I. Supplemental Methods for BSM 3.0: System Architecture and Input Sources

Transcription

1 Online Appendices I. Supplemental Methods for BSM 3.: System Architecture and Input Sources The Biomass Scenario Model (BSM) has been developed since 25 in coordination with the National Renewable Energy Lab (NREL), the US Department of Energy (DOE), and affiliated contractors and sponsors. We give a brief overview of the BSM here with more detailed information available in earlier publications [1, 2] and in other articles in this special session. The BSM includes a full representation of the biofuels supply chain (Figure 1), using a systems dynamics approach and simplified functional representations of the numerous processes. The purpose of the BSM is to examine the behavior of the system and its many interacting processes, rather than to make specific quantitative predictions. In order to gain a clear view into the evolution of the supply chain for biofuels, BSM focuses on the interplay between marketplace structures, various input scenarios, and government policy sets. There are 1 interconnected modules in the BSM representing the industry supply chain. The geographic structure of the BSM uses the 1 U.S. Department of Agriculture (USDA) farm production regions as a basis (Supplementary Figure 1), which facilitates analysis of regional differences in key variables. Each sector (feedstock production, feedstock logistics, biofuels production, biofuels distribution, and biofuels end use) is modeled as a standalone module but is linked to the others to receive and provide dynamic feedback. The model is solved numerically at a sub-monthly level and reports output for the timeframe of 25 to 25. The feedstock supply module (FSM) simulates the production of energy biomass (five types: herbaceous perennials, woody perennials, agricultural residue, urban residue, forest residue, and algae) as well as commodity crops (five types: corn, wheat, soybeans, cotton, and other grains) through farmer decision logic, land allocation dynamics, new agricultural practices, markets, and prices. Much of the feedstock production module is built from the USDA s dynamic agricultural economics POLYSYS [3-5]. POLYSYS has been used extensively and was the primary modeling tool for the DOE s Billion Ton Study and subsequent updates [6]. Land allocation in the BSM is driven by the farmers decisions and is nested in the FSM, where land is allocated by a modified nested logit model that tracks correlations among decisions to allocate land to commodity crops (with or without the collection of crop residues), hay, and energy crops. The logit model accounts for economic contributions (e.g., expected net revenue per acre) and non-economic contributions to the utility of land-allocation choices, which was calibrated by comparison to long-term agricultural forecasts annually published by the USDA. Available cropland is divided into three categories: active cropland, pastureland, and Conservation Reserve Program (CRP). Allocation of land within the FSM is based on net revenue calculations for the different crops. Within the FSM, potential net payments to growers are calculated for each of the 1 USDA farm production regions. Net per-acre grower payments reflect the profitability of land, including subsidies, across its various uses less the costs of production, harvesting, storage, and transportation. Subsidies contained in the FSM include the Biomass Crop Assistance Program, which is administered by the U.S. Department of Agriculture and which provides a per-ton payment to farmers producing energy crops, an establishment payment for the establishment of woody and herbaceous crops, and a per-acre annual payment for

2 those in designated project areas. Production costs are taken from POLYSYS for commodity crops, primary and secondary crops, and crop residues include collection and plant nutrient replacement (e.g., fertilization). The gross value of the primary crop is the crop price multiplied by the yield (tons per acre) plus any government subsidy; the gross value of the secondary crop is specified as a fraction of that for the primary crop. The net grower payment is calculated as the difference between the gross value and the production costs. Similarly, the value of residue from annual crops is the residue price minus the production costs. For perennial energy crops, the production costs vary annually over the life cycle of the project, which is generally 1 years, but can vary regionally. Additional details on how the BSM models Farmers decision making and other dynamics of Feedstock production are published elsewhere [1]. Simply put, in the BSM there is a balancing loop (also known as a negative feedback loop) that controls feedstock production; this loop is shown in the upper right hand portion of the causal loop diagram in Supplementary Figure 2. In this balancing loop, feedstock prices (received by the farmers) directly affect the attractiveness of growing cellulosic crops; the higher the feedstock price being paid, the more attractive it is for farmers to reallocate land from commodity or hay crops. When land is allocated to growing energy crops, at the expense of producing commodity crops, the supply of the former is reduced, which can in some situations, result in higher regional prices for the commodity crops. Conversely, as more farmers switch to new practices and allocate land to producing cellulosic crops, the availability of cellulosic material increases, which will cause the price paid for cellulosic feedstocks to be reduced, thereby lowering the attractiveness of producing cellulosic crops. Implicit in the very simple causal loop diagram depicted in Supplementary Figure 2 are numerous complex feedback loops, both balancing and reinforcing (also known as positive feedback loop), that interact across all modules of the BSM. Both types of feedbacks play important roles: balancing loops often encourage stability (in feedstock prices, for instance), and reinforcing loops encourage development (in growth of overall production capacity). The feedstock logistics system models the harvesting, collection, storage, preprocessing, and transportation of biomass feedstocks from the site (field, forest, or city) to the biorefinery, and was built largely from the Integrated Biomass Supply and Logistics (IBSAL) model from Idaho National Lab (INL) [7, 8]. Because transporting bulky feedstock over long inter-regional distances is costly, it was necessary to model separate markets for feedstock in each USDA region. The production levels of annuals and their prices were calibrated to USDA baseline projections. For commodity crops, the regional production is represented as a single price index and is generated for each crop; multipliers are then used to provide regional price variation. The structures of the cellulosic feedstock and hay markets are similar to the commodities markets, but they are regional as opposed to national. The production volume of commodity crops and their prices are calibrated to USDA annual baseline projections [9]. Transport distances for cellulosic feedstock are estimated regionally from combining (1) the endogenously computed weighted average feedstock yields for cellulosic energy crops and agricultural residues with (2) biorefinery size, (3) an assessment of the fraction of arable land available for cellulosic harvesting, and (4) geometric factors accounting for the layout of the road network. Collection radii and transportation distances are typically observed in most scenarios at around 3 5 miles.

3 The conversion sector is responsible for transforming feedstock into liquid fuels, including ethanol, butanol, and refinery-ready fuels (gasoline, diesel, jet fuel) suitable for insertion into the existing fuel infrastructure as refinery feedstocks, blendstocks, or finished products. Figure 1 and Supplementary Table 1 provide an overview of the structure of the conversion sector module. In the BSM, the conversion module comprises a significant fraction of the overall model structure [1]. It consists of seven sub-modules. Six of these sub-modules look at the dynamics of industry development for sets of conversion pathway. These dynamics include operations at different scale factors, learning along multiple dimensions, logic surrounding the attractiveness of investment in new facilities, and utilization of existing facilities. A seventh sub-module compares investment attractiveness across all conversion options, allocating investment capacity among these options based on their net present value. The BSM explicitly represents learning-by-doing in the refining of cellulosic feedstocks into ethanol via a modeling technique known as cascading learning curves [1]. This technique is an elaboration of common power-law representation of industrial learning that is typically expressed in the form y = ax b, where y is the cumulative average cost per unit, x is the cumulative number of units produced, a is the cost of the first unit, and b is a constant characterizing the cost reduction that occurs with increasing experience. Learning occurs separately for each biofuels pathway (starch-ethanol, biochemically converted cellulosic ethanol, and thermochemically converted cellulosic ethanol), and the BSM actually tracks four scales of operation and maturity: (1) pilot, (2) demonstration, (3) pioneer commercial, and (4) full-scale commercial. Experience accumulates at each of these four scales, and each scale has a unique techno-economic characterization. The BSM uses a standard set of financial computations that mimic those that might be used by a potential investor to initiate the construction of a new pioneer or full-scale commercial biorefinery. These calculations compute the expected rate of return for the investment using major categories of revenue and expenses, assuming that ethanol price and other factors are constant over the plant lifetime. The BSM assumes straight-line depreciation, which significantly reduces detail complexity, constant tax and interest rates, and maturity-based capital costs and access to credit. The conversion module in the BSM is responsive to a number of potential policies that improve the financial prospects of new biorefineries: Ethanol Subsidy: Pays a fixed pre-tax amount to the ethanol producer at the plant gate for each gallon of cellulosic ethanol produced, improves the revenue stream in financial calculations, and enables regulators to indirectly manage the selling price of ethanol. Feedstock Subsidy: Pays a fixed amount to the non-corn feedstock suppliers (farmers) to lower the price paid by the cellulosic ethanol producer, which affects the expenses stream in the financial calculations and is not a direct subsidy of the ethanol production industry. Capital Cost Reduction Subsidy: Pays a percentage of the initial "cash" payment that is needed to start construction of a cellulosic ethanol pioneer-scale plant. The subsidy improves the construction cost of the pioneer-scale plants only and is a direct payment to the cellulosic ethanol producer.

4 Loan Guarantee: Covers a fraction of loan given to a cellulosic ethanol producer to construct a pioneer-scale plant. The guarantee improves the ability of cellulosic ethanol producers to obtain financing for pioneer-scale facilities from banks and does not necessarily equate to a cost for the government if the ethanol plant is successful. We give additional information on the assumptions underpinning the drop-in fuels because of their prominent role in industry development according to the BSM 3. (Supplementary Table 2). In general, NREL aimed for realistic technological and economic assumptions for the various infrastructurecompatible ( drop in or fungible ) biofuel pathways, but the quality of those assumptions vary depending on the maturity of the analyses on which they are based and on the laboratory- and pilotscale information publicly available. Although the technological and economic parameters are based on DOE-sponsored design reports and peer-reviewed publications, our qualitative assessment is that the fast pyrolysis, Fischer-Tropsch, methanol-to-gasoline, and sugars-fermentation-to-hydrocarbons cost and performance estimates may be somewhat optimistic and the cellulosic butanol ones slightly pessimistic; the aqueous phase reforming inputs are particularly uncertain (Supplementary Table 2). Nevertheless, the systematic errors that these potential biases may induce do not substantially affect the overall conclusions of our analysis of the interaction between biofuel policy and land-use-constraint (e.g. CRP) scenarios: those conclusions are robust with respect to minor variations in the technoeconomic inputs to the BSM. The fuel distribution logistics module provides a very simple representation of the build-out of ethanolfriendly distribution infrastructure based primarily on the Integrated Biomass Supply and Logistics (IBSAL) model from Idaho National Lab (INL) [7, 8]. Because ethanol cannot be transported in the same pipelines as gasoline due to their differing chemical properties, ethanol is generally transported via truck or rail to regional hub stations, where it is blended with gasoline and transferred to final dispensing stations. Not all terminals are suited to store or blend ethanol, so the BSM includes logic for terminals that do not yet have ethanol infrastructure and a means by which they can acquire that structure. A two-stage supply-push approach is embedded within the module. This supply push works both within and across regions. First, the model seeks to balance ethanol production capacity within each region against terminal capacity to distribute that ethanol. As production capacity within a region grows, there is pressure within the region for terminals to acquire ethanol infrastructure. Second, as build-out occurs within each region, any excess regional production capacity is pooled across regions. This capacity surplus drives acquisition of infrastructure in other regions, in proportion to the terminal density within each region. Some powerful insights can be gained just by reviewing the dynamics of building ethanol capable terminals in conjunction with the rest of the supply chain [2]. In the absence of subsidies, the lack of distribution infrastructure seriously hinders downstream availability and adoption of high-blend fuel [1, 2] The dispensing station module focuses the decision making associated with the acquisition and use of high-blend tankage and equipment by retail dispensing stations [1]. The module considers roughly 12, stations, distributed both regionally and by ownership among oil-owned, branded independents, unbranded independents, and hypermart. The fundamental decision for each station is

5 the acquisition of tankage and dispensing equipment required to dispense high-ethanol blends into flexfuel vehicles. The module assumes that ten percent of stations have repurposable mid-grade tanks. The capital cost of repurposing is assumed to be significantly lower than investment in new tankage and equipment for highblends ($2k vs. $6k). The basic logic within the dispensing station module reflects both the physics of high blend availability and the economics of the investment decision. Stations will not consider investment unless distribution infrastructure is sufficient within the region. They will not invest unless the investment makes economic sense, as reflected in a net present value calculation that captures the discounted stream of expected costs and benefits from the investment. The fuel use module captures the both the effects of regional high-blend fuel availability and the effects of relative gasoline/high-blend pricing on the decision making for flex fuel vehicle (FFV) owners, with respect to the use of high-ethanol fuel blends. The module contains two major interconnected components. The first component accounts for the affinity of FFV owners toward high-blend fuels. The second uses a logit function to allocate fuel use between for FFV owners who are occasional and regular users of high-blend fuels. Under conditions of price parity between high blend and regular gasoline, occasional users are assumed to fill 2% of their fuel requirements using high blend. Regular users, on the other hand, are assumed to fill 8% of their fuel requirements using high blend under conditions of price parity. Movement between occasional and regular users is driven by a long-term retail price differential between the two products. Although the description above implies a linear flow of information between the modules, in reality the modules receive and react to information in a complex, nonlinear fashion that depends on, among other things, industrial learning, project economics, installed infrastructure, consumer choices, and investment dynamics. These complexities are described in more detail in earlier publications [1, 2].

6 Supplemental References 1. Newes, E., Inman, D, Bush, B, Understanding the Developing Cellulosic Biofuels Industry through Dynamic Modeling. In Economic Effects of Biofuel Production, dos Santos Bernardes, M. A., Ed. 211; pp Vimmerstedt, L. J.; Bush, B.; Peterson, S., Ethanol Distribution, Dispensing, and Use: Analysis of a Portion of the Biomass-to-Biofuels Supply Chain Using System Dynamics. Plos One 212, 7(5) e3582. doi:1.1371/journal.pone Langholtz, M.; Graham, R.; Eaton, L.; Perlack, R.; Hellwinkel, C.; Ugarte, D. G. D. l. T., Price projections of feedstocks for biofuels and biopower in the U.S. Energy Policy 212, 41, Ugarte, D. G. D.; Ray, D. E., Biomass and bioenergy applications of the POLYSYS modeling framework. Biomass Bioenerg. 2, 18, (4), Walsh, M. E.; Ugarte, D. G. D.; Shapouri, H.; Slinsky, S. P., Bioenergy crop production in the United States - Potential quantities, land use changes, and economic impacts on the agricultural sector. Environmental & Resource Economics 23, 24, (4), Perlack, R. D., L. L. Wright, A. F. Turhollow, R. L. Graham, B. J. Stokes, and D. C. Erbach Biomass as feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton annual supply; Technical report DOE/GO ; U.S. Department of Energy: Washington D.C., Sokhansanj, S.; Kumar, A.; Turhollow, A. F., Development and implementation of integrated biomass supply analysis and logistics model (IBSAL). Biomass Bioenerg. 26, 3, (1), Mobini, M.; Sowlati, T.; Sokhansanj, S., Forest biomass supply logistics for a power plant using the discrete-event simulation approach. Applied Energy 211, 88, (4), Interagency Agricultural Projections Committee. 27. USDA Agricultural Projections to 216. U.S. Department of Agriculture. 1. Peterson, S. An Overview of the Biomass Scenario Model. A Report for the National Renewable Energy Lab. Subcontract ACO ; July 211.

7 Supplementary Table 1: Overview of Conversion Module. The structure of the Conversion Module (CM) includes information on various conversion processes for each pathway, whether the simulation is based on the 1 USDA regions or national, source feedstock, fuel type produced, whether there are different scales of operation (pilot, pioneer, full-scale), and whether learning curve dynamics are included [1]. Conversion Options Regional? Feedstock Products Pilot & Demo Ops? Pioneerscale Ops? Full-scale Ops Learning Curve Dynamics? Starch to Ethanol Single pathway Yes Corn Ethanol No No Yes No (assume mature industry) Cellulose to Ethanol Biochemical Thermochemical Yes Cellulosic Feedstock Ethanol Yes Yes Yes Yes Cellulose to Butanol Single Pathway Yes Cellulosic Feedstock Butanol Yes Yes Yes Yes Cellulose to Refinery Fast Pyrolysis Fischer-Tropsch Methanol to Gasoline Fermentation APR Yes Cellulosic Feedstock Gasoline Diesel Jet fuel (3 drop-in points) Yes Yes Yes Yes Oil Crop to Refinery Algae to Refinery Soy Other Pond Photobioreactor Heterotrophic No No Oil crop Algae treated as part of conversion process Diesel Jet fuel (3 dropin points) Diesel Jet fuel (3 dropin points) Yes Yes Yes Yes Yes Yes Yes Yes (feedstock supply considered endogenous to module and subject to learning curve)

8 Supplementary Table 2: Overview of information for drop in fuels in the BSM 3. Process Aqueous Phase Reforming Cellulosic Butanol Degree of optimism and certainty Possibly realistic, but mostly uncertain Realistic to pessimistic General source notes Economics based on taking the 211 Thermochem design report and then scaling the output based on the theoretical yield of APR to the theoretical yield of thermochem. Virent uses APR and is a member of the NABC. At some point, we should be able to get better data. Techno-economics based on NREL milestone. Capital costs are comparable to biochem, but the yield of fuel is much lower. Given the number of companies involved in butanol (Gevo, BP/DuPont, Cobalt with Gevo in litigation against BP/DuPont), we may not have enough information on what yields are possible. Fast Pyrolysis Optimistic Techno-economics based on PNNL design report. First generation design report likely will understate costs. The design report assumes that the Role in BSM Little, if any, APR gets started. This may be a result of the economics and also the lack of any learning. APR has no pilot/demo plants getting started. APR also does not gain learning from other pathways. Little, if any, butanol gets started. Fast pyrolysis is one of the pathways that can take off given the right scenario. Key references G. W. Huber et al. Production of Liquid Alkanes by Aqueous- Phase Processing of Biomass-Derived Carbohydrates. Science, 25. L. Tao and A. Aden. Technoeconomic analysis of corn butanol and corn stover butanol. NREL milestone, 211. S.B. Jones et al. Production of Gasoline and Diesel from Biomass via Fast Pyrolysis, Hydrotreating and

9 Fischer-Tropsch Realistic to optimistic cuts of hydrocarbons will be usable as gasoline/diesel, which may not be realistic. Economics based on NREL milestone. Milestone predates the 211 Thermochem update, so capital costs may be understated. Oil industry is skeptical of FT, and apparently 2 of the IBRs that shut down were FT. Green Diesel Realistic Economics based on analysis by Argo of UOP technology. FT is one of the pathways that can take off given the right scenario. Little green diesel produced. Feedstock costs are too high. Hydrocracking: A Design Case. PNNL Technical Report, 29. R. Davis. Technoeconomic analysis of current technology for Fischer-Tropsch fuels production. NREL milestone, 29. J. Holmgren et al. A New Development in Renewable Fuels: Green Diesel. UOP report AM-7-11, 27. T. Kalnes, T. Marker, D. R. Shonnard. Green Diesel: A Second Generation Biofuel. International Journal of Chemical Reactor Engineering, 27. Methanol-togasoline Realistic to optimistic Economics based on NREL design report. Capital costs appear to be quite low compared to MTG gets started in some scenarios. S. D. Phillips, J. K. Tarud, M. J. Biddy, A. Dutta. Gasoline from Wood via

10 Sugars fermentation to hydrocarbons Realistic to optimistic thermochem. Economics based on scaling the 211 biochem design report by the theoretical yield of a fermentative process to the biochem ethanol process. This may overstate the current possible yields with this process. Recent media reports have said that the Amyris process with sugar cane has operating costs of almost $3/gallon. Fermentation has some output with government subsidies. Integrated Gasification, Synthesis, and Methanol-to- Gasoline Technologies. NREL Technical Report, 211. M. A. Rude, A. Schirmer. New microbial fuels: a biotech perspective. Current Opinion in Microbiology, 29.

11 Supplementary Figure 1: Map of the 1 U.S. Department of Agriculture (USDA) farm production regions [1].

12 Supplementary Figure 2: Causal loop diagram representing key variable interactions in the BSM (copied with permission from [1]).

13 II. Supplemental Results The full modeling results for the five land-use scenarios and the seven economic policy scenarios summarized in Table 3 are shown in the following 56 figures. Also shown are the results for the three blend level scenarios: no E15, slow rollout of E15 by 221 ( 8% E15 by 217 ), and rapid rollout of E15 by 215 ( 1% E15 by 215 ) (Supplementary Figure 3, SF3). Thus, the results are displayed in a loop, with no E15, slow E15, and rapid E15 for each output. These results are looped in order for: Land use nationally through time (SF4-6), Land use regionally at the end of simulation (23: SF7-9), Land use nationally through time by land use type (SF1-12), Cellulosic biomass production nationally through time (SF13-15), Cellulosic biomass production regionally in 23 (SF16-18), Cellulosic biomass production nationally through time by biomass-type (SF19-21), Estimated aggregate subsidy cost per gallon through time (SF22-24), Annual crop production nationally through time (SF25-27), Annual crop production regionally in 23 (SF28-3), Annual crop production nationally through time by crop-type (SF31-33), Biofuel production nationally through time (SF34-36), Biofuel production regionally in 23 (SF37-39), Biofuel production nationally through time by land-use scenario (SF4-42), Biomass price regionally through time (SF43-45), Biomass price regionally through time transposed (SF46-48), Annual crop prices nationally through time (SF49-51), Annual crop prices nationally through time transposed (SF52-54), Hay price regionally through time (SF55-57). Hay price regionally through time transposed (SF58-6).

14 1% E15 Schedules Supplementary Figure 3 E15 Schedule a. No E15 b. 8% E15 by 217 c. 1% E15 by 215 9% 8% 7% Fraction E15 in Low-Blend Fuel 6% 5% 4% 3% 2% 1% %

15 Land [acre] 3 A. base B. 4% dedicated CRP Land Use - a. No E15 Supplementary Figure 4 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land Use Cellulosics CRP Pasture Hay Annuals Land [acre] Land [acre] 3 3 Land [acre] 3 Land [acre] 3

16 Land [acre] 3 A. base B. 4% dedicated CRP Supplementary Figure 5 Land Use - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land Use Cellulosics CRP Pasture Hay Annuals Land [acre] Land [acre] 3 3 Land [acre] 3 Land [acre] 3

17 Land [acre] 3 A. base B. 4% dedicated CRP Supplementary Figure 6 Land Use - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land Use Cellulosics CRP Pasture Hay Annuals Land [acre] Land [acre] 3 3 Land [acre] 3 Land [acre] 3

18 1. Minimal.. Supplementary Figure 7 Land Use Map 23 - a. No E15 A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land [acre] 9,358,313 2,, 4,, 6,, 8,, 93,957,9 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. 2. Ethanol.. Land Use Cellulosics CRP Pasture Hay Annuals

19 1. Minimal.. Supplementary Figure 8 Land Use Map 23 - b. 8% E15 by 217 A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land [acre] 9,358,313 2,, 4,, 6,, 8,, 93,957,9 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. 2. Ethanol.. Land Use Cellulosics CRP Pasture Hay Annuals

20 1. Minimal.. Supplementary Figure 9 Land Use Map 23 - c. 1% E15 by 215 A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land [acre] 9,358,313 2,, 4,, 6,, 8,, 93,957,9 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. 2. Ethanol.. Land Use Cellulosics CRP Pasture Hay Annuals

21 Land [acre] Land Use (cont'd) - a. No E15 Supplementary Figure 1 Annuals Cellulosics CRP Hay Pasture A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land [acre] Land [acre] Land [acre] Land [acre]

22 Land [acre] Supplementary Figure 11 Land Use (cont'd) - b. 8% E15 by 217 Annuals Cellulosics CRP Hay Pasture A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land [acre] Land [acre] Land [acre] Land [acre]

23 Land [acre] Supplementary Figure 12 Land Use (cont'd) - c. 1% E15 by 215 Annuals Cellulosics CRP Hay Pasture A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Land [acre] Land [acre] Land [acre] Land [acre]

24 Biomass [ton/yr] 4 A. base B. 4% dedicated CRP Supplementary Figure 13 Biomass Production - a. No E15 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biomass Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue Biomass [ton/yr] 4 Biomass [ton/yr] 4 Biomass [ton/yr] 4 Biomass [ton/yr] 4

25 Biomass [ton/yr] 4 A. base B. 4% dedicated CRP Supplementary Figure 14 Biomass Production - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biomass Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue Biomass [ton/yr] 4 Biomass [ton/yr] 4 Biomass [ton/yr] 4 Biomass [ton/yr] 4

26 Biomass [ton/yr] 4 A. base B. 4% dedicated CRP Supplementary Figure 15 Biomass Production - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biomass Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue Biomass [ton/yr] 4 Biomass [ton/yr] 4 Biomass [ton/yr] 4 Biomass [ton/yr] 4

27 1. Minimal.. 2. Ethanol.. A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. Supplementary Figure 16 Biomass Production Map 23 - a. No E15 Biomass [ton/yr] 5,, 1,, 143,524,386 Biomass Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue

28 1. Minimal.. 2. Ethanol.. A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. Supplementary Figure 17 Biomass Production Map 23 - b. 8% E15 by 217 Biomass [ton/yr] 5,, 1,, 143,524,386 Biomass Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue

29 1. Minimal.. 2. Ethanol.. A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. Supplementary Figure 18 Biomass Production Map 23 - c. 1% E15 by 215 Biomass [ton/yr] 5,, 1,, 143,524,386 Biomass Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue

30 Biomass [ton/yr] 3 Supplementary Figure 19 Biomass Production (cont'd) - a. No E15 Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biomass [ton/yr] 3 Biomass [ton/yr] 3 Biomass [ton/yr] 3 Biomass [ton/yr] 3

31 Biomass [ton/yr] 3 Supplementary Figure 2 Biomass Production (cont'd) - b. 8% E15 by 217 Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biomass [ton/yr] 3 Biomass [ton/yr] 3 Biomass [ton/yr] 3 Biomass [ton/yr] 3

32 Biomass [ton/yr] 3 Supplementary Figure 21 Biomass Production (cont'd) - c. 1% E15 by 215 Herbaceous Perrenials Woody Perennials Agricultural Residue Urban Residue Forest Residue A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biomass [ton/yr] 3 Biomass [ton/yr] 3 Biomass [ton/yr] 3 Biomass [ton/yr] 3

33 A. base B. 4% dedicated CRP Supplementary Figure 22 Cost per Output - a. No E15 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal]

34 A. base B. 4% dedicated CRP Supplementary Figure 23 Cost per Output - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal]

35 A. base B. 4% dedicated CRP Supplementary Figure 24 Cost per Output - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal] Subsidy (cumulative) [$/gal]

36 Annuals [ton/yr] 6 4 A. base B. 4% dedicated CRP Supplementary Figure 25 Annuals Production - a. No E15 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annual Crop Other Grains Soy Wheat Corn Cotton Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4

37 Annuals [ton/yr] 6 4 A. base B. 4% dedicated CRP Supplementary Figure 26 Annuals Production - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annual Crop Other Grains Soy Wheat Corn Cotton Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4

38 Annuals [ton/yr] 6 4 A. base B. 4% dedicated CRP Supplementary Figure 27 Annuals Production - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annual Crop Other Grains Soy Wheat Corn Cotton Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4 Annuals [ton/yr] 6 4

39 1. Minimal.. 2. Ethanol.. A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. Supplementary Figure 28 Annuals Production Map 23 - a. No E15 Annuals [ton/yr] 4,96,959 1,, 2,, 33,776,342 Annual Crop Other Grains Soy Wheat Corn Cotton

40 1. Minimal.. 2. Ethanol.. A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. Supplementary Figure 29 Annuals Production Map 23 - b. 8% E15 by 217 Annuals [ton/yr] 4,96,959 1,, 2,, 33,776,342 Annual Crop Other Grains Soy Wheat Corn Cotton

41 1. Minimal.. 2. Ethanol.. A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 5. Diversity.. 4. Output F.. 3. RFS2 Fo.. Supplementary Figure 3 Annuals Production Map 23 - c. 1% E15 by 215 Annuals [ton/yr] 4,96,959 1,, 2,, 33,776,342 Annual Crop Other Grains Soy Wheat Corn Cotton

42 Annuals [ton/yr] 4 Supplementary Figure 31 Annuals Production (cont'd) - a. No E15 Other Grains Soy Wheat Corn Cotton A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annuals [ton/yr] 4 Annuals [ton/yr] 4 Annuals [ton/yr] 4 Annuals [ton/yr] 4

43 Annuals [ton/yr] 4 Supplementary Figure 32 Annuals Production (cont'd) - b. 8% E15 by 217 Other Grains Soy Wheat Corn Cotton A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annuals [ton/yr] 4 Annuals [ton/yr] 4 Annuals [ton/yr] 4 Annuals [ton/yr] 4

44 Annuals [ton/yr] 4 Supplementary Figure 33 Annuals Production (cont'd) - c. 1% E15 by 215 Other Grains Soy Wheat Corn Cotton A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annuals [ton/yr] 4 Annuals [ton/yr] 4 Annuals [ton/yr] 4 Annuals [ton/yr] 4

45 Biofuel [gal/yr] 6B 4B 2B A. base B. 4% dedicated CRP Supplementary Figure 34 Biofuel Production - a. No E15 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Pathway Algal Drop-In Oilseed Drop-In Cellulosic Drop-In Cellulosic Butanol Cellulosic Ethanol Starch Ethanol B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B

46 Biofuel [gal/yr] 6B 4B 2B A. base B. 4% dedicated CRP Supplementary Figure 35 Biofuel Production - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Pathway Algal Drop-In Oilseed Drop-In Cellulosic Drop-In Cellulosic Butanol Cellulosic Ethanol Starch Ethanol B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B

47 Biofuel [gal/yr] 6B 4B 2B A. base B. 4% dedicated CRP Supplementary Figure 36 Biofuel Production - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Pathway Algal Drop-In Oilseed Drop-In Cellulosic Drop-In Cellulosic Butanol Cellulosic Ethanol Starch Ethanol B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B 6B Biofuel [gal/yr] 4B 2B B

48 3. RFS2 Fo.. 2. Ethanol.. 1. Minimal.. Supplementary Figure 37 Biofuel Production Map 23 - a. No E15 A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biofuel [gal/yr] 5,,, 1,,, 15,87,152,64 Pathway Algal Drop-In Oilseed Drop-In Cellulosic Drop-In Cellulosic Butanol Cellulosic Ethanol Starch Ethanol 5. Diversity.. 4. Output F..

49 3. RFS2 Fo.. 2. Ethanol.. 1. Minimal.. Supplementary Figure 38 Biofuel Production Map 23 - b. 8% E15 by 217 A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biofuel [gal/yr] 5,,, 1,,, 15,87,152,64 Pathway Algal Drop-In Oilseed Drop-In Cellulosic Drop-In Cellulosic Butanol Cellulosic Ethanol Starch Ethanol 5. Diversity.. 4. Output F..

50 3. RFS2 Fo.. 2. Ethanol.. 1. Minimal.. Supplementary Figure 39 Biofuel Production Map 23 - c. 1% E15 by 215 A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biofuel [gal/yr] 5,,, 1,,, 15,87,152,64 Pathway Algal Drop-In Oilseed Drop-In Cellulosic Drop-In Cellulosic Butanol Cellulosic Ethanol Starch Ethanol 5. Diversity.. 4. Output F..

51 Biofuel [gal/yr] 4B 3B 2B 1B B 4B Supplementary Figure 4 Biofuel Production (cont'd) - a. No E15 Pathway Algal Drop-In Cellulosic Butanol Cellulosic Drop-In Cellulosic Ethanol Oilseed Drop-In Starch Ethanol A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B

52 Biofuel [gal/yr] 4B 3B 2B 1B B 4B Supplementary Figure 41 Biofuel Production (cont'd) - b. 8% E15 by 217 Pathway Algal Drop-In Cellulosic Butanol Cellulosic Drop-In Cellulosic Ethanol Oilseed Drop-In Starch Ethanol A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B

53 Biofuel [gal/yr] 4B 3B 2B 1B B 4B Supplementary Figure 42 Biofuel Production (cont'd) - c. 1% E15 by 215 Pathway Algal Drop-In Cellulosic Butanol Cellulosic Drop-In Cellulosic Ethanol Oilseed Drop-In Starch Ethanol A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B 4B Biofuel [gal/yr] 3B 2B 1B B

54 Biomass [$/ton] A. base B. 4% dedicated CRP Supplementary Figure 43 Biomass Price - a. No E15 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Region Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] 2 1

55 Biomass [$/ton] A. base B. 4% dedicated CRP Supplementary Figure 44 Biomass Price - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Region Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] 2 1

56 Biomass [$/ton] A. base B. 4% dedicated CRP Supplementary Figure 45 Biomass Price - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Region Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] 2 1

57 Biomass [$/ton] Supplementary Figure 46 Biomass Price (cont'd) - a. No E15 Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 3 Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] Biomass [$/ton]

58 Biomass [$/ton] Supplementary Figure 47 Biomass Price (cont'd) - b. 8% E15 by 217 Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 3 Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] Biomass [$/ton]

59 Biomass [$/ton] Supplementary Figure 48 Biomass Price (cont'd) - c. 1% E15 by 215 Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 3 Biomass [$/ton] Biomass [$/ton] Biomass [$/ton] Biomass [$/ton]

60 Crop [$/ton] A. base B. 4% dedicated CRP Crop Price - a. No E15 Supplementary Figure 49 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annual Crop Other Grains Soy Wheat Corn Cotton Crop [$/ton] Crop [$/ton] Crop [$/ton] Crop [$/ton]

61 Crop [$/ton] A. base B. 4% dedicated CRP Supplementary Figure 5 Crop Price - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annual Crop Other Grains Soy Wheat Corn Cotton Crop [$/ton] Crop [$/ton] Crop [$/ton] Crop [$/ton]

62 Crop [$/ton] A. base B. 4% dedicated CRP Supplementary Figure 51 Crop Price - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Annual Crop Other Grains Soy Wheat Corn Cotton Crop [$/ton] Crop [$/ton] Crop [$/ton] Crop [$/ton]

63 Crop [$/ton] Crop Price (cont'd) - a. No E15 Supplementary Figure 52 Other Grains Soy Wheat Corn Cotton A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Crop [$/ton] Crop [$/ton] Crop [$/ton] Crop [$/ton]

64 Crop [$/ton] Supplementary Figure 53 Crop Price (cont'd) - b. 8% E15 by 217 Other Grains Soy Wheat Corn Cotton A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Crop [$/ton] Crop [$/ton] Crop [$/ton] Crop [$/ton]

65 Crop [$/ton] Supplementary Figure 54 Crop Price (cont'd) - c. 1% E15 by 215 Other Grains Soy Wheat Corn Cotton A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Crop [$/ton] Crop [$/ton] Crop [$/ton] Crop [$/ton]

66 Hay [relative $] A. base B. 4% dedicated CRP Hay Price - a. No E15 Supplementary Figure 55 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Region Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains Hay [relative $] Hay [relative $] Hay [relative $] Hay [relative $]

67 Hay [relative $] A. base B. 4% dedicated CRP Supplementary Figure 56 Hay Price - b. 8% E15 by 217 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Region Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains Hay [relative $] Hay [relative $] Hay [relative $] Hay [relative $]

68 Hay [relative $] A. base B. 4% dedicated CRP Supplementary Figure 57 Hay Price - c. 1% E15 by 215 C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP Region Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains Hay [relative $] Hay [relative $] Hay [relative $] Hay [relative $]

69 Hay [relative $] Hay Price (cont'd) - a. No E15 Supplementary Figure 58 Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 2 Hay [relative $] Hay [relative $] Hay [relative $] Hay [relative $]

70 Hay [relative $] Supplementary Figure 59 Hay Price (cont'd) - b. 8% E15 by 217 Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 2 Hay [relative $] Hay [relative $] Hay [relative $] Hay [relative $]

71 Hay [relative $] Supplementary Figure 6 Hay Price (cont'd) - c. 1% E15 by 215 Appalachia Corn Belt Delta States Mountain Northeast Northern Plains Pacific Region Southeast Southern Plains A. base B. 4% dedicated CRP C. 1% dedicated CRP D. harvest CRP E. migrate 4% CRP F. migrate 7% CRP G. migrate 1% CRP 2 Hay [relative $] Hay [relative $] Hay [relative $] Hay [relative $]