### 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 Aven ue 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

Rob ert 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)
Eri c White (Oregon State University) RTI International 3040 Cornwallis Road Research Triangle Park, NC 27709iii 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

- 37iv

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 Dis counting

................................ ................ 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 Penetrat ion Constraints

................................ ..... 5

- 32v

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, Expor t 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 Em issions

................................ ................................ ..... 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 f

or Bioenergy Production

................................ ................... 7

- 37

7.4 Mitigation Strategies

................................ ................................ ...................... 7

- 38

8 Model Output

................................ ................................ ................................ ............ 8

- 1vi

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 Bioener gy

................................ ................................ ............................. 9

- 4
9.2.3 Climate Impacts

................................ ................................ .................... 9

- 4

10 References

................................ ................................ ................................ .............. 10

- 1vii 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

- 36viii 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 Quantit ies

................................ ....... 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 Comm odities 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 Require ments 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, B ase 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

- 15ix

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 Potent ial 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 Quan tity 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

– 20 04)

................................ ................................ ................................ ............ 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 Liv estock 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 Per iod (2000

- 2004)

................................ ................................ ........................... 7

- 5 7

- 5 Regional GHG Emissions from Electricity Generation (g CO 2 eq/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 Regi on, 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

- 15x

7

- 11 Lime Usage (tons of lime/acre) b y 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

- lif e for Forest Products in End Uses

................................ ................................ 7

- 31 7

- 14 GHG Mitigation Strategies

................................ ................................ ...................... 7

- 391

- 1 SECTION 1
FASOMGHG OVERVIEW AN D UPDATES

The Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASO MGHG) is a dynamic nonlinear programming model of the U.S. forest and agricultural sectors. The model solves a constrained dynamic optimization problem that maximizes the net present value of the sum of producer and consumer surplus across the two sectors over time. The model is constrained such that total production is equal to total consumption, technical input/output relationships hold, and total land use must remain constant. FASOM GHG 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 green house 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. FASOM GHG has been used in
numerous studies to examine i ssues 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 comprehe nsive sectoral coverage provided by FASO MGHG 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 pr oduction (pasture) and landowner responses to changing relative prices,
FASOM GHG 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
subse t of alternative land uses . Using FASOM GHG 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 a gricultural
commodities due to changes in relative commodity prices

. FASOM GHG 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 ac counts for changes in the primary agricultural GHGs ( carbon dioxide [ CO 2 ] , methane [ CH 4 ], and nitrous oxide [ N 2 O] ) , from the majority of emitting agricultural activities and tracks carbon sequestration and carbon losses over
time

. The intertemporal dynamic s of the economic and biophysical s ystems 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, providing1

- 2

additional i nsight s 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 secto rs. Section 5 presents information on the latest methods and assumptions used for bioenergy production in the model. In Section 6 , we descri be 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. FASOM GHG simulates a dynamic baseline and changes from that baseline in response to changes in public policy or other factors affecting the sector. For instance, t he model is often used to evaluate the joint economic and biophysical effects of GHG mitigati on and/or bioenergy scenarios in U.S. forestry and agriculture. FASOM GHG combines component models of agricultural crop and livestock production, renewable fuel s production, livestock feeding, agricultural processing, log production, forest processing, car bon 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 implication s of changes in policies . FASOM GHG 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 produ ction 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 compari son of the net present value of timber harvest from a future period versus the

1 See Bruce McCarl ’ s w ebsite at http://agecon2.tamu.edu/people/faculty/mccarl

- bruce/FASOM.html for ad ditional documentation of the model and http://agecon2.tamu.edu/people/faculty/mccarl

bruce/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 agr iculture v ersus 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 6 0 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 quanti ties;

§ 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/ sequestratio n of CO 2 , CH 4 , and N 2 O 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 FAS OMGHG 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 mod el, 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. A SM has been used for analyses of renewable fuel s dating back to the late
1970s and 1980s (Tyner et al., 1979 ; Chattin, 1982 ; Hickenbotham , 1987 ) . In addition, ASM was applied to st udy 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 ; R eilly 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 co mponent of the U.S. Forest Service ’ s decennial Forest and Rangeland Renewable Resources Planning Act (RPA) assessment process 2 : 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
A REACHANGE (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 U nited S tate s 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
AR EACHANGE 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 l umber) and all product conversion coefficients for both solid wood and fiber commodities. Trade between the
U nited S tates 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 ad dition, detailed GHG accounting for CO 2 and major non

- CO 2 GHGs was added into
a model denoted FASOM GHG . The forest carbon accounting component of FASOM GHG 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., TA MM, 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 f orest carbon accounting structure on F ORCARB
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

a gricultural GHG sources and management possibilities for mitigating GHG. Schneider (2000 )1

- 6

and McCarl and Schneide r (2001 ) e xpanded 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 exc hange 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 FASOM GHG
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 t o 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 u pdated 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 si nks and inclusion of update d 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 categorie s 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 reflec t 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:
afforestati on, forest management, soil carbon sequestration, biomass

- based changes in emissions from energy use 3 , CH 4 and N 2 O

- specific practices such as enteric fermentation and fertilizer management, CO 2 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 pr ocessing and transport ation 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 p roducts, 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 ethan ol 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 tradit ional 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 D G 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 oi l 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 commoditie s 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 inter national 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 gr oups 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

§ ble nded 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 so ftwood 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 feed stocks). 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 are2

- 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 gi ven level of outputs.

In the model, p rimary 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 g o to livestock use, feed mixing, processing, domestic consumption, or export s. A mixture of primary commodities and p rocessed products are supplied to meet national

- level demands in each market

. Table 2

- 1 summarizes the primary commodities currently includ ed 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 48 0 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 p ound 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) Proces sing 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)
Ora nge, 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 Commod ities (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 swe et 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, process ing Processing tomatoes in U.S. tons Wheat, durum Durum wheat in bushels Wheat, hard red sp ring Hard red spring wheat in bushels Wheat, hard red w inter Hard red winter wheat in bushels Wheat, soft red win ter Soft red winter wheat in bushels Wheat, soft w hite 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 (l iveweight)
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 (liveweigh t)
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)
StockedSYear l 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)

Fo rest and Agricultural Residues
S oftwoodres Softwood logging residues in U.S. tons

H ardwoodres Hardwood logging residues in U.S. tons
S oftmillres Softwood milling residues in U.S. tons

H ardmillres Hardwood milling residues in U.S. tons
C ornres Corn cr op residues in U.S. tons
S orghumres Sorghum crop residues in U.S. tons

W heatres Wheat crop residues in U.S. tons
O atsres Oat crop residues in U.S. tons

B arleyres Barley crop residues in U.S. tons

R iceres Rice crop residues in U.S. tons Biomanure, b ee f Beef cattle manure for use in bioenergy production in U.S. tons

B i omanure, d airy 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 woo ds 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

P UB_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_SW FLOG_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 s awlog 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. deliv ered 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 Impor ted 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, FASOM GHG contains a set of process ing 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 prod ucts. 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 s ector. 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 product s included in the model as secondary commodities.

Primary agricultural and forestry products are converted i nto processed products using processing budgets

. These budgets are generally reflective of a somewhat simplified view of the resources used in proc essing, 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 agric ultural2

- 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 pro cessing 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 Soyb ean 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 g allons Beverages Sweetened beverages in 1,000 g allons
Confection Sweetened c onfectionaries in 1,000 lbs Baking Sweetened baked goods in 1,000 lbs Canning Sweetened canned goods in 1,000 g allons 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 Distil lers grains for export in 1,000 lbs DG, corn Distillers grains from corn in 1,000 lbs DG, non corn 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 gallon s Canola meal Canola meal in U.S. tons
Corn starch Corn s tarch in 1,000 lbs
Corn oil Corn oil in 100 gallons Corn oil, non food Non food 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)

1 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

- 7

Table 2

- 2. Secondary ( Processed ) Commodities (continued)

Secondary Products Units
Livestock Products
Fed beef Feedlot fed beef in 100 lbs (carcass weight)
Non fed beef Non fed (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

Wo ol, clean Clean w ool in lbs Fluid milk, whole Whole fluid m ilk in 100 l bs Fluid milk, low

- fat Fat reduced fluid milk in lbs
Skim milk Skim milk in lbs
Cream Cream in l bs Evaporated condensed milk Evaporated co ndensed m ilk in l bs
Nonf at dry milk Non f a t d ry m ilk in Lbs

Butter Butter in l bs
American cheese American c heese in l bs
Other cheese Other c heese in l bs Cottage cheese Cottage c heese in l bs

Ice cream Ice c ream in l bs Processing Byproducts Bagasse Sugarcane bagasse in tons
Lard Lard from swine slaughter in U.S. tons Lignin L ignin produced from non wood 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 e thanol 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, non edible Non edible tallow from beef cattle s laughter 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 Har dwood 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 resid ues 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

ce llulosic 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 Ol d 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 a re 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 g eneration

. 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 a ddition 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

Bi oenergy 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 trill ion 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 b lending of livestock feeds from a number of
different alternativ e 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 fe ed and feed blending alternative and vary by market region. The actual mixtures t hat 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 F eeds

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 operation s 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 ope rations 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 (cw t) 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 product ion 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 commoditie s identifi ed above

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

. In addition to land, FASOM GHG depicts the factor supply of other resources, including water, labor, and other agricultural inputs in agriculture, as well as non wood 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, representin g 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

. Numer ous o ther2

- 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
incl ude hauling, harvesting, and product processing costs

. Other forest inputs are assumed to be infinitely available at a fixed price.

Budgets are included for all crop s included in the model based on data drawn from a variety of USDA and agricultural extensi on 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 oth er 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, producti on 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) repr esenting 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 Productio n 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 Gas2

- 12

Table 2

- 6 summarizes the inputs included in FASOMGHG livestock production budgets in terms of quantities (not all inputs are included in all live stock budgets). A number of categories track manure management systems because th ey 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 aggre gated 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 possibil ities (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 Livesto ck 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 generate d 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 t o 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 p rivate
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, 2 ownership class, forest type, site condition, management regime, and suitability of the land for agricul tural 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 assu med 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 ne t 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) i ntroduce 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 fo r bio fuels production, soybean crushing, wet milling, and bioelectricity generation. Processing budgets for other agricultural products are defined at a national leve l. These budgets are generally reflective of a somewhat simplified view of the resources u sed 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 proc essed 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 o f 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 ” ) U nited S tates , broken into 63 subregions for agricultural production and 11 market regions (see Table 2

- 7 ) (forestry production is not disaggregated in to 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 Southwe st 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 m arket 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

. Definiti on 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, Wes t 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

o nly) 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) Okla homa, 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 m ountain range

PNWW Pacific Northwest

— West

side (forestry only) Oregon and Washington, west of the Cascad e mountain range2

- 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 a griculture explicitly in all 63 regions
for an i nitial 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 agg regated regional definition shown
in Table 2

- 8, al though the aggregated version of the model is more typically used for model development and testing. In addition, t he 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 Pla ins 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 U nited S tates

. The conceptual foundation for land allocation is described below. In terms of transferability between agriculture and forestry, FASOMGHG includes five land suitability classes , a s described in
Section 2.4.4.

FASOMGHG includes all cropland, pastureland, rangeland, and private timberland 4 throughout the conterminous U nited S tates . The model tracks both area used for production and idled (if any) within each land category. In additio n, 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 t he 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 pu blic timberland is not explicitly modeled because the focus of the mod el is on private decision

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

- 17

T hese data bases rely on different sampling methods and define land use categories in
separate ways that each have advantages and disadvantages. T o maintain consistency with other
FASOM GHG input data, we rely on the ERS depiction of cropped acres to define our cropla nd 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, w e 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 FASOM GHG wit h a more realistic representation of publi c and private grazing lands as well as regional land transition possibilities between alternative uses.

Land categories included in the model a re specified as follows:

§ Cropland is land suitable for crop production that is being used to produce either trad itional 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 harve sted 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 prod uctivity) 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 pr ior 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 W eb site.

§ P asture was defined in an attempt to maintain a consistent definition with the NRI classification of grassland pasture but to elimina te 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) (Cropland NRI + Grassland Pasture NRI )

– (Cropland ERS + Cropland Pasture ERS ) or 2) 0. This proced ure is necessary to avoid double counting of pasturelands between the NRI and ERS data.

5 Note that FASOMGHG does not include every cropp ing 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 f

orest 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 inclu ded 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.
§ Publi c grazed forest is c omputed as the difference between the ERS

- MLU total forest pasture stock and the private portion given by the A gricultural Census as described above

. § Private rangeland is defined in FASOMGHG using a combination of NRI and ERS

MLU 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, g rass

- 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 practice s, 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 shru b communities, such as mesquite, chaparral, mountain shrub, and pinyon

- juniper, are also included as rangeland. ” Thus, r angeland generally has low forage productivity and is unsuitable for cultivation and it is assumed that rangeland cannot be used for cro p 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 defi ned 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 priv ate 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
appr oximately 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 calc ulated 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 con stant 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 as sumed 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 i nto 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 r ates 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 a nd 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. S tate and county

- level land area enrolled in the CRP was obtained from the USDA F arm S ervice A gency (2009 )

.
Figure 2

- 2 shows the baseline land allocation in FASOMGHG at the national level across
each o f 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 differe nce 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. Ba seline FASOMGHG U.S. Land Base by Land Use Category (million

acres)

2

. 4

. 1 Agricultural Land

As described above, c ropland 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 pr imary agricultural commodities, although cropland in some regions is more productive than in others. Therefore, crop yields v ary 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 in cluded in other categories rather than being reported under cropland). The average annual areas of cropland with failed crops 7 are not included in the reported FASOMGHG cropland and are not explicitly tracked in FASOMGHG . Cropland can potentially be conver ted to cropland pasture or private forestland. In addition to tracking

7 US DA data for planted area exceed the harvested area because there will inevitably be some fraction of planted cropland are a 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 system 8 to allow tracki ng 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 enco urage 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 conve rted 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

enr ollment 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 pe nalty), 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
mainta ining 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, FAS OMGHG model runs generally place a floor of 32 million acres in CRP land in future years.

C ropland pasture , pasture, and private and public grazed forest are all suitable for
li vestock grazing (i.e., land that provides sufficient forage to support the nee ds 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 c onverted to forestland. Pasture, which is considered less productive , can be converted to forestland but not cropland. Private and public

8 Crop tillage syste ms 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 s oil, thereby potentially in creasing carbon inputs. Tillage changes from more intensive conventional tillage practices, such as moldboard p lowing, to conservation or zero tillage practices will generally increase levels of soil carbon over time. In additio n , 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 fertil ization), 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 . F orage 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 bo th 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 considerabl y across regions of the U nited S tates . Therefore, the area of rangeland required

per animal for a given species can be very different acro ss 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 U nited S tates 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 FASO MGHG
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 FASO MGHG , defined on a regional
basis. 9 The area of pastureland used in livestock production is limited to the pastureland inventory by time period and region. It is possible to have idle pastureland in FASOMGHG and idle pastureland area and associated soil ca rbon sequestration are tracked in the model. In particular, changes in livestock populations will affect pasture and rangeland us ed for animal production and could i ncrease or decrease idle land in the model. Changes in animal populations
over time and imp acts 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 h eld 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 rath er than responses to market conditions. However, an exogenous quantity of timber harvested on U.S. public lands is accounted for
within the model. Projected r egional public harvest levels are drawn from the assumptions used
in the baseline case of the US F orest Service’s 2005 RPA Timber Assessment (Haynes et al., 2007). T imber inventory levels for public timberlands are simulated based on the se harvest
levels

.
Private timberland is tracked by its quality and its transferability between forestry and agricul tural 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 p er 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 forest s (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 orga nizations (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 t here 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 reg ion codes in parentheses) based on regional data and defin itions. 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 Par tial 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 th e 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 develo ped 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
compet ition between agriculture and forestry for the remaining land base available for these uses
over time. It is assumed that d eveloped land does not convert back to other uses.2

- 25

2

. 4

. 4 Land Allocation

In FASOMGHG , the initial land endowment is fixed. However, becau se land can move between forest s 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 agricult ure.

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 agricultura l uses

§ FORCROP

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

§ FORPAST

— includes acres that begin in timberland bu t can potentially be converted to pastureland

§ CROPFOR

— includes acres that begin in cropland but can potent ially be converted to timberland

§ PASTFOR

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

Land can fl ow between the agricultural and forestry sectors or vice versa in the FORCROP, FORPAST, CROPFOR, and PASTFOR land suita bility 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 additi on, 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 endog enous land allocation decision, land also moves out of agricultural and forestry uses into developed uses (e.g., shopping centers, housing, and other2

- 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 agricu ltural 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

FASOM GHG uses commodity supply and demand curves for the U.S. market that are calibrated to historic price and p roduction data with constant price differentials between region al
and nation al 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 pric es 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 ;
§ region al 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 transpor tation
costs.2

- 27

§ regional or national (depending on commodity) import supply ;
§ regional feed supply and demand ;
§ regional direct livestock demand ;
§ interregional t ransport 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, commod ities are typically produced regionally and are then transported to meet a national demand at a fixed regional transport cost. Harvests from public forestland s are included in the model but are treated as exogenously determined by the
government. For agric ultural 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 nat ional 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 product s 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).

P roduct movements from Canadian producing regions to the United States are endogenous and subject to appropriate transport costs, e xchange 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, extrac tion 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 200 5 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 comm odity trade arrangements are represented

. Agricultural primary and seconda ry commodities may be portrayed :
§ w ith trade occurring in explicit international markets using a Takayama and Judge (1973) style, spatial equilibrium sub model that portrays country/re gion 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 U nited S tates;

§ w ith the United St ates facing a single excess supply and / or excess demand relationship on behalf of the ROW ; or
§ w ithout being subject to international trade.

FASOMGHG has explicit trade functions between the United States and 29 distinct foreign trading partners for agricu ltural 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) d atabase an d USDA annual statistics.

International regions are generally defined in a more simple way than domestic regions, with individual region

- level su pply 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

commo dities 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 U nited S tates

. 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 fac ing the U nited S tates . In addition,2

- 29

there are many commodities without any explicit opportunities for internatio nal trade, such as hay, silage, energy crops, livestock, and many processed commodities . Generally, trade is not explicitly modeled for commodit ies 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 sub model s , then trading is portrayed among the 29 i ndividual coun tries/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. T able 2

- 10 presents the commod ities 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 explicit ly defined country /foreign region or to the U nited
S tates

. Similar ly

, demand in a country/region can be met from imports from other countries or
from the U nited S tates

.
Table 2

- 10

. Explicitly Traded Commodities and Countries/Regions Trading with the

U nited S tates

FASOMGHG Co mmodity Exporting C ountries Importing C ountrie s Canola Canada NA
Canola o il Canada NA
Canola m eal Canada NA
Corn Argentina, Brazil, China,

USSR, W

- Africa Canada, Caribbean, E

- Mexico, Indonesia, Japan, N

- Africa, NC

Euro, 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, W

Africa, 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, Jap an, 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, NC

Euro Brazil, China, E

- Mexico, Indonesia, Japan, N

- Africa, Philippines, SE

- Asia, S

- Korea, Taiwan, USSR, W

- Africa, W

- Asia2

- 30

For commodities where trade is important to the U.S. market, but data on trade flows
with individual coun tries/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 with out 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 a s well as all of the blended feed s.2

- 31

Table 2

- 11

. Commodities with Only ROW Export or Import Possibilities

FASOMGHG Commodity Imported into the U nited S tates Exported from the U nited S tates 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 C hipped potatoes N Y Refined sugar Y Y Fed beef N Y
Non f ed beef Y N Feedlot beef sla ughter Y N Stocked calf Y N Stocked steer c alf Y N Pork Y Y Chicken N Y Turkey N Y Wool, clean Y Y Evaporated condensed m ilk Y Y Nonf at dry mil k Y Y
Butter Y Y American cheese Y Y Other cheese Y Y2

- 32

Table 2

- 12

. Commodities without International Trade Possibilities Modeled

Baking Feeder pigs Oranges, process ing (75 lb. box) Beverages Fluid milk Orange s, proc ess ing ( 85 lb. box )
Biodiesel Gr a p e fr uit, fresh (6 7 lb. box ) Orange s, process ing ( 90 lb. box )
Broilers Gr a p e fr uit, fresh (80 lb. box ) Ref ined sugar Calf slaugh ter Gr a p e fr ui t , processing ( 67 lb. box ) Silage Canning Gr a p e fr ui t , processing (80 lb. box ) Skim milk
Corn oil Gr a p e fr ui t , processing (85 lb. bo x ) Steer calve s Corn starch Hay Stocked h eifer c alves
Corn syrup Heif er calve s Stocked h eifer y earl ings Cottage che ese Hogs for slaughter Stocked s teer yea rl ings
Cream Horses and mules Stocked yearling s Cull beef c o ws Hybr i d poplar Sugarbeet
Cull dai ry co ws Ice cream Switch g rass Cull ewes Lamb slaugh ter Tbtus
Cull sow milk Turkeys Dairy calves Non f ed sla ughter Willow

Dextrose Orange s, fresh ( 85 lb. box ) Wool
Ethanol Orange s, fres h ( 90 lb. box )

Note: FASOMGHG does not explicitly include ethano l 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 seq uester ed by agriculture and forestry as well as the carbon stock on lands in the model that are converted to nonagricult ural, 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 CO 2 , CH 4 , and N 2 O. Given the multi

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

- offs between
model variables and net GHG emissions. To cons ider these trade

- offs, all G HGs 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 CO 2 . The GWP allows one to convert emissions of various GHGs into a common measure, which allows for aggregating the radiative impacts of vario us GHGs into a single measure2

- 33

denominated in CO 2 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 e stimates are still under debate. As a result, the FASOMGHG model continues to use the 1996 GWPs for the GHGs covered by the
model:

§ CO 2 = 1

§ CH 4 = 21

§ N 2 O = 310

When CO 2 equivalent results are converted to a C equivalent basis, a transformation is
done based on the molecular weight of C in the CO 2 . This means that the CO 2 equivalent quantities of gas are divided by 3.667 to compute the carbon equivalent quantities.

A list of all categories in cluded 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

. Categorie s 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_AfforestSoilSequ est 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 prod ucing 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 prod ucts Forest_USImport Carbon in U

. S

. consumed but imported from non

- C anadian 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 pr oducing 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 FASOM GHG (cont inued)

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 ag ricultural soil with initial tillag e AgSoil_CropSequest_TillChange Carbon in cropped agricultural soil with change in tillage AgSoil_PastureSequest Carbon in pastureland Carbon_AgFuel Carbon emissions from ag ricultural use of fossil fuel s 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 emi ssions 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 ha uling for bioelecticity production
Carbon_BioElec_Process Carbon emissions in processing of for bioelecticity production Carbon_Biodiesel_Offset Carbon emission offset from b iodiesel production
Carbon_Biodiesel_Process Carbon emissions in processing of b iodiesel production Methane_Liquidmanagement Methane from e mission savings from improved manure technologies Methane_ EntericFerment Methane from enteric fermentation
Methane_Manure Methane from manure management
Methane_RiceCult Methane from r ice c ult ivation Methane_AgResid_Burn Methane from a gricultural r esidue b urning Methane_BioElec Net change in m ethane emissions from bioelectricity relative to c oal

- fired
Methane_Biodiesel Net change in methane emissions from biodiesel production relative to die sel
Methane_Ethl Net change in methane emissions from ethanol production relative to gasoline Methane_CEth Net change in m ethane emission s from cellulosic ethanol production relative to gasoline NitrousOxide_Manure Livestock m anure practices under mana ged s oil c ategories under AgSoilMgmt
NitrousOxide_BioElec Net change in n itrous oxide emissions from bioelectricity relative to coal

- fired NitrousOxide_Biodiesel Net change in n itrous o xide emissions from biodiesel production rather than diesel
NitrousO xide_Ethl Net change in n itrous oxide emission s from non

- cellulosic ethanol processing relative to gasoline
(continued)2

- 35

Table 2

- 13

. Categories of GHG Sources and Sinks in FASOM GHG (continued)

NitrousOxide_CEth Net change in n itrous oxide emissions from ce llulosic ethanol processing relative to gasoline NitrousOxide_Fert Nitrous oxide emissions from nitrogen inputs including nitrogen f ert ilizer

a pplication practices, crop residue retention, and symbiotic nitrogen fixation under managed s oil c ategories unde r AgSoilMgmt
NitrousOxide_Pasture Nitrous oxide emissions from p asture NitrousOxide_Histosol Em issions from t emperate histosol area NitrousOxide_Volat Indirect soils volatilization
NitrousOxide_Leach Indirect soils l eaching r unoff NitrousOxide_AgResi d_Burn Agricultural r esidue b urning

2

. 7

. 1 Forest GHG Accounts

As identified in Table 2

- 13 , f orest GHG accounting includes carbon sequestered, carbon emitted, and fossil fuel

- related carbon emissions avoided. Sequestration accounting encompasses
carbon in stand ing (live and dead) trees, forest soils, the forest understor y vegetation, forest floor including litter and large woody debris, and wood product s both in use and in landfills

. The sequestration accounting involves both increases and reductions in stocks

, with change s in specific accounts to reflect land move ment 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 agr iculture 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 product s are combusted in place of
fossil fuels , particularly when milling residues are burned to provide energy (generally for use at the mill). In addition, wood y biomass may be used as a bioenergy feed stock

.
Forest carbon accounts also include the carbon content of products imported into

, or exported out of

, the U nited S tates

. 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

§ p rocessed in and coming from Canada,

§ i mported from other countries

, and

§ e xport ed to other countrie s

.

T hese 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 po licy 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 fertiliza tion 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 invo lves 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 sequest ration.

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 c ommodities can be used as feedstocks for biofuel production processes in FASOMGHG, possi bly affecting fossil fuel usage and associated GHG emissions after accounting for emissions during hauling and processing of bioenergy feedstocks.
Four major forms of bio energy production are included :
§ Biodiesel: u sage of canola oil, corn oil, lard, poult ry fat, soybean oil , tallow, or yellow grease in the production of biodiesel, which replaces petroleum

- based diesel fuel

§ Bioelectricity: u sage of bagasse, crop residues, energy sorghum, hybrid poplar, lignin, manure, miscanthus, sweet sorghum pulp, switchg rass, willow, wood chips, logging residues, or m il l ing 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, hybri d 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: u sage of barley, corn, oats, rice, sorghum, sugar, sweet sorghum, or wheat for conversion to ethano l 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

pr oducing, 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

. Simplif ied

acc ounting 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 p hosphorus application and runoff, soil erosion, irrigation water usage, and a number of descriptions of total resource use an d activity within the agricultural and forestry sectors ( e.g., total land use, total pasture use, manure load, livestock numbers, t otal 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, agric ultural, 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 (195 1) and Samuelson (19 52 ) 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 o f 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 equilib rium 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 pro ducer and consumer surplus will be maximized where all markets are at a perfectly competitive market equilibrium. This approach is implemented using a

n onlinear programming model written in the Generalized Algebraic Modeling System (GAMS) language . The res ultant objective function value is consumer plus producer surplus.

Suppose we have a demand curve

P d = f (Q d

, ) (3.1)

and a supply curve

P s = f (Q s , ) (3.2)

where P d is the price demanders would be willing to pay to purchase Q d units of output and P s is the price at which suppliers would be willing to supply Q s units. Demand curves are typically downward sloping, reflecting declining willingness to pay with increasing quantity. Supply3

- 2

curves, on the other hand, are expected to be upward

- sloping, reflectin g 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 :

P d = P s (3.3)

Q d = Q s (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 :

s d s d s d

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 t he equilibrium price (P*) is greater than or equal to the demand price :

* d P ) f(Q ≤ (3.5)

Simultaneously, the market price may be less than the supply price :

*

s P ) f(Q ≥ (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 quant ity should be suppl ied. Simultaneously, when a non zero quantity3

- 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(Q d )

– P* ) Q d = 0 ( 3. 7)

(f(Q d )

– P* ) Q d = 0 ( 3. 8)

One should also recognize that the quantity supplied must be greater than or equal to the quantity demanded :

d s 

### Q Q ≥ (3.9)

However, if the quantity supplied is strictly greater than the quantity dem anded, then the equilibrium price should be zero. Mathematically this relationship is as follows:


## ( ) 0

P Q Q *

d s = +

− (3.10)

Finally, we state nonnegativity conditions for price and quantities :

0. P
, Q

, Q *

s d ≥ (3.11)

The above equations are si milar 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 :

0 Q

, Q 0

Q Q s.t. Q ) f(Q Q ) f(Q Max

s

d s d 0 s s 0 d d *
s Q * d Q

≥ ≤

− − 

## ∫ ∫ d d

w here the Lagrangian and Kuhn

- Tuck er conditions are :3

- 4 0

, , 0 ) ( )

, , ( 0 ) ) ( ( )

, , ( 0 ) ) ( ( )

, , ( 0

) ( )

, , ( 0

) ( )

, , ( 0

) ( )

, , ( ) ( Q ) f(Q Q ) f(Q )

, , ( * s Q * d Q

0 s 0 d d

≥ =

− − = ∂ ∂ = +

− = ∂ ∂ = − = ∂ ∂ ≥

− − = ∂ ∂ ≤ +

− = ∂ ∂ ≤

− = ∂ ∂ − − − = 

## ∫ ∫

λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ λ

s d s d s d s s s

s s d d d d d s d s d s d s

s s d d d s d s d s s d

Q Q Q

Q Q Q L Q

Q f Q Q Q Q L Q

Q f Q Q Q Q L Q

Q Q Q L Q

f Q Q Q L Q

f Q Q Q L Q Q d d Q Q L

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 process es 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

and k h, i, all for 0 Q

, X
, Z and j all for Y Q b i all for 0 Q a X

- h all for 0 Q C

- Z s.t. dX X P

- dZ Z P Max

k i h j k k k j k k k i i k k k h h i i i X

0 si h h h Z

0 dh i h

≥ ≤ ≤ + ≤



## ∑ ∑ ∑ ∑ ∑ ∑ ∫ ∑ ∫

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 combining3

- 5

fixed factors (j) with purchased factors (i) to produce commodities (h)

. The symbols in the
formulation are as follows :
§ P dh (Z h ) is the inverse demand function for the h th commodity

. § Z h is the quantity of commodity h that is consumed

. § P si (X i ) is the inverse supply curve for the i th purchased input

. § X i is the quantity of the i th factor supplied

. § Q ßk is the level of pro duction process k undertaken by firm ß

. § C hßk is the yield of output h from production process k

. § b jßk is the quantity of the j th owned fixed factor used in producing Q ßk

. § a ißk is the amount of the i th purchased factor used in producing Q ßk

. § Y jß is the endowment of the j th 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 Q β k
.

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 b y 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 val ue 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 demand3

- 6

schedules as input, rather these schedules are derived internally based upon production possibilities, output demand and purchased input supply

.
The competitive behavi or simulating properties of this formulation provide s 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 polic ie s to be specified and then simulated in terms of projected sectoral response to the policy change with endo genous 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 an y) . 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 va riables 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/h arvest, if the land stays in agriculture (potentially with accompanying land use change between cropland and eligibl e 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 ove r 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 ye ar 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 l and to f orest

— L and use change from cropland, cropland pastu re, 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 f orestry and agriculture are modeled ( 8 of the 11 market regions)

. § Agricultural l and from f orest

— L and use change from forestland into cropland, cropland pasture, or pasture (deforestation) on a regional basis. These variables reflect supply of additional l and 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 land owner makes a decision to either reforest or move the la nd 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 p astur e

— L an d 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 p astur e

— Land use change from cropland pasture into cropland on a regiona l basis. This cropland is assumed to have the same productivity as any other cropland in the same region.

§ Tillage /i rrigation

— These variables depict the selection of tillage and irrigation alternatives at a regional level. An initial distribution across ti llage 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 i rrigation 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 p rodu ction

— Crop production possibilities, defined by crop, irrigation/dryland

c omponent , fertilization alternative, and tillage alternative. Production of each crop available within a region uses inputs defined by the crop budgets, competing for

2 Tillage conver sion 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 co nventional 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 produc ed historically but may experience large increases in acreage under certain policy scenarios.

§ Livestock p roduction

— Livestock production possibilities, defined by animal type, livestock management alternative, manure management alternative, and enteric fer mentation 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 i ntermediate 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 de fined 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 b lending

— 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 m anageme nt

— Choice of manure management system for use with certain livestock types. Adoption of improved manure management systems requires production o f 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 i ncurs system capital and operating and maintenance costs. Thus, improved systems are typically not adopted without incentives.

§ Water s upply

— Supply of water from fixed

- price sources and sources with upward

sloping water supply curves. The fixed

- price suppl ied 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 priv ate surface water markets and ground water.3

- 9

§ Labor s uppl y

— 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 fun ction.

§ 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
pro cessed 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 int o 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 d eman d

— Domestic consumption of primary and secondary commoditie s. 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 commodit y from the relevant primary or secondary commodity balance at the national level. These variables are defined by commodity on a national basis.

§ Export d eman d

— Export of goods which are specified with explicit rest of world excess demand equations. The vari ables 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 s upply

— 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 commodi ty into the primary or secondary balance. These variables are defined by commodity on a national basis.

§ International t rade

— International transport between the U nited S tates and foreign countries/regions and among foreign countries/regions. These variable s are defined for all pairs of movements allowed in the data between the U nited S tates and foreign countries/regions and among the foreign countries/regions themselves.

§ International s upply/ d emand

— Import and export of goods which are specified with explic it 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 p aymen t

— Payments to producers or payments by producers based on net changes in GHG emissions and the GHG incentives in p lace 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

N umerous 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 region 3 ( 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 m ix

— Balances acreage by crop against the ac reage 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 s ystem u se

— Limits total acreage of each tillage system in use within the cropping variables to the acreage produced by the tillage choice variables. Limits regiona l 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 pla ce )

. § CRP l and

— 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.

§ Pastur e

— 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 cropla nd and forestland as well as conversion to developed uses.

§ Grazed f

orest

— 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 m ix

— 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 mixe s for that region.

3 The constraints included in the list are generally defined at the most disaggregated re gional 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 fee ds 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 groun d 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

c omes 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 p roduct s

— 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. A vaila bility comes from crop and livestock production and imports. This constraint is defined on an 11

- region basis when the commodity is regio nalized and on a national basis for those commodities where production is not disaggregated by region.

§ Secondary p roduc ts

— 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 consumpti on, or e xports. Availability com es from processing or imports

. This constraint may be defined on an 11

- region basis or national basis, depending on the commodity.

§ International m arket

— 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 s ize for m anure m anagement

— Balances herd size for animals that can potentially be treated with improved manure manage ment 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 n e t e missions

— Accumulates net GHG emissions by category and balances against the GHG payment variable by GHG account. By default, t hat variable equals total net emissions and is unrestricted in sign reflecting a possible net emission reduction or increase

. H owever, individual GHG accounts subject to carbon incentives can be defined in each model scenario.

§ Forestl and

— 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 bal ances; 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 forestlan d 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 so

called “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 pos sibility 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 r egions 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 thin ning 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 valu ation 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 agricu ltural 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 cle arcut 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 manag ement 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 rat es 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

e nter 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 affor estation (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

. F inally, 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 do llars 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 sawtimbe r 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 (a nd some wood products are used in the production of other wood products), generating or using milling residues. P rocessing capacity use is tracked by the model. Manufactured products also require one unit from the re gional and process

- specific non wood inpu t balance, which in turn reflects
an upw ard sloping supply curve of non wood 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 produ cts.

§ Terminal period manufactur ing

— 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 b eyond 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 m anufacture 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.

§ Non wood inputs

— Supply of non wood input items for wood product manufacturing. They are defined by wood product, process, region, and time period and derive from an upward sloping supply curv e for nonwoo d input items. Non wood 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 perio d. 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 future3

- 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 th e forest sector within FASOMGHG include the following :
§ Bare forest land

— 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 equa tion 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. Harveste d 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 th e 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 d ifferent 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.

§ F orest 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 w ood 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 i mports. 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.

§ W ood products processing capacit y

— Imposes capacity limits on wood product manufacture. The capacity is defined by time period, region, process, and wood product manufactured.

§ Mill residues ba lance

— 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.

§ Non wood input balance

— Balances wood product manufacturin g activity with

n onwood input supply (i.e., aggregate of inputs other than wood required to make wood products) . Non wood 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, re forested, 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 suppl y 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 dom estic 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 i n for es ts 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 re duction activity, and bioenergy

- related activity. Under a specific mitigation policy or3

- 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 baseli ne 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 f unction 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 subt racting 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 rel ative 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 s ign 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 eligibl e 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 valu e 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 of3

- 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 FOR ESTRY AND AGRICULTUR E

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 objectiv e 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 i ntersectoral 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:
FORC ROP (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 for est 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 emplo yed 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.

§ W hen 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 come s 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 regi onal 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 coupli ng 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 an d 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 hu rdle 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 i n 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 framewor k 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, 200 6). 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 t rees are harvested.4

- 3

Given these model attributes, the economic c onditions 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 rema ining 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 transf er 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.

I n 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 c lass

. 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 cl ass.

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 y oungest 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 l and 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 rem oved 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 l evel of disaggregation consistent with FASOMGHG region s; 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 thes e 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 pasturelan d eligible for conversion to timberland was established using information from Moulton and Richards (1990 )

.
Forest to agriculture conversion c osts are represented in each region by step functions to reflect increasing co sts, 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 in creasing 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 h igh 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
Total Acres Low Medium High
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 24,8 00,000 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 Availa ble 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 L and ($/acre) and

Acres Available for Conversion (continued)

Land Class Defi nition Region Land Conversion Cost Level Total Acres Low Medium High
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

Ava ilable 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 acreag e 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 FORO NLY 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 CROPF OR 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 i n 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 bio energy 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 commo dity 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 :
§ T he agricultural commodity hybrid poplar that can be trans ferred 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 et hanol 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 c haracteristics 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 withi n 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 log s 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 conver t 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 annual ized 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 fi rst 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 als o 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

- 85

- 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 describ e 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 f eedstocks 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 st ate

- level government subsidies are included in FASOM GHG , 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. FASOM GHG simulates the supply of biodiesel by feedstock
for each of the 10 FASOM GHG 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),

w hich 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 “agri

biodiesel” ( 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

- biodies el and biodiesel such that all biodiesel qualified for the $1 per gallon subsidy.5

- 2

response to the demand for biodiesel under s pecified 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 p rocesses. These assumptions are summarized
below.

Starch

- or Sugar

- Based Ethanol

FASOM GHG assumes standard sizes for ethanol production plants. The majority of plants producing ethanol from starch or sugar feedstocks are assumed to produce 75 million gall ons 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 readil y 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 assume d 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 FASOM GHG as well as the ass umed 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, t he level of sugar in the crop d ecreas es 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 feed stocks. 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 pro duce 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), Ba se Period ( 20 00

– 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 1.1 0 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 FASOM GHG , 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, 2010 a )

. These costs we re based on
bioche mical 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 cr acking 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 conv ersion to ethanol, which changes over time. Table 5

- 2 lists the categories of cellulosic feedstocks currently included in
FASOM GHG 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 .25

- 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 F e edstocks 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 res idues 71.9 1, 390, 8 2 1 Corn crop residues 71.9 1, 390, 8 2 1 Oat crop residues 71.9 1,390,281 Rice crop residues 71.9 1, 390, 8 2 1 Sorghum crop residues 71.9 1, 390, 8 2 1 Wheat crop residues 71.9 1, 390, 8 2 1 Energy Crops Energy sorghum 71.9 1, 390, 8 2 1 Hyb rid poplar 79.1 1,264 ,22 3
Miscanthus 71.9 1, 390, 8 2 1 Switchgrass 71.9 1, 390, 8 2 1
Willow 79.1 1,264,223
Wood Products Hardwood pulpwood 79.1 1,264,223 Softwood pulpwood 71.9 1,390,8 2 1 Logging Residues Hardwood logging residues 79.1 1,264,22 3 Softwood logging residues 71.9 1, 390, 8 2 1 Processing Residues Bagasse 71.9 1, 390, 8 2 1 Hardwood milling residues 79.1 1,264,223 Softwood milling residues 71.9 1, 390, 8 2 1 Sweet sorghum pulp 71.9 1, 390, 8 2 1

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 suga r

- 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 FASOM GHG , 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 FASOM GHG 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. Biodie sel producers within a region can produce any quantity of biodiesel, and FASOM GHG 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 g allon of biodie sel produced used within FASOM GHG we re based on analyses included in the RIA for the RFS2 program (EPA, 2010 a )

.
The quantity of feedstock required to produce a gallon of biodiesel varies across feedstocks to some extent, but with less varia nce than for ethanol production. Table 5

- 3 lists the categories of biodiesel feedstocks being included in FASOM GHG , the assumed biodiesel yield, and the quantity of feedstock required per 1,000 gallons of biodiesel produced. Feedstock requirements and proc essing 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 acti vities 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 Biodi esel Produced

(units) Soybean oil Pounds 0.1288 7,763.0

Canola oil Gallons 1.0053 994.7

Corn oil (non food grade) Gallons 1.0239 976.7

Nonedible tallow Pounds 0.1302 7,679.0

Edible tallow Pounds 0.1302 7,679.0

Lard Pounds 0.1302 7,679.05

- 6

5

. 1

. 2 Feedstock Co sts
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 cr op). 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 r elevant 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

- le vel 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, expor ts, 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 producti on 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 discussio n 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

- B ased Ethanol

Feedstocks , Base Period (2000

– 2004)

Crop CB GP LS NE PNWE PSW RM SC SE SW
Barley (bu/acre) 6 0.0 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 1 16.9 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, Dur u m (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 re d 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 a pplicable

The m arket price for each alternative ethanol fee dstock 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 fe edstocks in the market, eight of the 11 FASOMGHG market regions offer state subsidies that offset some of the ethanol production costs. These

6 The exception is sweet so rghum, 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 averag es 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 Ethano l
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 rem oval), 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 l evel 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 gallon s 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 plant

level feedstock costs.

The regional farm

- level market price for a crop residue feedstock reflects the fact that
farmer s 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 stat e

- level subsidies within each FASOM region.5

- 9

assumed harvesting and handling costs of $1 3.14 per wet ton (200 7$ ) 9 and increased fertilization requirements due to nutrient removal that vary by crop. The farm

- level f eedstock 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 co sts 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
cel lulosic 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,
ethano l 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

- Gr ain 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 o f 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 to ns of residue and which varies by crop.

9 Because 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.
10 The farm

- level price that processing plants are willing to pay for each fe edstock 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 r esidue 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 d iffer 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 (thes e 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 Facto r 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 residu e production

per acre. For instance, average corn residue production per acre in the Corn Belt in 2000 to 2004 is equal to 1 45.3 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 m oisture 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 a5

- 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 ad justing 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 p ercentages 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 (Wil helm 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 becau se 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 region s (the exception is hard red winter wheat residue in the Northeast region). 14

11 Many s ite 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 th e 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 conservat ion 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 P lains; 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 individua l 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 Th us, 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
bioele ctricity 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 t echnical 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 quan tities 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 ter ms 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 utilizin g 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 mar ket 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 describe d 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 c ropland. 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 feeds tocks 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 S elected Bioethanol Production Pathways in the United States”( ANL/ESD/06

- 7 )

.5

- 14

F or energy crops to be adopted widely, they need to provide high enough returns to farmers to
induce them to switch from alternative land use s to energy crops.

For energy crops, assumed baseline yields pe r 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 FA SOMGHG 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 re gions, 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

- 1 0 presents regional average energy crop yields for the base period for the three energy crops included in FASOMGHG

. Other th ings 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 descr ibed earlier, the entire yield of energy crops presented in
Table 5

- 1 0 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 s ummarizes 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 conversi on 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 plan t 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 feeds tock 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 f or 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

Switc hgrass 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, if5

- 16

feedstock prices reach high enough levels, energy crops become competitive with alternative land uses in some regions.

Pulpwood, Logging

, and Processing Residu es 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 resid ues, 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 ass umed to be harvestable based on Helynen, Hakkila, and Nousiainen (2000)

. Y ields 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, milli ng 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 product ion 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

- 1 2

.5

- 17

Table 5

- 1 2

. Ethanol Conversion Rates for Logging and Processing Residues, Base Perio d

(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 7 1.90 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 = Soft w ood 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 ethan ol 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 bioener gy 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, the re 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 ten ds 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 in5

- 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 c orn oils over time. Table 5

- 1 3 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
C anola 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 col lecting 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, an d soybean oils under policies encouraging expanded biodiesel5

- 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

Hau ling 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 l and 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 lim iting 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 Mile

per Cost D Cost Fixed TC )

2 ( × × +

= , where (5.2)

) mi sq acres 640 ( 4714

. 0 Yld

Den 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

- way 19 hauling distance in miles that is calculated b ased 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 50,00 0 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 additi on, because of their higher moisture contents and lower densities, the average load size for crop residues, sweet sorghum, switchgrass, miscanthus, and energy sorghum w as assumed to be 40,000 lbs. For

19 A multiplicative factor of 2 is included in the ca lculation 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 multipl ying by the practical forest density of 0.8. N o historical data are available for energy crop densities. All

energy c rops were assumed to have b iomass densities of 10% , with the expectation that they would be planted primarily around bioenergy plants using these feedstocks . Biomass density from crop residues w as calculated using a weighted average of the total acres of c orn, 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 Res idue 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

- 1 5 summarizes base period values for average hauling distances by feedstock by region. Hauling c osts will generally decline over time as feedstock yields rise (and ethanol conversion rates improve in the case of cellulosic fe edstocks) 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 haul ing distance is positive, but l ess 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 processin g 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 feedsto cks. Because crop residues and energy
crops cannot be harvested year

- round and agricultural processing residues are not available year

round , bioenergy producers incur storage costs to ensure availability of sufficient feedstock throughout the year. For cr op 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

an d 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 deli vered 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 i s $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 reflec ted 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 handli ng 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) Stor age

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 1,390,8 21 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 converti ng 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 te rms. 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 eth anol 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 for5

- 24

feedstocks and remain constant over time in real terms. These values were based on inform ation provided by EPA.

5

. 1

. 4 Additional Ethanol Market Penetration Costs

A t higher ethanol volume s , 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 a re 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 rate s 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 penetrat ion 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 o ver 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 esti mate 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 th at 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 a nd penetration of E85

, and we use the 2014 to 2030 projections of prices and E85 consumption to estimate a relationship that we i nterpret as the cost of E85 penetration. Based on the relationships estimated, we incorporated a step function into the model t hat 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

- 1 7

. 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 , g luten 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 cor n 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 be cause 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 t he 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 v ersus 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 rates5

- 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 s ame 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 r ecent 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 Cor n 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 po unds 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 w ithout 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 cor n oil as coproducts. Although fractionation produces food grade corn oil, the dry mill extraction process produces non food grade corn oil that cannot be used as vegetable oil for food production but can be used to produce biodiesel

. Based on projections in cluded 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 proce ss, 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 us ing 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 a ffecting 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 swit chgrass, 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 producti on levels5

- 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 Et hanol 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 t hat 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 a re 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, a lthough 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 biodiese l 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
de dicated 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 of5

- 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 ar e 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 reduc e 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 Mo deled
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]) a nd 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

- 1 9 summarizes the higher heating values (measure of energy content) used for each feedst ock 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 Feeds toc k 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 cost s 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 source s) 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 ( 20 00

– 2004)

Feedstock Higher Heating Value (Btu/we t ton) Qu antity 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

S oftwood pulpwood 12,000, 007 766,500
Logging Residues Hardwood logging residues 10,666,667 862,312

S oftwood 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. Us ing 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 m uch 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 th e 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 assoc iated 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 co sts 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 t akes 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 agricultura l 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 for5

- 31

feedstocks that would be transported to 100 MW dedicated biomass electricity plants. 22 Based on

t he reduced biomass feedstock quantities required for cofiring options in FASOMGHG,
d istances 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 N A 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 Southwe st; 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 dis tances 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. I n a region like the Corn Belt, w hich 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 t he 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, second

stage hauling distances increase from 15.0 miles for the first 500 MW to 950.9 miles by 1,500 MW of cofiring bec ause 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 dif fer 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 w ere calculated base d 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 Bioelectric i ty Market Penetration Constraints
In addition to rising hauling costs that reflect higher costs of inc reasing 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 to tal 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 sec tors 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 international ly 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 informa tion on expected future conditions. Another important consideration for any forward

- looking optimization model is discounting to appropriately compare economic returns realize d at different points in time. I n 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 adj ustments to key variables over time.

6

. 1 Dynamics of Timber Harvest
FASOMGHG endogenizes the harvest date f or a timber stand

, assuming it i s past a n exogenously determined region

- specific min im um harvest age . T he 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

year s from now, 10 year s from now

, etc. The decision whether to harvest a st and or extend i t 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 produc tion costs plus the difference in the opportunity cost of the land

. The value of the timber when with held from current harvesting is the price 5 year s from now times the yield at that point

. T his is taken into account in the optimization for all the possib le 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 t he 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 produ ction and price formation in competitive markets over time for both agricultural and6

- 2

wood product s

. 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 fol low, 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

. I n 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 informa tion 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 e conomic 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 . T he 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 tim e 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 la nd use, investment and harvest decisions with effects reaching broadly across both sectors in the model.

T he 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 ar i se 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 d p

P p r d P p r d

p t t p

= = −

≤ ≤ + = 

## ∑

=

for 1 and 1 0 for ) 1 ( 1 4

0 (8.1)6

- 3

w here

§ r is the discount rate for period p and

§ P is the last tim e period included in a model run

.

T he forestry and agricultural market components of the objective function are

) ( ) 1 ( 1

0 5 p p p P

p p AgWel

ForWel d

r W + + = 

## ∑

=

where

§ W is the net present value of forestry and agricultural welfare

§ d p is the period specific annuity factor

§ p id entifies the 5

- year time period

§ AgWel p represent s the multiterm agricultural consumer and producer surplus objective function in the 5

- year time period p

§ ForWel p represents the multiterm forestry consumer and producer surplus objective function in the 5

- y ear 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 d p converts the r eturns 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 po ssibility 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 standin g 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 la nd values at the end of the period must be6

- 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 condit ions are imposed on the model that value ending immature trees 1 and 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 assum es 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 period s 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 harv est 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 i s treated as if it continue s 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 experien ce 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) a nd 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

.

Simult aneous 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 a ffect 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 wil l 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 internatio nally, constitute one of the most influential factors of agricultural analys e s 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 increase s are dampened and land competition is less intense.

Yields for most crops have increased significantl y 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 price

induced 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 Pr oduction 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 combinat ion 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 adop ted 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 a re n ot directly incorporate d

. 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.
3 We 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 off set any potential price

- induced yield increase s.6

- 6

Table 6

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

Agricultural Commodity Units 20 00

– 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 pro duced a 0.75%

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 4 1.6 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, dur um 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 th at takes place along with changes in yields is the calculation of inpu t 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 i n 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

. S ubsequently, base d 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. B ased 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 avai lable data are assumed to be 0.6.

Table 6

- 2

. Input Elasticities with Respect to Changes in Yield

CropNitrogenPhosphorusManagementChemicalsSeedsCustomOperationsMarketingCapitalRepairsInsuranceTotal PesticidesTotalFuels

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)

CropNitrogenPhosphorusManagementChemicalsSeedsCustomOperationsMarketingCapitalRepairsInsuranceTotal PesticidesTotal Fuels

Soybeans 0.80 0.80 0 1.69 0 0.52 0 1.67 0.34 0 1.69 1.6 6 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 tren ds 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

B roilers 100 lbs. (liveweight) 2.25 % Eggs Dozens (farm level) 0.39%
Feeder pig 100 lbs. (liveweight) 0

. 48 %
Feedlot beef slau ghter 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%
Non f ed beef slau ghter 100 lbs. (liveweight) 0. 46 %
Steer Ca l ves 100 lbs. (liveweight) 0.17%
Stocked heifer ca lves 100 lbs. (liveweight) 0.17 %
Stocker heifer yea rlings 100 lbs. (liveweight) 0.17%
Stocked steer cal ves 100 lbs. (liveweig ht) 0.17 %
Stocked steer yearl ings 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 pro ducts 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.

T able 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

E ggs 0.64% 0.77% 0.96% Evaporated condensed mil k 0.72% NA NA

Fed beef 0.75% 1.00%

- 0.16%

Fluid milk, low

- f

at 0.72% NA NA

Fluid milk, w hole 0.72% NA NA Grapefruit, fresh NA 0.04% NA Hay 0.69% NA NA

Ice cream 0.72% NA NA

No nf at dry mi lk 0.72% NA NA

N on f ed beef 0.75% 0.70% 1.29%

(continued)

4 Supply and demand are pri ce

- 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% Orang es, 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 s pring 0.48% 0.51% NA
Wheat, hard red w inter 0.54% 0.36% NA
Wheat, soft red wi nter 0.54% 0.36% NA
Wheat, soft w hite 0.54% 0.56% NA

Wh eat 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 tr ack 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 giving6

- 11

location, forest t ype, 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 incr eases in timber yields incorporated into the model. 5 However, o nce 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 . Rege neration 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 re gion, 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

FASOMG HG 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 feed stock 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 tec hnology 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, cellulosi c 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 d epend 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

. Bio fuel s 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
Non edible tallow 0.13 gallons/lb 100
Lard 0.13 gallons/lb 100
Cellulosic Ethanol Barley crop residues 92.30 g allons/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 to n 128.46

- 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
Har dwood 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 Pro posed 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 Bioe nergy 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 plant

level processing costs for those technologi es. 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 th e RFS2 program, processing costs were assumed to decline from starting values of $3.29 per gallon for c ellulosic 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)

.

6 As noted above, there are assumed to be continued improvements in feedstock conversion rates to electri city 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 impro vements 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 haul ing 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 GHG s emitted from and sequestered by forestry and agri cultural activities and land use change in the
U nited S tates , 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 account s 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 foss il 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 CO 2 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 incl udes 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 T ables 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
Sil age 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 N A 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, f

resh 13.1 NA 9.1 14.2 NA 13.1 NA 17.3 13.1 13.1
Tomatoes, p rocessing 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 appli cable ( 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 ). T here is no dryland grapefruit, orange, pot ato, 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 S E 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

W heat, 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 N A 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, f

resh 7.5 NA NA NA NA 7.5 NA 6.6 7.5 7.5
Tomatoes, p rocessing NA NA NA NA NA 3.7 N A 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 FA SOMGHG region in the base period or that input is not used in the budgets for the crop/region/irrigation combination specified). T here 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

. Elect ricity 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 , gra in 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 combinatio ns 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 cro p/region/irrigation combination specified). T here 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 th e budgets for the crop/region/irrigation combination specified ). T here 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, e nergy u se 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 ene rgy type

.

GHG emissions from direct agricultural use of fossil fuels in crop production are
calculated in FASOM GHG by multiplying the quantity of each energy category used (diesel fuel, gasoline, natural gas, electricity , and propane ) by the GHG content o f that fuel . Diesel, gasoline, natural gas, and propane are assumed to have emissions factors of 10.2 kg CO 2 /gallon, 8.3 kg
CO 2 /gallon, 54.5 kg CO 2 /1

, 000 cu ft, and 5.7 kg CO 2 / 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 i n livestock production budgets

. However, because chang es 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 CO 2 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 gasoli ne 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 a ssumed 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. CO 2 emissions from forestry use of fossil fuels are calculated using
the emissions factors for die sel and gasoline presented above.

2 These assumptions are based on the Drying Costs for Corn spreadsheet developed by the University of Missouri Extension Pro gram and available at http://agebb.missouri.edu/download/index.htm7

- 7

Table 7

- 5

. Regional GHG Emissions from Electricity Generation (g CO 2 eq/kWh)

FASOMGHG Subregion CO 2 CH 4 N 2 O

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
Cali forniaS 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 CO 2 eq/kWh)

(continued)

FASOMGHG Subregion CO 2 CH 4 N 2 O

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 1.2 8
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
T xTranspec 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 pract ices, and region

. There is often considerable variation in the inputs used per7

- 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

.

B oth CO 2 emissions from fertilizer production (discussed in this section) and N 2 O 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
i rrigation status for FASOMGHG market regions where agricultural crops are produced

, assuming no residue removal

. 3

Table 7

- 6

. Nitrogen Fertilizer Usag e (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 10 3.6 103.8 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 p oplar 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 10 0.0 100.0 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 u se 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

Wi llow 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, f

resh NA NA NA NA NA 80. 0 80.0 NA 128.0 210.0 Grapefruit, p rocessing 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, f

resh NA NA NA NA NA 100.0 100.0 NA 148.0 210.0 Oranges, p rocessing 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

So rghum , 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, f

resh 104.0 NA 95.0 113.3 NA 295.0 NA 146.2 184.0 144.0
Tomatoes, p rocessing 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/irriga tion combination specified). T here 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, Irrigat ion 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 1 5.0 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

S oybeans 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 winte r 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
Co rn 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 S E SW Sugarcane NA NA NA NA NA NA NA 50.0 50.0 50.0 Tomatoes, f

resh 45.0 NA 35.0 50.0 NA 275.0 NA 41.7 45.0 35.0 Tomatoes, p rocessing 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/ir rigation 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). T here is no dryland grapefruit, orange, potato, rice

, sugarcane , or tomato production or irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG

.
Table 7

- 8

. P otassium 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

. P otassium Fertilizer Usage (lbs/acre) by Crop, Irrigation Status, and Region,

Base Period (2000

- 2004) (c ontinued)

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 winte r 37.5 NA 20.0 32.3 N A 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
Oa ts 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, f

resh 55.0 NA 50.0 100.0 NA NA NA 62.8 85.0 65.0 Tomatoes, p rocessing 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 re d 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 t he budgets for the crop/region/irrigation combination specified). T here is no dryland grapefruit, orange, potato, rice

, sugarcane , or tomato production or irrigated hybrid poplar, switchgrass, or willow production in FASOMGHG

.

FASOMGHG calculates energy us e 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

T able 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, phos phoru s, and potassium range from 1.6 to 2.8 kg CO 2 eq/ lb N, 1.4 to 5.2 kg CO 2 eq/ lb P, and 0.5 to 1.6
CO 2 eq/ 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 consultatio ns 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

assum e 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 . W e 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 i n the production of some crops and is included in those crop budgets in FASOMGHG. Although FASOMGHG does not directly calculate CO 2 emissions from lime production, the model does provide information on the amount of lime used

. Thus, CO 2 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 CO 2 emissions are includ ed in fertilizer emissions calculations), with appropriate unit conversions. The emissions factor used for fuel oil is 74.9 kg CO 2 eq/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 spr ing 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, f

resh NA NA NA NA NA 9.84 16.31 NA 32.51 16.40 Grapefruit, p rocessing 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, f

resh NA NA NA NA NA 7.00 3.66 NA 4.34 2.61 Oranges, p rocessing NA NA NA NA NA 7.00 3.66 NA 4.34 2.61
Potatoes 5.91 6.4 4 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 , swe et 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, f

resh 3.38 NA 12.79 6.02 NA 10.99 NA 3.74 16.20 7.53 Tomatoes, p rocessing 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: Value s 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 pe riod or that input is not used in the budgets for the crop/region/irrigation combination specified). T here is no dryland grapefruit, orange, potato, rice

, sugarcane , or tomato production or irrigated hybrid poplar, switchgrass, or willow production in FASO MGHG

.
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 N A
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 N A 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 combi nations 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 th e crop/region/irrigation combination specified). T here 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 CO 2 emissions associated with energy use, there are several important agricultural sources of the non

- CO 2 GHGs methane and nitrous oxide

. In this section, we describe the GHG accounting framework applied to the agricultural sources of CH 4 tracked7

- 17

within the model, incl uding livestock manure management, livestock enteric fermentation, rice cultivation, and agricultural residue burning. Section 7.1.3 describes the FASOMGHG methodology used for tracking N 2 O 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
CH 4 emissions per head by livestock type based on the 2001 emissions values by livestock type
and the numbe r 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 CO 2 eq/head for poultry, 23.31 kg CO 2 eq/head for sheep, 35.49 kg CO 2 eq/head for beef cattle, 214.20 kg
CO 2 eq/head for swine, and 1,612.17 kg CO 2 eq/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 CH 4 emissions associated with manu re 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 norma l 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 val ue 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 calcu lated to be 2 54.52 kg
CO 2 eq/head for sheep, 947.31 kg CO 2 eq/head for beef cattle, 23.52 kg CO 2 eq/head for swine,
and 2,898.84 kg CO 2 eq/head for dairy cattle. These average values for enteric fermentation emissions per head are multiplied by the number of l ivestock 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 methanogeni c 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 meth ane 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 cha nges 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 fa ctors used range from 1,783.44 kg CO 2 eq/acre in the California North subregion to 4,375.0 0 kg CO 2 eq/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 CH 4 (and N 2 O) 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 cr op based on 2001 data contained in the EPA GHG inventory for 1990

– 2003 (EPA, 2005b ). Emissions from this source r ang e from 3.16 kg CO 2 eq/acre to 25.35 kg CO 2 eq/acre across crops and FASOMGHG subregions for the subset of crops assumed to have these emissions.

7

. 1

. 3 Nitrous Oxide Emissions
Until recently, FASOM GHG used an explicit representation of direct and indirect N 2 O
emissions from nitrogen fertilizer application and nitrogen

- fixing crops based on IPCC default
factors

. 5 However, recent re search suggests N 2 O 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 N 2 O 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 N 2 O

5 See the 2006 IPCC Guidelines for National Greenhouse Gas Inventorie s , Chapter 11, N 2 O 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 N 2 O emissions and pr ovide 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 croppin g trends and GHG emissions in the FASOMGHG assessment, including the following:

§ Direct N 2 O emissions based on DAYCENT 6 simulations 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, N 2 O emissions from cropping activities and grassl ands 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 bioe nergy crop in the 63 FASOM regions and (2) total emissions for each N source

. § Indirect N 2 O emissions on a per

- acre basis using results from DAYCENT simulations of volatilization, leaching

, and runoff of nitrogen from each conventional and bioenergy crop in cluded in the analysis for the 63 FASOMGHG regions, combined with IPCC factors for the N 2 O emission associated with the simulated nitrogen losses

.
Rather than relying simply on the rate of fertilizer application and the IPCC default for N 2 O emissions per unit of fertilizer, we now account for N cycling within DAYCENT and can generate emissions estimates 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 N 2 O 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 invent ory 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 (CH 4 , N 2 O, NO x and N 2 ), 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 N 2 O emissions

. Nitrogen Fertilizer

, Crop Residues, and Symbiotic N Fixation

To calculate direct N 2 O 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/manag ement/region combination in FASOMGHG. Estimates of emissions per acre from DAYCENT a re defined as a function of (1) continuous management variables, (2) indicator management variables, and (3) continuous geophysical variables. The NREL modeling team has pr ovided individual statistical meta

- models expressing direct and indirect N 2 O emissions as a function of the aforementioned sets of
variables and their interactions.

To calculate direct N 2 O emissions from N inputs to croplands and grasslands (primarily
fer tilizer 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 appli cation rate: T his 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 N 2 O 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 am endments 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: T his variable is the percentage of crop resi due 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 r elevant for hay or grass systems.7

- 21

§ Indicator (0/1) management variables

– Crop or grass types: C rop and grass types vary by region depending on the types simulated in the U.S. Soil N 2 O national inventory

. This set of variables is treated as a set of indicator variables with a 1 assigned to the crop/grass of 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 r epresents 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 t o 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).

– Irri gation status: T his 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: T hese variables are also treat ed 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 varia ble is only relevant for crop models.

– Land use: T his 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 nat ive 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: T his variable indicates whether nitri fication inhibitors are applied. A v alue 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 count y 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 s oil m

oisture i ndex: A n 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 indi cator 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 t emperature i ndex: This variable represents the long

- term average temperature from weather stations.

– Sand f

raction: T his variab le 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
N 2 O emissions per acre for each crop, region, and production practice combination available in
the FASOMGHG crop budgets

.
Changes in FASOMGHG direct N 2 O emissions fr om croplands result pr imarily 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
N 2 O emissions are only adopted in the model under poli cies creating incentives for reducing these emissions (e.g., GHG mitigation incentives providing compensation for reduced N 2 O emissions). Ongoing work is expanding the range of mitigation options available within FASOMGHG to include other N management opti ons that can reduce N 2 O 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 N 2 O emissions. While these model updates are currently in process, FASOMGHG will soon be capable of exploring a variety of N management options for reducing N 2 O emissions beyond simply reducing N use or cropped acreage.

Indirect Emissions from Volatilization and Leaching

In addition to direct N 2 O emissions from croplands, there are indirect emissions
associated with v olatilization 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 N 2 O emissions

. In FASOMGHG , these emissions are calculated based on the estimates available from DAYCENT modeling

. Estimates of emissions due to N lost fro m a managed field through volatilization were calculated as a function of the same set of variables identified above under modeling of direct N 2 O emissions per acre

. The results of this modeling are then multiplied by the IPCC indirect emissions factor for volatilization (0.010 kg N 2 O

N/kgNH 2 - N+NO x - N/yr) and the conversion factor of 44/28 to convert the loss of nitrogen to volatilization through multiple pathways into N 2 O emissions

.7

- 23

After fertilizer application or heavy rain, large amounts of N may leach f rom the soil into drainage ditches, streams, rivers

, and eventually estuaries

. Some of this N is emitted as N 2 O when the leached nitrogen fertilizer undergoes the process of nitrification or denitrification

. In
FASOMGHG , these emissions are calculated usin g a similar modeling structure to that described
above for the calculation of direct N 2 O emissions

. The same variables are used in models estimated to generate estimates of emissions from leaching, multiplied by 0.0075 kgN 2 O

N/kgNO 3 - N/yr and the conversion factor of 44/28 to convert to N 2 O emissions

. IPCC guidelines from 2006 recommend that N leaching not be 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 evapotransp iration level.

Agricultural Residue Burning

As mentioned above, FASOMGHG assumes that a certain fraction of fields is burned each year, which results in N 2 O (and methane) emissions

. These emissions are calculated using
the IPCC default value of 0.7% of N contained in the residue that is burned being emitted as
N 2 O

. This results in emissions ranging from 0.02 kg CO 2 eq/acre to 2.26 kg CO 2 eq/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 IP CC guidance for other sources of N 2 O, direct emissions for histosols are based on area rather than annual N inputs. Emissions were assumed to equal 8 kg N 2 O

- N per hectare for cultivated temperate histosols . In the absence of data to assign emissions to cro p/region combinations, the
total emissions for histosols in the United States are evenly divided among all cropland ( 0.04 kg
CO 2 eq/acre)

. Livestock Manure Management
Livestock manure under aerobic conditions produces N 2 O as part of the nitrogen cycle
throu gh the nitrification and denitrification of the organic nitrogen in livestock manure and urine

. To calculate N 2 O emissions from manure management, FASOM GHG calculates the average
N 2 O 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 EPA7

- 24

GHG inventory for 1990

– 2003 (EPA, 2005b )

. Average emissions per head are 6.2 kg
CO 2 eq/head for swine, 21.7 kg CO 2 eq/head for sh eep, 71.3 kg CO 2 eq/head for beef cattle, and 421.6 kg CO 2 eq/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 N 2 O emissions
associated with manure ma nagement

.

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. T he level of sequestration per a cre varies by region, land use, and tillage and irrigation practices with dynamic adjustments taking place over time following a change in land use, till age, 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 m anagement 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 peri odic 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
de rived from Smith et al. ( 200 6 )

. 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 r efers 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 H eath, Birdsey, and Williams (2002 ). L ittle 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 st eady

- 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 Bird sey (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 larg est 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 FASOM GHG.
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 )

.

C oarse 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 f or 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 log ging 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 curren t 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 till age 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. Ojim a. 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 h arvested 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

- 1 2 presents an example of carbon disposition over time based on FA SOMGHG 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/mccarl

bruce/papers/1212FASOMGHG_doc.pdf.

Carbon Disposition a fter Harvest

Harvested Logs Removed from S ite

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 Manufacturing7

- 28

Table 7

- 1 2

. Example of Disposition Patterns of Harvested Wood b y Region and Harvest

Type, 100

- Year Period: Southeast

Region Type Product Disposi

tion 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 Softwoo d 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 0.4 0 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 Landf ills 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

Southeas t 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 t o 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 pro ducts:

§ softwood sawlogs for export7

- 29

§ hardwood sawlogs for export

§ softwood lumber

§ softwood plywood

§ oriented strand board

§ hardwood lumber

§ hardwood plywood

§ so ftwood 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 t racks 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 approx imately 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 ei ther 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 woo d products and 44% of paper products going into landfills remain7

- 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 poo ls goes into CO 2 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 R emaining in P roduct Landfill

Landfill (Permanent) Landfill (Temporary) Carbon L eaving P roduct7

- 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. r ailroads 67

Railroad ties 12 Railcar repair 12 Manufacturing

Household furnit ure 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 burn ed for fuel, wi th 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 pr oduce 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 t o 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 tracked 12 (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 describ ed 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

- S ite 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/acre7

- 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 consi stent 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 calc ulated 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 reallocatio n 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 c arbon 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

cr eate 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 CO 2 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 pract ices 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 till age 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 tra cks 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 se questration

. § 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 suc h 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 estima ted 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 sta tus, 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

consta nt over time, FASOMGHG yields non uniform changes in soil carbon consistent with the generally accepted scientific finding that carbon sequestered in an ecosystem approaches steady

state 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 s tate, 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 30Change 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 r eflect 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 s oil 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 va lues

. 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 h ave taken place at all points between the bas e period and 20 20

- 2024 based on the
assumed rate of saturation over time

. For analogous reasons, changes in land use in the 20 20

- 2024 model period would continue to affect soil carbon emissions calculated in the model for the following 25 years

.7

- 37

7

. 3 GHG Accounting for Bioenergy Production

FASOMGHG account s for changes in emissions associated with bioenergy production
and use in lieu of fossil fuels

, including emissions associated with hauling and processing bioenerg y 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 gaso line with ethanol, the avoided CO 2 emissions in FASOMGHG are 5.49 kg CO 2 eq/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 re duction in emissions of 6.50 kg CO 2 eq/gallon of diesel replaced. Bioelectricity is assumed to
reduce CO 2 emissions by 625,515.27 metric tons CO 2 eq per 100MW generation capacity (using 9.198 TBtu of feedstock per year) for nonresidue crops and 628,998.13 me tric tons CO 2 eq 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 CO 2 eq/gallon and
those from starch

- based feedstocks are 2.00 kg CO 2 eq/gallon. For feedstocks used to produce cellulosic ethanol, emissions from processing are between 0.55 kg CO 2 eq/gallon and 1.00 kg
CO 2 eq/gallon. For biodiesel production, emissions from processing are assumed to be 0.60 kg
CO 2 eq/gallon for canola oil, corn oil, and soybean oil, and 0 kg CO 2 eq/gallon for waste fats and oils. Emissions from processing biomass feedstocks are assumed to be 69.45 metric tons CO 2 eq 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 CO 2 emissions, there are also n et changes in non

- CO 2 emissions during processing and combustion when bioenergy feedstock s are used in place of fossil fuels
that are included in FASOMGHG

. Based on calculations of the net change in emissions, usi ng ethanol in place of gasoline reduces CH 4 emissions by 0.0113 kg CO 2 eq/gallon and N 2 O emissions by 0.1758 kg CO 2 eq/gallon, using b iodiesel ins tea d of diesel reduces CH 4 emissions by 0.1871 kg CO 2 eq/gallon and N 2 O emissions by 0.0924 kg CO 2 eq/gallon , and using biomass
feedstocks instead of coal increases CH 4 emissions by 68,250.00 metric tons CO 2 eq per 100MW generation capacity and N 2 O emissions by 833.13 metric tons CO 2 eq 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 e nhance and preserve carbon sinks and include afforestation, forest management, and agricultural soil tillage practices. Agricultural sources of
CH 4 , N 2 O, and fossil fuel CO 2 can be reduced through changes in fertilizer applications and
livestock and manure management. CO 2 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 inc reasing), 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

- CO 2 , fo ssil fuel CO 2 , 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 A ffected

Source/Sink Mitigation Strategy CO 2 CH 4 N 2 O
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, s equestration X X Crop fertilization alter ation Emission, s equestration X X Crop input alteration Emission X X Crop tillage alteration Emission, s equestration X X

Grassland conversion Sequestration X Irrigated/dry land conversion E mission 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 f uel s ubstitution X X X

Cellulosic ethanol Fossil f uel s ubsti tution X X X

Biodiesel Fossil f uel s ubstitution X X X Bioelectricity Fossil f uel s ubstitution X X X

Afforestation

, Avoided Deforestation , Forest Management, and Wood Products

The following fundamental mechanisms within forestry allow GHG mitigation :
§ E xpansions 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 choice s 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 f uel usage.

§ U sage of harvested wood to potentially reduce fossil fuel usage by employing it as a biofuel feedstock for electric ity or biofuels generation.

The principal GHG implications related to the above forestry manipulations involve changes in net sequ estration 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 c hoose 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 regardi ng 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 emissi ons 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 N 2 O emissions from incorporation of residues into the soil as described above

. In addition, agricultu ral fields are
sometimes burned to remove excess residue

. This burning results in N 2 O and CH 4 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 pot ential 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. Th is 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

Anothe r 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 e xclusively 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 an d 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 rota tions 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 fer tilizer 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 re sult.

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 opt ions for reducing these emissions (e.g., intermittent drainage, shallow flooding), the se options may be labor

- intensive and have generally been considered more appropriate for mitigation of rice cultivation emissions in Asia. There are no explicit mitigati on 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 th e model solution with sufficient incentives for reducing non

- CO 2 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 rea dily alter the set of standard predefined tables generated, 1 but 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 ar e currently organized into nine different output files:

§ n ational

- level model summary results ;
§ s cenario description ;
§ r egional

- level agricultural summary results ;
§ d etailed agricultural results ;
§ b ioenergy results ;
§ r egional forestry summary results ;
§ d etailed f

orestry results ;
§ GHG accounting results ; and

§ a dditional 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 a s 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:

§ n ational welfare and its distribution across consumers and producers and across the United States , Canada, and ROW for the markets included within the model ;
§ n ational income derived from GHG payments ;

1 In addition, custom output tables are created as needed for particular model applications.8

- 2

§ n ational index numbers for aggregated agricultural activities, including prices, production, import s, and exports ;
§ n ational agricultural commodity prices ;
§ n ational agricultural commodity production ;
§ n ational livestock feed use by feed type ;
§ n ational agricultural exports by product ;
§ i ndex numbers for international trade and production for international regions ;
§ n ational land use summary and land transfers by land use category ;
§ n ational and regional agricultural sector consumer and producer surplus ;
§ n ational crop acres by crop ;
§ r egional land value and use by land type ; and

§ c omparison 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:

§ s pecific information on model sett ings 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

§ a lternatives 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 sum mary results file, there are 24 tables containing model output on:

§ t otal producer surplus at the regional level ;
§ r egional use of individual available feedstocks for bioenergy production ;
§ p roducer surplus for crop production at the regional level ;
§ p roducer surplus for livestock production at the regional level ;
§ p roducer surplus associated with afforestation at the regional level ;8

- 3

§ r egional agricultural commodity production ;
§ i ndices of regional agricultural production by aggregated category ;
§ r egional crop acre s by crop , and

§ GHG payments for afforestation by region

.
The detailed agricultural results file contains 20 tables with model output for:

§ n ational 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 ;
§ n ational ag ricultural 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 ;
§ n ational use of primary com modities in secondary processing by production process ;
§ t otal national use of primary commodities in secondary processing ;
§ r egional and national water, energy, and fertilizer use by crop and irrigation status ;
§ n ational estimates of indirect and induced far m activity and nonfarm activity induced by agricultural production and revenue based on input

- output relationships ; and

§ r egional and national number of head of livestock by type

.

In the bio energy results file, there are six tables summarizing model outp ut for:

§ n ational use of agricultural commodities in bioenergy production by feedstock and bioenergy process, along with bioenergy yields per unit of feedstock ;
§ n ational production of bioenergy in quantity of bioenergy produced by feedstock ;
§ r egional bioene rgy production by process ; and

§ r egional bioenergy feedstock use by process

.
The regional forest summary results file contains a single large table summarizing:

§ f orestland area by remaining forest that has not been harvested during the model run and new fo restland (from either reforestation or afforestation) by region ;
§ a verage age of standing forests by region ;8

- 4

§ h arvested, reforested, and afforested areas by region ;
§ a verage rotation length for softwoods and hardwoods by region and forest type ;
§ p lanting and h arvest by ownership by region ;
§ p lanting and harvest by MIC by region ;
§ f orest inventory levels for hardwoods and softwoods by region ;
§ f orest inventory levels by ownership by region ;
§ r egional prices of logs and manufactured forest products by product by regi on ;
§ o utput of logs and manufactured products by region by region ;
§ r egional consumption of forest products by product ; and

§ i mports and exports of forestry products by product and trade region

In the detailed fo restry results file, there are two large tabl es 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

§ t otal national and regional stocks of GHG emissions and sequestration over time by GHG accounting category ;
§ a nnual GHG flux, i.e., annual average changes in emissions or sequestration for each model period at the national and regional levels by GHG accounting category 2 ;
§ c hanges 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 ;
§ r egional and national summaries of the use of GHG mitigation strategies and levels of specific agricultural and forest activities that impact GHG emissions ; and

§ d etailed 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 c onsistent 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:

§ n ational pasture use by pasture c ategory and livestock type ; and

§ n ational disposition of cropland, pasture, and forestland over time, tracking area in production, idled, and moving into and out of alternative land use categories9

- 1 SECTION 9
SELECTED ENVIRONMENT AL, RESOURCE, AND PO LICY ANALYSIS
A PPLICATIONS

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
bio energy

- related studies.

9

. 1 Types of Policies and Issues that can be Evaluated using FASOMGHG
FASOMGHG remains very capabl e 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 resou rce use or terms of trade in forestry and agriculture can, in prin ciple, be evaluated using the model. In addition to climate and bioenergy

- related applications, e xa mples include trade policy, non climate 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 for est 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 in to FASOMGHG, a much more complete characterization of GHG mitigati on 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 entir ely 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 technologic al 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 p ast 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 appli cations to climate or bioenergy

- related issues withi n the p ast five years. For more complete and periodically updated references to related publications and policy applications of FASOMGHG, refer to Dr. McCarl ’ s W eb site (http://agecon2.tamu.edu/people/faculty/mccarl

- bruce/)

.
9

. 2

. 1 GHG Mitigation

FASOMGHG has be en 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 t he Climate Stewardship and Innovation Act of 2007 (S. 280), t he Low
Carbon Econo my 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 u sed 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,

c ompetitiveness 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 m itigation potential within the p ast 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. (20 1 0 ); 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 for est 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 meas ure 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 t he original version of FASOM) include :
§ c arbon sequestration in agriculture ;
§ f ossil fuel mitigation through agricultural practices ;
§ m itigation of non

- CO 2 GHGs from agriculture ; and

§ r educed 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 a nalytical 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 ha ve been examined using FASOMGHG

, and the modeling team continues to update and further develop the model for conti nued 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 relati vely 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 strateg y 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 bio energy production is a potentially im portant component of GHG mitigation
strateg ies , 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 generatio n and in production of liquid transportation fuels. FASOMGHG i ncorporates 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 po licy 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 asses sment 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

. For9

- 5

instance, geo

- specific c hanges in temperature, precipitation, and CO 2 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 well

being 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 yie ld 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 FASOM GHG was linke d 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 4 th Assessment Report and the Environmental Policy Integrated Climate (EPIC) model, which was used to simulate changes in crop yields and irrigatio n 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 affect ing 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 leve ls of output and prices for timber and agricultural commodities and the welfare of producers and consumers in these

sectors . For instance, u nder scenarios showing a climate change

- induced increase in aggregate forest productivity, timber output rises, pric es 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 produ ctivity effects

.10

- 1 SECTION 10
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- 2

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