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 27709
iii 

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 and 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 Agriculture 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.................................................................................. 2-28
iv 

2.2.2 Food Prices............................................................................................. 2-29

References..............................................................................................................................R-1

Appendixes 

A: Description of the Forestry and Agricultural Sector Optimization Model .................. A-1 

B: Modeling Starch-Based and Sugar-Based Ethanol Production ....................................B-1 

C: Modeling Biodiesel Production ....................................................................................C-1 

D: Modeling Cellulosic Ethanol Production..................................................................... D-1 

E: High Yield Sensitivity Scenario....................................................................................E-1 
v 

LIST OF FIGURES 

Number Page

1-1. FASOM Agricultural Sector Modeling Structure......................................................... 1-9

2-1. Corn Prices under the Reference Case and Control Case (2007$) ............................... 2-5

2-2. Corn Production under the Reference 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 Reference 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 Reference Case and Control Case ....................... 2-11

2-13. Percentage Change in Commodity Production 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 Use 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 over 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................................................................................. 2-19
vi 

2-20. DG Prices under the Reference Case and Control Case (2007$)................................ 2-21

2-21. Gluten Feed and Gluten Meal 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 Reference 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 Price Indices under the Control Case ...... 2-30
vii 

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 Fertilizer Usage under the Reference Case 

and Control Case, 2022 (thousands of pounds) .......................................................... 2-28
1-1 

SECTION 1 

OVERVIEW OF FASOM 

The Forest and Agricultural Sector Optimization Model (FASOM), developed by 

Professor Bruce McCarl of Texas A&M University and others, is a dynamic nonlinear 

programming model of the U.S. forest and agricultural 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 consumption, technical input/output relationships 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 sectors and the associated impacts on commodity 

markets. In addition, the model simulates environmental impacts resulting from changing land 

allocation and production practices, including accounting for changes in net greenhouse gas 

(GHG) emissions for particular agricultural activities. The model was developed to evaluate the 

welfare and market impacts of policies that influence land allocation 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 programs, bioenergy production, and a variety of other policies 

affecting the forest and agricultural sectors. 

The comprehensive sectoral coverage provided by FASOM is advantageous for analyzing 

the impacts of the Energy Independence and Security 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 associated 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, movements between cropland and pasture, 

movements between agricultural land and forestland, 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 (CO2), methane (CH4), and nitrous oxide (N2O), from the 

majority of emitting agricultural activities and tracks carbon sequestration and carbon losses over 

time. The intertemporal dynamics of the economic and biophysical systems within FASOM 

allow for an accounting of environmental impacts over time and by region. This allows for a 
1-2 

more complete quantification of net impacts than other models are generally able to produce, 

which provides insight into the multiple environmental 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, forestry, and environmental impacts of implementing the 

renewable fuel standard (RFS2) volumes consistent with EISA. Section 1.2 describes the 

application of FASOM to modeling the proposed renewable fuel volumes specified under EISA. 

In Section 1.3, we highlight modifications to the model that have been made since the analysis 

conducted for the RFS2 proposal to reflect comments received and availability of new data. In 

Section 2, we present key model results for this analysis. In addition, a set of four appendixes 

provides additional detail on the FASOM model, focusing on assumptions and parameters most 

directly relevant for the analysis 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 incorporation of starch- and sugar-based ethanol 

production in the model. Appendix C describes similar information for the production of 

biodiesel in FASOM. Finally, Appendix D summarizes assumptions underlying 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, modeling methodology, and GHG accounting. 

1.1.1 Model Development 

The current version of FASOM reflects numerous model enhancements that have been 

made over time, dating back to the first version of the Agricultural Sector Model (ASM).2

 Since 

the initial version of ASM, there have been many changes to the model, including 

 

1

See http://agecon2.tamu.edu/people/faculty/mccarl-bruce/papers/1212FASOMGHG_doc.pdf for additional detail 

on the FASOM model. 2

Baumes, H. 1978. “A Partial Equilibrium Sector Model 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 environmental and resource analyses. ASM has been 

used for analyses of renewable fuel production dating back to the 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 Herbicide 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. McCarl, W.L. Miller, B. Liljedahl, R. Peart, C. Richey, S. 

Barber, and V. Lechtenberg. 1979. “The Potential of Producing Energy from Agriculture.” 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. 10Adams, R.M., J.M. Callaway, and B.A. McCarl. 1986. “Pollution, Agriculture and Social Welfare: The Case of 

Acid Deposition.” Canadian Journal of Agricultural Economics 34:3-19. 11Chang, 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. 12Adams, 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. 13Adams, R.M., C. Rosenzweig, R.M. Peart, 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. 14Adams, 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. 15Adams, 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. 16McCarl, B.A. 1999. “Economic Assessments under National Climate Change Assessment.” Presented at Meeting 

of National Climate Change Assessment Group, Washington, DC. 17Reilly, J., F. Tubiello, B. McCarl, and J. Melillo. 2000. “Climate 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. 18Reilly, 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. 

19Adams, R.M., D.M. Adams, J.M. Callaway, 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. 20McCarl, 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 with forest-sector models to create FASOM 

was an ASM study examining issues regarding joint forestry and agricultural GHG mitigation.21 

Attempting to reconcile forestry production possibilities 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 with land allocation between forestry and agriculture. Thus, the 

initial FASOM model was constructed to address these limitations by linking a simple 

intertemporal model of the forest sector with 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 horizon of the model. Management investment 

decisions in both sectors, including harvest timing in forestry, were made endogenous, so they 

too would be based on the expected profitability of an additional dollar spent on expanding 

future output (both timber and carbon, 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 (RPA) 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 

 

21Adams, R.M., D.M. Adams, J.M. Callaway, 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. 22Adams, 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. 23Adams, 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 Agriculture, 

Forest Service, Pacific Northwest Research Station, 58 p. 24Haynes, 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. 25Ince, P.J. 1994. Recycling and Long-Range Timber Outlook. General Technical Report RM-242. Ft. Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 

23 p. 26Zhang, 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. 27Zhang, 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. 28Mills, 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 Northwest Research Station. 160 p. 

29Alig, 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 future timber yields were taken in large part from the ATLAS inputs 

used for the 2000 RPA Timber Assessment (these data 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 allocations to the ATLAS model. TAMM and NAPAP 

are “myopic” market projection models (they project 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. Product 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 conversion coefficients for both solid wood and 

fiber commodities. Trade between the United States and Canada in all major classes of wood 

products is endogenous and subject to the full array of potential trade barriers 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 classes directly from 

ATLAS), and the selection of management intensity is endogenous. 

In addition, detailed GHG accounting for CO2 and major non-CO2 GHGs was added into 

a model denoted FASOMGHG.32 The forest carbon accounting component of FASOM is largely 

derived from the Forestry Carbon (FORCARB) modeling system, which is an empirical model of 

forest carbon budgets simulated across regions, forest 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 over 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 potential in particular.33,34 Deriving FASOMGHG’s forest carbon 

accounting structure from FORCARB ensures that forest carbon estimates from FASOM can be 

 30Alig, 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. 31Haynes, 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. 32Throughout the remainder of this report, references to FASOM refer to the most recent version of FASOMGHG 

used to conduct this analysis. 33Joyce, 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. 34Joyce, 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 Service, Rocky Mountain Research Station. 
1-6 

analyzed and compared with ongoing efforts by the 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 incorporate improved science. 

Following the inclusion of forest carbon accounting and some limited coverage of soil 

carbon changes associated with land use change, work began to widen the coverage of 

agricultural GHG sources and management possibilities for mitigating GHG. Schneider35 and 

McCarl and Schneider36 expanded the model to account for numerous categories of GHGs and to 

include a detailed set of agricultural-related GHG management possibilities. That work 

expanded ASM to include changes in tillage, land use exchange between pasture and crops, 

afforestation, nitrogen fertilization alternatives, 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 carbon sequestration coverage included in 

FASOM and the agricultural coverage in ASMGHG, it was decided to merge the agricultural 

alternatives into the FASOMGHG structure. This was manifest 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 alternatives in ASMGHG with the additional 

coverage of dynamics. More recently, additional model modifications have been made to 

enhance FASOM’s ability to provide detailed analyses of the agricultural and environmental 

impacts of large-scale renewable fuel production under EISA. These modifications are discussed 

in Section 1.1.3. 

1.1.2 Model Structure 

Examining the dynamic effects of policies affecting the forestry and agricultural sectors 

requires an analytical framework that can simulate 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 affecting the sector. FASOM combines component 

models of agricultural crop and livestock production, renewable fuels production, livestock 

feeding, agricultural processing, log production, forest processing, carbon sequestration, GHG 

 

35Schneider, U.A. December 2000. “Agricultural Sector Analysis on Greenhouse Gas Emission Mitigation in the 

U.S.” PhD dissertation. College Station, TX: Department of Agricultural Economics, Texas A&M University. 36McCarl, B.A., and U.A. Schneider. 2001. “The Cost of Greenhouse Gas Mitigation in US Agriculture and 

Forestry.” Science 294(Dec):2481-2482. 37Lee, H.C. 2002. “The Dynamic Role for Carbon Sequestration by the U.S. Agricultural and Forest Sectors in 

Greenhouse Gas Emission Mitigation.” PhD thesis. College 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 biophysical and economic processes that will determine 

the technical, economic, and environmental implications of changes in policies. FASOM covers 

private timberlands and all agricultural activity across 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 field crops, livestock, and renewable fuel (see 

Appendix A for additional detail). 

As noted above, FASOM assumes intertemporal 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 rather than harvesting it now is based on a comparison of the net 

present value of timber harvests from future periods versus the net present value of harvesting 

now and replanting (or not replanting and shifting 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 value of returns in agriculture and forestry. Land can also move 

between cropland and pasture depending on relative returns. This process establishes an 

equilibrium price for land across the sectors (reflecting 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 factor supply curves (an aggregate measure of producer costs). Such 

an approach involves solving a nonlinear programming model with endogenous product and 

factor prices. The resultant objective function value is consumer plus producer surplus. 

Operationally, FASOM is a multiperiod, intertemporal, price-endogenous, mathematical 

programming model depicting land transfers and other resource allocations between and within 

the agricultural and forest sectors in the United States. The model solution portrays 

simultaneous market equilibrium over an extended time, typically 70 to 100 years on a 5-year 

time step basis when running the combined agriculture-forest version of the model. Results yield 

a dynamic simulation of prices, production, management, consumption, GHG effects, and other 

environmental and economic indicators within these 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 CO2, CH4, and N2O and 

– total nitrogen and phosphorous applications. 

The subsections below provide an overview of the overall scope and 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; and dynamic yield, cost, and demand updating.

1.1.2.1 Resources, Production, Processing, and Commodity Flows 

The basic conceptual framework of the agricultural sector in FASOM is presented in 

Figure 1-1. Land, water, labor, natural resources, and other resources (e.g., fertilizer, capital) are 

used to produce raw primary commodities, including 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 fuels), 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 meeting 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 raw, processed, used for bioenergy, or mixed for 

livestock feed. These commodity groups are 

 raw crop, livestock, forestry, and renewable fuel feedstock primary commodities 

grown on the land; 

 processed, secondary commodities made from 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 sugarcane. In addition, a number of feed grains are 

substitutes in terms of livestock feeding. Similarly, many forestry products are substitutes for 

one another, such as sawtimber or pulpwood derived from alternative hardwood and softwood 

species groups. In addition, bioenergy feedstocks 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 other potential feedstocks). 
1-10 

FASOM contains a set of processing activities 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 being crushed into soybean meal and soybean oil because these 

secondary commodities frequently flow into different markets. Similar 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 Plains and Southwest regions are kept separate 

because they reflect important differences in agricultural characteristics. Likewise, there are 

important differences in the two Pacific Northwest regions (PNWW, PNWE) for forestry, so 

they are maintained separately, although only the PNWE region is considered a significant 

producer of agricultural commodities tracked in the model. 

1.1.2.4 Land Base Coverage 

FASOM includes all cropland, pastureland, rangeland, and private timberland39

throughout the conterminous United States. The model tracks both area used for production and 

idled (if any) within each land category. In addition, the model tracks the movement of forest 

and agricultural lands into developed uses. Land categories included in the model are based on 

USDA Economic Research Service land use data40 and are specified as follows: 

 Cropland is land suitable for crop production that 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 and irrigated/dryland status as well as the 

amount of time it has been in such a system to allow tracking of sequestered soil 

 

39 Although public timberland is not explicitly modeled because the focus of the model is on private decision-maker 

responses to changing incentives, FASOM 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 Eastern 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 carbon 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 amounts 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 (pasture on forested public lands that can be 

grazed).41,42 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. 

 

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-GTR-68. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 
1-12 

 Rangeland comprises both public and private rangeland, which is typically 

unimproved land where a significant portion of the natural vegetation is native 

grasses and shrubs. Rangeland generally has low forage productivity and is 

unsuitable for cultivation. In addition, much of the rangeland in the U.S. is publicly 

owned. It is assumed that rangeland cannot be used for crop production or forestland. 

 Forestland in FASOM refers to private timberland, with a number of subcategories 

(e.g., different levels of productivity, 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 harvested, 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 pasture 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 development is sufficiently high that the 

movement of forest and agricultural land into 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 exogenous rate (with the exception 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 vegetation 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 subject to the restrictions discussed under 

each category above. The conversion costs of moving between land categories are set at the 

present value of the difference in the land rental rates between the alternative 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 timberland that is reserved for other uses are not 

considered part of the U.S. timber base and are therefore not tracked by the model. In FASOM, 

endogenous land use modeling is only done for privately held parcels, not publicly owned or 
1-13 

managed timberlands. The reason is that management of public lands is significantly influenced 

by government decisions on management, harvesting, and other issues that account for multiple 

public uses of these lands rather than private responses to market conditions. However, an 

exogenous quantity of timber harvested on U.S. public lands is accounted for within the model. 

Regional public harvest levels are set at exogenous levels based 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 set by government administrative decree. The public 

land managers could change allowable harvest levels at any time, but those changes are not 

predictable, so harvest is assumed to remain fixed over time. 

Private timberland is tracked by its quality and its transferability between forestry and 

agricultural use. FASOM includes three different site classes to reflect differences in forestland 

productivity (these site groups were defined based on ATLAS inputs43), where yields vary 

substantially between groups: 

 HIGH—high site productivity group (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 (NIPF). The traditional definitions are used for 

these ownership groups: industrial timberland owners possess processing capacity 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 different possible management intensity classes 

depending on whether thinning, partial cutting, passive management, or other management 

methods are used. 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 

hardwood types in the South). Stand age is explicitly accounted for in 5-year cohorts ranging 

from 0 to 4 years up to 100+ years. 

 

43Haynes, 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 Agriculture, 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 public forest lands are included in the model but 

are treated as exogenously determined by the government. 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, depending 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 feedstocks included in the 

model. For each crop, production budgets are differentiated by region, tillage choice (three 

choices: conventional tillage, conservation tillage, or no-till), and irrigated or dryland and 

cropland type (four as discussed in land use section above). The differentiation included results 

in thousands of cropping production possibilities (budgets) representing agricultural production 

in each 5-year period. Energy crop production possibilities are similar, except that irrigation is 

not an available option in the current FASOM production possibilities; all energy crops are 

assumed to be produced under nonirrigated conditions 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 livestock production possibilities (budgets) represent agricultural 

production in each 5-year period. 

Supply curves for agricultural products are generated implicitly 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-scale tree-planting programs or implementation of mandatory GHG 

mitigation policies). 

The forest production component of FASOM depicts the use of existing private 

timberland as well as the reforestation decision on harvested land. Timberland is differentiated 

 

44Because 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 using 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 agricultural use. Decisions pertaining to timber 

management investment are endogenous. Actions on the inventory are depicted in a framework 

that allows timberland owners to institute management activities that alter the inventory 

consistent with maximizing the net present value 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 lands that are converted from agriculture to forestry (afforested) 

introduce decisions involving the choice 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 allocation across sectors sets FASOM apart from 

the majority of other forest and agricultural sector models of the United States. The conceptual 

foundation for land allocation is described below. 

In terms of transferability between agriculture and forestry, FASOM includes five land 

suitability classes: 

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

 FORCROP—includes acres that begin in timberland but can potentially be converted 

to cropland 

 FORPAST—includes acres that begin in timberland but can potentially be converted 

to pastureland 

 CROPFOR—includes acres that begin in cropland but can potentially be converted to 

timberland 

 PASTFOR—includes acres that begin in pasture 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 pastureland occur within the medium-quality forest 

 

45Timberlands are grouped in 21 5-year cohorts, 0 to 4 years, 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 movements in forestry are only allowed in the NIPF 

owner category, reflecting an assumption that land held by the FI ownership group will not be 

converted out of timberland. 

As mentioned above, the decision to move land between uses depends on the net present 

value of returns to alternative uses, including the costs of land conversion. Land transfers from 

forestry to agriculture take place only upon timber harvest and require an investment to clear 

stumps, level, and otherwise prepare the land for planting agricultural crops. Agricultural land 

can move to other uses during any of the 5-year model periods, but when afforested it begins in 

the youngest age cohort of timberland. 

In addition to the endogenous land allocation decision, land also moves out of 

agricultural and forestry uses into developed uses (e.g., shopping centers, housing, and other 

developed and infrastructural uses) at an exogenous rate. Thus, although land can move between 

forest, cropland, and pasture, the total land area devoted to agricultural 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 analysis, CRP land is permitted to convert back to 

cropland under the constraint that a minimum of 32 million acres of land remains in the CRP to 

be consistent with the 2008 Farm Bill and USDA assumptions.46

1.1.2.7 Nonland Factor Modeling 

In addition to land, FASOM depicts factor supply of other resources, including water, 

labor, and other agricultural inputs in agriculture, as well as nonwood inputs in the forest sector. 

In agricultural production, water and labor availability are specified on a regional basis. 

Supply curves for both items have a fixed price component and an upward-sloping component, 

representing rising marginal costs of higher supply 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 subject 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 product processing costs. Other forest inputs are assumed to be 

infinitely available at a fixed price. 

 

46In addition, we explore a sensitivity analysis where the land area remaining in the CRP is allowed to fall to about 

half of the baseline CRP area in FASOM (which 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 generally 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 secondary products, and processing costs to convert 

primary products into processed products. Processing costs for the production of processed 

agricultural products are usually assumed to equal the observed price differential between the 

value of the outputs and the value of the inputs based on USDA Agricultural Statistics.47 On the 

forestry side, the nonwood input supply curve 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 renewable fuels production, soybean crushing, wet 

milling, and bioelectricity generation. Processing 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 price 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 corn, wheat, soybeans, rice, and sorghum across 

37 foreign regions.48 FASOM includes information on transportation costs to all regions, which 

affect equilibrium exports. 

The model solution requires that all markets are in equilibrium (i.e., quantity supplied is 

equal to the quantity demanded in every market modeled at the set of market prices in the model 

solution). The demand and supply curves included within the model and that need to be in 

equilibrium in each 5-year period include 

 regional product supply, 

 national raw product demand, 

 

47U.S. Department of Agriculture, National Agricultural Statistics Service. Various years. USDA Agricultural 

Statistics (1990–2002). Available at http://www.nass.usda.gov/Publications/Ag_Statistics/. 48FASOM foreign regions include the European Economic Community, North Central Europe, Southwest Europe, 

Eastern Europe, Adriatic, Eastern Mediterranean, Former Soviet Union, North Africa, East Africa, West Africa, 

South Africa, Red Sea, Iran, India, Taiwan, Japan, South 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 perfectly 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 CO2 and non-CO2 GHGs emitted from and sequestered 

by agriculture and forestry, plus the stock on lands in the model that are converted to developed 

use. In addition, the model tracks GHG emission reductions in selected other sectors that result 

from mitigation actions in the forest and agricultural sectors. For instance, FASOM accounts for 

reduced GHG emissions from fossil fuel use in the energy sector due to the supply of renewable 

fuel feedstocks from agriculture. GHG accounting in FASOM covers both the agricultural and 

forest sectors as well as production of renewable fuels. Additional 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 years 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, landowners 
1-19 

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 simulation of prices, production, management, 

consumption, and GHG effects within these two sectors under the scenarios defined for a 

particular model run. 

Given the long rotation lengths in forestry, an important consideration for modeling the 

dynamics of landowner decision making is the possibility that trees could be planted with a 

rotation length that exceeds the amount of time remaining in a model simulation. Producers 

would need to anticipate net returns that justify keeping land in forestry (or moving land from 

agriculture to forestry) and incurring stand establishment 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 present value of an asset for all time periods after 

the end of the explicit model projection. Several types of terminal assets are valued in FASOM, 

including initial timber stands that are not harvested during the simulation period, reforested 

stands remaining at the end of the simulation period, and agricultural land retained in agriculture. 

1.1.2.12 Dynamic Yield, Cost, and Demand Updating 

FASOM also incorporates a number of assumptions 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 growth and projections of future yields. In 

addition, certain processing activities, particularly those that rely on relatively new technologies, 

are expected to experience increases in production efficiency and corresponding reductions in 

processing costs in the future. For these activities (e.g., cellulosic ethanol production), 

processing yields (quantity of secondary product output per unit of primary commodity input) 

and production costs are assumed to change over time 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, production and processing costs, and demand over time 

 

49For instance, if a stand with a 30-year rotation were being considered in year 90 of a 100-year simulation, the 

anticipated harvest date would fall outside the time period modeled, and the 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 relative returns to production of different commodities and will affect producer 

decisions. Other things being equal, for commodities where demand is growing faster than 

productivity, real prices will tend to increase over time. For commodities where demand growth 

is slower than productivity improvements, real prices will generally 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 model applied in this analysis has been 

improved over previous versions in several ways, including the addition of a more detailed 

representation of GHG sources and sinks and inclusion of updated 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 sector in the latest version of FASOM include reflection of large 

transfers of forest land from traditional industrial owners to timber investment management 

organizations (TIMOs) and real estate 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 plantations on private timberlands, and a change in 

outlook for Canadian timber harvests associated with pest outbreaks51. In addition, assumptions 

about growth in demand for developed land have been updated to reflect recent projections of 

income and population growth. 

There have also been significant improvements in the representation of the renewable 

fuels sector in FASOM. The model now includes multiple feedstocks that can meet the demand 

for producing liquid renewable fuels, including starch- and sugar-based ethanol production, 

biodiesel, and cellulosic ethanol production. Some of the key inputs for this sector that have 

been reviewed and updated recently, where possible, include production and delivery costs, 

energy crop and crop residue yields, ethanol conversion rates, and the rates of technological 

change. There is also an updated secondary market for renewable fuel production, namely the 

use of distillers grains (DG) for livestock feed. 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 quantities 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 implementing 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 feedstocks 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 agricultural feedstocks modeled in FASOM. 

For this analysis, we refer to baseline conditions without EISA requirements, which are based on 

projected volumes of ethanol and biodiesel in Annual Energy Outlook 2007 (AEO2007),52 as the 

“Reference Case” and the case where EISA volume 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 are 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 would 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 solution in some years in small volumes. Given 

projected increases in ethanol production under 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 modeled 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 sorghum pulp in 2022), the volume of cellulosic 

ethanol is increased to a much larger degree than starch-based ethanol (increase of 13.45 BGY). 

Although some of the projected increases in cellulosic renewable fuels are expected to be in the 

form of a diesel substitute, FASOM does not currently include this renewable diesel or biomass 

to liquids as potential processing pathways. Therefore, for the FASOM analysis all of the 

cellulosic volumes are assumed to be cellulosic ethanol. In addition 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 equivalents) above the Reference Case volume. 

A number of potential feedstocks can be used in FASOM to produce ethanol or biodiesel. 

The proposed renewable fuel volumes are implemented in the model as constraints on national 

production of each of the three categories of renewable 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 renewable fuels production by category. Generally, the 

model solution in such a case will include production in regions where feedstocks to produce the 

required volumes of starch-based ethanol, cellulosic 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 availability of an updated version of the combined forest-agriculture 

version of the model. At the time of the previous analysis, the combined forest-agriculture 

version was undergoing revisions and 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 yields, 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 categories, including the replacement of rangeland 

measured in animal unit months (AUMs) with rangeland 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 only, the agriculture sector only, or for both 

sectors combined. Running the model with both sectors simultaneously allows for analyzing of 

interactions between the two sectors, which compete for land. However, because the forestry 

component of the model was undergoing a substantial update at the time of the analyses 

conducted for the RFS2 proposal and was not ready 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 presented in this report to enable better assessment 
1-23 

of the impacts on land use as well as other interactions between the forest and agricultural 

sectors. In addition, this modeling change allowed market modeling of potential use of forestderived feedstocks for cellulosic ethanol production 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 yields derived from alternative cellulosic 

feedstocks. Rather than having several 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, sweet sorghum pulp, and softwood residues was assumed to be 

92.3 gallons of ethanol per dry ton of feedstock in 2022. The yield for hybrid poplar, willow, 

and hardwood residues was assumed to be 101.5 gallons of ethanol per dry ton of feedstock in 

2022. These updates resulted in higher 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 for corn residue, sorghum residue, bagasse, and 

softwood logging and milling residues than those used in the analysis for the proposal.54 

1.3.3 Updated Switchgrass Yields 

Switchgrass yields per acre were updated based 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 switchgrass 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 prior 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. 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. 

 
1-24 

fractionation is assumed to be food grade and a perfect 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. Based on EPA research on expected technological 

adoption, it was assumed that the share of plants employing extraction 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 Replacement 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 such as corn and soybean meal, and its use has been growing 

substantially in recent years as ethanol production has expanded. In the previous analyses 

conducted for the RFS2 proposal, it was assumed 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 content 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 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 and poultry by 2017. 

In addition, DG produced as a by-product of a dry milling process with corn oil 

fractionation/extraction has different nutritional characteristics 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 traditional DG when used for swine or 

poultry feed, although the total replacement 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 Distillers 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-Protein 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 proposal, it was assumed that all DG produced in 

the United States would go into the domestic livestock feed market. In addition to the changes to 

replacement rates described above, the model was modified to account for potential exports of 

DG. This provides a larger market for the DG produced and is more consistent with recent 

experience, where DG is being shipped overseas to be used in livestock 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 agricultural land tracked within 

the model more closely with national land use data.58 One major change was to split the former 

pasture category into separate categories of cropland pasture and forest pasture, 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 and vice versa was respecified such that exchanges 

between cropland and pasture are now only permitted for the higher productivity cropland 

pasture category. In addition, rangeland 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 estimates 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 depending on the AUM data that had been used in 

the past, similar to each of the pasture categories). Whereas rangeland and pasture were 

incorporated independently in FASOM livestock budgets previously, producers can now 

substitute directly between them to provide sufficient forage for their livestock. The area 

included in each of the new categories 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 proposal is the use of updated 

estimates of N2O emissions from soil management. The Natural Resource Ecology Laboratory 

at Colorado State University generated new estimates of direct N2O emissions from fertilizer 

application, as well as indirect N2O emissions from leaching and volatilization that were 

incorporated into FASOM. The emissions estimates were generated using the DAYCENT 

model60 and reflect recent updates to the data and model. Emissions estimates vary by region, 

crop, fertilizer application rate, tillage, irrigation status, and residue removal rate and better 

capture the heterogeneity of emissions than previously available estimates of N2O emissions 

from soil management. Because these updated values from DAYCENT simulations reflect 

emissions from the complete N cycling process, there is no longer a need for separate 

calculations of N2O 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 Appendix A for additional information on the model. 
2-1 

SECTION 2 

SUMMARY OF DOMESTIC AGRICULTURE AND FORESTRY IMPACTS 

In this section, we summarize the key impacts of the Renewable Fuel Standards required 

by the EISA on the U.S. agricultural sector. The impacts presented in this section are based on 

FASOM simulations of agricultural and forestry market outcomes under a Reference Case 

without EISA as well as under a Control Case with 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 primary source of feedstocks for the production of 

ethanol and biodiesel, required increases in production of these fuels will increase the demand 

for agricultural feedstocks and will affect agricultural 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 returns to production of alternative commodities 

will lead landowners to alter their choice of commodities to produce as well as the management 

practices used (e.g., irrigation, tillage). 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 result in different quantities of by-products and 

coproducts being produced, which will affect 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 impacts of EISA on key commodity prices and 

quantities, land use, renewable fuel by-products, livestock and related industries, international 

agricultural commodity trade, and environmental measures based on the Control Case are 

presented below. Results presented focus 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 conditions. Table 2-1 presents the distribution of ethanol and 

biodiesel production across feedstocks under both the Reference and Control Cases based on 

FASOM model results, which provides an indication of the commodity markets expected to be 

most directly affected by increasing renewable fuels production. 
2-2 

Table 2-1. Renewable Fuel Production by Feedstock 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 ethanol production is starch-based ethanol 

derived from corn, although there is a substantial increase in cellulosic ethanol production as 

well, primarily from switchgrass with smaller increases in bagasse, sweet sorghum pulp, wheat 

residue, and forestry residues. By 2022, almost half of the total increased ethanol production 
2-3 

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 the increase between the Reference and Control 

Cases is derived from soybean oil and corn oil (nonfood grade), with very little change in the use 

of the other feedstocks included in FASOM relative 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 increases due to higher demand, but changes in relative 

prices will also have implications for prices and quantities in other agricultural markets. Using 

FASOM to quantify these impacts over time enables 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 such 
2-4 

as cellulosic ethanol rather than starch-based ethanol mitigates these impacts. The rapid growth 

in starch-based ethanol production in recent years and continued increases anticipated under 

baseline conditions will result in a relatively small increment in production necessary to reach 

the 15 billion gallons per year (BGY) of starch-based 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 ethanol, as well as the demand for corn oil as a 

biodiesel feedstock, will affect 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 induces greater corn production (see Figure 2-2).61 

Under the Control Case, corn prices increase by 8.3% in 2017 (from $3.45/bushel to 

$3.74/bushel) and by 8.2% in 2022 (from $3.32/bushel 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 feedstock used for renewable fuels increases 

substantially. Although total corn production and consumption increase under the Control Case, 

this increase in allocation of corn to renewable fuels production results in decreases in corn use 

for both livestock feed and exports. Corn used 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 Reference Case. However, some of the decline in 

corn used for feed is replaced by DG, gluten feed, and gluten meal, which are all coproducts of 

ethanol production. In the Control 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 bushels. 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 million 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 allocation of corn use in 2017 and 2022, respectively. 

 

61Note that projected prices are declining slightly over time, 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 (inflation-adjusted) prices indicate that 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 Reference Case and Control Case (2007$) 

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2017 2022

Million Bushels

Year

Reference Case Control Case

Figure 2-2. Corn Production under the Reference Case and Control Case 
2-6 

0

1,000

2,000

3,000

4,000

5,000

6,000

Ethanol Livestock Feed Exports Other Food, Seed, 

and Industrial

Million Bushels

Reference Case Control Case

Figure 2-3. Distribution of Corn Usage 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 Industrial

Million Bushels

Reference Case Control Case

Figure 2-4. Distribution of Corn Usage across Consumption Categories, 2022 
2-7 

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 gallon) 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 soybean oil being the product that 

is in increased demand due to EISA, whereas soybean 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 bushel 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 soybean meal prices of about 1.8% in 2017 and 0.1% in 2022, which contributes 

to holding down soybean price increases. Although there is an increased 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 than the increase in demand, leading to a reduction in equilibrium 

meal prices. 
2-8 

Changes in soybean and soybean oil prices are shown in Figures 2-6 and 2-7, 

respectively. Prices for soybeans increase by 9.4% in 2017 (from $10.02/bushel to 

$10.97/bushel) and by 10.3% in 2022 (from $9.85/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 production 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 results 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 collection processes improve, lower cost tallow and 

waste oil feedstocks might constitute a larger share of biodiesel at some point in the future, but 

based on current modeling assumptions, most of the 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 portion of the biodiesel market. If these technologies 

become widespread and more cost-effective, the 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 2022

Million Bushels

Year

Reference Case Control Case

Figure 2-8. Soybean Production under the Reference Case and Control Case 
2-10 

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 Industrial

Million 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 Industrial

Million Bushels

Reference Case Control Case

Figure 2-10. Soybean Usage, 2022 
2-11 

0

5

10

15

20

25

2017 2022

Billion 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 show cellulosic ethanol production from 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 model simulations, switchgrass 

is the largest source of cellulosic feedstock (see Table 2-3). FASOM model results indicate that 

3.9 BGY of ethanol are produced 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 produce high yields per acre compared to residue crops. However, in 

general, a lower price is required to induce farmers to collect residues from grain production to 

sell as a cellulosic ethanol feedstock 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 residue. Because corn produces the most residue per 

acre among the crops considered and is the highest density crop in major agricultural production 

regions of the United States, corn residues are a particularly attractive feedstock to meet 

cellulosic ethanol production requirements. 

Because moisture content and ethanol yields vary across feedstocks and these yields are 

assumed to improve over time for cellulosic ethanol production, the actual quantity of feedstock 

required for a given level of ethanol production varies across feedstocks and time. Table 2-3 

summarizes the quantities of residues estimated to be used for cellulosic 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 market for cellulosic ethanol under the Reference 

Case, market prices estimated in FASOM for residue 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 market for switchgrass or corn residues in the 

Reference Case (i.e., market equilibrium quantities 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 residue prices are $19.24 per dry ton in 2017 and 

$39.19 per dry ton in 2022 as a market for these residues develops. Table 2-4 summarizes prices 

reported by FASOM for all cellulosic feedstocks that are used in the market equilibrium. As 

shown in Table 2-4, only seven cellulosic feedstocks (corn residue, 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 acquiring alternative cellulosic feedstocks 

sufficient to generate a given quantity of ethanol within a region should be very close in 

equilibrium, there may be differences in the farm-level price paid for alternative feedstocks for a 

number of reasons. First, a dry ton of different feedstocks will provide differing quantities of 

ethanol because of the variance in energy density and ethanol conversion rates. 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 handling and hauling costs per dry ton (and similarly per gallon of 

ethanol produced after adjusting for energy content) 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 Cellulosic 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 commodity 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 land use change and production shifts 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 increases for the majority of commodities tracked, 

with some of the largest price increases taking place 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 refined sugar production away 

from using sugarbeets and toward using sugarcane. 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 across products modeled. Livestock prices are 

generally increased, but the prices of some meats are declining. 
2-14 

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

Cotton

SoftWhiteWheat

HardRedWinterWheat

DurhamWheat

HardRedSpringWheat

Sorghum

Rice

Oats

Barley

Rye

Silage

Hay

Sugarcane

Sugarbeet

Potatoes

TomatoFrsh

TomatoProc

FeedlotBeefSlaughter

Milk

HogsforSlaughter

HorsesandMules

Eggs

Broilers

Turkeys

FedBeef

NonFedBeef

Pork

Chicken

Turkey

2017 2022

Figure 2-13. Percentage Change in Commodity Production under the Control Case 

Relative to the Reference Case 

2.1.2 Land Use 

Increases in demand for agricultural commodities used to produce large quantities of 

renewable fuels, such as corn and soybeans, are expected 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 constraints, 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 increasing faster than the use of cropland in the 

Reference Case. Some lands in the CRP move back into cropland over time. 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%

Cotton

SoftWhiteWheat

HardRedWinterWheat

DurhamWheat

HardRedSpringWheat

Sorghum

Rice

Oats

Barley

Rye

Silage

Hay

Sugarcane

Sugarbeet

Potatoes

TomatoFrsh

TomatoProc

FeedlotBeefSlaughter

Milk

HogsforSlaughter

HorsesandMules

Eggs

Broilers

Turkeys

FedBeef

NonFedBeef

Pork

Chicken

Turkey

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 produce ethanol feedstocks, the Control Case 

increases the use of cropland and pasture in production, while contributing to a decrease in land 

allocation to private forestland relative to the Reference Case. Much of the increase in land in 

agricultural production is drawn from 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 both increases land allocation to cropland and 

reduces the area of idle cropland relative to the Reference Case. Cropland area in production is 

increased by 4.6 million acres in 2017 and 8.1 million acres in 2022 relative to the Reference 

Case. Pasture in use increases by 11.0 million 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 2022

Million 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 year). Under this assumption, as 

long as productivity increases outpace assumed annual demand growth, total cropland acreage 

tends to fall while relative land use values increase. 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 expansion 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 conversion of cropland to 

developed use than in the Reference Case. Overall, 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 2022

Million 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 Allocation over Time, Reference Case 

The higher prices of agricultural commodities associated with increasing demand for use 

in renewable fuels production will have a direct impact on the value of U.S. agricultural land. As 

demand increases for corn and soybeans and feed inputs for livestock increase in price, the value 

of agricultural land is expected to increase, as shown in Figure 2-18. In 2017, the Control Case 

results in an increase in cropland 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 increases 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 from $1,846 per acre to $2,091 per acre in 2022 

(13.3% increase). 
2-18 

0

5

10

15

20

25

30

2017 2022

Million 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 Allocation 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 residue) increases, more acres of cropland are 

expected to be used in the production of corn, as shown in Figure 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 renewable 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 increase in demand for soybeans for use in biodiesel 

production under EISA, the biodiesel volume requirements are relatively small compared to the 

required volumes of ethanol. Soybean acreage 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 2022

Million 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 long-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 annual allocation of agricultural 

land to different crops. 
2-20 

FASOM assumes annual yield increases for corn and soybeans to be approximately 1.6% 

and 0.4%, respectively. In 2022, total production of corn in the Control Case is projected to be 

15.1 billion bushels, an increase of 3.9% over the Reference Case. FASOM estimates soybean 

production to be 3.0 billion bushels in 2022 under the 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 renewable 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 livestock feed. Because production of these coproducts increases along 

with renewable fuels production, enabling 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 corn and soybean meal used in feed (see Appendix 

A for more information on DG replacement of other feeds). As corn and 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 the 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 commercial 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 ethanol industry continues to 

make progress in being able to improve the quality 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 

 

65See 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 Reference Case and Control Case (2007$) 

 

while the price of gluten feed increases. 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 Prices under the Reference Case and Control 

Case (2007$) 
2-22 

Overall, the Control Case results in an increased 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 change 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 2022

Millions of Corn Bushels Replaced

Year

Reference Case Control Case

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

Another product that can be used in livestock feed is soybean meal, which is produced in 

greater quantities because of the increase in biodiesel production 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 market 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 increases in feed prices associated 

with the proposed increase in renewable fuels’ production volume. Although ethanol coproducts 

can be used as livestock feed, they do not fully offset the increased cost of livestock feed due to 

increased demand for corn in alternative uses. Generally, production of individual livestock 
2-23 

$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 percentage 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 international trade patterns. In particular, the United States is 

a major exporter of corn, soybeans, and soybean 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 ethanol raises the price of corn, which has a 

direct impact on the competing uses of corn. FASOM results show that higher U.S. corn prices 

lead to lower U.S. exports of corn, as shown in Figure 2-24. In 2017, U.S. corn exports drop 

from about 2.19 billion bushels in the Reference Case to 2.01 billion in the Control Case (8.4% 

decrease). Similarly, corn exports in 2022 fall from 2.28 billion to 2.09 billion (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 corn 
2-24 

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2017 2022

Million Bushels

Year

Reference Case Control Case

Figure 2-24. Corn Exports 

prices increase under the Control Case, they do not increase sufficiently to offset the reduction in 

exports, and the value of corn exports declines 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 2022

Million $

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 increase 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 than 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 2022

Million Bushels

Year

Reference Case Control Case

Figure 2-26. Soybean Exports 

Figure 2-28 shows the decrease in soybean oil exports associated with the increase in 

biodiesel volumes in the Control Case. In 2017, the 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 are 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 2022

Milion $

Year

Reference Case Control Case

Figure 2-27. Value of Soybean Exports 

0

1

2

3

4

5

2017 2022

Billion lbs

Year

Reference Case Control Case

Figure 2-28. Soybean Oil Exports 

In contrast to the large decline in U.S. exports of soybean oil, soybean meal exports 

increase in the Control Case, as shown in Figure 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 increase 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 production goes into increased use in both livestock 

feed and exports. 
2-27 

0

2

4

6

8

10

12

14

16

18

2017 2022

Million US tons

Year

Reference Case Control Case

Figure 2-29. Soybean Meal Exports 

2.1.6 Environmental Impacts 

The changes in acreage allocation and agricultural production practices that result under 

the EISA requirements lead to changes in environmental impacts as well. FASOM models a 

number of different types of environmental impacts resulting from policy changes. In this 

section, we focus on changes in fertilizer use and GHG emissions. 

2.1.6.1 Fertilizer Impacts 

Corn and soybean production require significant 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 predicted 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 cellulosic ethanol. As the corn residue is harvested, 

some of the vital nutrients in the soil are removed 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. Total use of phosphorus fertilizer increases 

by 714,137,000 pounds (12.7%) in 2022. 
2-28 

Table 2-5. Corn, Soybean, and Total Agricultural 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 result from increasing renewable 

fuels production are changes in farm income and the prices that consumers will pay for their 

food. Implications for both are discussed below based on FASOM results. 

2.2.1 U.S. Farm Income 

The increase in renewable fuel production provides a significant increase in farm income 

to the U.S. agricultural sector. FASOM model results show 2017 U.S. farm income from the 

sale of agricultural commodities increasing by 27.4% ($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 expected, increasing commodity prices and higher production levels lead 

to a large increase in farm income under the Control 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 2022

Billion $

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 derived 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 feedstocks. FASOM does not 

directly track retail food prices but estimates an increase in wholesale U.S. prices for meat and 

agricultural products associated with the higher renewable fuel volumes. To evaluate changes in 

overall U.S. food prices, FASOM generates an index 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 for 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 are only a portion of ultimate 

 

66The FASOM All Farm Products Price Index includes cotton, corn, soybeans, 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 Product and Food Price Indices under the Control 

Case 

household food costs, so significant increases in corn prices and, to a lesser degree, soybean 

prices are expected to result in a much smaller relative increase in household food costs.67

As seen in Figure 2-31, the percentage change in the All Crops Price Index68 is very close 

to the change in the All Farm Products Price Index. The impact of increases in grain prices on 

the Meat Price Index69 is much less than the impact on crop prices (and actually a decrease in 

price in 2017). This likely reflects substitution 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; therefore, the total cost of 

meat production will increase by less than the increase in feed prices. In addition, the higher 

value of tallow and lard as biodiesel feedstocks increases revenue from beef cattle and hog 

 

67According 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 fact, these interim processes can absorb 

price shocks to some extent, thereby dampening the impacts on the consumer. Therefore, only a portion of farm 

gate price changes are often reflected at the retail level. See http://www.ers.usda.gov/publications/foodreview/ 

septdec00/FRsept00e.pdf. 68 The FASOM All Crops Price Index includes cotton, corn, soybeans, wheat, 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. These increases in production of beef and pork are 

contributing to the observed patterns in modeled meat prices, where the Meat Price Index 

actually experiences a decline in 2017 and only a small 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 required increases in cellulosic ethanol production is expected to rely 

partially on forest feedstocks; consequently, forest management could 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 actual quantity of feedstock coming from forests 

is relatively low compared to other cellulosic feedstocks (total ethanol production from all forest 

feedstocks is expected to be 109 million gallons 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 for 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. Under 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 Reference Case and would be left in 

the woods. The exogenous increase in demand for hardwood and softwood logging residues 

under EISA will potentially create a market for these residues. Table 2-1 illustrates the 

production of renewable fuels across feedstocks, which provides an indication of the relative 

impact on the commodities markets most directly affected by increasing renewable fuels 

production. Implications for prices and quantities of hardwood and softwood logging residues 

are discussed below. 
2-32 

2.3.1.1 Hardwood Residues 

Currently, FASOM does not consider the possibility of cellulosic ethanol production 

competing for the wood used in the wood products sector. Instead, it is only the residues from 

processing mills or logging residues that would otherwise be left in the woods that are 

considered as potential feedstocks. Therefore, there are no impacts on the wood products market 

associated with using logging residues in ethanol production. Logging residues 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 and paper. However, as discussed above for 

hardwoods, FASOM is not currently modeling the possibility that 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 price 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.” Available at http://agecon2.tamu.edu/people/faculty/ 

mccarl-bruce/papers/1212FASOMGHG_doc.pdf. 

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 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. 

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APPENDIX A: 

DESCRIPTION OF THE FORESTRY AND AGRICULTURAL SECTOR 

OPTIMIZATION MODEL 
A-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 large-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 management decisions. In addition, changes in 

crop prices will affect animal feed prices and returns to livestock production. Section A.1 

provides an overview of agricultural 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 make the model particularly well-suited to 

analyses of major policies affecting the agricultural and forest sectors, such as the impacts of 

EISA renewable fuels standards, is FASOM’s ability 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 time and the resultant consequences for the 

commodity markets supplied by these lands as well as changes in net GHG emissions and other 

environmental impacts. In this section, we discuss the regions included in FASOM; the types of 

land tracked; potential land uses modeled; assumptions regarding land conversion costs; crop 

yields and technology change over time; and GHG emissions associated with land use and land 

use change. 

A.1.1 Regions 

The FASOM model includes the 48 states in the contiguous United States divided into 63 

production regions and 11 market regions, 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/faculty/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 Algebraic 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 Eastern 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 production regions are defined at the state level. 

However, for selected major production areas with significant differences in production 

conditions within states, the states are broken into subregions. In addition to the regions defined 

for the United States, international regions are defined 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 generally 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 importers 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 export demand functions facing the United States 

rather than trying to model individual region supply 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 opportunities for international trade, such as 

hay, silage, citrus fruits, energy crops, and livestock, among others. Generally, trade is not 

explicitly modeled for commodities where international 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

West
A-4 

A.1.2 Types of Land 

FASOM accounts for land used in agricultural and forestry production within the 

conterminous United States, separating the land area into multiple categories that are tracked 

separately based on differences in their characteristics 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 constraints, though, as discussed in Sections A.1.3 

and A.1.4. 

A.1.2.1 Agricultural Land 

The major categories of agricultural land modeled within FASOM are 

 cropland, 

 Conservation Reserve Program (CRP) land, 

 cropland pasture, 

 forest pasture, and 

 rangeland. 

Cropland is land that is suitable for crop production and can potentially be used in the 

production of any of the crops included in FASOM for the particular production 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 implicitly included in the pastureland category in 

FASOM (i.e., both of these categories of cropland are included in other categories rather than 

being reported under cropland). The average annual areas of cropland with failed crops72 or idle 

cropland are not included in the reported FASOM cropland and are not explicitly tracked in 

FASOM. Cropland can potentially be converted to cropland pasture or private forestland. 

 

72 USDA data for planted area exceeds the harvested area because 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 the crop. Thus, farmers will choose not 

to harvest those areas. 
A-5 

In addition to tracking aggregate cropland area, cropland is also tracked by crop tillage 

system and irrigated/dryland status as well as the duration of time the land has been in such a 

system73 to allow tracking of sequestered soil carbon and the transition to a new soil carbon 

equilibrium following a change in tillage. Also, there are differences in crop yields between 

irrigated and dryland systems as well as differences in input use, GHG emissions, and other 

environmental impacts. Different tillage systems 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 farmers choose to enroll in the program is typically 

marginal cropland that farmers have agreed to retire from production for a contracted period. 

The land is generally converted to vegetative cover such as grass, trees, or woody vegetation to 

conserve soil, improve water quality, enhance wildlife habitat, or produce 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 program when their contract expires (or before 

expiration, subject to a financial penalty), FASOM also tracks 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 agricultural production exceed the returns associated with 

maintaining land in the CRP. However, based on the 2008 Farm Bill, which specifies a 

maximum of 32 million acres in the 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 constraint 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 livestock 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 conventional 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 increasing carbon inputs. Tillage 

changes from more intensive conventional tillage practices, such as moldboard plowing, 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 typically involves less direct fossil fuel use for tractors. 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 manufacture 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?area=home&subject=copr&topic=rns-css. 
A-6 

pasture tends to be more productive. 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 the other hand, refers to land that has varying 

amounts of tree cover but can also be used as pasture. 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 public lands that can be grazed). 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 rangeland 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 for each livestock species and estimated forage 

productivity per acre for each category of pasture in FASOM, defined on a regional basis.75 The 

area of pastureland used in livestock production is limited to the pastureland inventory by time 

period and region. It is possible to have idle pastureland in FASOM 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 used for animal production and an 

increase in idle pastureland in the model. Changes in feed costs are one reason why livestock 

populations may be impacted by EISA. The impacts of these changes on livestock markets and 

input use, including pastureland, are tracked within FASOM. 

Rangeland in FASOM includes both public and private rangeland. Rangeland differs 

from pastureland primarily in that it is assumed to be generally unimproved land where a 

significant portion of the land cover is native grasses and shrubs. The productivity of rangeland 

varies considerably across regions of the U.S. Therefore, the area of rangeland required per 

animal for a given species can be very different across regions. Overall, rangeland typically 

provides lower forage production per acre than pastureland 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. 

 

75The 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 distinction between the two is that timberland 

refers to forestland that provides at least a minimum level of productivity (at least 20 cubic feet 

of live growing stock per acre per year) and is 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 

acre per year, called unproductive forestland, and timberland 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 only 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 assumption, and timber inventory levels for public 

timberlands are simulated based on the exogenous timber harvest levels set by government 

administrative decree. On private lands, landowners choose their forest management activities 

and decide when to harvest and whether to replant or convert their land to another use following 

a harvest based on the relative returns to alternative actions. 

Private timberland is tracked by its quality and its transferability between forestry and 

agricultural use. FASOM includes three different site classes to reflect differences in forestland 

productivity (these site groups were defined based on ATLAS inputs77), 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 (sites that produce between 50 and 85 

cubic feet of live growing stock 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 productivity in timber production are also assumed to provide higher 

agricultural productivity if they are transferred to agricultural production following timber 

harvest. These differences in assumed productivity affect potential transferability between 

timberland and agricultural uses, as described in Section A.1.3. 

 

76 Because these unproductive forestlands are assumed to offer low productivity 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 Agriculture, Forest Service, Pacific Northwest Research 

Station, 66 p. 
A-8 

Another distinction in land ownership tracked in FASOM is between two major classes 

of private forest owner groups: forest industry (FI) and non-industrial private forest (NIPF) 

landowners. The traditional definitions are used for these ownership groups: industrial 

timberland owners are those that possess processing capacity for the timber, and NIPF owners do 

not. In FASOM, it is assumed that timberland owner by the FI cannot be converted to 

agriculture, whereas NIPF lands can move into agricultural production, subject to the constraints, 

opportunity costs, and conversion costs described 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, partial cutting, passive 

management, or other management methods are used. 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 hardwood types in the South). Stand 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 rotation length, and carbon sequestration. 

A.1.2.3 Developed Land 

FASOM also tracks the movement of agricultural and forest land into developed uses. 

The economic returns to developed land uses typically exceed the returns available to 

agricultural or forestry land uses. Thus, FASOM assumes an exogenous rate of land conversion 

into developed uses by region for each of the agricultural and forest land 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 income, 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 the primary agricultural and forestry 

commodities produced and production practices employed on a given type of land as well as 

movements between the different types of land described above. Underlying the commodity 

production decisions and the associated environmental impacts is the decision by landowners on 

how much, where, and when to allocate land across 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 describe key model assumptions driving results 

related to land use. 
A-9 

A.1.3.1 Cropland 

Cropland can move freely between production of alternative crops (that are available for 

production within a region) with no conversion costs associated with moving between crops. 

However, opportunity costs of switching from the original crop influence switching between 

different crops produced on available cropland. In addition, there 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 adoption 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’ consideration of all production possibilities and 

constraints imposed by rotation requirements, available resources, and other technical factors. 

Consequently, forcing the model to use a convex combination of observed regional crop mixes 

implicitly incorporates all firm production processes and constraints.78 

In addition, cropland tillage can be altered at no explicit cost, but there are differences in 

input use that are taken into consideration. In many cases, the choice of tillage may change 

along with a change in crop to be produced based 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 production (subject to regional constraints on 

production possibilities). Irrigated cropland provides higher yields for a given crop-region 

combination, but also requires additional inputs and is subject to water resource 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 historical 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 decline from the starting value of just over 37 million 

acres to the 32 million acre maximum level authorized by the 2008 Farm Bill. Cropland can be 

converted to pasture for a minimal conversion cost. However, the opportunity cost of the land 

being kept in cropland rather than converted is generally a major cost of switching from cropland 

 

78 In applications where FASOM is run over an extended time period, these crop mix 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 region, the less likely that 

cropland will move to pastureland, other things being equal. Transferability between cropland 

and timberland is described below under Section A.1.3.4. 

Finally, there is exogenous movement of cropland 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 discussed 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 regarding 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 cropland or timberland and forest pasture in 

agriculture can be converted to forestland. The other forest pasture categories cannot be 

converted to another land category, but any of the pasture categories can move to idle 

pastureland. The cost of converting pastureland to cropland 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 cost80 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 opportunity cost associated with the returns to keeping the land in pasture used 

for livestock production. Thus, the net present value of land use in cropland has to exceed that of 

use in pastureland by a large enough margin to justify incurring the initial conversion costs. 

Transferability between pastureland and timberland 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 constraints on pastureland 

conversion, it is also possible for 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 costs 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 discount rates devalue future revenue and cost streams, while 

lower discount rates have just the opposite effect. 
A-11 

is less than the available pastureland in that region, but the costs of moving the land into another 

use (for those pasture categories that can be converted) are too high to justify conversion. 

In addition, as in the case of cropland, there is exogenous movement of pastureland to 

developed land. Pastureland conversion to developed land is discussed in more detail in Section 

A.1.3.5. 

A.1.3.4 Timberland 

In addition to tracking endogenous land transfers between agricultural uses, FASOM is 

one of the few models that incorporates endogenous movements of land between the U.S. 

agriculture and forestry sectors. 

To enable the modeling of land exchanges, additional 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 cannot be converted to agricultural uses 

 FORCROP—includes acres that begin in timberland but can potentially be converted 

to cropland 

 FORPAST—includes acres that begin in timberland but can potentially be converted 

to pastureland 

 CROPFOR—includes acres that begin in cropland but can potentially be converted to 

timberland 

 PASTFOR—includes acres that begin in pasture but can potentially be converted to 

timberland 

FASOM reflects the mobility of the land resource between the sectors subject to controls 

for land quality/growing conditions and investments 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 plains areas of the country, and would thus be suitable only for some 

agricultural uses. 

In the timberland inventory, acres that could potentially be converted from forest to crop 

or pasture use were included in the FORCROP and FORPAST classes, respectively. To reflect 

differences in land productivity and feasibility 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 allocation involving pastureland occur only within 

the medium-quality forest site productivity class. Thus, there are limits on the area of timberland 
A-12 

within a region that can potentially move to cropland or to pastureland that reflect the regional 

distribution of timberland site productivity. In addition, land movements in forestry are only 

allowed in the NIPF owner category, 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 sectors were assigned to the FORONLY land class. 

For land beginning in agricultural production, acres by region that could potentially be 

converted from crop or pastureland to forestland were included in the CROPFOR and PASTFOR 

land classes, respectively, based on National Resource Inventory data of the USDA Natural 

Resources Conservation Service82,83 and a study by Moulton and Richards.84 Agricultural land 

can move to other uses during any of the 5-year model periods, but when afforested it begins in 

the youngest age cohort of timberland. The land then 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 include additional hurdle 

costs to reflect conditions where it may take an income differential beyond the opportunity cost 

in agriculture to get agricultural producers to switch to forestry. Given these model attributes, 

the economic conditions for land movement are that 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 the net present value of the land remaining in 

agriculture by at least the hurdle cost. For land moving from forest to agriculture, the net present 

value of land in agriculture must exceed returns to a rotation in forestry plus 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 value plus the investment and market 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 land 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 agricultural land is assumed to occur after harvest of any merchantable 

trees, and 75% of timber volume removed in land clearing is assumed to be hauled to market.85 

Constraints on the amount of timberland 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 medium or high potential for conversion to 

cropland and pastureland. The data were checked against that for NRI prime farmland, 

representing forest, pastureland, cropland, rangeland, or other minor land uses that have good 

potential for cultivated crops (e.g., slope less than 5%, not excessively eroded, not wetlands). 

The published NRI data do not identify forestland 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 cost 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 used to estimate these land areas. To 

determine the proportion of forested cropland and pastureland available in each state, the ratio of 

“active” crop and pastureland was used to disaggregate the total prime agricultural land in forest 

cover. The amount of crop and pastureland eligible 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 pasture to forest conversion 

establishment costs.90 

Costs are represented in each region by step functions 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 Northwest 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 Northwest 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, moisture, and other factors.91

Table A-2 summarizes the costs of land conversion between agriculture and forest for 

each of these three steps and baseline acreage available for conversion by region. All conversion 

of agricultural lands to forestland are in the low cost category because 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 establishment costs include those for 

site preparation, tree seedlings, and tree planting. Intermediate timber management costs include 

those for pre-commercial thinning, prescribed burning, fertilization, and any other practices 

between stand establishment and harvest. Establishment costs vary by FASOM land class, with 

generally higher costs for reforested acres, such as those for FORONLY acres, and lower costs 

for afforesting CROPFOR and PASTFOR acres. Costs are lower on agricultural lands because 

they are typically well-suited to planting trees and 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 with a high percentage of labor experienced the greatest cost 

increases.95 Updated cost functions were used from 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. Murray. 2005. “FASOMGHG Conceptual Structure and 

Specification: Documentation.” Available at http://agecon2.tamu.edu/people/faculty/ 

mccarl-bruce/papers/1212FASOMGHG_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 Northwest 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 States 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 3,692,449 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 significantly less than for planted 

stands. Sources for updated cost estimates included Dubois, McNabb, and Straka97 and 

Cathcart,98 primarily for establishment of planted stands 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 Department of Forestry. Salem, OR. 
A-18 

A.1.3.5 Developed Land 

As noted in the sections above, land use change in FASOM reflects endogenous transfers 

of land between the agriculture and forest sectors based on relative returns to alternative land 

allocations. However, the model also incorporates exogenous projections of the conversion of 

cropland, pastureland, and timberland to developed 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 the United States over the next 50 years, who on 

average will have higher personal incomes, increasing demand for housing and infrastructure. 

With developed uses typically occupying the top of the economic hierarchy of land use in the 

United States,99 a consistent approach to modeling land conversion in FASOM is to satisfy 

demand for developed land before the endogenous competition for the reduced rural land base 

between the agricultural and 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 depend on agricultural or forest products commodity 

prices or land values. This assumption is made because the land value for developed uses in 

those areas where development is taking place is expected to greatly exceed the land value of 

keeping the land in agricultural 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 provides 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 Natural 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 forest and developed use (e.g., urban use). This 

detail in the land-use data is unique among major sources of land-use 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 land movement from cropland, pastureland, and 

timberland to developed uses change over time based on the RPA projections data. In general, 

growth in the area of developed land is projected to slow as one moves 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 agricultural land to developed land 

assumed in FASOM for 2022. 

Table A-3. Exogenous Land Movement 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 incorporated 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 influential 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 projected 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 significantly over time due to technological 

improvements. Thus, FASOM adjusts crop yields over time based on historical growth in yields. 

Assumed yield increases for corn and soybeans were modified for this study to ensure 

consistency of the national weighted average 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 summarizes 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 increasing productivity on existing land were lower than the value of the 

increased production, then agricultural landowners would presumably have already adopted 

these productivity-enhancing actions. Although it is possible that sufficient increases in 

commodity prices could induce farmers to adopt higher cost practices that increase productivity 
A-20 

Table A-4. FASOM National Average 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, landowners can potentially switch from dryland to 

irrigated production, change tillage practices, or make other 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-based 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 increases in conversion rates 

anticipated over time as cellulosic feedstock conversion technology 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 Conversion 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 index of 2022 conversion rates relative to baseline 

values. 

A.1.4.4 Impact of Yield Changes on Input Requirements 

Another major update in FASOM that takes place along with changes in yields is the 

calculation of input adjustments for 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 quantity and quality of inputs. The procedure used in FASOM 

employs an elasticity term that gives the percentage response of input usage per acre to a 

percentage change in yield. The elasticity of input change with respect to crops has been derived 

from historical data where available. Subsequently, 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. Based on data showing little relationship between 

yields and N fertilizer application since the early 1980s (see Figure A-2), we held fertilizer use 

constant in this analysis rather than allowing 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 harvest schedules or management inputs), forest 
A-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 preserve carbon sinks and include afforestation, forest management, 

and agricultural soil tillage practices. Agricultural sources of CH4, N2O, and fossil fuel CO2 can 

be reduced through changes in fertilizer applications and livestock and manure management. 

CO2 emissions can be offset through renewable fuels, 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 emissions and sequestration related to land use 

change. The remaining sections in this appendix provide information on GHG emissions 

associated with crop production, land management, and livestock production. 

In addition to quantifying GHG emissions and 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 reversibility of carbon benefits (e.g., due to changes in tillage, forest 

harvests, wildfires), and fate of carbon stored in products after forest harvest. In contrast, these 

can be compared with the options for agricultural non-CO2, fossil fuel CO2, and renewable fuels 

that do not exhibit saturation 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. Sequestration refers to storage of the GHGs for more than one 

year. As a consequence, the sequestration definition used in the model for standing vegetation is 

limited to carbon storage in trees, understory, and litter within both forests and plantations of 

woody renewable fuel feedstocks (poplar 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 Sources 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 Methane from enteric fermentation 

Methane_Manure Methane from manure management 

(continued) 
A-25 

Table A-6. Categories of GHG Sources and Sinks Included in FASOM (continued) 

Methane_RiceCult Methane from rice cultivation 

Methane_AgResid_Burn Methane from agricultural residue burning 

Methane_BioElec Methane emissions of biomass 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 fertilizer application practices under managed soil categories under 

AgSoilMgmt 

NitrousOxide_Pasture Nitrous oxide emissions from pasture 

NitrousOxide_Sludge Emissions from sewage 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 areaa

NitrousOxide_Volat Indirect soils volatilization 

NitrousOxide_Leach Indirect soils leaching runoff 

NitrousOxide_AgResid_Burn Agricultural residue burning 

a Histosols are soils that are composed primarily of organic 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. Unlike IPCC guidance for other sources of N2O, direct emissions for histosols 

are based on area rather than annual N inputs. Emissions were assumed to equal 8 kg N2O-N per hectare for 

cultivated temperate histosols. 

In addition, changes in sequestration for lands that move out of forestry and agricultural 

production into some form of developed usage such as housing, shopping centers, 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 sequestration, although some 

of the carbon that was in the harvested trees will continue to be stored in forest products for some 

time afterward. If harvested stands are replanted, then there is little loss in forest soil carbon, and 
A-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 carbon accounting procedures used 

in FASOM below in Section A.1.5.1.1.102

In addition, converting grasslands (pastureland or land in the CRP) to crop production 

typically results in a reduction in carbon sequestration per acre. FASOM tracks dynamic 

changes in soil carbon sequestration associated with land conversion. These carbon accounting 

calculations are described in Section A.1.5.1.2. 

A.1.5.1.1 Forest Carbon Accounting 

Forest carbon accounting in FASOM follows the FORCARB model developed by the 

U.S. Forest Service and used in the periodic aggregate assessments 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 ratio of growing stock volume to merchantable 

volume, and (3) and parameters of a forest volume-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 volume of wood in each 

unharvested stand, which, in turn, is used in computing 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 type, 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 from the timber growth and yield tables included 

in FASOM. The volume factors and biomass model parameters vary by species and region and 

are obtained from105,106 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 management 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 calculated using the parameters of the forest 

volume-to-biomass model equations for live and dead tree mass densities (above- and belowground) in Smith et al.,108 weighted for the FASOM region/forest type designations. Forestland 

area data reported by the RPA assessment109 are used to calculate the appropriate weights. 

Birdsey’s assumption that the mass of wood is approximately 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 

Birdsey111,112 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 Williams116

noted, little change in soil carbon occurs if forests are regenerated immediately after harvest. As 

a result, FASOM assumes soil carbon on a reforested 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, electronic file 2002_RPA_Tables.xls to Brooks Depro, RTI International, July 30, 

2003. 110 Birdsey, R.A. 1992. Changes in forest carbon storage from increasing forest area 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 management 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. “Methodology 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 management 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. “Methodology 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 initial soil carbon value for that land/region combination reported by 

the Century Model, which was developed by Colorado State University.119 The land then 

accumulates carbon until reaching the steady-state value for forests of the type planted in the 

region afforestation takes place (where 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 largest carbon storage pool, but is much smaller 

than tree or soil carbon pools. Smith and Heath120 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 excludes 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 debris carbon to live tree carbon.122 This value is then added to the forest 

floor carbon values generated by Smith and Heath’s forest floor model. The model for net 

accumulation of forest floor carbon is a continuous and increasing function of age, although the 

rate of accumulation eventually approaches zero (i.e., forest floor carbon reaches a steady state). 

Understory vegetation comprises the smallest component of total carbon stock and 

includes all live vegetation except trees larger than seedlings. FASOM assumes that understory 

carbon is a fixed fraction of live tree carbon and uses published ratios reported in U.S. EPA123 as 

the basis for these calculations. 

When timber is harvested, FASOM tracks the 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 management 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 over time scales and centuries and millennia. 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 levels 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 the 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 exclude carbon in logging residue left onsite. 

Logging residue is tracked separately in the forest floor carbon pool described 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 at http://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 Manufacturing 
A-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. 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. 

Harvested logs removed from site are converted into three types of outputs through 

primary manufacturing processes: FASOM wood and paper products, mill residues, and fuel 

wood. The fate of each of these outputs is discussed in turn below. 

FASOM contains the following 13 wood and paper products: 

 softwood sawlogs for export 
A-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 carbon changes over time and FASOM tracks the fate of product 

carbon for each end-use using two pools: carbon remaining in-product and carbon leaving the 

product (Figure A-4). Carbon that leaves the product 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 half-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 Nicholson127 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/1212FASOMGHG_doc.pdf. 

 As shown in Figure A-4, carbon leaving the product pool moves to either the emissions 

or landfill pools.128 Skog and Nicholson129 assumed that 67% of carbon leaving the wood product 

pool and 34% of carbon leaving the paper product pool goes to landfills. The remainder of the 

carbon leaving the wood and paper product pools goes into CO2 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 intermediate input in the production of wood and paper 

products. This carbon is tracked using the appropriate product category as described above. The 

second pool is for carbon in mill residue that is burned for fuel, with the fraction burned in each 

 

128 There are two landfill pools that are tracked: permanent and temporary. Carbon in permanent landfills is 

sequestered forever, but carbon in temporary landfills decays and is eventually released 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

product 
A-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/1212FASOMGHG_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 developed use, resulting in a 

dynamic change in carbon storage levels on the 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 Report 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, two 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-Site 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 profile 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 forested land switches to agriculture, soil carbon 

levels are assumed to be consistent with Century Model data on agricultural soil carbon for the 

appropriate category of agricultural 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 States 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/acre 
A-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 carbon pools tracked between the model simulation with the policy 

in place and the baseline model simulation. Thus, any potential foregone sequestration that may 

be associated with reallocation of forestland to agricultural land in response to a change in policy 

would be captured in the calculation of the difference between the forest carbon sequestration 

values under baseline and policy conditions. 

A.1.5.2 Soil Carbon Sequestration 

Agricultural soil carbon sequestration 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 remove restrictions to crop 

root growth. However, by loosening the soil, tillage breaks up soil aggregates and 

increases the exposure of soil organic matter to oxygen, which speeds oxidation and 

results in reduced soil carbon with an associated release of CO2 into the atmosphere. 

The use of tillage alternatives that reduce soil disturbance and therefore reduce 

oxidation of soil organic matter will increase 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 given region between irrigated and dryland 

cropland systems.133 For sites receiving irrigation, the increased yields are expected 

to increase biological activity and hence soil carbon sequestration throughout the 

year. FASOM incorporates these differences in soil carbon storage 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-intensive 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 agricultural chemical production and use. FASOM 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, pasturelands and land in the CRP 

experience less soil disturbance than actively tilled croplands and tend to store more 

carbon per acre. Thus, changes in the distribution of land between 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 disturbance relative to 

actively tilled annual crops. By definition 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 sequestration per acre. 

Baseline carbon storage is estimated from the baseline distribution of land across tillage 

practices, irrigation status, land use, and cropping patterns, assuming carbon sequestration rates 

are equal to those at equilibrium. Soil carbon accounting for changes in tillage practices is done 

as if land remained in the initial tillage forever. 

Changes in soil carbon due to changes in tillage, 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 years (see Figure A-6).134 

Because movement of soil carbon sequestration towards equilibrium levels is not 

constant over time, FASOM yields non-uniform changes in soil carbon consistent with the 

generally accepted scientific finding that carbon sequestered in an ecosystem approaches steadystate equilibrium under any management alternative. As shown above, the rate of change in 

carbon storage decreases over time and eventually 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. For instance, Figure A-7 presents examples of 

changes in soil carbon for the Northern California region in FASOM. In the cases shown, soil 

carbon initially decreases when moving from the initial equilibrium 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 dynamics discussed and shown in Figure A-5 refer to the 

change over time from the initial state under new management/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 Time 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 occurred in earlier periods, but where soil carbon 

levels continue to adjust to their new equilibrium values. For instance, emissions from changes 

in soil carbon sequestration reported for 2022 would reflect the appropriate 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 30

Change 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 fuels volumes, the control case reduces net soil 

carbon sequestration and increases CO2 emissions. There are increases in cropland soil carbon 

relative to the reference case, reflecting 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 than 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 associated with changes in forest carbon and 

agricultural soil carbon sequestration, 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 described 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 often 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 application per acre by crop and irrigation status for major crops 

in each of the FASOM market regions where agricultural crops are produced. FASOM 

calculates energy use in fertilizer production as a function of the quantity of fertilizer produced. 

In addition, both CO2 emissions from fertilizer production and N2O 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. Because residue collection increases fertilizer 

requirements due to the removal of nutrients,136 regional average fertilizer use is reported both 

with and without residue removal in these tables 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 use requirements and CO2

emissions associated with pesticide production, 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 chemicals, respectively, by crop and FASOM region on a per acre basis. 

In addition, lime is used as an input in the production of some crops and is included in 

those crop budgets in FASOM. Although FASOM does not directly calculate CO2 emissions 

from lime production, the model does provide information on the amount of lime used. Thus, 

CO2 emissions from this category can be calculated 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 emissions from lime use based on GREET assumptions 

for limestone production and CO2 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 active 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 active 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 N2O 

emissions from nitrogen fertilizer application and nitrogen-fixing crops based on IPCC default 

factors.137 However, recent research suggests N2O 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 N2O emissions in the 

2006 IPCC guidance. In addition, emissions are more heterogeneous than can be captured using 

IPCC default emissions factors. Thus, to obtain more accurate estimates of N2O emissions that 

better reflect heterogeneity of emissions across crops and production practices and that 

incorporate the entire N cycling process, EPA worked with the Natural Resource Ecology 

Laboratory at Colorado State University to conduct further analyses of N2O emissions and 

provide updated emissions data. 

Specifically, Colorado State University provided several key refinements for a reanalysis of land 

use and cropping trends and GHG emissions in the FASOM assessment, including the following: 

 Direct N2O emissions based on DAYCENT139 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. Colorado 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 N2O 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 the analysis for the 63 FASOM regions, combined with 

IPCC factors for the N2O emission associated with the simulated nitrogen losses. 

Rather than relying simply on the rate of fertilizer application and the IPCC default for N2O 

emissions per unit of fertilizer, we now account for N cycling within DAYCENT and can 

generate emissions estimates that vary by region, crop, fertilizer 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 Greenhouse Gas Inventories, Chapter 11, N2O emissions from 

managed soils. 138 Crutzen, P.J., A.R. Mosier, K.A. Smith, and W. Winiwarter. 2008. N2O 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 and temperature dynamics and trace gas fluxes (CH4, N2O, NOx

and N2), which are not simulated in CENTURY. 
A-48 

FASOM applies the estimates available from DAYCENT summary models for direct 

N2O emissions, nitrogen leaching/runoff and ammonia 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 changes 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 relationships 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., irrigation status, tillage, residue removal rate); 

with separate parameter estimates for crop and grassland 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 N2O emissions. 

A.2.2.1 Direct Emissions and Emissions Factors 

The primary category of direct N2O emissions is emissions resulting from the application 

of nitrogen fertilizer. Changes in FASOM direct N2O 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 reduced fertilization and other mitigation options, but 

these options are only expected to be selected 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 N2O emissions from croplands below. 

A.2.2.1.1 Nitrogen Fertilizer 

To calculate direct N2O 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 N2O national inventory. This set of variables is treated as a 

set of indicator variables with a 1 assigned to the crop/grass of interest and 0 for the 

rest to estimate emissions from each crop relative to the excluded cases of corn for 

crops and grass pasture for the grass category. 

 Mineral fertilizer: this variable is the natural log of the amount of fertilizer applied 

per unit of area. 

 Irrigation status: this variable is an indicator variable indicating whether or not the 

crop or grass is irrigated. A value of 1 means the crop is irrigated, whereas a value of 

0 means that the crop is not irrigated 
A-49 

 Tillage practice: these variables are also treated as indicator variables representing 

tillage practice, with 0/1 variables 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 variable 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 variables 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 percentage of crop residue removed from the 

field (including removals for use as a cellulosic feedstock). This variable can take on 

values between 0% and 100%, although removal 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 variables 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 generate estimates of 

direct N2O emissions per acre for each crop, region, and production practice combination 

available in the FASOM crop budgets. 

A.2.2.2 Indirect Emissions and Emissions Factors 

In addition to direct N2O emissions from croplands, there are several additional sources 

of indirect emissions, including emissions from 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 nitrogen. The volatilized N subsequently returns to 

soils through N deposition and then contributes to N2O emissions. In FASOM, these emissions 

are calculated based on the estimates available 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 variables identified above under modeling of direct N2O emissions per acre. 

The results of this modeling are then multiplied by the IPCC indirect emissions factor for 

volatilization (0.010 kg N2O-N/kgNH2-N+NOx-N/yr) and the conversion factor of 44/28 to 

convert the loss of nitrogen to volatilization through multiple pathways into N2O emissions. 
A-50 

A.2.2.2.2 Leaching 

After fertilizer application or heavy rain, large 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 nitrogen fertilizer undergoes the process of nitrification or 

denitrification. In FASOM, these emissions are calculated using a similar modeling structure to 

that described above for the calculation of direct N2O emissions. The same variables are used in 

models estimated to generate estimates of emissions from leaching, multiplied by 0.0075 kgN2ON/kgNO3-N/yr and the conversion factor of 44/28 to convert to N2O emissions. IPCC guidelines 

from 2006 recommend that N leaching not be included in estimates of indirect N2O emissions if 

annual precipitation minus potential evapotranspiration does not exceed field water holding 

capacity (with the exception of irrigated lands, which should always be included). Therefore, 

emissions from nitrate leaching are not included 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 certain fraction of fields is burned each 

year, which results in N2O (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 N2O. 

In addition, methane emissions are calculated based 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 contained 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 grown in flooded fields 

and has associated methane emissions. Although there are potentially changes in water and soil 

management practices that could be implemented 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 available for reducing methane emissions from rice 

cultivation is to reduce rice acreage. Thus, changes 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 emissions factors per acre 

calculated for each region based on 2001 data 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 budgets142, and energy use associated with input production (e.g., 

fertilizer and pesticide production), grain drying, and transportation of primary agricultural 

commodities, which are calculated outside of the 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, irrigation 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, natural 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 electricity and natural gas in crop 

production for irrigation water pumping. Rice and sugarbeets are the only crops that are 

assumed to use natural gas in pumping in some regions. For the rest of the irrigated crops that 

have private energy use for water pumping, electricity is the assumed energy source. 

Tables A-17 and A-18 summarize assumed electricity 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 calculated 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 are 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.htm 
A-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 fertilizer. 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 associated energy use per acre for 

that crop. 

Energy use in pesticide manufacturing is estimated in FASOM based on the quantity of 

active ingredients applied per acre multiplied by an assumed energy requirement per ton of 
A-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 emissions from pesticide and herbicide production in this analysis. 

GHG emissions from agricultural use of fossil fuels in crop production are calculated in 

FASOM by multiplying the quantity of each energy category used (diesel fuel, gasoline, natural 

gas, liquefied petroleum gas, propane, and electricity) 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 calculations of energy use for grain drying and product transportation. 

As with the categories above, GREET factors were used in this analysis to calculate GHG 

emissions. 

A.3 Land Management 

Other actions that can potentially influence GHG emissions in FASOM are decisions 

regarding land management. Some of these actions have already been discussed above, such as 

changes in soil carbon sequestration due to tillage changes, but below we briefly summarize key 

land management decisions that can influence energy use and GHG emissions in FASOM. 

A.3.1 Tillage and Residue Burning 

As noted earlier in this appendix, changing tillage practices will 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 tractors to plow the land. In addition, the tillage practices selected 

on a farm will affect the level of residue per acre on the farm, which affects N2O emissions from 

incorporation of residues into the soil as described above. In addition, agricultural fields are 

sometimes burned to remove excess residue. This burning results in N2O and CH4 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 continuous corn production. This type of change in 

rotation patterns would be expected to have effects on soil productivity, chemical and fertilizer 

use, crop yields and GHG emissions over time. 

 

144 The assumed allocation of energy requirements 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 generally been applied to shorter-term decisions 

such as changes in rotation patterns. Rather, the model data implicitly reflect average conditions 

for crop production (e.g., yields, input 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 certain crops is assumed to be exclusively dryland 

or exclusively irrigated, but for some crop-region combinations, there is some ability of 

production to move dryland and irrigation. Irrigated 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 production 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 different crops. To the 

extent that there was a change in rotation practices to focus more heavily on corn production, 

that shift in rotations would tend to increase fertilizer inputs, energy use, and associated GHG 

emissions. FASOM captures a wide array of interactions between agricultural markets and 

producer allocation among crops as well as the selection of management practices, enabling 

examination of the complex relationships between 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 possible in some cases that emissions per unit of production 

could decrease even if emissions per acre increase. 
A-57 

instance, in the case of beef cattle production, there is a cow-calf stage in the model, followed by 

alternatives for intermediate stages, and finally moving to a feedlot for finishing on grain. 

One of the primary differences between regional livestock budgets is the availability of 

pasture and rangeland forage and forage productivity per acre. Thus, 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 this section, we focus on the use of 

renewable fuel coproducts such as DGs as an alternative livestock feed, non-CO2 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 under 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 coproducts also helps increase the 

returns to renewable fuels production. 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 displacing livestock feed, dry mill ethanol plants that employ a 

fractionation or extraction process yield corn oil as a coproduct. The corn oil provides a 

secondary revenue stream, further helping increase the returns to renewable fuels production. 

The primary coproducts of starch-based ethanol production that are used as livestock feed 

are DG, gluten meal, and gluten feed. In particular, DG is produced in large quantities. This 

coproduct of ethanol production can be used as feed 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 where these coproducts were not 

available. The reduced input use also results in lower GHG emissions. 

A.4.2 Methane and Nitrous Oxide Emissions 

GHG emissions associated with livestock production are primarily associated with 

manure management (both CH4 and N2O) and enteric fermentation (CH4), although there are also 

emissions from energy use. Livestock manure under aerobic conditions produces N2O as part of 

the nitrogen cycle through the nitrification and denitrification of the organic nitrogen in livestock 

manure and urine. Under anaerobic conditions, such as in large-scale livestock waste storage 
A-58 

facilities, methane is produced 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 management, FASOM calculates the average 

emissions per head by livestock type for both CH4 and N2O emissions. These values are based 

on the 2001 emissions values by livestock type and 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 livestock category in each model period to 

estimate the emissions associated with manure management. Thus, emissions from this source 

are currently affected only by the number of animals 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 livestock 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 number 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 these options do not enter the market in the absence of incentives 

for reducing CH4 emissions. Thus, similar to the case of manure management, emissions from 

this source are currently affected only by the number of animals in each livestock category. 

A.4.3 Energy Use 

In livestock production budgets, FASOM does 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.epa.gov/climatechange/emissions/downloads06/05CR.pdf. 147 Ibid. 
APPENDIX B: 

MODELING STARCH-BASED AND SUGAR-BASED ETHANOL PRODUCTION 
B-1 

This appendix contains an overview of the key methods and assumptions used to model 

starch-based and sugar-based 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 discussed 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 discussed briefly in Section B.5. 

B.1 Process Types Included in FASOM 

FASOM assumes standard sizes for ethanol production plants. The majority of plants 

producing ethanol from starch or sugar feedstocks 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. Starches in plants such as corn, wheat, 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 sugarbeets contain simple sugars that can readily be 

extracted and fermented. 

Feedstock requirements to supply the standard ethanol plant sizes assumed vary by 

feedstock based primarily on differences in starch/sugar content that lead to variation in ethanol 

yield per unit of feedstock. Table B-1 lists the categories of starch- 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 harvest. 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 size. Therefore, to reduce average transportation 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. 

149As described earlier in the paragraph, the yields assumed for starch- and sugar-based feedstock conversion to 

ethanol are constant over time, starting 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 coproducts produced during the processing of certain 

feedstocks (e.g., DG from ethanol production 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 alternative policy scenarios. 

In addition, FASOM provides estimates 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 production include the cost of the crop, the cost of 

hauling the crop from the roadside at the farm to the ethanol production plant, and the per-gallon 

processing cost of the crop to ethanol. This section describes the costs of purchasing sufficient 
B-3 

crops to produce a given amount of starch ethanol, 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 unit 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 relevant comparison for ethanol production plants is 

the cost of purchasing the quantity of feedstock that will generate a given amount of ethanol. 

For a plant using a given crop, the plant-level quantity requirements 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, exports, 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 inflation-adjusted equilibrium prices could remain 

steady or even decline over time. Increasing yields also mitigate pressure to convert land to 

agricultural production both in the United States and abroad. 

Thus, projected crop yields, both in the United States and internationally, are one of the 

most influential factors influencing the market and environmental outcomes of this analysis. The 

regional average crop yields presented in Table B-2 are based on USDA projections through 

2017150 (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 Agricultural Outlook Board. USDA Agricultural Projections to 

2018. Report prepared by the Interagency Agricultural Projections Committee. Long-term Projections Report 

OCE-2009-1. Available at http://www.ers.usda.gov/Publications/OCE091/ 151This implicitly assumes that there are no price-induced yield 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 research 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 States; NE = Northeast; PNWE = Pacific Northwest—East 

side (agriculture only); PSW = Pacific 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 being used in numerous 

competing markets, they have positive market prices, unlike the case of cellulosic ethanol where 

there is no market for most of the feedstocks in FASOM in the Reference Case.152 Table B-3 

summarizes market equilibrium prices in FASOM 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 price for these feedstocks, including positive values 

for sweet sorghum. 

 

152The exception is sweet sorghum, which is in experimental stages and not widely traded in markets. 
B-5 

Table B-3. FASOM Market Prices for Ethanol 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 production 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 States; 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 Volumetric Ethanol Excise Tax Credit (VEETC), which was 

created in 2004 as part of H.R. 4520, 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, gluten feed, and corn oil, all of which are 

produced during the process of converting starches 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 assumes 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 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 timeframe 

for swine and poultry. We also implemented maximum 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 and 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 different nutritional characteristics 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 traditional DG when used for swine or 

poultry feed, although the total replacement 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 since the proposal is the incorporation of an 

export market for DG into FASOM. In the analysis for the proposal, it was assumed that all DG 

produced would be supplied into the domestic market. However, the model has been modified to 

 

155 Salil, A., M. Wu, and M. Wang. 2008. “Update of Distillers 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-Protein 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 shipped to Mexico and Canada, but there has also 

been expansion in exports to the Middle East 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 process is assumed to produce 17 pounds of DG per 

bushel of corn, or about 510,000,000 pounds of DG per 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 coproducts. Although fractionation produces food 

grade corn oil, the dry mill extraction process produces non-food grade corn oil that cannot be 

used as vegetable oil for food 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 from 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 gluten 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 corn 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, which experiences a price decline of 0.1%. Gluten 

feed, traditional DG, and fractionated/extracted 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 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. Although 

use of gluten meal decreases by 4.5% under the 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- and 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 assumed an average that takes into account older, 

less efficient plants. In addition, processing costs per gallon of ethanol are assumed to remain 

constant over time. This is in contrast to cellulosic ethanol, which, as described in Appendix D, 

is assumed to experience substantial improvements in ethanol yield over time as well as 

reductions in processing costs per gallon. As a result, 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 assumed between 2002 and 2022 vary across the crops 

that can potentially be used as feedstocks in ethanol production. Other things being equal, those 

crops with greater yield increases over time will become 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 markets 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 expansion 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 Changes 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 experience the greatest 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 will affect livestock production. Because FASOM 

captures the opportunity costs of land and other resources, results reflect the changes in market 

equilibrium associated with introducing a renewable fuels policy that increases the market 

demand for certain feedstocks. 

Because EISA focuses on increasing renewable fuels production from advanced sources 

such as cellulosic ethanol production, the incremental volume of starch- and sugar-based ethanol 
B-10 

under EISA is relatively small. This reduces the market impacts of increasing renewable fuels 

production considerably by creating a market for crop residues, which would be collected from 

lands being used primarily for other crop production 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, sugarcane, and other feedstocks; 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 landowners 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 residues, particularly corn 

stover, which further increases the returns to corn production. 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 PRODUCTION 
C-1 

In this appendix, we present key assumptions and methods used to model biodiesel 

production in FASOM. The processes used for biodiesel production in the 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 Section 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 biodiesel production are briefly discussed in Section 

C.5. 

C.1 Process Types Included in FASOM 

In FASOM, biodiesel production is treated differently than starch- or sugar-based ethanol 

or cellulosic ethanol in that biodiesel production is assumed to rely on inputs that are already 

produced as part of existing processing activities. For instance, existing regional soybean 

crushing and corn milling processing budgets produce a supply of soybean and corn oil. In 

addition, fed and nonfed cattle slaughter activities 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 production, which relies on a 

specific standard size plant to calculate the average regional hauling costs 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 producers within a region can produce any quantity of 

biodiesel, and FASOM measures production in thousands 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 within FASOM were based on analyses included in 

the RIA for this rule. 

The quantity of feedstock required to produce a gallon of biodiesel varies across 

feedstocks to some extent, but with less variance than for ethanol production. Table C-1 lists the 

categories of biodiesel feedstocks being included in FASOM, the assumed biodiesel yield, and 

the quantity of feedstock required per 1,000 gallons of biodiesel produced. Feedstock 

requirements and processing costs per gallon of biodiesel are assumed to remain constant over 

time (in inflation-adjusted dollars). In addition, the production of biodiesel 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 consist 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 biodiesel processing. However, there 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 for biodiesel under specified 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 competing 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 represent a substantial proportion 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. 

159Only 10 of the 11 market regions in FASOM have agricultural production. The PNWW region has only forestry 

production. Because there are no biodiesel production options in FASOM involving 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 gallons of biodiesel. The prices 

of the primary biodiesel feedstocks in the Reference and Control Cases for 2022 are presented in 

Table C-2. 

Table C-2. FASOM Market Prices for Biodiesel 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 biodiesel feedstocks reflects the equalizing of the 

supply and demand for that particular product and will vary based on the specific scenario being 

modeled. Changes in soybean and corn yields over time relative to changes in demand for oils 

derived from these crops, along with agricultural producer adjustments in land use and 

production due to changing relative crop prices, will determine changes in prices of soybean and 

corn oils over time. Table C-3 presents information 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 efficient techniques for collecting and using waste oils 

in biodiesel production are developed, 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 soybean 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 Production 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 States; NE = Northeast; PNWE = Pacific Northwest—East 

side (agriculture only); PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; 

SW = Southwest (agriculture only). 

C.3 Coproducts 

No coproducts are associated with biodiesel production in FASOM. Although soybean 

crushing to produce soybean oil generates a coproduct of 47.6 lbs of soybean meal per bushel of 

soybeans along with 11.16 lbs of soybean oil, that 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 stream of revenue for soybean crushing plants 

that are also producing biodiesel. Table C-4 presents soybean 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 changes in the technology of biodiesel 

production over time that would affect the biodiesel conversion rate or processing costs. 

Because the cost of biodiesel feedstocks depends on market forces, increases in corn and 

soybean yields over time will tend to dampen potential price increases associated with increases 

in demand for these crops because of renewable fuels’ 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 markets associated with changing production 

practices to allocate different levels 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 implications for corn and soybean 

oil prices, 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 experience the greatest 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 will affect livestock production. Because FASOM 

captures the opportunity costs of land and other resources, results reflect the changes in market 

equilibrium associated with introducing a renewable 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 decreased soybean meal prices. Although more is 

being used in livestock production, the increase in demand for soybean 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 quantities of these waste feedstocks produced, their 

costs, and biodiesel conversion rates, there are limited opportunities for competitive expansion of 
C-6 

the use of waste oils in biodiesel production. To the extent that 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 PRODUCTION 
D-1 

This appendix contains an overview of the key assumptions and methods used to model 

cellulosic ethanol production in FASOM. Ethanol processes included in FASOM are described 

in Section D.1, additional detail on feedstock costs is provided in Section D.2, coproducts are 

described briefly in Section D.3, assumed technology change over time is discussed in Section 

D.4, and the types of agricultural market impacts associated with cellulosic ethanol production 

are discussed in Section D.5. 

D.1 Process Types Included in FASOM 

In FASOM, there is currently one standard size for cellulosic ethanol plants. All ethanol 

plants relying on cellulosic feedstocks are assumed to produce 100 million gallons per year 

(MGY) of ethanol using a single type 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. Processing costs are based on 

information presented in the RIA for this rule. These costs were based on biochemical 

conversion using acid prehydrolysis, enzymatic hydrolysis, and fermentation, followed by 

distillation of ethanol and separation of coproducts. Thermochemical conversion plants, which 

rely on processes such as gasification, pyrolysis, and catalytic cracking to produce synthesis gas 

that can be converted to ethanol, are not included within FASOM. 

The quantity of feedstock required per plant varies by feedstock type based on moisture 

content; energy density; and available technology for feedstock 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 for 2022 and the corresponding number of dry tons 

of each feedstock required per plant to produce 100 MGY. Section D.2 provides additional 

information on the underlying sources of the initial assumptions on ethanol yield and moisture 

content, and Section D.4 discusses assumed technology changes over time. 

 

160 Carolan, J., S. Joshi, and B. Dale. 2007. “Technical 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 ethanol by feedstock over time in response to 

the demand for cellulosic ethanol 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 FASOM 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 harvesting as well as opportunity costs associated with 

residue removal (e.g., need for additional 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 section discusses the costs of purchasing sufficient 
D-3 

feedstock inputs to produce a given level of cellulosic ethanol, 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 handling costs, as described 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 feedstock to produce 100 million gallons of ethanol. 

Although the unit of quantity varies by feedstock for grain ethanol, 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 quantity requirements are multiplied by the regional 

farm-level market price for that feedstock generated by FASOM to calculate the total plant-level 

feedstock costs. 

The regional farm-level market price for a crop 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 these 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 order 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 production, and an opportunity cost is associated with diverting 

those residues to cellulosic 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 addition, opportunity costs are associated with loss of nutrients from 

removing residues. This section describes the calculation of the total quantity of residue, the 

 

161Because this value is specified as a constant cost per wet ton, costs of residue harvesting and handling per dry ton 

will clearly increase with feedstock moisture content. However, because 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. 162The farm-level price that processing plants are willing 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 removable, feedstock density, moisture content, energy content, 

ethanol yields, and other factors affecting the potential ethanol production from crop residues. 

D.2.1.1 Sustainable Crop Residue Production 

The quantity of crop residue produced after harvest (measured in wet tons) is calculated 

as 

 Residue = Crop Yield * Straw-to-Grain Ratio * 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, and rate of fertilizer application; straw-to-grain ratio is the 

average quantity of residue produced for every unit of grain production, which varies by 

crop163,164; and weight conversion factor is a factor used to convert from crop harvest units (e.g., 

bushel [bu], hundredweight [cwt]) to the common metric of tons of residue and which varies by 

crop. 

Because crop yields are assumed to increase 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 also 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 adjustments, if appropriate. In addition, there is an option to specify a 

maximum quantity of residue removal per acre within FASOM. 

Table D-2 presents the assumed straw-to-grain ratio and weight conversion factor for 

each of the six crops (these values were assumed to be the same for all varieties of wheat 

modeled). These values are constant across 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 residue/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 

 

163Tyner, W., M. Abdallah, C. Bottum, O. Doering, B.A. McCarl, W.L. Miller, B. Liljedahl, R. Peart, C. Richey, S. 

Barber, and V. Lechtenberg. 1979. “The Potential of Producing Energy from Agriculture.” Report to the Office 

of Technology Assessment. W. Lafayette, IN: Purdue University School of Agriculture. 164Lal, 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 winter 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 content 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 consistent with good management practices and is not 

sustainable. 

A number of site-specific factors affect the maximum amount of crop residue that can be 

sustainably removed, including crop type, soil type, soil fertility, slope, tillage, and climate. As a 

general rule, though, USDA National Resources Conservation Service recommends that about 

30% residue cover is adequate to control soil erosion.165 Removable residue values used in 

FASOM were calculated by adjusting 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 tillage, as shown in Table D-3. Another potential measure of 

sustainable residue availability is the minimum quantity of residues per acre that must be 

 

165Maung, 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. 166Graham, 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. 167Perlack, 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. 168Many site specific factors associated with the sustainable removal of residue (e.g., crop type, soil type, soil 

fertility, slope, and climate) affect which geographic regions 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 erosion and maintain soil carbon levels.169 However, insufficient 

data on minimum sustainable residues per acre were 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 limited 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 for 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 residues that are removed from fields under 

equilibrium market conditions in FASOM can then either be converted to ethanol or used in 

bioelectricity production. Baseline ethanol conversion rates used in FASOM to produce ethanol 

from crop residue feedstocks are based on several different sources. These baseline values are 

 

169Wilhelm, 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. 170Kerstetter J.D., and J.K. Lyons. 2001. “Logging and Agricultural Residue Supply Curves for the Pacific 

Northwest.” Washington State University Energy Publication. 171Banowetz, 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. 172Having the highest sustainably removal residue per acre does 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 Removable 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 States; NE = Northeast; PNWE = Pacific Northwest—East 

side (agriculture only); PSW = Pacific 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, reaching 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 sustainably removed are converted to dry 

tons by adjusting for their average moisture content (i.e., quantity in wet tons * [1 – proportion of 

feedstock that is moisture]). Thus, ethanol conversion 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 declines commensurately. 

D.2.1.3 Crop Residue Feedstock Costs 

The cost of acquiring crop residues for cellulosic ethanol production (excluding hauling 

and handling) is determined in FASOM by calculating 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, there are increased fertilization requirements due 

to nutrient removal that vary by crop. FASOM assumes that there is an increase 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 essentially no market for cellulosic feedstocks. 

Thus, the market prices of crop residues are zero. Under the Control Case or other scenarios 

requiring cellulosic ethanol production, a market for 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 that are being traded in the market in the Control Case 

equilibrium are corn residues in the Corn Belt, Great Plains, and Lake States 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 Gate 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 feedstock 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 adopted widely, they need to provide high enough returns to farmers 

to induce them to switch from alternative 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, while yields for willow and hybrid poplar increase 

at a rate of 0.75% per year. It was assumed that switchgrass could potentially be produced in 

eight of the FASOM regions (Corn Belt, Great Plains, Lake States, Northeast, Rocky Mountains, 

South Central, Southeast, Southwest); willow could be produced in four regions (Corn Belt, Lake 

States, Northeast, Southeast); and hybrid poplar could be produced in eight regions, matching up 

with the regions for switchgrass 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 for the year 2022 for the three 

energy crops included in FASOM. Other things being equal, the higher the energy crop yield in 

a region, the more likely that energy crop will offer competitive returns to landowners. In all 

regions where switchgrass is included in FASOM as a production possibility, switchgrass has the 

 

175Thomson, 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 model. 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 yields 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 described earlier, the entire yield 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 States; NE = Northeast; PNWE = Pacific Northwest—East 

side (agriculture only); PSW = Pacific 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 Section D.4. Table D-8 summarizes the ethanol 

conversion values used for 2022. 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 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 tons of switchgrass per year 

(1,231,024 wet tons). With increases in the ethanol conversion rate over time, the quantity of 

feedstock required by an ethanol plant of a given output level declines commensurately. 
D-11 

D.2.2.2 Costs of Using Energy Crop Feedstocks 

Similar to crop residues, the cost of acquiring 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 not only the cost of harvesting the feedstock, but 

also crop production costs and the opportunity cost of land because it is a dedicated crop rather 

than residues associated with other crop production. Ethanol feedstock yields per acre are 

generally much higher for these crops than for residues, however. Thus, if feedstock prices reach 

high enough levels, energy crops become competitive with 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 under 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 market solution under the 

Control Case is switchgrass, which is produced principally in the Southwest but also in the 

Northeast and Southeast. Although 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 yield 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 Prices 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 processing residues as potential feedstocks for 

cellulosic ethanol production. This section outlines assumptions 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 higher 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 generated by applying assumed logging residue 

percentages to FASOM data on round wood yields. Yields were averaged by FASOM market 

region using the FASOM forestry inventory data as weights. Two-thirds of the logging residue 

was assumed to be harvestable based on Helynen, Hakkila, and Nousiainen.179 Logging residue 

yields in 1,000 cu ft per acre were 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 pulp 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 residues are currently used on site for energy 

production in the sugar refining and the wood product 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 conversion technology take place over time, as 

discussed in Section D.4. Biomass from logging and processing residues can be converted to 

ethanol in FASOM in the year 2022 using the conversion rates shown 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 supply for a 100 MGY plant was calculated for 

each feedstock (see Table D-1). As with all of the other feedstocks, increases in ethanol 

 

179Helynen, 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. 180Carpenter, 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 Central Forest Experiment Station. 
D-13 

conversion rates over time result in a reduction in the quantity of feedstock needed by an ethanol 

plant. This lowers hauling costs because the smaller quantity of feedstock required can be 

collected from a smaller area. 

D.2.3.3 Costs of Using Logging and Processing Residues as Ethanol Feedstocks 

The cost of acquiring logging and processing residues for ethanol production (excluding 

hauling and handling costs) includes opportunity costs associated with diverting processing 

residues from heat and power production to ethanol production. Unlike most of the other 

cellulosic feedstocks, the Reference Case includes markets for selected processing residues 

(bagasse and sweet sorghum pulp). However, the increase in demand associated with the 

Control Case results in large increases in prices. 

Table D-11 summarizes the market prices for logging and processing 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 production that is tracked within FASOM is 

lignin, which is calculated as a proportion of feedstock use that varies across 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 sorghum pulp 12.7%. Lignin production levels 

were derived from data in Kadam et al.181 and Kadam182. Lignin can be used as a fuel for 

generating heat or could potentially be used for electricity generation. 

 

181Kadam, K.L., V.J. Camobreco, B.E. Glazebrook, L.H. Forrest, W.A. Jacobson, D.C. Simeroth, W.J. Blackburn, 

and K.C. Nehoda. 1999. Environmental Life Cycle Implications of Fuel Oxygenate Production from California 
D-14 

FASOM includes electricity production using lignin as a production possibility. In 

FASOM budgets, 384,151 tons of lignin from crops, 329,567 tons of lignin from hardwoods, and 

308,642 tons of lignin from softwoods are required by the standard 100 megawatt (MW) 

electricity generation plant included in FASOM. In addition, there 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. Currently, cellulosic ethanol 

production is not cost competitive, but increases in ethanol conversion rates and feedstock yields 

could potentially help to make large-scale cellulosic ethanol production competitive with other 

fuels. The cost of feedstock per unit over time will depend on market forces, but the quantity 

required to produce a given amount of ethanol is expected to decline over time with 

improvements in ethanol conversion technology, which will tend to reduce feedstock costs per 

gallon. The reduction in the quantity of feedstock needed to meet the EISA volume requirement 

over time (due to increasing cellulosic ethanol yields) will tend to reduce the demand for these 

feedstocks and is expected to contribute to lower feedstock prices farther into the future, other 

things being equal. As less feedstock is required per gallon of ethanol, hauling and handling 

costs will also tend to decline because less feedstock needs to be hauled and handled. In 

addition, the plant can acquire the necessary quantity of feedstock from fewer acres so average 

hauling distance declines. The assumed increase in feedstock yields over time also contributes to 

these cost reductions by reducing 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 the RIA for this rule. 

Baseline values for ethanol yield for all feedstocks were based on data from a study 

conducted by NREL.183 These yields were then increased over time until reaching the maximum 

feasible ethanol conversion rates 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/docs/fy99osti/25688.pdf. 182Kadam, 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 different feedstocks are assumed to progress towards 

their maximum levels were assumed to be the same for all feedstocks. 

Figures D-1 and D-2 show the ethanol yield 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 crops, 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 assumed 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 affecting 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 

Residue

Ethanol Yield (gallons/dry ton)

Figure D-1. Ethanol Yield per Dry Ton from Crop Residues, 2022 
D-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 

Residue

Plant Level Feedstock Requirement (dry tons/year)

Figure D-2. Dry Tons of Crop Residue Feedstocks Required for a 100 MGY Ethanol 

Production Plant, 2022 

0

15

30

45

60

75

90

105

120

Hybrid Poplar Switchgrass Willow

Ethanol Yield (gallons/dry ton)

Figure D-3. Ethanol Yield per Dry Ton from Energy Crops, 2022 
D-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 Willow

Plant Level Feedstock Requirements (dry tons/year)

Figure D-4. Dry Tons of Energy Crop Feedstocks Required for a 100 MGY Ethanol 

Production Plant, 2022 

0

20

40

60

80

100

120

Hardwood Residue Softwood Residue Bagasse Sweet Sorghum 

Pulp

Ethanol Yield (gallons/dry ton)

Figure D-5. Ethanol Yield per Dry Ton from Logging and Processing Residues, 2022 
D-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 

Pulp

Plant Level Feedstock Requirements (dry 

tons/year)

Figure D-6. Dry Tons of Logging and Processing 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 initial 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 productivity increases putting downward pressure 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 (Change 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 markets associated with changing production 

practices to collect crop residues, produce energy crops, collect logging residues, and to begin 

using or diverting processing residues from production of heat and power to ethanol. The largescale production of energy crops could have substantial 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 land that can be used for primary production of grains, energy 

crop production is generally less attractive unless these crops could be grown at high density 

around ethanol plants in regions where the opportunity costs of land are low. 
D-20 

Crop residues could potentially be used for grazing in cow-calf operations or as livestock 

bedding and provide some value as a fertilizer due to potash, potassium, and nitrogen content. 

However, the opportunity cost of retaining these residues in other uses is relatively low. In 

FASOM, there are crop budgets both with and without residue collection for barley, corn, oats, 

rice, sorghum, and wheat. As described earlier, the primary difference between the budgets with 

and without residue collection is that additional fertilizer is required if residues are removed. 

Using the baseline FASOM fertilizer 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 are costs of harvesting, 

baling, and moving the residue to the roadside at the farm. Crop yields 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 without residue collection to production with 

residue collection. 

For cellulosic ethanol production in FASOM, all feedstocks have the same processing 

costs per gallon to convert delivered feedstock to ethanol. In addition, handling costs per wet ton 

are assumed to be equal across all crop residues and energy crops. Because there are some 

differences in energy content per wet ton, feedstocks 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 feedstocks purchased by an ethanol plant rests largely 

on the differences in feedstock costs and biomass 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 land to energy crops. Regions where there are few attractive 

alternative crops and energy crops can be grown at very high density surrounding an ethanol 

production plant would provide the greatest potential for energy crop production. Otherwise, 

ethanol production will tend to be concentrated in the regions where large quantities of crop 

residues are available at high density. 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-year harvesting cycle). Consistent with these 

expectations, FASOM results for the Control Case show the majority of cellulosic ethanol 

feedstocks coming from corn residues in the Corn Belt, Great Plains, and Lake States regions 

and switchgrass in the Southcentral, Southwest, and Southeast regions as well as Kansas and 

Missouri. 

 

184 See: http://www.ers.usda.gov/Data/FertilizerUse/Tables/Table7.xls (values have been adjusted to 2007$). 
D-21 

Because there is such a large quantity of residue available at relatively low cost, a large 

amount of land in FASOM is reallocated from crop 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 tillage 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 residue 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 conditions, potentially with little change 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 residue 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 collecting and transporting crop residues to ethanol 

production plants. However, these sectors are not included within the FASOM model. The 

sizable production from switchgrass has larger market effects, resulting in a reallocation of land 

toward switchgrass production and contributing to an increase in land values and commodity 

prices. 
E-1 

APPENDIX E: 

HIGH YIELD SENSITIVITY SCENARIO 

One important assumption influencing the amount of land required to produce sufficient 

feedstocks to meet the required EISA renewable fuels volumes is the rate of increase in crop 

yields over time. The greater the rate of increase 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 Control Case relative to the Reference Case under a primary set of 

key assumptions, FASOM was used to analyze the effects of increasing the rates of increase for 

two key crops, corn and soybeans. In this appendix, 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, soybeans, and switchgrass; and corn and soybean exports across 

scenarios. This sensitivity analysis enables examination of the implications 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, the yields of both corn and soybeans were 

assumed to increase at faster rates than assumed in Section 2. These higher yield growth rates 

were applied to both the Reference Case and Control Case, with the impacts associated with 

EISA requirements for a particular scenario based on the differences between those cases. 

Therefore, the impacts being measured in this scenario are the incremental 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 analysis that EISA would lead to 

faster increases in corn productivity, but that the impacts may differ if corn yields were to 

increase more rapidly over time than assumed in the Base Scenario presented in Section 2. 

This increase in the technological 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 Yield 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 the 
E-2 

increases in technological rates of progress for corn and soybeans were applied to the export 

supply functions for non-U.S. corn and soybean 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 Change 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 comparisons 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 uncertainties 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 Results 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 soybean 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

90

Barley

Corn

Cotton

Hay

Oats

Potatoes

Rice

Rye

Silage

Sorghum

Soybeans

Sugarbeet

Sugarcane

SwitchgrassTomatoFrsh

TomatoProc

Wheat, Durham

Wheat, Hard Red Spring

Wheat, Soft White

White, Hard Red Winter

Millions 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 the 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 difference is that the increase in switchgrass acres 

for the High Yield Sensitivity Scenario is less than half the increase experienced under the Base 

Scenario. In addition, corn acreage increases more and soybean acreage decreases less under the 

High Yield Sensitivity Scenario. At higher corn yields, there is also greater production of corn 

stover per acre. As a result, the use of corn residues as a cellulosic ethanol feedstock almost 

doubles in the High Yield Sensitivity 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 

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-2

0

2

4

6

8

10

12

14

Barley

Corn

Cotton

Hay

Oats

Potatoes

Rice

Rye

Silage

Sorghum

Soybeans

Sugarbeet

Sugarcane

Switchgrass

TomatoFrsh

TomatoProc

Wheat, Durham

Wheat, Hard Red Spring

Wheat, Soft White

White, Hard Red Winter

Millions of Acres

Base Scenario High Yield Sensitivity Scenario

Figure E-2. Change in U.S. Harvested Acreage 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 acres in the High Yield Sensitivity Scenario. In 

addition, meeting the EISA renewable fuels volume targets requires 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 cropland 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 higher rate. Not only are corn 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 crops, other things being equal. There are competing effects in 

this case because a higher rate of technical progress increases corn and soybean production per 
E-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 Yield 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 and 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 Prices 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 sources of feedstocks for renewable 

fuels) under the Reference Case and Control Case 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, increasing prices are entirely consistent with expectations. For corn 

under the High Yield Sensitivity Scenario, with less land in crops and lower commodity prices, 

the development of a market for corn stover increases the returns for planting corn relative to 

alternative crops sufficiently that corn acreage 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 increases 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 acreage and production will increase enough to meet 

demand even with a decrease in corn price. Under this scenario, substantially higher yields for 

corn and soybeans result in lower agricultural commodity prices and land values in the Reference 

Case. With these market conditions, the development of a market for corn residues in the 
E-6 

Table E-3. Changes in Simulated Market Prices 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 returns to corn production (and the corn residue coproduct) relative to 

alternative crops enough to make corn production relatively more attractive than alternative 

crops even though the expanded production leads to a decline 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 acreage is larger in the High Yield 

Sensitivity Scenario than the Base Scenario. The 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 meet the increased demand for corn under EISA. 

E.2.3 Sensitivity Analysis Results for Selected Exports 

Table E-4 compares model results for exports of key commodities most directly affected 

by expanded volume requirements for renewable fuels. 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 domestic soybean processing expands even more 

than soybean production, resulting in a large increase in domestic soybean oil production and 
E-7 

Table E-4. Changes in Simulated Exports for 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 Scenario 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 residues 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 exports 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 domestic demand for soybean oil for use in biodiesel production 

increases more than the increase in domestic production of soybeans and soybean oil. However, 

the percentage decline in exports is much smaller under the High Yield Sensitivity Scenario.