U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page i

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008. Climate Change 

Program Office, Office of the Chief Economist, U.S. Department of Agriculture. Technical

Bulletin No. 1930. 159 pp. June, 2011.

Abstract

Emissions of the three most important long-lived greenhouse gases (GHG) have increased 

measurably over the past two centuries. Carbon dioxide (CO2), methane (CH4), and nitrous oxide 

(N2O) concentrations in the atmosphere have increased by approximately 38%, 143%, and 18%, 

respectively, since 1750. In the U.S., agriculture accounted for approximately 6% of total GHG 

emissions (6,957 Tg CO2 eq. [teragrams of carbon dioxide equivalent]) in 2008. Livestock, 

grasslands, crop production, and energy use contributed a total of 462 Tg CO2 eq. to the 

atmosphere in 2008. This total includes an offset from agricultural soil carbon sequestration of 

roughly 40 Tg CO2 eq. The primary agricultural sources are N2O emissions from cropped and 

grazed soils (214 Tg CO2 eq.), CH4 emissions from enteric fermentation (141 Tg CO2 eq.), CO2

emissions from energy use (72 Tg CO2 eq.), and CH4 emissions from managed livestock waste 

(45 Tg CO2 eq.). Forests in the United States contributed a net reduction in atmospheric GHG of 

approximately 886 Tg CO2 eq. in 2008, which offset total U.S. GHG emissions by approximately 

13%. In aggregate, the U.S. agricultural sector (including GHG sources from crop and livestock 

production, grasslands, energy use and GHG sinks for forests and urban trees) was estimated to 

be a net sink of 424 Tg CO2 eq. in 2008.

Keywords: climate change, greenhouse gas, land use, carbon stocks, carbon sequestration, 

enteric fermentation, livestock waste, nitrous oxide, methane, rice cultivation, energy 

consumption.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page ii

June, 2011

Dear Reader:

I am pleased to present you with this report, The U.S. Agriculture and Forestry 

Greenhouse Gas Inventory: 1990-2008, an update to USDA Technical Bulletin 1921 (2008),

which accounted for greenhouse gas emissions and sinks for the agricultural and forestry sectors 

through 2005. 

This report is consistent with the U.S. Environmental Protection Agency’s (EPA)

Inventory of U.S. Greenhouse Gas Emissions and Sinks (April, 2010) in its assessment methods. 

However, EPA’s national-scale reporting here has been disaggregated to provide a State-by-State 

presentation. We believe this format will serve as a useful resource to land managers, planners, 

and others with an interest in greenhouse gas dynamics and their relationships to land use and 

land use change.

Data collection and analysis, as well as coordination of this Inventory, could not have 

been accomplished without the contributions of Stephen Del Grosso, Ronald Follett, and others 

within USDA’s Agricultural Research Service. I also express my thanks to Linda Heath, James 

Smith, and Rich Birdsey of the USDA Forest Service; James Duffield of USDA’s Office of 

Energy Policy and New Uses; Jerry Hatfield of USDA’s Agricultural Research Service; Stephen 

Ogle at the Natural Resources Ecology Laboratory of Colorado State University; and Tom Wirth 

in EPA’s Office of Atmospheric Programs for their data, analysis, and review. Their thoughtful 

and diligent efforts compose the foundation of this report, which we hope will serve as a useful 

resource for a broad spectrum of land management-focused professionals and other interested 

individuals.

Sincerely,

William Hohenstein

Director, USDA Climate Change Program Office
Contributors

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page iii

Stephen Del Grosso (editor), Agricultural Research Service, USDA

James Duffield, Office of Energy Policy and New Uses, USDA

Marlen D. Eve (co-editor), Climate Change Program Office, USDA

Linda Heath, Forest Service, USDA

Stephen Ogle, Natural Resources Ecology Laboratory, Colorado State University

James Smith, Forest Service, USDA

Tom Wirth, Office of Atmospheric Programs, EPA
Table of Contents

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page iv

Page

List of Boxes, Maps, Tables, and Figures......................................................... vi

Acknowledgments ............................................................................................ ix

Glossary of Terms and Units ............................................................................ x

Chapter 1: Introduction .................................................................................... 1

1.1 Global Change and Global Greenhouse Gas Emissions in Agriculture 

 and Forestry ..........................................................................................................1

1.2 Sources and Mechanisms for GHG Emissions..........................................................4

1.3 Strategies for GHG Mitigation...................................................................................5

1.4 Purpose of this Report................................................................................................6

1.5 Overview of the Report Structure .............................................................................8

1.6 Summary of Changes and Additions for the Third Edition of the Inventory ............8

Chapter 2: Livestock and Grazed Land Emissions.......................................... 10

2.1 Summary of U.S. GHG Emissions from Livestock...................................................10

2.2 Sources of GHG Emissions from Livestock..............................................................12

2.3 U.S. Livestock Populations........................................................................................15

2.4 Enteric Fermentation..................................................................................................16

2.5 Managed Livestock Waste.........................................................................................20

2.6 Grazed Lands.............................................................................................................27

2.7 Mitigating Greenhouse Gas Emissions from Livestock ............................................33

Chapter 3: Cropland Agriculture ..................................................................... 36

 3.1 Summary of U.S. Greenhouse Gas Emissions from Cropland Agriculture...............36

3.2 Sources of Greenhouse Gas Emissions in Cropland Agriculture ..............................40

3.3 Nitrous Oxide Emissions from Cropped Soils...........................................................43

3.4 Methane Emissions from Rice Cultivation................................................................53

3.5 Residue Burning.........................................................................................................56

3.6 Carbon Stock Changes in Cropped Soils...................................................................60

 3.7 Uncertainty in Estimating Carbon Stock Changes in Agricultural Soils...................65

3.8 Mitigation of Carbon Dioxide Emissions..................................................................67
Table of Contents

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page v

Chapter 4: Carbon Stocks & Stock Changes in U.S. Forests.......................... 68

4.1 Summary....................................................................................................................68

4.2 Concepts and Conventions ........................................................................................72

4.3 Carbon Stocks and Stock Changes by Forest Type, Region, and Ownership ...........72

4.4 Mechanisms of Carbon Transfer................................................................................76

4.5 Methods......................................................................................................................78

4.6 Major Changes Compared to Previous Inventories ..................................................81

4.7 Uncertainty.................................................................................................................81

Chapter 5: Energy Use in Agriculture ............................................................. 83

5.1 Summary of Greenhouse Gas Emissions from Energy Use in Agriculture...............83

5.2 Spatial and Temporal Trends in Greenhouse Gas Emissions from

 Energy Use in Agriculture .....................................................................................83

5.3 Sources of Greenhouse Gas Emissions from Energy Use on 

Agricultural Operations .............................................................................................85

5.4 Methods for Estimating Carbon Dioxide Emissions from Energy 

 Use in Agriculture......................................................................................................87

5.5. Major Changes Compared to Previous Inventories..................................................88

References......................................................................................................... 90

Appendix A....................................................................................................... A-1

Appendix B ....................................................................................................... B-1

Appendix C ....................................................................................................... C-1
List of Boxes, Maps, Tables and Figures

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page vi

Boxes

Box 1-1 Greenhouse Gas Emissions Units......................................................................2

Maps

Map 2-1 GHG Emissions from Livestock in 2008 ..........................................................12

Map 2-2 Methane Emissions from Enteric Fermentation in 2008...................................16

Map 2-3 GHG Emissions from Managed Waste in 2008................................................22

Map 2-4 Nitrous Oxide Emissions from Grazed Soils in 2008 .......................................29

Map 3-1 County Level Nitrous Oxide Emissions from Cropped Soils in 2005 ..............37

Map 3-2 U.S. Cropped Land ...........................................................................................38

Map 3-3 State Level Carbon Dioxide Fluxes from Cropped Soils in 2005.....................62

Map 3-4 Percentage of No Till Adoption by County ......................................................64

Map 3-5 Percentage of Reduced Till Adoption by County .............................................64

Map 4-1 Carbon Stock Change by State in 2008.............................................................69

Map 4-2 U.S. Forest Carbon Stocks in 2008 ...................................................................70

Map 4-3 Regions Used for Carbon Stock Summaries.....................................................70

Tables

Table 1-1 Agriculture and Forestry Greenhouse Gas Emission Estimates and 

Uncertainty Intervals, 2008..................................................................................1

Table 1-2 Summary of Agriculture and Forestry Emissions and 

Offsets, 1990, 1995, 2000-2008...........................................................................5

Table 2-1 Greenhouse Gas Emission Estimates and Uncertainty 

Intervals in 2008 ..................................................................................................10

Table 2-2 Greenhouse Gas Emissions by Livestock Category 

and Source in 2008...............................................................................................11

Table 2-3 Descriptions of Livestock Waste Deposition and 

Storage Pathways.................................................................................................13

Table 2-4 U.S. Methane Emissions from Enteric Fermentation 

in 1990, 1995, 2000-2008 ....................................................................................17

Table 2-5 Greenhouse Gas Emissions from Managed Livestock 

Waste in 1990, 1995, 2000-2008 .........................................................................21

Table 2-6 Greenhouse Gas Emissions from Grazed Lands 

in 1990, 1995, 2000-2008 ....................................................................................28

Table 3-1 Estimates and Uncertainties for Cropland Greenhouse 

Gas Emissions, 2008............................................................................................36

Table 3-2 Summary of Greenhouse Gas Emissions from 

Cropland Agriculture, 1990, 1995, 2000-2008....................................................37

Table 3-3 Nitrous Oxide Emissions from Different Cropped 

Soils, 1990, 1995, 2000-2008 ..............................................................................40
List of Boxes, Maps, Tables and Figures

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page vii

Table 3-4 Methane from Rice Cultivation from Primary and Ratoon Operations

 by State, 1990, 1995, 2000-2008 ...........................................................................53

Table 3-5 Change in Methane Emissions from Rice Cultivation, 1990-2008 ................54

Table 3-6 Greenhouse Gas Emissions from Agriculture 

Burning by Crop, 1990, 1995, 2000-2008 ...........................................................57

Table 4-1 Forest Carbon Stock Change Estimates and Uncertainty Intervals

for 2008................................................................................................................68

Table 4-2 Carbon Stocks and Annual Change for Forest and Wood Pools, 

1990, 1995, 2000-2008 ........................................................................................68

Table 4-3 Forest Area, Carbon Stocks, and Net Annual Stock Change by Forest 

 Type Group .............................................................................................................71

Table 5-1 Energy Use and Carbon Dioxide Emissions 

by Fuel Source on U.S. Farms in 2008 ................................................................83

Table 5-2 Definitions of Regions Used in Figure 5-1......................................................84

Figures

Figure 1-1 Agricultural Sources of Greenhouse Gas 

Emissions in 2008................................................................................................3

Figure 1-2 Agricultural Sinks of Carbon Dioxide in 2008 ..............................................3

Figure 1-3 Agriculture and Forestry Emissions and Offsets for 1990, 1995, 

2000-2008 ............................................................................................................4

Figure 2-1 Greenhouse Gas Emission from 

Livestock in 2008.................................................................................................10

Figure 2-2 Greenhouse Gas Emissions from Managed Livestock 

Waste by Livestock Type in 2008 .......................................................................21

Figure 2-3 Greenhouse Gas Emissions from 

Managed Livestock Waste, 1990-2008................................................................22

Figure 2-4 Estimated Reductions in Methane Emissions from Anaerobic 

Digesters, 1990-2008 ...........................................................................................34

Figure 3-1 U.S. Planted Cropland Area by Crop Type, 1990-2008 ................................38

Figure 3-2 Annual Nitrogen Inputs to Cropland Soil ......................................................39

Figure 3-3 Methane from Rice Cultivation by State, 1990 & 2008.................................53

Figure 3-4 Greenhouse Gas Emissions from Field Burning by Crop Type, 2008...........56

Figure 3-5 Change in Commodity Production, 1990-2008 .............................................57

Figure 3-6 Change in Commodity Production, 

1990-2008 ............................................................................................................58

Figure 3-7 Carbon Dioxide Emissions and Sequestration 

Sources from Cropland Soils, 2008 .....................................................................61

Figure 4-1(a) Forest Ecosystem Carbon Stocks; (b) Forest Ecosystem Average 

Stock Density.......................................................................................................74

Figure 4-2 Net Annual Forest Carbon Stock Change ......................................................75

Figure 4-3 Summary Diagram of Forest Carbon Stocks and Carbon Transfer 
List of Boxes, Maps, Tables and Figures

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page viii

Among Stocks......................................................................................................77

Figure 5-1 Carbon Dioxide Emissions from Energy Use in Agriculture 

by Region, 2008...................................................................................................84

 Figure 5-2 Energy Use in Agriculture by Source, 1965-2008 .........................................85

Figure 5-3 Carbon Dioxide Emissions from Energy Use in Agriculture by Fuel 

Source, 2001, 2005, and 2008..............................................................................89
Acknowledgments

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page ix

This report was made possible by contributions from a number of individuals and collaboration 

between the U.S. Department of Agriculture (USDA), the U.S. Environmental Protection 

Agency (EPA), and Colorado State University.

The U.S. Agriculture and Forestry Greenhouse Gas Inventory (USDA GHG Inventory) is 

supplemental to the official Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHG 

Inventory) submitted by EPA to the United Nations Framework Convention on Climate Change 

each April. We thank the EPA for permission to reprint estimates and methodologies from the 

official U.S. GHG Inventory. We would like to acknowledge the contribution of Tom Wirth of 

EPA’s Office of Atmospheric Programs, who provided detailed emissions data for livestock 

sources of methane and nitrous oxide reported in Chapter 2. Stephen Del Grosso, Melissa 

Reyes-Fox, and Kris Nichols of USDA Agriculture Research Service (ARS) Northern Plains 

Area (NPA) Soil Plant Nutrient Research unit in Fort Collins, CO, in collaboration with Stephen 

Ogle, William Parton, and Cindy Keough of Colorado State University, provided estimates for 

N2O emissions from agricultural soils, wrote most of the text for Chapters 2 and 3, and all of the 

text for Chapter 1. The Natural Resources Ecology Laboratory (NREL) of Colorado State 

University prepared the agricultural soil carbon estimates and supporting text for Chapters 2 and 

3. Amy Swan of the NREL assisted with maps for Chapter 3. James Smith and Linda Heath of 

the USDA Forest Service, Northern Research Station, provided estimates and text for Chapter 4.

The USDA Forest Service tailored its analysis for this report to partition regional and land 

ownership trends in forest carbon. James Duffield of the Office of Energy Policy and New Uses 

prepared Chapter 5. The estimates presented in Chapter 5 are unique to this report and derive 

from ongoing work in the Office of Energy Policy and New Uses to track fuel consumption in 

agriculture. We acknowledge Mary F. Smith and Morgan Carey of USDA ARS NPA Soil Plant 

Nutrient unit for formatting the document for printing. 

Comments provided by reviewers from the USDA ARS, EPA, USDA Forest Service, and 

Colorado State University greatly improved this document. Brenda Chapin, Office of the Chief 

Economist, and the USDA Office of Communications provided assistance with publishing.
Glossary of Terms and Units

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page x

CO2 Carbon dioxide

CH4 Methane

N2O Nitrous oxide

NOx Nitrogen oxides

C Carbon

GHG Greenhouse gas

GWP Global warming potential

Tg Teragram (1012 grams)

Tg CO2 eq. Teragrams of carbon dioxide equivalent 

Gg Gigagram (109 grams)

Mg Megagram (106 grams)

t Metric ton (1,000 kg)

ha Hectares

DE Digestible energy (percent)

Ym Fraction of gross energy converted to CH4

TDN Total digestible nutrients

VOCs Volatile organic compounds

VS Volatile solids

Bo Maximum CH4-producing capacity for domestic 

wastewater

DM Dry matter

Btu British thermal unit

Qbtu Quadrillion British thermal units

Tbtu Trillion British thermal units

EF Emission factor

MCF Methane conversion factor
Chapter 1: Introduction

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 1

1.1 Global Change and Global Greenhouse Gas Emissions in 

Agriculture and Forestry

In 2008, total U.S. greenhouse gas emissions measured 6,957 teragrams of carbon dioxide 

equivalents (Tg CO2 eq.), rising nearly 14 percent from 1990 estimates (EPA 2010). Global 

concentrations of the three most important long-lived greenhouse gases (GHGs) in the

atmosphere have increased measurably since the onset of the Industrial Revolution in 1750. 

Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) concentrations in the atmosphere 

have increased by approximately 36%, 148%, and 18% respectively (EPA 2010, Keeling & 

Whorf 2005, Dlugokencky et al. 2005, Prinn et al. 2000). Agriculture and forestry practices may 

either contribute to or remove GHGs from the atmosphere. Agriculture and forestry have 

contributed to GHG levels in the atmosphere through cultivation and fertilization of soils, 

production of ruminant livestock, management of livestock manure, land use conversions, and 

fuel consumption. The primary GHG sources for agriculture are N2O emissions from cropped 

and grazed soils, CH4 emissions from ruminant livestock production and rice cultivation, and 

CH4 and N2O emissions from managed livestock waste. The management of cropped, grazed, 

and forestland has helped offset GHG emissions by promoting the biological uptake of CO2 

through the incorporation of carbon into biomass, wood products, and soils, yielding a total U.S.

net emissions of 6,016 Tg CO2 eq. (net CO2 flux from Land Use, Land Use Change, and 

Forestry, EPA 2010). This report serves to estimate U.S. GHG emissions for the agricultural and 

forestry sectors, to quantify uncertainty in emission estimates, and to estimate the potential of 

agriculture to mitigate U.S. GHG emissions.

Observed increases in atmospheric 

GHG concentrations are primarily a 

result of fossil fuel combustion for 

power generation, transportation, 

and construction. In the United 

States, agriculture accounted for 

6.1% of total GHG emissions in 

2008 (EPA 2010). Greenhouse gas 

emissions estimates reported here 

are in units of CO2 equivalents. Box 

1-1 describes this reporting 

convention, which normalizes all 

GHG emissions to CO2 equivalents 

using Global Warming Potentials 

(GWP). Agriculture in the United States, including livestock, grasslands, crop production, and 

energy use, contributed a total of 462 Tg CO2 eq. to the atmosphere in 2008 (Table 1-1). This 

total includes an offset, or sink, from agricultural (cropped and grazed lands) soil carbon 

sequestration of roughly 40 Tg CO2 eq (Table 1-2). Forests in the United States contributed a net 

reduction in atmospheric GHGs of approximately 886 Tg CO2 eq. in 2008, which offset total 

U.S. GHG emissions by almost 13% (EPA 2010). After accounting for GHG sources and C 

sequestration, agricultural and forested lands in the U.S. were estimated to be a net sink of 424 

Tg CO2 eq. (Table 1-1). The 95% confidence interval for this estimate ranges from a sink of 587 

to 240 Tg 

Table 1-1 Agriculture and Forestry Greenhouse Gas 

Emission Estimates and Uncertainty Intervals, 2008

Estimate

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Source Tg CO2 eq.1 percent

Livestock 203 185 230 -9 +14 

Crops2 154 84 215 -34 +71 

Grassland2 33 5 132 -84 +298 

Energy Use3 72 

Forestry (792) (935) (651) -18 +18 

Urban Trees (94)

Net Emissions (424) (587) (240) -38 +44 

Note: Parentheses indicate net sequestration. 1 Teragrams of carbon dioxide equivalent.

2 Includes sequestration in agricultural soils.

3 Confidence intervals were not available for this component.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 2

CO2 eq. (Table 1-1). Approximately one-third of agriculture’s GHG emissions in 2008 were due 

to crop production. Most of the emissions from crop production were from non-rice soils, with 

residue burning and rice cropping accounting for about 2% of overall agricultural emissions 

BOX 1-1

The USDA greenhouse gas (GHG) Inventory report follows the international convention for 

reporting GHG emissions, as described in the introduction of the U.S. GHG Inventory 

(EPA, 2006). Emissions of GHGs are expressed in equivalent terms, normalized to carbon 

dioxide using Global Warming Potentials (GWPs) published by the Intergovernmental 

Panel on Climate Change (IPCC) Second Assessment Report (SAR). Global Warming 

Potentials, which are based on physical and chemical properties of gases, represent the 

relative effect of a given GHG on the climate, integrated over a given time period, relative 

to carbon dioxide (CO2) (IPCC, 2001). The GWP values used in the U.S. GHG Inventory 

and this report are recommended by the IPCC for national GHG inventory reporting (Table 

B1-1). These values for methane (CH4) and nitrous oxide (N2O) are referenced to CO2 and 

based on a 100-year time period (IPCC, 1996).

Table B1-1 (Reproduced from U.S. GHG Inventory 2003, Table 1-2) 

Global Warming Potentials of Selected Greenhouse Gases

Gas Atmospheric lifetime (yrs) GWP*

CO2 50-200 1

CH4 12 21

N2O 120 310

*For consistency with international reporting standards, the U.S. GHG Inventory 

uses GWP values published in the IPCC Second Assessment Report (1996). 

Global Warming Potential and estimated atmospheric lifetime values were revised 

for some gases in the IPCC Third Assessment Report (2001).

In the USDA and U.S.GHG Inventories, units are expressed as teragrams carbon dioxide 

equivalent (Tg CO2 Eq.). One teragram equals one million metric tons. The formula for 

converting gigagrams (1Gg = 109 grams) of a GHG to teragrams (1Tg = 1012 grams) of 

carbon dioxide equivalent (Tg CO2 eq.) is provided in the U.S. GHG Inventory and is 

repeated here for clarity:

In the land use sector, where carbon dioxide gas is sequestered and stored as carbon (C) in 

biomass and soils, greenhouse gas removals are often expressed in units of million metric 

tons of carbon equivalent (MMTCE). The formula below shows how to convert MMTCE 

to Tg CO2 eq., and is based on the molecular weights of carbon and carbon dioxide.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 3

(Figure 1-1). Livestock production is responsible for most of the remaining agricultural 

emissions, with about 28% from enteric fermentation, 12% from managed waste, and 13% from 

grazed lands. The remaining 14% of total emissions result from agriculturally related energy 

usage, which is listed under 

the Energy heading by EPA 

(2010), but is provided here 

for comprehensiveness. It 

should be noted that the 

estimates in Figure 1-1 are 

for emissions only and do not 

account for C storage in 

agricultural soils and forests. 

Regarding sequestration, 

forests are by far the leading 

sink, followed by harvested 

wood products, urban trees, 

and agricultural soils (Figure 

1-2).

Sources and sinks of 

emissions are conveniently 

partitioned (sinks are less 

than 0) in Figure 1-3. Overall emissions profiles of agricultural sources, including energy use but 

excluding storage by soils and forestry, show that sources increased 8% between 1990 and 2008 

(Table 1-2, Figure 1-3). The sink strength of the forest pool has increased 13% since 1990 (Table 

1-2, Figure 1-3). However, the sink strength of agricultural soils has decreased by approximately 

57% since 1990. In 

aggregate, the net emissions 

decreased slightly from 1990 

to 2008 by about 2%.

Annual CO2 emissions from 

on-farm energy use in 

agriculture are small relative 

to total energy use across all 

sectors in the United States. 

In 2008, fuel and electricity 

consumption associated with 

crop and livestock operations 

resulted in 72 Tg CO2 (Table 

1-1), which is about 1% of 

overall energy-related CO2

emissions for 2008, equaling 

5,572.8 Tg CO2 (EPA 2010). 

Electricity use led to about 

38% of CO2 emissions from 

Tg CO2 eq. is teragrams carbon dioxide equivalent.

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 4

energy use in agriculture; diesel fuel use led to about 38%, while gasoline, liquefied petroleum 

gas, and natural gas contributed 11%, 7%, and 5%, respectively, to total CO2 emissions from 

energy use in agriculture.

1.2 Sources and Mechanisms for Greenhouse Gas Emissions

Over half of global annual emissions of CH4 and roughly a third of global annual emissions of 

N2O are believed to derive from human sources, mainly from agriculture (IPCC 2007). 

Agricultural activities contribute to these emissions in a number of ways. While losses of N2O to 

the atmosphere occur naturally, the application of nitrogen to amend soil fertility increases the 

natural rate of emissions. The rate is amplified when more nitrogen is applied than can be used 

by the plants, either due to volume or timing. In agricultural practices, nitrogen is added to soils 

through the use of synthetic fertilizers, application of manure, cultivation of nitrogen-fixing 

crops/forages (e.g., legumes), and retention of crop residues. Rice cultivation involves periodic 

flooding of rice paddies, which promotes anaerobic decomposition of organic matter in soil from 

rice residue and organic fertilizers by CH4-emitting soil microbes. Finally, burning of residues in 

agricultural fields produces CH4 and N2O as byproducts.

Livestock grazing, production, and waste cause CH4 and N2O emissions to the atmosphere. 

Ruminant livestock such as cattle, sheep, and goats emit CH4 as a byproduct of their digestive 

processes (called “enteric fermentation”). Managed livestock waste can release CH4 through the 

Tg CO2 eq. is teragrams carbon dioxide equivalent. CH4 is methane. N2O is nitrous oxide. GHG is greenhouse gases.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 5

biological breakdown of organic compounds and N2O through nitrification and denitrification of 

nitrogen contained in manure; the magnitude of emissions depends in large part on manure 

management practices and to some degree on the energy content of livestock feed. Grazed lands 

have enhanced N2O emissions from nitrogen additions through manure and urine and from 

biological fixation of nitrogen by legumes, which are typically seeded in heavily grazed pastures. 

Some pastures are also amended with nitrogen fertilizers, managed manure, and sewage sludge, 

which also contribute to GHG emissions on those lands.

1.3 Strategies for Greenhouse Gas Mitigation

Agriculture and forest management can offset GHG emissions by increasing capacity for carbon 

uptake and storage in biomass, wood products, and soils. This process is referred to as carbon 

sequestration. The net flux of CO2 between the land and the atmosphere is a balance between 

carbon losses from land use conversion and land management practices, and carbon gains from 

forest growth and sequestration in soils (IPCC 2001). Improved forest regeneration and 

management practices such as density control, nutrient management, and genetic tree 

Table 1-2 Summary of Agriculture and Forestry Emissions and Offsets, 1990, 1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source GHG Tg CO2 eq.

Livestock 176.1 193.0 192.0 192.6 194.3 189.3 191.1 195.6 198.6 204.3 203.0 

Enteric 

Fermentation

CH4

132.4 143.7 136.8 136.0 136.3 134.5 134.6 136.7 139.0 141.2 140.8 

Managed Waste CH4 29.3 33.9 38.6 40.1 41.2 38.4 40.2 42.2 42.3 45.9 45.0 

Managed Waste N2O 14.4 15.5 16.7 16.5 16.8 16.3 16.4 16.6 17.3 17.3 17.1 

Grassland (2.1) 5.7 (22.5) 8.9 (4.5) 30.6 31.6 33.4 32.1 31.1 33.2 

Grassland CH4 2.9 3.0 2.7 2.8 2.7 2.7 2.8 2.9 3.0 3.0 2.9 

Grassland N2O 64.0 61.6 58.2 63.9 64.2 59.1 60.1 61.8 60.5 59.5 61.7 

Grassland CO2 (69.0) (58.9) (83.4) (57.7) (71.4) (31.2) (31.2) (31.3) (31.3) (31.4) (31.4)

Crops 124.8 136.9 136.7 164.5 156.1 148.4 152.7 153.6 148.5 149.2 153.8 

Cropland Soils1 N2O 139.1 143.7 151.3 159.7 149.7 147.3 151.9 153.2 150.0 150.7 153.4 

Cropland Soils2 CO2 (22.6) (15.6) (23.5) (4.3) (1.7) (7.2) (8.3) (8.0) (8.9) (9.2) (8.3)

Rice Cultivation CH4 7.1 7.6 7.5 7.6 6.8 6.9 7.6 6.8 5.9 6.2 7.2 

Residue Burning CH4 0.8 0.7 0.9 0.9 0.8 0.9 1.0 0.9 0.9 1.0 1.0 

Residue Burning N2O 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

Energy Use3 CO2 73.9 73.9 73.9 73.9 72.9 71.9 70.9 69.9 70.5 71.0 71.6 

Forestry (786.9) (759.9) (545.2) (593.9) (737.4) (790.7) (805.5) (894.4) (902.3) (898.8) (885.8)

Forests CO2 (598.1) (574.2) (354.8) (500.5) (639.2) (695.9) (700.2) (701.2) (703.9) (703.9) (703.9)

Harvested Wood CO2 (131.8) (118.4) (112.9) (93.4) (98.2) (94.8) (105.3) (105.4) (108.6) (103.0) (88.0)

Urban Trees4 CO2 (57.1) (67.3) (77.5) n/a n/a n/a n/a (87.8) (89.8) (91.9) (93.9)

Net Emissions

All 

GHGs (414.2) (350.3) (165.1) (154.0) (318.7) (350.5) (359.1) (441.9) (452.6) (443.2) (424.2)

Note: Parentheses indicate a net sequestration. Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is nitrous oxide; CO2 is carbon 

dioxide.

1

Includes emissions from managed manure during storage and transport before soil application.

2

Agricultural soil C sequestration includes sequestration on land set aside under the Conservation Reserve Program (CRP), in addition to cultivated mineral 

and organic soils.

3

Data interpolated for all years except 2001, 2005 and 2008.

4

Data not reported for years 2001-2004.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 6

improvement promote tree growth and enhance carbon accumulation in biomass. In addition, 

wood products harvested from forests can serve as long-term carbon storage pools. The adoption 

of agroforestry practices like windbreaks and riparian forest buffers, which incorporate trees and 

shrubs into ongoing farm operations, represents a potentially large GHG sink nationally. While 

deforestation is a large global source of CO2, within the United States, net forestland area has 

experienced a relatively small net loss of roughly 4.2 million hectares (Kimble et al. 2003). 

Avoidance of large scale deforestation and adoption of the practices mentioned above have 

resulted in the forestry sector being a net GHG sink in the United States.

Agricultural practices such as conservation tillage and grassland practices such as rotational 

grazing can also reduce carbon losses and promote carbon sequestration in agricultural soils. 

These practices offset CO2 emissions caused by land use activities such as conventional tillage 

and cultivation of organic soils. However, strategies intended to sequester carbon in soils can 

also impact the fluxes of two important non-CO2 GHGs, N2O and CH4. Consequently, the net 

impact of different management strategies on all three biogenic GHGs must be considered when 

comparing alternatives (Robertson et al. 2000, Del Grosso et al. 2005). Innovative practices to 

reduce GHG emissions from livestock include modifying energy content of livestock feed, 

supplementing feed with agents that reduce CH4 emissions from digestive processes, and 

managing manure in controlled systems that reduce or eliminate GHG emissions. For example, 

anaerobic digesters are a promising technology, whereby CH4 emissions from livestock waste 

are captured and used as an alternative energy source. Nitrous oxide emissions from soils can be 

reduced by precision application of nitrogen fertilizers and use of nitrification inhibitors. These 

and other practices, many of which have additional benefits beyond GHG emission reductions, 

are discussed further in this report.

1.4 Purpose of this Report

The U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 was developed to 

include emission estimates for years not included in the U.S. Agriculture and Forestry 

Greenhouse Gas Inventories: 1990-2001 (USDA 2004) and 1990-2005 (USDA 2008) and to 

revise estimates for previous years based on improved methodologies. This inventory provides a 

comprehensive assessment of the contribution of U.S. agriculture (i.e., livestock and crop 

production) and forestry to the national greenhouse gas emissions inventory. The document was 

prepared to support and expand on information provided in the official Inventory of U.S. GHG 

Emissions and Sinks (U.S. GHG Inventory), which is prepared annually by the U.S. 

Environmental Protection Agency to meet U.S. commitments under the United Nations 

Framework Convention on Climate Change (UNFCCC) (EPA 2010). This report, the U.S. 

Agriculture and Forestry GHG Inventory (USDA GHG Inventory) supplements the U.S. GHG 

Inventory, providing an in depth look at agriculture and forestry emissions and sinks of GHG and 

presenting additional information on GHG emissions from fuel consumption on U.S. farms and 

ranches. The methodologies and emissions reported here are consistent with the EPA (2010) 

inventory. There are, however, important differences in reporting that should be noted and 

understood by the reader. For clarity, two examples of these differences will be noted. First, for 

the EPA inventory, source and sink categories are defined by the UNFCCC. Because of this, CO2

emissions from agricultural soils are included in the Land Use, Land Use Change, and Forestry 

(LULUCF) chapter instead of the Agriculture chapter. In this report, CO2 emissions from grazed 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 7

and cropped soils are included in the Livestock and Grazed Land Emissions and Cropland 

Agriculture chapters, respectively. Second, how energy is distributed among economic sectors is 

context dependent. This report includes emissions from on-farm energy use, but not the energy 

emissions associated with the production and transport of farm inputs. The UNFCCC submission 

(EPA, 2010) combines on-farm energy use with energy associated with farm inputs and reports it 

in the chapter with energy use from other sectors. Finally, readers should be aware of total 

versus net emissions. For example, in 2008 total emissions for the United States were 6,957 Tg 

CO2 eq., but net emissions were 6,016 Tg CO2 eq. after accounting for carbon storage in forests, 

harvested wood products, and agricultural soils.

The U.S. GHG Inventory provides national-level estimates of emissions of the primary longlived GHGs (carbon dioxide, methane, nitrous oxide, and fluorinated gases) across a broad range 

of sectors (energy, industrial processes, solvent use, agriculture, land use change and forestry, 

and waste). Due to the national-level scale of reporting in the U.S. GHG Inventory, that report 

does not always provide regional or state GHG emissions data. However, in some cases county, 

state, and regional emissions data are part of the inventory development process and can be used 

for more disaggregated analyses.

This report customizes the data from the U.S. GHG Inventory in a manner that is useful to 

agriculture and forestry producers and related industries, natural resource and agricultural 

professionals, as well as technical assistance providers, researchers, and policymakers. The 

information provided in this inventory will be useful in improving our understanding of the 

magnitude of GHG emissions by county, state, region, and land use, and by crop, pasture, range, 

livestock and forest management systems. The potential to mitigate emissions from cropped soils 

is also quantified in this edition of the inventory. The analyses presented in this report are the 

result of a collaborative process and direct contributions from EPA, USDA (Forest Service, 

Natural Resources Conservation Service, Agricultural Research Service, Office of Energy Policy 

and New Uses, and the Climate Change Program Office), and the Natural Resources Ecology 

Laboratory (NREL) of Colorado State University. 

USDA administers a portfolio of conservation programs that have multiple environmental 

benefits, including reductions in GHG emissions and increases in carbon sequestration. This and 

future USDA GHG Inventory reports will facilitate tracking of progress in promoting carbon 

sequestration and reducing GHG emissions through agriculture and forest management. The 

USDA GHG Inventory describes the role of agriculture and forestry in GHG emissions and 

sinks, including quantitative estimates of GHG emissions reductions and carbon sequestration 

through agriculture and forest management. Extensive and indepth emissions estimates are 

presented for all agricultural and forestry GHG sources and sinks for which internationally 

recognized methods are available. Where possible, emissions estimates are provided at county, 

state and regional scales in addition to the national levels provided in the U.S. GHG Inventory. 

Emissions are categorized by additional information such as land ownership and management 

practices where possible. This report will help to:

• Quantify current levels of emissions and sinks at county, state, regional, and national 

scales in agriculture and forestry,

• Identify activities that are driving GHG emissions and sinks and trends in these activities,
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 8

• Quantify the uncertainty associated with GHG emission and sink estimates, and

• Quantify the mitigation potential of land management practices intended to reduce GHG 

emissions.

1.5 Overview of the Report Structure

The report provides detailed trends in agriculture and forestry GHG emissions and sinks, with 

information by source and sink at county, state and regional levels. The report is structured 

mainly from a land use perspective, addressing livestock operations, croplands, and forests 

separately, but it also includes a chapter on energy use. The livestock chapter inventories GHG 

emissions from livestock and livestock waste stored and managed in confined livestock 

operations as well as pasture and range operations. The cropland agriculture chapter addresses 

emissions from cropland soil amendments, rice production, and residue burning, as well as 

carbon sequestration in agricultural soils. The forest chapter details carbon sequestration in forest 

biomass and soils, urban trees, and wood products. Fluxes of methane and nitrous oxide in 

forestry are not addressed since little information is currently available to develop estimates for 

these sources for forests. Qualitatively, forest soils are net methane sinks in the United States and 

soil N2O emissions are small because forests do not receive large N additions (<1% of N 

fertilizer nationally is applied to forest soils, EPA, 2010). The energy chapter provides 

information on carbon dioxide emissions from energy consumption on U.S. farms, covering 

GHG emissions from fuel use in livestock and cropland agriculture. While the U.S. GHG 

Inventory provides estimates of GHG emissions from energy consumption in the production of 

fertilizer, this indirect source of agricultural GHG emissions is not covered in this report.

Chapters 2 through 5 present a summary of sources of GHG emissions and sinks in the land use 

or category of emissions covered by each chapter. A summary of GHG emissions at the national 

level is provided initially, followed by more detailed descriptions of emissions by each source at 

national and sub-national scales where available. Methodologies used to estimate GHG 

emissions and quantify uncertainty are summarized. Changes from the second edition of this 

inventory are indicated. Text describing the methods and uncertainty for some chapters is 

summarized from the U.S. GHG Inventory, with permission from the EPA.

1.6 Summary of Changes and Additions for the Third Edition of the 

Inventory

Compared to previous years, more sophisticated methodologies were used in this report to 

estimate GHG fluxes form all the major categories. When adjustments are made to existing 

methodologies (e.g., using new data sources), recalculations are made for the entire time series of 

estimates to ensure consistency. In addition to updating GHG flux estimates for 1990-2005 

(based on current methodologies), estimates for 2006-2008 are also included.

Major changes impacting livestock emissions involved revising animal population estimates or 

diet assumptions, refining the models used to calculate emissions, using updated activity data, 

applying anima specific emissions factors, and accounting for sources previously neglected (see 

Chapter 2 for details). Methane conversion rate, digestible energy values for cattle, and feedlot 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 9

diets were updated. As a result of these changes, emissions from enteric fermentation increased 

by approximately 18% on average compared to the previous inventory (USDA 2008). The 

biggest change for emissions from managed livestock waste is that the inventory now includes 

indirect N2O emissions associated with N losses from volatilization of N as ammonia (NH3), 

nitrogen oxides (NOx), and leaching and runoff, as recommended by IPCC (2006). These 

indirect N2O emissions are added to the direct N2O emissions to present a more complete picture 

of N2O emissions from manure management. As a result of this change in methodology, N2O 

emission estimates from manure management systems have increased by approximately 60 

percent compared to the previous inventory. In this edition, N additions to soils from grazing 

animals are consistent with N excretion data. Nitrate leaching was assumed to be an insignificant 

source of indirect N2O in grassland systems where the amount of precipitation plus irrigation did 

not exceed the potential evapotranspiration, as recommended by IPCC (2006). These changes 

resulted in an approximately 40 percent decrease in grazed soil N2O emissions. The biggest 

change that impacted estimates of carbon dioxide fluxes for grazed lands involved using annual 

survey data from the USDA National Resources Inventory (NRI). Availability of new data 

extended the time series of activity data beyond 1997 to 2003. In previous inventories, activity

data were only available through 1997 at 5-year intervals, and subsequent years were treated as 

the same land use practice occurring in 1997. NRI area data were reconciled with the forest area 

estimates in the Forest Inventory and Analysis (FIA) dataset, and were incorporated into the 

estimation of soil C stock changes. These changes resulted in an average annual increase in C 

stocks of soils used for livestock grazing of approximately 40 Tg CO2 eq. for the time series, 

compared to the previous Inventory.

Although there were no major changes in methodologies for cropland emissions (Chapter 3) 

compared to the previous edition (USDA 2008), a series of improvements were implemented. 

Instead of assuming that nitrate leaching can occur anywhere, a criterion was used to designate 

lands where nitrate is susceptible to be leached into waterways, as suggested by IPCC (2006). 

Other changes include: using state-level N data for on-farm use of fertilizers to estimate synthetic 

N fertilizer application on non-major crops, including uncertainty in model outputs of N 

volatilization and N leaching/runoff in the calculation of uncertainty for indirect emissions, using 

a default uncertainty of ±50 percent for Tier 1 uncertainties that were not addressed in the 

previous inventory (e.g., crop yields and organic fertilizer amendments), improved estimates of 

manure N available for land application, revising the model parameterization for sorghum, and 

correcting uncertainty calculations. The main results of these changes are lower N2O emissions 

and wider confidence intervals. 

Estimates of forest carbon stock changes (Chapter 4) reflect a substantial number of incremental 

changes in methods and data compared to the previous inventory. The accumulation of newer 

inventory data for most states, including stocks for coastal (southern and eastern) Alaska and 

western Texas, affect carbon stock totals and changes compared to previous inventories. Updated 

land area analysis resulted in reduction of grassland area in the United States, because woodlands 

previously designated as grassland are now considered forest land, thus increasing the estimation 

of soil C stock changes in these areas. However, redefining forestland also led to the removal of 

low cover, lower productivity woodlands areas from the surveys (Smith et al. 2009), which were 

included in the previous USDA (2008) inventory. On average, these changes increased carbon 

stock estimates by approximately 8 percent.
Chapter 2: Livestock and Grazed Land Emissions 

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 10

2.1 Summary of U.S. Greenhouse Gas Emissions from Livestock

A total of 234 Tg CO2 eq. of greenhouse gases (GHGs) were emitted from livestock, managed 

livestock waste, and grazed land in 2008 (Table 2-1, Figure 2-1). This represents about 55% of 

total emissions from the agricultural sector (EPA 2010). Compared to the baseline year (1990), 

emissions from this source were about 26% higher in 2008. The 95% confidence interval for 

2008 was estimated to lie between 204 and 332 Tg CO2 eq. (Table 2-1). 

Table 2-1 Greenhouse Gas Emission Estimates and Uncertainty Intervals in 2008

Estimate Lower Bound

Upper 

Bound Lower Bound

Upper 

Bound

Source Tg CO2 eq. percent

CH4 enteric fermentation 141 125 166 -11 +18

CH4 managed waste + grazed land 48 39 57 -18 +20 

N2O managed waste 14 12 18 -16 +24 

N2O grazed land 62 39 156 -37 +153 

CO2 grazed land remaining grazed land (5) (7) (3) -53 +42 

CO2 land converted to grazed land (27) (29) (24) -8 +9 

Total 234 204 332 -13 +42 

Note: Parentheses indicate a net sequestration. Tg CO2 eq. is teragrams carbon dioxide equivalent. CH4 is methane. N2O is nitrous oxide. 

CO2 is carbon dioxide.

Enteric fermentation was responsible for over half (141 Tg CO2 eq.) of all emissions associated 

with livestock production, while grazed lands (62 Tg CO2 eq.) and managed waste (48 Tg CO2

eq.) accounted for approximately 26% and 20% of the total emissions. All of the emissions from 

enteric fermentation and about 77% of emissions from managed livestock waste were in the form 

of methane (CH4). Of the emissions from grazed lands, 96% were in the form of nitrous oxide 

(N2O) from soils (Table 

2-2). Soils in grazed 

lands do not often 

experience the anaerobic 

conditions required for 

CH4 production to exceed 

CH4 uptake. However, a 

small portion of manure 

from grazing animals is 

converted to CH4. Grazed 

lands served as a sink for 

CO2 emissions,

sequestering 31.4 Tg in 

2008 (Table 2-2). The 

largest total emissions 

associated with livestock 

production were from Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 11

Texas and California (Map 2-1). Emissions were high in Texas primarily because of the large

numbers of beef cattle, while dairy cattle emissions are responsible for most emissions in 

California. Emissions were also high in Iowa, Nebraska, Kansas, Oklahoma, and Missouri. 

Beef cattle were responsible for the largest fraction (55%) of GHG emissions from livestock in 

2008, with the majority of emissions in the form of CH4 from enteric fermentation and N2O from 

grazed land soils (Figure 2-1, Table 2-2). Dairy cattle were the second largest livestock source of 

GHG emissions (28%), primarily CH4 from enteric fermentation and managed waste. The third 

largest GHG source from livestock was swine (12%), nearly all of which was CH4 from waste. 

Horses, goats, and sheep caused relatively small GHG emissions when compared to other animal 

groups, because populations of these types are relatively small.

Livestock contribute GHGs to the atmosphere both directly and indirectly. Livestock emit CH4

directly as a byproduct of digestion through a process called enteric fermentation. In addition, 

livestock manure and urine (“waste”) cause CH4 and N2O emissions to the atmosphere through 

increased decomposition and nitrification/denitrification. Managed waste that is collected and 

stored emits CH4 and N2O. Grazing animals influence soil processes 

(nitrification/denitrification) that result in N2O emissions from the nitrogen (N) in their waste, 

which increases N2O emissions. Forage legumes on grazed lands also contribute to N2O 

emissions because legumes fix nitrogen from the atmosphere which can become mineralized in 

the soil and contribute to nitrification and denitrification. Grazed lands can also act as a source or 

sink for atmospheric carbon dioxide (CO2), depending on whether carbon inputs to the soil from 

plant residues and manure exceed carbon losses from decomposition of soil organic matter. Soils 

that have been historically cropped using conventional tillage are often depleted of carbon 

because tillage disturbs soil aggregates and warms soil, both of which increase decomposition 

rates. Carbon depleted soils can act as CO2 sinks upon conversion to grazing because grazed 

soils are typically not plowed. Factors such as grazing intensity and weather patterns also 

influence net CO2 fluxes, so a particular parcel of grazed land may be a net source or sink of 

carbon during any given year.

Table 2-2 Greenhouse Gas Emissions by Livestock Category and Source in 2008

Enteric Fermentation Managed Livestock Waste Grazed Land

CH4 CH4 N2O N2O1 CH4 CO2 Total

Animal Type Tg CO2 eq.

Beef cattle 100.77 2.47 7.44 51.90 1.97 (26.40) 138.2

Dairy cattle 33.09 19.43 5.48 1.68 0.05 (0.85) 58.9

Swine 3.59 19.58 1.65 0.20 0.01 (0.10) 24.9

Horses 1.00 0.82 0.41 6.92 0.76 (3.52) 6.4

Poultry 0.00 2.63 1.77 0.12 0.01 (0.06) 4.5

Sheep 2.12 0.08 0.34 0.51 0.04 (0.26) 2.8

Goats 0.27 0.02 0.02 0.39 0.02 (0.20) 0.5

Total 140.8 45.0 17.1 61.7 2.85 (31.4) 236.2

Note: Parentheses indicate a net sequestration. Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is 

nitrous oxide; CO2 is carbon dioxide. 1

Includes direct and indirect emissions.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 12

This chapter provides national and state-level data on CH4 emissions from enteric fermentation, 

CH4 and N2O emissions from managed livestock waste, and CO2, N2O and CH4 fluxes for grazed 

lands. Emissions 

associated with 

waste applied to 

grazed land are 

included in this 

chapter, while 

nitrous oxide 

emissions from 

managed livestock 

waste applied to 

cropped soils are 

included in the 

Cropland 

Agriculture 

chapter (Chapter 

3). State-level

livestock 

population data 

also are presented 

in this chapter 

because GHG 

emissions from 

livestock are related to livestock population sizes.

2.2 Sources of Greenhouse Gas Emissions from Livestock

The mechanisms and important factors in generating GHG fluxes from livestock, waste 

management, and grazed lands are detailed below.

2.2.1 Enteric Fermentation

Enteric fermentation is a normal digestive process where anaerobic microbial populations in the 

digestive tract ferment food and produce CH4 gas as a byproduct. Methane is then emitted from 

the animal to the atmosphere through exhaling or eructation. Ruminant livestock, including 

cattle, sheep, and goats, have greater rates of enteric fermentation because of their unique 

digestive system, which includes a large rumen or fore-stomach where enteric fermentation takes 

place. Non-ruminant livestock such as swine, horses, and mules produce less CH4 from enteric 

fermentation because it takes place in the large intestine, which has a smaller capacity to produce 

CH4 than the rumen. The energy content and quantity of animal feed also affect the amount of 

CH4 produced in enteric fermentation, with lower quality and higher quantities of feed causing 

greater emissions.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 13

2.2.2 Managed Livestock Waste

Livestock waste can be “managed” in storage and treatment systems, or spread on fields in lieu 

of long-term storage. Alternatively, livestock waste is termed “unmanaged” when it is deposited 

directly on grazed lands and not transported. Many livestock producers in the U.S. manage 

livestock waste in systems such as solid storage, dry lots, liquid-slurry storage, deep pit storage, 

and anaerobic lagoons. Table 2-3 provides descriptions of managed and unmanaged pathways 

for livestock waste, indicating the relative impacts of different pathways on GHG emissions. 

Sometimes livestock waste that is stored and treated is subsequently applied as a nutrient 

amendment to agricultural soils. GHG emissions from the application of treated waste to cropped 

soils as a nutrient amendment are discussed in the next chapter along with GHG emissions from 

other nutrient amendments for crop production. 

Table 2-3 Descriptions of Livestock Waste Deposition and Storage Pathways

Manure 

Management 

System Description

Relative Emissions

CH4 N2O

Pasture /

Range / Paddock

Manure and urine from pasture and range grazing animals is 

deposited directly onto the soil. low high

Daily Spread Manure and urine are collected and spread on fields, there is little or 

no storage of the manure/urine before it is applied to soils. low zero1

Solid Storage Manure and urine (with or without litter) are collected by some means 

and placed under long-term bulk storage. low high

Dry Lot

Manure and urine are deposited directly onto unpaved feedlots 

where the manure is allowed to dry and it is periodically removed (after 

removal it is sometime spread onto fields).

low high

Liquid / Slurry

Manure and urine are collected and transported in a liquid state to 

tanks for storage. The liquid/slurry mixture may be stored for a longtime and water may be added to facilitate handling.moderate to high low

Anaerobic 

Lagoon

Manure and urine are collected using a flush system and transported 

to lagoons for storage. Manure/urine resides in lagoons for 30-200 

days. 

variable low

Pit Storage Combined storage of manure and urine in pits below livestock 

confinements. moderate to high low

Poultry with 

Litter

Enclosed poultry houses use bedding derived from wood shavings, 

chopped straw, or other products depending on availability. The 

bedding absorbs moisture and dilutes manure. Litter is cleaned out 

once a year. This system is used for breeder flocks and meat chickens 

(broilers) and other fowl.

low high

Poultry without 

Litter

In high-rise cages or scrape-out/belt systems, manure is excreted 

onto the floor below with no bedding to absorb moisture. The 

ventilation system dries the manure as it is stored. This high-rise 

system is a form of passive windrow composting.

low low

Adapted from IPCC (2000) Chapter 4. CH4 is methane; N2O is nitrous oxide.

1

N2O emissions are assumed to be zero during the transport/storage phase but not after the waste has been applied to soils.

The magnitude of CH4 and N2O emissions from managed livestock waste depends in large part 

on environmental conditions. Methane is emitted under anaerobic conditions, when oxygen is not 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 14

available to the bacteria which decompose waste. Storage in ponds, tanks, or pits such as those 

that are coupled with liquid/slurry flushing systems often promote anaerobic conditions (i.e., 

where oxygen is not available and CH4 is produced), whereas solid waste stored in stacks or 

shallow dry pits tends to provide aerobic conditions (i.e., where oxygen is available and little or 

no CH4 is produced). High temperatures generally accelerate the rate of decomposition of 

organic compounds in waste, increasing CH4 emissions under anaerobic conditions. In addition, 

longer residency time in a storage system can increase CH4 production, and moisture additions, 

particularly in solid storage systems that normally experience aerobic conditions, can amplify 

CH4 emissions.

While environmental conditions are important factors affecting CH4 emissions from the 

management of livestock waste, diet and feed characteristics are also influential. Livestock feed 

refers to the mixture of grains, hay and byproducts from processed foods that is fed to animals at 

feedlots and supplemental feed for grazing animals, while diet includes the mixture of plants that 

animals graze. Livestock feed, diet, and growth rates affect both the amount and quality of 

manure. Not only do greater amounts of manure lead to higher CH4 production, but higher 

energy feed also produces manure with more volatile solids, increasing the substrate from which 

CH4 is produced. However, this impact is somewhat offset because some higher energy feeds are 

more digestible than lower quality forages, and thus less waste is excreted.

The production of N2O from managed livestock waste depends on the composition of the waste, 

the type of bacteria involved, and the conditions following excretion. For N2O emissions to 

occur, the waste must first be handled aerobically where ammonia or organic nitrogen is 

converted to nitrates and nitrites (nitrification), and if conditions become sufficiently anaerobic, 

nitrates and nitrites can be denitrified, i.e., reduced to N oxides and nitrogen gas (N2) (Groffman 

et al. 2000). Nitrous oxide is produced as an intermediate product of both nitrification and 

denitrification and can be directly emitted from soil as a result of both of these processes. These 

emissions are most likely to occur in dry waste handling systems that have aerobic conditions, 

but that also contain pockets of anaerobic conditions due to high water contents and high oxygen 

gas (O2) demand from decomposition. For example, waste in dry lots is deposited on soil, is 

oxidized to nitrite and nitrate, and encounters anaerobic conditions following precipitation events 

that increase water content, enhance decomposition, and deplete the supply of O2.

Managed livestock waste can also contribute to indirect N2O emissions. Indirect emissions result 

from nitrogen that was emitted or leached from the manure management system in a form other 

than N2O and was then converted to N2O offsite. These sources of indirect N2O emission from 

animal waste are from ammonia (NH3) volatilization, nitric oxide (NO) emissions from 

nitrification and denitrification, and nitrate (NO3) leached or run off into ground or surface 

waters. The gaseous losses of NH3 and NO to the atmosphere can then be deposited to the soil 

and converted to N2O by nitrification. The nitrate leached or run off into waterways can be 

converted to N2O by aquatic denitrification.

2.2.3 Grazed Lands

Nitrous oxide from soils is the primary GHG associated with grazed lands. Grazed lands 

contribute to N2O emissions by adding nitrogen to soils from animal wastes and from forage 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 15

legumes. Legumes fix atmospheric N2 into forms that can be used by plants and by soil 

microbes. Nitrogen from manure and legumes is cycled into the soil and can provide substrates 

for nitrification and denitrification. Nitrous oxide is a by-product of this cycle; thus more 

nitrogen added to soils yields more N2O released to the atmosphere. A portion of the nitrogen 

cycled within the plant-animal-soil system volatilizes to the atmosphere in various gaseous forms 

and is eventually re-deposited onto the soils where it can contribute to indirect N2O emissions. 

Some nitrogen in the form of nitrate can leach into groundwater and surface runoff, undergo 

denitrification, and contribute to indirect N2O emissions. In addition to nitrogen additions, 

weather, soil type, grazing intensity and other factors influence emissions from grazed lands.

Manure deposited on grazed lands also produces CH4 emissions. Methane emissions from this 

source are relatively small, less than 3% of total grazed land GHG emissions, because of the 

predominately aerobic conditions that exist on most pastures and ranges.

Grazed lands can be emission sources or net sinks for CO2. Typically, cropland that has recently 

been converted to grazed land stores CO2 from the atmosphere in the form of soil organic 

carbon. But after sufficient time, soil organic carbon reaches a steady state, given consistent 

weather patterns. Long-term soil carbon levels are sensitive to climate change and soils that were 

previously sinks can revert to being sources of CO2.

2.3 U.S. Livestock Populations

Greenhouse gas emissions from livestock are related to population size. Livestock population 

data are collected annually by USDA’s National Agricultural Statistics Service (NASS). Those 

data are an input into the GHG estimates from livestock in the U.S. GHG Inventory.

Beef and dairy cattle, swine, sheep, goats, poultry, and horses are raised throughout the United 

States. Detailed livestock population numbers for each state in 2008 are provided in Appendix 

Table A-1. Appendix Table A-2 shows total national livestock population sizes from 1990 to 

2008 by livestock categories. Trends for beef cattle, dairy cattle, and swine are described in more 

detail below because of their relatively high population numbers and consequently high 

contributions to GHG emissions.

Texas raised by far the most beef cattle, at over 14 million head in 2008 (Appendix Table A-1). 

Kansas, Nebraska, Oklahoma, Iowa, and Missouri each raised from 4 to 6 million head of beef 

cattle, while several other states raised approximately (~)2 million head. Fewer dairy cattle than 

beef cattle are raised currently in the United States. Dairy cattle populations were highest in 

California (~2.6 million) and Wisconsin (~1.9 million) (Appendix Table A-1). New York, Idaho, 

Pennsylvania, and Minnesota had the next largest populations of dairy cattle, ranging from 

730,000 to 970,000 head in each state. Most states had fewer than 500,000 head of dairy cattle.

Iowa was the largest swine producer with 19.5 million head in 2008 (Appendix Table A-1). 

North Carolina housed the second largest swine population at 10 million head. Illinois, Indiana, 

Minnesota, Missouri, Nebraska, and Oklahoma also have sizeable swine populations.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 16

Arkansas and Georgia had the largest poultry populations in 2008, with roughly 240 million and 

280 million head of poultry in each state, respectively (Appendix Table A-1). Alabama, North 

Carolina, Mississippi, and Texas also had large populations of poultry, between 141 and 209 

million head each. Iowa, Indiana, Kentucky, Maryland, and Virginia had poultry populations 

between 51 and 84 million head.

2.4 Enteric Fermentation

Just over half (53%) of emissions associated with livestock production were from CH4 produced 

by enteric fermentation. Cattle were responsible for the vast majority of enteric CH4 emissions 

(95%) in 2008 (Table 2-2). Texas (17.9 Tg CO2 eq.) and California (9.1 Tg CO2 eq.) had the 

largest CH4 emissions from enteric fermentation for beef cattle and dairy cows in 2008 (Map 2-2, 

Appendix Table A-4). These emissions were largely tied to the sizable populations of cattle in 

both states. However, enteric fermentation emissions in Texas were mostly from beef cattle, 

whereas in California they were

mostly from dairy cattle (Appendix Table A-4). State-level data for non-cattle livestock (i.e.,

swine, sheep, goats, and horses) or bulls was not generated due to the relatively low 

contributions of these animals to total enteric emissions. Central, Northern Plains, and some 

Great Lakes states also had relatively high CH4 emissions from enteric fermentation, ranging 

between 3 and 8.5 Tg CO2 eq. per state in 2008 (Appendix Table A-4). Emissions tended to be 

lower from some 

states in the 

Northeast, 

Southeast, and the 

desert Southwest, 

mainly because 

cattle populations 

are low in these 

states.

Annual emissions 

of CH4 from 

enteric 

fermentation 

fluctuated by 

approximately 10 

Tg CO2 eq. 

between 1990 and 

2008 (Table 2-4). 

Emissions peaked 

in 1995, then 

decreased by 

about 10 Tg CO2 eq. by 2005 and were back up near 1995 emissions by 2008. Overall, by 2008, 

CH4 emissions from enteric fermentation increased by about 6% compared to 1990 levels. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 17

2.4.1 Methods for Estimating Methane Emissions from Enteric Fermentation

The official U.S. GHG Inventory estimates for enteric fermentation are calculated according to 

the methodological framework provided by the Intergovernmental Panel on Climate Change 

(IPCC) for preparing national GHG inventories. The IPCC guidance is 

organized into a hierarchical, tiered analytical structure, in which higher tiers correspond to more 

complex and detailed methodologies. The methods detailed below correspond to both Tier 1 and 

Tier 2 approaches. With the permission of EPA, Annex 3.9 from the official U.S. GHG 

Inventory is summarized below. Methane emissions from enteric fermentation were estimated 

for five livestock categories: cattle, horses, sheep, swine, and goats. Emissions from cattle 

represent the majority of U.S. emissions; consequently, the more detailed IPCC Tier 2 

methodology was used to estimate emissions from cattle, and the IPCC Tier 1 methodology was 

used to estimate emissions from the other types of livestock.

2.4.1.1 Estimating Methane Emissions from Cattle

This section describes the process used to estimate enteric fermentation emissions of CH4 from 

cattle on a regional basis. A Cattle Enteric Fermentation Model (CEFM) based on 

recommendations provided in IPCC (2006, 1997) was developed that uses information on 

population, energy requirements, digestible energy, and the fraction of energy converted to 

methane to estimate CH4 emissions. The emission estimation methodology consists of the 

following three steps: (1) characterize the cattle population to account for cattle population 

categories with different emissions profiles; (2) characterize cattle diets to generate information 

needed to estimate emissions factors; and (3) estimate emissions using these data and the IPCC 

Tier 2 equations.

Step 1: Characterize U.S. Cattle Population

Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to 

slaughter. This level of detail accounts for the variability in CH4 emissions associated with each 

life stage. Given that the time in which cattle can be in a stage can be less than 1 year (e.g., beef 

calves are weaned at 7 months), the stages are modeled on a per-month basis. The type of cattle 

Table 2-4 U.S. Methane Emissions from Enteric Fermentation in 1990, 1995, 

2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Animal Type Tg CO2 eq.

Beef cattle 94.5 107.7 100.6 99.9 100.0 100.0 98.3 99.3 100.9 101.6 100.8

Dairy cattle 32.0 30.5 30.9 30.7 30.8 28.7 30.1 30.6 31.3 32.7 33.1

Horses 1.9 1.9 2.0 2.1 2.3 2.6 3.0 3.5 3.6 3.6 3.6

Sheep 1.9 1.5 1.2 1.2 1.1 1.1 1.0 1.0 1.0 1.0 1.0

Swine 1.7 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 2.1 2.1

Goats 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Total 132.4 143.7 136.8 136.0 136.3 134.5 134.6 136.7 139.0 141.2 140.8

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 18

use also impacts CH4 emissions (e.g., beef versus dairy). Consequently, cattle life stages were 

modeled for several categories of dairy and beef cattle. These categories are listed in Appendix 

Table A-5.

The key variables tracked for each of these cattle population categories1 includes calving rates, 

pregnancy and lactation (Appendix Table A-6), average weights and weight gains (Appendix 

Table A-7), feedlot placements (Appendix Table A-8), death rates, number of animals per 

category each month, and animal characteristics (i.e., age, gender, etc.) data.

Cattle population data were taken from USDA National Agricultural Statistics Survice (NASS)

(Appendix Table A-2). The USDA NASS publishes monthly, annual, and multi-year livestock 

population and production estimates. Multi-year reports include revisions to earlier published 

data. Cattle and calf populations, feedlot placement statistics (e.g., number of animals placed in 

feedlots by weight class), slaughter numbers, and lactation data were obtained from the USDA 

NASS (Cattle: USDA NASS 2004, 1999). Beef calf birth percentages were obtained from the 

USDA Animal and Plant Health Inspection Service (APHIS) National Animal Health 

Monitoring System (USDA APHIS NAHMS 2008, 1997).

Step 2: Characterize U.S. Cattle Diets

To support development of digestible energy (DE), the percent of gross energy intake digestible 

to the animal and CH4 conversion rate (Ym) (i.e., the fraction of gross energy converted to CH4

values for each of the cattle population categories) data were collected on diets considered 

representative of different regions. For both grazing animals and animals being fed mixed 

rations, representative regional diets were estimated using information collected from state 

livestock specialists and from USDA APHIS NAHMS (2008). The data for each of the diets 

(e.g., proportions of different feed constituents, such as hay or grains) were used to determine 

chemical composition for use in estimating DE and Ym for each animal type. Region- and cattletype-specific estimates for DE and Ym were developed for the U.S. (Appendix Table A-9). 

Regions are defined in (Appendix Table A-10). Additional detail on the regional diet 

characterization is provided in EPA (2010).

Step 3: Estimate Methane Emissions from Cattle

Emissions were estimated in three steps: (a) determine gross energy intake using the IPCC 

(2006) equations, (b) determine an emissions factor using the DE values and other factors, and 

(c) sum the daily emissions for each animal type. The necessary data values include:

• Body weight (kg)

• Weight gain (kg/day)

• Net energy for activity (Mj/day)

• Standard reference weight (dairy = 1,324 lbs; beef = 1,195 lbs)

• Milk production (kg/day)

 1 Except bulls. Only end-of-year census population statistics and a national emission factor are used to estimate CH4

emissions from the bull population.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 19

• Milk fat (% of fat in milk = 4)

• Pregnancy (% of population that is pregnant)

• DE (% of gross energy intake digestible)

• Ym (the fraction of gross energy converted to CH4)

This process was repeated for each month, and the totals for each subcategory were summed to 

achieve an emissions estimate for the entire year. The estimates for each of the 10 subcategories 

of cattle are listed in Appendix Table A-11. The CH4 emissions for each subcategory were then 

summed to estimate total emissions from beef cattle and dairy cattle for the entire year. The 

cattle emissions calculation model estimates emissions on a regional scale. Individual state-level 

estimates were developed from these regional estimates using the proportion of each cattle 

population subcategory in the state relative to the population in the region.

2.4.1.2 Emission Estimates From Other Livestock

All livestock population data, except for horses, were taken from USDA NASS (1994) reports 

(Hogs and pigs; Sheep and goats). Appendix Table A-2 shows the population data for all 

livestock that were used for estimating all livestock-related emissions. For each animal category, 

the USDA publishes monthly, annual, and multi-year livestock population and production 

estimates. Multi-year reports include revisions to earlier published data. Recent reports were 

obtained from the USDA Economics and Statistics System, while historical data were 

downloaded from USDA NASS. The Food and Agriculture Organization (FAO) of the United 

Nations publishes horse population data. These data were accessed from the FAOSTAT database 

(FAO 2009). National-level emission calculations for other livestock were developed from 

national population totals. State-level emissions for each livestock type were developed from 

these national totals based on the proportion of livestock population in each state relative to the 

national total population for the particular livestock category and by assuming that emissions are 

proportional to populations. Appendix Table A-12 shows the emission factors used for these 

other livestock.

2.4.2 Uncertainty in Estimating Methane Emissions from Enteric Fermentation

The following discussion of uncertainty in the enteric fermentation estimates is from the U.S. 

GHG Inventory (EPA 2010) and reproduced here with permission from EPA.

Uncertainty is estimated using the Monte Carlo Stochastic Simulation technique. Emission 

factors and animal population data are the primary sources of uncertainty in estimating CH4

emissions from enteric fermentation. One hundred eighty-five input variables were identified as 

key input variables for uncertainty analysis (e.g., estimates of births by month, weight gain of 

animals by age class, and placement of animals into feedlots based on placement statistics and 

slaughter weight data). The uncertainty associated with these input variables is ±10% or lower. 

However, the uncertainty for many of the emission factors is over ± 20%. The overall 95% 

confidence interval around the estimate of 141 Tg CO2 eq. ranges from 125 to 166 Tg CO2 eq. 

(Table 2-1).
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 20

2.4.3 Changes Compared to the 2nd Edition of the USDA GHG Report

There were several important modifications made to the emissions estimates for this edition of 

the USDA GHG report relative to the previous Inventory (USDA 2008b). Most of the changes 

involved revising animal population estimates or diet assumptions, or refining the model used to 

calculate emissions. Heifer and steer stocker populations previously left out of the emissions 

calculations are now included, and beef, dairy, swine, and horse populations were revised. The 

FAO horse population estimates increased dramatically between the current and previous 

Inventory. Enteric fermentation data for bull populations are no longer averaged between 

January and July because of the high degree of uncertainty related to July estimates, so 

populations are based solely on January estimates. An adjustment was made to the CEFM to 

allow feedlot placements for the 700–800 lbs category to use excess animals from the over 800 

lbs category if insufficient animals are available to place in a given month at 700–800 lbs. Calf 

weight at 7 months was adjusted to be equal for all months, as current research indicated that 

evidence was not sufficient to suggest that calf weight at weaning differs by birth month. Mature 

weight for beef cows was revised based on annual data collected from 1989 through 2007, as 

was replacement weight at 15 and 24 months. Mature weight for dairy cows was adjusted to 

1,550 for all years, and replacement weight at 15 and 24 months was adjusted accordingly. 

Monthly weight gain for stockers and coefficients used for calculating the net energy required for 

maintenance used for lactating cattle were increased. 

Methane conversion rate (Ym) and digestible energy (DE) values for cattle were updated based 

on model evaluations (Kebreab et al. 2008) and literature values. Feedlot diets were updated 

based on current survey data from Galyean and Gleghorn (2001) and Vasconcelos and Galyean 

(2007). Further modifications were made to feedlot placement methodology when discrepancies 

existed between simulated USDA placement data for weight class and number of calculated 

animals available by weight. The models would account for these differences by pulling 

available stockers from the higher weight category. If a minimum number of available stockers 

still could not be reached, animals were pulled from the next lower weight category. 

As a result of the changes outlined above, emissions from enteric fermentation increased by 

approximately 18% on average compared to the previous Inventory (USDA 2008).

2.5 Managed Livestock Waste

Greenhouse gas emissions from managed livestock waste are composed of CH4 and N2O from 

livestock waste storage and treatment and CH4 emissions from the daily spread of livestock 

waste. Emissions from these sources are discussed below, with estimates disaggregated spatially 

and by livestock category where possible. Methane was the predominant GHG emitted from 

managed livestock waste in 2008, accounting for 72% of 62 Tg CO2 eq. total emissions from this 

source (Table 2-5). The remaining 28% of GHG emissions from managed livestock waste was 

N2O. Dairy cattle and swine were each responsible for 37% and 36% of total managed waste 

emissions respectively (Figure 2-2). Poultry (7%) and beef cattle (17%) were also important 

sources in 2008. For beef cattle, N2O was the predominate form (75%) of waste emissions. Over 

time, emissions from managed waste increased by ~40% from 1990 to 2008 (Figure 2-3). Most 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 21

of the increase was from higher CH4 emissions due to the trend of storing more waste in liquid 

systems and anaerobic lagoons, which facilitate CH4 production. 

Table 2-5 Greenhouse Gas Emissions from Managed Livestock Waste in 1990, 

1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

GHG Type Tg CO2 eq.3

Nitrous Oxide1 14.4 15.5 16.7 16.5 16.8 16.3 16.4 16.6 17.3 17.3 17.1

Methane 2 29.3 33.9 38.6 40.1 41.2 38.4 40.2 42.2 42.3 45.9 45.0

Total 43.7 49.3 55.2 56.6 57.9 54.7 56.5 58.9 59.6 63.2 62.1

1 Does not include emissions from managed manure applied to cropped soils.

2 Includes CH4 from managed sources and from grazed grasslands. Manure deposited on grasslands produces little CH4 due to 

predominantly aerobic conditions

3 Teragrams carbon dioxide equivalent.

While beef cattle are responsible for the largest overall emissions from all livestock, (Table 2-2, 

Figure 2-1), emissions from beef cattle managed waste are relatively small (Figure 2-2) because 

most waste generated by beef cattle is unmanaged. Emissions from beef cattle managed manure 

changed little between 1990 and 2008. Managed manure emissions from horses, sheep, and goats 

are small due to the relatively small population of these animals (Appendix Table A-2), and, as 

for beef cattle, most of the manure is unmanaged or managed in dry systems (EPA 2010). Statelevel GHG emissions from managed livestock waste varied across states in 2008, with a small 

number of states responsible for the larger contributions to national GHG emissions. California 

and Iowa had the largest GHG emissions from managed livestock waste (4.8 and 4.6 Tg CO2 eq., 

respectively; Map 2-3). In North Carolina, this was primarily from swine. In Texas, however, 

most emissions were from both beef and dairy cattle waste, with a smaller portion from swine 

(Appendix Table A-14, A-15).
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 22

Figure 2-3

Greenhouse Gas Emissions from Managed Livestock Waste, 1990-2008

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Tg CO2 eq.

Total

CH4

N2O

Tg CO2 eq. is teragrams carbon dioxide equivalent Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is nitrous oxide.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 23

2.5.1 Methods for Estimating Methane and Nitrous Oxide Emissions from Managed 

Livestock Waste

This section summarizes how CH4 and N2O emissions from livestock waste were calculated in 

the U.S. GHG Inventory (EPA 2010) as well as for this inventory report. Animal population data 

are used to estimate CH4 production potential and nitrogen in waste, and these are multiplied by 

a methane conversion factor (MCF) and direct and indirect N2O emission factors. MCFs are used 

to determine the amount of CH4 emissions that are potentially produced by each unit of livestock 

waste. MCFs vary by livestock type, manure storage system, and the waste storage temperature. 

The IPCC (2006) default direct N2O emission factor was used while indirect N2O emission 

factors varied by region and waste management system. The EPA provides the USDA with state 

and national estimates of GHG emissions from managed livestock waste. The estimates of GHG 

emissions from managed livestock waste were prepared following a methodology developed by 

EPA and consistent with international guidance, and are described in detail in Annex 3.10 of the 

U.S. GHG Inventory (EPA 2010).

Data required to calculate CH4 emissions from livestock waste:

• Animal population data (by animal type and state); 

• Typical Animal Mass (TAM) data (by animal type); 

• Portion of manure managed in each Waste Management System (WMS), by state and 

animal type; 

• Volatile solids (VS) production rate (by animal type and state or U.S.); 

• CH4 producing potential (Bo) of the volatile solids (by animal type); 

• Methane Conversion Factors (MCF), the extent to which the CH4 producing potential is 

realized for each type of WMS (by state and manure management system, including the 

impacts of any biogas collection efforts). 

Seven livestock types are considered: dairy cattle, beef cattle, swine, sheep, goats, poultry, and 

horses. For swine and dairy cattle, manure management system usage is determined for different 

farm size categories using data from the USDA (USDA 2000a, 2000b, 2000c, 1998b, 1996) and 

EPA (EPA 2002a, 2002b, ERG 2008, 2000). For beef cattle and poultry, manure management 

system usage is not tied to farm size and is based on other sources (ERG 2008, 2000, USDA 

2000d, UEP 1999). For other animal types, manure management system usage is based on

previous estimates (EPA 1992).

Appendix Table A-16 presents a summary of the waste characteristics used in the emissions 

estimates. The method for calculating volatile solids production from beef and dairy cows, 

heifers, and steers is based on the relationship between animal diet and energy utilization, which 

is modeled in the enteric fermentation portion of the inventory. Volatile solids content of manure 

equals the fraction of the diet consumed by cattle that is not digested and thus excreted as fecal 

material which, when combined with urinary excretions, constitutes manure. Estimations of 

gross energy intake and digestible energy were used to calculate the indigestible energy per 

animal unit as gross energy minus digestible energy plus an additional 2% of gross energy for 

urinary energy excretion per animal unit. This was then converted to volatile solids production 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 24

per animal unit using the typical conversion of dietary gross energy to dry organic matter of 20.1 

MJ/kg (Garrett & Johnson 1983). Appendix Table A-17 shows volatile solid production rates by 

state and livestock category.

Methane conversion factors for dry manure management systems were set equal to the default 

IPCC factors for temperate climates (IPCC 2006). MCFs for liquid slurry, anaerobic lagoon, and 

deep pit systems were calculated based on the forecast performance of biological systems 

relative to temperature changes. These calculations account for the following: average monthly 

ambient temperature, minimum system temperature, the carryover of volatile solids from month 

to month, and a factor to account for management and design practices that result in loss of 

volatile solids form lagoon systems. State-level emissions factors for liquid slurry, deep pit, and 

anaerobic lagoon are shown in Appendix Table A-18. Appendix Table A-19 has national-scale 

emission factors for other waste management systems. For each animal type, the base emission 

factors were weighted to incorporate the distribution of waste management systems within each 

state to get a state-level weighted emission factor (Appendix Table A-20).

Methane emissions were estimated by multiplying regional or national animal type-specific 

volatile solid production by the animal type-specific maximum CH4 production capacity of the 

waste and the state-specific MCF.

The following inputs were used in the calculation of direct and indirect N2O emissions: 

• Animal population data (by animal type and state); 

• TAM data (by animal type); 

• Portion of manure managed in each WMS (by state and animal type); 

• Total Kjeldahl N excretion rate (Nex); 

• Direct N2O emission factor (EFWMS); 

• Indirect N2O emission factor for volatilization (EFvolitalization); 

• Indirect N2O emission factor for runoff and leaching (EFrunoff/leach); 

• Fraction of N loss from volatilization of ammonia and NOx (Fracgas);

• Fraction of N loss from runoff and leaching (Fracrunoff/leach)

N2O emissions were estimated by first determining activity data, including animal population, 

typical animal mass (TAM), WMS usage, and waste characteristics. 

N2O emissions factors for all manure management systems were set equal to the default IPCC 

(2006) factors for temperate climates (Appendix A-19).

• Nex rates for all cattle except for bull and calves were calculated for each state and 

animal type in the Cattle Enteric Fermentation Model (CEFM), which is described in 

section 6.1, Enteric Fermentation and in more detail in Annex 3.9, Methodology for 

Estimating CH4 Emissions from Enteric Fermentation. Nex rates for all other animals 

were determined using data from USDA’s Agricultural Waste Management Field 

Handbook (USDA 1996) and data from the American Society of Agricultural Engineers, 

Standard D384.1 (ASAE 2003). 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 25

• All N2O emissions factors (direct and indirect) were taken from IPCC (IPCC 2006). 

• Country-specific estimates were developed for the fraction of N loss from volatilization 

(Fracgas) and runoff and leaching (Fracrunoff/leach). Fracgas values were based on 

WMS-specific volatilization values as estimated from U.S. EPA’s National Emission 

Inventory - Ammonia Emissions from Animal Agriculture Operations (EPA 2005). 

Fracrunoff/leaching values were based on regional cattle runoff data from EPA’s Office 

of Water (EPA 2002b; see Table A-9 in Annex 3.1). 

To estimate N2O emissions, first, the amount of N excreted (kg per year) in manure in each 

WMS for each animal type, state, and year was calculated. The population (head) for each state 

and animal was multiplied by TAM (kg animal mass per head) divided by 1,000, the N 

excretion rate (Nex, in kg N per 1000 kg animal mass per day), WMS distribution (percent), 

and the number of days per year. 

Direct N2O emissions were calculated by multiplying the amount of Nex (kg per year) in each 

WMS by the N2O direct emission factor for that WMS (EFWMS, in kg N2O-N per kg N) and 

the conversion factor of N2O-N to N2O. These emissions were summed over state, animal and 

WMS to determine the total direct N2O emissions (kg of N2O per year). 

Then, indirect N2O emissions from volatilization (kg N2O per year) were calculated by 

multiplying the amount of N excreted (kg per year) in each WMS by the fraction of N lost 

through volatilization (Fractas) divided by 100, and the emission factor for volatilization 

(EFvolatilization in kg N2O per kg N), and the conversion factor of N2O-N to N2O. Next, 

indirect N2O emissions from runoff and leaching (kg N2O per year) were calculated by 

multiplying the amount of N excreted (kg per year) in each WMS by the fraction of N lost 

through runoff and leaching (Fracrunoff/leach) divided by 100, and the emission factor for runoff 

and leaching (EFrunoff/leach in kg N2O per kg N), and the conversion factor of N2O-N to N2O. 

The indirect N2O emissions from volatilization and runoff and leaching were summed to 

determine the total indirect N2O emissions. 

2.5.2 Uncertainty in Estimating Methane and Nitrous Oxide Emissions from Managed 

Livestock Waste

The following discussion of uncertainty in estimating GHG emissions from livestock waste is 

modified from information provided in the U.S. GHG Inventory (EPA 2010, 2007, 2003). The 

information is reproduced here with permission from EPA.

An uncertainty analysis based on the Monte Carlo Stochastic Simulation technique was 

conducted on the manure management inventory considering the issues described below and 

based on published data from scientific and statistical literature, the IPCC, and experts in the 

industry. The results of the uncertainty analysis showed that the manure management CH4

inventory has a 95% confidence interval from 39 to 57 Tg CO2 eq. around the inventory value of 

48 Tg CO2 eq., and the manure management N2O inventory has a 95% confidence interval from 

12 to 18 Tg CO2 eq. around the inventory value of 14 Tg CO2 eq (Table 2-1).
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 26

Uncertainties derive from limited information on regional patterns in the use of manure 

management systems and CH4-generating characteristics of each system. It is assumed that shifts 

in the swine and dairy sectors toward larger farms causes more manure to be managed in liquid 

manure management systems. Farm-size data from 1992, 1997 and 2002 are used to modify 

MCFs based on this assumption. However, the assumption of a direct relationship between farm 

size and liquid system usage may not apply in all cases and may vary based on geographic 

location. In addition, the CH4-generating characteristics of manure management systems are 

based on relatively few laboratory and field measurements. Good Practice Guidance and 

Uncertainty Management in National Greenhouse Gas Inventories (IPCC 2000) published a 

default range of MCFs for anaerobic lagoon systems of 0% to 100%, reflecting the wide range in 

performance of these systems globally.

There are potential classification errors when naming manure management systems. For 

example, many livestock waste treatment systems classified as anaerobic lagoons are actually 

holding ponds, which may be organically overloaded, thus producing CH4 at a different rate than 

estimated. In addition, the performance of manure management systems depends on how they 

are operated, which undoubtedly varies across facilities. An MCF based on optimized lagoon 

systems does not take into consideration the actual variation in performance across operational 

systems. Therefore, an MCF methodology was developed to better match observed system 

performance and account for the impact of temperature on system performance. The MCF 

methodology used in the inventory includes a factor to account for management and design 

practices that result in the loss of volatile solids from the management system. This factor, 

estimated with data from three systems, all in anaerobic lagoons in temperate climates, was 

applied broadly to systems across a range of management practices. Additional data are needed 

on animal waste lagoon systems across the country to verify and refine this methodology. Data 

are also needed on how lagoon temperatures relate to ambient air temperatures and whether the 

lower bound estimate of temperature used for lagoons and other liquid systems should be 

revised. The inventory relies on the IPCC MCF for poultry waste management operations of 

1.5%. This factor needs further evaluation to assess if poultry high-rise houses promote sufficient 

aerobic conditions to warrant a lower MCF.

The default N2O emission factors published in Good Practice Guidance and Uncertainty 

Management in National Greenhouse Gas Inventories (IPCC 2000) were derived using limited 

information. The IPCC factors are global averages; U.S.-specific emission factors may be 

significantly different. Manure and urine in anaerobic lagoons and liquid/slurry management 

systems produce CH4 at different rates, and would in all likelihood produce N2O at different 

rates, although a single N2O emission factor was used for both system types. In addition, there 

are little data available to determine the extent to which nitrification and denitrification occur in 

animal waste management systems. Ammonia concentrations that are present in poultry and 

swine systems suggest that N2O emissions from these systems may be 

lower than predicted by the IPCC default factors. At this time, there are insufficient data 

available to develop U.S.-specific N2O emission factors; however, this is an area of ongoing 

research, and warrants further study as more data become available. Similar approaches will be 

studied for other animal sub-groups.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 27

Additional data would help confirm and track diet changes over time, which are used to 

introduce variability in volatile solids for beef and dairy cows, heifers, and steers. A similar 

approach for swine volatile solids production may improve the accuracy of future inventory 

estimates. Uncertainty also exists with the maximum CH4-producing potential of volatile solids 

excreted by different animal groups. The maximum CH4-producing values used in the CH4

calculations are published values for U.S. animal waste. However, there are several studies that 

provide a range of maximum CH4-producing values for certain animals, including dairy and 

swine. The maximum CH4-producing values chosen for dairy assign separate values for dairy 

cows and dairy heifers to better represent the feeding regimens of these animal groups. For 

example, dairy heifers do not receive an abundance of high-energy feed and, consequently, their 

waste will not produce as much CH4 as would that from milking cows. 

2.5.3 Changes Compared to the 2nd Edition of the USDA GHG Report

There were several changes in the methods used to calculate emissions from managed livestock 

waste. One of the biggest changes is that the Inventory now includes indirect N2O emissions in 

the manure management sector associated with N losses from volatilization of N as ammonia 

(NH3), nitrogen oxides (NOx), and leaching and runoff, as recommended by IPCC (2006). These 

indirect N2O emissions are added to the direct N2O emissions to present a more complete picture 

of N2O emissions from manure management. The days per year used in N2O calculations was 

changed from 365 to 365.25 to include leap years and to be consistent with the CH4 inventory 

calculations. Instead of calculating state weighted average N2O emission factors and methane 

conversion factors (MCFs), N2O and CH4 emissions are now calculated from the “bottom up” 

such that CH4 and N2O are calculated for each animal group, manure management system, and 

state. These values are then summed to calculate the total greenhouse gas emissions from manure 

management in the United States. Animal population data were updated to reflect the final 

estimates reports from USDA NASS (USDA 1994, 1998a-b, 2000a, 2004, 2005, 2006, 2007). 

The FAO (2007) horse population estimates for recent years increased dramatically between the 

current and previous Inventories, resulting in a much larger estimated horse population, and 

therefore greater greenhouse gas emissions from this source category. On average, annual CH4

emission estimates are more than those of the previous Inventory by about one percent. Nitrous 

oxide emission estimates from manure management systems have increased by approximately 60 

percent for all years of the current Inventory compared to the previous Inventory mainly due to 

accounting both direct and indirect N2O emissions. The most significant changes in N2O 

emissions compared to the previous Inventory occurred in the poultry and swine sectors, whose

emissions were approximately 70 percent higher due to the inclusion of indirect N2O emissions. 

2.6 Grazed Lands

For the purposes of this report, the term “grazed lands” refers to all lands grazed by livestock 

regardless of management intensity (ie., rangeland, pasture, paddock, etc.). Grazed land soils 

emit N2O due to enhanced nitrogen cycling as well as a relatively small amount of CH4

emissions from manure deposits. Manure deposited on grazed land (i.e., unmanaged manure) 

produces little CH4 due to predominant aerobic conditions. Nitrous oxide sources include direct 

and indirect emissions of N2O associated with increased nitrogen from forage legumes and waste 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 28

from grazing animals. Grazed lands can be either a source or a sink of CO2, depending on the

level of soil disturbance and grazing intensity but generally sequester carbon because these lands 

are not plowed.

Nitrous oxide was the predominant GHG emitted from grazed land soils in 2008, accounting for 

96% of all emissions from this source (Table 2-6). The remaining 4% of GHG emissions from 

grazed lands was CH4. Grazed lands served as a CO2 sink in 2008, with an uptake of 31.4 Tg 

CO2 eq. via the sequestration of CO2 into soil organic carbon. Nitrous oxide emissions from 

grazed land totaled 60.5 Tg CO2 eq. in 2008 (Table 2-6), including direct and indirect sources. 

Beef cattle are responsible for the highest proportion of direct N2O emissions from grazed lands 

because the vast majority of grazed lands in the U.S. are used for beef production. Texas and 

Oklahoma had the largest emissions from grazed lands due to the large amounts of rangeland in 

these states. In aggregate, emissions from grazed lands were roughly four times those of 

managed manure in 2008 and have been since 1990, when national emissions from this source 

were first estimated (Tables 2-5, 2-6). This is due to large numbers of beef cattle on grazing land 

(more than 80% of all cattle) compared to feedlots, which are a source of managed waste (Map 

2-4).

Table 2-6 Greenhouse Gas Emissions from Grazed Lands in 1990, 1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

GHG Type Tg CO2 eq.

Nitrous Oxide1 64.0 62.9 64.2 56.8 58.2 63.9 64.2 59.1 60.1 61.8 60.5 

Direct 53.7 53.3 54.5 49.3 50.2 54.1 54.5 49.6 51.2 52.6 51.3 

Indirect Volatilization 5.6 5.5 5.6 5.4 5.1 5.3 5.2 5.3 5.3 5.3 5.3 

Indirect Leaching & 

Run-Off 4.8 4.1 4.2 2.0 2.9 4.5 4.4 4.2 3.6 4.0 3.9 

Methane2 2.9 3.0 2.7 2.8 2.7 2.7 2.8 2.9 3.0 3.0 2.9 

Carbon Dioxide (69.0) (58.9) (83.4) (57.7) (71.4) (31.2) (31.2) (31.3) (31.3) (31.4) (31.4)

Grazed Lands 

Remaining Grazed (46.7) (36.4) (51.4) (27.5) (43.1) (4.5) (4.5) (4.6) (4.6) (4.7) (4.7)

Land Converted to 

Grazed Land (22.3) (22.5) (32.0) (30.2) (28.3) (26.7) (26.7) (26.7) (26.7) (26.7) (26.7)

Total (2.1) 6.9 (16.4) 1.8 (10.5) 35.4 35.7 30.7 31.8 33.4 31.9 

1 Does not include emissions from managed manure applied to cropped soils.

Tg CO2 eq. is teragrams carbon dioxide equivalent.

2.6.1 Methodology to Estimate Nitrous Oxide Emissions from Grazed Lands

Estimates of N2O emissions from this component were based on DAYCENT model simulations 

of non-federal grazed lands, estimates of animal waste production (Appendix Table A-21), and 

IPCC (2006) methodology for emissions from federal grazed lands (EPA 2010). Both managed 

manure applications and unmanaged manure are considered here. Managed manure is defined as 

manure that was transported and temporarily stored in a management system before soil

application. Unmanaged manure is not managed in manure management systems, but instead 

remains on soils after being deposited by grazing animals in pastures, rangelands, and paddocks. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 29

The livestock 

included in this 

component were 

dairy cattle, beef 

cattle, swine, 

sheep, goats, 

poultry, and 

horses.

The DAYCENT 

ecosystem model 

simulated 

improved 

pastures and 

rangelands at 

county-level 

resolution for 

non-federal 

grasslands. 

Improved 

pastures are 

defined as 

grazing lands that have either been seeded with legumes and/or amended with organic nitrogen 

(e.g., managed manure) or synthetic fertilizer nitrogen. Grazing intensity on improved pastures 

was assumed to be moderate to heavy, while intensity on rangelands was assumed to be light to 

moderate. Key model inputs are daily weather, soil texture class, vegetation mix, animal waste 

N inputs, and grazing intensity. The model simulates soil water and temperature flows, plant 

growth and senescence, decomposition of dead plant material and soil organic matter, 

mineralization of nutrients, and trace gas fluxes. Nitrous oxide emissions, nitrate (NO3) leaching, 

nitrogen volatilization, animal waste deposition, and nitrogen fixation by legumes were 

simulated on a per unit area basis, and multiplied by the estimated grazed area (NRI USDA 

2000b) in each county to obtain total county level nitrogen losses. The DAYCENT simulations 

are described in more detail in Chapter 3 of this report and in EPA (2010) and Del Grosso et al. 

(2006). Manure N deposition from grazing animals (i.e., pasture, range and paddock or PRP 

manure) was an input to the DAYCENT model (see Annex 3.10 EPA 2010), and included 

approximately 91 percent of total PRP manure. The remainder of the PRP manure N excretions 

in each county was assumed to be excreted on federal grasslands, and the N2O

emissions were estimated using the IPCC (2006) Tier 1 method with IPCC default emission 

factors. Waste nitrogen deposited on grazed lands not accounted for by the DAYCENT 

simulations were multiplied by the default IPCC (2006) emission factor of 0.02 kg N20-N/kg N 

to estimate direct N2O-nitrogen emissions, as opposed to the 0.01 kg N2O-N/kg N used to 

estimate N additions from managed soils (including mineral fertilizers, organic amendments, 

crop residues, and N mineralization from soil carbon losses). 

The amounts of PRP manure N applied on non-federal and federal grasslands in each county 

were based on the proportion of non-federal grassland area according to data from the NRI 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 30

(USDA 2000b, relative to the area of federal grasslands from the National Land Cover Dataset 

(Vogelman et al. 2001). Sewage sludge was assumed to be applied on grasslands because of the 

heavy metal content and other pollutants in human waste that limit its use as an amendment to 

croplands. Sewage sludge application was estimated from data compiled by EPA (2003), 

McFarland (2001), and NEBRA (2007). 

Indirect N2O emissions due to volatilization of applied nitrogen and indirect N2O emissions due 

to leaching were calculated using DAYCENT and IPCC (2006) estimates of volatilization and 

NO3 leaching and IPCC estimates of the portion of volatilized or leached/runoff nitrogen that is 

converted to N2O. Nitrogen volatilized, leached, or runoff N are all outputs for the grazed lands 

simulated by DAYCENT. For animal waste not accounted for by the DAYCENT simulations, 

10% of animal waste nitrogen was assumed to volatilize and 30% of animal waste nitrogen was 

assumed to be leached or runoff. The total volatilized nitrogen was multiplied by the IPCC 

default emission factor of 0.01 kg N20- N/kg N (IPCC 2006). The total nitrogen leached or 

runoff was multiplied by the IPCC (2006) default emission factor of 0.0075 kg N20-N/kg N.

Total grazed land N2O emissions were partitioned among different animal types by assuming 

that emissions are linearly proportional to waste nitrogen production.

2.6.2 Uncertainty in Nitrous Oxide Emissions for Grazed Lands

Uncertainty due to model inputs and model structure were quantified. Model inputs used to 

represent weather, N inputs, and soil texture are not known precisely, and each of these has an 

associated range of uncertainty represented by a probability density function. Model structural 

uncertainty refers to the errors inherent in the model. That is, the model is not expected to yield 

perfect results even if model inputs were precisely known. Combining uncertainties related to 

model input and model structure yields uncertainty ranges for N2O in grazed lands that are larger 

than those reported in the previous Inventory. To address uncertainty in model inputs, a series of 

Monte Carlo simulations were performed. To address model structural uncertainty, DAYCENTsimulated N2O emissions were compared with measured emissions from over 10 grassland 

experiments in North America. IPCC (2006) methodology was used to estimate uncertainties for 

federal grazed lands not accounted for by the DAYCENT simulations. Uncertainty from the 

DAYCENT simulated grazed land was combined with uncertainty for remaining grazed lands 

calculated using IPCC (2006) methodology by using simple error propagation. The calculated 

95% confidence interval around the estimate of 62 Tg CO2 eq. for grazed soil N2O emissions 

was 39 to 156 TgCO2 eq (Table 2-1). Uncertainty calculations are described in detail in Chapter 

3 of this report.

2.6.3 Methodology to Estimate Methane Emissions from Grazed Lands

Methane emissions were estimated by multiplying regional or national animal type-specific 

volatile solid production by the animal type-specific maximum CH4-production capacity of the 

waste and the national MCF for manure deposited on grazed lands.

2.6.4 Changes Compared to the 2nd Edition of the USDA GHG Report
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 31

In accordance with 2006 IPCC suggested protocol, the N2O emission factor for grazed land 

manure associated with horses, sheep, and goats was set to 1 percent. Previously, the 2% 

emission factor for cattle, swine, and poultry was applied to all livestock categories. In the 

previous edition, internal calculations in the DAYCENT model were used to derive N additions 

from unmanaged animal waste. In this edition, these N additions are based on animal population 

data. This improvement ensures that the data on PRP manure N in the DAYCENT model 

simulations is consistent with N excretion data from the Managed Livestock Waste section of 

this Inventory. Another important change relates to indirect emissions form NO3 leaching. 

Nitrate leaching was assumed to be an insignificant source of indirect N2O in grassland systems 

where the amount of precipitation plus irrigation did not exceed the potential evapotranspiration, 

as recommended by IPCC (2006). These areas are typically semi-arid to arid, and nitrate 

leaching to groundwater is a relatively uncommon event. Adopting this recommendation 

reduced indirect N2O emissions significantly. In aggregate, these changes resulted in an 

approximately 40-percent decrease in N2O emissions from grazed lands on average, primarily 

due to the new operational version of DAYCENT, revised N additions from grazing animal 

waste, and reduced impact of NO3 leaching on indirect N2O emissions in arid and semi-arid 

regions. 

2.6.5 Methodology to Estimate Carbon Dioxide Fluxes for Grazed Lands

As with N2O emissions, carbon dioxide (CO2) fluxes for grasslands were estimated using results 

from an ecosystem model (CENTURY) and IPCC (2006) methodology. CENTURY (Parton et 

al. 1994) uses monthly weather data, surface soil texture class, and current and historical 

vegetation type and land management information to simulate plant growth and senescence, 

decomposition of dead plant material and soil organic matter, soil water content and temperature, 

and other ecosystem variables. CENTURY has been parameterized to simulate continuous 

grasslands and croplands converted to grasslands but not other land uses converted to grasslands. 

Consequently, IPCC (2006) methodology was used to estimate CO2 fluxes for land converted 

from non-agricultural uses to grazed land. Also, CENTURY has not been well tested with 

organic soils, so IPCC (2006) methodology was also used for grazed organic soils.

Both CENTURY and IPCC (2006) methodologies rely on land use classifications and land use 

histories. The National Resources Inventory (NRI USDA 2000b) was used to identify grassland 

remaining grassland and land converted to grassland. Grassland includes pasture and rangeland 

where the primary land use is livestock grazing. The NRI is a statistically based sample of all 

non-federal land and includes ~400,000 points in agricultural land. Data have been reported 

every five years starting in 1982, and 2003 is the most recent year that has been reported. 

According to NRI data, ~17 million ha of grassland (out of a total ~261 million ha reported in 

2003) were converted to grassland between 1997 and 2003. An example of land converted to 

grassland is land that was cropped historically but then converted to pasture use. Carbon dioxide 

fluxes for grazed lands were calculated using estimates of changes in soil organic carbon stocks 

and molecular stoichiometry. 

Mineral soil carbon stocks and stock changes for NRI points classified as grasslands remaining 

grasslands and cropland converted to grassland were estimated using the CENTURY model. In 

addition to accounting for weather and soil texture, these simulations also included estimates of 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 32

managed manure additions to grasslands. Waste from grazing animals deposited directly onto 

grasslands is calculated by the model based on grazing intensity and forage availability. 

CENTURY estimates carbon stock changes by accounting for carbon inputs from plant material 

and manure and carbon outputs from grazing and decomposition. For details on sources of the 

input data required to run CENTURY and how the simulations were conducted, see Chapter 3 of 

this report and Chapter 7 and Annex 3.13 of the U.S. GHG Inventory (EPA 2010).

Mineral soil carbon stocks and stock changes for NRI points classified as land other than 

cropland converted to grassland and all grasslands growing on organic soils were estimated using 

IPCC (2006, 1997) methodology. U.S.-specific stock change factors based on field data were 

developed for land converted to grassland and for drained histosols used for grazing. As with 

grazed land N2O emissions, CO2 fluxes were partitioned among different animal types by 

assuming that fluxes are linearly proportional to waste nitrogen production.

2.6.6 Uncertainty in Carbon Dioxide Fluxes for Grazed Lands

Uncertainty for the estimates of CO2 fluxes from mineral soil grassland remaining grassland and 

cropland converted to grassland provided by CENTURY model simulations used a Monte Carlo 

approach, which addresses uncertainties in model inputs and uncertainties from scaling NRI 

points to cover all grasslands remaining grassland in the U.S. Uncertainty for estimates from 

other land uses converted to grassland and all organic soil grasslands provided by IPCC (2006, 

1997) methodology used a Monte Carlo approach that addressed uncertainties in carbon stock 

change factors and in land use data. Uncertainties were combined using simple error 

propagation, the results yielded an uncertainty of (7) to (3) around the estimate of (5) Tg CO2 eq. 

in 2008 for land remaining grazed land and (29) to (24) around the estimate of (27) Tg CO2 eq. 

for land converted to grazed land in 2008, where parentheses indicate a net sequestration of CO2

(Table 2-1).

2.6.7 Changes Compared to the 2nd Edition of the USDA GHG Report

There are several important changes that impacted estimate of carbon dioxide fluxes for grazed 

lands. Annual survey data from the USDA National Resources Inventory (NRI) were 

incorporated into this year’s Inventory. This resulted in the availability of new data, which

extended the time series of activity data beyond 1997 to 2003. In previous Inventories, activity 

data were only available through 1997 at 5-year intervals, and subsequent years were treated as 

the same land use practice occurring in 1997. Each NRI point was simulated separately, instead 

of simulating clusters of points that had common land use histories and soil characteristics in a 

county as was done previously. NRI area data were reconciled with the forest area estimates in 

the Forest Inventory and Analysis (FIA) dataset, and were incorporated into the estimation of 

soil C stock changes. Overall, these changes resulted in an average annual increase in soil C 

stocks of approximately 40 Tg CO2 eq. for the time series, compared to the previous Inventory.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 33

2.7 Mitigating Greenhouse Gas Emissions from Livestock

2.7.1 Enteric Fermentation

Emissions of CH4 from enteric fermentation in ruminant and non-ruminant animals are 

dependent on the animal’s digestive system and the amount and type of feed consumed. On 

average, beef and dairy cattle convert 6% of gross energy intake from feed into CH4 through 

enteric fermentation, constituting a loss of energy from the perspective of the animal (Johnson & 

Johnson 1995). Research on animal nutrition has focused on reducing this energy loss, which 

consequently reduces CH4 emissions and increases nutritional efficiency. Through such research, 

a number of potential strategies have been identified to reduce CH4 emissions from enteric 

fermentation, including (Mosier et al. 1998):

• Increasing the digestibility of forages and feeds;

• Providing feed additives which may tie up hydrogen in the rumen;

• Inhibiting the formation of CH4 by rumen bacteria;

• Increasing acetic acid in the rumen;

• Improving production efficiency; and 

• Modifying bacteria in the rumen. 

Currently, government research programs indirectly address mitigation of CH4 emissions 

through improved livestock production. Ongoing research development and deployment efforts 

related to mitigating CH4 emissions include:

• Decreasing feed digestion time by improving grazing management to increase the 

digestibility of forages, increasing the digestibility of feed grains, and increasing the 

feeding of concentrated supplements;

• Adding edible oils in feed to sequester hydrogen making it unavailable for 

methanogens;

• Using feed additives, ionophores, which inhibit the formation of CH4 by rumen 

bacteria;

• Improving livestock production efficiency by feed additives such as hormones to 

increase milk production and growth regulators for beef production or by improved 

diet or genetics;

• Enhancing rumen microbes to produce usable products rather than CH4.

2.7.2 Livestock Waste

Livestock and poultry waste from production facilities has the potential to produce significant 

quantities of CH4 and N2O, depending on the waste management practices used. In the United 

States, livestock and poultry manure is managed in a myriad of ways, suggesting there are 

multiple options for reducing CH4 and N2O emissions. When manure is stored or treated in 

systems that promote anaerobic conditions, such as lagoons and tanks, the decomposition of the 

biodegradable fraction of the waste tends to produce CH4. When manure is handled as a solid, 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 34

such as in stacks or deposits on pastures, the biodegradable fraction tends to decompose 

aerobically and produce little or no CH4, although it produces N2O.

A relatively large portion of CH4 is emitted from livestock and poultry waste in anaerobic 

lagoons. Current, commercially available technologies that have been the most successful in 

reducing CH4 emissions from manure management are anaerobic digestion systems. Unlike 

conventional lagoons, digestion technologies keep waste treatment and storage functions 

separate and allow for gas recovery and combustion, pathogen and organic stabilization, odor 

and other air quality pollution control, and flexible approaches to nutrient management.

The EPA tracks installation and usage of anaerobic digesters under voluntary programs such as 

AgStar (http://www.epa.gov/agstar/) and uses this data to estimate how much anaerobic digesters 

have reduced overall CH4 emissions from livestock waste over the last 11 years. Figure 2-4 

shows an increasing trend in emissions reductions annually from the use of anaerobic digesters, 

reflecting increasing numbers of digester systems being installed each year. 

Other emission reduction processes can include separation, aeration, or shifts to solid handling or 

storage management systems. These strategies, however, could be limited by other farm or 

environmental constraints and costs.

Figure 2-4

Estimated Reductions in Methane Emissions from Anaerobic Digesters, 1990-2008

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Tg CO2 eq. 

Swine Dairy Cattle

Tg CO2 eq. is teragrams carbon dioxide equivalent

2.7.3 Grazed Lands

Nitrous oxide is by far the largest source of emissions from grazed lands so it also provides the 

largest mitigation potential (Table 2-6). However, because grazed lands are not highly managed, 

particularly the large expanses of rangeland in the Western U.S., mitigation options are limited. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 35

One strategy that may be feasible for more intensely managed pastures in the Eastern U.S. is 

nitrification inhibitors. Although synthetic nitrogen fertilizer inputs are low, grazing lands 

usually have large nitrogen inputs from biological nitrogen fixation because they are seeded with 

legumes. This mitigation potential has not been quantified but will be in future DAYCENT 

model simulations. 

Recent model simulations indicate grazed lands are currently providing a net sequestration of 

CO2 emissions (Table 2-6) and have the potential to store over 100 Tg CO2 per year across the 

U.S. (Follett et al. 2001). The largest potential is by decreasing soil erosion and restoring eroded 

and degraded soils so that they become net carbon sinks. Other management practices which 

enhance carbon storage include nutrient/manure additions, legume seeding, and improved 

grazing management. However, the benefits of increased carbon storage must be compared with 

the costs of increased N2O emissions associated with nutrient/manure additions and legume 

seeding. 
Chapter 3: Cropland Agriculture 

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 36

3.1 Summary of U.S. Greenhouse Gas Emissions from Cropland 

Agriculture

In 2008, cropland agriculture resulted in total emissions of 196 Tg CO2 eq. of greenhouse gases 

(GHG) (Table 3-1). Cropland agriculture is responsible for almost half (46%) of all emissions 

from the agricultural sector (EPA 2010). Nitrous oxide (N2O), carbon dioxide (CO2), and 

methane (CH4) emissions from cropped soils totaled 154, 34, and 8 Tg CO2 eq., respectively, in 

2008. However, that amount was offset by a storage, or carbon sequestration, of 42 Tg CO2 eq. 

in cropped soils in 2008. When carbon sequestration is taken into account, net emissions of GHG 

from cropland agriculture amount to approximately 154 Tg CO2 eq. The 95% confidence interval 

for net emissions in 2008 is estimated to lie between 104 and 246 Tg CO2 eq. (Table 3-1).

Table 3-1 Estimates and Uncertainties for Cropland Greenhouse Gas Emissions, 2008

GHG Emissions Lower Bound Upper Bound Lower Bound Upper Bound

Source Tg CO2 eq. percent

N2O 154 114 241 -26 +57 

Soils Direct 118 84 181 -29 +53 

Soils Indirect1 35 14 96 -59 +173 

Residue Burning 1 0 1 -71 +83 

CH4 8 4 19 -57 +127 

Residue Burning 1 0 2 -68 +88 

Rice Cultivation 7 3 18 -64 +143 

CO2 (8) (38) 20 -360 +347 

Mineral Soils (42) (69) (16) -63 +63 

Organic Soils 30 17 40 -43 +33 

Liming of Soils 4 0 8 -97 +102 

Total Emissions 196 154 285 -22 +45 

Net Emissions2 154 104 246 -33 +60 

Note: Parentheses indicate a net sequestration. Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is nitrous oxide; 

CO2 is carbon dioxide. 1 Accounts for loss of manure N during transport, treatment and storage, including volatilization and leaching/runoff. 2 Includes sources and sinks.

Net emissions in 2008 were 23% higher than the baseline year (1990). Greenhouse gas emissions 

from agricultural soils fluctuated between 1990 and 2008, with CH4 and N2O reaching their 

highest levels in 2001 (Table 3-2). Net CO2 flux showed substantial interannual variability, 

mainly due to fluctuations in the mineral soil CO2 sink. Annual fluctuations in CO2 sequestration 

are primarily a result of variability in weather patterns and land use changes. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 37

Table 3-2 Summary of Greenhouse Gas Emissions from Cropland Agriculture, 1990, 1995, 

2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source Tg CO2 eq.

N2O 139.5 144.1 151.8 160.2 150.2 147.8 152.4 153.8 150.5 151.2 153.9 

Soils Direct 103.0 109.8 115.6 122.3 115.3 111.4 118.5 117.9 114.7 116.7 118.3 

Soils Indirect1 36.0 33.9 35.7 37.5 34.4 35.9 33.4 35.4 35.3 34.1 35.1 

Residue Burning 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

CH4 7.9 8.4 8.4 8.5 7.6 7.8 8.5 7.8 6.8 7.1 8.2 

Residue Burning 0.8 0.7 0.9 0.9 0.8 0.9 1.0 0.9 0.9 1.0 1.0 

Rice Cultivation 7.1 7.6 7.5 7.6 6.8 6.9 7.6 6.8 5.9 6.2 7.2 

CO2 (22.6) (15.6) (23.5) (4.3) (1.7) (7.2) (8.3) (8.0) (8.9) (9.2) (8.3)

Mineral Soils (57.1) (50.3) (58.1) (39.0) (37.0) (42.0) (42.5) (42.6) (43.4) (44.0) (42.4)

Organic Soils 29.8 30.3 30.3 30.3 30.3 30.3 30.3 30.3 30.3 30.3 30.3 

Liming of Soils 4.7 4.4 4.3 4.4 5.0 4.6 3.9 4.3 4.2 4.5 3.8

Total Emissions 181.9 187.2 194.8 203.4 193.1 190.5 195.2 196.2 191.9 193.2 196.2

Net Emissions2 124.8 136.9 136.7 164.5 156.1 148.4 152.7 153.6 148.5 149.2 153.8

Note: Parentheses indicate a net sequestration. Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is nitrous oxide; CO2 is 

carbon dioxide.

1 SoilsIndirect N2O emissions account for volatilization and leaching/runoff. 2 Includes sources and sinks.

Greenhouse gas emission from agricultural soils, primarily N2O, were responsible for the 

majority of total emissions, while CH4 and N2O from residue burning and rice cultivation caused 

about 4% of emissions in 2008 (Tables 3-1, 3-2). Soil CO2 emissions from cultivation of organic 

soils (15%) and from liming (2%) are the remaining sources. Nitrous oxide emissions from soils 

are the largest source in the U.S. because N2O is a potent greenhouse gas (see Chapter 1 Box 1-

1) and due to the large amounts of nitrogen added to crops in fertilizer that stimulate N2O 

production. Emissions from residue burning are minor because only ~3% of crop residue is 

assumed to be burned in the U.S. (EPA 2010). Cropped soils in the U.S. are a net CO2 sink 

mainly because reduced tillage 

intensity has become more 

popular in recent years and 

lands used for perennial hay 

cropping, as well as idle 

cropland enrolled in the 

Conservation Reserve 

Program (CRP), continue to 

store carbon. 

Nitrous oxide emissions were 

largest in areas where a large 

portion of land is used for 

intensive agriculture (Map 3-

1). For example, 90% or more 

of the land in many counties 

in the Corn Belt is intensively 

cropped (Map 3-2). Corn is 

the leading crop for N2O 

Tg CO2 eq. is teragrams carbon dioxide equivalent; N2O is nitrous oxide.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 38

emissions followed by soybean and hay (Table 3-3). 

Emissions from corn cropping are high because large amounts of nitrogen (N) fertilizer are 

routinely applied and the land area used for corn production is the most extensive (Figure 3-1). 

Although little N fertilizer is applied for soybean cropping, N2O emissions are high because 

soybeans supply large amounts of N to the soil from biological fixation of atmospheric nitrogen 

(N2). In general, N2O emissions are highly correlated with crop areas and nitrogen inputs. 

Biological fixation makes 

up about half of total N 

additions, followed by 

synthetic fertilizer addition 

and manure (Figure 3-2). 

Note that Map 3-1 does not 

include emissions from 

non-major crops, which 

make up a significant 

portion of total emissions 

in California and Florida. 

The cropped soil emissions 

reported here are consistent 

with those in EPA (2010).

Cropland agriculture 

results in GHG emissions 

from multiple sources, with 

Map 3.2 U.S. Cropped Land
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 39

the magnitude of emissions determined, in part, by land management practices. Application of 

synthetic and organic fertilizers, cultivation of N fixing crops and rice, cultivation and 

management of soils, and field burning of crop residues leads to emissions of N2O, CH4, and 

CO2. However, agricultural soils can also mitigate GHG emissions through the biological uptake 

of organic carbon in soils resulting in CO2 removals from the atmosphere. This chapter covers 

both GHG emissions from cropland agriculture and biological uptake of CO2 in agricultural 

soils. National estimates of these sources, published in the U.S. GHG Inventory, are reported in 

this section and, where appropriate, county and state-level emissions estimates are provided. 

Sources and sinks of N2O, CH4, and CO2 and the mechanisms that control fluxes are discussed in 

detail. Methodologies used to estimate emissions are summarized and mitigation opportunities 

are discussed and quantified where possible. The methodologies used here are similar to those 

reported in the second edition of the USDA GHG report (USDA 2008), with some improvements 

in model algorithms and model input data. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 40

3.2 Sources of Greenhouse Gas Emissions in Cropland Agriculture

3.2.1 Cropped Soils

Agricultural soils serve as both a source of GHG and a mechanism to remove CO2 from the 

atmosphere. Nitrous oxide, CH4, and CO2 emissions and sinks are a function of underlying 

biochemical processes. Nitrous oxide is produced as an intermediate during nitrification and 

Table 3-3 Nitrous Oxide Emissions from Differently Cropped Soils, 1990, 1995, 2000-

20081

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source Tg CO2 eq.

Corn 47.5 42.8 49.7 53.6 49.3 47.8 51.6 51.6 47.1 59.3 54.0

Direct 36.1 34.8 40.0 42.8 40.3 37.4 42.5 41.7 38.0 48.0 43.7

Volatilization 1.1 1.1 1.3 1.2 1.3 1.2 1.3 1.2 1.2 1.5 1.3

Leaching & Runoff 10.2 6.9 8.3 9.6 7.7 9.2 7.9 8.7 7.8 9.8 9.0

Soybean 23.8 22.2 29.7 33.1 28.7 29.0 29.9 28.7 30.1 25.4 28.8

Direct 17.1 17.7 22.5 24.5 22.0 21.2 22.5 21.6 22.8 19.3 21.8

Volatilization 0.9 0.9 1.2 1.1 1.1 1.1 1.2 1.0 1.1 1.0 1.1

Leaching & Runoff 5.8 3.6 5.9 7.4 5.7 6.6 6.2 6.1 6.2 5.2 5.9

Hay 16.8 16.4 17.5 18.6 16.8 17.2 17.0 17.9 16.8 17.3 17.4

Direct 14.3 13.7 15.4 15.8 14.4 14.6 15.0 15.3 14.7 14.9 15.2

Volatilization 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Leaching & Runoff 2.2 2.4 1.8 2.4 2.1 2.3 1.7 2.3 1.7 2.0 1.9

Wheat 13.0 17.8 10.8 10.7 11.5 11.9 9.9 8.6 10.6 8.7 8.2

Direct 10.1 8.8 8.1 8.3 8.7 7.7 7.9 7.1 7.1 6.4 6.3

Volatilization 0.6 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.3

Leaching & Runoff 2.4 8.5 2.3 1.9 2.4 3.8 1.6 1.2 3.1 1.9 1.6

Cotton 3.9 5.5 5.9 5.8 5.4 4.5 4.6 5.3 4.7 3.6 3.4

Direct 3.2 4.1 4.4 4.7 3.9 3.7 3.6 4.2 4.1 2.8 2.6

Volatilization 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Leaching & Runoff 0.6 1.3 1.4 0.9 1.4 0.7 0.9 1.0 0.5 0.8 0.7

Sorghum 2.8 2.2 2.2 2.7 2.5 2.0 1.6 1.5 1.6 1.7 1.9

Direct 2.1 1.8 1.8 2.3 2.0 1.6 1.3 1.2 1.2 1.4 1.5

Volatilization 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0

Leaching & Runoff 0.7 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.4 0.3 0.4

Non-major crops 18.8 23.4 22.0 21.2 21.5 21.8 23.8 26.0 25.1 20.5 25.7

Direct 14.1 17.6 16.5 16.0 16.3 16.6 18.1 19.7 18.9 15.6 19.5

Volatilization 1.8 2.2 2.2 2.1 2.2 2.1 2.3 2.5 2.5 2.1 2.5

Leaching & Runoff 2.9 3.6 3.3 3.1 3.1 3.1 3.4 3.8 3.7 2.9 3.8

Histosol Cultivation2 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9

Managed Manure3 9.9 10.8 11.2 11.6 11.6 10.6 11.1 11.3 11.6 11.7 11.6

All Direct 109.8 112.2 122.8 129.0 121.9 116.4 124.8 125.0 121.3 123.0 125.1

All Volatilization 5.0 5.2 5.7 5.5 5.5 5.4 5.7 5.6 5.7 5.3 5.6

All Leaching & Runoff 24.7 26.6 23.4 25.7 22.9 25.9 21.9 23.3 23.4 22.9 23.2

Total 139.5 144.1 151.8 160.2 150.2 147.8 152.4 153.8 150.5 151.2 153.9

Note: Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. 1 Emissions from residue burning are not included. 2 Direct emissions.

3 Accounts for loss of manure N during transport, treatment and storage, including volatilization and leaching/runoff.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 41

denitrification in soils (Firestone & Davidson 1989). In nitrification, soil micro-organisms 

(“microbes”) convert ammonium (NH4) to nitrate (NO3) through aerobic oxidation (IPCC 1996). 

In denitrification, microbes convert nitrate to nitrogen oxides (NOx) and dinitrogen gas (N2) by 

anaerobic reduction. During nitrification and denitrification, soil microbes release N2O, which 

can diffuse from the soil and enter the earth’s atmosphere (IPCC 1996). Cropland soil 

amendments that add nitrogen to soils drive the production of N2O by providing additional 

substrate for nitrification and denitrification. Commercial fertilizer, livestock manure, sewage 

sludge, cultivation of N-fixing crops, and incorporation of crop residues all add N to soils. In 

addition, cultivation, particularly of soils high in organic matter (i.e., histosols), enhances 

mineralization of nitrogen-rich organic matter, making more nitrogen available for nitrification 

and denitrification (EPA 2010, 2007). Compared to soil N2O emissions, other GHG sources from 

croplands are relatively small. Methane gas is produced and emitted primarily from rice paddies. 

This, however, is responsible only for a small portion of total emissions from cropped soils in the 

U.S. due to the small land area cropped with rice in this country. Emissions from crop residue 

burning are also not a large source compared to soils due to the small portion of residues burned 

in the U.S. 

Nitrous oxide is the major GHG emitted from cropland agriculture in the U.S. Nitrogen can be 

converted to N2O and emitted directly from agricultural fields (direct emissions), or it can be 

transported from the field in a form other than N2O and then converted to N2O elsewhere 

(indirect emissions). A major source of indirect N2O emissions is from nitrate that either leaches 

into the groundwater or runs off the soil surface and then is converted to N2O via aquatic 

denitrification (Del Grosso et al. 2006). A second source of indirect N2O emissions comes from 

N that is volatilized to the atmosphere, then is deposited back onto soils, and converted to N2O 

(Del Grosso et al. 2006). 

The size of CO2 sources and sinks from soils is related to the amount of organic carbon stored in 

the soil (IPCC 1996). Changes in soil organic carbon (SOC) content are related to inputs (e.g., 

atmospheric CO2 fixed as carbon in plants through photosynthesis) and losses from 

decomposition of soil organic matter which causes CO2 emissions (IPCC 1996). The net balance 

of CO2 uptake and loss in soils is driven in part by biological processes, which are affected by 

soil characteristics and climate. In addition, land use and management can affect the net balance 

of CO2 through modifying inputs and rates of decomposition (IPCC 1996). Changes in 

agricultural practices such as clearing, drainage, tillage, crop selection, irrigation, grazing, crop 

residue management, fertilization, and flooding can modify both organic matter inputs and 

decomposition, and thereby result in a net flux of CO2 to or from soils. 

Most agricultural soils contain comparatively low amounts of organic carbon as a percentage of 

total soil mass, typically in the range of 1 to 6 % organic C by weight, and are thus classified as 

mineral soils (NRCS 1999). However, on an area basis, this amount of carbon typically exceeds 

that stored in vegetation in most ecosystems (including forests). Historically, conversion of 

native ecosystems to agricultural uses resulted in large soil carbon losses, as much as 30-50% or 

more of the C present in the native condition (Haas et al. 1957, Schlesinger 1986, Guo & Gifford 

2002, Lal 2004). Presently, after many decades of cultivation, most soils have likely stabilized at 

lower carbon levels or are increasing their organic matter levels as a result of increasing crop 

productivity (providing more residues), less intensive tillage, and other improvements in 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 42

agricultural management practices (Paustian et al. 1997, Allmaras et al. 2000, Follett 2001). 

Changes in land-use or management practices that result in increased organic inputs or decreased 

oxidation of organic matter (e.g., taking cropland out of production, improved crop rotations, 

cover crops, application of organic amendments and manure, and reduction or elimination of 

tillage) usually result in a net accumulation of SOC until a new equilibrium is achieved.

Cultivated organic soils, also referred to as histosols, contain more than 12 to 20% organic 

matter by weight, and constitute a special case (NRCS 1999, Brady & Weil 1999). Organic soils 

form as a result of water-logged conditions, in which decomposition of plant residue is retarded. 

When organic soils are drained and cultivated, the rate of decomposition, and hence CO2

emissions, is greatly accelerated. Due to the depth and richness of the organic layers, carbon loss 

from cultivated organic soils can continue over long periods of time. 

In addition, lime, often added to mineral and organic agricultural soils to reduce acidic 

conditions, contains carbonate compounds (e.g., limestone and dolomite) that when added to 

soils release CO2 through the bicarbonate equilibrium reaction (IPCC 1996).

3.2.2 Rice Cultivation

Rice cultivation is unique because it takes place almost exclusively on flooded fields, including 

in the U.S. where rice is almost grown entirely on flooded fields (EPA 2010). This water regime 

causes CH4 emissions as a result of waterlogged soils restricting oxygen diffusion and creating 

conditions for anaerobic decomposition of organic matter, facilitated by CH4 emitting 

“methanogenic” bacteria (IPCC 1996, Le Mer & Roger 2001). Methane from rice fields reaches 

the atmosphere in three ways: bubbling up through the soil, diffusion losses from the water 

surface, and diffusion through the vascular elements of plants (IPCC 1996). Diffusion through 

plants is considered the primary pathway, with diffusion losses from surface water being the 

least important process (IPCC 1996). Soil composition, texture, and temperature are important 

variables affecting CH4 emissions from rice cultivation, as are the availability of carbon substrate 

and other nutrients, soil pH, and partial pressure of CH4 (IPCC 1996). Since U.S. rice acreage is 

relatively small compared to other crops, CH4 emissions from rice cultivation are small 

compared to other cropland agriculture sources (EPA 2007).

3.2.3 Residue Burning

In the U.S., 7-8 million acres of crop residues in fields are burned annually to prepare for 

cultivation and to control for pests (EPA 2010). While CO2 is a product of residue combustion, 

residue burning is not considered a net source of CO2 to the atmosphere because CO2 released 

from burning crop biomass is replaced by uptake of CO2 in crops growing the following season 

(IPCC 1996). However, CH4 and N2O, also products of residue combustion, are not recycled into 

crop biomass through biological uptake the following season. Therefore, residue burning is 

considered a net source of CH4 and N2O to the atmosphere. Overall, GHG emissions from field 

burning of crop residues are comparatively small in the U.S. relative to other countries (EPA 

2010).
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 43

3.2.4 Agroforestry

Agroforestry practices such as establishing windbreaks and riparian forest buffers represent 

another potential carbon sink in cropland agriculture. Comprehensive data on agroforestry

practices are not available to estimate the current national levels of carbon sequestration from 

such practices. However, published research studies have estimated the potential agroforestry

carbon sink in the U.S. In temperate systems, agroforestry practices store large amounts of 

carbon (Kort & Turlock 1999, Schroeder 1994), with the potential ranging from 15 to 198 metric 

tons of carbon per hectare (modal value of 34 metric tons of carbon per hectare) (Dixon 1995). 

Nair and Nair (2003) estimated that by the year 2025, the potential carbon sequestration of 

agroforestry in the United States will be 90 million metric tons of carbon per year. There is a 

need to better quantify and track agroforestry practices nationally, particularly to inform USDA 

programs like the Conservation Reserve Program, Environmental Quality Incentives Program, 

and Forest Land Enhancement Program, which may provide incentives to land owners to 

implement agroforestry.

3.3 Nitrous Oxide Emissions from Cropped Soils

In 2008, 80% of total cropland soil N2O emissions were direct soil emissions (Table 3-3). Of the 

20% of total emissions from indirect N2O, 81% are from NO3 leaching/runoff and the remainder 

are associated with volatilization. Corn cropland has the highest emissions, roughly 35% of the 

total, followed by soybean and hay (Table 3-3). Emissions are highest from corn because corn 

covers the largest land area (Figure 3-1) of all crops and synthetic nitrogen inputs with corn are 

high. Emissions from soybeans are high due to large crop area and high rates of nitrogen 

fixation. Other factors contributing to high emissions for these crops are: they are grown mostly 

in the north central region where many of the soils are high in organic matter and some of the 

soils are poorly drained, both of which enhance denitrification rates. In the previous report, 

emissions from wheat were third highest, but recent declines in wheat area have resulted in fewer 

emissions for this particular crop. Emissions from hay cropping are substantial, despite minimal 

fertilizer N additions, because a large portion of hay includes N-fixing plants (e.g., alfalfa). 

Emissions from cotton and sorghum are low, as the cropland area for these crops is small 

compared to the other major crops simulated by DAYCENT. In addition, emissions from 

sorghum are low because this crop tends to be grown in drier areas in the eastern Great Plains, 

and cotton is grown mostly in the South, where soils tend to be low in organic matter. Non-major 

crop types were responsible for ~17% of total emissions in 2008 (Table 3-3). Emissions from 

histosol cultivation are small (~2% of total) because histosols represent only ~750,000 ha, which 

is less than 1% of U.S. cropped land. 

Nitrous oxide emissions are largely driven by nitrogen additions, weather, and soil physical 

properties. External nitrogen inputs (i.e., addition of synthetic fertilizers and manure, as well as 

biological fixation) to cropped soils varied between ~24 and 27 Tg N between 1990 and 2008 

(Fig. 3-2), while N2O emissions varied between 142 and 165 Tg CO2 eq. (Table 3-3). Variation 

in N inputs explained roughly 46% of the variability in soil N2O emissions. Also, the years with 

highest nitrogen inputs did not necessarily lead to the highest N2O emissions. This indicates that 

other factors such as changes in weather patterns strongly influence the annual variability in 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 44

estimated N2O emissions. Specifically, amount and timing of precipitation, temperature patterns, 

and soil carbon and nitrogen availability interact to control N2O emissions.

3.3.1 Methods for Estimating N2O Emissions from Cropped Soils

Emissions of N2O from nitrogen additions to cropland soils and cultivation of histosol soils are 

source categories analogous to those covered in Agricultural Soil Management in the U.S. GHG 

Inventory (EPA 2010), with some exceptions. The U.S. GHG Inventory (EPA 2010) includes in 

the Agricultural Soils Management section direct emissions of N2O from livestock on grazed 

lands, while the USDA GHG Inventory includes this source under Livestock GHG Emissions in 

Chapter 2 of this report. Also, the U.S. GHG Inventory (EPA 2010) includes in the Agricultural 

Soils Management section indirect emissions of N2O from all sources, including indirect N2O 

from livestock grazing and from urban areas. For this report, indirect N2O from grazing is 

included in the livestock chapter, while indirect emissions from urban areas and other nonagricultural sources are not covered at all. 

Briefly, the DAYCENT ecosystem model was used to estimate direct soil N2O emissions, NO3

leaching, and nitrogen volatilization from major crop types. IPCC (2006) methodology was used 

to estimate direct and indirect emissions from cropped soils not included in the DAYCENT 

simulations and to calculate indirect emissions from DAYCENT estimates of NO3 leaching and 

volatilization. IPCC (2006) methodology was also used to estimate emissions from cultivation of 

organic soils. Use of a process-based model for inventories is known as a Tier 3 approach, while 

use of IPCC (2006) methodology is referred to as a Tier 1 approach. The methodology described 

below shows how the Tier 1 and Tier 3 approaches can be combined to derive overall emission 

estimates. Refer to EPA (2010, 2007) for a complete description of the methodologies used to 

estimate N2O emissions. 

3.3.2.1 DAYCENT Simulations for Major Crop Types

The DAYCENT ecosystem model (Del Grosso et al. 2001, Parton et al. 1998) was used to 

estimate direct N2O emissions from mineral soils producing major crops, (corn, soybean, wheat, 

alfalfa hay, other hay, sorghum, and cotton) which represent approximately 86% of total 

cropland in the United States. DAYCENT simulated crop growth, soil organic matter 

decomposition, greenhouse gas fluxes, and key biogeochemical processes affecting N2O 

emissions. The simulations were driven by model input data generated from daily weather 

records, land management, and soil physical properties determined in national soil surveys.

DAYCENT simulations were conducted for each major crop at the county scale in the U.S. The 

county scale was selected because soil, weather, and crop area data were available for every 

county. However, land management data (e.g., timing of planting, harvesting, and fertilizer 

application; intensity of cultivation; rate of fertilizer application) were only available at the 

agricultural region level as defined by the Agricultural Sector Model (McCarl et al. 1993). There 

are 63 agricultural regions in the contiguous United States; most states correspond to one region, 

except for those with greater heterogeneity in agricultural practices, which led to further 

subdivisions. Therefore, while several cropping systems were simulated for each county in an 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 45

agricultural region, the model parameters that determined the influence of management activities 

on soil N2O emissions (e.g., when crops were planted/harvested, amount of fertilizer added), did 

not differ among those counties.

Corn, soybeans, wheat, alfalfa hay, other hay, sorghum, and cotton are defined as major crops 

and were simulated in every county where they were grown. For rotations that include a cycle 

that repeats every 2 or more years (e.g., corn/soybeans, wheat/corn/fallow) different simulations 

were performed where each phase of the rotation was simulated every year. For example, in 

regions where wheat/corn/fallow cropping is used, three rotations were simulated: one with 

wheat grown the first year, a second with corn the first year, and a third with fallow the first year. 

This ensured that each crop was represented during each year in one of the three simulations. In 

cases where the same crop was grown in the same year in two or more distinct rotations for a 

region, N2O emissions were averaged across the different rotations to obtain a value for that 

crop. Emissions from cultivated fallow land were also included. Fallow area was assumed to be 

equal to winter wheat area in regions where winter wheat/fallow rotations are the dominant land 

management for winter wheat. 

The simulations reported here assumed conventional tillage cultivation, gradual improvement of 

cultivars, and gradual increases in fertilizer application until 1989. We accounted for 

improvements of cultivars (cultivated varieties) because it is unrealistic to assume that modern 

corn is identical, in terms of yield potential, nitrogen demand, etc., as corn grown in 1900. 

Realistic simulations of historical land management and vegetation type are important because 

they influence present day soil carbon and nitrogen levels, which influence present day nitrogen 

cycling and associated N2O emissions. 

Nitrous oxide emission estimates from DAYCENT include the influence of N additions, crop 

type, irrigation, and other factors in aggregate, and therefore it is not possible to reliably partition 

N2O emissions by anthropogenic activity (e.g., N2O emissions from synthetic fertilizer 

applications cannot be distinguished from those resulting from manure applications). 

Consequently, emissions are not subdivided according to activity (e.g., N fertilization, manure 

amendments), as is suggested in the IPCC Guidelines, but the overall estimates are likely more 

accurate than the more simplistic IPCC method, which is not capable of addressing the broader 

set of driving variables influencing N2O emissions. Thus, DAYCENT forms the basis for a more 

complete estimation of N2O emissions than is possible with the IPCC methodology.

Uncertainty in the three major model inputs (weather, soil class, and N addition) was addressed 

using Monte Carlo analysis (Del Grosso et al. 2010). For example, although mean amounts of N 

fertilizer applied to different crops are known, the amounts of fertilizer applied by particular 

farmers are uncertain. Monte Carlo analysis provides a method to quantify how this type of 

uncertainty impacts N2O emissions. There are three main steps in this analysis. First, a set of 

simulations was performed using mean N fertilizer additions, median weather, and the dominant 

soil texture class. These were designated the 0th simulations. Second, probability distribution 

functions were derived for N additions, weather, and soil texture class. Third, Monte Carlo 

simulations were performed for a subset of counties in each agricultural region. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 46

In addition to uncertainty in model inputs, model structural error was also addressed. Model 

structural error stems from models not being perfect representations of reality. That is, models 

contain assumptions and imperfectly represent the processes that control crop growth and N2O 

emissions. To quantify model structural error, N2O emissions generated by DAYCENT were 

compared with emissions measured in field plots at various locations in North America.

3.3.2.2 0th Simulations 

For each crop in each county, simulations were performed assuming the most common land 

management practice, the weather most representative of the land area in the county where each 

crop is grown, and the most common soil type for the land area where each crop is grown (0th

simulations). Simulations included native vegetation (year one to plow out), historical 

agricultural practices (plow out to 1970) and modern agriculture (1971 through 2008). Plow out 

(the year when native soils were initially cropped) was assumed to occur between 1600 and 

1850, depending on the state in which the county lies. Simulation of at least 1,600 years of native 

vegetation was needed to initialize soil organic matter (SOM) pools in the model. Modern 

weather was used to drive the simulations of native vegetation and historical cropping. 

Simulation of native vegetation and the historical cropping period was needed to establish 

modern day SOM levels, which is important because N2O emissions are sensitive to the amount 

of SOM. Annual model outputs for N2O emissions, NO3 leached/runoff, and N volatilized were 

compiled for the years 1990-2008.

3.3.2.3 Probability Distribution Functions

Probability distribution functions (PDFs) were derived for key model inputs, including weather, 

soil type, and N amendments. In each county selected for the Monte Carlo analysis, all of the 1 

km2 cells with daily weather that correspond to the land area where row crops and small grains 

dominate were identified and assigned an equal probability of being selected in an individual 

Monte Carlo simulation. Cells with daily weather were similarly identified for the areas cropped 

with hay. The three dominant soil map units were identified for the land area with row crops and 

small grains, and each was assigned a probability given their relative level of dominance. Three 

soil map units were similarly identified and assigned probabilities for the areas where hay

dominates. 

Mineral N fertilization rates were based on two sets of PDFs, which were specified for individual 

crop types and hay. The first PDF was the probability of a fertilization event and the second PDF 

was a log-normal distribution of fertilization rates. Both PDFs were derived from USDA surveys 

and supplemental information (ERS 1997, USDA NASS 2009, 2004, 1999, Grant & Krenz 

1985). Irrigated and rain-fed crops were treated separately due to significantly different 

fertilization rates. State-level PDFs were developed for crops and hay if a minimum of 15 survey 

data points existed in the state. Where data were insufficient at the state-level, PDFs were 

developed for multi-state Farm Production Regions. 

Uncertainty in manure amendments for crops and hay was incorporated in the analysis based on 

total manure available for application in each county, a weighted average amendment rate, and 

the crop-specific land area amended with manure for 1997 (Edmonds et al. 2003). Edmonds et al. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 47

(2003) provided county-level estimates of the proportion of specific crops and hay land amended 

with manure in 1997. EPA (2010) provided supplemental data on county-level variation in 

manure production across the time series from 1990 to 2008. We used the EPA data to scale the 

amended area in 1997 for each crop and hay under the assumption that more manure production 

would increase the area amended with manure, and vice versa. The estimated area was then 

divided by the respective total areas in the county for each crop and hay, yielding a probability of 

either including a manure amendment or not in the Monte Carlo analysis. If soils were amended 

with manure, a reduction factor was applied to the N fertilization rate accounting for the 

interaction between fertilization and manure N amendments (i.e., farmers usually reduce mineral 

fertilization rates if applying manure). Reduction factors were randomly selected from PDFs 

based on relationships between manure N application and fertilizer rates (ERS 1997). 

3.3.2.4 Monte Carlo Simulations

In each agricultural region, two counties were randomly selected for Monte Carlo simulations. 

Additional counties were selected based on the variance in N2O emissions across regions from 

previous simulations (Del Grosso et al. 2010) by using a Neyman allocation (Cochran 1977). 

Neyman’s optimization apportions samples based on an estimated variance in soil N2O 

emissions. Using this approach, greater variance leads to a higher sampling density within the 

respective region with the goal of optimally capturing variation across the croplands in the 

conterminous U.S. Regions with greater variance in N2O emissions were assumed to have more 

variability in weather, soil characteristics, and agronomic practices, suggesting that more 

counties needed to be included in the Monte Carlo analysis. In total, 300 counties were selected 

for the Monte Carlo simulations. As with the 0th simulations, simulations of pre-settlement native 

vegetation and historical cropping patterns were performed in each county using the median 

weather for the county in combination with the three most dominant soil types. 

One hundred Monte Carlo simulations were performed for each crop and hay type in the 300 

counties selected for the Monte Carlo analysis. Random draws were made to select a soil type 

and weather file for the simulation from their respective PDFs, and the appropriate historical 

simulation was identified based on the soil type. Random draws were made to determine if 

mineral N fertilizer would be applied, the rate, and if the crop would be amended with manure. If 

manure was added, synthetic fertilizer rates were reduced based on an additional draw from the 

PDF for the reduction factors. The DAYCENT simulation was executed following the PDF 

draws and the process was repeated for a total of 100 iterations. 

3.3.2.5 Nitrous Oxide Emission Estimates

Nitrous oxide emissions from the 0th simulation for each crop in each county in each agricultural 

region were adjusted by comparing the 0th simulation emissions to the mean emissions from the 

Monte Carlo simulations for that agricultural region. DAYCENT emissions for each crop in units 

of g N2O-N m-2 were multiplied by the county-level crop area based on NASS data. Lastly, 

emissions from all crops were summed to obtain county-level and national emissions from 

cropped soils. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 48

3.3.2.6 Activity Data for DAYCENT Simulations

The activity data requirements for estimating N2O emissions from major crop types include the 

following: daily weather, soil texture, native vegetation, crop rotation and land management 

information, N fertilizer rates and timing, manure amendment N rates and timing, and countylevel crop areas. Unlike the IPCC approach, N inputs from crop residues are not considered 

activity data in the DAYCENT analysis because N availability from this source is internally 

generated by the model. That is, while the model accounts for the contribution of crop residues to 

the soil profile and subsequent N2O emissions, this source of mineral soil N is not activity data in 

the sense that it is not a model input.

Daily Weather Data: Daily maximum/minimum temperature and precipitation were obtained 

from the DAYMET model, which generates daily surface precipitation, temperature, and other 

meteorological data at 1 km2 resolution driven by weather station observations and an elevation 

model (Thornton et al. 2000, 1997, Thornton & Running, 1999). DAYMET weather data are

available for the United States at 1 km2 resolution for 1980 through 2003. 

Soil Properties: Soil texture data required by DAYCENT were obtained from STATSGO (Soil 

Survey Staff, USDA Natural Resources Conservation Service, 2005) and were based on 

observations. Observed data for soil hydraulic properties needed for model inputs were not 

available, so they were calculated from STATSGO texture class and Saxton et al.’s (1986) 

hydraulic properties calculator. 

Native Vegetation by County: Pre-agricultural land cover for each county was designated 

according to the potential native vegetation used in the VEMAP (1995) analysis, which was

based on the Kuchler (1964) Potential Vegetation Map for the conterminous United States. 

 

Crop Rotation and Land Management Information by Agricultural Region: Data for the 63 

agricultural regions were obtained for specific timing and type of cultivation, timing of 

planting/harvest, and crop rotation schedules (Hurd 1930, 1929, Latta 1938, Iowa State College 

Staff Members 1946, Bogue 1963, Hurt 1994, USDA 2000a, USDA 2000c, CTIC 1998, Piper et 

al. 1924, Hardies & Hume 1927, Holmes 1902, 1929, Spillman 1902, 1905, 1907, 1908, Chilcott 

1910, Smith 1911, Kezer ca. 1917, Hargreaves 1993, ERS 2002, Warren 1911, Langston et al. 

1922, Russell et al. 1922, Elliot & Tapp 1928, Elliot 1933, Ellsworth 1929, Garey 1929, Hodges 

et al. 1930, Bonnen & Elliot 1931, Brenner et al. 2001, 2002, Smith et al. 2002). 

Nitrogen Fertilizer Amendment Rates and Timing by Agricultural Region: Fertilizer application 

rates and timing of applications within each of the 63 agricultural regions were determined from 

regional, state, or sub-state estimates for different crops. Estimates were obtained primarily from 

the USDA Economic Research Service Cropping Practices Survey (ERS 1997) with additional 

data from other sources, including the National Agricultural Statistics Service (USDA NASS 

2009, 2004, 1999). Prior to 1990, estimates for crop-specific regional fertilizer rates were based 

largely on extrapolation/interpolation of fertilizer rates from the years with available data. For 

crops in some agricultural regions, little or no data were available, and therefore a geographic 

regional mean was used to simulate N fertilization rates. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 49

Managed Livestock Manure2 Nitrogen Amendment Rates and Timing by Agricultural Region: 

Data on managed manure N amendments to soils were available for 1997 (Kellogg et al. 2000), 

and demonstrated that less than half of manure N produced on an annual basis was applied to 

soils. Crop-specific application rates of manure N for other years between 1990 and 2008 were 

obtained by multiplying the 1997 crop-specific rates by the ratio of managed manure N produced 

in that year to the managed manure N produced in 1997; the amount of land receiving manure 

(approximately 5 percent of total cropped land) was assumed to be constant during 1990 through 

2008. Nitrogen available for application was estimated for managed systems based on the total 

amount of N produced in manure minus N losses and including the addition of N from bedding 

materials. Nitrogen losses include direct nitrous oxide emissions, volatilization of ammonia and 

NOx, and runoff and leaching. The remaining manure N that was not applied to major crops and 

grassland was assumed to be applied to non-major crop types. Manure was applied during spring 

at the same time as synthetic N fertilizer. Prior to 1990, manure application rates and timing were 

based on various sources (Brooks 1901, Anonymous 1924, Fraps & Asbury 1931, Ross & 

Mehring 1938, Saltzer & Schollenberger 1938, Alexander & Smith 1990). As with mineral N 

fertilization, data for manure were incomplete, so regional averages were used to fill spatial gaps 

in data and interpolation/extrapolation was used to fill temporal gaps. Manure N application rates 

during 1990 through 2004 were based on Kellogg et al. (2000). 

Crop Areas by Crop Type and by County: County-level total crop area data were downloaded 

from the USDA NASS Web site for the years 1990 through 2008 (USDA NASS 2009), and 

these data formed the basis to scale emissions from individual crop types across the entire 

county. 

3.3.3 IPCC Methodology for Non-Major Crop Types

3.3.3.1 Mineral Soils

For mineral agricultural soils producing non-major crop types, the Tier 1 IPCC methodology was 

used to estimate direct N2O emissions. Estimates of direct N2O emissions from N applications to 

non-major crop types were based on the annual increase in mineral soil N from the following 

practices: (1) the application of synthetic commercial fertilizers, (2) the retention of crop 

residues, and (3) manure and non-manure organic fertilizers. 

IPCC methodology for emissions from mineral soils is based on nitrogen inputs. Nitrogen inputs 

from synthetic and organic fertilizer and aboveground and belowground crop residues were 

added together. This sum was multiplied by the IPCC default emission factor (1.0%) to derive an

estimate of cropland direct N2O emissions from non-major crop types. Nitrate leached or runoff 

and N volatilized from non-major crop types are calculated by multiplying N fertilizer applied by 

the IPCC (2006) default factors (30% and 10%, respectively). 

 2 For purposes of the Inventory, total livestock manure is divided into two general categories: (1) managed manure, 

and (2) unmanaged manure. Managed manure includes manure that is stored in manure management systems such 

as pits and lagoons, as well as manure applied to soils through daily spread operations. Unmanaged manure 

encompasses all manure deposited on soils by animals on pasture, range, and paddock.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 50

Annual synthetic fertilizer nitrogen additions to non-major crop types are calculated by process 

of elimination. For each year, fertilizer applied to major crops and grazed lands (as simulated by 

DAYCENT – approximately 80% of the U.S. total fertilizer used on farms) was subtracted from 

total fertilizer used on farms in the United States. The difference, approximately 20% of total 

synthetic fertilizer N used on farms in the U.S., was assumed to be applied to non-major crop 

types. Non-major crop types include fruits, nuts, and vegetables, which is estimated at 

approximately 5% of total U.S. N fertilizer use (TFI 2000), and other annual crops not simulated 

by DAYCENT, barley, oats, tobacco, sugarcane, sugar beets, sunflower, millet, peanuts, etc., 

which account for approximately 15% of total U.S. fertilizer used on farms. Manure N applied to 

non-major crops was estimated in a similar manner; manure applied to major crops and grazed 

lands as simulated by DAYCENT was subtracted from total manure available for soil 

application. This difference was assumed to be applied to non-major crops. In addition to 

synthetic fertilizer and manure N, nitrogen in soils due to the cultivation of non-major N-fixing 

crops (e.g., edible legumes) was included in these estimates. Finally, crop residue nitrogen was 

derived from information on crop production yields, residue management (retained vs. burned or 

removed), mass ratios of aboveground residue to crop product, dry matter fractions, and nitrogen 

contents of the residues (IPCC 2006). The activity data for these practices were obtained from 

the following sources:

• Annual production statistics for crops whose residues are left on the field: USDA (2003, 

2002, 2001, 2000a, 1998, 1994), Schueneman (2001, 1999a, 1999b, 1999c), Deren 

(2002), Schueneman and Deren (2002), Cantens (2004), Lee (2004, 2003). 

• Crop residue N was derived by combining amounts of above- and below-ground biomass, 

which were determined based on crop production yield statistics (USDA 2006, 2005, 

2003, 1998, 1994), dry matter fractions (IPCC 2006), linear equations to estimate aboveground biomass given dry matter crop yields (IPCC 2006), ratios of below-to-aboveground biomass (IPCC 2006), and N contents of the residues (IPCC 2006). 

Annual Applications of Commercial Non-Manure Organic Fertilizers by Agricultural Region:

Estimates of total national annual N additions from land application of other organic fertilizers 

were derived from organic fertilizer statistics (TVA 1994, 1993, 1992a, AAPFCO 2006, 2005, 

2004, 2003, 2002, 2000a, 2000b, 1999, 1998, 1997, 1996, 1995). The organic fertilizer data, 

which are recorded in mass units of fertilizer, had to be converted to mass units of N by 

multiplying by the average organic fertilizer N contents provided in the annual fertilizer 

publications. These N contents are weighted average values, and vary from year to year (ranging 

from 2.3 percent to 3.9 percent over the period 1990 through 2004). Annual on-farm use of these 

organic fertilizers is very small, less than 0.03 Tg N.

3.3.3.2 Cultivation of Histosols 

The IPCC Tier 1 method is used to estimate direct N2O emissions from the drainage and 

cultivation of organic cropland soils. Estimates of the total U.S. acreage of drained organic soils 

cultivated annually for temperate and sub-tropical climate regions was obtained for 1982, 1992, 

and 1997 from the Natural Resources Inventory (USDA 2000b, as extracted by Eve 2001 and 

amended by Ogle 2002), using temperature and precipitation data from Daly et al. (1998, 1994). 

To estimate annual N2O emissions from histosol cultivation, the temperate histosol area is 

multiplied by the IPCC default emission factor for temperate soils (8 kg N2O-N/ha cultivated; 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 51

IPCC 2006), and the sub-tropical histosol area is multiplied by the average of the temperate and 

tropical IPCC default emission factors (12 kg N2O-N/ha cultivated; IPCC 2006).

3.3.3.3 Total N2O Emissions

Total direct emissions were obtained by summing DAYCENT-generated emissions from major 

crops on mineral soils, IPCC-generated estimates for non-major crops on mineral soils, and 

IPCC estimates of emissions from organic soils. Total indirect emissions from NO3 leaching or 

runoff were obtained by adding DAYCENT estimates for major crops on mineral soils to IPCC 

(2006) estimates for non-major crops on mineral soils and multiplying by the default emission 

factor (0.75% of N leached/runoff). Total indirect emissions from nitrogen volatilization were 

obtained by adding DAYCENT estimates for major crops on mineral soils to IPCC (2006) 

estimates for non-major crops on mineral soils and multiplying by the default emission factor 

(1% of N volatilized). Indirect emissions from NO3 leaching or runoff were added to those from 

nitrogen volatilization to get total indirect emissions. Total direct and indirect emissions were 

then summed to get total N2O emissions from cropped soils.

3.3.4 Uncertainty in N2O Emissions

Uncertainty was estimated differently for each of the following components of N2O emissions 

from cropped soils: direct emissions from major crops calculated by DAYCENT due to model 

input uncertainty, direct emissions from major crops calculated by DAYCENT due to model 

structure uncertainty, direct emissions from minor crops not calculated by DAYCENT, and 

indirect emissions from all crops. For direct emissions calculated using DAYCENT, model input 

uncertainty was quantified using the Monte Carlo analysis described above in section 3.3.2 and 

in more detail by Del Grosso et al. (2010). Model structure uncertainty was quantified by 

comparing DAYCENT estimates of N2O emissions with measured values (Del Grosso et al. 

2010). Uncertainty for direct emissions from minor crops was estimated using simple error 

propagation (IPCC 2006). Uncertainty in indirect emissions for major crops combined 

uncertainty in DAYCENT estimates of nitrate leaching and N gas volatilization based in the 

Monte Carlo simulations with uncertainty in the IPCC Tier 1 emissions factors used to convert 

these N loss vectors to N2O emissions. Uncertainty in indirect emissions for minor crops 

combined uncertainty in IPCC Tier 1 emissions factors for nitrate leaching and N gas 

volatilization with uncertainty in the IPCC Tier 1 emissions factors used to convert these N loss 

vectors to N2O emissions. Error propagation was used to combine uncertainties in the various 

components by taking the square root of the sum of the squares of the standard deviations of the 

components (IPCC 2006). The 95% confidence interval in N2O emissions was estimated to lie 

between 114 and 241 Tg CO2 eq. (Table 3-1). 

3.3.5 Changes Compared to the 2nd Edition of the USDA GHG Report

Although there were no major changes in methodologies compared to the previous edition 

(USDA 2008), a series of improvements were implemented. Instead of assuming that nitrate 

leaching can occur anywhere, a criterion was used to designate lands where nitrate is susceptible 

to be leached into waterways, as suggested by IPCC (2006). This is based on observations that in 

semi-arid and arid areas, nitrate can be leached below the rooting zone, but it does not enter 

waterways because water tables in dry areas are low or non-existent. Other changes include: 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 52

using state-level N data for on-farm use of fertilizers to estimate synthetic N fertilizer application 

on non-major crops, including uncertainty in DAYCENT outputs of N volatilization and N 

leaching/runoff in the calculation of uncertainty for indirect emissions; using a default 

uncertainty of ±50 percent for Tier 1 uncertainties that were not addressed in the previous 

inventory (e.g., crop yields and organic fertilizer amendments); assuming that manure N 

available for land application not accounted for by the DAYCENT simulations was applied to 

non-major crop types; revising DAYCENT parameterization for sorghum; correcting an error in 

the empirically based uncertainty estimator; improved estimates of manure additions to 

croplands; and using sugar-cane-specific information for calculating the residue/crop ratio, 

fraction of residue burned, dry matter fraction, burning efficiency, and combustion efficiency for 

this crop. The main results of these changes are lower N2O emissions and wider confidence 

intervals. Lower N2O emission estimates were primarily due to the new operational version of 

DAYCENT and the revised structural uncertainty associated with the model. Earlier versions of 

DAYCENT tended to overestimate emissions, and although these emissions were adjusted using 

the structural uncertainty estimator, there was considerable uncertainty in those adjustments and 

it is likely that high estimates were not sufficiently adjusted downwards. The new operational 

version of DAYCENT does not systematically overestimate N2O emissions for the majority of 

crops so overall emissions are lower. Including residual error from the linear mixed-effect model 

as a component of the structural uncertainty and accounting for additional sources of uncertainty 

mentioned above that were previously neglected are responsible for the wider uncertainty 

intervals.

3.3.6 Mitigation of N2O Emissions

Mitigation of N2O emissions is based on optimizing the amount and timing of nitrogen fertilizer 

additions. Excess fertilizer applied to crops increases the nitrogen available for N2O, N oxide and 

NH3 emissions, and for NO3 leaching. Using time-released fertilizers and applying fertilizer in 

multiple applications improves the synchrony between nitrogen supply and plant nitrogen 

demand. However, multiple applications of fertilizer require increased time and equipment usage 

by farmers and time-released fertilizers are more expensive than conventional fertilizers. Use of 

nitrification inhibitors has been shown to decrease N2O emissions (Halvorson et al. 2010a, 

2010b, Weiske et al. 2001, McTaggert et al. 1997). The capability to simulate their impact has 

been incorporated into the DAYCENT ecosystem model. National-scale DAYCENT simulations 

suggest that universal use of nitrification inhibitors could reduce total N2O emissions by 10-20% 

while maintaining, or slightly increasing, crop yields. The model showed lower direct N2O and 

NOx emissions because nitrification rates were decreased, but also lower NO3 leaching rates 

because reduced nitrification also reduces inputs to the soil NO3 pool. However, fertilizer 

amended with nitrification inhibitors, as with time-released fertilizer, is more expensive. Further 

analyses of the environmental and economic costs and benefits of improved N source fertilizers 

need to be performed before optimum region-specific mitigation strategies can be identified. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 53

3.4 Methane Emissions from Rice Cultivation

Methane emissions from rice cultivation3 are limited to seven U.S. states (Figure 3-3). In four 

states (Arkansas, Florida, Louisiana, and Texas), the climate allows for cultivation of two rice 

crops per season, 

the second of which 

is referred to as a 

ratoon crop (EPA 

2010). Methane 

emissions from 

primary and ratoon 

crops are accounted 

for separately 

because emissions 

from ratoon crops 

are higher (EPA 

2010). Overall, rice 

cultivation is a 

small source of 

CH4 in the United 

States. In 2008, 

CH4 emissions totaled 7.2 Tg CO2 eq, of which 5.3 Tg CO2 eq. were from primary crops in all 

seven states and 1.9 Tg CO2 was from ratoon crops in four states (Table 3-4). 

Table 3-4 Methane from Rice Cultivation from Primary and Ratoon 

Operations by State, 1990, 1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source Tg CO2 eq.

Primary 5.1 5.6 5.5 5.9 5.7 5.4 6.0 6.0 5.1 4.9 5.3

Arkansas 2.1 2.4 2.5 2.9 2.7 2.6 2.8 2.9 2.5 2.4 2.5

California 0.7 0.8 1.0 0.8 0.9 0.9 1.1 0.9 0.9 1.0 0.9

Florida 0.02 0.04 0.03 0.02 0.02 0.01 0.02 0.02 0.02 0.03 0.02

Louisiana 1.0 1.0 0.9 1.0 1.0 0.8 1.0 0.9 0.6 0.7 0.8

Mississippi 0.4 0.5 0.4 0.5 0.5 0.4 0.4 0.5 0.3 0.3 0.4

Missouri 0.1 0.2 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.3 0.4

Texas 0.6 0.6 0.4 0.4 0.4 0.3 0.4 0.4 0.3 0.3 0.3

Ratoon 2.1 2.1 2.0 1.7 1.1 1.5 1.6 0.8 0.9 1.3 1.9

Arkansas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Florida 0.04 0.08 0.05 0.05 0.04 0.04 0.05 0.00 0.02 0.03 0.03

Louisiana 1.1 1.1 1.3 1.1 0.5 1.0 1.1 0.5 0.5 0.9 1.2

Texas 0.9 0.8 0.7 0.6 0.5 0.5 0.5 0.4 0.4 0.3 0.6

Total 7.1 7.6 7.5 7.6 6.8 6.9 7.6 6.8 5.9 6.2 7.2

Note: Tg CO2 eq. is teragrams carbon dioxide equivalent.

 3 This source focuses on CH4 emissions resulting from anaerobic decomposition and does not include emissions 

from burning of rice residues. The latter is covered in section 3.5.

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 54

Arkansas and Louisiana had the highest CH4 emissions (2.91 Tg CO2 eq. and 1.4 Tg CO2 eq. 

respectively) from rice cultivation in 2008, followed by California and Texas. Mississippi, 

Missouri, and Florida each had emissions less than or equal to 0.5 Tg CO2 eq. (Table 3-4). 

Overall since 1990, CH4 emissions from rice cultivation have increased by 1% (Table 3-5). 

While national-scale changes were small between 1990 and 2008 (1% increase), sizeable shifts 

occurred at state levels during that time period. For example, CH4 emissions in Missouri and 

California increased by 149% and 31%, respectively, while emissions in Texas declined by 42% 

(Table 3-5). Although CH4 emissions from Missouri increased by 149% between 1990 and 2008, 

they remained small in magnitude relative to emissions from other states because of the small 

land area used for rice production in this state. State-level shifts in CH4 emissions since 1990 are 

positively correlated with changes in area of rice cultivation (Appendix Table B-1). Appendix 

Table B-1 provides a complete time series of areas harvested for rice by state with primary 

versus ratoon crops from 1990-2008.

3.4.1 Methods for Estimating CH4

Emissions from Rice Cultivation 

The EPA provided estimates for CH4

emissions from rice cultivation for this 

report. Details on the methods are 

provided below and are excerpted, with 

permission from EPA, from Chapter 6 of 

the U.S. GHG Inventory report (EPA 

2010). The method used by EPA applies 

area-based seasonally integrated 

emission factors (i.e., amount of CH4

emitted over a growing season per unit 

harvested area) to harvested rice areas to estimate annual CH4 emissions from rice cultivation. 

The EPA derives specific CH4 emission factors from published studies containing rice field 

measurements in the United States, with separate emissions factors for ratoon and primary crops 

to account for higher seasonal emissions in ratoon crops.

A review of published experiments was used to develop emissions factors for primary and ratoon 

crops. Experiments where nitrate or sulfate fertilizers or other substances believed to suppress 

CH4 formation were applied, and experiments where measurements were not made over an entire 

flooding season or where floodwaters were drained mid-season, were excluded from the analysis. 

The remaining experimental results were then sorted by season (i.e., primary and ratoon) and 

type of fertilizer amendment (i.e., no fertilizer added, organic fertilizer added, and synthetic and 

organic fertilizer added). The experimental results from primary crops with synthetic and organic 

fertilizer added (Bossio et al. 1999, Cicerone et al. 1992, Sass et al. 1991a and 1991b) were 

averaged to derive an emission factor for the primary crop, and the experimental results from 

ratoon crops with synthetic fertilizer added (Lindau et al. 1995, Lindau & Bollich 1993) were 

averaged to derive an emission factor for the ratoon crop. The resultant emission factor for the 

primary crop is 210 kg CH4/ha per season, and the resultant emission factor for the ratoon crop is 

780 kg CH4/ha per season.

Table 3-5 Change in Methane Emissions from Rice 

Cultivation, 1990-2008

1990 2008 1990-2008

State Tg CO2 eq. % Change

Arkansas 2.14 2.49 16%

California 0.70 0.92 31%

Florida 0.06 0.05 -19%

Louisiana 2.06 2.06 0%

Mississippi 0.45 0.41 -8%

Missouri 0.14 0.36 149%

Texas 1.57 0.91 -42%

Total 7.12 7.20 1%

Note: Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 55

The harvested rice areas for the primary and ratoon crops in each state are presented in Appendix 

Table B-1. Primary crop areas for 1990 through 2008 for all states except Florida and Oklahoma 

were taken from USDA NASS Field Crops Final Estimates 1987-1992 (USDA 1994), Field 

Crops Final Estimates 1992-1997 ( USDA 1998a), Crop Production 2000 Summary (USDA 

2003), and Crop Production 2001 Summary (USDA 2005-2009). Harvested rice areas in Florida, 

which are not reported by USDA, were obtained from Tom Schueneman (2001, 2000, 1999b, 

1999c), a Florida agricultural extension agent, and Chris Deren (2002) of the Everglades 

Research and Education Center at the University of Florida. Acreages for the ratoon crops were 

derived from conversations with the agricultural extension agents in each state. California, 

Mississippi, Missouri, and Oklahoma have not ratooned rice over the period 1990 through 2008 

(Guethle 1999, 2000, 2001, 2002 through 2008; Lee 2003 through 2007; Mutters 2002 through 

2005; Street 1999 through 2003; Walker 2005, 2007, 2008). In Arkansas, ratooning occurred 

only in 1998 and 1999, when the ratoon area was less than 1% of the primary area (Slaton 2001, 

2000, 1999). In Florida, the ratoon area was 50% of the primary area from 1990 to 1998 

(Schueneman 1999a), about 65% of the primary area in 1999 (Schueneman 2000), around 41% 

of the primary area in 2000 (Schueneman 2001), and about 70% of the primary area in 

2001(Deren 2002). In Louisiana, the percentage of the primary area in ratoon was constant at 

30% over the 1990 to 1999 period, but increased to approximately 40% in 2000 before returning 

to 30% in 2001 (Linscombe 2002, 2001,1999a, Bollich 2000). In Texas, the percentage of the 

primary area in ratoon was constant at 40% over the entire 1990 to 1999 period and in 2001, but 

increased to 50% in 2000 due to an early primary crop (Klosterboer 2002, 2001, 2000, 

1999a,1999b).

3.4.2 Uncertainty in Estimating Methane Emissions from Rice Cultivation

The following discussion of uncertainty in estimating GHG emissions from rice cultivation is 

modified from information provided in the U.S. GHG Inventory (EPA 2010). The information is 

reproduced here with permissions from the EPA.

Methane emission factors are the largest source of uncertainty in estimates for rice cultivation. 

Seasonal emissions, derived from field measurements in the United States, vary by more than an 

order of magnitude resulting from a variation in cultivation practices, fertilizer applications, 

cultivar types, soil, and climatic conditions. Some variability is accounted for by separating 

primary from ratoon areas. However, even within a cropping season, measured emissions vary 

significantly. Of the experiments that were used to derive the emission factors used here, primary 

emissions ranged from 22 to 479 kg CH4/ha per season and ratoon emissions ranged from 481 to 

1,490 kg CH4/ha per season. 

Data are not collected regularly on the area of rice crops in ratoon, creating another source of 

uncertainty. The area estimates are derived from expert opinion and account for 1 to 5% of the 

total area of rice cultivation. A final source of uncertainty is the practice of flooding outside of 

the normal rice season. According to agriculture extension agents, this occurs in all rice-growing 

states. No uncertainties were calculated for the practice of flooding outside of the normal rice 

season because CH4 flux measurements have not been undertaken over a sufficient geographic 

range or under a broad enough range of representative conditions to account for this source in the 

emission estimates or its associated uncertainty. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 56

To quantify the uncertainties for emissions from rice cultivation, a Monte Carlo (Tier 2) 

uncertainty analysis was performed using the information provided above. The results of the Tier 

2 quantitative uncertainty analysis are summarized in Table 3-1. Rice cultivation CH4 emissions 

in 2008 were estimated to be between 2.6 and 17.5 Tg CO2 eq. at a 95-percent confidence level, 

which indicates a range of 64 percent below to 143 percent above the actual 2008 emission 

estimate of 7.2 Tg CO2 eq.

3.5 Residue Burning

Greenhouse gas emissions from field burning of crop residues are a function of the amount and 

type of residues burned. In the U.S., crops burned include wheat, rice, sugarcane, corn, barley, 

soybeans, and peanuts (EPA 2010). For most crops, less than 5% of residues are burned per year, 

but a higher portion of rice residues is burned annually (EPA 2010). Consequently, emissions 

from residue burning are a small source of overall crop-related emissions in the U.S. About 

three-fifths of GHG emissions from residue burning, across all crop types, consisted of CH4 in 

2008; the remaining was N2O (Table 3-6, Figure 3-4). CO2 burning is not considered a GHG 

source because the 

CO2 lost from 

burning was 

assimilated during the 

year by growing 

vegetation. The 

highest GHG 

emissions were from 

burning of corn and 

soybean crop 

residues, at 38 and 

34% respectively. 

Burning of wheat, 

rice, sugarcane, and 

barely crop residues 

each contributed 11% 

or less to overall 

GHG emissions; 

burning of peanut crop residues contributed almost nothing to this source of GHG due to the 

relatively small amount of land area planted with this crop.

Total greenhouse gas emissions from residue burning increased 29% from 1990 to 2008. Trends 

in relative GHG emissions were similar across crop types in 1990 compared to 2008 with a few 

exceptions. In both 1990 and 2008, burning of corn residues contributed the most to GHG 

emissions from residue burning, while burning of soybeans was the second largest source. 

Between 1990 and 2008, soybean and corn production both increased in absolute amounts 

(Figure 3-5). However, proportionally, soybean production increased slightly more than corn 

(soybean production increased by 54% and corn by 53%) (Figure 3-6). Despite the higher 

nitrogen content in soybeans relative to corn, corn production was still greater than soybean 

Tg CO2 eq. is teragrams carbon dioxide equivalent; N2O is nitrous oxide; CH4 is methane.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 57

production in 2008, thus resulting in higher GHG emissions from residue burning.

Table 3-6 Greenhouse Gas Emissions from Agriculture Burning by Crop, 

1990, 1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source Tg CO2 eq.

CH4 0.77 0.75 0.89 0.87 0.80 0.89 0.96 0.93 0.90 0.96 0.97

Wheat 0.14 0.11 0.11 0.10 0.08 0.12 0.11 0.11 0.09 0.10 0.13

Rice 0.09 0.09 0.08 0.08 0.06 0.10 0.08 0.09 0.09 0.08 0.07

Sugarcane 0.09 0.10 0.12 0.11 0.12 0.11 0.10 0.09 0.10 0.10 0.10

Corn 0.28 0.26 0.35 0.34 0.32 0.36 0.42 0.40 0.37 0.46 0.43

Barley 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Soybeans 0.15 0.17 0.21 0.22 0.21 0.19 0.24 0.24 0.25 0.21 0.23

Peanuts 0.002 0.002 0.002 0.003 0.002 0.003 0.003 0.003 0.002 0.002 0.003

N2O 0.39 0.40 0.48 0.49 0.45 0.47 0.53 0.52 0.52 0.50 0.52

Wheat 0.05 0.04 0.04 0.03 0.03 0.04 0.04 0.04 0.03 0.04 0.04

Rice 0.04 0.04 0.04 0.04 0.03 0.05 0.04 0.04 0.04 0.04 0.03

Sugarcane 0.021 0.023 0.028 0.026 0.027 0.026 0.022 0.020 0.023 0.023 0.023

Corn 0.09 0.08 0.11 0.11 0.10 0.11 0.13 0.12 0.12 0.15 0.14

Barley 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Soybeans 0.18 0.21 0.26 0.28 0.26 0.23 0.30 0.29 0.31 0.26 0.28

Peanuts 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 0.001 0.002

Total 1.16 1.14 1.37 1.35 1.26 1.36 1.49 1.45 1.43 1.46 1.49

Note: Tg CO2 eq. is teragrams carbon dioxide equivalent; CH4 is methane; N2O is nitrous oxide.

Appendix Table B-2 provides the complete time series of crop production from 1990 to 2008 for

crop types that 

contribute to GHG 

emissions from 

burning. Appendix 

Table B-3 provides 

nationwide data for 

crop production 

managed with burning 

by year. Production of 

crops such as corn and 

soybeans has been 

slowly increasing since 

1990, with other crops 

like wheat, rice, and 

sugarcane remaining 

relatively constant or 

decreasing. Barley 

production has 

declined since the mid1990s.

The state-level rice 

harvest estimates were provided directly by EPA based on state production data.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 58

3.5.1 Methods for Estimating CH4 and N2O Emissions from Residue Burning

EPA provided nationallevel estimates of GHG 

emissions from 

agricultural residue 

burning for all crop 

types, and state-level 

estimates for GHG 

emissions from rice 

residue burning for this 

report. In addition, 

state-level estimates 

were derived by USDA 

for all crop types 

(except rice) using the 

same method. Details 

on the methods used by 

EPA are provided 

below, including 

excerpts from Chapter 6 of the U.S. GHG Inventory report (EPA 2010). This information is 

reproduced with permission from EPA.

The equations below were used to estimate the amounts of carbon and nitrogen released during 

burning. 

Carbon Released = (Annual Crop Production) × (Residue/Crop Product Ratio)

× (Fraction of Residues Burned in situ) × (Dry Matter Content of the Residue)

× (Burning Efficiency) × (Carbon Content of the Residue) × (Combustion Efficiency)

Nitrogen Released = (Annual Crop Production) × (Residue/Crop Product Ratio)

× (Fraction of Residues Burned in situ) × (Dry Matter Content of the Residue)

× (Burning Efficiency) × (Nitrogen Content of the Residue) × (Combustion Efficiency)

Values used in the above equations to estimate emissions from residue burning are summarized 

in Appendix Table B-4. National and state-level crop production statistics are provided in 

Appendix Table B-2 and Appendix Table B-3. The sources for developing these input data are 

described for each parameter below.

Annual Crop Production: 

Crop production data for all crops except rice in Florida and Oklahoma were taken from the 

USDA’s Field Crops, Final Estimates 1987–1992, 1992–1997, 1997–2002 (USDA 1994, 1998, 

2003), and Crop Production Summary (USDA 2005 through 2009). Rice production data for 

Florida and Oklahoma, which are not collected by USDA, were estimated separately. Average 

primary and ratoon crop yields for Florida (Schueneman & Deren 2002) were applied to Florida 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 59

acreages (Schueneman 1999b, 2001; Deren 2002; Kirstein 2003, 2004; Cantens 2004, 2005; 

Gonzalez 2007a, 2008, 2009), and crop yields for Arkansas (USDA 1994, 1998, 2003, 2005 

through 2009) were applied to Oklahoma acreages(Lee 2003 through 2006; Anderson 2008, 

2009).

Residue-to-Crop Product Mass Ratios: 

All residue/crop product mass ratios except sugarcane were obtained from Strehler and Stützle 

(1987) The ratio for sugarcane is from Kinoshita (1988). 

Fraction of Residues Burned:

The percentage of crop residue burned was assumed to be 3 percent for all crops in all years, 

except rice and sugarcane, based on state inventory data (Noller 1996, Cibrowski 1996, Oregon 

Department of Energy 1995, ILENR 1993, and the Wisconsin Department of Natural Resources 

1993). Estimates of the percentage of rice residue burned were derived from state-level estimates 

of the percentage of rice area burned each year, which were multiplied by state-level annual rice 

production statistics. The annual percentages of rice area burned in each state were obtained 

from agricultural extension agents in each state and reports of the California Air Resources 

Board (Buehring 2009; Guethle 2009, 2008, 2007; Lancero 2006 through 2009; Texas 

Agricultural Experiment Station 2006 through 2009; Wilson 2003, 2004, 2005, 2007, and 2009;

Lee 2005 through 2007; Sacramento Valley Basinwide Air Pollution Control Council 2005 and 

2007; Walker 2004 through 2008; anonymous 2006; Cantens 2005; Stansel 2004, 2005;

Lindberg 2002, 2003; Deren 2002; Najita 2001 and 2000; California Air Resources Board 2001, 

1999; Bollich 2000; Fife 1999; Street 2001 through 2003; Klosterboer 2000 through 2003, 

1999a, 1999b; Linscombe 2001 through 2009, 1999a, 1999b; Schueneman 2001, 1999a, 1999b).

The estimates provided for Florida remained constant over the entire 1990 through 2008 period. 

While the estimates for all other states varied over the time series, estimates for Missouri 

remained constant through 2005, dropped in 2006, and remained constant near the 2006 value in 

2007 and 2008. For California, the annual percentages of rice area burned in the Sacramento 

Valley are assumed to be representative of burning in the entire state, because the Sacramento 

Valley accounts for over 95 percent of the rice acreage in California (Fife 1999). These values 

generally declined between 1990 and 2008 because of a legislated reduction in rice straw burning 

(Lindberg 2002), although there was a slight increase from 2004 to 2005 and from 2006 to 2007. 

Estimates for percent of sugarcane burned were obtained from Ashman (2008).

Residue Dry-Matter Content: 

Residue dry-matter contents for all crops except soybeans and peanuts were obtained from Turn 

et al. (1997). Soybean dry-matter content was obtained from Strehler and Stützle (1987). Peanut 

dry-matter content was obtained through personal communications with Jen Ketzis (1999), who 

accessed Cornell University’s Department of Animal Science’s computer model, Cornell Net 

Carbohydrate and Protein System.

Burning and Combustion Efficiency:

Burning efficiency refers to the fraction of dry biomass exposed to burning that actually burns

and the combustion efficiency refers to the fraction of carbon in the fire that is oxidized 

completely to CO2. The burning efficiency was assumed to be 93% and the combustion 

efficiency was assumed to be 88%, for all crop types, except sugarcane (EPA 1994). For 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 60

sugarcane, the burning efficiency was assumed to be 81% (Kinoshita 1988) and the combustion 

efficiency was assumed to be 68% (Turn et al. 1997). Emission ratios and conversion factors for 

all gases were taken from IPCC Guidelines (1996).

Carbon and Nitrogen Content: 

The residue carbon contents and nitrogen contents for all crops except soybeans and peanuts are 

from Turn et al. (1997). The residue carbon content for soybeans and peanuts is the IPCC default 

(IPCC UNEP OECD IEA 1997). The nitrogen content of soybeans is from Barnard and 

Kristoferson (1985) and the nitrogen content of peanuts is from Ketzis (1999).

3.5.2 Uncertainty in Estimating Methane and Nitrous Oxide Emissions from Residue 

Burning

The following discussion of uncertainty in estimating GHG emissions from residue burning is 

modified from information provided in the U.S. GHG Inventory (EPA 2010). The information is 

reproduced here with permission from EPA.

Assumptions about the annual amount of residues burned by crop type are the largest source of 

uncertainty in estimating GHG emissions from field burning of agricultural residues. Data on the 

fraction burned, as well as the gross amount of residue burned each year, is not collected at either 

the national or state level. In addition, burning practices are highly variable among crops and 

states. The fractions of residue burned used in these calculations are based upon information 

collected by state agencies and in published literature. These emissions estimates may continue 

to change as more information becomes available in the future. Other sources of uncertainty 

include the residue/crop product mass ratios, residue dry matter contents, burning and 

combustion efficiencies, and emission ratios. Residue/crop product ratios for specific crops can 

vary among cultivars and, for all crops except sugarcane, generic global residue/crop product 

ratios were used rather than ratios specific to the United States. In addition, residue dry matter 

contents, burning and combustion efficiencies, and emission ratios can vary due to weather and 

other combustion conditions, such as fuel geometry. Values for these variables were taken from 

literature on agricultural biomass burning. 

A Monte Carlo analysis was performed to quantify the uncertainties mentioned above. The 

calculated 95% confidence interval was 0.2 to 1.0 Tg CO2 eq. for N2O emissions from residue 

burning, or 71% below and 83% above the estimate of 0.5 Tg CO2 eq. and 0.3 to 1.8 Tg CO2 eq. 

for CH4 emissions from residue burning, or 68% below and 88% above the estimate of 1.0 Tg 

CO2 eq. (Table 3-1). 

3.6 Carbon Stock Changes in Cropped Soils

Except for cultivated organic soils and liming practices, cropped soils in the U.S. were estimated 

to accumulate about 42 Tg CO2 eq. in 2008 (Table 3-1)4. Much of the carbon change is 

 4 Emissions and sinks of carbon in agricultural soils are expressed in terms of CO2 equivalents; carbon sequestration 

is a result of changes in stocks of carbon in soils, from which CO2 fluxes are inferred. Units of CO2 equivalent can 

be converted to carbon using a multiplier of 0.272.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 61

attributable to the Conservation Reserve Program, land use conversions between annual 

croplands and perennial hay and grazing lands, and land management (Figure 3-7). Practices 

such as the adoption of conservation tillage, including no-till, which have taken place over the 

past two decades, and reduced frequency of summer-fallow are important drivers of carbon stock 

changes. Manure applications to cropland also impact the estimated soil carbon stock increase.

In contrast, the small area of cultivated organic soils – less than 1 million hectares of a total 386 

million hectares of 

agricultural and forest land

– concentrated in Florida, 

California, the Gulf and 

Southeastern coastal region,

and parts of the upper 

Midwest was a net source of 

CO2 emissions for all years 

covered by the inventory 

(1990-2008). In 2008, about 

30 Tg CO2 eq. was emitted 

from cultivation of these 

soils (Table 3-1). Liming of 

agricultural soils resulted in 

emissions of about 4 Tg 

CO2 eq per year. Total net 

carbon sequestration in 

2008 equaled ~8 Tg CO2 eq. 

when all of the above 

components were taken into 

consideration. Carbon 

uptake on agricultural soils varied between 1990 and 2008 (Table 3-2), driven largely by land 

use changes and weather fluctuations.

Most states in the Corn Belt and northern Great Plains are storing C in cropped soils due to 

adoption of reduced tillage and other practices (Map 3-3). The exception to this is Minnesota, 

which is losing C at the state level. Carbon losses from cropping of organic soils exceed C gains 

in mineral soil cropping for this state. Florida has the highest C losses, primarily due to 

sugarcane cropping on organic soils.

3.6.1 Methods for Estimating Carbon Stock Changes in Agricultural Soils

Two broad categories of cropland were considered, cropland remaining cropland and land 

converted to cropland. Within both of these categories, Tier 2 and Tier 3 methodologies were 

used. The Tier 2 approach is based on relatively simple equations used in IPCC (2003) 

methodology that have been modified to better represent nations or regions within nations. The 

Tier 3 approach (CENTURY model) uses a more complex ecosystem model to simulate carbon 

fluxes for cropped systems. Both tiers used land use and management data based primarily on the 

National Resources Inventory (NRI) (USDA 2000b). The NRI represents a robust statistical 

sampling of land use and management on all non-federal land in the United States, and greater 

-20

-10

0

10

20

30

40

 Low Residue Crop

Hay/Pasture in Rotation

Row Crop

Continuous Small Grains

Conservation Reserve ProgramHay

Other Crops & Irrigated 

Organic Soilstotal

Tg CO2 eq.

Figure 3-7

Carbon Dioxide Emissions and Sequestration Sources from Cropland Soils

Annual mean 1997-2008

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 62

than 400,000 NRI survey points occurred in agricultural lands and were used in the inventory 

analysis. The methodology summarized below is described in detail in the U.S. GHG Inventory 

(EPA 2010). 

3.6.2.1 CENTURY Model Simulations for Most Cropped Mineral Soils

CENTURY simulates 

carbon and nitrogen 

dynamics, soil water 

content and 

temperature, and 

other ecosystem 

variables (Parton et 

al.1994). Key 

submodels include: 

plant growth, 

senescence of 

biomass, 

decomposition of 

dead plant material 

and soil organic 

matter, and 

mineralization of 

nitrogen. Model 

inputs are monthly 

maximum/minimum 

air temperature and 

precipitation, surface 

soil texture class, soil hydric condition, vegetation type, and land management information (e.g., 

cultivation timing and intensity, timing and amount of fertilizer and organic matter amendments). 

Soil organic matter is simulated to a depth of 20 cm, while water, temperature, and mineral 

nitrogen are simulated throughout the soil profile. Soil organic matter is divided into three pools 

based on decomposability: active (turns over in months to years), slow (turns over in decades), 

and passive (turns over in centuries). The model accounts for the effects of nutrient availability, 

water, and temperature on plant growth (CO2 uptake) and the effects of these factors, as well as 

cultivation, on decomposition (CO2 release). The ability of the model to integrate carbon gains 

and losses and simulate plant growth and soil carbon levels reliably has been demonstrated using 

data from many sites in the U.S. and around the world (Parton et al.1994, Cerri et al. 2007, Ogle 

et al. 2007). The model has been shown to work in all the major biomes of the earth and can 

accurately reproduce the impacts of climate, soil texture, and land management on carbon fluxes 

(Parton et al. 1993, Kelly et al. 1997, Lugato 2007, Bricklemyer 2007). CENTURY has been 

parameterized to represent the major crops grown in the U.S. The major crops simulated by 

CENTURY for this analysis were corn, soybeans, small grains, hay, sorghum, millet, and cotton, 

which cover ~90% of U.S. cropland. Crops not simulated by CENTURY include; rice, 

sugarcane, tobacco, vegetables, orchards, and horticultural crops.

Three sets of simulations were performed: one to represent pre-settlement native vegetation, one 

to represent historical cropping, and one to represent modern cropping. This is important because 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 63

previous vegetation types and land management activities influence the capacity of present day 

soils to lose or sequester carbon. Native vegetation was represented at the MLRA (Major Land 

Resource Area, USDA NRCS 1981) level. MLRAs represent geographical units with relatively 

similar soils, climate, water resources, and land use. Data on historical cropping practices for 

different regions were obtained from various sources including historical accounts and from 

NASS. Beginning in 1979, the first year of the NRI survey, simulations of crops and 

management practices were based on NRI data. Additional data for tillage practices used (Maps 

3-4, 3-5) were from the Conservation Technology Information Center (CTIC 1998). Cropspecific N fertilization rates were from the USDA Economic Research Service survey (ERS 

1997) and other sources (e.g., NASS). Manure application rates were estimated from data 

compiled by the USDA Natural Resources Conservation Service (Edmonds et al. 2003). Monthly 

weather data required to run CENTURY were from the PRISM database. PRISM (Daly et al. 

1994) is based on observed weather, and the resolution is 4x4 km grid cells. The data were area 

weighted to represent the agricultural land in each county in the U.S. Soil texture and drainage 

capacity (hydric vs. non-hydric) were derived from the NRI. 

3.6.2.2 Tier 2 Approach for Remaining Cropped Mineral Soils, Organic Soils, and Liming

A Tier 2 approach was used to estimate soil carbon stock changes for crops not simulated by the 

CENTURY model, for non-agricultural lands that were converted to cropland, and for organic 

soils. Data on climate, soil type, and land use were used to classify land area and to apply 

appropriate stock change factors. U.S.-specific carbon stock change factors were derived from 

published literature to estimate the impact of management practices (e.g., changes in tillage or 

crop rotation) on soil carbon fluxes (Ogle et al. 2006b, 2003). Cultivated histosol areas are listed 

in Appendix Table B-5, carbon loss rates from organic soils under agricultural management in 

the United States are listed in Appendix Table B-6, state-level estimates of annual soil carbon 

stock changes by major land use and management type are listed in Appendix Table B-7, and 

state-level estimates of mineral soil carbon changes on cropland by major activity are listed in 

Appendix Table B-8.

Stock change factors and reference carbon stocks can vary for different climate regimes and soil 

types. The IPCC method defines eight climate types according to mean annual temperature, 

precipitation, and potential evapotranspiration. Six of these occur in the continental United 

States. The PRISM long-term monthly climate data set (Daly et al. 1998) was used to classify 

each of the 180 Major Land Resource Areas (MLRAs) in the United States into climate zones. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 64

Map 3.4 Percentage of No-Till Adoption by County

Map 3.5 Percentage of Reduced Till Adoption by County

Reference soil carbon stocks were stratified by climate region and categorized into six major 

groupings, based on taxonomic orders that relate to soil development and physical characteristics 

that influence soil carbon contents. Estimates for carbon stocks under conventionally managed 

cropland (defined as the reference land use) were derived from the National Soil Survey 

Characterization Database (USDA NRCS 1997).
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 65

Based on the NRI, crop management systems were aggregated into 22 different categories. 

State-level estimates of mineral soil carbon changes on cropland by major activity are listed in 

Appendix Table B-9. Tillage practices are not included in the NRI. Thus, supplemental data were 

used from the Conservation Technology Information Center (CTIC 1998), which provides spatial 

information on tillage practices (Maps 3-4, 3-5). Data for wetland restoration under the 

Conservation Reserve Program (CRP) were obtained from Euliss and Gleason (2002). Manure N 

amendments over the inventory time period were based on application rates and areas amended 

with manure N from Edmonds et al. (2003). 

Organic soils (i.e., peat, mucks) that have been drained and converted to cropland or pasture are 

subject to potentially high rates of carbon loss. Annual C losses were estimated using IPCC 

(2006, 1997) methodology, except that U.S.-specific carbon loss rates were used in the 

calculations instead of the default IPCC rates (Ogle et al. 2003).

Limestone and dolomite are often applied to acidic soils to raise the pH. However, CO2 is 

emitted when these materials degrade. Emissions were estimated using a Tier 2 approach. 

Application rates were derived from estimates and industry sources (Minerals Yearbook, 

published by the U.S. Bureau of Mines through 1994 and by the U.S. Geological Survey from 

1994 to present). The emission factors used, 0.059 ton CO2-C/1 ton limestone and 0.064 ton 

CO2-C/1 ton dolomite, are lower than the default IPCC emission factors because they account for 

a portion of limestone that may leach through soils and travel through waterways to the ocean 

(West & McBride 2005). The methodology summarized above is described in detail in Chapter 7 

of the U.S. GHG Inventory (EPA 2010). 

3.7 Uncertainty in Estimating Carbon Stock Changes in Agricultural 

Soils

Uncertainty was calculated separately for the Tier 3 and Tier 2 approaches used to estimate CO2

fluxes. The methodologies summarized below are described in detail in Chapter 7 and Annex 

3.13 of the U.S. GHG Inventory (EPA 2010). 

3.7.1 Tier 3 Approach for Cropped Mineral Soils Simulated by CENTURY

As estimated by the CENTURY model, mineral soils on which major crops are grown 

sequestered 42 Tg CO2 eq. in 2008 with a 95% confidence interval of +/- 64%. This uncertainty 

has three components: Monte Carlo approach to address uncertainties in CENTURY model 

inputs, an empirical approach to address structural uncertainty inherent in the model, and scaling 

uncertainty associated the NRI survey data. For model input uncertainty, probability distribution 

functions were developed for fertilizer rates, manure application, and tillage practices. A Monte 

Carlo analysis was conducted with 100 iterations in which input values were randomly drawn 

from the probability density functions to simulate the soil carbon stocks for each NRI cluster of 

points using CENTURY. An empirically based estimator was used to assess model structural 

error. This estimator was derived from a linear effects mixing model analysis of comparisons 

between modeled soil carbon stocks and measurements from 45 long-term experiments with over 

800 treatments representing a variety of cropping, fertilizer, and tillage management practices 

(Ogle et al. 2006a). The model included variables that accounted for significant biases (alpha 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 66

level of 0.05) in CENTURY model estimates. For each carbon stock estimate from the Monte 

Carlo simulations, 

the structural uncertainty estimator was applied to adjust the model output for bias and prediction 

error. Uncertainty in land use statistics from the NRI were incorporated based on the sampling 

variance of the cluster of NRI points. 

3.7.2 Tier 2 Approach for Remaining Cropped Mineral Soils, Organic Soils, and Liming

The CENTURY model has not been adequately tested with organic soils and soils used to grow 

non-major crop types (e.g., commodity crops, vineyards, fruit and nut trees) so an IPCC Tier 2 

methodology was used for these soils. As estimated by Tier 2 methodology, mineral soils for 

non-major crops lost ~1 Tg CO2 eq. in 2008 with a 95% confidence interval of –380% and 

+377% and organic soils emitted 30 Tg CO2 eq. in 2008 with a 95% confidence interval of –39% 

and +31%. A Monte Carlo approach was used to simulate a range of values with 50,000 

iterations by selecting values from probability distribution functions (Ogle et al. 2003). For 

mineral soils, probability distribution functions were derived from a synthesis of 91 published 

studies that addressed the impact of land management on soil carbon stock changes. For organic 

soils, probability distribution functions for emission factors were derived from a synthesis of 10 

studies and combined with uncertainties in the NRI land use data for organic soils.

As estimated by Tier 2 methodology, liming of soils led to emissions of ~4.0 Tg CO2 eq. in 

2008 with a 95% confidence interval of –97% and +102%. Uncertainty in the emissions factors 

and uncertainty in data for agricultural use of limestone and dolomite were included in the 

analysis.

3.7.3 Combined Uncertainties

Uncertainties for the above components were combined using simple error propagation (IPCC 

2006). That is, the combined uncertainty was calculated by taking the square root of the sum of 

the squares of the standard deviations of the components. The combined 95% confidence interval 

for CO2 storage in cropped soils in 2008 ranged from -38 to 20 Tg CO2 eq. around the estimate 

of -8 Tg CO2 eq. (Table 3-1). 

3.7.4 Changes Compared to the 2nd Edition of the USDA GHG Report

There were important changes in land classification data that effected C stock change estimates. 

Data from the USDA National Resources Inventory (NRI) are used to classify land use and 

management practices. In previous inventories, NRI data were collected in 5-year increments,

and the last available year was 1997. Availability of new annual data extended the time series of 

activity data beyond 1997 to 2003. Also, each NRI point was simulated separately instead of 

simulating clusters of points that had common cropping rotation histories, and more exact 

cropping histories were simulated instead of generalized cropping rotations. Overall, these 

changes resulted in an average annual decline in soil C sequestration in mineral soils of close to 

20 Tg CO2 eq. for the reporting period compared to the previous Inventory. Uncertainties are 

also higher because soil C stock changes were estimated for each year from new annual NRI data 

instead of averaging over 5-year periods.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 67

In addition, annual C flux estimates for mineral soils between 1990 and 2008 were adjusted to 

account for additional C stock changes associated with sewage sludge amendments using a Tier 

2 method provided in IPCC (2003, 2006), which utilizes U.S.-specific C loss rates (Ogle et al. 

2003) rather than default IPCC rates. Estimates of the amounts of sewage sludge N applied to 

agricultural land were derived from national data on sewage sludge generation, disposition, and 

nitrogen content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass 

units, were obtained from an EPA report (EPA 1999), and estimates for 2004 were obtained from 

an independent national biosolids survey (NEBRA 2007). These values were linearly 

interpolated to estimate values for the intervening years. The stock change rate is based on 

country-specific factors and the IPCC default method.

3.8 Mitigation of CO2 Emissions

Currently, cropped soils in the U.S. are estimated to be storing carbon at the gross rate of 

approximately 43 Tg CO2 and a net rate of ~8 Tg CO2 per year. However, the potential to store 

carbon is thought to be much higher (e.g., Sperow et al. (2003) estimated a potential of 220 – 255 

Tg CO2 per year). Strategies to increase soil C stocks include: reduction in tillage intensity, 

reduced cropping of organic soils, reduced summer fallow, planting non-growing season cover 

crops, increased land in CRP, and increased use of hay or pasture in crop rotations. Organic soils 

provide an opportunity to mitigate emissions because they make up less than 1% of total cropped 

land in the U.S., but are a source of 30 Tg CO2 per year (Table 3-7). Summer fallow tends to 

decrease soil carbon because during a large part of the growing season plants are not present to 

provide carbon inputs but decomposition of soil carbon by microbes continues. Cropped land 

converted to CRP stores carbon because the land is not cultivated and trees or grasses are planted 

to provide carbon inputs. Including hay or pasture in rotations also increases carbon inputs, and 

carbon losses are lower because the land is not tilled during the hay or pasture phase of the 

rotation. We do not quantitatively estimate mitigation potential for this report because no recent 

nationwide analyses have been conducted. 

Recent data suggest that a large portion of the cropped land in the U.S. is currently under 

reduced or no till cultivation (Maps 3-4, 3-5), thus the potential for further soil carbon gains by 

reducing tillage intensity may be limited. However, reduced tillage intensity does imply reduced 

on-farm energy consumption and lower CO2 emissions. Similarly, the potential to convert 

cropland to idle CRP land is limited because the demand for biofuel feedstocks has incentivized 

keeping lands in production. Currently, about one-third of the corn crop is used for ethanol 

production, and the amount of cropland dedicated to biofuel feedstock production is expected to 

continue to increase as the nation moves towards the goal outlined in the Energy Independence 

and Security Act of 2007 to increase domestic ethanol production from the current level of ~11 

billion to 36 billion gallons by 2022. A large portion of future biofuel feedstocks are expected to 

be supplied by perennial crops which can increase soil C stocks, but no national analyses to 

quantify this potential have yet been published.
Chapter 4: Carbon Stocks & Stock Changes in U.S. Forests

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 68

4.1 Summary 

Forest ecosystems, urban trees, and forest products represent significant carbon sinks in the 

United States, offsetting approximately 12.7% of total U.S. greenhouse gas emissions. The net 

amount of carbon stored—that is, annual incremental change—by forests during 2008 in the 

United States is an estimated 704 and 88 Tg CO2 eq. for forest ecosystems and harvested wood 

products (HWP), respectively. Net forest system (ecosystems plus HWP) total sequestration in 

2008 was estimated to be 792 Tg CO2 eq., with a 95% confidence interval of 935 to 651 Tg 

CO2 eq. (Table 4-1). Compared to 1990, CO2 sequestered by forest systems in 2008 was about 

8% greater (Table 4-2). Although 

the net effect was zero, an 

additional 194 Tg CO2 eq. was 

sequestered by trees, but harvested 

and burned to produce energy. 

Urban trees also sequestered 

carbon, about 94 Tg CO2 eq. in 

2008. Current total carbon stocks 

in forest ecosystems of the 

conterminous United States are 

Table 4-1 Forest Carbon Stock Change Estimates and 

Uncertainty Intervals for 2008

Estimate 95% Confidence Interval

Source Tg CO2 eq.

Forest (704) (846) to (567)

Harvested Wood (88) (110) to (67)

Total (792) (935) to (651)

Note: Parentheses indicate net sequestration. Tg CO2 eq. is teragrams carbon dioxide 

equivalent.

Table 4-2 Carbon Stocks and Annual Change for Forest and Wood Pools and Forest Area, 1990, 

1995, 2000, 2005-20081

1990 1995 2000 2005 2006 2007 2008

Annual Change Tg CO2 eq. yr-1

Forest (598) (574) (355) (701) (704) (704) (704)

Aboveground Biomass (378) (398) (309) (397) (397) (397) (397)

Belowground Biomass (74) (79) (62) (79) (79) (79) (79)

Dead Wood (29) (31) (16) (23) (26) (26) (26)

Litter (47) (28) 3 (56) (56) (56) (56)

Soil Organic Carbon2 (70) (37) 29 (146) (146) (146) (146)

Harvested Wood (132) (118) (113) (105) (109) (103) (88)

Wood Products (65) (55) (47) (45) (45) (39) (24)

Landfilled Wood (67) (63) (66) (60) (63) (64) (64)

Total (730) (693) (468) (807) (812) (807) (792)

Carbon Stock Tg CO2 eq. 

Forest 155,981 158,884 161,235 164,126 164,827 165,531 166,235

Aboveground Biomass 55,098 57,016 58,775 60,606 61,003 61,400 61,797

Belowground Biomass 10,948 11,328 11,677 12,041 12,120 12,199 12,278

Dead Wood 10,814 10,964 11,093 11,193 11,216 11,242 11,269

Litter 17,436 17,644 17,715 17,892 17,948 18,004 18,060

Soil Organic Carbon 61,685 61,932 61,974 62,394 62,539 62,685 62,831

Harvested Wood 6,817 7,440 8,021 8,525 8,631 8,739 8,842

Wood Products 4,514 4,807 5,069 5,264 5,309 5,354 5,393

Landfilled Wood 2,303 2,633 2,952 3,262 3,322 3,385 3,449

Total 162,798 166,323 169,256 172,651 173,458 174,270 175,077

1,000 ha

Forest Area 267,986 271,194 273,767 276,796 277,536 278,276 279,016

Note: Parentheses indicate net sequestration. Tg CO2 eq. is teragrams carbon dioxide equivalent.

1

Based on interpolation and extrapolation after aggregating plot-level data to state totals according to Smith et al. (2010). 

2

Soil carbon does not include effects of land use history.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 69

about 167 Pg CO2 eq. (Table 4-2, Pg=1,000 Tg). 

Periodic summary statistics on forestland in the conterminous United States indicate about a 3% 

increase in area between the compilation years 1987 to 2007, that is, about 9 million additional 

hectares (Smith et al. 2009). In addition to the net accumulation of carbon in harvested wood 

pools, sequestration is a reflection of net forest growth and increasing forest area over this 

period. Generally, the largest stocks and net annual changes are in biomass carbon. 

Carbon sequestration rates for forests and harvested wood products are greatest in California, 

followed by Missouri, Georgia, Washington, Oregon, North Carolina, Wisconsin, and 

Mississippi (Map 4-1). Only six States are emitting more carbon than they are sequestering. The 

distribution of forestland in the conterminous United States is illustrated in Map 4-2; carbon 

stock and change summaries provided below are according to the 10- or 4-region sets specified 

in Map 4-3. Among the four regions, total carbon stocks and net annual change (sequestration) 

are greatest in the North. However, stock and change are greatest in the Pacific Coast region 

when expressed on a per-hectare basis (see Table C-1 for details of this summary). Hardwood 

forest type groups in the East formed the largest stock of carbon in biomass; this was about 27 Pg 

CO2 eq. in comparison to about 15 Pg CO2 eq. in softwood and mixed type groups in the East 

(Table 4-3). Softwood type groups in the West included about 25 Pg CO2 eq. in biomass,

whereas hardwood type groups accounted for about 4 Pg CO2 eq. 

Forestlands of the United States constitute 33% (304 million hectares) of total U.S. land area. 

These forestlands are surveyed by the USDA Forest Service, Forest Inventory and Analysis 

(FIA) program. A large proportion of these forests are managed for timber production. About 

75% of forestland, 277 million hectares are classified as timberland, meaning they meet 

minimum levels of productivity and are available for timber harvest. Effects of management and 

land use change are implicitly part of the forest survey and are thus reflected in carbon stocks 

and stock changes. This chapter summarizes carbon stocks and stock changes on an average 273

million 

hectares 

located in 

the 

conterminou

s 48 States 

and coastal 

Alaska. 

Summaries 

of 

information 

included in 

this chapter 

represent 

updates of 

inventories 

and carbon 

estimations 

relative to 

the national 

Map 4-1

Carbon Stock Change by State1

 Note: Tg CO2 eq. is teragrams carbon dioxide equivalent. 1 Net change values are model estimates for 2008 according to Smith et al. (2010) 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 70

forest carbon 

budgets reported 

in the second 

edition of the 

USDA 

Greenhouse Gas 

Inventory (Smith 

& Heath 2008).

Estimates of 

stocks and net 

annual stock 

change for carbon 

on forestlands and 

in harvested wood 

products for the 

conterminous 

United States 

presented here 

expand on the 

information reported for forestlands in Heath et al. (in press) and Chapter 7 of the most recent 

U.S. GHG Inventory (EPA 2010), and are consistent with reporting recommendations of the 

Intergovernmental Panel on Climate Change (IPCC) Good Practice Guidance for Land Use, 

Land-Use Change, and Forestry (Penman et al. 2003). The summary tables provided in this 

chapter and in appendix C provide additional detail by summarizing data according to forest 

types, ownerships, or other classifications. 

Although annual estimates are available beginning in 1990, we present estimates for a logical 

subset of years (Table 4.3). The post-2000 large increase in sequestration (Table 4-2) is due 

Map 4-2

U.S. Forest Carbon Stocks in 20081

 Note: Mg CO2 Eq/ha is Megagrams carbon dioxide equivalent per hectare. 1 Stocks are based on the most recent inventory per state.

Map 4-3

Regions Used for Carbon Stock and Stock-Change Summaries1

1 Regions used for 10- or 4-region carbon summaries are: Pacific Northwest, West (PNWW); Pacific Northwest, East (PNWE); Pacific Southwest 

(PSW); Rocky Mountain, North (RMN); Rocky Mountain, South (RMS); Northern Prairie States (NPS); Northern Lake States (NLS); Northeast (NE); 

South Central (SC); and Southeast (SE). Note that regions are merged for some tables, these combinations include: PNWW, PNWE, PSW, and 

coastal Alaska as Pacific Coast; RMN and RMS as Rocky Mountain; NLS, NPS, and NE as North; and SC and SE as South. Pacific Coast and Rocky 

Mountain are collectively called West, and North and South are collectively referred to as East. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 71

mainly to additional increases in the East, mostly in the North. The main factor in the increase is 

the relatively steady increase in forest area in many states; stocks follow this area trend. There is 

an additional increase in soil organic carbon (SOC) beyond that expected from increases in forest 

area. This additional SOC change is a function of forest type groups. The total effect in SOC 

net change over the entire interval (1990-2008) suggests there was a slight shift to low specific 

SOC density forest types in the 1990s relative to earlier and later inventories (see Annex 3.12 of 

EPA (2010) for SOC associated with forest types). 

Table 4-3 Forest Area, Carbon Stocks, and Net Annual Stock Change by Forest Type Group1

Forest Area 

Carbon Stocks Net Annual Stock Change

Biomass 

Dead Plant 

Matter SOC2 Biomass 

Dead Plant 

Matter Per Hectare

Forest Type 1,000 ha Tg CO2 eq. Tg CO2 eq. Yr-1 kg CO2/ha

East 176,735 43,766 12,592 45,666 (321) (35) (18,820)

Aspen/Birch 6,963 1,293 453 3,353 10.8 3.4 2,041 

Elm/Ash/Cottonwood 8,805 2,116 488 2,963 (0.7) 1.7 113 

Loblolly/Shortleaf Pine 22,901 4,757 1,447 4,622 (84.2) (18.3) (4,476)

Longleaf/Slash Pine 5,320 842 314 1,907 (9.0) (1.2) (1,919)

Maple/Beech/Birch 19,912 6,729 2,855 6,116 (28.8) 0.5 (1,420)

Oak/Gum/Cypress 9,716 3,177 569 3,606 5.3 3.2 867 

Oak/Hickory 63,590 18,414 3,613 11,218 (240.8) (35.1) (4,339)

Oak/Pine 11,800 2,724 856 2,326 33.3 11.1 3,765 

Pinyon/Juniper 3,575 245 190 499 n/a n/a n/a

Spruce/Fir 6,129 1,242 956 4,111 2.8 (0.3) 415

White/Red/Jack Pine 4,170 1,376 362 1,440 4.4 4.1 2,033 

Woodland Hardwoods 7,441 304 158 1,773 n/a n/a n/a

Other Hardwood Type Groups 850 141 34 357 (7.9) (1.5) (11,022)

Other Softwood Type Groups 1,902 319 170 349 (6.2) (3.0) (4,879)

Nonstocked 3,661 86 128 1,024 n/a n/a n/a

West 99,971 28,739 16,536 16,878 (11) (12) (9,784)

Alder/Maple 1,353 503 162 556 n/a n/a n/a

Aspen/Birch 3,506 789 502 724 n/a n/a n/a

California Mixed Conifer 3,167 1,817 823 578 n/a n/a n/a

Douglas Fir 15,785 7,229 3,490 3,831 n/a n/a n/a

Fir/Spruce/Mountain Hemlock 14,756 5,929 3,541 2,560 n/a n/a n/a

Hemlock/Sitka Spruce 5,011 3,280 1,547 2,018 n/a n/a n/a

Lodgepole Pine 6,467 1,527 865 873 n/a n/a n/a

Other Western Softwoods 3,425 488 549 670 n/a n/a n/a

Pinyon/Juniper 19,890 1,995 1,616 1,493 (3.7) (3.1) (340)

Ponderosa Pine 9,313 2,008 1,128 1,236 n/a n/a n/a

Redwood 285 262 112 56 n/a n/a n/a

Spruce/Fir 383 39 55 87 n/a n/a n/a

Tanoak/Laurel 1,101 594 176 218 n/a n/a n/a

Western Larch 697 190 135 95 (3.1) (1.7) (6,937)

Western Oak 4,304 1,219 570 504 n/a n/a n/a

Western White Pine 112 31 17 19 n/a n/a n/a

Woodland Hardwoods 4,583 396 552 470 (4.0) (7.4) (2,506.5)

Other Hardwood Type Groups 1,163 315 135 249 n/a n/a n/a

Nonstocked 4,669 128 561 641 n/a n/a n/a

Total 276,706 72,505 29,127 62,544 (332) (48) (28,603) 1

Net change is determined from the two most recent inventories for all forests. Stock change does not include soil carbon changes. Stocks and 

area are based on the most recent inventory per state. Tg CO2 eq. is teragrams carbon dioxide equivalent. Kg CO2 is Kilograms carbon dioxide. 2

SOC (soil organic carbon) does not include effects of past land use history.

Note: “n/a” Indicates not available because large area of comparable forest type groups is not defined for two surveys within the Forest 

Inventory Analysis database (FIADB v4.0). Totals over these columns should be interpreted accordingly.

Note: Parentheses indicate net sequestration. Other Hardwood Type Groups and Other Softwood Type Groups represent aggregates of minor type 

groups. However, "Other Western Softwoods" is a specific type group within the FIADB.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 72

The transition from FIA’s older periodic inventories to the current annualized inventories does 

not affect the estimates in an identifiable way. Both types of surveys provide estimates of 

quantities of forest land and forest characteristics on that land; we have identified and corrected 

for known differences occurring over time (Smith et al. 2010, EPA 2010).

4.2 Concepts and Conventions

For reporting purposes, carbon estimates in forest ecosystems are allocated to the following 

pools (Penman et al. 2003): 

• Aboveground biomass, which includes all living biomass above the soil including 

stem, stump, branches, bark, seeds, and foliage. This category includes not only live 

trees, but live understory.

• Belowground biomass, which includes all living biomass of coarse living roots 

greater than 2 mm diameter. 

• Dead wood, which includes all non-living woody biomass either standing, lying on 

the ground (but not including litter), or in the soil. 

• Litter, which includes the litter, fumic, and humic layers, and all non-living biomass 

with a diameter less than 7.5 cm at transect intersection, lying on the ground. 

• Soil organic carbon (SOC), all organic material, including fine roots, in soil to a depth 

of 1 meter but excluding the coarse roots of the belowground pools. 

The two harvested wood products carbon pools are: 

• Harvested wood products in use. 

• Harvested wood products in solid waste disposal sites (SWDS). 

Continuous, regular annual surveys are not available over the entire time period of interest for 

each state; therefore, estimates for non-survey years were derived by interpolation between 

known data points. Survey years vary by state and the list of survey years and data can be found 

in Table 2 in Smith et al. (2010). Thus, the national estimates in Table 4-2 are a composite of 

individual state surveys, broken out in more detail in Appendix Table C-1. The same process 

applies to forest area for each year – annual data are not available throughout the interval so 

annualized average information between inventory years is presented here (Smith et al. 2010). 

4.3 Carbon Stocks and Stock Changes by Forest Type, Region, and 

Ownership

Total forest ecosystem areas, carbon stocks, and net annual stock change according to forest type 

group are listed in Table 4-3. Minor type groups in the East and West are pooled, for example, 

tropical and exotic hardwood groups in both regions. Carbon classifications in this table are for 

biomass, nonliving plant mass, and soil organic carbon. Biomass includes live trees plus live 

understory vegetation. Non-living plant mass includes standing dead trees, down dead wood, and 

the forest floor. Carbon estimates include aboveground and belowground components. 

Estimates of stock change according to forest type group were developed by subdividing the 

state or sub-state classifications according to forest type group (USDA FS 2010) before 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 73

calculating annualized stock or stock change. Note that not all forestland includes at least two 

surveys in the current inventory format, which limits the estimates of change available in Table 

4-3. Thus, change calculated for selected subsets does not necessarily add to totals calculated as 

more aggregate stocks.

Regional summaries were developed for the regions indicated in Map 4-3; the 10-region 

classifications are used in Figures 4-1 and 4-2, while the 4-region set is used for additional tables 

in the appendix. Total forest ecosystem carbon stocks are generally greater in eastern regions 

than in the West (Figure 4-1a). This is in contrast to regional average values for carbon density,

which are greater in the West than in the East (Figure 4-1b). Mass of carbon per unit area is 

greatest in the Pacific Northwest-Westside and the Northern Lake States due to large pools of 

biomass and SOC, respectively. The most notable regional features in ecosystem pool carbon 

density are: greater carbon in biomass in the Pacific Northwest-Westside; greater SOC pools in 

northern regions; and smaller pools of down dead wood and forest floor in the South. Net annual 

stock changes are shown in Figure 4-2, which includes estimated changes in harvested wood 

product pools. 

Forestland in the conterminous United States is distributed throughout the 48 States. Carbon 

density of live trees, both aboveground and belowground, is shown in Map 4-2, which illustrates 

both the spatial distribution of forest ecosystem carbon and average carbon density over the 

lower 48 States. Large areas of high live-tree carbon density include the Pacific Coast states and 

the Appalachian Mountains. This map is based on the most recent inventory data available per 

state. (State-wide summaries of total forest area and non-soil ecosystem carbon stock are 

presented in Appendix Table C-1.) This table also includes net change for area, non-soil 

ecosystem carbon stock, and stock of carbon in harvested wood products for 2008. Carbon stock 

change in harvested wood is allocated according to total roundwood removals per state reported 

for nominal year 2006 in the Timber Product Output tables available at USDA FS (2010). 

Calculated values for net annual change in forest ecosystem carbon reflect estimated carbon 

densities and forest areas reported in the two most recent surveys per state. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 74
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 75

Estimates of net annual change calculated as the difference between two successive inventories 

are sensitive to changes in forestland over the interval as well as changes in average carbon 

density. Even small differences in carbon density can contribute to large differences if the 

change is applied to large areas. Whether change in area or density is the controlling factor is 

dependent on the situation (Smith and Heath 2010). Most estimates of net ecosystem carbon 

change provided in Tables 4-2 and 4-3, Figure 4-2, and Appendix Table C-1 correspond well to 

changes in forest area. That is, net gains in forest carbon are most often accompanied by 

increases in forestland and vice versa. There are exceptions, and most of these involve net gains 

in forest carbon (negative flux) despite decreases in area. This is the case in Table 4-3 for Eastern 

Maple Beech Birch forests which are decreasing in overall area (data not shown), yet total 

carbon stock in biomass is increasing. Similarly, Appendix Table C-1 shows the pattern of 

carbon stock trend counter to forest area trend in 16 of the lower 48 States listed. The two 

instances of net carbon loss accompanying area gains involve relatively low rates of area change 

(0.2% or less). 

Additional tabular summaries of forest ecosystem carbon stocks are provided in Appendix 

Tables C-2 through C-5. The distribution of carbon stocks among forest age classes is shown in 

Appendix Table C-2 for privately owned and Appendix Table C-3 for publicly owned forests. 

The tables illustrate that the greater proportion of forest carbon stocks in the East is under private 

ownership whereas the greater proportion in the West is under public ownership. Distributions 

according to age are shifted toward older forests on public lands; this is the case for all four 

regions but is more apparent in the West. Similarly, distribution according to stand size class 

(Appendix Table C-4) shows a greater proportion in larger size class stands in the West. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 76

Patterns of carbon stocks among forest types and ownerships are presented by forest ecosystem 

pools (excluding soils) in Appendix Table C-5. Ownership is classified as public or private for 

timberlands (forests of minimum productivity and available for harvesting). The remaining 

forestland, both public and private, is either reserved from harvesting or is considered less 

productive (and thus probably not managed for commercial wood products). The net annual 

stock change corresponding to Appendix Table C-5 is provided in Appendix Table C-6. Note 

that Appendix Table C-6 is affected by the same data limitations as discussed above for Table 4-

3. For more information about forest inventory variables such as forest classifications of 

ownership, productivity, forest type, and stand size class, see Smith et al. (2009) and USDA 

Forest Service (2010). 

A large proportion of non-forest trees in the United States is in urban areas – approximately 3% 

of total tree cover in the conterminous United States, with notable urban expansion projected in 

the future (Nowak and Walton 2005). Advances in design and deployment of trees in urban 

environments can provide significant fossil fuel savings for heating and cooling through 

microclimate management (Dwyer et al. 2000). Development of urban tree waste management 

and recycling processes and systems would reduce emissions and increase sequestration 

opportunities. Methods have been developed for estimating carbon sequestration rates for urban 

trees of the United States (Nowak & Crane 2002). Net flux of carbon into urban trees for 2008 is 

estimated to be -94 Tg CO2 eq. per year (EPA 2010). 

4.4 Mechanisms of Carbon Transfer

Carbon sequestration is a function of the continuous exchange of carbon dioxide between forest 

ecosystems and the atmosphere, which is illustrated by Figure 4-3. Forest carbon balance also 

includes some non-CO2 emissions, but the majority of exchange is in terms of CO2, which is the 

focus of this chapter. Tree growth results in the net accumulation of CO2 in forests (removal 

from the atmosphere), whereas other processes such as respiration, decomposition, or 

combustion emit CO2 to the atmosphere. Photosynthesis provides the energy for the conversion 

of carbon dioxide to organic carbon; this assimilation of CO2 by trees most often exceeds any 

simultaneous losses through respiration, resulting in net tree growth. Forests convert much of the 

accumulated carbon to wood, which stores carbon and energy. Processes that control the fate of 

wood grown in a forest largely determine the subsequent loss of CO2 to the atmosphere. 

Mortality and disturbance add to the pools of down dead wood and forest floor, which are 

subject to decay. Carbon can also be removed from forest ecosystems through runoff or leaching 

through soil. Mechanisms of relatively rapid carbon loss from specific forestlands include 

disturbances such as fire or the harvest of wood. However, a portion of the carbon in harvested 

wood is not immediately returned to the atmosphere, rather it is retained in wood products. Once 

in a product pool, the carbon is emitted as CO2 over time through combustion or decay of the 

wood product. Net release of carbon from wood products can vary considerably depending on 

the product, its end use, and the means of disposal (Heath et al. 1996, Smith et al. 2006, Skog 

2008). 

Forest management affects carbon stocks and stock changes by controlling mechanisms 

associated with carbon gain and loss (Houghton & Hackler 2000, Johnson & Curtis 2001). Forest 

management can be defined as activities involving the regeneration, tending, protection, harvest, 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 77

Figure 4-3. Summary diagram of forest carbon stocks and carbon transfer among stocks

and utilization of forest resources to meet goals defined by the forestland owner. Management 

often focuses on more than one outcome and can vary by forest ecosystem, landowner 

objectives, and economic possibilities. Example goals, or expected outcomes, of management 

include productivity and resource conservation. Relatively passive management may include tree 

harvest and removal, followed by natural regeneration, or riparian area management such as 

consciously retaining a buffer strip of trees along a watercourse. Intensive management may 

consist of site preparation, improved stocking, species conversion, planting genetically improved 

stock, application of pesticides or fertilizer, and improvement cuttings such as thinning or precommercial thinning. 

Increased net carbon sequestration is generally associated with forest systems under improved 

forest management practices, although some practices may reduce carbon storage for a given 

site-age-type dynamic. Examples of improved management practices include afforestation, 

increased productivity, reduced conversion to non-forest uses, lengthened rotations in some 

systems, and increased proportion and retention of carbon in harvested wood products. 

Afforestation offers significant opportunities to capture and store carbon on lands that are not 

currently forested (Houghton & Goodale 2004, Woodbury et al. 2006). This is a particularly 

useful approach to increasing carbon sequestration for marginal agricultural lands. Similarly, 

reductions in conversion to non-forest land uses contribute to maintaining carbon stocks, 

particularly through the additional organic carbon storage in forest soils (Lal 2005). Sustainable 

short-rotation woody crops systems offer the opportunity to rapidly deploy new, faster growing 

genetic material, sequester carbon in the soil, add to the wood products pool, and provide energy 

feedstocks as fossil fuel offsets. Improvements in the management of wood products in use and 

in landfills provide a number of opportunities to reduce emissions and increase sequestration. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 78

Continuing development of wood products can increase their use as substitutes for nonrenewable 

materials and extend their durability and thus expected lifespan (Perez-Garcia et al. 2005). 

Some of the carbon in harvested wood products may remain sequestered for a long time before 

returning to the atmosphere, depending on the lifespan of the individual products. Emissions can 

occur from wood burned for energy, or from decay or burning of wood without energy capture 

(Figure 4-3). This distinction between the two paths for carbon emitted to the atmosphere is 

useful to assess potential displacement of other fuel sources. Average annual carbon emissions 

from harvested wood are estimated at 373 Tg CO2 eq. over the period 1990 through 2008 (EPA 

2010, see Table A-220 of Annex 3.12). In 2008, it is estimated that 194 Tg CO2 eq. was emitted 

by combustion of wood biomass to produce energy (EPA 2010). Net annual carbon 

sequestration via harvested wood, after accounting for these emissions, is presented in Table 4-2. 

Information about emissions released when processing or producing harvested wood carbon 

pools in the U.S. can be found in Heath et al. (2010).

4.5 Methods

Estimates of forest ecosystem carbon as reported here are based on the stock-change method, 

which uses forest inventory data to produce a series of successive carbon stock estimates for an 

individual state, for example (Penman et al. 2003, Smith et al. 2010). The FIA data consist of a 

series of annual partial surveys per state each year with re-measurements at 5- to 10-year 

intervals, depending on the state (USDA FS 2010). Carbon stocks for each forest classification, 

ecosystem carbon pool, and inventory are separately calculated and aggregated to total stocks for 

a specific year for each state. The term “survey” is used here to describe a complete inventory for 

a state, which is repeated at regular intervals. The inventories for some states are further divided 

into separate sub-state classifications for consistency in each consecutive series of carbon stocks. 

Net annual stock change (sometimes referred to as flux) is the difference between successive 

stocks divided by the interval of time between surveys. Carbon estimates for harvested wood 

products are based on a separate stock change method and input data that are not directly related 

to forest inventory data. 

The overall goal in reporting these pools is to be as consistent as possible with: (1) the format 

and estimates provided in the previous USDA forest carbon inventory (Smith & Heath 2008); (2) 

current forest carbon estimates (EPA 2010, Heath et al. in press); and (3) the carbon estimation 

methods applied to the available inventory data. As a result, the sequence and identity of figures 

and tables describing forest carbon are similar to the previous inventory, but the estimates are 

updated to those in EPA (2010). Classifications, or groupings, of values within tables or figures 

have changed somewhat due to corresponding changes in forest inventories. Methods are 

summarized below, with additional details in EPA (2010), Skog (2008), and Smith et al. (2010). 

Current forest survey data for the United States are available from the Forest Inventory and 

Analysis DataBase (FIADB), version 4.0 (USDA FS 2010). Surveys from the FIADB are 

supplemented with some older surveys from FIA Resources Planning Act Assessment (RPA) 

databases, which are periodic summaries of state inventories, along with older FIA tree-level 

data for some states. More complete information about FIA forest inventories is available on the 

Internet (USDA FS 2010). All FIADB surveys used for carbon stock estimates were obtained 

from the FIADB Web site on December 4, 2009. See Table 2 of Smith et al. (2010) for a list of 

the specific surveys used for the estimate provided here. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 79

Carbon estimation factors (described below) are applied to the plot-level inventory data and 

summed to calculate carbon stocks for each survey of each state. Each survey is associated with 

an average year for field collection of data. Carbon stocks for each state or sub-state 

classification are assigned to those average years with net stock change based on the interval (in 

years) between the stocks. In this way, state-wide annualized estimates of ecosystem stock and 

change can be calculated and summed to U.S. totals as presented in EPA (2010) and Table 4-2. 

A similar approach was used to produce the additional estimates disaggregated by categories 

presented in the figures and tables. 

Forest ecosystem carbon is estimated for each inventory plot as six separate pools: live tree, 

understory vegetation, standing dead tree, down dead wood, forest floor, and soil organic carbon. 

Live tree and understory are also allocated to aboveground and belowground portions. For each 

inventory summary in each state, each carbon pool is estimated using available conversion 

factors, which are generally based on empirical or process-based models (Birdsey & Heath 1995, 

Birdsey & Heath 2001, Heath et al. 2003, Smith et al. 2004). Coefficients are applied to the 

survey data at the scale of FIA inventory plots; the results are estimates of carbon density (Mg 

per hectare). These densities are then converted to CO2 equivalents. Live tree and understory 

carbon pools are combined as biomass in this inventory. Similarly, standing dead trees and down 

dead wood are combined as dead wood in this inventory. Definitions of forest floor and SOC 

correspond to litter and forest soils, respectively. 

Tree carbon includes aboveground and belowground (coarse root) carbon mass of live trees. 

Separate estimates are made for whole-tree and aboveground-only biomass. Thus, the 

belowground portion is determined as the difference between the two estimates. Tree carbon 

estimates are based on equations in Jenkins et al. (2003) and are functions of tree species and 

diameter as well as forest type and region. Tree carbon in the RPA plots, which do not include 

individual tree data, are estimated from plot-level growing stock volume of live trees and 

equations based on Smith et al. (2003). Carbon mass of wood is 50% of dry weight (Eggleston et 

al. 2006). The minimum-sized tree included in the tree carbon pool data is one-inch diameter 

(2.54 cm) at breast height (1.3 meter). Understory vegetation is defined as all biomass of 

undergrowth plants in a forest, including woody shrubs and trees less than one-inch diameter, 

measured at breast height. We estimated that 10% of understory carbon mass is belowground. 

This general root-to-shoot ratio (0.11) is near the lower range of temperate forest values provided 

in Penman et al. (2003), and was selected based on two general assumptions: (1) ratios are likely 

to be lower for light-limited understory vegetation as compared with larger trees, and (2) a 

greater proportion of all root mass will be less than 2 mm diameter. Understory carbon density 

estimates are based on Birdsey (1996). Coefficients used for estimating understory or the 

volume-based standing dead tree carbon are presented in Annex 3.12 of EPA (2010).

Dead wood includes down dead wood and standing dead trees. Down dead wood is defined as 

pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached 

to live or standing dead trees. Down dead wood includes stumps and roots of harvested trees. 

Ratio estimates of down dead wood to live tree biomass were developed by FORCARB2 

simulations and applied at the plot level (Heath et al. in press). The standing dead tree carbon 

pool includes aboveground and belowground (coarse root) mass. Estimates are based on Smith et 

al. (2003) and are functions of plot-level growing stock volume of live trees, carbon density of 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 80

live trees, forest type, and region. Coefficients used for estimating dead wood carbon are 

presented in Annex 3.12 of EPA (2010). 

Estimates of forest floor and SOC are not based on carbon density of trees, but are functions of 

plot age, forest type, and region. Forest floor carbon is the pool of organic carbon (litter, duff, 

humus, and fine woody debris) above the mineral soil and includes woody fragments with 

diameters of up to 7.5 cm. Estimates are based on equations of Smith and Heath (2002) applied 

at the plot level. Forest floor and woody debris remaining after harvests are also included as part 

of calculations of forest ecosystem carbon pools. Estimates of SOC are based on the national 

STATSGO spatial database (USDA SCS 1991, USDA NRCS 2006) and the general approach 

described by Amichev & Galbraith (2004). In their procedure, SOC was calculated for the

conterminous United States using the STATSGO database, and data gaps were filled by 

representative values from similar soils. The SOC estimates are based on region and forest type 

only. Links to region and forest type groups were developed with the assistance of the USDA 

Forest Service FIA Geospatial Service Center by overlaying FIA forest inventory plots on the 

soil carbon map. Historical land use change effects are currently not included in the estimate of 

the soil carbon pool (Johnson & Curtis 2001, Woodbury et al. 2006). That is, soil carbon for 

areas which were cleared and plowed at one time, and then reverted to forest, are probably still 

accruing soil carbon. However, we currently assume that all forests of a given forest type within 

a region have the same amount of SOC. The regional averages for SOC according to forest type 

group are included in Annex 3.12 of EPA (2010).

The tabular forest carbon summary values are based on a short sequence of calculations, these 

are: (1) determine carbon density for individual inventory plots; (2) identify the date (year) 

associated with each survey based on when data were collected; (3) sum total carbon within each 

state or sub-state classification for each survey to get carbon stock according to specific 

classification and year; and (4) linearly interpolate, or extrapolate, to determine annualized 

stocks and net stock change (Smith et al. 2010). In this way, carbon stocks are calculated 

separately for each state based on inventories available since 1990 and for the most recent 

inventory prior to 1990. With this method, stock and flux since the most recent survey are based 

on extrapolation. Thus, the annualized estimates for 2008 will not exactly match the most recent 

data per state. In the results presented in this chapter, all estimates of 2008 net stock change (or 

flux) are based on the difference between the two most recent surveys (extrapolated values). 

Most values for carbon stock or forest area are based on the most recent data available for each 

state; the only exception is the set of annualized stocks provided in Table 4-2. 

Calculations of carbon in harvested wood products are completely separate from the ecosystem 

estimates because the datasets and methods are largely unrelated. These estimates focus on 

carbon in wood removed from the forest; logging residues are part of the ecosystem pools. 

Carbon in harvested wood that is either in products in use or in products discarded in solid waste 

disposal sites (SWDS) is based on the methods described in Skog (2008). Estimates were 

developed for years from 1910 onward based on historical data from the USDA Forest Service 

(USDA 1964, Ulrich 1989, Howard 2001), and historical data as implemented in the framework 

underlying the North American Pulp and Paper (NAPAP, Ince 1994), the Timber Assessment 

Market, and the Aggregate Timberland Assessment System Timber Inventory models 

(TAMM/ATLAS, Haynes 2003, Mills & Kincaid 1992). From these data on annual wood and 

paper production, the fate of carbon in harvested wood was tracked for each year from 1910 

through 2008; this included the change in carbon stocks in wood products, in SWDS, and carbon 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 81

emitted to the atmosphere. The carbon conversion factors and decay rates for harvested carbon 

removed from the forest are taken from Skog (2008). To account for imports and exports, the 

production approach is used, meaning that carbon in exported wood is counted as if it remained 

in the United States, and carbon in imported wood is not counted. The carbon stock changes 

presented in this chapter represent the net amounts of carbon that continue to be stored in a 

product pool. Allocation of the national estimates from EPA (2010) to regions or states is based 

on roundwood removals per state from the FIA Timber Products Output reports for RPA nominal 

year 2006 (see Other Reporting Tools at USDA FS 2010). 

4.6 Major Changes Compared to Previous Inventories

The estimates provided in Table 4-2 reflect a substantial number of incremental changes in 

methods and data between EPA (2007) and EPA (2010) in terms of net stock change since 1990. 

The accumulation of newer inventory data for most states, including stocks for coastal (southern 

and eastern) Alaska and western Texas, affect totals and changes compared to previous 

inventories. Any modifications or adjustments in a survey are accompanied by corresponding 

modifications to related older data as necessary to assure consistency over time within each 

series. Reassessment of the definition of forestland resulted in reduction of grassland area in the 

U.S. because woodlands previously designated as grassland are now considered forest land, thus 

increasing the estimation of soil C stock changes in these areas (EPA 2010). However, redefining forestland also led to the removal of low cover, lower productivity woodlands areas 

from the surveys (Smith et al. 2009), which were included in the previous USDA (2008) 

inventory. In addition, a few older western inventories were removed from the stock-change 

calculations to improve year-to-year consistency between successive stocks. Lastly, the 

estimation procedures for obtaining carbon stocks from inventory have changed (see Bechtold & 

Patterson 2005) as well as the approaches to selecting available inventory data. See Smith et al. 

(2010) for more discussion of how inventory data were used to develop the current 1990-2008 

estimates. For comparison of the respective inventory sets, see Tables A-186 and A-207 of EPA 

(2007) and EPA (2010), respectively. On average, these changes increased carbon stock 

estimates by approximately 8 percent.

4.7 Uncertainty

Uncertainty about forest inventory data and the carbon conversion factors applied to the 

inventory contributes to overall uncertainty of the carbon estimates. Contributing components 

include errors in sampling or measurements as well as unknowns or errors in the largely 

empirical models used to develop the carbon factors applied at the plot level. 

Sampling error is determined separately for each carbon pool according to Bechtold and 

Patterson (2005). Additional related errors in this use of inventory data are based on resolving a 

state’s forest inventory to carbon stock for a defined forest area at a single point in time. Some 

small error is possible if surveys conducted over a multi-year period are averaged to a single year 

before calculating stock change. However, if significant portions of a state’s forest inventory 

were sampled on a completely different schedule as was the case with some of the older 

inventories, then the error would increase. For this reason, stocks and stock changes were 

separately determined at sub-state levels, such as national forests, in some Western states (Smith 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2008 Page 82

et al. 2010). The potential for an additional error comes from the use of successive surveys and 

the need for consistent definition, identification and inclusion of all forestlands within a state. 

Small errors in carbon stocks are reflected in stock-change calculations; for example, if small 

areas or ownerships are omitted from one of a pair of successive surveys, then a portion of the 

resulting state-wide change is due to the apparent change in forestland. Such problems with 

definition or inclusion of forestlands can have significant effects on calculated net flux. 

Uncertainty associated with the estimates of specific carbon stocks varies by carbon pool and 

forest type. Carbon in trees is relatively well defined, and information on errors in estimates 

(Jenkins et al. 2003) makes it possible to develop quantitative estimates of uncertainty. Relative 

errors in the estimates for other ecosystem carbon pools are greater; these carbon conversion 

factors are generally based on extrapolations of site-specific studies, which may not adequately 

represent regional averages. Additionally, representative data are not available for all forest 

types; this also increases uncertainty as substitutions are required. An important source of 

uncertainty is high variability and general lack of precision possible in assigning estimates of 

SOC. Soil carbon is a large pool, but it changes relatively slowly. There is limited information 

available for assessing soil carbon or the cumulative effects of land use change, which can 

amount to significant stock changes when summed over large forest areas (Woodbury et al. 

2006). 

A quantitative uncertainty analysis was developed for estimates of total carbon flux. The analysis 

incorporated the information from preliminary uncertainty analyses and estimates of uncertainty 

in the carbon conversion factors (Heath & Smith 2000, Smith & Heath 2001, Skog et al. 2004, 

Skog 2008). Additional details on the analysis are provided in Chapter 7 and Annex 3.12 of EPA 

(2010). The uncertainty analysis was performed using the IPCC-recommended Tier 2 uncertainty 

estimation methodology, that is, the Monte Carlo simulation technique. The 2008 95% 

confidence interval for forest carbon stock changes is -935 to -651 Tg CO2 eq. , with a mean 

sink of -792 Tg CO2 eq. (Table 4-1). The 95% confidence interval for forest ecosystem 

sequestration is -846 to -567 Tg CO2 eq., and is -110 to -67 Tg CO2 eq. for harvested wood 

products. 
Chapter 5: Energy Use in Agriculture

U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 83

5.1 Summary of Greenhouse Gas Emissions from Energy Use in 

Agriculture

Approximately 0.8 quadrillion btu of direct energy was used in agriculture in 2008, resulting in 

almost 72 Tg of CO2 emissions (Table 5-1). The total energy consumption for all sectors in the 

U.S., including agriculture, was approximately 100.9 quadrillion btu, resulting in 5,572.8 Tg of 

CO2 emissions (EPA 2010). Production agriculture’s contribution to this total was very small at a 

little less than 1.3%. Within production agriculture, diesel fuel accounted for about 38% of CO2

emissions and electricity contributed about another 38% of CO2 emissions. Gasoline 

consumption accounted for about 11% of CO2 emissions, while LP gas and natural gas 

accounted for about 7% and 5% respectively.

Table 5-1 Energy use and carbon dioxide emissions by fuel source on U.S. farms in 

2008 

 Energy Carbon Fraction

Fuels consumed content oxidized CO2 emissions

 Qbtu Tg C/Qbtu Tg CO2 eq.

Diesel 0.377 19.95 1 27.58

Gasoline 0.113 19.33 1 8.02

LP gas 0.080 17.18 1 5.08

Natural gas 0.069 14.47 1 3.64

Electricity 0.156 ** ** 27.25

Total 0.795 71.57

Qbtu is quadrillion British thermal units; Tg C/Qbtu is teragrams carbon per quadrillion British thermal units; Tg CO2 eq. is 

teragrams carbon dioxide equivalent; LP is liquefied petroleum; CO2 is carbon dioxide.

** Varies dependent on fuel used to generate electricity and heat rate of the power generating plant.

5.2 Spatial and Temporal Trends in Greenhouse Gas Emissions 

from Energy Use in Agriculture

The highest emissions in 2008 were in the Corn Belt and Northern Plains (Figure 5-1). Regions 

are defined in Table 5-2. Intermediate emissions occurred in the Pacific, Mountain, Southern 

Plains, and Lake States. Relatively small emissions were estimated for the Southeast, Northeast, 

Delta, and Appalachian states. There is a strong correlation between production and energy 

use/emissions. Generally, the states with the most agriculture production use the most energy and 

therefore have the highest CO2 emissions (Figure 5-1). However, emissions also vary by the 

types of energy used for farm production in each region. For example, even though the Pacific 

region was the third highest energy user among the regions, it ranked sixth in CO2 emissions 

(Figure 5-1). The Pacific region has the lowest electricity emission factor among the regions 

because the western part of the United States has the most hydroelectric power. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 84

 

Agricultural energy use and resulting CO2 emissions grew throughout the 1960s and 1970s, 

peaking in the late 1970s (Figure 5-2). High prices, stemming from the oil crisis of the 1970s and 

early 1980s, drove farmers to be more energy efficient, resulting in a decline in energy use and 

CO2 emissions throughout most of the 1980s (Miranowski 2005). This decline is attributed to 

Table 5-2: Definition of Regions Used in Figure 5-1

Region States of Region Region States of Region Region States of Region

Corn Belt Illinois Pacific California Southeast Alabama

Indiana Oregon Florida

Iowa Washington Georgia

Missouri Southern Plains Oklahoma South Carolina

Ohio Texas Northeast Connecticut

Mountain Arizona Lake States Michigan Delaware

Colorado Minnesota Maine

Idaho Wisconsin Maryland

Montana Appalachian Kentucky Massachusetts

Nevada North Carolina New Hampshire

New Mexico Tennessee New Jersey

Utah Virginia New York

Wyoming West Virginia Pennsylvania

Northern Plains Kansas Delta States Arkansas Rhode Island

Nebraska Louisiana Vermont

North Dakota Mississippi

South Dakota
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 85

switching from gasoline-powered to more fuel-efficient diesel-powered engines, adopting 

energy-conserving tillage practices, shifting to larger multifunction machines, and adopting 

energy-saving methods of crop drying and irrigation (Uri & Day 1991; Sandretto & Payne 2006; 

Lin et al. 1995). Another major change in farm energy consumption began around 1979 when 

automobile manufacturers began to produce more fuel-efficient vehicles. Laws, such as the 

Energy Policy and Conservation Act of 1975, increased average fuel economy standards, and 

both gasoline and diesel powered equipment became increasingly energy efficient throughout the 

1980s and 1990s. Declines in farm energy use leveled off in the late 1980s as increases in energy 

prices subsided (Figure 5-2). Energy use increased throughout most of the 1990s, but since 2000,

energy use has gone up and down with no apparent trend. However, energy productivity (i.e., 

output per unit of energy input) has increased significantly. 

5.3 Sources of Greenhouse Gas Emissions from Energy Use on 

Agricultural Operations

Agricultural operations, including crop and livestock farms, dairies, nurseries, and greenhouses,

require a variety of energy sources. Energy use in agriculture varies across agricultural 

operations by crop or livestock type, size of operation, and geographic location. Energy use also 

varies over time, depending on weather conditions, changes in energy prices, and changes in 

total annual crop and livestock production. While energy use in agriculture causes CO2

emissions, this source is small relative to the total U.S. CO2 emissions from energy. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 86

Different forms of energy are used for different purposes in U.S. agriculture. Energy used on 

farms is typically categorized as direct and indirect energy (Miranowski 2005). Direct energy is 

used on the farm for various operations, whereas indirect energy is the energy used to produce 

energy-intensive farm inputs, such as commercial fertilizers. 

Liquid fuel is the most versatile form of direct energy used on farms. Crop production uses large 

amounts of diesel fuel, gasoline, and liquefied petroleum (LP) gas for field operations. Most 

large farms use diesel-fueled vehicles for tilling, planting, cultivating, disking, harvesting, and 

applying fertilizers and pesticides. Gasoline is used for small trucks and older harvesting 

equipment. Smaller farms are more likely to use gasoline-powered equipment, but as farms get 

larger they tend to use more diesel fuel. 

Farmers use a significant amount of energy to dry crops, such as grain, tobacco, and peanuts. 

Several types of energy can be used for crop drying, including LP gas, electricity, diesel fuel, and 

natural gas. Annual rainfall can have a significant effect on the amount of energy used to dry 

crops from year to year. For example, above-average rainfall, especially just prior to harvest 

time, can increase the moisture level of grain. In order to meet quality standards, it may require 

more energy to dry the grain. Weather can also affect the energy used in livestock facilities and 

other farm buildings that use various forms of energy for heating, cooling, and air circulation. 

Natural gas is commonly used to control greenhouse temperatures, and dairies rely heavily on 

electricity to power milking machines and other equipment. 

While many irrigation systems in the U.S. are gravity flow systems that require little or no 

energy for water distribution, irrigation systems that use pumps to distribute water use energy. 

Based on the 2008 USDA Farm and Ranch Irrigation Survey, about 49 million acres of U.S. 

farmland were irrigated with pumps powered by liquid fuels, natural gas and electricity, costing a 

total of $2.68 billion (USDA NASS 2009). Electricity was the principal power source for these 

pumps, costing $1.5 billion to irrigate about 30 million acres. Diesel fuel was used to power 

pumps on about 13 million acres and natural gas was used on about 4.7 million acres (USDA

NASS 2009). 

Irrigated land (including gravity flow irrigation) went up in 2008 to about 55 million acres 

compared to the 52.6 million acres reported by the 2003 USDA Farm and Ranch Irrigation 

Survey. Irrigated farmland has been increasing over time, however, and acreage can vary 

substantially from year to year, depending on environmental conditions and economic factors 

(Gollehon & Quinby 2006). Corn for grain or seed, soybeans, and alfalfa required the most water 

in 2008. The leading states in irrigated land in 2008 are Nebraska with 15% of U.S. total, 

California with 13% of U.S. total, and Texas with almost 10% of U.S. total. 

A significant amount of indirect energy is used off the farm to manufacture farm inputs that are 

ultimately consumed on the farm. Some farm inputs such as fertilizers and pesticides are 

produced by energy-intensive industries. For example, commercial nitrogen fertilizer is made 

primarily from natural gas, and synthetic pesticides are made from a variety of chemicals. 

Although GHG emissions result from the energy consumption used in manufacturing energyintensive agricultural inputs, these indirect emissions are not detailed in this inventory. For 

information on the GHG emissions of manufacturing commercial fertilizers see EPA’s 2010 U.S. 

Greenhouse Gas Inventory Report (EPA 2010).
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 87

The amount and type of energy used in agricultural operations affect overall CO2 emissions, and 

generally CO2 levels increase with higher energy use in agriculture (Figure 5-2). Some fuels 

have higher carbon content than others, resulting in higher CO2 emissions per btu (British 

thermal unit) used. However, some fuel/engine applications are more energy efficient than others 

and require less fuel to perform similar operations. For example, diesel fuel has a higher btu 

content than gasoline on a volumetric basis, but diesel engines have a higher performance rating 

compared to gasoline engines. Therefore, even though diesel fuel has higher carbon content per 

btu compared to gasoline, using diesel engines to perform farm operations may result in lower 

CO2 emissions. 

5.4 Methods for Estimating Carbon Dioxide Emissions from Energy 

Use in Agriculture

The CO2 emission estimates for energy use are constructed from fuel consumption data using 

standardized methods published in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 

1990 – 2008 (EPA 2010). Emission estimates from fuel use in agriculture are not explicitly 

published in the U.S. Greenhouse Gas Inventory Report, however they are contained in the 

estimates of fuel consumption and emissions by sectors. The emissions estimates presented in 

this chapter were prepared separately from the U.S. Greenhouse Gas Inventory Report.

Estimates of CO2 from agricultural operations are based on energy expense data from the 

Agricultural Resource Management Survey (ARMS) conducted by the National Agricultural 

Statistics Service (NASS) of the USDA. The ARMS collects information on farm production 

expenditures, including expenditures on diesel fuel, gasoline, LP gas, natural gas, and electricity 

(USDA/NASS, 2009). NASS also collects data on price per gallon paid by farmers for gasoline, 

diesel, and LP gas (USDA/NASS, 2008). Energy expenditures are divided by fuel prices to 

approximate gallons of fuel consumed by farmers. Gallons of gasoline, diesel, and LP gas are 

then converted to btu based on the heating value of each of the fuels. The individual farm data 

are aggregated by state, and the state data are divided into 10 production regions, allowing fuel 

consumption to be estimated at the national and regional levels. Farm consumption estimates for 

electricity and natural gas are also approximated by dividing prices into expenditures. Since 

electricity and natural gas prices are not collected by NASS, we use data from the Energy 

Information Administration (EIA) that reports average prices by state (EIA, 2009a; EIA 2009b). 

NASS regional prices were derived by aggregating the EIA state data into NASS production 

regions.

Following the method outlined in Annex 2 of the U.S. Greenhouse Gas Inventory, consumption 

of diesel fuel, gasoline, LP gas and natural gas were converted to CO2 emissions using the 

coefficients for carbon content of fuels and fraction of carbon oxidized during combustion (Table 

5-2). These carbon content coefficients were derived by EIA and are similar to those published 

by the Intergovernmental Panel on Climate Change (IPCC). For each fuel type, fuel consumption 

in units of quadrillion btu was multiplied by the carbon content coefficient to estimate the Tg of 

carbon contained in the fuel consumed. This value is sometimes referred to as “potential 

emissions” because it represents the maximum amount of carbon that could be released to the 
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 88

atmosphere if all carbon were oxidized (EPA 2010). To convert from carbon content to CO2, it 

was assumed that the fraction of carbon that is oxidized was 100%.

A different approach was used to estimate emissions from electricity, since a number of fuel 

sources can be used to generate electricity. Also, fuel sources vary significantly by region, for 

example, some regions of the country rely more on coal for electricity generation, while other 

regions use more natural gas to generate electricity. Also, the mix of fuel sources used in a 

region can change overtime. To account for these variables, the CO2 emission estimates from 

electricity generation in this chapter are derived from the most current state data available from 

EIA. In response to a special request from USDA, EIA tabulated state emission factors for the 

NASS production regions. The regional-level electricity emission factors represent average CO2

emissions generated by utility and non-utility electric generators for the 1998-2000 time period. 

These regional emission factors were multiplied by estimated electricity use in each farm 

production region to calculate CO2 emissions. As reported above, electricity use is estimated 

from farm expenditure data collected by NASS. Price estimates for electricity published by EIA 

are divided into electricity expenditures to derive the kilowatt hours consumed by farmers. The 

kilowatt hours of electricity used on the farm are converted to btu, based on a standard 

conversion rate of 3,413 btu per kilowatt hour.

5.5. Major Changes Compared to Previous Inventories

This report is the third edition of the U.S. Agriculture and Forestry Greenhouse Inventory, which 

estimates GHG emissions for the year 2008. Figure 5-3 compares the 2008 results with the two 

previous study periods, 2005 and 2001. Annual GHG emissions are expected to vary with 

changes in crop and livestock production levels. In addition, weather conditions can have a 

significant influence on energy use in agriculture, thereby affecting GHG emissions from year to

year. Total emissions in 2001 are slightly greater than the previous two reports, with most of the 

difference related to a higher use of diesel fuel (Figure 5-3). The similarity among the 3 years is 

an indication that changes in GHG emissions generally follow long-term energy trends as shown 

in Figure 5-3. When a short-term spike in GHG emissions occurs, it probably is related to a 

major weather event or other factors significantly affecting agricultural production.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page 89

 
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Bulletin number 215. U.S. Department of Agriculture, Bureau of Plant Industry.

Weiske, A., G. Benckiser, T. Herbert, and G. Ottow (2001) Influence of the nitrification inhibitor 

3,4-dimethylpyrazole phosphate (DMPP) in comparison to dicyandiamide (DCD) on nitrous 

oxide emissions, carbon dioxide fluxes and methane oxidation during three years of repeated 

application in field experiments. Biology and Fertility of Soils, 34:109-117.

West, T.O. and A.C. McBride (2005) The contribution of agricultural lime to carbon dioxide 

emissions in the United States: dissolution, transport, and net emissions. Agricultural Ecosystems 

& Environment, 108:145-154. 

Wilson, C. (2003) Personal Communication. Chuck Wilson, Rice Specialist at the University of 

Arkansas Cooperative Extension Service and Caren Mintz, ICF International. June 11, 2003.

Wilson, C. (2004) Telephone conversation between Lauren Flinn of ICF Consulting and Chuck 

Wilson, Rice Specialist at the University of Arkansas Cooperative Extension Service. 23 June.

Wilson, C. (2005) Email correspondence between Lauren Flinn of ICF Consulting and Chuck 

Wilson, Rice Specialist at the University of Arkansas Cooperative Extension Service. July 2005. 

Wilson, C. (2007) Personal Communication. Chuck Wilson, Rice Specialist at the University of 

Arkansas Cooperative Extension Service and Victoria Thompson, ICF International. August 22, 

2007.

Wilson, C. (2009) Personal Communication. Chuck Wilson, Rice Specialist at the University of 

Arkansas Cooperative Extension Service and Sarah Menassian, ICF International. August 14, 

2009. 

Wisconsin Department of Natural Resources (1993) Wisconsin greenhouse gas emissions: 

estimates for 1990. Bureau of Air Management, Wisconsin Department of Natural Resources, 

Madison, WI. 

Woodbury, P.B., L.S. Heath, and J.E. Smith (2006) Land use change effects on forest carbon 

cycling throughout the southern USA. Journal of Environmental Quality, 35: 1348-13.
Appendix A: Livestock Emissions

U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 1

Appendix A

A-1 Population of Animals by State in 2008

A-2 U.S. Livestock Population, 1990, 1995, 2000-2008

A-3 State-Level Methane Emissions from Enteric Fermentation in 1990, 1995, 2000-2008

A-4 State-Level Methane Emissions from Enteric Fermentation by Livestock Category in 

2008

A-5 Cattle Population Categories Used for Estimating Methane Emissions

A-6 Dairy Lactation by Region

A-7 Typical Livestock Weights

A-8 U.S. Feedlot Placement in 2008

A-9 Regional Estimates of Digestible Energy and Methane Conversion Rates for Enteric 

Fermentation in 2008

A-10 Definition of Regions in the Enteric Fermentation Model

A-11 Methane Emissions from Cattle Enteric Fermentation, 1990, 1995, 2000-2008

A-12 IPCC Emission Factors for Livestock

A-13 Summary of Greenhouse Gas Emissions from Managed Waste by State in 2008

A-14 Methane Emissions from Manure Management by State and Animal in 2008

A-15 Nitrous Oxide Emissions from Manure Management by State and Animal in 2008

A-16 Waste Characteristics Data

A-17 State Volatile Solids Production Rates in 2008

A-18 State-Based Methane Conversion Factors for Liquid Waste Management Systems in 

2008

A-19 Additional Nitrous Oxide and Methane Emission Factors

A-20 State-Weighted Methane Conversion Factors for Livestock Waste Emissions in 2008

A-21 Nitrogen in Livestock Waste on Grazed Lands
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 2

Appendix Table A-1 Population of Animals by State in 2008

Beef Cattle Dairy Cattle Swine Goat Horse Sheep Poultry

State Head

Alabama 1,288,531 17,984 175,000 50,574 170,731 11,091 209,274,989

Alaska 15,461 799 900 277 5,010 11,091 1,129,194

Arizona 938,433 234,842 165,000 35,374 122,172 155,000 1,129,194

Arkansas 1,822,556 23,978 301,250 32,580 188,414 11,091 241,991,424

California 3,376,306 2,632,471 80,000 103,122 343,973 620,000 45,115,151

Colorado 2,850,938 195,779 750,000 18,561 279,168 420,000 5,016,444

Connecticut 22,921 29,967 3,100 2,586 24,762 8,333 19,283,263

Delaware 11,892 9,191 9,000 1,521 9,001 11,091 45,292,830

Florida 1,663,713 154,889 20,000 39,964 260,451 11,091 24,493,444

Georgia 1,103,618 98,930 230,000 69,498 192,926 11,091 283,713,626

Hawaii 154,364 3,297 13,000 5,364 11,944 11,091 678,444

Idaho 1,443,753 834,036 33,000 11,520 216,945 235,000 1,164,444

Illinois 1,111,217 159,820 4,375,000 17,192 155,495 60,000 6,253,444

Indiana 737,838 242,757 3,600,000 27,801 256,496 52,000 51,345,151

Iowa 4,023,659 324,652 19,525,000 18,898 201,046 225,000 83,990,818

Kansas 7,058,167 184,779 1,800,000 24,763 175,773 90,000 1,129,194

Kentucky 2,270,885 135,858 350,000 68,412 389,629 37,000 62,688,989

Louisiana 891,828 34,975 11,000 14,633 124,575 11,091 18,232,263

Maine 42,550 50,445 4,400 3,162 33,206 8,333 5,316,444

Maryland 125,284 80,924 31,000 9,601 67,647 24,000 57,216,353

Massachusetts 25,058 22,975 10,000 6,022 40,401 8,333 434,444

Michigan 693,922 480,567 1,020,000 21,094 273,584 82,000 27,396,263

Minnesota 1,835,394 732,146 7,525,000 19,768 241,835 145,000 37,610,636

Mississippi 951,906 27,978 375,000 26,738 174,172 11,091 163,385,989

Missouri 4,114,674 169,810 3,112,500 48,654 368,506 82,000 32,169,818

Montana 2,334,925 28,962 175,000 8,613 246,074 270,000 815,444

Nebraska 6,843,288 76,937 3,325,000 11,718 153,615 80,000 13,028,626

Nevada 406,104 36,968 3,500 6,506 42,030 70,000 1,129,194

New Hampshire 16,740 21,479 2,800 3,774 20,662 8,333 663,444

New Jersey 22,817 14,984 8,000 8,312 70,113 11,091 1,942,444

New Mexico 1,152,538 449,621 2,000 19,128 121,702 130,000 1,129,194

New York 531,039 969,909 95,000 33,130 196,216 65,000 21,625,263

North Carolina 816,671 70,927 10,025,000 67,276 167,327 27,000 177,687,788

North Dakota 1,609,165 40,953 151,000 2,523 113,175 93,000 1,129,194

Ohio 1,002,125 383,652 1,897,500 45,061 350,274 125,000 45,704,545

Oklahoma 5,147,362 96,905 2,390,000 82,792 391,394 80,000 48,329,808

Oregon 1,167,345 179,795 20,000 30,628 240,933 220,000 18,824,263

Pennsylvania 874,861 826,131 1,140,000 39,932 295,614 98,000 58,913,878

Rhode Island 3,352 1,698 1,800 468 5,096 8,333 1,129,194

South Carolina 395,427 24,978 240,000 41,192 106,160 11,091 53,460,394

South Dakota 3,378,498 120,889 1,412,500 7,021 181,352 340,000 4,403,667

Tennessee 2,084,215 105,858 205,000 114,664 387,513 30,000 39,377,535

Texas 14,226,582 579,431 1,100,000 1,194,289 970,628 960,000 141,632,535

Utah 700,011 124,874 740,000 9,092 159,976 280,000 5,278,667

Vermont 80,428 197,817 2,600 4,133 29,309 8,333 555,444

Virginia 1,479,084 142,858 350,000 41,275 212,050 81,000 55,693,091

Washington 810,855 347,668 25,000 23,217 197,991 52,000 22,656,263

West Virginia 398,721 15,987 7,000 17,484 83,035 35,000 18,525,485

Wisconsin 1,721,129 1,902,929 385,000 35,179 265,886 90,000 15,713,444

Wyoming 1,240,402 11,984 89,000 5,380 164,011 425,000 318,444

Total 87,018,553 13,658,043 67,311,850 2,530,466 9,499,998 5,950,000 2,175,119,513

Source: EPA 2010
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 3

Appendix Table A-2 U.S. Livestock Population, 1990, 1995, 2000-

2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Animal Type 1 million head

Dairy Cattle 14 14 13 13 13 13 13 13 13 13 14

Dairy Cows 10 9 9 9 9 9 9 9 9 9 9

Dairy Heifers 4 4 4 4 4 4 4 4 4 4 4

Swine 54 59 59 59 60 60 61 61 62 65 67

Market <60 lbs. 18 20 20 20 20 20 20 20 21 22 22

Market 60-119 lbs. 12 13 13 13 13 13 13 14 14 15 15

Market 120-179 lbs. 9 11 11 11 11 11 11 11 11 12 13

Market >180 lbs. 8 9 9 9 10 10 10 10 10 11 11

Breeding Swine 7 7 6 6 6 6 6 6 6 6 6

Beef cattle 89 97 91 90 89 88 87 87 88 88 87

Feedlot Steers 6 7 8 8 8 8 8 8 9 9 9

Feedlot Heifers 3 4 5 5 5 5 5 5 5 5 5

Bulls NOF1 2 2 2 2 2 2 2 2 2 2 2

Calves NOF 24 25 24 23 23 22 22 22 22 22 22

Heifers NOF 10 12 10 10 10 10 9 10 10 10 9

Steers NOF 10 12 9 9 9 8 8 8 8 8 8

Cows NOF 32 35 34 33 33 33 33 33 33 33 32

Sheep 11 9 7 7 7 6 6 6 6 6 6

Goats 3 2 2 2 3 3 3 3 3 3 3

Poultry 1,537 1,827 2,033 2,060 2,098 2,085 2,131 2,150 2,154 2,167 2,175

Hens >1 yr. 273 299 334 340 340 341 344 348 350 347 340

Pullets 73 81 95 96 95 100 101 97 97 104 99

Chickens 7 8 8 8 8 8 8 8 8 8 8

Broilers 1,066 1,332 1,506 1,525 1,562 1,544 1,589 1,613 1,612 1,619 1,638

Turkeys 118 107 90 91 92 91 88 84 87 89 91

Horses 5 5 5 6 6 7 8 9 9 9 9

Source: USDA NASS 2009, 2008, 2007, 2006, 2005, 2000, 1995.

Note: Totals may not sum due to independent rounding.

1

(NOF) Not on feed.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 4

Appendix Table A-3 State-Level Methane Emissions from Enteric Fermentation in 1990, 

1995, 2000-2008 

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

State Tg CO2 eq.

Alabama 1.98 2.21 1.85 1.72 1.74 1.81 1.73 1.69 1.62 1.63 1.57

Alaska 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Arizona 1.30 1.35 1.33 1.38 1.41 1.37 1.43 1.54 1.61 1.67 1.71

Arkansas 2.19 2.46 2.26 2.23 2.26 2.33 2.41 2.34 2.19 2.22 2.29

California 6.99 7.27 7.98 8.14 8.37 8.02 8.36 8.62 8.69 9.12 9.10

Colorado 3.48 3.96 4.03 4.03 3.98 3.54 3.12 3.39 3.56 3.74 3.79

Connecticut 0.14 0.13 0.12 0.11 0.11 0.09 0.09 0.09 0.09 0.09 0.09

Delaware 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.03

Florida 2.82 3.04 2.70 2.65 2.61 2.56 2.60 2.57 2.53 2.61 2.55

Georgia 1.77 1.96 1.71 1.67 1.64 1.70 1.69 1.66 1.63 1.62 1.55

Hawaii 0.27 0.25 0.24 0.22 0.22 0.23 0.23 0.23 0.24 0.24 0.23

Idaho 2.41 2.80 3.11 3.18 3.26 3.22 3.32 3.46 3.55 3.73 3.80

Illinois 2.17 2.13 1.89 1.81 1.78 1.70 1.67 1.72 1.72 1.71 1.63

Indiana 1.56 1.49 1.23 1.16 1.18 1.14 1.15 1.19 1.20 1.26 1.26

Iowa 5.22 5.16 4.76 4.61 4.56 4.62 4.47 4.59 4.82 5.06 5.11

Kansas 6.26 7.56 7.55 7.70 7.77 7.54 7.71 7.73 7.91 7.82 7.97

Kentucky 3.10 3.41 2.85 2.91 2.96 3.03 3.03 2.99 3.05 3.25 3.14

Louisiana 1.44 1.35 1.26 1.20 1.19 1.19 1.22 1.22 1.17 1.21 1.26

Maine 0.19 0.18 0.17 0.16 0.17 0.15 0.15 0.15 0.15 0.15 0.15

Maryland 0.49 0.49 0.41 0.38 0.39 0.36 0.36 0.36 0.36 0.34 0.32

Massachusetts 0.13 0.12 0.10 0.09 0.09 0.08 0.08 0.08 0.08 0.07 0.07

Michigan 1.68 1.70 1.51 1.48 1.48 1.46 1.52 1.54 1.60 1.69 1.71

Minnesota 3.50 3.54 3.30 3.24 3.16 3.01 3.03 3.02 3.00 3.12 3.12

Mississippi 1.67 1.71 1.41 1.42 1.43 1.39 1.36 1.40 1.26 1.24 1.25

Missouri 5.23 5.78 5.31 5.26 5.30 5.47 5.34 5.37 5.60 5.49 5.32

Montana 3.11 3.88 3.78 3.70 3.61 3.57 3.55 3.48 3.55 3.55 3.80

Nebraska 7.11 7.88 8.43 8.38 8.21 8.10 7.96 8.19 8.45 8.76 8.53

Nevada 0.75 0.78 0.77 0.79 0.77 0.78 0.78 0.77 0.77 0.76 0.74

New Hampshire 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

New Jersey 0.11 0.10 0.08 0.08 0.07 0.07 0.07 0.07 0.06 0.06 0.06

New Mexico 1.99 2.36 2.54 2.52 2.54 2.48 2.51 2.57 2.71 2.73 2.84

New York 2.79 2.64 2.70 2.59 2.63 2.48 2.54 2.57 2.59 2.70 2.74

North Carolina 1.20 1.43 1.25 1.26 1.25 1.18 1.17 1.16 1.12 1.13 1.08

North Dakota 2.21 2.69 2.52 2.61 2.61 2.51 2.41 2.41 2.40 2.49 2.42

Ohio 2.04 1.96 1.71 1.71 1.72 1.64 1.70 1.81 1.82 1.80 1.83

Oklahoma 5.71 6.37 5.80 5.68 5.82 6.15 5.81 6.02 6.24 6.15 6.17

Oregon 2.06 2.37 2.23 2.09 2.17 2.11 2.24 2.29 2.25 2.11 2.22

Pennsylvania 2.94 2.80 2.75 2.70 2.68 2.49 2.54 2.58 2.56 2.61 2.62

Rhode Island 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

South Carolina 0.72 0.68 0.61 0.59 0.56 0.56 0.57 0.58 0.55 0.54 0.53

South Dakota 4.21 5.07 4.92 5.14 5.07 4.84 4.82 4.92 4.96 4.92 4.85

Tennessee 2.85 3.17 2.68 2.71 2.76 2.88 2.79 2.76 2.83 2.92 2.77

Texas 16.34 19.44 17.66 17.49 17.58 17.85 17.62 17.60 18.16 18.08 17.95

Utah 1.21 1.37 1.38 1.38 1.38 1.32 1.34 1.34 1.28 1.34 1.39

Vermont 0.58 0.55 0.57 0.56 0.56 0.51 0.53 0.52 0.52 0.52 0.52

Virginia 2.17 2.24 2.05 2.11 2.13 2.08 2.03 2.12 2.17 2.12 2.09

Washington 2.16 2.17 2.09 2.02 1.98 1.86 1.93 1.85 1.91 1.98 1.94

West Virginia 0.60 0.65 0.54 0.52 0.54 0.52 0.50 0.53 0.54 0.56 0.54

Wisconsin 6.00 5.34 5.14 5.06 5.01 4.72 4.96 4.99 5.03 5.25 5.29

Wyoming 1.59 2.00 2.09 2.06 2.00 1.78 1.83 1.80 1.91 1.93 1.82

Total 126.58 138.16 131.50 130.63 130.80 128.66 128.37 130.00 132.14 134.23 133.86

Source: EPA 2010

Note: State-level emissions do not include data for non-cattle or bulls. Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 5

Appendix Table A-4 State-Level Methane Emissions from Enteric Fermentation by Livestock 

Category in 2008

Beef cattle Dairy cattle Total

State Tg C02 eq.

Alabama 1.522 0.047 1.57

Alaska 0.019 0.002 0.02

Arizona 1.053 0.656 1.71

Arkansas 2.233 0.052 2.29

California 2.390 6.706 9.10

Colorado 3.307 0.463 3.77

Connecticut 0.015 0.073 0.09

Delaware 0.012 0.022 0.03

Florida 2.116 0.436 2.55

Georgia 1.270 0.284 1.55

Hawaii 0.221 0.006 0.23

Idaho 1.740 2.048 3.79

Illinois 1.280 0.343 1.62

Indiana 0.706 0.549 1.25

Iowa 4.374 0.728 5.10

Kansas 7.519 0.409 7.93

Kentucky 2.817 0.325 3.14

Louisiana 1.182 0.077 1.26

Maine 0.032 0.121 0.15

Maryland 0.118 0.201 0.32

Massachusetts 0.021 0.053 0.07

Michigan 0.533 1.176 1.71

Minnesota 1.554 1.570 3.12

Mississippi 1.175 0.072 1.25

Missouri 4.990 0.331 5.32

Montana 3.735 0.060 3.79

Nebraska 8.303 0.176 8.48

Nevada 0.649 0.093 0.74

New Hampshire 0.014 0.055 0.07

New Jersey 0.024 0.035 0.06

New Mexico 1.638 1.207 2.85

New York 0.344 2.399 2.74

North Carolina 0.886 0.196 1.08

North Dakota 2.334 0.082 2.42

Ohio 0.971 0.855 1.83

Oklahoma 5.934 0.232 6.17

Oregon 1.797 0.417 2.21

Pennsylvania 0.571 2.046 2.62

Rhode Island 0.004 0.004 0.01

South Carolina 0.456 0.069 0.53

South Dakota 4.558 0.280 4.84

Tennessee 2.510 0.256 2.77

Texas 16.404 1.510 17.91

Utah 1.088 0.306 1.39

Vermont 0.032 0.492 0.52

Virginia 1.701 0.385 2.09

Washington 1.020 0.912 1.93

West Virginia 0.501 0.037 0.54

Wisconsin 1.076 4.208 5.28

Wyoming 1.793 0.025 1.82

Total 100.54 33.09 133.63

Source: EPA 2010. Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 6

Appendix Table A-5 Cattle Population Categories Used for 

Estimating Methane Emissions

Dairy Cattle Beef Cattle

Calves Calves

Heifer Replacements Heifer Replacements

Cows Heifer and Steer Stockers

Animals in Feedlots (Heifers and Steers)

Cows

Bulls

Source: EPA 2010

Appendix Table A-6 Dairy Lactation by Region1

California West

Northern 

Great Plains Southcentral Northeast Midwest Southeast

Year (kg * year)/cow

1990 8,372 66,559 42,812 22,736 77,236 52,303 52,743

1991 8,407 67,689 43,171 22,567 79,184 53,321 53,373

1992 8,492 70,688 44,561 23,321 81,808 54,986 55,075

1993 8,551 70,754 44,780 23,648 81,325 55,167 56,636

1994 9,164 72,957 46,045 24,015 81,694 55,789 57,970

1995 8,878 72,463 46,522 24,001 83,709 57,073 58,719

1996 8,691 73,672 47,248 23,977 84,164 56,593 58,149

1997 8,994 74,496 47,655 23,971 86,676 58,160 59,389

1998 8,823 75,345 49,205 24,167 88,487 59,843 59,252

1999 9,426 75,633 50,450 24,450 89,617 60,676 60,901

2000 9,585 77,056 52,718 25,135 90,412 62,644 62,241

2001 9,482 76,278 52,854 25,002 92,829 61,693 63,078

2002 9,651 78,322 54,951 25,684 94,469 63,499 63,785

2003 9,522 77,600 55,450 26,275 93,256 65,910 62,099

2004 9,589 77,455 55,707 27,711 94,080 66,746 63,946

2005 9,709 78,956 57,794 28,155 95,091 68,757 65,091

2006 9,895 79,414 59,844 27,854 96,716 69,234 65,889

2007 10,179 80,809 60,320 27,459 96,675 69,393 68,011

2008 10,135 80,141 61,700 28,157 98,318 68,914 67,452

Source: USDA 2005d, 2004d, 2003d, 2002d, 2001d, 2000d, 1999a, 1995a.

1 Beef lactation data developed using methodology described in EPA 2010.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 7

Appendix Table A-7 Typical Livestock Weights 

Cattle Type lbs.

Beef Replacement Heifer

Replacement Weight, 15 Months 715

Replacement Weight, 24 Months 1,078

Mature Weight, 36 Months 1,172

Dairy Replacement Heifer

Replacement Weight, 15 months 800

Replacement Weight, 24 Months 1,225

Mature Weight, 36 Months 1,350

Stockers– Grazing/Forage Based Only

Steer Weight Gain/Month to 12 Months 45

Steer Weight Gain/Month to 24 Months 35

Heifer Weight Gain/Month to 12 Months 35

Heifer Weight Gain/Month to 24 Months 30

Source: Feedstuffs (1998), Western Dairyman (1998), Johnson (1999), 

NRC (1999), EPA 2010.

Appendix Table A-8 U.S. Feedlot Placement in 2008

Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Total

Weight Placed Number of animals placed, 1,000 head

< 600 lbs. 400 330 335 315 340 365 360 365 445 700 565 425 4,945

600 - 700 lbs. 467 385 330 278 350 325 315 410 415 615 630 490 5,010

700 - 800 lbs. 525 533 561 428 565 383 481 566 541 543 451 407 5,984

> 800 lbs. 395 475 510 515 645 445 500 720 880 580 370 325 6,360

Total 1,787 1,723 1,736 1,536 1,900 1,518 1,656 2,061 2,281 2,438 2,016 1,647 22,299

Source: USDA (2002f, 2001f, 2000f, 1999a, 1995a), EPA 2010.

Note: Totals may not sum due to independent rounding.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 8

Appendix Table A-9 Regional Estimates of Digestible Energy and Methane Conversion 

Rates for Enteric Fermentation in 2008

Animal Type Data California West Northern

Great 

Plains

Southcentral Northeast Midwest Southeast

Beef Repl. Heif. DE 1 65 59 66 64 65 65 64

Ym 2 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5%

Dairy Repl. Heif. DE 64 64 64 64 64 64 64

Ym 5.6% 5.6% 5.3% 6.0% 5.9% 5.3% 6.5%

Steer Stockers DE 65 59 66 64 65 65 64

Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5%

Heifer Stockers DE 65 59 66 64 65 65 64

Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5%

Steer Feedlot DE 83 83 83 83 83 83 83

Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9%

Heifer Feedlot DE 83 83 83 83 83 83 83

Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9%

Beef Cows DE 63 57 64 62 63 63 62

Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5%

Dairy Cows DE 67 67 67 67 67 67 67

Ym 5.5% 5.5% 5.2% 5.9% 5.8% 5.2% 6.4%

Steer Step-Up DE 72 72 72 72 72 72 72

Ym 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2%

Heifer Step-Up DE 72 72 72 72 72 72 72

Ym 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2%

Source: EPA 2010

1 (DE) Digestible energy; in units of percent gross energy (GE) in MJ/Day.

2 (Ym) Methane conversion rate is the fraction of gross energy (GE) in feed converted to methane.

Appendix Table A-10 Definition of Regions in the Enteric Fermentation 

Model 

Region & State(s)

California

Northern Great 

Plains Northeast Southeast West

California Colorado Connecticut Alabama Alaska

Midwest Kansas Delaware Florida Arizona

Illinois Montana Maine Georgia Hawaii

Indiana Nebraska Maryland Kentucky Idaho

Iowa North Dakota Massachusetts Mississippi Nevada

Michigan South Dakota New Hampshire North Carolina New Mexico

Minnesota Wyoming New Jersey South Carolina Oregon

Missouri South Central New York Tennessee Utah

Ohio Arkansas Pennsylvania Virginia Washington

Wisconsin Louisiana Rhode Island

Oklahoma Vermont

Texas West Virginia

Source: EPA 2010
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 9

Appendix Table A-11 Methane Emissions from Cattle Enteric Fermentation, 1990, 

1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Animal Type Gg CH4

Dairy 1,526 1,452 1,471 1,464 1,468 1,364 1,433 1,459 1,490 1,555 1,576

Cows 1,257 1,197 1,222 1,213 1,218 1,136 1,190 1,210 1,232 1,285 1,302

Replacements 7-11 months 58 55 55 55 55 49 54 56 56 60 60

Replacements 12-23 months 211 199 194 196 195 179 189 194 201 211 214

Beef 4,502 5,128 4,790 4,757 4,761 4,762 4,680 4,731 4,803 4,837 4,799

Bulls 114 126 122 121 119 119 117 117 120 117 117

Cows 2,887 3,223 3,059 3,041 3,022 3,056 3,037 3,056 3,079 3,083 3,065

Replacements 7-11 months 69 85 74 74 75 76 77 80 82 81 79

Replacements 12-23 months 188 241 204 207 207 214 211 217 228 228 220

Steer Stockers 563 661 508 506 516 484 464 472 475 478 473

Heifer Stockers 305 374 322 320 322 304 291 298 298 294 288

Total Feedlot Cattle 375 416 502 488 499 508 482 489 521 556 557

Total 6,028 6,579 6,262 6,220 6,229 6,127 6,113 6,190 6,292 6,392 6,374

Source: EPA 2010.

Note: Totals may not sum due to independent rounding. Gg CH4 is gigagrams methane.

Appendix Table A-12 IPCC 

Emission Factors for Livestock

Emission Factors

Animal Type (kg CH4/head/year)

Dairy 128

Other Cattle 53

Calves 0

Swine 1.5

Sheep 8

Goats 5

Horses 18

Source: EPA 2010, IPCC 2006.

IPCC is the Intergovernmental Panel on Climate 

Change; kg CH4 is kilograms methane.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 10

Appendix Table A-13 Summary of 

Greenhouse Gas Emissions from Managed1

Waste by State in 2008

CH4 N2O Total

State Tg C02 eq.

Alabama 0.40 0.16 0.55

Alaska 0.01 0.00 0.01

Arizona 1.00 0.29 1.29

Arkansas 0.29 0.21 0.50

California 7.18 1.51 8.68

Colorado 0.76 0.72 1.48

Connecticut 0.03 0.02 0.06

Delaware 0.03 0.03 0.06

Florida 0.54 0.08 0.62

Georgia 0.66 0.23 0.89

Hawaii 0.02 0.00 0.03

Idaho 1.94 0.57 2.52

Illinois 1.19 0.30 1.49

Indiana 1.04 0.32 1.35

Iowa 6.69 1.41 8.10

Kansas 0.94 1.48 2.42

Kentucky 0.27 0.10 0.37

Louisiana 0.11 0.03 0.14

Maine 0.04 0.02 0.06

Maryland 0.08 0.07 0.15

Massachusetts 0.01 0.01 0.02

Michigan 0.84 0.38 1.22

Minnesota 2.10 0.78 2.88

Mississippi 0.45 0.13 0.58

Missouri 0.94 0.24 1.18

Montana 0.15 0.06 0.22

Nebraska 1.03 1.50 2.53

Nevada 0.12 0.03 0.15

New Hampshire 0.01 0.01 0.02

New Jersey 0.02 0.01 0.03

New Mexico 1.31 0.28 1.59

New York 0.53 0.31 0.83

North Carolina 4.45 0.42 4.87

North Dakota 0.11 0.07 0.18

Ohio 0.76 0.37 1.13

Oklahoma 1.41 0.34 1.75

Oregon 0.32 0.15 0.47

Pennsylvania 0.60 0.35 0.95

Rhode Island 0.00 0.00 0.01

South Carolina 0.28 0.06 0.34

South Dakota 0.53 0.34 0.88

Tennessee 0.20 0.07 0.27

Texas 2.65 1.95 4.61

Utah 0.46 0.12 0.58

Vermont 0.10 0.05 0.15

Virginia 0.27 0.12 0.39

Washington 0.72 0.26 0.98

West Virginia 0.04 0.03 0.07

Wisconsin 1.32 1.03 2.35

Wyoming 0.07 0.07 0.14

Total 45.02 17.11 62.13

Source: EPA 2010. Tg CO2 eq. is teragrams carbon dioxide 

equivalent. CH4 is methane. N2O is nitrous oxide. 1

Methane totals include emissions from grazed land manure.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 11

Appendix Table A-14 Methane Emissions from Manure Management by State and Animal in 2008

Dairy cattle Beef cattle Poultry Swine Goats Horses Sheep Total

State Tg CO2 eq.

Alabama 0.0441 0.0148 0.0004 0.0193 0.2546 0.0002 0.0637 0.3971

Alaska 0.0003 0.0006 0.0000 0.0004 0.0043 0.0001 0.0001 0.0058

Arizona 0.0406 0.8717 0.0003 0.0138 0.0142 0.0025 0.0601 1.0032

Arkansas 0.0426 0.0102 0.0003 0.0213 0.1080 0.0002 0.1095 0.2920

California 0.0982 6.8946 0.0008 0.0389 0.1061 0.0102 0.0278 7.1766

Colorado 0.0767 0.4251 0.0001 0.0211 0.0658 0.0046 0.1637 0.7571

Connecticut 0.0004 0.0165 0.0000 0.0019 0.0122 0.0001 0.0003 0.0314

Delaware 0.0002 0.0061 0.0000 0.0007 0.0183 0.0001 0.0024 0.0280

Florida 0.0599 0.2984 0.0003 0.0295 0.1539 0.0002 0.0024 0.5445

Georgia 0.0370 0.1050 0.0005 0.0218 0.4080 0.0002 0.0865 0.6590

Hawaii 0.0069 0.0077 0.0000 0.0014 0.0033 0.0002 0.0045 0.0239

Idaho 0.0411 1.8665 0.0001 0.0164 0.0127 0.0000 0.0037 1.9405

Illinois 0.0274 0.1198 0.0001 0.0117 0.0067 0.0007 1.0216 1.1880

Indiana 0.0161 0.1675 0.0001 0.0193 0.0312 0.0006 0.8007 1.0354

Iowa 0.1050 0.2629 0.0001 0.0152 0.0465 0.0025 6.2592 6.6913

Kansas 0.1856 0.2655 0.0001 0.0133 0.0014 0.0010 0.4693 0.9362

Kentucky 0.0541 0.0285 0.0004 0.0294 0.0334 0.0004 0.1279 0.2740

Louisiana 0.0331 0.0170 0.0001 0.0141 0.0455 0.0002 0.0021 0.1121

Maine 0.0007 0.0232 0.0000 0.0025 0.0088 0.0001 0.0003 0.0356

Maryland 0.0026 0.0440 0.0001 0.0051 0.0253 0.0003 0.0068 0.0842

Massachusetts 0.0005 0.0087 0.0000 0.0030 0.0008 0.0001 0.0017 0.0148

Michigan 0.0142 0.5704 0.0001 0.0206 0.0180 0.0009 0.2134 0.8376

Minnesota 0.0378 0.4754 0.0001 0.0182 0.0391 0.0016 1.5277 2.0999

Mississippi 0.0339 0.0161 0.0002 0.0197 0.2256 0.0002 0.1533 0.4490

Missouri 0.0956 0.0842 0.0003 0.0278 0.0235 0.0009 0.7062 0.9385

Montana 0.0649 0.0292 0.0000 0.0186 0.0074 0.0030 0.0318 0.1548

Nebraska 0.1921 0.0938 0.0001 0.0116 0.0128 0.0009 0.7177 1.0290

Nevada 0.0133 0.1004 0.0000 0.0032 0.0010 0.0008 0.0008 0.1195

New Hampshire 0.0003 0.0110 0.0000 0.0016 0.0011 0.0001 0.0003 0.0144

New Jersey 0.0005 0.0062 0.0000 0.0053 0.0035 0.0001 0.0015 0.0172

New Mexico 0.0360 1.2519 0.0001 0.0092 0.0129 0.0014 0.0001 1.3115

New York 0.0089 0.4692 0.0002 0.0148 0.0161 0.0007 0.0169 0.5269

North Carolina 0.0177 0.0316 0.0005 0.0189 0.2986 0.0004 4.0787 4.4465

North Dakota 0.0420 0.0234 0.0000 0.0085 0.0014 0.0010 0.0305 0.1068

Ohio 0.0226 0.2818 0.0002 0.0264 0.0254 0.0014 0.4019 0.7597

Oklahoma 0.1211 0.1785 0.0004 0.0295 0.0950 0.0009 0.9873 1.4128

Oregon 0.0384 0.2330 0.0002 0.0182 0.0246 0.0024 0.0021 0.3187

Pennsylvania 0.0151 0.2724 0.0002 0.0223 0.0316 0.0011 0.2588 0.6015

Rhode Island 0.0001 0.0006 0.0000 0.0004 0.0020 0.0001 0.0002 0.0033

South Carolina 0.0135 0.0181 0.0003 0.0120 0.1252 0.0002 0.1066 0.2759

South Dakota 0.0874 0.1153 0.0000 0.0137 0.0057 0.0037 0.3083 0.5341

Tennessee 0.0482 0.0294 0.0006 0.0292 0.0190 0.0003 0.0738 0.2006

Texas 0.5353 1.4399 0.0094 0.1098 0.1372 0.0157 0.4053 2.6527

Utah 0.0228 0.2497 0.0000 0.0121 0.0587 0.0031 0.1124 0.4587

Vermont 0.0010 0.0919 0.0000 0.0022 0.0010 0.0001 0.0001 0.0963

Virginia 0.0335 0.0475 0.0002 0.0160 0.0352 0.0009 0.1367 0.2699

Washington 0.0250 0.6460 0.0001 0.0149 0.0280 0.0006 0.0034 0.7180

West Virginia 0.0096 0.0074 0.0001 0.0063 0.0110 0.0004 0.0010 0.0357

Wisconsin 0.0293 1.1895 0.0002 0.0200 0.0101 0.0010 0.0733 1.3234

Wyoming 0.0324 0.0113 0.0000 0.0124 0.0007 0.0046 0.0119 0.0734

Total 2.4656 19.4292 0.0177 0.8232 2.6325 0.0726 19.5762 45.0170

Source: EPA 2010. Tg CO2 eq. is teragrams carbon dioxide equivalent.

Managed manure includes emissions from grazed lands.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 12

Appendix Table A-15 Nitrous Oxide Emissions from Manure 

Management by State and Animal in 2008

Dairy cattle Beef cattle Poultry Swine Total

State Tg CO2 eq.

Alabama 0.0032 0.0019 0.1395 0.0039 0.1484

Alaska 0.0003 0.0000 0.0018 0.0000 0.0022

Arizona 0.0937 0.1811 0.0019 0.0035 0.2802

Arkansas 0.0032 0.0015 0.1860 0.0075 0.1981

California 1.1314 0.2832 0.0512 0.0018 1.4676

Colorado 0.0997 0.5705 0.0062 0.0201 0.6965

Connecticut 0.0084 0.0001 0.0141 0.0000 0.0226

Delaware 0.0024 0.0001 0.0295 0.0002 0.0322

Florida 0.0411 0.0012 0.0219 0.0002 0.0643

Georgia 0.0189 0.0019 0.1914 0.0055 0.2177

Hawaii 0.0014 0.0003 0.0014 0.0003 0.0034

Idaho 0.4286 0.1248 0.0019 0.0005 0.5559

Illinois 0.0768 0.0900 0.0068 0.1117 0.2854

Indiana 0.1023 0.0553 0.0555 0.0887 0.3019

Iowa 0.1492 0.6690 0.0834 0.4775 1.3791

Kansas 0.0944 1.3219 0.0018 0.0490 1.4671

Kentucky 0.0175 0.0081 0.0426 0.0089 0.0771

Louisiana 0.0040 0.0010 0.0134 0.0002 0.0185

Maine 0.0059 0.0001 0.0059 0.0000 0.0120

Maryland 0.0208 0.0047 0.0377 0.0007 0.0638

Massachusetts 0.0059 0.0001 0.0011 0.0002 0.0073

Michigan 0.2315 0.0860 0.0221 0.0254 0.3650

Minnesota 0.3451 0.1594 0.0694 0.1803 0.7541

Mississippi 0.0037 0.0018 0.1085 0.0089 0.1229

Missouri 0.0702 0.0337 0.0415 0.0743 0.2198

Montana 0.0148 0.0216 0.0015 0.0044 0.0423

Nebraska 0.0347 1.3581 0.0131 0.0826 1.4884

Nevada 0.0170 0.0036 0.0018 0.0001 0.0224

New Hampshire 0.0056 0.0000 0.0013 0.0000 0.0070

New Jersey 0.0038 0.0001 0.0026 0.0002 0.0066

New Mexico 0.1899 0.0778 0.0019 0.0000 0.2696

New York 0.2593 0.0142 0.0165 0.0021 0.2920

North Carolina 0.0118 0.0016 0.1548 0.2407 0.4089

North Dakota 0.0184 0.0342 0.0018 0.0040 0.0584

Ohio 0.1569 0.0951 0.0455 0.0442 0.3417

Oklahoma 0.0438 0.0204 0.0335 0.0606 0.1584

Oregon 0.0743 0.0416 0.0138 0.0003 0.1300

Pennsylvania 0.2021 0.0380 0.0559 0.0283 0.3243

Rhode Island 0.0004 0.0000 0.0018 0.0000 0.0022

South Carolina 0.0038 0.0006 0.0473 0.0065 0.0582

South Dakota 0.0554 0.2032 0.0078 0.0359 0.3024

Tennessee 0.0161 0.0023 0.0265 0.0050 0.0500

Texas 0.2600 1.4839 0.0992 0.0265 1.8697

Utah 0.0612 0.0170 0.0087 0.0192 0.1062

Vermont 0.0502 0.0002 0.0012 0.0000 0.0517

Virginia 0.0213 0.0152 0.0519 0.0088 0.0972

Washington 0.1454 0.0860 0.0176 0.0004 0.2494

West Virginia 0.0037 0.0030 0.0155 0.0001 0.0224

Wisconsin 0.8598 0.1247 0.0128 0.0087 1.0060

Wyoming 0.0054 0.0371 0.0010 0.0027 0.0462

Total 5.475 7.278 1.772 1.650 16.1746

Source: EPA 2010. 

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 13

Appendix Table A-16 Waste Characteristics Data

Average 

TAM1 Source

Nitrogen, 

Nex2 Source

Max Methane 

Generation 

Potential Source

Volatile 

Solids 

(VS) Source

Livestock kg kg/day per 1,000 kg mass

Bo (m3 CH4/kg VS added) kg/day per 1,000 kg mass

Dairy Cows 604 Safley 2000 0.44

USDA 

1996 0.24 Morris 1976 8.8

Lieberman and 

Pape, 2005

Dairy Heifers 476 Safley 2000 0.31

USDA 

1996 0.17

Bryant et. al. 

1976 6.7

Lieberman and 

Pape, 2005

Feedlot Steers 420 USDA 1996 0.3

USDA 

1996 0.33 Hashimoto 1981 3.86

Lieberman and 

Pape, 2005

Feedlot Heifers 420 USDA 1996 0.3

USDA 

1996 0.33 Hashimoto 1981 3.98

Lieberman and 

Pape, 2005

Bulls NOF3 750 Safley 2000 0.31

USDA 

1996 0.17 Hashimoto 1981 6.04 USDA 1996

Calves NOF 118 ERG 2003 0.3

USDA 

1996 0.17 Hashimoto 1981 6.41 USDA 1996

Heifers NOF 420 USDA 1996 0.31

USDA 

1996 0.17 Hashimoto 1981 7.09

Lieberman and 

Pape, 2005

Steers NOF 318 Safley 2000 0.31

USDA 

1996 0.17 Hashimoto 1981 7.93

Lieberman and 

Pape, 2005

Cows NOF 533 NRC 2000 0.33

USDA 

1996 0.17 Hashimoto 1981 6.97

Lieberman and 

Pape, 2005

Market Swine 

<60 lbs. 16 Safley 2000 0.6

USDA 

1996 0.48 Hashimoto 1984 8.8 USDA 1996

Market Swine 

60-119 lbs. 41 Safley 2000 0.42

USDA 

1996 0.48 Hashimoto 1984 5.4 USDA 1996

Market Swine 

120-179 lbs. 68 Safley 2000 0.42

USDA 

1996 0.48 Hashimoto 1984 5.4 USDA 1996

Market Swine 

>180 lbs. 91 Safley 2000 0.42

USDA 

1996 0.48 Hashimoto 1984 5.4 USDA 1996

Breeding Swine 198 Safley 2000 0.24

USDA 

1996 0.48 Hashimoto 1984 2.6 USDA 1996

Feedlot Sheep 25 EPA 1992 0.42

ASAE 

1999 0.36 EPA 1992 9.2 EPA 1992

Sheep NOF 80 EPA 1992 0.42

ASAE 

1999 0.19 EPA 1992 9.2 EPA 1992

Goats 64 ASAE 1999 0.45

ASAE 

1999 0.17 EPA 1992 9.5 EPA 1992

Horses 450 ASAE 1999 0.3

ASAE 

1999 0.33 EPA 1992 10 EPA 1992

Hens ≥ 1 yr 1.8 ASAE 1999 0.83

USDA 

1996 0.39 Hill 1982 10.8 USDA 1996

Pullets 1.8 ASAE 1999 0.62

USDA 

1996 0.39 Hill 1982 9.7 USDA 1996

Other Chickens 1.8 ASAE 1999 0.83

USDA 

1996 0.39 Hill 1982 10.8 USDA 1996

Broilers 0.9 ASAE 1999 1.1

USDA 

1996 0.36 Hill 1984 15 USDA 1996

Turkeys 6.8 ASAE 1999 0.74

USDA 

1996 0.36 Hill 1984 9.7 USDA 1996

Source: EPA 2010. Bo is maximum methane producing capacity for domestic waste water. CH4 is methane. 1

(TAM) Typical animal mass.

2

Nitrogen excretion source. 3

(NOF) Not on feed.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 14

Appendix Table A-17 State Volatile Solids Production Rates in 2008

Dairy Cow Dairy Heifer

Beef Cow 

NOF1

Beef Heifer 

NOF

Beef Steer 

NOF

Beef Heifer 

OF1

Beef Steer 

OF

State kg/day/1,000 kg mass

Alabama 8.40 8.35 7.02 7.81 8.22 4.74 4.27

Alaska 7.30 8.35 9.02 10.05 10.81 4.58 4.15

Arizona 10.37 8.35 9.02 10.34 10.81 4.27 3.91

Arkansas 7.59 8.35 7 7.86 8.19 4.35 3.98

California 10.02 8.35 6.85 7.95 8.00 4.33 3.96

Colorado 10.25 8.35 6.46 7.69 7.52 4.34 3.97

Connecticut 9.22 8.35 6.9 7.67 8.07 4.93 4.41

Delaware 8.63 8.35 6.9 7.72 8.07 4.64 4.19

Florida 8.90 8.35 7.02 7.75 8.22 4.58 4.15

Georgia 9.07 8.35 7.02 7.85 8.22 4.63 4.18

Hawaii 7.00 8.35 9.02 10.26 10.81 4.58 4.15

Idaho 10.11 8.35 9.02 10.82 10.81 4.42 4.03

Illinois 9.07 8.35 6.91 8.07 8.07 4.59 4.15

Indiana 9.38 8.35 6.91 7.98 8.07 4.35 3.98

Iowa 9.46 8.35 3.91 8.27 8.07 4.28 3.93

Kansas 9.63 8.35 6.46 7.75 7.52 4.35 3.97

Kentucky 7.89 8.35 7.02 7.91 8.22 4.65 4.20

Louisiana 7.39 8.35 7 7.73 8.19 4.48 4.07

Maine 8.99 8.35 6.9 7.76 8.07 4.47 4.07

Maryland 9.02 8.35 6.9 7.76 8.07 4.45 4.05

Massachusetts 8.63 8.35 6.9 7.74 8.07 4.58 4.15

Michigan 10.05 8.35 6.91 7.99 8.07 4.38 4.00

Minnesota 9.17 8.35 6.91 8.04 8.07 4.24 3.89

Mississippi 8.19 8.35 7.02 7.82 8.22 4.57 4.14

Missouri 8.02 8.35 6.91 7.85 8.07 4.49 4.08

Montana 9.03 8.35 6.46 7.17 7.52 4.69 4.23

Nebraska 9.09 8.35 6.46 7.71 7.52 4.35 3.98

Nevada 9.65 8.35 9.02 10.49 10.81 4.48 4.07

New Hampshire 9.44 8.35 6.9 7.74 8.07 4.30 3.94

New Jersey 8.51 8.35 6.9 7.89 8.07 4.36 3.98

New Mexico 10.34 8.35 9.02 10.56 10.81 4.22 3.88

New York 9.42 8.35 6.9 8.02 8.07 4.05 3.75

North Carolina 9.38 8.35 7.02 7.83 8.22 4.65 4.20

North Dakota 8.40 8.35 6.46 7.43 7.52 4.22 3.88

Ohio 9.01 8.35 6.91 7.93 8.07 4.33 3.96

Oklahoma 8.58 8.35 7 8.08 8.19 4.35 3.98

Oregon 9.40 8.35 9.02 10.54 10.81 4.46 4.06

Pennsylvania 9.26 8.35 6.9 8.00 8.07 4.35 3.98

Rhode Island 8.94 8.35 6.9 7.60 8.07 4.87 4.36

South Carolina 9.05 8.35 7.02 7.81 8.22 4.58 4.15

South Dakota 9.45 8.35 6.46 7.50 7.52 4.39 4.01

Tennessee 8.60 8.35 7.02 7.86 8.22 5.02 4.48

Texas 9.51 8.35 7 8.21 8.19 4.32 3.95

Utah 9.70 8.35 9.02 10.51 10.81 4.22 3.88

Vermont 9.03 8.35 6.9 7.89 8.07 4.52 4.10

Virginia 9.02 8.35 7.02 7.87 8.22 4.35 3.98

Washington 10.36 8.35 9.02 10.77 10.81 4.47 4.07

West Virginia 8.13 8.35 6.9 7.74 8.07 5.25 4.65

Wisconsin 9.34 8.35 6.91 7.87 8.07 4.31 3.95

Wyoming 9.29 8.35 6.46 7.30 7.52 4.61 4.17

Source: EPA 2010.

1

(NOF) Not on feed or (OF) On feed.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 15

Appendix Table A-18 State-Based Methane Conversion Factors1

for Liquid Waste Management Systems in 2008

Liquid/Slurry and Deep Pit Anaerobic Lagoon

State percent

Alabama 0.38 0.75

Alaska 0.14 0.46

Arizona 0.43 0.76

Arkansas 0.35 0.74

California 0.36 0.75

Colorado 0.22 0.65

Connecticut 0.24 0.68

Delaware 0.31 0.73

Florida 0.51 0.76

Georgia 0.37 0.75

Hawaii 0.58 0.76

Idaho 0.22 0.65

Illinois 0.28 0.71

Indiana 0.27 0.70

Iowa 0.25 0.68

Kansas 0.30 0.73

Kentucky 0.31 0.73

Louisiana 0.46 0.76

Maine 0.20 0.62

Maryland 0.30 0.72

Massachusetts 0.24 0.67

Michigan 0.24 0.67

Minnesota 0.23 0.67

Mississippi 0.24 0.75

Missouri 0.30 0.72

Montana 0.20 0.61

Nebraska 0.26 0.70

Nevada 0.26 0.71

New Hampshire 0.21 0.64

New Jersey 0.29 0.71

New Mexico 0.28 0.70

New York 0.23 0.65

North Carolina 0.34 0.73

North Dakota 0.21 0.64

Ohio 0.27 0.70

Oklahoma 0.36 0.74

Oregon 0.21 0.64

Pennsylvania 0.26 0.69

Rhode Island 0.26 0.69

South Carolina 0.39 0.75

South Dakota 0.24 0.68

Tennessee 0.33 0.73

Texas 0.43 0.76

Utah 0.22 0.66

Vermont 0.21 0.63

Virginia 0.30 0.72

Washington 0.21 0.64

West Virginia 0.26 0.69

Wisconsin 0.23 0.66

Wyoming 0.20 0.62

Source: EPA 2010, IPCC 2006. 

1

(MCF) Methane conversion factors represent weighted average of multiple animal types.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 16

Appendix Table A-19 Additional Methane and 

Nitrous Oxide Emission Factors

Methane Nitrous Oxide

Manure Management System

Pasture 0.015 0.02

Daily spread 0.005 0

Solid storage 0.04 0.005

Dry lot 0.015 0.02

Poultry with bedding 0.015 0.001

Poultry without bedding 0.015 0.001

Liquid/Slurry 0.25 0.005

Aerobic treatments 0 0.005

Source: IPCC 2006.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 17

Appendix Table A-20 State-Weighted Methane Conversion Factors for Livestock Waste 

Emissions in 20081

Beef 

Feed 

Lot 

Heifer

Beef 

Feed 

Lot 

Steer

Dairy 

Cow

Dairy 

Heifer

Swine 

Market

Swine 

Breeding Layer Broiler Turkey Sheep Goats Horses

State percent

Alabama 0.01 2.09 0.69 0.02 2.49 0.54 8.69 3.41 0.03 0.01 0.02 0.92

Alaska 0.00 0.02 0.03 0.00 0.00 0.00 0.18 + 0.03 0.01 0.00 0.02

Arizona 0.75 1.19 41.37 0.14 2.37 0.49 0.65 + 0.03 0.12 0.01 0.66

Arkansas 0.01 2.02 0.47 0.02 2.39 2.83 0.54 3.72 0.89 0.01 0.01 1.01

California 1.39 3.29 325.90 2.41 1.03 0.29 4.32 0.27 0.46 0.48 0.04 1.85

Colorado 1.51 2.14 20.12 0.12 5.15 2.65 3.11 + 0.03 0.22 0.00 1.00

Connecticut 0.00 0.02 0.77 0.02 0.01 0.01 0.28 0.27 0.03 0.00 0.00 0.09

Delaware 0.00 0.01 0.29 0.01 0.07 0.05 0.07 0.78 0.03 0.01 0.00 0.03

Florida 0.01 2.84 14.10 0.12 0.06 0.05 7.10 0.20 0.03 0.01 0.02 1.40

Georgia 0.01 1.75 4.93 0.07 2.99 1.13 14.89 4.52 0.03 0.01 0.03 1.04

Hawaii 0.00 0.33 0.36 0.00 0.14 0.08 0.13 + 0.03 0.01 0.00 0.06

Idaho 0.33 1.63 88.35 0.53 0.12 0.06 0.58 + 0.03 0.12 0.00 0.78

Illinois 0.25 1.05 5.70 0.11 39.37 9.28 0.29 + 0.03 0.03 0.00 0.56

Indiana 0.16 0.61 7.83 0.14 32.66 5.47 0.80 0.27 0.41 0.03 0.01 0.92

Iowa 1.87 3.13 12.32 0.20 269.44 28.62 1.68 0.27 0.26 0.12 0.00 0.72

Kansas 3.79 5.05 12.51 0.13 18.95 3.39 0.04 + 0.03 0.05 0.01 0.63

Kentucky 0.03 2.55 1.26 0.09 4.96 1.13 0.59 0.98 0.03 0.02 0.02 1.40

Louisiana 0.01 1.57 0.78 0.03 0.07 0.03 1.86 0.27 0.03 0.01 0.01 0.67

Maine 0.00 0.03 1.07 0.03 0.01 0.01 0.39 + 0.03 0.00 0.00 0.12

Maryland 0.01 0.11 2.05 0.05 0.25 0.07 0.23 0.95 0.03 0.01 0.00 0.24

Massachusetts 0.00 0.02 0.40 0.02 0.06 0.02 0.01 + 0.03 0.00 0.00 0.15

Michigan 0.24 0.44 26.91 0.25 8.39 1.77 0.56 0.27 0.03 0.04 0.01 0.98

Minnesota 0.44 1.36 22.15 0.49 62.43 10.32 0.35 0.14 1.37 0.08 0.00 0.87

Mississippi 0.01 1.60 0.75 0.02 6.06 1.24 8.02 2.69 0.03 0.01 0.01 0.94

Missouri 0.10 4.46 3.90 0.11 26.17 7.46 0.25 0.27 0.60 0.04 0.01 1.32

Montana 0.06 3.03 1.37 0.02 1.19 0.32 0.33 + 0.03 0.14 0.00 0.88

Nebraska 3.79 5.36 4.43 0.04 27.49 6.69 0.56 0.02 0.03 0.04 0.00 0.55

Nevada 0.01 0.62 4.77 0.02 0.03 0.01 0.02 + 0.03 0.04 0.00 0.15

New Hampshire 0.00 0.01 0.51 0.01 0.01 0.00 0.03 + 0.03 0.00 0.00 0.07

New Jersey 0.00 0.02 0.28 0.01 0.06 0.02 0.14 + 0.03 0.01 0.00 0.25

New Mexico 0.21 1.50 59.40 0.21 0.00 0.00 0.59 + 0.03 0.07 0.00 0.44

New York 0.04 0.38 21.68 0.66 0.62 0.18 0.47 0.27 0.03 0.03 0.01 0.70

North Carolina 0.01 0.84 1.46 0.05 159.74 34.48 10.52 2.55 1.15 0.02 0.03 0.90

North Dakota 0.09 1.91 1.09 0.03 0.85 0.60 0.04 + 0.03 0.05 0.00 0.41

Ohio 0.27 0.81 13.22 0.20 16.11 3.03 0.85 0.18 0.17 0.07 0.01 1.26

Oklahoma 0.49 5.27 8.42 0.08 33.71 13.30 3.74 0.76 0.03 0.04 0.02 1.41

Oregon 0.13 1.70 10.97 0.13 0.07 0.03 0.87 0.27 0.03 0.11 0.01 0.87

Pennsylvania 0.12 0.60 12.43 0.54 10.61 1.72 0.66 0.51 0.33 0.05 0.01 1.06

Rhode Island + 0.00 0.03 0.00 0.01 0.00 0.07 + 0.03 0.00 0.00 0.02

South Carolina 0.00 0.64 0.84 0.02 4.50 0.57 4.84 0.76 0.36 0.01 0.02 0.57

South Dakota 0.56 3.60 5.43 0.06 11.93 2.75 0.14 + 0.13 0.18 0.00 0.65

Tennessee 0.01 2.29 1.31 0.09 2.98 0.54 0.24 0.64 0.03 0.02 0.03 1.39

Texas 5.95 19.54 68.09 0.48 16.29 3.01 4.45 2.05 0.03 0.75 0.45 5.23

Utah 0.05 1.04 11.82 0.07 4.07 1.28 2.68 + 0.12 0.15 0.00 0.57

Vermont 0.00 0.05 4.27 0.11 0.00 0.00 0.02 + 0.03 0.00 0.00 0.11

Virginia 0.05 1.55 2.17 0.09 5.59 0.92 0.36 0.80 0.52 0.04 0.01 0.76

Washington 0.27 0.92 30.56 0.21 0.12 0.04 1.04 0.27 0.03 0.03 0.01 0.71

West Virginia 0.01 0.45 0.35 0.01 0.03 0.01 0.14 0.27 0.11 0.02 0.00 0.30

Wisconsin 0.34 1.05 55.46 1.18 2.80 0.69 0.29 0.17 0.03 0.05 0.01 0.95

Wyoming 0.10 1.45 0.53 0.01 0.29 0.28 0.01 + 0.03 0.22 0.00 0.59

Source: EPA 2010

1

(MCFs) Methane conversion factors are weighted by the distribution of waste management systems for each animal type within a state.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page A - 18

Appendix Table A-21 Nitrogen 

in Livestock Waste on Grazed 

Lands

Year

Teragrams 

Nitrogen

1990 3.9

1991 3.9

1992 4.0

1993 4.0

1994 4.0

1995 4.1

1996 4.0

1997 3.9

1998 3.8

1999 3.8

2000 3.7

2001 3.7

2002 3.7

2003 3.7

2004 3.7

2005 3.8

2006 3.8

2007 3.8

2008 3.7

Source: EPA 2010
Appendix B: Crop Emissions

U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 1

Appendix B

B-1 Rice Harvested Area, 1990, 1995, 2000-2008

B-2 Total U.S. Production of Crops Managed with Burning, 1990, 1995, 2000-2008

B-3 State Production of Crops Managed with Burning in 2008

B-4 Information Used in Estimating Methane and Nitrous Oxide Emissions from Crop 

Residue Burning in 2008; (a) Crop Assumptions and Coefficients; (b) Emissions 

Factors and Global Warming Potentials; (c) Rice Area Burned by State

B-5 Soil Carbon Stocks by Climate Region and U.S. Soil Groupings

B-6 Stock Change Factors for the U.S. and IPCC Default Values for Impacts on Mineral 

Soils

B-7 Cultivated Histosol (Organic Soils) Area

B-8 Carbon Loss Rates from Organic Soils under Agricultural Management in the U.S.

B-9 State-Level Estimates of Annual Soil Carbon Stock Changes by Major Land Use and 

Management Type, 2008

B-10 State-Level Estimates of Soil Carbon Changes on Cropland by Major Activity, 2008
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 2

Appendix Table B-1 Rice Harvested Area, 1990, 1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

State and Crop 1,000 Hectares

Arkansas 486 542 571 656 608 589 629 662 567 536 565

 Primary 486 542 571 656 608 589 629 662 567 536 565

 Ratoon 0 0 0 0 0 0 0 1 0 0 0

California 160 188 222 191 214 205 239 213 212 216 209

Florida 7 15 11 7 8 5 7 5 6 8 7

 Primary 5 10 8 5 5 2 4 5 5 6 5

 Ratoon 2 5 3 3 3 2 3 0 1 2 2

Louisiana 287 276 329 281 283 294 280 264 279 303 269

 Primary 221 206 251 214 251 231 216 236 251 249 194

 Ratoon 66 69 78 66 32 64 65 28 28 54 75

Mississippi 101 117 88 102 102 95 95 106 76 76 93

Missouri 32 45 68 84 74 69 79 87 87 72 81

Texas 200 180 130 122 114 101 119 103 84 80 106

 Primary 143 129 87 87 83 73 88 81 61 59 70

 Ratoon 57 51 43 35 31 28 31 22 24 21 37

Total 1,273 1,363 1,418 1,443 1,403 1,358 1,448 1,440 1,310 1,291 1,330

Appendix Table B-2 Total U.S. Production of Crops Managed with Burning, 

1990, 1995, 2000-2008

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008

Crop Million Metric Tons

Wheat 74.3 59.4 60.6 53.0 43.7 63.8 58.7 57.3 49.3 55.8 68.0

Rice 7.1 7.9 8.7 9.8 9.6 9.1 10.6 10.2 8.8 9.0 9.3

Sugarcane 25.5 27.9 32.8 31.4 32.3 30.7 26.3 24.1 26.8 27.2 27.8

Corn 201.5 188.0 251.9 241.4 227.8 256.3 299.9 282.3 267.6 331.2 307.4

Barley 9.2 7.8 6.9 5.4 4.9 6.1 6.1 4.6 3.9 4.6 5.2

Soybeans 52.4 59.2 75.1 78.7 75.0 66.8 85.0 83.4 86.8 72.9 80.5

Peanuts 1.6 1.6 1.5 1.9 1.5 1.9 1.9 2.2 1.6 1.7 2.3

Total 371.7 351.8 437.5 422.0 394.8 434.6 488.6 464.1 444.8 502.3 500.6
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 3

Appendix Table B-3 Production of Crops Managed with Burning

Corn Soybeans Barley Wheat Peanuts Rice Sugarcane

Year 1,000 bushels 1,000 lbs. 1,000 cwt 1,000 tons

1990 7,933,894 2,063,480 361,876 2,924,713 3,603,593 156,830 25,525

1991 7,474,639 2,128,399 397,987 2,121,542 4,926,477 160,554 27,444

1992 9,476,538 2,346,768 390,071 2,642,953 4,284,347 180,876 27,545

1993 6,337,623 2,003,236 341,172 2,567,571 3,392,350 157,379 28,188

1994 10,050,350 2,694,457 321,305 2,486,723 4,247,377 199,104 28,057

1995 7,399,926 2,329,519 308,031 2,338,576 3,461,420 175,197 27,922

1996 9,232,401 2,550,250 336,365 2,440,017 3,661,133 172,767 26,729

1997 9,206,676 2,880,755 308,462 2,658,669 3,539,330 184,000 28,766

1998 9,758,520 2,936,751 301,340 2,729,227 3,963,382 185,505 31,486

1999 9,430,452 2,843,264 233,135 2,459,487 3,829,431 207,052 32,023

2000 9,914,883 2,954,747 272,399 2,387,274 3,265,454 191,903 32,762

2001 9,502,419 3,097,107 212,850 2,086,522 4,276,631 215,925 31,377

2002 8,966,635 2,952,965 194,488 1,720,555 3,320,988 211,672 32,253

2003 10,089,051 2,628,882 238,524 2,512,200 4,144,070 200,265 30,715

2004 11,806,886 3,346,750 239,775 2,312,366 4,288,117 232,920 26,320

2005 11,113,894 3,281,984 181,622 2,254,987 4,869,775 223,769 24,137

2006 10,534,690 3,415,921 154,425 1,941,434 3,464,198 194,292 26,820

2007 13,037,654 2,868,291 180,091 2,197,557 3,672,178 199,148 27,187

2008 12,101,033 3,170,490 205,280 2,678,016 5,147,817 204,409 27,842
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 4

Appendix Table B-4 Information Used in Estimating Methane and Nitrous Oxide 

Emissions from Crop Residue Burning in 2008

B-4(a) Crop Assumptions and Coefficients

Assumption/Coefficient Corn Peanuts Soybeans Barley Wheat Rice Sugarcane

Residue/Crop Ratio 1 1 2.1 1.2 1.3 1.4 0.19

Fraction Residue Burned 0.03 0.03 0.03 0.03 0.03 variable 0.95 

Fraction Dry Matter 0.91 0.86 0.87 0.93 0.93 0.91 0.62

Burning Efficiency 0.93 0.93 0.93 0.93 0.93 0.93 0.93 

Combustion Efficiency 0.88 0.88 0.88 0.88 0.88 0.88 0.88 

Fraction Carbon 0.4478 0.45 0.45 0.4485 0.4428 0.3806 0.4235

Fraction Nitrogen 0.0058 0.0106 0.023 0.0077 0.0062 0.0072 0.004

B-4(b) Emissions Factors and Global Warming 

Potentials

Greenhouse Gas

Factor & Global 

Warming Potential

Emissions Factor

Methane 0.005

Nitrous Oxide 0.007

Global Warming Potential

Methane 21

Nitrous Oxide 310

B-4(c) Rice Area Burned by State

State % Burned

Arkansas 20

California 11

Florida1 0

Louisiana 5

Mississippi 25

Missouri 3

Texas 0

1

Crop residue burning is illegal in Florida.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 5

Appendix Table B-5 Soil Carbon Stocks by Climate Region and U.S. Soil 

Groupings1

IPCC USDA CTD CTM WTD WTM STD STM

Inventory Soil Categories Taxonomic Soil Orders Metric Tons C/ha

High Clay Activity Mineral 

Soils

Vertisols, Mollisols, 

Inceptisols, Aridisols, & High 

Base Status Alfisols 42 65 37 51 42 57

Low Clay Activity Mineral 

Soils

Ultisols, Oxisols, Acidic 

Alfisols, & Many Entisols 45 52 25 40 39 47

Sandy Soils >70% Sand, <8% Clay

24 40 16 30 33 50

Volcanic Soils Andisols 124 114 124 124 124 128

Spodic Soils Spodosols 86 74 86 107 86 86

Aquic Soils Soils With Aquic Suborder 86 89 48 51 63 48

Organic Soils Histosols2 n/a n/a n/a n/a n/a n/a

IPCC is Intergovernmental Panel on Climate Change. C/ha is Carbon per hectare.

1

U.S. soil groupings are based on the IPCC Soil Inventory categories and the USDA taxonomic soil orders.

2

Carbon stocks are not needed for organic soils and are thus represented by n/a or "not applicable".

Note: Carbon stocks are for the top 30 cm of the soil profile, and were estimated from pedon data available in the NSSC 

database (NRCS 1997).

Climate regions: Cold temperate dry (CTD), cold temperate moist (CTM), warm temperate dry (WTD), warm temperate moist 

(WTM), subtropical temperate dry (STD), and subtropical temperate moist (STM).

Appendix Table B-6 Stock Change Factors for the U.S. and IPCC 

Default Values for Impacts on Mineral Soils

Factors

IPCC 

Default

Warm Moist 

Climate

Warm Dry 

Climate

Cool Moist 

Climate

Cool Dry 

Climate

Land Use Change

Cultivated1 1 1 1 1 1

General Uncultivated1 1.4 1.42 + 0.06 1.37 + 0.05 1.24 + 0.06 1.20 + 0.06

Set Aside 1.25 1.31 + 0.06 1.26 + 0.04 1.14 + 0.06 1.10 + 0.05

Improved Grassland

Medium Input 1.1 1.14 + 0.06 1.14 + 0.06 1.14 + 0.06 1.14 + 0.06

High Input n/a2 1.11 + 0.04 1.11 + 0.04 1.11 + 0.04 1.11 + 0.04

Wetland Rice Production 1.1 1.1 1.1 1.1 1.1

Tillage

Conventional Till 1 1 1 1 1

Reduced Till 1.05 1.08 + 0.03 1.09 + 0.09 1.08 + 0.03 1.01 + 0.03

No-till 1.1 1.13 + 0.02 1.17 + 0.08 1.13 + 0.02 1.05 + 0.03

Cropland Input

Low 0.9 0.94 + 0.01 0.94 + 0.01 0.94 + 0.01 0.94 + 0.01

Medium 1 1 1 1 1

High (without manure) 1.1 1.07 + 0.02 1.07 + 0.02 1.07 + 0.02 1.07 + 0.02

High (with manure) 1.2 1.38 + 0.06 1.34 + 0.08 1.38 + 0.06 1.34 + 0.08

IPCC is Intergovernmental Panel on Climate Change.

1

Factors in the IPCC report (2006) were converted to represent changes in soil organic content storage from a cultivated 

condition rather than a native condition.

2

n/a indicates "non applicable".
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 6

Appendix Table B-7 Cultivated Histosol 

(Organic Soils) Area

Temperate Sub-Tropical

Year 1,000 ha

1990 444 194

1991 444 194

1992 444 194

1993 450 196

1994 450 196

1995 450 196

1996 450 196

1997 450 196

1998 450 196

1999 450 196

2000 450 196

2001 450 196

2002 450 196

2003 450 196

2004 450 196

2005 450 196

2006 450 196

2007 450 196

2008 450 196

Appendix Table B-8 Carbon Loss Rates from Organic Soils 

Under Agricultural Management in the United States

Cropland Grassland1

Climate Regions Metric Tons C/ha-yr

CTD & CTM 11.2 ± 2.5 2.8 ± 0.51

WTD & WTM 14.0 ± 2.5 3.5 ± 0.81

STD & STM 14.0 ± 3.3 3.5 ± 0.81

C/ha-yr is carbon per hectare per year. 1

There is not enough data available to estimate values for C losses from grasslands. Estimates are 

25% of the values for cropland (the IPCC default organic soil C losses on grasslands).

Climate regions: Cold temperate dry (CTD), cold temperate moist (CTM), warm temperate dry 

(WTD), warm temperate moist (WTM), subtropical temperate dry (STD), and subtropical 

temperate moist (STM).
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 7

Appendix Table B-9 State-Level Estimates of Annual Soil Carbon Stock Changes by 

Major Land Use and Management Type, 2008

Net Change, 

Cropland 1 Net Change, Hay CRP

Ag. Land on Organic 

Soils Total 2 

State Tg CO2 eq.

Alabama (0.14) (0.15) (0.12) 0.00 (0.42)

Alaska ND ND ND ND 0.00 

Arizona 0.04 (0.01) 0.00 0.00 0.03 

Arkansas 0.43 (0.10) (0.06) 0.00 0.28 

California (0.14) 0.19 (0.02) 2.27 2.30 

Colorado 0.33 (0.25) (0.13) 0.00 (0.04)

Connecticut (0.02) (0.01) 0.00 0.00 (0.03)

Delaware 0.02 0.00 (0.00) 0.00 0.02 

Florida 0.55 0.10 (0.01) 10.00 10.64 

Georgia 0.14 (0.10) (0.02) 0.00 0.03 

Hawaii ND ND ND 0.29 0.29 

Idaho 0.29 (0.34) (0.52) 0.09 (0.49)

Illinois (2.71) (0.36) (0.77) 0.50 (3.34)

Indiana (0.53) (0.30) (0.18) 2.80 1.78 

Iowa (1.39) (0.57) (1.48) 0.73 (2.71)

Kansas (0.98) (0.64) (1.00) 0.00 (2.61)

Kentucky 0.12 (0.47) (0.12) 0.00 (0.47)

Louisiana (0.57) (0.08) (0.09) 0.00 (0.74)

Maine 0.03 (0.09) (0.03) 0.00 (0.09)

Maryland 0.03 (0.10) (0.00) 0.03 (0.04)

Massachusetts (0.00) (0.02) 0.00 0.03 0.01 

Michigan (0.33) (0.45) (0.12) 2.29 1.39 

Minnesota 0.45 (0.73) (0.81) 5.72 4.64 

Mississippi 0.10 (0.11) (0.28) 0.00 (0.29)

Missouri (0.47) (1.66) (1.50) 0.00 (3.63)

Montana (1.69) (1.59) (2.09) 0.03 (5.34)

Nebraska (0.50) (0.37) (0.51) 0.00 (1.39)

Nevada (0.02) (0.01) 0.00 0.00 (0.03)

New Hampshire 0.00 (0.03) 0.00 0.01 (0.02)

New Jersey 0.01 (0.09) 0.00 0.01 (0.07)

New Mexico 0.12 (0.08) (0.14) 0.00 (0.11)

New York 0.43 (1.03) (0.06) 0.54 (0.13)

North Carolina (0.04) (0.12) (0.03) 2.22 2.04 

North Dakota (2.48) (1.81) (1.73) 0.00 (6.03)

Ohio (1.05) (0.75) (0.26) 0.41 (1.65)

Oklahoma (0.55) (0.34) (0.22) 0.00 (1.10)

Oregon 0.23 (0.19) (0.27) 0.06 (0.16)

Pennsylvania 0.04 (1.03) (0.06) 0.00 (1.04)

Rhode Island 0.00 0.00 0.00 0.00 0.00 

South Carolina 0.08 (0.07) (0.03) 0.04 0.02 

South Dakota (1.15) (1.41) (0.84) 0.00 (3.40)

Tennessee (0.19) (0.45) (0.14) 0.00 (0.79)

Texas (0.10) 0.04 (0.75) 0.00 (0.80)

Utah (0.16) 0.17 (0.09) 0.00 (0.08)

Vermont 0.08 (0.15) 0.00 0.00 (0.06)

Virginia 0.04 (0.45) (0.01) 0.02 (0.40)

Washington 0.26 (0.50) (0.67) 0.08 (0.82)

West Virginia (0.03) (0.27) 0.00 0.00 (0.30)

Wisconsin 0.17 (0.45) (0.57) 2.15 1.30 

Wyoming 0.06 (0.19) (0.04) 0.01 (0.15)

Total (11.16) (17.41) (15.78) 30.35 (14.00) 1 Annual cropping systems on mineral soils (e.g., corn, soybean, cotton, and wheat).

2 Total does not include change in soil organic carbon storage on federal lands, including those that were previously under private 

ownership, and does not include carbon storage due to sewage sludge applications.

Tg CO2 eq. is teragrams carbon dioxide equivalent. ND= No data.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page B- 8

Appendix Table B-10 State-Level Estimates of Mineral Soil Carbon Changes on 

Cropland1 by Major Activity, 2008

Cropland Remaining Cropland Grassland Converted to Cropland

Irrigated Non-irrigated Irrigated Non-irrigated Net Total

State Tg CO2 eq.

Alabama (0.01) (0.54) 0.00 0.14 (0.40)

Alaska ND ND ND ND 0.00 

Arizona 0.02 (0.00) 0.00 0.00 0.02 

Arkansas 0.21 (0.06) 0.06 0.10 0.30 

California 0.09 (0.10) 0.12 (0.04) 0.07 

Colorado 0.06 (0.09) 0.00 (0.01) (0.04)

Connecticut 0.00 (0.03) 0.00 0.00 (0.03)

Delaware (0.01) 0.01 0.00 0.00 0.01 

Florida (0.01) (0.00) 0.14 0.51 0.64 

Georgia (0.03) (0.09) 0.00 0.17 0.06 

Hawaii ND ND ND ND 0.00 

Idaho 0.14 (0.73) 0.08 (0.06) (0.58)

Illinois (0.11) (3.76) 0.00 0.06 (3.81)

Indiana (0.03) (1.01) (0.00) 0.08 (0.96)

Iowa (0.03) (2.95) (0.01) (0.40) (3.38)

Kansas 0.31 (2.79) (0.01) (0.09) (2.59)

Kentucky (0.02) (0.44) 0.00 0.05 (0.40)

Louisiana (0.23) (0.55) 0.01 0.05 (0.72)

Maine 0.01 (0.11) 0.00 0.00 (0.10)

Maryland (0.01) (0.07) 0.00 0.02 (0.05)

Massachusetts (0.00) (0.02) 0.00 0.00 (0.03)

Michigan (0.00) (0.64) 0.00 (0.09) (0.73)

Minnesota 0.03 (0.84) (0.01) (0.13) (0.95)

Mississippi 0.05 (0.57) 0.00 0.23 (0.29)

Missouri (0.02) (2.81) 0.04 (0.76) (3.55)

Montana (0.44) (4.40) (0.09) (0.36) (5.29)

Nebraska 0.43 (1.82) 0.08 (0.07) (1.39)

Nevada (0.03) (0.01) 0.01 0.00 (0.03)

New Hampshire 0.00 (0.02) 0.00 0.00 (0.02)

New Jersey 0.01 (0.10) 0.00 0.00 (0.08)

New Mexico (0.04) (0.06) 0.03 (0.04) (0.11)

New York (0.01) (0.70) 0.00 0.04 (0.66)

North Carolina 0.01 (0.25) 0.00 0.00 (0.23)

North Dakota (0.08) (5.76) (0.00) (0.17) (6.02)

Ohio (0.01) (1.72) 0.00 (0.25) (1.98)

Oklahoma (0.01) (1.01) 0.00 0.03 (0.99)

Oregon 0.10 (0.29) 0.04 (0.06) (0.21)

Pennsylvania 0.01 (0.88) 0.00 (0.13) (1.00)

Rhode Island ND ND ND ND 0.00 

South Carolina (0.01) (0.09) 0.00 0.09 (0.01)

South Dakota 0.01 (3.08) (0.01) (0.29) (3.37)

Tennessee (0.03) (0.87) 0.00 0.14 (0.75)

Texas (0.19) (0.64) 0.03 0.04 (0.75)

Utah (0.06) (0.07) 0.05 0.00 (0.07)

Vermont 0.00 (0.04) 0.00 (0.00) (0.05)

Virginia 0.01 (0.35) 0.00 (0.07) (0.41)

Washington (0.08) (0.81) 0.06 (0.07) (0.90)

West Virginia 0.00 (0.21) 0.00 (0.08) (0.28)

Wisconsin (0.01) (0.63) (0.00) (0.11) (0.76)

Wyoming (0.03) (0.06) 0.01 (0.07) (0.15)

Total (0.00) (42.03) 0.64 (1.60) (42.99)

1 Data from mineral soils used; includes soil C sequestration on CRP lands.

2

Losses from annual cropping systems due to plow-out of pastures, rangeland, hayland, and perennial/horticultural cropland.

Tg CO2 eq. is teragrams carbon dioxide equivalent. ND= No data.

Note: Parenthesis indicate a net sequestration.
Appendix C: Forest Carbon Stocks

U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 1

Appendix C

C-1 Forest Area, Stock, and Stock Change by State

C-2 Carbon Stock Pools on Private Forestland by Region and Age-Class

C-3 Carbon Stock Pools on Public Forestland by Region and Age-Class

C-4 Carbon Stock Pools on Timberlands by Region and Stand Size Class

C-5 Carbon Stocks on all Forestland by Forest Type Group and Ownership

C-6 Net Annual Carbon Stock Change on all Forestland by Forest Type Group and 

Ownership
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 2

Appendix Table C-1 Forest Area, Stock, and Stock Change by State1

State Forest Area 

Net Area 

Change

Non-Soil

Stocks SOC2

Non-Soil 

Change

Harvested Wood 

Products Change

1000 ha 1000 ha yr-1 Tg CO2 eq. Tg CO2 eq. Tg CO2 eq. yr-1

Alabama 9,184 (28.3) 2,455 1445 (4.4) (6.4)

Alaska 6,192 n/a 4,510 1970 n/a (0.4)

Arizona 7,558 (24.7) 1,601 620 7.4 (0.3)

Arkansas 7,396 (27.0) 2,301 1138 (3.5) (4.2)

California 13,511 54.2 7,989 1908 (66.4) (3.7)

Colorado 9,244 48.2 3,373 1035 (20.5) (0.2)

Connecticut 707 (6.7) 333 159 1.6 (0.1)

Delaware 148 (1.1) 65 34 (0.2) (0.1)

Florida 6,838 26.1 1,727 2580 (22.4) (3.1)

Georgia 10,046 3.8 2,942 3020 (28.2) (7.1)

Idaho 8,622 (11.8) 3,854 1294 7.5 (1.5)

Illinois 1,831 13.3 723 356 (4.1) (0.6)

Indiana 1,920 14.3 826 372 (14.1) (0.6)

Iowa 1,227 34.8 415 253 (10.8) (0.2)

Kansas 902 15.6 273 268 (5.6) (0.1)

Kentucky 4,860 (17.9) 1,745 719 (3.8) (1.2)

Louisiana 5,722 11.5 1,639 965 0.9 (4.4)

Maine 7,161 (1.4) 2,698 2157 (8.3) (3.3)

Maryland 987 (8.2) 481 225 (5.2) (0.4)

Massachusetts 1,236 (3.8) 620 315 (2.6) (0.3)

Michigan 7,815 1.0 2,930 4176 6.8 (2.2)

Minnesota 6,876 64.1 1,964 4201 (16.2) (1.9)

Mississippi 7,941 31.8 2,236 1270 (21.5) (5.5)

Missouri 6,231 73.2 2,217 1098 (40.6) (1.0)

Montana 10,360 67.5 4,161 1497 (16.2) (1.3)

Nebraska 504 13.5 157 129 (3.4) (0.1)

Nevada 4,488 21.0 900 354 (2.6) (0.1)

New Hampshire 1,914 (4.9) 912 518 (0.4) (0.2)

New Jersey 843 (2.7) 327 207 (1.5) (0.2)

New Mexico 6,753 21.9 1,774 574 (14.2) (0.1)

New York 7,645 20.7 3,418 1977 (23.1) (1.4)

North Carolina 7,525 25.6 2,655 1878 (25.2) (5.0)

North Dakota 289 (1.2) 72 81 1.1 (0.0)

Ohio 3,205 2.0 1,253 742 (11.6) (0.4)

Oklahoma 3,102 37.2 829 465 (15.7) (0.8)

Oregon 12,176 8.3 7,276 3543 (28.1) (6.2)

Pennsylvania 6,707 (10.6) 2,867 1546 (13.6) (1.3)

Rhode Island 148 (1.8) 65 35 (0.4) (0.0)

South Carolina 5,218 47.5 1,589 1532 (20.8) (3.5)

South Dakota 757 23.4 191 156 (4.6) (0.2)

Tennessee 5,659 26.3 2,113 838 (21.2) (2.0)

Texas2 24,363 (0.1) 3,521 4451 (0.1) (4.0)

Utah 7,374 94.2 1,873 702 (13.1) (0.1)

Vermont 1,850 (2.3) 881 499 2.5 (0.4)

Virginia 6,364 (9.7) 2,473 1318 (12.4) (3.1)

Washington 9,061 22.7 6,248 2881 (28.8) (5.6)

West Virginia 4,857 (0.4) 2,172 1059 (19.6) (1.0)

Wisconsin 6,757 70.1 2,274 3486 (24.9) (2.4)

Wyoming 4,633 10.7 1,715 501 (5.8) (0.1)

Total 276,706 740 101,632 62,544 (558) (88) 1 Net change values are model outputs for 2008 (Smith et al. 2010); stocks and area are based on the most recent inventory per state.

Parentheses indicate negative values, which are a net decrease of forest area or net increase in carbon sequestration.

A value of “n/a indicates not available. Tg CO2 eq. is teragrams carbon dioxide equivalent. 2 SOC (soil organic carbon) does not include effects of past land use history. 3 The central and western portions of these states are represented by a single survey; therefore estimates of change are based only on forests in the eastern 

forests in the eastern portions of the states.

portions of the states.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 3

Appendix Table C-2 Carbon Stock Pools on Private Forestland by 

Region and Age-Class1

Age Class SOC2

Dead Plant 

Matter Biomass Total 

Region Years Tg CO2 eq.

North 16,399 5,159 15,523 37,080

<20 1,420 273 275 1,967

20-40 2,408 528 1,409 4,346

40-60 4,648 1,359 4,332 10,339

60-80 4,892 1,770 5,695 12,356

80-100 2,232 902 2,808 5,942

100-150 740 305 953 1,998

150-200 37 14 32 83

200+ 3 1 3 8

Unknown 19 7 17 42

South 18,620 4,650 19,006 42,276

<20 5,968 1,093 2,774 9,835

20-40 4,656 1,059 4,116 9,831

40-60 4,080 1,196 5,475 10,751

60-80 2,450 818 4,247 7,516

80-100 814 274 1,403 2,490

100-150 267 85 410 762

150-200 16 5 22 43

Unknown 371 119 558 1,047

Pacific Coast 3,587 2,444 4,536 10,567

<20 775 353 217 1,345

20-40 654 360 740 1,754

40-60 660 447 1,042 2,149

60-80 586 435 891 1,912

80-100 383 314 628 1,325

100-150 291 266 533 1,091

150-200 67 71 147 285

200+ 88 87 151 326

Unknown 83 111 187 381

Rocky 

Mountain 1,487 1,647 1,981 5,116

<20 322 330 140 792

20-40 79 74 56 209

40-60 127 122 136 385

60-80 236 241 321 797

80-100 267 305 445 1,016

100-150 299 376 587 1,262

150-200 99 129 190 418

200+ 58 71 107 236

Total 40,093 13,899 41,047 95,039 1 Stocks are based on the most recent inventory per state. 2 SOC (soil organic carbon) does not include effects of past land use history.

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 4

Appendix Table C-3 Carbon Stock Pools on Public Forestland 

by Region and Age-Class1

Age 

class SOC2 Dead Plant Matter Biomass Total 

Region Years Tg CO2 eq.

North 7,649 1,977 5,475 15,101

<20 762 100 108 969

20-40 951 146 361 1,459

40-60 1,625 344 972 2,941

60-80 2,246 670 1,972 4,889

80-100 1,339 472 1,431 3,243

100-150 649 216 559 1,424

150-200 61 20 49 130

200+ 8 4 13 25

Unknown 8 3 10 21

South 2,998 807 3,762 7,567

<20 437 64 159 660

20-40 531 102 386 1,020

40-60 695 189 874 1,758

60-80 818 271 1,365 2,454

80-100 356 121 660 1,138

100-150 121 45 252 418

150-200 1 1 4 6

Unknown 38 14 61 113

Pacific Coast 6,714 6,084 12,958 25,757

<20 443 255 118 816

20-40 569 302 503 1,374

40-60 516 324 654 1,494

60-80 785 601 1,289 2,674

80-100 744 630 1,365 2,740

100-150 1,171 1,133 2,573 4,877

150-200 722 790 1,778 3,289

200+ 1,714 1,987 4,577 8,278

Unknown 52 62 101 215

Rocky 

Mountain 5,090 6,360 9,263 20,712

<20 825 814 314 1,953

20-40 216 205 158 579

40-60 198 197 235 630

60-80 548 595 923 2,065

80-100 832 1,003 1,676 3,512

100-150 1,416 1,975 3,310 6,701

150-200 708 1,060 1,791 3,558

200+ 346 511 857 1,714

Total 22,452 15,228 31,458 69,138 1 Stocks are based on the most recent inventory per state.

2 SOC (soil organic carbon) does not include effects of past land use history.

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 5

Appendix Table C-4 Carbon Stock Pools on Timberlands by Region 

and Stand Size Class1

Stand Size 

Class SOC2 Dead Plant Matter Biomass Total 

Region Tg CO2 eq.

North 22,786 6,725 19,930 49,441

Nonstocked 286 40 12 338

Seedling/ 

Sapling 4,768 843 1,045 6,656

Poletimber 7,523 2,011 5,190 14,723

Sawtimber 10,209 3,831 13,684 27,723

South 17,573 4,753 20,812 43,138

Nonstocked 273 29 23 325

Seedling/ 

Sapling 4,042 727 1,438 6,207

Poletimber 4,606 1,158 4,676 10,441

Sawtimber 8,652 2,838 14,674 26,165

Pacific Coast 7,401 5,756 12,370 25,527

Nonstocked 180 113 26 319

Seedling/ 

Sapling 1,128 539 320 1,987

Poletimber 825 451 809 2,086

Sawtimber 5,267 4,653 11,215 21,135

Rocky 

Mountain 3,631 4,534 7,182 15,346

Nonstocked 181 175 42 398

Seedling/ 

Sapling 527 483 303 1,313

Poletimber 729 752 1,205 2,686

Sawtimber 2,194 3,123 5,632 10,949

Total 51,391 21,767 60,294 133,452 1 Stocks are based on the most recent inventory per state. 2 SOC (soil organic carbon) does not include effects of past land use history.

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 6

Appendix Table C-5 Carbon Stocks1 on all Forestland by 

Forest Type Group and Ownership Class2

Private Public Reserve/Other 

Forest Type Group Tg CO2 eq.

East 42,264 9,956 4,138

Aspen/Birch 1,026 623 96

Elm/Ash/Cottonwood 2,036 384 184

Loblolly/Shortleaf Pine 5,335 823 46

Longleaf/Slash Pine 860 284 12

Maple/Beech/Birch 6,977 1,910 698

Oak/Gum/Cypress 2,934 654 158

Oak/Hickory 17,239 3,282 1,506

Oak/Pine 2,876 603 100

Pinyon/Juniper 8 3 424

Spruce/Fir 1,338 688 172

White/Red/Jack Pine 1,131 498 109

Woodland Hardwoods 2 0 460

Other Hardwood Type 

Groups 109 43 24

Other Softwood Type 

Groups 309 141 39

Nonstocked 84 20 109

West 8,447 21,395 15,433

Alder/Maple 437 198 30

Aspen/Birch 265 797 229

California Mixed Conifer 528 1,566 546

Douglas-fir 2,894 6,557 1,266

Fir/Spruce/Mountain 

Hemlock 683 4,927 3,860

Hemlock/Sitka Spruce 768 2,491 1,567

Lodgepole Pine 163 1,428 801

Other Western Softwoods 61 313 663

Pinyon/Juniper 1 8 3,602

Ponderosa Pine 1,067 1,825 244

Redwood 240 15 119

Spruce/Fir 13 9 72

Tanoak/Laurel 428 216 126

Western Larch 50 243 31

Western Oak 488 414 887

Western White Pine 2 15 31

Woodland Hardwoods 14 22 911

Other Hardwood Type 

Groups 214 122 114

Nonstocked 129 227 333

Total 50,710 31,351 19,571

1 Excluding soils.

2 SOC (soil organic carbon) Stocks are based on the most recent inventory per state.

Note: Other Hardwood Type Groups and Other Softwood Type Groups represent aggregates of 

minor type groups. However, "Other Western Softwoods" is a specific type group within the 

Forest Inventory Analysis DataBase (FIADB).

Note: The Private and Public ownership classes represent timberlands only. The Reserved or 

Other (lower productivity) forests include both public and private owners.

Tg CO2 eq. is teragrams carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 Page C- 7

Appendix Table C-6 Net Annual Carbon Stock Change1 on all 

Forestland by Forest Type Group and Ownership Class2

Private Public Reserve/Other 

Forest Type Group Tg CO2 eq. yr-1

East

Aspen/Birch 6.3 6.1 1.2 

Elm/Ash/Cottonwood 3.8 (4.9) 1.6 

Loblolly/Shortleaf Pine (87.0) (17.7) 2.6 

Longleaf/Slash Pine (1.7) (8.2) (0.7)

Maple/Beech/Birch (3.3) (32.2) 4.3 

Oak/Gum/Cypress 32.2 (23.5) 0.4 

Oak/Hickory (209.5) (83.4) 17.3 

Oak/Pine 47.2 (4.2) 1.9 

Pinyon/Juniper n/a n/a n/a

Spruce/Fir 8.8 (10.0) 2.0 

White/Red/Jack Pine 13.8 (8.0) 2.6 

Woodland Hardwoods n/a n/a n/a

Other Hardwood Type 

Groups (5.6) (1.7) (2.1)

Other Softwood Type Groups (9.1) (0.6) 0.7 

Nonstocked n/a n/a n/a

West

Alder/Maple n/a n/a n/a

Aspen/Birch n/a n/a n/a

California Mixed Conifer n/a n/a n/a

Douglas-fir n/a n/a n/a

Fir/Spruce/Mountain Hemlock n/a n/a n/a

Hemlock/Sitka Spruce n/a n/a n/a

Lodgepole Pine n/a n/a n/a

Other Western Softwoods n/a n/a n/a

Pinyon/Juniper n/a n/a (6.9)

Ponderosa Pine n/a n/a n/a

Redwood n/a n/a n/a

Spruce/Fir n/a n/a n/a

Tanoak/Laurel n/a n/a n/a

Western Larch 0.4 (5.6) 0.3 

Western Oak n/a n/a n/a

Western White Pine n/a n/a n/a

Woodland Hardwoods n/a n/a (11.5)

Other Hardwood Type 

Groups n/a n/a n/a

Nonstocked n/a n/a n/a

Total3

1 Excluding soils.

2 Net change values are model estimates for 2008 (Smith et al. 2010). Parentheses indicate negative 

values, which are a net decrease of forest area or net increase in carbon sequestration. A value of “n/a” 

indicates not available, and totals from columns that include “n/a” should be interpreted accordingly. 3 Totals would not be the sum of the change of individual forest types because at a more aggregate 

resolution, more data are available. 

Note: Other Hardwood Type Groups and Other Softwood Type Groups represent aggregates of minor 

type groups. However, "Other Western Softwoods" is a specific type group within the Forest Inventory 

Analysis DataBase (FIADB).

Note: The Private and Public ownership classes represent timberlands only. The Reserved or Other 

(lower productivity) forests include both public and private owners.
U.S. Agriculture and Forestry Greenhouse Gas Inventory 1990-2008 

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http://www.usda.gov/oce/climate_change/AFGGInventory1990_2008.htm.

Printed copies may be purchased from the National Technical Information Service. Call 1-800-

553-NTIS (6847) or 703-605-6000, or visit http://www.ntis.gov.

June, 2011

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