chain logistical costs associated with cellulosic ethanol production in Western Canada. These default values are presented and justified in Table 6. TABLE 6 BASIC DEFAULT SCENARIO IN THE MODEL FOR A CELLULOSIC ETHANOL PLANT IN WESTERN CANADA Availability of Feedstocks Plant Capacity Element Default Value of Variable Justifications and Observations Feedstock needs total Feedstock needs tonnes/day metric dry tonnes Industry information suggests that this represents the optimal scale of a second generation biofuel plant metric dry tonnes/day Ethanol plant operates 24/7 for 350 days/year (96%) Total capacity litres/year Calculated annual production capacity Feedstock Proportions Element Default Value of Variable Justifications and Observations Cereal straw 75% of supply Main feedstock option Switchgrass 25% of supply Complementary feedstock option Basic Financial Data Element Default Value of Variable Justifications and Observations Annual interest rate 5.6% Estimated on-farm field basis (unharvested) Grower s price/tonne Straw = $12.50/tonne Switchgrass = $0/tonne Harvesting Scheme Used in the Farm Machinery Custom and Rental Rate Guide , published by Saskatchewan Agriculture and Food (2006) Straw: For Western Canada, straw cost in the windrow usually varies between $10-$15/tonne (field dry basis) Switchgrass: Price used will be based on production costs, established in the Feedstock Characteristics sheet Element Default Value of Variable Justifications and Observations Federal tax on purchase of equipment Provincial tax on purchase of equipment Farmer's ownership equity on harvesting equipment 0% Federal tax break on equipment is assumed 0% Not province-specific 50% Industry standard

69 Profit margin - farm (% of total cost associated with harvesting) Additional margin - farm (to cover unforeseen additional costs) Wage rate farm 15% Industry standard 0% No unforeseen costs expected $21.00/hr Based on wages for farm specialized labour and labour shortages in the agricultural sector Twine cost for bales $0.76 /bale Based on Saskatchewan Agriculture and Food (2006) Diesel price farm $0.7465/litre Main Options Based on price averages between and in main western Canadian cities (Calgary, Edmonton, Red Deer, Brandon, Winnipeg, Prince Albert, Regina and Saskatoon), less the amounts of federal and provincial taxes Element Default Value of Variable Justification(s) and Observations Powered equipment - tractor's PTO HP Harvester Baler Collector 200 Minimum horsepower for operating chosen baler Switchgrass: Rotary cutter disk 2 = large rectangular bales (L x W x H = 4 ft x 4 ft x 8 ft = 128 cubic feet) Stinger system Storage Scheme Most used equipment Most common in Western Canada Equipment with lowest operating and maintenance cost on the market Element Default Value of Variable Justifications and Observations Number of contracted growers (i.e. storage sites) Grower's compensation ($/year) Total quantity of feedstock (tonnes) Average quantity of biomass stored per storage site (tonnes/site) Straw = 600 Switchgrass = 200 $0/year Straw = tonnes Switchgrass = tonnes 1000 tonnes/storage site (farm) Transportation Scheme Expected average quantity of biomass stored per storage site (per farm) = tonnes/site (farm) Usually implicitly included in the grower s compensation established in the Basic Financial Data category of variables Established in Feedstock Proportions category of variables Average value considered realistic Element Default Value of Variable Justifications and Observations Federal tax on purchase of equipment 5% Current GST rate

70 Provincial tax on purchase of equipment 0% Not province-specific Average radius of supply 100 km Considered reasonable for Western Canada Average distance between storage sites Owner's equity on purchase of equipment 10 km Considered reasonable for Western Canada 50% Industry standard Profit margin - owner 15% Industry standard Additional margin (to cover unforeseen additional costs) 0% No unforeseen costs expected Wage rate trucks $21.00 Based on wages for specialized labour Diesel price truck $ Licensing and insurance fees ($/truck/year) Based on price averages between and in main western Canadian cities (Calgary, Edmonton, Red Deer, Brandon, Winnipeg, Prince Albert, Regina and Saskatoon) including federal and provincial taxes $8500 Average value in the industry Feedstock characteristics The Feedstock Characteristics sheet contains information related to the different types of feedstock whose costs are then estimated by the model. The main feedstock option for the western model has been established as cereal straw. Switchgrass is the second feedstock option available to the user for scenariobuilding. Although data on the true availability of straw residue is incomplete, this feedstock remains the most promising source of biomass for biofuel production in Western Canada and will support the industry in its early years. However, some of the barriers to the economic use of straw that remain are related to availability, quality, cost of collection, transport, and storage (Sokhansanj et al., 2006). Only a portion of farmers holding excess straw will be willing to sell it, and those who will sell typically do not commit 100% of their excess straw in a given year. The amount that a producer is prepared to remove and supply to an industrial user depends on the producer s perceived value of straw. For the Canadian Prairie provinces, research results show that straw is only available reliably in the black soil zone. The total available amount of cereal straw and chaff was thus estimated at two million tonnes of material for use as a biomass resource (Watson et al., 1998). These elements need to be considered when formulating a probable scenario to be entered into the model.

71 On the other hand, switchgrass has yet to be largely adopted as an energy crop production in the Prairies. Although it presents a significant potential for the second generation biofuel industy, this feedstock will not likely become the main source of biomass for cellulosic ethanol plants in the short-term. Furthermore, perennial grasses, still at the development stage, may prove to be better adapted to the Prairie ecosystems as well. Assumption: straw moisture content = 13% When considering the feedstock characteristics of cereal straw, the first input element to be entered into the model is the percentage of moisture content [cell D13 in the Feedstock Characteristics sheet]. This parameter is identified as an independent variable cell whose value is based on data in the literate (green cell). Consequently, the default value of this variable is 13%, although moisture content of cereal straw can slightly vary from region to region and from year to year. As for all feedstock sources considered in a given scenario, conversions of cost elements to a metric dry tonne basis are inescapably based on the entered moisture content of each feedstock. Assumption: estimated yield = 2 tonnes/ha for cereal straw Secondly, the estimated yield, in tonnes per hectare, must be established [cell D14 in the Feedstock Characteristics sheet]. The default value of this independent variable cell is 2 tonnes/ha for cereal straw, representing an average yield for the Prairie provinces. This same variable, along with the identified feedstock availability and plant capacity options (described in sections and 1.3.2) are then utilized in the calculation of the number of hectares that need to be harvested [cell D19 in the Feedstock Characteristics sheet] and the amount of straw in tonnes [cell D20 in the Feedstock Characteristics sheet] that will be supplied to the biofuel plant on an annual basis. Assumptions: large round bales, 141 cubic feet = 0.43 tonnes/bale large rectangular bales, 128 cubic feet = 0.48 tonnes/bale Thirdly, other independent variable cells establish the tonnage of the different baling options. Although the dimensions of large round bales amount to 141 cubic feet and those of large rectangular bales amount to a total of 128 cubic feet, differences in bale density give rise to approximately the same weight in tonnes per bale. Thus, the weight of large round bales [cell D15 in the Feedstock Characteristics sheet] is set by default at 0.43 tonnes/bale. The weight of large rectangular bales [cell D17 in the Feedstock Characteristics sheet] is attributed the default value of 0.48 tonnes/bale. In this way, the amount of bales

72 produced per hectare [calculated in cells D16 and D18 in the Feedstock Characteristics sheet] can be calculated. Assumption: estimated on-farm field basis (unharvested) price = $12.50/tonne for cereal straw Finally, the estimated on-farm field basis (unharvested) price is transcribed in cell D22 [ Feedstock Characteristics sheet]. It is to be noted that this value must be changed in the Scenario sheet if the user wishes to attribute a different value than the default value of $12.50/tonne for cereal straw. Where the feedstock characteristics of switchgrass differ from those of cereal straw is in the estimated onfarm field basis (unharvested) price. If an absolute value is attributed to this variable in the Scenario sheet, this same value will be used correspondingly [cell D34 in the Feedstock Characteristics sheet]. However, if no value is attributed to this variable in the Scenario sheet, the model will consider an estimated on-farm field basis price based on production costs. Assumption: switchgrass production costs are based on the following elements: Seeding density [cell D38 in the Feedstock Characteristics sheet] o default value = 8.3 kg/ha Seed price [cell D39 in the Feedstock Characteristics sheet] o default value = $10/kg Total seed cost [equation (purple) cell D40 in the Feedstock Characteristics sheet] Land development and seeding cost [cell D41 in the Feedstock Characteristics sheet] o default value = $18.68/hectare Fertilizer and lime costs [cell D42 in the Feedstock Characteristics sheet] o default value = $37.15/hectare Cost of chemicals [cell D43 in the Feedstock Characteristics sheet] o default value = $33.42/hectare Cost of fuel and repairs [cell D44 in the Feedstock Characteristics sheet] o default value = $10.82/hectare Land taxes [cell D45 in the Feedstock Characteristics sheet] o default value = $8.65/hectare These cost elements are related to establishment costs. Their default values are based on pasture establishment costs found in the Guidelines for Estimating Crop Production Costs, published by Manitoba Agriculture, Food and Rural Initiatives (2008) and a recent study reporting farm-scale production cost of switchgrass (Perrin et al., 2008). It is therefore assumed that costs related to switchgrass establishment in the conditions of the Prairie provinces will not differ considerably from these default values. Assumption: average number of years of switchgrass production period = five years

73 What can differ from regular forage productions is the number of years on which this total estimated establishment cost for switchgrass production can be allocated. Thus this number of years needs to be entered by the user in an independent variable cell [cell D47 in the Feedstock Characteristics sheet]. Although regular forage productions tend to last an average of three years, switchgrass has the advantage of being able to produce considerable amounts of biomass for a more extended period of time. The default value for this input cell is five, representing the average production period, in years, for a switchgrass crop. Thus, if a high value is entered in this input cell, the establishment cost allocated on a $/year/ha basis [calculated in cell D48 in the Feedstock Characteristics sheet] will be lowered. Furthermore, production (establishment) costs are allocated on a yearly basis using a basic annuity equation as shown in the comment box of cell 48: Assumption: basic land rental rate = $62/year/hectare. A basic land rental rate in a $/year/hectare [cell D49 in the Feedstock Characteristics sheet] is then added to the annual establishment costs and converted to a $/tonne value [cell D50 in the Feedstock Characteristics sheet]. For Western Canada, the default value of the land rental rate is established at $62/year/hectare. The other alternative feedstock options in this model, identified as Alternative Feestock A, Alternative Feestock B and Alternative Feestock C can eventually be attributed to other feedstocks. Due to the fact that these new sources of biomass will most likely emerge from the production of new perennial grass crops on a larger scale, the feedstock characteristics of these alternative feedstock options are based on those of the switchgrass crop Harvesting costs This segment of the biomass supply chain is modelled using the Harvesting sheet, contained in the Excel file. The different cells in this sheet calculate the fixed and operating costs of utilizing the equipment options available to the user in the Scenario sheet. These calculations are based on a set of independent variables entered by the user and a number of built-in default-value parameters. In order to

74 facilitate future modifications to the model, no elements contained in this sheet are protected. However, the equation cells (purple cells) and independent variable cells (green cells) that take into account assumptions derived from the literature should not be modified. Assumptions are accordingly reported in corresponding comment boxes. In brief, the main assumptions considered for calculating harvesting costs computed by the model relating logistical costs of a cellulosic ethanol plant in Western Canada are as follows: Most assumptions and equations used for calculating the costs of harvesting operations are derived from the annual Saskatchewan Farm Machinery Custom Rate and Rental Guide (Saskatchewan Agriculture and Food, 2006). The machine cost data in this document is given for a wide variety of farm equipment under different ranges of annual hours of use. The data is specific to each individual piece of machinery and includes fixed costs (depreciation, investment cost, insurance and housing) and variable costs (repairs, fuel, oil and grease). Thus, the total cost per hour represents the total cost of owning and operating the harvesting machinery, with an additional profit margin. Fuel use of farm equipment is calculated based on tractor s horsepower at the power take-off (PTO). Oil and lube prices are calculated at 15% of fuel (diesel) costs. Field efficiency (FE) is factored into the use of every piece of machinery. This FE is based on a field efficiency factor, depending on the options chosen by the user. The harvesting equipment (rotary disc cutter) has an efficiency factor of 0.8, the large round bail bailer an efficiency factor of 0.5 and the large rectangular bale baler an efficiency factor of Some pieces of equipment are considered to not only serve the purpose of harvesting biomass for the biofuel plant. Therefore, a percentage of use is factored into the model. This percentage is used to calculate the fixed costs for the time only dedicated to harvesting biomass for the biofuel plant. Balers are exclusively used for harvesting biomass for the biofuel plant (100%), while tractors are considered to be used for other on-farm operations (44%).

75 We assume that harvesting operations will take place over six week s time. Harvesting workers will work 12-hour days, four days/week, for a total of 288 hours dedicated exclusively to harvesting biomass for the biofuel plant. Such use would theoretically reduce the normal lifetime of the harvesting equipment. Consequently, a five-year lifetime is set by default for balers. For the purpose of this study, the same number of baling hours is assumed for square and round baling. Extensive use of baling equipment reduces not only its useful life period, but also its salvage value. In consequence, it is assumed that balers salvage value is of $0. Bales will be scattered throughout the field (no bale accumulator will be installed on the baler). A Stinger Stacker will be used to transport the bales from the field to the roadside stacking location. This machine has the capacity to pickup bales at speeds of three to five miles per hour and carries up to eight square bales at a time and eight round bales at a time. Also, the Stinger Stacker stacks the bales appropriately, ready for loading onto transportation units. Wages for farm labour is established by default at $21/hour. Based on wages of specialized farm machinery operators (i.e. custom labour), which have a high opportunity cost, and the fact that labour shortages often occur in the fall when the biomass feedstocks will be harvested, this amount is considered to be reasonable. However, some landowners might be able to hire operators at a lower rate. In such a case, the difference between this lower rate and the 2$1/hour wage established in the model will compensate the landowner for time spent training and managing employees. Due to the time required for lubricating and servicing machinery, as well as time delays in getting to and from the field, we assume that the actual man-hours of labour exceeds actual field time by 20 percent. Hourly labour costs are therefore estimated by multiplying the farm labour wage entered by the user by 120% of the machine hours used for a particular operation Storage costs Assumption: storage segment of the supply chain is facilitated by on-farm storage sites

76 The storage segment of the supply chain is facilitated by on-farm storage sites in both costing models. This storage option currently represents the most cost-efficient way to store large amounts of biomass. It is assumed that the biofuel plant will also need to have biomass storage facilities that can store at least one or two weeks worth of production if transportation difficulties, such as limited seasonal road access, arise. In our opinion, this supply chain scheme will be adopted by the second generation biofuel industry in its formative years, until a more diversified biomass industry, based on a greater number of bioproducts, becomes a reality. Nevertheless, certain costs are associated with this type of storage facility. The Storage sheet incorporated in both models establishes the details of these costs. For storage costs applying to Western Canada, the number of contracted growers and, as a result, the number of storage sites, which is established by the user in the Scenario sheet [cells in line 48 and columns C to G in the Scenario sheet], is reported back to the Storage sheet [cells in line 13 and columns D to H in the Storage sheet]. In the same way, yearly compensations for growers [cells in line 49 and columns C to G in the Scenario sheet], are copied into the Storage sheet [cells in line 14 and columns D to H in the Storage sheet]. The total quantity of each feedstock supplied to the biofuel plant is also copied from the Scenario sheet to the Storage sheet [cells in lines 19 to 23 in column E in the Scenario sheet are reported back to cells in line 15, columns D to H in the Storage sheet]. Although all these elements are marked as being independent variables cells (blue cells), if the user desires to change one or more of these values, he or she must do so in the Scenario sheet. The average number of bales per storage site is then calculated [cells in line 19 and columns E to N in the Storage sheet]. These calculations are made for the different feedstock options and for the different baling options (large round bales and large rectangular bales 3 ) using variables established by the user in the Scenario sheet, as well as in the Feedstock Characteristics sheet. Assumption: yearly compensation to the farmer for a snow clearing service = $100/year/storage site Certain cost elements related to storage of biomass are established by independent variable cells (blue cells) that are only present in the Storage sheet. One of these cost elements shows a yearly compensation to the farmer for a snow clearing service he will provide [blue cell - D21 in the Storage sheet], permitting a year-round access to the storage site. By default, this compensation is valued at $100/year/storage site. This cost element is added to the other cost elements reported from the Scenario 3 Assumptions pertaining to the dimensions of each type of bale are reinstituted in the comment boxes of cells identifying the type of bale in question [cells in 18 and columns E to N in the Storage sheet]

77 sheet in order to provide the total yearly cost on a per site basis [purple cells line 23, columns E to I in the Storage sheet] and consequently on a per tonne basis [purple cells line 24, columns E to I in the Storage sheet]. This total cost therefore takes into account yearly compensations awarded to growers for storage and snow clearing services they provide. Tarping cost = $0/tonne stored The other cost element introduced by an independent variable cell (blue cell) exclusive to the Storage sheet, is the cost of protecting biomass bales from the stresses of weather conditions with tarpaulins (tarps). These large sheets of flexible cloth-like material can be either made out of canvas or polyester, can be water resistant or waterproof, and can also be coated with plastics such as latex or polyvinyl chloride (PVC). Costs related to this aspect of storage can be entered by the user on a $/tonne basis [blue cell D26 in the Storage sheet]. These costs will greatly depend on the attributes of the materials utilized and the local weather conditions. As a result, these costs can vary considerably. Very little information exists on the type of material that will be promoted and used by the second generation biofuel industry for covering stored biomass in this way, which will ultimately depend on future industry requirements. Furthermore, in some cases, the use of tarpaulins could even be unnecessary. As far as we know, the incremental difference in biomass quality, made possible by the use of tarpaulins for storage and transportation, does not justify its cost. Therefore, the default value of this cost element is fixed at $0/tonne, but can be amended by the user. At last, the total storage cost is calculated in the Storage sheet, using all the different cost elements described in this section. The result is reported on a $/tonne basis [purple cells line 29, columns E to I in the Storage sheet]. In summary, the main and most important assumptions considered in calculating storage costs computed by the model relating logistical costs of a cellulosic ethanol plant in Western Canada are: Stacks are located in areas that are well-drained and free of standing moisture year round. It is assumed that tarps and covers are unnecessary under these conditions. Cost associated with these farm-gate roadside stacking facilities is assumed by the grower. The details concerning year round access and upholding of these facilities are negotiated between the biofuel plant and the grower and described in the provisions of the straw supply contracts signed.

78 A yearly compensation to the farmer for a snow clearing services, permitting a yearround access to the storage site, is valued at $100/year/storage site Transportation costs This segment of the biomass supply chain is modelled using two different sheets, the Truck Loading sheet and the Transportation sheet respectively, in the Excel file. These sheets calculate the fixed and operating costs of using telescopic bale handlers for loading the transportation units (trucks), and those of utilizing these same transportation units. These calculations are based on the independent variables entered by the user in the various sheets previously described. In practice, all elements contained in these sheets can be changed by the user; however, many of these are equation cells (purple cells) and independent variable cells (green cells) that take into account assumptions derived from the literature and should not be altered. The content of each of these cells [line 13, columns G to BV in the Truck Loading and Transportation sheets] and of their corresponding comment boxes (assumptions) will not be described here. The following subsections briefly describe the foundation of each specific sheet.

79 Truck Loading Sheet This sheet calculates the fixed and operational costs of using telescopic bale handlers for the loading of the transportation units (trucks). An example of this type of equipment is shown in Figure 7. FIGURE 7 EXAMPLES OF LOADING EQUIPMENT USED Source: The main assumptions considered for calculating truck loading costs are: Telescopic bale handlers are assumed to have 120 HP, handle one bale per grab for large round bales and two bales per grab for large rectangular bales. Each one of these loads takes an estimated time of two minutes per grab. This piece of machinery has an efficiency factor of 0.8, which covers time required for lubrication and servicing, as well as operator s break time. Loading operations take place year round: an equivalent of 48 weeks. Machine operators will work 12-hour days, four days/week, for a total of 2,304 hours in a year, dedicated exclusively to loading biomass onto transports for delivery to the biofuel plant. Telescopic bale handlers (loaders) are loaded onto transports, in order to travel from site to site. Cost related to these transports (fuel, repairs etc.) is considered to be incorporated into the profit margin of machine owner(s), whether they be agricultural producers or independent contractors. We assume that harvesting operations will have a ten-year lifetime. Extensive use of baling equipment could reduce this variable. Its salvage value is however estimated at 10% of initial purchase price.

80 Many equations used for calculating the costs related to this machinery s use are derived from the source used to calculate cost of harvesting operations: the annual Saskatchewan Farm Machinery Custom Rate and Rental Guide (Saskatchewan Agriculture and Food, 2006). Thus, the total cost per hour represents the total cost of owning and operating the harvesting machinery, with an additional profit margin. Transportation Sheet This sheet calculates the fixed and operating costs of using transportation units (trucks) for moving biomass from the on-farm storage sites to the biofuel plant s gate. One of the main assumptions related to this segment of the supply chain is that it will utilize only roadway facilities. This is based on the fact that the minimum economic rail shipping distance for straw in the context of Western Canada has been estimated at 170 km (Mahmudi et al., 2006). This distance exceeds the average radius from which an economically sized and centrally located biofuel plant would need to source biomass feedstocks in the short term. Although biofuel plants are typically located near the railway network, these facilities are usually used to ship the end product (ethanol) out of the plant, rather than to ship feedstocks to the plant. Moreover, rail line layout in Canada is not ideal for moving feedstocks for the biofuel industry. The bulk density of these feedstocks and the unavailability of short rail lines near each contracted farm favour truck transportation. In the long term, shipment by railway could be preferred if certain impediments, such as greater road congestion, arise and affect delivery schedules. Rail transportation could eventually represent a cost saving option. Generally speaking, rail shipment of biomass has a lower variable cost but a higher fixed cost. As a result, short hauls of biomass feedstock would need to be done by truck and long hauls by rail. For the moment, rail shipment is manifestly less cost-efficient for the second generation biofuel industry, as demonstrated in Table 7. TABLE 7 COSTS ASSOCIATED WITH DIFFERENT TRANSPORTATION NETWORKS Cost of Biomass Transport by Truck Only and Truck Plus Train for the Straw and Wood Chip Power Plant ($/t) Truck only Truck plus train Straw plant Source: Mahmudi et al., 2006 Because of their dispersed production sites, materials such as straw and other energy crops need to start their transportation to a biofuel plant by truck. This gives a serious disadvantage to rail shipment from a

81 logistical and economic perspective. In the event that a more diversified biomass industry arises in Canada, feedstocks for the second generation biofuel industry could likely be shipped over greater distances. However, because it is simply not an economically feasible option at the moment, offloading truck transported biomass onto the rail network is not considered in the model. Since transportation represents one of the most important cost components of a supply chain, it needs to be as efficient as possible. In addition, the annual quantity of biomass that a second generation biofuel plant requires will force the industry to adopt technologies that are beneficial in terms of economies of scale. For that reason, the B-train truck with flat decks, portrayed in Figure 8, is at the centre of the trucking segment of the costing model. FIGURE 8 EIGHT AXLE B-TRAIN WITH FLAT DECKS FOR STRAW TRANSPORTATION Source: Transport Canada, Besides the elements explained in this section, the main assumptions considered for calculating trucking costs are: Trucks are assumed to have 475 HP and carry two flat-decks (trailers) in a B-train configuration. The number of bales that fit onto flat deck trailer varies according to the bale type. It is assumed that 16 large round bales and 20 large rectangular bales can fit onto one trailer. Trucks have an efficiency factor of 0.9, which covers time required for lubrication and servicing, as well as operator s break time.

82 Cost for driver time resulting from loading and unloading of truckloads is included using the appropriate hourly rate. Furthermore, return trips are taken into account in the calculation of costs related to road use. Average speed of trucks is 80 km/h. Transporting operations will take place year round, an equivalent of 48 weeks. Drivers are expected to work 12-hour days, four days/week, for a total of 2,304 hours in a year, dedicated exclusively to transporting biomass to the biofuel plant. It is assumed that tractor-trailer trucks consume 42.8 litres of diesel per 100 km and have a useful life of 12 years Diesel prices entered by default are based on price averages between January 3 rd 2006 and February 19 th 2008, using Natural Resources Canada s Fuel Focus Web-based database ( in the following cities: o Calgary, Alberta o Edmonton, Alberta o Red Deer, Alberta o Brandon, Manitoba o Winnipeg, Manitoba o Prince Albert, Saskatchewan o Regina, Saskatchewan o Saskatoon, Saskatchewan Oil and lube prices are calculated at 15% of fuel (diesel) costs. Tire costs are calculated based on the default value of $0,035/km. Wages for transport operators is established by default at $21/hour. Due to time required for lubricating and servicing trucks, as well as time allocated for breaks and road mishaps, we assume that the actual man-hours of labour exceeds actual field time by 20 percent. Hourly labour costs are therefore estimated by multiplying the labour wage entered by the user by 120% of the machine hours used for a particular operation.

83 3.3 Specific assumptions - model for a cellulosic ethanol plant in Eastern Canada This section details the input cells and assumptions that are considered in the analysis of feedstock supply chain scenarios for Eastern Canada. In these scenarios, different elements related to feedstock characteristics, harvesting equipment, storage compensation and transportation issues must be understood and correctly entered into the model by the user. The main difference between the model described previously and this model specific to Eastern Canada (Québec and Ontario), is that this model allows the user to estimate the delivered cost of corn stover, perennial grasses such as switchgrass and/or an alfalfa variety with a low lignin content, developed as a new energy crop. Corn stover differs significantly from all of the other modeled feedstocks with regard to harvesting, storage and transportation activities. Although field drying and baling of corn stover has been achieved in certain regions, normal fall weather conditions in Eastern Canada would not favour such a harvesting technique. From a cost perspective, harvesting wet corn stover eliminates baling, bale gathering, stacking, and loading steps, thereby potentially reducing cost. However, these benefits can be narrowed by greater transport and storage costs resulting from lower stover density and greater moisture content (Shinners et al., 2003). The different sheets of the Excel-based model used in estimating the total feedstock logistical cost associated with cellulosic ethanol production in Eastern Canada, are explained individually in each of the following sub-sections Basic scenario The model does not present a completely blank scenario for analysis. In fact, a basic scenario, based on plausible default values, has been established to portray the modelled supply chain logistical costs associated with cellulosic ethanol production in Eastern Canada. These default values are presented and justified in Table 8.

84 TABLE 8 BASIC DEFAULT SCENARIO IN THE MODEL FOR A CELLULOSIC ETHANOL PLANT IN EASTERN CANADA Availability of Feedstocks Plant Capacity Element Default Value of Variable Justifications and Observations Feedstock needs total Feedstock needs tonnes/day metric dry tonnes Industry information suggests that this represents the optimal scale of a second generation biofuel plant 1989 metric dry tonnes/day Ethanol plant operates 24/7 for 350 days/year (96%) Total capacity litres/year Calculated annual production capacity Feedstock Proportions Element Default Value of Variable Justifications and Observations Corn stover 100% of supply Main feedstock option Switchgrass 0% of supply Potential complementary feedstock option Low-lignin alfalfa 0% of supply Potential complementary feedstock option Basic Financial Data Element Default Value of Variable Justifications and Observations Annual interest rate 5.6% Estimated on-farm field basis (unharvested) Grower s price/tonne corn stover = $150/tonne switchgrass = $0/tonne low-lignin Alfalfa = $0/tonne Harvesting Scheme Used in the Farm Machinery Custom and Rental Rate Guide , published by Saskatchewan Agriculture and Food (2006) Stover: for Eastern Canada, $15/tonne (field basis) is the strict minimum price at which farmers are willing to sell this residue Switchgrass and low-lignin alfalfa: price used will be based on production costs, established in the Feedstock Characteristics sheet Element Default Value of Variable Justifications and Observations Federal tax on purchase of equipment Provincial tax on purchase of equipment Farmer's ownership equity on harvesting equipment Profit margin - farm (% of total cost associated with harvesting) Additional margin - farm (to cover unforeseen additional costs) 0% Federal tax break on equipment is assumed 0% Not province-specific 50% Industry standard 15% Industry standard 0% No unforeseen costs expected

85 Wage rate farm $21.00/hr Based on wages for farm specialized labour Twine cost for bales $0.76/bale Based on Saskatchewan Agriculture and Food (2006) Diesel price farm $0.7294/litre Main Options Based on price averages between and in main Eatern Canadian cities (Chicoutimi, Gaspé, Hamilton, London, Montréal, North Bay, Ottawa, Sault Ste Marie, Sherbrooke, St.Catharines, Sudbury, Thunder Bay, Toronto, Windsor) less the amounts of federal and provincial taxes Element Default Value of Variable Justifications and Observations Power equipment - tractor's PTO HP Harvester Baler Collector does not apply for corn stover switchgrass = 200 low-lignin alfalfa = 200 corn stover: self-propelled forage harvester switchgrass and low-lignin alfalfa: rotary cutter disk 2 = large rectangular bales (L x W x H = 4 ft x 4 ft x 8 ft = 128 cubic feet) corn stover: tractor with container-type wagon others: Stinger System Storage Scheme Minimum horsepower for operating chosen baler Most used and most efficient equipment Most common in Western Canada Equipment with lowest operating and maintenance cost on the market Element Default Value of Variable Justifications and Observations Number of contracted growers (i.e. storage sites) Grower's compensation ($/year) Total quantity of feedstock (tonnes) Average quantity of biomass stored per storage site (tonnes/site) corn stover = 696 others = 0 $0/year corn stover = tonnes tonnes/storage site (farm) Transportation Scheme Expected average quantity of biomass stored per storage site (per farm) = tonnes/site (farm) Usually implicitly included in the grower s compensation established in the Basic Financial Data category of variables Established in Feedstock Proportions category of variables Average value considered realistic Element Default Value of Variable Justifications and Observations Federal tax on purchase of equipment Provincial tax on purchase of equipment 5% Current GST rate 0% Not province-specific

86 Average radius of supply 100 km Considered reasonable for Eastern Canada Average distance between storage sites Owner's equity on purchase of equipment 10 km Considered reasonable for Eastern Canada 50% Industry standard Profit margin - owner 15% Industry standard Additional margin (to cover unforeseen additional costs) 0% No unforeseen costs expected Wage rate trucks $21.00 Based on wages for specialized labour Diesel price truck $ Licensing and insurance fees ($/truck/year) Based on price averages between and in main Eastern Canadian cities (Chicoutimi, Gaspé, Hamilton, London, Montréal, North Bay, Ottawa, Sault Ste Marie, Sherbrooke, St.Catharines, Sudbury, Thunder Bay, Toronto, Windsor), including federal and provincial taxes $8500 Average value in the industry Feedstock characteristics The Feedstock Characteristics sheet contains information related to different feedstock types, whose costs are then estimated by the model. The main feedstock option for the eastern model has been established as corn stover. Switchgrass and low-lignin alfalfa are the second and third feedstock options that are readily available to the user for scenario building. Corn stover consists of the non-grain, non-root portions of the corn plant (stalks, leaves, corn husks, and cobs) of a corn crop left in a field after harvest. Corn stover has a fertilizer value (mostly phosphorus and potassium) but soil incorporation of corn stover usually requires additional nitrogen fertilizer application to allow decomposition to take place. Therefore, the net effect should be considered when farmers decide on their asking price for corn stover in the windrow. Also, experience with large-scale collection of corn stover is very limited. The stover supply chain configuration for Québec and Ontario presented in this model requires further analysis and a trial period before it can completely be put into place. These elements must be taken into account when formulating a probable scenario to be entered into the model. When considering the feedstock characteristics of corn stover, the first input element to be entered into the model is the percentage of moisture content [cell D13 in the Feedstock Characteristics sheet]. This parameter is identified as an independent variable cell whose value is based on data in the literature (green cell). The default value of this variable is 56%, representing an average initial moisture content (Savoie and Descôteaux, 2004). As for the two other feedstock options in this model, conversions of cost elements

87 to a metric dry tonne basis in the Results sheet are based on the entered moisture content in this Feedstock Characteristics sheet. Next, the estimated yield, in tonnes per hectare, must be established [cell D14 in the Feedstock Characteristics sheet]. The default value of this independent variable cell for corn stover is 9.66 tonnes (at 56% moisture content)/ha. Representing a yield of five dry tonnes/ha, this is considered to be an average yield value, which can be lower or higher in certain cases. From a practical standpoint, such amounts should be feasible using proper collection methods while considering sustainability issues and weather conditions at the time of harvest. Lower collectable yields have been reported in the literature but ethanol plants considering purchasing corn stover will likely stay away from those regions or collection systems. This same variable, along with the identified feedstock availability and plant capacity options, are then used in the calculation of the number of hectares to be harvested [cell D19 in the Feedstock Characteristics sheet] and the amount of stover in tonnes (wet basis) [cell D20 in the Feedstock Characteristics sheet] that will be supplied to the biofuel plant on an annual basis. As described in the following subsection, corn stover will be harvested using a two-pass system, using a self-propelled forage harvester which is able to propel the raw material into a container-type wagon pulled by a tractor, as is often the case in corn silage harvesting activities. The third independent variable cell to be established by the user [cell D15 in the Feedstock Characteristics sheet] is the tonnage contained in this piece of equipment. Based on the literature, the value of this tonnage is set at 10.2 tonnes/wagon (Shinners et al., 2003). As a result, the amount of wagons needed to harvest one hectare is calculated [cell D16 in the Feedstock Characteristics sheet]. The total amount of hectares of corn stover to be collected in order to provide the quantity of this feedstock desired by the biofuel plant is also calculated using these different parameters [cell D17 in the Feedstock Characteristics sheet]. Finally, the estimated on-farm field basis (unharvested) price is copied in cell D20 [ Feedstock Characteristics sheet]. This value must be changed in the Scenario sheet if the user wishes to attribute a different value than the default value of $15/tonne for corn stover. For the characteristics of the two other feedstocks incorporated in this model, namely switchgrass and low-lignin alfalfa, the elements that can be entered or changed by the user are structured in the same way as in the model specific to Western Canada. The moisture content and estimated yields of these feedstocks are the first elements to consider [cells D23, D34, D50 and D51 in the Feedstock Characteristics sheet]. The default values of these variables (15% for switchgrass and 13% for low-lignin alfalfa) are based on the literature. The other main cells to consider are those that establish the tonnage of the different baling options. The weight of large round bales [cell D25 and D52 in the Feedstock Characteristics

88 sheet] is set at a default value of 0.43 tonnes/bale. The weight of large rectangular bales [cell D27 and D54 in the Feedstock Characteristics sheet] is attributed the default value of 0.48 tonnes/bale. As a result, the amount of bales produced per hectare is calculated in subsequent cells in the same way as in the model specific to Western Canada. The on-farm field basis (unharvested) price of switchgrass and low-lignin alfalfa can be attributed an absolute value in the Scenario [cells D29 and E29 in the Scenario sheet]. However, if no value is attributed to one or more of these variables in this way, the model will consider an estimated on-farm field basis price based on production costs. The costs for each of the alternative feedstocks are based on the following elements:

89 Switchgrass: Seeding density [cell D36 in the Feedstock Characteristics sheet] o default value = 8.3 kg/ha 1. Seed price [cell D37 in the Feedstock Characteristics sheet] o default value = $10/kg Total seed cost [equation (purple) cell D38 in the Feedstock Characteristics sheet] Land development and seeding cost [cell D39 in the Feedstock Characteristics sheet] o default value = $18.68/hectare Fertilizer and lime costs [cell D40 in the Feedstock Characteristics sheet] o default value = $37.15/hectare Cost of chemicals [cell D43 in the Feedstock Characteristics sheet] o default value = $33.42/hectare Cost of fuel and repairs [cell D44 in the Feedstock Characteristics sheet] o default value = $10.82/hectare Land taxes [cell D45 in the Feedstock Characteristics sheet] o default value = $0/hectare Low-lignin alfalfa: Seeding density [cell D64 in the Feedstock Characteristics sheet] o default value = 13.4 kg/ha Seed price [cell D65 in the Feedstock Characteristics sheet] o default value = $8.79/kg Total seed cost [equation (purple) cell D66 in the Feedstock Characteristics sheet] Land development and seeding cost [cell D67 in the Feedstock Characteristics sheet] o default value = $37/hectare Fertilizer and lime costs [cell D68 in the Feedstock Characteristics sheet] o default value = $24.55/hectare Cost of chemicals [cell D69 in the Feedstock Characteristics sheet] o default value = $23.05/hectare Cost of fuel and repairs [cell D70 in the Feedstock Characteristics sheet] o default value = $20.35/hectare Land taxes [cell D45 in the Feedstock Characteristics sheet] o default value = $0/hectare These cost elements are related to establishment costs. Their default values are based on the field crop budgets, published by the Ontario Ministry of Agriculture, Food and Rural Affairs (2008) and on a recent study reporting farm-scale production cost of switchgrass (Perrin et al., 2008). They are also in tune with the yearly budgets of Guy Beauregard and André Brunelle (MAPAQ Centre-du-Québec). For switchgrass, the default number of years on which the total estimated establishment cost is allocated is five [cell D45 in the Feedstock Characteristics sheet], representing the average production period for this crop. For low-lignin alfalfa, this default value is four [cell D73 in the Feedstock Characteristics sheet]. These values can be altered by the user as they are marked as being independent variable cells (blue cells). As in the model specific to Western Canada, production (establishment) costs are allocated on a yearly basis using a basic annuity equation as is stated in the comment boxes of cells 46 and 74 [ Feedstock Characteristics sheet].

90 A basic land rental rate in a $/year/hectare [cells D47 and D75 in the Feedstock Characteristics sheet] is then added to the annual establishment costs of each energy crop and converted to a $/tonne value [cell D48 and D76 in the Feedstock Characteristics sheet]. For Eastern Canada, the default value of the land rental rate is established at $120/year/hectare. The other alternative feedstock options in this model, identified as Alternative Feedstock B and Alternative Feedstock C can eventually be attributed to other feedstocks. Due to the fact that these new sources of biomass will most likely emerge from the production of new perennial grass crops on a larger scale, the feedstock characteristics of these alternative feedstock options are factually based on those of the switchgrass and low-lignin alfalfa crops Harvesting costs This segment of the biomass supply chain is modeled using the Harvesting sheet, contained in the Excel file. The different cells in this sheet calculate the fixed and operating costs of the equipment options available to the user in the Scenario sheet. These calculations are essentially based on a set of independent variables entered by the user and a number of built-in default-value parameters. In order to facilitate future modifications to the model, no elements contained in this sheet are protected. However, the equation cells (purple cells) and independent variable cells (green cells) take into account assumptions derived from the literature, which are accordingly reported in the comment boxes. Each cost element and calculation is plainly isolated by a column, while lines give the results concerning the different feedstock options. In order for the user to trace back each of these elements and calculations, line 11 of the Harvesting sheet clearly identifies the elements and assumptions taken into consideration. For corn stover, a two-pass system, utilizing a self-propelled forage harvester which is able to propel the raw material into a container-type wagon pulled by a tractor (see Figure 9 below), is at the centre of the modelled harvesting scheme. Ideally, wet stover would be collected using a single-pass whole-plant harvesting system. This includes a grain combine with a modified unit that chops and blows the stalk and leaf fraction of the crop into a separate container-type wagon. Such systems are said to significantly reduce cost of collecting corn stover and although they are being developed, they are not yet commercially available. On the other hand, the two-pass system has a notable advantage: the forage harvesters are proven technology and they are versatile enough to harvest other crops, thereby spreading the fixed costs over a greater number of hours of annual use. In fact, corn stover harvesting costs have proven to be higher when using a specialized one-pass system (modified ear corn harvesters or corn shellers) because

91 their use was limited to corn only. Also, a two-pass system using a self-propelled forage harvester is said to reduce delivered cost of stover by 19%, compared to dry baling systems (Shinners et al., 2003). FIGURE 9 CONTAINER-TYPE WAGONS USED TO COLLECT CORN STOVER For the switchgrass and low-lignin alfalfa crops, harvesting using balers is assumed, in the same way as in the model specific to Western Canada. In brief, the main assumptions considered for calculating harvesting costs computed by the model for Eastern Canada are as follows: Most assumptions and equations used for calculating the costs of harvesting operations are derived from the annual Saskatchewan Farm Machinery Custom Rate and Rental Guide (Saskatchewan Agriculture and Food, 2006). The machine cost data in this document is given for a wide variety of farm equipment under different ranges of annual hours of use. The data is specific to each individual piece of machinery and includes fixed costs (depreciation, investment cost, insurance and housing) and variable costs (repairs, fuel, oil and grease). Thus, the total cost per hour represents the total cost of owning and operating the harvesting machinery, with an additional profit margin. Self-propelled forage harvesters with 300 HP and a 25 foot width are assumed to be used to collect the corn stover in the windrow.

92 Fuel use of farm equipment is calculated based on equipment s horsepower. Oil and lube prices are calculated at 15% of fuel (diesel) costs For corn stover, the raw material (wet basis) is propel into a container-type wagon pulled by a tractor with a capacity of 10.2 tonnes. Field efficiency (FE) is factored into the use of every piece of machinery. This is based on a field efficiency factor, which depends on the options chosen by the user. The harvesting equipment (self-propelled forage harvester and rotary disc cutter) has an efficiency factor of 0.8, the large round bail baler has an efficiency factor of 0.5 and the large rectangular bale baler has an efficiency factor of Some pieces of equipment are used for more than harvesting biomass for the biofuel plant. Therefore, a percentage of use is factored into the model. This percentage is used to calculate the fixed costs for the time dedicated specifically to harvesting biomass for the biofuel plant. Self-propelled forage harvester and balers are exclusively used for harvesting biomass for the biofuel plant (100%), while tractors are used for other on-farm operations (44%). Harvesting of switchgrass and low-lignin alfalfa feedstock takes place over six weeks. Harvesting workers will work 12-hour days, four days/week, for a total of 288 hours dedicated exclusively to harvesting these sources of biomass for the biofuel plant. It is assumed that corn stover collection takes place over seven weeks, six days/week and with 12-hour days, for a total of 504 hours for harvesting this biomass source. Extensive use of equipment reduces not only its useful life period, but also its salvage value. In consequence, it is assumed that the salvage value of balers and wagons will be $0, while other pieces of equipment have a salvage value equal to 10% of their initial price. Bales will be scattered throughout the field (no bale accumulator will be installed on the baler). A Stinger Stacker will be used to transport the bales from the field to the roadside stacking location. This machine has the capacity to pickup bales at speeds of three to five miles per hour and carries up to eight square bales at a time and eight

93 round bales at a time. Also, the Stinger Stacker stacks the bales so that they are ready for loading onto transportation units. The default value of wages for farm labour is established at $21/hour. Based on wages of specialized farm machinery operators (i.e. custom labour), which have a high opportunity cost, and the fact that labour shortages often occur in the fall when the biomass feedstocks will need to be harvested, this amount is considered to be reasonable. However, some landowners might be able to hire operators at a lower rate. In such a case, the difference between this lower rate and the $21/hour wage established in the model will compensate the landowner for time spent training and managing his employees. Due to time required for lubricating and servicing machinery, as well as time delays in getting to and from the field, we assume that the actual man-hours of labour exceeds actual field time by 20 percent. Hourly labour costs are therefore estimated by multiplying the farm labour wage entered by the user by 120% of the machine hours used for a particular operation Storage costs The storage segment of the supply chain is facilitated by on-farm storage. The Storage sheet incorporated in the model establishes the details of costs related to storing the different feedstock. The number of contracted growers and, as a result, the number of storage sites, which is established by the user in the Scenario sheet [cells in line 49 and columns C to G in the Scenario sheet], is copied into the Storage sheet [cells in line 13 and columns D to H in the Storage sheet]. Yearly compensations for growers [cells in line 49 and columns C to G in the Scenario sheet], are copied in the Storage sheet [cells in line 14 and columns D to H in the Storage sheet] as well as is the total quantity of each feedstock supplied to the biofuel plant [cells in lines 19 to 23 in column E in the Scenario sheet are reported back to cells in line 15, columns D to H in the Storage sheet]. Although all these elements are marked as independent variables cells (blue cells), if the user desires to change one or more of these values, he or she must do so in the Scenario sheet. Taking into account the end-application requirements of corn stover, this feedstock is stored using an ensiling technique. Similar to ensiling forages for animal feed, where oxygen is restricted and proper moisture content is maintained, fermentation of the stover generates limited losses (Shinners et al., 2003).

94 In fact, ensiling wet stover generates lower losses and maintains more uniform moisture when compared to storing dry stover bales outdoors (Shinners et al., 2007). In this model, chopped material was assumed to be ensiled in a pile silo. Tower silos are not considered to be cost-effective for corn stover. Therefore, the cost related to using a tractor-operated silage blower to shape the pile silo is modelled in the Storage sheet. This modelling approach is indistinguishable from the method used to model costs of the harvesting and transportation segments of the supply chain. Nonetheless, certain parameters (machine horsepower, efficiency, purchase price, etc.) are adapted to these pieces of machinery. For switchgrass and low-lignin alfalfa feedstocks, the average number of bales per storage site is calculated [cells in line 28 and columns G to N in the Storage sheet]. These calculations are computed for the different feedstock options as well as for the different baling options (large round bales and large rectangular bales 4 ) using variables established by the user in the Scenario sheet, as well as in the Feedstock Characteristics sheet. Certain cost elements related to storage of biomass are established by independent variable cells (blue cells) that are only present in the Storage sheet. One of these cost elements relates a yearly compensation to the farmer for a snow clearing service he or she will provide [blue cell D31 in the Storage sheet], permitting a year-round access to the storage site. By default, this compensation is valued at 100$/year/storage site. This cost element is copied from the Scenario sheet and provided on a per-site basis for different feedstock options [purple cells line 33, columns E to I in the Storage sheet]. It is also provided on a per tonne basis [purple cells line 24, columns E to I in the Storage sheet] using the appropriate calculations. The other cost element introduced by an independent variable cell (blue cell) and exclusive to the Storage sheet is the cost of protecting biomass from the stresses of weather conditions by the means of tarpaulins (tarps). Costs related to this aspect of storage can be entered by the user on a $/tonne basis for different feedstock options [blue cells D24, G29, I29, K29 and M29 in the Storage sheet]. These costs will greatly depend on the attributes of the materials used. The default value of this cost element is fixed at $0/tonne for all feedstock options, but can be eventually changed by the user. 4 Assumptions pertaining to the dimensions of each type of bale are reinstituted in the comment boxes of cells identifying the type of bale in question [cells in 18 and columns E to N in the Storage sheet]

95 Finally, the total storage cost is calculated in the Storage sheet, using all the different cost elements described in this section. As expected, the result is reported on a $/tonne basis [purple cells line 38, columns E to I in the Storage sheet]. In summary, the main assumptions that are considered in calculating storage costs for a cellulosic ethanol plant in Eastern Canada are: Stacks are located in areas that are well drained and free of standing moisture year round. Costs associated with these farm-gate roadside stacking and pile silo facilities are assumed by the grower. The details concerning year round access and maintenance of these facilities are negotiated between the biofuel plant and the grower and described in the provisions of the straw supply contracts signed. For storing corn stover, pile silos are formed using an eight foot wide piler/blower machine (purchase price = $14,000), operated by a tractor of 95 HP at 5.5 mph. Fixed costs (depreciation, investment cost, insurance and housing) and variable costs (repairs, fuel, oil and grease) related to operating this piling machinery is considered in the model. Thus, the total cost per hour represents the total cost of owning and operating this machinery for the storage segment of the supply chain, with an additional profit margin. Storage activities are assumed to follow corn stover harvesting operations and take place over seven weeks, for six days/week and 12-hour days, for a total of 504 hours. Due to time required for lubricating and servicing machinery, as well as time delays in getting to and from the field, we assume that the actual man-hours of labour exceeds actual field time by 20 percent. Hourly labour costs are therefore estimated by multiplying the farm labour wage entered by the user by 120% of the machine hours used for a particular operation. A yearly compensation to the farmer for a snow clearing services, permitting a yearround access to the storage site, is valued at $100/year/storage site.

96 3.3.5 Transportation costs This segment of the biomass supply chain is modelled using two different sheets, the Truck Loading sheet and the Transportation sheet, in the Excel file. The first sheet calculates the fixed and operating costs of using front-end loaders (tractor with bucket) for corn stover and telescopic bale handlers for the other feedstock options (switchgrass and low-lignin alfalfa). These pieces of equipment are used to load the transportation units (trucks). The costs of utilizing these same transportation units are modelled in the Transportation sheet. These calculations are based on the independent variables entered by the user in the various sheets described previously. In practice, all elements contained in these sheets can be changed by the user. Many of these are equation cells (purple cells) and independent variable cells (green cells) that take into account assumptions derived from the literature. Corn stover transportation is subject to the same cost inefficiencies as bale transportation. Rail line layout in Canada is not ideal for moving this feedstock, whose bulk density inevitably favours truck transportation from the farm-gate to the biofuel plant. In other words, it would not be cost effective to remove stover from the on-farm storage sites, ship it by truck to a rail site and then transfer it to a rail car. Each cost element and calculation is plainly isolated by a column, while lines give the results concerning the different feedstock options. In order for the user to trace back each of these elements and calculations, line 13 of both the Truck Loading sheet and the Transportation sheet, clearly identifies the elements and assumptions taken into consideration. Truck Loading sheet This sheet calculates the fixed and operating costs of utilizing front-end loaders (tractor with bucket) for corn stover and telescopic bale handlers for the other feedstock options (switchgrass and low-lignin alfalfa). Telescopic bale handlers are assumed to have 120 HP, handle one bale per grab for large round bales and two bales per grab for large rectangular bales. For corn stover, each frontend loader (tractor with bucket) uses 160 HP and loads 1.32 tonnes per load. Each one of these loads takes an estimated time of two minutes per grab. These pieces of machinery have an efficiency factor of 0.8, which covers time required for lubrication and servicing, as well as operator s break time.

97 Loading operations take place year round: an equivalent of 48 weeks. Machine operators will work 12-hour days, four days/week, for a total of 2,304 hours in a year dedicated exclusively to loading biomass onto transports for delivery to the biofuel plant. Telescopic bale handlers and front-end loaders are themselves loaded onto transports, in order to travel from storage site to storage site. Costs related to these transports (fuel, repairs etc.) are considered to be incorporated into the profit margin of the machine owner(s), whether they are agricultural producers or independent contractors. We assume that harvesting operations will have a ten-year lifetime. Extensive use of baling equipment could reduce this variable. Its salvage value is however estimated at 10% of initial purchase price. Many equations used for calculating the costs related to this machinery s use derive from the source used to calculate cost of harvesting operations: the annual Saskatchewan Farm Machinery Custom Rate and Rental Guide (Saskatchewan Agriculture and Food, 2006). Thus, the total cost per hour represents the total cost of owning and operating the harvesting machinery, with an additional profit margin. Wages for transport operators is established by default at $21/hour. Due to time required for lubricating and servicing machinery, as well as time delays in getting to and from the field, we assume that the actual man-hours of labour exceeds actual field time by 20 percent. Hourly labour costs are therefore estimated by multiplying the farm labour wage entered by the user by 120% of the machine hours used for a particular operation. Transportation sheet This sheet calculates the fixed and operating costs of using transportation units (trucks) for moving biomass from the on-farm storage sites to the biofuel plant s gate. One of the main assumptions related to this segment of the supply chain is that it will utilize only roadway facilities. This is based on the fact that rail line layout in Canada is not ideal for moving feedstocks for the biofuel industry. The bulk density of these feedstocks, as well as the unavailability of short rail lines near each contracted farm favour truck transportation. Because it currently is simply not an economically feasible option, offloading truck transported biomass onto the rail network is not considered in the model.

98 Since transportation represents one of the most important cost components of a supply chain, it needs to be as efficient as possible. In addition, the annual quantity of biomass that a second generation biofuel plant requires will force the industry to adopt technologies that are beneficial in terms of economies of scale. For corn stover transportation, it is assumed that a bulk dump trailer (see figure 10) hitched to a diesel road truck is utilized. For the other feedstock options (switchgrass and low-lignin alfalfa), the same transportation units as in the model specific to Western Canada (B-train truck with flat decks) are at the center of the transportation segment of the supply chain. FIGURE 10 BULK DUMP TRAILER Besides the elements explained in this section, the main assumptions considered for calculating trucking costs are as follows: Trucks are assumed to have 475 HP and carry two flat-decks (trailers) in a B-Train configuration for baled biomass and a bulk dump trailer for corn stover. This type of trailer can hold 108 m 3 of raw material. To calculate the complete tonnage of such a transport, it is assumed that bulk density of chopped corn stover equals 220 kg of wet material (±60% moisture) per cubic metre (Shinners et al., 2003).

99 The number of bales that fit onto flat deck trailer varies according to the bale type. It is assumed that 16 large round bales and 20 large rectangular bales can fit onto one trailer. Trucks have an efficiency factor of 0.9, which covers time required for lubrication and servicing, as well as operator s break time. Cost for driver time resulting from loading and unloading of truckloads is included using the appropriate hourly rate. Furthermore, return trips are taken into account in the calculation of costs related to road use. Average speed of trucks is considered to be 80 km/h Transporting operations will take place year-round: an equivalent of 48 weeks. Drivers are expected to work 12-hour days, four days/week, for a total of 2,304 hours in a year, dedicated exclusively to transporting biomass to the biofuel plant. It is assumed that tractor-trailer trucks consume 42.8 litres of diesel per 100 km and have a useful life of 12 years. Diesel prices entered by default are based on price averages between January 3 rd 2006 and February 19 th 2008, using Natural Resources Canada s Fuel Focus webbased database ( in the following cities: o Chicoutimi, Québec o Gaspé, Québec o Hamilton, Ontario o London, Ontario o Montréal, Québec o North Bay, Ontario o Ottawa, Ontario o Sault Ste Marie, Ontario o Sherbrooke, Québec o St.Catharines, Ontario o Sudbury, Ontario o Thunder Bay, Ontario o Toronto, Ontario o Windsor, Ontario Oil and lube prices are calculated at 15% of fuel (diesel) costs Tire costs are calculated based on the default value of $0,035/km.

100 Due to time required for lubricating and servicing trucks, as well as time allocated for breaks and road mishaps, we assume that the actual man-hours of labour exceeds field time by 20 percent. Hourly labour costs are therefore estimated by multiplying the labour wage entered by the user by 120% of the machine hours used for a particular operation.

101 4. ANALYSES OF DIFFERENT SCENARIOS AND LIMITS OF THE ECONOMIC MODELS In this section, the results of the basic default scenarios established in sections and for supply chain logistics in both Western and Eastern Canada are presented. Three supplementary scenarios are then analyzed. The first examines and compares the use of agricultural residues exclusively, in both regions of Canada. The second supplementary scenario builds on a 100% straw scenario for Western Canada, and compares the difference in cost of using large rectangular bales against that of large round bales. Lastly, using each model individually, we compare the total cost of a supply chain exclusively based on agricultural residues to a supply chain based exclusively on switchgrass. 4.1 Analyses of different supply chain scenarios The results of the basic default scenarios are presented in Table 9, which demonstrates that the logistical costs of transporting large amounts of biomass for a commercial-sized biofuel plant are higher in the scenario for Eastern Canada than in that for Western Canada. Although corn stover in Eastern Canada is less expensive to harvest, mainly because it presents considerably higher yields than straw or switchgrass in Western Canada, it is more expensive to store and to transport. This is essentially explained by the fact that corn stover is collected on a wet basis. When compared on a dry basis, it is significantly more costly to store and to transport corn stover than it is to store and transport the baled feedstock options in Western Canada.

102 TABLE 9 RESULTS OF BASIC DEFAULT SCENARIOS Model for Western Canada Model for Eastern Canada Total estimated $/dry tonne $67.63 $86.54 feedstock cost $/dry tonne/km $0.34 $0.43 Feedstock options Cereal straw Switchgrass Corn stover Grower s $/tonne $12.50 $15.75 $15.00 payment* $/dry tonne $14.37 $18.53 $34.09 Harvesting costs Storage costs $/tonne $24.03 $27.27 $5.11 $/dry tonne $27.62 $32.08 $11.61 $/tonne $0.10 $0.10 $4.90 $/dry tonne $0.11 $0.12 $11.15 Transportation $/tonne $20.22 $20.22 $13.06 costs ** $/dry tonne $23.24 $23.78 $29.68 *Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modeled parameters. As mentioned throughout this report, agricultural residues (corn stover for Eastern Canada and cereal straw for Western Canada) represent the most abundant feedstock source available in Canada to supply the quantities of biomass needed to provide a second generation biofuel plant of optimal scale (production capacity of around 200 million litres). Using the costing models, we simulated supply chains that are exclusively based on using agricultural residues in both regions of Canada. For the model specific to Eastern Canada, the basic default scenario already assumes a supply chain of 100% corn stover. For the model specific to Western Canada, the main parameters altered from the basic default scenario for this analysis are those pertaining to the feedstock proportions of the supply chain, in order to establish a 100% straw scenario. Also, the number of contracted growers and storage sites was modified to keep the quantity of stored biomass at an average of 1,000 tonnes/site. The results comparing the total logistical costs of supply chains based exclusively on agricultural residues for both regions of Canada are presented in Table 10.

103 TABLE 10 RESULTS OF SUPPLY CHAIN SCENARIOS BASED EXCLUSIVELY ON THE USE OF AGRICULTURAL RESIDUES Feedstock options: Western Canada Eastern Canada 100% crop residues Cereal straw Corn stover Total estimated $/dry tonne $65.34 $86.54 feedstock cost $/dry tonne/km $0.33 $0.43 Grower s payment* Harvesting costs Storage costs $/tonne $12.50 $15.00 $/dry tonne $14.37 $34.09 $/tonne $24.03 $5.11 $/dry tonne $27.62 $11.61 $/tonne $0.10 $4.90 $/dry tonne $0.11 $11.15 Transportation $/tonne $20.22 $13.06 costs** $/dry tonne $23.24 $29.68 * Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modelled parameters As expected, the total cost of harvesting, storing and transporting corn stover in Eastern Canada is higher than the logistical cost of using cereal straw in Western Canada. On the other hand, we notice that a 100% straw scenario slightly reduces cost when compared to the basic default scenario that utilizes switchgrass for 25% of total feedstock needs for a biofuel plant in Western Canada. This is essentially due to the fact that straw harvesting costs are not considered in the model. Only field collecting and storing costs are calculated for straw, where as the cost of field harvesting (mowing) is considered for the switchgrass feedstock. When using the costing model for a cellulosic ethanol plant in Western Canada, the user must choose to package the cereal straw feedstock after initial harvesting. In fact, the user can either select large round bales (diameter x height = 6 ft x 5 ft = 141 cubic feet) or large rectangular bales (length x width x height = 4 ft x 4 ft x 8 ft = 128 cubic feet). Although these two options represent the most cost efficient ways to supply the required amount of biomass to the biofuel plant, it is interesting to compare the difference in cost between these two collection options. Starting with the 100% straw supply chain analyzed in the previous table, the cost of each packaging option was analyzed. Results of each option are compared in Table 11.

104 TABLE 11 RESULTS OF A SUPPLY CHAIN SCENARIO EXCLUSIVELY BASED ON THE USE OF CEREAL STRAW IN DIFFERENT TYPES OF BALES Western Canada: 100% Cereal Straw Supply Chain Large round bales Large rectangular bales Total estimated $/dry tonne $80.89 $65.34 feedstock cost $/dry tonne/km $0.40 $0.33 Grower s payment* Harvesting costs Storage costs $/tonne $12.50 $12.50 $/dry tonne $14.37 $14.37 $/tonne $21.58 $24.03 $/dry tonne $24.81 $27.62 $/tonne $0.10 $0.10 $/dry tonne $0.11 $0.11 Transportation $/tonne $36.19 $20.22 costs* $/dry tonne $41.60 $23.24 * Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modelled parameters While large round bales are less costly for harvesting operation when one takes into account the initial purchase price of the machinery and the fact that less powerful tractors can be used, these bales are much more costly to transport as a result of their shape and density. It should be noted that transportation costs (including loading and unloading) are considerably high for large round bales based on this modelling approach. One possible explanation for this is that the model assumes the same times for loading the bales at the farms and the unloading of the same bales at the plant. The plant should be expected to unload a lot faster, which would lead to cost reductions versus what has been modeled. As discussed in section 6, economies of scale will also allow greater efficiency in these operations in the long term. The global feedstock supply chain of the second generation biofuel industry could eventually need to incorporate other sources of biomass. Using each model individually, we can compare the total cost of a supply chain exclusively based on agricultural residues to a supply chain based exclusively on switchgrass for both regions of Canada. The main parameters altered from the basic default scenarios for this analysis are those pertaining to the feedstock proportions of the supply chain, as well as the number of contracted growers and storage sites required to keep the quantity of stored biomass at an average of 1,000 tonnes/site. Furthermore, taking into consideration the previous analysis between large round bales and large rectangular bales, cereal straw and switchgrass are assumed to be packaged in large rectangular bales in these scenarios. The result of this analysis is presented in Table 12.

105 TABLE 12 RESULTS OF DIFFERENT FEEDSTOCK SCENARIOS Feedstock Options Model for Western Canada 100% cereal straw 100% switchgrass Model for Eastern Canada 100% corn stover 100% switchgrass Total estimated $/dry tonne $65.34 $74.50 $86.54 $63.41 feedstock cost $/dry tonne/km $0.33 $0.37 $0.43 $0.32 Grower s $/tonne $12.50 $15.75 $15.00 $15.72 payment* $/dry tonne $14.37 $18.53 $34.09 $18.49 Harvesting $/tonne $24.03 $27.27 $5.11 $19.15 costs $/dry tonne $27.62 $32.08 $11.61 $22.52 Storage costs $/tonne $0.10 $0.10 $4.90 $0.10 $/dry tonne $0.11 $0.12 $11.15 $0.12 Transportation $/tonne $20.22 $20.21 $13.06 $18.94 costs * $/dry tonne $23.24 $23.77 $29.68 $22.28 * Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modelled parameters Although such 100% switchgrass scenarios are not likely to become a reality, this analysis demonstrates that switchgrass is not significantly more costly than cereal straw in Western Canada. Modelled parameters pertaining to yields, moisture content and production costs are at the root of this end result. However, this analysis demonstrates that switchgrass could be a more cost-effective feedstock source in Eastern Canada, especially compared to the logistical costs of a supply chain based exclusively on corn stover as a feedstock source, provided, of course, that switchgrass can be produced at a reasonable price in the same regions as corn for grain and use only marginal land for production. If switchgrass were to compete for the same land as that used for corn production, production costs would be significantly higher than those modelled in this study. Moreover, in the event that switchgrass would be grown on the outskirt of the corn regions and trucked to cellulosic ethanol plant located within the corn regions, additional trucking would have to be factored-in. If better-adapted varieties and production technologies are developed in the long term, new energy crops such as switchgrass may be called upon to play a more important role in the development of the second generation biofuel industry as a significant, cost-effective feedstock source. From a general perspective, the results of the different scenarios analyzed in this section are considerably higher than data found in the literature. They analyses clearly demonstrate that a delivered cost of $35/dry tonne for agricultural residues is unlikely to be a reality in the present economic context. For several years,

106 this value has been adopted as a rule of thumb in order for a second generation biofuel plant to be economically viable (Sokhansanj and Turhollow, 2005). However, the recent rapid-rise of the price of crude oil and steel has had an impact on supply chain logistical costs for the second generation biofuel industry. This new business environment is leading the way to a new price discovery process between farmers and cellulosic ethanol plants. 4.2 Other logistical costs not taken into account The supply chain logistical costs associated with cellulosic ethanol production that were modelled in this study only incorporate the basic harvesting, storing and transportation costs. A biofuel plant will have to consider other cost elements before and after it starts production operations, including costs related to contracting, scheduling pick-up and deliveries, field quality assurance and inventory management. Although they are related to logistics, these types of costs were not considered in this study because they are to be assumed internally by the biofuel plant. 4.3 Sensitivity analysis of impact of on-farm labour costs on total feedstock price In the model, wages for farm labour is established at a default value of $21/hour. Based on wages of specialized farm machinery operators (i.e. custom labour), which have a high opportunity cost, and the fact that labour shortages often occur in the fall when the biomass feedstocks will be harvested, this amount is reasonable. However, some landowners might be able to hire operators at a lower rate, or might consider their time to be more valuable. Accordingly, different farm wages were entered into the model for further analysis of this parameter s impact on the total delivered price of feedstock. These sensitivity analyses were made using supply chains scenarios that are exclusively based on the utilization of agricultural residues in both regions of Canada. Therefore, for the model specific to Eastern Canada, the scenario assumed a supply chain of 100% corn stover. For the model specific to Western Canada, a 100% straw scenario was considered. Also, the number of contracted growers and storage sites was modified to keep the quantity of stored biomass at an average of 1,000 tonnes/site. The result of these sensitivity analyses are reported in Table 13.

107 TABLE 13 FARM WAGES SENSITIVITY ANALYSES Model for Western Canada Model for Eastern Canada Feedstock options Cereal straw Corn stover Custom farm wage $/hour $14 $21 $30 $14 $21 $30 Total estimated $/dry tonne $64.19 $65.34 $66.82 $84.28 $86.54 $89.43 feedstock cost $/dry tonne/km $0.32 $0.33 $0.33 $0.42 $0.43 $0.45 Harvesting costs Total $/dry tonne difference -1.76% % -2.61% % $/tonne $23.03 $24.03 $25.32 $4.64 $5.11 $5.71 $/dry tonne $26.47 $27.62 $29.10 $10.55 $11.61 $12.97 $/dry tonne harvesting cost difference -4.16% % -9.13% % As the above table illustrates, for a 100% straw scenario in Western Canada, a 33% decrease in hourly farm wages ($21 to $14) reduces the feedstock harvesting cost by 4.16% and the total estimated feedstock cost by only 1.76%. In contrast, a 43% increase in hourly farm wages ($21 to $30) only increases the feedstock harvesting cost by 5.36% and the total estimated feedstock cost by 2.27% in the same scenario. For the 100% corn stover scenario in Eastern Canada, variations are much more important. A 33% decrease in hourly farm wages ($21 to $14) reduces the feedstock harvesting cost by 9.13% and the total estimated feedstock cost by only 2.61%, while a 43% increase in hourly farm wages ($21 to $30), increases the feedstock harvesting cost by 11.71% and the total estimated feedstock cost by 3.34%. As changes in farm wages do not affect the transportation segment of the supply chain, the total estimated feedstock cost on a $/dry tonne/km basis is not significantly affected by these variations.

108 4.4 Examples of crop residue estimates and radius of supply In order to demonstrate the use of the Crop Residue Estimate sheet, two different census regions were assessed and analyzed using each model individually. Figure 11 illustrates the two regions from which the census data was used for this exercise. The first is in Saskatchewan, while the second census region, which includes numerous census divisions, is in Ontario. FIGURE 11 CENSUS REGIONS USED TO DEMONSTRATE THE CROP RESIDUE ESTIMATE SHEET For the Ontario region, only grain corn was identified as a crop of interest, corn stover being the main source of crop residue in this part of Canada. For the census division in Saskatchewan, crop residues from wheat, barley and canola were calculated using census data. The main parameters considered in these examples, extracted from the 2006 Census of Agriculture conducted by Statistics Canada, are presented in Table 14. This same table also presents the calculated results related to the gross and net total amounts of crop residue available in the different regions, as well as the calculated radius of supply based on each region s total area of all land tenures. Once these results established in the Crop Residue Estimate sheet, they were not automatically copied into the Scenario sheet.

109 TABLE 14 PARAMETERS AND RESULTS OF THE CENSUS REGIONS USED TO DEMONSTRATE THE CROP RESIDUE ESTIMATE SHEET Region's characteristics Total area of all land tenures minus total area used by others Eastern Canada Western Canada Crop of interest Corn Total wheat Barley ha 1,702,688 1,078,941 1,078,941 Total farm area in the covered territory ha 1,592,343 1,009,356 1,009,356 Total land in crops (excluding Christmas trees) ha 1,353, , ,790 Cultivated area dedicated to crop of interest (ha) 291, , ,545 Cultivated area dedicated to crop of interest (%) 22% 33% 14% Production Gross amount of crop residue available (dry tonnes) 1,267, , ,763 Producers' characteristics Total number farms reporting the production of feedstock of interest Total number farms reporting the production of feedstock of interest # 6,176 1,513 1,076 % of total farms with crop area 37% 67% 47% Average area cultivated by each farmer ha Producers' willingness and losses Farmers willing to sell their crop residues % 50% 50% 50% Percentage of area each farmer is willing to harvest and sell biomass feedstock Feedstock availability and radius of supply % 50% 50% 50% Gross total amount of feedstock dry tonnes 1,267, , ,763 Net total amount of feedstock dry tonnes 316, ,484 38,691 Calculated radius of supply km Calculated average trucking distance km Net total amount of feedstock available d. t./km of radius 4,304 1, The calculated net total amount of feedstock available in each region and the corresponding radii of supply were then entered into the Scenario sheet in order to estimate the cost of using such parameters for a second generation biofuel plant. The number of contracted growers was also altered accordingly. The remaining parameters are those of the supply chain scenarios that are exclusively based on the use of crop

110 residues in both regions of Canada. Table 15 presents the modified parameters and the results of the analysis. TABLE 15 REGIONAL ANALYSES Feedstock options: 100% crop residues Parameters for Census Regions Net total amount of feedstock Calculated average trucking distance (radius) Number of contracted growers (i.e. storage sites) Western Canada Region 8B Cereal straw Eastern Canada Southern Ontario region Corn stover dry tonnes 316, ,175 km # 647 3,088 Estimated feedstock costs Total estimated $/dry tonne $59.56 $80.61 feedstock cost $/dry tonne/km $0.57 $0.96 Grower s payment* Harvesting costs Storage costs $/tonne $12.50 $15.00 $/dry tonne $14.37 $34.09 $/tonne $24.03 $5.11 $/dry tonne $27.62 $11.61 $/tonne $0.20 $6.87 $/dry tonne $0.23 $15.62 Transportation $/tonne $15.09 $8.48 costs** $/dry tonne $17.34 $19.28 * Price of feedstock field basis (unharvested) **Includes loading and unloading See tables in sections and for default values of modelled parameters The results of these regional analyses do not present significantly lower costs than the default supply chain scenarios based exclusively on the use of crop residues (see Table 10). This demonstrates that the main assumptions of the default scenarios reflect what can be actually witnessed. However, the total estimated feedstock costs on a dollars per dry tonne basis (including harvesting, storage and transportation costs) differ substantially from the rule of thumb pertaining to the delivered cost of feedstock adopted by industry analysts ($35/dry tonne). Taking the present economic context into account, more recent studies

111 have suggested that the minimum delivered cost should be around $50/dry tonne. With this in mind, the parameters used for the regional analysis (see tables above) were used to conduct sensitivity analyses of the radius impact on the total feedstock price. The results of these analyses are presented in Table 16. TABLE 16 RADIUS OF SUPPLY SENSITIVITY ANALYSES ON REGIONAL SCENARIOS Model for Western Canada Region 8B Model for Eastern Canada Southern Ontario region Feedstock options Cereal straw Corn stover Radius of supply km Total estimated $/dry tonne $57.10 $58.33 $59.56 $77.03 $78.82 $80.61 feedstock cost $/dry tonne/km $0.89 $0.69 $0.57 $1.75 $1.23 $0.96 Total $/dry tonne difference -4.13% -2.07% % -2.22% - Transportation $/tonne $12.95 $14.02 $15.09 $6.91 $7.70 $8.48 costs** $/dry tonne $14.88 $16.11 $17.34 $15.70 $17.49 $19.28 $/dry tonne harvesting cost difference % -7.09% % -9.28% - **Includes loading and unloading Although reducing the radius of supply can decrease the transportation costs substantially, the total estimated feedstock cost is not significantly affected by these variations. Along with the analyses related to farm wages, the conducted sensitivity analyses for this parameter demonstrate that an isolated parameter does not radically impact the total estimated feedstock cost in a given scenario. This testifies to the fact that the total estimated feedstock cost calculated by the model is based on numerous parameters that can fluctuate greatly from one region to another. Future users of this model who wish to establish scenarios that successfully reach the $50/dry tonne objective need to be realistic about the parameters entered. Other parameters that could further help in establishing a regional scenario that could attain the recognized objective include the number of farmers willing to sell their crop residues, as well as the percentage of area each farmer is willing to harvest and sell its residues. These parameters can be altered in the Crop Residue Estimate sheet of the model [lines 34 and 35].

112 5. PORTRAIT OF BIOMASS PRODUCTION IN CANADA USING POTENTIALLY EXPLOITABLE FEEDSTOCKS This section provides an overview of biomass production regarding the feedstocks that are most fit to be at the centre of the development of the Canadian second generation biofuel industry in the coming years. Key feedstock production basins are identified throughout Western and Eastern Canada. The main attributes of each basin, positive and negative, for second generation biofuels are briefly discussed. This analysis will help in developing the optimum road map to commercializing second generation biofuels in each feedstock basin. Development of those commercialization plans, however, is beyond the scope of this study. 5.1 Feedstock production basins in Western Canada Industry information suggests that the optimal scale of a second generation biofuel plant is an annual production capacity of around 200 million litres. Depending on the type of technologies utilized, such a plant requires approximately dry tonnes of feedstock, which must be supplied equally throughout the year. In Western Canada, cereal straw is by far the most abundant source of lignocellulosic biomass feedstock that can supply such quantities. More specifically most of Canada s cereal straw is produced in the Prairie provinces. Therefore, second generation biofuel plants that wish to use cereal straw as their main feedstock should be located in Alberta, Saskatchewan and/or Manitoba. However, strong uncertainties exist in straw availability on a regional basis. Data on the true availability of straw residue is incomplete. The inherent problem with this biomass production is its annual variation, which depends largely on weather conditions. Furthermore, the amount of surface residues required for erosion control vary depending on soil texture and field slope, so not all of the straw produced can be removed (Sokhansanj et al., 2006). Barriers to the economic use of cereal straw include uncertainty in its availability, quality, cost of collection, transport, storage, and location. Only a portion of farmers with excess straw are willing to sell it and farmers typically do not commit 100% of their excess straw in a given year. In fact, the amount that a producer is prepared to remove and supply to an industrial user will depend on the producer s perceived value of straw. Hence, in establishing an industrial plant, it is essential to investigate the amount of land in production and the local yields and harvest practices, as well as the general economic, environmental, and social contexts.

113 To determine which specific regions within the Prairies present the greatest potential for the development of the second generation biofuel industry with cereal straw as a central feedstock, we based our analysis on the cereal production level of each region, including wheat, oat, barley, flax and rye productions. Using different governmental sources, data concerning production area and local yields for the main cereal crops over a ten year-period were compiled for the different Census Agricultural Regions (CAR). These are commonly used by Statistics Canada in conducting the Census of Agriculture. In order to incorporate the effect of periodic fluctuations in production, a ten-year average of total cereal production for each CAR was calculated using data compiled from 1996 to 2006.,To determine the gross straw production of each CAR, the straw-to-cereal ratios used by Sokhansanj et al. (2006) were applied to the total average cereal production levels of each region. Taking into consideration sustainability and land protection issues, a quantity of residue was subtracted from the gross straw production levels of each CAR. This represents an amount of one ton of straw per hectare of cereal production, according to Sokhansanj et al. (2006). The use of straw by the livestock sector in each region was also accounted for. Because the livestock industry uses straw for feeding and bedding during only part of the year, the amount of cattle surveyed in a given region along with the region s climate will inevitably affect the net straw availability. To compensate for such use, the estimation of straw consumption by the livestock sector was based on coefficients established by Sokhansanj et al. (2006). As described in Table 17, these coefficients were applied to the total number of cows surveyed for each CAR in the 2006 Census of Agriculture, conducted by Statistics Canada. TABLE 13 ESTIMATION OF STRAW CONSUMPTION BY THE LIVESTOCK SECTOR Applied equation: [(A x B) + (C x D)] x E = Straw consumption by the livestock sector Province Region A B C D E Feeding Bedding Days kg/day Days kg/day Southern Total number of cows Alberta Central For each Census Northern Agricultural Saskatchewan All Region* Manitoba All *Data from 2006 Census of Agriculture, Coefficient source: Sokhansanj et al., 2006

114 The equation detailed in Table 17 was used to calculate the quantity of straw used by the livestock sector in each CAR. This quantity was then subtracted from the amount of straw available once organic amendments (one ton of straw per hectare) were taken into consideration. The results of these calculations, representing the net available quantity of straw in each Census Agricultural Region, are illustrated in Figure 12. FIGURE 12 STRAW AVAILABILITY IN WESTERN CANADA The straw densities presented in this map are based on the total number of square kilometers within each census region. No adjustment was made for forested areas, rivers, lakes, cities, etc. Each census region covers a fairly large area and significant variability in crop production, forested areas, etc., may exist within each region which is not reflected in the computation. Areas near Prince Albert (northern Saskatchewan) should have a higher straw density than depicted in this map as there is a significant amount of forested areas within those regions. In Alberta, the area with the largest straw density on this map (Calgary Lethbridge) is outside of the black soil zone and is likely over-estimated as a result of the larger amounts of straw produced per hectare in the Lethbridge region (irrigated land). Ideally, this mapping should be done at the county level and including only areas used for agricultural production. This was outside of the scope of this study. Source: ÉcoRessources Consultants For the Prairies, results show that straw is only reliably available in the black soil zone, which coincides with the results of previous studies. In fact, the available amount of cereal straw and chaff in these provinces was estimated at two million tonnes of material for use as a biomass resource (Watson et al., 1998).

115 The most suitable locations for biofuel plants using straw as a feedstock are in areas where straw availability per square kilometre is high. In Figure 12, the darker Census Agricultural Regions present the most potential for the second generation biofuel industry in Western Canada. Although these regions are somehow scattered, three main biomass production basins are depicted. As indicated by the circles in Figure 12, the first of these basins is located east of Edmonton, Alberta (the Lethbridge region being excluded), the second one is found in central Saskatchewan, between Saskatoon and Prince Albert, and the third is situated in southern Manitoba. Although the shades of green portray straw availability in the different Census Agricultural Regions in Figure 12, it is important to note that, in reality, straw availability can vary within a given CAR. Also, infrastructure accessibility and geographical factors such as the presence of rivers could increase the cost of transporting feedstock to a biofuel plant centrally located in one or more of these identified production basins. Switchgrass, the other most promising source of biomass for the second generation biofuel industry, can be grown on almost all types of soils, especially sandy soils and loams. This factor alone makes the Prairie provinces particularly suitable for production. However, because this crop is not yet widely implemented in the agricultural landscape, it is likely to be grown in regions where cash crops such as cereals and oilseeds cannot provide satisfactory yields. Therefore, regions which currently present a greater potential for the cultivation of switchgrass are actually marginal regions where agricultural lands are mainly devoted to hay production. Because hay production represents the opportunity cost of cultivating switchgrass for the biomass industry, it is also important to consider that regions with strong needs for forages are less likely to use lands for switchgrass production. Therefore, cattle herds should also be considered in the evaluation of potential production basins. For the purpose of this study, it is assumed that switchgrass production areas will mirror the regions where straw is produced as it would be used as a complement to the straw supply of second generation biofuel plants. 5.2 Feedstock production basins in Eastern Canada In Eastern Canada, because the ratio of cereal production to animal production is significantly lower than in Western Canada, the amount of straw available for biofuel production is considerably lower. This situation results in a higher price for this potential feedstock. Corn stover represents the most abundant source of feedstock that can potentially supply the biofuel industry in Eastern Canada. The development of biofuel production based on this crop residue should be concentrated in areas where corn production is significant. Therefore, biofuel plants using this feedstock could potentially operate in Ontario and Québec. For these two provinces, Figure 13 illustrates the average quantities of corn produced for grain, as opposed to those grown for silage, per square kilometre. Results for Ontario represent average grain

116 production over a seven-year timeframe ( ), while a ten-year average ( ) was calculated for Québec. In Ontario, Statistics Canada s Census Divisions are used, whereas the Census Agricultural Regions represent the Québec portion of the map. FIGURE 13 CORN PRODUCTION IN ONTARIO AND QUÉBEC Although the term tons is used in the legend, the quantities used in this analysis were in metric tonnes Source: ÉcoRessource Consultants As Figure 13 indicates, there are two potential corn stover production basins where biofuel plants using this feedstock could imaginably be established. As the circles indicate, the first basin is located in the Ontario Peninsula and the second includes southwestern Québec and eastern Ontario. Since switchgrass production presents greater potential on marginal lands, which are currently dedicated mainly to hay production, only certain regions can be identified as presenting a real potential for this production. On the other hand, regions which produce sufficient amounts of corn stover for the biofuel industry are not likely to reallocate a significant number of hectares to this crop. This is evidenced by the short supply of hay in regions where cash crops are extensively cultivated.

117 Unfortunately, it is difficult to map out the potential production basins of switchgrass in Eastern Canada in the same way that production basins were illustrated for other feedstocks. Nevertheless, certain regions, counties or districts, in both Ontario and Québec, which present somewhat of a potential for switchgrass production are listed in Table 18. It is important to note that this information is preliminary and would require further investigation to be confirmed. TABLE 18 REGIONS PRELIMINARILY IDENTIFIED AS HAVING POTENTIAL FOR SWITCHGRASS PRODUCTION Ontario Prescott Russel Stormont Dundas and Glengarry Ottawa Valley Leeds Greenville Lanark County Québec Bas-St-Laurent-Gaspésie Estrie Cantons de l Est Chaudière-Appalaches Saguenay-Lac-St-Jean Abitibi-Témiscamingue These regions present a low corn-to-forage ratio in their cultivated area, while a significant portion of their total cultivated area is dedicated to forage crops. The first biofuel plants to come into being will most likely aim to utilize the most abundant and most reliable feedstock sources, i.e. crop residues (straw and/or corn stover). If these same plants eventually wish to be supplied with switchgrass, it is unlikely that they will be able to keep the same geographical radius of supply in Eastern Canada. In this scenario, the cost of transportation could increase considerably and make the switchgrass option less economically attractive. Another scenario could incorporate smaller biofuel plants that primarily utilize switchgrass and alternative energy crops as feedstock. This scenario is only viable in the long term, as the cost of second generation technologies for biofuel production requires that biofuel plants produce large volumes of biofuel in order to benefit from economies of scale. Furthermore, smaller plants established in remote regions would have

118 to transport their end product (biofuel) longer distances in order to market it. This would increase the overall production cost of such plants and would impact, to a certain degree, economic viability.

119 6. DISCUSSION OF OTHER COSTS AND BENEFITS TO SOCIETY In this section, other costs and benefits to society created by the second generation biofuel industry are briefly discussed. Economies of scale, which can ultimately lead to cost reductions and greater benefits, are firstly discussed. Secondly, environmental costs and benefits of the biofuel industry and second generation technologies are analyzed. 6.1 Economies of scale Economies of scale play an important role in the economics of chemical and oil and gas facilities; a similar trend is expected for the second generation biofuel industry. As this industry develops and technologies mature, the annual ethanol production capacity of plants built should increase to capture economies of scale. Similar, in this respect, to oil refineries, cellulosic ethanol plants are capital-intensive projects that benefit from larger annual end-product throughput. Beyond technology and access to capital, the capacity to build ever-larger plants will likely be constrained by access to cellulosic raw materials at an economically viable price. Cellulosic raw materials are bulky products that cannot be shipped over long distances without incurring a significant cost premium. Economies of scale will allow plants to pay a premium for feedstocks sourced from a greater distance, but sourcing feedstocks from a different feedstock basin will likely require new and more efficient feedstock handling technologies than modelled in this study. The order of magnitude of economies of scale can usually be estimated by simply applying a factor to a given plant investment cost and annual throughput. The formula typically goes as follows (Alpert, 1959): Where: Cost new /Cost default = (Capacity new /Capacity default ) 0.7 Cost new = capital cost of facility of different plant size Cost default = capital cost of facility of default plant size Capacity new = annual production capacity of new plant size Capacity default = annual production capacity of default plant size Using this formula, the capital cost of a plant with twice the annual production capacity of the default plant would require only 63% more investment dollars. The exponential factor may vary amongst industries; in order to improve the accuracy of this estimate for second generation biofuel facilities,

120 current factors for the chemical and/or the oil and gas industries should be obtained. A more in-depth discussion of economies of scale and the expansion of the feedstock collection radius is beyond the scope of this study. Beyond economies of scale realized at the plant level, feedstock supply chains developed for second generation biofuel plants allow them to reap some economic benefits from the optimal as opposed to the traditional use of the supply chain equipment. The large and steady volume of feedstock required by biofuel plants allows maximization of the annual use of farm balers, collection units and trucks, which in turn spreads the capital cost of such equipment over a larger number of working hours and tonnes of product. The economies realized would then allow new custom rate equilibrium prices to be achieved by the industry. 6.2 Environmental Costs and Benefits Overall greenhouse gas emissions There is growing awareness of our impact on the environment and governments have expressed specific concern over climate change caused by human combustion of fossil fuels. In particular, the transportation sector accounted for 30% of final energy use in 2005 and for 36% of overall greenhouse gas (GHG) emissions (including electricity) in Canada (OEE, 2007). The transportation sector is the second largest energy-using sector after the industrial sector. It is the largest GHG-emitting sector due to its heavy reliance on fossil fuels (mostly gasoline and diesel). Biofuels, which emit lower levels of pollutants when burned, can be used as an alternative to the use of common fossil fuels in the transportation. In this section, emission coefficients associated with the entire lifecycle of gasoline and different types of ethanol are presented. Lifecycle analyses are a method for evaluating the impacts of fuel production, distribution and consumption from the cradle to the grave by looking at all possible sources of pollution from the first extraction of raw materials from the environment to the final end-use of the product (Sheehan et al., 1998). By taking into account all potential emission sources, a lifecycle analysis allows us to compare the total impacts of different fuels. For the purpose of this study, emission coefficients, covering greenhouse gas emissions and criteria air contaminants (CAC), were obtained from the GHGenius lifecycle analysis model. Emissions of three gases carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) are included in order to provide an account of their respective roles in global warming. CAC emissions are responsible for urban smog and

121 acid rain; the main pollutants are nitrogen oxides (NO x ), sulphur oxides (SO x ), volatile organic compounds (VOC) and particulate matter (PM). Numerous lifecycle analysis models exist, but the advantages associated with GHGenius are significant: the model is public, supported by Natural Resources Canada, specific to Canada and in particular to the transportation sector, transparent and well-documented. The main source of difference among lifecycle analysis models is each system s boundaries. The GHGenius model is recognized as a very complete transportation model which covers most energy sources, as well as materials manufacturing processes and land-use changes (Natural Resources Canada, 2007). Finally, the database is continuously updated with the most recent available data for Canada in order to take into account the impacts of new developments in the energy sector, such as the oil sand industry in Alberta. This model provides a detailed analysis of emissions resulting from each step in a fuel s entire well-to-wheel lifecycle (Figure 14). More than 200 transportation fuel pathways are analyzed in the model using Canadian specific data. FIGURE 14 FUEL LIFECYCLE ANALYSIS Source: UCS, 2007 Figure 15 shows the GHG and CAC emission coefficients (in grams of CO 2 equivalent per litre of fuel: grams of CO 2 -eq/litre) for the entire lifecycle of conventional and reformulated gasoline as well as for different generations of ethanol. Emission factors for the production of ethanol derived from corn and wheat using first generation technology are presented, as are emission factors for ethanol derived from second generation technology using corn stover and switchgrass. Results show that the environmental benefit of using an 85% ethanol blend in light-duty vehicles occurs at the tank-to-wheel portion of the lifecycle. For gasoline, the large majority of GHG is emitted during the final use stage, i.e. during the fuel combustion in vehicles. Consequently the use of ethanol is commonly heralded as decreasing tailpipe emissions of CO 2. Indeed, any biomass combustion (solid, liquid or gaseous) for energy production is