### January 2010


# U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description

Final Report

Prepared for U.S. Environmental Protection Agency Office of Transportation and Air Quality Ariel Rios Building 1200 Pennsylvania Avenue, N.W. Mail Code: 6401A Washington, DC 20460

Prepared by

Robert H. Beach Bruce A. McCarl (Texas A&M University) RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709

RTI Project Number 0210826.003_________________________________ RTI International is a trade name of Research Triangle Institute. RTI Project Number 0210826.003 

# U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description

Final Report

January 2010

Prepared for U.S. Environmental Protection Agency Office of Transportation and Air Quality Ariel Rios Building 1200 Pennsylvania Avenue, N.W. Mail Code: 6401A Washington, DC 20460

Prepared by

Robert H. Beach Bruce A. McCarl (Texas A&M University) RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709iii CONTENTS

Section Page

1 Overview of FASOM.................................................................................................... 1-1
1.1 Model Description ............................................................................................... 1-2 1.1.1 Model Development................................................................................. 1-2 1.1.2 Model Structure ....................................................................................... 1-6 1.1.3 Model Updates for Renewable Fuels Analyses ..................................... 1-20
1.2 Modeling the Energy Independence a nd Security Act (EISA) Using FASOM.............................................................................................................. 1-21 1.2.1 Proposed Renewable Fuel Volumes ...................................................... 1-21
1.3 Model Modifications Since the RFS2 Proposal................................................. 1-22 1.3.1 Use of Combined Forest-Agriculture Model ......................................... 1-22 1.3.2 Updated Yields for Cellulosic Ethanol Production................................ 1-23 1.3.3 Updated Switchgrass Yields .................................................................. 1-23 1.3.4 Addition of Corn Oil Extraction as a Biodiesel Pathway ...................... 1-23 1.3.5 Modifications to Feed Replacement Rates for Dried Distillers Grains..................................................................................................... 1-24 1.3.6 Added Export Market for DG................................................................ 1-25 1.3.7 Modifications to Land Use Categories .................................................. 1-25 1.3.8 Updated Data for Nitrous Oxide Emissions from Soil Management .... 1-26

2 Summary of Domestic Agricultu re and Forestry Impacts ............................................ 2-1
2.1 Agricultural Commodity Impacts ........................................................................ 2-1 2.1.1 Commodity Prices and Quantities ........................................................... 2-1 2.1.2 Land Use ................................................................................................ 2-14 2.1.3 Coproducts of Renewable Fuel Production ........................................... 2-20 2.1.4 Livestock and Related Industries........................................................... 2-22 2.1.5 International Agricultural Trade ............................................................ 2-23 2.1.6 Environmental Impacts .......................................................................... 2-27
2.2 Changes in Farm Income and Food Prices ........................................................ 2-28 2.2.1 U.S. Farm Income.................................................................................. 2-28iv 2.2.2 Food Prices............................................................................................. 2-29

References..................................................................................................................... .........R-1 Appendixes A: Description of the Forestry and Ag ricultural Sector Optimization Model .................. A-1 B: Modeling Starch-Based and S ugar-Based Ethanol Production ....................................B-1 C: Modeling Biodiesel Production ....................................................................................C-1 D: Modeling Cellulosic Ethanol Production..................................................................... D-1 E: High Yield Sensitivity Scenario....................................................................................E-1v LIST OF FIGURES

Number Page
1-1. FASOM Agricultural Sector Modeling Structure......................................................... 1-9
2-1. Corn Prices under the Reference Ca se and Control Case (2007$) ............................... 2-5
2-2. Corn Production under the Referen ce Case and Control Case ..................................... 2-5
2-3. Distribution of Corn Usage across Consumption Categories, 2017 ............................. 2-6
2-4. Distribution of Corn Usage across Consumption Categories, 2022 ............................. 2-6
2-5. Corn Oil (Non-food Grade) Prices under the Reference Case and Control Case (2007$)........................................................................................................................ .. 2-7
2-6. Soybean Prices under the Reference Case and Control Case (2007$).......................... 2-8
2-7. Soybean Oil Prices under the Reference Case and Control Case (2007$) ................... 2-8
2-8. Soybean Production under the Refere nce Case and Control Case ............................... 2-9
2-9. Soybean Usage, 2017.................................................................................................. 2-10
2-10. Soybean Usage, 2022.................................................................................................. 2-10
2-11. Soybean Oil Production under the Refere nce Case and Control Case ....................... 2-11
2-13. Percentage Change in Commodity Producti on under the Control Case Relative to the Reference Case...................................................................................................... 2-14
2-14. Percentage Change in Commodity Prices under the Control Case Relative to the Reference Case............................................................................................................ 2-15
2-15. Total U.S. Cropland and Pasture in Us e under the Reference Case and Control Case........................................................................................................................... .. 2-16
2-16. Changes in Land Allocation over Time, Reference Case........................................... 2-17
2-17. Changes in Land Allocation ove r Time, Control Case............................................... 2-18
2-18. U.S. Land Values for Cropland under the Reference Case and Control Case (2007$)........................................................................................................................ 2-18
2-19. U.S. Corn and Soybean Acreage................................................................................. 2-19vi 2-20. DG Prices under the Reference Case and Control Case (2007$)................................ 2-21
2-21. Gluten Feed and Gluten M eal Prices under the Reference Case and Control Case (2007$)........................................................................................................................ 2-21
2-22. Ethanol By-products Used in Livestock Feed ............................................................ 2-22
2-23. Soybean Meal Prices under the Referen ce Case and Control Case (2007$) .............. 2-23
2-24. Corn Exports ............................................................................................................... 2-24
2-25. Value of Corn Exports (2007$)................................................................................... 2-24
2-26. Soybean Exports ......................................................................................................... 2-25
2-27. Value of Soybean Exports .......................................................................................... 2-26
2-28. Soybean Oil Exports ................................................................................................... 2-26
2-29. Soybean Meal Exports ................................................................................................ 2-27
2-30. U.S. Farm Income under the Reference Case and Control Case (2007$)................... 2-29
2-31. Change in Agricultural Product and Food Pr ice Indices under the Control Case ...... 2-30vii LIST OF TABLES

Number Page
1-1. Definitions of 11 Market Regions in FASOM............................................................ 1-11
2-1. Renewable fuel Production by Feedstock Used under the Reference Case and Control Case (millions of gallons per year).................................................................. 2-2
2-2. Acreage, Production, and Price of Corn and Soybeans under the Reference Case and Control Case........................................................................................................... 2-3
2-3. Cellulosic Feedstock Use (millions of dry tons per year)........................................... 2-12
2-4. National Average Farm-Level Cellulosic Feedstock Prices ($2007/dry ton)............. 2-13
2-5. Corn, Soybean, and Total Agricultural Fer tilizer Usage under the Reference Case and Control Case, 2022 (thousands of pounds) .......................................................... 2-281-1 SECTION 1 OVERVIEW OF FASOM
The Forest and Agricultural Sector Op timization Model (FASOM), developed by Professor Bruce McCarl of Texas A&M Univ ersity and others, is a dynamic nonlinear programming model of the U.S. forest and agricu ltural sectors. The model solves a constrained dynamic optimization problem that maximizes the net present value of the sum of producer and consumer surplus across the two sectors over time. The model is constrained such that total production is equal to total cons umption, technical input/output re lationships hold, and total land use must remain constant. FASOM simulates the allocation of land over time to competing activities in both the forest and agricultural se ctors and the associated impacts on commodity markets. In addition, the model simulates envi ronmental impacts resulting from changing land allocation and production pract ices, including accounting for changes in net greenhouse gas (GHG) emissions for particular agricultural activi ties. The model was developed to evaluate the welfare and market impacts of policies that influence land alloca tion and alter production activities within these sectors. FASOM has been used in numerous studies to examine issues including GHG mitigation policy, potential impacts of climate change, timber harvest policy on public lands, federal farm progr ams, bioenergy production, and a variety of other policies affecting the forest and agricultural sectors.
The comprehensive sectoral coverage provide d by FASOM is advantageous for analyzing the impacts of the Energy Independence and Secu rity Act of 2007 (EISA) renewable fuel volume requirements for a number of reasons. Because the model accounts for land competition and landowner responses to changing relative prices, FASOM provides a more complete assessment of the net market impacts associ ated with increasing the demand for renewable fuel feedstocks than models that focus only on the feedstocks. Using FASOM enables determination of secondary impacts such as crop switching, m ovements between cropland and pasture, movements between agricultural land and forestla nd, and reductions in equilibrium quantities of agricultural and forest commodities due to higher prices. FASOM also captures changes in the livestock market due to higher feed costs as well as changes in U.S. exports and imports of major agricultural commodities. In addition, FASOM accounts for changes in the primary agricultural GHGs, which are carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O), from the majority of emitting agricultural activities a nd tracks carbon sequestration and carbon losses over time. The intertemporal dynamics of the ec onomic and biophysical systems within FASOM allow for an accounting of environmental impacts over time and by region. This allows for a1-2 more complete quantification of net impacts than other models are generally able to produce, which provides insight into the multiple environmen tal and economic impacts in these sectors.
Section 1.1 provides a synopsis of the most recently updated version of FASOM used in analyses of the domestic agricultural, forestr y, and environmental impacts of implementing the renewable fuel standard (RFS2) volumes consis tent with EISA. Section 1.2 describes the application of FASOM to modeli ng the proposed renewable fuel vol umes specified under EISA. In Section 1.3, we highlight modi fications to the model that have been made since the analysis conducted for the RFS2 proposal to reflect comment s received and availability of new data. In Section 2, we present key model re sults for this analysis. In a ddition, a set of four appendixes provides additional detail on the FASOM model, focusing on assumptions and parameters most directly relevant for the analys is of large-scale production of renewable fuels. Appendix A provides more detail on the FASOM model assumptions and GHG accounting. Appendix B presents additional information regarding the in corporation of starch- and sugar-based ethanol production in the model. Appendix C describe s similar information for the production of biodiesel in FASOM. Finally, Appendix D summ arizes assumptions under lying the modeling of ethanol production using cellulosic feedstocks. A fifth appendix, Appendix E, presents selected results from a sensitivity analysis examining the effects of assuming larger increases in corn and soybean yields over time. 1.1 Model Description This section provides a brief history of the FASOM model and its predecessor models as well as an overview of model structure and a discussion of recent major updates, particularly those related to modeling renewable fuels markets. 1 Appendix A provides additional detail on FASOM model assumptions, modeli ng methodology, and GHG accounting. 1.1.1 Model Development
The current version of FASOM reflects numer ous model enhancements that have been made over time, dating back to the first vers ion of the Agricultural Sector Model (ASM). 2 Since the initial version of ASM, there have b een many changes to the model, including

1 See http://agecon2.tamu.edu/people/f aculty/mccarl-bruce/papers/1212FASO MGHG_doc.pdf for additional detail on the FASOM model. 2 Baumes, H. 1978. “A Partial Equilibrium Sector Mode l of U.S. Agriculture Open to Trade: A Domestic Agricultural and Agricultural Trade Policy Analysis.” Ph.D . dissertation. W. Lafayette, IN: Purdue University.1-3 improvements for pesticide analysis by Burton, 3 as reported in Burton and Martin, 4 and a number of model additions to enable more detailed envi ronmental and resource analyses. ASM has been used for analyses of renewable fuel produc tion dating back to th e late 1970s and 1980s. 5 , 6 , 7 In addition, ASM was applied to study ozone impacts, 8 , 9 acid rain, 10 soil conservation policy, 11 global climate change impacts, 12 , 13 , 14 , 15 , 16 , 17 , 18 and GHG mitigation. 19 , 20

3 Burton, R.O. 1982. “Reduced Herbicide Availability: An Analysis of the Economic Impacts on U.S. Agriculture.” Ph.D. thesis. W. Lafayette, IN: Purdue University. 4 Burton, R.O., and M.A. Martin. 1987. “Restrictions on He rbicide Use: An Analysis of Economic Impacts on U.S. Agriculture.” North Central Journal of Agricultural Economics 9:181-194. 5 Tyner, W., M. Abdallah, C. Bottum, O. Doering, B.A. McCa rl, W.L. Miller, B. Liljedahl, R. Peart, C. Richey, S. Barber, and V. Lechtenberg. 1979. “The Potential of Pr oducing Energy from Agricultu re.” Report to the Office of Technology Assessment. W. Lafayette, IN: Purdue University School of Agriculture. 6 Chattin, B.L. 1982. By-product Utilization from Biomass Conversion to Ethanol . Ph.D. dissertation. W. Lafayette, IN: Purdue University. 7 Hickenbotham, T.L. 1987. Vegetable Oil as a Diesel Fuel Alternative: An Investigation of Selected Impacts on U.S. Agricultural Sector . Ph.D. dissertation. St. Paul, MN: University of Minnesota. 8 Hamilton, S.A. 1985. The Economic Effects of Ozone on U.S. Agriculture: A Sector Modeling Approach . PhD. Dissertation. Oregon State University. 9 Adams, R.M., S.A. Hamilton, and B.A. McCarl. September 1984. “The Economic Effects of Ozone on Agriculture.” Research Monograph. EPA/600-3-84-90. Corvallis, OR: USEPA, Office of Research and Development. 10 Adams, R.M., J.M. Callaway, and B.A. McCarl. 1986. “P ollution, Agriculture and Social Welfare: The Case of Acid Deposition.” Canadian Journal of Agricultural Economics 34:3-19. 11 Chang, C.C., J.D. Atwood, K. Alt, and B.A. McCarl. 1994. “Economic Impacts of Erosion Management Measures in Coastal Drainage Basins.” Journal of Soil and Water Conservation 49(6):606-611. 12 Adams, R.M., J.D. Glyer, B.A. McCarl, and D.J. Dudek. 1988. “The Implications of Global Change for Western Agriculture.” Western Journal of Agricultural Economics 13(December):348-356. 13 Adams, R.M., C. Rosenzweig, R.M. Pear t, J.T. Ritchie, B.A. McCarl, J.D. Glyer, R.B. Curry, J.W. Jones, K.J. Boote, and L.H. Allen Jr. 1990. Global Change and U.S. Agriculture. Nature 345:219-224. 14 Adams, R.M., B.A. McCarl, K. Segerson, C. Rosenzweig, K. J. Bryant, B.L. Dixon, R. Connor, R.E. Evenson, and D. Ojima. 1999. “The Economic Effects of Climate Change on U.S. Agriculture. In The Economics of Climate Change , R. Mendelsohn and J. Neumann, eds., pp. 19-54. New York: Cambridge University Press. 15 Adams, R.M., C.C. Chen, B.A. McCarl , and D.E. Schimmelpfenning. 2001. “Climate Variability and Climate Change.” In Advances in the Economics of Environmental Resources , Vol. 3, D. Hall and R. Howarth, eds., pp. 115-148. London: JAI Press. 16 McCarl, B.A. 1999. “Economic Assessments under Nati onal Climate Change Assessment.” Presented at Meeting of National Climate Change Assessment Group, Washington, DC. 17 Reilly, J., F. Tubiello, B. McCarl, and J. Melillo. 2000. “C limate Change and Agriculture in the United States.” In Climate Change Impacts on the United States: The Potential Consequences of Climate Variability and Change , pp. 379-403. Report for the U.S. Global Change Research Program. New York: Cambridge University Press. 18 Reilly, J.M., F. Tubiello, B.A. McCarl, D.G. Abler, R. Darwin, K. Fuglie, S.E. Hollinger, R.C. Izaurralde, S. Jagtap, J.W. Jones, L.O. Mearns, D.S. Ojima, E.A. Paul, K. Paustian, S.J. Riha, N.J. Rosenberg, and C. Rosenzweig. 2002. “U.S. Agriculture and Climate Change: New Results.” Climatic Change 57:43-69. 19 Adams, R.M., D.M. Adams, J.M. Callaway, C.C. Chang, and B.A. Mc Carl. 1993. “Sequestering Carbon on Agricultural Land: Social Cost an d Impacts on Timber Markets.” Contemporary Policy Issues 11:76-87. 20 McCarl, B.A., and U.A. Schneider. 2001. “The Cost of Greenhouse Gas Mitigation in US Agriculture and Forestry.” Science 294(Dec):2481-2482.1-4 One of the drivers behind integrating ASM w ith forest-sector models to create FASOM was an ASM study examining issues regarding jo int forestry and agri cultural GHG mitigation. 21 Attempting to reconcile forestry production possib ilities with the static single-year equilibrium representation in ASM led to the recognition that the model did not adequately reflect a number of dynamic issues associated w ith land allocation between forest ry and agriculture. Thus, the initial FASOM model was constructed to a ddress these limitations by linking a simple intertemporal model of the forest sector w ith a version of the ASM model in a dynamic framework, allowing some portion of the land base in each sector to be shifted to the alternative use. Land could transfer between sectors based on its marginal profitability in all alternative forest and agricultural uses over the time horiz on of the model. Management investment decisions in both sectors, incl uding harvest timing in forestry, were made endogenous, so they too would be based on the expected profitabili ty of an additional dollar spent on expanding future output (both timber and carb on, if valued monetarily).

The basic structure of the forest sector was based on the family of models developed to support the timber assessment component of the U.S. Forest Service’s decennial Forest and Rangeland Renewable Resources Planning Act (R PA) assessment process: TAMM (Timber Assessment Market Model), 22 , 23 , 24 NAPAP (North American Pulp and Paper model), 25 , 26 , 27 ATLAS (Aggregate Timberland Assessment System), 28 and AREACHANGE. 29 , 30 Timber inventory data

21 Adams, R.M., D.M. Adams, J.M. Callaway, C.C. Chang, and B.A. Mc Carl. 1993. “Sequestering Carbon on Agricultural Land: Social Cost an d Impacts on Timber Markets.” Contemporary Policy Issues 11:76-87. 22 Adams, D.M. and R.W. Haynes. 1980. The 1980 Softwood Timber Assessment Market Model: Structure, Projections, and Policy Simulations . Forest Science Monograph 22, 64 p. 23 Adams, D.M. and R.W. Haynes. 1996. The 1993 Timber Assessment Market Model: Structure, Projections, and Policy Simulations . General Technical Report PNW-GTR-368. Por tland, OR: U.S. Depart ment of Agriculture, Forest Service, Pacific Northw est Research Station, 58 p. 24 Haynes, R.W. (Technical coordinator). 2003. An Analysis of the Timber Situation in the United States: 1952 to 2050 . General Technical Report PNW-GTR-560. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 254 p. 25 Ince, P.J. 1994. Recycling and Long-R ange Timber Outlook . General Technical Repo rt RM-242. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 23 p. 26 Zhang, D., J. Buongiorno, and P. Ince. 1993. PELPS III: A Microcomputer Price Endogenous linear Programming System for Economic Modeling: Version 1.0 . Research Paper FPL-526. Madison, WI: USDA, Forest Service, Forest Products Laboratory, 43 p. 27 Zhang, D., J. Buongiorno, and P. Ince. 1996. “A Recursive Linear Programming Analysis of the Future of the Pulp and Paper Industry in the United States: Changes in Supplies and Demands, and the Effects of Recycling.” Annals of Operations Research 68:109-139. 28 Mills, J., and J. Kincaid. 1992. The Aggregate Timberland Assessment System—ATLAS: A Comprehensive Timber Projection Model . General Technical Report PNW-281. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwes t Research Station. 160 p. 29 Alig, R.J., A. Plantinga, S. Ahn, and J. Kline. 2003. Land Use Changes Involving Forestry for the United States: 1952 to 1997, with Projections to 2050 . U.S. Forest Service General Technical Report 587, Pacific Northwest Research Station. Portland, OR, 92 p.1-5 and estimates of current and futu re timber yields were taken in large part from the ATLAS inputs used for the 2000 RPA Timber Assessment (these da ta have since been updated with information from the 2005 interim RPA assessment, as described below). 31 The AREACHANGE models provide timberland area and forest type allo cations to the ATLAS model. TAMM and NAPAP are “myopic” market projection models (they proj ect ahead one period at a time) of the solid wood and fiber products sectors in the United States and Canada. In ATLAS, harvested lands are regenerated (grown) according to exogenous assumptions regarding the intensity of management and associated yield volume changes. The timberland base is adjusted for gains and losses projected over time by the AREACHANGE models, including afforestation of the area moving from agriculture into forestry. Pr oduct demand relations were extracted directly from the latest versions of TAMM and NAPAP, as were product supply relations for the solid wood products (such as lumber) and all product c onversion coefficients for both solid wood and fiber commodities. Trade between the United Stat es and Canada in all major classes of wood products is endogenous and subject to the full array of potential trade barr iers and exchange rates. Timber supply also uses nearly the full set of management intensity options available in ATLAS (e.g., for the South, seven planted pine management intensity cl asses directly from ATLAS), and the selection of mana gement intensity is endogenous.
In addition, detailed GHG accounting for CO 2 and major non-CO 2 GHGs was added into a model denoted FASOMGHG. 32 The forest carbon accounting co mponent of FASOM is largely derived from the Forestry Carbon (FORCARB) mode ling system, which is an empirical model of forest carbon budgets simulated across regions, fo rest types, land classes, forest age classes, ownership groups, and carbon pools. The U.S. Forest Service uses FORCARB, in conjunction with their economic forest-sector models (e.g., TAMM, NAPAP, ATLAS, and AREACHANGE) to estimate the total amount of carbon stored in U.S. forests ove r time as part of the Forest Service’s ongoing assessment of forest resources in general (i.e., pursuant to the RPA) and forest carbon sequestration pote ntial in particular. 33 , 34 Deriving FASOMGHG’s forest carbon accounting structure from FORCARB ensures that forest carbon estimates from FASOM can be

30 Alig, R.J., J.D. Kline, and M. Lichtenstein. 2004. “Urb anization on the U.S. Landscape: Looking Ahead in the 21st Century.” Landscape and Urban Planning 69(2-3):219-234. 31 Haynes, R.W. (Technical coordinator). 2003. An Analysis of the Timber Situation in the United States: 1952 to 2050 . General Technical Report PNW-GTR-560. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 32 Throughout the remainder of this re port, references to FASOM refer to the most recent version of FASOMGHG used to conduct this analysis. 33 Joyce, L.A., ed. 1995. Productivity of America's Forests and Climate Change . General Technical Report RM271. Fort Collins, CO: U.S. Forest Service, Rocky Mountain Forest and Range Experiment Station. 34 Joyce, L.A., and R.A. Birdsey, eds. 2000. The Impact of Climate Change on America’s Forests . RMRS-GTR-59. Fort Collins, CO: U.S. Forest Servi ce, Rocky Mountain Research Station.1-6 analyzed and compared with ongoing efforts by th e U.S. Forest Service to estimate and project forest carbon estimates at the national level. It also enables FASOM to be updated over time as the FORCARB system evolves to in corporate improved science.
Following the inclusion of forest carbon acc ounting and some limited coverage of soil carbon changes associated with land use cha nge, work began to widen the coverage of agricultural GHG sources and management po ssibilities for mitigating GHG. Schneider 35 and McCarl and Schneider 36 expanded the model to account for numerous categories of GHGs and to include a detailed set of agricultural-relate d GHG management possibilities. That work expanded ASM to include changes in tillage, la nd use exchange between pasture and crops, afforestation, nitrogen fertilization alterna tives, enteric fermentation, manure management, renewable fuel offsets, fossil fuel use reduction, and changes in rice cultivation. The resulting model was labeled ASMGHG.
Given the dynamic modeling and forest ca rbon sequestration c overage included in FASOM and the agricultural coverage in ASM GHG, it was decided to merge the agricultural alternatives into the FASOMGHG structure. This was manife st in the first version of FASOMGHG that was built in the context of Lee. 37 In that work, the agricultural model was expanded to have all the GHG management a lternatives in ASMGHG with the additional coverage of dynamics. More recently, additiona l model modifications have been made to enhance FASOM’s ability to provide detailed an alyses of the agricultu ral and environmental impacts of large-scale renewabl e fuel production under EISA. Thes e modifications are discussed in Section 1.1.3.
1.1.2 Model Structure Examining the dynamic effects of policies aff ecting the forestry and agricultural sectors requires an analytical framework that can simula te the time path of market and environmental impacts. FASOM simulates a dynamic baseline and changes from that baseline in response to changes in public policy or other factors aff ecting the sector. FASOM combines component models of agricultural crop and livestock pr oduction, renewable fuels production, livestock feeding, agricultural processing, log production, forest processing, carbon sequestration, GHG

35 Schneider, U.A. December 2000. “A gricultural Sector Analys is on Greenhouse Gas Em ission Mitigation in the U.S.” PhD dissertation. College Station, TX: Depart ment of Agricultural Economics, Texas A&M University. 36 McCarl, B.A., and U.A. Schneider. 2001. “The Cost of Greenhouse Gas Mitigation in US Agriculture and Forestry.” Science 294(Dec):2481-2482. 37 Lee, H.C. 2002. “The Dynamic Role for Carbon Sequestration by the U.S. Agricultural and Forest Sectors in Greenhouse Gas Emission Mitigation.” PhD thesis. Co llege Station, TX: Department of Agricultural Economics, Texas A&M University.1-7 emissions, wood product markets, agricultural markets, GHG payments, and land use to systematically capture the rich mix of biophysic al and economic processes that will determine the technical, economic, and environmental implica tions of changes in policies. FASOM covers private timberlands and all ag ricultural activity ac ross the conterminous (“lower 48”) United States, broken into 11 market regions. Finally , FASOM tracks five forest product categories and more than 2,000 production possibilities for fi eld crops, livestock, a nd renewable fuel (see Appendix A for additional detail).
As noted above, FASOM assumes intertem poral optimizing behavior by economic agents. Landowners are assumed to have perfect foresight and base decisions in a given period on the net present value of the future returns to alternative activities. 38 For instance, the decision to continue growing a stand ra ther than harvesting it now is based on a comparison of the net present value of timber harvests from future pe riods versus the net present value of harvesting now and replanting (or not replanting and shif ting the land to agricultural use). Similarly, landowners make a decision to keep their land in agriculture vs. afforestation based on a comparison of the net present valu e of returns in agriculture and forestry. Land can also move between cropland and pasture depending on relativ e returns. This process establishes an equilibrium price for land across the sectors (ref lecting productivity in alternative uses and land conversion costs) and, given the land base interaction, a link between contemporaneous commodity prices in the two sectors as well.
Mathematically, FASOM solves an objective function to maximize net market surplus, represented by the area under the product demand function (an aggregate measure of consumer welfare) less the area under fact or supply curves (an aggregate m easure of producer costs). Such an approach involves solving a nonlinear pr ogramming model with e ndogenous product and factor prices. The resultant objective functi on value is consumer plus producer surplus.
Operationally, FASOM is a multiperiod, inte rtemporal, price-endogenous, mathematical programming model depicting land transfers and other resource al locations between and within the agricultural and forest sectors in th e United States. The model solution portrays simultaneous market equilibrium over an extende d time, typically 70 to 100 years on a 5-year time step basis when running the combined agricultu re-forest version of the model. Results yield a dynamic simulation of prices, production, mana gement, consumption, GHG effects, and other environmental and economic indicators within thes e sectors under each scenario defined in the model run.

38 FASOM calculates net present values using a 4% discount rate.1-8 The key endogenous variables in FASOM include

 commodity and factor prices;

 production, consumption, export and import quantities;

 land use allocations between sectors;

 management strategy adoption;

 resource use;

 economic welfare measures;

– producer and consumer surplus,

– transfer payments,

– net welfare effects; and

 environmental impact indicators:

– GHG emission/absorption of CO 2 , CH 4 , and N 2 O and

– total nitrogen and phosphorous applications. The subsections below provide an overview of the overall scope a nd structure of FASOM in terms of resources, production, processing, and commodity flows; land coverage; geographic scope; market modeling; land allocation; GHG accounting; renewable fuels; baseline; environmental indicators; dynamic scope; a nd dynamic yield, cost, and demand updating. 1.1.2.1 Resources, Production, Pro cessing, and Commodity Flows The basic conceptual framework of the agri cultural sector in FASOM is presented in Figure 1-1. Land, water, labor, natu ral resources, and other resource s (e.g., fertilizer, capital) are used to produce raw primary commodities, includi ng renewable fuels feedstocks. These primary commodities may move directly to markets or they may be used as inputs to processing activities generating secondary commodities (e.g., renewable fu els), as direct livestock feed, or in the production of blended livestock feeds. The primary and secondary commodities, renewable fuels, blended feeds, and imports go to m eeting household demand, other domestic demand, livestock feeding, and exports.1-9 Figure 1-1. FASOM Agricultural Sector Modeling Structure

1.1.2.2 Commodities Modeled FASOM includes several major groupings of agricultural and forest commodities, depending on the sector and whether they are ra w, processed, used for bioenergy, or mixed for livestock feed. These commodity groups are

 raw crop, livestock, forestr y, and renewable fuel feed stock primary commodities grown on the land;

 processed, secondary commodities made fr om the raw crop, livestock, and wood products;

 energy products made from renewable fuel feedstocks; and

 blended feeds for livestock consumption.
Agricultural commodities are quite frequently substitutable in demand. For example, sorghum is a close substitute for corn on a calorie -for-calorie basis in many uses, and beet sugar is a perfect substitute for sugar derived from suga rcane. In addition, a number of feed grains are substitutes in terms of livestock feeding. Similarly, many forest ry products are substitutes for one another, such as sawtimber or pulpwood de rived from alternative hardwood and softwood species groups. In addition, bioenergy feedstoc ks derived from agricultural and forestry commodities are substitutes for one another (e.g. , ethanol can be produced using either crop residues or logging residues, among ot her potential feedstocks).1-10 FASOM contains a set of processing activ ities that make secondary commodities. Secondary commodities are generally included in the model either to represent substitution or to depict demand for components of products. For example, processing possibilities for soybeans are included depicting soybeans be ing crushed into soybean meal and soybean oil because these secondary commodities frequently fl ow into different markets. Si milar possibilities exist in the forest sector. For instance, paper could be made from pulp logs or from logging residues. Thus, the model reflects a large degree of demand substitution. 1.1.2.3 Geographic Coverage
FASOM includes all states in the conterminous United States, broken into 63 subregions for agricultural production and 11 market regions (see Table 1-1). The 11-region breakdown reflects the existence of regions for which there is agricultural activity but no forestry, and vice versa. Forestry production is included in 9 of the market regions (all but Great Plains and Southwest), whereas agricultural production is included in 10 of the market regions (all but Pacific Northwest—West side). The Great Plai ns and Southwest regions are kept separate because they reflect important differences in ag ricultural characteristics. Likewise, there are important differences in the two Pacific Nort hwest regions (PNWW, PNWE) for forestry, so they are maintained separatel y, although only the PNWE region is considered a significant producer of agricultural commod ities tracked in the model. 1.1.2.4 Land Base Coverage FASOM includes all cropland, pasturela nd, rangeland, and private timberland 39 throughout the conterminous United States. The model tracks both area used for production and idled (if any) within each land category. In a ddition, the model tracks th e movement of forest and agricultural lands into developed uses. La nd categories included in the model are based on USDA Economic Research Service land use data 40 and are specified as follows:

 Cropland is land suitable for crop production th at is being used to produce either traditional crops (e.g., corn, soybeans) or dedicated energy crops (e.g., switchgrass). The 1997 U.S. Department of Agriculture (USDA) National Resource Inventory (NRI) data (most recent NRI dataset that is publicly available at a spatially disaggregated level) coupled with USDA National Agricultural Statistics Service (NASS) data on county-level harvested acreage were used to specify land availability. Cropland is tracked by crop tillage system a nd irrigated/dryland st atus as well as the amount of time it has been in such a syst em to allow tracking of sequestered soil

39 Although public timberland is not explicitly modeled beca use the focus of the model is on private decision-maker responses to changing incentives, FASO M includes an exogenous timber supply from public forestlands. 40 USDA Economic Research Service. Major Land Uses . Dataset available at: http://www.ers.usda.gov/Data/MajorLandUses/

.1-11 Table 1-1. Definitions of 11 Market Regions in FASOM

Key Region States/Subregions NE Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia LS Lake States Michigan, Minnesota, Wisconsin CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS, IndianaN, IndianaS, IowaW, IowaCent, IowaNE, IowaS, OhioNW, OhioS, OhioNE) GP Great Plains Kansas, Nebraska, North Dakota, South Dakota SE Southeast Virginia, North Carolina, South Carolina, Georgia, Florida SC South Central Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, Eastern Texas SW Southwest (agriculture only) Oklahoma, All of Texas but the Easter n Part (Texas High Plains, Texas Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas Coastal Bend, Texas South, Texas Trans Pecos) RM Rocky Mountains Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming PSW Pacific Southwest All regions in California (CaliforniaN, CaliforniaS) PNWE Pacific Northwest—East side (agriculture only) Oregon and Washington, east of the Cascade mountain range (agriculture only) PNWW Pacific Northwest—West side (forestry only) Oregon and Washington, west of the Cascade mountain range (forestry only)

carbon and the transition to a new soil car bon equilibrium after a change in tillage. Cropland can be converted to cropland pasture or forestland.

 Cropland pasture is managed land suitable for crop production (i.e., relatively high productivity) that is being used as pasture, but it can potentially be converted to crop production or forestland.

 Forest pasture is pasture on land with varying amount s of tree cover that can also be used for livestock production, although forage productivity of these lands tends to be relatively low. This land category is further subdivided into forest pasture in forest (pasture on private timberland), forest pasture in agriculture (woodland pasture on farmland), and forest pasture in public (pastu re on forested public lands that can be grazed). 41 , 42 Forest pasture in agriculture can be converted to priv ate timberland, but the other two categories of forest pastur e cannot be converted to any other uses.

41 USDA Economic Research Service. Major Land Uses . Dataset available at: http://www.ers.usda.gov/Data/MajorLandUses/

. 42 Mitchell, J.E. 2000. Rangeland resource trends in the United States: A technical document supporting the 2000 USDA Forest Service RPA Assessment . General Technical Report RMRS-G TR-68. Fort Collins, CO: U.S. Department of Agriculture, Forest Servi ce, Rocky Mountain Research Station.1-12  Rangeland comprises both public and privat e rangeland, which is typically unimproved land where a significant portion of the natural vegetation is native grasses and shrubs. Rangeland genera lly has low forage productivity and is unsuitable for cultivation. In addition, much of the rangela nd in the U.S. is publicly owned. It is assumed that rangeland cannot be used for crop produc tion or forestland.

 Forestland in FASOM refers to private timberla nd, with a number of subcategories (e.g., different levels of produc tivity, management practices, age classes) tracked (see below for additional details). The model also reports the number of acres of private forestland existing at the starting point of the model that remains in standing forests (i.e., have not yet been harvested), the number of acres harves ted, the number of harvested acres that have been reforested, and the area converted from other land uses (afforested). Public forestland area is not explicitly tracked because it is assumed to remain constant over time. Forestland can be converted to cropland, cropland pasture, or forest pastur e in agriculture.

 Developed (urban) land is assumed to increase over time at an exogenous rate for each region based on projected changes in population and economic growth. It is assumed that the land value for use in de velopment is sufficiently high that the movement of forest and agricultural land in to developed land will not vary between the policy cases analyzed. Each of the four land categories described above moves into the developed land category at an exoge nous rate (with the ex ception of forest pasture in forest and forest pasture in public), decreasing the total land base available for forestry and agriculture over time.

 Conservation Reserve Program (CRP) land is specified as land that is voluntarily taken out of crop production and enrolled in the USDA’s CRP. Land in the CRP is generally marginal cropland retired from production and converted to vegetative cover, such as grass, trees, or woody ve getation to conserve soil, improve water quality, enhance wildlife habitat, or produce other environmental benefits. However, it is possible for this land to move back into cropland as landowner commitments to maintain land in CRP expire.
Land is allowed to move between categories su bject to the restrict ions discussed under each category above. The conversion costs of mo ving between land categories are set at the present value of the difference in the land rental rates between the altern ative uses based on the assumed equilibration of land markets.
Timberland refers to productive forestlands able to generate at least 20 cubic feet of live growing stock per acre per year and that are not reserved for uses other than timber production (e.g., wilderness use). Lands under forest cover that do not produce at least 20 cubic feet per year, called unproductive forestland, and timberla nd that is reserved for other uses are not considered part of the U.S. timber base and are therefore not tracked by th e model. In FASOM, endogenous land use modeling is only done for priv ately held parcels, not publicly owned or1-13 managed timberlands. The reason is that manageme nt of public lands is significantly influenced by government decisions on management, harvesti ng, and other issues that account for multiple public uses of these lands rather than private responses to ma rket conditions. However, an exogenous quantity of timber harvested on U.S. pub lic lands is accounted for within the model. Regional public harvest levels ar e set at exogenous levels base d on past harvesting within the region and timber inventory levels for public timberlands are simulated based on the exogenous timber harvest levels that are assumed to be se t by government administrative decree. The public land managers could change allowable harvest le vels at any time, but those changes are not predictable, so harvest is assu med to remain fixed over time.
Private timberland is tracked by its quality a nd its transferability between forestry and agricultural use. FASOM includes three different site classes to reflect differences in forestland productivity (these site groups we re defined based on ATLAS inputs 43 ), where yields vary substantially between groups:

 HIGH—high site productivity grou p (as defined in ATLAS);

 MEDIUM—medium site productivity group; and

 LOW—low site productivity group.
FASOM also tracks land ownership including two private forest owner groups: forest industry (FI) and nonindustrial private forests (N IPF). The traditional definitions are used for these ownership groups: industrial timberland owne rs possess processing cap acity for the timber, and NIPF owners do not.
In addition, FASOM tracks land in terms of the type of timber management, the species on the land, and the stand age. There are 18 di fferent possible management intensity classes depending on whether thinning, partial cutting, passive management, or other management methods are used. There are al so 25 different forest species types, which vary by region (e.g., Douglas fir and other species t ypes in the West and planted pi ne, natural pine, and various hardwood types in the South). Stand age is expl icitly accounted for in 5-year cohorts ranging from 0 to 4 years up to 100+ years.

43 Haynes, R.W., D.M. Adams, and J. Mills. 1995. The 1993 RPA Timber Assessment Update . General Technical Report RM-259. Portland, OR: U.S. Department of Ag riculture, Forest Service, Pacific Northwest Research Station, 66 p.1-14 1.1.2.5 Production Modeling The production component includes agricultural crop and livestock operations, as well as FI and NIPF forestry operations. Harvests from p ublic forest lands are included in the model but are treated as exogenously determined by the gov ernment. FASOM contains an agricultural production model for each of the primary crop, livestock, and renewable fuels feedstock commodities. Production of agricultural crops, renewable fuel feedstocks, and livestock compete for land, labor, and irrigation water at the 63 or 11-region level, depend ing on the regional level selected for a given model run. 44 The costs of these and other inputs are included in the budgets for regional production variables.
Budgets are included for all primary crop and renewable fuel feedst ocks included in the model. For each crop, production budgets are di fferentiated by region, tillage choice (three choices: conventional tillage, conservation tillage , or no-till), and irri gated or dryland and cropland type (four as discussed in land use sec tion above). The differentiation included results in thousands of cropping production possibilitie s (budgets) representing agricultural production in each 5-year period. Energy crop production possibi lities are similar, except that irrigation is not an available option in th e current FASOM production possi bilities; all energy crops are assumed to be produced under nonirrigated conditi ons and do not compete for irrigation water.
For livestock production, budgets are included that are defined by region, animal type, enteric fermentation management alternative, manure management alternative, and feeding alternative. Hundreds of liv estock production possi bilities (budgets) repr esent agricultural production in each 5-year period.
Supply curves for agricultural products are generated implicitl y within the system as the outcome of competitive market forces and market adjustments. This is in contrast to supply curves that are estimated from observed, historical data. This approach is useful here in part because FASOM is often used to simulate conditions that fall well outside the range of historical observation (such as large-scal e tree-planting programs or implementation of mandatory GHG mitigation policies).
The forest production component of FASO M depicts the use of existing private timberland as well as the reforestation decision on harvested land. Timberland is differentiated

44 Because of model size and run time considerations, FASOM is often run with 63 regions in initial years (e.g., first 20 years of an analysis) and then switched to usin g only the 11 market regions in later years.1-15 by region, the age cohort of trees, 45 ownership class, cover type , site condition, management regime, and suitability of the land for agricu ltural use. Decisions pertaining to timber management investment are endogenous. Actions on the inventory are depicted in a framework that allows timberland owners to institute ma nagement activities that alter the inventory consistent with maximizing the net present valu e of the returns from the activities. The key decision for existing timber stands involves selecting the harvest age. Lands that are harvested and subsequently reforested or la nds that are converted from agri culture to forestry (afforested) introduce decisions involving the ch oice of species type, management type, and future harvest age.
1.1.2.6 Land Allocation Underlying the commodity production described above and the associated environmental impacts is the decision by landowners on how much, where, and when to allocate land across the two sectors. The inclusion of endogenous land a llocation across sectors sets FASOM apart from the majority of other forest and agricultural sect or models of the United States. The conceptual foundation for land allocation is described below.
In terms of transferability between agricultu re and forestry, FASOM includes five land suitability classes:

 FORONLY—includes timberland acres that can not be converted to agricultural uses

 FORCROP—includes acres that begin in timb erland but can potentially be converted to cropland

 FORPAST—includes acres that begin in timb erland but can potentially be converted to pastureland

 CROPFOR—includes acres that begin in cropl and but can potentiall y be converted to timberland

 PASTFOR—includes acres that begin in past ure but can potentially be converted to timberland
Land can flow between the agricultural and forestry sectors or vice versa in the FORCROP, FORPAST, CROPFOR, and PASTFOR land suitability categories. Movements between forestry and cropland are only permitted within the high-quality forest site productivity class. Changes in land allocation involving past ureland occur within the medium-quality forest

45 Timberlands are grouped in 21 5-year cohorts, 0 to 4 year s, 5 to 9, up to 100+ years. Harvesting is assumed to occur at the midyear of the cohort.1-16 site productivity class. In addition, land moveme nts in forestry are only allowed in the NIPF owner category, reflecting an assumption that la nd held by the FI ownership group will not be converted out of timberland.
As mentioned above, the decision to move la nd between uses depends on the net present value of returns to alte rnative uses, including the costs of la nd conversion. Land transfers from forestry to agriculture take place only upon timbe r harvest and require an investment to clear stumps, level, and otherwise prepare the land for planting agricultural crop s. Agricultural land can move to other uses during any of the 5-year m odel periods, but when afforested it begins in the youngest age cohort of timberland.
In addition to the endogenous land alloca tion decision, land also moves out of agricultural and forestry uses into developed uses (e.g., shopping centers, housing, and other developed and infrastructural us es) at an exogenous rate. Th us, although land can move between forest, cropland, and pasture, the total land area devoted to agricu ltural and forestry production is trending downward over time as more land is developed. FASOM generally holds CRP land area fixed at initial levels, but for the EISA an alysis, CRP land is permitted to convert back to cropland under the constraint th at a minimum of 32 million acres of land remains in the CRP to be consistent with the 2008 Fa rm Bill and USDA assumptions. 46 1.1.2.7 Nonland Factor Modeling In addition to land, FASOM depicts factor supply of other resour ces, including water, labor, and other agricultural input s in agriculture, as we ll as nonwood inputs in the forest sector.
In agricultural production, water and labor availability are sp ecified on a regional basis. Supply curves for both items have a fixed pr ice component and an upward-sloping component, representing rising marginal costs of higher supp ly quantities. For water, the fixed price is available to a maximum quantity of federally provided agricultural water, while pumped water has an upward-sloping supply curve and is subjec t to maximum availability. Other inputs are assumed to be infinitely available at a fixed price (i.e., the agricultural sector is a price taker in these markets).
On the forestry side, nonwood inputs are available on an upward-sloping basis and include hauling, harvesting, and prod uct processing costs. Other fo rest inputs are assumed to be infinitely available at a fixed price.

46 In addition, we explore a sensitivity analysis where the la nd area remaining in the CRP is allowed to fall to about half of the baseline CRP area in FASOM (w hich is just over 37 million acres).1-17 1.1.2.8 Product Processing Modeling Raw agricultural and forestry products are converted into processed products in FASOM using processing budgets. These budgets are generall y reflective of a somewhat simplified view of the resources used in processing, where the primary factors in the budgets are the use of primary commodities as inputs, the yield of seco ndary products, and processing costs to convert primary products into processed products. Pr ocessing costs for the pr oduction of processed agricultural products are usually assumed to equal the observed price differential between the value of the outputs and the va lue of the inputs based on US DA Agricultural Statistics. 47 On the forestry side, the nonwood input supply cu rve provides the cost of processing wood.
The processing budgets for wood products are regionalized for all forest products with different data in the nine domestic forest production regions and the Canadian regions. Agricultural processing is regionalized for re newable fuels production, soybean crushing, wet milling, and bioelectricity generation. Processi ng budgets for other agricultural products are defined at a national level. 1.1.2.9 Market Modeling FASOM uses commodity supply and demand curves for the U.S. market that are calibrated to historic price and production data with constant pr ice differentials between regions and the nation for some crops. In addition, the model includes supply and demand data for major commodities traded on world markets such as co rn, wheat, soybeans, rice, and sorghum across 37 foreign regions. 48 FASOM includes information on transp ortation costs to all regions, which affect equilibrium exports.
The model solution requires that all markets ar e in equilibrium (i.e., quantity supplied is equal to the quantity demanded in every market mode led at the set of market prices in the model solution). The demand and supply curves included within the model and th at need to be in equilibrium in each 5-year period include

 regional product supply,

 national raw product demand,

47 U.S. Department of Agriculture, National Agricultural St atistics Service. Various years. USDA Agricultural Statistics (1990–2002). Available at http://www .nass.usda.gov/Publications/Ag_Statistics/. 48 FASOM foreign regions include the European Economic Community, North Central Europe, Southwest Europe, Eastern Europe, Adriatic, Eastern Med iterranean, Former Soviet Union, Nort h Africa, East Afri ca, West Africa, South Africa, Red Sea, Iran, India, Taiwan, Japan, So uth Korea, North Korea, China, Bangladesh, Indonesia, Myanmar, Pakistan, Philippines, Thailand, Vietnam, West Asia, Southeast Asia, Australia, Caribbean, Eastern Mexico, Eastern South America, Western South America, Argentina, Brazil, Canada, and Other.1-18  regional or national processed commodity demand,

 regional or national supply of processed commodities,

 regional or national (depending on commodity) export demand,

 regional or national (depending on commodity) import supply,

 regional feed supply and demand,

 regional direct livestock demand,

 interregional transport perfectly elastic supply,

 international transport perf ectly elastic supply, and

 country-specific excess demand and supply of rice, sorghum, corn, soybeans, and the individual types of wheat modeled.
In the case of forestry products, commodities are typically produced regionally and are then transported to meet a national demand at a fixed regional transport cost. For agricultural products, processed commodities such as soybean meal, gluten feed, starch, and all livestock feeds are manufactured and used on the 11-market region basis but are supplied into a single national domestic market as well to meet export demand. 1.1.2.10 GHG Accounting FASOM quantifies the stocks of CO 2 and non-CO 2 GHGs emitted from and sequestered by agriculture and forestry, plus the stock on land s in the model that are converted to developed use. In addition, the model tracks GHG emission re ductions in selected othe r sectors that result from mitigation actions in the fo rest and agricultural sectors. For instance, FASOM accounts for reduced GHG emissions from fossil fuel use in th e energy sector due to the supply of renewable fuel feedstocks from agriculture. GHG account ing in FASOM covers bo th the agricultural and forest sectors as well as produc tion of renewable fuels. Addi tional detail on GHG accounting in FASOM is provided in Appendix A. 1.1.2.11 Temporal Scope and Dynamics FASOM is typically run for periods up to 100 ye ars to depict land use, land transfers, and other resource allocations between and within the U.S. agricultural and forest sectors. The model solution portrays a multiperiod equilibrium on a 5-year time step basis. As noted earlier, FASOM incorporates deterministic expectations of future prices , or perfect foresight, where expected future prices are identical to the prices that are realized in the future. Thus, landowners1-19 are able to foresee and account for the consequences of their land use, management, and production decisions on future commodity prices and incorporate that information into their decisions. It is assumed that producers maximize the net present value of future returns. The results from FASOM yield a dynamic simu lation of prices, production, management, consumption, and GHG effects within these tw o sectors under the scenarios defined for a particular model run.
Given the long rotation lengths in forestry, an important consider ation for modeling the dynamics of landowner decision making is the possi bility that trees could be planted with a rotation length that exceeds the amount of tim e remaining in a model simulation. Producers would need to anticipate net re turns that justify keeping land in forestry (or moving land from agriculture to forestry) and incurring stand esta blishment costs in order to plant trees. To account for cases where the anticipated harvest date of a stand that could potentially be planted in a given period is past the end date being modeled, 49 “terminal conditions” must be defined. Terminal conditions represent the projected net pres ent value of an asset fo r all time periods after the end of the explicit model projection. Several t ypes of terminal assets are valued in FASOM, including initial timber stands that are not ha rvested during the simula tion period, reforested stands remaining at the end of the simulation peri od, and agricultural land re tained in agriculture. 1.1.2.12 Dynamic Yield, Cost, and Demand Updating FASOM also incorporates a number of a ssumptions regarding changes in yields, production costs, and demand over time. Assumed rates of technological progress that vary by commodity are included based on historical yield gr owth and projections of future yields. In addition, certain processing activitie s, particularly thos e that rely on relatively new technologies, are expected to experience incr eases in production efficiency a nd corresponding reductions in processing costs in the future. For these activities (e.g., cellulosic ethanol production), processing yields (quantity of secondary product output per un it of primary commodity input) and production costs are assumed to change over tim e at rates that vary by process. Finally, domestic and export demand are assumed to change over time at growth rates that vary across commodities based on historical experience and USDA projections.
Simultaneous changes assumed for each of these variables over time are reflected in the baseline simulation. Changes in yield, producti on and processing costs, and demand over time

49 For instance, if a stand with a 30-year rotation were bein g considered in year 90 of a 100-year simulation, the anticipated harvest date would fall ou tside the time period modeled, and th e producer would not receive revenue from harvesting (and would therefore not be expected to plant trees in this period in the absence of terminal conditions assigning a value to future harvests).1-20 will alter the relativ e returns to production of different commodities and will affect producer decisions. Other things being equal, for commodities where de mand is growing faster than productivity, real prices will tend to increase over time. For commodities where demand growth is slower than productivity impr ovements, real prices will genera lly trend downward. Of course, these changes in relative returns will lead to shifts in land allocation and production practices until the point where a new equilibrium is reached. 1.1.3 Model Updates for Renewable Fuels Analyses The recently updated version of the FASOM mo del applied in this analysis has been improved over previous versions in several ways, including the addition of a more detailed representation of GHG sources a nd sinks and inclusion of update d baseline data on land use and agricultural policies, input costs, and output prices . In addition, the forest sector component of the model has been modified to include updates from the most recent 2005 interim RPA data. 50 Important changes to the forest se ctor in the latest ve rsion of FASOM include reflection of large transfers of forest land from traditional industr ial owners to timber investment management organizations (TIMOs) and real es tate investment trusts (REITs), domestic production costs that are high relative to prices in some regions, changing trade patterns, increasing reliance on nonfederal timber and expanded use of plantati ons on private timberlands, and a change in outlook for Canadian timber harvests associated with pest outbreaks 51 . In addition, assumptions about growth in demand for developed land have been updated to reflec t recent projections of income and population growth.
There have also been significant improvement s in the representation of the renewable fuels sector in FASOM. The model now include s multiple feedstocks that can meet the demand for producing liquid renewable fu els, including starch- and su gar-based ethanol production, biodiesel, and cellulo sic ethanol production. Some of the ke y inputs for this sector that have been reviewed and updated recently, where possi ble, include production and delivery costs, energy crop and crop residue yiel ds, ethanol conversion rates, and the rates of technological change. There is also an updated secondary ma rket for renewable fuel production, namely the use of distillers grains (DG) for livestock f eed. Many of these improvements are explained in more detail in Appendixes B, C, and D.

50 USDA Forest Service. 2007. Interim Update of the 2000 Renewable Resource Planning Act Assessment. FS874. Washington, DC. 51 Canadian timber harvests are incorporated into FASOM in more detail than other foreign trade because Canadian timber has a major impact on U.S. markets (large qu antities are imported and processed into wood products in the United States).1-21 1.2 Modeling the Energy Independence and Security Act (EISA) Using FASOM For this analysis, FASOM was applied to assess the domestic agricultural and environmental impacts associated with implemen ting EISA. EISA calls for 36 billion gallons per year (BGY) of renewable fuels by 2022, but not all of this production is expected to be derived from agricultural and forestry feedst ocks modeled in FASOM. Thus, the volume of renewable fuels modeled in FASOM is less than the full 36 BGY (about 30.2 BGY or 30.9 BGY in ethanol equivalents), with the remainder distributed across municipal solid waste, yellow grease, and imported renewable fuels exogenous to FASOM.
Below we discuss the proposed volumes of ag ricultural feedstocks modeled in FASOM. For this analysis, we refer to baseline conditions without EISA requirements, which are based on projected volumes of ethanol and biodie sel in Annual Energy Outlook 2007 (AEO2007), 52 as the “Reference Case” and the case where EISA volum e requirements are binding and set at their primary expected volumes under base CRP acreage, energy price, and corn yield assumptions as the “Control Case.” Results for the Reference and Control Cases ar e presented in Section 2. 1.2.1 Proposed Renewable Fuel Volumes For the domestic agricultural sector analysis of the RFS2 renewable fuels volumes, we assumed that 15 BGY of transportation fuels w ould come from domestically produced starchand sugar-based ethanol by 2022. Although the overwhelming majority comes from corn ethanol, FASOM includes production possibilities for using other feedstocks as well, and feedstocks other than corn enter the model so lution in some years in small volumes. Given projected increases in ethanol production unde r baseline conditions (Reference Case) in FASOM, a requirement of 15 BGY by 2022 under the Control Case is an increase of only 2.7 BGY relative to the Reference Case. We also modeled a volume of 13.7 BGY of cellulosic ethanol from agricultural sources in 2022, with another 2.3 BGY of cellulosic ethanol derived from municipal solid waste, which is being mode led outside of FASOM. Because there is very little cellulosic ethanol being produced in the Reference Case (only 0.25 BGY of cellulosic ethanol produced from bagasse and sweet so rghum pulp in 2022), the volume of cellulosic ethanol is increased to a much larger degree than starch-based ethanol (i ncrease of 13.45 BGY). Although some of the projected incr eases in cellulosic renewable fuels are expected to be in the form of a diesel substitute, FASOM does not curr ently include this renewable diesel or biomass to liquids as potential proce ssing pathways. Therefore, for the FASOM analysis all of the cellulosic volumes are assumed to be cellulosic ethanol. In ad dition to ethanol production, we

52 Energy Information Administration, U.S. Department of Energy. 2006. Annual Energy Outlook 2007. DOE/EIA-0383(2007). Available at: http://tonto. eia.doe.gov/ftproot/forecasting/0383(2007).pdf.1-22 modeled the use of 1.47 BGY of biodiesel (2.2 BGY in ethanol equivalents) in 2022, which is an increase of 1.25 BGY (1.88 BGY in ethanol equiva lents) above the Reference Case volume.
A number of potential feedstoc ks can be used in FASOM to produce ethanol or biodiesel. The proposed renewable fuel volumes are implemen ted in the model as constraints on national production of each of the three categories of re newable fuels modeled, with the model solution reflecting the mix of feedstocks in each region and year that maximizes welfare based on model assumptions and the constraints on renewabl e fuels production by category. Generally, the model solution in such a case will include producti on in regions where feedstocks to produce the required volumes of starch-based ethanol, cellulo sic ethanol, or biodiesel are available at the lowest delivered cost per gallon of renewable fuel produced. 1.3 Model Modifications Since the RFS2 Proposal
A number of modifications have been made to FASOM since the analysis conducted for the proposed revisions to the National RFS2 program rule published in the Federal Register on May 16, 2009. These changes were made to reflect comments received and the availability of new data, including the availabil ity of an updated version of th e combined forest-agriculture version of the model. At the time of the prev ious analysis, the combined forest-agriculture version was undergoing revisions an d was not available for use, but those revisions have since been completed. Other major changes to the modeling framework used include using updated values for cellulosic ethanol yields, switchgrass yiel ds, and distillers grains replacement rates of corn and soybean meal in animal feed; adding corn oil from extraction as a biodiesel fuel pathway; and adding additional land categorie s, including the replacement of rangeland measured in animal unit months (AUMs) with ra ngeland measured in acres, consistent with the other land categories included in the model. Each of these significant modifications is described in more detail below. 1.3.1 Use of Combined Forest-Agriculture Model
FASOM can be run for the forest sector onl y, the agriculture sect or only, or for both sectors combined. Running the model with both sectors simultaneously allows for analyzing of interactions between the two s ectors, which compete for land. However, because the forestry component of the model was undergoing a substa ntial update at the time of the analyses conducted for the RFS2 proposal and was not rea dy for use in the renewable fuels analyses conducted, we used the agriculture- only version of the model for the proposal. Since that time, the updates to the forestry component that were underway have been completed. Thus, we used combined model runs to generate the results pres ented in this report to enable better assessment1-23 of the impacts on land use as we ll as other interactions betwee n the forest and agricultural sectors. In addition, this mode ling change allowed market mode ling of potential use of forestderived feedstocks for cellulosic ethanol pr oduction rather than the exogenous assumption applied for the proposal. 1.3.2 Updated Yields for Cellulosic Ethanol Production Based on new research conducted by the National Renewable Energy Laboratory (NREL), 53 we updated the assumed cellulosic ethanol yi elds derived from alternative cellulosic feedstocks. Rather than having se veral different yields across types of feedstocks, all feedstocks included in FASOM were assumed to reach one of two yields by 2022. The yield for all crop residues, switchgrass, bagasse, sw eet sorghum pulp, and softwood residues was assumed to be 92.3 gallons of ethanol per dry ton of feedstoc k in 2022. The yield for hybrid poplar, willow, and hardwood residues was assumed to be 101.5 gall ons of ethanol per dry ton of feedstock in 2022. These updates resulted in high er ethanol yields per ton of feedstock for all crop residues other than corn and sorghum residues, sweet sorghum pulp, hybrid poplar, willow, and hardwood logging and milling residues, but lower yields fo r corn residue, sorghum residue, bagasse, and softwood logging and milling resi dues than those used in th e analysis for the proposal. 54 1.3.3 Updated Switchgrass Yields Switchgrass yields per acre were updated ba sed on new work conducted by the Pacific Northwest National Laboratory, 55 which increased average switchgrass yields at the national level. In the analysis for the RFS2 proposal, national average switchgra ss yields in 2022 were 6.3 wet tons per acre in the Control Case. In the current analysis for the final rulemaking, national average switchgrass yields in 2022 are now 7.8 wet tons per acre in the Control Case. 1.3.4 Addition of Corn Oil Extraction as a Biodiesel Pathway FASOM did not explicitly include corn oil extracted from DG as a potential source for making biodiesel in the analysis for the proposal . The model has been modified to add this pathway as part of the dry milling process, with the potential for corn oil to be derived from either extraction from DG or fractionation pr ior to the creation of DG. Corn oil from

53 Tao, L. and A. Aden, Technoeconomic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR) , November 2008. 54 Beach, R.H., B.A. McCarl, and A.W. Lentz. Agricultural Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , October 2008. 55 Thomson, A.M., R.C. Izarrualde, T.O. West, D.J. Pa rrish, D.D. Tyler, and J.R. Williams. 2009. Simulating Potential Switchgrass Production in the United States . PNNL-19072. College Park, MD: Pacific Northwest National Laboratory.1-24 fractionation is assumed to be food grade and a pe rfect substitute for corn oil produced from the wet milling process. Corn oil from extraction, however, is nonfood grade and can only be used in biodiesel production in the model. Base d on EPA research on e xpected technological adoption, it was assumed that the share of plants employing extrac tion or fractionation in their production processes would increase over time. Based on Chapter 1.4 of the Regulatory Impact Analysis (RIA), it was assumed that by 2022, 70% of dry mill ethanol plants will withdraw corn oil via extraction, 20% will withdraw corn oil via fractionation, and 10% will do neither. 1.3.5 Modifications to Feed Replacemen t Rates for Dried Distillers Grains As mentioned above, one of the by-products of the dry mill ethanol production process is DG. This by-product can be used as feed for beef cattle, dairy cattle, swine, and poultry in place of traditional feed sources su ch as corn and soybean meal, and its use has been growing substantially in recent years as ethanol produc tion has expanded. In the previous analyses conducted for the RFS2 proposal, it was assume d that one pound of DG would replace one pound of a combination of corn and soybean meal , consistent with current livestock production practices. However, based on research conducted by Argonne National Laboratory, 56 one pound of DG can potentially substitute for 1.196 pounds of total corn and soybean meal for cattle because the DG has higher nutritional cont ent per pound. Thus, we have updated the replacement rates used in the model such that they increase over time from a 1:1 replacement rate of DG for corn and soybean meal initially to the maximum technological replacement rate estimated by Argonne of 1:1.196 in 2017 for beef and dairy cattle. We continue to use a replacement rate of 1:1 throughout the entire modeling time frame for swine and poultry. We also implemented maximum DG inclus ion rates in livestock feed as a percentage of total feed based on the Argonne study. These limits vary by species and are assumed to increase between 2007 and 2017, reaching maximum levels of 50% for beef cattle, 30% for dairy cattle, and 25% for both swine and poultry by 2017.
In addition, DG produced as a by-product of a dry milling process with corn oil fractionation/extraction has diffe rent nutritional characteristic s than traditional DG, which contain higher levels of oil. 57 Based on this research, the proportion of soybean meal vs. corn replaced by fractionated/extracted DG is higher than for traditi onal DG when used for swine or poultry feed, although the total repl acement rate of DG for a combination of corn and soybean meal remains 1:1. Therefore, we have modified the model to apply different replacement rates

56 Salil, A., M. Wu, and M. Wang. 2008. “Update of Di stillers Grains Replacement Ratios for Corn Ethanol LifeCycle Analysis.” Available at http://www .transportation.anl.gov/pdfs/AF/527.pdf. 57 Shurson, G.C. 2006. “The Value of High-P rotein Distillers Compounds in Swine Feeds.” Distillers Grains Quarterly , First Quarter 2006:22-25.1-25 for fractionated/extracted DG and traditional DG when used in swine and poultry feed. Because there was no comparable research identified for cattle diets, we assumed that replacement rates for cattle remain the same for fractionated/extracted DG as for traditional DG. 1.3.6 Added Export Market for DG In the version of the model used for the pr oposal, it was assumed that all DG produced in the United States would go into the domestic livest ock feed market. In ad dition to the changes to replacement rates described above, the model wa s modified to account for potential exports of DG. This provides a larger market for the DG pr oduced and is more c onsistent with recent experience, where DG is being shipped oversea s to be used in liv estock feed. 1.3.7 Modifications to Land Use Categories The land use categories defined and modeled within FASOM have been updated since the proposal to add more detail and to match up total forest and agricultu ral land tracked within the model more closely w ith national land use data. 58 One major change was to split the former pasture category into separate categories of cropland pasture and forest pa sture, with three separate subcategories of forest pasture, as described in Section 1.1.2.4. Along with this change, the ability of pasture to move into cropland a nd vice versa was respecified such that exchanges between cropland and pasture are now only pe rmitted for the higher productivity cropland pasture category. In addition, ra ngeland was formerly tracked and reported in terms of animal unit months (AUMs) rather than acres. 59 The model has been modified to specify all livestock budgets in terms of animal unit grazing requirements, with estim ates of acres per animal unit developed for each pasture type present in each region. Thus, rangeland is now specified in terms of acres (with productivity of rangeland depe nding on the AUM data th at had been used in the past, similar to each of the pasture categories). Wher eas rangeland and pasture were incorporated independently in FASOM livestock budgets pr eviously, producers can now substitute directly between them to provide su fficient forage for their livestock. The area included in each of the new cate gories was also updated to track additional idle lands and match more closely with total rural land available for forests and agriculture in other land use assessments.

58 USDA Economic Research Service. Major Land Uses . Dataset available at: http://www.ers.usda.gov/Data/MajorLandUses/

. 59 An AUM is defined as the amount of forage required for one month to sustain 1 animal unit or its equivalent, where an “animal unit” is defined as one mature 1,000-pound cow and her calf.1-26 1.3.8 Updated Data for Nitrous Oxide Emissions from Soil Management Another change that has been incorporated since the pr oposal is the use of updated estimates of N 2 O emissions from soil management. Th e Natural Resource Ecology Laboratory at Colorado State University gene rated new estimates of direct N 2 O emissions from fertilizer application, as well as indirect N 2 O emissions from leaching and volatilization that were incorporated into FASOM. The emissions estimates were generated using the DAYCENT model 60 and reflect recent updates to the data and model. Emissions estimates vary by region, crop, fertilizer application rate, tillage, irrigation status, and re sidue removal rate and better capture the heterogeneity of emissions th an previously available estimates of N 2 O emissions from soil management. Because these updated values from DAYCENT simulations reflect emissions from the complete N cycling proce ss, there is no longer a need for separate calculations of N 2 O emissions from crop residues or nitrogen fixing by legumes.

60 The DAYCENT model was developed by Colorado State University. This model simulates soil dynamics and trace gas fluxes at a daily time step. See Append ix A for additional information on the model.2-1 SECTION 2 SUMMARY OF DOMESTIC AGRICU LTURE AND FORESTRY IMPACTS
In this section, we summarize the key impact s of the Renewable Fuel Standards required by the EISA on the U.S. agricultura l sector. The impacts presente d in this section are based on FASOM simulations of agricultu ral and forestry market out comes under a Reference Case without EISA as well as under a Control Case wi th EISA renewable fuel requirements. The differences in market outcomes between these two scenarios provide an estimate of the net impacts of EISA. 2.1 Agricultural Commodity Impacts Because the agricultural sector is a primar y source of feedstocks for the production of ethanol and biodiesel, required in creases in production of these fuels will increase the demand for agricultural feedstocks and will affect agricult ural market outcomes. Not only will prices and quantities of directly affected feedstock markets be affected, but also effects will be transmitted throughout the agricultural sector. Changing retu rns to production of alternative commodities will lead landowners to alter th eir choice of commodities to produce as well as the management practices used (e.g., irrigation, tilla ge). Sectors that use products derived from crops as inputs, such as the livestock sector, will be affected by changes in the absolute and relative prices of those inputs. Changes in practices may also re sult in different quantities of by-products and coproducts being produced, which will a ffect those markets as well.
In addition to market effects, the changes in agricultural production associated with EISA have implications for environmental outcomes by affecting fertilizer use, use of agricultural chemicals, and GHG emissions. Estimated impa cts of EISA on key commodity prices and quantities, land use, renewable fuel by-products, livestock and related i ndustries, in ternational agricultural commodity trade, and environmen tal measures based on the Control Case are presented below. Results presented fo cus on estimated impacts in 2017 and 2022. 2.1.1 Commodity Prices and Quantities Because of increases in demand for certain agricultural products as feedstocks for renewable fuels production under EISA, prices for those products are generally expected to increase relative to baseline c onditions. Table 2-1 presents the distribution of ethanol and biodiesel production across feedstocks under bot h the Reference and Control Cases based on FASOM model results, which provi des an indication of the commodity markets expected to be most directly affected by increa sing renewable fuels production.2-2 Table 2-1. Renewable Fuel Production by Fe edstock Used under the Reference Case and Control Case (millions of gallons per year)

2017 2022

Feedstock Reference Case Control Case Reference Case Control Case
Biodiesel Corn oil (nonfood grade) 0 542 0 681
Edible tallow 23 23 24 24
Lard 16 53 23 55
Nonedible tallow 46 47 48 48 Soybean oil 104 659 120 659
Cellulosic ethanol

Bagasse 185 581 229 614 Corn residue 0 0 0 4,871 Hardwood logging residue 0 75 0 73 Softwood logging residue 0 33 0 36 Sweet sorghum pulp 65 107 22 110 Switchgrass 0 3,879 0 7,912
Wheat residue 0 42 0 77
Starch-based ethanol Sorghum 3 0 16 0 Corn (wet milling process) 1,281 1,391 1,311 1,391 Corn (dry milling process) 10,009 13,594 10,969 13,594 Sweet sorghum 18 30 6 30 Total ethanol 11,562 19,729 12,553 28,728 Total biodiesel 189 1,324 214 1,467 Total renewable fuels from agricultural feedstocks in FASOM 11,750 21,053 12,766 30,195

In 2017, the majority of the incremental et hanol production is st arch-based ethanol derived from corn, although there is a substantial increase in ce llulosic ethanol production as well, primarily from switchgrass with smaller increases in bagasse, sw eet sorghum pulp, wheat residue, and forestry residues. By 2022, almost half of the total incr eased ethanol production2-3 associated with the Control Case is cellulosic ethanol derived from switchgrass. Corn residue also enters as a major feedstock, while the use of other ethanol feedstocks changes little between 2017 and 2022. For biodiesel, the majority of th e increase between the Reference and Control Cases is derived from soybean o il and corn oil (n onfood grade), with very lit tle change in the use of the other feedstocks included in FASOM re lative to the Reference Case. Table 2-2 summarizes acreage, production, and price for corn and soybeans under the Reference and Control Cases, as well as the changes between cases. Table 2-2. Acreage, Production, and Price of Corn and Soybeans under the Reference Case and Control Case

2017 2022

Crop Reference Case Control Case Change Reference Case Control Case Change
Corn

Acreage (million acres) 78.7 83.6 4.9 77.9 81.5 3.6 Price ($2007/bushel) $3.45 $3.74 $0.29 $3.32 $3.60 $0.27 Production (million bushels) 13,812.1 14,586.1 774.0 14,511.7 15,079.2 567.5 Soybeans

Acreage (million acres) 67.3 67.2 -0.1 68.1 66.6 -1.4 Price ($2007/bushel) $10.02 $10.97 $0.95 $9.85 $10.87 $1.02 Production (million bushels) 2,988.7 2,966.2 -22.5 3,080.5 3,028.1 -52.4

Over time, changes in relative prices will induce increased production of these products that may at least partially offset price increas es due to higher demand, but changes in relative prices will also have implications for prices and quantities in othe r agricultural markets. Using FASOM to quantify these impacts over time en ables us to account for supply and demand conditions in the markets for all major agricultural commodities produced in the United States simultaneously. In the subsections below, we summarize impacts in the markets for corn, soybeans, cellulosic feedstocks, and other agricultural commodities.
2.1.1.1 Corn As the principal feedstock for starch-based ethanol production in the United States, corn is the commodity most likely to be affected by proposed increases in starch-based ethanol production. However, EISA’s focus on increasing production of advanced renewable fuels such2-4 as cellulosic ethanol rather than starch-based et hanol mitigates these impacts. The rapid growth in starch-based ethanol production in recent y ears and continued incr eases anticipated under baseline conditions will result in a relatively sm all increment in production necessary to reach the 15 billion gallons per year (BGY) of starch-b ased ethanol specified by the EISA (about 3.7 BGY increase in 2017 and 2.7 BGY increase in 2022 relative to the Reference Case). Nonetheless, the increased demand for corn etha nol, as well as the demand for corn oil as a biodiesel feedstock, will a ffect U.S. corn markets and utilization patterns.
This increase in demand results in higher equilibrium corn prices relative to the Reference Case, as shown in Figure 2-1, and indu ces greater corn production (see Figure 2-2). 61 Under the Control Case, corn prices in crease by 8.3% in 2017 (from $3.45/bushel to $3.74/bushel) and by 8.2% in 2022 (from $3.32/bush el to $3.60/bushel). Corn production increases by 5.6% in 2017 and 3.9% in 2022 relative to the Reference Case.
Within FASOM, U.S. corn consumption is broken down into four major categories: livestock feed; ethanol production; corn exports; and other food, seed, and industrial uses. Under the Control Case, the percentage of corn f eedstock used for renewable fuels increases substantially. Although total corn production and consumption incr ease under the Control Case, this increase in allocat ion of corn to renewable fuels producti on results in decreases in corn use for both livestock feed and exports. Corn us ed for livestock feed falls 5.7% (261 million bushels) in 2017 relative to the Reference Case. In 2022, corn used for feed is expected to fall by 2.5% (118 million bushels), relative to the Refere nce Case. However, some of the decline in corn used for feed is replaced by DG, gluten f eed, and gluten meal, which are all coproducts of ethanol production. In the Contro l Case, model results show that the use of ethanol coproducts to replace corn as livestock feed increases by 261 million bushels of corn replaced relative to the Reference Case, to a total of 1,076 million bushe ls. For 2022, model results indicate that the Control Case results in an increase of 148 million bushels of corn replaced by ethanol coproducts, with a total of 1,175 m illion bushels of corn replaced. In addition, corn exports decline by 184 million bushels in 2017 (8.4%) and by 188 million bushels in 2022 (8.2%). Figures 2-3 and 2-4 display the allocati on of corn use in 2017 and 2022, respectively.

61 Note that projected prices are declining slightly over ti me, reflecting future model equilibrium values accounting for supply adjustments, both increased land allocation to corn production and increasing corn yields over time due to improvements in technology. Falling real (inflatio n-adjusted) prices indicate th at supply is growing more rapidly than demand between 2017 and 2022 in both the Reference Case and Control Case.2-5 $0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00

2017 2022$/Bushel

Year Reference Case Control Case

Figure 2-1. Corn Prices under the Re ference Case and Control Case (2007$)

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000

2017 2022Million Bushels

Year Reference Case Control Case
Figure 2-2. Corn Production under the Reference Case and Control Case2-6 0 1,000 2,000 3,000 4,000 5,000 6,000

Ethanol Livestock Feed Exports Other Food, Seed, and IndustrialMillion Bushels

Reference Case Control Case
Figure 2-3. Distribution of Corn Us age across Consumption Categories, 2017

0 1,000 2,000 3,000 4,000 5,000 6,000

Ethanol Livestock Feed Exports Other Food, Seed, and IndustrialMillion Bushels

Reference Case Control Case
Figure 2-4. Distribution of Corn Us age across Consumption Categories, 20222-7 Increased demand for corn oil use in biodiesel results in large increases in non-food grade corn oil prices as well, as shown in Figure 2-5. Non-food grade corn oil prices increase by 304% in 2017 (from $0.28 per gallon to $1.14 per gall on) and by 271% in 2022 (from $0.34 per gallon to $1.26 per gallon) under the Control Case.

$0.00 $0.20 $0.40 $0.60 $0.80 $1.00 $1.20

2017 2022$/gallon

Year

Reference Case Control Case
Figure 2-5. Corn Oil (Non-food Grade) Prices under the Reference Case and Control Case (2007$)

2.1.1.2 Soybeans In the markets for soybeans and soybean oil, prices increase because of greater demand for soybean oil in biodiesel production under the Control Case and increases in corn production that shift land away from soybeans, reducing the supply. Soybean meal prices, on the other hand, decline under the Control Case. This is consistent with s oybean oil being the product that is in increased demand due to EISA, whereas so ybean crushing to produce oil also generates large quantities of soybean meal as a coproduct. Thus, the price of soybeans depends on both the price of soybean oil and soybean meal, and a bus hel of soybeans produces more meal than oil, about 11.16 pounds of soybean oil and 45.57 pounds of soybean meal. Large increases in soybean meal associated with increased soybean oil production for use in making biodiesel lead to a reduction in soyb ean meal prices of about 1.8% in 2017 and 0.1% in 2022, which contributes to holding down soybean price increases. Alth ough there is an increas ed demand for soybean meal as a feed source due to increasing prices of corn, soybeans, and other feed sources, the increase in supply is larger th an the increase in demand, leading to a reduction in equilibrium meal prices.2-8 Changes in soybean and soybean oil pri ces are shown in Figures 2-6 and 2-7, respectively. Prices for soybeans in crease by 9.4% in 2017 (from $10.02/bushel to $10.97/bushel) and by 10.3% in 2022 (from $9.8 5/bushel to $10.87/bushel) relative to the Reference Case. Soybean oil prices increase by far larger percentages of 37.0% in 2017 (from $249.89/1,000 lbs to $342.35/1,000 lbs) and by 37.9% in 2022 (from $241.55/1,000 lbs to $333.21/1,000 lbs).

$0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10 $11 $12

2017 2022$/Bushel

Year

Reference Case Control Case Figure 2-6. Soybean Prices under the Reference Case and Control Case (2007$)

$0 $50 $100 $150 $200 $250 $300 $350 $400

2017 2022$/1,000 lbs

Year Reference Case Control Case Figure 2-7. Soybean Oil Prices under the Reference Case and Control Case (2007$)2-9 As shown in Figure 2-8, U.S. soybean produc tion declines slightly under the Control Case as more cropland is allocated to corn production, contributing to a decrease in soybean acreage. Soybean oil production, on the other hand, increases under the Control Case. The demand for soybean oil for biodiesel production re sults in more soybeans being used to produce soybean oil domestically with a sizable reduction in exports, as shown in Figures 2-9 and 2-10. Figure 2-11 summarizes the modeled change in soybean oil production between the Reference and Control Cases.
FASOM allows for the production of biodiesel from a variety of sources (e.g., soybean oil, corn oil (food grade or non- food grade), edible tallow from beef slaughter, nonedible tallow from beef slaughter, lard). To the extent that collec tion processes improve, lower cost tallow and waste oil feedstocks might constitu te a larger share of biodiesel atsome point in the future, but based on current modeling assumptions, most of th e incremental biodiesel associated with EISA is expected to be produced from oils that have competing uses in food production (corn and soybean oils). In addition, FASOM does not currently model biodiesel from algae or poultry waste, which have historically been a small porti on of the biodiesel market . If these technologies become widespread and more cost-effective, th e impact of higher biodiesel volumes on soybean and corn oil prices would be reduced.

0 500 1,000 1,500 2,000 2,500 3,000 3,500

2017 2022Million Bushels

Year
Reference Case Control Case

Figure 2-8. Soybean Production under the Reference Case and Control Case2-10 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

Soybean Crushing (for uses other than biodiesel) Biodiesel Exports Other Food, Seed, and IndustrialMillion Bushels

Reference Case Control Case Figure 2-9. Soybean Usage, 2017

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

Soybean Crushing (for uses other than biodiesel) Biodiesel Exports Other Food, Seed, and IndustrialMillion Bushels

Reference Case Control Case
Figure 2-10. Soybean Usage, 20222-11 0 5 10 15 20 25

2017 2022Billion lbs

Year
Reference Case Control Case Figure 2-11. Soybean Oil Production under the Reference Case and Control Case

2.1.1.3 Cellulosic Feedstocks
FASOM model results s how cellulosic ethanol production fr om agricultural feedstocks reaching 4.7 BGY by 2017 and 13.7 BGY by 2022. To achieve these production levels, large quantities of feedstocks must be available for processing. In our mode l simulations, switchgrass is the largest source of cellulosic feedstock (see Table 2-3). FASOM model results indicate that 3.9 BGY of ethanol are produ ced from switchgrass in 2017, with production doubling to 7.9 BGY in 2022. Switchgrass and other energy crops are attractive cellulosic feedstocks because they have the potential to produ ce high yields per acre compared to residue crops. However, in general, a lower price is require d to induce farmers to collect re sidues from grain production to sell as a cellulosic ethanol f eedstock than to replace other production with dedicated energy crops. As a result, a large share of feedstock in major crop-producing regions is expected to come from crop residues, particularly corn resi due. Because corn produces the most residue per acre among the crops considered and is the highe st density crop in major agricultural production regions of the United States, corn residues are a particularly attractive feedstock to meet cellulosic ethanol producti on requirements.
Because moisture content and ethanol yields vary across feedstocks and these yields are assumed to improve over time for cellulosic etha nol production, the actual quantity of feedstock required for a given level of et hanol production varies across feed stocks and time. Table 2-3 summarizes the quantities of residues estimated to be used for cellulo sic ethanol production under the Control Case.2-12 Table 2-3. Cellulosic Feedstock Use (millions of dry tons per year)

Feedstock 2017 2022 Bagasse 6.465 6.647 Corn residue 0 52.693 Hardwood logging residue 0.756 0.724 Softwood logging residue 0.372 0.391 Sweet sorghum pulp 1.192 1.192 Switchgrass 43.166 86.012 Wheat residue 0.466 0.837 Total across all 52.418 148.496

Because there is very little commercial mark et for cellulosic ethanol under the Reference Case, market prices estimated in FASOM for re sidue feedstocks and energy crops are very low or zero in the Reference Case. Under the Control Case, however, a significant market for these feedstocks develops. Although there is no mark et for switchgrass or corn residues in the Reference Case (i.e., market equilibrium quantiti es are zero), the average national farm-level price of switchgrass in the model results is $43.22 per dry ton in 2017 and $46.42 per dry ton in 2022. 62 Similarly, equilibrium farm-level corn re sidue prices are $19.24 per dry ton in 2017 and $39.19 per dry ton in 2022 as a market for these re sidues develops. Table 2-4 summarizes prices reported by FASOM for all cellulosic feedstocks th at are used in the market equilibrium. As shown in Table 2-4, only seven cellulosic feedstocks (corn resi due, wheat residue, switchgrass, bagasse, hardwood logging residue, softwood logging residue, and sweet sorghum pulp) are used in ethanol production in the model solution for the Control Case.
Note that although the delivered cost of ac quiring alternative ce llulosic feedstocks sufficient to generate a given quantity of ethanol within a re gion should be very close in equilibrium, there may be differences in the farm-l evel price paid for alte rnative feedstocks for a number of reasons. First, a dr y ton of different feedstocks w ill provide differing quantities of ethanol because of the variance in energy densit y and ethanol conversion ra tes. Because ethanol processors consider the amount they are paying for feedstock in terms of the amount per gallon of ethanol produced, farm-level prices will vary to reflect the difference in energy content. In addition, feedstocks vary in ha ndling and hauling costs per dry ton (and similarly per gallon of ethanol produced after adjusting for energy cont ent) and those differences will contribute to differences in equilibrium prices that would be paid for cellulosic feedstocks at the farm level.

62 These prices represent the feedstock price at the farm level, not including storage, transportation, and processing at the ethanol plant.2-13 Table 2-4. National Average Farm-Level Ce llulosic Feedstock Prices ($2007/dry ton)

2017 2022

Feedstock Reference Case Control Case Reference Case Control Case Crop residues Corn residue $0 $19.24 $0 $39.19 Wheat residue $0 $34.15 $0 $36.25 Energy crops Switchgrass $0 $43.22 $0 $46.42 Processing residues Bagasse $7.81 $39.93 $9.32 $43.06 Hardwood logging residue $0 $30.27 $0 $34.83 Softwood logging residue $0 $23.41 $0 $27.55 Sweet sorghum pulp $81.05 $119.46 $79.26 $115.45 Note: Prices are presented only for those feedstocks that enter the market solution in the Control Case.
2.1.1.4 Other Commodities In addition to the most directly affected co mmodity markets discussed above, all markets for agricultural commodities are affected to some extent because of competing uses for land and other resources that result in la nd use change and production shif ts in response to changes in relative prices. Figures 2-13 and 2-14 show estimated percentage changes in production and prices, respectively, for other key commodities under the Control Case. Production of most other crops declines because land has moved out of these commodities into corn production. These reductions in production result in price in creases for the majority of commodities tracked, with some of the largest price increases taking pl ace in markets for grains such as oats, barley, and rye. The sugar market also experiences substantial effects on price and production, where the increased value of sugarcane bagasse results in a large shift in refine d sugar production away from using sugarbeets and toward using sugar cane. In addition to the value of the sugar produced using sugarcane, there is also the additional value associated with the bagasse used in ethanol production. Production of livestock products experiences relatively small percentage changes with both increases and decreases acr oss products modeled. Livestock prices are generally increased, but th e prices of some meats are declining.2-14 -40% -30% -20% -10%

0% 10% 20% 30% 40%CottonSoftWhiteWheatHardRedWinterWheatDurhamWheatHardRedSpringWheatSorghumRiceOatsBarleyRyeSilageHaySugarcaneSugarbeetPotatoesTomatoFrshTomatoProcFeedlotBeefSlaughterMilkHogsforSlaughterHorsesandMulesEggsBroilersTurkeysFedBeefNonFedBeefPorkChickenTurkey

2017 2022

Figure 2-13. Percentage Change in Commodi ty Production under the Control Case Relative to the Reference Case

2.1.2 Land Use Increases in demand for agricultural commod ities used to produce large quantities of renewable fuels, such as corn and soybeans, are e xpected to affect U.S. land usage substantially. Total U.S. cropland and pasture acreage are both declining over time in the Reference Case because of assumed exogenous rates of conversion of agricultural land for development, although land in production is increasing in both categories. Land can move back and forth between cropland and pasture subject to c onstraints, but based on FASOM assumptions regarding baseline demand growth for crop and livestock commodities and changes in yields over time, the use of pasture in production is in creasing faster than the use of cropland in the Reference Case. Some lands in the CRP move b ack into cropland over ti me. FASOM allows up to 5.3 million acres of CRP lands to revert to cropland by 2022 under both scenarios (such that CRP land will reach 32 million acres, as authorized in the 2008 Farm Bill).2-15 -10%

-5% 0% 5% 10% 15% 20% 25% 30% 35% 40%CottonSoftWhiteWheatHardRedWinterWheatDurhamWheatHardRedSpringWheatSorghumRiceOatsBarleyRyeSilageHaySugarcaneSugarbeetPotatoesTomatoFrshTomatoProcFeedlotBeefSlaughterMilkHogsforSlaughterHorsesandMulesEggsBroilersTurkeysFedBeefNonFedBeefPorkChickenTurkey

2017 2022
Figure 2-14. Percentage Change in Commodity Prices under the Control Case Relative to the Reference Case

By increasing the demand for cropland to pr oduce ethanol feedstocks , the Control Case increases the use of cropland and pasture in production, while cont ributing to a decrease in land allocation to private forestland relative to the Re ference Case. Much of the increase in land in agricultural production is drawn fr om previously idle pastureland. As shown in Figure 2-15, total U.S. cropland in production increases by about 0.6 million acres between 2017 and 2022 under the Reference Case. The Control Case bo th increases land allo cation to cropland and reduces the area of idle cropland relative to th e Reference Case. Cropl and area in production is increased by 4.6 million acres in 2017 and 8.1 mi llion acres in 2022 relative to the Reference Case. Pasture in use increases by 11.0 m illion acres in 2017 and 3.1 million acres in 2022 relative to the Reference Case.2-16 0 50 100 150 200 250 300 350 400

2017 2022Million Acres

Year Cropland - Reference Case Cropland - Control Case Pasture in Use - Reference Case Pasture in Use - Control Case Private Forestland - Reference Case Private Forestland - Control Case Figure 2-15. Total U.S. Cropland and Pasture in Use under the Reference Case and Control Case

FASOM assumes a constant annual yield increase in cropland productivity (for instance, FASOM assumes that corn yields increase at just over 1.6% per yea r). Under this assumption, as long as productivity increases outpace assume d annual demand growth, total cropland acreage tends to fall while relative land use values in crease. As cropland competes with development and other uses of land, continued increases in cropland productivity are very important for reducing the costs of large-scale expa nsion of renewable fuels production.
Figures 2-16 and 2-17 show changes in land allocation between cropland, pasture, CRP, and developed uses under the Reference Case and Control Case, respectively. 63 The Control Case results in more pastureland converted to cropland and less conve rsion of cropland to developed use than in the Reference Case. Overa ll, as a cumulative result of changes in land allocation over time, total cropland sent to pasture is 16.6% lower (0.78 million acres) in 2017 and 16.4% lower (0.77 million acres) in 2022 under the Control Case.

63 Developed use includes land converted to use for homes, businesses, roads, parking lots, etc. FASOM assumes that this land is no longer available for any forestry or agricultural production in the future after conversion to developed use.2-17 0 5 10 15 20 25 30

2017 2022Million Acres

Year Pasture to Cropland Pasture to Forest Pasture to Developed Cropland to Pasture Cropland to Developed CRP to Cropland Forest to Pasture Forest to Cropland Figure 2-16. Changes in Land Alloca tion over Time, Reference Case

The higher prices of agricultural commodities associated with increasing demand for use in renewable fuels production will have a direct im pact on the value of U.S. agricultural land. As demand increases for corn and soybeans and feed i nputs for livestock increase in price, the value of agricultural land is expected to increase, as shown in Figur e 2-18. In 2017, the Control Case results in an increase in cropl and value of 37.9% relative to the Reference Case (from $4,571 per acre to $6,305 per acre). In 2022, the Control Case increases cropland value by 39.5% (from $4,526 per acre to $6,315 per acre). The value of cropland pasture increas es as well, though by a much smaller percentage. The average cropland pasture value increases from $1,801 per acre to $1,900 per acre in 2017 (5.5% increase) and fr om $1,846 per acre to $2,0 91 per acre in 2022 (13.3% increase).2-18 0 5 10 15 20 25 30

2017 2022Million Acres

Year Pasture to Cropland Pasture to Forest Pasture to Developed Cropland to Pasture Cropland to Developed CRP to Cropland Forest to Pasture Forest to Cropland Figure 2-17. Changes in Land Allo cation over Time, Control Case

$0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000

2017 2022$/Acre

Year Cropland - Reference Case Cropland - Control Case Cropland pasture - Reference Case Cropland pasture - Control Case
Figure 2-18. U.S. Land Values for Cropland under the Reference Case and Control Case (2007$)2-19 As demand for corn and corn stover (corn re sidue) increases, more acres of cropland are expected to be used in the pr oduction of corn, as shown in Fi gure 2-19. FASOM results indicate that acreage devoted to corn production will increase by 6.2% (4.9 million acres) in 2017 under the Control Case. By 2022, cropland devoted to corn production is expected to increase 4.6% (3.6 million acres), relative to the Reference Case. 64 As noted earlier, although there is an increase in the demand for soybeans as a renewa ble fuel feedstock as well, there is still a reduction in acreage as land shifts into corn production, although the percentage change is relatively small. Although there is an increas e in demand for soybeans for use in biodiesel production under EISA, the biodies el volume requirements are rela tively small compared to the required volumes of ethanol. Soybean acreag e falls by about 0.1 million acres (0.1%) in 2017 and 1.4 million acres (2.1%) in 2022 relative to the Reference Case.

50 55 60 65 70 75 80 85 90

2017 2022Million Acres

Year
Corn - Reference Case Corn - Control Case Soybeans - Control Case Soybeans - Reference Case
Figure 2-19. U.S. Corn and Soybean Acreage

64 FASOM does not explicitly include corn -on-corn rotations because it is a lo ng-term model solving in 5-year time steps and is focused on capturing changes in the long-run trends rather than short-term effects on practices such as crop rotations. Crop rotation practices are implicitly reflected in the average annu al allocation of agricultural land to different crops.2-20 FASOM assumes annual yield increases for co rn and soybeans to be approximately 1.6% and 0.4%, respectively. In 2022, tota l production of corn in the Cont rol Case is projected to be 15.1 billion bushels, an increase of 3.9% over th e Reference Case. FASOM estimates soybean production to be 3.0 billion bushels in 2022 under th e Control Case, which represents a decrease of about 1.7% relative to the Reference Case. 2.1.3 Coproducts of Renewable Fuel Production Rapid increases in the production of renewable fuels have also resulted in large increases in the production of coproducts of renewabl e fuel production (e.g., DG and soybean meal). These coproducts can account for a substantial share of ethanol plant revenue. In addition, they can be used to partially substitute for grains in animal feeds and play an important and growing role in the market for livesto ck feed. Because production of these coproducts increases along with renewable fuels production, en abling them to replace a larger share of grain used for animal feed, they can potentially help mitigate environmental impacts of renewable fuels production by reducing land conversion and associated increases in chemical inputs.
FASOM assumes that DG, an ethanol processing by-product from the corn dry milling process, could be used to replace some of the co rn and soybean meal used in feed (see Appendix A for more information on DG replacement of othe r feeds). As corn a nd other feed prices increase, the demand for DG increases and results in higher DG prices in our model simulations. By 2017, the price of DG increases 6.1% under th e Control Case and in 2022, DG prices are 6.8% higher than the Reference Case, as shown in Figure 2-20.
The cost of producing ethanol depends on, among other factors, the price of corn and the price of related by-products. However, the comm ercial uses for DG are still being developed. This analysis assumes DG technology improves to pelletize and distribute DG to a wider market. At the renewable fuel volumes analyzed in this rule, the amount of DG in feed is unlikely to reach the maximum inclusion levels for cattle, 65 particularly if the etha nol industry continues to make progress in being able to improve the qualit y of DG and adjust the nutritional content so that it is better suited for pork and poultry production.
In addition to DG, gluten feed and gluten meal produced as coproducts of the corn wet milling process can be used as livestock feed. The prices of these products under the Reference and Control Cases are shown in Figure 2-21. Although the quantities of both increase by the same proportion (equal to the increase in corn wet milling), the price of gluten meal declines

65 See http://www.ers.usda.gov/Publications/FDS/2007/05May/FDS07D01/fds07D01.pdf.2-21 $0 $10 $20 $30 $40 $50 $60 $70

2017 2022$/1,000 lbs

Year

Reference Case Control Case

Figure 2-20. DG Prices under the Referen ce Case and Control Case (2007$)

while the price of gluten feed in creases. One of the primary reasons for this difference in price change is the increase in soybean meal, which is a closer substitute for gluten meal than gluten feed, under the Control Case.

$0 $20 $40 $60 $80 $100 $120

2017 2022$/1,000 lbs

Year

Reference Case - Gluten Meal Control Case - Gluten Meal Reference Case - Gluten Feed Control Case - Gluten Feed
Figure 2-21. Gluten Feed and Gluten Meal Pric es under the Reference Case and Control Case (2007$)2-22 Overall, the Control Case results in an in creased use of these ethanol coproducts in livestock feed as they replace some of the corn that is being diverted to renewable fuels production. Figure 2-22 shows the ch ange in use of ethanol coproducts as a replacement for corn in livestock feed uses.

0 200 400 600 800 1,000 1,200 1,400 1,600

2017 2022Millions of Corn Bushels Replaced

Year

Reference Case Control Case
Figure 2-22. Ethanol By-products Used in Livestock Feed

Another product that can be us ed in livestock feed is soyb ean meal, which is produced in greater quantities because of th e increase in biodiesel producti on using soybean oil. Although there is increasing demand for soybean meal in livestock feed as a replacement for corn, the increase in supply exceeds the increase in demand and the ma rket price declines under the Control Case, as shown in Figure 2-23.
2.1.4 Livestock and Related Industries As described above, the livestock sector is affected by increase s in feed prices associated with the proposed increase in renewable fuel s’ production volume. A lthough ethanol coproducts can be used as livestock feed, they do not fully o ffset the increased cost of livestock feed due to increased demand for corn in a lternative uses. Generally, prod uction of individual livestock2-23 $0 $20 $40 $60 $80 $100 $120 $140 $160 $180 $200 $220

2017 2022$/Ton

Year
Reference Case Control Case Figure 2-23. Soybean Meal Prices under the Reference Case and Control Case (2007$)

products tracked in FASOM experiences small pe rcentage changes, as shown in Figure 2-13, while prices of most livestock products increase slightly in response to increased production costs. 2.1.5 International Agricultural Trade In addition to the domestic market impacts, the changes in equilibrium acreage, prices, and production lead to changes in in ternational trade patterns. In pa rticular, the United States is a major exporter of corn, soybeans, and soyb ean products, which are major feedstocks for renewable fuels production. Thus, the changes taking place in markets for these commodities will affect international markets. Some of the key changes taking place under the Control Case are summarized below. 2.1.5.1 Corn Exports The increasing demand for corn to make etha nol raises the price of corn, which has a direct impact on the competing uses of corn. FA SOM results show that higher U.S. corn prices lead to lower U.S. exports of corn, as show n in Figure 2-24. In 2017, U.S. corn exports drop from about 2.19 billion bushels in the Reference Ca se to 2.01 billion in the Control Case (8.4% decrease). Similarly, corn exports in 2022 fall from 2.28 billion to 2.09 billi on (8.2% decrease). Corn exports rise between 2017 and 2022 under both the Reference and Control Cases as corn production increases faster than the requirement for use in ethanol production. Although corn2-24 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

2017 2022Million Bushels

Year Reference Case Control Case
Figure 2-24. Corn Exports

prices increase under the Control Case, they do not increase sufficien tly to offset the reduction in exports, and the value of corn exports declin es by about $56.6 million (0.7% decrease) in 2017 and $57.2 million (0.8% decrease) in 2022, as shown in Figure 2-25. Lower exports of corn could affect world food prices and consumption patterns, although changes in international food consumption associated with EISA are modeled outside of FASOM.

$0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000

2017 2022Million $

Year
Reference Case Control Case
Figure 2-25. Value of Corn Exports (2007$)2-25 2.1.5.2 Soybean, Soybean Oil, and Soybean Meal Exports As discussed earlier, between increases in biodiesel volumes and reductions in soybean acreage and supply, soybean prices increase over time. This in crease in price and reduced supply both contribute to reduced soybean exports relative to the Reference Case, as shown in Figure 2-26. In the model results for 2017, exports of soybeans are 137 million bushels (14.2%) lower under the Control Case th an the Reference Case. In 2022, soybean exports fall by 134 million bushels (13.6%) under the Control Case. In value terms, soybean exports decrease by $590 million in 2017 and by $453 million in 2022 (see Figure 2-27).

0 100 200 300 400 500 600 700 800 900 1000 1100

2017 2022Million Bushels

Year Reference Case Control Case
Figure 2-26. Soybean Exports

Figure 2-28 shows the decrease in soybean oil expor ts associated with the increase in biodiesel volumes in the Control Case. In 2017, th e quantity of soybean oil exported falls from 4.2 billion pounds under the Reference Case to 2.1 billion pounds in the Control Case, a decrease of 50.0%. Similarly, exports of soybean oil ar e 51.2% lower under the Control Case than the Reference Case in 2022.2-26 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000

2017 2022Milion $

Year Reference Case Control Case Figure 2-27. Value of Soybean Exports

0 1 2 3 4 5

2017 2022Billion lbs

Year
Reference Case Control Case Figure 2-28. Soybean Oil Exports

In contrast to the large dec line in U.S. exports of soybean oil, soybean meal exports increase in the Control Case, as shown in Fi gure 2-29. The volume exported in 2017 increases from 15.2 million tons to 16.2 million tons (6.5% increase), while increasing from 15.8 million tons to 16.3 million tons (2.8%) in 2022. The incr ease in domestic demand is for soybean oil for biodiesel production, but soybean meal is produced in large quantities as a coproduct of this process. Thus, this increased soybean meal pr oduction goes into increased use in both livestock feed and exports.2-27 0 2 4 6 8 10 12 14 16 18

2017 2022Million US tons

Year
Reference Case Control Case
Figure 2-29. Soybean Meal Exports

2.1.6 Environmental Impacts The changes in acreage allocation and agricu ltural production practic es that result under the EISA requirements lead to changes in envi ronmental impacts as well. FASOM models a number of different types of environmental impa cts resulting from policy changes. In this section, we focus on changes in fe rtilizer use and GHG emissions. 2.1.6.1 Fertilizer Impacts Corn and soybean production require significan t inputs of fertilizer. By 2022, nitrogen inputs are expected to rise 6.8% and 5.8% for corn and soybean production, respectively, as shown in Table 2-5. Phosphorus inputs are pred icted to rise 12.6% for corn, but to fall by 1.5% for soybeans, relative to the Reference Case. In addition to increasing corn acreage under the Control Case, the large increase in fertilizer use in corn production is also directly related to the harvesting of corn residue used to make cellulo sic ethanol. As the corn residue is harvested, some of the vital nutrients in the soil are re moved with the residue, and this requires more fertilizer-intensive corn production. Because there are currently no markets for soybean residues, increased fertilizer usage for soybean production is a function of increased soybean production. Summing across all crops included in FASOM, total use of nitrogen fertilizer increases by 1,501,258,000 pounds (5.7%) in 2022. Tota l use of phosphorus fertilizer increases by 714,137,000 pounds (12.7%) in 2022.2-28 Table 2-5. Corn, Soybean, and Total Agricult ural Fertilizer Usage under the Reference Case and Control Case, 2022 (thousands of pounds)

2022
Crop Reference Case Control Case Change Percentage Change Corn

Nitrogen 9,592,165 10,240,942 648,778 6.8% Phosphorus 2,250,311 2,533,221 282,910 12.6% Soybeans

Nitrogen 418,568 442,712 24,144 5.8% Phosphorus 683,121 673,161 -9,960 -1.5% All crops in FASOM

Nitrogen 26,208,645 27,709,903 1,501,258 5.7% Phosphorus 5,614,163 6,328,300 714,137 12.7%

2.2 Changes in Farm Income and Food Prices Additional effects of changing market conditions that resu lt from increasing renewable fuels production are changes in farm income and the prices that consumers will pay for their food. Implications for both are discu ssed below based on FASOM results.
2.2.1 U.S. Farm Income The increase in renewable fuel production provi des a significant increase in farm income to the U.S. agricultural sector. FASOM mode l results show 2017 U.S. farm income from the sale of agricultural commodities increasing by 2 7.4% ($13.0 billion) under the Control Case. For 2022, farm income is projected to be 47.4% ( $20.3 billion) higher than in the Reference Case (see Figure 2-30). As expecte d, increasing commodity prices a nd higher production levels lead to a large increase in farm income under the Contro l Case. Most of the increase in net income is likely to be concentrated in rural areas.2-29 $0 $10 $20 $30 $40 $50 $60 $70

2017 2022Billion $

Year
Reference Case Control Case Figure 2-30. U.S. Farm Income under the Reference Case and Control Case (2007$)

2.2.2 Food Prices Despite the wider use of U.S. agricultural feedstocks for renewable fuels, FASOM estimates only a modest increase in U.S. household food costs. One of the key reasons for this is that the majority of the required increase in renewable fuels is de rived from cellulosic feedstocks. The use of these feedstocks, which have no competing use as food, has less of an impact on food costs than the use of starch and sugar-based feedstoc ks. FASOM does not directly track retail food prices but estimates an increase in wholesale U.S. prices for meat and agricultural products associated w ith the higher renewable fuel volum es. To evaluate changes in overall U.S. food prices, FASOM generates an inde x of prices for all farm products included in the model (All Farm Products Price Index), which is a weighted average of prices received by farmers at the farm gate fo r crop and livestock products. 66 In 2017, FASOM predicts an increase in the All Farm Products Price Index of 6.4% and an increase of 6.1% in 2022, as shown in Figure 2-31. However, the costs of wholesale agricultural inputs ar e only a portion of ultimate

66 The FASOM All Farm Products Price Index includes cotton, corn, soybea ns, wheat, sorghum, rice, oats, barley, silage, hay, sugarcane, sugar beets, potatoes, tomatoes, oranges, grapefruit, crop residues, switch grass, hybrid poplar, willow, sweet sorghum, beef, cattle, milk, hogs, sheep , lambs, wool, horses and mules, eggs, chicken, and turkey.2-30 -1 0 1 2 3 4 5 6 7

2017 2022% Change

Year All Crops Meats All Farm Products
Figure 2-31. Change in Agricultural Produc t and Food Price Indices under the Control Case

household food costs, so significan t increases in corn prices a nd, to a lesser degree, soybean prices are expected to result in a much sm aller relative increase in household food costs. 67
As seen in Figure 2-31, the percentage change in the All Crops Price Index 68 is very close to the change in the All Farm Products Price Inde x. The impact of increa ses in grain prices on the Meat Price Index 69 is much less than the impact on cr op prices (and actually a decrease in price in 2017). This likely reflects substituti on among meats toward those that are relatively less affected by the simulated increases in feed prices . Feed is an important component in producing meat products, but other costs are associated with meat production; therefor e, the total cost of meat production will increase by less than the in crease in feed prices. In addition, the higher value of tallow and lard as biodiesel feedstoc ks increases revenue from beef cattle and hog

67 According to the U.S. Department of Agriculture (USDA), approximately 80% of consumer food expenditures are marketing costs (e.g., processing, wholesaling, packaging, and distributing). The price at the farm gate is only 20% of the retail price of food items. Marketing costs consist of a complex set of variables and do not necessarily change proportionately to an increase in farm gate costs. In fa ct, these interim processes can absorb price shocks to some extent, thereby dampening the impact s on the consumer. Therefore, only a portion of farm gate price changes are often reflected at the retail leve l. See http://www.ers.usda. gov/publications/foodreview/ septdec00/FRsept00e.pdf. 68 The FASOM All Crops Pr ice Index includes cotton, corn, soybeans, wh eat, sorghum, rice, oats, barley, silage, hay, sugarcane, sugar beets, potatoes, tomatoes, oranges, grapefruit, crop residues, switch grass, hybrid poplar, willow, and sweet sorghum. 69 The FASOM Meat Price Index includes beef, pork, turkey, and chicken.2-31 production and leads to increased production of non-fed beef and hogs in 2017 and 2022 as well as increased production of fed beef in 2017. Thes e increases in production of beef and pork are contributing to the observed patterns in mode led meat prices, where the Meat Price Index actually experiences a dec line in 2017 and only a sm all increase in 2022.
2.3 Forest Commodity Impacts
The forestry sector is a second source of feedstock for the production of cellulosic ethanol. Production of the require d increases in cellulosic ethanol production is expected to rely partially on forest feedstocks; c onsequently, forest management c ould be affected as well. As modeled in FASOM, the amount of cellulosic ethanol expected to be produced from forest feedstocks is low, approximately about 0.05% of the total volume of renewable fuels production in 2022 in our model simulations. Because the act ual quantity of feedstoc k coming from forests is relatively low compared to other cellulosic feed stocks (total ethanol pr oduction from all forest feedstocks is expected to be 109 million ga llons in 2022, whereas sweet sorghum pulp is expected to produce 110 million gallons), the direct impacts of using forest-derived feedstocks are small. Estimated impacts of EISA on the use of forest feedstocks for ethanol production are presented below. As for results presented fo r agricultural feedstocks, we focus on estimated impacts in 2017 and 2022.
2.3.1 Commodity Prices and Quantities
Within FASOM, U.S. forest products are broken down into two major categories: softwoods and hardwoods. Currently, the model assumes that either milling residues or logging residues can be used to produce ethanol. U nder the Control Case, hardwood logging residues and softwood logging residues are expected to contribute 73 million gallons and 36 million gallons of ethanol in 2022, respectively. The logging residues used as cellulosic ethanol feedstock are generally considered non-merchantable in the Refere nce Case and would be left in the woods. The exogenous increase in dema nd for hardwood and softwood logging residues under EISA will potentially creat e a market for these residues. Table 2-1 illustrates the production of renewable fuels acr oss feedstocks, which provides an indication of the relative impact on the commodities markets most dire ctly affected by increasing renewable fuels production. Implications for pr ices and quantities of hardw ood and softwood logging residues are discussed below.2-32 2.3.1.1 Hardwood Residues Currently, FASOM does not consider the pos sibility of cellulosic ethanol production competing for the wood used in the wood products sector. Instea d, it is only the residues from processing mills or logging residues that woul d otherwise be left in the woods that are considered as potential feedstocks. Therefor e, there are no impacts on the wood products market associated with using logging residues in et hanol production. Logging re sidues from harvesting comprise the majority of the entire feedstock supply generated by hardwoods. As a market is created for hardwood logging residues, their price reaches $20.18/dry ton in 2017 and $23.22/dry ton in 2022 under the Control Case. 2.3.1.2 Softwood Residues
Softwoods constitute 80 to 90% of the timber used for lumber in the U.S. and are used extensively in the manufacture of fiberboard a nd paper. However, as discussed above for hardwoods, FASOM is not curren tly modeling the possibility th at cellulosic ethanol production would compete with the wood products industry. It is the milling and logging residues that can potentially be used in ethanol production. In this analysis, the pr ice for softwood logging residues rises from $0 in the Reference Case to $15.61/dry ton in 2017 and $18.37/dry ton in 2022 under the Control Case.R-1 REFERENCES Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG Conceptual Structure and Specification: Documentation.” Availabl e at http://agecon2.tam u.edu/people/faculty/ mccarl-bruce/papers/1212FASOMGHG_doc.pdf. Adams, D.M., R.J. Alig, J.M. Callawa y, B.A. McCarl and S.M. Winnett. 1996. The Forest and Agricultural Sector Optimization Mode l (FASOM): Model Structure and Policy Applications. PNW-RP-495. U.S. Department of Agriculture, Pacific Northwest Research Station, Portland, Oregon. Adams, D.M. and R.W. Haynes. 1980. The 1980 Softwood Timber Assessment Market Model: Structure, Projections, and Policy Simulations . Forest Science Monograph 22, 64 p. Adams, D.M. and R.W. Haynes. 1996. The 1993 Timber Assessment Market Model: Structure, Projections, and Policy Simulations . General Technical Report PNW-GTR-368. Portland, OR: U.S. Department of Agricult ure, Forest Service, Pacific Northwest Research Station, 58 p. Adams, R.M., D.M. Adams, J.M. Callawa y, C.C. Chang, and B.A. McCarl. 1993. “Sequestering Carbon on Agricultural Land: Social Cost and Impacts on Timber Markets.” Contemporary Policy Issues 11:76-87. Adams, R.M., S.A. Hamilton, and B.A. McCarl . September 1984. “The Economic Effects of Ozone on Agriculture.” Research Monogr aph. EPA/600-3-84-90. Corvallis, OR: USEPA, Office of Research and Development. Adams, R.M., J.D. Glyer, B.A. McCarl, and D. J. 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Lafayette, IN: Purdue University. Beach, R.H., B.A. McCarl, and A.W. Lentz. Agricultural Impacts of the Energy Independence and Security Act: FASOM Results and Model Description , October 2008. Birdsey, R.A. 1992. “Changes in forest carbon storage from increasing forest area and timber growth.” In Forest and Global Change, Volume 1: Opportunities for Increasing Forest Cover , R.N. Sampson and H. Dwight, H., eds. Washington, DC: American Forests: 23- 29. Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating Carbon Emissions , D. Hair, and Neil R. Sampson, eds. Washington, DC: American Forests: 309-333. Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. 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. U.S. Environmental Protection Agency. 2003. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2001 . Annex O. U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2003 . EPA 430-R-05-003. Available at http://www.epa.gov/climatechange /emissions/downloads06/05CR.pdf. Walsh, M.E., D.G. de la Torre Ugarte, H. Shapouri, and S.P. Slinsky. 2003. “Bioenergy Crop Production in the United States: Potential Quantities, Land Use Changes, and Economic Impacts on the Agricultural Sector.” Environmental and Resource Economics 24:313- 333. West, T.O., and W.M. Post. 2002. “Soil Organi c Carbon Sequesteration Rates by Tillage and Crop Rotation.” Soil Science Society of America Journal 66:1930-1936. Wilhelm, W.W., J.M.F. Johnson, D.L. Karlen, a nd D.T. Lightle. 2007. “Corn Stover to Sustain Soil Organic Carbon Further C onstrains Biomass Supply.” Agronomy Journal 99:1665- 1667. Wu, M., M. Wang, and H. Huo. November 200 6. Fuel-Cycle Assessment of Selected Bioethanol Production Pathways in th e United States. ANL/ESD/06-7. Zhang, D., J. Buongiorno, and P. Ince. 1993. PELPS III: A Microcomputer Price Endogenous linear Programming System for Economic Modeling : Version 1.0. Research Paper FPL526. Madison, WI: USDA, Forest Servi ce, Forest Products Laboratory, 43 p. Zhang, D., J. Buongiorno, and P. Ince. 1996. “A Recursive Linear Programming Analysis of the Future of the Pulp and Paper Industry in the United States: Changes in Supplies and Demands, and the Effects of Recycling.” Annals of Operations Research 68:109-139.APPENDIX A: DESCRIPTION OF THE FOREST RY AND AGRICULTURAL SECTOR OPTIMIZATION MODELA-1 In this appendix, we include a more detailed description of the Forestry and Agricultural Sector Optimization Model (FASOM). Our focus in this report is on model assumptions and parameters most directly relevant to analyses of the impacts of larg e-scale renewable fuels production on agricultural and forestry market and environmental conditions. 70 The increase in demand for renewable fuels feedstocks associated with EISA is expected to affect the relative prices of alternative agricultural and forestry products, which will influence landowner decisions regarding land allocation, input use, and crop ma nagement decisions. In addition, changes in crop prices will affect animal feed prices and returns to li vestock production. Section A.1 provides an overview of agricu ltural and forest land use and land use change modeling in FASOM. Section A.2 discusses chemical and energy inputs modeled in FASOM as well as GHG accounting for crop production activities. In Section A.3, modeling of land management decisions such as changes in tillage and implications for energy use and GHG emissions are presented. Finally, in Section A .4 we discuss treatment of the livestock and poultry sectors in FASOM. A.1 Agricultural and Forestry Land Use One of the unique features of FASOM that ma ke the model particularly well-suited to analyses of major policies affecting the agricultur al and forest sectors, such as the impacts of EISA renewable fuels standards, is FASOM’s ab ility to simulate the allocation of land across competing uses over time. The model simulates the allocation of land to multiple activities in both the forest and agricultural sectors over ti me and the resultant consequences for the commodity markets supplied by these lands as we ll as changes in net GHG emissions and other environmental impacts. In this section, we disc uss the regions included in FASOM; the types of land tracked; potential land uses modeled; assumptions regardi ng land conversion costs; crop yields and technology change ove r time; and GHG emissions asso ciated with land use and land use change. A.1.1 Regions
The FASOM model includes the 48 states in the contiguous Un ited States divided into 63 production regions and 11 market re gions, as listed in Table A-1. In addition, these regions are displayed in Figure A-1. Each of the production regions is uniquely mapped to one of the 11

70 The focus of this appendix is primarily on key data and assumptions expected to influence analyses of the impacts of EISA. See http://agecon2.tamu.edu/people/facu lty/mccarl-bruce/papers/1212FASOMGHG_doc.pdf for a more detailed description of FASOM, particularly for the underlying equations and model programming structure and implementation in the General Al gebraic Modeling System (GAMS) language.A-2 Table A-1. Definitions of 11 Market Regions in FASOM

Key Market Region Production Region (States/Subregions) NE Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia LS Lake States Michigan, Minnesota, Wisconsin CB Corn Belt All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS, IndianaN, IndianaS, IowaW, IowaCent, IowaNE, IowaS, OhioNW, OhioS, OhioNE) GP Great Plains Kansas, Nebraska, North Dakota, South Dakota SE Southeast Virginia, North Carolina, South Carolina, Georgia, Florida SC South Central Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, Eastern Texas SW Southwest (agriculture only) Oklahoma, All of Texas but the Easter n Part (Texas High Plains, Texas Rolling Plains, Texas Central Blacklands, Texas Edwards Plateau, Texas Coastal Bend, Texas South, Texas Trans Pecos) RM Rocky Mountains Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming PSW Pacific Southwest All regions in California (CaliforniaN, CaliforniaS)
PNWE Pacific Northwest— East side (agriculture only) Oregon and Washington, east of the Cascade mountain range

PNWW Pacific Northwest— West side (forestry only) Oregon and Washington, west of the Cascade mountain range

larger market regions. The majority of produc tion regions are defined at the state level. However, for selected major production area s with significant diffe rences in production conditions within states, the states are broken into subregions. In addition to the regions defined for the United States, international regions are de fined for the purposes of modeling international trade in agricultural products. 71

71 FASOM’s forest sector includes endogenous trade with only Canada.A-3 Figure A-1. Map of FASOM Regions

International regions are genera lly defined in a more simple way than domestic regions, with individual region-level supply and demand curves specified only for the commodities with the largest trade volumes, such as corn, wheat, soybeans, sorghum, and rice. In addition, only certain regions are defined for exporters and im porters of a given commodity. In cases where commodities are traded in markets with spatial equilibrium sub-models defined, then the regions that can supply and demand that commodity in the model can either export them to another explicit region or to the United States. Similarly, demand in a region can be met through imports from the United States or from other countries. The model solves for the spatial market equilibrium and trading patterns for these heavily traded commodities.
For many other commodities (e.g., cotton, oats , barley, beef, pork, poultry), trade is modeled as total excess import supply and expor t demand functions facing the United States rather than trying to model individual region su pply and demand. In these cases, there are single curves representing the import supply and export demand facing the United States. In addition, there are many commodities without any explicit oppor tunities for internati onal trade, such as hay, silage, citrus fruits, ener gy crops, and livestock, among othe rs. Generally, trade is not explicitly modeled for commodities where interna tional trade volumes for the United States are small or the commodity is not actively traded. South Central Northeast

Southeast Lake States

Pacific Southwest Great Plains Rocky Mountains Pacific Northwest West East

Corn Belt

South Wes tA-4 A.1.2 Types of Land FASOM accounts for land used in agricultu ral and forestry production within the conterminous United States, separating the land ar ea into multiple categories that are tracked separately based on differences in their characteri stics and potential uses. Different types of land tracked in the model are used as inputs into different agricultural and forestry production processes or may provide different levels of productivity. There is potential for certain types of land to move between categories subject to cons traints, though, as disc ussed in Sections A.1.3 and A.1.4. A.1.2.1 Agricultural Land The major categories of agricultura l land modeled within FASOM are

 cropland,

 Conservation Reserve Program (CRP) land,

 cropland pasture,

 forest pasture, and

 rangeland. Cropland is land that is suita ble for crop production and can potentially be used in the production of any of the crops included in FASO M for the particular pr oduction region being considered. Land in the cropland category is the most productive land available for producing primary agricultural commodities, although cropland in some regions is more productive than in others. Therefore, crop yields vary across regions based on historical data . The area of baseline cropland included in the model is land in crop production based on USDA National Resources Inventory (NRI) data and USDA National Agricultural Statistics Service (NASS) data on country-level harvested acreage, i.e., cropland area included in FASOM is equivalent to estimated harvested cropland. Cropland enrolled in the CRP is included under the CRP land category and cropland used as pasture is implic itly included in the pastureland category in FASOM (i.e., both of these categor ies of cropland are included in other categories rather than being reported under cropland). The average annual areas of cropland with failed crops 72 or idle cropland are not included in the reported FASOM cropland and ar e not explicitly tracked in FASOM. Cropland can potentially be converted to cropland past ure or private forestland.

72 USDA data for planted area exceeds the harvested area be cause there will inevitably be some fraction of planted cropland area that is not harvested due to crop failure associated with poor weather, extreme events, or other conditions. In that case, the cost of harvesting may exceed the value of th e crop. Thus, farmers will choose not to harvest those areas.A-5 In addition to tracking aggregate cropland area, cropland is also tr acked by crop tillage system and irrigated/dryland status as well as the duration of time the land has been in such a system 73 to allow tracking of sequestered soil car bon and the transition to a new soil carbon equilibrium following a change in tillage. Als o, there are differences in crop yields between irrigated and dryland systems as well as diffe rences in input use, GHG emissions, and other environmental impacts. Different tillage syst ems also have differences in input usage and environmental impacts in FASOM.
CRP land is cropland that has been enrolled in the Conservation Reserve Program (CRP), which is a USDA program providing payments to encourage activities providing conservation and environmental benefits. The land that farmer s choose to enroll in th e program is typically marginal cropland that farmers have agreed to retire from production fo r a contracted period. The land is generally converted to vegetative cover such as gra ss, trees, or woody vegetation to conserve soil, improve water quality, enhance wi ldlife habitat, or produ ce other environmental benefits. The area of CRP land in FASOM in the baseline is based on 2007 data on CRP enrollment by state available from the USDA Farm Service Agency. 74 Because landowners can choose to remove their land from the CRP progr am when their contract expires (or before expiration, subject to a financ ial penalty), FASOM also track s the area of CRP land with expiring contracts in each year. As CRP contracts expire, landowners will move land back into agricultural production if the returns to agricultur al production exceed the returns associated with maintaining land in the CRP. However, base d on the 2008 Farm Bill, which specifies a maximum of 32 million acres in th e CRP, and indications from USDA that they plan to provide sufficient funding to maintain that maximum level of 32 million acres in the CRP, FASOM model runs generally place a constr aint of 32 million acres in CRP land in future years. That was the assumption employed in modeling the reference and control cases.
Both cropland pasture and forest pasture are suitable for live stock grazing (i.e., land that provides sufficient forage to support the needs of grazing livestock within a region), but cropland

73 Crop tillage systems in FASOM include conventiona l tillage, conservation tillage, and no-till. Conservation tillage and no-till reduce the exposure of carbon in the soil to oxidation and allow larger soil aggregates to form. These practices also leave crop residues on the soil, thereby potentially increasin g carbon inputs. Tillage changes from more intensive conventional tillage practices , such as moldboard plowin g, to conservation or zero tillage practices will generally increase levels of soil carbon over time. In addition, emission reductions may also result because less-intensive tillage typi cally involves less direct fossil fuel use for tractor s. However, there are also alterations in chemical usage (possibly increases in pesticide usage and alterations in rate of fertilization), which can potentially increase emissions from their manuf acture and usage. FASOM has the ability to track these indirectly induced GHG effects associated with changes in tillage. 74 USDA Farm Service Agency. CRP Contract Summary and Statistics. http://www.fsa.usda.gov/FSA/webapp?ar ea=home&subject=copr&topic=rns-css.A-6 pasture tends to be more producti ve. Cropland pasture is managed land that is suitable for crop production and is classified specifically in this category in NRI and NASS data, but that is currently being used as pasture. Because it has sufficient quality to be used in crop production, cropland pasture can potentially be converted to crop production within the model. It can also be converted to forestland. Forest pasture, on th e other hand, refers to land that has varying amounts of tree cover but can also be used as past ure. Forage production on these lands tends to be relatively low, however. This land category is further subdivided into forest pasture in forest (pasture on private timberland), forest pasture in agriculture (woodland pasture on farmland), and forest pasture in public (pasture on forested publi c lands that can be graz ed). Forest pasture in agriculture can be converted to private timberland, but the other two categories of forest pasture cannot be converted to any other uses.
The area of pastureland or ra ngeland required per animal is calculated in FASOM for each combination of livestock type and pasture or rangeland category available in each region. These values are based on forage requirements fo r each livestock species and estimated forage productivity per acre for each category of pa sture in FASOM, defined on a regional basis. 75 The area of pastureland used in livestock production is limited to the pastur eland inventory by time period and region. It is possible to have idle pastureland in FA SOM and idle pastureland area and associated soil carbon sequestration are tracked in the model. In particular, reductions in livestock populations may result in a reduction in pastureland us ed for animal production and an increase in idle pastureland in the model. Ch anges in feed costs are one reason why livestock populations may be impacted by EISA. The imp acts of these changes on livestock markets and input use, including pastureland, are tracked with in FASOM.
Rangeland in FASOM includes both public a nd private rangeland. Rangeland differs from pastureland primarily in that it is a ssumed to be generally unimproved land where a significant portion of the land cover is native gr asses and shrubs. The productivity of rangeland varies considerably across region s of the U.S. Therefore, th e area of rangeland required per animal for a given species can be very different across regions. Over all, rangeland typically provides lower forage production per acre than pa stureland and is considered unsuitable for cultivation. In addition, much of the rangeland in the U.S. is publicly owned. Thus, it is assumed that rangeland cannot be used for crop production or forestland.

75 The calculation of acres of pasture required by a given type of livestock in a particular region is implicitly based on estimates of AUMs available for each category of pastureland in that region.A-7 A.1.2.2 Forestland
FASOM does not track all forestland in the United States. Instead, the model focuses on timberland available for timber production. The di stinction between the tw o is that timberland refers to forestland that provide s at least a minimum level of pr oductivity (at leas t 20 cubic feet of live growing stock per acre per year) and is not rese rved for uses other than timber production (e.g., wilderness use). Lands under forest cover that do not produce at least 20 cubic feet per acre per year, called unproductive forestland, and tim berland that is reserved for other uses are not considered part of the U.S. timber base and are therefore not tracked by the model. 76
In FASOM, endogenous land use modeling is onl y done for privately held parcels, not publicly owned or managed timberlands. However, the quantity of timber harvested on U.S. public lands is accounted for as an exogenous as sumption, and timber inventory levels for public timberlands are simulated based on the exoge nous timber harvest levels set by government administrative decree. On priv ate lands, landowners choose their forest management activities and decide when to harvest and whether to replan t or convert their land to another use following a harvest based on the relative re turns to alternative actions.
Private timberland is tracked by its quality a nd its transferability between forestry and agricultural use. FASOM includes three different site classes to reflect differences in forestland productivity (these site groups we re defined based on ATLAS inputs 77 ), where yields vary substantially between groups:

 HIGH—high site productivity group (sites that produce >85 cubic feet of live growing stock per acre per year);

 MEDIUM—medium site productivity group (s ites that produce between 50 and 85 cubic feet of live growing stoc k per acre per year); and

 LOW—low site productivity group (sites that produce between 20 and 50 cubic feet of live growing stock per acre per year). Sites that offer higher productiv ity in timber production are also assumed to provide higher agricultural productivity if they are transf erred to agricultural production following timber harvest. These differences in assumed producti vity affect potential tr ansferability between timberland and agricultural uses, as described in Section A.1.3.

76 Because these unproductive fo restlands are assumed to offer low producti vity for agricultural uses as well, these land areas are not available for conversion to agricultural uses in FASOM. 77 Haynes, R.W., D.M. Adams, and J. Mills. 1995. The 1993 RPA Timber Assessment Update . General Technical Report RM-259. Portland, OR: U.S. Department of Ag riculture, Forest Service, Pacific Northwest Research Station, 66 p.A-8 Another distinction in land ow nership tracked in FASOM is between two major classes of private forest owner groups: forest industr y (FI) and non-industrial private forest (NIPF) landowners. The traditional definitions are used for these ownership groups: industrial timberland owners are those that possess processi ng capacity for the timber, and NIPF owners do not. In FASOM, it is assumed that timberla nd owner by the FI cannot be converted to agriculture, whereas NIPF lands can move into agricultural producti on, subject to the constraints, opportunity costs, and conversion costs desc ribed in the following two sections.
In addition, FASOM tracks land in terms of the type of timber management, the species group on the land, and the stand age. There are 18 different possible management intensity classes included in the model depending on whether thinning, part ial cutting, passive management, or other management methods are use d. There are also 25 different forest species types, which vary by region (e.g., Douglas fir and other species types in the West and planted pine, natural pine, and various ha rdwood types in the South). Sta nd age is explicitly accounted for in 5-year cohorts ranging from 0 to 4 years up to 100+ years. Differences in these variables affect forest growth rates, optimal ro tation length, and car bon sequestration. A.1.2.3 Developed Land FASOM also tracks the movement of agricultur al and forest land in to developed uses. The economic returns to devel oped land uses typically exceed the returns available to agricultural or forestry land us es. Thus, FASOM assumes an exogenous rate of land conversion into developed uses by region for each of the ag ricultural and forest la nd categories included in the model (with the exception of forest pasture in forest and forest pasture in public) based on projections of future U.S. population and in come, with endogenous competition between agriculture and forestry for the remaining land base available for these uses over time. A.1.3 Potential Land Uses, Opportunity Costs, and Conversion Costs FASOM tracks many possible changes in th e primary agricultural and forestry commodities produced and production practices em ployed on a given type of land as well as movements between the different types of land described above. U nderlying the commodity production decisions and the associated environm ental impacts is the decision by landowners on how much, where, and when to allocate land acro ss potential uses. The primary factor driving changes in production practices and land use in FASOM is the relative returns available, accounting for all relevant costs. Below we de scribe key model assumptions driving results related to land use.A-9 A.1.3.1 Cropland Cropland can move freely between production of alternative cr ops (that are available for production within a region) with no conversion co sts associated with moving between crops. However, opportunity costs of switching from the original crop influence switching between different crops produced on availa ble cropland. In addition, ther e are crop mix constraints in place that constrain regional production to be a convex combination of typical historical crop mixes (with adjustments to allow for the adoptio n of new crops that have not been produced historically, such as energy crops). The primary rationale for this constraint is that observed historical outcomes reflect landowners’ cons ideration of all produc tion possibilities and constraints imposed by rotation requirements, ava ilable resources, and othe r technical factors. Consequently, forcing the model to use a conve x combination of observed regional crop mixes implicitly incorporates all firm production processes and constraints. 78
In addition, cropland tillage can be altered at no exp licit cost, but there are differences in input use that are taken into c onsideration. In many cases, the choice of tillage may change along with a change in crop to be produced base d on the relative returns to alternative tillage types across different crops. FASOM includes constraints on the number of acres that can change tillage status in a given time period and land changing tillage types is forced to remain in the new tillage category for a minimum amount of time before changing again. Cropland can also move between irrigated and dryland pr oduction (subject to regi onal constraints on production possibilities). Irrigated cropland pr ovides higher yields for a given crop-region combination, but also requires additional inputs a nd is subject to water re source availability on a regional level.
Cropland can also be converted to CRP land, pastureland, or timberland. 79 The quantity of cropland in the CRP is typically based on hist orical data and held constant over time unless changes to CRP policy are being considered in a model run. In this study, our primary assumption for CRP land is that it will declin e from the starting value of just over 37 million acres to the 32 million acre maximum level auth orized by the 2008 Farm Bill. Cropland can be converted to pasture for a minimal conversion co st. However, the opportunity cost of the land being kept in cropland rather th an converted is generally a majo r cost of switching from cropland

78 In applications where FASOM is run over an extended time period, these crop mi x constraints are typically phased out beginning after about 35 years. This reflects an assumption that observed crop mixes become less representative of future production possibilities the further one moves out into the future given technological and other advancements in production. 79 There is no explicit movement of any land types into or out of rangeland (with the exception of rangeland moving to developed land) because that category of land use is considered to be unsuitable for cultivation and to offer lower productivity than the pasture categories.A-10 to pastureland. The higher the returns to crop production in a given regi on, the less likely that cropland will move to pastureland, other things being equal. Transfer ability between cropland and timberland is described below under Section A.1.3.4.
Finally, there is exogenous movement of cr opland to developed land based on increases in future developed land area associated with projected future U.S. population and income. Conversion to developed land is discusse d in more detail in Section 1.3.5.
A.1.3.2 CRP Land The acreage of CRP land in the model is based on historical data rega rding the acreage in CRP by state as described above. In this study, acres in the CRP are permitted to revert to cropland if the returns to crop production exceed available CRP payments. However, we assume that CRP acreage will not fall below the 32 million acre maximum level authorized by the 2008 Farm Bill. A.1.3.3 Pastureland
Cropland pasture can be converted to cropl and or timberland and forest pasture in agriculture can be converted to forestland. Th e other forest pasture categories cannot be converted to another land category, but any of the pasture categorie s can move to idle pastureland. The cost of convert ing pastureland to cr opland in FASOM is equal to the difference in their regional land rental rates based on the assumed equilibration of land markets. For instance, if regional pasture rents for $20 per acre and regional cropland rents for $100 per acre, then the conversion cost 80 is the net present value of $80 per year over an infinite horizon, which is equal to $2,080 based on the 4% discount rate used in FASOM. 81 In addition to the conversion cost, there is an opportun ity cost associated with the returns to keeping the land in pasture used for livestock production. Thus, the ne t present value of land use in cr opland has to exceed that of use in pastureland by a large enough margin to ju stify incurring th e initial conversion costs. Transferability between pastureland and timbe rland is described below under Section A.1.3.4.
Depending on the demand for pastureland from the livestock sector, the costs of land conversion to cropland or timberland, and the overall regional constr aints on pastureland conversion, it is also possible fo r pastureland to move into an idle pastureland category. Essentially, this indicates that the amount of pasture being used in regional livestock production

80 The conversion cost used in FASOM embodies both cost s of converting land as well as the difference in land productivity. 81 The real (inflation-adjusted) discount rate used in FASOM is 4%, which is broadly consistent with opportunity costs of capital in agriculture and forestry. Higher disco unt rates devalue future revenue and cost streams, while lower discount rates have just the opposite effect.A-11 is less than the available pastur eland in that region, but the cost s of moving the land into another use (for those pasture categories that can be converted) are to o high to justify conversion.
In addition, as in the case of cropland, there is exogenous mo vement of pastureland to developed land. Pastureland convers ion to developed land is discusse d in more detail in Section A.1.3.5.
A.1.3.4 Timberland In addition to tracking endogenous land transf ers between agricultural uses, FASOM is one of the few models that incorporates e ndogenous movements of land between the U.S. agriculture and fore stry sectors. To enable the modeling of land exchanges, a dditional information on land suitability was added beyond what was used in the ATLAS modeling. There were five land suitability classes defined that relate to the movement of land between forestry and agriculture:

 FORONLY—includes timberland acres that can not be converted to agricultural uses

 FORCROP—includes acres that begin in timb erland but can potentially be converted to cropland

 FORPAST—includes acres that begin in timb erland but can potentially be converted to pastureland

 CROPFOR—includes acres that begin in cropl and but can potentiall y be converted to timberland

 PASTFOR—includes acres that begin in past ure but can potentially be converted to timberland FASOM reflects the mobility of the land resour ce between the sectors subject to controls for land quality/growing conditions and investment s needed to mobilize land. The land quality factors generally restrict some lands to be only in forest, due to topography or soil characteristics. Likewise, the growing conditions render some lands unsuitable for forest uses at all, particularly in the drier pl ains areas of the country, and woul d thus be suitable only for some agricultural uses.
In the timberland inventory, acres that could potentially be c onverted from forest to crop or pasture use were included in the FORCROP a nd FORPAST classes, resp ectively. To reflect differences in land productivity and feasibili ty of converting timberland to crop production, movements between forestry and cropland are only permitted within the high-quality forest site productivity class. Similarly, changes in land al location involving pastur eland occur only within the medium-quality forest site productivity class. Thus, there are limits on the area of timberlandA-12 within a region that can potentially move to cropland or to pastur eland that reflect the regional distribution of timberland site productivity. In add ition, land movements in forestry are only allowed in the NIPF owner categor y, reflecting an assumption that land held by the FI ownership group will not be converted out of timberland. All private timberland acres that were not eligible for transferring between s ectors were assigned to th e FORONLY land class.
For land beginning in agricultur al production, acres by region that could potentially be converted from crop or pastureland to forestla nd were included in the CROPFOR and PASTFOR land classes, respectively, based on National Resource Inventory data of the USDA Natural Resources Conservation Service 82 , 83 and a study by Moulton and Richards. 84 Agricultural land can move to other uses during any of the 5-year m odel periods, but when afforested it begins in the youngest age cohort of timberland. The land th en remains in timberland until timber harvest, at which time the land could potentially be converted back to agricultural use.

The investments to mobilize land from forest to agriculture generally involve stump clearing, leveling, etc., of forested lands. In addition, the model can in clude additional hurdle costs to reflect conditions where it may take an income differential be yond the opportunity cost in agriculture to get agricultura l producers to switch to forestry. Given these model attributes, the economic conditions for land movement are th at when land moves into forestry, the net present value of the returns from one rotation in forestry plus the future value of forestland beyond the first rotation must be greater than th e net present value of the land remaining in agriculture by at least the hurdle cost. For land moving from forest to ag riculture, the net present value of land in agriculture must exceed returns to a rotation in forestry pl us the future value of forested land by the investment cost to transfer land plus any hurdle cost (this term is currently set to zero). In both land transfer cases, the land moves between sectors until the markets equilibrate and the net present va lue plus the investment and ma rket wedges are equal across the sectors for lands on the margin. Naturally, land movement does not occur if the differences in the land returns are less than the hurdle cost plus the land transformation investment costs.

Land conversion costs include those for la nd clearing, wind rowing, and burning, and any necessary leveling and removing large chunks of woody debris for seedbed preparation. Any

82 U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS) (formerly Soil Conservation Service) (SCS). 1989. 1987 National Resources Inventory Summary Report . USDA, NRCS. 88p. 83 U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS). 2001. 1997 National Resources Inventory Summary Report . USDA, NRCS. 90 p. 84 Moulton, R., and K.R. Richards. 1990. Costs of sequestering carbon through tree planting and forest management in the United States . General Technical Report WO-58. Washington, D.C.: U.S. Department of Agriculture, Forest Service, Washington Office. 17 p.A-13 timberland converted to agricultura l land is assumed to occur afte r harvest of any merchantable trees, and 75% of timber volume re moved in land clearing is assu med to be hauled to market. 85 Constraints on the amount of tim berland that could be converted to agricultural uses were derived from data from the Natural Resource Inventory by the Natural Resources Conservation Service, 86 pertaining to NIPF timberland with medi um or high potential for conversion to cropland and pastureland. The data were checked against that for NRI prime farmland, representing forest, pasturela nd, cropland, rangeland, or other minor land uses that have good potential for cultivated crops (e.g., slope less than 5%, not exce ssively eroded, not wetlands). The published NRI data do not identify fores tland qualifying as prime cropland below our FASOM region, thus allocation of prime cropland by forest type, management intensity class, and age cohort is by assumption (proportional to what is in the highest forestry site group).
Another important portion of the conversion co st information is estimates of acres of available prime cropland and pastureland in each state that are currently under the cover of forestland. 87 , 88 NRI data (1997 national survey) were us ed to estimate these land areas. To determine the proportion of forested cropland and pa stureland available in each state, the ratio of “active” crop and pastureland was used to disaggrega te the total prime agricultural land in forest cover. The amount of crop and pastureland eligib le for conversion to timberland was established using information from Moulton and Richards. 89 One stipulation is that reforestation establishment costs must be greater than or equal to crop to forest or past ure to forest conversion establishment costs. 90
Costs are represented in each region by step f unctions to reflect increasing costs, as the percentage of land base being converted increases . Costs for land conversion in a region are less expensive for the initial 10% of land converted and subsequently higher for the next 50%, and

85 Adams, D.M., R.J. Alig, J.M. Callaway, B.A. McCarl , and S.M. Winnett. 1996. The Forest and Agricultural Sector Optimization Model (FASOM): Model Structure and Policy Applications. PNW-RP-495. U.S. Department of Agriculture, Pacific Northw est Research Station, Portland, Oregon. 86 U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS). 2001. 1997 National Resources Inventory Summary Report . USDA, NRCS. 90 p. 87 Ibid. 88 These areas are included under the timberland category in either the high or medium site productivity classes. 89 Moulton, R., and K.R. Richards. 1990. Costs of sequestering carbon through tree planting and forest management in the United States . General Technical Report WO-58. Washington, D.C.: U.S. Department of Agriculture, Forest Service, Washington Office. 17 p. 90 Adams, D.M., R.J. Alig, J.M. Callaway, B.A. McCarl , and S.M. Winnett. 1996. The Forest and Agricultural Sector Optimization Model (FASOM): Model Structure and Policy Applications. PNW-RP-495. U.S. Department of Agriculture, Pacific Northw est Research Station, Portland, Oregon.A-14 even more so for the final 40%. This reflects the increasing marginal cost of land conversion due to varying topography, mois ture, and other factors. 91

Table A-2 summarizes the costs of land conve rsion between agricultu re and forest for each of these three steps and baseline acreage av ailable for conversion by region. All conversion of agricultural lands to forestla nd are in the low cost category b ecause site preparation costs are expected to be minimal for these sites. In general, it is more expensive to convert forestland to cropland than pastureland because there is more site preparation work involved.
In addition to the land conversion costs, some forest establishment costs are considered by FASOM as part of the land allocation decision. Forest es tablishment costs include those for site preparation, tree seedlings, and tree planting. Intermediate timber management costs include those for pre-commercial thinning, prescribed burning, fertilization, a nd any other practices between stand establishment and harvest. Esta blishment costs vary by FASOM land class, with generally higher costs for refore sted acres, such as those for FORONLY acres, and lower costs for afforesting CROPFOR and PASTFOR acres. Co sts are lower on agricultural lands because they are typically well-suited to planting trees an d there is less preparation work required than for reforestation of forestland, which requires more land clearing and site preparation.
For regional averages of forest establishment in the South, Forest Farmer reports cost trends every 2 to 3 years in the Manual Edition . Cost estimates and trends over the period 1952 to 1994 were reported for specific forestry practices common to the South. 92 , 93 , 94 These cost data are based on surveys of forest industry, consultants, and public agencies, and most are on a per acre basis. Those practices w ith a high percentage of labor experienced the greatest cost increases. 95 Updated cost functions were used fro m the previous FASOM model to estimate establishment and growing costs on a per acre basis. 96 Establishment costs for naturally

91 Adams, D., R. Alig, B.A. McCarl, and B.C. Murr ay. 2005. “FASOMGHG Co nceptual Structure and Specification: Documentation.” Available at http://agecon2.tamu.edu/people/faculty/ mccarl-bruce/papers/1 212FASOMGHG_doc.pdf. 92 Dubois, M.R., K. McNabb, and T.J. Straka, 1999. Costs and cost trends for forestry practices in the South. The Forest Landowner , 32nd Manual Edition, 58(2):3-8. 93 Dubois, M.R., K. McNabb, and T.J. Straka, 2000. Costs and cost trends for forestry practices in the South. The Forest Landowner , 33rd Manual Edition. 94 Dubois, M.R., K. McNabb, and T.J. Straka, 2003. Costs and cost trends for forestry practices in the South. The Forest Landowner , 34th Manual Edition. 95 Dubois, M.R., K. McNabb, and T.J. Straka, 1999. Costs and cost trends for forestry practices in the South. The Forest Landowner , 32nd Manual Edition, 58(2):3-8. 96 Adams, D.M., R.J. Alig, J.M. Callaway, B.A. McCarl , and S.M. Winnett. 1996. The Forest and Agricultural Sector Optimization Model (FASOM): Model Structure and Policy Applications. PNW-RP-495. U.S. Department of Agriculture, Pacific Northw est Research Station, Portland, Oregon.A-15 Table A-2. FASOM Conversion Costs Between Agricultural and Forest Land ($/acre) and Acres Available for Conversion

Land Conversion Cost Level Land Class Definition Region Low Medium High Total Acres FORCROP Cost per acre Corn Belt $434 $761 $986 Available acreage Corn Belt 422,259 2,111,293 1,689,035 4,222,587 FORPAST Cost per acre Corn Belt $282 $479 $609 Available acreage Corn Belt 38,391 191,957 153,565 383,913 CROPFOR Cost per acre Corn Belt $434 Available acreage Corn Belt 78,000,000 78,000,000 PASTFOR Cost per acre Corn Belt $282 Available acreage Corn Belt 10,200,000 10,200,000 FORCROP Cost per acre Lake States $423 $676 $902 Available acreage Lake States 561,685 2,808,424 2,246,740 5,616,849 FORPAST Cost per acre Lake States $254 $423 $592 Available acreage Lake States 58,195 290,976 232,780 581,951 CROPFOR Cost per acre Lake States $423 Available acreage Lake Stat es 24,800,000 24,800,000 PASTFOR Cost per acre Lake States $254 Available acreage Lake States 2,600,000 2,600,000 FORCROP Cost per acre New England $451 $789 $1,014 Available acreage New England 395,726 1,978,629 1,582,903 3,957,258 FORPAST Cost per acre New England $282 $507 $620 Available acreage New England 77,324 386,621 309,297 773,242 CROPFOR Cost per acre New England $451 Available acreage New England 11,100,000 11,100,000 PASTFOR Cost per acre New England $282 Available acreage New England 3,900,000 3,900,000 (continued)A-16 Table A-2. FASOM Conversion Costs Between Agricultural and Forest Land ($/acre) and Acres Available for Conversion (continued)

Land Conversion Cost Level Land Class Definition Region Low Medium High Total Acres FORCROP Cost per acre Rocky Mountains $733 $902 $1,014 Available acreage Rocky Mountains 3,261 16,306 13,045 32,612 FORPAST Cost per acre Rocky Mountains $451 $564 $676 Available acreage Rocky Mountains 219 1,094 875 2,188 CROPFOR Cost per acre Rocky Mountains $733 Available acreage Rocky Mountains 10,900,000 10,900,000 PASTFOR Cost per acre Rocky Mountains $451 Available acreage Rocky Mountains 1,800,000 1,800,000 FORCROP Cost per acre Pacific SW $423 $648 $874 Available acreage Pacific SW 1,052 5,260 4,208 10,520 FORPAST Cost per acre Pacific SW $282 $423 $564 Available acreage Pacific SW 48 240 192 480 CROPFOR Cost per acre Pacific SW $423 Available acreage Pacific SW 1,100,000 1,100,000 PASTFOR Cost per acre Pacific SW $282 Available acreage Pacific SW 400,000 400,000 FORCROP Cost per acre Pacific NW East $366 $592 $817 Available acreage Pacific NW East 37,970 189,852 151,881 379,703 FORPAST Cost per acre Pacific NW East $254 $372 $507 Available acreage Pacific NW East 9,567 47,836 38,269 95,672 CROPFOR Cost per acre Pacific NW East $366 Available acreage Pacific NW East 2,600,000 2,600,000 PASTFOR Cost per acre Pacific NW East $254 Available acreage Pacific NW East 300,000 300,000 (continued)A-17 Table A-2. FASOM Conversion Costs Between Agricultural and Forest Land ($/acre) and Acres Available for Conversion (continued)

Land Conversion Cost Level Land Class Definition Region Low Medium High Total Acres FORCROP Cost per acre South Central $254 $451 $564 Available acreage South Central 1,301,600 6,508,000 5,206,400 13,016,000 FORPAST Cost per acre South Central $169 $282 $366 Available acreage South Central 520,800 2,604,000 2,083,200 5,208,000 CROPFOR Cost per acre South Central $254 Available acreage South Central 38,900,000 38,900,000 PASTFOR Cost per acre South Central $169 Available acreage South Central 15,200,000 15,200,000 FORCROP Cost per acre Southeast $282 $434 $535 Available acreage Southeast 968,896 4,844,481 3,875,585 9,688,962 FORPAST Cost per acre Southeast $208 $276 $344 Available acreage Southeast 370,574 1,852,869 1,482,296 3,705,739 CROPFOR Cost per acre Southeast $282 Available acreage Southeast 13,900,000 13,900,000 PASTFOR Cost per acre Southeast $208 Available acreage Southeast 5,200,000 5,200,000 FORCROP Available acreage US 3,692,44 9 18,462,245 14,769,797 36,924,491 FORPAST Available acreage US 1,075,118 5,375,593 4,300,474 10,751,185 CROPFOR Available acreage US 181,300,000 181,300,000 PASTFOR Available acreage US 39,600,000 39,600,000

regenerated stands include site preparation costs, but are si gnificantly less than for planted stands. Sources for updated cost esti mates included Dubois, McNabb, and Straka 97 and Cathcart, 98 primarily for establishment of planted st ands in the Southern regions and Pacific Northwest Westside, respectively.

97 Dubois, M.R., K. McNabb, and T.J. Straka, 2003. Costs and cost trends for forestry practices in the South. The Forest Landowner , 34th Manual Edition. 98 Cathcart, J. 2003. Oregon Department of Forestry Cost Share Rates for the Forest Land Enhancement Program (FLEP). [Internal Report]. Oregon De partment of Forestry. Salem, OR.A-18 A.1.3.5 Developed Land As noted in the sections above, land use ch ange in FASOM reflect s endogenous transfers of land between the agriculture and forest sect ors based on relative returns to alternative land allocations. However, the model also incorpor ates exogenous projections of the conversion of cropland, pastureland, and timberland to develo ped uses such as shopping centers, housing, and other developed and infrastructural uses. Demand for such developed land is driven by an addition of more than 130 million people in th e United States over the next 50 years, who on average will have higher personal incomes, in creasing demand for housing and infrastructure. With developed uses typically occupying the t op of the economic hierar chy of land use in the United States, 99 a consistent approach to modeling la nd conversion in FASOM is to satisfy demand for developed land before the endogenous competition for the reduced rural land base between the agricultural a nd forest sectors. In other words, FASOM assumes that the conversion of agricultural and forest land to developed land is a function of population, personal income, and other demographic factors, but does not de pend on agricultural or forest products commodity prices or land values. This assumption is ma de because the land value for developed uses in those areas where development is taking place is expected to greatly ex ceed the land value of keeping the land in agricultu ral or forestry uses.
Projections of conversion of forest and agricultural land to developed use were obtained from land-use modeling for the 2010 Resources Planning Act (RPA) Land Base Assessment by the U.S. Forest Service. The RPA model prov ides area projections of forest, pasture, crop, range, and developed uses by region and projection decade. The main land-use data source is the NRI developed by the USDA Natura l Resources Conservation Service. 100 The NRI data allow matrices to be estimated that contain regional transition rates among the major land uses, such as between forest and pasture uses or between fore st and developed use (e.g., urban use). This detail in the land-use data is unique among major sources of land-us e data that provide coverage for the contiguous 48 states and informs the exogenous land conversion assumptions used in FASOM.
The values used in FASOM for regional la nd movement from cropl and, pastureland, and timberland to developed uses change over time ba sed on the RPA projections data. In general, growth in the area of developed land is projected to slow as one m oves farther out into the future.

99 Alig, R.J., J.D. Kline, and M. Lichtenstein. 2004. “Urbanization on the U.S. Landscape: Looking Ahead in the 21st Century.” Landscape and Urban Planning 69(2-3):219-234. 100 U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS). 2001. 1997 National Resources Inventory Summary Report . USDA, NRCS. 90 p.A-19 Table A-3 presents a snapshot of the exogenous movement of agri cultural land to developed land assumed in FASOM for 2022. Table A-3. Exogenous Land Move ment from Forestry and Agriculture to Developed Uses (Acres per Year), 2022

CB GP LS NE PNWE PNWW PSW RM SC SE SW Cropland 129,204 19,194 44,524 20,330 11, 301 NA 27,252 2,321 38,311 19,899 22,055 Pastureland 17,641 3,899 29,574 19, 247 24,920 NA 33,385 588 50,474 32,245 21,206 Rangeland 252 8,100 NA NA 6,456 NA 31,138 1,154 2,758 2,265 55,681 Timberland 94,644 NA 80,974 125,146 27, 641 61,206 35,825 5,525 164,649 132,446 NA

A.1.4 Crop Yields and Technology Change Changes in technology over time are incor porated within a number of different components of FASOM, including crop yields, input use, ethanol conversion rates, electricity generation, and other processes. Projected crop yields , both domestically and internationally, constitute one of the most influe ntial factors of this agricultural analysis. Conversion rates of feedstocks to renewable fuels over time also play an important role in determining the market equilibrium outcome. Below we discuss project ed crop yields, assumptions regarding priceinduced yield changes, renewable fuel feedstock conversion rates, and assumptions regarding the impacts of yield changes on input use requirements. A.1.4.1 Crop Yields Yields for most crops have increased si gnificantly over time due to technological improvements. Thus, FASOM adjusts crop yields ove r time based on historical growth in yields. Assumed yield increases for corn and soybean s were modified for this study to ensure consistency of the national weighted aver age yields of these commodities with USDA projections through 2017 (the last year included in their baseline projections report) and then extrapolated out to 2022. Table A-4 summa rizes 2022 national average yields for major commodities in FASOM as well as the assumed rate of annual growth. A.1.4.2 Price-Induced Changes in Yields
If the costs of increas ing productivity on existing land were lower than the value of the increased production, then agricu ltural landowners would presum ably have already adopted these productivity-enhancing actions. Although it is possible that sufficient increases in commodity prices could induce fa rmers to adopt higher cost pract ices that increase productivityA-20 Table A-4. FASOM National Av erage Yields for Major Commodities and Annual Yield Growth

Agricultural Commodity Units 2022 Yield (units/acre) Annual Yield Growth Rate Barley bu 57.3 0.10% Corn bu 186.3 1.62% Cotton 480 lb bales 1.7 0.43% Hay tons 3.2 0.84% Hybrid Poplar dry tons 4.6 0.75% Oats bu 60.6 0.02% Rice cwt 79.2 1.33% Silage tons 19.8 1.90% Sorghum cwt 63.5 0.09% Soybeans bu 45.3 0.43% Switchgrass dry tons 9.1 2.04% Sugarcane tons 38.3 0.00% Willow dry tons 4.2 0.75% Wheat, Durham bu 34.9 1.11% Wheat, Hard Red Spring bu 44.0 1.00% Wheat, Hard Red Winter bu 73.1 1.31% Wheat, Soft White bu 78.3 1.11%

but are not profitable at lower commodity prices, FASOM does not directly incorporate yield responses to changes in price. However, la ndowners can potentially switch from dryland to irrigated production, change ti llage practices, or make othe r management adjustments in response to the changes in price. 101
A.1.4.3 Renewable Fuel Feedstock Conversion Rates FASOM assumes that starch- and sugar-b ased ethanol production and biodiesel production are both mature technologies that have essentially already reached technical limits on feedstock conversion. Thus, while conversion rates differ across feedstocks, all feedstock conversion rates for producing these fuels remain constant over time in the model. For cellulosic ethanol production, on the other hand, there are substantial in creases in conversion rates anticipated over time as cellulo sic feedstock conversion technol ogy improves and enables us to approach the technical limits on conversion. Table A-5 summarizes 2022 renewable fuel

101 In addition, we did not model decreases in yields that might occur due to increased planting on marginal land in response to higher commodity prices. This effect is expected to at least partially offset any potential priceinduced increase in yield that could be associated with expansion of biofuels production to meet the RFS2 volumes.A-21 Table A-5. FASOM Biofuel Feedstock Conv ersion Rates and Index Value (2002=100)

Feedstock 2022 Yield (gallons/unit) 2022 Index Value (2002=100) Starch- and Sugar-Based Ethanol Barley 1.66 gallons/bu 100 Corn (dry mill process) 2.71 gallons/bu 100 Corn (wet mill process) 2.50 gallons/bu 100 Oats 1.10 gallons/bu 100 Rice 3.98 gallons/cwt 100 Sorghum 4.25 gallons/cwt 100 Refined Sugar 141.00 gallons/ton 100 Sweet Sorghum 9.00 gallons/ton 100 Sweet Sorghum (ratooned) 11.00 gallons/ton 100 Wheat, Durham 2.56 gallons/bu 100 Wheat, Hard Red Spring 2.56 gallons/bu 100 Wheat, Hard Red Winter 2.56 gallons/bu 100 Wheat, Soft White 2.56 gallons/bu 100 Biodiesel Soybean Oil 0.13 gallons/lb 100 Corn Oil 1.02 gallons/gallon 100 Edible Tallow 0.13 gallons/lb 100 Non-edible Tallow 0.13 gallons/lb 100 Lard 0.13 gallons/lb 100 Cellulosic Ethanol Barley crop residues 92.30 gallons/dry ton 128.4 Corn crop residues 92.30 gallons/dry ton 128.4 Oat crop residues 92.30 gallons/dry ton 128.4 Rice crop residues 92.30 gallons/dry ton 128.4 Sorghum crop residues 92.30 gallons/dry ton 128.4 Wheat crop residues 92.30 gallons/dry ton 128.4 Hybrid poplar 101.50 gallons/dry ton 128.4 Switchgrass 92.30 gallons/dry ton 128.4 Willow 101.50 gallons/dry ton 128.4 Hardwood logging residues 101.50 gallons/dry ton 128.4 Softwood logging residues 92.30 gallons/dry ton 128.4 Bagasse 92.30 gallons/dry ton 128.4 Hardwood milling residues 101.50 gallons/dry ton 128.4 Softwood milling residues 92.30 gallons/dry ton 128.4 Sweet sorghum pulp 92.30 gallons/dry ton 128.4 Source: Tao, Ling and Aden, Andy. November 2008. Techno-economic Modeling to Support the EPA Notice of

Proposed Rulemaking. National Renewable Energy Laboratory (NREL).A-22 feedstock conversion rates in FASOM and an in dex of 2022 conversion rate s relative to baseline values. A.1.4.4 Impact of Yield Changes on Input Requirements Another major update in FASOM that takes pl ace along with changes in yields is the calculation of input adjustments fo r a given change in yield. This adjustment is included to account for the fact that increases in yield over time are related to greater expenditures on inputs, including increases in both qua ntity and quality of inputs. The procedure used in FASOM employs an elasticity term that gives the per centage response of input usage per acre to a percentage change in yield. The elasticity of inpu t change with respect to crops has been derived from historical data where available. Subse quently, based on the yields, input uses in the production budgets can be updated using the elasticity of input use change with respect to yield change times the projected yield change. Ba sed on data showing little relationship between yields and N fertilizer applica tion since the early 1980s (see Figure A-2), we held fertilizer use constant in this analysis rather than allo wing fertilizer use to increase with yield.

0 20 40 60 80 100 120 140 160

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Nitrogen Application Rates
Figure A-2. Nitrogen Application Rates A.1.5 GHG Emissions Factors for Land Use Change and Crop Production GHG mitigation opportunities in forestry and agriculture include afforestation (tree planting), forest management (e.g., altering harves t schedules or management inputs), forestA-23 preservation, agricultural soil tillage practices, grazing management, riparian buffers, renewable fuel substitutes, fertilization management, and livestock and manure management. Sequestration activities can enhance and preser ve carbon sinks and in clude afforestation, forest management, and agricultural soil tillage practic es. Agricultural sources of CH 4 , N 2 O, and fossil fuel CO 2 can be reduced through changes in fe rtilizer applications and livestock and manure management. CO 2 emissions can be offset through renewable fuel s, such as switchgrass and short-rotation tree species, which can be grown and used instead of fossil fuels to generate electricity or transportation fuels. A detailed list of the categories of GHG sources and sinks quantified in FASOM is shown in Table A-6.
This section focuses on changes in GHG emi ssions and sequestration related to land use change. The remaining sections in this appendix provide info rmation on GHG emissions associated with crop production, land management, and livestock production.
In addition to quantifying GHG emissions a nd sinks, FASOM also can distinguish the unique time dynamics and accounting issues of carbon sequestration options. These include issues such as saturation of carbon sequestration over time (i.e., carbon sequestration in a particular sink reaches an equilibrium such that carbon storage is maintained, but is no longer increasing), potential reversib ility of carbon benefits (e.g., due to changes in tillage, forest harvests, wildfires), and fate of carbon stored in products after fore st harvest. In contrast, these can be compared with the options for agricultural non-CO 2 , fossil fuel CO 2 , and renewable fuels that do not exhibit satura tion or reversibility and are permanent reductions.
GHGs, generally in the form of carbon, can be sequestered in soils, standing trees, other vegetation, and wood products. Sequ estration refers to storage of the GHGs for more than one year. As a consequence, the sequestration defini tion used in the model for standing vegetation is limited to carbon storage in trees , understory, and litte r within both forests and plantations of woody renewable fuel feedstocks (p oplar and willow) but excludes, for instance, carbon stored in annually cultivated crops.
Carbon sequestration is also modeled within

 cropland soils,

 pastureland soils,

 soils in idled lands,

 timberland soils, and

 harvested wood products.A-24 Table A-6. Categories of GHG Sou rces and Sinks Included in FASOM

Forest_SoilSequest Carbon in forest soil Forest_LitterUnder Carbon in litter and understory of forests that remain forests Forest_ContinueTree Carbon in trees of forests that remain forests Forest_AfforestSoilSequest Carbon in forest soil of afforested forests Forest_AfforestLitterUnder Carbon in litter and understory of afforested forests Forest_AfforestTree Carbon in trees of afforested forests Forest_USpvtProduct Carbon from US private forests consumed producing forest products Forest_USpubProduct Carbon from US public forests consumed producing forest products Forest_CANProduct Carbon in US consumed but Canadian produced forest products Forest_USExport Carbon in US produced but exported forest products Forest_USImport Carbon in US consumed but imported from non-Canadian source Forest_USFuelWood Carbon in US consumed fuelwood Forest_USFuelResidue Carbon in US residue that is burned Forest_USresidProduct Carbon from US residues consumed producing forest products Forest_CANresidProduct Carbon from Canadian residues consumed producing forest products Carbon_For_Fuel Carbon emissions from forest use of fossil fuel Dev_Land Carbon on land after it moves into developed use AgSoil_CropSequest Carbon in cropped agricultural soil AgSoil_PastureSequest Carbon in pastureland Carbon_AgFuel Carbon emissions from agricultural use of fossil fuels Carbon_Dryg Carbon emissions from grain drying Carbon_Fert Carbon emissions from fertilizer production Carbon_Pest Carbon emissions from pesticide production Carbon_Irrg Carbon emissions from water pumping Carbon_Ethl_Offset Carbon emission offset by conventional ethanol production Carbon_Ethl_Haul Carbon emissions in hauling for conventional ethanol production Carbon_Ethl_Process Carbon emissions in processing of conventional ethanol production Carbon_CEth_Offset Carbon emission offset by cellulosic ethanol production Carbon_CEth_Haul Carbon emissions in hauling for cellulosic ethanol production Carbon_CEth_Process Carbon emissions in processing of cellulosic ethanol production Carbon_BioElec_Offset Carbon emission offset from bioelecticity production Carbon_BioElec_Haul Carbon emissions in hauling for bioelecticity production Carbon_BioElec_Process Carbon emissions in processing of for bioelecticity production Carbon_Biodiesel_Offset Carbon emission offset from biodiesel production Carbon_Biodiesel_Haul Carbon emissions in hauling for biodiesel production Carbon_Biodiesel_Process Carbon emissions in processing of biodiesel production Methane_Liquidmanagement Methane from emission savings from improved manure technologies Methane_EntericFerment Methan e from enteric fermentation Methane_Manure Methane from manure management (continued)A-25 Table A-6. Categories of GHG Sources a nd Sinks Included in FASOM (continued)

Methane_RiceCult Methane from rice cultivation Methane_AgResid_Burn Methane from agricultural residue burning Methane_BioElec Methane emissions of bioma ss power plants below coal power plants Methane_Biodiesel Methane emissions from biodiesel production Methane_Ethl Methane emission savings from corn ethanol processing Methane_CEth Methane emission savings from cellulosic ethanol processing NitrousOxide_Manure Livestock manure practices under managed soil categories under AgSoilMgmt NitrousOxide_BioElec Nitrous oxide emissions of biomass power plants over coal power plants NitrousOxide_Biodiesel Nitrous oxide emissions from biodiesel production NitrousOxide_Ethl Nitrous oxide emission savings from corn ethanol processing NitrousOxide_CEth Nitrous oxide emissions from cellulosic ethanol processing NitrousOxide_Fert Nitrogen fer tilizer application practices under managed soil categories under AgSoilMgmt NitrousOxide_Pasture Nitrous oxide emissions from pasture NitrousOxide_Sludge Emissions from se wage sludge used as crop fertilizer NitrousOxide_Nfixing Emissions from nitrogen-fixing crops NitrousOxide_CropResid Emissions from crop residue retention NitrousOxide_Histosol Emissions from temperate histosol area a NitrousOxide_Volat Indi rect soils volatilization NitrousOxide_Leach Indir ect soils leaching runoff NitrousOxide_AgResid_Burn Agricultural residue burning

a Histosols are soils that are composed primarily of organi c materials and that form in settings such as wetlands

where restricted drainage inhibits the decomposition of plant and animal remains, which enables these organic materials to accumulate over time. Unlik e IPCC guidance for other sources of N 2 O, direct emissions for histosols are based on area rather than annual N inputs . Emissions were assumed to equal 8 kg N 2 O-N per hectare for cultivated temperate histosols. In addition, changes in sequestration for lands that move out of fore stry and agricultural production into some form of developed usage such as housing, shopping cen ters, roads, and so forth are tracked in the model. In the subsections below, we discuss biomass clearing and changes in soil carbon sequestration—the two major categories of changes in carbon sequestration in the model—in more detail. A.1.5.1 Biomass Clearing One of the largest carbon pools is carbon sequestered in forests. Carbon is stored not just in the live and standing dead trees , but also in understory, forest floor and coarse woody debris, and forest soil. Harvesting timber will cause a reduction in carbon se questration, although some of the carbon that was in the harvested trees will c ontinue to be stored in forest products for some time afterward. If harvested stands are replanted, then th ere is little loss in fo rest soil carbon, andA-26 carbon sequestration in trees planted in that stand will increase over time. However, land converted from forestry to agricultural or other uses will have a much greater permanent reduction in carbon sequestration. We summarize the forest car bon accounting procedures used in FASOM below in Section A.1.5.1.1. 102
In addition, converting grasslands (pasture land or land in the CRP) to crop production typically results in a reduction in carbon se questration per acre. FASOM tracks dynamic changes in soil carbon sequestration associated with land conversion. These carbon accounting calculations are describe d in Section A.1.5.1.2.
A.1.5.1.1 Forest Carbon Accounting Forest carbon accounting in FASOM follo ws the FORCARB model developed by the U.S. Forest Service and used in the period ic aggregate assessme nts of forest carbon sequestration. Tree carbon is the largest forest carbon pool and is modeled as a function of three factors: (1) merchantable volume, (2) the ra tio of growing stock volume to merchantable volume, and (3) and parameters of a forest vo lume-to-biomass model developed by U.S. Forest Service researchers. 103 Harvest age is allowed to vary ; thus, the growth of existing and regenerated/afforested stands must be modeled. Timber growth and yield data are included for existing stands, reforested stands, and afforested lands that track the vo lume of wood in each unharvested stand, which, in turn, is used in co mputing forest carbon sequestration. These data indicate the wood volume per acre in unharvested timber stands for each timber stand strata (e.g., a stand giving location, forest t ype, management intensity class) by age cohort. The data used are derived largely from the U.S. Forest Service RPA modeling system. 104 Merchantable volume, by age, on each representative stand is obtained fr om the timber growth and yield tables included in FASOM. The volume factors and biomass mo del parameters vary by species and region and are obtained from 105 , 106 and Smith et al. (2003). 107

102 See Adams et al. (2005) for additional detail on the FASOM forest carbon accounting procedures. 103 Smith, J., L.S. Heath, and J. Jenkins. 2003. Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U. S. Forests. Newton Square, PA: USDA Forest Services. 104 Haynes, R.W. (Technical coordinator). 2003. An Analysis of the Timber Situation in the United States: 1952 to 2050 . General Technical Report PNW-GTR-560. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 254 p. 105 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333. 106 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest man agement opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25.A-27 Carbon in live and standing dead trees is calc ulated using the parameters of the forest volume-to-biomass model equations for live a nd dead tree mass densities (above- and belowground) in Smith et al., 108 weighted for the FASOM region/for est type designations. Forestland area data reported by the RPA assessment 109 are used to calculate the appropriate weights. Birdsey’s assumption that the mass of wood is a pproximately 50% carbon is used to derive the associated levels of carbon. 110
Soil carbon is the second-largest carbon pool of carbon. Treatment of soil carbon follows Birdsey 111 , 112 and recent work by Heath, Birdsey, and Williams. 113 FASOM computes soil carbon profiles using soil carbon data over time from Birdsey. 114 , 115 As Heath, Birdsey, and Williams 116 noted, little change in soil carbon occurs if forests are regenerated immediately after harvest. As a result, FASOM assumes soil carbon on a reforest ed stand remains at a steady-state value. Currently, the age that this value is reached is assumed to be the minimum harvest age for FASOM region/forest type. This assumption is generally consistent with the ages at which steady-state levels of soil carbon are achieved in Birdsey. 117 , 118 Afforested land coming from crop

107 Smith, J., L.S. Heath, and J. Jenkins. 2003. Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U. S. Forests. Newton Square, PA: USDA Forest Services. 108 Ibid. 109 Miles, P., U.S. Forest Service, elect ronic file 2002_RPA_Tables.xls to Brooks Depro, RTI International, July 30, 2003. 110 Birdsey, R.A. 1992. Changes in forest carbon stor age from increasing forest ar ea and timber growth. In: Sampson, R.N.; Dwight, H.; eds. Forest and Global Change, Volume 1: Opportunities for Increasing Forest Cover . Washington, DC: American Forests: 23-29. 111 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333. 112 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest man agement opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25. 113 Heath, L.S.; Birdsey, R.A.; Williams, D.W. 2002. “Met hodology for estimating soil carbon for the forest carbon budget model of the United States, 2001.” Environmental Pollution , 116: 373-380. 114 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333. 115 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest man agement opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25. 116 Heath, L.S.; Birdsey, R.A.; Williams, D.W. 2002. “Met hodology for estimating soil carbon for the forest carbon budget model of the United States, 2001.” Environmental Pollution , 116: 373-380. 117 Birdsey, R.A. 1996a. “Regional Estimates of Timber Volume and Forest Carbon for Fully Stocked Timberland, Average Management after Cropland and Pasture Revision to Forest.” In Forests and Global Change. Volume II: Forest Management Opportunities for Mitigating Carbon Emissions , D. Hair, and Neil R. Sampson, eds., Washington, DC: American Forests: 309-333.A-28 or pasture use start with the in itial soil carbon value for that land/region combination reported by the Century Model, which was deve loped by Colorado State University. 119 The land then accumulates carbon until reaching the steady-state va lue for forests of the type planted in the region afforestation takes place (w here steady state is assumed to be reached at the minimum harvest age in FASOM for that region/forest type).
Forest floor carbon constitutes the third larg est carbon storage pool, but is much smaller than tree or soil carbon pools. Smith and Heath 120 developed a model estimating forest floor carbon mass and it forms the basis for forest floor carbon estimates in FASOM. The model’s definition of forest floor exclude s coarse woody debris materials; that is, pieces of down dead wood with a diameter of at least 7.5 cm that are not attached to trees. 121 In order to account for this material, coarse woody debris is assumed to be a fixed fraction of live tree carbon based on ratios of coarse woody debr is carbon to live tree carbon. 122 This value is then added to the forest floor carbon values generated by Smith and Heat h’s forest floor model. The model for net accumulation of forest floor carbon is a continuo us and increasing function of age, although the rate of accumulation eventually approaches zero (i.e ., forest floor carbon reach es a steady state).
Understory vegetation comprises the smalle st component of total carbon stock and includes all live vegetation excep t trees larger than seedlings. FASOM assumes that understory carbon is a fixed fraction of liv e tree carbon and uses published ra tios reported in U.S. EPA 123 as the basis for these calculations.
When timber is harvested, FASOM tracks th e fate of the carbon that had been sequestered on the harvested land. Figure A-3 summarizes the disposition of carbon following harvest. To calculate carbon in harvested logs , cubic feet of roundwood (the units in which

118 Birdsey, R.A. 1996b. “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In Forests and Global Change. Volume II: Forest man agement opportunities for mitigating carbon emissions, Chapter 1, D. Hair and Neil R. Sampson, eds. Washington, DC: American Forests: 1-25. 119 The current version of the CENTURY agroecosystem model simulates carbon, nitrogen, phosphorus, and sulfur dynamics through an annual cycle ove r time scales and centuries and mill ennia. CENTURY is capable of modeling a wide range of cropping system rotations and tillage practices for analysis of the effects of management and climate on agroecosystem productivity and sustainability. The model has undergone numerous enhancements since the original version developed in Parton, W.J., D.S. Schimel, C.V. Cole, and D.S. Ojima. 1987. Analysis of factors controlling soil organic matter le vels in Great Plains grasslands. Soil Science Society of America Journal 51:1173-1179. 120 Smith, J., and L.S. Heath. 2002. A Model of Forest Floor Carbon Mass for United States Forest Types. Newton Square, PA: USDA Forest Service. 121 Smith, J., USDA Forest Service, email communication to Brian Murray, RTI International. August 11, 2004. 122 U.S. Environmental Protection Agency. 2003. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2001 . Annex O. 123 U.S. Environmental Protection Agency. 2003. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2001 . Annex O.A-29 timber is quantified in th e model) is converted into metric tons of carbon using factors reported in Skog and Nicholson. 124 These factors vary by region and are reported for logs coming from an aggregate softwood and hardwood stand. They ex clude carbon in logging residue left onsite. Logging residue is tracked separately in the fore st floor carbon pool desc ribed above. Table A-7 presents an example of carbon disposition over time based on FASOM accounting procedures. 125

Figure A-3. Carbon Disposition after Timber Harvest Source: Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG Conceptual Structure and

Specification: Documentation.” Available athttp://agecon2.tamu.edu/people/faculty/mccarlbruce/papers/1212FASOMGHG_doc.pdf.

124 Skog, K, and G. Nicholson. 2000. Carbon Sequestration in Wood and Paper Products. In The Impact of Climate Change on American Forests, Chapter 5, L. Joyce and R. Birdsey, eds. USDA Forest Service, General Technical Report RMRS-GTR-59, Chap. 5:79-88. 125 Depro, B.M., B.C. Murray, R.J. Alig, and A. Shanks. 2008. Public land, timber harvests, and climate mitigation: Quantifying carbon sequestration potential on U.S. public timberlands. Forest Ecology and Management 255(3- 4):1122-1134. Carbon Disposition After Harvest

Harvested Logs Removed from site

Primary FASOM Manufacturing

Wood and Paper Products (i = 1,...,13)

Mill Residue

Fuelwood Logging Residue Down Dead Wood (Coarse Woody Debris) Accounted for in on-site module

Wood and Paper Product Disposition over Time

Emission Fuel Offset Emission In-product or Landfill

Secondary ManufacturingA-30 Table A-7. Example of Disposition Patterns of Harvested Wood by Region and Harvest Type, 100-Year Period: Southeast

Years after Harvest Region Type Product Disposition 0 10 20 30 40 50 60 70 80 90 100 Southeast Softwood Pulpwood Products 0.30 0.07 0.05 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.02 Southeast Softwood Pulpwood Landfills 0.00 0.16 0.16 0.16 0.10 0.14 0.14 0.13 0.12 0.11 0.11 Southeast Softwood Pulpwood Energy 0.44 0.45 0.45 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 Southeast Softwood Pulpwood Emissions 0.26 0.32 0.34 0.35 0.41 0.37 0.38 0.39 0.40 0.41 0.41

Southeast Softwood Sawtimber Products 0.47 0.28 0.24 0.21 0.18 0.17 0.15 0.14 0.13 0.13 0.12
Southeast Softwood Sawtimber Landfills 0.00 0.13 0.16 0.17 0.18 0.19 0.19 0.19 0.18 0.18 0.18 Southeast Softwood Sawtimber Energy 0.38 0.40 0.40 0.40 0.40 0.40 0.41 0.41 0.41 0.41 0.41
Southeast Softwood Sawtimber Emissions 0.15 0.19 0.20 0.22 0.24 0.24 0.25 0.26 0.28 0.28 0.29

Southeast Hardwood Pulpwood Products 0.30 0.07 0.05 0.04 0.04 0.03 0.03 0.03 0.03 0.03 0.03 Southeast Hardwood Pulpwood Landfills 0.00 0.16 0.16 0.15 0.15 0.14 0.13 0.12 0.12 0.11 0.10 Southeast Hardwood Pulpwood Energy 0.39 0.40 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 Southeast Hardwood Pulpwood Emissions 0.31 0.37 0.38 0.40 0.40 0.42 0.43 0.44 0.44 0.45 0.46

Southeast Hardwood Sawtimber Products 0.27 0.12 0.08 0.07 0.06 0.05 0.05 0.04 0.04 0.04 0.04
Southeast Hardwood Sawtimber Landfills 0.00 0.11 0.13 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.12 Southeast Hardwood Sawtimber Energy 0.42 0.43 0.43 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44
Southeast Hardwood Sawtimber Emissions 0.31 0.34 0.36 0.35 0.36 0.37 0.38 0.39 0.39 0.39 0.40 Note: These are proportions of the harvested stock allocated to each pool in the years following harvest. Column

totals may not sum to one due to independent rounding. Source: Depro, B.M., B.C. Murray, R.J. Alig, and A. Shan ks. 2008. Public Land, Timber Harvests, and Climate

Mitigation: Quantifying Carbon Sequestration Potential on U.S. Public Timberlands. Forest Ecology and Management 255(3-4):1122-1134. Harvested logs removed from site are c onverted into three types of outputs through primary manufacturing processes: FASOM wood and paper produc ts, mill residues, and fuel wood. The fate of each of these outputs is discussed in turn below.

FASOM contains the followi ng 13 wood and paper products:

 softwood sawlogs for exportA-31  hardwood sawlogs for export

 softwood lumber

 softwood plywood

 oriented strand board

 hardwood lumber

 hardwood plywood

 softwood miscellaneous products

 hardwood miscellaneous products

 softwood used in non-OSB reconstituted panel

 hardwood used in non-OSB reconstituted panel

 softwood pulpwood

 hardwood pulpwood The distribution of product ca rbon changes over time and FASOM tracks the fate of product carbon for each end-use using tw o pools: carbon remaining in-product and carbon leaving the product (Figure A-4). Carbon that leaves the pro duct ultimately makes its way to emissions or is permanently sequestered in landfills.
The fraction remaining in the product is based on a model specifying half-life values for a set of end-use categories (Table A-8). 126 The half-life represents the time it takes for approximately half of the product to decompose. For instance, carbon that is stored in paper products is assumed to have a relatively short ha lf-life, with 50% of carbon decomposing within two years, whereas carbon stored in wood used for single-family homes has a half-life of 100 years. These values from Skog and Nicholson 127 were mapped to FASOM wood and paper products categories, weighting by wood and paper product use in various end uses.

126 Skog, K, and G. Nicholson. 2000. Carbon Sequestration in Wood and Paper Products. In The Impact of Climate Change on American Forests, Chapter 5, L. Joyce and R. Birdsey, eds. USDA Forest Service, General Technical Report RMRS-GTR-59, Chap. 5:79-88. 127 Ibid.A-32 Figure A-4. Wood and Paper Product Carbon Disposition Source: Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG Conceptual Structure and

Specification: Documentation.” Available at http://agecon2.tamu.edu/people/faculty/ mccarl-bruce/papers/1 212FASOMGHG_doc.pdf. As shown in Figure A-4, carbon leaving the product pool moves to either the emissions or landfill pools. 128 Skog and Nicholson 129 assumed that 67% of car bon leaving the wood product pool and 34% of carbon leaving th e paper product pool goes to landf ills. The remainder of the carbon leaving the wood and paper product pools goes into CO 2 emissions to the atmosphere.
In addition, FASOM tracks the fate of mill residue using two different pools. The first is for mill residue that is used as an intermed iate input in the production of wood and paper products. This carbon is tracked using the appropriate product categ ory as described above. The second pool is for carbon in mill residue that is bu rned for fuel, with the fraction burned in each

128 There are two landfill pools that are tracked: perman ent and temporary. Carbon in permanent landfills is sequestered forever, but carbon in temporary landfills d ecays and is eventually releas ed as emissions. The model assumes that approximately 77% of wood products and 44% of paper products going into landfills remain permanently sequestered. The rest is eventually released to the atmosphere as emissions based on an assumed half-life of 14 years. 129 Skog, K, and G. Nicholson. 2000. Carbon Sequestration in Wood and Paper Products. In The Impact of Climate Change on American Forests, Chapter 5, L. Joyce and R. Birdsey, eds. USDA Forest Service, General Technical Report RMRS-GTR-59, Chap. 5:79-88. Total Carbon in Wood and Paper Products

Emission

Carbon remaining in product Landfill

Landfill (Permanent) Landfill (Temporary) Carbon leaving productA-33 Table A-8. Half-life for Forest Products in End Uses

End Use or Product Half-Life in Years Paper 2 New residential construction Single family 100 Multifamily 70 Mobile homes 12 Residential upkeep & improvement 30 New nonresidential construction All ex. railroads 67 Railroad ties 12 Railcar repair 12 Manufacturing Household furniture 30 Commercial furniture 30 Other products 12 Shipping Wooden containers 6 Pallets 6 Dunnage etc. 6 Other uses for lumber and panels 12 Uses for other industrial timber products 12 Exports 12 Source: Adams, D., R. Alig, B.A. McCarl, and B.C. Murray. 2005. “FASOMGHG Conceptual Structure and

Specification: Documentation.” Available at http://agecon2.tamu.edu/people/faculty/ mccarl-bruce/papers/1 212FASOMGHG_doc.pdf. region based on Smith et al. 130 It was assumed that one-third of mill residue burned is used to offset fossil fuels.
Harvested fuel logs and the associated carbon are used as to produce energy at mills. For fuel wood, FASOM assumes that 100% of fuel wood burned in the sawtimber and pulpwood production process is used to offset fossil fuels.
A.1.5.1.2 Effects of Changes in Forest Area on Carbon Storage In FASOM, land used in forestry can move to agriculture or develope d use, resulting in a dynamic change in carbon storage levels on th e previously forested land. When land moves

130 Smith, W.B., J.S. Vissage, D.R. Darr, and R.M. Sheffield. 2001. Forest Resources of the United States, 1997. US Forest Service General Technical Re port NC-219. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Research Station.A-34 from forestry to one of these other uses, tw o carbon pools associated with the land are tracked (Figure A-5):

 residual forest floor carbon

 soil carbon (in agricultural or in developed use)

Figure A-5. Disposition of On-Sit e Carbon after Deforestation

For agriculture and developed land uses, the path of residual forest floor carbon stock is assumed to be the same as for the forest floor carbon prof ile after a harvest, which is described above. This model of decay is based on the average forest floor of mature forests and regional averages for decay rates, as described in Smith and Heath. 131
The approaches used in FASOM to account for transition paths of soil carbon following deforestation are defined as follows. When fo rested land switches to agriculture, soil carbon levels are assumed to be consistent with Cent ury Model data on agricultural soil carbon for the appropriate category of agricultu ral land and do not vary over time. In the case of timberland

131 Smith, J. and L.S. Heath. 2002. A Model of Forest Floor Carbon Mass for United Stat es Forest Types. Newton Square, PA: USDA Forest Service. T Years in Agriculture or Developed Land Use Residual Forest Floor

Soil Carbon Total On-Site Carbon Tons of C/acreA-35 switching to developed land uses, the soil carbon levels are assumed to be consistent with the steady-state value of the minimum harvest age. As for land moving to agriculture, the soil carbon level does not vary over time.
In addition, the carbon stored in the forest products produced from the harvest that cleared the land is tracked over time as described in the previous section. The change in forest sequestration associated with a policy change is calculated as the difference in carbon sequestration in each of the ca rbon pools tracked between the m odel simulation with the policy in place and the baseline model simulation. Thus, any potential fo regone sequestration that may be associated with reallocation of forestland to ag ricultural land in response to a change in policy would be captured in the calcu lation of the difference between the forest carbon sequestration values under baseline and policy conditions. A.1.5.2 Soil Carbon Sequestration Agricultural soil carbon sequest ration depends on management activities that influence carbon storage per acre. Key factors that affect soil carbon sequestration include the following:

 Intensity of agricultural tillage . Agricultural soils have traditionally been tilled to create a suitable seedbed, reduce weed competition, and re move restrictions to crop root growth. However, by loosening the soil, tillage breaks up soil aggregates and increases the exposure of soil organic ma tter to oxygen, which speeds oxidation and results in reduced soil carbon w ith an associated release of CO 2 into the atmosphere. The use of tillage alternatives that re duce soil disturbance and therefore reduce oxidation of soil organic matter will incr ease soil carbon sequestration. Reduced tillage practices also leave crop residues on the soil, thereby potentially increasing carbon inputs. Typically, reduced tillage involves movement from intensive tillage practices such as moldboard plowing to conservation or zero tillage practices. 132

 Irrigation status . Based on data from the Century model, there are differences in soil carbon sequestration per acre for a gi ven region between irrigated and dryland cropland systems. 133 For sites receiving irrigation, th e increased yields are expected to increase biological activity and hen ce soil carbon sequest ration throughout the year. FASOM incorporates these differences in soil carbon storag e within tables of soil carbon storage that vary by irrigation status.

132 In addition to changes in soil carbon, there are additional changes in emissions associated with tillage changes that are tracked in FASOM. Less-inte nsive tillage typically reduces emissions from fossil fuel use by tractors, but may result in increases in the use of pesticides and changes in the rate of fertilization, which can increase emissions associated with agricultura l chemical production and use. FASO M tracks these indirect effects on GHG emissions. 133 All pastureland and CRP land in FASOM are assumed to be produced in dryland systems.A-36  Relative abundance of grasslands . Normally, pasturela nds and land in the CRP experience less soil disturbance than activel y tilled croplands a nd tend to store more carbon per acre. Thus, changes in the di stribution of land be tween pastureland, cropland, and land in the CRP will affect agricultural soil carbon sequestration.

 Mix of annuals versus perennials . Because perennial crops would not be tilled on an annual basis, there will typically be a reduction in soil dist urbance relative to actively tilled annual crops. By defin ition in FASOM, perennial crops such as switchgrass, hybrid poplar, and willow are produced under zero tillage. Similarly, as described in the previous section on forest carbon sequestration, forest soils have higher rates of carbon seque stration per acre. Baseline carbon storage is estimated from the baseline distribution of land across tillage practices, irrigation status, la nd use, and cropping patterns, assuming carbon sequestration rates are equal to those at equilibrium. Soil carbon acc ounting for changes in tillage practices is done as if land remained in the initial tillage forever.
Changes in soil carbon due to changes in ti llage, irrigation status, or land use are generally assumed to take place over a number of years as the soil carbon levels adjust to a new equilibrium. In FASOM, soil carbon levels are assumed to reach a new equilibrium after 25 years, although almost 94% of the adjustment takes place within 15 ye ars (see Figure A-6). 134
Because movement of soil carbon sequestra tion towards equilibrium levels is not constant over time, FASOM yields non-uniform changes in soil carbon consistent with the generally accepted scientific fi nding that carbon sequestered in an ecosystem approaches steadystate equilibrium under any manage ment alternative. As shown above, the rate of change in carbon storage decreases over time and eventual ly reaches zero at the new equilibrium (saturation). 135 Soil carbon per acre may increase or decrease depending on the land use change or change in land management taking place. Fo r instance, Figure A-7 presents examples of changes in soil carbon for the Northern Californi a region in FASOM. In the cases shown, soil carbon initially decreases wh en moving from the initial equilibriu m state to a new state, but then it increases per acre over time until reaching a new equilibrium at a higher level of carbon storage per acre.

134 There is an immediate jump in carbon storage in year 0 due to changing tillage, irrigation, and/or land use that depends on the initial state and the new state. The dyna mics discussed and shown in Figure A-5 refer to the change over time from the initial state under new manage ment/land use conditions to the equilibrium for that state. 135 West, T.O., and W.M. Post. 2002. “Soil Organic Carbon Sequestration Rates by Tillage and Crop Rotation.” Soil Science Society of America Journal 66:1930-1936.A-37 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Year 0 Year 5 Year 10 Year 15 Year 20 Year 25% Adjustment over Time

Figure A-6. Percentage Adjustment over Ti me to New Soil Carbon Equilibrium Following Change in Land Use or Management

To reflect the timing of changes in soil carbon, FASOM output on GHG emissions associated with increases or decreases in agricultural soil carbon represents changes in cumulative soil carbon relative to the baseline. Values reported reflect all changes in soil carbon that are taking place in a given period, including those changes associated with land use change or alterations in management practices that o ccurred in earlier periods, but where soil carbon levels continue to adjust to th eir new equilibrium values. For instance, emissions from changes in soil carbon sequestration repor ted for 2022 would reflect the a ppropriate portion of emissions related to all changes in tillage, irrigation status, land use change, and cropping patterns that have taken place at all points between the baseline and 2022 based on the assumed rate of saturation over time. For analogous reasons, changes in land use in 2022 would continue to affect soil carbon emissions calculated in the model for the next 25 years.A-38 -4 -2 0 2 4 6 8

Year 0 Year 5 Year 10 Year 15 Year 20 Year 25 Year 30Change in Soil C (tons/acre)

Dryland, Conventional Tillage to Irrigated No-Till Dryland, Conventional Tillage to Pasture Figure A-7. Change in Soil Carbon for FASOM Northern California Region for Selected Changes in Land Use and Management

For the analysis of the RFS2 renewable fu els volumes, the contro l case reduces net soil carbon sequestration and increases CO 2 emissions. There are increases in cropland soil carbon relative to the reference case, re flecting the reduced conversion of cropland to pasture over time relative to the baseline simulation when demand for agricultural commodities is increased by the policy. However, reductions in pastureland soil carbon more th an outweigh the increases in cropland soil carbon, resulting in net reductions in soil carbon sequestration under the control

case. A.2 Chemical and Energy Inputs and GHG Emissions In addition to changes in carbon storage asso ciated with changes in forest carbon and agricultural soil carbon sequestrati on, agricultural chemical and energy use also result in GHG emissions that are calculated and tracked within FASOM. Key assumptions used in calculating these emissions within the model are de scribed in the subsections below.A-39 A.2.1 Fertilizer, Pesticide, and Lime Use The crop budgets included in the FASOM model include data on input use that varies by crop, management practices, and region. There is of ten considerable variation in the inputs used per acre, which implies that total input use and associated GHG emissions and other environmental impacts will be affected by changes in crop mix and management practices that result under alternative policies.

Tables A-9 and A-10 summarize the use of nitrogen and phosphorus fertilizers, respectively, based on average ap plication per acre by crop and irrigation status for major crops in each of the FASOM market regions wher e agricultural crops are produced. FASOM calculates energy use in fertilizer production as a function of the quantity of fertilizer produced.
In addition, both CO 2 emissions from fertilizer production and N 2 O emissions resulting from nitrogen fertilizer use are calculated in the model based on the quantity of fertilizer produced and applied as discussed in Section A.2.2. Becau se residue collection increases fertilizer requirements due to the removal of nutrients, 136 regional average fertiliz er use is reported both with and without residue removal in these tabl es for those crops where residue removal is included as a possibility in the model. Table A-9. Nitrogen Fertilizer Usage (lbs/acre ) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland

Barley (no residue harvest) 66.3 45. 5 63.3 75.1 59.9 41.1 43.3 71.9 72.6 45.4 Barley (with residue

harvest) 71.0 49.7 67.4 80.5 64.7 44.3 46.8 77.7 78.5 49.0 Corn (no residue harvest) 130.8 103.6 103.8 91.8 NA NA 79.3 125.5 109.5 94.9 Corn (with residue

harvest) 143.0 113.8 115.2 101. 0 NA NA 86.7 136.7 119.5 101.7 Cotton 100.9 55.0 NA NA NA NA NA 88.2 81.4 43.6 Hay 162.0 173.4 173.4 122.0 173. 4 173.4 165.3 165.2 155.2 169.1 Hybrid Poplar 171.5 173.5 173. 5 123.3 173.5 NA NA 161.3 151.6 181.0 Oats (no residue harvest) 50.1 33. 7 44.4 47.5 49.1 59.7 30.3 39.1 53.0 36.3 Oats (with residue harvest) 53.8 36. 8 47.8 51.0 53.4 63.7 33.0 41.3 56.3 38.7 (continued)

136 Additional fertilizer application amounts are based on the GREET defaults, as described in the November 7, 2006 report by M. Wu, M. Wang, and H. Huo, "Fuel-Cycle Assessment of Selected Bioethanol Production Pathways in the United States"(ANL/ESD/06-7).A-40 Table A-9. Nitrogen Fertilizer Usage (lbs/acre ) by Crop, Irrigation Status, and Region, 2022 (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW Silage 137.7 75.7 123.7 173.0 179.9 143.0 87.9 172.7 180.7 167.3 Sorghum (no residue

harvest) 74.4 57.0 NA 67.0 NA 50. 2 34.1 88.1 50.3 57.1 Sorghum (with residue

harvest) 85.0 64.1 NA 75.7 NA 55. 9 39.1 97.4 56.7 62.5 Soybeans 7.8 7.2 3.5 7. 8 NA NA NA 4.9 8.2 6.3 Sugarbeets 110.0 NA 95.0 NA NA NA NA NA NA NA Switchgrass 100 100 100 100 NA NA 100 100 100 100 Wheat, Durham (no

residue harvest) NA 52.9 51.0 NA NA NA 41.0 NA NA NA Wheat, Durham (with

residue harvest) NA 55.4 54.8 NA NA NA 43.2 NA NA NA Wheat, hard red spring (no

residue harvest) NA 70.9 84.7 NA 66.0 NA 48.5 NA NA NA Wheat, hard red spring

(with residue harvest) NA 74.2 88.8 NA 70.3 NA 51.0 NA NA NA Wheat, hard red winter (no

residue harvest) 77.4 50.3 68. 1 73.8 NA 60.0 37.3 84.1 91.3 58.6 Wheat, hard red winter

(with residue harvest) 86.7 56.3 77.5 83.3 NA 66.3 42. 4 91.7 99.5 63.5 Wheat, soft white (no

residue harvest) NA NA NA NA 57.8 NA 86.4 NA NA NA Wheat, soft white (with

residue harvest) NA NA NA NA 64.2 NA 92.6 NA NA NA Willow 173.5 NA 173.4 123.3 NA NA NA NA 161 NA Irrigated

Barley (no residue

harvest) NA 63.9 NA NA 85.0 92.4 88.5 NA NA NA Barley (with residue

harvest) NA 69.4 NA NA 91.4 98.9 94.6 NA NA NA Corn (no residue harvest) NA 137.3 NA NA 214.3 197.1 129.5 141.8 NA 185.8 Corn (with residue

harvest) NA 151.4 NA NA 229.1 210. 9 141.0 151.3 NA 199.8 Cotton NA NA NA NA NA 109.3 117.7 115.2 NA 118.2 Hay NA 173.4 NA NA NA NA 171.2 NA NA NA Oats (no residue harvest) NA 48.0 NA NA 96.0 96.0 51.8 NA NA NA Oats (with residue

harvest) NA 51.0 NA NA 101.7 101.5 56.1 NA NA NA Rice 100.0 NA NA NA NA 156.0 NA 144.3 NA 142.3 Silage NA 222.0 NA NA NA NA 247.8 NA NA 230.0 (continued)A-41 Table A-9. Nitrogen Fertilizer Usage (lbs/acre ) by Crop, Irrigation Status, and Region, 2022 (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW Sorghum (no residue

harvest) NA 96.2 NA NA NA NA 77.1 82.0 NA 85.2 Sorghum (with residue

harvest) NA 106.8 NA NA NA NA 83.4 90.1 NA 93.3 Soybeans NA 5.3 NA NA NA NA NA 4.2 NA 9.9 Sugarbeets 140.0 98.4 140.0 NA 157.1 160.0 190.6 NA NA 200.0 Sugarcane NA NA NA NA NA NA NA 40.0 40.0 40.0 Wheat, Durham (no

residue harvest) NA 118.7 NA NA NA 98.0 176.8 NA NA NA Wheat, Durham (with

residue harvest) NA 124.1 NA NA NA 108.1 185.5 NA NA NA Wheat, hard red spring

(no residue harvest) NA 114.8 NA NA 122.0 NA 124.8 NA NA NA Wheat, hard red spring

(with residue harvest) NA 119.7 NA NA 130.4 NA 131.2 NA NA NA Wheat, hard red winter

(no residue harvest) NA 69.0 NA NA NA 117.0 81.5 75.0 NA 91.3 Wheat, hard red winter

(with residue harvest) NA 77.5 NA NA NA 129.6 91.8 83.5 NA 99.6 Wheat, soft white (no

residue harvest) NA NA NA NA 91.1 NA 73.8 NA NA NA Wheat, soft white (with

residue harvest) NA NA NA NA 102.9 NA 122.1 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM. Table A-10. Phosphorus Fertilizer Usage (lbs /acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland

Barley (no residue harvest) 10.2 5. 2 21.4 17.8 5.0 10.0 8.3 9.6 10.0 11.7 Barley (with residue

harvest) 12.6 7.4 23.5 20.6 7.5 11.7 10.1 12.6 13.1 13.5 Corn (no residue harvest) 22.9 27.6 40.1 36.2 NA NA 18.5 37.3 32.6 16.5 Corn (with residue harvest) 29.2 32.8 46.0 40.9 NA NA 22.3 43.0 37.8 20.0 Cotton 29.2 15.0 NA NA NA NA NA 36.2 41.0 14.5 Hay 17.0 19.0 18.2 15.3 18. 7 15.0 11.1 16.8 15.4 15.0 Oats (no residue harvest) 15.3 10. 0 10.6 15.0 13.0 14.8 10.2 14.4 15.0 14.7 (continued)A-42 Table A-10. Phosphorus Fertilizer Usage (lbs /acre) by Crop, Irrigation Status, and Region, 2022 (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW Oats (with residue harvest) 17.3 11. 6 12.3 16.8 15.2 16.9 11.6 15.5 16.7 15.9 Silage 25.0 12.4 23.4 24.7 20.5 20.0 10.3 25.0 24.1 22.5 Sorghum (no residue

harvest) 18.8 11.1 NA 15.0 NA 19. 9 11.5 23.7 21.2 18.6 Sorghum (with residue

harvest) 24.2 14.8 NA 19.5 NA 22. 8 14.1 28.5 24.5 21.3 Soybeans 6.7 13.7 7.7 5. 4 NA NA NA 16.1 21.4 7.0 Sugarbeets 22.0 NA 20.0 NA NA NA NA NA NA NA Switchgrass 40.0 40.0 40.0 40. 0 NA NA 40.0 40.0 40.0 40.0 Wheat, Durham (no residue

harvest) NA 14.5 5.0 NA NA NA 5.0 NA NA NA Wheat, Durham (with

residue harvest) NA 15.7 6.9 NA NA NA 6.1 NA NA NA Wheat, hard red spring (no

residue harvest) NA 24.5 25.9 NA 10.0 NA 20.4 NA NA NA Wheat, hard red spring

(with residue harvest) NA 26.1 28.0 NA 12.2 NA 21.7 NA NA NA Wheat, hard red winter (no

residue harvest) 17.7 17.7 9.9 10.0 NA 5. 0 8.2 15.0 15.2 18.1 Wheat, hard red winter

(with residue harvest) 22.5 20.9 14.7 14.9 NA 8. 2 10.8 19.0 19.4 20.6 Wheat, soft white (no

residue harvest) NA NA NA NA 5.2 NA 13.8 NA NA NA Wheat, soft white (with

residue harvest) NA NA NA NA 8.5 NA 17.0 NA NA NA Irrigated

Barley (no residue harvest) NA 10.0 NA NA 15.8 25.0 14.9 NA NA NA Barley (with residue harvest) NA 12.8 NA NA 19.1 28.4 18.0 NA NA NA Corn (no residue harvest) NA 31.3 NA NA 41.3 25.0 26.6 25.0 NA 39.7 Corn (with residue harvest) NA 38.5 NA NA 48.9 32.1 32.5 29.9 NA 46.9 Cotton NA NA NA NA NA 35.0 16.5 30.7 NA 24.7 Hay NA 30.0 NA NA NA NA 27.4 NA NA NA Oats (no residue harvest) NA 15.0 NA NA 25.0 25.0 17.3 NA NA NA Oats (with residue harvest) NA 16.5 NA NA 27.9 27.8 19.5 NA NA NA Rice 20.0 NA NA NA NA 35.0 NA 20.0 NA 20.0 Silage NA 30.5 NA NA NA NA 35.0 NA NA 30.0 Sorghum (no residue harvest) NA 35.0 NA NA NA NA 26.4 35.0 NA 35.0 Sorghum (with residue harvest) NA 40.5 NA NA NA NA 29.7 39.2 NA 39.2 Soybeans NA 13.5 NA NA NA NA NA 16.6 NA 15.0 Sugarbeets 50.0 26.7 50.0 NA 50.0 50.0 46.5 NA NA 50.0 (continued)A-43 Table A-10. Phosphorus Fertilizer Usage (lbs /acre) by Crop, Irrigation Status, and Region, 2022 (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW Sugarcane NA NA NA NA NA NA NA 50.0 50.0 50.0 Wheat, Durham (no residue

harvest) NA 32.5 NA NA NA 10.0 24.1 NA NA NA Wheat, Durham (with residue

harvest) NA 35.3 NA NA NA 15.2 28.6 NA NA NA Wheat, hard red spring (no

residue harvest) NA 39.0 NA NA 5.3 NA 25.0 NA NA NA Wheat, hard red spring (with

residue harvest) NA 41.5 NA NA 6.4 NA 28.3 NA NA NA Wheat, hard red winter (no

residue harvest) NA 24.7 NA NA NA 10.0 14.5 15.0 NA 19.1 Wheat, hard red winter (with

residue harvest) NA 29.0 NA NA NA 16.5 19.8 19.4 NA 23.4 Wheat, soft white (no residue

harvest) NA NA NA NA 5.2 NA 18.2 NA NA NA Wheat, soft white (with residue

harvest) NA NA NA NA 13.6 NA 22.4 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM. The three primary categories of pesticides that are tracked in FASOM are herbicides, insecticides, and fungicides. FASOM crop budgets contain hundreds of different active ingredients. However, for the purposes of calculating energy us e requirements and CO 2 emissions associated with pesticide producti on, FASOM uses the total quantity of active ingredients applied. Thus, we summarize the use of pesticides in terms of the combined use of active ingredients in pounds per acre. Tables A-11, A-12, and A-13 present data on the use of these three categories of chemi cals, respectively, by crop and FASO M region on a per acre basis.
In addition, lime is used as an input in th e production of some crops and is included in those crop budgets in FASOM. Although FASOM does not directly calculate CO 2 emissions from lime production, the model does provide info rmation on the amount of lime used. Thus, CO 2 emissions from this category can be calcu lated based on the quantity of lime used and emissions factors. Table A-14 presents average lime use per acre for crops that use lime as an input in the model. We calculate GHG emi ssions from lime use based on GREET assumptions for limestone production and CO 2 emissions associated with calcining limestone to lime.A-44 Table A-11. Herbicide Usage (pounds of active ingredients/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland Barley 0.04 0.04 0.04 0.04 0.03 0.03 0.04 0.04 0.04 0.04 Corn 2.91 2.36 2.34 2.85 NA NA 1.96 2.16 2.63 2.40 Cotton 1.79 2.17 NA NA NA NA NA 2.76 2.55 1.77 Hay 0.01 0.01 0.01 0.01 0. 01 0.01 0.01 0.01 0.01 0.01 Hybrid poplar 0.01 0.01 0.01 0.01 0.01 NA NA 0.01 0.01 0.01 Oats 0.02 0.02 0.02 0.03 0. 02 0.02 0.02 0.02 0.02 0.02 Silage 2.47 1.49 2.00 1.22 0.85 0.85 0.84 1.42 1.11 1.07 Sorghum 0.11 0.26 NA 0.11 NA 0.11 0.11 0.11 0.11 0.08 Soybeans 1.18 1.29 1.03 0. 47 NA NA NA 1.18 0.73 0.27 Sugarbeets 1.85 NA 1.74 NA NA NA NA NA NA NA Switchgrass 0.01 0.01 0.01 0. 01 NA NA 0.01 0.01 0.01 0.01 Wheat, Durham NA 0.89 0.13 NA NA NA 0.16 NA NA NA Wheat, Hard Red Spring NA 0.76 0.58 NA 0.16 NA 0.96 NA NA NA Wheat, Hard Red Winter 0.24 0.23 0.26 0.22 NA 0.11 0. 34 0.27 0.29 0.24 Wheat, Soft White NA NA NA NA 0.49 NA 0.41 NA NA NA Willow 0.01 NA 0.01 0.01 NA NA NA NA 0.01 NA Irrigated Barley NA 0.04 NA NA 0.03 0.03 0.04 NA NA NA Corn NA 2.44 NA NA 1.78 1.78 1.92 2.48 NA 2.35 Cotton NA NA NA NA NA 2.57 2.53 2.61 NA 1.76 Hay NA 0.01 NA NA NA NA 0.01 NA NA NA Oats NA 0.02 NA NA 0.02 0.02 0.02 NA NA NA Rice 0.78 NA NA NA NA 0.78 NA 0.55 NA 0.83 Silage NA 1.93 NA NA NA NA 0.83 NA NA 1.12 Sorghum NA 0.27 NA NA NA NA 0.11 0.08 NA 0.08 Soybeans NA 1.32 NA NA NA NA NA 1.10 NA 0.28 Sugarbeets 1.85 2.11 1.85 NA 2.06 1.92 1.73 NA NA 1.85 Wheat, Durham NA 0.90 NA NA NA 0.12 0.26 NA NA NA Wheat, Hard Red Spring NA 0.78 NA NA 0.16 NA 0.38 NA NA NA Wheat, Hard Red Winter NA 0. 22 NA NA NA 0.11 0.27 0.27 NA 0.27 Wheat, Soft White NA NA NA NA 0.57 NA 0.41 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM.A-45 Table A-12. Insecticide Usage (pounds of acti ve ingredients/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland

Barley 0.01 0.02 0.01 0.01 0.01 0.01 0.02 0.01 0.01 0.01 Corn 0.69 0.47 0.49 0.74 NA NA 0.68 0.57 0.78 0.47 Cotton 4.78 4.76 NA NA NA NA NA 6.53 4.57 4.11 Hay 0.01 0.01 0.01 0.01 0. 01 0.01 0.01 0.02 0.02 0.02 Hybrid poplar 0.01 0.01 0.01 0.01 0.01 NA NA 0.02 0.02 0.02 Oats 0.01 0.01 0.01 0.01 0. 01 0.01 0.01 0.01 0.01 0.01 Silage 0.71 0.37 0.54 0.43 0.31 0.31 0.30 0.37 0.32 0.31 Sorghum 0.03 0.09 NA 0.03 NA 0.03 0.03 0.03 0.03 0.03 Soybeans 0.70 0.73 0.66 0. 26 NA NA NA 0.78 0.46 0.12 Sugarbeets 32.39 NA 32.07 NA NA NA NA NA NA NA Switchgrass 0.01 0.01 0.01 0. 01 NA NA 0.01 0.02 0.02 0.02 Wheat, Durham NA 0.39 0.05 NA NA NA 0.05 NA NA NA Wheat, Hard Red Spring NA 0.39 0.36 NA 0.03 NA 0.50 NA NA NA Wheat, Hard Red Winter 0.09 0.12 0.10 0.07 NA 0.03 0. 16 0.09 0.09 0.10 Wheat, Soft White NA NA NA NA 0.20 NA 0.15 NA NA NA Willow 0.01 NA 0.01 0.01 NA NA NA NA 0.02 NA Irrigated

Barley NA 0.02 NA NA 0.01 0.01 0.02 NA NA NA Corn NA 0.46 NA NA 0.48 0.48 0.64 0.48 NA 0.47 Cotton NA NA NA NA NA 5.28 5.30 5.04 NA 4.10 Hay NA 0.01 NA NA NA NA 0.01 NA NA NA Oats NA 0.01 NA NA 0.01 0.01 0.01 NA NA NA Rice 0.98 NA NA NA NA 0.98 NA 0.75 NA 1.07 Silage NA 0.60 NA NA NA NA 0.29 NA NA 0.33 Sorghum NA 0.09 NA NA NA NA 0.03 0.03 NA 0.03 Soybeans NA 0.78 NA NA NA NA NA 0.73 NA 0.12

Sugarbeets 32.39 32.60 32.39 NA 16.00 59.2 9 16.81 NA NA 32.39 Wheat, Durham NA 0.39 NA NA NA 0.04 0.10 NA NA NA Wheat, Hard Red Spring NA 0.40 NA NA 0.03 NA 0.17 NA NA NA Wheat, Hard Red Winter NA 0. 11 NA NA NA 0.03 0.11 0.10 NA 0.10 Wheat, Soft White NA NA NA NA 0.23 NA 0.15 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM.A-46 Table A-13. Fungicide Usage (pounds of acti ve ingredients/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland

Barley 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 Cotton 0.087 0.087 NA NA NA NA NA 0.112 0.065 0.087 Sugarbeets 30.637 NA 30.904 NA NA NA NA NA NA NA Wheat, Durham NA 0.018 0.006 NA NA NA 0.018 NA NA NA Wheat, Hard Red Spring NA 0.018 0.006 NA 0.008 NA 0.018 NA NA NA Wheat, Hard Red Winter 0.020 0.023 0.022 0.018 NA 0.006 0. 023 0.037 0.062 0.023 Wheat, Soft White NA NA NA NA 0.006 NA 0.005 NA NA NA Irrigated

Barley NA 0.001 NA NA 0.001 0.001 0.001 NA NA NA Cotton NA NA NA NA NA 0.087 0.087 0.086 NA 0.086 Rice 0.096 NA NA NA NA 0.096 NA 0.048 NA 0.108 Sugarbeets 30.780 30.822 30.668 NA 14.406 57.365 15.290 NA NA 30.780 Wheat, Durham NA 0.018 NA NA NA 0.006 0.018 NA NA NA Wheat, Hard Red Spring NA 0.018 NA NA 0.009 NA 0.010 NA NA NA Wheat, Hard Red Winter NA 0. 023 NA NA NA 0.006 0.023 0.023 NA 0.023 Wheat, Soft White NA NA NA NA 0.010 NA 0.005 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM. Table A-14. Lime Usage (tons of lime/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland Barley 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.07 0.00 Corn 0.11 0.00 0.00 0.26 NA NA 0.00 0.00 0.03 0.00 Cotton 0.00 0.00 NA NA NA NA NA 0.11 0.11 0.00 Hay 0.02 0.00 0.00 0.02 0. 00 0.00 0.00 0.11 0.10 0.00 Silage 0.00 0.00 0.00 0.26 0.00 0.00 0.00 0.08 0.08 0.00 Soybeans 0.11 0.00 0.00 0. 26 NA NA NA 0.00 0.04 0.00 Sugarbeets 0.13 NA 0.00 NA NA NA NA NA NA NA Wheat, Durham NA 0.00 0.20 NA NA NA 0.00 NA NA NA Wheat, Hard Red Spring NA 0. 00 0.20 NA 0.00 NA 0.00 NA NA NA Wheat, Hard Red Winter 0.14 0.00 0.00 0.50 NA 0.00 0. 00 0.03 0.07 0.00 Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM.A-47 A.2.2 Nitrous Oxide Emissions For the analysis of the proposed rule, FASOM estimated direct and indirect N 2 O emissions from nitrogen fertilizer application and nitrogen-fixing crops based on IPCC default factors. 137 However, recent research suggests N 2 O emissions may be significantly higher than those estimated based on current IPCC guidance, 138 with the exception of emissions from nitrogen-fixing crops, which are no longer considered as a direct source of N 2 O emissions in the 2006 IPCC guidance. In addition, em issions are more heterogeneous than can be captured using IPCC default emissions factors. Thus, to obtain more accurate estimates of N 2 O emissions that better reflect heterogeneity of emissions acr oss crops and production practices and that incorporate the entire N cycling process, EP A worked with the Natural Resource Ecology Laboratory at Colorado State University to conduct further analyses of N 2 O emissions and provide updated emissions data.
Specifically, Colorado State University provided se veral key refinements for a reanalysis of land use and cropping trends and GHG emissions in th e FASOM assessment, in cluding the following:

 Direct N 2 O emissions based on DAYCENT 139 simulations with an accounting of all nitrogen inputs to agricultural soils, including mineral N fertilizer, organic amendments, symbiotic N fixation, asymbiotic N fixation, crop residue N, and mineralization of soil organic matter. Co lorado State University provided (1) the total emission rate on a per-acre basis for each simulated conventional and bioenergy crop in the 63 FASOM regions and (2) total emissions for each N source.

 Indirect N 2 O emissions on a per-acre basis using results from DAYCENT simulations of volatilization, leaching and runoff of nitrogen from each conventional and bioenergy crop included in th e analysis for the 63 FASO M regions, combined with IPCC factors for the N 2 O emission associated with the simulated nitrogen losses. Rather than relying simply on the rate of fe rtilizer application and the IPCC default for N 2 O emissions per unit of fertilizer, we now account for N cycling within DAYCENT and can generate emissions estimates th at vary by region, crop, fertiliz er application rate, tillage, irrigation status, and residue removal rate based on a full accounting of all nitrogen inputs.

137 See the 2006 IPCC Guidelines for National Gr eenhouse Gas Inventories, Chapter 11, N 2 O emissions from managed soils. 138 Crutzen, P.J., A.R. Mosier, K.A. Smith, and W. Winiwarter. 2008. N 2 O Release from Agro -biofuel Production Negates Global Warming Reduction by Replacing Fossil Fuels. Atmospheric Chemistry and Physics 8:389-395. 139 The DAYCENT model is similar to the CENTURY model described earlier in this section, except that it is capable of simulating detailed daily soil water an d temperature dynamics and trace gas fluxes (CH 4 , N 2 O, NO x and N 2 ), which are not simulated in CENTURY.A-48 FASOM applies the estimates available fr om DAYCENT summary models for direct N 2 O emissions, nitrogen leaching/runoff and am monia volatilization, which were developed based on output from the DAYCENT simulation model using the U.S. national greenhouse gas inventory framework. Simulations were executed to track change s in emissions, leaching/runoff and volatilization for 30 years following a change in crop, land use, tillage, or crop residue removal rate. Models summarizing the rela tionships between emissions and cropping characteristics were developed for each FASOM region. The data contain parameter estimates for the effects of each predictor variable (e.g., ir rigation status, tillage, residue removal rate); with separate parameter estimates for crop and gr assland systems (hay is grouped with the crops because hay is considered a crop in the U.S. national GHG inventory framework). Below we briefly describe the procedures used in FASOM to generate estimates of N 2 O emissions.
A.2.2.1 Direct Emissions and Emissions Factors The primary category of direct N 2 O emissions is emissions resulting from the application of nitrogen fertilizer. Ch anges in FASOM direct N 2 O emissions from croplands result from either changes in crop acreage or crop mix. There are mitigation options included in the model that enable selection of alternatives such as re duced fertilization and other mitigation options, but these options are only expected to be select ed under policies creating incentives to reduce fertilizer N use. Thus, we do not examine those other mitigation options in this analysis. We discuss model calculation of direct N 2 O emissions from croplands below.
A.2.2.1.1 Nitrogen Fertilizer
To calculate direct N 2 O emissions from N inputs to croplands and grasslands (primarily fertilizer use), FASOM uses DAYCENT data on direct emissions per acre by crop and FASOM region. Estimates of emissions per acre from DAYCENT are defined as a function of the following variables:

 Crop or grass types: crop and grass types vary by region depending on the types simulated in the U.S. Soil N 2 O national inventory. This set of variables is treated as a set of indicator variables with a 1 assigned to the crop/grass of in terest and 0 for the rest to estimate emissions from each crop rela tive to the excluded cases of corn for crops and grass pasture for the grass category.

 Mineral fertilizer: this variab le is the natural log of th e amount of fertilizer applied per unit of area.

 Irrigation status: this variable is an indi cator variable indicating whether or not the crop or grass is irrigated. A value of 1 m eans the crop is irrigated, whereas a value of 0 means that the crop is not irrigatedA-49  Tillage practice: these variables are also treated as indicator variables representing tillage practice, with 0/1 vari ables for reduced tillage and no-tillage practices. If both reduced tillage and no-tillage variables are zero, then the resulting estimates correspond to conventional tillage. This vari able is only relevant for crop models.

 Land use: this set of variables is treated as indicator variables for land use change. There are 0/1 variables for the cases when land use change takes place from CRP land and from native land. If both the CRP and native land indicator va riables are 0, then the result represents no land use change (i.e ., shift in specific crop or grass on a given piece of land, but no change in broad land use category).

 Residue removal: this variable is the per centage of crop residue removed from the field (including removals for use as a cellulosi c feedstock). This variable can take on values between 0% and 100%, although remova l rates would be limited in practice by technical and sustainability constraints. This variable is not relevant for hay or grass systems.

 First-order interactions between the variab les listed above: first- order interactions between the variables were tested and those that were significant were included in the summary models.
The parameter values from DAYCENT modeling are then used to ge nerate estimates of direct N 2 O emissions per acre for each crop, regi on, and production practice combination available in the FASOM crop budgets.
A.2.2.2 Indirect Emissions and Emissions Factors
In addition to direct N 2 O emissions from croplands, ther e are several additional sources of indirect emissions, including emissions fr om volatilization, leaching, crop residues, and residue burning. FASOM calculation of each is described below.

A.2.2.2.1 Volatilization Some of the N applied to agricultural soils as fertilizer volatilizes, entering the atmosphere as ammonia and other oxides of nitr ogen. The volatilized N s ubsequently returns to soils through N deposition a nd then contributes to N 2 O emissions. In FASOM, these emissions are calculated based on the estimates avai lable from DAYCENT modeling. Estimates of emissions due to N lost from a managed field through volatilization were calculated as a function of the same set of vari ables identified above unde r modeling of direct N 2 O emissions per acre. The results of this modeling are then multiplied by the IPCC indirect emissions factor for volatilization (0.010 kg N 2 O-N/kgNH 2 -N+NO x -N/yr) and the conversion factor of 44/28 to convert the loss of nitrogen to volatili zation through multiple pathways into N 2 O emissions.A-50 A.2.2.2.2 Leaching After fertilizer application or heavy rain, la rge amounts of N may leach from the soil into drainage ditches, streams, rivers and eventually estuaries. Some of this N is emitted as nitrous oxide when the leached nitrog en fertilizer undergoes the pr ocess of nitrification or denitrification. In FASOM, these emissions are calculated using a similar modeling structure to that described above for the calculation of direct N 2 O emissions. The same variables are used in models estimated to generate estimates of emissions from leaching, multiplied by 0.0075 kgN 2 ON/kgNO 3 -N/yr and the conversion factor of 44/28 to convert to N 2 O emissions. IPCC guidelines from 2006 recommend that N leaching not be include d in estimates of indirect N2O emissions if annual precipitation minus potential evapotrans piration does not exceed field water holding capacity (with the exception of irrigated lands, wh ich should always be included). Therefore, emissions from nitrate leaching are not incl uded for FASOM regions where the long term average annual precipitation level is less than 70% of the potential evapotranspiration level.
A.2.2.2.4 Residue Burning As mentioned above, FASOM assumes that a ce rtain fraction of fields is burned each year, which results in N 2 O (and methane) emissions. These emissions are calculated using the IPCC default value of 0.7% of N contained in the residue that is burned being emitted as N 2 O. In addition, methane emissions are calculated ba sed on the average methane emissions per acre, calculated using total emissions data from this source and the percentage of acreage that is burned by crop based on 2001 data containe d in the EPA GHG inventory for 1990–2003. 140 These emissions are typically quite small relative to the other emissions tracked in FASOM.
A.2.3 Rice Methane Emissions Another source of GHG emissions from crop production is methane production from rice cultivation. The majority of rice produced in the world is grown in flooded fields commonly known as paddies. However, when fields are flooded, aerobic decomposition of organic matter depletes the oxygen present in the soil, leading to anaerobic soil conditions in the flooded fields. Methane is then produced through anaerobic decomposition of soil organic matter by methanogenic bacteria.
FASOM assumes that all rice produced in the United States is grow n in flooded fields and has associated methane emissions. Although th ere are potentially cha nges in water and soil management practices that could be imple mented to reduce methane emissions, FASOM

140 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2003 . EPA 430-R-05-003. Available at http://www.epa. gov/climatechange/emissions/downloads06/05CR.pdf.A-51 currently assumes that the only method availa ble for reducing methane emissions from rice cultivation is to reduce rice acreage. Thus, ch anges in methane emissions from rice cultivation will result only from changes in the acreage planted to rice.
Methane emissions per acre are calculated based on regional emissi ons factors per acre calculated for each region based on 2001 da ta from the EPA GHG inventory for 1990–2003. 141 The model then calculates emissions from rice production based on emissions factors for each region and the distribution of rice acreage in the model solution. A.2.4 Energy Use Energy is another key input in crop production, including transportation fuels, electricity, and fuels for heating. FASOM includes data on both direct energy use in crop production, which is included within the crop budgets 142 , and energy use associated with input production (e.g., fertilizer and pesticide producti on), grain drying, and transpor tation of primary agricultural commodities, which are calculated outside of th e crop budgets as described in the sections below. Tables A-15 and A-16 summarize FASOM values for on-farm diesel fuel and gasoline use in crop production by crop, ir rigation status, and region. Table A-15. Diesel Fuel Usage (gallons/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland

Barley 5.4 5.3 8.6 8.2 8.3 10.5 4.7 3.1 2.5 8.2 Corn 11.0 9.0 9.6 7.9 NA NA 11.1 9.2 8.1 10.2 Cotton 3.7 4.1 NA NA NA NA NA 8.2 4.9 16.9 Hay 6.7 7.8 7.5 5.0 9. 3 7.2 5.8 8.4 7.5 9.2 Oats 10.4 5.1 9.5 7.2 4. 3 4.0 5.7 3.6 2.5 6.7 Silage 14.8 15.5 14.1 7.9 18.8 20.0 20.3 8.8 12.0 18.2 Sorghum 8.9 11.4 NA 8.8 NA 2.6 10.3 10.4 9.0 10.8 Soybeans 9.7 8.3 9.7 8. 3 NA NA NA 4.1 7.9 8.1 Sugarbeets 13.8 NA 15.5 NA NA NA NA NA NA NA Switchgrass 3.0 2.8 2.9 2. 9 NA NA 2.8 3.0 3.0 2.9 Wheat, Durham NA 10.2 8.2 NA NA NA 6.4 NA NA NA Wheat, Hard Red Spring NA 9. 5 8.2 NA 9.4 NA 6.5 NA NA NA Wheat, Hard Red Winter 10.1 11.1 7.6 7.3 NA 4.5 10.7 6.6 7.9 9.1 (continued)

141 Ibid. 142 Energy data (e.g., diesel fuel, gasoline, electricity, na tural gas) included within the crop budgets are based on USDA Agricultural Resource Management Survey (ARMS) data ( http://www.ers.usda.gov/Data/ARMS/ ) and crop budgets developed by university extension offices.A-52 Table A-15. Diesel Fuel Usage (gallons/acre) by Crop, Irrigation Status, and Region, 2022 (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW Wheat, Soft White NA NA NA NA 9.6 NA 6.9 NA NA NA Irrigated

Barley NA 5.9 NA NA 8.2 10.5 6.0 NA NA NA Corn NA 10.9 NA NA 13.3 14.7 13.9 8.2 NA 13.5 Cotton NA NA NA NA NA 34.7 14.0 10.1 NA 16.3 Hay NA 12.2 NA NA 10.4 NA 10.4 NA NA NA Oats NA 4.1 NA NA 4.9 4.0 5.2 NA NA NA Rice 7.4 NA NA NA NA 18.7 NA 12.0 NA 22.9 Silage NA 16.4 NA NA NA NA 17.5 NA NA 15.1 Sorghum NA 11.7 NA NA NA 3.1 7.9 7.0 NA 9.7 Soybeans NA 9.3 NA NA NA NA NA 4.4 NA 8.1 Sugarbeets 12.5 15.9 12. 5 NA NA NA 17.7 NA NA 22.3 Sugarcane NA NA NA NA NA NA NA 24.8 24.8 24.8 Wheat, Durham NA 10.1 NA NA NA 3.8 7.2 NA NA NA Wheat, Hard Red Spring NA 10.1 NA NA 11.4 NA 6.8 NA NA NA Wheat, Hard Red Winter NA 11. 1 NA NA NA 4.5 8.3 4.1 NA 6.3 Wheat, Soft White NA NA NA NA 11.3 NA 6.3 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM. Table A-16. Gasoline Usage (gallons/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland

Barley 2.1 NA NA 0.1 3.8 NA NA 3.4 3.7 NA Corn NA NA NA 0.7 NA NA NA 3.7 3.7 NA Cotton NA NA NA NA NA NA NA 2.9 0.3 NA Hay 3.7 1.7 4.1 NA 2.8 3.7 1.1 NA NA NA Oats 3.2 NA NA 3.7 1. 5 3.7 NA 2.4 3.7 NA Silage NA NA NA 0.3 NA 0.0 NA 3.8 1.6 0.0 Sorghum NA NA NA NA NA 3.7 NA 3.2 3.7 NA Soybeans 3.7 2.3 NA 3.7 NA NA NA NA 2.1 NA Wheat, Hard Red Spring NA NA NA NA 0.4 NA NA NA NA NA Wheat, Hard Red Winter 3.7 0.6 NA 3.7 NA 3.7 NA 3.7 3.7 NA (continued)A-53 Table A-16. Gasoline Usage (gallons/acre) by Crop, Irrigation Status, and Region, 2022 (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW Irrigated

Barley NA NA NA NA 3.2 NA NA NA NA NA Corn NA 0.0 NA NA 2.8 3.7 NA NA NA NA Cotton NA NA NA NA NA 3.7 0.0 2.5 NA NA Hay NA 3.7 NA NA NA NA 0.9 NA NA NA Oats NA NA NA NA NA 3.7 0.2 NA NA NA Rice 3.7 NA NA NA NA 3.7 NA 4.2 NA NA Silage NA 0.0 NA NA NA NA 0.0 NA NA NA Soybeans NA 3.7 NA NA NA NA NA NA NA NA Sugarcane NA NA NA NA NA NA NA 3.7 3.7 3.7 Wheat, Durham NA NA NA NA NA 3.7 NA NA NA NA Wheat, Hard Red Spring NA NA NA NA 1.0 NA NA NA NA NA Wheat, Hard Red Winter NA 0.8 NA NA NA 3.7 NA NA NA NA Wheat, Soft White NA NA NA NA 1.4 NA NA NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM. Crop budgets for some crops also contain direct use of electri city and natural gas in crop production for irrigation water pumping. Rice an d sugarbeets are the only crops that are assumed to use natural gas in pumping in some regi ons. For the rest of th e irrigated crops that have private energy use for water pumping, el ectricity is the assumed energy source. Tables A-17 and A-18 summarize assumed electri city and natural gas use included within the FASOM crop budgets.
Energy use for grain drying is calculated in FASOM based on assumptions that removing 10 percentage points of moisture from 100 bushels of grain requires 17.5 gallons of propane and 9 kWh of electricity. 143 Thus, energy use per acre is calc ulated as the number of percentage points of moisture to be removed multiplied by the yield per acre and the energy use per percentage point and yield unit for each crop that is dried. Emissions ar e then calculated based on assumed emissions factors per unit of energy use by energy type.

143 These assumptions are based on the Drying Costs for Corn spreadsheet developed by the University of Missouri Extension Program and available at http://agebb.missouri.edu/download/index.htmA-54 Table A-17. Electricity Usage (kWh/acre) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Dryland Hay 0 0 0 0 0 0 0.1 0 0 0 Irrigated Barley NA 3.6 NA NA 5.9 3.6 4.0 NA NA NA Corn NA 11.0 NA NA 10.9 10. 2 9.5 2.8 NA 5.1

Cotton NA NA NA NA NA 0 0 1.8 NA 2.9 Hay NA 7.6 NA NA NA NA 2.1 NA NA NA Oats NA 3.8 NA NA 9.1 3.6 1.5 NA NA NA Silage NA 11.3 NA NA NA NA 3.5 NA NA 10.6 Sorghum NA 7.5 NA NA NA 7.9 3.1 5.9 NA 4.5 Soybeans NA 7.4 NA NA NA NA NA 7.5 NA 4.3 Sugarbeets 0 0 0 NA 0 0 4.2 NA NA 4.5

Wheat, Durham NA 6.2 NA NA NA 3.7 0 NA NA NA Wheat, Hard Red Spring NA 6.2 NA NA 5.3 0 3.7 NA NA NA Wheat, Hard Red Winter NA 2. 4 NA NA NA 5.2 2.1 6.8 NA 4.8 Wheat, Soft White NA 0 NA NA 5.3 NA 5.3 NA NA NA Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there is no dryland rice or sugarcane production or irrigated hybrid poplar, switchgrass, or willow production in FASOM. Table A-18. Natural Gas Usage (1000 cu ft/acre ) by Crop, Irrigation Status, and Region, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Irrigated

Rice 20.8 NA NA NA NA 0.0 NA 17.9 NA 0.0 Sugarbeets 25.9 9.6 25.9 NA 3.8 0.0 0.0 NA NA 0.0 Note: NA indicates not applicable, i.e ., those crops were not cultivated under that irrigation status in that FASOM

region. In addition, there are only two irrigated crops that are assumed to use natural gas in irrigation water pumping in FASOM. In addition to the use of energy on-farm in crop production, FASOM calculates indirect energy requirements associated with the use of fe rtilizer. However, for this analysis EPA used GHG emissions factors from the GREET model to represent GHG emissions from producing fertilizer. Because fertilizer production requires substantial energy inputs, the more fertilizer required for a given crop-region calculation, the greater the asso ciated energy use per acre for that crop.
Energy use in pesticide manufacturing is es timated in FASOM based on the quantity of active ingredients applied per acre multiplied by an assumed energy requirement per ton ofA-55 active ingredients that varies across chemicals, assuming that 70% of the energy requirements are met with natural gas and the other 30% with electricity. 144 However, GREET factors were again used to represent GHG emissi ons from pesticide and herbicide production in this analysis.
GHG emissions from agricultural use of foss il fuels in crop production are calculated in FASOM by multiplying the quantity of each energy category used (d iesel fuel, gasoline, natural gas, liquefied petroleum gas, propane, and elec tricity) by the GHG content of that fuel. Both direct energy uses from the crop budgets as well as indirect use for input production are included. There are also calcula tions of energy use for grain dr ying and product transportation. As with the categories above, GREET factors we re used in this anal ysis to calculate GHG emissions. A.3 Land Management Other actions that can potentially influe nce GHG emissions in FASOM are decisions regarding land management. Some of these actions have already been discussed above, such as changes in soil carbon sequestrati on due to tillage changes, but below we briefly summarize key land management decisions that can influen ce energy use and GHG emissions in FASOM. A.3.1 Tillage and Residue Burning As noted earlier in this appendi x, changing tillage practices wi ll result in changes in soil carbon sequestration that will take place over a 25 -year period. More intensive tillage also requires more diesel fuel for trac tors to plow the land. In additi on, the tillage practices selected on a farm will affect the level of residue per acre on the farm, which affects N 2 O emissions from incorporation of residues into the soil as descri bed above. In addition, ag ricultural fields are sometimes burned to remove excess residue. This burning results in N 2 O and CH 4 emissions that are tracked in FASOM. Thus, reductions in the reliance on agricultural burning will reduce those emissions in the model. A.3.2 Crop Rotations One potential response that farmers may have to higher corn prices is to move from cornsoybean or other rotations patterns into conti nuous corn production. This type of change in rotation patterns would be expect ed to have effects on soil produc tivity, chemical and fertilizer use, crop yields and GHG emissions over time.

144 The assumed allocation of energy re quirements between natural gas and electricity is currently based on professional judgment.A-56 FASOM does not explicitly model the selection of alternative crop rotations. Because the model operates in 5-year time steps, it has not ge nerally been applied to shorter-term decisions such as changes in rotation patte rns. Rather, the model data imp licitly reflect average conditions for crop production (e.g., yields, in put use, etc.) associated with historical rotation patterns on a regional level. A.3.3 Irrigation Another important management decision that substantially affects fertilizer and other input use as well as energy use for irrigation water pumping is the allocation of dryland vs. irrigated land. In some regions, production of cer tain crops is assumed to be exclusively dryland or exclusively irrigated, but for some crop-regi on combinations, there is some ability of production to move dryland and irrigation. Irrig ated lands typically have higher yields, but require more fertilizer and more energy use per acre in FASOM. A.3.4 Impacts on Chemical and Energy Inputs In addition to the allocation of crop producti on across crops, the selection of tillage and irrigation practices will affect the use of both chemical inputs and energy inputs. More intensive tillage practices and the use of irrigation typically increase the energy requirements per acre of production for a given crop. 145 Although FASOM does not explicitly include decisions regarding crop rotations because it focuses on modeling long-run responses, changes in rotations will implicitly be reflected in changes in the average annual land allocation to di fferent crops. To the extent that there was a change in rotation practices to focu s more heavily on corn production, that shift in rotations would tend to increase fertilizer i nputs, energy use, and associated GHG emissions. FASOM captures a wide array of in teractions between agricultural markets and producer allocation among crops as well as the selection of management practices, enabling examination of the complex relationships betwee n sectors and the overall impacts on agricultural chemical and energy use that result. A.4 Livestock and Poultry Sectors In addition to the extensive coverage of crop production within FASOM, the model also tracks livestock and poultry production. Analogous to the crop budgets, there are alternative livestock budgets included in the model that vary by region and species. In addition, the budgets are split into multiple stages for livestock products with multi-year production cycles. For

145 Irrigation also increases yields, however. Thus, it is po ssible in some cases that em issions per unit of production could decrease even if emissions per acre increase.A-57 instance, in the case of beef cattle production, ther e is a cow-calf stage in the model, followed by alternatives for intermediate stages, and fina lly moving to a feedlot for finishing on grain.
One of the primary differences between regiona l livestock budgets is the availability of pasture and rangeland forage and fo rage productivity per acre. T hus, the allocation of feed and forage included within each of the livestock budget for a particular region varies based on regional availability of alternative forage sources. In th is section, we focus on the use of renewable fuel coproducts such as DGs as an alternative livestock feed, non-CO 2 GHG emissions from livestock, and energy use in livestock production. A.4.1 Use of Renewable Fuel Coproducts One factor that can potentially help to mitigate impacts of higher feed prices for the traditional commodities used in livestock feed un der EISA is the increased availability of renewable fuel coproducts that can potentially substitute for current feed commodities. The increased availability of these products helps to keep the costs of feeding livestock lower than they would be otherwise. The availability of a market for co products also helps increase the returns to renewable fuels produc tion. Use of these products as livestock feed in FASOM is expected to increase rapidly over time under the Reference case and even faster under the Control case. In addition to di splacing livestock feed, dry mill ethanol plants that employ a fractionation or extraction process yield corn o il as a coproduct. The corn oil provides a secondary revenue stream, furthe r helping increase the returns to renewable fuels production.
The primary coproducts of starch-based ethano l production that are used as livestock feed are DG, gluten meal, and gluten feed. In partic ular, DG is produced in large quantities. This coproduct of ethanol production can be used as f eed for beef cattle, dairy cattle, swine, and poultry in place of traditional feed sources such as corn and soybean meal (for additional detail, see Appendix B). In addition to replacing some of the corn and soybean-derived livestock feeds, substitution of these renewable fuel coproducts reduces the demand for land, fertilizer, energy, and other inputs needed for crop production relative to a case wh ere these coproducts were not available. The reduced input use al so results in lower GHG emissions.
A.4.2 Methane and Nitr ous Oxide Emissions
GHG emissions associated with livestock production are primarily associated with manure management (both CH 4 and N 2 O) and enteric fermentation (CH 4 ), although there are also emissions from energy use. Livestock manure under aerobic conditions produces N 2 O as part of the nitrogen cycle through the nitrification and denitrification of the organic nitrogen in livestock manure and urine. Under anaerobic conditions, su ch as in large-scale livestock waste storageA-58 facilities, methane is produ ced during decomposition of the manure. In addition, enteric fermentation is part of the normal digestive process in ruminants such as cattle that results in the production of methane within the rumen, which is then emitted. Calculation of these livestock emissions in FASOM is described below. A.4.2.1 Manure Management
To calculate emissions from manure mana gement, FASOM calcul ates the average emissions per head by livestock type for both CH 4 and N 2 O emissions. These values are based on the 2001 emissions values by livestock type a nd the number of livestock in each livestock category reported in the EPA GHG inventory for 1990–2003. 146 The average values per head are multiplied by the number of livestock in each liv estock category in each model period to estimate the emissions associated with manure ma nagement. Thus, emissions from this source are currently affected only by the number of anim als in each livestock category. To the extent that EISA requirements result in higher feed costs, livestock herds are expected to be reduced and manure management emissions will tend to decline relative to the Reference case.
A.4.2.2 Enteric Fermentation
Enteric fermentation emissions from livesto ck are calculated based on the number of animals of each type and a calculated value of average emissions per head based on the 2001 emissions values by livestock type and the num ber of livestock in each livestock category reported in the EPA GHG inventory report for 1990–2003. 147 These average values for enteric fermentation emissions per head are multiplied by the number of livestock of each type in a given year to generate the emissions estimate. There are emissions mitigation options included within the FASOM model, but thes e options do not enter the market in the absence of incentives for reducing CH 4 emissions. Thus, similar to the case of manure management, emissions from this source are currently affected only by the nu mber of animals in each livestock category. A.4.3 Energy Use In livestock production budgets, FASOM doe s not include direct energy inputs. However, because changes in the livestock population influence the demand for crops to be used as livestock feed, changes in livestock populations will have an indirect effect on energy use by inducing changes in crop production.

146 U.S. Environmental Protection Agency. 2005. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990– 2003. EPA 430-R-05-003. Available at http://www.ep a.gov/climatechange/emissions/downloads06/05CR.pdf. 147 Ibid.APPENDIX B: MODELING STARCH-BASED AND SUG AR-BASED ETHANOL PRODUCTIONB-1 This appendix contains an overview of the key methods and assumptions used to model starch-based and sugar-b ased ethanol production in FASOM. Ethanol processes included in FASOM are described in Section B.1, detailed information on feedstock costs is presented in Section B.2, coproducts are disc ussed in Section B.3, assumptions regarding technology change are presented in Section B.4, and the types of agricultural market impacts associated with starch ethanol production are discu ssed briefly in Section B.5. B.1 Process Types Included in FASOM FASOM assumes standard sizes for ethanol pr oduction plants. The majority of plants producing ethanol from starch or sugar feedstoc ks are assumed to produce 75 million gallons per year (MGY) of ethanol using a single feedstock. The exception is plants using sweet sorghum as a feedstock, which are assumed to be 40 MGY plants. 148 Plant-level production costs used within the model are based on sources in the literature, adjusted based on communication with EPA to account for updated input costs. These costs are based on standard procedures for fermentation of sugars to produce ethanol. Star ches in plants such as corn, wh eat, and other grains are chains of sugars that can be readily broken down into simple sugars before fermentation, whereas sugarbased feedstocks such as sugarcane or sugarbee ts contain simple sugars that can readily be extracted and fermented.
Feedstock requirements to supply the standa rd ethanol plant sizes assumed vary by feedstock based primarily on differences in starch/s ugar content that lead to variation in ethanol yield per unit of feedstock. Tabl e B-1 lists the categories of star ch- and sugar-based feedstocks currently included in FASOM as well as the assumed ethanol yield and corresponding units required per ethanol plant in 2022. 149

148 The advantage of sweet sorghum as an ethanol feedstock is its high sugar content. However, the level of sugar in the crop decreases rapidly following ha rvest. Thus, to efficiently capture these sugars for ethanol processing, sweet sorghum needs to be transported to the ethanol plant as soon as possible after the sweet sorghum is harvested. In addition, sweet sorghum is assumed to be 65% moisture, which greatly increases hauling costs per dry ton and decreases optimal plant si ze. Therefore, to reduce average tr ansportation distance and hauling cost, FASOM assumes a smaller plant size for sweet sorghum than for any of the other feedstocks. Because smaller plants have higher processing costs per gallon of ethanol produced, sweet sorghum plants also have higher assumed processing costs per gallon than the other feedstocks. 149 As described earlier in the paragraph, the yields assumed for starch- and sugar-based feedstock conversion to ethanol are constant over time, star ting with the 2002 FASOM baseline.B-2 Table B-1. Starch- and Sugar-Based Ethanol Feedstocks Included in FASOM and Annual Quantity Requirements for a 75 MGY Ethanol Plant (40 MGY for Sweet Sorghum), 2022

Feedstock Units Ethanol Yield (gallons/unit) Quantity Required per Plant (units) Barley Bushels 1.66 45,276,754 Corn (dry milling process) Bushels 2.71 27,675,277 Corn (wet milling process) Bushels 2.50 30,000,000 Oats Bushels 1.10 68,428,344 Refined sugar Tons 141.00 531,915 Rice 100 pounds (cwt) 3.98 18,844,221 Sorghum 100 pounds (cwt) 4.25 18,453,427 Sweet sorghum 100 pounds (cwt) 9.00 4,444,444 Sweet sorghum (ratooned) 100 pounds (cwt) 11.00 3,636,364 Wheat, Durham Bushels 2.56 29,264,758 Wheat, hard red spring Bushels 2.56 29,264,758 Wheat, hard red winter Bushels 2.56 29,264,758 Wheat, soft white Bushels 2.56 29,264,758

Production costs for starch- and sugar-based ethanol are broken into feedstock costs, hauling costs, and processing costs. Revenue is derived from ethanol sales at the market price, government subsidies, and the sale of coproduc ts produced during the processing of certain feedstocks (e.g., DG from ethanol pr oduction using grain feedstocks).
FASOM is used to simulate the quantity of each of the starch- and sugar-based feedstocks considered that would be used in ethanol production, accounting for competing demands for those feedstocks for use in food production or in animal feed under alterna tive policy scenarios. In addition, FASOM provides estimat es of the number of standard size starch- or sugar-based ethanol plants that would be located in each of the 11 FASOM market regions over time.
B.2 Feedstock Costs Costs of using starch crops for ethanol producti on include the cost of the crop, the cost of hauling the crop from the roadside at the farm to the ethanol production pl ant, and the per-gallon processing cost of the crop to ethanol. This se ction describes the costs of purchasing sufficientB-3 crops to produce a given amount of starch etha nol, which depends on crop energy content (i.e., quantity of crop required to produce a given amount of ethanol varies across crop). The quantity of crop required also affects hauling costs.
Plant-level crop costs reflect the cost per uni t of crop multiplied by the number of units required to provide enough crop to produce 75 million gallons of ethanol. Crop quantity requirements for starch ethanol vary by crop and are measured in bushels (bu), hundred weight (cwt), or tons, depending on the crop. The rele vant comparison for ethanol production plants is the cost of purchasing the quantit y of feedstock that will genera te a given amount of ethanol.
For a plant using a given crop, the plant-le vel quantity requirement s are multiplied by the regional farm-level market price for that crop generated by FASOM to calculate the total plantlevel feedstock costs. The regional farm-level market price reflects the market-clearing price based on all possible uses for the crop, including feed use in livestock, ex ports, international and domestic food consumption, and use in ethanol production. Increases in demand for crops used to produce starch- and sugar-based ethanol place upward pressure on the market-clearing price for crops used in ethanol production. However, if increases in crop acreage and yield increases meet or exceed increases in demand, then inflat ion-adjusted equilibrium prices could remain steady or even decline over time. Increasing yi elds also mitigate pressure to convert land to agricultural production both in th e United States and abroad.
Thus, projected crop yields, both in the United States and internati onally, are one of the most influential factors influencing the market a nd environmental outcomes of this analysis. The regional average crop yields presented in Ta ble B-2 are based on US DA projections through 2017 150 (the final year reported in the baseline projections report) and then extrapolated out to 2022 assuming yield continues to grow at the same annual rate in the future . Baseline yields are based on historical averages for each region, and yields are assumed to increase at the same rate over time in both the Reference and Control Cases. 151

150 USDA Office of the Chief Economist, World Agricultura l Outlook Board. USDA Agricultural Projections to 2018. Report prepared by the Interagency Agricultural Projections Committee. L ong-term Projections Report OCE-2009-1. Available at http://www .ers.usda.gov/Publications/OCE091/ 151 This implicitly assumes that there are no price-induced yiel d increases. It is possible that higher prices may spur landowners to modify their practices to adopt more expensive production technologies that increase yields, for instance. Also, higher returns to production may lead to an increase in resear ch funding and speed up technological improvements. On the other hand, higher prices may induce planting on marginal land that would have lower yields than land currently in production.B-4 Table B-2. Regional Average Crop Yields, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Barley (bu/acre) 61.5 62.9 54.6 70.5 72.9 64.3 62.4 75.7 77.7 46.9 Corn (bu/acre) 197.2 196.1 185.2 147.1 NA 223.1 154.0 166.9 160.7 168.3 Oats (bu/acre) 72.8 59.4 65.1 67.8 97.0 91.8 67.5 42.2 62.7 46.4 Sugarcane (tons/acre) NA NA NA NA NA NA NA 31.1 44.6 46.2

Rice (cwt/acre) 94.4 NA NA NA NA 129.4 NA 97.3 NA 108.6 Sorghum (bu/acre) 100.0 83.8 NA 82.3 NA 53.9 53.9 82.4 60.1 63.6
Sweet sorghum (tons/acre) 32.3 24.3 NA NA NA NA 17.2 28.7 NA 17.8
Sweet sorghum (ratooned) (tons/acre) 43.8 33.0 NA NA NA NA 23.3 38.9 NA 24.2

Wheat, Durham (bu/acre) NA 50.5 48.2 NA NA 129.8 70.4 NA NA NA

Wheat, hard red spring (bu/acre) NA 51.1 51.2 NA 80.5 NA 55.8 NA NA NA

Wheat, hard red winter (bu/acre) 110.2 85.4 110.8 111.2 NA 110.2 90.0 94.7 96.1 77.7

Wheat, soft white (bu/acre) NA NA NA NA 92.8 NA 80.9 NA NA NA

Note: CB = Corn Belt; GP = Great Plains; LS = Lake St ates; NE = Northeast; PNWE = Pacific Northwest—East

side (agriculture only); PSW = Paci fic Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; SW = Southwest (agriculture only). NA = Not applicable

The market price for each alternative ethanol feedstock will vary by scenario. Because starch- and sugar-based feedstocks considered in FASOM are generally be ing used in numerous competing markets, they have positive market pr ices, unlike the case of cellulosic ethanol where there is no market for most of the feed stocks in FASOM in the Reference Case. 152 Table B-3 summarizes market equilibrium prices in FASO M under the Reference and Control Cases. As expected, the introduction of increased demand for ethanol feedstocks to increase ethanol production under EISA leads to increases in pri ce for these feedstocks, including positive values for sweet sorghum.

152 The exception is sweet sorghum, which is in experime ntal stages and not widely traded in markets.B-5 Table B-3. FASOM Market Prices for Ethano l Feedstock Crops (2007$/unit) in Reference and Control Cases, 2022

Feedstock Unit Reference Case Price ($/unit) Control Case Price ($/unit) % Change in Price Barley bu 4.70 5.81 23.6 Corn bu 3.32 3.60 8.2 Oats bu 3.01 4.04 34.2 Sugarcane tons 31.87 37.46 17.5 Rice cwt 10.61 10.86 2.4 Sorghum cwt 6.35 6.52 2.6 Sweet sorghum tons 10.68 10.86 9.9

Wheat, Durham bu 8.57 9.12 6.4 Wheat, hard red spring bu 5.91 5.71 -3.3 Wheat, hard red winter bu 5.61 6.16 9.9 Wheat, soft white bu 5.20 4.91 -5.6 NA = Not applicable In addition to revenue from selling feedstocks in the market, eight of the eleven FASOM market regions offer state subsidies that offset some of the ethanol pr oduction costs. These subsidies are in addition to the $0.45 per gallon national ethanol subsidy included in the model. 153 Regional averages for subsidies per gallon of ethanol produced are presented in Table B-4. 154 Table B-4. Average State Ethanol Subsidy by Region Used in FASOM (cents/gallon of ethanol produced)

CB GP LS NE PNWE PSW RM SC SE SW Average state subsidy 0.02 15.0 15.4 0.01 0 0 0.86 0.50 2.00 10.4

Note: CB = Corn Belt; GP = Great Plains; LS = Lake St ates; NE = Northeast; PNWE = Pacific Northwest—East

side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; SW = Southwest.

153 The national subsidy included in FASOM is the Volu metric Ethanol Excise Tax Credit (VEETC), which was created in 2004 as part of H.R. 452 0, the American Jobs Creation Act of 2004. The credit was 51 cents per gallon on pure ethanol (5.1 cents per gallon for E10 and 42 cents per gallon for E85) until January 1, 2009, when it was reduced to 45 cents per gallon of pure ethanol. 154 The values used were based on a review of available state-level subsidies within each FASOM region.B-6 B.3 Coproducts There are four primary coproducts of the starch-to-ethanol production process tracked within FASOM and they are DG, gluten meal, gl uten feed, and corn oi l, all of which are produced during the process of converting starch es in grain crops into ethanol. DG has the potential to replace some of the corn used for feed, and FASOM includes feed substitution using DG as a corn and soybean meal replacement possibility. Based on research conducted by Argonne National Laboratory, 155 FASOM assumes that one pound of DG can potentially substitute for 1.196 pounds of total corn and soybean meal for cattle because the DG has higher nutritional content per pound. The model assume s that replacement rates increase over time from a 1:1 replacement rate of DG for corn and soybean meal initially to the maximum technological replacement rate estimated by Ar gonne of 1:1.196 in 2017 for beef and dairy cattle. We continue to use a replacement rate of 1:1 throughout the en tire modeling timeframe for swine and poultry. We also implemented maximu m DG inclusion rates in livestock feed as a percentage of total feed based on the Argonne study. These limits vary by species and are assumed to increase between 2007 and 2017, reaching maximum levels of 50% for beef cattle, 30% for dairy cattle, and 25% for both swine a nd poultry by 2017 and remaining at those levels after 2017. In addition, DG produced as a byproduct of a dry milling process with corn oil fractionation/extraction has diffe rent nutritional characteristic s than traditional DG, which contain higher levels of oil. 156 Based on this research, the proportion of soybean meal vs. corn replaced by fractionated/extracted DG is higher than for traditi onal DG when used for swine or poultry feed, although the total repl acement rate of DG for a combination of corn and soybean meal remains 1:1. Therefore, we have modified the model to apply different replacement rates for fractionated/extracted DG and traditional DG when used in swine and poultry feed. Because there was no comparable research identified for cattle diets, we assumed that replacement rates for cattle remain the same for fractionated/extracted DG as for traditional DG.

Another change that has been implemented si nce the proposal is the incorporation of an export market for DG into FASOM. In the analys is for the proposal, it was assumed that all DG produced would be supplied into th e domestic market. However, th e model has been modified to

155 Salil, A., M. Wu, and M. Wang. 2008. “Update of Di stillers Grains Replacement Ratios for Corn Ethanol LifeCycle Analysis.” Available at http://www.transportation.anl.gov/pdfs/AF/527.pdf

. 156 Shurson, G.C. 2006. “The Value of High-P rotein Distillers Compounds in Swine Feeds.” Distillers Grains Quarterly , First Quarter 2006:22-25.B-7 account for potential exports of DG. This change expands the market for DG and is consistent with historical experience in recent years. Exports of DG have increased rapidly over the last few years. The majority of U.S. exports are sh ipped to Mexico and Canada, but there has also been expansion in exports to the Middle Ea st and Asia. Based on values reported by the National Corn Growers Association, 157 the corn wet mill process is assumed to produce 13.5 pounds of gluten feed, 2.5 pounds of gluten meal , and 0.2078 gallons of corn oil per bushel of corn. Thus, the standard 75 MGY plant used in FASOM will produce 405,000 pounds of gluten feed, 75,000 pounds of gluten meal, and 62,337 gallons of corn oil per year as coproducts of ethanol production. The corn dry mill proces s is assumed to produce 17 pounds of DG per bushel of corn, or about 510,000,000 pounds of DG pe r year without using fractionation or extraction. However, plants can also use two different processes to separate corn oil during the dry milling process: the “front-end” fractionation process and the “back-end” extraction process. Both processes yield DG and corn oil as c oproducts. Although frac tionation produces food grade corn oil, the dry mill ex traction process produces non-food grade corn oil that cannot be used as vegetable oil for f ood production but can be used to produce biodiesel. Based on projections included in Chapter 1.4 of the RIA, beginning in 2010 10% of dry mill ethanol plants will use extraction and 3% will use fractionation. These shares are assumed to climb to 70% for the extraction process, 20% for the fractionation process, and 10% that use neither fractionation nor extraction by 2022. In addition to by-products fr om using corn, the use of grains other than corn as ethanol feedstocks can also produce DG as a coproduct. Table B-5 summarizes the coproducts produced when using different grain feedstocks for ethanol production. Table B-5. Production of Ethanol Coproducts by Feedstock

Gluten Feed

(lbs/bu) Gluten Meal (lbs/bu) Corn Oil (gallons/bu) DG (lbs/bu) Barley NA NA NA 14.6 Corn (wet milling process) 13.5 2.5 0.2078 NA Corn (dry milling process – no fractionation or extraction) NA NA NA 17.0
Corn (dry milling process – fractionation) NA NA 0.1439 15.9 Corn (dry milling process – extraction) NA NA 0.1929 15.5 Oats NA NA NA 9.7 Rice NA NA NA 30.4 Sorghum NA NA NA 30.4 Wheat NA NA NA 18.2 NA = Not applicable; those coproducts are not produced from that feedstock/process combination.

157 National Corn Growers Association. 2006 World of Corn.B-8 Revenue provided by selling these coproducts is an important factor affecting the economic viability of ethanol processing plants. In the Reference Case, the 2022 prices of these feed coproducts were $93.62 per ton for gl uten feed, $201.05 per ton for gluten meal, $116.75 per ton for traditional DG, and $118.88 per ton for DG from the fractionation or extraction process. The added revenue from biodiesel and co rn oil will be another factor affecting ethanol producer decisions to fractionate or extract. Under the Control Case, prices for these coproducts increase with the exception of gluten meal, whic h experiences a price decline of 0.1%. Gluten feed, traditional DG, and fractionated/extrac ted DG prices increase by 13.6%, 6.8%, and 6.5%, respectively. In addition, these coproducts play a vital role in mitigating impacts on the livestock sector by providing substitutes for grain feeds and keeping price increases lower than they would be otherwise. Total use of thes e products as livestock feed in FASOM is expected to increase rapidly over time under the Reference Case and ev en faster under the Control Case. Although use of gluten meal decreases by 4.5% under th e Control Case in 2022, use of gluten feed increases by 6.4%, and use of DG increases by 15.2% . Overall, use of ethanol by-products in livestock feed increases by 12.6% in 2022 relative to the Reference Case.
B.4 Technology Change As noted earlier, fermentation using starch- a nd sugar-based feedstocks is assumed to be a relatively well-developed technology that has reached physical limits on ethanol conversion potential, and ethanol conversion yields are assumed to remain constant over time. Since FASOM uses an “average” ethanol plant, we assume d an average that takes into account older, less efficient plants. In additi on, processing costs per gallon of ethanol are assumed to remain constant over time. This is in contrast to cellu losic ethanol, which, as de scribed in Appendix D, is assumed to experience substantial improve ments in ethanol yield over time as well as reductions in processing costs per gallon. As a re sult, the quantities of feedstock required at standard FASOM ethanol plants and the inflation- adjusted costs of processing those feedstocks remain the same over time.
However, improvements in feedstock yields will reduce hauling costs over time. As shown in Table B-6, yield improvements assume d between 2002 and 2022 vary across the crops that can potentially be used as feedstocks in et hanol production. Other thi ngs being equal, those crops with greater yield increases over time will b ecome more attractive feedstocks over time as their higher yields reduce hauling costs and tend to mitigate future price increases.B-9 B.5 Agricultural Market Impacts FASOM tracks the impacts on agricultural mark ets associated with changing production practices to allocate different levels of grain crops and sugar from production of food and animal feed to ethanol production. Large-scale expans ion of crop production to provide starch- and sugar-based feedstocks for ethanol production will potentially have major implications for land Table B-6. FASOM Assumptions Regarding Ch anges in Crop Yields Over Time

Average Annual Change in Yield (%) Aggregate Change in Yield, 2002–2022 (%) Barley (bu) 0.10% 1.9% Corn (dry milling process) (bu) 1.62% 38.0% Corn (wet milling process) (bu) 1.62% 38.0% Oats (bu) 0.02% 0.3% Sugarcane (tons) 0.00% 0.1% Rice (cwt) 1.33% 30.3% Sorghum (cwt) 0.09% 1.8% Sweet sorghum (cwt) 0.00% 0.0% Wheat, Durham (bu) 1.11% 24.6% Wheat, hard red spring (bu) 1.00% 21.9% Wheat, hard red winter (bu) 1.31% 29.7% Wheat, soft white (bu) 1.11% 24.6%

uses and values as well as the production of competing food and animal feed products. Production increases must be induced through higher equilibrium market prices and will result in production shifting toward the crops that experi ence the greatest increase in demand and price and away from other crops. However, as land sh ifts out of other crops, the supply of those crops will decline and result in price increases in thos e markets until a new equilibrium is reached. In addition, changes in prices of animal feeds w ill affect livestock produc tion. Because FASOM captures the opportunity costs of la nd and other resources, results reflect the changes in market equilibrium associated with introducing a rene wable fuels policy that increases the market demand for certain feedstocks.
Because EISA focuses on increasing renewabl e fuels production from advanced sources such as cellulosic ethanol produc tion, the incremental volume of starch- and sugar-based ethanolB-10 under EISA is relatively small. This reduces the market impacts of in creasing renewable fuels production considerably by creating a market for crop residues, which would be collected from lands being used primarily for other crop produc tion purposes rather than competing for land. Nonetheless, the increased volumes of ethanol production will affect the agricultural sector by further increasing the demand for corn, sugarc ane, and other feedst ocks; increasing the equilibrium prices for agricultural products; and increasing the competition for agricultural land.
Generally, the demand for agricultural crops for use as feedstocks in ethanol production is expected to shift land use in FASOM as landow ners respond to changes in the relative returns to alternative crops under EISA. Because of relatively high ethanol yields, hauling cost advantages, and valuable coproducts , corn is likely to continue to be the primary feedstock for expanded noncellulosic ethanol production under EISA . In addition, the creation of a market for cellulosic ethanol leads to a substantial increase in the value of crop residu es, particularly corn stover, which further increases the returns to corn producti on. One of the major factors influencing market outcomes is the future change in crop yields, particularly corn yields. Given the yield improvements incorporated into FASOM, corn production is likely to grow faster than corn demand in the future even with additional use as an ethanol feedstock. Thus, improvements in corn yield will contribute to reductions in the price of corn and other commodities in the future and will help mitigate land use and environmental impacts.APPENDIX C: MODELING BIODIESEL PRODUCTIONC-1 In this appendix, we present key assumpti ons and methods used to model biodiesel production in FASOM. The processes used for bi odiesel production in th e model are described in Section C.1, information on feedstock costs is presented in Section C.2, assumptions regarding coproducts of biodiesel production are briefly described in Sec tion C.3, assumptions regarding biodiesel production technology change over time are presented in Section C.4, and the types of agricultural market impacts associated with biodi esel production are briefl y discussed in Section C.5. C.1 Process Types Included in FASOM In FASOM, biodiesel production is treated differently than st arch- or sugar-based ethanol or cellulosic ethanol in that bi odiesel production is assumed to re ly on inputs that are already produced as part of existing pr ocessing activities. For inst ance, existing regional soybean crushing and corn milling processing budgets produ ce a supply of soybean and corn oil. In addition, fed and nonfed cattle slaughter activitie s produce edible and nonedible tallow, and pork slaughter produces lard. Thus, it was assumed in FASOM that biodiesel would be produced at the same site where the inputs are produced. Unlike ethanol producti on, which relies on a specific standard size plant to calculate the aver age regional hauling cost s to supply a plant of that size, biodiesel production is assumed to require no hauling costs and to be available at constant returns to scale. Biodiesel producer s within a region can produce any quantity of biodiesel, and FASOM measures production in t housands of gallons of biodiesel without estimating a specific number of plants that are generating that quantity of biodiesel. Processing costs per gallon of biodiesel produced used with in FASOM were based on analyses included in the RIA for this rule.
The quantity of feedstock required to pr oduce a gallon of biodiesel varies across feedstocks to some extent, but with less varian ce than for ethanol producti on. Table C-1 lists the categories of biodiesel feedstocks being include d in FASOM, the assumed biodiesel yield, and the quantity of feedstock required per 1,000 ga llons of biodiesel produced. Feedstock requirements and processing costs per gallon of bi odiesel are assumed to remain constant over time (in inflation-adjusted dollars). In addi tion, the production of biodi esel feedstocks as a proportion of the amount of soybean crushing, corn milling, and livestock slaughter activities taking place remains constant over time.C-2 Table C-1. Biodiesel Feedstocks Included in FASOM and Quantity Requirements per 1,000 Gallons of Biodiesel, 2022

Feedstock Unit Biodiesel Yield (gallons/unit) Quantity Required per 1,000 Gallons of Biodiesel Produced (units) Soybean oil Pounds 0.1288 7,763.0 Corn oil (non-food grade) Gallons 1.0239 976.7 Nonedible tallow Pounds 0.1302 7,679.0 Edible tallow Pounds 0.1302 7,679.0 Lard Pounds 0.1302 7,679.0

Production costs for biodiesel c onsist of feedstock costs and processing costs. Revenue at biodiesel plants is derived only from biodiesel sales at the market price; unlike ethanol production, no state-level government subsidies are included in FASOM, and no valuable coproducts are produced during biod iesel processing. However, ther e are federal subsidies of $1 per gallon for both virgin oil and waste oil and greases included in the model. 158 These subsidies are assumed to remain constant at those levels indefinitely. FASOM simulates the supply of biodiesel by feedstock for each of the 10 FASOM market regions with agricultural production over time in response to the demand fo r biodiesel under sp ecified scenarios. 159
C.2 Feedstock Costs The costs of using biodiesel feedstocks for biodiesel production include only the opportunity costs of not using the oils to produce alternative comp eting products and the costs of processing the feedstocks into biodiesel. Because it is assumed that the crop feedstock used in biodiesel production is located on site, FASOM assumes zero handling and hauling costs in biodiesel production, whereas these costs repres ent a substantial propor tion of total production costs in ethanol production.

158 The national subsidy included in FASOM is based on the subsidy included in the Emergency Economic Stabilization Act of 2008 (H.R.1424), signed into law in October 2008. The Energy Policy Act of 2005 (H.R.6), which extended the biodiesel credit specified as part of the Volumetric Ethanol Excise Tax Credit (VEETC)under the American Jobs Creation Act of 2004 (H.R. 4520), provided a subsidy equal to $1 per gallon for “agribiodiesel” (diesel fuel made from virgin oils derived from agricultural commodities and animal fats) and $0.50 per gallon for “biodiesel” (diesel fuel made from agricultural products and animal fats). The Emergency Economic Stabilization Act of 2008 eliminated the distinction between agri-biodiesel and biodiesel such that all biodiesel now qualifies for the $1 per gallon subsidy. 159 Only 10 of the 11 market regions in FASOM have agricultural production. The PNWW region has only forestry production. Because there are no bi odiesel production options in FASOM in volving feedstocks derived from the forestry sector, no biodiesel production is available in the PNWW region.C-3 Plant-level feedstock costs reflect the cost per unit of feedstock multiplied by the number of units required to provide enough feedstock to produce 1,000 gall ons of biodiesel. The prices of the primary biodiesel feedstocks in the Refe rence and Control Cases for 2022 are presented in Table C-2.
Table C-2. FASOM Market Prices for Biodiese l Feedstocks (2007$/unit) in Reference and Control Cases, 2022

Feedstock Unit Reference Case Price ($/unit) Control Case Price ($/unit) % Change in Price Soybean oil Pounds $0.24 $0.33 37.9% Corn oil (non-food) Gallons $0.34 $1.26 270.9%

For a plant using a given feedstock type, the plant-level quantity requirements are multiplied by the regional market price for that feedstock generated by FASOM to calculate the total plant-level feedstock costs. The price for bi odiesel feedstocks reflec ts the equalizing of the supply and demand for that particular product and will vary based on the sp ecific scenario being modeled. Changes in soybean and corn yields ov er time relative to changes in demand for oils derived from these crops, along with agricu ltural producer adjustments in land use and production due to changing relative crop prices, will determine change s in prices of soybean and corn oils over time. Table C-3 presents informa tion on the quantity of corn and soybean oils that can be produced per acre of crops by region.
In addition, to the extent that more efficien t techniques for collecti ng and using waste oils in biodiesel production are develope d, those feedstocks may also play a larger role in biodiesel production in the future. Expanded use of waste oil feedstocks could contribute to moderating price increases for corn and soyb ean oils to meet EISA biodiesel production levels, but currently it is expected that the majority of the increase in biodiesel production would be derived from soybean and corn oils.C-4 Table C-3. Regional Average Potential Prod uction of Soybean and Corn Oil per Acre, 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Corn (bu/acre) 197.2 196.1 185.2 147.1 NA 223.1 154.0 166.9 160.7 168.3
Corn oil (gallons/acre) (wet mill) 41.0 40.8 38.5 30.6 NA 46.4 32.0 34.7 33.4 35.0

Soybeans (bu/acre) 50.3 40.1 42.6 38.0 NA NA NA 36.7 31.5 23.8 Soybean oil (lbs/acre) 561.1 447.5 475.4 424.1 NA NA NA 409.6 351.5 265.6

Note: CB = Corn Belt; GP = Great Plains; LS = Lake St ates; NE = Northeast; PNWE = Pacific Northwest—East

side (agriculture only); PSW = Paci fic Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; SW = Southwest (agriculture only). C.3 Coproducts No coproducts are associated with biodies el production in FASOM. Although soybean crushing to produce soybean oil generates a copr oduct of 47.6 lbs of soybean meal per bushel of soybeans along with 11.16 lbs of soybean oil, th at process is separated from the biodiesel production process that uses soybean oil outputs from soybean crushing to produce biodiesel. Nonetheless, soybean meal is a highly marketable input in livestock feed production, and the sale of soybean meal would represent an additional st ream of revenue for soybean crushing plants that are also producing biodiesel. Table C-4 presents soybe an meal production, exports, and prices under the Reference and Control Cases. The increase in biodiesel production under the Control Case results in increased soybean meal production, which results in a slightly lower market equilibrium price. C.4 Technology Change Unlike ethanol production, we assumed no ch anges in the technology of biodiesel production over time that would affect the biodi esel conversion rate or processing costs. Because the cost of biodiesel feedstocks depe nds on market forces, increases in corn and soybean yields over time will tend to dampen poten tial price increases associated with increases in demand for these crops because of renewable fu els’ requirements or other changing market conditions. However, we assumed no changes in technology would occur that would directly affect the cost of biodiesel production.C-5 Table C-4. Prices, Exports, and Production of Soybean Meal, 2022

Variable Units Value in Reference Case (units) Value in Control Case (units) Percentage Change
Production tons 43.83 45.91 4.7% Exports tons 15.81 16.26 2.8% Price $/ton 201.05 200.82

− 0.1%
C.5 Agricultural Market Impacts FASOM tracks the impacts on agricultural mark ets associated with changing production practices to allocate different le vels of corn and soybean oils from production of food and animal feed to biodiesel production. Large-scale expansion of biodiesel production to provide feedstocks for biodiesel production will potentially have major imp lications for corn and soybean oil prices, as well as the produc tion of competing food and animal feed products. Production increases must be induced through higher equilibr ium market prices and will result in production shifting toward the crops that experience the gr eatest increase in demand and price and away from other crops. However, as land shifts out of other crops, the supply of those crops will decline and result in price increases in those markets until a new equilibrium is reached. In addition, changes in prices of animal feeds w ill affect livestock production. Because FASOM captures the opportunity costs of la nd and other resources, results reflect the changes in market equilibrium associated with introducing a rene wable fuels policy that increases the market demand for certain feedstocks.
Expansion of soybean oil production for use as a biodiesel feedstock will also result in increased production of soybean meal, which can be used in livestock feed. Thus, this coproduct will help mitigate impacts on livestock producers. In the Control Case modeled in FASOM, increases in soybean meal production result in d ecreased soybean meal prices. Although more is being used in livestock producti on, the increase in demand for soybe an meal as a livestock feed is more than outweighed by the increase in supply associated with expanded soybean crushing.

The use of waste oils as a feedstock in FASOM does not require any additional land because the land use is already accounted for in production of the primary product. In addition, the substitution of waste oils for soybean and corn oils used extensively in food and feed production reduces market impacts in the soybean and corn oil markets. However, based on current model assumptions regarding the quanti ties of these waste feedstocks produced, their costs, and biodiesel conversion rates, there ar e limited opportunities for competitive expansion ofC-6 the use of waste oils in biodies el production. To the extent th at more efficient ways to use additional waste oils in biodiesel production can be identified, impacts of increased biodiesel production on food and feed markets could be reduced.APPENDIX D: MODELING CELLULOSIC ETHANOL PRODUCTIOND-1 This appendix contains an overview of the key assumptions and methods used to model cellulosic ethanol produc tion in FASOM. Ethanol processe s included in FASOM are described in Section D.1, additional detail on feedstock cost s is provided in Sect ion D.2, coproducts are described briefly in Section D.3, assumed technol ogy change over time is discussed in Section D.4, and the types of agricultural market imp acts associated with cel lulosic ethanol production are discussed in Section D.5. D.1 Process Types Included in FASOM In FASOM, there is currently one standard size for cellulosi c ethanol plants. All ethanol plants relying on cellulosic feedstocks are assumed to produce 100 million gallons per year (MGY) of ethanol using a single t ype of feedstock. The selection of this plant size was based on Carolan, Joshi, and Dale. 160 Plant-level production costs used within the model are calculated based on the assumption of a representative 100 MGY plant. Proce ssing costs are based on information presented in the RIA for this ru le. These costs were based on biochemical conversion using acid prehydrolysis, enzyma tic hydrolysis, and fermentation, followed by distillation of ethanol and sepa ration of coproducts. Thermochem ical conversion plants, which rely on processes such as gasi fication, pyrolysis, and catalytic cracking to produce synthesis gas that can be converted to ethanol, ar e not included within FASOM.
The quantity of feedstock required per plant varies by feedstock type based on moisture content; energy density; and av ailable technology for feedstoc k conversion to ethanol, which changes over time. Table D-1 lists the categories of cellulosic feedstocks currently included in FASOM as well as the assumed ethanol yield fo r 2022 and the corresponding number of dry tons of each feedstock required per plant to produ ce 100 MGY. Section D.2 provides additional information on the underlying sources of the init ial assumptions on ethanol yield and moisture content, and Section D.4 discusses as sumed technology changes over time.

160 Carolan, J., S. Joshi, and B. Dale. 2007. “Techni cal and Financial Feasibility Analysis of Distributed Bioprocessing Using Regional Biomass Pre-Processing Centers.” Journal of Agricultural & Food Industrial Organization 5(2), Article 10. Available at http ://www.bepress.com/jafio/vol5/iss2/art10.D-2 Table D-1. Cellulosic Feedstocks Included in FASOM and Annual Quantity Requirements for a 100 MGY Ethanol Plant, 2022

Feedstock Ethanol Yield (gallons/dry ton) Quantity Required per Plant (dry tons) Crop Residues Barley crop residues 92.30 1,083,424 Corn crop residues 92.30 1,083,424 Oat crop residues 92.30 1,083,424 Rice crop residues 92.30 1,083,424 Sorghum crop residues 92.30 1,083,424 Wheat crop residues 92.30 1,083,424 Energy Crops Hybrid poplar 101.50 985,222 Switchgrass 92.30 1,083,424 Willow 101.50 985,222 Logging Residues Hardwood logging residues 101.50 985,222 Softwood logging residues 92.30 1,083,424 Processing Residues Bagasse 92.30 1,083,424 Hardwood milling residues 101.50 985,222 Softwood milling residues 92.30 1,083,424 Sweet sorghum pulp 92.30 1,083,424 Note: Ethanol yields are based on Tao, L. and A. Aden, Technoeconomic Modeling to Support the EPA Notice of

Proposed Rulemaking (NOPR) , November 2008. FASOM simulates the supply of cellulosic etha nol by feedstock over time in response to the demand for cellulosic ethano l to meet the proposed renewable fuel volumes and provides estimates of total cellulosic ethanol production, and the number of cellulosic ethanol plants located in each of the 11 FASO M market regions over time.
D.2 Feedstock Costs Costs of using cellulosic feedstocks for ethanol production include the cost of the feedstock, which must cover the costs of harvestin g as well as opportunity costs associated with residue removal (e.g., need for addi tional fertilizer due to nutrient removal), the cost of hauling the feedstock from the roadside at the farm to the ethanol production plant, and handling costs associated with storage and grinding. This se ction discusses the costs of purchasing sufficientD-3 feedstock inputs to produce a gi ven level of cellulosic ethano l, which depends on feedstock energy content (i.e., tons of feedstock required to produce a given amount of ethanol vary across feedstocks because they have different moisture levels and energy contents). The quantity of feedstock required also affects hauling and ha ndling costs, as descri bed in Section D.3.
Plant-level feedstock costs reflect the cost per unit of feedstock multiplied by the number of units required to provide enough feedstoc k to produce 100 million gallons of ethanol. Although the unit of quantity vari es by feedstock for grain et hanol, all cellulosic ethanol feedstocks are measured in U.S. short tons (1 short ton is equal to 2,000 pounds). For a plant using a given feedstock type, the plant-level qua ntity requirements are multiplied by the regional farm-level market price for that feedstock gene rated by FASOM to calculate the total plant-level feedstock costs.
The regional farm-level market price for a cr op residue feedstock reflects the fact that farmers require compensation for additional costs associated with residue removal, including assumed harvesting and handling costs of $13.14 per wet ton (2007$) 161 and increased fertilization requirements due to nutrient removal that vary by crop. The farm-level feedstock price must be at least high enough to cover thes e costs before farmers will be willing to supply any residues to the market. 162
For energy crops, land opportunity costs and production costs must be covered by the market price available for the feedstock in orde r for farmers to be willing to move land into energy crop production. In addition, some processing residues are already used in applications other than cellulosic ethanol pr oduction, and an opportunity cost is associated with diverting those residues to cellulo sic ethanol production. D.2.1 Crop Residues For crop residues, there is a limit to how much residue can be removed before negatively affecting soil erosion. In addi tion, opportunity costs are associat ed with loss of nutrients from removing residues. This section describes the calculation of the total quantity of residue, the

161 Because this value is specified as a constant cost per we t ton, costs of residue harv esting and handling per dry ton will clearly increase with f eedstock moisture content. However, b ecause the assumed moisture content is relatively similar across the crop residues included, the cost per dry ton varies only from $14.42 per dry ton for wheat residue to $15.46 per dry ton for rice residue. 162 The farm-level price that processing plants are willi ng to pay for each feedstock depends on the hauling and handling costs and ethanol yield associated with that feedstock. In the FASOM model solution, ethanol processing plants are located in the regions where production of EISA-required volumes can be achieved at the lowest cost.D-4 portion that is sustainably rem ovable, feedstock density, moisture content, energy content, ethanol yields, and other factor s affecting the potential ethanol production from crop residues. D.2.1.1 Sustainable Crop Residue Production The quantity of crop residue produced after harv est (measured in wet tons) is calculated

as
Residue = Crop Yield * Straw-to-Grain Ra tio * Weight Conversion Factor, (D.1)
where crop yield is the value for grain yield from FASOM crop budgets and varies by crop, region, irrigation status, tillage, a nd rate of fertilizer applicati on; straw-to-grain ratio is the average quantity of residue produced for ev ery unit of grain production, which varies by crop 163 , 164 ; and weight conversion factor is a factor used to convert from crop harvest units (e.g., bushel [bu], hundredweight [cwt]) to the common me tric of tons of residue and which varies by crop. Because crop yields are assumed to incr ease over time due to continued technical progress in crop production (see Table B-3), the amount of residue being produced per acre and the sustainably removable levels of residue will al so increase over time. Currently, it is assumed that both crop residue yield and grain yield change at the same average annual rate. However, FASOM includes an adjustment factor that can be applied to allow residue yield adjustments that differ from grain yield adjustment s, if appropriate. In addition, there is an option to specify a maximum quantity of residue rem oval per acre within FASOM.
Table D-2 presents the assumed straw-to-gra in ratio and weight conversion factor for each of the six crops (these values were assume d to be the same for all varieties of wheat modeled). These values are constant acros s regions and remain constant over time.
Based on the values presented in Tables B-3 and D-2, we can calculate residue production per acre. For instance, average corn residue production per acre in the Corn Belt in 2022 is equal to 197.2 bu grain/acre * 1 bu resi due/bu grain * 0.028 tons/bu = 5.52 wet tons of corn residue/acre. Corn residue is assumed to have a moisture content of 12%, so 5.522 wet tons of corn residue/acre correspond to 4.85 dry tons of corn residue/acre after removing the 12% of

163 Tyner, W., M. Abdallah, C. Bottum, O. Doering, B.A. McCa rl, W.L. Miller, B. Liljedahl, R. Peart, C. Richey, S. Barber, and V. Lechtenberg. 1979. “The Potential of Pr oducing Energy from Agricultu re.” Report to the Office of Technology Assessment. W. Lafayette, IN: Purdue University School of Agriculture. 164 Lal, R. 2005. “World Crop Residues Production and Implications of Its Use as a Biofuel.” Environment International 31(4):575-584.D-5 Table D-2. Straw-to-Grain Ratio and Weight Conversion Factor

Crop Straw to Grain Ratio Weight Conversion Factor Barley 1.5 : 1 0.024 tons/bu
Corn 1.0 : 1 0.028 tons/bu
Oats 1.0 : 1 0.024 tons/bu
Rice 1.0 : 1 0.050 tons/cwt Sorghum 1.0 : 1 0.050 tons/cwt
Wheat 1.5 : 1 0.030 tons/bu

corn residue that is water. For hard red wi nter wheat in the Great Plains, average residue production per acre in 2022 is 85.4 bu grain/ acre * 1.5 bu residue/bu grain * 0.030 tons/bu = 3.84 wet tons of wheat residue/acre. Moisture cont ent for wheat is assumed to be 8.9%. Thus, 3.84 wet tons of wheat residue/acre is equivalent to 3.50 dry tons of wheat residue/acre. However, removing all residue production is not consiste nt with good management practices and is not sustainable.
A number of site-specific fact ors affect the maximum amount of crop residue that can be sustainably removed, including crop t ype, soil type, soil fertility, slope, tillage, and climate. As a general rule, though, USDA Nationa l Resources Conservation Se rvice recommends that about 30% residue cover is adequa te to control soil erosion. 165 Removable residue values used in FASOM were calculated by adju sting the residue production per acre based on the harvestable percentages provided in Graham et al. 166 and Perlack et al., 167 which consider the effects of erosion and runoff. This approach uses a maximum percentage removal of residues. 168 These percentages vary by crop and ti llage, as shown in Table D-3. Another potential measure of sustainable residue availability is the minimum quantity of resi dues per acre that must be

165 Maung, T.A. 2007. “Economics of Biomass Fuels for Electricity Generation: A Case Study with Crop Residues.” Draft unpublished dissertation. College Station, TX: Texas A&M University. 166 Graham, R.L., R. Nelson, J. Sheehan, R.D. Perlack, and L. L. Wright. 2007. “Current and Potential U.S. Corn Stover Supplies.” Agronomy Journal 99:1-11. 167 Perlack, R.D., L.L. Wright, A.F. Turhollow, R.L. Graham, B.J. Stokes, and D.C. Erbach. 2005. Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply

. Report prepared for the U.S. Department of Energy and the U.S. Department of Agriculture. Available at http://feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf. 168 Many site specific factors associated with the sustainable removal of residue (e.g., crop type, soil type, soil fertility, slope, and climate) affect which geographic re gions are suitable for crop residue removal. Detailed modeling of these factors was beyond the scope of this analysis.D-6 Table D-3. Sustainably Harvestable Residue Percentages

Crop Conventional Tillage Conservation Tillage Zero Tillage Barley 0% 24.7% 35.3%
Corn 0% 35.0% 50.0%
Oats 0% 24.7% 35.3% Rice 0% 24.7% 35.3% Sorghum 0% 24.7% 35.3% Wheat 0% 24.7% 35.3%

retained on the land to prevent erosio n and maintain soil carbon levels. 169 However, insufficient data on minimum sustainable residues per acre we re available for use in this analysis. In addition, other than corn, removal rates for all crops in FASOM with potential residue use were assumed to be equal because there is lim ited residue-specific information available. 170 , 171
Table D-4 presents average removable residue per acre in 2022, reflecting adjustments to regional residue production per acre to account fo r the sustainably removable percentages. These values change over time with changes in crop yields and tillage practices. However, corn provides the greatest volume of sustainably removable crop residue per acre in all years in almost every region (the exception is hard red winter wheat residue in the Northeast region). 172 D.2.1.2 Ethanol Production from Crop Residues Available sustainably removable crop resi dues that are removed from fields under equilibrium market conditions in FASOM can then either be converted to ethanol or used in bioelectricity production. Base line ethanol conversion rates used in FASOM to produce ethanol from crop residue feedstocks are based on several different sources. These baseline values are

169 Wilhelm, W.W., J.M.F. Johnson, D.L. Karlen, and D.T. Lightle. 2007. “Corn Stover to Sustain Soil Organic Carbon Further Constrains Biomass Supply.” Agronomy Journal 99:1665-1667. 170 Kerstetter J.D., and J.K. Lyons. 2001. “Logging an d Agricultural Residue Supply Curves for the Pacific Northwest.” Washington State Un iversity Energy Publication. 171 Banowetz, G.M., A. Boateng, J.J. Steiner, S.M. Griffith, V. Sethi, and H. El-Nashaar. 2008. “Assessment of Straw Biomass Feedstock Resources in the Pacific Northwest.” Biomass and Bioenergy 32(7):629-634. 172 Having the highest sustainably removal residue per acre do es not necessarily mean that corn residues will be the preferred crop residue feedstock in a given region, however. The choice of feedstock by cellulosic ethanol processing plant depends on the relative cost of feedstocks per unit of ethanol produced. Thus, other factors such as crop density and energy content will also influence optimal feedstock selection.D-7 Table D-4. Regional Average Sustainably Rem ovable Crop Residue (Dry Tons per Acre), 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Barley residue 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.6 0.7 0.4
Corn residue 1.8 1.7 1.6 1.3 1.9 1.7 1.2 1.7 1.6 1.3
Oats residue 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.4 0.3
Rice residue NA NA NA NA NA NA NA NA NA NA Sorghum residue 1.2 0.9 NA 1.0 NA 0.5 0.5 1.0 0.8 0.6 Wheat, Durham NA 0.5 0.5 NA NA 1.1 0.7 NA NA NA Wheat, hard red spring NA 0.6 0.6 NA 0.8 NA 0.6 NA NA NA

Wheat, hard red winter 1.4 0.9 1.3 1.4 NA 1.2 0.9 1.2 1.3 0.8

Wheat, soft white NA NA NA NA 1.0 NA 0.9 NA NA NA
Note: CB = Corn Belt; GP = Great Plains; LS = Lake St ates; NE = Northeast; PNWE = Pacific Northwest—East

side (agriculture only); PSW = Paci fic Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; SW = Southwest (agriculture only). Values are only presented for an individual crop for regions where that crop can be produced with residue collection in FASOM. Although it is theoretically possible that residues could be collected from rice production, all rice production in FASOM is assumed to use conventional tillage. 173 Thus, rice in FASOM has 0% sustainably removable residues as shown in Table D-3.

then increased over time based on assumed rates of technical progress, re aching the levels shown in Table D-5 by 2022. To place feedstocks with varying moisture contents on a more readily comparable basis, the ethanol conversion rate is often reported in terms of gallons of ethanol per dry ton, and the quantities of feedstocks that can be sustai nably removed are converted to dry tons by adjusting for their average moisture conten t (i.e., quantity in wet tons * [1 – proportion of feedstock that is moisture]). Thus, ethanol conve rsion rates are provided in terms of both gallons per wet ton and gallons per dry ton in Table D-5.
Using these conversion rates, the required supply for a 100 MGY plant was calculated for each feedstock (see Table D-1). For instance, an ethanol plant using corn residue as a feedstock in 2022 would need 100 million gallons of ethanol per year/92.3 gallons per dry ton of corn residue = 1,083,424 dry tons of corn residue per year (1,231,163 wet tons ). With increases in the

173 Historically, there was very little U.S. rice production utilizing reduced tillage, but this practice has become more common in recent years.D-8 Table D-5. Ethanol Conversion Rates for Crop Residues, 2022

Crop Residue Moisture (%) Ethanol Conversion Rate (gallons/wet ton) Ethanol Conversion Rate (gallons/dry ton) Barley 10.3% 82.79 92.30
Corn 12.0% 81.22 92.30
Oats 10.3% 82.79 92.30 Rice 15.0% 78.46 92.30 Sorghum 10.0% 83.07 92.30 Wheat 8.9% 84.08 92.30 Source for ethanol conversion rate (gallons/dry ton): NREL (2008)

ethanol conversion rate over time , the quantity of feedstock required by an ethanol plant of a given output level decl ines commensurately. D.2.1.3 Crop Residue Feedstock Costs The cost of acquiring crop residues for cellu losic ethanol producti on (excluding hauling and handling) is determined in FASOM by calcu lating the market price of each alternative feedstock. These prices will vary by scenario but must be high enough to provide farmers with compensation for additional costs associated with residue removal. In addition to the on-farm harvesting and handling costs described above, ther e are increased fertilization requirements due to nutrient removal that vary by crop. FASOM assu mes that there is an in crease in fertilization requirement relative to baseline fertilizer use for each ton of residue removed from an acre of cropland. 174 In the Reference Case, there is essent ially no market for cellulosic feedstocks. Thus, the market prices of crop residues are zer o. Under the Control Ca se or other scenarios requiring cellulosic ethanol production, a market fo r cellulosic feedstocks is created, and residue prices rise to positive market-clearing levels under the new market conditions.
Table D-6 summarizes the market prices for crop residues in FASOM in the Control Case in 2022. The only crop residues th at are being traded in the market in the Control Case equilibrium are corn residues in the Corn Belt, Great Plains, and Lake St ates and wheat residues in the Great Plains, Rocky Mountains, and Pacific Northwest-East regions.

174 Additional fertilizer application amounts are based on the GREET defaults, as described in the November 7, 2006 report by M Wu, M. Wang, and H. Huo, “Fuel-Cycle Assessment of Selected Bioethanol Production Pathways in the United States”(ANL/ESD/06-7).D-9 Table D-6. FASOM National Average Farm Ga te Prices for Crop Residues (2007$/dry ton) in Control Case, 2022

Feedstock Price (2007$/dry ton) Corn crop residues $39.19 Wheat crop residues $36.25
D.2.2 Energy Crops Because there is little field experience with energy crops, more assumptions are involved in generating estimates of feedst ock availability and costs than for crop residues. This section outlines assumptions underlying the use of energy crops for ethanol production in FASOM. In order for energy crops to be adopt ed widely, they need to provide high enough returns to farmers to induce them to switch from altern ative land uses to energy crops.
For energy crops, assumed baseline yields per acre are based on Thomson et al. 175 for switchgrass and Walsh et al. for willow and hybrid poplar. 176 Switchgrass yields are assumed to increase over time at a rate of 2.04% per year, wh ile yields for willow and hybrid poplar increase at a rate of 0.75% per year. It was assumed that switchgrass c ould potentially be produced in eight of the FASOM regions (Cor n Belt, Great Plains, Lake Stat es, Northeast, Rocky Mountains, South Central, Southeast, Southwest); willow coul d be produced in four regions (Corn Belt, Lake States, Northeast, Southeast); a nd hybrid poplar could be produ ced in eight regions, matching up with the regions for switchgra ss except for the exclusion of the Rocky Mountains region and addition of the Pacific Northwest—East side region. Although it is technically possible for energy crops to be produced in each of these regions, they will only enter the market solution if they are competitive with alternative land uses available in those regions. 177
Table D-7 presents regional average energy crop yields fo r the year 2022 for the three energy crops included in FASOM. Other things being equal, th e higher the energy crop yield in a region, the more likely that en ergy crop will offer competitive returns to landowners. In all regions where switchgrass is incl uded in FASOM as a production pos sibility, switchgrass has the

175 Thomson, A.M., R.C. Izarrualde, T.O. West, D.J. Parrish, D.D. Tyler, and J.R. Williams. 2009. Simulating Potential Switchgrass Production in the United States . PNNL-19072. College Park, MD: Pacific Northwest National Laboratory. 176 Walsh, M.E., D.G. de la Torre Ugarte, H. Shapouri, and S.P. Slinsky. 2003. “Bioenergy Crop Production in the United States: Potential Quantities, Land Use Changes, and Economic Impacts on the Agricultural Sector.” Environmental and Resource Economics 24:313-333. 177 To reflect constraints on land conversion that may not be fully captured within FASOM, energy crop penetration was limited to a maximum of 12.5% of cropland in each region.D-10 highest potential yield among the energy crops included in the mode l. However, it is important to keep in mind that regions with high yields available for energy crops will also tend to have relatively high yields for competing crops. Thus, it is not necessarily the case that regions with the highest potential energy crop yi elds will be the regions that produce those crops in the model solution.
Unlike crop residues, where only a portion of the residues is removed based on the sustainably removable fractions de scribed earlier, the entire yiel d of energy crops presented in Table D-8 can be harvested and used for cellulosic ethanol production. Table D-7. Regional Average Energy Crop Yields (Dry Tons per Acre), 2022

Crop CB GP LS NE PNWE PSW RM SC SE SW Hybrid poplar 3.7 3.0 3.5 3.1 4.6 NA NA 3.3 3.2 3.0 Switchgrass 9.9 6.1 7.7 5.5 NA NA 3.4 9.3 7.7 8.6
Willow 3.6 NA 3.6 3.7 NA NA NA NA 3.5 NA
Note: CB = Corn Belt; GP = Great Plains; LS = Lake St ates; NE = Northeast; PNWE = Pacific Northwest—East

side (agriculture only); PSW = Paci fic Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; SW = Southwest (agriculture only). Table D-8. Ethanol Conversion Rates for Energy Crops, 2022

Energy Crop Moisture (%) Ethanol Conversion Rate (gallons/wet ton) Ethanol Conversion Rate (gallons/dry ton) Hybrid poplar 31.03% 70.00 101.50 Switchgrass 11.99% 81.23 92.30 Willow 33.33% 67.67 101.50 Source for ethanol conversion rate (gallons/dry ton): NREL (2008) D.2.2.1 Ethanol Production from Energy Crops Biomass from energy crops is converted to ethanol in FASOM using conversion rates that increase over time as discussed in S ection D.4. Table D-8 summarizes the ethanol conversion values used for 2022. Using these c onversion rates, the requ ired supply for a 100 MGY plant was calculated for each feedstock (see Table D-1). For instance, an ethanol plant using switchgrass as a feedstock in 2022 would need 100 million gallons of ethanol per year divided by 92.3 gallons per dry ton of switchgrass = 1,083,424 dry t ons of switchgrass per year (1,231,024 wet tons). With increases in the etha nol conversion rate over time, the quantity of feedstock required by an ethanol plant of a gi ven output level declin es commensurately.D-11 D.2.2.2 Costs of Using Energy Crop Feedstocks Similar to crop residues, the cost of acqui ring energy crops as a feedstock (excluding hauling and off-farm handling) is determined by the market-clearing farm-level price for each energy crop commodity as estimated by FASOM. It is important to note that opportunity costs associated with energy crop production include no t only the cost of harvesting the feedstock, but also crop production costs and the op portunity cost of land because it is a dedicated crop rather than residues associated with other crop production. Ethanol f eedstock yields per acre are generally much higher for these crops than for resi dues, however. Thus, if feedstock prices reach high enough levels, energy crops become competitive w ith alternative land uses in some regions.
As is the case for crop residues, there is almost no market for energy crops in the Reference Case, but when a market is created unde r the Control Case, prices of these feedstocks increase substantially. Table D-9 summarizes the market prices for energy crops in FASOM under the Control Case in 2022. The only energy crop that enters the ma rket solution under the Control Case is switchgrass, which is produced principally in the Southwest but also in the Northeast and Southeast. A lthough the ethanol conversion rate (gallons per dry ton) of switchgrass is lower than that of hybrid poplar or willow, switchgrass is assumed to have a higher crop density, higher crop yi eld per acre, and contains less moisture than hybrid poplar or willow (all of which tend to reduce hauling costs), which may help explain the market focus on switchgrass in the model. 178 Table D-9. FASOM National Average Pri ces for Energy Crops (2007$/Dry Ton) in Control Case, 2022

Feedstock Price (2007$/dry ton) Switchgrass $46.42

D.2.3 Logging and Processing Residues Finally, FASOM also includes logging and proce ssing residues as potential feedstocks for cellulosic ethanol production. This section outlines assumpti ons underlying the use of these residues for cellulosic ethanol production.

178 Note that although switchgrass has a lower ethanol conversion rate per dry ton than the other energy crops considered, its ethanol conversion rate per wet ton is hi gher because moisture content of switchgrass is much less than that of hybrid poplar or willow.D-12 D.2.3.1 Logging and Processing Residues Production For logging residues, yields are genera ted by applying assumed logging residue percentages to FASOM data on round wood yields. Yields we re averaged by FASOM market region using the FASOM forestry i nventory data as weights. Two- thirds of the logging residue was assumed to be harvestable base d on Helynen, Hakkila, and Nousiainen. 179 Logging residue yields in 1,000 cu ft per acre we re then converted to tons per acre using 27.5 lbs per cu ft for softwood and 33.0 lbs per cu ft for hardwood based on Carpenter. 180
Bagasse, milling residues, and sweet sorghum pul p are produced as coproducts of sugar, wood products, and sweet sorghum production in FASOM and depend on production of these products by region. Bagasse and milling residue s are currently used on site for energy production in the sugar refining and the wood pr oduct and paper industries, so use of these products for ethanol production competes with those alternative uses. D.2.3.2 Ethanol Production from Logging and Processing Residues FASOM assumes improvements in conversi on technology take place over time, as discussed in Section D.4. Biomass from loggi ng and processing residues can be converted to ethanol in FASOM in the year 2022 using th e conversion rates show n in Table D-10. Table D-10. Ethanol Conversion Rates for Logging and Processing Residues, 2022

Residue Moisture (%) Ethanol Conversion Rate (gallons/wet ton) Ethanol Conversion Rate (gallons/dry ton) HW logging residue 33.33% 67.67 101.50 SW logging residue 33.33% 61.54 92.30 Bagasse 31.03% 63.66 92.30 HW milling residue 33.33% 67.67 101.50 SW milling residue 33.33% 61.54 92.30 Sweet sorghum pulp 35.00% 60.00 92.30

HW = Hardwood, SW = Soft Wood Source for ethanol conversion rate (gallons/dry ton): NREL (2008) Using these conversion rates, the required supp ly for a 100 MGY plant was calculated for each feedstock (see Table D-1). As with all of the other feedstocks, increases in ethanol

179 Helynen, S., P. Hakkila, and I. Nousiainen. 2000. “Wood Energy 1999-2003: A New National Technology Programme in Finland.” New Zealand Journal of Forestry Science 1-2:46-53. 180 Carpenter, E.M. 1980. Wood Fuel Potential from Harvested Areas in the Eastern United States . U.S. Department of Agriculture Forest Service, Resource Bulletin NC-51, 14 p. St. Paul, MN: U.S. Department of Agriculture Forest Service, North Ce ntral Forest Experiment Station.D-13 conversion rates over time result in a reduction in th e quantity of feedstock needed by an ethanol plant. This lowers hauling costs because th e smaller quantity of f eedstock required can be collected from a smaller area. D.2.3.3 Costs of Using Logging and Proces sing Residues as Et hanol Feedstocks The cost of acquiring logging and processi ng residues for ethanol production (excluding hauling and handling costs) in cludes opportunity costs associat ed with diverting processing residues from heat and power production to ethanol production. Unlike most of the other cellulosic feedstocks, the Reference Case incl udes markets for selected processing residues (bagasse and sweet sorghum pulp). However, the increase in demand associated with the Control Case results in larg e increases in prices.
Table D-11 summarizes the market prices fo r logging and processi ng residues in FASOM under the Reference and Control Cases in 2022. Table D-11. FASOM National Average Prices for Processing Residues (2007$/Dry Ton) in the Reference and Control Cases, 2022

Feedstock Reference Case Price (2007$/dry ton) Control Case Price (2007$/dry ton) HW logging residue $0 $34.83 SW logging residue $0 $27.55 Bagasse $9.32 $43.06 Sweet sorghum pulp $79.26 $115.45 Note: Prices are only included for those feedstocks and cases where the feedstock is used in ethanol production in

the model solution. D.3 Coproducts The only coproduct of cellulosic ethanol produc tion that is tracked within FASOM is lignin, which is calculated as a proportion of feed stock use that varies acr oss feedstock types. For switchgrass, 26.4% of switchgrass volume is lignin; hybrid poplar 20.7%; willow, softwood, hardwood 20%; bagasse 28.5%; corn residue 22.2% ; wheat residue 27.3%; sorghum and barley residue 27.0%; rice residue 25.5%; and sweet so rghum pulp 12.7%. Lignin production levels were derived from data in Kadam et al. 181 and Kadam 182 . Lignin can be used as a fuel for generating heat or could potentially be used for electricity generation.

181 Kadam, K.L., V.J. Camobreco, B.E. Glazebrook, L.H. Forr est, W.A. Jacobson, D.C. Simeroth, W.J. Blackburn, and K.C. Nehoda. 1999. Environmental Life Cycle Implications of Fuel Oxygenate Production from CaliforniaD-14 FASOM includes electricity production using li gnin as a production possibility. In FASOM budgets, 384,151 tons of lignin from cr ops, 329,567 tons of lignin from hardwoods, and 308,642 tons of lignin from softwoods are re quired by the standard 100 megawatt (MW) electricity generation plant include d in FASOM. In addition, th ere are production possibilities in FASOM for co-firing with lignin at levels ranging from 5% to 20%. D.4 Technology Change One of the vital issues affecting the potential future large-scale adoption of cellulosic ethanol as a transportation fuel is the rate of technical change. Curre ntly, cellulosic ethanol production is not cost competitive, but increases in ethanol conversion rates and feedstock yields could potentially help to make large-scale cel lulosic ethanol production competitive with other fuels. The cost of feedstock per unit over time will depend on market fo rces, but the quantity required to produce a given amount of ethanol is expected to decline over time with improvements in ethanol conversion technology, wh ich will tend to reduce feedstock costs per gallon. The reduction in the quantity of feedstoc k needed to meet the EISA volume requirement over time (due to increasing cellulosic ethanol yi elds) will tend to reduce the demand for these feedstocks and is expected to contribute to lowe r feedstock prices farther into the future, other things being equal. As less feedstock is requ ired per gallon of ethanol, hauling and handling costs will also tend to decline because less f eedstock needs to be hauled and handled. In addition, the plant can acquire the necessary quan tity of feedstock from fewer acres so average hauling distance declines. The assumed increase in feedstock yields over tim e also contributes to these cost reductions by reduci ng average hauling distance and cost. Finally, the cost of processing delivered feedstock into ethanol at the plant was also assumed to decrease over time based on data presented in th e RIA for this rule.
Baseline values for ethanol yield for all f eedstocks were based on data from a study conducted by NREL. 183 These yields were then increas ed over time until reaching the maximum feasible ethanol conversion rate s assumed for each feedstock based on the work at NREL. After reaching that maximum yield, yields are assumed to remain constant at that level in subsequent

Biomass . Golden, CO: National Renewable Energy Laboratory. Available at http://www.nrel.gov/do cs/fy99osti/25688.pdf. 182 Kadam, K.L. 2000. Environmental Life Cycle Implications of Using Bagasse Derived Ethanol as a Gasoline Oxygenate in Mumbai . NREL/TP-580-28705. U.S. Department of Energy, National Renewable Energy Laboratory. 183 Tao, L. and A. Aden, Technoeconomic Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR) , November 2008.D-15 years. The rates at which ethanol yields for di fferent feedstocks are as sumed to progress towards their maximum levels were assumed to be the same for all feedstocks.
Figures D-1 and D-2 show the ethanol yiel d per dry ton and corresponding feedstock quantity requirements for crop residues over time . Figures D-3 and D-4 present analogous yield and feedstock quantity information for energy cr ops, and Figures D-5 and D-6 provide this information for logging and processing residues. As described above, the time frame at which the yield from a particular feedstock is assume d to reach its maximum is the same across all feedstocks considered, with each cellulosic feedstock reaching its maximum level in the 2022 FASOM model period. The magnitude of reductions in feedstock quantity required over time are an important factor affecti ng the costs of cellulosic ethanol production in FASOM over time.

0 10 20 30 40 50 60 70 80 90 100

Barley Residue Corn Residue Oats Residue Rice Residue Sorghum Residue Wheat ResidueEthanol Yield (gallons/dry ton)

Figure D-1. Ethanol Yield per Dry Ton from Crop Residues, 2022D-16 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000

Barley Residue Corn Residue Oats Residue Rice Residue Sorghum Residue Wheat ResiduePlant Level Feedstock Requirement (dry tons/year)

Figure D-2. Dry Tons of Crop Residue F eedstocks Required for a 100 MGY Ethanol Production Plant, 2022

0 15 30 45 60 75 90 105 120

Hybrid Poplar Switchgrass WillowEthanol Yield (gallons/dry ton)

Figure D-3. Ethanol Yield per Dry Ton from Energy Crops, 2022D-17 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 1,100,000 1,200,000

Hybrid Poplar Switchgrass WillowPlant Level Feedstock Requirements (dry tons/year)

Figure D-4. Dry Tons of Energy Crop F eedstocks Required for a 100 MGY Ethanol Production Plant, 2022

0 20 40 60 80 100 120

Hardwood Residue Softwood Residue Bagasse Sweet Sorghum PulpEthanol Yield (gallons/dry ton)

Figure D-5. Ethanol Yield per Dry Ton from Logging and Processing Residues, 2022D-18 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 1,100,000 1,200,000

Hardwood Residue Softwood Residue Bagasse Sweet Sorghum PulpPlant Level Feedstock Requirements (drytons/year)

Figure D-6. Dry Tons of Logging and Processi ng Residue Feedstocks Required for a 100 MGY Ethanol Production Plant, 2022

Increases in feedstock yields also contribute to reductions in production costs. Table D-12 summarizes changes in yield over time for cellulosic feedstocks. Changes are presented as an index relative to the year 2002. Although in itial yields differ across FASOM regions, the percentage change in yield for a given crop is assumed to be the same in each region.
In addition to these produc tivity increases put ting downward pressu re on cellulosic ethanol production costs, the assumed costs of converting delivered feedstock to ethanol are decreasing over time between 2007 and 2022, then remain constant at the 2022 level in later years.D-19 Table D-12. Cellulosic Feedstock Yield Index (C hange in Tons of Feedstock per Acre), 2022 (2002 = 100)

Feedstock 2022 Crop Residues Barley crop residues 101.9 Corn crop residues 138.0 Oat crop residues 100.3 Rice crop residues 130.3 Sorghum crop residues 101.8 Wheat crop residues 129.7 Energy Crops Hybrid poplar 116.0 Switchgrass 149.8
Willow 116.0 Logging Residues Hardwood logging residues 138.0 Softwood logging residues 138.0 Processing Residues Bagasse 100.1 Hardwood milling residues 138.0 Softwood milling residues 138.0 Sweet sorghum pulp 101.8

D.5 Agricultural Market Impacts FASOM tracks the impacts on agricultural mark ets associated with changing production practices to collect crop residue s, produce energy crops, collect logging residues, and to begin using or diverting processing resi dues from production of heat a nd power to ethanol. The largescale production of energy crops could have subs tantial implications for land use and production of food products, but returns to those crops must exceed the opportunity costs of land for producers to switch to production of energy crops. When the alternative feedstock source involves collecting residues on la nd that can be used for prim ary production of grains, energy crop production is generally less attractive unless these crops c ould be grown at high density around ethanol plants in regions where th e opportunity costs of land are low.D-20 Crop residues could potentially be used for gr azing in cow-calf operations or as livestock bedding and provide some value as a fertilizer du e to potash, potassium, and nitrogen content. However, the opportunity cost of retaining these re sidues in other uses is relatively low. In FASOM, there are crop budgets both with and without residue coll ection for barley, corn, oats, rice, sorghum, and wheat. As described earlier, the primary difference between the budgets with and without residue collection is th at additional fertilizer is required if residues are removed. Using the baseline FASOM fertil izer prices of $0.293 per pound for nitrogen fertilizer and $0.309 per pound for phosphorus fertilizer, 184 the additional fertilizer costs for these nutrients would be about $3.16 per ton of residues removed. In addition, there ar e costs of harvesting, baling, and moving the residue to th e roadside at the farm. Crop yi elds and other input uses are assumed to be the same with and without residue collection. Thus, as long as the market prices for crop residues are equal to or higher than the sum of these opportunity costs, farmers in FASOM will choose to switch from production w ithout residue collection to production with residue collection.
For cellulosic ethanol producti on in FASOM, all feedstocks have the same processing costs per gallon to convert deliver ed feedstock to ethanol. In a ddition, handling costs per wet ton are assumed to be equal acro ss all crop residues and energy cr ops. Because there are some differences in energy content per wet ton, feedst ocks with higher energy content per ton have lower handling costs per gallon of ethanol produced, but these costs tend to be fairly close across feedstocks. Thus, the allocation between feedstoc ks purchased by an ethanol plant rests largely on the differences in feedstock costs and biom ass hauling costs. As noted above, residue removal from land where traditional crops can be produced as well will tend to have lower opportunity costs than devoting la nd to energy crops. Regions where there are few attractive alternative crops and energy crops can be grow n at very high density surrounding an ethanol production plant would provide th e greatest potential for energy crop production. Otherwise, ethanol production will tend to be concentrated in the regions where la rge quantities of crop residues are available at high dens ity. In FASOM, the density of energy crops is assumed to be 10% for all regions where production of those crops is feasible (and adjusted density is lower for hybrid poplar and willow because of the multiple-y ear harvesting cycle). Consistent with these expectations, FASOM results for the Control Case show the ma jority of cellulosic ethanol feedstocks coming from corn residues in the Corn Belt, Great Plains, an d Lake States regions and switchgrass in the Southcentr al, Southwest, and Southeast re gions as well as Kansas and Missouri.

184 See: http://www.ers.usda.gov/Data/F ertilizerUse/Tables/Table7.xls (values have been adjusted to 2007$).D-21 Because there is such a large quantity of resi due available at relatively low cost, a large amount of land in FASOM is reallocated from cr op production without residue collection to crop production with residue collection, especially in the Corn Belt, Great Plains, and Lake States regions. Because more residue can be removed from no-till acres, if the price of residues is high enough, it could induce switching from conventional t illage to conservation tillage or no-till. However, there is relatively little change in tillage in FASOM model results for the Control Case. The switch from production without resi due collection to crop production with residue collection in FASOM is much more pronounced. This implies that in the regions where ethanol production from crop residues is concentrated, many farmers begin collecting residues where they did not under baseline conditi ons, potentially with little ch ange in other aspects of their production practices (e.g., tillage, irrigation).

Because FASOM assumes there are no effects on crop yields or input uses other than fertilizer associated with resi due collection in the Control Case , agricultural market impacts of cellulosic ethanol production from crop residues are relatively small. There may be sizable impacts on sectors such as the custom baling and transportation sectors associated with introducing a large-scale market for collecti ng and transporting crop residues to ethanol production plants. However, these sectors are not included within th e FASOM model. The sizable production from sw itchgrass has larger market effects, resulting in a re allocation of land toward switchgrass production a nd contributing to an increase in land values and commodity prices.E-1 APPENDIX E: HIGH YIELD SENSITIVITY SCENARIO
One important assumption influencing the amoun t of land required to produce sufficient feedstocks to meet the required EISA renewable fuels volumes is the ra te of increase in crop yields over time. The greater the rate of incr ease in crop yields, the less land that will required over time to provide the feedstock necessary to meet the volume requirements. In addition to analyzing the impacts of the Cont rol Case relative to the Referen ce Case under a primary set of key assumptions, FASOM was used to analyze the e ffects of increasing the rates of increase for two key crops, corn and soybeans. In this appe ndix, we present selected key 2022 results for the High Yield Sensitivity Scenario. Our comparisons focus on the impacts on U.S. harvested acres by crop; prices for corn, soyb eans, and switchgrass; and corn and soybean exports across scenarios. This sensitivity analysis enables exam ination of the implicatio ns of higher crop yields for the modeled impacts of the renewable fuel volumes required by EISA. E.1 Specification of High-Yield Sensitivity Scenario For the High Yield Sensitivity Scenario, th e yields of both corn and soybeans were assumed to increase at faster rates than assume d in Section 2. These higher yield growth rates were applied to both the Reference Case and C ontrol Case, with the im pacts associated with EISA requirements for a particular scenario based on the differences between those cases. Therefore, the impacts being measured in this sc enario are the incrementa l effects of EISA if corn yields grow at a faster rate than assumed in the Base Reference and Control Scenarios described in Section 2. The reason that corn yields are increased in both the Reference and Control Cases is that we are not assuming in this sensitivity anal ysis that EISA would lead to faster increases in corn producti vity, but that the impacts may di ffer if corn yields were to increase more rapidly over time than assumed in the Base Scenario presented in Section 2.
This increase in the technologi cal rate of progress begins in the 2012 model period in the FASOM model (the next future time period). By 2012, national average corn yields in the U.S. are 7.9 percent higher in the High Yi eld Control Case than in the Base Yield Control Case. By 2017, they are 16.2 percent higher, and by 2022 they are 24.6 percent higher. Similarly, soybean yields in the U.S. are 10.1 percent higher in 2012, 21.0 percent higher in 2017, and 32.6 percent higher in 2022 in the High Yield Control Case than in the Base Yield Control Case. 1 Similar

1 Corn yields in individual FASOM regions are all increased by 7%, 17%, and 27% in 2012, 2017, and 2022, respectively. Similarly, soybean yields in individual FASOM regions are all increased by 7%, 19%, and 30% in 2012, 2017, and 2022, respectively. However, because some regions have higher yields than others and theE-2 increases in technological rate s of progress for corn and soybe ans were applied to the export supply functions for non-U.S. corn and soyb ean exporters. Table E-1 summarizes the differences in average corn and soybean yields at the national level. Table E-1. U.S. Average Yields (Bushels per Acre) for Corn and Soybeans for the Control Case under the Base and High Yield Sensitivity Scenarios, 2022

Crop Base Scenario High Yield Sensitivity Scenario Ch ange Percentage Change
Corn 185.1 232.9 47.8 25.8% Soybeans 45.4 60.2 14.8 32.6%

In the following subsection, we present compar isons of the FASOM results generated for the High Yield Sensitivity Scenario to those from the Base Scenario, focusing on impacts on crop acreage, commodity prices, and exports. E.2 Comparison of Acreage, Prices, and Exports Although the assumptions employed in the Base Scenario were selected as reasonable estimates of future conditions, a number of uncertain ties are associated with projecting values far into the future. Increases in yield over time can mitigate competition for land and increases in agricultural commodity prices because more output is being produced per acre of land. To the extent that corn yields increase at a faster rate than assumed in the Base Scenario, there will be less future competition for land and a reduction in the upward pressure on agricultural commodity prices. E.2.1 Sensitivity Analysis Resu lts for Harvested Acreage Figure E-1 summarizes the harvested area for U.S. crops in the Reference Case under the Base and High Yield Sensitivity Scenarios. The biggest differences between the Scenarios considered are the reductions in corn and soyb ean acreage as their yields increase and fewer acres are required to meet market demand for these commodities.

regional allocation of production across regions changes under this scenario, the weighted average national yields do not increase at exactly the same rate as the increases applied to each individual region.E-3 0 10 20 30 40 50 60 70 80 90BarleyCornCottonHayOatsPotatoesRiceRyeSilageSorghumSoybeansSugarbeetSugarcaneSwitchgrassTomatoFrshTomatoProcWheat, DurhamWheat, Hard Red SpringWheat, Soft WhiteWhite, Hard Red WinterMillions of Acres

Base Scenario High Yield Sensitivity Scenario Figure E-1. U.S. Harvested Acreage by Crop for the Reference Case under the Base and High Yield Sensitivity Scenarios, 2022

Figure E-2 shows the change between th e Reference and Control Cases for 2022 harvested acreage for U.S. crops simulated using FASOM under both Base and High Yield Sensitivity Scenario assumptions. The biggest differe nce is that the increase in switchgrass acres for the High Yield Sensitivity Scenario is less th an half the increase experienced under the Base Scenario. In addition, corn acr eage increases more and soybean acreage decreases less under the High Yield Sensitivity Scenario. At higher corn yi elds, there is also greater production of corn stover per acre. As a result, the use of corn re sidues as a cellulosic et hanol feedstock almost doubles in the High Yield Sensitiv ity Scenario, while the use of switchgrass declines by nearly 60%. In general, there are smaller declines in acreage for crops other than corn and soybeans under the High Yield Sensitivity Scenario because less land is required for renewable fuels feedstock production with higher yields.E-4 -4 -2 0 2 4 6 8 10 12 14BarleyCornCottonHayOatsPotatoesRiceRyeSilageSorghumSoybeansSugarbeetSugarcaneSwitchgrassTomatoFrshTomatoProcWheat, DurhamWheat, Hard Red SpringWheat, Soft WhiteWhite, Hard Red WinterMillions of Acres

Base Scenario High Yield Sensitivity Scenario Figure E-2. Change in U.S. Harvested A creage between the Reference Case and the Control Case for the Base and High Yield Sensitivity Scenarios, 2022

Similarly, there is less land conversion from pasture and forest under the High Yield Sensitivity Scenario. As shown in Table E-2, total harvested cropland in 2022 under the Reference Case is reduced by about 11 million acre s in the High Yield Sensitivity Scenario. In addition, meeting the EISA rene wable fuels volume targets requi res an increase in harvested cropland that is 2.7 million acres less under the High Yield Sensitivity Scenario, resulting in a total of 13.7 million acres less harvested cropla nd under the Control Case for the High Yield Sensitivity Scenario than the Base Scenario.
As expected, there are substantial differences in U.S. land allocation if the yields for corn and soybeans are increased at a substantially high er rate. Not only are co rn and soybeans two of the most important U.S. crops, but they also provide key feedstocks for renewable fuels production. When corn and soybeans have higher yields, the market equilibrium simulated in FASOM has less acreage in these cr ops, other things being equal. There are competing effects in this case because a higher rate of technical progress increases corn and soybean production perE-5 Table E-2. Total Harvested Cropland under the Base and High Yield Sensitivity Scenarios, 2022

Reference Case Control Case Change Percentage Change Base Scenario 306,279 314,385 8,106 2.6% High Yield Sensitivity Scenario 295,243 300,678 5,435 1.8% Difference between High Yi eld and Base Scenarios

− 11,036
− 13,707
− 2,671 Percentage Change

− 3.6%
− 4.4%
− 33.0%

acre, which would tend to increase the returns to production and draw more acreage into these crops. However, increased production per acre will reduce the number of acres needed to meet the demand for corn, and prices fall until corn an d soybean acreage have decreased sufficiently to equilibrate supply and demand. In this case, we see that the net effect of higher yields is to reduce equilibrium harvested acreage. E.2.2 Sensitivity Analysis Results for Pri ces of Key Renewable Fuels Feedstocks In Table E-3, we present a comparison of FASOM model results for equilibrium U.S. corn, corn residues, soybean, and switchgrass prices (largest sour ces of feedstocks for renewable fuels) under the Reference Case and Control Ca se simulated for both the Base and High Yield Sensitivity Scenarios. The prices of these commodities increase when EISA requirements are introduced in every case with the exception of corn prices under the High Yield Sensitivity Scenario. Given the increase in demand for these feedstocks associated with EISA in order to increase ethanol production, increasi ng prices are entirely consistent with expectations. For corn under the High Yield Sensitivity Scenario, with le ss land in crops and lower commodity prices, the development of a market for corn stover incr eases the returns for planting corn relative to alternative crops sufficiently that corn acreag e and production actually increase enough relative to the increased demand for corn due to EISA that equilibrium corn prices fall by 4.2%.
As expected, equilibrium prices are lower in the Reference Case under the High Yield Sensitivity Scenario. In addition, price incr eases required to induce sufficient feedstock production to meet EISA volume requirements are smaller under the High Yield Sensitivity Scenario. In fact, we observe that corn acr eage and production will increase enough to meet demand even with a decrease in corn price. Unde r this scenario, substant ially higher yields for corn and soybeans result in lower agricultural comm odity prices and land values in the Reference Case. With these market conditions, the develo pment of a market for corn residues in theE-6 Table E-3. Changes in Simulated Market Pr ices for Corn, Soybeans, and Switchgrass between the Reference and Control Cases under the Base and High Yield Sensitivity Scenarios, 2022 (2007$)

Reference Case Control Case Change Percentage Change
Base Scenario Corn ($/bu) $3.32 $3.60 $0.27 8.2% Corn residues ($/dry ton) NA $34.49 $34.49 NA Soybeans ($/bu) $9.85 $10.87 $1.02 10.3% Switchgrass ($/dry ton) NA $37.22 $37.22 NA High Yield Sensitivity Scenario Corn ($/bu) $2.74 $2.62

− $0.12

− 4.2% Corn residues ($/dry ton) NA $21.34 $21.34 NA Soybeans ($/bu) $7.69 $8.44 $0.24 2.9% Switchgrass ($/dry ton) NA $32.66 $32.66 NA

Control Case increases the return s to corn production (and the corn residue coproduc t) relative to alternative crops enough to make corn production relatively more attrac tive than alternative crops even though the expanded pro duction leads to a dec line in the price of corn. With higher yields, less additional corn acreage is needed to produce enough feedstock to meet the renewable fuels volume requirements of EISA , but the increase in corn acreag e is larger in the High Yield Sensitivity Scenario than the Base Scenario. Th e supply of corn increases more than the demand for corn under the High Yield Sensitivity Scenario as the value of corn residues provides enough net income to induce greater switching towards corn in this scenario with lower commodity prices, resulting in a decrease in the equilibrium price of corn. Not surprisingly, one of the implications of this sensitivity analysis is that the faster corn productivity increases over time, the smaller the adjustments required to m eet the increased demand for corn under EISA. E.2.3 Sensitivity Analysis Resu lts for Selected Exports Table E-4 compares model results for exports of key commodities most directly affected by expanded volume requirements for renewable fu els. With higher yields for corn and soybeans, the model results show an increase in corn exports under the High Yield Sensitivity Scenario. Exports of soybeans are lower as dom estic soybean processing expands even more than soybean production, resulti ng in a large increase in domesti c soybean oil production andE-7 Table E-4. Changes in Simulated Exports fo r Corn, Soybeans, and Soybean Oil between the Reference and Control Cases under the Base and High Yield Sensitivity Scenarios, 2022

Reference Case Control Case Change Percentage Change
Base Scenario Corn (million bu) 2,281.4 2,093.4

− 187.9
− 8.2% Soybeans (million bu) 993.2 858.5

− 134.7
− 13.6% Soybean oil (million tons) 2.3 1.1

− 1.2
− 51.2% High Yield Sensitivity Scenario Corn (million bu) 2,690.9 2,812.9 122.0 4.5% Soybeans (million bu) 896.1 839.0

− 57.2
− 6.4% Soybean oil (million tons) 7.0 5.0

− 2.1
− 29.6%

soybean oil exports that are more than three times as high in the High Yield Sensitivity Scenario under the Reference Case.
As described above, the High Yield Sensitivity Scenario leads to a greater expansion in corn acreage and production than the Base Scenar io because land values and commodity prices are substantially lower and the relative impact of revenue from corn residues is higher. As a result, the development of a market for corn resi dues increases the returns to corn production to the point that corn supply increases more than the increase in domestic demand associated with EISA renewable fuels volume requirements and e xports of corn increase. Soybeans do not have a similar coproduct used for cellulosic ethanol production. Thus, exports of soybeans and soybean oil decline as the dom estic demand for soybean oil for use in biodiesel production increases more than the increase in domestic pr oduction of soybeans and soybean oil. However, the percentage decline in expor ts is much smaller under the Hi gh Yield Sensitivity Scenario.