August 2010

Model Documentation for the 

Forest and Agricultural Sector 

Optimization Model with 

Greenhouse Gases (FASOMGHG)

Draft Report

Prepared for

Sara Bushey Ohrel

U.S. Environmental Protection Agency

Climate Change Division

1200 Pennsylvania Avenue

Washington, DC 20460

Prepared by

Robert H. Beach

Darius Adams (Oregon State University)

Ralph Alig (U.S. Forest Service)

Justin Baker (Duke University)

Gregory S. Latta (Oregon State University)

Bruce A. McCarl (Texas A&M University)

Brian C. Murray (Duke University

Steven K. Rose (EPRI)

Eric White (Oregon State University)

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709

RTI Project Number 0210826.016
_________________________________

RTI International is a trade name of Research Triangle Institute.

RTI Project Number

0210826.016

Model Documentation for the 

Forest and Agricultural Sector 

Optimization Model with 

Greenhouse Gases (FASOMGHG)

Draft Report

August 2010

Prepared for

Sara Bushey Ohrel

U.S. Environmental Protection Agency

Climate Change Division

1200 Pennsylvania Avenue

Washington, DC 20460

Prepared by

Robert H. Beach

Darius Adams (Oregon State University)

Ralph Alig (U.S. Forest Service)

Justin Baker (Duke University)

Gregory S. Latta (Oregon State University)

Bruce A. McCarl (Texas A&M University)

Brian C. Murray (Duke University

Steven K. Rose (EPRI)

Eric White (Oregon State University)

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709
iii

CONTENTS

Section Page

1 FASOMGHG Overview and Updates........................................................................ 1-1

1.1 Model Summary............................................................................................... 1-2

1.2 Brief History and Previous Applications........................................................... 1-4

1.3 Recent Developments and Model Updates........................................................ 1-6

2 Model Scope ............................................................................................................. 2-1

2.1 Commodities.................................................................................................... 2-1

2.1.1 Primary Commodities........................................................................... 2-1

2.1.2 Secondary Commodities ....................................................................... 2-5

2.1.3 Bioenergy Products............................................................................... 2-9

2.1.4 Blended Livestock Feeds ...................................................................... 2-9

2.2 Inputs to Production ....................................................................................... 2-10

2.3 U.S. Regional Disaggregation......................................................................... 2-13

2.4 Land Use Categories....................................................................................... 2-16

2.4.1 Agricultural Land................................................................................ 2-20

2.4.2 Forestland ........................................................................................... 2-22

2.4.3 Developed Land.................................................................................. 2-24

2.4.4 Land Allocation .................................................................................. 2-25

2.5 Market Modeling............................................................................................ 2-26

2.6 International Trade ......................................................................................... 2-27

2.6.1 Forestry .............................................................................................. 2-27

2.6.2 Agriculture.......................................................................................... 2-28

2.7 GHG Accounts............................................................................................... 2-32

2.7.1 Forest GHG Accounts......................................................................... 2-35

2.7.2 Agricultural GHG Accounts................................................................ 2-36

2.7.3 Bioenergy GHG Accounts .................................................................. 2-37

2.7.4 Developed Land GHG ........................................................................ 2-37
iv

2.8 Other Environmental Impacts......................................................................... 2-37

3 Economic Theory and Model Structure ..................................................................... 3-1

3.1 Theoretical Basis.............................................................................................. 3-1

3.2 Agricultural Sector ........................................................................................... 3-6

3.2.1 Key Variables....................................................................................... 3-6

3.2.2 Constraints on Agricultural Production ............................................... 3-10

3.3 Forest Sector .................................................................................................. 3-12

3.3.1 Key Variables..................................................................................... 3-12

3.3.2 Constraints on Forestry Production ..................................................... 3-15

3.4 GHG Payments............................................................................................... 3-16

4 Linkages between Forestry and Agriculture............................................................... 4-1

4.1 Land Transfers ................................................................................................. 4-1

4.2 Commodity Transfers....................................................................................... 4-6

4.3 Consistent Treatment of Time and Discounting ................................................ 4-7

5 Bioenergy ................................................................................................................. 5-1

5.1 Transportation Biofuels.................................................................................... 5-1

5.1.1 Processes Modeled................................................................................ 5-2

5.1.2 Feedstock Costs.................................................................................... 5-6

5.1.3 Hauling, Storage, and Processing Costs............................................... 5-19

5.1.4 Additional Ethanol Market Penetration Costs...................................... 5-24

5.1.5 Coproducts ......................................................................................... 5-25

5.2 Bioelectricity.................................................................................................. 5-27

5.2.1 Processes Modeled.............................................................................. 5-28

5.2.2 Feedstock Costs.................................................................................. 5-28

5.2.3 Hauling, Storage, and Processing Costs............................................... 5-30

5.2.4 Bioelectricity Market Penetration Constraints ..................................... 5-32
v

6 Dynamics.................................................................................................................. 6-1

6.1 Dynamics of Timber Harvest............................................................................ 6-1

6.2 Discounting and Annuities................................................................................ 6-1

6.3 Terminal Conditions......................................................................................... 6-3

6.4 Updating Yields, Demand, and Other Factors over Time .................................. 6-4

6.4.1 Crop Yields........................................................................................... 6-5

6.4.2 Livestock Yields................................................................................... 6-8

6.4.3 Domestic Demand, Export Demand, and Import Supply ....................... 6-9

6.4.4 Timber Yields..................................................................................... 6-10

6.4.5 Bioenergy Feedstock Conversion Rates .............................................. 6-11

6.4.6 Bioenergy Processing Costs................................................................ 6-14

7 GHG Accounting ...................................................................................................... 7-1

7.1 GHG Emissions................................................................................................ 7-1

7.1.1 Carbon Dioxide Emissions.................................................................... 7-1

7.1.2 Methane Emissions............................................................................. 7-16

7.1.3 Nitrous Oxide Emissions..................................................................... 7-18

7.2 Carbon Sequestration...................................................................................... 7-24

7.2.1 Forest Carbon Accounting .................................................................. 7-24

7.2.2 Soil Carbon Sequestration................................................................... 7-33

7.3 GHG Accounting for Bioenergy Production ................................................... 7-37

7.4 Mitigation Strategies ...................................................................................... 7-38

8 Model Output............................................................................................................ 8-1
vi

9 Selected Environmental, Resource, and Policy Analysis Applications....................... 9-1

9.1 Types of Policies and Issues that can be Evaluated using FASOMGHG ........... 9-1

9.2 Selected Recent Applications ........................................................................... 9-2

9.2.1 GHG Mitigation.................................................................................... 9-2

9.2.2 Bioenergy ............................................................................................. 9-4

9.2.3 Climate Impacts.................................................................................... 9-4

10 References .............................................................................................................. 10-1
vii

LIST OF FIGURES

Number Page

2-1 Map of the FASOMGHG Regions .......................................................................... 2-15

2-2 Baseline FASOMGHG US Land Base by Land Use Category (million acres) ......... 2-20

7-1 Carbon Disposition after Timber Harvest ................................................................ 7-27

7-2 Wood and Paper Product Carbon Disposition.......................................................... 7-30

7-3 Disposition of On-Site Carbon after Deforestation .................................................. 7-32

7-4 Percentage Adjustment over Time to New Soil Carbon Equilibrium Following 

Change in Land Use or Management....................................................................... 7-35

7-5 Change in Soil Carbon for FASOMGHG Northern California Region for 

Selected Changes in Land Use and Management..................................................... 7-36
viii

LIST OF TABLES

Number Page

2-1 Primary Commodities ............................................................................................... 2-2

2-2 Secondary (Processed) Commodities......................................................................... 2-6

2-3 Bioenergy Products................................................................................................... 2-9

2-4 Blended Livestock Feeds......................................................................................... 2-10

2-5 Major Categories Included in Crop Budgets in Quantities ....................................... 2-11

2-6 Major Categories included in Livestock Budgets in Quantities................................ 2-12

2-7 Definition of FASOMGHG Production Regions and Market Regions ..................... 2-14

2-8 Aggregated US Regions.......................................................................................... 2-16

2-9 Forest Management Intensity Classes...................................................................... 2-24

2-10 Explicitly Traded Commodities and Countries/Regions Trading with the United 

States...................................................................................................................... 2-29

2-11 Commodities with Only ROW Export or Import Possibilities.................................. 2-31

2-12 Commodities without International Trade Possibilities Modeled ............................. 2-32

2-13 Categories of GHG Sources and Sinks in FASOMGHG.......................................... 2-33

4-1 Conversion Costs Between Agricultural and Forestland ($/acre) and Acres 

Available for Conversion .......................................................................................... 4-5

5-1 Starch- and Sugar-Based Ethanol Feedstocks Included in FASOMGHG and 

Annual Quantity Requirements for a 75 MGY Ethanol Plant (40 MGY for 

Sweet Sorghum), Base Period (2000–2004) .............................................................. 5-3

5-2 Cellulosic Feedstocks Included in FASOMGHG and Annual Quantity 

Requirements for a 100 MGY Ethanol Plant, Base Period (2000–2004) .................... 5-4

5-3 Biodiesel Feedstocks Included in FASOMGHG and Quantity Requirements per 

1,000 Gallons of Biodiesel (2022)............................................................................. 5-5

5-4 Regional Average Crop Yields for Starch- and Sugar-Based Ethanol 

Feedstocks, Base Period (2000–2004)....................................................................... 5-7

5-5 Average State Ethanol Subsidy by Region Used in FASOMGHG (cents/gallon 

of ethanol produced) ................................................................................................. 5-8

5-6 Straw-to-Grain Ratio and Weight Conversion Factor............................................... 5-10

5-7 Sustainably Harvestable Residue Percentages ......................................................... 5-11

5-8 Regional Average Sustainably Removable Crop Residue for Acres where 

Residue Can be Removed (Wet Tons per Acre), Base Period (2000–2004) ............. 5-12

5-9 Ethanol Conversion Rates for Crop Residues, Base Period (2000–2004)................. 5-13

5-10 Regional Average Energy Crop Yields (Wet Tons per Acre), Base Period 

(2000–2004)............................................................................................................ 5-15
ix

5-11 Ethanol Conversion Rates for Energy Crops, Base Period (2000–2004) .................. 5-15

5-12 Ethanol Conversion Rates for Logging and Processing Residues, Base Period 

(2000–2004)............................................................................................................ 5-17

5-13 Regional Average Potential Production of Soybean, Canola, and Corn Oil per 

Acre, Base Period (2000–2004)............................................................................... 5-18

5-14 Regional Crop Residue Density (Percentage of Region with Available 

Feedstock)............................................................................................................... 5-20

5-15 Regional Values for Average One-Way Hauling Distance (Miles) for 

FASOMGHG Standard Plant Sizes, Base Period (2000–2004)................................ 5-21

5-16 Assumed Storage Costs for Ethanol Feedstocks for FASOMGHG Standard Size 

Plant, Base Period (2000–2004) .............................................................................. 5-23

5-17 Market Penetration Costs for Ethanol...................................................................... 5-25

5-18 Production of Ethanol Coproducts by Feedstock ..................................................... 5-27

5-19 Bioelectricity Feedstocks Included in FASOMGHG and Annual Quantity 

Requirements per 100 MW Generation Capacity, Base Period (2000–2004)............ 5-29

5-20 Regional Values for Average One-Way Hauling Distance (Miles) for 

FASOMGHG Standard 100 MW Dedicated Bioelectricity Plant, Base Period 

(2000–2004)............................................................................................................ 5-31

6-1 National Average Yields for Major Crops and Annual Yield Growth ........................ 6-6

6-2 Input Elasticities with Respect to Changes in Yield ................................................... 6-7

6-3 Yield Growth for Livestock Products over Time ....................................................... 6-8

6-4 Annual Shifts in Agricultural Commodity Import Supply, Domestic Demand, 

and Export Demand Curves....................................................................................... 6-9

6-5 Biofuels Feedstock Conversion Rates over Time..................................................... 6-12

7-1 Diesel Fuel Usage (gallons/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)................................................................................................... 7-2

7-2 Gasoline Usage (gallons/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)................................................................................................... 7-3

7-3 Electricity Usage (kWh/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)................................................................................................... 7-5

7-4 Natural Gas Usage (1,000 cu ft/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)........................................................................................... 7-5

7-5 Regional GHG Emissions from Electricity Generation (g CO2eq/kWh)..................... 7-7

7-6 Nitrogen Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)................................................................................................... 7-9

7-7 Phosphorus Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)......................................................................................... 7-11

7-8 Potassium Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)......................................................................................... 7-12

7-10 Pesticide Usage (pounds of active ingredients/acre) by Crop, Irrigation Status, 

and Region, Base Period (2000-2004)..................................................................... 7-15
x

7-11 Lime Usage (tons of lime/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)................................................................................................. 7-16

7-12 Example of Disposition Patterns of Harvested Wood by Region and Harvest 

Type, 100-Year Period: Southeast ........................................................................... 7-28

7-13 Half-life for Forest Products in End Uses................................................................ 7-31

7-14 GHG Mitigation Strategies...................................................................................... 7-39
1-1

SECTION 1

FASOMGHG OVERVIEW AND UPDATES 

The Forest and Agricultural Sector Optimization Model with Greenhouse Gases 

(FASOMGHG) 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. FASOMGHG 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 detailed accounting for changes in net greenhouse gas (GHG) emissions. The model 

was developed to evaluate the welfare and market impacts of policies that influence land 

allocation and alter production activities within these sectors. FASOMGHG has been used in 

numerous studies to examine issues including the potential impacts of GHG mitigation policy, 

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 FASOMGHG is advantageous for 

analysis of policies impacting the forest and agricultural sectors for a number of reasons. 

Because the model accounts for land competition between forestry, crop production, and 

livestock production (pasture) and landowner responses to changing relative prices, 

FASOMGHG provides a more complete assessment of the net market impacts associated with a 

policy than models that focus only on direct policy impacts on an individual commodity or 

subset of alternative land uses. Using FASOMGHG enables determination of secondary impacts,

such as crop switching, movements between cropland and pasture, movements between 

forestland and agricultural land, and changes in equilibrium quantities of forest and agricultural 

commodities due to changes in relative commodity prices. FASOMGHG 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, the model accounts for changes in the primary 

agricultural GHGs (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 allow for an 

accounting of environmental impacts over time and by region. This allows for a more complete 

quantification of net impacts than other models are generally able to produce, providing 
1-2

additional insights into the numerous important environmental and economic impacts in these 

sectors. 

The remainder of Section 1 provides a brief overview of the model, its history, and recent 

major updates and modifications that have been made. In Section 2, we discuss the scope of the 

model in terms of commodities, regions, inputs, land categories, GHG accounts, and trade. 

Section 3 presents a summary of the underlying theory and model structure. Section 4 discusses 

model linkages between the forestry and agricultural sectors. Section 5 presents information on 

the latest methods and assumptions used for bioenergy production in the model. In Section 6, we 

describe the model dynamics, including updates to yields, bioenergy conversion technology, 

demand, and other factors over time. Section 7 explains the GHG accounting used in the model. 

In Section 8, we discuss the outputs provided by FASOMGHG following completion of a model 

simulation. Finally, Section 9 summarizes selected recent model applications related to GHG 

mitigation, bioenergy, and climate impacts and adaptation.1

1.1 Model Summary

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. FASOMGHG simulates a dynamic baseline and changes from that baseline in response 

to changes in public policy or other factors affecting the sector. For instance, the model is often 

used to evaluate the joint economic and biophysical effects of GHG mitigation and/or bioenergy 

scenarios in U.S. forestry and agriculture. FASOMGHG combines component models of 

agricultural crop and livestock production, renewable fuels production, livestock feeding, 

agricultural processing, log production, forest processing, carbon sequestration, GHG 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. FASOMGHG covers private 

timberlands and all agricultural activity across the conterminous (“lower 48”) United States, 

broken into 11 market regions. Finally, FASOMGHG tracks five forest product categories and 

more than 2,000 production possibilities for field crops, livestock, and renewable fuel. 

FASOMGHG assumes intertemporal optimizing behavior by economic agents. For 

instance, the decision to continue growing a stand of timber rather than harvesting it now is 

based on a comparison of the net present value of timber harvest from a future period versus the 

 

1

See Bruce McCarl’s website at http://agecon2.tamu.edu/people/faculty/mccarl-bruce/FASOM.html for additional 

documentation of the model and http://agecon2.tamu.edu/people/faculty/mccarlbruce/McCarlClimateChange.htm for selected climate change-related applications. 
1-3

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 versus

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 a land price equilibrium 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.

The model solution portrays simultaneous multi-period, multi-commodity, multi-factor 

market equilibria, typically over 60 to 100 years on a 5-year time step basis when running the 

combined forest-agriculture 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.

The key endogenous variables in FASOMGHG include:

§ commodity and factor prices;

§ production, consumption, and 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, such as

– GHG emission/sequestration of CO2, CH4, and N2O and

– total nitrogen and phosphorous applications.

Additional detail on each of the key model characteristics is provided in subsequent 

sections of this documentation. 
1-4

1.2 Brief History and Previous Applications

The current version of FASOMGHG reflects numerous model enhancements that have

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

(Baumes, 1978). Since the initial version of ASM, there have been many changes to the model, 

including improvements for pesticide analysis by Burton (1982), as reported in Burton and 

Martin (1987), and a number of model additions to enable more detailed environmental and 

resource analyses. ASM has been used for analyses of renewable fuels dating back to the late 

1970s and 1980s (Tyner et al., 1979; Chattin, 1982; Hickenbotham, 1987). In addition, ASM was 

applied to study ozone impacts (Hamilton, 1985; Adams, Hamilton, and McCarl, 1984), acid rain

(Adams, Callaway, and McCarl, 1986), soil conservation policy (Chang et al., 1994), global 

climate change impacts (Adams et al., 1988, 1990, 1999, 2001; McCarl, 1999; Reilly et al., 2000, 

2002), and GHG mitigation (Adams et al., 1993; McCarl and Schneider, 2001). 

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

(Adams et al., 1993). 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 was constructed to address these limitations by linking 

a simple intertemporal model of the forest sector with a version of the ASM 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 process2: TAMM (Timber

Assessment Market Model) (Adams and Haynes, 1980, 1996; Haynes, 2003), NAPAP (North 

American Pulp and Paper model) (Ince, 1994; Zhang, Buongiorno, and Ince, 1993, 1996), 

ATLAS (Aggregate Timberland Assessment System) (Mills and Kincaid, 1992), and 

AREACHANGE (Alig et al., 2003, 2010; Alig, Kline, and Lichtenstein, 2004; and Alig and 

Plantinga, 2007). Timber inventory data and estimates of current and future timber yields were 

 

2 Adams and Haynes (2007) give a complete description of the full modeling system.
1-5

taken in large part from the ATLAS inputs used for the 2000 RPA Timber Assessment (Haynes, 

2003) (these data have since been updated with information from the 2005 RPA Update 

assessment, as described below). 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. The forest carbon accounting component of FASOMGHG is 

largely derived from the U.S. Forest Service’s 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 its economic forest-sector models (e.g., TAMM, NAPAP, 

ATLAS, 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 (Joyce, 1995; Joyce 

and Birdsey, 2000). Basing the model’s forest carbon accounting structure on FORCARB 

ensures that forest carbon estimates from FASOMGHG can be compared with ongoing efforts by 

the U.S. Forest Service to estimate and project national forest carbon sequestration. It also 

enables FASOMGHG to be updated over time as the FORCARB system evolves to incorporate 

the latest 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. Schneider (2000) 
1-6

and McCarl and Schneider (2001) 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 FASOM structure. This was manifest in the first version of FASOMGHG

that was built in the context of Lee (2002). 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, model modifications have been made to enhance FASOM’s ability to 

provide detailed analyses of the agricultural and environmental impacts of bioenergy production

from forest and agricultural feedstocks, both liquid transportation fuels and bioelectricity. 

Selected recent applications of the updated FASOMGHG are discussed in Section 9. 

1.3 Recent Developments and Model Updates 

The most recent version of FASOMGHG 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, land conversion costs, GHG emissions, and 

agricultural policies. The land characterization included in the model has been significantly 

modified to provide greater disaggregation of land use categories and to explicitly incorporate 

rangeland acreage. Other improvements include updates to rates of technological change (largely 

to provide more consistency with USDA long-term agricultural projections through 2019), input 

costs, and output prices to reflect the current state of the market. From a GHG accounting 

standpoint, the number of categories has been expanded to account for more than 50 categories 

of stocks and fluxes in forestry and agriculture that can be summed up to 7 major categories: 

afforestation, forest management, soil carbon sequestration, biomass-based changes in emissions 

from energy use3, CH4 and N2O-specific practices such as enteric fermentation and fertilizer 

management, CO2 from fossil fuels emitted during agricultural production, and carbon 

sequestered on developed land. Finally, the assumptions about growth in demand for developed 

land have been updated to reflect recent projections of income and population growth. 

 

3 These reductions represent the net emissions changes from replacing fossil fuels in the transportation and electric 

power sectors after accounting for emissions during processing and transportation of the biomass.
1-7

In the forestry component of the model, there have been updates from the most recent 

Forest Service’s 2005 RPA Update data (U.S. Forest Service, 2007; Haynes, 2007), including the 

basic forest inventory data, addition of log processing activities for the full range of solid wood 

and fiber products, expansion of the forms of trade between the US and Canada to include all 

forest products, recognition of off-shore imports of softwood lumber, addition of multiple classes 

of silvicultural regimes (management intensity classes [MICs]) in the South and PNWW, 

revisions in the outlook for Canadian timber harvests associated with recent pest outbreaks and 

changes in provincial forestry policies, and improvements and updates in the GHG accounting 

approach.

4

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

fuels sector in FASOMGHG. 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. Canola production was added to the 

model to allow for canola oil use in vegetable oil and biodiesel markets. There are also numerous 

feedstocks that can be utilized in the model for bioelectricity production, through either cofiring 

at coal plants or in dedicated biomass plants. 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, bioelectricity generation, and the 

rates of technological change. There have been recent modifications to better account for 

locations of existing generation where cofiring could potentially take place and to reflect 

increasing transportation distances (and costs) with higher bioenergy production. 

There is also an updated market for coproducts of biofuels production, primarily the use 

of distillers grains (DG) for livestock feed. DG 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. The model 

also now incorporates pathways for both fractionation to remove corn oil prior to the production 

of DG and extraction of corn oil from DG as part of the dry milling process for corn, accounting 

for differences in the DG characteristics associated with oil removal. Corn oil from fractionation 

is assumed to be food grade and a perfect substitute for corn oil produced from the wet milling 

process. Corn oil from extraction is nonfood grade but can be used in biodiesel production. 

 

4 Canadian timber harvest and forest products output are incorporated into FASOM in more detail than other foreign 

trade flows because trade with Canada comprises the largest part of U.S. forest products trade. 
2-1

SECTION 2

MODEL SCOPE 

In the following subsections, we provide an overview of the overall scope of 

FASOMGHG in terms of the commodities included and commodity flows between primary and 

secondary (processed) products, inputs used in production, U.S. regional disaggregation, land 

categories and allocation, GHG accounts tracked, other environmental impacts calculated, and 

treatment of international trade. 

2.1 Commodities 

FASOMGHG includes several major groupings of agricultural and forest commodities,

depending on the sector and whether they are primary commodities, 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 essentially 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 individual 

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). Thus, 

the mix of commodities that will be produced in a given model run depends on interactions 

between numerous related markets. 

2.1.1 Primary Commodities

Primary commodity production is derived from allocation decisions based on the set of 

production possibilities for field crops, livestock, and biofuels. The allocation decisions are 
2-2

based on optimizing across the budgets associated with each production possibility, given prices 

for outputs and inputs. Budgets are based on using inputs to produce a given level of outputs.

In the model, primary commodities can be used directly or converted to secondary 

products via processing activities with associated costs (e.g., soybean crushing to meal and oil, 

livestock to meat and dairy). Primary commodities can go to livestock use, feed mixing, 

processing, domestic consumption, or exports. A mixture of primary commodities and processed 

products are supplied to meet national-level demands in each market. Table 2-1 summarizes the 

primary commodities currently included within FASOMGHG and their units. There are currently 

40 primary crop products (including multiple subcategories of crops such as grapefruit, oranges, 

and tomatoes), 25 primary livestock products, 12 categories of forest and agricultural residues, 

and 32 categories of public and private domestic and imported logs included in the model.

Table 2-1. Primary Commodities 

Commodities Units

Crop Products

Barley Barley in bushels

Canola Canola in hundredweight (cwt)

Corn Corn in bushels

Cotton Cotton in 480 lb bales

Grapefruit, fresh (67 lb. box) Fresh market grapefruit in 1,000 67 pound boxes (CA, AZ) 

Grapefruit, fresh (80 lb. box) Fresh market grapefruit in 1,000 80 pound boxes (TX) 

Grapefruit, fresh (85 lb. box) Fresh market grapefruit in 1,000 85 pound boxes (FL) 

Grapefruit, processing (67 lb. box) Processing market grapefruit in 1,000 67 pound boxes (CA, AZ) 

Grapefruit, processing (80 lb. box) Processing market grapefruit in 1,000 80 pound boxes (TX) 

Grapefruit, processing (85 lb. box) Processing market grapefruit in 1,000 85 pound boxes (FL) 

Hay Hay in U.S. tons

Hybrid poplar Hybrid poplar in U.S. tons

Miscanthus Miscanthus in U.S. tons

Oats Oats in bushels

Orange, fresh (75 lb. box) Fresh market oranges in 1,000 75 pound boxes (CA, AZ)

Orange, fresh (85 lb. box) Fresh market oranges in 1,000 85 pound boxes (TX) 

Orange, fresh (90 lb. box) Fresh market oranges in 1,000 90 pound boxes (FL) 

Orange, processing (75 lb. box) Processing market oranges in 1,000 75 pound boxes (CA, AZ) 

Orange, processing (85 lb. box) Processing market oranges in 1,000 85 pound boxes (TX) 

Orange, processing (90 lb. box) Processing market oranges in 1,000 90 pound boxes (FL) 

Potatoes Potatoes in cwt

Rice Rice in cwt

(continued)
2-3

Table 2-1. Primary Commodities (continued)

Commodities Units

Rye Rye in bushels

Silage Silage in U.S. tons

Sorghum, energy Energy sorghum in dry metric tons 

Sorghum, grain Grain sorghum in cwt

Sorghum, sweet Sweet sorghum in U.S. tons

Sorghum, sweet (ratooned) Ratooned sweet sorghum in U.S. tons

Soybeans Soybeans in bushels

Sugarbeets Sugarbeets in U.S. tons

Sugarcane Sugarcane in U.S. tons

Switchgrass Switchgrass in U.S. tons

Tomatoes, fresh Fresh tomatoes in cwt

Tomatoes, processing Processing tomatoes in U.S. tons

Wheat, durum Durum wheat in bushels

Wheat, hard red spring Hard red spring wheat in bushels

Wheat, hard red winter Hard red winter wheat in bushels

Wheat, soft red winter Soft red winter wheat in bushels

Wheat, soft white Soft white wheat in bushels

Willow Willow in U.S. tons

Livestock Products

NonFedSlaughter 100 lbs non fed beef (liveweight)

FeedlotBeefSlaughter 100 lbs fed beef (liveweight)

CalfSlaughter 100 lbs of calf (liveweight)

CullBeefCo 100 lbs of cull beef cow (liveweight)

Milk 100 lbs of raw milk

CullDairyCows 100 lbs of cull dairy cow (liveweight)

HogsforSlaughter 100 lbs of hogs for slaughter (liveweight)

FeederPig 100 lbs feeder pigs (liveweight)

CullSow 100 lbs cull sows (liveweight)

LambSlaugh 100 lbs of slaughter lambs (liveweight)

CullEwes 100 lbs of cull ewes (liveweight)

Wool Raw wool in lbs

SteerCalve 100 lbs of steer calves (liveweight)

HeifCalve 100 lbs of heifer calves (liveweight)

StockedCalf 100 lbs of calves after first stocker phase ready to feed (liveweight)

StockedHCalf 100 lbs of heifer calves after first stocker phase ready to feed (liveweight)

StockedSCalf 100 lbs of steer calves after first stocker phase ready to feed (liveweight)

DairyCalves 100 lbs of dairy calves (liveweight)

(continued)
2-4

Table 2-1. Primary Commodities (continued)

Commodities Units

StockedYearling 100 lbs of yearlings after second stocker phase ready to feed (liveweight)

StockedHYearl 100 lbs of heifer yearlings after second stocker phase ready to feed 

(liveweight)

StockedSYearl 100 lbs of steer yearlings after second stocker phase ready to feed 

(liveweight)

HorsesandMules Number of horses and mules in head

Eggs Dozens of eggs at farm level

Broilers Broilers in 100 lbs (liveweight)

Turkeys Turkeys in 100 lbs (liveweight)

Forest and Agricultural Residues

Softwoodres Softwood logging residues in U.S. tons

Hardwoodres Hardwood logging residues in U.S. tons

Softmillres Softwood milling residues in U.S. tons

Hardmillres Hardwood milling residues in U.S. tons

Cornres Corn crop residues in U.S. tons

Sorghumres Sorghum crop residues in U.S. tons

Wheatres Wheat crop residues in U.S. tons

Oatsres Oat crop residues in U.S. tons

Barleyres Barley crop residues in U.S. tons

Riceres Rice crop residues in U.S. tons

Biomanure, beef Beef cattle manure for use in bioenergy production in U.S. tons

Biomanure, dairy Dairy cattle manure for use in bioenergy production in U.S. tons

Logs From Timber Harvest

PVT_SWSLOG_WOODS Softwood privately-produced sawlog in 1,000 cu. ft. in the woods

PVT_HWSLOG_WOODS Hardwood privately-produced sawlog in 1,000 cu. ft. in the woods

PVT_SWPLOG_WOODS Softwood privately-produced pulplog in 1,000 cu. ft. in the woods

PVT_HWPLOG_WOODS Hardwood privately-produced pulplog in 1,000 cu. ft. in the woods

PVT_SWFLOG_WOODS Softwood privately produced fuellog in 1,000 cu. ft. in the woods

PVT_HWFLOG_WOODS Hardwood privately produced fuellog in 1,000 cu. ft. in the woods

PUB_SWSLOG_WOODS Softwood publicly produced sawlog in 1,000 cu. ft. in the woods

PUB_HWSLOG_WOODS Hardwood publicly produced sawlog in 1,000 cu. ft. in the woods

PUB_SWPLOG_WOODS Softwood publicly produced pulplog in 1,000 cu. ft. in the woods

PUB_HWPLOG_WOODS Hardwood publicly produced pulplog in 1,000 cu. ft. in the woods

PUB_SWFLOG_WOODS Softwood publicly produced fuellog in 1,000 cu. ft. in the woods

PUB_HWFLOG_WOODS Hardwood publicly produced fuellog in 1,000 cu. ft. in the woods

IMP_SWSLOG_WOODS Imported softwood sawlog in the woods

IMP_HWSLOG_WOODS Imported hardwood sawlog in the woods

IMP_SWPLOG_WOODS Imported softwood pulplog in the woods

(continued)
2-5

Table 2-1. Primary Commodities (continued)

Commodities Units

IMP_HWPLOG_WOODS Imported hardwood pulplog in the woods

IMP_SWFLOG_WOODS Imported softwood fuellog in the woods

IMP_HWFLOG_WOODS Imported hardwood fuellog in the woods

PVT_SWFLOG_MILL Softwood privately produced fuellog in 1,000 cu. ft. delivered to the mill

PVT_HWFLOG_MILL Hardwood privately produced fuellog in 1,000 cu. ft. delivered to the mill

PUB_SWSLOG_MILL Softwood publicly produced sawlog in 1,000 cu. ft. delivered to the mill

PUB_HWSLOG_MILL Hardwood publicly produced sawlog in 1,000 cu. ft. delivered to the mill

PUB_SWPLOG_MILL Softwood publicly produced pulplog in 1,000 cu. ft. delivered to the mill

PUB_HWPLOG_MILL Hardwood publicly produced pulplog in 1,000 cu. ft. delivered to the mill

PUB_SWFLOG_MILL Softwood publicly produced fuellog in 1,000 cu. ft. delivered to the mill

PUB_HWFLOG_MILL Hardwood publicly produced fuellog in 1,000 cu. ft. delivered to the mill

IMP_SWSLOG_MILL Imported softwood sawlog delivered to the mill

IMP_HWSLOG_MILL Imported hardwood sawlog delivered to the mill

IMP_SWPLOG_MILL Imported softwood pulplog delivered to the mill

IMP_HWPLOG_MILL Imported hardwood pulplog delivered to the mill

IMP_SWFLOG_MILL Imported softwood fuellog delivered to the mill

IMP_HWFLOG_MILL Imported hardwood fuellog delivered to the mill

2.1.2 Secondary Commodities

As shown in Table 2-2, FASOMGHG contains a set of processing activities that make 

secondary commodities using primary commodities and other inputs (included as a processing 

cost). 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. There are 

currently 27 crop products, 17 livestock products, 10 processing byproducts, and 40 forestry 

products included in the model as secondary commodities.

Primary agricultural and forestry products are converted into processed products 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 
2-6

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.1 On the forestry side, 

the nonwood input supply curve provides the cost of processing wood.

Table 2-2. Secondary (Processed) Commodities 

Secondary Products Units

Crop Products

Orange juice Orange juice in 1,000 gallons at 42 brix

Grapefruit juice Grapefruit juice in 1,000 gallons at single-strength equivalent

Soybean meal Soybean meal in U.S. tons

Soybean meal equivalent Soybean meal equivalency in U.S. tons

Soybean oil Soybean oil in 1,000 lbs of oil

HFCS High fructose corn syrup (HFCS) in 1,000 gallons

Beverages Sweetened beverages in 1,000 gallons

Confection Sweetened confectionaries in 1,000 lbs

Baking Sweetened baked goods in 1,000 lbs

Canning Sweetened canned goods in 1,000 gallons

Refined sugar Refined sugar in U.S. tons

Gluten meal Gluten meal in 1,000 lbs

Gluten feed Gluten feed in 1,000 lbs

DG, export Distillers grains for export in 1,000 lbs

DG, corn Distillers grains from corn in 1,000 lbs

DG, noncorn Distillers grains not from corn in 1,000 lbs

DG, corn fractionation Distillers grains after fractionation in 1,000 lbs

Canola oil Canola oil in 100 gallons

Canola meal Canola meal in U.S. tons

Corn starch Corn starch in 1,000 lbs

Corn oil Corn oil in 100 gallons

Corn oil, nonfood Nonfood grade corn oil from DDG extraction in 100 gallons

Corn syrup Corn syrup in 1,000 gallons

Dextrose Dextrose in 1,000 lbs

Potatoes, chipped Potato chips in 100 lbs

Potatoes, dried Dried potatoes in 100 lbs

Potatoes, frozen Frozen potatoes in 100 lbs

(continued)

 

1U.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/.
2-7

Table 2-2. Secondary (Processed) Commodities (continued)

Secondary Products Units

Livestock Products

Fed beef Feedlot fed beef in 100 lbs (carcass weight)

Nonfed beef Nonfed (grass-fed) beef in 100 lbs (carcass weight)

Pork Pork in 100 lbs after dressing

Chicken Chicken in 100 lbs on ready to cook basis

Turkey Turkey in 100 lbs on ready to cook basis

Wool, clean Clean wool in lbs

Fluid milk, whole Whole fluid milk in 100 lbs

Fluid milk, low-fat Fat reduced fluid milk in lbs

Skim milk Skim milk in lbs

Cream Cream in lbs

Evaporated condensed milk Evaporated condensed milk in lbs

Nonfat dry milk Nonfat dry milk in Lbs

Butter Butter in lbs

American cheese American cheese in lbs

Other cheese Other cheese in lbs

Cottage cheese Cottage cheese in lbs

Ice cream Ice cream in lbs

Processing Byproducts 

Bagasse Sugarcane bagasse in tons

Lard Lard from swine slaughter in U.S. tons

Lignin Lignin produced from nonwood cellulosic ethanol processes in U.S. tons

Lignin, hardwood Lignin produced from hardwood cellulosic ethanol process in U.S. tons

Lignin, softwood Lignin produced from softwood cellulosic ethanol process in U.S. tons

Poultry fat Fat from chicken and turkey slaughter in lbs

Sweet sorghum pulp Sweet sorghum pulp in U.S. tons

Tallow, edible Edible tallow from beef cattle slaughter in lbs

Tallow, nonedible Nonedible tallow from beef cattle slaughter in lbs 

Yellow grease Waste cooking oil in lbs

Wood Products

SLUM Softwood lumber in million board feet, lumber tally 

SPLY Softwood plywood in million square feet, 3/8”

OSB Oriented strand board (OSB) in million square feet, 3/8”

HLUM Hardwood lumber in million board feet, lumber tally 

HPLY Hardwood plywood in million square feet, 3/8”

SWPANEL Softwood used in non-OSB reconstituted panel in million square feet, 3/8”

HWPANEL Hardwood used in non-OSB reconstituted panel in million square feet, 3/8”

(continued)
2-8

Table 2-2. Secondary (Processed) Commodities (continued)

Secondary Products Units

SWMISC Softwood miscellaneous products in million cubic feet

HWMISC Hardwood miscellaneous products in million cubic feet

SRESIDUES Softwood residues in million cubic meters

HRESIDUES Hardwood residues in million cubic meters

HWPULP Hardwood pulp in million cubic meters

SWPULP Softwood pulp in million cubic meters

Hardwood pulp Hardwood pulp moved to agricultural component of model for use in 

cellulosic ethanol production in U.S. tons

Softwood pulp Softwood pulp moved to agricultural component of model for use in 

cellulosic ethanol production in U.S. tons

AGFIBERLONG Agrifiber, long fiber

AGRIFIBERSHORT Agrifiber, short fiber

OLDNEWSPAPERS Old newspapers in million metric tons

OLDCORRUGATED Old corrugated paper in million metric tons

WASTEPAPER Mixed wastepaper in million metric tons

PULPSUBSTITUTE Pulp substitutes in million metric tons

HIGDEINKING Hi-grade deinking in million metric tons

NEWSPRINT Newsprint in million metric tons 

UNCFREESHEET Uncoated free sheet in million metric tons 

CFREESHEET Coated free sheet in million metric tons 

UNCROUNDWOOD Uncoated roundwood in million metric tons 

CROUNDWOOD Coated roundwood in million metric tons 

TISSUE Tissue and sanitary in million metric tons

SPECIALTYPKG Specialty packaging in million metric tons

KRAFTPKG Kraft packaging in million metric tons

LINERBOARD Linerboard in million metric tons

CORRUGMED Corrugated medium in million metric tons

SBLBOARD Solid bl. board in million metric tons 

RECBOARD Recycled board in million metric tons

CONSTPAPER Construction paper and board in million metric tons

DISPULP Dissolving pulp in million metric tons

SWKMPULP Softwood kraft market pulp in million metric tons

HWKMPULP Hardwood kraft market pulp in million metric tons

RECMPULP Recycled market pulp in million metric tons

CTMPMPULP Chemi-thermomechanical market pulp in million metric tons 

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. 
2-9

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. 

2.1.3 Bioenergy Products

Another category of processed product that can be produced in FASOMGHG using a 

subset of primary and secondary commodities is bioenergy. In addition to the category totals 

shown in Table 2-3, the model tracks the quantity of each bioenergy product produced using 

each individual feedstock. The bioenergy sector is a very important component of the 

FASOMGHG specification that has received a great deal of enhancement since the last major 

model update. Given recent policy interest and promulgation of rules greatly expanding 

renewable energy production and consumption as well as the sizable potential role for bioenergy 

in GHG mitigation, we have been engaged in a major effort to update this component of the 

model in recent years. This has included updates to data and parameters as well as incorporation 

of additional feedstocks, all of which are highlighted in Section 6.

Table 2-3. Bioenergy Products

Bioenergy Products Units

Crop ethanol Ethanol from crop grains and sugar in 1,000 gallons

Cellulosic ethanol Ethanol from cellulosic processes in 1,000 gallons

Biodiesel Biodiesel in 1,000 gallons

TBtus Bioenergy inputs to electricity production in trillion British thermal units (Btus)

2.1.4 Blended Livestock Feeds

In addition to using the primary and/or secondary commodities identified above directly 

as livestock feed, FASOMGHG also allows for blending of livestock feeds from a number of 

different alternative formulas. Table 2-4 summarizes the categories of blended livestock feeds 

that can be used to meet livestock feed demand. These blends are defined to meet nutritional 

requirements of the individual livestock types, but each of the blends identified below can be 

made using a variety of different mixtures of primary and secondary commodities to deliver the 

appropriate nutrient levels. These alternative mixtures are defined by feed and feed blending 

alternative and vary by market region. The actual mixtures that will be used in the market 

equilibrium will depend on relative prices and availability as well as nutrient requirements. The 

resultant feeds are supplied for consumption by each livestock type included within the model. 
2-10

Table 2-4. Blended Livestock Feeds 

Feed Item Units

StockPro0 Protein feed for stockers in 100 lbs (cwt) 

CatGrain0 Blend of grains for cattle in 100 lbs (cwt) 

HighProtCa Protein feed for cattle in 100 lbs (cwt) 

CowGrain0 Blend of grains for cow calf operations in 100 lbs (cwt) 

CowHiPro0 Protein feed for cow calf operations in 100 lbs (cwt) 

FinGrain0 Blend of grains for pig finishing in 100 lbs (cwt) 

FinProSwn0 Protein feed for pig finishing in 100 lbs (cwt) 

FarGrain0 Blend of grains for farrowing operations in 100 lbs (cwt) 

FarProSwn0 Protein feed for farrowing operations in 100 lbs (cwt) 

FPGGrain0 Blend of grains for feeder pigs in 100 lbs (cwt) 

FPGProSwn0 Protein feed for feeder pigs in 100 lbs (cwt) 

DairyCon0 Blend of grains for dairy operations in 100 lbs (cwt) 

BroilGrn0 Blend of grains for broilers in 100 lbs (cwt) 

BroilPro0 Protein feed for broilers in 100 lbs (cwt) 

TurkeyGrn0 Blend of grains for turkeys in 100 lbs (cwt) 

TurkeyPro0 Protein feed for turkeys in 100 lbs (cwt) 

EggGrain0 Blend of grains for eggs in 100 lbs (cwt) 

EggPro0 Protein feed for eggs in 100 lbs (cwt) 

SheepGrn0 Blend of grains for sheep in 100 lbs (cwt) 

SheepPro0 Protein feed for sheep in 100 lbs (cwt) 

2.2 Inputs to Production

The production component includes agricultural crop and livestock operations, as well as

forest industry (FI) and nonindustrial private forests (NIPF) forestry operations. FASOMGHG

contains an agricultural production model for each of the primary commodities identified above. 

Production of traditional agricultural crops, bioenergy crops, livestock and forestry compete for 

suitable land. In addition to land, FASOMGHG depicts the 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. Numerous other 
2-11

inputs (e.g., fossil fuels, capital) 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.

Budgets are included for all crops included in the model based on data drawn from a 

variety of USDA and agricultural extension sources. Table 2-5 summarizes major categories of 

inputs included within the crop budgets that are defined and tracked in terms of quantities, 

typically because those quantities provide information on key energy, natural resource, GHG 

emissions, and other environmental impacts under a policy scenario (not all inputs are included 

in all crop budgets). The remainder of budget items are defined only in terms of dollars and 

largely aggregated for the purposes of the model. For each traditional crop, production budgets 

are differentiated by region, tillage choice (three choices: conventional tillage, conservation 

tillage, or no-till), and irrigated or dryland. 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 FASOMGHG production possibilities; all energy crops are assumed to be 

produced under nonirrigated conditions and do not compete for irrigation water.

Table 2-5. Major Categories Included in Crop Budgets in Quantities 

Carbon—Fuel Use Gasoline Nitrogen

Carbon—Grain Drying Herbicide Nitrous Oxide—Residue Burning

Carbon—Fertilizer Production Insecticide Nitrous Oxide—Fertilizer

Carbon—Irrigation Water Pumping Irrigation Water Nitrous Oxide—Histosol

Carbon—Pesticide Production Labor Nitrous Oxide—Leaching

Crop Residue Land Nitrous Oxide—Volatilization

Crop Yield Lime & Gypsum Phosphorus

Diesel Fuel Methane—Residue Burning Potassium

Electricity Methane—Rice Cultivation

Fungicide Natural Gas
2-12

Table 2-6 summarizes the inputs included in FASOMGHG livestock production budgets 

in terms of quantities (not all inputs are included in all livestock budgets). A number of 

categories track manure management systems because they are a key source of emissions for 

livestock. As for crops, the remainder of the inputs identified in available livestock budgets are 

included only in dollar terms and aggregated for model purposes. 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.

Table 2-6. Major Categories included in Livestock Budgets in Quantities 

Barley Liquid Volatile Solids Volume Oats

Biomanure Livestock Head Pasture

Blended Feed Requirements Livestock Product Output Silage

Corn Managed Manure Fraction Soybean Meal

Hay Methane—Enteric Fermentation Volatile Solids in Manure

Head in Liquid Systems Methane—Manure Wheat

Labor Nitrous Oxide—Manure

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 FASOMGHG 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 FASOMGHG depicts the use of existing private

timberland as well as the reforestation decision on harvested land. The forest sector relies on a 

series of forest growth and yield values to grow the forest inventory over time and to convert 

harvested area into forest products. In addition, forest carbon sequestration is calculated over 

time based on the inventory characteristics. Timberland is differentiated by region, the age 

cohort of trees,2ownership class, forest type, site condition, management regime, and suitability 

of the land for agricultural use. Decisions pertaining to timber management investment are 

 

2 Timberlands 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.
2-13

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 forest type, management regime, and future harvest age.

Raw agricultural and forestry products are converted into processed products in 

FASOMGHG using processing budgets. Agricultural processing is regionalized for biofuels 

production, soybean crushing, wet milling, and bioelectricity generation. Processing budgets for 

other agricultural products are defined at a national level. 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.3 For production of bioenergy, the model also calculates net changes in 

carbon, methane, and nitrous oxide emissions from replacing fossil fuels with biofuels after 

accounting for emissions associated with hauling and processing bioenergy feedstocks. 

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 Canadian regions. Carbon 

sequestered in wood products is also calculated and tracked over time. 

2.3 U.S. Regional Disaggregation 

FASOMGHG includes all states in the conterminous (“lower 48”) United States, broken 

into 63 subregions for agricultural production and 11 market regions (see Table 2-7) (forestry 

production is not disaggregated into the 63 subregions, just the 11 market regions). These regions 

are graphically displayed in Figure 2-1. The 11 market regions provide a consolidation of 

regional definitions that would otherwise differ if the forest and agricultural sectors were treated 

separately. 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, “PNWW”). The Great Plains and Southwest regions are kept 

 

3 U.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/.
2-14

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

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

production, and the PNWE region is considered a significant producer of agricultural 

commodities tracked in the model, whereas PNWW is not. Thus, the two model regions that 

make up the Pacific Northwest are tracked separately. Each of the production regions is uniquely 

mapped to one of the 11 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. 

Table 2-7. Definition of FASOMGHG Production Regions and Market Regions 

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 (agriculture 

only)

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

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
2-15

South Central

Northeast

Southeast

Lake States

Pacific

Southwest

Great

Plains

Rocky Mountains

Pacific

Northwest

West East

Corn Belt

South

West

Figure 2-1. Map of the FASOMGHG Regions

When running the model, one can choose whether to keep the 63 regions or collapse to 

11 regions to reduce run time. It is also possible to model agriculture explicitly in all 63 regions 

for an initial time period to provide maximum regional detail for the near to intermediate term 

and then collapse to 11 regions at a specified future time period for model size control purposes.

The full FASOMGHG can also be run at the more aggregated regional definition shown 

in Table 2-8, although the aggregated version of the model is more typically used for model 

development and testing. In addition, the wood products production and GHG accounting 

calculations employ an even more aggregated set of U.S. regions, following the regional 

definition in the North American Pulp and Paper (NAPAP) model (Zhang et al., 1993, 1996; 

Ince, 1994). This specification combines the Midwest and Northeast regions into a North region 

and does not include the Plains region because there are no forests tracked in that region. 
2-16

Table 2-8. Aggregated U.S. Regions 

Region FASOMGHG Market Regions Included

Midwest CB, LS

Northeast NE

Plains GP, SW

PNW_West_side PNWW

Southern_US SE, SC

Western_US PNWE, RM, and PSW

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

2.4 Land Use Categories

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 FASOMGHG 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, FASOMGHG includes five land suitability classes, as described in 

Section 2.4.4.

FASOMGHG includes all cropland, pastureland, rangeland, and private timberland4

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 accounts for the movement of 

forest and agricultural lands into developed uses. We recently updated our land use 

categorization system to represent a more comprehensive range of land use categories. This 

process included expanding our coverage of pasturelands to explicitly represent multiple forms 

of public and private grazing lands (each with different animal unit grazing potential per unit of 

land). The FASOMGHG land base was developed based on land classifications from multiple 

sources, with the USDA Economic Research Service Major Land Use (MLU) database (USDA 

ERS, 2007) and the Natural Resources Inventory (NRI) published by the USDA-Natural 

Resource Conservation Service serving as our primary data sources. 

 

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

responses to changing incentives, FASOMGHG includes an exogenous timber supply from public forestlands. 
2-17

These databases rely on different sampling methods and define land use categories in 

separate ways that each have advantages and disadvantages. To maintain consistency with other 

FASOMGHG input data, we rely on the ERS depiction of cropped acres to define our cropland 

base. However, the ERS lacks a clear distinction between grassland pasture and rangeland, while 

the NRI defines these as separate land categories, a distinction that we also wish to maintain 

given differences in ownership and productivity. Therefore, we make use of both datasets and 

attempt to avoid overlap between different land use categories as outlined below. This “hybrid”

NRI-MLU land categorization system is unique, and we feel that it provides FASOMGHG with 

a more realistic representation of public and private grazing lands as well as regional land 

transition possibilities between alternative uses. 

Land categories included in the model 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). 

This category includes only cropland from which one or more crops included in 

FASOMGHG were harvested.5 Cropland used for livestock grazing before or after 

crops were harvested is included within this category as long as crops are harvested 

from the land. Data used to define cropland area are directly from the ERS-MLU 

(USDA ERS, 2007). 

§ Cropland pasture is managed land suitable for crop production (i.e., relatively high 

productivity) that is being used as pasture. The ERS-MLU database defines this area 

as “used only for pasture or grazing that could have been used for crops without 

additional improvement. Also included were acres of crops hogged or grazed but not 

harvested prior to grazing.” Not requiring additional improvement to be suitable for 

crop production is a key distinction between cropland pasture and other forms of 

grassland pasture or rangeland. This land is assumed to be more freely transferable 

with cropland than other grassland types. State totals for cropland pasture used in the 

model are drawn directly from the ERS-MLU Web site. 

§ Pasture was defined in an attempt to maintain a consistent definition with the NRI 

classification of grassland pasture but to eliminate overlap with ERS cropland or 

cropland pasture as defined above. For each region, we compute the initial stock of 

“pasture” algebraically as the maximum of 1) (CroplandNRI + Grassland PastureNRI) –

(CroplandERS + Cropland PastureERS) or 2) 0. This procedure is necessary to avoid 

double counting of pasturelands between the NRI and ERS data.

 

5 Note that FASOMGHG does not include every cropping activity conducted in the United States. For instance, 

tobacco, vineyards, and most fruits and vegetables are not included within the model. 
2-18

§ Private grazed forest is calculated based on woodland areas of farms reported in the 

Agricultural Census to be used for grazing (woodland pasture).6 Woodland pasture is

defined as “all woodland used for pasture or grazing during the census year. 

Woodland or forestland pastured under a per-head grazing permit was not counted as 

land in farms and, therefore, was not included in woodland pastured.” These lands are 

not included in the private timberland areas defined in the model, and there are no 

forest products harvested from these lands in FASOMGHG. The area in this category 

is fixed over time and is not allowed to transfer into forestland or other alternative 

uses. 

§ Public grazed forest is computed as the difference between the ERS-MLU total 

forest pasture stock and the private portion given by the Agricultural Census as 

described above.

§ Private rangeland is defined in FASOMGHG using a combination of NRI and ERSMLU data. Rangeland is typically unimproved land where a significant portion of the 

natural vegetation is native grasses and shrubs. The NRI database defines rangeland 

as “land on which the climax or potential plant cover is composed principally of 

native grasses, grass-like plants, forbs or shrubs suitable for grazing and browsing, 

and introduced forage species that are managed like rangeland. This would include 

areas where introduced hardy and persistent grasses, such as crested wheatgrass, are 

planted and practices, such as deferred grazing, burning, chaining, and rotational 

grazing, are used with little or no chemicals or fertilizer being applied. Grassland, 

savannas, many wetlands, some deserts, and tundra are considered to be rangeland. 

Certain low forb and shrub communities, such as mesquite, chaparral, mountain 

shrub, and pinyon-juniper, are also included as rangeland.” Thus, rangeland generally 

has low forage productivity and is unsuitable for cultivation and it is assumed that 

rangeland cannot be used for crop production or forestland. To calculate rangeland 

acres while avoiding double-counting, we first use 2003 NRI data to provide a base 

definition for the rangeland class. States with no reported rangeland acres in the NRI 

database (USDA NRCS, 2003) are defined to have no rangeland area in 

FASOMGHG to be consistent with the NRI definition and to limit overlap between 

the NRI classification of rangeland and the ERS-MLU classification of “grassland 

pasture and range.” Then, to determine the state totals of private rangeland, ERS 

(2008) data defining regional totals of privately held grazing land by type was used. 

These regional proportions were multiplied by corresponding state-level totals to 

define the private rangeland stock by state. For example, the private rangeland stock 

in Wyoming was calculated by multiplying the total ERS estimate for Wyoming by 

the proportion of private to total rangeland for the “Mountain” region in which 

Wyoming is located. In solving for the private rangeland area used in FASOMGHG, 

it is important to maintain the relationship between all grazing lands for consistency. 

The ERS defines all privately owned grazing lands to be equal to the sum of cropland 

pasture, grazed forest, and grassland pasture and range and reports a total of 

approximately 488 million acres. Following all of our adjustments to develop a 

 

6 Data are available at 

http://www.agcensus.usda.gov/Publications/2002/Volume_1,_Chapter_2_US_State_Level/st99_2_008_008.pdf.
2-19

consistent land use definition based on both NRI and ERS-MLU data, the total private 

grazing land base in the baseline is approximately 484 million acres. 

§ Public rangeland was calculated using the proportions described above under private 

rangeland and totals about 182 million acres. This includes federal, state, and local 

sources. 

§ Forestland in FASOMGHG 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. Regional timberland stocks, as well as 

timber demand, inventory, and additional forestry sector information are drawn from 

the 2005 RPA Timber Assessment (Adams and Haynes, 2007).

§ 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. All private land uses (except CRP and grazed forest) are 

able to convert to developed land, decreasing the total land base available for forestry 

and agriculture over time. Land transfer rates vary by land use type over time and are 

consistent with the national land base assessment by Alig et al. (2010). 

§ 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. State and 

county-level land area enrolled in the CRP was obtained from the USDA Farm 

Service Agency (2009).

Figure 2-2 shows the baseline land allocation in FASOMGHG at the national level across 

each of the land categories defined above. Land is allowed to move between categories over time 

subject to restrictions based on productivity and land suitability. 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 (see the 

subsections below for additional detail on each land use category and its potential conversion to 

alternative land uses).
2-20

Figure 2-2. Baseline FASOMGHG U.S. Land Base by Land Use Category (million 

acres)

2.4.1 Agricultural Land

As described above, cropland is land that is suitable for crop production and can 

potentially be used in the production of any of the crops included in FASOMGHG 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 total area of baseline cropland is based on ERS-MLU data as described 

above, with baseline land in production of individual crops based on USDA National 

Agricultural Statistics Service (NASS) historical data on county-level harvested acreage by crop. 

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 FASOMGHG (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 crops7are not included in the 

reported FASOMGHG cropland and are not explicitly tracked in FASOMGHG. Cropland can 

potentially be converted to cropland pasture or private forestland. In addition to tracking 

 

7 USDA data for planted area exceed 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.
2-21

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 system8to 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 FASOMGHG.

CRP land is cropland that has been enrolled in the Conservation Reserve Program, 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 FASOMGHG in the baseline is based on 2007 data on CRP

enrollment by state available from the USDA Farm Service Agency (2009). Because landowners 

can choose to remove their land from the CRP program when their contract expires (or before

expiration, subject to a financial penalty), FASOMGHG 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, FASOMGHG 

model runs generally place a floor of 32 million acres in CRP land in future years. 

Cropland pasture, pasture, and private and public grazed forest are all suitable for 

livestock grazing (i.e., land that provides sufficient forage to support the needs of grazing 

livestock within a region), but cropland pasture tends to be more productive. 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. Pasture, which is 

considered less productive, can be converted to forestland but not cropland. Private and public 

 

8 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 associated with increased manufacture and usage. FASOM has the ability to 

track these indirectly induced GHG effects associated with changes in tillage.
2-22

grazed forest 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. Neither private 

nor public grazed forest can be converted to any other uses. As mentioned above, FASOMGHG 

assumes that no timber is produced from private grazed forest. 

Rangeland in FASOMGHG 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 United States. Therefore, the area of rangeland required 

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

lower forage production per acre than pastureland and is considered unsuitable for cultivation. In 

addition, much of the rangeland in the United States is publicly owned. Thus, it is assumed that 

rangeland cannot be used for crop production or forestland.

The area of pastureland or rangeland required per animal is calculated in FASOMGHG

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 FASOMGHG, defined on a regional 

basis.9The 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 FASOMGHG and 

idle pastureland area and associated soil carbon sequestration are tracked in the model. In 

particular, changes in livestock populations will affect pasture and rangeland used for animal 

production and could increase or decrease idle land in the model. Changes in animal populations 

over time and impacts of policies affecting livestock markets, including use of each of the 

pasture and rangeland categories by each type of livestock, are tracked within FASOMGHG.

2.4.2 Forestland 

Timberland refers to productive forestlands able to grow at least 20 cubic feet of 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 acre per 

year, called unproductive forestland, and timberland that is reserved for other uses are not 

considered part of the U.S. timber base (Haynes et al., 2007) and are therefore not tracked by the 

model.

 

9 The calculation of acres of pasture required by a given type of livestock in a particular region is implicitly based on 

estimates of AUMs available for each category of pastureland in that region. 
2-23

In FASOMGHG, endogenous land use modeling is only done for privately held parcels, 

not publicly owned or publicly managed timberlands. The reason is that management of public 

lands is largely dictated by government decisions on management, harvesting, and other issues 

that account for multiple public uses of these lands rather than responses to market conditions. 

However, an exogenous quantity of timber harvested on U.S. public lands is accounted for 

within the model. Projected regional public harvest levels are drawn from the assumptions used 

in the baseline case of the US Forest Service’s 2005 RPA Timber Assessment (Haynes et al., 

2007). Timber inventory levels for public timberlands are simulated based on these harvest 

levels. 

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

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

forestland productivity (these site groups were defined based on ATLAS inputs [Haynes et al., 

2007]), 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)

§ LOW—low site productivity group (sites that produce between 20 and 50 cubic feet 

of live growing stock per acre per year)

FASOMGHG 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. As a result the NIPF group includes lands owned by timber 

investment management organizations (TIMOs) and real estae investment trusts (REITs).

In addition, FASOMGHG tracks land in terms of the type of timber management

practised, forest type (identified by dominant species), and stand age. As shown in Table 2-9, 

across all regions there are 18 management intensity classes defined based on whether thinning, 

partial cutting, passive management, or other management methods are used. Note that some 

management intensity classes are only defined for a subset of regions (as identified by the region 

codes in parentheses) based on regional data and definitions. There are also 25 different forest 

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.
2-24

Table 2-9. Forest Management Intensity Classes (regions of application in parentheses)

MIC Code Description

AFFOR Afforestation of bottomland hardwood (SE, SC)

AFFOR_CB Afforestation of hardwood and softwood forest types (CB)

LO Natural regeneration (or afforestation) with low management

NAT_REGEN Natural regeneration with low management (PNWW)

NAT_REGEN_PART_CUT_HI Partial cutting with high level of management (PNWW)

NAT_REGEN_PART_CUT_LO Partial cutting with medium level of management (PNWW)

NAT_REGEN_PART_CUT_MED Partial cutting with low level of management (PNWW)

NAT_REGEN_THIN Natural regeneration with a commercial thin (PNWW)

PART_CUT_HI Partial cutting with medium level of management (SE, SC)

PART_CUT_HI+ Partial cutting with high level of management (SE, SC)

PART_CUT_LO Partial cutting with low level of management (SE, SC)

PASSIVE Passive management (minimal amount of management)

PLANT Plant with no intermediate treatments (PNWW)

PLANT_THIN Plant with medium level of management (PNWW)

PLANT+ Plant with high level of management (PNWW)

PLNT_HI Planted pine with high level of management (SE, SC)

PLNT_HI_THIN Planted pine with commercial thin and high level of management (SE, SC)

PLNT_LO_THIN Planted pine with commercial thin and no intermediate treatments (SE, SC)

PLNT_MED Planted pine with medium level of management (SE, SC)

PLNT_MED_THIN Planted pine with commercial thin and medium level of management (SE, 

SC)

RESERVED Reserved from harvest

SHORT_ROTSWDS Short rotation softwoods with high level of management (SE, SC)

TRAD_PLNT_PINE Planted pine with no intermediate treatments (SE, SC)

2.4.3 Developed Land

FASOMGHG also accounts for the movement of agricultural and forestland into 

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

available to agricultural or forestry land uses. Thus, FASOMGHG assumes an exogenous rate of 

land conversion into developed uses by region for each of the agricultural and forestland

categories included in the model (with the exception of private and public grazed forest pasture 

and CRP lands) 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. It is assumed that developed land does not convert back to other uses. 
2-25

2.4.4 Land Allocation

In FASOMGHG, the initial land endowment is fixed. However, because land can move 

between forests and agriculture, agricultural production faces, in effect, an endogenous excess 

land supply “equation” from forestry. Forestry production, in turn, effectively faces an 

endogenous excess land supply “equation” from agriculture. 

The conceptual foundation for land allocation is described below. In terms of 

transferability between agriculture and forestry, FASOMGHG 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 

site productivity class. In addition, land movements in forestry are only allowed in the NIPF 

owner category, reflecting an assumption (and lengthy historical observation) that land held by 

the FI ownership group will not be converted from timberland to agriculture.

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
2-26

developed and infrastructural uses) at an exogenous rate. Rates at which forest and agricultural 

land are converted to developed uses in FASOMGHG are based on land-use modeling for a 

national land base assessment by the U.S. Forest Service and cooperators. 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 shifted to developed uses. 

An additional potential source of land is CRP land moving back into production. There 

are, however, environmental benefits associated with land in CRP and plans to retain some 

portion of that land in the program. In recent analyses, FASOMGHG has generally been applied 

allowing CRP land to convert back to cropland under the constraint that a minimum of 32 

million acres of land remains in the CRP. This is consistent with the 2008 Farm Bill and

information provided by USDA on their intentions to maintain that level of CRP acreage.

2.5 Market Modeling

FASOMGHG 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 regional 

and national prices 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 

(see Section 2.6 for additional discussion of international trade modeling and foreign regions 

included). Transportation costs clearly influence equilibrium exports and FASOMGHG includes 

data on transportation costs to all regions included within the model and between foreign regions 

for those commodities where trade is explicitly modeled.10

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 that need to be in 

equilibrium in each 5-year period include:

§ regional product supply;

§ national raw product demand;

§ regional or national processed commodity demand;

§ regional or national supply of processed commodities;

§ regional or national (depending on commodity) export demand;

 

10 Real transportation costs are assumed to increase at a rate of 0.5% per year relative to the base transportation 

costs.
2-27

§ 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 

five 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. Harvests from 

public forestlands are included in the model but are treated as exogenously determined by the 

government. 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 to meet export demand. 

2.6 International Trade

FASOMGHG accounts for international trade in both forestry and agricultural products, 

with the commodities included in the trade component and their treatment varying based on the 

importance of trade to the U.S. market and available data. 

2.6.1 Forestry

For the forest sector, forest products trade with Canada and softwood lumber trade with 

the rest of the world are endogenous. These are the largest (by volume or weight) US forest 

products trade flows. All other product movements are exogenous and, in the baseline case, 

follow projections derived from the Forest Service’s 2005 RPA Timber Assessment Update 

(Haynes et al., 2007).

Product movements from Canadian producing regions to the United States are 

endogenous and subject to appropriate transport costs, exchange rates, and tariffs. Supplies of 

logs in Canada derive primarily from public lands (“Crown” lands) governed by individual 

provinces, with small volumes from private lands. Harvests from these lands vary over time 

based on provincial policies, extraction and delivery costs and market prices for logs. These 

supplies are represented by a set of (log price sensitive) delivered log supply equations for both 

sawlogs and pulpwood in each Canadian region. 
2-28

Softwood lumber imports into the United States from non-Canadian sources are based on 

a linear import supply function drawn from the 2005 RPA Timber Assessment Update (Haynes 

et al., 2007), which shifts over time to correspond to the base scenario in the Update. 

2.6.2 Agriculture

Three types of agricultural commodity trade arrangements are represented. Agricultural 

primary and secondary commodities may be portrayed:

§ with trade occurring in explicit international markets using a Takayama and Judge 

(1973) style, spatial equilibrium submodel that portrays country/region level excess 

demand on behalf of a set of foreign countries/regions, excess supply on behalf of a 

set of foreign countries/regions and interregional trade between the foreign 

countries/regions themselves and with the United States;

§ with the United States facing a single excess supply and/or excess demand 

relationship on behalf of the ROW; or 

§ without being subject to international trade.

FASOMGHG has explicit trade functions between the United States and 29 distinct 

foreign trading partners for agricultural commodities having such detailed trade data available. 

For the remaining commodities traded internationally, excess supply/demand functions are 

specified to capture net trade flows with the rest of the world as one composite trade region with 

the U.S. Demand levels are parameterized based on the USDA Static World Policy Simulation 

Model (SWOPSIM) database and USDA annual statistics.

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 submodels 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 for the ROW) facing the 

United States rather than 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, 
2-29

there are many commodities without any explicit opportunities for international trade, such as 

hay, silage, energy crops, livestock, and many processed commodities. 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.

When commodities are subject to explicit spatial interregional trade with spatial 

equilibrium submodels, then trading is portrayed among the 29 individual countries/foreign 

regions currently included in FASOMGHG. In those countries/foreign regions that are major 

importers or exporters of an explicitly traded commodity, explicit supply and demand functions

are defined. Table 2-10 presents the commodities that are traded and the countries/regions that

supply and demand them in the model. Note that when a country supplies certain commodities, it 

can either export them to another explicitly defined country/foreign region or to the United 

States. Similarly, demand in a country/region can be met from imports from other countries or 

from the United States.

Table 2-10. Explicitly Traded Commodities and Countries/Regions Trading with the 

United States

FASOMGHG 

Commodity Exporting Countries Importing Countries

Canola Canada NA

Canola oil Canada NA

Canola meal Canada NA

Corn Argentina, Brazil, China, 

USSR, W-Africa

Canada, Caribbean, E-Mexico, Indonesia, Japan, N-Africa, NCEuro, Philippines, SE-Asia, S-Korea, Taiwan, W-Asia

Rice E-Medit, India, Myanmar, 

N-Africa, Pakistan, 

Thailand, Vietnam

Bangladesh, Brazil, Caribbean, China, Indonesia, Japan, N-Korea, 

NC-Euro, Philippines, S-Africa, SE-Asia, Taiwan, USSR, WAfrica, WS-America

Sorghum Argentina, Australia, China E-Mexico, Japan, NC-Euro, S-Korea, Taiwan

Soybeans Argentina, Brazil, Canada, 

Caribbean, USSR

China, E-Europe, E-Mexico, Indonesia, Japan, N-Africa, NC-Euro, 

SE-Asia, S-Korea, Taiwan, W-Africa, W-Asia

Wheat, Durum Canada Brazil, Indonesia, Japan, N-Africa, Philippines, SE-Asia, S-Korea, 

Taiwan, USSR

Wheat, Hard Red 

Spring

Australia, Canada Brazil, Caribbean, China, Indonesia, Japan, N-Africa, Philippines, 

SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia

Wheat, Hard Red 

Winter

Argentina, Australia, 

Canada

Brazil, China, E-Mexico, Indonesia, Japan, N-Africa, Philippines, 

SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia

Wheat, Soft Red 

Winter

Argentina, Australia, 

Canada

Brazil, China, E-Mexico, Indonesia, Japan, N-Africa, Philippines, 

SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia

Wheat, Soft

White

Australia, Canada, NCEuroBrazil, China, E-Mexico, Indonesia, Japan, N-Africa, Philippines, 

SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia
2-30

For commodities where trade is important to the U.S. market, but data on trade flows 

with individual countries/foreign regions are more limited, U.S. trade is modeled at an aggregate 

level with the ROW. When U.S. trade is included in the model with only ROW excess import 

supply and export demand functions, then the curves represent the sum of ROW exports and 

imports that are faced at the national U.S. market level. The commodities currently included in 

the model in this way are listed in Table 2-11, identifying whether they are included in the 

import supply and/or export demand functions. 

Commodities without explicit trade are generally specified as such because either the 

trade numbers are small or the commodity is not traded. These include the commodities listed in 

Table 2-12 as well as all of the blended feeds.
2-31

Table 2-11. Commodities with Only ROW Export or Import Possibilities 

FASOMGHG Commodity Imported into the United States Exported from the United States

Canola Y N

Canola oil Y N

Canola meal Y N

Cotton N Y

DG N Y

Oats N N

Barley Y Y

Sugarcane N N

Potatoes Y Y

Tomatoes, fresh Y Y

Tomatoes, processed N N

Oranges, fresh (75 lb. box) Y Y

Grapefruit, fresh (85 lb. box) Y Y

Eggs Y Y

Orange juice Y Y

Grapefruit juice Y Y

Soybean meal N Y

Soybean oil N Y

HFCS N Y

Confection Y N

Gluten feed N Y

Frozen potatoes Y Y

Dried potatoes Y Y

Chipped potatoes N Y

Refined sugar Y Y

Fed beef N Y

Nonfed beef Y N

Feedlot beef slaughter Y N

Stocked calf Y N

Stocked steer calf Y N

Pork Y Y

Chicken N Y

Turkey N Y

Wool, clean Y Y

Evaporated condensed milk Y Y

Nonfat dry milk Y Y

Butter Y Y

American cheese Y Y

Other cheese Y Y
2-32

Table 2-12. Commodities without International Trade Possibilities Modeled 

Baking Feeder pigs Oranges, processing (75 lb. box)

Beverages Fluid milk Oranges, processing (85 lb. box)

Biodiesel Grapefruit, fresh (67 lb. box) Oranges, processing (90 lb. box)

Broilers Grapefruit, fresh (80 lb. box) Refined sugar

Calf slaughter Grapefruit, processing (67 lb. box) Silage

Canning Grapefruit, processing (80 lb. box) Skim milk

Corn oil Grapefruit, processing (85 lb. box) Steer calves

Corn starch Hay Stocked heifer calves

Corn syrup Heifer calves Stocked heifer yearlings

Cottage cheese Hogs for slaughter Stocked steer yearlings

Cream Horses and mules Stocked yearlings

Cull beef cows Hybrid poplar Sugarbeet

Cull dairy cows Ice cream Switchgrass

Cull ewes Lamb slaughter Tbtus

Cull sow milk Turkeys 

Dairy calves Nonfed slaughter Willow 

Dextrose Oranges, fresh (85 lb. box) Wool

Ethanol Oranges, fresh (90 lb. box)

Note: FASOMGHG does not explicitly include ethanol trade, but in applications for biofuels analyses, we have 

assumed exogenous levels of mandated ethanol volumes would be provided by imports based on information from 

other models. 

2.7 GHG Accounts 

FASOMGHG quantifies the stocks of GHGs emitted from and sequestered by agriculture 

and forestry as well as the carbon stock on lands in the model that are converted to 

nonagricultural, nonforest developed usage. In addition, the model tracks GHG emission 

reductions in other sectors caused by mitigation actions in the forest and agricultural sectors.

The GHGs tracked by the model include CO2, CH4, and N2O. Given the multi-GHG 

impact of the agricultural and forestry sectors, there are multidimensional trade-offs between 

model variables and net GHG emissions. To consider these trade-offs, all GHGs are converted to 

carbon or carbon dioxide equivalent basis using 100-year global warming potential (GWP) 

values for application of GHG incentives.

GWPs compare the abilities of different GHGs to trap heat in the atmosphere. They are 

based on the radiative forcing (heat-absorbing ability) and decay rate of each gas relative to that 

of CO2. The GWP allows one to convert emissions of various GHGs into a common measure, 

which allows for aggregating the radiative impacts of various GHGs into a single measure 
2-33

denominated in CO2 or C equivalents. Extensive discussion of GWPs can be found in the 

documents of the Intergovernmental Panel on Climate Change (IPCC). In 2001, the IPCC 

updated its estimates of GWPs for key GHGs, but these estimates are still under debate. As a 

result, the FASOMGHG model continues to use the 1996 GWPs for the GHGs covered by the 

model: 

§ CO2 = 1

§ CH4 = 21

§ N2O = 310

When CO2 equivalent results are converted to a C equivalent basis, a transformation is 

done based on the molecular weight of C in the CO2. This means that the CO2 equivalent 

quantities of gas are divided by 3.667 to compute the carbon equivalent quantities.

A list of all categories included in the model’s GHG accounting appears in Table 2-13, 

totaling 57 categories. Brief summaries of the major categories of GHG accounts are presented 

in the subsections below. Additional detail on the GHG accounting and mitigation opportunities 

included within the model is presented in Section 6. 

Table 2-13. Categories of GHG Sources and Sinks in FASOMGHG

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 U.S. private forests consumed producing forest products

Forest_USpubProduct Carbon from U.S. public forests consumed producing forest products

Forest_CANProduct Carbon in U.S. consumed but Canadian produced forest products

Forest_USExport Carbon in U.S. produced but exported forest products

Forest_USImport Carbon in U.S. consumed but imported from non-Canadian source

Forest_USFuelWood Carbon in U.S. consumed fuelwood

Forest_USFuelResidue Carbon in U.S. residue that is burned

Forest_USresidProduct Carbon from U.S. 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

(continued)
2-34

Table 2-13. Categories of GHG Sources and Sinks in FASOMGHG (continued)

Dev_Land_from_Ag Carbon on land after it moves from agriculture into developed use

Dev_Land_from_Forest Carbon on land after it moves from forest into developed use

AgSoil_CropSequest_Initial Carbon in cropped agricultural soil with initial tillage

AgSoil_CropSequest_TillChange Carbon in cropped agricultural soil with change in tillage

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_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

Methane_RiceCult Methane from rice cultivation

Methane_AgResid_Burn Methane from agricultural residue burning

Methane_BioElec Net change in methane emissions from bioelectricity relative to coal-fired

Methane_Biodiesel Net change in methane emissions from biodiesel production relative to diesel

Methane_Ethl Net change in methane emissions from ethanol production relative to gasoline 

Methane_CEth Net change in methane emissions from cellulosic ethanol production relative to 

gasoline

NitrousOxide_Manure Livestock manure practices under managed soil categories under AgSoilMgmt

NitrousOxide_BioElec Net change in nitrous oxide emissions from bioelectricity relative to coal-fired

NitrousOxide_Biodiesel Net change in nitrous oxide emissions from biodiesel production rather than 

diesel

NitrousOxide_Ethl Net change in nitrous oxide emissions from non-cellulosic ethanol processing

relative to gasoline

(continued)
2-35

Table 2-13. Categories of GHG Sources and Sinks in FASOMGHG (continued)

NitrousOxide_CEth Net change in nitrous oxide emissions from cellulosic ethanol processing relative 

to gasoline

NitrousOxide_Fert Nitrous oxide emissions from nitrogen inputs including nitrogen fertilizer

application practices, crop residue retention, and symbiotic nitrogen fixation 

under managed soil categories under AgSoilMgmt

NitrousOxide_Pasture Nitrous oxide emissions from pasture

NitrousOxide_Histosol Emissions from temperate histosol area

NitrousOxide_Volat Indirect soils volatilization

NitrousOxide_Leach Indirect soils leaching runoff

NitrousOxide_AgResid_Burn Agricultural residue burning

2.7.1 Forest GHG Accounts

As identified in Table 2-13, forest GHG accounting includes carbon sequestered, carbon 

emitted, and fossil fuel-related carbon emissions avoided. Sequestration accounting encompasses 

carbon in standing (live and dead) trees, forest soils, the forest understory vegetation, forest 

floor including litter and large woody debris, and wood products both in use and in landfills. The 

sequestration accounting involves both increases and reductions in stocks, with changes in 

specific accounts to reflect land movement into forest use through afforesation, net growth of 

forests not of afforestation origin, and placement of products in long-lasting uses or landfills.

11

Reductions arise when land is migrated to agriculture or development and products decay in their 

current uses. 

Forest-related emissions accounting includes GHGs emitted when fossil fuels are used in 

forest production. Forest-related GHG accounting calculates the estimated amount of fossil fuels 

(and associated GHG emissions) that are saved when wood products are combusted in place of 

fossil fuels, particularly when milling residues are burned to provide energy (generally for use at 

the mill). In addition, woody biomass may be used as a bioenergy feedstock. 

Forest carbon accounts also include the carbon content of products imported into, or 

exported out of, the United States. In particular, there is explicit accounting for products:

 

11 In the case of wood product accounts, note that these accounts have increases in C sequestration when more 

products are made, but the forest carbon accounts are simultaneously reduced to account for C reduced by 

harvesting. 
2-36

§ processed in and coming from Canada, 

§ imported from other countries, and 

§ exported to other countries. 

These categories may or may not be included in an incentive scheme for GHG mitigation,

as they will generally be accounted for elsewhere. Nonetheless, the accounts are included in the 

model in case they are needed for policy analysis.

12

2.7.2 Agricultural GHG Accounts

On the agricultural side, the categories tracked in the model are also listed in Table 2-13. 

Agricultural emissions arise from crop and livestock production, principally from:

§ fossil fuel use, 

§ nitrogen fertilization usage,

§ other nitrogen inputs to crop production,

§ agricultural residue burning,

§ rice production, 

§ enteric fermentation, and 

§ manure management.

In addition, changes in carbon sequestration are tracked within the model. Agricultural 

sequestration involves the amount of carbon sequestered in agricultural soils, due principally to 

choice of tillage, and irrigation along with changes to crop mix choice. Sequestration is also 

considered in terms of grasslands versus cropland/or mixed usage, where cropland can be moved 

to pasture use or vice versa. The sequestration accounting can yield either positive or negative 

quantities, depending on the direction of change in tillage between the three available options 

(conventional, conservation, or zero tillage) and irrigation choices, along with pasture land 

(grassland)/cropland conversions and movements between agriculture and forestry. With 

movements from forestry to agriculture, gains in the agricultural soil carbon account are typically 

more than offset by losses in the forest soil carbon account (e.g., forest soils typically store more 

carbon per acre than soils in agricultural uses). When moving from agricultural land uses to 

forestland, on the other hand, there are typically net increases in soil carbon sequestration.

 

12 The specific categories of emissions that receive GHG incentives in a given scenario are defined by the analyst in 

constructing the scenario. 
2-37

As with forest products, certain agricultural commodities can also be used as bioenergy 

feedstocks. 

2.7.3 Bioenergy GHG Accounts

Selected agricultural and forestry commodities can be used as feedstocks for biofuel 

production processes in FASOMGHG, possibly affecting fossil fuel usage and associated GHG 

emissions after accounting for emissions during hauling and processing of bioenergy feedstocks. 

Four major forms of bioenergy production are included: 

§ Biodiesel: usage of canola oil, corn oil, lard, poultry fat, soybean oil, tallow, or 

yellow grease in the production of biodiesel, which replaces petroleum-based diesel 

fuel

§ Bioelectricity: usage of bagasse, crop residues, energy sorghum, hybrid poplar, lignin, 

manure, miscanthus, sweet sorghum pulp, switchgrass, willow, wood chips, logging 

residues, or milling residues as inputs to electric generating power plants in place of 

coal (through either cofiring or dedicated biomass plants) 

§ Cellulosic ethanol: usage of bagasse, crop residues, energy sorghum, hybrid poplar, 

miscanthus, sweet sorghum pulp, switchgrass, willow, wood chips, logging residues, 

or milling residues to produce cellulosic ethanol, which replaces gasoline

§ Starch or sugar-based ethanol: usage of barley, corn, oats, rice, sorghum, sugar, sweet 

sorghum, or wheat for conversion to ethanol and replacement of gasoline 

In all of these cases, the GHG reduction provided by bioenergy production is equal to the 

GHGs emitted from burning and producing the fossil fuel replaced less the GHG emissions of 

producing, transporting, and processing the bioenergy feedstock. 

2.7.4 Developed Land GHG 

FASOMGHG incorporates exogenous data that specify the rate of conversion of 

agriculture and forestry lands to nonagricultural and nonforestry developed uses. Simplified

accounting is employed to estimate the carbon sequestered on these lands, as described in 

Section 6. 

2.8 Other Environmental Impacts

FASOMGHG considers a number of environmental indicators above and beyond the 

GHG accounts. The main components are nitrogen and phosphorus application and runoff, soil 

erosion, irrigation water usage, and a number of descriptions of total resource use and activity 

within the agricultural and forestry sectors (e.g., total land use, total pasture use, manure load, 

livestock numbers, total afforestation).
3-1

SECTION 3

ECONOMIC THEORY AND MODEL STRUCTURE 

In this section, we present a brief overview of the economic theory underlying 

FASOMGHG (see Adams et al., 2005 for more detail). We then summarize the forestry, 

agricultural, and GHG payment subcomponents of the model. 

3.1 Theoretical Basis 

FASOMGHG employs a price-endogenous mathematical programming approach 

following the spatial equilibrium concept originally motivated by Enke (1951) and Samuelson 

(1952) and more fully developed by Takayama and Judge (1973) as explained in the literature 

review by McCarl and Spreen (1980). In this approach, an optimization problem is defined and 

solved using a set of equations that specify attainment of a competitive market equilibrium under 

a given set of supply and demand conditions. The objective function is the summation of all 

areas beneath product demand curves minus the sum of all areas under import and factor supply 

curves (i.e., the area between the demand and supply curves to the left of their intersection), 

which represents the producer surplus (area below the equilibrium price) and consumer surplus 

(area above the equilibrium price). Commodity and factor prices are endogenous, determined by 

the supply and demand relationships in all markets included within the model. 

Mathematically, FASOMGHG solves an objective function to maximize net market 

surplus, where the sum of producer and consumer surplus will be maximized where all markets 

are at a perfectly competitive market equilibrium. This approach is implemented using a 

nonlinear programming model written in the Generalized Algebraic Modeling System (GAMS) 

language. The resultant objective function value is consumer plus producer surplus.

Suppose we have a demand curve 

Pd = f (Qd,) (3.1)

and a supply curve 

Ps = f (Qs,) (3.2)

where Pd is the price demanders would be willing to pay to purchase Qd units of output and Ps is 

the price at which suppliers would be willing to supply Qs units. Demand curves are typically 

downward sloping, reflecting declining willingness to pay with increasing quantity. Supply 
3-2

curves, on the other hand, are expected to be upward-sloping, reflecting increasing marginal 

production costs that lead suppliers to require higher prices in order to expand production. 

In a perfectly competitive market, we would have a set of market equilibrium conditions 

requiring prices and quantities to equilibrate:

Pd = Ps (3.3)

Qd = Qs (3.4)

The mathematical programming approach to formulating such problems was motivated 

by Samuelson (1952), who suggested solving optimization problems whose first-order conditions 

constituted a system of equations characterizing an equilibrium. Suppose we follow this 

approach by first defining a system of equations, then posing the related optimization problem. 

In this case, the equilibrium would simultaneously solve the following equations:

d s

d s

d s

Q Q

and

f(Q ) f(Q )

or

P P

=

=

=

However, there are cases where these relationships would not necessarily hold. In 

particular, it is possible that markets could clear at zero quantity, in which case the supply price 

might be greater than or equal to the demand price. Thus, we can write the condition that the 

equilibrium price (P*) is greater than or equal to the demand price:

*

f(Qd) ≤ P

(3.5)

Simultaneously, the market price may be less than the supply price:

*

f(Qs) ≥ P

(3.6)

One can also argue that these two relations should only be inequalities when the quantity 

supplied or demanded equals zero. Namely, when the demand price is less than the equilibrium 

price, then no quantity should be demanded. Similarly, when the supply price is greater than the 

equilibrium price, then no quantity should be supplied. Simultaneously, when a nonzero quantity 
3-3

is supplied or demanded, then the equilibrium price should equal the supply or demand price. 

This relationship can be expressed through complementary slackness relations where:

(f(Qd) – P* ) Qd = 0 (3.7)

(f(Qd) – P* ) Qd = 0 (3.8)

One should also recognize that the quantity supplied must be greater than or equal to the 

quantity demanded:

s Qd Q ≥ (3.9)

However, if the quantity supplied is strictly greater than the quantity demanded, then the 

equilibrium price should be zero. Mathematically this relationship is as follows:

( Q Q )P 0

*

− s + d =

(3.10)

Finally, we state nonnegativity conditions for price and quantities:

Q , Q , P 0. *

d s ≥

(3.11)

The above equations are similar to Kuhn-Tucker conditions. In particular, if P* is taken 

to be a dual variable, then the above equation system is equivalent to the Kuhn-Tucker 

conditions of the following optimization model:

Q , Q 0

s.t. Q Q 0

Max f(Q ) Q f(Q ) Q

d s

d s

0

s s

0

d d

* Qs* Qd

≥

− ≤

− ∫ ∫d d

where the Lagrangian and Kuhn-Tucker conditions are:
3-4

, , 0

( ) 0

( , , )

( ( ) ) 0

( , , )

( ( ) ) 0

( , , )

( ) 0

( , , )

( ) 0

( , , )

( ) 0

( , , )

( , , ) f(Q ) Q f(Q ) Q ( )

* Qs* Qd

0

s

0

d d

≥

=− − =

∂

∂

= − + =

∂

∂

= − =

∂

∂

= − − ≥

∂

∂

= − + ≤

∂

∂

= − ≤

∂

∂

= − − − ∫ ∫

λ

λ λ

λ

λ

λ

λ

λ

λ

λ

λ

λ

λ

λ

λ

λ λ

d s

d s

d s

s s s

s

d s

d d d

d

d s

d s

d s

s

s

d s

d

d

d s

d s s d s

Q Q

Q Q

L Q Q

Q f Q Q

Q

L Q Q

Q f Q Q

Q

L Q Q

Q Q

L Q Q

f Q

Q

L Q Q

f Q

Q

L Q Q

L Q Q d d Q Q

and if P* is used in place of ? then the systems are mathematically the same.

The above presentation was restricted to a single commodity with explicit supply and 

demand curves. However, one can depict commodity demand for multiple products without 

specifying explicit supply functions for those products, but rather defining production processes

and factor supply functions for inputs. Such models have exogenous factor supply and product 

demand curves, but implicit factor demand and product supply. Such a model can be expressed 

as follows:

( ) ( )

β

β

β

β β β

β

β β

β

β β

Z , X , Q 0 for all i, h, k and

 b Q Y for all j and

- X a Q 0 for all i

s.t. Z - C Q 0 for all h

Max P Z dZ - P X dX

h i k

j

k

j k k

k

i i k k

k

h h k k

i i

i

X

0

h h si

h

Z

0

dh

h i

≥

≤

+ ≤

≤

∑

∑∑

∑∑

∑ ∫ ∑ ∫

This problem assumes that a number of different types of firms (ß) are being modeled. 

Each firm has a finite set of production processes (k), which depict particular ways of combining 
3-5

fixed factors (j) with purchased factors (i) to produce commodities (h). The symbols in the 

formulation are as follows:

§ Pdh(Zh) is the inverse demand function for the hth commodity.

§ Z h is the quantity of commodity h that is consumed.

§ Psi (Xi) is the inverse supply curve for the ith purchased input.

§ Xi is the quantity of the ith factor supplied.

§ Qßk is the level of production process k undertaken by firm ß.

§ Chßk is the yield of output h from production process k.

§ bjßk is the quantity of the jth owned fixed factor used in producing Qßk.

§ aißk is the amount of the ith purchased factor used in producing Qßk.

§ Yjß is the endowment of the jth owned factor available to firm ß.

An investigation of the Kuhn-Tucker conditions would show that the shadow price on the 

first and second rows are, respectively, the demand and supply prices. The conditions for the Q 

variable indicate that production levels are set so the marginal value of the commodities 

produced is less than or equal to the marginal costs of the owned and fixed factors for each Qβk.

The model formulation assumes that (1) the supply and demand equations are integrable 

and (2) that product demand and factor supply functions are exogenous to the model.

The area under the product demand and factor supply functions makes the objective 

function equal consumer plus producer surplus, which is the net social benefit generated by the 

market exchange of these goods. The solution of the model generates equilibrium price and 

quantity for each output, and purchased input, along with the imputed values for the owned 

factors of production.

The model formulation assumes that the sector is composed of many buyers and sellers, 

none of which can individually influence output or factor prices. Each producer supplies output 

at the point where marginal cost equals product price, and utilizes purchased inputs at the point 

where the marginal value product of each purchased input equals its market price. Thus, the 

sectoral supply of output schedule corresponds to an aggregate marginal cost schedule, and the 

sectoral derived demand schedule for purchased inputs corresponds to the aggregate marginal

value product schedule. Hence, the model does not take product supply or factor demand 
3-6

schedules as input, rather these schedules are derived internally based upon production 

possibilities, output demand and purchased input supply. 

The competitive behavior simulating properties of this formulation provides a powerful 

tool for policy simulation. Except in centrally planned economies, the government cannot dictate 

production patterns consistent with its objectives. This formulation recognizes the difference and 

possible conflict between government and producer objectives (see Candler, Fortuny, and 

McCarl, 1981, for elaboration). The model allows policies to be specified and then simulated in 

terms of projected sectoral response to the policy change with endogenous producer adjustment.

3.2 Agricultural Sector

The agricultural component of the model maximizes total sectoral welfare (consumer 

plus producer surplus, including costs and revenue associated with net GHG emissions), where 

primary variables include those associated with land allocation and transformation, crop 

production mix, tillage system, irrigation, livestock production mix, feed blending, agricultural 

processing mix, domestic consumption, exports, imports, and adoption of GHG mitigation 

options (if any). Equilibrium values of these variables are determined subject to constraints on 

crop and livestock mixes; resource limits for land, water, and labor; balances on primary, 

secondary, and blended feed commodities; trade balances; and GHG balances. Both variables 

and constraints in the agricultural sector component are explained in more detail below.

3.2.1 Key Variables

The agriculture-related land use decision simulated in FASOMGHG is that, in each 5-

year time period,

1

owners of agricultural land can decide (1) whether to keep an acre of land in 

agricultural production or change land use to afforestation; (2) what crop/livestock mix to 

plant/raise/harvest, if the land stays in agriculture (potentially with accompanying land use 

change between cropland and eligible pasture categories); and (3) what type of forest and timber 

management system to select, if the land is to be planted in trees. These decisions are made 

entirely on the basis of relative profitability of land in its various competing alternative uses over 

the life-span of the foreseeable choices (for land in either crops, pasture, or trees). These 

producers are simultaneously making decisions regarding production practices (including 

adoption of GHG mitigation options) and all of these decisions regarding production interact 

with the demand side characterization to determine market outcomes. 

 

1 The values reported in the model for each 5-year time period are assumed to represent typical annual activity 

within each year of that period. Activities within the final 5-year period of any model run are treated as if that 

level of activity persisted indefinitely into the future for the purpose of calculating net present values within the 

objective function. 
3-7

Key categories of variables in the agricultural component of FASOMGHG include the 

following:

§ Agricultural land to forest—Land use change from cropland, cropland pasture, or 

pasture into forestland on a regional basis. These land movements decrease land 

availability in the categories moving into forestland and increase it in forestland on a 

1:1 basis. These land movements can only occur in the model regions where both 

forestry and agriculture are modeled (8 of the 11 market regions). 

§ Agricultural land from forest—Land use change from forestland into cropland, 

cropland pasture, or pasture (deforestation) on a regional basis. These variables 

reflect supply of additional land into the appropriate agricultural land category and 

removal of that area from forestland availability on a 1:1 basis. Land only moves 

from forestland when harvested. At that point, the forest landowner makes a decision 

to either reforest or move the land into an agricultural use. If moved into an 

agricultural land use, the category into which forestland can move is constrained by 

land quality (e.g., only the highest quality forestland is eligible to move into 

cropland). 

§ Cropland to cropland pasture—Land use change from cropland into cropland 

pasture on a regional basis. These variables reflect removal of land from cropland and 

addition into cropland pasture. 

§ Cropland from cropland pasture—Land use change from cropland pasture into 

cropland on a regional basis. This cropland is assumed to have the same productivity 

as any other cropland in the same region. 

§ Tillage/irrigation—These variables depict the selection of tillage and irrigation 

alternatives at a regional level. An initial distribution across tillage systems 

(conventional, conservation tillage, and no tillage) and irrigation status is assigned to 

the starting cropland inventory, defined by region. Allocation of cropland across 

tillage systems and irrigation status can change over time using land eligible to 

change tillage, including land moving over from forestry (which is assumed to move 

initially into dryland conventional tillage).2 Land in a particular tillage/irrigation 

status is then supplied to crop production using that tillage system and irrigation 

status. Land that has changed status can potentially change again in a later time 

period. These variables enter the GHG calculations during current and future periods, 

reflecting the trajectory of changes in soil carbon sequestration. 

§ Crop production—Crop production possibilities, defined by crop, irrigation/dryland 

component, fertilization alternative, and tillage alternative. Production of each crop 

available within a region uses inputs defined by the crop budgets, competing for 

 

2 Tillage conversion is based on relative returns to alternative tillage practices. In addition, there are tillage hurdle 

costs associated with moving to less intensive tillage included within the model. They are currently set equal to 

$7 per acre for switching between conventional and conservation tillage, $10 per acre for moving between 

conventional and zero tillage, and $5 per acre for moving between conservation and zero tillage. 
3-8

cropland, water, and labor while producing crop products. Components of crop 

production budgets also enter the GHG balance in several accounts, including fossil 

fuel usage related emissions, nitrous oxide emissions, methane emissions, and 

changes in carbon sequestration. The mixture of crops produced within a region is 

generally required to fall within a convex combination of historical crop mixes for a 

length of time defined within the model scenario. The exception is for energy crops, 

where they may not have been produced historically but may experience large 

increases in acreage under certain policy scenarios. 

§ Livestock production—Livestock production possibilities, defined by animal type, 

livestock management alternative, manure management alternative, and enteric 

fermentation methane mitigation alternative. Livestock production budgets use inputs 

as defined by the livestock budgets, competing for land to be in cropland pasture and 

pasture against other potential uses, available grazed forestland and rangeland 

between alternative livestock types, and labor while producing livestock products. 

Livestock production budgets use some primary and secondary crop products directly 

as feed input and use blended feeds to meet nutritional requirements. Livestock 

budgets also use intermediate animal stages as inputs to the final stage (e.g., feeder 

pigs to finishing). The mix of livestock produced within a region is required to fall 

within a convex combination of historical regional livestock production mixes for a 

length of time defined within the model scenario. Components of livestock budgets 

enter the GHG balance in several accounts including fossil fuel usage related 

emissions, manure management emissions, and enteric fermentation emissions. 

§ Feed blending—Blending of livestock feeds from a number of different alternative 

formulas utilizing primary and secondary commodities and defined on a regional 

basis. The resultant feeds are supplied for eventual consumption by the livestock 

production variables. These variables are defined by feed and feed blending 

alternative to meet nutritional requirements for each livestock type. 

§ Manure management—Choice of manure management system for use with certain 

livestock types. Adoption of improved manure management systems requires 

production of livestock types in quantities consistent with the level of adoption of the 

mitigation alternatives within the region where improved manure management 

options are being applied. Adoption of these systems reduces manure management 

GHG emissions, but also incurs system capital and operating and maintenance costs. 

Thus, improved systems are typically not adopted without incentives. 

§ Water supply—Supply of water from fixed-price sources and sources with upwardsloping water supply curves. The fixed-price supplied water is assumed to be 

available at a fixed price up to a maximum quantity and largely reflects surface water 

supplied by public sources (e.g., Bureau of Land Management). Water available 

according to a supply function represents pumped water from private surface water 

markets and ground water. 
3-9

§ Labor supply—Supply of labor from family and hired sources. The family labor is 

assumed to be available at a fixed reservation wage up to a maximum quantity. The 

hired labor is supplied according to a supply function. 

§ Processing—Agricultural processing activity that transforms primary commodities 

into secondary commodities. The variables reflect the cost of processing, the use of 

primary and some secondary products as inputs to processing and the creation of 

processed secondary products. The processing variables also enter the GHG balances 

reflecting GHG emissions from inputs used in processing and GHG reductions 

relative to the use of fossil fuels when they represent transformation of agricultural 

feedstocks into bioenergy. These variables are defined by processing alternative and 

are defined on either an 11-region or a national basis depending on the secondary 

commodity being manufactured. 

§ Domestic demand—Domestic consumption of primary and secondary commodities. 

This includes the disappearance of commodities into markets at the wholesale or 

retail levels as well as potential movement into any processing alternatives that are 

not formally included in FASOMGHG. These variables reflect a withdrawal of the 

commodity from the relevant primary or secondary commodity balance at the 

national level. These variables are defined by commodity on a national basis. 

§ Export demand—Export of goods which are specified with explicit rest of world 

excess demand equations. The variables represent the area underneath the excess 

demand curve in the objective function and reflect withdrawal of the commodity from 

the relevant primary or secondary balance at the national level. These variables are 

defined by commodity on a national basis. 

§ Import supply—Import of goods which are specified with explicit rest of world 

excess supply equations. The variables represent the negative of the area underneath 

the import excess supply curve in the objective function and reflect supply of the 

commodity into the primary or secondary balance. These variables are defined by 

commodity on a national basis. 

§ International trade—International transport between the United States and foreign 

countries/regions and among foreign countries/regions. These variables are defined 

for all pairs of movements allowed in the data between the United States and foreign 

countries/regions and among the foreign countries/regions themselves. 

§ International supply/demand—Import and export of goods which are specified with 

explicit foreign country/region excess supply or demand equations. The variables 

represent the area underneath the country-level export excess demand or import 

excess supply curve in the objective function and reflect addition/withdrawal of the 

commodity in the foreign country/region commodity balance. These variables are 

defined by commodity for each relevant foreign country/region basis. 

§ GHG payment—Payments to producers or payments by producers based on net 

changes in GHG emissions and the GHG incentives in place for a given scenario. 
3-10

These variables are defined for each of the GHG accounts and enter the welfare 

function through effects on net revenue.

3.2.2 Constraints on Agricultural Production

Numerous constraints are applied within the model for consistency with historical 

observations or available projections. Some of the most important constraints placed on the 

agricultural sector within FASOMGHG include limits on the following:

§ Cropland—Limits total regional cropland usage to the acres available in that region3

(i.e., limited to the initial agricultural endowment of cropland adjusted for land use 

changes to and from CRP land, cropland pasture, pasture, and forestland as well as 

land converted to developed uses). 

§ Crop mix—Balances acreage by crop against the acreage allowed by the crop mix 

possibilities, defined for each crop under both irrigated and total cropland. Regional 

crop mix is generally forced to be within a convex combination of historical crop 

mixes for that region. An exception to this constraint is applied for emerging 

bioenergy crops that may not have been produced historically. 

§ Tillage system use—Limits total acreage of each tillage system in use within the 

cropping variables to the acreage produced by the tillage choice variables. Limits 

regional cropland usage by tillage system to the acres available in that region for that 

tillage system (i.e., limited to the initial agricultural endowment of cropland across 

tillage systems adjusted for land conversion and changes in tillage that have taken 

place). 

§ CRP land—CRP land is generally constrained to reach 32 million acres by the 2020 

model period for consistency with the 2008 Farm Bill and information provided by 

USDA. However, limits on CRP land can be modified as needed for model scenarios. 

§ Pasture—Limits total cropland pasture and total pasture use in livestock production 

to the area of each category available in each region. Availability is based on the 

initial amount of cropland pasture and pasture adjusted for transformations to and 

from cropland and forestland as well as conversion to developed uses. 

§ Grazed forest—Limits livestock use of private and public grazed forest to the initial 

areas of each category available. The model does not permit these land use categories 

to be converted to other uses. 

§ Livestock mix—Balances the number of head by livestock type against the 

distribution of head allowed by the livestock mix possibilities, where the constraint 

forces each regional livestock mix to be a convex combination of historical livestock 

mixes for that region. 

 

3 The constraints included in the list are generally defined at the most disaggregated regional level being used in a 

given model scenario and time period unless specified otherwise (i.e., at the 63-region level in time periods for 

which the model is run at that level of disaggregation). 
3-11

§ Blended feed—Balances the usage of blended feeds with the amount produced. This

constraint is defined on an 11-region basis.

§ Water—Limits regional irrigation water use to the quantity available in that region 

within the model. Available water can come from one of two sources. The first is 

water available from the Bureau of Land Management and other public surface water 

sources available up to a maximum quantity in each region at a fixed price. The 

second source is pumped water from ground and private surface water sources, which 

is represented by an upward sloping supply curve.

§ Labor—Limits the labor used to the quantity available within the market region. 

Labor is used by the crop and livestock production variables. The labor available 

comes from a mixture of fixed price family labor that is available up to a maximum 

quantity and an upward sloping supply curve component of hired labor. The limits 

control the labor market at the geographic level of the 11 FASOMGHG regions. 

§ Primary products—Balances primary product usage so that it cannot exceed 

primary product availability. Primary product usage arises through the combined 

effects of usage for processing, feed blending, direct feeding to livestock, domestic 

consumption, and exports. Availability comes from crop and livestock production and 

imports. This constraint is defined on an 11-region basis when the commodity is 

regionalized and on a national basis for those commodities where production is not 

disaggregated by region.

§ Secondary products—Balances processed or secondary product usage so it cannot 

exceed secondary product availability. Secondary product usage arises through the 

combined effects of usage for processing, feed blending, direct feeding to livestock, 

domestic consumption, or exports. Availability comes from processing or imports. 

This constraint may be defined on an 11-region basis or national basis, depending on 

the commodity. 

§ International market—Balances foreign country/region markets so that demand is 

met in those regions accounting for net trade between regions for those commodities 

and regions that have explicit spatial international market modeling. 

§ Herd size for manure management—Balances herd size for animals that can 

potentially be treated with improved manure management against the number of 

animals treated with improved manure management alternatives. The constraint is 

defined only for the categories of livestock that are defined as eligible for improved 

manure management for GHG emission reduction purposes. 

§ GHG net emissions—Accumulates net GHG emissions by category and balances 

against the GHG payment variable by GHG account. By default, that variable equals 

total net emissions and is unrestricted in sign reflecting a possible net emission 

reduction or increase. However, individual GHG accounts subject to carbon 

incentives can be defined in each model scenario. 

§ Forestland—Forestry land use balances as explained in Section 3.3.
3-12

3.3 Forest Sector

The forestry sector component depicts forestry production, manufacturing, input supply, 

product consumption, interregional transport, international trade, and terminal forest inventory 

valuation. The feasible solution values for the forestry variables are limited by constraints on 

land; input/factor supply; log supply/demand balances; intermediate and final product balances; 

processing capacity; and terminal inventory valuation. The forestry submodel also affects the 

GHG balance. Below we discuss key variables and constraints incorporated in FASOMGHG.

3.3.1 Key Variables

Similar to the decision on the agricultural side of the model, forest landowners make an 

endogenous decision regarding whether to harvest or not in each 5-year period for forests that 

have reached the minimum harvest age defined by forest type and region. Once forestland has 

been harvested, there is an additional decision whether to reforest that land or convert it to 

agricultural use, either cropland or pasture (subject to land quality constraints). In the event the 

land is reforested, there is also a decision regarding the timber management strategy to adopt on 

the land and the forest type to which the cut-over area will be regenerated. In FASOMGHG, each 

of these decisions is made based on the relative discounted returns to alternative forest rotation 

lengths, land uses, and management practices over the model simulation period. 

Key categories of variables that make up the FASOMGHG forestry submodel include the 

following categories. Treatment of the forest inventory will be recognized as employing the socalled “type 2” format of Johnson and Scheurman (1977).

§ Existing stand management—Allocation of forestland acres to forest production 

alternatives for stands that exist at the beginning of the model run. The variable 

involves determination of harvest timing, with one possibility being that the stand is 

not harvested during the timeframe simulated. Forest stands are differentiated by 

region, land class (reflecting suitability for agriculture), site quality, type of forest 

stand (generally softwoods and hardwoods; in some regions more detailed 

characterizations such as planted pine, natural pine, etc. are possible), type of private 

forest owner (industrial or nonindustrial private), age of stand, and prior stand 

management (including prior fertilization, partial cut and thinning regimes) and grow 

at different rates. If an acre of land in this category is clearcut, it creates an acre that 

can be reforested or converted to agricultural use. Clearcut harvests, thinning, or 

partial cuts produce harvested logs and incur production costs from the initial model 

time period up to the period of clearcut (or through the model end period if they are 

not cut). Existing stands that are not clearcut during the specified model timeframe 

are assigned a terminal inventory valuation relation to ensure that a value is placed on 

standing forest inventory at the end of model simulations. Finally, this land enters the 

GHG balances between the first period and the period just before clearcut (after 

clearcut, it moves into reforested land or an agricultural land category) reflecting (a) 
3-13

GHG sequestration in soils, trees, and other ecosystem components; and (b) fossil 

fuel emissions incurred during forestry production.

§ Reforested land management—Variables tracking acres allocated to reforestation 

on clearcut forestland and their management, including determination of harvest 

timing (including the possibility of no harvest during the timeframe of a model 

scenario), type of forest (softwoods, hardwoods or more detailed class depending on 

region), and management applied (including possible fertilization, partial cut, and 

thinning regimes). As with existing stands, the reforested land area is characterized by 

region, land class, site quality, stand age, and type of forest owner and trees grow at 

different rates over time based on these characteristics. If reforested acres are clearcut 

during the course of a model simulation, they can be reforested again. In periods 

when reforested stands are clearcut, thinned, or partially cut, they produce harvested 

logs and incur production costs across time from the establishment period to the time 

when they are clearcut (or the model end period if they are not cut). If these stands are 

not clearcut, they are recognized in a terminal inventory valuation process. Also, they 

enter the GHG balances for forest sequestration between the time of stand 

establishment up to just before clearcut and for fossil fuel emissions incurred during 

production.

§ Afforested land establishment and management—Afforestation involves the 

transfer of land from agriculture (cropland, cropland pasture, and/or pasture) to forest 

production. Afforested land is characterized by forest type, management intensity, 

and harvest timing, including possible choice of partial cut or thinning regime. 

Afforested land is classified by region, suitability for agriculture, and site quality 

(high or medium). All afforested land is assumed to belong to nonindustrial private 

forest owners. The variables use one acre of land transferring in from agriculture per 

acre of afforestation (1:1 ratio). In turn, if clearcut, they create an acre that can 

potentially be reforested or converted back to agricultural use. As for other categories 

of forest stands, when the stand is clearcut, thinned, or partially cut, it produces 

harvested logs, while incurring production costs from the periods of establishment 

through the clearcut period (or terminal period defined in the model simulation). If 

these stands are not clearcut during the model timeframe, they yield terminal forest 

inventory. Finally, they enter the GHG balances for forest sequestration during the 

periods between establishment up to just before the clearcut period and for fossil fuel 

emissions incurred during production.

§ Forest production cost—Total forest production costs in dollars by each region and 

time period based on the selected forest management practices.

§ Manufactured wood products—Quantity of wood product manufacturing 

(processing) activity in units of product made by region, type of product, type of 

process, input mix, and time period. These variables not only embody manufacturing, 

they also incorporate: (a) harvested logs and forest-to-mill hauling, (b) log-to-product 

transformation factors, and (c) the possibility to downgrade logs in manufacturing 

(e.g., use sawtimber as pulpwood, in order to balance log supply and demand). This 

variable is defined for all FASOMGHG wood product manufacturing regions, 
3-14

including those in Canada. Logs are used as inputs to produce wood products based 

on conversion factors in the model (and some wood products are used in the 

production of other wood products), generating or using milling residues. Processing 

capacity use is tracked by the model. Manufactured products also require one unit 

from the regional and process-specific nonwood input balance, which in turn reflects 

an upward sloping supply curve of nonwood inputs. Finally, these products enter the 

GHG balance in the period of wood product manufacture and all subsequent periods, 

reflecting carbon sequestered in manufactured wood products.

§ Terminal period manufacturing—Terminal-period activity of wood product 

manufacturing by region, wood product made, process, and input mix. The variables 

have the same characteristics as those above for manufactured wood products, except 

that the logs come from the standing inventory at the end of the model across all tree 

ages under the assumption of a perpetually regulated forest as depicted in the terminal 

inventory balance equation. These variables are included to provide value for 

standing timber beyond the model time horizon, thereby avoiding unrealistic 

harvesting activity toward the end of the explicit model time horizon.

§ Forest domestic transportation—Quantity of wood products transported between 

regions. The variable is defined by product type, originating region, destination 

region, and time period. It reflects transportation costs, while removing one unit of 

wood product in the region of origin and supplying one unit into the region of 

destination. All regions are covered where wood products manufacture is defined, 

including those in Canada.

§ Endogenous public supply (Canada)—Canadian public supply (from provincial 

lands) of harvested logs by region, time period, and log type. This supply source 

provides logs into the harvested log balance with production costs included in the 

objective function. 

§ Nonwood inputs—Supply of nonwood input items for wood product manufacturing. 

They are defined by wood product, process, region, and time period and derive from 

an upward sloping supply curve for nonwood input items. Nonwood inputs include 

those for manufacturing inputs (such as labor and non-wood materials), harvest, and 

woods-to-mill hauling costs. 

§ Wood product demand—Wood product demand by type of wood product at the 

U.S. national level by time period. This is represented by a downward sloping 

demand curve for each product. 

§ Wood product imports—Import supply of wood products (softwood lumber from 

non-Canadian sources only) at the regional level by time period. This is represented 

by an upward sloping import supply curve defined for each wood product 

manufacturing region. 

§ Terminal forest product demand—Terminal period demand for wood products at 

the U.S. national level. This represents a downward sloping demand curve for future 
3-15

wood products, assuming all stands are subjected to perpetual even-aged 

management. As discussed above, terminal demand is included to provide value for 

standing timber beyond the defined model horizon. 

3.3.2 Constraints on Forestry Production

Some of the key constraints placed on the forest sector within FASOMGHG include the 

following:

§ Bare forestland—Balances clearcut timberland with its use for reforestation, transfer 

to agriculture, and transfer to developed use. The stands are defined by region, land 

class, site quality, type of eligible succeeding forest stand, and forest owner. Clearcut 

acreage comes from existing stands, reforested stands, and afforested stands after 

their first harvest rotation. Clearcuts cannot occur before forests reach a minimum 

harvest age defined for each forest type and region.

§ Bare land from agriculture—Balances agricultural land transferring into forestry 

with its use for afforestation. Stands are defined by region, land classes suitable for 

agriculture, and forest site quality (high, medium). This equation only treats 

afforested stands during their first rotation. Subsequent stands go into the bare 

forestland balance.

§ Harvested log balance—Balances the harvested logs from private and public sources 

with their use in wood products manufacturing. Harvested logs from private sources 

come from existing, reforested, or afforested stands. They include logs obtained from 

clearcut harvests, thinning, and partial cuts. Logs from public sources come from U.S. 

public lands harvested at an exogenous rate and from the endogenous public supply 

variable for Canada that depicts upward sloping supply of Canadian logs. Wood 

products manufacturing uses logs at rates defined by region, log type, and time 

period.

§ Minimum hardwood area—Requires the standing hardwood area to be greater than 

or equal to a minimum acreage level. The constraint is defined by region, type of 

forest owner, and time period. Given the slow rate of hardwood growth, and because 

FASOMGHG does not differentiate between different quality grades (and hence 

different prices) of hardwood logs, model simulations often show sharp reductions in 

the area of hardwoods. This has not happened historically, even on industrial 

ownerships, which commonly place a high premium on rapid growth and higher rates 

of return. To reflect issues not directly included in the model that influence hardwood 

area, minimum hardwood constraints were added that require a minimum absolute 

area of hardwood forest be retained in each region and time period on each private 

forest owner type.

§ Forest production inputs—Balances the use of pre-harvest forest production inputs 

with their supply for management of existing, reforested, and afforested stands 

defined by log producing region and time period. 
3-16

§ Wood products balance—Balances manufactured wood product supply and 

demand. These equations are defined by region, wood product (including logs), and 

time period. Regional supply is obtained from wood products manufacturing in that 

region plus products transported in from other domestic regions and imports. The 

demand arises from intermediate product use by manufacturing and outgoing 

products moved to other regions plus domestic demand and exports. Finally, there are 

adjustments for commodity movements to and from agriculture for selected products. 

§ Wood products processing capacity—Imposes capacity limits on wood product 

manufacture. The capacity is defined by time period, region, process, and wood 

product manufactured. 

§ Mill residues balance—Balances milling residue supply and demand. These 

equations are defined by region, residue type, and time period. Milling residues are 

obtained as a byproduct of wood products manufacturing. Demand arises from use as 

an input into the manufacturing process. 

§ Nonwood input balance—Balances wood product manufacturing activity with 

nonwood input supply (i.e., aggregate of inputs other than wood required to make 

wood products). Nonwood input supply is from an upward sloping supply curve. The 

equation is defined by wood product, processing alternative, region, and time period. 

§ Terminal forest inventory—Balances logs in terminal forest stands (those that 

persist beyond the explicit model time period) with their use in terminal wood 

products manufacturing. These logs come from private lands that are either in 

existing, reforested, or afforested stands. These equations are defined by region, log 

type, and time period. Use of this inventory in establishing a terminal inventory value 

is decribed in section 6.3 below.

§ Terminal forest products—Balances the terminal period supply and demand for 

manufactured wood products. Supply arises from wood products manufacturing plus 

incoming domestic transportation across regions and imports. Demand arises from 

intermediate product use by manufacturing, outgoing transportation to other domestic 

regions, domestic demand, and exports. These equations are defined by region and 

wood product.

§ GHG balance—Balances net GHG emissions by type of GHG account with the 

GHG payments variable. The equations incorporate terms that reflect sequestration in 

forests and wood products along with GHG emissions from fossil fuel use. GHG 

terms are also included to reflect land use change between agriculture and forests as 

discussed in Section 6.

3.4 GHG Payments 

The GHG submodel accounts for and reflects payments to net GHG emission reductions 

above those in the baseline from agriculture and forestry, including sequestration activity, 

emission reduction activity, and bioenergy-related activity. Under a specific mitigation policy or 
3-17

GHG price signal, different land use, production possibilities, and management activities can be 

employed in the model to reduce net emissions. 

The GHG payment variable reflects payments associated with the cumulative changes in 

each GHG account shown in Table 2-13 relative to the baseline for scenarios with GHG 

reduction incentives. The payments can be either positive or negative in each account based on 

the net change in GHG computed by the account-specific GHG balance equations. The GHG 

payment term component of the overall objective function is somewhat complex, encompassing 

several different factors. Specifically, it involves the following: 

§ Computation of the net gain in the GHG account accrued since the last time period 

that is above and beyond the net gain in the baseline. This is done by incorporating 

the difference between the GHG payment variable in the current period and the 

variable value in the last period to calculate the gain under a model scenario. Then we 

subtract off the difference in gain observed in the baseline by subtracting the baseline 

number for this period minus the baseline for the previous period. Note that baseline 

values can be set to fixed levels (e.g., emissions in a specific year) such that the 

model would reflect payments and credit changes taking place relative to those fixed 

base levels. 

§ Conversion of all accounts to a carbon dioxide equivalent basis by multiplying by the 

appropriate GWP for each GHG. 

§ Attaching the proper signs to emissions, bioenergy offsets, and sequestration—

specifically, a negative sign to emissions and a positive to offsets and sequestration. 

This sign convention means that increases in values of any account earn positive 

payments. 

§ Multiplying individual account terms by 0/1 indicator variables for whether the GHG 

account is eligible for GHG payments in a given model year in a given scenario (e.g., 

it is possible to control the accounts that receive GHG incentives as well as time 

periods when those incentives are in effect for that account). 

§ Multiplying individual accounts by a value between zero and one to reflect any 

discounting of emissions reductions for a given account that may be included in 

policy provisions.

§ Introducing discounting provisions to convert all dollar flows to a net present value. 

This involves two stages. First, we multiply by an annuity factor that converts the 

calculated average annual payments in each 5-year period to the net present value of 

total payments for that period. Then we multiply by a discount factor that reduces the 

value back to base year dollars (currently 2004). The annuity factor used is the value 

of a 5-year annuity for all time periods except for the last time period explicitly 

included in a model simulation. In that terminal period, the factor for present value of 
3-18

an infinite annuity is used, assuming that activity in the terminal period persists at 

those levels forever (see Section 6.3 for more details).
4-1

SECTION 4

LINKAGES BETWEEN FORESTRY AND AGRICULTURE 

Although the forestry and agriculture submodels can be run independently, the ability to 

incorporate interactions between these sectors offers valuable insights and is one of the key 

factors differentiating FASOMGHG from other modeling frameworks. For combined forestry 

and agriculture model runs, FASOMGHG is solved with a composite objective function that 

spans the forestry, agriculture, and GHG payment sectors described in the previous section. 

When policies are imposed that provide incentives or require adjustments, the unifying force of 

the objective function causes the model to consider the possibility of adjusting activity anywhere 

within the forestry and agricultural sectors as well as the amount of land transfers and 

commodity movements. The intersectoral transfer submodel depicts movements of land and 

commodities between the forestry and agricultural sectors. Such activities are also adjusted 

temporally and geographically. In turn, the model equilibrates the land, commodity, and factor 

markets across all sectors, time periods, and geographic locations.

4.1 Land Transfers 

Before discussing land transfers, it is important to discuss region and land type 

synchronization. Land in the forestry sector is defined at the 11 FASOMGHG region geographic 

level, with quality dimensions including land classes relevant for exchange with agriculture: 

FORCROP (land in forest that can be converted to crops), CROPFOR (land in crops that can be 

converted to forest), FORPAST (land in forest that can be converted to pasture), and PASTFOR

(land in pasture that can be converted to forest). There are also three forest site productivity 

indices (high, medium, and low). On the agricultural side, the land base can be defined on a 63-

region basis or can be aggregated to the 11 FASOMGHG regions defined in Section 2.3. Land is 

also differentiated by tillage practice employed and irrigation status. 

To best model sequestration and control land movements, several assumptions are used to 

synchronize these disparate regional definitions: 

§ When cropland moves into forestry, it is assumed to have high forest site 

productivity.

§ When pasture moves into forestry, it is assumed to have medium forest site 

productivity.

§ When forestland moves into cropland, it comes from either the FORCROP or 

CROPFOR classes with high forest site productivity.
4-2

§ When forestland moves into pasture, it comes from either the FORPAST or

PASTFOR classes with medium forest site productivity.

§ When forestland moves between one of the FASOMGHG 11 regions into the 

agricultural sector during periods in which the agricultural sector is disaggregated 

into 63 subregions, that land moves into the subregions located within the larger 

region in proportion to each subregion’s share of the relevant agricultural land type in 

the larger region. For instance, suppose the initial land endowment in the PSW region 

shows 20% of regional cropland in the Southern California subregion and 80% in the 

Northern California subregion. In that case, an acre of PSW forestland moving into 

cropland would be placed into the subregions as 0.2 acres in Southern California and 

0.8 acres in Northern California. 

§ When forestland exchanges with cropland, it always comes from or goes into the 

inventory of acres with dryland irrigation status that are conventionally tilled. The 

model can move land from or to other tillage/irrigation status cases by coupling the 

land transfer decision with a tillage change decision.

§ Although this discussion is centered on land transferring at the FASOMGHG 11-

region level, not all such transfers are allowed. Land transfers only occur in regions 

that have both agricultural and forestry production. This is true for only eight of the 

11 FASOMGHG regions. 

FASOMGHG reflects the mobility of the land resource between the two sectors subject 

to controls for land quality/growing conditions, investments needed to mobilize land, and hurdle 

costs to changing land use. For instance, the land quality factors generally restrict some lands to 

only be in forest, due to topography or soil characteristics. Likewise, growing conditions render 

some lands unsuitable for forest uses, particularly in the drier plains areas of the country.

Investments to mobilize land from forest to agriculture generally involve, for example,

stump clearing and leveling of forested lands and result in a three step depiction of land 

transformation processes. A framework to include hurdle costs was included in FASOMGHG to 

reflect the fact that it may require an income differential above and beyond the opportunity cost 

in agriculture to get agricultural producers to switch to forestry (Lubowski, Plantinga and 

Stavins, 2006).1 Given lack of information on the appropriate values for hurdle costs in each 

region, this term is currently set to zero in the model, but can readily be modified given 

additional information on these costs.

 

1 One potential explanation for the presence of hurdle costs is that agricultural landowners require higher returns in 

order for them to be willing to give up the annual revenue stream provided by agricultural land uses to receive 

forestry revenues that will not be realized until decades into the future when trees are harvested. 
4-3

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 (e.g., site 

preparation costs, which are typically higher for conversion to cropland than pasture) plus any 

hurdle cost, if incorporated.

2

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.

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 

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 Service (USDA, 1989, 2001) and a study by Moulton and Richards

(1990). 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. 

 

2 Note that the types of forests that could be established on agricultural lands and the associated yield characteristics 

of those forest types differ by region.
4-4

Forestry to agricultural use 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 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 (Adams et al., 1996). 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 (USDA, 2001), 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 at a level of disaggregation consistent with FASOMGHG

regions; 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 (USDA, 2001).

3 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 (1990). 

Forest to agriculture conversion 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 even more so for the final 40%. This reflects the increasing 

marginal cost of land conversion due to varying topography, moisture, and other factors (Adams 

et al., 2005).

Table 4-1 summarizes the costs of land conversion between forest and agriculture for 

each of these three steps and baseline acreage available for conversion by region. In general, it is 

more expensive to convert forestland to cropland than pastureland because more site preparation 

work is involved. Conversion costs from cropland to forest and pasture to forest are embedded in 

 

3 These areas are included under the timberland category in either the high or medium site productivity classes. 
4-5

the management costs for new stands and vary by MIC. We do not include those costs here, but 

summarize the acres available for conversion for the CROPFOR and PASTFOR categories. 

Table 4-1. Conversion Costs Between Forestland and Agricultural Land ($/acre) and 

Acres Available for Conversion 

Land Class Definition Region

Land Conversion Cost Level

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 Available acreage Corn Belt 78,000,000 78,000,000

PASTFOR 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 Available acreage Lake States 24,800,000 24,800,000

PASTFOR 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 Available acreage New England 11,100,000 11,100,000

PASTFOR Available acreage New England 3,900,000 3,900,000

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 Available acreage Rocky Mountains 10,900,000 10,900,000

PASTFOR 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 Available acreage Pacific SW 1,100,000 1,100,000

PASTFOR Available acreage Pacific SW 400,000 400,000

(continued)
4-6

Table 4-1. Conversion Costs Between Forestland and Agricultural Land ($/acre) and 

Acres Available for Conversion (continued)

Land Class Definition Region

Land Conversion Cost Level

Low Medium High Total Acres

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 Available acreage Pacific NW East 2,600,000 2,600,000

PASTFOR Available acreage Pacific NW East 300,000 300,000

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 Available acreage South Central 38,900,000 38,900,000

PASTFOR 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 Available acreage Southeast 13,900,000 13,900,000

PASTFOR 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

Note: Acreages presented above include only private timberland by FASOMGHG forest class, where the 

FORONLY class refers to timberland that cannot be converted to agricultural uses; the FORCROP class begins in 

timberland but can potentially be converted to cropland; the FORPAST class begins in timberland but can 

potentially be converted to pasture; the CROPFOR class begins in cropland but can potentially be converted to 

timberland; and the PASTFOR class begins in pasture but can potentially be converted to timberland. There are 

no protected, public, or woodland (private forest pasture) forest areas included in this table because those 

categories are ineligible for conversion to other uses in FASOMGHG. 

4.2 Commodity Transfers 

FASOMGHG also reflects movement of commodities between the forest and agriculture 

sectors, largely in the form of bioenergy feedstocks and short rotation woody crops, which are 

included within the agricultural sector but can potentially provide wood products. Unlike land 

transfers, regional synchronization procedures are not needed on the commodity side because the 

agricultural regional commodity model is always defined at the FASOMGHG 11-region level 

and the commodities can move on a 1:1 basis between comparable regions.
4-7

The following specific commodities can move between the sectors:

§ The agricultural commodity hybrid poplar that can be transferred into forest products 

markets for use in pulpwood forest products markets.

§ The forestry commodities hardwood and softwood pulp logs and milling residues can 

move into the agricultural sector and then into processing activities producing 

cellulosic ethanol or bioelectricity.

§ Logging residues can be collected and moved over to the agricultural sector of the 

model and then into processing activities producing cellulosic ethanol or 

bioelectricity.

4.3 Consistent Treatment of Time and Discounting

The dynamic characteristics of the submodels for forestry, agriculture, and GHG 

payments are somewhat different. This requires model features to synchronize the sectoral 

objective functions, as well as some alterations in the interpretations of levels of activity within 

the model time periods. Efforts were made in the forestry and agricultural submodels to represent 

typical steady-state activity in each year during a 5-year period. 

For the forestry component, this necessitated model features regarding the volume of logs 

obtained from a harvested acre. Mainly, the harvested acreage gives the total acreage harvested 

during each 5-year period and if one multiplies that acreage by the per acre yield, one would get 

the total volume of logs across the 5-year period. To convert this to an annual amount, the yield 

terms were divided by five under the assumption that the acres harvested are cut at an equal rate 

in each year of the 5-year period. This leads to the processing and demand portions of the forest 

submodel having annualized amounts in a 5-year period. Also, the cost data for forestry are 

typically specified as annual costs and, in the case of the variable cost items, were adjusted to be 

average annual cost during a 5-year period. In turn, the forest returns were treated as constant 

annuities across the 5-year period and were multiplied by annuity factors. Subsequently, the 

returns across each of the 5-year periods were each multiplied by the relevant discount rate to 

adjust back to net present value on the basis of the first model time period. Dynamics and 

discounting are discussed in more detail in Section 6.2.

For the agricultural component, the model represents typical activity during each year of 

a 5-year period. Thus, agricultural returns in each time period were also treated as a continuing 

annual annuity and were multiplied by annual factors and then converted to a net present value. 

The GHG payment part of the model was also treated with net present value terms but 

was not converted to an annuity.
4-8
5-1

SECTION 5

BIOENERGY 

Given the importance of bioenergy in recent model development and application, we 

include a separate section in this model documentation focused on summarizing key data and 

methods used to characterize bioenergy production in FASOMGHG. Here we describe the 

bioenergy processes included in the model; feedstock, hauling, storage, and processing costs; and 

coproducts associated with bioenergy production. Technology change over time, including 

changes in crop yields, conversion rates of cellulosic ethanol feedstocks to biofuels, and 

cellulosic ethanol processing costs also play a vital role in the evolution of the bioenergy market 

over time and is discussed in Section 6.

5.1 Transportation Biofuels

FASOMGHG includes three major types of liquid biofuels for use in transportation: 

ethanol made from starch or sugar feedstocks, cellulosic ethanol, and biodiesel. Each of these 

biofuels can be made using a variety of different potential forest and agricultural feedstocks 

included within the model. Production costs for ethanol are broken into feedstock costs, hauling 

costs, storage 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). 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 FASOMGHG, 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.1 These subsidies are assumed to remain 

constant at those levels indefinitely. FASOMGHG simulates the supply of biodiesel by feedstock 

for each of the 10 FASOMGHG market regions with agricultural production over time in 

 

1 The national subsidy included in FASOMGHG 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 qualified for the $1 per gallon subsidy. 
5-2

response to the demand for biodiesel under specified scenarios.2 Below we describe current data 

and model assumptions used to characterize this sector. 

5.1.1 Processes Modeled

FASOMGHG includes modeling of starch- and sugar-based ethanol, cellulosic ethanol, 

and biodiesel using standard plant sizes and processes. These assumptions are summarized 

below. 

Starch- or Sugar-Based Ethanol

FASOMGHG 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.3 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 sugar-based 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 5-1 lists the categories of starch- and sugar-based feedstocks 

currently included in FASOMGHG as well as the assumed ethanol yield and corresponding units 

required per ethanol plant in the base period.

4

 

2 Only 10 of the 11 market regions in FASOMGHG have agricultural production. The PNWW region has only 

forestry production. Because there are no biodiesel production options in FASOMGHG involving feedstocks 

derived from the forestry sector, no biodiesel production is available in the PNWW region. 

3 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, 

FASOMGHG 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 included.

4 The use of starch- and sugar-based feedstocks to produce ethanol is assumed to be a mature technology. Thus, 

conversion rates assumed for production of ethanol from these feedstocks are assumed to be constant over time 

starting with the 2000 to 2004 base period. 
5-3

Table 5-1. Starch- and Sugar-Based Ethanol Feedstocks Included in FASOMGHG and 

Annual Quantity Requirements for a 75 MGY Ethanol Plant (40 MGY for 

Sweet Sorghum), Base Period (2000–2004)

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, durum 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 red winter Bushels 2.56 29,264,758

Wheat, soft white Bushels 2.56 29,264,758

Cellulosic Ethanol

In FASOMGHG, there is currently one standard size for cellulosic ethanol plants. All 

ethanol plants relying on cellulosic feedstocks are assumed to produce 100 MGY of ethanol 

using a single type of feedstock. The selection of this plant size was based on Carolan, Joshi, and 

Dale (2007). 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 the RFS2 program (EPA, 2010a). 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 FASOMGHG.

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 5-2 lists the categories of cellulosic feedstocks currently included in 

FASOMGHG as well as the assumed ethanol yield for the base period and the corresponding 

number of dry tons of each feedstock required per plant to produce 100 MGY. Section 5.2 
5-4

provides additional information on the underlying sources of the initial assumptions on ethanol 

yield and moisture content for cellulosic feedstocks, and Section 6 discusses assumed technology 

changes over time.5

Table 5-2. Cellulosic Feedstocks Included in FASOMGHG and Annual Quantity 

Requirements for a 100 MGY Ethanol Plant, Base Period (2000–2004)

Feedstock

Ethanol Yield 

(gallons/dry ton)

Quantity Required per Plant 

(dry tons)

Crop Residues

Barley crop residues 71.9 1,390,821

Corn crop residues 71.9 1,390,821

Oat crop residues 71.9 1,390,281

Rice crop residues 71.9 1,390,821

Sorghum crop residues 71.9 1,390,821

Wheat crop residues 71.9 1,390,821

Energy Crops

Energy sorghum 71.9 1,390,821

Hybrid poplar 79.1 1,264,223

Miscanthus 71.9 1,390,821

Switchgrass 71.9 1,390,821

Willow 79.1 1,264,223

Wood Products

Hardwood pulpwood 79.1 1,264,223

Softwood pulpwood 71.9 1,390,821

Logging Residues

Hardwood logging residues 79.1 1,264,223

Softwood logging residues 71.9 1,390,821

Processing Residues

Bagasse 71.9 1,390,821

Hardwood milling residues 79.1 1,264,223

Softwood milling residues 71.9 1,390,821

Sweet sorghum pulp 71.9 1,390,821

Note: Ethanol yields are based on Tao, L. and A. Aden. November 2008. Technoeconomic Modeling to Support the 

EPA Notice of Proposed Rulemaking (NOPR).

 

5 Unlike conversion rates of starch and sugar-based feedstocks to ethanol, there are assumed to be substantial 

improvements in conversion rates between the base period and the 2020 to 2025 model period as technology for 

converting cellulosic feedstocks to ethanol advances.
5-5

Biodiesel

In FASOMGHG, 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 

and canola crushing and corn milling processing budgets produce a supply of soybean, canola,

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 FASOMGHG 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 FASOMGHG 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 FASOMGHG were 

based on analyses included in the RIA for the RFS2 program (EPA, 2010a).

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 5-3 lists the 

categories of biodiesel feedstocks being included in FASOMGHG, 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. 

Table 5-3. Biodiesel Feedstocks Included in FASOMGHG 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

Canola oil Gallons 1.0053 994.7

Corn oil (nonfood 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
5-6

5.1.2 Feedstock Costs

Costs of using crops for biofuels production include the cost of the crop, the cost of 

hauling the crop from the roadside at the farm to the biofuels production plant, the cost of storing 

harvested feedstock for use through the year, and the per-gallon processing cost of the crop. This 

section describes the costs of purchasing sufficient crops to produce a given amount of biofuels, 

which depends on crop energy content (i.e., quantity of crop required to produce a given amount 

of biofuel varies across crop). The quantity of crop required also affects hauling costs, which are 

described in Section 5.1.3 along with storage and processing costs.

Starch- or Sugar-Based Ethanol

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 starch- or sugar-based 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 FASOMGHG to calculate the total 

plant-level 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 bioenergy policy 

(see Section 6 for discussion of yield adjustments over time). The regional average crop yields 

for the base period presented in Table 5-4 are based on historical averages for each region

calculated from USDA yield data.
5-7

Table 5-4. Regional Average Crop Yields for Starch- and Sugar-Based Ethanol 

Feedstocks, Base Period (2000–2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Barley (bu/acre) 60.0 52.9 53.6 69.2 63.0 64.1 60.9 74.3 76.2 46.0

Corn (bu/acre) 145.3 139.2 136.0 108.5 175.9 163.9 123.4 132.3 118.6 121.2

Oats (bu/acre) 72.6 60.7 64.9 67.6 90.9 77.6 62.0 42.1 62.5 46.2

Sugarbeet 

(tons/acre)

17.1 18.2 20.4 NA 22.6 40.4 33.8 NA NA 29.7

Sugarcane 

(tons/acre)

NA NA NA NA NA NA NA 31.1 44.6 46.2

Rice (cwt/acre) 40.4 NA NA NA NA 116.9 NA 85.1 NA 100.1

Sorghum 

(bu/acre)

59.1 41.0 NA 48.6 NA 74.9 29.8 52.1 35.6 33.4

Sweet sorghum 

(tons/acre)

32.8 29.8 NA 26.7 NA 38.5 19.0 28.7 19.5 22.6

Wheat, Durum 

(bu/acre)

NA 28.9 44.3 NA NA 119.5 39.9 NA NA NA

Wheat, hard red 

spring (bu/acre)

NA 36.3 45.6 NA 55.6 NA 34.7 NA NA NA

Wheat, hard red 

winter (bu/acre)

NA 35.3 NA NA NA 64.1 29.8 NA NA 29.9

Wheat, soft red 

winter (bu/acre)

72.6 NA 79.6 69.0 NA NA NA 63.7 58.1 NA

Wheat, soft 

white (bu/acre)

NA NA NA NA 58.9 NA 71.7 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 FASOMGHG are generally being used in 

numerous competing markets, use in bioenergy will compete for feedstocks with these markets.

6

In addition to revenue from selling feedstocks in the market, eight of the 11 FASOMGHG

market regions offer state subsidies that offset some of the ethanol production costs. These 

 

6The exception is sweet sorghum, which is in experimental stages and not widely traded in markets. 
5-8

subsidies are in addition to the $0.45 per gallon national ethanol subsidy included in the model.7

Regional averages for subsidies per gallon of ethanol produced are presented in Table 5-5.

8

Table 5-5. Average State Ethanol Subsidy by Region Used in FASOMGHG (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.

Cellulosic Ethanol

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 

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, storage, and handling costs, as described in Section 5.3.

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 FASOMGHG to calculate the total plantlevel 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 

 

7 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. 

8 The values used were based on a review of available state-level subsidies within each FASOM region. 
5-9

assumed harvesting and handling costs of $13.14 per wet ton (2007$)

9

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.10

For energy crops, in addition to harvesting and handling costs of $13.14 per wet ton, 

there are also land opportunity costs and production costs that 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.

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 

portion that is sustainably removable, feedstock density, moisture content, energy content, 

ethanol yields, and other factors affecting the potential ethanol production from crop residues.

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, (5.1)

where crop yield is the value for grain yield from FASOMGHG 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 crop

(Tyner et al, 1979; Lal, 2005);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.

 

9Because 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. 

10The 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 FASOMGHG model solution, ethanol 

processing plants are generally located in the regions where production can be achieved at the lowest cost.
5-10

Because crop yields are assumed to increase over time due to continued technical 

progress in crop production (see Section 6.4), 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, 

FASOMGHG 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.

Table 5-6 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.

Table 5-6. 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

Based on the values presented in Tables 5-4 and 5-6, we can calculate residue production 

per acre. For instance, average corn residue production per acre in the Corn Belt in 2000 to 2004

is equal to 145.3 bu grain/acre * 1 bu residue/bu grain * 0.028 tons/bu = 4.07 wet tons of corn 

residue/acre. Corn residue is assumed to have a moisture content of 12%, so 4.07 wet tons of 

corn residue/acre correspond to 3.58 dry tons of corn residue/acre after removing the 12% of 

corn residue that is water. For hard red winter wheat in the Great Plains, average residue 

production per acre in 2022 is 35.3 bu grain/acre * 1.5 bu residue/bu grain * 0.030 tons/bu = 1.59

wet tons of wheat residue/acre. Moisture content for wheat is assumed to be 8.9%. Thus, 1.59

wet tons of wheat residue/acre is equivalent to 1.45 dry tons of wheat residue/acre. However, 

removing all residue production is not consistent with good management practices nor 

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 
5-11

general rule, though, USDA National Resources Conservation Service recommends that about 

30% residue cover is adequate to control soil erosion (Muang, 2007).Removable residue values 

used in FASOMGHG were calculated by adjusting the residue production per acre based on the 

harvestable percentages provided in Graham et al. (2007) and Perlack et al. (2005), which 

consider the effects of erosion and runoff. This approach uses a maximum percentage removal of 

residues.11 These percentages vary by crop and tillage, as shown in Table 5-7. Another potential 

measure of sustainable residue availability is the minimum quantity of residues per acre that 

must be retained on the land to prevent erosion and maintain soil carbon levels (Wilhelm et al., 

2007). 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 FASOMGHG with 

potential residue use were assumed to be equal because there is limited residue-specific 

information available (Kerstetter and Lyons, 2001; Banowetz et al., 2008).

Table 5-7. 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%

Table 5-8 presents average removable residue per acre in the base period, reflecting 

adjustments to regional residue production per acre to account for the sustainably removable 

percentages.12 These values change over time with changes in crop yields and tillage practices.13

However, corn typically provides the greatest volume of sustainably removable crop residue per 

acre in all years in all regions (the exception is hard red winter wheat residue in the Northeast 

region).14

 

11 Many site specific factors associated with the sustainable removal of residue (e.g., crop type, soil type, soil 

fertility, slope, and climate) affect which geographic regions are suitable for crop residue removal. Detailed 

modeling of these factors was beyond the scope of this analysis. 

12 See Table 5-9 for assumed moisture content for these feedstocks. 

13 The sustainably removable crop residue per acre not only changes with tillage adjustments, but also the acreage 

eligible for residue removal (area in conservation or zero tillage). 

14 Having 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. 
5-12

Table 5-8. Regional Average Sustainably Removable Crop Residue for Acres where 

Residue Can be Removed (Wet Tons per Acre), Base Period (2000–2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Barley residue 0.6 0.5 0.5 0.7 0.6 0.5 0.5 0.7 0.8 0.4

Corn residue 1.7 1.6 1.5 1.3 1.8 1.6 1.2 1.5 1.5 1.2

Oats residue 0.5 0.4 0.4 0.4 0.5 0.5 0.4 0.3 0.4 0.3

Rice residue NA NA NA NA NA NA NA NA NA NA

Sorghum residue 1.4 1.1 NA 1.2 NA 1.2 0.7 1.2 0.9 0.8

Wheat, durum NA 0.4 0.4 NA NA 1.0 0.6 NA NA NA

Wheat, hard red spring NA 0.4 0.4 NA 0.6 NA 0.4 NA NA NA

Wheat, hard red winter NA 0.4 NA NA NA 0.4 0.3 NA NA 0.3

Wheat, soft white NA NA NA NA 0.6 NA 0.7 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 FASOMGHG. Although it is theoretically possible that residues could 

be collected from rice production, all rice production in FASOMGHG is assumed to use conventional tillage.

15

Thus, rice in FASOMGHG has 0% sustainably removable residues in all regions. 

Available sustainably removable crop residues that are removed from fields under 

equilibrium market conditions in FASOMGHG can then either be converted to ethanol or used in 

bioelectricity production (see Section 5.2). Baseline ethanol conversion rates used in 

FASOMGHG to produce ethanol from crop residue feedstocks are based on several different 

sources. These baseline values are then increased over time based on assumed rates of technical

progress, reaching the levels shown in Table 5-9 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 5-9.

 

15 Historically, there was very little U.S. rice production utilizing reduced tillage, but this practice has become more 

common in recent years. 
5-13

Table 5-9. Ethanol Conversion Rates for Crop Residues, Base Period (2000–2004)

Crop Residue Moisture (%)

Ethanol Conversion Rate 

(gallons/wet ton)

Ethanol Conversion Rate 

(gallons/dry ton)

Barley 10.3% 64.49 71.90

Corn 12.0% 63.27 71.90

Oats 10.3% 64.49 71.90

Rice 15.0% 61.12 71.90

Sorghum 10.0% 64.71 71.90

Wheat 8.9% 65.50 71.90

Source for ethanol conversion rate (gallons/dry ton): Tao, L. and A. Aden. November 2008. Technoeconomic

Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR).

Using these conversion rates, the required supply for a 100 MGY plant was calculated for 

each feedstock (see Table 5-2). For instance, an ethanol plant using corn residue as a feedstock in 

the initial model period (2000 to 2004) would need 100 million gallons of ethanol per year/71.9

gallons per dry ton of corn residue = 1,390,821 dry tons of corn residue per year (1,580,478 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.

The cost of acquiring crop residues for cellulosic ethanol production (excluding hauling 

and handling) is determined in FASOMGHG 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. FASOMGHG 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.16 In the absence of bioenergy policy incentives, there is essentially no 

market for cellulosic feedstocks. However, under policies requiring cellulosic ethanol production 

and/or providing incentives for GHG mitigation, a market for cellulosic feedstocks is created, 

and residue prices rise to positive market-clearing levels.

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 FASOMGHG.

 

16 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).
5-14

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. (2009)

for switchgrass and Walsh et al. (2003) for willow and hybrid poplar. It was assumed that 

switchgrass could potentially be produced in eight of the FASOMGHG 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.17

Table 5-10 presents regional average energy crop yields for the base period for the three 

energy crops included in FASOMGHG. 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 FASOMGHG as a production possibility, 

switchgrass has the 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 5-10 can be harvested and used for cellulosic ethanol production.18

Biomass from energy crops is converted to ethanol in FASOMGHG using conversion 

rates that increase over time. Table 5-11 summarizes the ethanol conversion values used for the 

base period (see Section 6.4 for discussion of assumed yield improvements over time). Using 

these conversion rates, the required supply for a 100 MGY plant was calculated for each 

feedstock. For instance, an ethanol plant using switchgrass as a feedstock would need 

100 million gallons of ethanol per year divided by 71.9 gallons per dry ton of switchgrass = 

 

17 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. 

18 See Table 5-11 for assumed moisture content for these feedstocks. 
5-15

1,390,821 dry tons of switchgrass per year (1,580,298 wet tons). If using willow, the same size 

plant would still need 1,390,821 dry tons of feedstock but would require more wet tons of 

feedstock than for switchgrass because of the higher moisture content (1,896,454 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.

Table 5-10. Regional Average Energy Crop Yields (Wet Tons per Acre), Base Period 

(2000–2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Energy sorghum 21.0 17.4 NA 17.1 NA 22.3 11.2 17.1 12.5 13.2

Hybrid poplar 4.6 3.8 4.4 3.9 5.8 NA NA 4.1 4.1 3.7

Miscanthus 21.1 16.5 16.0 17.4 NA NA 6.3 19.4 18.8 11.4

Switchgrass 7.5 4.6 5.8 4.0 NA NA 2.4 7.0 6.1 6.4

Willow 4.7 NA 4.6 4.8 NA NA NA NA 4.5 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 5-11. Ethanol Conversion Rates for Energy Crops, Base Period (2000–2004)

Energy Crop Moisture (%)

Ethanol Conversion Rate 

(gallons/wet ton)

Ethanol Conversion Rate 

(gallons/dry ton)

Energy sorghum 11.99% 63.36 71.90

Hybrid poplar 31.03% 54.56 79.10

Miscanthus 11.99% 63.36 71.90

Switchgrass 11.99% 63.36 71.90

Willow 33.33% 52.73 79.10

Source for ethanol conversion rate (gallons/dry ton): Tao, L. and A. Aden. November 2008. Technoeconomic 

Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR). 

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 FASOMGHG. 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 
5-16

feedstock prices reach high enough levels, energy crops become competitive with alternative 

land uses in some regions.

Pulpwood, Logging, and Processing Residues Production

Finally, FASOMGHG also includes pulpwood and logging and processing residues as 

potential feedstocks for cellulosic ethanol production. Pulpwood is smaller diameter timber that 

is typically used in the pulp and paper sector but could also be used in bioenergy production 

rather than relying on residues. 

For pulpwood, volumes are calculated by using the round wood yield factors in 

FASOMGHG for trees grown under a given set of conditions and harvested at a particular stand 

age. For logging residues, yields are generated by applying assumed logging residue percentages 

to FASOMGHG data on round wood yields. Yields were averaged by FASOMGHG market 

region using the FASOMGHG forestry inventory data as weights. Two-thirds of the logging 

residue was assumed to be harvestable based on Helynen, Hakkila, and Nousiainen (2000).

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 (1980).

Bagasse, milling residues, and sweet sorghum pulp are produced as coproducts of sugar, 

wood products, and sweet sorghum production in FASOMGHG and depend on production of 

these products by region. Bagasse and milling residues are currently used onsite 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.

FASOMGHG assumes improvements in conversion technology take place over time for 

these feedstocks, as discussed in Section 6.4. Biomass from pulpwood, logging, and processing 

residues can be converted to ethanol in FASOMGHG using the conversion rates shown in 

Table 5-12.
5-17

Table 5-12. Ethanol Conversion Rates for Logging and Processing Residues, Base Period 

(2000–2004)

Residue Moisture (%)

Ethanol Conversion Rate 

(gallons/wet ton)

Ethanol Conversion Rate 

(gallons/dry ton)

HW pulpwood 33.33% 52.73 79.10

SW pulpwood 33.33% 47.93 71.90

HW logging residue 33.33% 52.73 79.10

SW logging residue 33.33% 47.93 71.90

Bagasse 31.03% 49.59 71.90

HW milling residue 33.33% 52.73 79.10

SW milling residue 33.33% 47.93 71.90

Sweet sorghum pulp 35.00% 46.74 71.90

HW = Hardwood, SW = Softwood 

Source for ethanol conversion rate (gallons/dry ton): Tao, L. and A. Aden. November 2008. Technoeconomic 

Modeling to Support the EPA Notice of Proposed Rulemaking (NOPR).

Using these conversion rates, the required supply for a 100 MGY plant was calculated for 

each feedstock. As with all of the other feedstocks, increases in ethanol 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.

The use of pulpwood for bioenergy production competes directly with existing use in the 

pulp and paper sector and bioenergy producers would need to pay market prices for these 

feedstocks, where the addition of demand for bioenergy use is likely to increase the price of 

pulpwood relative to baseline conditions. 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, there are markets for selected 

processing residues (bagasse and sweet sorghum pulp) in the absence of bioenergy or climate 

policy. However, the increase in demand associated with such policies tends to result in large 

increases in the prices of these residues. 

Biodiesel

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 
5-18

biodiesel production is located on site (transportation is reflected in the oilseed processing 

budgets), FASOMGHG assumes zero incremental handling and hauling costs in biodiesel 

production at an oilseed processing facility, whereas these costs represent a substantial 

proportion of total production costs in ethanol production.

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. 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 FASOMGHG 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, canola, 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, 

canola, and corn oils over time. Table 5-13 presents information on the average quantity of these

oils that can be produced per acre by region.

Table 5-13. Regional Average Potential Production of Soybean, Canola, and Corn Oil per 

Acre, Base Period (2000–2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Canola (cwt/acre) NA 12.9 12.9 NA 16.9 NA 12.3 NA NA 9.9

Canola oil 

(gallons/acre)

NA 66.1 66.2 NA 86.3 NA 62.8 NA NA 50.6

Corn (bu/acre) 145.3 139.2 136.0 108.5 175.9 163.9 123.4 132.3 118.6 121.2

Corn oil 

(gallons/acre) 

(wet mill)

30.2 28.9 28.3 22.5 36.6 34.1 25.6 27.5 24.6 25.2

Soybeans (bu/acre) 46.1 39.4 42.6 38.0 NA NA NA 36.9 31.5 25.0

Soybean oil 

(gallons/acre)

66.2 56.6 61.2 54.6 NA NA NA 53.0 45.3 36.0

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).

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, canola, and soybean oils under policies encouraging expanded biodiesel 
5-19

production, but currently it is expected that the majority of the increase in biodiesel production 

would be derived from soybean, canola, and corn oils.

5.1.3 Hauling, Storage, and Processing Costs

Hauling costs represent the costs of biomass transportation from road side at the farm to 

the ethanol production plant. These costs are a function of distance, biomass yield, density of the 

biomass being used as a feedstock (defined as the proportion of the land area around an ethanol 

plant with available biomass of the type being used as a feedstock), and the truck-hauling rate. 

The dispersion of biomass residues, their bulkiness, and their relatively low energy density 

suggest that hauling costs may be a limiting factor on plant size as larger plants will need to 

acquire biomass from an increasing distance. Based on an approach developed by French (1960) 

and described in McCarl et al. (2000), average transportation costs per ton are calculated as: 

Loadsize

Fixed Cost D Cost per Mile TC + ( 2 × × )

=

, where (5.2)

)

sq mi

acres (640

0.4714

Den Yld

M

D

× ×

= ×

Fixed Cost represents costs that do not vary with distance, including loading and 

unloading costs and other fixed costs of operating a truck. In addition, there is a variable hauling 

cost component that is assumed to increase with average distance at the constant rate of Cost per 

Mile. Given a square grid system of roads as described in French (1960), D is denoted as an 

average one-way19 hauling distance in miles that is calculated based on M, the biomass quantity 

requirements of an ethanol plant (see Tables 5-1 and 5-2 for quantities in the base period); Den, 

the density of biomass residue production (proportion of area surrounding plant with available 

biomass); and Yld, the regional average value for harvestable feedstock yield in wet tons per 

acre. Loadsize is the average load size of a truck in wet tons hauled per load. Fixed Cost, Cost 

per Mile, and Loadsize for grain crops are assumed to be $45 per load, $1.10 per mile, and 

50,000 lbs, respectively (converted into the appropriate unit for a given crop to be consistent 

with the yield measure, for example, bushels). Costs for crop residues and energy crops are 

assumed to be twice as high at $90 per load and $2.20 per mile. In addition, because of their 

higher moisture contents and lower densities, the average load size for crop residues, sweet 

sorghum, switchgrass, miscanthus, and energy sorghum was assumed to be 40,000 lbs. For 

 

19 A multiplicative factor of 2 is included in the calculation to represent round-trip costs. 
5-20

processing residues, there are no hauling costs because they are assumed to be used at the site 

where they are generated. Similarly, no hauling costs are included in biodiesel production 

because they are already included in other stages of production where fats and oils are produced. 

Logging residue densities were determined for each FASOMGHG region by calculating a 

weighted average stand rotation volume based on the forest inventory and multiplying by the 

practical forest density of 0.8. No historical data are available for energy crop densities. All 

energy crops were assumed to have biomass densities of 10%, with the expectation that they 

would be planted primarily around bioenergy plants using these feedstocks. Biomass density 

from crop residues was calculated using a weighted average of the total acres of corn, sorghum, 

wheat, barley, oats, and rice produced in the top five counties in each agricultural region relative 

to the total available acres of land. Table 5-14 summarizes regional crop residue densities used in 

the model. 

Table 5-14. Regional Crop Residue Density (Percentage of Region with Available 

Feedstock)

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

Barley NA NA NA 0.6 NA NA NA NA NA NA

Corn 18.5 6.0 7.2 3.2 NA NA NA NA 0.7 0.4

Oats NA NA NA NA NA NA NA NA NA NA

Rice NA NA NA NA NA 1.1 NA 1.6 NA NA

Sorghum NA 2.5 NA NA NA NA NA NA NA 1.1

Wheat NA 25.5 6.6 NA 40.0 8.8 30.0 NA NA 27.8

Note: Values are provided only for those crop/region combinations with available feedstock yields and densities 

high enough to be considered feasible bioenergy sources in the model.

Table 5-15 summarizes base period values for average hauling distances by feedstock by 

region. Hauling costs will generally decline over time as feedstock yields rise (and ethanol 

conversion rates improve in the case of cellulosic feedstocks) and more feedstock is collected per 

acre (and less feedstock is needed for cellulosic ethanol production), reducing average hauling 

distance for a given plant size. Hauling distances are only reported for those crop/region 

combinations where hauling distance is positive, but less than 400 miles because combinations 

with distances greater than 400 miles are assumed to be infeasible and are dropped from the 

model. 
5-21

Table 5-15. Regional Values for Average One-Way Hauling Distance (Miles) for 

FASOMGHG Standard Plant Sizes, Base Period (2000–2004)

Feedstock CB GP LS NE PNWE PSW RM SC SE SW

Crops

Barley NA 213.7 NA 208.4 346.5 352.4 259.3 NA NA NA

Corn (dry milling) 19.0 34.9 31.4 52.9 NA 241.4 219.3 86.2 107.4 138.5

Corn (wet milling) 19.8 36.3 32.7 55.1 NA 251.4 228.4 89.7 111.8 144.2

Oats NA NA 391.6 246.0 NA NA NA NA NA NA

Rice NA NA NA NA NA 70.2 NA 70.2 NA 183.9

Sorghum, grain 377.8 74.6 NA 254.2 NA 208.3 307.6 294.1 NA 129.7

Sorghum, sweet 255.1 50.3 NA 172.2 NA 131.4 201.3 199.0 301.2 86.5

Wheat, durum NA 71.9 NA NA NA 60.0 87.8 NA NA NA

Wheat, hard red spring NA 31.7 58.1 NA 46.0 NA 31.8 NA NA NA

Wheat, hard red winter NA 47.3 NA NA NA 46.5 41.7 NA NA 34.3

Wheat, soft red winter 67.0 NA 87.8 54.1 NA NA NA 47.4 47.9 NA

Wheat, soft white NA NA NA NA 21.2 NA 22.4 NA NA NA

Residues

Barley residue NA NA NA 395.6 NA NA NA NA NA NA

Corn residue 42.0 80.4 73.3 118.1 NA NA NA 186.3 230.8 330.8

Oats residue NA NA NA NA NA NA NA NA NA NA

Rice residue NA NA NA NA NA NA NA NA NA NA

Sorghum residue NA 148.3 NA NA NA NA NA NA NA 269.9

Wheat residue NA 81.2 141.8 NA 52.2 91.3 66.6 NA NA 84.8

Energy Crops

Energy sorghum NA 37.9 NA 128.3 NA 161.4 NA 148.4 NA 69.9

Hybrid poplar 37.0 41.0 38.0 40.2 33.2 NA NA 39.1 39.7 41.2

Miscanthus 15.9 18.3 18.5 17.6 NA NA 30.5 16.2 16.9 19.0

Switchgrass 27.0 34.6 30.6 35.9 NA NA 46.2 28.0 30.0 29.0

Willow 37.3 NA 37.8 37.1 NA NA NA NA 38.2 NA

Wood Products

HW pulpwood 94.5 NA 89.5 82.9 110.6 64.5 110.6 56.8 54.0 NA

SW pulpwood 178.3 NA 150.6 125.0 104.2 63.0 104.2 73.3 62.7 NA

Logging Residues 

HW logging residues 94.5 NA 89.5 82.9 110.6 64.5 110.6 56.8 54.0 NA

SW logging residues 178.3 NA 150.6 125.0 104.2 63.0 104.2 73.3 62.7 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). There are also wood products and logging residues available for ethanol 

production in PNWW (Pacific Northwest—West side) region, with average distances of 60.6 miles for softwood 

and 73.0 miles for hardwood. 
5-22

Storage costs are calculated for crop residues, energy crops, and processing residues 

based on the amount of time these feedstocks would need to be stored.20 It was assumed that 

wood products, logging residues, and milling residues could be harvested throughout the year; 

thus, storage costs are assumed to be zero for these feedstocks. Because crop residues and energy 

crops cannot be harvested year-round and agricultural processing residues are not available yearround, bioenergy producers incur storage costs to ensure availability of sufficient feedstock 

throughout the year. For crop residues, dedicated energy crops, and agricultural processing 

residues, storage costs are calculated based on the assumed harvest window, maximum time in 

storage (which affects storage capacity requirements), costs for placing feedstocks into storage 

and taking them back out of storage, and a monthly cost associated with feedstock storage 

calculated based on the average number of months that feedstock is stored.21 Costs of storage are 

assumed to be $7.15/dry ton of storage capacity, $4.59/dry ton to place feedstock into storage 

and then later take it back out of storage, and $2.72/dry ton per month while in storage. 

Table 5-16 summarizes the assumptions and storage costs for the feedstocks with storage costs 

included in FASOMGHG for the base period.

Plant-level ethanol processing costs in FASOMGHG are calculated as the sum of costs 

associated with handling a given feedstock prior to processing at the ethanol plant (estimated as a 

cost per wet ton of feedstock) and costs incurred at the plant to convert delivered feedstock to 

ethanol (estimated as a cost per gallon of ethanol produced). The costs of feedstock preparation 

and grinding at the ethanol production plant prior to processing for crop residues, energy crops, 

pulpwood, and logging residues is assumed to be $13.54 per wet ton based on rice straw 

feedstock supply studies for California conducted by Fife and Miller (1999) and Summers 

(2001). For pulpwood and logging residues, there is also a cost of $8.71 per green ton to gather 

and chip the pulpwood and logging residue that is included in this category, whereas for crop 

residues and energy crops, the harvesting cost is part of the crop budget and reflected as part of 

the feedstock cost. Thus, the total cost included in this category for logging residues is $22.25 

per green ton. Processing residues (bagasse, sweet sorghum pulp, and softwood and hardwood 

mill residues) were assigned a $5.00 per ton handling cost. These costs do not vary over time or 

 

20 No storage costs are included for grain crops because they are routinely stored for year-round consumption in 

other markets using a well-established infrastructure and their storage costs are assumed to be reflected in their 

market prices. 

21 The average number of months feedstock is stored is calculated based on an assumption of equal monthly 

withdrawals from storage over the number of months that feedstock is stored, that is, if residues are stored for up 

to 10 months, then it was assumed that 10/12 of total plant feedstock requirements are stored for 1 month, 9/12 

for 2 months, and so on. In this example, the average number of months that a ton of crop residues would be 

stored is 4.5833 months. 
5-23

across regions. No additional costs are included for handling starch and sugar-based feedstocks 

or in biodiesel production.

Table 5-16. Assumed Storage Costs for Ethanol Feedstocks for FASOMGHG Standard 

Size Plant, Base Period (2000–2004)

Feedstock

Tons of 

Feedstock 

Required (dry 

tons)

Harvest 

Window 

(months)

Storage 

Capacity for 

Peak Quantity 

(dry tons)

Average Time 

in Storage 

(months)

Total Annual 

Storage Costs 

per Plant

Residues

Barley residue 1,390,821 2 1,159,017 4.58 $30,945,632

Corn residue 1,390,821 2 1,159,017 4.58 $30,945,632

Oats residue 1,390,821 2 1,159,017 4.58 $30,945,632

Rice residue 1,390,821 2 1,159,017 4.58 $30,945,632

Sorghum residue 1,390,821 2 1,159,017 4.58 $30,945,632

Wheat residue 1,390,821 2 1,159,017 4.58 $30,945,632

Energy Crops

Energy sorghum 1,390,821 6 695,410 1.75 $14,781,851

Hybrid poplar 1,264,223 8 421,408 0.83 $7,801,440

Miscanthus 1,390,821 6 695,410 1.75 $14,781,851

Switchgrass 1,390,821 3 1,043,115 3.75 $26,432,544

Willow 1,264,223 8 421,408 0.83 $7,801,440

Processing Residues 

Bagasse 1,390,821 2 1,159,017 4.58 $30,945,632

Sweet sorghum pulp 1,390,821 7 579,509 1.25 $11,532,227

The second component of processing costs is the assumed cost per gallon of converting 

delivered feedstock that has been through a grinding process into ethanol or converting fats and 

oils into biodiesel. This cost is assumed to be $0.62 per gallon for sugar, $1.64 per gallon for 

sweet sorghum, and $0.71 per gallon for all other starch or sugar-based feedstocks. These costs 

are assumed to remain constant over time in real terms. For cellulosic ethanol, production costs 

are assumed to be $3.29 per gallon for all feedstocks other than sweet sorghum pulp (assumed to 

be $1.39 per gallon) in the base period, with considerable reductions in costs assumed over time 

as cellulosic ethanol production technology advances and takes advantage of economies of scale 

(see Section 6.4). Production costs for biodiesel are assumed to be $0.40 per gallon for 
5-24

feedstocks and remain constant over time in real terms. These values were based on information 

provided by EPA.

5.1.4 Additional Ethanol Market Penetration Costs

At higher ethanol volumes, increasing use of fuel mixes with 85% ethanol and 15%

gasoline (E85) will be required due to the E10 blending wall (e.g., once essentially all gasoline 

vehicles are already using E10), as well as constraints preventing the penetration of mid-level 

blending. More E85 requires more Flex Fuel Vehicles (FFVs) and distribution infrastructure 

(transportation, distribution, and dispensing) and/or higher FFV refueling rates and stations 

carrying E85. The number of FFVs increases at an increasing rate and E85 has a lower return 

compared to E10 due to E85’s lower energy content as well as more limited E85 fueling 

availability. These issues imply an increasing ethanol penetration cost with increasing total 

ethanol volume in order to reflect the growing share of E85.

Data from the Energy Information Administration (EIA) 2009 Annual Energy Outlook 

(AEO2009) project increasing divergence between motor gasoline and ethanol prices over time. 

These data reveal a clear relationship between the difference in these prices and E85 

consumption, with the difference between the price of gasoline and wholesale ethanol increasing 

with increased E85 consumption. We used this information to estimate a function for the cost of 

E85 penetration under the assumption that the difference between the price of motor gasoline and 

wholesale ethanol should reflect the cost and performance differences between the fuels 

(according to EIA). 

AEO2009 assumes that the E10 blending wall is reached around 2013 and that additional 

ethanol volumes beyond that are E85. Thus, the growing difference between the gasoline and 

wholesale ethanol prices from 2014 to 2030 can presumably be attributed to increased 

production and penetration of E85, and we use the 2014 to 2030 projections of prices and E85 

consumption to estimate a relationship that we interpret as the cost of E85 penetration. Based on 

the relationships estimated, we incorporated a step function into the model that increases the cost 

of ethanol production as the volume of ethanol increases, as shown in Table 5-17. These costs 

are in addition to the cost of producing and transporting the feedstocks to refineries and 

conversion to ethanol presented above.
5-25

Table 5-17. Market Penetration Costs for Ethanol

Ethanol Production Volume (bgy) Penetration Cost ($/gallon)

=5 0

>5 to 10 0.03

>10 to 15 0.20

>15 to 20 0.40

>20 to 25 0.65

>25 to 30 0.98

>30 to 35 1.20

>35 to 40 1.43

>40 to 45 1.70

>45 1.80

5.1.5 Coproducts

Four primary coproducts of the starch-to-ethanol production process are tracked within 

FASOMGHG—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 FASOMGHG includes feed substitution using DG as a corn 

and soybean meal replacement possibility. Based on research conducted by Argonne National 

Laboratory (Salil, Wu, and Wang, 2008), FASOMGHG assumes that 1 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.

DG produced as a byproduct of a dry milling process with corn oil fractionation or 

extraction has different nutritional characteristics than traditional DG, which contain higher 

levels of oil (Shurson, 2006). Based on this research, the proportion of soybean meal versus 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 
5-26

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.

In addition to using DG in domestic markets, the model was recently modified to account 

for potential exports. 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 (2006), 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 FASOMGHG 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 nonfood 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 for the RFS2 program, 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 5-18 summarizes the 

coproducts produced when using different grain feedstocks for ethanol production.

Revenue provided by selling these coproducts is an important factor affecting the 

economic viability of ethanol processing plants. The added revenue from biodiesel and corn oil 

will be another factor affecting ethanol producer decisions to fractionate or extract. 

The only coproduct of cellulosic ethanol production that is tracked within FASOMGHG

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 
5-27

were derived from data in Kadam et al. (1999) and Kadam (2000). Lignin can be used as a fuel 

for generating heat or could potentially be used for electricity generation.

Table 5-18. 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.

FASOMGHG includes electricity production using lignin as a production possibility. In 

FASOMGHG 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 FASOMGHG. In addition, there are 

production possibilities in FASOMGHG for cofiring with lignin at levels ranging from 5% to 

20%. 

No coproducts are explicitly associated with biodiesel production in FASOMGHG. For 

instance, 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. Similarly, canola meal 

would provide revenue for canola crushing plants producing biodiesel.

5.2 Bioelectricity

FASOMGHG includes both cofiring with biomass at coal-fired plants and the use of 

dedicated biomass plants. There are a number of similarities in the assumptions used for 

bioelectricity production and cellulosic ethanol production, both of which rely on the same set of 
5-28

feedstocks except for the addition of manure as a potential feedstock for bioelectricity. As for 

liquid transportation fuels, costs in the model are broken into feedstock costs, hauling costs, 

storage costs, and processing costs. Unlike cellulosic ethanol, all revenue is assumed to come 

from the sale of electricity; there are no government subsidies or coproducts. A key driver of 

bioelectricity production is policy incentives for GHG mitigation. The use of bioenergy 

feedstocks in electricity production in place of coal enables firms generating electricity to 

potentially reduce GHG emissions and thus reduce their compliance costs under a GHG 

mitigation policy. In this section, we highlight differences in assumptions and point the reader to 

Section 5.1 for additional information on many of the feedstock assumptions.

5.2.1 Processes Modeled

FASOMGHG models the production of bioelectricity using a number of different 

feedstocks in increments of 100 megawatts (MW) electricity generation capacity, which is 

assumed to operate at 75% of capacity (producing 657 million kilowatt-hours [kWh]) and to 

require 9.198 trillion British thermal units (TBtu) of feedstock. Based on a dedicated biomass 

plant heat rate of 14,000 Btu input per kWh of generation, Table 5-19 summarizes the higher 

heating values (measure of energy content) used for each feedstock and the corresponding 

quantity of that feedstock required for a dedicated biomass plant in FASOMGHG. For cofiring, 

the model includes options for cofiring at a standard coal-fired plant size of 750 MW at 5%, 

10%, 15%, and 20% (replacing 37.5, 75.0, 112.5, and 150.0 MW of coal-fired generation, 

respectively).

5.2.2 Feedstock Costs

The cost of acquiring feedstocks for use in bioelectricity production is very similar to that 

described in Section 5.1.2 for cellulosic ethanol production. Plant-level feedstock costs reflect 

the cost per unit of feedstock multiplied by the number of units required to provide enough 

feedstock to power 100 MW dedicated plants or cofiring at 750 MW coal-fired plants at rates of 

5%, 10%, 15%, or 20% of total plant electricity generation. Quantity requirements at the plant

level are multiplied by the endogenous regional farm-level market price for that feedstock to 

calculate total plant-level feedstock costs. Generally, the same feedstocks that can be used in 

cellulosic ethanol production within the model can also be used for bioelectricity production (see 

Section 5.1.2 for information on regional crop yields, moisture content, and other assumptions 

regarding these feedstocks). In addition, lignin (from hardwood, softwood, or nonwood sources) 

and manure can be used as bioelectricity feedstocks in the model.
5-29

Table 5-19. Bioelectricity Feedstocks Included in FASOMGHG and Annual Quantity 

Requirements per 100 MW Generation Capacity, Base Period (2000–2004)

Feedstock

Higher Heating Value

(Btu/wet ton)

Quantity Required per Plant

(wet tons)

Residues

Barley residue 14,883,920 617,982

Corn residue 9,226,139 996,950

Oats residue 14,883,920 617,982

Rice residue 11,109,200 827,962

Sorghum residue 11,917,456 771,809

Wheat residue 15,055,891 610,924

Energy Crops

Energy sorghum 13,749,781 668,956

Hybrid poplar 11,492,271 800,364

Miscanthus 13,920,708 660,742

Switchgrass 13,749,781 668,956

Willow 14,153,995 649,852

Pulpwood

Hardwood pulpwood 10,666,667 862,312

Softwood pulpwood 12,000,007 766,500

Logging Residues

Hardwood logging residues 10,666,667 862,312

Softwood logging residues 12,000,007 766,500

Processing Residues 

Bagasse 10,276,632 895,040

Hardwood milling residues 10,666,667 862,312

Lignin, nonforest 18,222,001 504,774

Lignin, hardwood 21,239,991 433,051

Lignin, softwood 22,679,998 405,556

Manure 8,114,032 1,133,592

Softwood milling residues 12,000,007 766,500

Sweet sorghum pulp 11,406,208 806,403

Note: Values included in the table are for dedicated biomass. Using biomass in cofiring requires a less than 

proportionate amount of feedstock because combustion is more complete and the heat rate is lower when burned 

along with coal. Cofiring at 5%, 10%, 15%, and 20% requires only 3.78%, 7.80%, 12.08%, and 16.64% as much 

feedstock, respectively, as is required for a 100% biomass-dedicated plant producing the same kWh.
5-30

Crop residues and energy crops are assumed to have harvesting and handling costs of 

$13.14 per wet ton (2007$) for bioelectricity production based on the costs associated with 

cellulosic ethanol production. Manure is assumed to have the same harvesting and handling costs 

as well. Lignin, however, is assumed to have higher costs of $30 per wet ton. There are 

additional fertilization requirements (and associated costs) due to nutrient removal when crop 

residues are taken off the land that vary by crop. There are also limits placed on crop residue 

removal based on requirements for erosion control and sustainability as described in 

Section 5.1.2. In addition, land opportunity costs and production costs are included within the 

model for energy crops. Competition for these feedstocks with cellulosic ethanol production as 

well as nonbioenergy processing uses for selected feedstocks will increase the opportunity costs 

of using these feedstocks in bioelectricity production.

5.2.3 Hauling, Storage, and Processing Costs

Hauling and storage costs for bioelectricity production are calculated in a similar way to 

that presented in Section 5.1.3, but it was assumed that hauling takes place in two stages rather 

than one for cofiring (dedicated plants require only the first stage). In the first stage, feedstocks 

are assumed to be transported to a central location and this is done in the same way as for 

cellulosic ethanol. From there, they are transported to coal-fired plants that could potentially 

cofire biomass in the second stage with increasing transportation costs as the total quantity of 

cofiring increases. These adjustments were incorporated to reflect the increasing distances 

between concentrations of agricultural feedstocks and plants where these feedstocks could 

potentially be cofired as more cofiring takes place. Unlike cellulosic ethanol production, where 

ethanol plants are located with proximity to forestry and agricultural feedstocks playing an 

important role in siting decisions, power plants are very unlikely to have considered bioenergy 

feedstock availability in coal-fired plant location decisions. In many cases, existing coal-fired 

plants and bioenergy feedstocks may be located in substantially different areas within a region 

because the plants are located near population centers, whereas the forestry and agricultural 

feedstocks are concentrated in more rural areas.

Thus, hauling costs for the first stage are calculated in an identical way to that described 

in Section 5.1.3. Although feedstock yields and densities are the same for feedstocks whether 

they are used in cellulosic ethanol or bioelectricity production, the average hauling distance and 

associated costs per ton will vary based on differences in feedstock quantity requirements for 

different types of bioenergy plants. Table 5-20 summarizes average hauling distances for 
5-31

feedstocks that would be transported to 100 MW dedicated biomass electricity plants.22 Based on 

the reduced biomass feedstock quantities required for cofiring options in FASOMGHG, 

distances for 5%, 10%, 15%, and 20% cofiring at a 750 MW plant would be equal to 53.3%, 

76.5%, 98.3%, and 111.7%, respectively, of the distance shown in Table 5-20 for a 100 MW 

dedicated bioelectricity plant for the first stage of hauling to a plant that is cofiring biomass.

Table 5-20. Regional Values for Average One-Way Hauling Distance (Miles) for 

FASOMGHG Standard 100 MW Dedicated Bioelectricity Plant, Base Period 

(2000–2004)

Feedstock CB GP LS NE PNWE PSW RM SC SE SW

Residues

Barley residue NA 265.0 NA 249.8 NA NA 329.6 NA NA NA

Corn residue 33.4 63.9 58.2 93.8 NA NA NA 148.0 183.3 262.7

Oats residue NA NA NA 296.3 NA NA NA NA NA NA

Rice residue NA NA NA NA NA 141.6 NA 141.5 NA 370.8

Sorghum residue NA 104.8 NA 341.6 NA 312.5 NA 388.9 NA 190.7

Wheat residue NA 51.4 89.7 NA 40.0 57.8 30.0 NA NA 27.8

Energy Crops

Energy sorghum NA 24.7 NA 83.5 NA 105.0 NA 96.5 NA 45.5

Hybrid poplar 24.5 27.1 25.1 26.6 22.0 NA NA 25.9 26.2 27.2

Miscanthus 10.3 11.8 11.9 11.4 NA NA 19.7 10.5 10.9 12.2

Switchgrass 17.5 22.5 19.9 23.3 NA NA 30.0 18.2 19.5 18.8

Willow 21.8 NA 22.2 21.7 NA NA NA NA 22.4 NA

Wood Products

HW pulpwood 94.5 NA 89.5 82.9 110.6 64.5 110.6 56.8 54.0 NA

SW pulpwood 178.3 NA 150.6 125.0 104.2 63.0 104.2 73.3 62.7 NA

Logging Residues 

HW logging residues 94.5 NA 89.5 82.9 110.6 64.5 110.6 56.8 54.0 NA

SW logging residues 178.3 NA 150.6 125.0 104.2 63.0 104.2 73.3 62.7 NA

Processing Residues

Manure 111.8 111.8 111.8 111.8 111.8 111.8 111.8 111.8 111.8 111.8

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). There are also wood products and logging residues available for bioelectricity 

production in PNWW (Pacific Northwest—West side) region, with average distances of 60.6 miles for softwood 

and 73.0 miles for hardwood. 

 

22 No hauling distances were assumed for processing residues that are assumed to be used on site to generate 

electricity, such as lignin and milling residues. 
5-32

In the second stage included for cofiring, feedstocks are hauled from a central location to 

the bioelectricity plants that will cofire with average hauling distances calculated based on an 

assessment of the location of available biomass within a region and existing generation. These 

distances are adjusted upward using a step function defined at 500 MW intervals of cofiring as 

an increasing quantity of feedstocks is used in bioelectricity production. In a region like the Corn 

Belt, which has both large quantities of potential feedstocks and coal-fired plants, the average 

distance increases slowly with the amount of cofiring. Second-stage hauling distance for the 

Corn Belt increases from 14.1 miles for the first 500 MW to 16.7 miles at 15,000 MW cofired 

and to 32.42 miles at 25,000 MW cofired. In the Pacific Southwest, on the other hand, secondstage hauling distances increase from 15.0 miles for the first 500 MW to 950.9 miles by 1,500 

MW of cofiring because of a combination of lower feedstock availability and less coal-fired 

generation.

Storage costs for bioelectricity are calculated in the same way as described for cellulosic 

ethanol in Section 5.1.3, although the magnitude of the storage costs will differ based on 

differences in the quantity of feedstock being stored. 

Annual power plant operating costs were assumed to be $1,838,800 for a 100 MW 

dedicated biomass plant, and operating costs for the biomass component of cofired plants were

calculated based on the relative feedstock requirements for each level of cofiring included 

relative to the quantity required for a 100 MW dedicated plant. 

5.2.4 Bioelectricity Market Penetration Constraints

In addition to rising hauling costs that reflect higher costs of increasing levels of 

bioelectricity production and the expected increases in feedstock costs as bioelectricity expands, 

market penetration constraints are also placed on bioelectricity. These constraints are included to 

reflect construction lags, limits on total potential cofiring based on regional coal-fired electricity 

generation, depreciation and potential retirement of existing coal-fired plants, and anticipated 

increases in electricity demand over time. 
6-1

SECTION 6

DYNAMICS 

The forest and agricultural sectors have experienced considerable changes in supply and 

demand conditions over time due to changes in available technology and productivity on the 

supply side as well as population and income on the demand side. Changes both domestically 

and internationally will impact U.S. market conditions. To capture expected future changes in 

key characteristics affecting market outcomes, FASOMGHG incorporates adjustments to market 

and production conditions over time based on historical experience and available information on 

expected future conditions. Another important consideration for any forward-looking 

optimization model is discounting to appropriately compare economic returns realized at 

different points in time. In addition, given long rotations in the forestry sector, it is necessary to 

define terminal conditions that account for the future value of standing forests at the end of the 

model period. This section discusses the dynamics of timber harvest, discounting and annuities, 

terminal conditions, and model adjustments to key variables over time. 

6.1 Dynamics of Timber Harvest

FASOMGHG endogenizes the harvest date for a timber stand, assuming it is past an 

exogenously determined region-specific minimum harvest age. The harvest alternatives are 

differentiated by the model’s 5-year time periods. Thus a 40-year-old timber stand in the PNWW 

region (which is beyond the minimum harvest age for that region) could be harvested now, 5 

years from now, 10 years from now, etc. The decision whether to harvest a stand or extend it 

until the next period depends on whether the discounted value of its 5-year growth plus the value 

of any price change by extending the harvesting period exceeds the discounted cost of holding it

into the next period, which consists of any realized production costs plus the difference in the 

opportunity cost of the land. The value of the timber when withheld from current harvesting is 

the price 5 years from now times the yield at that point. This is taken into account in the 

optimization for all the possible future harvest periods.

6.2 Discounting and Annuities

Given the modeling of multiyear timber production, FASOMGHG needs to handle 

economic returns over time. This is done by solving for multiple interlinked market equilibria in 

adjacent 5-year periods for the model duration, rather than for just one single period (as would be 

the case in a static equilibrium model). The FASOMGHG objective function depicts 

maximization of the net present value of the sum of producer and consumer surplus, associated 

with production and price formation in competitive markets over time for both agricultural and 
6-2

wood products. In that sense, the first-order (Kuhn-Tucker) conditions for the choice variables in 

the model provide a set of optimization rules for economic agents to follow, leading to the 

establishment of a competitive equilibrium.

Because these choices occur over time, the optimizing nature of the model holds that 

producers and consumers have perfect foresight regarding future demand, yields, technologies, 

and prices. In other words, choices made at the beginning of the projection period are based on 

correct expectations of what the model predicts will occur in the future.

Perfect foresight employs the assumption that agents are rational and respond with the 

best information they have available at the time. This draws on well-established economic 

theories about rational expectations (Muth, 1961) and intertermporally efficient capital markets 

(Fama, 1970).

The multiperiod nature of the economic problem requires transforming future revenues 

and costs to the present using a real (inflation-adjusted) annual discount rate. The default rate 

used is 4%, which is broadly consistent with opportunity costs of capital in agriculture and 

forestry. The default rate can be altered to test the sensitivity of model results to alternative 

discount rates. Higher discount rates devalue future revenue and cost streams. Because forestry is 

an enterprise with revenue streams that are deferred often several time periods beyond the 

incidence of initial establishment and ongoing management costs, forestry returns can generally 

be expected to be particularly sensitive to the chosen discount rate. Moreover, the discount rate 

can significantly affect the timing of land use, investment and harvest decisions with effects 

reaching broadly across both sectors in the model. 

The forestry and agricultural model returns and costs are assumed to represent typical 

activity during each year of a 5-year period. Thus, returns in each explicit period are treated as a 

continuing annual series of five equal amounts discounted to the start of each period under the 

assumption that the same level of returns arise in each year of the period. In the terminal period,

returns arising in all subsequent years (beyond the end of the projection) are treated as an infinite 

annuity as discussed in Section 6.3. Thus, the period specific annuity factor dp

p P

r

d

p P

r

d

p

t

p t

= =

≤ ≤ −

+

=∑=

for 1

and

for 0 1

(1 )

1

4

0

(8.1)
6-3

where

§ r is the discount rate for period p and

§ P is the last time period included in a model run.

The forestry and agricultural market components of the objective function are

( )

(1 )

1

0

5 p p p

P

p

p

d ForWel AgWel

r

W +

+

=∑=

where 

§ W is the net present value of forestry and agricultural welfare

§ dp is the period specific annuity factor 

§ p identifies the 5-year time period

§ AgWelp represents the multiterm agricultural consumer and producer surplus 

objective function in the 5-year time period p 

§ ForWelp represents the multiterm forestry consumer and producer surplus objective 

function in the 5-year time period p

The term involving (1 + r) exponentiated to the 5p provides a discount factor to take the 

net present value of the returns in each time period back to the first model period. Multiplying by 

period-specific annuity factor dp converts the returns in each 5-year period to net present value in 

the dollars of the first year in each period. 

6.3 Terminal Conditions

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. Because the 

model is defined for a finite time period, there will be immature trees of varying ages at the end 

of the time period modeled. 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. If the model did not place a value on these standing forests, the 

optimizing nature of the model would be inclined to leave no standing forests at the end of the 

model simulation period rather than leave them for harvests in future periods. Adding valuation 

of existing forests requires that agricultural land values at the end of the period must be 
6-4

considered to avoid land conversions out of agriculture and into forestry due to the perceived 

lack of any opportunity costs after the final model period. 

To counter these ending-period anomalies, terminal conditions are imposed on the model 

that value ending immature trees1and land remaining in agriculture. Terminal conditions 

represent the projected net present value of an asset for all time periods after the end of the 

explicit model projection. FASOMGHG assumes that forest management is, from the last period 

onward, a continuous or constant flow process with a forest inventory that is fully regulated on 

rotations equivalent to those observed in the last time periods of the projection. A perpetual 

harvest flow is estimated using these rotation ages, the total terminal inventory volume and von 

Mantel’s formula (see Adams et al., 1996 for details). Demand functions for timber are used in 

this period assuming that the fully regulated harvest volume persists forever. The terminal value 

of land remaining in agriculture is formed by assuming that the last period persists forever, that 

is, activity in the last period is treated as if it continues forever where we assume market demand, 

supply, agricultural yields, and technology will remain the same forever. 

6.4 Updating Yields, Demand, and Other Factors over Time

FASOMGHG 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 

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 

 

1

For 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). 
6-5

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.

6.4.1 Crop Yields

Projected crop yields, both domestically and internationally, constitute one of the most 

influential factors of agricultural analyses looking far into the future. The faster crop yields 

increase, the easier it is to meet future demands for food, fiber, feed, and bioenergy feedstocks so 

potential price increases are dampened and land competition is less intense. 

Yields for most crops have increased significantly over time because of technological 

improvements and FASOMGHG adjusts crop yields for future model periods based on historical 

growth in yields. In this section, we discuss projected crop yields, assumptions regarding priceinduced yield changes, and assumptions regarding the impacts of yield changes on input use 

requirements. Table 6-1 summarizes national average yields for major commodities in 

FASOMGHG in the base period as well as the assumed rate of annual growth. Although each 

practice/region combination for a given crop may start at a different yield in the base period, they 

are all assumed to experience the same annual average changes in yields.2 Production of residues 

are assumed to increase at the same rate as the assumed value for the primary product, for 

example, an annual increase in corn yields of 1.48% means that the corn residue produced per 

acre for the corresponding practice/region combination would also increase by 1.48% per year. 

Price-Induced Changes in Crop 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 

but are not profitable at lower commodity prices, these effects are not directly incorporated. 

However, landowners can potentially switch from dryland to irrigated production, change tillage 

practices, or make other management adjustments in response to price changes.3

 

2 Note that the national average yield calculated by FASOMGHG will generally not increase at the rate shown in 

Table 6-1 over time due to reallocation of acreage planted to a given crop across practices and regions over time. 

3We also did not model decreases in yields that might occur due to increased planting on marginal land in response 

to higher output prices. This effect is expected to at least partially offset any potential price-induced yield increases.
6-6

Table 6-1. National Average Yields for Major Crops and Annual Yield Growth

Agricultural Commodity Units

2000–2004 Yield 

(units/acre) Annual Yield Growth Rate

Barley bu 56.1 0.10%

Canola cwt 12.0 1.00%

Corn bu 139.5 1.48%

Cotton 480 lb bales 1.7 0.43%

Hay tons 2.6 0.84%

Hybrid poplar dry tons None produceda0.75%

Miscanthus tons None produced 0.75%

Oats bu 60.4 0.02%

Potatoes cwt 351.5 0.50%

Rice cwt 87.4 1.33%

Rye bu 18.9 0%

Silage tons 21.3 1.90%

Sorghum, energy tons None produced 0.09%

Sorghum, grain Cwt 38.3 0.09%

Sorghum, sweet Tons None produced 0.09%

Soybeans bu 41.6 0.43%

Switchgrass dry tons None produced 0.75%

Sugarbeet tons 23.0 0.07%

Sugarcane tons 38.0 0.00%

Tomatoes, fresh cwt 325.5 0.97%

Tomatoes, processing tons 34.8 0.90%

Willow dry tons None produced 0.75%

Wheat, durum bu 34.7 1.11%

Wheat, hard red spring bu 36.7 1.00%

Wheat, hard red winter bu 32.9 1.31%

Wheat, soft red winter bu 66.5 1.31%

Wheat, soft white bu 61.4 1.11%

a

For those crops that are not produced in the base period, there are still potential yields defined for each region (see 

Table 5-10), and those potential yields are increased at the rates shown in this table. 
6-7

Impact of Yield Changes on Input Requirements

Another adjustment 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 FASOMGHG 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. The default input elasticities assumed in FASOMGHG are shown in Table 6-2. Based on

results derived by Avila and Evenson (2004), the elasticities for total inputs, pesticide use, liquid 

fuels use, and nonliquid fuels use for which no data are available for an individual crop are set at 

0.5. Elasticities for other input costs without available data are assumed to be 0.6. 

Table 6-2. Input Elasticities with Respect to Changes in Yield

Crop

NitrogenPhosphorusManagementChemicalsSeedsCustom OperationsMarketingCapitalRepairsInsuranceTotal PesticidesTotal Fuels

Barley 0.94 0.94 0 1.38 - 0.11 1.31 0 0.45 0.85 0 1.38 0.78

Canola 0 0 0 0 0 0 0 0 0 0 0.50 0.50

Corn 0.16 0.16 0 0.77 1.14 1.00 0 0.80 0.87 0 0.77 0.79

Cotton 0.30 0.30 0 1.50 - 0.01 0 2.00 0.06 - 0.06 0 1.50 - 0.08

Grapefruit, all 0 0 0 0 0 0 0 0 0 0 0.50 0.50

Hay 0.16 0.16 0 0.77 1.14 0.73 0 0.80 0.87 0 0.77 0.79

Hybrid poplar 1.00 1.00 0 0 0 0 0 0 0 0 0.50 0.50

Miscanthus 1.00 1.00 0 0 0 0 0 0 0 0 0.50 0.50

Oats 0.69 0.69 0 1.45 1.13 1.67 0 - 0.18 0.91 0 1.45 - 0.12

Oranges, all 0 0 0 0 0 0 0 0 0 0 0.50 0.50

Potatoes 0 0 0 0 0 0 0 0 0 0 0.50 0.50

Rice 0.40 0 0 0.93 0 0.60 0 0 0 0 0.93 0.40

Rye 0 0 0 0 0 0 0 0 0 0 0.50 0.50

Silage 0.16 0.16 0 0.77 1.14 1.00 0 0.80 0.87 0 0.77 0.79

Sorghum, all 0.96 0.96 0 1.60 1.22 0.54 0 0.76 0.70 0 1.60 1.05

(continued)
6-8

Table 6-2. Input Elasticities with Respect to Changes in Yield (continued)

Crop

NitrogenPhosphorusManagementChemicalsSeedsCustom OperationsMarketingCapitalRepairsInsuranceTotal PesticidesTotal Fuels

Soybeans 0.80 0.80 0 1.69 0 0.52 0 1.67 0.34 0 1.69 1.66

Sugarbeets 0.85 0.85 1.00 1.00 0.80 0.95 0 0 1.00 1.00 1.00 0.50

Sugarcane 0.40 0 0.42 0.07 0 0 0 0 1.97 0 0.07 0.50

Switchgrass 1.00 1.00 0 0 0 0 0 0 0 0 0.50 0.50

Tomatoes, all 0 00 0 0 0 0 0 0 0 0 0.50 0.50

Wheat, all 0.90 0.90 0 1.26 0.10 0.89 0 0.84 0.81 0 0 0

Willow 1.00 1.00 0 0 0 0 0 0 0 0 0.50 0.50

6.4.2 Livestock Yields

Just as there are crop yield increases over time incorporated into the model, there are also 

productivity improvements assumed for livestock production based on historical trends using 

USDA data, as shown in Table 6-3. Improvements in this sector typically involve greater 

production per head of livestock.

Table 6-3. Yield Growth for Livestock Products over Time

Agricultural Commodity Units Annual Yield Growth Rate

Broilers 100 lbs. (liveweight) 2.25%

Eggs Dozens (farm level) 0.39%

Feeder pig 100 lbs. (liveweight) 0.48%

Feedlot beef slaughter 100 lbs. (liveweight) 0.78%

Hogs for slaughter 100 lbs. (liveweight) 0.61%

Lamb slaughter 100 lbs. (liveweight) 0.54%

Milk 100 lbs. of raw milk 2.33%

Nonfed beef slaughter 100 lbs. (liveweight) 0.46%

Steer Calves 100 lbs. (liveweight) 0.17%

Stocked heifer calves 100 lbs. (liveweight) 0.17%

Stocker heifer yearlings 100 lbs. (liveweight) 0.17%

Stocked steer calves 100 lbs. (liveweight) 0.17%

Stocked steer yearlings 100 lbs. (liveweight) 0.17%

Turkeys 100 lbs. (liveweight) 1.57%
6-9

6.4.3 Domestic Demand, Export Demand, and Import Supply

FASOMGHG also incorporates exogenous shifts in the domestic and export demand 

curves for agricultural products as well as the import supply curve that take place over time.

4

These values are based on USDA long-term commodity projections and historical trends. 

Table 6-4 summarizes the default assumed annual rates of change in these supply and demand 

curves. 

Table 6-4. Annual Shifts in Agricultural Commodity Import Supply, Domestic Demand, 

and Export Demand Curves

Agricultural Commodity Domestic Demand Export Demand Import Supply

American cheese 0.72% NA NA

Barley 2.23% 0.75% - 0.07%

Butter 0.72% NA NA

Canola 0.30% NA NA

Canola meal NA 0.10% 1.50%

Canola oil 0.06% 0.10% 17.43%

Chicken 0.75% 0.77% NA

Corn 0.26% 0.50% 7.53%

Cottage cheese 0.72% NA NA

Cotton 0.53% 1.00% 0.96%

Cream 0.72% NA NA

Dairy calves 0.27% NA NA

Eggs 0.64% 0.77% 0.96%

Evaporated condensed milk 0.72% NA NA

Fed beef 0.75% 1.00% - 0.16%

Fluid milk, low-fat 0.72% NA NA

Fluid milk, whole 0.72% NA NA

Grapefruit, fresh NA 0.04% NA

Hay 0.69% NA NA

Ice cream 0.72% NA NA

Nonfat dry milk 0.72% NA NA

Nonfed beef 0.75% 0.70% 1.29%

(continued)

 

4

Supply and demand are price-elastic. These shifts are referring to movement in the entire relevant supply or 

demand curve and then the quantity supplied or demanded will be determined by moving along the curves until 

reaching equilibrium simultaneously in all markets. 
6-10

Table 6-4. Annual Shifts in Agricultural Commodity Import Supply, Domestic Demand, 

and Export Demand Curves (continued)

Agricultural Commodity Domestic Demand Export Demand Import Supply

Oats 0.00% NA 0.39%

Oranges, fresh 3.70% 1.91% NA

Orange juice 0.12% NA 0.60%

Other cheese 0.72% NA NA

Pork 0.71% 1.60% 1.36%

Potatoes 0.50% NA NA

Refined sugar 1.06% 0.56% - 0.89%

Rice 0.50% 1.00% 2.89%

Silage 0.69% NA NA

Skim milk 0.72% NA NA

Sorghum 0.50% 0.50% NA

Soybean meal NA 0.10% 1.50%

Soybean oil 0.06% 0.10% 17.43%

Soybeans 0.30% 0.30% 2.65%

Sugarbeets 0.02% NA NA

Sugarcane 0.03% NA NA

Tomatoes, fresh 1.66% 0.49% 0.90%

Tomatoes, processing 0.95% 1.08% 4.30%

Turkey 0.73% NA NA

Wheat, durum 0.64% 0.56% NA

Wheat, hard red spring 0.48% 0.51% NA

Wheat, hard red winter 0.54% 0.36% NA

Wheat, soft red winter 0.54% 0.36% NA

Wheat, soft white 0.54% 0.56% NA

Wheat NA NA 3.52%

6.4.4 Timber Yields

Harvest age is allowed to vary and 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 
6-11

location, forest type, management intensity class [MIC]) by age cohort. The data used are 

derived largely from the U.S. Forest Service RPA ATLAS modeling system as applied in the 

2005 RPA timber Assessment Update (Haynes et al., 2007).

There are currently no exogenous increases in timber yields incorporated into the model.5

However, once a cohort is harvested in FASOMGHG, the land can be regenerated and could 

move into a more intensively managed forest type with higher growth rates if prospective returns 

are favorable. Regeneration yields are the potential yields for regenerated stands by region, land 

suitability class, owner, forest type, site class, management intensity class, and age cohort. Upon 

regeneration, FASOMGHG assigns a new MIC. Timber growth by MIC differs by region, forest 

type, site class, and age cohort. For key timber producing regions in the PNWW and South, the 

model includes a spectrum of MIC options ranging from passive (minimal) management to 

intensive regimes.

6.4.5 Bioenergy Feedstock Conversion Rates

FASOMGHG 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. 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 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. 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.

 

5 The model does include exogenous trends in the efficiency of wood and fiber processing in both solid wood 

manufacture (lumber, plywood, OSB) and pulp and paper.
6-12

Baseline values for ethanol yield for all cellulosic feedstocks were based on data from a 

study conducted by NREL (Tao and Aden, 2008). 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. We assume this maximum feasible level is reached in the 2020 to 2025 

model period. After reaching that maximum yield, yields are assumed to remain constant at that 

level in subsequent 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. 

Table 6-5 summarizes renewable fuel feedstock conversion rates in FASOMGHG for all 

potential biofuels feedstocks in 2020 to 2025 and provides an index of conversion rates relative 

to the levels used in the base year. 

Table 6-5. Biofuels Feedstock Conversion Rates over Time

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, all 5 types 2.56 gallons/bu 100

Biodiesel

Soybean oil 0.13 gallons/lb 100

canola oil 1.01 gallons/gallon 100

Corn oil 1.02 gallons/gallon 100

Edible tallow 0.13 gallons/lb 100

Nonedible 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
6-13

Energy sorghum 92.30 gallons/dry ton 128.4

Hybrid poplar 101.50 gallons/dry ton 128.4

Miscanthus 92.30 gallons/dry ton 128.4

Switchgrass 92.30 gallons/dry ton 128.4

Willow 101.50 gallons/dry ton 128.4

(continued)
6-14

Table 6-5. Biofuels Feedstock Conversion Rates over Time (continued)

Feedstock 2022 Yield (gallons/unit)

2022 Index Value 

(2002=100)

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, L. and A. Aden. November 2008. Technoeconomic Modeling to Support the EPA Notice of Proposed 

Rulemaking (NOPR).

For electricity production, assumed technology improvements are such that less feedstock 

is required over time to produce the same electricity output. This is true for both coal and 

biomass feedstocks. Electricity generation using coal or any of the biomass feedstocks included 

in FASOMGHG is assumed to have technological improvements over time at an assumed rate of 

a 5% reduction in feedstock required for every 5-year model period after 2010 until needing only 

75% as much feedstock in the 2035 model period as in the 2010 model period. Feedstock 

requirements are then assumed to remain constant at 75% of 2010 requirements for all model 

periods after 2035.

6.4.6 Bioenergy Processing Costs

Finally, FASOMGHG makes adjustments to the processing costs assumed for cellulosic 

ethanol over time. Because production of starch- and sugar-based ethanol, biodiesel, and 

electricity are assumed to be mature technologies, there are no adjustments made to the plantlevel processing costs for those technologies.6 For cellulosic ethanol, on the other hand, there are 

assumed to be major advances in processing technology between the 2005 model period and the 

2020 model period that will greatly reduce processing costs. Based on analyses presented in the 

RIA for the RFS2 program, processing costs were assumed to decline from starting values of 

$3.29 per gallon for cellulosic ethanol processing in the 2000 and 2005 model periods to $2.30 in 

the 2010 model period (30% reduction in processing costs), $0.82 in the 2015 model period 

(75% reduction), and $0.66 in the 2020 and all subsequent model periods (80% reduction).

 

6As noted above, there are assumed to be continued improvements in feedstock conversion rates to electricity that 

require less feedstock and tend to reduce production costs over time, but plant-level processing costs to generate 

a given amount of electricity are assumed to remain constant. For starch- and sugar-based ethanol, there are 

assumed to be no improvements in feedstock conversion rates (because plants are already operating very close to 

the theoretical maximum yields for these feedstocks) or processing costs, but crop yield improvements will tend 

to lower costs of production over time as average hauling distances required for a given quantity of feedstock 

decline. 
7-1

SECTION 7

GHG ACCOUNTING 

As discussed in Section 2.7, FASOMGHG incorporates detailed accounting for GHGs

emitted from and sequestered by forestry and agricultural activities and land use change in the 

United States, including the dynamics of carbon sequestration in forests, soils, and wood 

products. In addition, the model tracks GHG emission changes in selected other sectors related to 

actions in the forest and agricultural sectors. In this section, we provide additional information on 

the GHG accounting calculations used within the model for GHG emissions, carbon 

sequestration, and bioenergy feedstock production (see Table 2-13 for a list of all GHG accounts 

incorporated within FASOMGHG). In addition, we discuss the GHG mitigation strategies 

available within the model. 

7.1 GHG Emissions

FASOMGHG quantifies GHG emissions produced in the forestry and agricultural 

sectors. These emissions primarily arise from fossil fuel-related processes in both sectors (e.g., 

energy consumption), livestock production, fertilization, and rice cultivation. However, they also 

come from other smaller sources, such as residue burning. In the subsections below, we describe 

the sources of emissions included in the model by GHG type. 

7.1.1 Carbon Dioxide Emissions

The major sources of CO2 emissions in the model are the direct use of fossil fuels and the 

use of energy-intensive agricultural inputs (e.g., fertilizer, pesticides), where FASOMGHG 

accounts for GHG emissions associated with production of these inputs. Key assumptions used 

in calculating these emissions within the model are described in the subsections below. 

Use of Fossil Fuels

Energy is a key input in crop production. FASOMGHG includes data on both direct 

energy use in crop production and indirect energy use associated with input production (e.g., 

fertilizer and pesticide production) within the crop budgets.

1 Tables 7-1 and 7-2 summarize data

for on-farm diesel fuel and gasoline use in crop production by crop, irrigation status, and market 

region. 

 

1 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. 
7-2

Table 7-1. Diesel Fuel Usage (gallons/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)

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

Canola NA 4.0 4.1 NA 5.4 NA 7.1 NA NA 4.3

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, energy 9.6 11.4 NA 9.2 NA 2.6 10.2 11.2 9.5 10.1

Sorghum, grain 8.9 11.4 NA 8.8 NA 2.6 10.3 10.4 9.0 10.8

Sorghum, sweet 9.6 11.4 NA 9.2 NA 2.6 10.2 11.2 9.5 10.1

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, durum 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 NA 11.1 NA NA NA 4.5 10.7 NA NA 9.1

Wheat, soft red winter 10.1 NA 7.6 7.3 NA NA NA 6.7 7.9 NA

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 NA NA 10.4 NA NA NA

Oats NA 4.1 NA NA 4.9 4.0 5.2 NA NA NA

Potatoes 28.1 28.1 18.6 11.0 31.1 32.1 30.2 8.8 15.1 8.8

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, energy NA 11.7 NA NA NA 3.1 7.6 7.3 NA 8.8

Sorghum, grain NA 11.7 NA NA NA 3.1 7.9 7.0 NA 9.7

(continued)
7-3

Table 7-1. Diesel Fuel Usage (gallons/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004) (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Sorghum, sweet NA 11.7 NA NA NA 3.1 7.6 7.3 NA 8.8

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

Tomatoes, fresh 13.1 NA 9.1 14.2 NA 13.1 NA 17.3 13.1 13.1

Tomatoes, processing 6.7 NA 6.7 6.8 NA 12.3 16.0 NA 6.7 NA

Wheat, durum 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 NA NA 6.3

Wheat, soft red winter NA NA NA NA NA NA NA 9.6 NA NA

Wheat, soft white NA NA NA NA 11.3 NA 6.3 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

Table 7-2. Gasoline Usage (gallons/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)

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

Canola NA NA NA NA NA NA 0.6 NA NA 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

Rye 3.7 NA NA 3.7 1.9 NA NA 3.7 3.7 NA

Silage NA NA NA 0.3 NA 0.0 NA 3.8 1.6 0.0

Sorghum, energy NA NA NA NA NA 3.7 NA 3.1 3.7 NA

Sorghum, grain NA NA NA NA NA 3.7 NA 3.2 3.7 NA

Sorghum, sweet NA NA NA NA NA 3.7 NA 3.1 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

(continued)
7-4

Table 7-2. Gasoline Usage (gallons/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004) (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Wheat, hard red winter NA 0.6 NA NA NA 3.7 NA NA NA NA

Wheat, soft red winter 3.7 NA NA 3.7 NA NA NA 3.7 3.7 NA

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

Potatoes NA NA NA NA NA NA 2.3 NA NA NA

Rice 3.7 NA NA NA NA 3.7 NA 3.7 NA NA

Silage NA 0.0 NA NA NA NA 0.0 NA NA NA

Sorghum, energy NA NA NA NA NA 3.7 NA NA NA NA

Sorghum, grain NA NA NA NA NA 3.7 NA NA NA NA

Sorghum, sweet NA NA NA NA NA 3.7 NA 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

Tomatoes, fresh 7.5 NA NA NA NA 7.5 NA 6.6 7.5 7.5

Tomatoes, processing NA NA NA NA NA 3.7 NA NA NA NA

Wheat, durum 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 red winter NA NA NA NA NA NA NA 3.7 NA NA

Wheat, soft white NA NA NA NA 1.4 NA NA NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

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 some regions. For the rest of the irrigated crops that have private energy use 

for water pumping, electricity is the assumed energy source. Tables 7-3 and 7-4 summarize 

assumed electricity and natural gas use included within the FASOMGHG crop budgets.
7-5

Table 7-3. Electricity Usage (kWh/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

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 NA NA 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

Potatoes 10.6 10.6 6.4 2.9 4.4 3.8 10.2 3.0 3.0 3.0

Silage NA 11.3 NA NA NA NA 3.5 NA NA 10.6

Sorghum, energy NA 6.0 NA NA NA 7.9 3.1 5.8 NA 4.4

Sorghum, grain NA 7.5 NA NA NA 7.9 3.1 5.9 NA 4.5

Sorghum, sweet NA 6.0 NA NA NA 7.9 3.1 5.8 NA 4.4

Soybeans NA 7.4 NA NA NA NA NA 7.5 NA 4.3

Sugarbeets NA NA NA NA NA NA 4.2 NA NA 4.5

Wheat, durum NA 6.2 NA NA NA 3.7 NA NA NA NA

Wheat, hard red spring NA 6.2 NA NA 5.3 NA 3.7 NA NA NA

Wheat, hard red winter NA 2.4 NA NA NA 5.2 2.1 NA NA 4.8

Wheat, soft red winter NA NA NA NA NA NA NA 6.8 NA NA

Wheat, soft white NA NA NA NA 5.3 NA 5.3 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

Table 7-4. Natural Gas Usage (1,000 cu ft/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Irrigated

Rice 20.8 NA NA NA NA NA NA 17.9 NA NA

Sugarbeets 25.9 9.6 25.9 NA NA NA 3.8 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There are only two irrigated crops that are assumed to use natural gas in irrigation water 

pumping in FASOMGHG.
7-6

In addition to the energy usage included directly in the crop budgets, energy use for grain 

drying is calculated in FASOMGHG 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.2

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. 

GHG emissions from direct agricultural use of fossil fuels in crop production are 

calculated in FASOMGHG by multiplying the quantity of each energy category used (diesel fuel, 

gasoline, natural gas, electricity, and propane) by the GHG content of that fuel. Diesel, gasoline, 

natural gas, and propane are assumed to have emissions factors of 10.2 kg CO2/gallon, 8.3 kg 

CO2/gallon, 54.5 kg CO2/1,000 cu ft, and 5.7 kg CO2/gallon, respectively, in all U.S. regions. 

Because of the widely varying mixture of energy sources used for electricity generation across 

U.S. regions, region-specific estimates of GHG emissions were estimated as shown in Table 7-5. 

FASOMGHG does not include direct energy inputs in livestock production budgets. 

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. 

In addition to the agricultural emissions from energy use, the model also includes CO2

emissions from the use of gasoline and diesel in forestry management operations, including 

forest establishment and planting costs, treatment of competing herbaceous plants, fertilization, 

precommercial thinning, and other periodic management activities. Annual fuel use varies by 

region, forest type, and management practices. Stand establishment is assumed to require diesel 

use between 0.35 and 18.2 gallons/acre and gasoline use between 0 and 0.91 gallons/acre. In all

regions where these options are available, precommercial thinning is assumed to require 1.21 

gallons of diesel and 0.27 gallons of gasoline per acre, treatment with herbicides is assumed to 

require 0.93 gallons of gasoline per acre, and fertilization is assumed to require 0.5 gallons of 

gasoline per acre. Periodic management activities are assumed to require between 0.11 and 0.34 

gallons of gasoline per acre. CO2 emissions from forestry use of fossil fuels are calculated using 

the emissions factors for diesel and gasoline presented above. 

 

2 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
7-7

Table 7-5. Regional GHG Emissions from Electricity Generation (g CO2eq/kWh)

FASOMGHG Subregion CO2 CH4 N2O

Alabama 594.21 0.21 1.93

Arizona 476.27 0.15 0.95

Arkansas 585.13 0.19 1.76

CaliforniaN 276.69 0.04 0.94

CaliforniaS 276.69 0.04 0.94

Colorado 875.43 0.28 1.79

Connecticut 426.38 0.11 2.45

Delaware 830.07 0.22 1.73

Florida 630.49 0.17 2.11

Georgia 621.42 0.22 1.81

Idaho 13.61 0.03 1.13

IllinoisN 526.17 0.17 1.15

IllinoisS 526.17 0.17 1.15

IndianaN 943.47 0.31 2.01

IndianaS 943.47 0.31 2.01

IowaW 852.75 0.28 1.94

IowaCent 852.75 0.28 1.94

IowaNE 852.75 0.28 1.94

IowaS 852.75 0.28 1.94

Kansas 762.04 0.24 1.57

Kentucky 911.72 0.31 1.97

Louisiana 535.24 0.11 1.32

Maine 385.55 0.26 7.95

Maryland 621.42 0.20 1.66

Massachusetts 580.60 0.15 2.45

Michigan 716.68 0.24 2.05

Minnesota 689.46 0.24 2.21

Mississippi 585.13 0.16 1.86

Missouri 834.61 0.27 1.77

Montana 648.64 0.22 1.52

Nebraska 635.03 0.21 1.34

Nevada 689.46 0.19 1.26

New Hampshire 308.44 0.13 2.42

New Jersey 322.05 0.08 1.08

New Mexico 916.26 0.28 1.84

(continued)
7-8

Table 7-5. Regional GHG Emissions from Electricity Generation (g CO2eq/kWh) 

(continued)

FASOMGHG Subregion CO2 CH4 N2O

New York 390.09 0.08 1.14

North Carolina 562.45 0.19 1.48

North Dakota 1016.05 0.32 2.07

OhioNW 816.47 0.27 1.83

OhioS 816.47 0.27 1.83

OhioNE 816.47 0.27 1.83

Oklahoma 780.18 0.21 1.55

Oregon 127.01 0.03 0.47

Pennsylvania 571.53 0.19 1.50

Rhode Island 476.27 0.04 0.95

South Carolina 376.48 0.14 1.28

South Dakota 362.87 0.12 0.74

Tennessee 589.67 0.20 1.48

TxHiPlains 662.24 0.14 1.08

TxRolingPl 662.24 0.14 1.08

TxCntBlack 662.24 0.14 1.08

TxEast 662.24 0.14 1.08

TxEdPlat 662.24 0.14 1.08

TxCoastBe 662.24 0.14 1.08

TxSouth 662.24 0.14 1.08

TxTranspec 662.24 0.14 1.08

Utah 875.43 0.29 1.88

Vermont 13.61 0.04 1.35

Virginia 526.17 0.18 1.93

Washington 113.40 0.04 0.52

West Virginia 898.11 0.30 1.93

Wisconsin 743.89 0.25 1.94

Wyoming 975.22 0.32 2.07

Agricultural Input Use

In addition to the direct use of energy on-farm in crop production, FASOMGHG

calculates indirect energy requirements associated with the use of energy-intensive inputs. The 

crop budgets included in FASOMGHG include data on input use that varies by crop, 

management practices, and region. There is often considerable variation in the inputs used per 
7-9

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.

Both CO2 emissions from fertilizer production (discussed in this section) and N2O 

emissions resulting from nitrogen fertilizer use (see Section 7.1.2) are calculated in the model 

based on the quantity of fertilizer applied. Tables 7-6 through 7-8 summarize the use of nitrogen, 

phosphorus, and potassium, respectively, based on average application per acre by crop and 

irrigation status for FASOMGHG market regions where agricultural crops are produced, 

assuming no residue removal.

3

Table 7-6. Nitrogen Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Dryland

Barley 66.2 45.5 63.3 75.1 59.9 41.1 43.3 71.9 72.6 45.4

Canola NA 64.6 65.2 NA 110.0 NA 61.0 NA NA 89.3

Corn 130.8 103.6 103.8 91.8 NA NA 79.3 125.5 109.5 94.9

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

Miscanthus 34.0 34.0 34.0 34.0 NA NA 34.0 34.0 34.0 34.0

Oats 50.1 33.7 44.4 47.5 49.1 59.7 30.3 39.1 53.0 36.3

Rye 41.0 35.4 44.1 46.5 47.5 NA 30.7 41.0 49.4 31.0

Silage 137.7 75.7 123.7 173.0 179.9 143.0 87.9 172.7 180.7 167.3

Sorghum, energy 74.6 57.0 NA 67.0 NA 58.0 34.1 88.1 50.3 57.1

Sorghum, grain 74.6 57.0 NA 67.0 NA 58.0 34.1 88.1 50.3 57.1

Sorghum, sweet 74.6 57.0 NA 67.0 NA 58.0 34.1 88.1 50.3 57.1

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 100.0 100.0 100.0 100.0 NA NA 100.0 100.0 100.0 100.0

Wheat, durum NA 52.9 51.0 NA NA NA 41.0 NA NA NA

Wheat, hard red spring NA 70.9 84.7 NA 66.0 NA 48.5 NA NA NA

Wheat, hard red winter NA 50.3 NA NA NA 60.0 37.3 NA NA 58.6

Wheat, soft red winter 71.7 NA 68.1 73.8 NA NA NA 76.9 71.9 NA

(continued)

 

3 Residue collection increases fertilizer requirements due to the removal of nutrients. FASOMGHG increases 

fertilizer application rates for crop production with residue removal (e.g., for use in bioenergy production) based 

on GREET default values described in Wu, Wang, and Huo (2006). 
7-10

Table 7-6. Nitrogen Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004) (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Wheat, soft white NA NA NA NA 57.7 NA 72.7 NA NA NA

Willow 173.5 NA 173.4 123.3 NA NA NA NA 161.0 NA

Irrigated

Barley NA 63.4 NA NA 85.0 92.4 88.5 NA NA NA

Corn NA 137.3 NA NA 214.3 197.1 129.5 141.8 NA 185.8

Cotton NA NA NA NA NA 109.3 117.7 115.2 NA 118.2

Grapefruit, fresh NA NA NA NA NA 80.0 80.0 NA 128.0 210.0

Grapefruit, processing NA NA NA NA NA 80.0 80.0 NA 128.0 210.0

Hay NA 173.4 NA NA NA NA 171.2 NA NA NA

Oats NA 48.0 NA NA 96.0 96.0 51.8 NA NA NA

Oranges, fresh NA NA NA NA NA 100.0 100.0 NA 148.0 210.0

Oranges, processing NA NA NA NA NA 100.0 70.0 NA 148.0 210.0

Potatoes 102.0 102.0 92.7 84.2 219.1 120.0 135.8 78.0 125.0 78.0

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

Sorghum, energy NA 96.2 NA NA NA 95.0 77.1 82.0 NA 85.2

Sorghum, grain NA 96.2 NA NA NA 95.0 77.1 82.0 NA 85.2

Sorghum, sweet NA 96.2 NA NA NA 95.0 77.1 82.0 NA 85.2

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

Tomatoes, fresh 104.0 NA 95.0 113.3 NA 295.0 NA 146.2 184.0 144.0

Tomatoes, processing 250.0 NA 250.0 155.9 NA 250.0 192.9 NA 160.0 NA

Wheat, durum NA 118.7 NA NA NA 98.0 176.8 NA NA NA

Wheat, hard red spring NA 114.8 NA NA 122.0 124.8 NA NA NA

Wheat, hard red winter NA 69.0 NA NA NA 117.0 81.5 NA NA 91.3

Wheat, soft red winter NA NA NA NA NA NA NA 91.0 NA NA

Wheat, soft white NA NA NA NA 67.2 NA 135.8 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.
7-11

Table 7-7. Phosphorus Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Dryland

Barley 10.4 5.3 21.4 17.8 5.0 10.0 8.3 9.6 10.0 11.7

Canola NA 20.0 20.0 NA NA NA 32.1 NA NA 23.0

Corn 22.9 27.6 40.1 36.2 NA NA 18.5 37.3 32.6 16.5

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

Miscanthus 6.0 6.0 6.0 6.0 NA NA 6.0 6.0 6.0 6.0

Oats 15.3 10.0 10.6 15.0 13.0 14.8 10.2 14.4 15.0 14.7

Rye 10.0 10.0 11.9 15.0 12.5 NA 10.6 13.3 15.0 10.0

Silage 25.0 12.4 23.4 24.7 20.5 20.0 10.3 25.0 24.1 22.5

Sorghum, energy 18.8 11.1 NA 15.0 NA 15.0 11.5 23.7 21.2 18.6

Sorghum, grain 18.8 11.1 NA 15.0 NA 15.0 11.5 23.7 21.2 18.6

Sorghum, sweet 18.8 11.1 NA 15.0 NA 15.0 11.5 23.7 21.2 18.6

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, durum NA 14.5 5.0 NA NA NA 5.0 NA NA NA

Wheat, hard red spring NA 24.5 25.9 NA 10.0 NA 20.4 NA NA NA

Wheat, hard red winter NA 17.7 NA NA NA 5.0 8.2 NA NA 18.1

Wheat, soft red winter 10.0 NA 9.9 10.0 NA NA NA 10.0 10.0 NA

Wheat, soft white NA NA NA NA 5.2 NA 11.6 NA NA NA

Irrigated

Barley NA 10.0 NA NA 15.8 25.0 14.9 NA NA NA

Corn NA 31.3 NA NA 41.3 25.0 26.6 25.0 NA 39.7

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 NA 15.0 NA NA 25.0 25.0 17.3 NA NA NA

Potatoes 35.0 35.0 29.5 25.0 137.2 50.0 172.9 25.0 35.0 25.0

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, energy NA 35.0 NA NA NA 30.0 26.4 35.0 NA 35.0

Sorghum, grain NA 35.0 NA NA NA 30.0 26.4 35.0 NA 35.0

Sorghum, sweet NA 35.0 NA NA NA 30.0 26.4 35.0 NA 35.0

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)
7-12

Table 7-7. Phosphorus Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004) (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

Tomatoes, fresh 45.0 NA 35.0 50.0 NA 275.0 NA 41.7 45.0 35.0

Tomatoes, processing 70.0 NA 70.0 70.0 NA 76.0 35.0 NA 70.0 NA

Wheat, durum NA 32.5 NA NA NA 10.0 24.1 NA NA NA

Wheat, hard red spring NA 39.0 NA NA 5.3 NA 25.0 NA NA NA

Wheat, hard red winter NA 24.7 NA NA NA 10.0 14.5 NA NA 19.1

Wheat, soft red winter NA NA NA NA NA NA NA 15.0 NA NA

Wheat, soft white NA NA NA NA 6.1 NA 21.7 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

Table 7-8. Potassium Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Dryland

Barley 24.9 5.0 9.1 14.1 NA 5.0 0.2 37.1 40.0 4.4

Canola NA 30.0 30.0 NA NA NA 9.3 NA NA NA

Corn 61.8 7.4 54.6 46.5 NA NA 5.3 33.2 54.1 NA

Cotton 83.5 55.0 NA NA NA NA NA 64.7 82.0 NA

Hay 12.6 NA NA 64.8 NA NA 2.8 56.9 69.8 19.3

Miscanthus 15.0 15.0 15.0 15.0 NA NA 15.0 15.0 15.0 15.0

Oats 14.0 3.1 NA 5.0 3.0 NA 5.0 NA 21.5 NA

Rye 5.0 2.0 NA 5.0 2.5 NA 5.0 NA 30.5 NA

Silage 17.1 NA 10.0 52.6 3.0 NA NA 53.1 56.5 NA

Sorghum, energy 10.0 10.1 NA 10.0 NA NA 10.4 11.2 33.3 NA

Sorghum, grain 10.0 10.1 NA 10.0 NA NA 10.4 11.2 33.3 NA

Sorghum, sweet 10.0 10.1 NA 10.0 NA NA 10.4 11.2 33.3 NA

Soybeans 20.9 3.1 22.6 13.2 NA NA NA 22.1 24.3 NA

Sugarbeets 70.0 NA NA 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

(continued)
7-13

Table 7-8. Potassium Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region, 

Base Period (2000-2004) (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Wheat, durum NA 0.6 20.0 NA NA NA NA NA NA NA

Wheat, hard red spring NA 1.9 14.7 NA NA NA 4.1 NA NA NA

Wheat, hard red winter NA 1.7 NA NA NA NA 7.8 NA NA 0.8

Wheat, soft red winter 37.5 NA 20.0 32.3 NA NA NA 23.7 35.1 NA

Wheat, soft white NA NA NA NA 0.7 NA 2.6 NA NA NA

Irrigated

Barley NA 10.0 NA NA NA 19.8 5.2 NA NA NA

Corn NA 6.9 NA NA 3.7 NA 6.3 NA NA NA

Cotton NA NA NA NA NA NA 2.5 72.4 NA NA

Hay NA NA NA NA NA NA 8.2 NA NA NA

Oats NA 5.0 NA NA 10.0 NA 6.5 NA NA NA

Potatoes 60.0 60.0 27.2 NA 15.2 10.0 148.1 40.0 78.0 40.0

Rice NA NA NA NA NA NA NA 0.1 NA 30.0

Sorghum, energy NA 25.0 NA NA NA NA 6.9 NA NA NA

Sorghum, grain NA 25.0 NA NA NA NA 6.9 NA NA NA

Sorghum, sweet NA 25.0 NA NA NA NA 6.9 NA NA NA

Soybeans NA 3.1 NA NA NA NA NA 23.3 NA NA

Tomatoes, fresh 55.0 NA 50.0 100.0 NA NA NA 62.8 85.0 65.0

Tomatoes, processing 150.0 NA 150.0 150.0 NA NA NA NA 150.0 NA

Wheat, durum NA 1.4 NA NA NA NA NA NA NA NA

Wheat, hard red spring NA 3.0 NA NA NA NA 3.8 NA NA NA

Wheat, hard red winter NA 3.4 NA NA NA NA 6.2 NA NA 0.3

Wheat, soft red winter NA NA NA NA NA NA NA 40.0 NA NA

Wheat, soft white NA NA NA NA 0.8 NA 4.9 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

FASOMGHG calculates energy use in fertilizer production and associated GHG 

emissions as a function of the quantity of fertilizer applied. Fertilizer production is assumed to 

require the energy inputs shown in Table 7-9. 
7-14

Table 7-9. Energy Requirements for Fertilizer Manufacturing (GJ per ton of nutrient)

Type of Energy Nitrogen Phosphorus Potassium

Natural gas 58.86 20.53 9.59

Electricity 9.81 31.39 9.18

Fuel oil 7.06 20.00 7.65

Steam 7.96 14.80 7.65

After applying emissions factors,

4 GHG emissions for the use of nitrogen, phosphorus, 

and potassium range from 1.6 to 2.8 kg CO2eq/lb N, 1.4 to 5.2 kg CO2eq/lb P, and 0.5 to 1.6 

CO2eq/lb K across FASOMGHG subregions. 

The three primary categories of pesticides that are tracked in FASOMGHG are 

herbicides, insecticides, and fungicides. Energy use in pesticide manufacturing is estimated in 

FASOMGHG based on the quantity of active ingredients applied per acre multiplied by an 

assumed energy requirement per ton of active ingredients that varies across chemicals based on 

expert consultations and energy requirements reported in Green, Hartley, and West (1987). 

Average values for energy requirements in the model are 214.93 GJ/metric ton, 245.06 GJ/metric 

ton, and 356.39 GJ/metric ton for herbicides, insecticides, and fungicides, respectively. We 

assume that 70% of the energy requirements are met with natural gas and the other 30% with 

electricity. FASOMGHG crop budgets contain hundreds of different active ingredients. We 

summarize the use of pesticides as the combined total use of pesticides (summed across 

herbicides, insecticides, and fungicides) in pounds of active ingredients per acre by crop and 

region in Table 7-10. Estimated emissions per acre vary widely but are generally less than for 

fertilizer.

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

those crop budgets in FASOMGHG. Although FASOMGHG 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 7-11 presents average lime use per acre for crops that use lime as an 

input in the model.

 

4 Emissions factors for natural gas and electricity are consistent with those reported in Section 7.1.1.1 (for electricity 

use, only CO2 emissions are included in fertilizer emissions calculations), with appropriate unit conversions. The 

emissions factor used for fuel oil is 74.9 kg CO2eq/GJ. There are no GHG emissions calculated for steam. 
7-15

Table 7-10. Pesticide Usage (pounds of active ingredients/acre) by Crop, Irrigation Status, 

and Region, Base Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Dryland

Barley 0.05 0.06 0.05 0.05 0.04 0.04 0.06 0.05 0.05 0.05

Canola NA 1.21 1.21 NA 0.53 NA 1.21 NA NA 1.33

Corn 3.58 2.82 2.82 3.58 NA NA 2.62 2.72 3.40 2.86

Cotton 6.66 7.01 NA NA NA NA NA 9.61 7.19 5.98

Hay 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Hybrid poplar 0.02 0.02 0.02 0.03 0.03 NA NA 0.03 0.03 0.03

Miscanthus 0.02 0.02 0.02 0.03 NA NA 0.03 0.03 0.03 0.03

Oats 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Silage 3.17 1.85 2.53 1.63 1.14 1.14 1.12 1.78 1.42 1.37

Sorghum, energy 0.14 0.35 NA 0.14 NA 0.14 0.14 0.14 0.14 0.11

Sorghum, grain 0.14 0.35 NA 0.14 NA 0.14 0.14 0.14 0.14 0.11

Sorghum, sweet 0.14 0.35 NA 0.14 NA 0.14 0.14 0.14 0.14 0.11

Soybeans 1.88 2.02 1.69 0.73 NA NA NA 1.96 1.18 0.39

Sugarbeets 65.02 NA 64.71 NA NA NA NA NA NA NA

Switchgrass 0.02 0.02 0.02 0.03 NA NA 0.03 0.03 0.03 0.03

Wheat, durum NA 1.31 0.19 NA NA NA 0.24 NA NA NA

Wheat, hard red spring NA 1.17 0.95 NA 0.20 NA 1.47 NA NA NA

Wheat, hard red winter NA 0.33 NA NA NA 0.14 0.49 NA NA 0.32

Wheat, soft red winter 0.31 NA 0.35 0.28 NA NA NA 0.36 0.40 NA

Wheat, soft white NA NA NA NA 0.72 NA 0.66 NA NA NA

Willow 0.02 NA 0.02 0.03 NA NA NA NA 0.03 NA

Irrigated

Barley NA 0.06 NA NA 0.04 0.04 0.06 NA NA NA

Corn NA 2.89 NA NA 2.25 2.25 2.54 2.94 NA 2.81

Cotton NA NA NA NA NA 7.94 7.92 8.03 NA 5.95

Grapefruit, fresh NA NA NA NA NA 9.84 16.31 NA 32.51 16.40

Grapefruit, processing NA NA NA NA NA 9.84 16.31 NA 32.51 16.40

Hay NA 0.02 NA NA NA NA 0.03 NA NA NA

Oats NA 0.03 NA NA 0.03 0.03 0.03 NA NA NA

Oranges, fresh NA NA NA NA NA 7.00 3.66 NA 4.34 2.61

Oranges, processing NA NA NA NA NA 7.00 3.66 NA 4.34 2.61

Potatoes 5.91 6.44 8.98 8.56 7.85 6.70 5.11 6.70 6.70 6.70

Rice 1.85 NA NA NA NA 1.85 NA 1.35 NA 2.01

Silage NA 2.51 NA NA NA NA 1.10 NA NA 1.44

Sorghum, energy NA 0.35 NA NA NA 0.14 0.14 0.11 NA 0.11

Sorghum, grain NA 0.35 NA NA NA 0.14 0.14 0.11 NA 0.11

Sorghum, sweet NA 0.35 NA NA NA 0.14 0.14 0.11 NA 0.11

Soybeans NA 2.11 NA NA NA NA NA 1.82 NA 0.39

(continued)
7-16

Table 7-10. Pesticide Usage (pounds of active ingredients/acre) by Crop, Irrigation Status, 

and Region, Base Period (2000-2004) (continued)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Sugarbeets 65.02 65.53 64.90 NA 32.46 118.57 33.82 NA NA 65.02

Tomatoes, fresh 3.38 NA 12.79 6.02 NA 10.99 NA 3.74 16.20 7.53

Tomatoes, processing 6.39 NA 19.50 4.56 NA 14.38 6.39 NA 6.39 NA

Wheat, durum NA 1.32 NA NA NA 0.17 0.39 NA NA NA

Wheat, hard red spring NA 1.21 NA NA 0.20 NA 0.56 NA NA NA

Wheat, hard red winter NA 0.32 NA NA NA 0.14 0.35 NA NA 0.35

Wheat, soft red winter NA NA NA NA NA NA NA 0.36 NA NA

Wheat, soft white NA NA NA NA 0.82 NA 0.66 NA NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

Table 7-11. Lime Usage (tons of lime/acre) by Crop, Irrigation Status, and Region, Base 

Period (2000-2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW

Dryland

Barley 0.04 NA NA 0.00 NA NA NA 0.06 0.07 NA

Corn 0.11 NA NA 0.26 NA NA NA NA 0.03 NA

Cotton NA NA NA NA NA NA NA 0.10 0.12 NA

Hay 0.02 NA NA 0.02 NA NA NA 0.11 0.10 NA

Silage NA NA NA 0.26 NA NA NA 0.09 0.08 NA

Soybeans 0.11 NA NA 0.26 NA NA NA NA 0.04 NA

Sugarbeets 0.14 NA NA NA NA NA NA NA NA NA

Wheat, durum NA NA 0.20 NA NA NA NA NA NA NA

Wheat, hard red spring NA NA 0.20 NA NA NA NA NA NA NA

Wheat, hard red winter 0.14 NA 0.00 0.51 NA NA NA 0.03 0.07 NA

Irrigated

Cotton NA NA NA NA NA NA NA 0.09 NA NA

Note: Values are presented only for those crop/irrigation combinations with nonzero usage in at least one region in 

the base period. NA indicates not applicable (i.e., those crops were not cultivated under that irrigation status in 

that FASOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation 

combination specified). There is no dryland grapefruit, orange, potato, rice, sugarcane, or tomato production or 

irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG.

7.1.2 Methane Emissions

In addition to CO2 emissions associated with energy use, there are several important 

agricultural sources of the non-CO2 GHGs methane and nitrous oxide. In this section, we 

describe the GHG accounting framework applied to the agricultural sources of CH4 tracked 
7-17

within the model, including livestock manure management, livestock enteric fermentation, rice 

cultivation, and agricultural residue burning. Section 7.1.3 describes the FASOMGHG 

methodology used for tracking N2O emissions.

Livestock Manure Management

Under anaerobic conditions, such as in large-scale livestock waste storage facilities, 

methane is produced during decomposition of the manure. FASOMGHG calculates the average 

CH4 emissions per head by livestock type 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 (EPA, 2005b). Average emissions are calculated to be 0.42 kg CO2eq/head for 

poultry, 23.31 kg CO2eq/head for sheep, 35.49 kg CO2eq/head for beef cattle, 214.20 kg 

CO2eq/head for swine, and 1,612.17 kg CO2eq/head for dairy cattle. The average values per head 

are then multiplied by the number of livestock in each livestock category in each model period to 

estimate the CH4 emissions associated with manure management. In the absence of policy 

incentives for adopting mitigation options, emissions from this source are typically affected only 

by the number of animals in each livestock category. 

Enteric Fermentation 

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. 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 (EPA, 2005b). Average emissions are calculated to be 254.52 kg 

CO2eq/head for sheep, 947.31 kg CO2eq/head for beef cattle, 23.52 kg CO2eq/head for swine, 

and 2,898.84 kg CO2eq/head for dairy cattle. 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. As for manure management, emissions from this source are 

typically affected only by the number of animals in each livestock category unless there are 

specific incentives for adopting mitigation options in place. 

Rice Cultivation

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. 
7-18

Methane is then produced through anaerobic decomposition of soil organic matter by 

methanogenic bacteria.

FASOMGHG 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, 

FASOMGHG currently assumes that the only method available for reducing methane emissions 

from rice cultivation in the United States 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

(EPA, 2005b). Emission factors used range from 1,783.44 kg CO2eq/acre in the California North

subregion to 4,375.00 kg CO2eq/acre in the rice-producing subregions in Texas. 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. 

Agricultural Residue Burning

FASOMGHG assumes that a certain fraction of fields used to grow certain crops (corn, 

rice, soybeans, sugarcane, and wheat) is burned each year after harvest, which results in CH4

(and N2O) emissions. These emissions are calculated based on the average methane emissions 

per acre associated with agricultural residue burning, 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 (EPA, 2005b). Emissions from this source range from 

3.16 kg CO2eq/acre to 25.35 kg CO2eq/acre across crops and FASOMGHG subregions for the 

subset of crops assumed to have these emissions.

7.1.3 Nitrous Oxide Emissions 

Until recently, FASOMGHG used an explicit representation of direct and indirect N2O

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

factors.

5 However, recent research suggests N2O emissions may be significantly higher than 

those estimated based on current IPCC guidance (Crutzen et al., 2008), 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 

 

5

See the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Chapter 11, N2O emissions from 

managed soils.
7-19

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

that incorporate the entire nitrogen (N) cycling process, we worked with the Natural Resource 

Ecology Laboratory (NREL) at Colorado State University to conduct further analyses of N2O 

emissions and provide updated emissions data. These updated and more detailed data have been 

used in recent model applications in place of the IPCC defaults.

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

of land use and cropping trends and GHG emissions in the FASOMGHG assessment, including 

the following:

§ Direct N2O emissions based on DAYCENT6simulations with an accounting of all 

nitrogen inputs to agricultural soils, including mineral N fertilizer application levels 

and timing (spring, fall, split spring, fall-spring), organic amendments, nitrification 

inhibitors, symbiotic N fixation, asymbiotic N fixation, crop residue N, and 

mineralization of soil organic matter. In addition, N2O emissions from cropping 

activities and grasslands were modeled as a function of geophysical factors, including 

the sand content of soils, average soil moisture, and temperature. 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 FASOMGHG 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, and a number of relevant crop 

management variables (described below) based on a full accounting of all nitrogen inputs.

FASOMGHG 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 (EPA, 2010b). 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 FASOMGHG region. The data 

 

6 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.
7-20

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 FASOMGHG to 

generate estimates of N2O emissions. 

Nitrogen Fertilizer, Crop Residues, and Symbiotic N Fixation

To calculate direct N2O emissions from N inputs to croplands and grassland, 

FASOMGHG uses DAYCENT data on direct emissions per acre to calculate emissions for the 

corresponding crop/management/region or grass/management/region combination in 

FASOMGHG. Estimates of emissions per acre from DAYCENT are defined as a function of 

(1) continuous management variables, (2) indicator management variables, and (3) continuous 

geophysical variables. The NREL modeling team has provided individual statistical meta-models 

expressing direct and indirect N2O emissions as a function of the aforementioned sets of 

variables and their interactions. 

To calculate direct N2O emissions from N inputs to croplands and grasslands (primarily 

fertilizer use), FASOMGHG uses DAYCENT data on direct emissions per acre by crop and 

FASOMGHG region. Estimates of emissions per acre from DAYCENT are defined as a function 

of the following variables:

§ Continuous management variables

– N mineral fertilizer application rate: This variable is the natural log of the amount 

of fertilizer applied per unit of area.

– N mineral fertilizer application rate squared: The fertilizer application rate 

squared, meant to capture nonlinear effects of N application on N2O emissions.

– Organic soil amendments: Application rate of organic soil amendment per unit of 

area.

– Organic soil amendments squared: The organic soil amendment rate squared, 

meant to capture nonlinear effects of soil amendments.

– Sum of N fertilizer and organic soil amendments applied: Sum of fertilizer and 

organic soil amendment application. This variable is used only in logit models 

used to determine the probability that leaching will occur in dry regions. 

– 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.
7-21

§ Indicator (0/1) management 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.

7

– N fertilizer application timing: This variable represents the timing of the N 

fertilizer application as a set of indicator variables. There are specific variables for 

fertilizer applied in (1) the fall only, (2) in the fall and spring, or (3) twice in the 

spring. A single spring application is assumed to be the base condition for this 

variable and is the excluded case. N fertilizer application is subdivided across 

applications in cases where there is more than one application of fertilizer 

according to this variable (e.g., split spring application).

– 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 the crop is not irrigated.

– 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). 

– Nitrogen inhibitor applied: This variable indicates whether nitrification inhibitors 

are applied. A value of 1 means a nitrogen inhibitor is applied, while a value of 0 

means it is not applied. 

§ Continuous geophysical variables: Average values for the climate and soil variables 

listed below were obtained by taking county level values for the corresponding 

variables, dividing by county-level harvested acreage estimates, and forming a 

weighted average value for each of the 63 FASOM regions by mapping from county 

to subregional level. 

– Average soil moisture index: An index value describing long-term soil moisture 

in a region. Soil moisture is particularly important in calculating N leaching.

 

7

For a set of indicator (dummy) variables used in regressions that include an intercept, one must be excluded to 

avoid introducing perfect multicollinearity between the categories (as the sum of the indicator variables would be 

equal to 1 for every observation if all were included). Model estimates with all included indicator variables equal 

to 0 for a category correspond to the estimated values for the excluded (base) case. 
7-22

– Average temperature index: This variable represents the long-term average 

temperature from weather stations.

– Sand fraction: This variable represents the average sand content of soils. 

In addition to the variables listed above, first-order interactions between the variables 

were tested and those that were statistically 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 FASOMGHG crop budgets. 

Changes in FASOMGHG direct N2O emissions from croplands result primarily from 

changes in crop acreage, reduced N fertilizer application (with 85% and 70% of base application 

levels modeled), or changes in the crop mix. Typically, mitigation options aimed at reducing 

N2O emissions are only adopted in the model under policies creating incentives for reducing 

these emissions (e.g., GHG mitigation incentives providing compensation for reduced N2O 

emissions). Ongoing work is expanding the range of mitigation options available within 

FASOMGHG to include other N management options that can reduce N2O emissions such as 

nitrification inhibitors and changes in the timing of fertilizer application using the available 

DAYCENT data. Application timing, in particular, can have a large impact on N2O emissions. 

While these model updates are currently in process, FASOMGHG will soon be capable of 

exploring a variety of N management options for reducing N2O emissions beyond simply 

reducing N use or cropped acreage. 

Indirect Emissions from Volatilization and Leaching

In addition to direct N2O emissions from croplands, there are indirect emissions 

associated with volatilization and leaching, which can be substantial. 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 FASOMGHG, 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 N2ON/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. 
7-23

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 N2O 

when the leached nitrogen fertilizer undergoes the process of nitrification or denitrification. In 

FASOMGHG, 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 FASOMGHG regions where the long-term 

average annual precipitation level is less than 70% of the potential evapotranspiration level. 

Agricultural Residue Burning

As mentioned above, FASOMGHG 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. This results in emissions ranging from 0.02 kg CO2eq/acre to 2.26 kg CO2eq/acre across 

regions and crops for the subset of crops where some fields are burned after harvest. 

Histosols

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 the absence of data to assign emissions to crop/region combinations, the 

total emissions for histosols in the United States are evenly divided among all cropland (0.04 kg 

CO2eq/acre).

Livestock Manure Management

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. 

To calculate N2O emissions from manure management, FASOMGHG calculates the average 

N2O emissions per head by livestock type. 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 
7-24

GHG inventory for 1990–2003 (EPA, 2005b). Average emissions per head are 6.2 kg 

CO2eq/head for swine, 21.7 kg CO2eq/head for sheep, 71.3 kg CO2eq/head for beef cattle, and 

421.6 kg CO2eq/head for dairy cattle. The average values per head are multiplied by the number 

of livestock in each livestock category in each model period to estimate the N2O emissions 

associated with manure management. 

7.2 Carbon Sequestration

In addition to GHG emissions, the forest and agricultural sectors provide a substantial 

GHG sink through carbon sequestration in soils, vegetation, and wood products.8 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 vegetation, forest floor litter 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 limited loss in forest soil carbon, 

and 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 FASOMGHG in Section 7.2.1.

9

Another carbon sequestration category is carbon storage in agricultural soils. The level of 

sequestration per acre varies by region, land use, and tillage and irrigation practices with 

dynamic adjustments taking place over time following a change in land use, tillage, and/or 

irrigation. In general, the conversion of grasslands (pasture or CRP land) to cropland results in a 

reduction in carbon sequestration per acre. The dynamics of soil carbon sequestration accounting 

associated with land conversion or changes in management are described in Section 7.2.2. 

7.2.1 Forest Carbon Accounting 

Forest carbon accounting in FASOMGHG follows the general structure of the early 

FORCARB model developed by the U.S. Forest Service. Subsequent revisions of this model are 

used in the periodic aggregate assessments of forest carbon sequestration by USEPA. Tree 

carbon (live and standing dead) is the largest forest carbon pool and is modeled using 

relationships between growing stock volume (from the yield tables decribed above) and biomass 

derived from Smith et al. (2006). Because the standing tree biomass in younger stands is poorly 

predicted from yield tables, we incorporate an adjustment for stand age consistent with Smith et 

 

8

Sequestration in FASOMGHG refers to carbon stored for more than one year. Thus, carbon stored in annually 

cultivated crops is not included. 

9

See Adams et al. (2005) for additional detail on the FASOMGHG forest carbon accounting procedures. 
7-25

al. (2006). Estimates of standing tree biomass are converted to estimates of standing tree carbon 

using the standard conversion factor of 0.5. 

Soil carbon is the second-largest forest carbon pool. Treatment of soil carbon follows 

Birdsey (1996a, 1996b) and Heath, Birdsey, and Williams (2002). Little change in soil carbon 

occurs if forests are regenerated immediately after harvest. As a result, FASOMGHG 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 FASOMGHG region/forest type. This 

assumption is generally consistent with the ages at which steady-state levels of soil carbon are 

achieved in Birdsey (1996a, 1996b). Afforested land coming from crop or pasture use start with 

the initial soil carbon value for that land/region combination reported by the Century Model.10

The land then accumulates carbon until reaching the steady-state value for forests of the type 

planted in the region where afforestation takes place (the steady state is assumed to be reached at 

the minimum harvest age in FASOMGHG for that region/forest type). 

Forest floor litter constitutes the third largest forest carbon storage pool but is much 

smaller than tree or soil carbon pools. Smith and Heath (2002) developed a model estimating 

forest floor carbon mass, and it forms the basis for forest floor carbon estimates in FASOMGHG. 

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 (Smith, 2004). 

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 (EPA, 2003). 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). 

When a stand is harvested, large woody debris in logging residue, if retained on the site under 

the scenario being simualted, is added to the forest floor carbon stock of the ensuing regeneration 

stand and assumed to decay at the rates described in Smith and Heath (2002).

Understory vegetation comprises the smallest component of total carbon stock and 

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

 

10 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.
7-26

understory carbon is a fixed fraction of live tree carbon and uses ratios reported in U.S. EPA

(2003) as the basis for these calculations. 

Biomass Removals 

When timber is harvested, FASOMGHG tracks the fate of the carbon in both products 

and residues on and off site. Figure 7-1 summarizes the disposition of carbon following harvest. 

To calculate carbon in harvested logs, cubic feet of roundwood (the units in which timber is 

quantified in the model) is converted into metric tons of carbon using factors reported in Skog 

and Nicholson (2000). 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 as described above. Table 7-

12 presents an example of carbon disposition over time based on FASOMGHG accounting 

procedures (Depro et al., 2008).
7-27

Figure 7-1. 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. 

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 
7-28

Table 7-12. Example of Disposition Patterns of Harvested Wood by Region and Harvest 

Type, 100-Year Period: Southeast

Region Type Product

Disposition

Years after Harvest

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 the site are converted into three types of outputs through 

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

fate of each of these outputs is discussed below.

FASOMGHG contains the following 13 wood and paper products:

§ softwood sawlogs for export
7-29

§ 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 FASOMGHG tracks the fate of 

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

leaving the product (Figure 7-2). 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 (Skog and Nicholson, 2000) and is shown in Table 7-13. 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 2 years, whereas carbon stored in wood used for single-family 

homes has a half-life of 100 years. These values from Skog and Nicholson (2000) were mapped 

to FASOMGHG wood and paper product categories, weighting by wood and paper product use 

in various end uses.

As shown in Figure 7-2, carbon leaving the product pool moves to either the emissions or 

landfill pools.11 Skog and Nicholson (2000) assumed that 67% of carbon leaving the wood 

 

11 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 
7-30

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. 

Figure 7-2. 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. 

 

permanently sequestered. The rest is eventually released to the atmosphere as emissions based on an assumed 

half-life of 14 years. 

Total Carbon in Wood and Paper 

Products

Emission

Carbon

Remaining in

Product

Landfill

Landfill

(Permanent)

Landfill

(Temporary)

Carbon

Leaving

Product
7-31

Table 7-13. 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 and 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. 

In addition, FASOMGHG 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 

region based on Smith et al. (2001). 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 to produce energy at mills. For 

fuel wood, FASOMGHG assumes that fuel wood burned in the sawtimber and pulpwood 

production process is used in lieu of fossil fuels.
7-32

Effects of Changes in Forest Area on Carbon Storage

In FASOMGHG, 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 from forestry to one of these other uses, two carbon pools associated with the land 

are tracked12 (Figure 7-3):

§ residual forest floor carbon

§ soil carbon (in agricultural or in developed use)

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 (2002).

Figure 7-3. Disposition of On-Site Carbon after Deforestation

 

12 In addition, there is a third carbon pool associated with the harvested timber, in the form of wood products, which 

is described later in this section.

Time

Years in Agriculture or Developed Land Use

Residual Forest Floor

Soil Carbon

Total On-Site 

Carbon

Tons of 

C/acre
7-33

The approaches used in FASOMGHG 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 decline to be consistent with Century Model data on agricultural 

soil carbon for the appropriate category of agricultural land. In the case of timberland switching 

to developed land uses, the soil carbon levels are assumed to remain constant at the steady-state 

value of the minimum harvest age.

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.

7.2.2 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.13

§ 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.14 For sites receiving irrigation, the increased yields are expected to 

increase biological activity and hence soil carbon sequestration throughout the year. 

 

13 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. 

14 All pastureland and CRP land in FASOM are assumed to be produced in dryland systems. 
7-34

FASOMGHG incorporates these differences in soil carbon storage within tables of 

soil carbon storage that vary by irrigation status. 

§ 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 FASOMGHG, 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.

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 FASOMGHG, 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 7-4).15

 

15 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 7-4 refer to the 

change over time from the initial state to a new equilibrium under new management/land use conditions. 
7-35

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 7-4. Percentage Adjustment over Time to New Soil Carbon Equilibrium 

Following Change in Land Use or Management 

Because movement of soil carbon sequestration towards equilibrium levels is not 

constant over time, FASOMGHG yields nonuniform 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) (West and Post, 2002). Soil carbon per acre may increase or decrease depending on 

the land use change or change in land management taking place. For instance, Figure 7-5

presents examples of changes in soil carbon for the Northern California region in FASOMGHG. 

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.
7-36

-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 7-5. Change in Soil Carbon for FASOMGHG Northern California Region for 

Selected Changes in Land Use and Management 

To reflect the timing of changes in soil carbon, FASOMGHG 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 the model period 2020-2024 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 base period and 2020-2024 based on the 

assumed rate of saturation over time. For analogous reasons, changes in land use in the 2020-

2024 model period would continue to affect soil carbon emissions calculated in the model for the 

following 25 years. 
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7.3 GHG Accounting for Bioenergy Production

FASOMGHG accounts for changes in emissions associated with bioenergy production

and use in lieu of fossil fuels, including emissions associated with hauling and processing 

bioenergy feedstocks. Emissions associated with land use change and changes in agricultural 

production processes as related to bioenergy production are captured within the appropriate 

emissions categories described earlier in this section. 

For replacement of gasoline with ethanol, the avoided CO2 emissions in FASOMGHG 

are 5.49 kg CO2eq/gallon of gasoline replaced on an energy-equivalent basis (assuming that 1.6 

gallons of ethanol are required to replace 1 gallon of gasoline). The analogous value for diesel is 

a reduction in emissions of 6.50 kg CO2eq/gallon of diesel replaced. Bioelectricity is assumed to 

reduce CO2 emissions by 625,515.27 metric tons CO2eq per 100MW generation capacity (using 

9.198 TBtu of feedstock per year) for nonresidue crops and 628,998.13 metric tons CO2eq per 

100MW generation capacity for residue crops relative to the use of coal. In addition to these 

emissions replacement factors, FASOMGHG also accounts for emissions associated with 

hauling and processing these feedstocks explicitly in the bioenergy GHG accounting (as well as 

emissions from growing feedstocks and land use change captured under other emissions 

categories). Emissions from processing sugar-based feedstocks are 1.00 kg CO2eq/gallon and 

those from starch-based feedstocks are 2.00 kg CO2eq/gallon. For feedstocks used to produce 

cellulosic ethanol, emissions from processing are between 0.55 kg CO2eq/gallon and 1.00 kg 

CO2eq/gallon. For biodiesel production, emissions from processing are assumed to be 0.60 kg 

CO2eq/gallon for canola oil, corn oil, and soybean oil, and 0 kg CO2eq/gallon for waste fats and 

oils. Emissions from processing biomass feedstocks are assumed to be 69.45 metric tons CO2eq 

per 100MW generation capacity. Hauling emissions vary by feedstock and region depending on

the average distance required to transport the required quantity of a given feedstock for use in 

bioenergy. 

In addition to changes in CO2 emissions, there are also net changes in non-CO2 emissions 

during processing and combustion when bioenergy feedstocks are used in place of fossil fuels

that are included in FASOMGHG. Based on calculations of the net change in emissions, using 

ethanol in place of gasoline reduces CH4 emissions by 0.0113 kg CO2eq/gallon and N2O 

emissions by 0.1758 kg CO2eq/gallon, using biodiesel instead of diesel reduces CH4 emissions 

by 0.1871 kg CO2eq/gallon and N2O emissions by 0.0924 kg CO2eq/gallon, and using biomass 

feedstocks instead of coal increases CH4 emissions by 68,250.00 metric tons CO2eq per 100MW 

generation capacity and N2O emissions by 833.13 metric tons CO2eq per 100MW generation 

capacity. 
7-38

7.4 Mitigation Strategies 

GHG mitigation opportunities in forestry and agriculture include afforestation (tree 

planting), forest management (e.g., altering harvest schedules or management inputs), forest 

preservation/avoided deforestation, agricultural soil tillage practices, grazing management, use of 

bioenergy feedstocks instead of fossil fuels, 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 from energy use can be reduced 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 list of the major

categories of GHG mitigation strategies included in FASOMGHG is shown in Table 7-14, along 

with the GHGs directly affected by each. 

In addition to quantifying GHG emissions and sinks, FASOMGHG also can distinguish 

the unique time dynamics and accounting issues of carbon sequestration mitigation 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.

Major categories of GHG mitigation are briefly described below. 
7-39

Table 7-14. GHG Mitigation Strategies

GHG Affected

Source/Sink Mitigation Strategy CO2 CH4 N2O

Forestry

Afforestation Sequestration X

Reforestation Sequestration X

Timberland management Sequestration X

Harvested wood products Sequestration X

Agriculture

Manure management Emission X X

Crop mix alteration Emission, sequestration X X

Crop fertilization alteration Emission, sequestration X X

Crop input alteration Emission X X

Crop tillage alteration Emission, sequestration X X

Grassland conversion Sequestration X

Irrigated/dry land conversion Emission X X

Rice acreage Emission X X X

Enteric fermentation Emission X

Livestock system change Emission X X

Livestock herd size Emission X X

Bioenergy

Conventional ethanol Fossil fuel substitution X X X

Cellulosic ethanol Fossil fuel substitution X X X

Biodiesel Fossil fuel substitution X X X

Bioelectricity Fossil fuel substitution X X X

Afforestation, Avoided Deforestation, Forest Management, and Wood Products

The following fundamental mechanisms within forestry allow GHG mitigation: 

§ Expansions in forested area either through conversion of agricultural lands into forest 

uses (afforestation) or by avoiding conversion of forest areas into agriculture (avoided 

deforestation).

§ Forest management manipulations in the form of lengthened timber rotations or 

altered species choices that provide higher levels of carbon sequestration over a given 

period of time.
7-40

§ Expanded usage of more intensive (higher input) management. For example, one can 

employ improved tree varieties, fertilization, thinning, partial cutting, and a number 

of other management alternatives to increase forest growth rates and carbon 

sequestration per acre.

§ Conversion of harvested wood into longer-lived wood products.

§ Usage of forest management techniques that require less fossil fuel usage.

§ Usage of harvested wood to potentially reduce fossil fuel usage by employing it as a 

biofuel feedstock for electricity or biofuels generation.

The principal GHG implications related to the above forestry manipulations involve

changes in net sequestration and net emissions, altering carbon and carbon dioxide. In addition to 

the net mitigation pathways listed above, the net GHG emissions from forests can be altered by 

choice of regions and sites where management is altered. For example, one could choose to 

introduce forests on highly productive, fast-growing sites to obtain higher yields and faster rates 

of carbon sequestration. 

Agricultural Land Management

Other actions that can potentially influence GHG emissions in FASOMGHG are 

decisions regarding land management. Some of these actions have already been discussed, such 

as changes in soil carbon sequestration due to tillage changes, but below we briefly summarize 

additional key land management decisions that can influence energy use and GHG emissions in 

FASOMGHG. 

Tillage and Residue Burning

As noted earlier in this section, 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 FASOMGHG. Thus, reductions in the reliance on agricultural burning will reduce 

those emissions in the model. 
7-41

Crop Rotations

One potential response that farmers may have to changes in relative crop prices is to alter 

rotation patterns to produce more of the crops with increases in their relative prices. 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. However, FASOMGHG 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) associated with historical rotation patterns on a regional level. 

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 versus 

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 FASOMGHG. 

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 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.16 Although FASOMGHG 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. FASOMGHG 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.

 

16 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. 
7-42

Rice Cultivation

Methane emissions result from anaerobic conditions used for producing rice. While there 

are water management and other potential options for reducing these emissions (e.g., intermittent 

drainage, shallow flooding), these options may be labor-intensive and have generally been 

considered more appropriate for mitigation of rice cultivation emissions in Asia. There are no 

explicit mitigation options included in FASOMGHG for this source [due to lack of national data 

on successful mitigation techniques and relatively small magnitude of U.S. emissions from this 

category]; mitigation comes only from reducing the acreage of U.S. rice cultivation.

Livestock Management

Emissions mitigation options for manure management (e.g., systems that capture methane 

from liquid manure management) or enteric fermentation (e.g., changes in feed, use of bovine 

somatotropin [bST]) included within FASOMGHG enter the model solution with sufficient 

incentives for reducing non-CO2 emissions.
8-1

SECTION 8

MODEL OUTPUT 

After a model simulation is completed, FASOMGHG generates a set of output tables in 

both HTML and Excel formats. The analyst conducting a model run can readily alter the set of 

standard predefined tables generated,1but the standard outputs include detailed information on 

net welfare, commodity markets, land use, bioenergy, GHG emissions, and trade for the baseline 

and all policy scenarios.

Output tables are currently organized into nine different output files:

§ national-level model summary results;

§ scenario description;

§ regional-level agricultural summary results;

§ detailed agricultural results;

§ bioenergy results;

§ regional forestry summary results;

§ detailed forestry results;

§ GHG accounting results; and

§ additional land use results.

Results are generally provided in both levels and percentage differences. For a subset of 

results, outputs are provided for each individual model period as well as being summarized as an 

annuity calculated from the 2010 model period through the final period selected for the model 

run. 

The national-level model summary results files currently contains 30 tables summarizing 

model results for:

§ national welfare and its distribution across consumers and producers and across the 

United States, Canada, and ROW for the markets included within the model;

§ national income derived from GHG payments;

 

1

In addition, custom output tables are created as needed for particular model applications. 
8-2

§ national index numbers for aggregated agricultural activities, including prices, 

production, imports, and exports;

§ national agricultural commodity prices;

§ national agricultural commodity production;

§ national livestock feed use by feed type;

§ national agricultural exports by product;

§ index numbers for international trade and production for international regions;

§ national land use summary and land transfers by land use category;

§ national and regional agricultural sector consumer and producer surplus;

§ national crop acres by crop;

§ regional land value and use by land type; and

§ comparison of selected FASOMGHG outputs to USDA baseline projections.

This HTML file also contains a list of all tables included in all output files and hyperlinks 

to the table locations. 

The scenario description file contains information describing:

§ specific information on model settings for a given run (e.g., whether agriculture 

and/or forestry sectors are active, end date for model run, constraints incorporated for 

crop and livestock mix and/or timing of phasing out of those constraints); and

§ alternatives selected for a given model run for energy price projections, RFS scenario, 

constraints on CRP reversion, export market responsiveness, carbon price, technical 

progress scenario, and potentially other scenario assumptions defined by the analyst.

In the regional-level agricultural summary results file, there are 24 tables containing 

model output on:

§ total producer surplus at the regional level;

§ regional use of individual available feedstocks for bioenergy production;

§ producer surplus for crop production at the regional level;

§ producer surplus for livestock production at the regional level;

§ producer surplus associated with afforestation at the regional level;
8-3

§ regional agricultural commodity production;

§ indices of regional agricultural production by aggregated category;

§ regional crop acres by crop, and

§ GHG payments for afforestation by region.

The detailed agricultural results file contains 20 tables with model output for:

§ national agricultural activity summary with more detailed information on land use 

(including by irrigation status and by tillage method), as well as yields, commodity 

production, and environmental impacts (e.g., water use in irrigation; applications of 

nitrogen, phosphorus, and potassium and runoff; erosion), as well as agricultural 

production and processing;

§ national agricultural commodity disappearance, showing commodity prices and 

production levels along with a disaggregation of the market balance across production 

and imports and usage for consumption, feed use, processing use, and exports;

§ national use of primary commodities in secondary processing by production process;

§ total national use of primary commodities in secondary processing;

§ regional and national water, energy, and fertilizer use by crop and irrigation status;

§ national estimates of indirect and induced farm activity and nonfarm activity induced 

by agricultural production and revenue based on input-output relationships; and 

§ regional and national number of head of livestock by type.

In the bioenergy results file, there are six tables summarizing model output for:

§ national use of agricultural commodities in bioenergy production by feedstock and 

bioenergy process, along with bioenergy yields per unit of feedstock;

§ national production of bioenergy in quantity of bioenergy produced by feedstock;

§ regional bioenergy production by process; and

§ regional bioenergy feedstock use by process.

The regional forest summary results file contains a single large table summarizing:

§ forestland area by remaining forest that has not been harvested during the model run 

and new forestland (from either reforestation or afforestation) by region;

§ average age of standing forests by region;
8-4

§ harvested, reforested, and afforested areas by region;

§ average rotation length for softwoods and hardwoods by region and forest type;

§ planting and harvest by ownership by region;

§ planting and harvest by MIC by region;

§ forest inventory levels for hardwoods and softwoods by region;

§ forest inventory levels by ownership by region;

§ regional prices of logs and manufactured forest products by product by region;

§ output of logs and manufactured products by region by region; 

§ regional consumption of forest products by product; and

§ imports and exports of forestry products by product and trade region

In the detailed forestry results file, there are two large tables summarizing the information 

presented in the regional forest summary results file at the national level. 

The GHG accounting results file contains 29 tables summarizing GHG emissions in a 

variety of ways, including

§ total national and regional stocks of GHG emissions and sequestration over time by 

GHG accounting category;

§ annual GHG flux, i.e., annual average changes in emissions or sequestration for each 

model period at the national and regional levels by GHG accounting category2;

§ changes in national and regional GHG stock or flux under policy scenarios relative to 

the baseline by GHG accounting category;

§ GHG emissions calculated for individual crops by category of GHG emission;

§ regional and national summaries of the use of GHG mitigation strategies and levels of 

specific agricultural and forest activities that impact GHG emissions; and

§ detailed regional and national information on the dynamic changes in soil 

sequestration and shifts of soil carbon between accounting categories as land moves 

between land use categories.

 

2

Stock and flux measures calculated are consistent with one another. The annual flux for a given period can be 

calculated as the difference in GHG stocks between the starting and ending years to get the change in emissions 

over that period, then dividing by five to calculate an annual average. 
8-5

These GHG accounting results are presented in a number of different formats and 

different levels of aggregation. 

Finally, there is a table providing some additional agricultural model results, focusing on:

§ national pasture use by pasture category and livestock type; and

§ national disposition of cropland, pasture, and forestland over time, tracking area in 

production, idled, and moving into and out of alternative land use categories 
9-1

SECTION 9

SELECTED ENVIRONMENTAL, RESOURCE, AND POLICY ANALYSIS 

APPLICATIONS

A core objective of FASOMGHG is to provide a tool for simulating the effects of various 

policy scenarios on a wide range of economic and environmental outcomes in the U.S. forest and 

agricultural sectors. In this section, we briefly discuss the types of policies and issues that can be 

evaluated using the model and present selected recent applications, focusing on climate and 

bioenergy-related studies. 

9.1 Types of Policies and Issues that can be Evaluated using FASOMGHG

FASOMGHG remains very capable of analyzing numerous other issues and providing 

unique insights into potential impacts. Given its breadth of coverage and depth of sectoral detail, 

FASOMGHG can be used to evaluate a wide range of policy issues. Almost any type of policy 

affecting resource use or terms of trade in forestry and agriculture can, in principle, be evaluated 

using the model. In addition to climate and bioenergy-related applications, examples include 

trade policy, nonclimate environmental policy, technical change, and resource conservation 

issues. Nonetheless, we will focus primarily on climate and bioenergy applications here as those 

areas have been the primary focus of recent model development and application.1

At its inception as FASOM, the primary focus of the combined forest and agricultural 

modeling system was to evaluate carbon sequestration policies in support of wide-ranging 

assessments of climate change mitigation strategies. As the model has evolved into 

FASOMGHG, a much more complete characterization of GHG mitigation options available in 

forestry and agriculture has been added. More recently, the model has undergone major 

modifications and enhancements to improve the treatment of bioenergy production, including the 

addition of numerous potential feedstocks; an entirely new definition of the U.S. land base 

providing greater disaggregation to better capture bioenergy competition for land; review and 

updating of key data on crop yields, production and processing costs, and feedstock conversion 

rates; updated technological progress rates; improved information on DG production and feed 

replacement; and the addition of corn fractionation as a processing pathway, among others. 

Moreover, the model is well-suited to work via integration with other models to assess the 

impacts of climate change on forestry and agriculture. While FASOMGHG predecessor models 

have been applied to bioenergy and climate policy studies since the late 1970s and late 1980s, 

 

1

For additional information on previous applications, see Adams et al. (2005). 
9-2

respectively, the relevance of the model in these areas has become even stronger over the past 

few years. A brief discussion of selected recent applications in these areas is presented below. 

9.2 Selected Recent Applications 

The following subsections briefly review selected model applications to climate or 

bioenergy-related issues within the past five years. For more complete and periodically updated 

references to related publications and policy applications of FASOMGHG, refer to Dr. McCarl’s 

Web site (http://agecon2.tamu.edu/people/faculty/mccarl-bruce/). 

9.2.1 GHG Mitigation

FASOMGHG has been used extensively by EPA in recent years to analyze climate 

change bills in both the U.S. Senate and House of Representatives. Bills analyzed by EPA using 

the model include the Climate Stewardship and Innovation Act of 2007 (S. 280), the Low 

Carbon Economy Act of 2007 (S. 1766), the Lieberman-Warner Climate Security Act of 2008 

(S. 2191), and the Waxman-Markey American Clean Energy and Security Act of 2009 (H.R. 

2454) and related discussion draft, and the American Power Act of 2010.

2

The model was also used for an EPA (2005) report, Greenhouse Gas Mitigation Potential 

in U.S. Forestry and Agriculture, which explored the potential for these sectors to play a role in 

U.S. GHG mitigation and provided substantial detail on the potential effects on land use, 

competitiveness of alternative mitigation opportunities by region and over time under different 

carbon price paths, and total sequestration and mitigation potential. Additional ASMGHG and 

FASOMGHG applications exploring mitigation potential within the past 5 years include Lee, 

McCarl, and Gillig (2005); Pattanayak et al. (2005); Schneider and McCarl (2005, 2006); Lee et 

al. (2006, 2007); McCarl (2006a); McCarl and Sands (2007); Schneider et al. (2007); Alig et al. 

(2010); Beach et al. (2009a, 2009b); Daigneault et al. (2009); and Baker et al. (2010). There are 

plans for another such report and application of FASOMGHG in the future.

As indicated above, the original impetus for FASOM was to evaluate carbon 

sequestration from land use change between agriculture and forestry and changes in forest 

management practices. By keeping carbon accounts across the landscape, the model can quantify 

the amount of carbon sequestered (stored) in forest and agricultural ecosystems under baseline 

(business-as-usual) conditions and under specific policies to induce additional sequestration. The 

difference in carbon sequestered with the policies and without the policies (baseline) can be 

viewed as a measure of GHG mitigation within these sectors. 

 

2 These analyses are available at http://www.epa.gov/climatechange/economics/economicanalyses.html.
9-3

However, as has been described above, FASOM’s evolution to FASOMGHG has 

substantially expanded the model’s ability to examine GHG mitigation strategies in forestry and 

agriculture. FASOMGHG enhancements (since the original version of FASOM) include:

§ carbon sequestration in agriculture;

§ fossil fuel mitigation through agricultural practices;

§ mitigation of non-CO2 GHGs from agriculture; and

§ reduced fossil fuel emissions through the use of biofuels.

Thus, the model has evolved from one that specialized primarily in carbon sequestration through 

land use change and forestry to one that provides more comprehensive coverage of GHG 

mitigation in forestry and agriculture. 

The coverage and structure of the model provides analytical capabilities to address key 

policy questions, such as the following:

§ What will be the net emissions of GHGs from U.S. forestry and agriculture over time 

if no mitigation policies are put in place (business-as-usual)?

§ How much GHG mitigation can be generated by the U.S. forest and agricultural 

sectors if various economic incentives are put in place?

§ What strategies and gases have the highest mitigation potential and lowest costs?

§ How are GHG mitigation opportunities distributed across regions?

§ How do GHG reductions from mitigation actions change over time, especially given 

the potential for re-releases of carbon sequestered in forests and soils?

§ What are the direct and indirect consequences of mitigation policies that are limited 

in their coverage of activities, regions, or time periods? Indirect consequences could 

include leakage or reversal of GHG reduction benefits.

§ How will changes in land use and management brought about by a GHG mitigation 

incentive program affect the production and prices of forest and agricultural 

commodities? 

§ How will changes in commodity production and prices affect the welfare of 

commodity producers and consumers?

§ What ancillary (non-GHG) environmental effects (e.g., water quality, habitat) could 

occur as a result of GHG mitigation actions on the landscape? Are these co-benefits

or ancillary costs of mitigation strategies?
9-4

Each of the issues identified above have been examined using FASOMGHG, and the 

modeling team continues to update and further develop the model for continued study of these 

and additional emerging research questions. 

In general, recent model applications have examined the potential for agricultural and 

forestry activities to reduce net GHG emissions and provide relatively low-cost mitigation 

opportunities relative to those available from other sectors. Recent FASOMGHG studies have 

generally found that at lower carbon prices and in the near term, forest management is the 

dominant GHG mitigation strategy from the forest and agricultural sectors. At higher carbon 

prices and in the longer term, biofuels and afforestation typically begin to dominate the 

mitigation portfolio. The results of FASOMGHG applications have indicated that mitigation 

from the forest and agricultural sectors may serve as an important near-term bridge that can 

contribute to holding down mitigation costs until technology is developed to significantly reduce 

energy-related emissions at lower costs. 

9.2.2 Bioenergy

While bioenergy production is a potentially important component of GHG mitigation 

strategies, it is also a vital component of energy policy more broadly. As a renewable resource, 

the use of forest and agricultural feedstocks has attracted a great deal of attention both for use in 

electricity generation and in production of liquid transportation fuels. FASOMGHG incorporates 

the costs of bioenergy production for numerous pathways and dynamic adjustments over time, as 

discussed in Sections 5 and 6. 

One major recent application of FASOMGHG to bioenergy policy assessment is Beach 

and McCarl (2010). In this study, we applied FASOMGHG to simulate potential impacts 

associated with the volume requirements specified by RFS2. This study provides considerable 

detail regarding potential effects on commodity prices and quantities, land use, renewable fuel 

by-products, livestock and related industries, international agricultural commodity trade, and 

environmental measures, including information used in life cycle analyses of alternative 

feedstocks as part of the assessment of the net GHG impacts of each individual feedstock under 

consideration for RFS2 eligibility. 

9.2.3 Climate Impacts

The model’s forest and agricultural sector activity and outcomes are driven by the 

biophysical productivity of the landscape. The economic returns to a particular land use or 

management practices are driven by the physical yields that can be generated. Clearly, climate is 

a significant factor in determining these yields in both the forest and agricultural sectors. For 
9-5

instance, geo-specific changes in temperature, precipitation, and CO2 concentrations over time 

(the “greenhouse effect”) can affect the level and distribution of forest and agricultural yield 

potential. FASOMGHG can simulate how farmers and foresters adapt to these climate-driven

yield changes in yield by altering land uses and management practices. Accounting for these 

adaptations, the model can show how changes in climate ultimately impact the economic wellbeing of producers and consumers in the forest and agricultural sectors.

In order for FASOMGHG to simulate future climate change scenarios, it must be 

integrated into a broader framework that includes global circulation models (GCMs) that project 

future climate change and biophysical models that translate climate change to yield changes in 

forestry and agriculture. Such an integration was performed as part of the U.S. National 

Assessment Synthesis Team (2001) report analyzing possible climate change impacts on 

forestry, agriculture, and other sectors, wherein FASOMGHG was linked with GCM scenarios 

from the Hadley Centre (UK) and The Canadian Centre climate models and corresponding 

simulations from biophysical models such as Century (Parton, 1996) and TEM (Melillo et al 

1993). More recently, the model was linked with updated GCM scenarios from the 

Intergovernmental Panel on Climate Change 4th Assessment Report and the Environmental 

Policy Integrated Climate (EPIC) model, which was used to simulate changes in crop yields and 

irrigation water requirements associated with alternative GCMs (Beach et al., 2010). 

FASOMGHG analyses show how future climate change scenarios could affect aggregate 

productivity of U.S. forests and agricultural lands, thereby affecting resource allocation across

and within the sectors, i.e., adaptations such as land use change, crops planted, and changes in 

management practices. The climate-induced changes and the adaptations they engender have 

economic consequences for the sectors in question, affecting the levels of output and prices for 

timber and agricultural commodities and the welfare of producers and consumers in these 

sectors. For instance, under scenarios showing a climate change-induced increase in aggregate 

forest productivity, timber output rises, prices fall, consumers are made better off, and producers 

are made worse off. FASOMGHG can show how these effects vary by region within the United 

States, by climate scenario, and by biophysical model employed for gauging productivity effects. 
10-1

SECTION 10

REFERENCES 

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., 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. and R.W. Haynes. 1980. The 1980 Softwood Timber Assessment Market Model:

Structure, Projections, and Policy Simulations. Forest Science Monograph 22, 64 p.

Adams, D.M. and R.W. Haynes. 1996. The 1993 Timber Assessment Market Model: Structure, 

Projections, and Policy Simulations. General Technical Report PNW-GTR-368. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 

58 p. 

Adams, D. M. and R. W. Haynes (eds.). 2007. Resource and Market Projections for Forest 

Policy Development: Twenty-five Years of Experience with the US RPA Timber 

Assessment. Managing Forest Ecosystems Series #14. Springer Publishers, Dordrecht, 

The Netherlands. 615 p.

Adams, 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.

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, 

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Adams, R.M., J.D. Glyer, B.A. McCarl, and D.J. Dudek. 1988. “The Implications of Global 

Change for Western Agriculture.” Western Journal of Agricultural Economics

13(December):348-356.

Adams, 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. 

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Adams, R.M., B.A. McCarl, K. Segerson, C. Rosenzweig, K.J. Bryant, B.L. Dixon, R. Connor, 

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pp. 19-54. New York: Cambridge University Press. 
10-2

Adams, R.M., C.C. Chen, B.A. McCarl, and D.E. Schimmelpfenning. 2001. “Climate Variability 

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D. Hall and R. Howarth, eds., pp. 115-148. London: JAI Press.

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Welfare: The Case of Acid Deposition.” Canadian Journal of Agricultural Economics

34:3-19. 

Alig, R.J., A. Plantinga, S. Ahn, and J. Kline. 2003. Land Use Changes Involving Forestry for 

the United States: 1952 to 1997, with Projections to 2050. U.S. Forest Service General 

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p.

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. 

Alig, R.J., Latta, G, Adams, D.M., and McCarl, B.A. 2010. “Mitigating greenhouse gases: The 

importance of land base interactions between forests, agriculture, and residential 

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Avila, A.F.D., and R.E. Evenson. 2004. “Total Factor Productivity Growth in Agriculture: The 

Role of Technological Capital.” Paper published by the Economic Growth Center, Yale 

University. 

Baker, J.S., B.A. McCarl, B.C. Murray, S.K. Rose, R.J. Alig, D.M. Adams, G.S. Latta, R.H. 

Beach, and A. Daigneault. (2010). “Net Farm Income and Land Use under a U.S. 

Greenhouse Gas Cap and Trade.” Policy Issues, PI7(April 2010):1-5. 

http://www.aaea.org/publications/policy-issues/PI7.pdf

Banowetz, G.M., A. Boateng, J.J. Steiner, S.M. Griffith, V. Sethi, and H. El-Nashaar. 2008. 

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Baumes, H. 1978. “A Partial Equilibrium Sector Model of U.S. Agriculture Open to Trade: A 

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Beach, R.H., A. Daigneault, B.A. McCarl, and B.C. Murray. (2009a, March). “Modeling 

Alternative Policies for Forestry and Agricultural GHG Mitigation: Allowances vs. 

Offsets.” Presented at Climate Change: Global Risks, Challenges, & Decisions, 

Copenhagen, Denmark, March 10-12, 2009. 
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Beach, R. H., Daigneault, A., McCarl, B. & Rose, S. (2009b, June). Modeling alternative 

policies for forestry and agricultural bioenergy production and GHG mitigation. In 

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