U . S . A G R I C U LT U R E A N D F O R E S T R Y

Greenhouse Gas Inventory

1990–2013

TECHNICAL BULLETIN NUMBER 1943. SEPTEMBER 2016.

Office of the Chief Economist | Climate Change Program Office

 United States Department of Agriculture
U . S . A G R I C U LT U R E A N D F O R E S T R Y

Greenhouse Gas Inventory

1990–2013
ii

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

United States Department of Agriculture, Office of the Chief Economist, Climate Change Program Office. 

Technical Bulletin No. 1943. 137 pp. September 2016. 

Data from this report can be downloaded from: http://dx.doi.org/10.15482/USDA.ADC/1264344

Abstract

The U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 was developed to update previous USDA 

greenhouse gas inventories 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 greenhouse gas (GHG) emissions. 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. Carbon dioxide (CO2

), methane (CH4), and nitrous oxide 

(N2

O) concentrations in the atmosphere have increased by approximately 43 percent, 152 percent, and 20 percent 

respectively since about 1750. In 2013, total U.S. GHG emissions were 6,673 million metric tons of carbon dioxide 

equivalents (MMT CO2

 eq.), rising 5.9 percent from 1990 estimates. Carbon sequestration in managed forests, urban 

trees, and harvested wood products (882 MMT CO2

 eq.) reduced these emissions to a net 5,791 MMT CO2 eq. in the 

United States in 2013. Agriculture alone accounted for about 9 percent of total U.S. emissions (595 MMT CO2

 eq.). 

The primary GHG sources from agriculture are N2

O emissions from cropped and grazed soils (264 MMT CO2 eq.), CH4

emissions from ruminant livestock production (165 MMT CO2

 eq.) and rice cultivation (8 MMT CO2 eq.), CH4 and N2O 

emissions from managed livestock waste (79 MMT CO2

 eq.), and CO2 emissions from on-farm energy use (74 MMT 

CO2

 eq.). The largest managed carbon sink in the United States is managed forests, which sequester 705 MMT CO2 eq. 

The U.S. agriculture and forestry sector in aggregate provided a net sink of 270 MMT CO2

 eq. in 2013 (including GHG 

sources from crop and livestock production, grasslands, energy use, and GHG sinks for forests and urban trees). This 

report serves to estimate U.S. GHG emissions for the agricultural sector, to quantify uncertainty in emission estimates, 

and to estimate the potential of agriculture to mitigate U.S. GHG emissions.

Keywords: climate change, greenhouse gas, land use, carbon stocks, carbon sequestration, enteric fermentation, livestock waste, nitrous oxide, methane, rice cultivation, energy consumption.
iii

September 1, 2016

Dear Reader:

I am pleased to present The U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013. This report supersedes 

USDA Technical Bulletin 1930 (2011), which accounted for greenhouse gas emissions and sinks for the agricultural and 

forestry sectors through 2008. 

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

Emissions and Sinks: 1990–2013 (April, 2015). However, EPA’s national-scale reporting here has been disaggregated by 

region or State when possible. Some categories are not directly comparable due to different greenhouse gas source 

grouping. 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.

As part of the USDA Building Blocks for Climate Smart Agriculture and Forestry, the Office of the Chief Economist 

is coordinating efforts to track greenhouse gas sources and sinks in agriculture. Over the next few years, we will be 

updating key agricultural management practice and technology data. We expect that these new data inputs will 

significantly refine estimates of soil carbon, methane emissions from manure management systems, and nitrous oxide 

emissions from fertilizers. We also anticipate future improvements due to the new U.S. Forest Carbon Accounting 

Framework.

Data collection and analysis, as well as coordination of this Inventory, could not have been accomplished without 

the contributions of Stephen Del Grosso, Melissa Reyes-Fox, and others within USDA’s Agricultural Research Service. I 

would also like to thank Rich Birdsey, Linda Heath, Coeli Hoover, and James Smith of the USDA Forest Service; James 

Duffield of USDA’s Office of Energy Policy and New Uses; Marlen Eve and Jerry Hatfield of USDA’s Agricultural Research 

Service; Tom Capehart, Elizabeth Marshall, and Ken Matthews of USDA’s Economic Research Service; Jan Lewandrowski 

of USDA’s Office of the Chief Economist; 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.

Sincerely,

William Hohenstein 

Director, USDA Climate Change Program Office
iv

Contributors

Marci Baranski (editor), Climate Change Program Office, USDA 

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

Grant Domke, Forest Service, USDA 

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

Marlen Eve, Agricultural Research Service, USDA 

Ernie Marx, Natural Resources Ecology Laboratory, Colorado State University 

Kristopher Nichols, Agricultural Research Service, USDA 

Stephen Ogle, Natural Resources Ecology Laboratory, Colorado State University 

Melissa Reyes-Fox, Agricultural Research Service, USDA

James Smith, Forest Service, USDA 

Amy Swan, Natural Resources Ecology Laboratory, Colorado State University 

Tom Wirth, Office of Atmospheric Programs, EPA 

Christopher Woodall, Forest Service, USDA
v

List of Maps and Figures........................................................................................................................................................................ vii

List of Tables..................................................................................................................................................................................................... viii

Acknowledgements .......................................................................................................................................................................................x 

Glossary of Terms and Units..................................................................................................................................................................xi

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

1.1 Global Change and Global Greenhouse Gas Emissions in Agriculture and Forestry...................................... 1

1.2 Sources and Mechanisms for Greenhouse Gas Emissions ........................................................................................... 4

1.3 Strategies for Greenhouse Gas Mitigation.......................................................................................................................... 4

1.4 Purpose of This Report.................................................................................................................................................................. 5

1.5 Overview of the Report Structure ........................................................................................................................................... 6

1.6 Summary of Changes and Additions for the Fourth Edition of the Inventory................................................... 7

1.7 References........................................................................................................................................................................................... 9

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

2.1 Summary of U.S. Greenhouse Gas Emissions from Livestock..................................................................................10

2.2 Sources of Greenhouse Gas Emissions from Livestock ...............................................................................................13

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

2.4 Enteric Fermentation...................................................................................................................................................................15

2.5 Managed Livestock Waste.........................................................................................................................................................18

2.6 Grazed Lands ...................................................................................................................................................................................22

2.7 Mitigating Greenhouse Gas Emissions from Livestock...............................................................................................26

2.8 Planned Improvements ..............................................................................................................................................................27

2.9 References.........................................................................................................................................................................................28

2.10 Appendix A.......................................................................................................................................................................................30

Chapter 3: Cropland Agriculture....................................................................................................................................................57

3.1 Summary of U.S. Greenhouse Gas Emissions from Cropland Agriculture.........................................................57

3.2 Sources and Sinks of Greenhouse Gas Emissions in Cropland Agriculture.......................................................60

3.3 Nitrous Oxide Emissions from Cropped Soils ..................................................................................................................61

3.4 Methane Emissions from Rice Cultivation.........................................................................................................................69

3.5 Residue Burning.............................................................................................................................................................................71

3.6 Carbon Stock Changes in Cropped Soils ............................................................................................................................75

3.7 Mitigation of CO2

 Emissions .....................................................................................................................................................78

3.8 Planned Improvements ..............................................................................................................................................................78

3.9 References.........................................................................................................................................................................................79

3.10 Appendix B .......................................................................................................................................................................................83

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

Table of Contents
vi

Chapter 4: Carbon Stocks and Stock Changes in U.S. Forests.......................................................................... 109

4.1 Summary......................................................................................................................................................................................... 109

4.2 Background Concepts and Conventions for Reporting Forest Carbon............................................................ 110

4.3 Carbon Stocks and Stock Changes by Forest Type, Region, and Ownership................................................. 112

4.4 Mechanisms of Carbon Transfer.......................................................................................................................................... 116

4.5 Methods .......................................................................................................................................................................................... 117

4.6 Major Changes Compared to Previous Inventories ................................................................................................... 119

4.7 Uncertainty.................................................................................................................................................................................... 119

4.8 Planned Improvements ........................................................................................................................................................... 120

4.9 References...................................................................................................................................................................................... 121 

4.10 Appendix C..................................................................................................................................................................................... 123

Chapter 5: Energy Use in Agriculture...................................................................................................................................... 133

5.1 Summary of Greenhouse Gas Emissions from Energy Use in Agriculture...................................................... 133

5.2 Spatial and Temporal Trends in Greenhouse Gas Emissions from Energy Use in Agriculture............... 133

5.3 Sources of Greenhouse Gas Emissions from Energy Use on Agricultural Operations .............................. 134

5.4 Methods for Estimating Carbon Dioxide Emissions from Energy Use in Agriculture................................ 135

5.5 Major Changes Compared to Previous Inventories ................................................................................................... 136

5.6 References...................................................................................................................................................................................... 137
vii

List of Maps and Figures

Figure 1-1 Agricultural Sources of Greenhouse Gas Emissions in 2013....................................................................................................................... 3

Figure 1-2 Agricultural and Forest Sinks of Carbon Dioxide in 2013............................................................................................................................. 3

Figure 1-3 Agriculture and Forestry Emissions and Offsets for 1990, 1995, 2000-2013......................................................................................... 4

Figure 2-1 Greenhouse Gas Emissions from Livestock in 2013.....................................................................................................................................11

Map 2-1 Greenhouse Gas Emissions from Livestock Production in 2013..............................................................................................................12

Map 2-2 Methane Emissions from Enteric Fermentation in 2013 ............................................................................................................................16

Figure 2-2 Greenhouse Gas Emissions from Managed Livestock Waste by Livestock Type in 2013................................................................19

Map 2-3 Greenhouse Gas Emissions from Managed Livestock Waste in 2013....................................................................................................19

Figure 2-3 Greenhouse Gas Emissions from Managed Livestock Waste, 1990-2013.............................................................................................19

Map 2-4 Nitrous Oxide Emissions from Grazed Soils in 2013.....................................................................................................................................23

Figure 2-4 Estimated Reductions in Methane Emissions from Anaerobic Digesters, 2000-2013.....................................................................26

Map 3-1a Total Nitrous Oxide (Direct and Indirect) for Major Land Resource Areas, Tier 3 Crops, Annual Means 2003–2007 ............58

Map 3-1b Unit Area Nitrous Oxide (Direct and Indirect) for Major Land Resource Areas, Tier 3 Crops, Annual Means 2003–2007 ...58

Figure 3-1a U.S. Planted Cropland Area by Rotation Category, 1990-2007 ................................................................................................................59

Figure 3-1b U.S. Planted Cropland Area by Crop Type, 1995-2013.................................................................................................................................59

Figure 3-2 Annual Nitrogen Inputs to Cropland Soil, 1990-2007 .................................................................................................................................61

Figure 3-3 Methane from Rice Cultivation by State, 1990 & 2013................................................................................................................................69

Figure 3-4 Greenhouse Gas Emissions from Field Burning by Crop Type, 2013......................................................................................................72

Figure 3-5 Change in Commodity Production, 1990-2013.............................................................................................................................................72

Figure 3-6 Change in Commodity Production, 1990-2013.............................................................................................................................................72

Map 3-3a Soil Carbon Changes for Major Land Resource Areas, Tier 3 Crops, Annual Means.........................................................................74

Map 3-3b Unit Area Soil Carbon Changes for Major Land Resource Areas, Tier 3 Crops, Annual Means 2003–2007 ..............................74

Map 3-4a Soil Carbon Changes for Major Land Resource Areas, Tier 3 Crops Conventional Till, Annual Means 2003-2007 ................75

Map 3-4b Soil Carbon Changes for Major Land Resource Areas, Tier 3 Crops Reduced Till, Annual Means 2003-2007..........................75

Map 3-4c Soil Carbon Changes for Major Land Resource Areas, Tier 3 Crops No Till, Annual Means 2003-2007......................................75

Figure 3-7 CO2 Emissions and Sequestration Sources from Cropland Soils, 2003-2007 ......................................................................................75

Map 4-1 Geographic Regions Used for Carbon Stock and Stock Change Summaries................................................................................... 112

Figure 4-1 Forest Ecosystem Carbon Stocks...................................................................................................................................................................... 114

Figure 4-2 Net Annual Forest Carbon Stock Change ..................................................................................................................................................... 114

Figure 4-3 Summary Diagram of Forest Carbon Pools and Carbon Transfer Among Pools............................................................................. 116

Figure 5-1 CO2 Emissions from Energy Use in Agriculture, by Region, 2013......................................................................................................... 134

Figure 5-2 Energy Use in Agriculture, by Source, 1965-2013...................................................................................................................................... 135

Figure 5-3 CO2 Emissions from Energy Use in Agriculture, by Fuel Source, 2001, 2005, 2008, and 2013.................................................... 136
viii

List of Tables

Table 1-1 Agriculture and Forestry Greenhouse Gas Emission Estimates and Uncertainty Intervals.............................................................. 1

Table 1-2 Summary of Agriculture and Forestry Emissions and Offsets, 1990, 1995, 2000, 2005, 2010-2013 ............................................. 3

Table 2-1 Greenhouse Gas Emission Estimates and Uncertainty Intervals in 2013 .............................................................................................11

Table 2-2 Greenhouse Gas Emissions by Livestock Category and Source in 2013...............................................................................................12

Table 2-3 Descriptions of Livestock Waste Deposition and Storage Pathways.....................................................................................................14

Table 2-4 U.S. Methane Emissions from Enteric Fermentation in 1990, 1995, 2000, 2005, 2010-2013.........................................................16

Table 2-5 Greenhouse Gas Emissions from Managed Livestock Waste in 1990, 1995, 2000, 2005, 2010-2013.........................................18

Table 2-6 Greenhouse Gas Emissions from Grazed Lands in 1990, 1995, 2000, 2005, 2010-2013..................................................................22

Table A-1 Population of Animals by State in 2013............................................................................................................................................................31

Table A-2 U.S. Livestock Population, 1990, 1995, 2000, 2005-2013............................................................................................................................32

Table A-3 State-Level Methane Emissions from Enteric Fermentation by Livestock Category in 2013........................................................33

Table A-4 State-Level Methane Emissions from Enteric Fermentation in 1990, 1995, 2000, 2005-2013......................................................34

Table A-5 Cattle Population Categories Used for Estimating Methane Emissions...............................................................................................34

Table A-6 Dairy Lactation by Region1....................................................................................................................................................................................35

Table A-7 Typical Livestock Weights for 2013.....................................................................................................................................................................35

Table A-8 U.S. Feedlot Placements for 2013........................................................................................................................................................................36

Table A-9 Regional Estimates of Digestible Energy and Methane Conversion Rates for Foraging Animals 2007-2013.........................36

Table A-10 Regional Estimates of Digestible Energy and Methane Conversion Rates for Dairy and Feedlot Cattle for 2013 ................36

Table A-11 Definition of Regions for Characterizing the Diets of Dairy Cattle (all years) and Foraging Cattle 1990-2006 ......................37

Table A-12 Definition of Regions for Characterizing the Diets of Foraging Cattle from 2007-2013.................................................................37

Table A-13 Methane Emissions from Cattle Enteric Fermentation, 1990-2013........................................................................................................38

Table A-14 IPCC Emission Factors for Livestock ...................................................................................................................................................................38

Table A-15 Summary of Greenhouse Gas Emissions from Managed1 Waste by State in 2013 ...........................................................................39

Table A-16 Methane Emissions from Manure Management by State and Animal in 2013..................................................................................40

Table A-17 Nitrous Oxide Emissions from Manure Management by State and Animal in 2013 ........................................................................41

Table A-18 Waste Characteristics Data....................................................................................................................................................................................42

Table A-19 State Volatile Solids Production Rates in 2013...............................................................................................................................................43

Table A-20 State-Based Methane Conversion Factors1 for Liquid Waste Management Systems in 2013.......................................................44

Table A-21 Maximum Methane Generation Potential, B0 .................................................................................................................................................45

Table A-22 Methane Conversion Factors for Dry Systems................................................................................................................................................45

Table A-23 Methane Conversion Factors for Dry Systems................................................................................................................................................46

Table A-24 Direct Nitrous Oxide Emission Factors for 2013 ............................................................................................................................................47

Table A-25 Nitrogen in Livestock Waste on Grazed Lands...............................................................................................................................................48

Table A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes from Non-Federal Grasslands, 2003-2007...................49

Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from Non-Federal Grasslands, 2003-2007......51

Table 3-1 Estimates and Uncertainties for Cropland Greenhouse Gas Emissions, 2013 ....................................................................................57

Table 3-2 Summary of Greenhouse Gas Emissions from Cropland Agriculture, 1990, 1995, 2000, 2005-2013.........................................58

Table 3-3 Tier 3 Cropland Area by Management Practice, 2013 .................................................................................................................................59

Table 3-4 Nitrous Oxide Emissions from Differently Cropped Soils, 5-year Means..............................................................................................62

Table 3-5 Methane from Rice Cultivation from Primary and Ratoon Operations by State, 1990, 1995, 2000, 2005-2013.....................70

Table 3-6 Change in Methane Emissions from Rice Cultivation, 1990-2013 ..........................................................................................................70

Table 3-7 Greenhouse Gas Emissions from Agriculture Burning by Crop, 1990, 1995, 2000, 2005–2013 ...................................................71

Table 3-8 Agricultural Crop Production ...............................................................................................................................................................................73
ix

Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007............................................................................................................84

Table B-2 MLRA-Level Estimates of Total Annual Direct N2

O Emissions by Major Crop Rotation, 2003-2007............................................88

Table B-3 MLRA-Level Estimates of Total Annual Indirect N2

O Emissions from Ammonia, Nitric Oxide, and Nitrogen Dioxide 

Volatilization, by Major Crop Rotation, 2003-2007.......................................................................................................................................92

Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching 

by Major Crop Rotation, 2003-2007...................................................................................................................................................................96

Table B-5 Rice Harvested Area, 1990, 1995, 2000-2013............................................................................................................................................... 100

Table B-6 Total U.S. Production of Crops Managed with Burning, 1990, 1995, 2000-2013 ............................................................................ 100

Table B-7 Production of Crops Managed with Burning............................................................................................................................................... 101

Table B-8a Crop Assumptions and Coefficients................................................................................................................................................................ 101

Table B-8b Emissions Factors and Global Warming Potentials.................................................................................................................................... 101

Table B-8c Rice Area Burned by State .................................................................................................................................................................................. 101

Table B-9 Cultivated Histosol (Organic Soils) Area........................................................................................................................................................ 102

Table B-10 Carbon Loss Rates from Organic Soils Under Agricultural Management in the United States................................................. 102

Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by Major Crop Rotation, 2003-2007........................................ 103

Table B-12 State-Level Estimates of Mineral Soil Carbon Changes on Cropland1 by Major Activity, 2013 ................................................. 107

Table 4-1 Forest Carbon Stock Change Annualized Estimates and Uncertainty Intervals, 2013 ................................................................. 109

Table 4-2 Forest Carbon Stock/Stock Change and Area Annualized Estimates, 1990, 1995, 2000, 2005, 2010, and 2013................. 110

Table 4-3 Carbon Stocks by Ownership and Forest Type and Groups by Region, 2013.................................................................................. 111

Table 4-4 Total Annualized Carbon Stock Change 1990-2013, With Uncertainty Interval for 2013............................................................ 113

Table 4-5 Total Annualized Forest Land 1990-2013, with Uncertainty Interval for 2013................................................................................ 113

Table 4-6 Mean Plot-level Carbon Densities According to Region and Ownership for Six Carbon Pools Based on the Most Recent 

Inventory Per State................................................................................................................................................................................................ 114

Table 4-7 Total Forest Ecosystem Carbon Stocks According to Region and Ownership for Six Carbon Pools Based on Annualized 

Estimates for 2013................................................................................................................................................................................................. 115

Table 4-8 Net Annual Forest Ecosystem Carbon Stock Change According to Region and Ownership for Six Carbon Pools Based on 

Annualized Estimates for 2013 ......................................................................................................................................................................... 115

Table C-1a Current Forest Land Area According to Region, Ownership, and Forest Type Group, 2013....................................................... 124

Table C-1b Current Forest Carbon Stocks in Live Trees According to Region, Ownership, and Forest Type Group, 2013..................... 125

Table C-2 Annualized Carbon Stock Estimates per State, 2013e.............................................................................................................................. 126

Table C-3a Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Publicly Owned Forestland (non-reserved) by 

Region and Stand-Age Class, 2013.................................................................................................................................................................. 127

Table C-3b Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Privately Owned Forestland (non-reserved) by 

Region and Stand-Age Class, 2013.................................................................................................................................................................. 128

Table C-3c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Reserved Forestland (both public and private 

ownerships) by Region and Stand-Age Class, 2013.................................................................................................................................. 129

Table C-4a Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Publicly Owned Forestland (non-reserved) by 

Region and Stand-Age Class, 2013.................................................................................................................................................................. 130

Table C-4b Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Privately Owned Forestland (non-reserved) by 

Region and Stand-Age Class, 2013.................................................................................................................................................................. 130

Table C-4c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Reserved Forestland (both public and private 

ownerships) by Region and Stand-Age Class, 2013.................................................................................................................................. 131

Table 5-1 Energy Use and Carbon Dioxide Emissions by Fuel Source on U.S. Farms, 2013............................................................................ 133

Table 5-2 Definition of Regions Used in Figure 5-1....................................................................................................................................................... 135
x

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

the United States 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. We also acknowledge 

William Parton, Keith Paustian, Stephen Williams, Kendrick Killian, Mark Easter, Shannon Spencer and 

Ram Gurung of the Natural Resource Ecology Laboratory (NREL) of Colorado State University who helped 

generate the agricultural soil carbon and nitrous oxide estimates for Chapters 2 and 3. 

We also thank reviewers including Marlen Eve and Jerry Hatfield of USDA ARS; Tom Capehart, Elizabeth 

Marshall, and Ken Matthews of USDA ERS; Rich Birdsey and Coeli Hoover of USDA Forest Service; Jan 

Lewandrowski of USDA’s Office of the Chief Economist; Tom Wirth of the EPA; and the staff of APHIS and ERS 

for additional review. Brenda Chapin and Susan Carter, Office of the Chief Economist, and the USDA Office 

of Communications provided assistance with publishing.

Acknowledgments
xi

Glossary of Terms and Units

Chemical identities

C Carbon 

CO2 Carbon dioxide 

CO2 eq. Carbon dioxide equivalent 

CH4 Methane

N2O Nitrous oxide 

NOx Nitrogen oxides

Metric units 

MT Metric ton (106 grams or 1,000 kilograms) 

Mg Megagram (106 grams) 

Gg Gigagram (109 grams) 

Tg Teragram (1012 grams) 

MMT Million metric tons (1012 grams) 

ha Hectares

Livestock specific 

Bo Maximum methane-producing capacity 

CEFM Cattle Enteric Fermentation Model 

DE Digestible energy 

MCF Methane conversion factor 

Nex Total Kjeldahl nitrogen excretion rate 

TAM Typical animal mass 

VS Volatile solids 

WMS Waste management system 

Ym Fraction of gross energy converted to methane

Cropland specific 

CRP USDA Conservation Reserve Program 

MLRA Major Land Resource Area

Forestry specific 

CRM Component ratio method 

dbh Diameter breast height 

FIA USDA Forest Inventory and Analysis 

FIADB USDA Forest Inventory and Analysis Database 

HWP Harvested wood products 

SOC Soil organic carbon

Energy specific 

BTU British thermal unit 

QBTU Quadrillion British thermal units 

EIA Energy Information Administration 

LP gas Liquid petroleum gas

Other 

EF Emission factor 

GHG Greenhouse gas 

GWP Global warming potential 

NRI U.S. National Resources Inventory

1

Chapter 1

Introduction

Table 1-1 Agriculture and Forestry Greenhouse Gas Emission Estimates 

and Uncertainty Intervals, 2013

1.1 Global Change and 

Greenhouse Gas Emissions in 

Agriculture and Forestry

In 2013, total U.S. greenhouse gas emissions 

measured 6,673 million metric tons of carbon dioxide 

equivalents (MMT CO2 eq.), rising 5.9 percent from 

1990 estimates (EPA 2015). Global concentrations of 

the three most important long-lived greenhouse gases 

(GHG) 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 43 percent, 152 percent, 

and 20 percent respectively (EPA 2015, Keeling and 

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 

GHGs 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 from agriculture are 

N2O emissions from cropped and grazed soils, 

CH4 emissions from ruminant livestock production 

and rice cultivation, CH4 and N2O emissions from 

managed livestock waste, and CO2 emissions from 

on-farm energy use. 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 U.S. 

net emissions of 5,791 MMT CO2 eq. in 2013. Net 

emissions equate to total greenhouse gas emissions 

minus CO2 sequestration or removal of CO2 from 

the atmosphere, including the net forest sink as well 

as the net soil sink from grazed lands and croplands. 

This report serves to estimate U.S. GHG emissions 

for the agricultural sector, 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 approximately 9 percent of total 

GHG emissions in 2013 (EPA 2015). Greenhouse 

gas emission 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). Note that GWPs for CH4 and N2O have 

changed compared to the previous edition of this 

inventory. 

Agriculture in the United States, including livestock, 

grasslands, crop production, and energy use, 

contributed a total of 595 MMT CO2 eq. to the 

atmosphere in 2013 (Table 1-1). This total includes 

a relatively small soil CO2 sink of 1.4 MMT CO2

eq. in cropped soils (Table 1-2). In previous USDA 

Inventory reports, grazed lands were a relatively 

large sink for CO2, but new simulations using more 

recent land cover data estimate that grazed lands are 

currently close to CO2 neutral. Forests and urban 

trees in the United States contributed to a total 

reduction in atmospheric GHGs of approximately 

865 MMT CO2 eq. in 2013, which offset total U.S. 

GHG emissions by 13 percent. After accounting for 

GHG sources and C sequestration, agricultural and 

forested lands in the United States were estimated 

to be a net sink of 270 MMT CO2 eq. (Table 1-1). 

The 95 percent confidence interval for this estimate 

ranges from a sink of 486 to 38 MMT CO2 eq. (Table 

1-1). 

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

Intervals, 2013

Estimate Lower Bound Upper Bound

Source MMT CO2 eq.

Livestock 243 222 276 

Crops1 175 129 249 

Grassland1 102 32 190 

Energy Use2 74 

Forestry (776) (973) (576)

Urban Trees (90) (133) (47)

Net Emissions (270) (486) (38)

Note: Parentheses indicate a net sequestration. MMT CO2 eq. is million metric tons carbon dioxide 

equivalent. 

1Includes sequestration in agricultural soils. 2Confidence intervals were not available for this component.

Download data: http://dx.doi.org/10.15482/USDA.ADC/1260729 
Chapter 1 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

2

Close to half (45 percent) of agriculture’s GHG 

emissions in 2013 were from soils (Figure 1-1). Most 

of the emissions from crop production were from 

non-rice soils, with residue burning and rice cropping 

accounting for about 1 percent of overall agricultural 

emissions (Figure 1-1). Enteric fermentation from 

livestock production was responsible for a large 

portion (28 percent) of the remaining agricultural 

emissions. Managed livestock waste and on-farm 

energy use each accounted for 13 percent of 

agricultural emissions. 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 urban trees and 

harvested wood products (Figure 1-2). 

Sources and sinks of emissions are conveniently 

partitioned in Figure 1-3 (sinks are values less than 

0). Overall emissions profiles of agricultural sources, 

including energy use but excluding storage by soils 

and forestry, show that sources increased 13 percent 

between 1990 and 2013 (Table 1-2, Figure 1-3). The 

sink strength of the forests, harvested wood, and 

urban trees pool has increased 24 percent since 1990 

(Table 1-2, Figure 1-3). However, the sink strength 

of agricultural soils has decreased by approximately 

104 percent since 1990. In sum, emissions increased 

from 1990 to 2013, but C storage related to forestry 

increased to an even greater extent. Because C 

sequestration exceeds sources, net emissions 

are negative (GHG sink), and the amount of net 

sequestration increased by about 23 percent since 

1990 (Table 1-2). 

Box 1-1

The USDA GHG Inventory report follows the international convention for reporting GHG emissions, 

as described in the introduction of the U.S. GHG Inventory (EPA 2015). Emissions of GHGs are 

expressed in equivalent terms, normalized to carbon dioxide (CO2) using Global Warming Potentials 

(GWPs) published by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment 

Report (Table B1-1). GWPs, which are based on physical and chemical properties of gases, represent 

the effect of a given GHG on the climate, integrated over a given period of time, relative to CO2

(IPCC 2006). Since the reference gas used is CO2, GWP-weighted emissions are measured in million 

metric tons of CO2 equivalent (MMT CO2 eq.). GWP values allow for a comparison of the impacts of 

emissions and reductions of different gases. These values for methane (CH4) and nitrous oxide (N2O) 

are referenced to CO2 and based on a 100-year time period (EPA 2015). These GWPs have been 

adjusted since the previous USDA Inventory Report was published.

Table B1-1 (reproduced from U.S. GHG Inventory Report (EPA 2015), Table 1-2) 

 Gas Atmospheric Lifetime GWPc

 CO2 b 1 

 CH4

a

 12 25 

 N2O 114 298 

Source: (IPCC 2007) 

a The GWP of CH4 includes the direct effects and those indirect effects due to the production of tropospheric ozone and 

stratospheric water vapor. The indirect effect due to the production of CO2 is not included. b For a given amount of carbon dioxide emitted, some fraction of the atmospheric increase in concentration is quickly absorbed 

by the oceans and terrestrial vegetation, thus will continue to cycle through aquatic and terrestrial ecosystems as carbon. Some 

fraction of the atmospheric carbon dioxide will only slowly decrease over a number of years, and depending on the amount of 

carbon dioxide emitted, between 15% and 40% can remain in the atmosphere for up to 2000 years (IPCC 2013).

c 100-year time horizon.

The relationship between kilotons (kt) of a gas and MMT CO2 eq. can be expressed as follows:

MMT CO2 eq. = (kt of gas)x(GWP)x(MMT/1000kt)

where, 

MMT CO2 eq. = Million metric tons of CO2 equivalent 

kt = Kilotons (equivalent to a thousand metric tons) 

GWP = Global warming potential 

MMT = Million metric tons
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 1

3

Table 1-2 Summary of Agriculture and Forestry Emissions and Offsets, 1990, 1995, 2000, 2005, 2010-2013 Table x-2 Summary of Agriculture and Forestry Emissions and Offsets, 1990, 1995, 2000, 

2005, 2010-2013

1990 1995 2000 2005 2010 2011 2012 2013

Source GHG MMT CO2 eq.

Livestock 215.1 236.9 236.9 241.6 249.1 247.4 247.4 243.2 

Enteric 

Fermentation

CH4 164.2 178.7 170.6 168.9 171.1 168.7 166.3 164.5 

Managed Waste CH4 37.2 43.3 50.0 56.3 60.9 61.4 63.7 61.4 

Managed Waste N2O 13.8 15.0 16.3 16.4 17.1 17.3 17.3 17.3 

Grassland 73.9 93.6 33.0 82.9 101.5 101.4 100.7 102.0 

Grassland CH4 2.7 2.9 2.7 2.7 2.6 2.6 2.5 2.8 

Grassland N2O 80.5 90.3 70.8 85.0 96.1 96.0 95.5 95.9 

Grassland CO2 (9.3) 0.3 (40.5) (4.8) 2.8 2.8 2.7 3.3 

Crops 117.0 161.5 133.1 164.0 174.7 173.0 177.1 175.1 

Cropland Soils1 N2O 143.5 158.2 141.8 158.6 168.1 169.8 170.5 167.8 

Cropland Soils2 CO2 (36.0) (6.9) (18.8) (3.9) (4.9) (5.7) (3.1) (1.4)

Rice Cultivation CH4 9.2 9.8 9.6 8.9 11.1 8.5 9.3 8.3 

Residue Burning CH4 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 

Residue Burning N2O 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

Energy Use3 CO2 73.9 73.9 73.9 69.9 72.7 73.3 73.9 74.4 

Forestry (699.8) (728.0) (563.2) (887.6) (851.5) (856.1) (860.7) (865.2)

Forests4 CO2 (508) (542) (376) (704) (705) (705) (705) (705)

Harvested Wood4 CO2 (132) (118) (113) (103) (60.5) (63.9) (67.3) (70.8)

Urban Trees5 CO2 (60.4) (67.1) (73.8) (80.5) (86.1) (87.3) (88.4) (89.5)

Net Emissions

All 

GHGs (219.8) (162.0) (86.2) (329.2) (253.5) (261.1) (261.6) (270.4)

Note: Parentheses indicate a net sequestration. MMT CO2 eq. is million metric tons carbon dioxide equivalent. CH4 is methane; N2O is nitrous 

oxide; CO2 is carbon dioxide. 1Includes emissions from managed manure during storage and transport before soil application.

2Agricultural soil C sequestration includes sequestration on land set aside under the USDA Conservation Reserve Program, in addition to 

cultivated mineral and organic soils.

3Data interpolated for all years except 2001, 2005, 2008, and 2013.

4Data were interpolated for years 2001-2004, 2006-2009, and 2011-2012. 5Data taken from EPA. Data were interpolated for years 1995 and 2000.

Figure 1-1 Agricultural Sources of Greenhouse Gas 

Emissions in 2013 (CH4 is methane; N2O is nitrous oxide; CO2

is carbon dioxide. MMT CO2 eq. is million metric tons of carbon 

dioxide equivalent)

Figure 1-1 Agricultural sources of greenhouse gas emissions in 2013

Figure 1-2

Agricultural and forest sinks of carbon dioxide in 2013

Cropland soils

(N2O) 168 MMT CO2

eq. 28%

Enteric fermentation 

(CH4) 165 MMT CO2

28%

Managed livestock waste 

(CH4, N2O)

79 MMT CO2 eq. 13%

Grazed lands

(CH4, N2O)

99 MMT CO2 

eq. 17%

Rice cultivation & 

residue burning 

(CH4, N2O) 9 MMT 

CO2 eq. 1%

Energy use

(CO2) 74 MMT 

CO2 eq. 13%

Forests

(705) MMT CO2 eq. 

81%

Harvested wood

(71) MMT CO2 eq. 

8%

Urban trees (90) MMT CO2

eq. 10%

Figure 1-2 Agricultural and Forest Sinks of Carbon 

Dioxide in 2013 (MMT CO2 eq. is million metric tons of carbon 

dioxide equivalent)

Figure 1-1 Agricultural sources of greenhouse gas emissions in 2013

Figure 1-2

Agricultural and forest sinks of carbon dioxide in 2013

ec eeo

(CH4) 165 MMT CO2

28%

(CH4, N2O)

79 MMT CO2 eq. 13%

Forests

(705) MMT CO2 eq. 

81%

Harvested wood

(71) MMT CO2 eq. 

8%

Urban trees (90) MMT CO2

eq. 10%
Chapter 1 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

4

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 2013, fuel 

and electricity consumption associated with crop and 

livestock operations resulted in 74 MMT CO2 (Table 

1-1), which equals 1.4 percent of overall energyrelated CO2

 emissions for 2013 (5332 MMT CO2, 

EPA 2015). Diesel fuel use led to about 42 percent 

of CO2 emissions from energy use in agriculture; 

electricity use led to about 37 percent; and gasoline, 

liquefied petroleum gas, and natural gas contributed 

10 percent, 7 percent, and 4 percent, respectively, to 

total CO2 emissions from energy use in agriculture.

1.2 Sources and Mechanisms for 

Greenhouse Gas Emissions

One-half to two-thirds of global annual CH4

emissions and roughly a third of global annual 

emissions of N2O are believed to derive from human 

sources, mainly from agriculture (IPCC 2013). 

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 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 (rice 

residue and organic fertilizers) in the soil by soil 

microbes, resulting in methane emissions. Finally, 

burning of residues in agricultural fields produces 

CH4 and N2O as combustion byproducts. 

Livestock grazing, production, and waste emit 

CH4 and N2O into 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 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 mitigate 

GHG emissions in two ways: sources can be reduced 

and emissions can be offset 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 

Figure 1-3 Agriculture and Forestry Emissions and Offsets for 1990, 1995, 2000-2013 

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

Figure 1-3

Agriculture and Forestry Emissions and Offsets for 1990, 1995, 2000-2013

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

(1000)

(800)

(600)

(400)

(200)

0

200

400

600

800

MMT CO2 eq.

Rice/Residue Burning

Managed Waste

Grazed Lands

Enteric Fermentation

Cropland Soils

Agricultural Soils

Urban Trees

Harvested Wood

Forests
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 1

5

growth and sequestration in soils (IPCC 2001). 

Improved forest regeneration and management 

practices such as density control, nutrient 

management, and genetic tree 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 increased in recent decades (see 

Chapter 4). 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. This sink could be increased 

by increasing afforestation and implementing more 

intensive management to increase forest growth 

(McKinley et al. 2011). 

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, inoculating 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. A recent USDA report (Eve et 

al. 2014) discusses these and other mitigation options 

in detail and quantifies expected GHG reductions (or 

increases) for various land management practices.

1.4 Purpose of This Report

The U.S. Agriculture and Forestry Greenhouse Gas 

Inventory: 1990–2013 was developed to update 

the U.S. Agriculture and Forestry Greenhouse Gas 

Inventories: 1990-2001 (USDA 2004), 1990-2005 

(USDA 2008) and 1990-2008 (USDA 2011) 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 greenhouse gas emissions. 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 (EPA 2015). 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.

The U.S. GHG Inventory provides national-level 

estimates of emissions of the primary long-lived 

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 Major Land Resource Area (MLRA), 

State, and regional emissions data are part of the 

inventory development process and can be used 

for more disaggregated analyses. For example, soil 

emissions are reported in this edition of the USDA 

Inventory disaggregated at the MLRA level.

Emissions reported here do not always exactly match 

the emissions reported in the U.S. GHG Inventory 

(EPA 2015) for some source categories. There are 

two main reasons for this; first the EPA (2015) report 

partitions emissions by IPCC (2006) categories, 

while the USDA report attempts to logically 

designate emissions due to agricultural production 

systems. For example, EPA (2015) includes CO2

emissions from lime and urea fertilizer applied to 

cropped and grazed soils in the land Use, Land-Use 

Change, and Forestry category, whereas emissions 

from these sources are included in the agricultural 

soils category in this report. Second, in some tables 

and figures EPA (2015) reports CO2 emissions from 
Chapter 1 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

6

energy (e.g., electric power generation) partitioned 

as its own category, whereas in other figures and 

tables, energy emissions are allocated to the end-use 

economic sector. In contrast, this report consistently 

accounts for CO2 emissions from on-farm energy use 

in the agricultural sector. Note that this report does 

not account for CO2 emissions from indirect energy, 

which is defined as energy used off the farm to 

manufacture farm inputs such as synthetic fertilizers. 

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 MLRA, State, region, and land use, and by crop, 

pasture, range, livestock, and forest management 

systems. 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. 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 MLRA, 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 MLRA, State, regional, and national scales in 

agriculture and forestry, 

• Identify activities that are driving GHG 

emissions and sinks and trends in these 

activities, 

• Quantify the uncertainty associated with GHG 

emission and sink estimates.

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 MLRA, 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 from 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 CH4

 and N2O in 

forestry are not addressed 

since little information is 

currently available to develop 

estimates for these sources for 

forests. Qualitatively, forest 

soils are net CH4 sinks in the 

United States, and soil N2O 

emissions are small because 

forests do not receive large N 

additions. The energy chapter 

provides information on 

CO2

 emissions from energy 

consumption on U.S. farms, 

covering GHG emissions 

from fuel use in livestock and 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 1

7

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 in each chapter, 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 previous 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 Fourth Edition of the 

Inventory

Compared to previous editions, more sophisticated 

methodologies were used in this report to estimate 

GHG fluxes from 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 1990-2008 (based on current 

methodologies), estimates for 2009-2013 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 

animal-specific emissions factors, and accounting 

for sources previously neglected (see Chapter 2 for 

details). Methane conversion rates, digestible energy 

values for cattle, and feedlot diets were also updated. 

As a result of these changes, emissions from enteric 

fermentation increased by approximately 17 percent 

on average compared to the previous inventory 

(USDA 2011). The biggest changes for emissions 

from managed livestock also relate to updated 

livestock population data and refined methodologies. 

Consequently, emission estimates from manure 

management systems (see Chapter 2, Table 2-3 

for full list of these systems) have increased by 

approximately 18 percent compared to the previous 

inventory. There were several changes in calculations 

of N2

O emissions from grazed soils which are 

generated primarily by DayCent model simulations. 

The most important change was performing model 

simulations at National Resources Inventory (NRI) 

resolution (simulations were conducted at the county 

level for the previous inventory). In contrast to 

the previous edition which used model-generated 

estimates of N additions from grazing livestock 

waste, these were based on county-level animal 

population data to be consistent with activity data 

for emissions from enteric fermentation. Additional 

changes include using updated and refined model 

activity data, expanding the observational data sets 

used to quantify model uncertainty, and improving 

model algorithms to better represent the processes 

that control soil GHG fluxes. These changes resulted 

in an approximate 40-percent increase in grazed soil 

N2

O emissions. The biggest changes that impacted 

estimates of carbon dioxide fluxes for grazed lands 

also involved using annual survey data from the NRI 

and DayCent model improvements. These changes 

resulted in an average annual decrease in estimated 

soil C sequestration of approximately 69 percent 

compared to the previous inventory.

There were several changes in calculations of 

cropland emissions compared to the previous edition 

of the inventory, mainly relating to DayCent model 

simulations for soil N2

O and CO2 emissions (see 

chapter 3 for details). The most important changes 
Chapter 1 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

8

were simulating more crops and using NRI for 

land cover information. In previous inventories, 

land cover was based on NASS statistics for areas 

of major crops (corn, soybeans, wheat, alfalfa hay, 

other hay, sorghum, and cotton) at the county level 

with region-specific assumptions regarding common 

cropping practices. In contrast, NRI data represent 

actual land use during any particular year. Another 

improvement relates to land area considered eligible 

to contribute to indirect N2O from NO3 leached or 

runoff from cropped fields. Instead of assuming that 

nitrate leaching and runoff can occur anywhere, a 

criterion was used to designate lands where nitrate is 

susceptible to be leached or runoff 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 does not 

enter waterways because water tables in dry areas 

are deep or non-existent. Other changes are related to 

improvements in the DayCent model and uncertainty 

estimation. These changes resulted in an increase 

in N2O emissions of approximately 4 percent and a 

decline in estimated C sequestration in mineral soils 

of 14 percent, relative to the previous inventory. 

The estimates of C storage in forests and wood 

products reflect a substantial number of incremental 

changes in methods and data between EPA (2010) 

and EPA (2015) in terms of net stock change 

since 1990 (see chapter 4 for details). New annual 

inventory data for most States and adjustments to 

the identification of land area classified as forests 

included in the inventories have affected stock 

totals and changes. In addition, major changes in 

carbon conversion factors as applied to live and 

standing dead trees as well as to down dead wood 

and litter pools affected estimates as each update 

was implemented. Overall, these changes decreased 

overall forest and wood product C stock estimates by 

15 percent and C stock changes by 7 percent relative 

to the previous inventory.

Aggregating across all sources and sinks, net 

emissions are approximately a 30-percent smaller 

sink than reported in the previous 

inventory. Although some of the 

changes compared to the previous 

inventory may appear to be large, they 

are within the calculated uncertainty 

ranges. Because of the relatively large 

uncertainty associated with GHG fluxes 

for agricultural and forestry production 

systems, it is difficult to predict the 

magnitude of changes that will be 

reported in subsequent inventories. 

However, both the observational 

measurements that are used to test and 

constrain the methods and models used, 

and the estimates derived from the 

methods and models, should improve 

as more extensive observational data 

sets become available. Similarly, 

availability of more refined model 

input data sets should improve the 

estimates reported in future editions of 

this volume. The individual chapters 

provide details regarding expected 

improvements.

SUGGESTED CITATION

Del Grosso, S.J., M. 

Baranski, M. Eve, and M. 

Reyes-Fox, 2016. Chapter 

1: Introduction. In U.S. 

Agriculture and Forestry 

Greenhouse Gas Inventory: 

1990–2013, Technical 

Bulletin No. 1943, United 

States Department of 

Agriculture, Office of 

the Chief Economist, 

Washington, DC. 137 pp. 

September 2016. Del Grosso 

S.J. and M. Baranski, Eds. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 1

9

1.7 References

Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005). 

DAYCENT model analysis of past and contemporary soil N2

O and 

net greenhouse gas flux for major crops in the USA. Soil Tillage 

and Research, 83:9-24. doi:10.1016/j.still.2005.02.007. 

Dlugokencky, E.J., R.C. Myers, P.M. Lang, K.A. Masarie, 

A.M. Crotwell, K.W. Thoning, B.D. Hall, J.W. Elkins, and 

L.P. Steele (2005). Conversion of NOAA atmospheric dry 

air CH4

 mole fractions to a gravimetrically prepared standard 

scale. Journal of Geophysical Research, 110:(D)18306. 

doi:10.1029/2005JD006035.

EPA (2010). Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990-2008. U.S. Environmental Protection Agency, Office 

of Atmospheric Programs, Washington, D.C. Available online at 

<http://www.epa.gov/climatechange/emissions/usinventoryreport.

html>.

EPA (2015). Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990-2013. Environmental Protection Agency, Office 

of Atmospheric Programs, Washington D.C. April, 2015. 

Available at http://www.epa.gov/climatechange/ghgemissions/

usinventoryreport.html. 

Eve, M., D. Pape, M. Flugge, R. Steele, D. Man, M. Riley-Gilbert, 

and S. Biggar, Eds. (2014). Quantifying Greenhouse Gas Fluxes 

in Agriculture and Forestry: Methods for Entity-Scale Inventory. 

Technical Bulletin Number 1939, Office of the Chief Economist, 

United States Department of Agriculture, Washington, DC. 606 

pages. July 2014.

IPCC (2001). Climate change 2001: the scientific basis, 

contribution of Working Group I to the third assessment report of 

the Intergovernmental Panel on Climate Change. J.T. Houghton, 

Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. 

Maskell, and C.A. Johnson, editors. Cambridge University Press, 

Cambridge, UK.

IPCC (2006). 2006 IPCC guidelines for national greenhouse gas 

inventories, vol. 4: agriculture, forestry and other land use. S. 

Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, editors. 

Intergovernmental Panel on Climate Change, National Greenhouse 

Gas Inventories Programme, Technical Support Unit, Kanagawa, 

Japan. Available online at < http://www.ipcc-nggip.iges.or.jp>.

IPCC (2007). Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, 

M. Tignor and H.L. Miller (eds.). Cambridge University Press. 

Cambridge, United Kingdom 996 pp. 

IPCC (2013). Climate Change 2013: The Physical Science Basis. 

Contribution of Working Group I to the Fifth Assessment Report 

of the Intergovernmental Panel on Climate Change. [Stocker, 

T.F., D. Qin, G.-K., Plattner, M. Tignor, S.K. Allen, J. Boschung, 

A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge 

University Press, Cambridge, United Kingdom and New York, NY, 

USA, 1535 pp. 

Keeling, C.D. and T.P. Whorf (2005). Atmospheric CO2

records from sites in the SIO air sampling network, in Trends: 

a compendium of data on global change. Carbon Dioxide 

Information Analysis Center, Oak Ridge National Laboratory, U.S. 

Department of Energy, Oak Ridge, TN.

Kimble, J.M., L.S. Heath, R.A. Birdsey, and R. Lal (2003). The 

potential of U.S. forest soils to sequester carbon and mitigate the 

greenhouse effect. CRC Press, Boca Raton, FL.

McKinley, D. C., Ryan, M. G., Birdsey, R. A., Giardina, C. P., 

Harmon, M. E., Heath, L. S., ... & Skog, K. E. (2011). A synthesis 

of current knowledge on forests and carbon storage in the United 

States. Ecological Applications, 21(6), 1902-1924.

Prinn, R.G., R.F. Weiss, P.J. Fraser, P.G. Simmonds, D.M. 

Cunnold, F.N. Alyea, S. O’Doherty, P. Salameh, B.R. Miller, 

J. Huang, R.H.J. Wang, D.E. Hartley, C. Harth, L.P. Steele, G. 

Sturrock, P.M. Midgely, and A. McCulloch (2000). A history 

of chemically and radiatively important gases in air deduced 

from ALE/GAGE/AGAGE. Journal of Geophysical Research, 

105:17751-17792.

Robertson, G.P., E.A. Paul, and R.R. Harwood (2000). Greenhouse 

gases in intensive agriculture: contributions of individual gases to 

the radiative forcing of the atmosphere. Science, 289:1922-1925.

USDA (2004). U.S. Agriculture and Forestry Greenhouse Gas 

Inventory: 1990-2001. Technical bulletin 1907. Office of the Chief 

Economist, United States Department of Agriculture, Washington, 

D.C. Available online at <http://www.usda.gov/oce/gcpo>.

USDA (2008). U.S. Agriculture and Forestry Greenhouse 

Gas Inventory: 1990-2005. Del Grosso, S.J. and M.K. Walsh 

(Eds.) Technical bulletin 1921. Office of the Chief Economist, 

United States Department of Agriculture, Washington, D.C. 

Available online at http://www.usda.gov/oce/global_change/

AFGGInventory1990_2005.htm.

USDA (2011). U.S. Agriculture and Forestry Greenhouse Gas 

Inventory: 1990-2008. Del Grosso, S.J. and M.K. Walsh (Eds.) 

T echnical bulletin 1930. Office of the Chief Economist, United 

States Department of Agriculture, Washington, D.C. Available 

online at http://www.usda.gov/oce/climate_change/AFGG_

Inventory/USDA_GHG_Inv_1990-2008_June2011.pdf

Zhang, F., Chen, J. M., Pan, Y., Birdsey, R. A., Shen, S., Ju, W., 

& He, L. (2012). Attributing carbon changes in conterminous US 

forests to disturbance and non-disturbance factors from 1901 to 

2010. Journal of Geophysical Research: Biogeosciences (2005–

2012), 117(G2).

11

2.1 Summary of U.S. Greenhouse Gas 

Emissions From Livestock

A total of 342 MMT CO2 eq. of greenhouse gases 

(GHGs) were emitted from livestock, managed 

livestock waste, and grazed land in 2013 (Table 2-1, 

Figure 2-1). This represents about 66 percent of 

total emissions from the agricultural sector, which 

totaled 516 MMT CO2 eq. (EPA 2015). Compared to 

the baseline year (1990), emissions from livestock 

sources were about 18 percent higher in 2013. There 

are three main reasons for this increase: methane 

(CH4) emissions from managed livestock waste 

increased, nitrous oxide (N2O) emissions from grazed 

lands increased, and the CO2 sink strength of grazed 

lands decreased. The 95 percent confidence interval 

for 2013 was estimated to lie between 293 and 407 

MMT CO2 eq. (Table 2-1).

Enteric fermentation contributed to a little less than 

half (165 MMT CO2 eq.) of all emissions associated 

with livestock production, while soils from grazed 

lands (102 MMT CO2 eq.) and managed waste (76 

MMT CO2 eq.) accounted for approximately 30 

and 22 percent, respectively, of the total livestock 

emissions. All of the emissions from enteric 

fermentation and about 77 percent of emissions from 

managed livestock waste were in the form of CH4. 

Of the emissions from grazed lands, 94 percent were 

in the form of 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 during 

the short period of time when paddies are drying. 

Although lands converted to grazing are estimated 

to be a C sink, this is balanced by long-term grazed 

lands being a C source in aggregate. Soils in grazed 

lands are estimated to be roughly CO2

 neutral, 

emitting an estimated net 3.3 MMT CO2 eq. in 2013 

(Table 2-2). Note that C storage in biomass is not 

accounted and the uncertainty ranges for both grazed 

land remaining grazed land and land converted 

to grazed land have lower bounds indicating 

sequestration and upper bounds indicating emissions 

(Table 2-1). Carbon (C) storage in grassland biomass 

is not accounted because biomass in these systems 

overturns quickly relative to soil C and does not 

contribute much to long term sequestration.

The largest total emissions associated with livestock 

production were from 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 relatively high in 

Idaho, Montana, South Dakota, Nebraska, Colorado, 

Kansas, Oklahoma, Wisconsin, Iowa, and Missouri.

Beef cattle contributed the largest fraction (63 

percent) of GHG emissions from livestock in 2013, 

with the majority of emissions in the form of CH4

Chapter 2

Livestock and Grazed Land Emissions

Table 2-1 Greenhouse Gas Emission Estimates and 

Uncertainty Intervals in 2013 Table Error! No text of specified style in document.-3 Greenhouse Gas Emission Estimates and 

Uncertainty Intervals in 2013

Estimate

Lower 

Bound

Upper 

Bound

Source MMT CO2 eq.

CH4 enteric fermentation 165 146 194

CH4 managed waste + grazed land 61 50 74

N2O managed waste 17 15 21

N2O grazed land 96 72 138

CO2 grazed land remaining grazed land 12 (24) 48

CO2 land converted to grazed land (9) (18) 1

Total 342 293 407

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent.

Figure 2-1 Greenhouse Gas Emissions from Livestock in 2013 (CH4 is 

methane; N2O is nitrous oxide; CO2 is carbon dioxide. MMT CO2 eq. is million metric 

tons of carbon dioxide equivalent)

Figure 2-1 Greenhouse Gas Emissions from Livestock in 2013.

CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

20

40

60

80

100

120

140

Beef

Cattle

Dairy

Cattle

Sheep Poultry Swine Horses Goats Bison Mules

MMT CO2 eq.

Enteric CH4

Manure CH4

N2O

68%

24%

7%

Total 240 MMT CO2 eq.

Download data: http://dx.doi.org/10.15482/USDA.ADC/1264149
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

12

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 

(25 percent), primarily CH4 from enteric fermentation 

and managed waste. The third-largest GHG source 

from livestock was swine (8 percent), nearly all of 

which was CH4 from waste. Horses, mules, goats, 

sheep, and bison caused relatively small GHG 

emissions when compared to other animal groups, 

because populations of these types are relatively 

small. Poultry have relatively low emissions despite 

comprising the largest livestock group, because this 

group does not produce enteric waste.

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 throughout 

its lifecycle. 

Grazing animals influence soil processes (e.g., 

nitrification/denitrification) that result in N2O 

emissions from the nitrogen (N) in their waste. 

Forage legumes on grazed lands also contribute to 

N2O emissions because when legumes fix N from the 

atmosphere, that N 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 C inputs to the soil—from plant residues 

and manure—exceed C losses from decomposition of 

soil organic matter. Soils that have been historically 

cropped using conventional tillage are often depleted 

of C because tillage disturbs soil aggregates and 

warms soil, which increases decomposition rates. 

Carbon-depleted soils can act as CO2

 sinks when 

converted to grazing land, 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 C during any given year.

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 N2O 

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

in 2013 Table Error! No text of specified style in document.-4 Greenhouse Gas Emissions by Livestock 

Category and Source in 2013

Enteric 

Fermentation

Managed Livestock 

Waste Grazed Land Total

CH4 CH4 N2O N2O1 CH4 CO2

Animal Type MMT CO2 eq.

Beef Cattle 117.10 0.62 7.65 85.16 2.38 2.95 215.87

Dairy Cattle 41.59 31.66 5.74 5.06 0.11 0.18 84.34

Swine 2.47 23.05 1.89 0.24 0.01 0.01 27.66

Horses 1.59 0.02 0.12 3.44 0.21 0.12 5.49

Poultry NA 3.22 1.58 0.17 0.01 0.01 4.98

Sheep 1.07 0.03 0.31 0.80 0.04 0.03 2.28

Goats 0.31 0.00 0.02 0.64 0.02 0.02 1.02

American Bison 0.32 NA NA 0.32 0.01 0.01 0.66

Mules and Asses 0.07 0.00 0.00 0.10 0.01 0.00 0.19

Total 164.53 58.61 17.3 95.93 2.78 3.33 342.49

Note: Methane emissions from manure deposited on grasslands is not partitioned by animal type. MMT CO2 eq. is million 

metric tons carbon dioxide equivalent. CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. 1Includes direct and indirect emissions.

Map 2-1 Greenhouse Gas Emissions from Livestock Production in 2013
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

13

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 that generate 

GHG fluxes from livestock, waste management, and 

grazed lands are detailed below.

2.2.1 Enteric Fermentation 

Enteric fermentation is a normal digestive process in 

animals 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 because enteric fermentation 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. Low quality feeds, such as dormant 

grasses and crop residues, are relatively low in 

protein and high in fiber which reduces digestibility 

and enhances CH4 production.

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 United States manage livestock 

waste in systems such as solid storage, dry lots, 

liquid/slurry storage, deep pit storage, and anaerobic 

lagoons. Table 2-3 (adapted from EPA 2015) 

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. Greenhouse gas emissions from 

treated waste applied to cropped soils as a nutrient 

amendment are discussed in the next chapter along 

with GHG emissions from other nutrient amendments 

for crop production.

The magnitude of CH4

 and N2O emissions from 

managed livestock waste depends in large part 

on storage system and environmental conditions. 

Methane is emitted under anaerobic conditions, 

when oxygen is not available to the bacteria that 

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). However, moist conditions 

(which are a function of rainfall and humidity) can 

promote CH4 production in non-liquid-based manure 

systems. 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 

added moisture, particularly in solid storage systems 

that normally experience aerobic conditions, can 

amplify CH4 emissions.

While storage system and 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 as 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 (NH3

) or organic N is converted to nitrates 

(NO3) and nitrites (NO2) (nitrification), and if 

conditions become sufficiently anaerobic, NO3 and 

NO2 can be denitrified, i.e., reduced to nitrogen 

oxides and nitrogen gas (N2) (Groffman et al. 2000; 

Archibeque et al. 2012). Nitrous oxide is produced 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

14

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 content and high oxygen gas (O2) 

demand from decomposition. For example, waste 

in dry lots is deposited on soil, oxidized to NO2 and 

NO3, 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 N that was volatilized or leached/runoff 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 NH3 volatilization and NO3 runoff 

into ground or surface waters. The gaseous losses 

of NH3 to the atmosphere can then be deposited to 

the soil and converted to N2O by nitrification. The 

NO3 runoff into waterways can be converted to N2O 

by aquatic denitrification. Note that in addition to 

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

Table Error! No text of specified style in document.-5 Descriptions of Livestock Waste 

Deposition and Storage Pathways

Manure Management System Description

Pasture/Range/Paddock Manure and urine from pasture and range grazing animals are deposited directly 

onto the soil (unmanaged).

Daily Spread Manure and urine are routinely collected and spread on fields within 24 hours of 

excretion; there is little or no storage of the manure/urine before it is applied to 

soils. Nitrous oxide emissions are assumed to be zero during the transport/storage 

phase but not after the waste has been applied to soils.

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

under long-term bulk storage.

Dry Lot Manure and urine are deposited directly onto a paved or unpaved open containment 

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

removal, it is sometime spread onto fields).

Liquid/Slurry Manure is stored as excreted or with some minimal addition of water to facilitate 

handling and is stored in either tanks or earthen ponds, usually for periods less than 

1 year.

Anaerobic Lagoon Uncovered anaerobic lagoons are designed and operated to combine waste 

stabilization and storage. Lagoon supernatant is usually used to remove manure from 

the associated confinement facilities to the lagoon. Anaerobic lagoons are designed 

with varying lengths of storage (up to a year or greater), depending on the climate 

region, the volatile solids loading rate, and other operational factors, and must be 

cleaned out every 5-15 years.

Anaerobic Digester Animal excrement with or without straw is collected and anaerobically digested in a 

large containment vessel (complete mix or plug flow digester) or covered lagoon. 

Digesters are designed and operated for waste stabilization by the microbial 

reduction of complex organic compounds to CO2 and CH4, which are captured and 

flared or used as a fuel.

Deep Pit Combined storage of manure and urine in pits (up to one year) below livestock 

confinements. Little to no water added to manure.

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.

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.

Adapted from IPCC 2006.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

15

NH3 losses, nitrogen oxides (NOx) can contribute 

to volatilization but because there are no quantified 

estimates available, losses due to volatilization are 

based solely on NH3 loss factors. Similarly, leached 

NO3 can contribute to indirect N2O, but because little 

is known about leaching from manure management 

systems, only emissions associated with runoff are 

calculated.

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 N to soils from animal 

wastes, forage legumes, and fertilizer additions. 

Legumes fix atmospheric N2 into forms that can 

be used by plants and by soil microbes. Nitrogen 

from manure, legumes, and fertilizers is cycled into 

the soil and can provide substrates for nitrification 

and denitrification. Nitrous oxide is a byproduct of 

this cycle; thus, more N added to soils yields more 

N2

O released to the atmosphere. A portion of the N 

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 N in the 

form of NO3 can leach into groundwater and surface 

runoff, undergo denitrification, and contribute to 

indirect N2O emissions. In addition to N 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 5 percent 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 C reaches a steady 

state, given consistent weather patterns. Long-term 

soil C levels are sensitive to climate change, and 

soils that were previously sinks can revert to being 

sources of CO2. Note that current methodology 

does not include CO2 fluxes resulting from growing 

(or senescing) biomass nor CO2 emissions from 

grassland fires.

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 2013 are provided in Appendix Table A-1. 

Appendix Table A-2 shows total national livestock 

population sizes from 1990 to 2013 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 

11 million head in 2013 (Appendix Table A-1). 

Kansas, Nebraska, and Oklahoma each raised from 

4 to 7 million head of beef cattle, while several 

other States raised ~2 million head. Fewer dairy 

cattle than beef cattle are raised currently in the 

United States. Dairy cattle populations were highest 

in California and Wisconsin (3.4 million and 2.6 

million, respectively) (Appendix Table A-1). New 

York, Idaho, Pennsylvania, and Minnesota had the 

next largest populations of dairy cattle, ranging from 

982,000 to 1.2 million head in each State. Most 

States had fewer than 100,000 head of dairy cattle. 

Iowa was the largest swine producer, with 20 million 

head in 2013 (Appendix Table A-1). North Carolina 

housed the second-largest swine population at nearly 

9 million head. Minnesota, Illinois, and Indiana also 

have sizeable swine populations.

2.4 Enteric Fermentation 

Just less than half (48 percent) of emissions 

associated with livestock production were from 

CH4 produced by enteric fermentation. Cattle were 

responsible for the majority of enteric CH4 emissions 

(71 percent) in 2013 (Table 2-2). Texas (19.3 MMT 

CO2 eq.) and California (11.3 MMT CO2 eq.) had 

the largest CH4 emissions from enteric fermentation 

for beef cattle and dairy cows in 2013 (Map 2-2, 

Appendix Table A-3). 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 derived mostly from dairy cattle 

(Appendix Table A-3). State-level data for non-cattle 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

16

livestock (i.e., swine, sheep, goats, mules, bison, 

and horses) were 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 

10 MMT CO2 eq. per State in 2013 (Appendix Table 

A-3). 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 14 MMT CO2 eq. 

between 1990 and 2013 (Table 2-4). Emissions 

peaked in 1995, then decreased by about 10 MMT 

CO2 eq. by 2005, then rose slightly by 2010. In recent 

years, CH4 emissions from enteric fermentation have 

declined. Overall, by 2013, CH4 emissions from 

enteric fermentation increased by over 0.2 percent 

compared to 1990 levels. Emissions increased 

slightly even though animal numbers of beef cattle 

(the major contributor) decreased (Appendix Table 

A-2) because the amount of feed consumed per 

animal increased. State-level emissions for 1990, 

1995, 2000 and 2005-2013 are presented in Appendix 

Table A-4.

2.4.1 Methods for Estimating Methane 

Emissions From Enteric Fermentation 

The official U.S. GHG Inventory estimates for 

enteric fermentation (as well as those for managed 

waste and grazed soils) 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 3.10 from the official U.S. GHG Inventory 

is summarized below. Methane emissions from 

enteric fermentation were estimated for seven 

livestock categories: cattle, horses, sheep, swine, 

goats, American bison, and mules. 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.

Map 2-2 Methane Emissions from Enteric Fermentation in 2013

(CH4 is methane. Tg CO2 eq. is teragrams of carbon dioxide equivalent)

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

1990, 1995, 2000, 2005, 2010-2013 Table Error! No text of specified style in document.-6 U.S. Methane Emissions From Enteric 

Fermentation in 1990, 1995, 2000, 2005, 2010-2013

 Animal Type MMT CO2 eq.

1990 1995 2000 2005 2010 2011 2012 2013

Beef Cattle 119.1 135.5 126.7 125.2 124.4 121.7 118.7 117.1

Dairy Cattle 39.4 37.5 38.0 37.6 40.7 41.1 41.7 41.6

Sheep 2.3 1.8 1.4 1.2 1.1 1.1 1.1 1.1

Horses 1.0 1.2 1.5 1.7 1.7 1.7 1.6 1.6

Swine 2.0 2.2 2.2 2.3 2.4 2.5 2.5 2.5

Goats 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3

American Bison 0.1 0.2 0.4 0.4 0.4 0.3 0.3 0.3

Mules and Asses 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1

Total 164.2 178.7 170.6 168.9 171.1 168.7 166.3 164.5

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

17

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

Calf birth rates, population statistics, feedlot 

placement information, and slaughter weight data 

were used to create a transition matrix that models 

cohorts of individual animal types and their specific 

emission profiles. 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 4 to 6 months or later), the stages are 

modeled on a per-month basis. The type of cattle 

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 NASS 

(National Agricultural Statistics Service) (Appendix 

Table A-2). 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, beef calf birth percentages, and lactation 

data were obtained from NASS QuickStats database 

(USDA 2013a). 

Step 2: Characterize U.S. Cattle Diets

Data were collected on diets considered 

representative of different regions to support 

development of digestible energy (DE), the percent 

of gross energy intake digestible to the animal, 

and CH4

 conversion rate (Ym), the fraction of gross 

energy converted to CH4, values for each of the cattle 

population categories. 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 VS (USDA 2010). 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 

cattle-type-specific estimates for DE and Ym were 

developed for the United States (Appendix Tables 

A-9 and A-10). Regions in the enteric fermentation 

model are defined in Appendix Table A-11, A-12. 

Additional detail on the regional diet characterization 

is provided in EPA (2015).

Step 3: Estimate Methane Emissions From Cattle

Emissions were estimated in three steps: (a) 

determine gross energy intake using the IPCC (2006) 

Tier 2 equations, (b) determine an emissions factor 

using the gross energy 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)

• 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)

• Population

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 12 subcategories of 

cattle are listed in Appendix Table A-13. 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.

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.
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

18

2.4.1.2 Emission Estimates From Other Livestock

Emissions other (non-cattle) livestock used the 

default Tier 1 emission factor recommended by 

IPCC (2006). Other livestock population data 

(sheep, goats, swine, horses, mules, poultry, and 

American bison) were taken from USDA NASS 

(2014) or earlier census data. 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. Nationallevel emission calculations for other livestock were 

developed from national population totals. Appendix 

Table A-14 shows the emission factors used for these 

other livestock types.

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 2015) and reproduced here with 

permission from EPA.

Uncertainty is estimated using an IPCCrecommended Tier 2 method based on 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. A total of 185 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 percent or lower. However, the uncertainty for 

many of the emission factors is over ± 20 percent. 

The overall 95-percent confidence interval around 

the estimate of 165 MMT CO2

 eq. ranges from 146 to 

194 MMT CO2 eq. (Table 2-1).

2.4.3 Changes Compared to the 3rd edition of 

the USDA GHG Report

There were several modifications made to the 

emissions estimates for this edition of the USDA 

GHG report relative to the previous inventory 

(USDA 2011). Most of the changes involved revising 

estimates of animal populations, average weights, 

and diet assumptions, or refining the models used 

to calculate emissions. American bison, which 

were previously excluded, are now included in the 

inventory. Enteric fermentation emissions from bull 

populations are now calculated with a Tier 2, instead 

of Tier 1, methodology. As a result of the changes 

outlined above, the amount of emissions estimated 

for enteric fermentation increased by approximately 

17 percent on average compared to the previous 

inventory (USDA 2011).

2.5 Managed Livestock Waste

Greenhouse gas emissions from managed livestock 

waste are composed of CH4 and N2O from livestock 

waste storage, transport, 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 2013, accounting for 78 percent of 78 MMT 

CO2 eq. total emissions from this source (Table 2-5). 

The remaining 22 percent of GHG emissions from 

managed livestock waste was N2O. Dairy cattle and 

swine were responsible for 37 and 25 percent of 

total managed waste emissions, respectively (Figure 

2-2). Poultry (5 percent) and beef cattle (8 percent) 

were also important sources in 2013. For beef cattle, 

N2O was the predominate form (93 percent) of waste 

emissions. Over time, emissions from managed waste 

increased by 14 percent from 1990 to 2013 (Figure 

2-3). Most 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.

While beef cattle contribute 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 2013. Managed manure emissions 

from horses, sheep, and goats are small due to 

the relatively small population of these animals 

(Appendix Table A-2), and most of the manure 

Table 2-5 Greenhouse Gas Emissions from Managed 

Livestock Waste in 1990, 1995, 2000, 2005, 2010-2013 Table Error! No text of specified style in document.-7 Greenhouse Gas Emissions Fro

Managed Livestock Waste in 1990, 1995, 2000, 2005, 2010-2013

GHG Type 1990 1995 2000 2005 2010 2011 2012 2013

MMT CO2 eq.

Nitrous Oxide1 13.8 15.0 16.3 16.4 17.1 17.3 17.3 17.3

Methane 2 37.2 43.3 50.0 56.3 60.9 61.4 63.7 61.4

Total 51.0 58.2 66.4 72.8 78.0 78.7 81.0 78.7

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. 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.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

19

is unmanaged or managed in dry systems (EPA 

2015). State-level GHG emissions from managed 

livestock waste varied across States in 2013, 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, 11.7 and 10.5 MMT 

CO2

 eq., respectively (Appendix Table A-15). In 

California, emissions were primarily from dairy 

cattle. In Iowa most emissions were from swine 

(Appendix Table A-16, A-17).

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 2015) as well as 

for this inventory report. Animal population data 

were used to estimate CH4

 production potential 

and N in waste, and these were multiplied by a 

methane conversion factor (MCF) and direct and 

indirect N2O emission factors. Methane conversion 

factors are used to determine the amount of CH4

emissions that are potentially produced by each unit 

of livestock waste. Methane conversion factors 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, consistent with international guidance, and are 

described in detail in Annex 3.11 of the U.S. GHG 

Inventory (EPA 2015).

Figure 2-2 Greenhouse Gas Emissions from Managed 

Livestock Waste by Livestock Type in 2013

(CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. 

MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

Figure 2-2 

Greenhouse Gas Emissions from Managed Livestock Waste by Livestock Type in 2013

CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

5

10

15

20

25

30

35

40

Dairy cattle Swine Beef cattle Poultry Horses Sheep Goats

MMT CO2 eq.

N2O

CH4

MMT CO2 eq. is million metric tons carbon dioxide equivalent

Total 78 MMT CO2 eq.

76%

24%

Map 2-3 Greenhouse Gas Emissions from Managed Livestock Waste in 2013

Figure 2-3 Greenhouse Gas Emissions from Managed 

Livestock Waste, 1990-2013

(CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. 

MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

Figure 2-3

Greenhouse Gas Emissions from Managed Livestock Waste, 1990-2013

CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

MMT CO2 eq.

Total

CH4

N2O
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

20

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 national); 

• 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). 

Eight livestock types are considered for this 

particular emissions category: dairy cattle, beef 

cattle, swine, sheep, goats, poultry, horses, and 

mules/asses. For swine and dairy cattle, manure 

management system usage is determined for different 

farm-size categories using data from the USDA 

(Ott 2000; USDA 1996a, 1998, 2009) and EPA 

(EPA 2002a, 2002b, ERG 2000). For beef cattle and 

poultry, manure management system usage is not 

tied to farm size and is based on other sources (ERG 

2000, UEP 1999, USDA 2000a). For other animal 

types, manure management system usage is based on 

previous estimates (EPA 1992).

Appendix Table A-18 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 percent of gross energy 

for urinary energy excretion per animal unit. This 

was then converted to volatile solids production 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-19 

shows volatile solid production rates by State and 

livestock category.

MCFs for liquid-slurry, anaerobic-lagoon, and deeppit 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 MCFs for liquid-slurry, deep-pit, and 

anaerobic-lagoon systems are shown in Appendix 

Table A-20. Appendix Table A-21 has national-scale 

maximum methane-generation potential (B0) by 

animal type, and Appendix Table A-22 has methane 

conversion factors for dry waste management 

systems equal to the default IPCC (2006) factors 

for temperate climates. 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 MCF 

(Appendix Table A-23).

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.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

21

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 NH3 and 

nitrogen oxides (NOx) (Fracgas); and

• Fraction of N loss from runoff and leaching 

(Fracrunoff/leach).

Nitrous oxide emissions were estimated by first 

determining activity data, including animal 

population, typical animal mass (TAM), WMS 

usage, and waste characteristics. Nitrous oxide 

emissions factors for all manure-management 

systems were set equal to the default IPCC (2006) 

factors for temperate climates (Appendix A-24). 

Nitrogen excretion 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. Nitrogen excretion rates 

for all other animals were determined using data 

from USDA’s Agricultural Waste Management Field 

Handbook (USDA 1996b, 2008; ERG 2010a, 2010b) 

and data from the American Society of Agricultural 

Engineers, Standard D384.1 (ASAE 2003). 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 N2

O 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 1,000 kg animal mass per day), WMS distribution 

(percent), and the number of days per year.

Direct N2

O 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, Appendix A-21) 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 

(Fracgas) 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 N2

O 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 2015). The information is reproduced 

here with permission from EPA.

Uncertainty is estimated using an IPCCrecommended Tier 2 method developed by EPA 

(2003) based on the Monte Carlo Stochastic 

Simulation technique. A normal probability 

distribution was assumed for each source data 

category. The series of equations used were 

condensed into a single equation for each animal 

type and State. The results of the uncertainty analysis 

showed that the manure management CH4

 inventory 

has a 95-percent confidence interval from 50 to 74 

MMT CO2 eq. around the inventory value of 61 

MMT CO2 eq., and the manure management N2O 

inventory has a 95-percent confidence interval from 

15 to 21 MMT CO2

 eq. around the inventory value of 

17 MMT CO2 eq. (Table 2-1).
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

22

2.5.3 Changes Compared to the 3rd edition of 

the USDA GHG Report

In addition to updating livestock population data, the 

total VS and Nex estimates from the CEFM were 

used in the manure management calculations for 

cattle in the current inventory. An error in the crude 

protein calculation in the CEFM was corrected. The 

VS and Nex for other animal types were updated 

using data from USDA’s updated Agricultural Waste 

Management Field Handbook (USDA 2010). For 

the current Inventory, USDA population data were 

used that included updated market swine categories. 

Data from the 2007 USDA Census of Agriculture 

were used to update goat populations and the WMS 

distributions for dairy and swine. Temperature data, 

which are used to estimate MCFs for liquid systems, 

were updated. Anaerobic digester data were updated 

using the AgSTAR database. In aggregate, these 

changes resulted in increased average emissions of 

13 percent for CH4

 and 3 percent for N2O.

2.6 Grazed Lands

Grazed-land soils emit N2O due to enhanced N 

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 N from 

synthetic fertilizer and managed manure application, 

forage legumes cultivation, and unmanaged waste 

from grazing animals. Grazed lands can be either a 

source or a sink of CO2, depending on the level of 

soil disturbance, soil type, previous land use, and 

grazing intensity. In general, grazed mineral soils that 

were previously cropped with annuals and then tilled 

sequester C upon conversion to perennial vegetation 

cover. However, drained organic soils (histosols) 

used for grazing are typically a CO2 source because 

draining enhances decomposition of soil organic 

matter. 

Nitrous oxide was the predominant GHG emitted 

from grazed land soils in 2013, accounting for 94 

percent of all emissions from this source (Table 2-6). 

The remaining 6 percent of GHG emissions from 

grazed lands was divided roughly equally between 

CH4

 and CO2. Grazed lands were sources of CO2 in 

2013, contributing 3 percent of emissions. Nitrous 

oxide emissions from grazed land totaled 102 MMT 

CO2 eq. in 2013 (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 United States are used for beef 

production. Texas and Montana had the largest 

emissions from grazed lands due to the large amounts 

of rangeland in these States (Map-2-4). Emissions 

tended to be high in most Great Plains States, 

again due to large areas of rangeland. In aggregate, 

emissions from managed grazed land were greater 

than those of managed manure in 2013 and for most 

years 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 

(about 80 percent of all cattle) compared to feedlots, 

which are a source of managed waste. In addition 

to Map 2-4, direct and indirect N2

O emisisons for 

non-Federal are reported in Gg CO2 eq.’s at the more 

resolved Major Land Resource Area (MLRA) level in 

Appendix Table A-25. Similarly, MLRA level soil C 

stock changes for non-Federal grasslands are reported 

in Appendix Table A-26. 

2.6.1 Methods for Estimating Nitrous Oxide 

Emissions From Grazed Lands

Estimates of N2O emissions from this component 

were based on DayCent model simulations of 

non-Federal grazed lands (IPCC Tier 3 approach), 

estimates of animal waste production and application 

on to grazed lands (Appendix Table A-27), estimates 

of synthetic N fertilizer applied to grazed lands, 

and IPCC (2006) methodology for emissions 

from Federal grazed lands, grazed organic soils, 

and sewage sludge N additions (EPA 2015). 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 remains on soils 

after being deposited by grazing animals in pastures, 

rangelands, and paddocks. The livestock included in 

this component were dairy cattle, beef cattle, swine, 

sheep, goats, poultry, and horses.

Table 2-6 Greenhouse Gas Emissions from Grazed Lands in 1990, 1995, 

2000, 2005, 2010-2013 Table 2-6 Greenhouse Gas Emissions From Grazed Lands in 1990, 1995, 2000, 2005, 2010-

2013 

1990 1995 2000 2005 2010 2011 2012 2013

GHG Type MMT CO2 eq. 

Nitrous Oxide1 80.5 90.3 70.8 85.0 96.1 96.0 95.5 95.9

Direct 73.7 83.4 64.8 78.1 89.2 89.1 88.5 89.0

Indirect Volatilization 4.2 4.3 4.0 4.5 4.5 4.5 4.5 4.4

Indirect Leaching & Runoff 2.7 2.6 2.0 2.4 2.5 2.5 2.5 2.5

Methane2 2.7 2.9 2.7 2.7 2.6 2.6 2.5 2.8

Carbon Dioxide (9.3) 0.3 (40.5) (4.8) 2.8 2.8 2.7 3.3

Grazed Lands Remaining Grazed (1.9) 8.1 (30.1) 4.2 11.7 11.7 11.5 12.1

Land Converted to Grazed Land (7.4) (7.7) (10.4) (9.0) (8.9) (8.9) (8.8) (8.8)

Total 73.9 93.6 33.0 82.9 101.5 101.4 100.7 102.0

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. 1 Does not include emissions from managed manure applied to cropped soils. 

 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

23

The DayCent ecosystem model simulated nonFederal pastures and rangelands at National 

Resources Inventory (NRI) survey (USDA 2013b) 

resolution. The NRI is a statistically based sample 

of all non-Federal land, and includes over 500,000 

points in agricultural land for the conterminous 

United States and Hawaii (note that not all of these 

points were simulated using the Tier 3 method). Data 

have been reported every 5 years starting in 1982, 

with 2007 being the most recent year. Each point is 

associated with an “expansion factor” that allows 

scaling of N2

O emissions from NRI points to the 

entire country (i.e., each expansion factor represents 

the amount of area with similar land-use/management 

history as the sample point). Land-use and some 

management information (e.g., vegetation type, soil 

attributes, and irrigation) were originally collected 

for each NRI point on a 5-year cycle beginning in 

1982. However, the NRI program began collecting 

annual data in 1998, and data are currently available 

through 2007. For subsequent years (2008-2013), 

raw model outputs for 2007 were repeated, but 

emissions were not identical because some expansion 

factors changed. 

Pastures are defined as grazing lands that are 

relatively intensively managed and may have been 

seeded with legumes and/or amended with organic N 

(e.g., managed manure) or synthetic fertilizer N and/

or irrigated. Rangelands are typically extensive areas 

of native grasslands that are not intensively managed. 

Grazing intensity on 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, NO3

 leaching and N (NOx, NH3) 

volatilization were simulated on a per unit area basis, 

and multiplied by the estimated expansion factor for 

each NRI point. Outputs for each NRI point were 

then aggregated to the State and national levels. The 

DayCent simulations are described in more detail in 

Chapter 3 of this report and in EPA (2015) and Del 

Grosso et al. (2010). 

Manure N deposition from grazing animals (i.e., 

PRP manure) on non-Federal grasslands was an 

input to the DayCent model (see Annex 3.12 EPA 

2015), and included approximately 92 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 N2

O 

emissions were estimated using the IPCC (2006) 

Tier 1 method with IPCC default emission factors. 

Waste N deposited on grazed lands not accounted for 

by the DayCent simulations and sewage sludge N 

additions were multiplied by the default IPCC (2006) 

emission factor of 0.02 kg N2

0-N/kg N to estimate 

direct N2O-N emissions, as opposed to the 0.01 

kg N2

O-N/kg N used to estimate N additions from 

Map 2-4 Nitrous Oxide Emissions from Grazed Soils in 2013

(N2O is nitrous oxide. Tg CO2 eq. is teragrams of carbon dioxide equivalent)
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

24

managed soils (including mineral fertilizers, organic 

amendments, crop residues, and N mineralization 

from soil C losses). Data available at the time the 

IPCC (2006) guidelines were developed suggested 

that the default emission factor should be greater 

for waste N deposited by grazing animals compared 

to other N sources, but more recent observations 

suggest that this factor should be close to the 0.01 kg 

N2O-N/kg N factor use for the other sources (van der 

Weerden 2011).

The amounts of PRP manure N applied on nonFederal grasslands in each NRI point were based 

on the proportion of non-Federal grassland area 

compared to total grassland area according to data 

from the NRI (USDA 2009, relative to the area of 

Federal grasslands from the U.S. Geological Survey 

(USGS) National Land Cover Dataset (Forest 

Inventory and Analysis Data, <http://fia.fs.us/

tools-data/data>). Managed manure N amendments 

to grasslands were estimated from Edmonds et 

al. (2003) and adjusted for annual variation using 

data on the availability of managed manure N for 

application to soils. All managed manure applied to 

grasslands was assumed to be applied to non-Federal 

grasslands. Sewage sludge was assumed to be applied 

on grasslands instead of cropped land 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 (1993), NEBRA (2007), 

and AAPFCO (1995-2014). 

Indirect N2

O emissions due to volatilization of 

applied N 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 N 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 percent of animal waste N was assumed to 

volatilize and 30 percent of animal waste N was 

assumed to be leached or runoff. The total volatilized 

N was multiplied by the IPCC default emission factor 

of 0.01 kg N20- N/kg N (IPCC 2006). The total N 

leached or runoff was multiplied by the IPCC (2006) 

default emission factor of 0.0075 kg N20-N/kg N.

Total grazed land N2

O emissions were partitioned 

among different animal types by assuming that 

emissions are linearly proportional to waste N 

production.

2.6.2 Uncertainty in Nitrous Oxide Emissions 

From Grazed Lands

Uncertainty due to model inputs and model structure 

were quantified. Model inputs used to represent N 

inputs from livestock waste and synthetic fertilizer 

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. 

To address uncertainty in model inputs, a series 

of 100 Monte Carlo simulations were performed 

for each NRI point. To address model structural 

uncertainty, DayCent-simulated N2

O emissions were 

compared with measured emissions from over 15 

grassland experiments. 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 

based on simple error propagation. The calculated 

95-percent confidence interval around the estimate 

of 96 MMT CO2 eq. for grazed-soil N2O emissions 

was 72 to 138 MMT CO2 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. As noted previously, these emissions are very 

small because of predominately aerobic conditions in 

deposited manure.

2.6.4 Changes Compared to the 3rd Edition of 

the USDA GHG Report

There were several changes compared to the 

previous inventory. The most important change 

was performing DayCent model simulations at NRI 

resolution (simulations were conducted at the county 

level for the previous inventory). Simulations also 

incorporated MODIS Enhanced Vegetation Index 

to reduce uncertainties in the estimation of crop 

production, and instead of using model-generated 

N and C deposited from animal waste, these were 

based on county-level animal population data to 

be consistent with activity data for emissions from 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

25

enteric fermentation and livestock waste. Additional 

changes include using updated and refined model 

activity data, better representing land use change 

and tillage practices, expanding the observational 

data sets used to quantify model uncertainty, and 

improving model algorithms to better represent the 

processes that control soil GHG fluxes. In aggregate, 

these changes resulted in an approximate 40-percent 

increase in N2O emissions from grazed lands on 

average.

2.6.5 Methods for Estimating Carbon Dioxide 

Fluxes for Grazed Lands

As with N2O emissions, carbon dioxide (CO2) fluxes 

for non-Federal grasslands were estimated using 

results from the DayCent ecosystem model and IPCC 

(2006) methodology. See section 2.6.1 for details 

on model simulations. Although model simulations 

for N2

O and CO2 fluxes were identical, model 

outputs for CO2 are portioned by land use (grassland 

remaining grassland versus land converted to 

grassland) whereas N2O emissions from grazed lands 

are not partitioned by land use. DayCent 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, DayCent has not been well tested 

with organic soils, so IPCC (2006) methodology was 

also used for grazed organic soils.

Both DayCent and IPCC (2006) methodologies rely 

on land use classifications and land use histories. 

The National Resources Inventory (USDA 2009) 

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. According to NRI data, 

~17 million ha of grassland (out of a total ~261 

million ha reported in 2007) were converted to 

grassland between 1997 and 2007. 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 

C stocks and molecular stoichiometry. 

Mineral soil C stocks and stock changes for NRI 

points classified as grasslands remaining grasslands 

and cropland converted to grassland were estimated 

using the DayCent model. In addition to accounting 

for weather and soil texture, these simulations also 

included estimates of managed manure additions 

to grasslands. DayCent estimates carbon-stock 

changes by accounting for C inputs from plant 

material and manure and C outputs from grazing 

and decomposition. For details on sources of the 

input data required to run DayCent and how the 

simulations were conducted, see Chapter 3 of this 

report and Chapter 7 and Annex 3.12 of the U.S. 

GHG Inventory (EPA 2015).

Mineral soil C 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 N2

O emissions, CO2

fluxes were partitioned among different animal types 

by assuming that fluxes are linearly proportional to 

waste N 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 DayCent model simulations used a Monte 

Carlo approach, which addresses uncertainties in 

model inputs, uncertainty in model structure, and 

uncertainties from scaling NRI points to cover all 

grasslands remaining grassland in the United States. 

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. 

To assess structural uncertainty, DayCent simulated 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

26

soil C-stock changes were compared with measured 

values from over 25 grassland experiments in North 

America. Uncertainties were combined using simple 

error propagation. The results yielded an uncertainty 

of (24) to 48 around the estimate of 12 MMT CO2 eq. 

in 2013 for land remaining grazed land and (18) to 

1 around the estimate of (9) MMT CO2 eq. for land 

converted to grazed land in 2013, where parentheses 

indicate a net sequestration of CO2 (Table 2-1). 

Uncertainty calculations are described in detail in 

Chapter 3 of this report.

2.6.7 Changes Compared to the 3rd edition of 

the USDA GHG Report

As with N2O, the major change compared to the 

previous inventory was performing DayCent model 

simulations at NRI resolution (see section 2.6.4 

for details). The implemented changes resulted 

in a decrease in estimated soil C sequestration 

of approximately 30 MMT CO2 eq. on average 

(69 percent decrease), compared to the previous 

inventory.

2.7 Mitigating Greenhouse Gas 

Emissions From Livestock

In addition to the mitigation strategies discussed 

below that are based primarily on implementation 

of improved technologies designed to decrease 

emissions from enteric fermentation, livestock 

waste management, and grazed lands, there are 

also mitigation options related to human behavior. 

Specifically, recent research suggests that consuming 

less animal products is likely to reduce GHG 

emissions and have co-benefits such as improved 

human health and increased biodiversity (Del Grosso 

and Cavigelli 2012, Smith et al. 2013, Eshel et al. 

2014, Machovina et al. 2015).

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 percent 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.

Although many of the mitigation options mentioned 

above have been extensively studied (Hristov et 

al. 2013), reliable quantitative estimates of these 

potentials remain elusive. Reasons for lack of 

reliable quantitative estimates include variability 

in observations and complex interactions with 

other GHG sources (e.g., emissions from livestock 

Figure 2-4 Estimated Reductions in Methane Emissions from Anaerobic waste) that compromise the efficacy of general 

Digesters, 2000-2013 Figure 2-4

Estimated Reductions in Methane Emissions from Anaerobic Digesters, 2000-2013

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

1

2

3

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

MMT CO2 eq. 

Direct

Indirect

Total
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

27

recommendations. Agroecosystem models have potential 

to account for these interactions, 

but empirical models are limited 

by simplistic assumptions that 

lead to large errors, and complex 

models are limited by difficulty 

in acquiring required input data 

(Kebreab et al. 2016).

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, 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 12 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.

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 most grazed lands are not highly managed, 

particularly the large expanses of rangeland in the 

Western United States, mitigation options are limited. 

One strategy that may be feasible for more intensely 

managed pastures in the Eastern United States is 

nitrification inhibitors. Although synthetic N fertilizer 

inputs are low, grazing lands usually have large N 

inputs from biological N fixation because they are 

seeded with legumes. Equations to estimate the 

mitigation potential of fertilizers formulated with 

nitrification inhibitors are included in a recent USDA 

report (Ogle et al. 2014). 

2.8 Planned Improvements

There are a few areas where changes could be made 

to improve upon the existing inventory. Regarding 

enteric CH4 emissions, changes involve updating 

and refining input values such as cattle births, 

DE, animal weight gains, emissions factors, and 

updating the uncertainty methodology. For managed 

manure emissions, the uncertainty analysis will be 

updated to more accurately assess uncertainty of 

emission calculations due to extensive changes in 

emission calculation methodology and the use of new 

calculations and variables for indirect N2O emissions. 

The 2012 Agricultural Census data will be used to 

update county-level animal population and WMS 

estimates. For grazing emission from soils, major 

improvements include refining the DayCent model 

and using more recent NRI data. Future inventories 

will attempt to quantify mitigation potentials from all 

sources related to livestock production.

SUGGESTED CITATION

Del Grosso, S.J., S.M. Ogle, 

M. Reyes-Fox, K.L. Nichols, 

and E. Marx, 2016. Chapter 2: 

Livestock and Grazed Lands 

Emissions. In U.S. Agriculture 

and Forestry Greenhouse 

Gas Inventory: 1990–2013, 

Technical Bulletin No. 1943, 

United States Department 

of Agriculture, Office 

of the Chief Economist, 

Washington, DC. 137 pp. 

September 2016. 

Del Grosso S.J. and M. 

Baranski, Eds. 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

28

2.9 References

AAPFCO (1995-2000a, 2002 -2007). Commercial Fertilizers. 

Association of American Plant Food Control Officials. University 

of Kentucky. Lexington, KY.

AAPFCO (2008- 2014). Commercial Fertilizers. Association of 

American Plant Food Control Officials. University of Missouri. 

Columbia, MO. 

Archibeque, S., K. Haugen-Kozyra, K. Johnson, E. Kebreab, W. 

Powers-Schilling, L.P. Olander, and A. Van de Bogert (2012). 

Near-term options for reducing greenhouse gas emissions from 

livestock systems in the United States. Nicholas Institute for 

Environmental Policy Solutions Report NI R 12-04.

ASAE (2003). Manure production and characteristics. American 

Society of Agricultural Engineers, St. Joseph, MI.

Del Grosso, S.J., S.M. Ogle, W.J. Parton, F.J. Breidt. 

(2010). Estimating uncertainty in N2

O emissions from US 

cropland soils, Global Biogeochemical Cycles, 24, GB1009, 

doi:10.1029/2009GB003544.

Del Grosso, S.J. and M.A. Cavigelli (2012). Climate stabilization 

wedges revisited: can agricultural production and greenhouse gas 

reduction goals be accomplished? Frontiers in Ecology and the 

Environment, 10: 571-578.

Edmonds, L., N. Gollehon, R.L. Kellogg, B. Kintzer, L. Knight, 

C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J. Schaeffer 

(2003). Costs associated with development and implementation 

of comprehensive nutrient management plans, part 1: nutrient 

management, land treatment, manure and wastewater handling 

and storage, and recordkeeping. Natural Resource Conservation 

Service, United States Department of Agriculture, Government 

Printing Office, Washington, D.C. 

Enns, M. (2008). Personal Communication. Mark Enns, Colorado 

State University and staff at ICF International.

EPA (1992). Global methane emissions from livestock and poultry 

manure. L.M. Safley, M.E. Casada, J.W. Woodbury, and K.F. Roos, 

authors. U.S. Environmental Protection Agency, Office of Air and 

Radiation, Washington, D.C. 

EPA (1993). Federal Register. Part II. Standards for the Use and 

Disposal of Sewage Sludge; Final Rules. U.S. Environmental 

Protection Agency, 40 CFR Parts 257, 403, and 503.

EPA (2002a). Development document for the final revisions to 

The National Pollutant Discharge Elimination System (NPDES) 

regulation and the effluent guidelines for Concentrated Animal 

Feeding Operations (CAFOS). EPA-821-R-03-001. U.S. 

Environmental Protection Agency, Washington, D.C.

EPA (2002b). Cost methodology for the final revisions to the 

National Pollutant Discharge Elimination System (NPDES) 

regulation and the effluent guidelines for Concentrated Animal 

Feeding Operations (CAFOS). EPA-821-R-03-004. U.S. 

Environmental Protection Agency, Washington, D.C.

EPA (2003). Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990–2001. Office of Atmospheric Programs, Washington, 

D.C.

EPA (2005). Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990–2003. Office of Atmospheric Programs, Washington, 

D.C.

EPA (2015). Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990–2013. Office of Atmospheric Programs, Washington, 

D.C.

ERG (2000). Calculations: percent distribution of manure for 

waste management systems. Eastern Research Group, Inc., 

Morrisville, NC.

ERG (2010a). Telecon with William Boyd of USDA Natural 

Resources Conservation Service and Cortney Itle of ERG 

Concerning Updated Volatile Solids and Nitrogen Excretion Rates. 

August 8, 2010. 

ERG (2010b). Updating Current Inventory Manure Characteristics: 

New USDA Agricultural Waste Management Field Handbook 

Values. Memorandum to EPA from ERG. August 13, 2010.

Eshel, G., A. Shepon, T. Makov., and R. Milo (2014). Land, 

irrigation water, greenhouse gas, and reactive nitrogen burdens of 

meat, eggs, and dairy production in the United States. Proceedings 

of the National Academy of Sciences, 111(33), 11996-12001.

Garrett, W.N. and D.E. Johnson (1983). Nutritional energetics of 

ruminants. Journal of Animal Science, 57(suppl.2): 478-497. 

Groffman, P.M., R. Brumme, K. Butterbach-Bahl, K.E. Dobbie, 

A.R. Mosier, D. Ojima, H. Papen, W.J. Parton, K.A. Smith, and C. 

Wagner-Riddle (2000) Evaluating annual nitrous oxide fluxes at 

the ecosystem scale. Global Biogeochemical Cycles, 14(4): 1061.

Holstein Association (2010). History of the Holstein Breed 

(website). Available online at <http://www.holsteinusa.com/

holstein_breed/breedhistory.html>.

Hristov, A.N., J. Oh, C. Lee, R. Meinen, F. Montes, T. Ott, J. 

Firkins, A. Rotz, C. Dell, A. Adesogan, W.Z. Yang, J. Tricarico, 

E. Kebreab, G. Waghorn, J. Dijkstra, and S. Oosting (2013). In: 

Gerber, P., B. Henderson, and H. Makkar, editors, Mitigation of 

greenhouse gas emissions in livestock production: A review of 

technical options for non-CO2

 emission. Food and Agriculture 

Organization of the United Nations, Rome.

IPCC (1997). Revised 1996 IPCC guidelines for national 

greenhouse gas inventories, vol. 1-3. Working Group 1, authors. 

Intergovernmental Panel on Climate Change, United Nations 

Environment Programme, Organization for Economic Cooperation 

and Development, International Energy Agency, Paris, France. 

IPCC (2006). 2006 IPCC guidelines for national greenhouse gas 

inventories, vol. 4: agriculture, forestry and other land use. S. 

Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, editors. 

Intergovernmental Panel on Climate Change, National Greenhouse 

Gas Inventories Programme, Technical Support Unit, Kanagawa, 

Japan. 

Johnson, K.A. and D.E. Johnson (1995). Methane emissions from 

cattle. Journal of Animal Science, 73: 2483-2492.

Johnson, K. (2010). Personal Communication. Kris Johnson, 

Washington State University, Pullman, and ICF International.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

29

Kebreab, E., L. Tedeschi, J. Dijkstra, J.L. Ellis, A. Bannink and 

J. France (2016). Modeling greenhouse gas emissions from 

enteric fermentation advances. In Agricultural Systems Modeling, 

Synthesis and Modeling of Greenhouse Gas Emissions and Carbon 

Storage in Agricultural and Forest Systems to Guide Mitigation 

and Adaptation, 6:173-196.

Machovina, B., K.J. Feeley, and W.J. Ripple (2015). Biodiversity 

conservation: The key is reducing meat consumption. Science of 

the Total Environment, 536, 419-431.

Mosier, A.R., J.M. Duxbury, J.R. Freney, O. Heinemeyer and K. 

Minami (1998). Mitigating agricultural emissions of methane. 

Climatic Change, 40:39-80.

NEBRA (2007). A National Biosolids Regulation, Quantity, End 

Use and Disposal Survey: Final report. Available online at <http://

www.nebiosolids.org/uploads/pdf/NtlBiosolidsReport-20July07.

pdf>.

Ogle, S.M., P.R. Adler, F.J. Breidt, S. Del Grosso, J. Derner, 

A. Franzluebbers, M. Liebig, B. Linquist, G.P. Robertson, M. 

Schoeneberger, J. Six, C. van Kessel, R. Venterea, T. West (2014). 

Chapter 3: Quantifying greenhouse gas sources and sinks in 

cropland and grazing land systems. In Quantifying Greenhouse 

Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale 

Inventory. Technical Bulletin Number 1939, Office of the Chief 

Economist, United States Department of Agriculture, Washington, 

DC. 606 pages. Eve, M., D. Pape, M. Flugge, R. Steele, D. Man, 

M. Riley-Gilbert, and S. Biggar, Eds.

Ott, S.L. (2000). Dairy ’96 Study. Stephen L. Ott, Animal and 

Plant Health Inspection Service, United States Department of 

Agriculture. June 19, 2000.

Smith, P., H. Haberl, A. Popp, K.H. Erb, C. Lauk, R. Harper,... and 

S. Rose (2013). How much land-based greenhouse gas mitigation 

can be achieved without compromising food security and 

environmental goals?. Global Change Biology, 19(8), 2285-2302.

UEP (1999). Voluntary survey results, estimated percentage 

participation/activity, caged layer environmental management 

practices. Industry data submissions for EPA profile development, 

United Egg Producers and National Chicken Council.

USDA (1996a). Swine ’95: grower/finisher part II: reference 

of 1995 U.S. grower/finisher health and management practices. 

United States Department of Agriculture, Animal Plant Health and 

Inspection Service, Washington, D.C.

USDA (1996b). Agricultural Waste Management Field Handbook, 

National Engineering Handbook (NEH), Part 651. Natural 

Resources Conservation Service, United States Department of 

Agriculture. July 1996.

USDA (1997). Beef cow/calf health and productivity audit. 

United States Department of Agriculture, Animal and Plant Health 

Inspection Service, National Animal Health Monitoring System, 

Fort Collins, CO. Available online at <http://www.aphis.usda.gov/

vs/ceah/cahm>.

USDA (1998). Re-aggregated data from the National Animal 

Health Monitoring System’s (NAHMS) swine ’95 study. 

Aggregated by E. Bush. United States Department of Agriculture, 

Centers for Epidemiology and Animal Health.

USDA (2000a). Layers ’99—Part II: References of 1999 Table 

Egg Layer Management in the U.S. USDA-APHIS-VS. Fort 

Collins, CO. <http://www.aphis.usda.gov/animal_health/nahms/

poultry/downloads/layers99/Layers99_dr_PartII.pdf>.

USDA (2000b). 1997 National Resources Inventory. United States 

Department of Agriculture, Natural Resources Conservation 

Service, Washington, D.C. Available online at <http://www.nrcs.

usda.gov/technical/NRI/>.

USDA (2008). Agricultural Waste Management Field Handbook, 

National Engineering Handbook (NEH), Part 651. Natural 

Resources Conservation Service, United States Department of 

Agriculture.

USDA (2009). Agricultural Statistics Annual. National Agricultural 

Statistics Service. Available online at <http://www.nass.usda.gov/

Publications/Ag_Statistics/2009/2009.pdf>.

USDA (2010). Beef 2007–08, Part V: Reference of Beef Cow-calf 

Management Practices in the United States, 2007–08. USDA–

APHIS–VS, CEAH. Fort Collins, CO. 

USDA (2013a). Quick Stats: Agricultural Statistics Database. 

National Agriculture Statistics Service, United States Department 

of Agriculture. Washington, DC. Available online at <http://

quickstats.nass.usda.gov/>.

USDA (2013b). Summary Report: 2010 National Resources 

Inventory. Natural Resources Conservation Service, Washington, 

D.C, and Center for Survey Statistics and Methodology, Iowa 

State University, Ames, Iowa. <http://www.nrcs.usda.gov/Internet/

FSE_DOCUMENTS/stelprdb1167354.pdf>

van der Weerden, T. J., J. Luo, C.A. de Klein, C.J. Hoogendoorn, 

R.P. Littlejohn., and G.J. Rys, (2011). Disaggregating nitrous 

oxide emission factors for ruminant urine and dung deposited onto 

pastoral soils. Agriculture, ecosystems & environment, 141(3), 

426-436.
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

30

2.10 Appendix A

A-1 Population of Animals by State in 2013

A-2 U.S. Livestock Population, 1990, 1995, 2000, 2005-2013

A-3 State-Level Methane Emissions From Enteric Fermentation by 

Livestock Category in 2013

A-4 State-Level Methane Emissions From Enteric Fermentation in 

1990, 1995, 2000, 2005-2013

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 2013

A-9 Regional Estimates of Digestible Energy and Methane 

Conversion Rates for Foraging Animals 2007-2013

A-10 Regional Estimates of Digestible Energy and Methane 

Conversion Rates for Dairy and Feedlot Cattle in 2013

A-11 Definition of Regions for Characterizing the Diets of Dairy 

Cattle (all years) and Foraging Cattle 1990-2006

A-12 Definition of Regions for Characterizing the Diets of 

Foraging Cattle from 2007-2013

A-13 Methane Emissions From Cattle Enteric Fermentation, 

1990-2013

A-14 IPCC Emission Factors for Livestock

A-15 Summary of Greenhouse Gas Emissions From Managed 

Waste by State in 2013

A-16 Methane Emissions From Manure Management by State and 

Animal in 2013

A-17 Nitrous Oxide Emissions From Manure Management by 

State and Animal in 2013

A-18 Waste Characteristics Data

A-19 State Volatile Solids Production Rates in 2013

A-20 State-Based Methane Conversion Factors for Liquid Waste 

Management Systems in 2013

A-21 Maximum Methane Generation Potential, B0

A-22 Methane Conversion Factors for Dry Systems

A-23 Methane Conversion Factors for Livestock Waste Emissions 

in 2013

A-24 Direct Nitrous Oxide Emission Factors for 2013

A-25 Nitrogen in Livestock Waste on Grazed Lands 

A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock 

Changes From Non-Federal Grasslands, 2003-2007

A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect 

N2

O Emissions From Non-Federal Grasslands, 2003-2007
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

31

Appendix Table A-1 Population of Animals by State in 2013 Appendix Table A-1 Population of Animals by State in 2013

Beef Cattle Dairy Cattle Swine Sheep Goat Horse Poultry

State Head

Alabama 1,164,323 17,605 85,000 12,083 47,212 59,026 206,577,762

Alaska 11,681 805 1,000 12,083 626 1,443 1,212,966

Arizona 716,787 360,285 175,000 140,000 77,557 97,124 1,212,966

Arkansas 1,529,163 20,585 115,000 12,083 39,816 57,514 211,111,970

California 1,948,078 3,470,786 95,000 570,000 141,886 134,921 35,115,697

Colorado 2,905,849 288,910 705,000 435,000 31,913 108,624 6,310,216

Connecticut 12,609 36,203 3,500 7,333 4,356 18,607 3,292,216

Delaware 8,354 9,796 6,000 12,083 1,704 6,596 40,412,966

Florida 1,544,585 219,544 15,000 12,083 50,923 121,118 22,346,307

Georgia 864,002 148,980 141,000 12,083 69,256 68,492 270,221,762

Hawaii 123,956 5,168 11,500 12,083 13,761 4,827 1,212,966

Idaho 1,368,084 1,186,413 38,200 235,000 18,208 58,921 1,212,966

Illinois 928,878 196,112 4,625,000 53,000 31,120 59,361 15,352,580

Indiana 505,098 319,163 3,625,000 55,000 36,940 100,629 51,466,697

Iowa 3,739,688 429,699 20,375,000 175,000 56,297 60,248 76,182,580

Kansas 6,075,096 299,269 1,812,500 65,000 40,878 71,868 1,212,966

Kentucky 1,946,299 158,722 315,000 43,000 57,308 135,110 63,290,034

Louisiana 800,046 29,200 8,000 12,083 18,220 59,645 13,121,580

Maine 24,191 63,864 4,500 7,333 6,558 11,953 3,912,216

Maryland 94,328 106,053 22,000 12,083 9,516 28,245 59,341,125

Massachusetts 14,149 24,893 8,500 7,333 8,674 20,288 442,216

Michigan 461,980 726,955 1,045,000 82,000 26,903 85,370 26,567,580

Minnesota 1,292,988 982,438 7,787,500 135,000 33,107 61,633 37,940,121

Mississippi 879,933 28,158 500,000 12,083 23,304 57,371 143,196,762

Missouri 3,298,804 180,591 2,800,000 75,000 105,113 110,921 150,681,212

Montana 2,958,072 29,151 166,000 235,000 9,937 96,457 896,216

Nebraska 7,339,137 103,169 3,037,500 80,000 24,087 64,066 21,772,580

Nevada 435,537 52,863 2,000 73,000 23,287 23,278 1,212,966

New Hampshire 7,792 26,904 3,800 7,333 5,072 8,936 1,212,966

New Jersey 17,937 14,576 9,000 12,083 7,785 27,161 1,212,966

New Mexico 789,115 608,835 1,200 100,000 30,044 50,144 1,212,966

New York 250,477 1,241,783 66,000 70,000 35,745 91,189 16,162,580

North Carolina 673,494 92,519 8,900,000 26,000 59,969 64,569 175,307,515

North Dakota 1,885,554 40,177 135,000 74,000 4,830 45,375 1,212,966

Ohio 780,309 533,110 2,140,000 121,000 47,969 113,113 51,455,788

Oklahoma 3,942,639 89,538 2,187,500 75,000 81,811 157,591 42,041,125

Oregon 1,109,398 253,845 8,500 210,000 32,249 66,628 12,974,580

Pennsylvania 488,239 1,118,260 1,127,500 86,000 48,366 120,614 63,279,242

Rhode Island 2,916 1,860 1,900 7,333 923 2,203 1,212,966

South Carolina 318,626 31,187 245,000 12,083 37,761 54,217 50,900,818

South Dakota 3,809,849 193,980 1,162,500 275,000 17,706 68,665 4,586,333

Tennessee 1,655,886 97,535 175,000 33,000 83,866 87,449 34,381,398

Texas 11,475,115 857,519 632,500 700,000 826,704 387,214 136,119,489

Utah 639,196 185,983 730,000 295,000 14,210 58,818 6,021,333

Vermont 30,796 261,563 3,200 7,333 11,388 11,342 459,216

Virginia 1,329,691 177,138 255,000 87,000 48,379 86,135 54,576,485

Washington 780,991 508,126 38,200 54,000 25,906 59,591 18,362,580

West Virginia 383,913 20,113 5,000 30,000 17,001 24,215 20,672,333

Wisconsin 908,325 2,618,905 305,000 84,000 62,145 100,168 16,784,762

Wyoming 1,428,100 13,061 90,000 375,000 9,416 70,858 329,216

Total 75,700,053 18,481,893 65,746,500 5,335,000 2,517,711 3,539,852 2,177,309,818

Source: EPA 2015
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

32

Appendix Table A-2 U.S. Livestock Population, 1990, 1995, 2000, 2005-2013 Appendix Table A-2 U.S. Livestock Population, 1990, 1995, 2000, 2005-2013

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013

Animal Type 1 million head

Dairy Cattle1 14 14 13 13 13 13 14 14 14 14 14 14

Dairy Cows 10 9 9 9 9 9 9 9 9 9 9 9

Dairy Heifers 4 4 4 4 4 4 4 4 5 5 5 5

Swine 54 59 59 61 62 65 67 66 65 66 66 66

Market <60 lbs. 18 20 20 20 21 22 20 19 19 19 19 19

Market 60-119 lbs. 12 13 13 14 14 15 17 17 17 17 17 17

Market 120-179 lbs. 9 11 11 11 11 12 13 13 12 12 13 13

Market >180 lbs. 8 9 9 10 10 11 11 11 11 11 11 11

Breeding Swine 7 7 6 6 6 6 6 6 6 6 6 6

Beef cattle 82 90 85 82 83 83 82 81 80 79 77 76

Feedlot Steers 17 18 17 17 17 17 16 16 16 16 15 15

Feedlot Heifers 6 7 8 8 9 9 8 8 9 9 8 8

Bulls NOF2 2 2 2 2 2 2 2 2 2 2 2 2

Calves NOF 32 35 34 33 33 33 32 32 31 31 30 29

Heifers NOF 10 12 9 8 8 8 8 9 8 8 7 8

Steers NOF 3 4 5 5 5 5 5 5 5 5 5 5

Cows NOF 10 12 10 10 10 10 9 9 9 9 9 9

Sheep 11 9 7 6 6 6 6 6 6 5 5 5

Goats 3 2 2 3 3 3 3 3 3 3 3 3

Poultry 1,537 1,827 2,033 2,150 2,154 2,167 2,176 2,089 2,104 2,096 2,169 2,177

Hens >1 yr. 273 299 334 348 350 347 340 341 342 339 347 353

Pullets 73 81 95 97 97 104 99 102 106 102 104 105

Chickens 7 8 8 8 8 8 8 8 7 7 7 7

Broilers 1,066 1,332 1,506 1,613 1,612 1,619 1,638 1,555 1,568 1,565 1,626 1,633

Turkeys 118 107 90 84 87 89 91 82 81 83 84 80

Horses 2 3 3 4 4 4 4 4 4 4 4 4

Source: EPA 2015

Note: Totals may not sum due to independent rounding.

1Dairy cattle does not include dairy calves.

2(NOF) Not on feed.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

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Appendix Table A-3 State-Level Methane Emissions from Enteric Fermentation by Livestock Category in 2013

Appendix Table A-3 State-Level Methane Emissions From 

Enteric Fermentation by Livestock Category in 2013

Beef cattle Dairy cattle Swine Horses Total*

State MMT CO2 eq.

Alabama 1.94 0.05 0.00 0.03 1.99

Alaska 0.02 0.00 0.00 0.00 0.02

Arizona 1.07 0.86 0.01 0.04 1.93

Arkansas 2.55 0.04 0.00 0.03 2.59

California 3.08 8.22 0.00 0.06 11.30

Colorado 4.30 0.60 0.03 0.05 4.91

Connecticut 0.02 0.09 0.00 0.01 0.11

Delaware 0.01 0.02 0.00 0.00 0.04

Florida 2.58 0.56 0.00 0.06 3.13

Georgia 1.43 0.38 0.01 0.03 1.81

Hawaii 0.22 0.01 0.00 0.00 0.23

Idaho 2.24 2.73 0.00 0.03 4.98

Illinois 1.40 0.40 0.17 0.03 1.80

Indiana 0.76 0.72 0.14 0.05 1.48

Iowa 5.23 0.96 0.76 0.03 6.19

Kansas 8.36 0.56 0.07 0.03 8.92

Kentucky 3.22 0.36 0.01 0.06 3.58

Louisiana 1.34 0.07 0.00 0.03 1.40

Maine 0.04 0.14 0.00 0.01 0.18

Maryland 0.15 0.24 0.00 0.01 0.39

Massachusetts 0.02 0.05 0.00 0.01 0.08

Michigan 0.66 1.62 0.04 0.04 2.27

Minnesota 1.89 1.97 0.29 0.03 3.86

Mississippi 1.47 0.06 0.02 0.03 1.53

Missouri 5.31 0.33 0.11 0.05 5.64

Montana 5.23 0.06 0.01 0.04 5.29

Nebraska 10.22 0.22 0.11 0.03 10.44

Nevada 0.77 0.13 0.00 0.01 0.89

New Hampshire 0.01 0.06 0.00 0.00 0.08

New Jersey 0.03 0.03 0.00 0.01 0.06

New Mexico 1.39 1.54 0.00 0.02 2.93

New York 0.41 2.93 0.00 0.04 3.34

North Carolina 1.13 0.23 0.33 0.03 1.36

North Dakota 3.01 0.07 0.01 0.02 3.09

Ohio 1.16 1.09 0.08 0.05 2.25

Oklahoma 6.27 0.22 0.08 0.07 6.49

Oregon 1.92 0.56 0.00 0.03 2.48

Pennsylvania 0.76 2.53 0.04 0.05 3.29

Rhode Island 0.00 0.00 0.00 0.00 0.01

South Carolina 0.53 0.07 0.01 0.02 0.61

South Dakota 5.93 0.37 0.04 0.03 6.29

Tennessee 2.76 0.24 0.01 0.04 3.00

Texas 17.20 2.11 0.02 0.17 19.31

Utah 1.11 0.42 0.03 0.03 1.53

Vermont 0.05 0.58 0.00 0.01 0.63

Virginia 2.18 0.45 0.01 0.04 2.64

Beef cattle Dairy cattle Swine Horses Total*

State MMT CO2 eq.

Washington 1.20 1.22 0.00 0.03 2.42

West Virginia 0.64 0.04 0.00 0.01 0.68

Wisconsin 1.32 5.47 0.01 0.05 6.79

Wyoming 2.48 0.03 0.00 0.03 2.50

Total 117.0 41.7 2.5 1.6 158.8

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. 

Source: EPA 2015

*State totals include all livestock categories
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

34

Appendix Table A-4 State-Level Methane Emissions from Enteric Fermentation in 1990, 1995, 2000, 2005-2013

Appendix Table A-4 State-Level Methane Emissions from Enteric Fermentation in 1990,

1995, 2000, 2005-2013

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013

State MMT CO2 eq.

Alabama 2.53 2.82 2.37 2.16 2.08 2.08 2.00 2.02 2.05 2.00 1.97 1.98

Alaska 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.03 0.02

Arizona 1.55 1.62 1.61 1.86 1.95 2.03 2.07 2.14 1.97 1.88 1.96 1.95

Arkansas 2.82 3.17 2.91 3.01 2.82 2.82 2.90 2.85 2.96 2.80 2.72 2.58

California 8.69 9.05 9.92 10.69 10.80 11.50 11.47 11.26 11.06 11.04 11.34 11.20

Colorado 4.34 4.96 5.08 4.27 4.50 5.02 5.08 4.93 4.92 5.04 5.24 5.05

Connecticut 0.17 0.16 0.15 0.12 0.11 0.11 0.11 0.11 0.10 0.10 0.10 0.10

Delaware 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

Florida 3.58 3.87 3.43 3.26 3.21 3.30 3.23 3.24 3.25 3.15 3.23 3.15

Georgia 2.25 2.49 2.18 2.10 2.07 2.04 1.96 1.91 1.88 1.82 1.86 1.82

Hawaii 0.32 0.30 0.29 0.28 0.29 0.28 0.27 0.27 0.26 0.26 0.25 0.23

Idaho 2.87 3.34 3.74 4.16 4.28 4.50 4.58 4.56 4.58 4.76 4.81 5.01

Illinois 2.71 2.67 2.38 2.17 2.17 2.12 2.03 1.98 1.91 1.78 1.72 1.81

Indiana 1.95 1.87 1.55 1.50 1.51 1.57 1.56 1.51 1.58 1.55 1.51 1.47

Iowa 6.49 6.44 5.96 5.76 6.05 6.26 6.31 6.35 6.29 6.19 6.14 6.24

Kansas 7.79 9.46 9.48 9.74 9.98 9.88 10.06 9.89 9.56 9.73 9.45 9.17

Kentucky 3.92 4.31 3.63 3.80 3.87 4.11 3.98 3.81 3.70 3.53 3.42 3.58

Louisiana 1.85 1.74 1.62 1.57 1.51 1.55 1.60 1.60 1.53 1.43 1.40 1.39

Maine 0.23 0.22 0.21 0.19 0.18 0.18 0.19 0.19 0.19 0.19 0.18 0.18

Maryland 0.61 0.61 0.51 0.44 0.45 0.43 0.40 0.39 0.40 0.39 0.40 0.39

Massachusetts 0.16 0.14 0.12 0.10 0.10 0.09 0.09 0.09 0.08 0.08 0.08 0.08

Michigan 2.08 2.11 1.87 1.92 1.99 2.09 2.12 2.14 2.21 2.21 2.27 2.32

Minnesota 4.35 4.40 4.11 3.77 3.75 3.85 3.86 3.89 3.89 3.84 3.82 3.88

Mississippi 2.12 2.18 1.81 1.79 1.61 1.58 1.59 1.58 1.62 1.55 1.55 1.53

Missouri 6.63 7.34 6.76 6.84 7.14 6.84 6.63 6.52 6.35 5.97 5.88 5.64

Montana 4.00 4.98 4.88 4.47 4.58 4.98 5.34 5.26 5.13 5.13 5.09 5.30

Nebraska 8.88 9.90 10.64 10.34 10.69 11.07 10.77 10.76 10.62 10.41 10.77 10.66

Nevada 0.90 0.92 0.92 0.91 0.92 0.90 0.88 0.89 0.90 0.91 0.91 0.89

New Hampshire 0.10 0.10 0.10 0.09 0.09 0.08 0.09 0.09 0.08 0.08 0.08 0.08

New Jersey 0.14 0.13 0.10 0.08 0.08 0.07 0.07 0.08 0.07 0.06 0.06 0.06

New Mexico 2.37 2.83 3.06 3.11 3.28 3.30 3.44 3.44 3.31 3.25 3.06 2.88

New York 3.44 3.26 3.34 3.16 3.19 3.32 3.38 3.28 3.30 3.31 3.35 3.36

North Carolina 1.51 1.81 1.59 1.47 1.42 1.43 1.37 1.40 1.35 1.33 1.35 1.36

North Dakota 2.83 3.44 3.23 3.10 3.09 3.20 3.09 3.08 3.00 2.99 2.91 3.10

Ohio 2.55 2.45 2.15 2.28 2.29 2.24 2.27 2.37 2.35 2.27 2.31 2.28

Oklahoma 7.24 8.08 7.39 7.69 7.97 7.76 7.78 7.86 8.01 7.53 6.70 6.50

Oregon 2.46 2.82 2.65 2.73 2.68 2.52 2.65 2.42 2.47 2.54 2.53 2.47

Pennsylvania 3.64 3.46 3.40 3.19 3.17 3.24 3.24 3.22 3.27 3.25 3.26 3.28

Rhode Island 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

South Carolina 0.91 0.87 0.78 0.74 0.70 0.69 0.67 0.65 0.65 0.65 0.64 0.61

South Dakota 5.35 6.44 6.28 6.26 6.32 6.27 6.17 6.23 6.32 6.16 6.08 6.35

Tennessee 3.61 4.02 3.42 3.52 3.60 3.71 3.51 3.21 3.31 3.25 3.16 2.98

Texas 20.76 24.75 22.55 22.48 23.16 22.83 22.63 23.08 22.73 22.42 20.74 19.55

Utah 1.44 1.63 1.65 1.61 1.53 1.60 1.67 1.60 1.58 1.55 1.57 1.53

Vermont 0.71 0.68 0.70 0.64 0.64 0.65 0.65 0.64 0.62 0.64 0.63 0.65

Virginia 2.73 2.83 2.58 2.67 2.73 2.66 2.62 2.47 2.57 2.53 2.50 2.63

Washington 2.57 2.61 2.52 2.23 2.31 2.39 2.34 2.34 2.24 2.35 2.38 2.42

West Virginia 0.76 0.82 0.69 0.67 0.69 0.72 0.69 0.70 0.65 0.66 0.66 0.68

Wisconsin 7.42 6.62 6.37 6.17 6.23 6.47 6.52 6.59 6.68 6.75 6.79 6.86

Wyoming 2.05 2.57 2.69 2.31 2.44 2.69 2.55 2.57 2.51 2.49 2.57 2.51

Total 158.44 173.40 165.42 163.51 166.29 169.12 168.55 167.53 166.13 163.87 161.49 159.82

Note: State level emissions do not include data for non-cattle. MMT CO2 eq. is million metric tons carbon dioxide equivalent.

Source: EPA 2015

Appendix Table A-5 Cattle Population Categories Used for Estimating Methane Emissions Appendix Table A-5 Cattle Population Categories Used for Estimating Methane Emissions

Dairy Cattle Beef Cattle

Calves (4-6 mo) Calves (4-6 mo)

Heifer Replacements Heifer Replacements

Cows Heifer and Steer Stockers

Animals in Feedlots (Heifers and Steers)

Cows

Bulls1

Source: EPA 2015

1 Bulls (beef and dairy) are accounted for in a single category.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

35

Appendix Table A-6 Dairy Lactation by Region1

Appendix Table A-6 Dairy Lactation by Region1

California West Northern Great 

Plains Southcentral Northeast Midwest Southeast 

Year (lbs * year)/cow

1990 18,456 146,737 94,384 50,123 170,274 115,308 116,277

1991 18,534 149,227 95,175 49,752 174,570 117,551 117,666

1992 18,722 155,838 98,240 51,413 180,353 121,223 121,419

1993 18,852 155,984 98,723 52,135 179,289 121,622 124,859

1994 20,203 160,840 101,511 52,944 180,102 122,992 127,801

1995 19,573 159,752 102,563 52,913 184,544 125,823 129,453

1996 19,161 162,417 104,164 52,860 185,547 124,764 128,195

1997 19,829 164,233 105,060 52,846 191,086 128,219 130,930

1998 19,451 166,106 108,478 53,279 195,078 131,930 130,626

1999 20,781 166,741 111,222 53,903 197,570 133,766 134,263

2000 21,130 169,877 116,222 55,413 199,323 138,105 137,216

2001 20,904 168,163 116,523 55,120 204,650 136,009 139,062

2002 21,277 172,668 121,146 56,623 208,267 139,990 140,620

2003 20,993 171,078 122,244 57,926 205,592 145,306 136,904

2004 21,139 170,757 122,811 61,092 207,408 147,148 140,976

2005 21,404 174,066 127,412 62,071 209,638 151,582 143,500

2006 21,815 175,077 131,933 61,406 213,221 152,633 145,258

2007 22,440 178,152 132,981 60,537 213,130 152,983 149,937

2008 22,344 176,679 136,074 61,381 217,190 151,903 148,871

2009 22,000 179,386 139,674 62,443 217,153 154,529 152,199

2010 23,025 184,540 143,910 63,000 220,024 157,303 150,623

2011 23,438 187,898 144,853 64,369 221,859 157,887 151,149

2012 23,457 190,031 149,516 66,056 227,856 162,671 152,473

2013 23,178 184,682 150,877 64,082 231,351 163,624 151,718

Source: EPA 2015

1 Beef lactation data developed using methodology described in EPA 2015.

Appendix Table A-7 Typical Livestock Weights for 2013 Appendix Table A-7 Typical Livestock Weights for 2013

Cattle Type lbs

Calves 270

Dairy Cows 1,500

Dairy Replacements 899

Beef Cows 1,348

Bulls 2,022

Beef Replacements 893

Steer Stockers 721

Heifer Stockers 711

Steer Feedlot 1,017

Heifer Feedlot 959

Source: Feedstuffs (1998), Western Dairyman 

(1998), Enns (2008), Johnson (2010), NRC 

(1999), Holstein Association 2010, USDA 

(2013b,) EPA (2015).
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

36

Appendix Table A-8 U.S. Feedlot Placements for 2013 Appendix Table A-8 U.S. Feedlot Placements for 2013

Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Total 

Weight Placed Number of animals placed, 1,000 head

< 600 lbs. 460 400 380 445 415 460 620 715 685 840 750 550 6,720

600 - 700 lbs. 475 365 360 310 355 380 400 365 415 590 500 385 4,900

700 - 800 lbs. 544 492 589 485 480 420 495 476 504 487 377 360 5,709

> 800 lbs. 410 410 585 545 560 435 620 690 865 575 410 378 6,483

Total 1,889 1,667 1,914 1,785 1,810 1,695 2,135 2,246 2,469 2,492 2,037 1,673 23,812

Source: USDA (2002f, 2001f, 2000f, 1999a, 1995a), EPA 2015.

Note: Totals may not sum due to independent rounding.

Appendix Table A-9 Regional Estimates of Digestible Energy and Methane Conversion Rates for Foraging Animals 

2007-2013

Appendix Table A-9 Regional Estimates of Digestible Energy and Methane Conversion 

Rates for Foraging Animals 2007-2013

Animal Type Data West Central Northeast Southeast

Beef Repl. Heif. DE 1 61.9 65.6 64.5 64.6

Ym 2 6.5% 6.5% 6.5% 6.5% 

Steer Stockers DE 61.9 65.6 64.5 64.6

Ym 6.5% 6.5% 6.5% 6.5%

Heifer Stockers DE 61.9 65.6 64.5 64.6

Ym 6.5% 6.5% 6.5% 6.5%

Beef Cows DE 59.9 63.6 62.5 62.6

Ym 6.5% 6.5% 6.5% 6.5%

Beef Calves (4-6 mo) DE 61.9 65.6 64.5 64.6

Ym 6.5% 6.5% 6.5% 6.5%

Bulls DE 59.9 63.6 62.5 62.6

Ym 6.5% 6.5% 6.5% 6.5%

Source: EPA 2015

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 Regional Estimates of Digestible Energy and Methane Conversion Rates for Dairy and Feedlot 

Cattle for 2013

Appendix Table A-10 Regional Estimates of Digestible Energy and Methane Conversion 

Rates for Dairy and Feedlot Cattle for 2013

Animal Type Data California West Northern 

Great Plains Southcentral Northeast Midwest Southeast

Dairy Repl. Heif. DE1 63.7 63.7 63.7 63.7 63.7 63.7 63.7

Ym2 6.0% 6.0% 5.7% 6.5% 6.4% 5.7% 7.0%

Steer Feedlot DE 82.5 82.5 82.5 82.5 82.5 82.5 82.5

Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9%

Heifer Feedlot DE 82.5 82.5 82.5 82.5 82.5 82.5 82.5

Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9%

Dairy Cows DE 66.7 66.7 66.7 66.7 66.7 66.7 66.7

Ym 5.9% 5.9% 5.6% 6.4% 6.3% 5.6% 6.9%

Dairy Calves (4-6 mo) DE 63.7 63.7 63.7 63.7 63.7 63.7 63.7

Ym 7.8% (4 mo), 8.03% (5 mo), 8.27% (6 mo) - all regions

Source: EPA 2015

1 (DE) Digestible energy; in units of percent gross energy (GE) in megajoules (MJ) per day.

2 (Ym) Methane conversion rate is the fraction of gross energy (GE) in feed converted to methane.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

37

Appendix Table A-11 Definition of Regions for Characterizing the Diets of Dairy Cattle (all years) and Foraging Cattle 

1990-2006

Appendix Table A-12 Definition of Regions for Characterizing the Diets of Foraging Cattle from 2007-2013

Appendix Table A-12 Definition of Regions for Characterizing the Diets of Foraging Cattle 

from 2007-2013

Region & State(s)

West Central Northeast Southeast

Alaska Illinois Connecticut Alabama

Arizona Indiana Delaware Arkansas

California Iowa Maine Florida

Colorado Kansas Maryland Georgia

Hawaii Michigan Massachusetts Kentucky

Idaho Minnesota New Hampshire Louisiana

Montana Missouri New Jersey Mississippi

Nevada Nebraska New York North Carolina

New Mexico North Dakota Pennsylvania Oklahoma

Oregon Ohio Rhode Island South Carolina

Utah South Dakota Vermont Tennessee

Washington Wisconsin West Virginia Texas

Wyoming Virginia

Source: EPA 2015

Appendix Table A-11 Definition of Regions for Characterizing the Diets of Dairy Cattle (all 

years) and Foraging Cattle 1990-2006

Region & State(s)

West

Northern Great 

Plains Midwest Northeast South Central Southeast

Alaska Colorado Illinois Connecticut Arkansas Alabama

Arizona Kansas Indiana Delaware Louisiana Florida

Hawaii Montana Iowa Maine Oklahoma Georgia

Idaho Nebraska Michigan Maryland Texas Kentucky

Nevada North Dakota Minnesota Massachusetts Mississippi

New Mexico South Dakota Missouri New Hampshire North Carolina

Oregon Wyoming Ohio New Jersey South Carolina

Utah Wisconsin New York Tennessee

Washington California Pennsylvania Virginia

California Rhode Island

Vermont

West Virginia

Source: EPA 2015
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

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Appendix Table A-13 Methane Emissions from Cattle Enteric Fermentation, 1990-2013 Appendix Table A-13 Methane Emissions from Cattle Enteric Fermentation, 1990-2013

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013

Animal Type kt CH4

Dairy 1,574 1,498 1,519 1,503 1,534 1,601 1,622 1,639 1,626 1,643 1,668 1,664 

Calves 62 59 59 54 55 58 58 58 57 57 58 58 

Cows 1,242 1,183 1,209 1,197 1,219 1,271 1,289 1,304 1,287 1,301 1,324 1,325 

Replacements 7-11 months 58 56 55 56 57 60 60 61 62 63 62 61 

Replacements 12-23 months 212 201 196 196 203 213 216 216 221 222 224 220 

Beef 4,763 5,419 5,070 5,007 5,081 5,123 5,077 5,022 4,976 4,867 4,747 4,684 

Bulls 196 225 215 214 220 217 216 214 215 211 205 202 

Calves 182 193 186 179 177 175 171 169 169 166 160 158 

Cows 2,884 3,222 3,058 3,056 3,079 3,089 3,070 3,002 2,970 2,921 2,855 2,774 

Replacements 7-11 months 69 85 74 80 82 82 79 78 75 74 75 77 

Replacements 12-23 months 188 241 204 217 228 229 221 216 213 202 207 210 

Steer Stockers 563 662 509 473 475 480 475 491 475 439 415 434 

Heifer Stockers 306 375 323 299 299 296 290 300 301 283 267 269 

Total Feedlot Cattle 375 416 502 488 521 556 554 552 559 570 559 560

Total 6,338 6,917 6,589 6,510 6,615 6,725 6,700 6,661 6,602 6,510 6,416 6,348

Note: Totals may not sum due to independent rounding; kt CH4 is kilotons methane.

Source: EPA 2015

Appendix Table A-14 IPCC Emission Factors for Livestock Appendix Table A-14 IPCC Emission Factors for Livestock

Emission Factors

Animal Type (kg CH4/head/year)

DAIRY 

Calves 12

Cows 144

Replacements 7-11 months 46

Replacements 12-23 months 69

BEEF

Bulls 98

Calves 11

Cows 95

Replacements 7-11 months 60

Replacements 12-23 months 70

Steer Stockers 58

Heifer Stockers 60

Total Feedlot 43

Sheep 8

Horses 18

Swine 2

Goats 5

American Bison 82

Mules and Asses 10

Note: kg CH4 is kilograms methane.

Source: EPA 2015, IPCC 2006.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

39

Appendix Table A-15 Summary of Greenhouse Gas Emissions from Managed1 Waste by State in 2013 Appendix Table A-15 Summary of Greenhouse Gas Emissions from Managed1 Waste by 

State in 2013

CH4 N2O Total

State MMT C02 eq.

Alabama 0.44 0.13 0.57

Alaska 0.01 0.00 0.01

Arizona 1.44 0.28 1.73

Arkansas 0.25 0.15 0.40

California 10.24 1.46 11.70

Colorado 1.15 0.76 1.90

Connecticut 0.03 0.01 0.05

Delaware 0.03 0.03 0.06

Florida 0.79 0.07 0.86

Georgia 0.80 0.20 1.00

Hawaii 0.03 0.01 0.04

Idaho 3.25 0.59 3.84

Illinois 1.55 0.32 1.86

Indiana 1.38 0.34 1.72

Iowa 8.93 1.56 10.49

Kansas 1.39 1.47 2.86

Kentucky 0.29 0.08 0.38

Louisiana 0.13 0.02 0.14

Maine 0.05 0.02 0.06

Maryland 0.11 0.06 0.18

Massachusetts 0.02 0.01 0.02

Michigan 1.50 0.43 1.92

Minnesota 2.77 0.82 3.59

Mississippi 0.55 0.11 0.66

Missouri 1.06 0.27 1.33

Montana 0.22 0.05 0.26

Nebraska 1.30 1.61 2.91

Nevada 0.19 0.03 0.21

New Hampshire 0.02 0.01 0.03

New Jersey 0.01 0.01 0.02

New Mexico 1.97 0.21 2.19

New York 0.83 0.30 1.13

North Carolina 4.60 0.40 5.00

North Dakota 0.13 0.06 0.19

Ohio 1.11 0.40 1.50

Oklahoma 1.59 0.33 1.92

Oregon 0.52 0.14 0.66

Pennsylvania 0.81 0.36 1.17

Rhode Island 0.00 0.00 0.01

South Carolina 0.32 0.06 0.37

South Dakota 0.75 0.33 1.08

Tennessee 0.19 0.05 0.23

Texas 3.62 2.00 5.62

Utah 0.72 0.13 0.84

Vermont 0.15 0.05 0.21

Virginia 0.28 0.10 0.37

Washington 1.24 0.32 1.57

West Virginia 0.04 0.02 0.06

Wisconsin 2.50 1.11 3.60

Wyoming 0.10 0.06 0.16

Total 61.39 17.31 78.70

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. 

CH4 is methane. N2O is nitrous oxide.

Source: EPA 2015

1Methane totals include emissions from grazed-land manure.
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

40

Appendix Table A-16 Methane Emissions from Manure Management by State and Animal in 2013 Appendix Table A-16 Methane Emissions from Manure Management by State and Animal 

in 2013

Dairy 

cattle

Beef cattle Poultry Swine Goats Horses Sheep Total

State MMT CO2 eq.

Alabama 0.0133 0.0602 0.3143 0.0460 0.0004 0.0048 0.0002 0.4392

Alaska 0.0005 0.0005 0.0056 0.0001 0.0000 0.0001 0.0001 0.0069

Arizona 1.2876 0.0414 0.0181 0.0858 0.0007 0.0080 0.0025 1.4441

Arkansas 0.0073 0.0522 0.1214 0.0665 0.0004 0.0047 0.0002 0.2527

California 9.9547 0.1175 0.1042 0.0399 0.0013 0.0111 0.0100 10.2387

Colorado 0.7402 0.1095 0.1029 0.1810 0.0002 0.0059 0.0051 1.1448

Connecticut 0.0253 0.0004 0.0068 0.0003 0.0000 0.0010 0.0001 0.0339

Delaware 0.0067 0.0003 0.0221 0.0019 0.0000 0.0004 0.0001 0.0315

Florida 0.5376 0.0799 0.1576 0.0024 0.0005 0.0099 0.0002 0.7881

Georgia 0.1877 0.0444 0.5008 0.0611 0.0006 0.0056 0.0002 0.8005

Hawaii 0.0103 0.0072 0.0084 0.0046 0.0001 0.0004 0.0002 0.0312

Idaho 3.1659 0.0518 0.0166 0.0091 0.0001 0.0032 0.0028 3.2495

Illinois 0.2116 0.0311 0.0119 1.2863 0.0002 0.0033 0.0006 1.5450

Indiana 0.3529 0.0172 0.0389 0.9647 0.0002 0.0055 0.0006 1.3802

Iowa 0.5249 0.1286 0.0482 8.2221 0.0004 0.0033 0.0021 8.9295

Kansas 0.5689 0.2131 0.0017 0.5982 0.0003 0.0039 0.0008 1.3869

Kentucky 0.0391 0.0662 0.0435 0.1371 0.0004 0.0074 0.0005 0.2943

Louisiana 0.0159 0.0413 0.0618 0.0006 0.0002 0.0049 0.0002 0.1248

Maine 0.0359 0.0008 0.0077 0.0004 0.0000 0.0007 0.0001 0.0457

Maryland 0.0634 0.0033 0.0359 0.0063 0.0001 0.0015 0.0001 0.1108

Massachusetts 0.0129 0.0005 0.0009 0.0011 0.0001 0.0011 0.0001 0.0166

Michigan 1.1848 0.0159 0.0252 0.2660 0.0002 0.0047 0.0010 1.4977

Minnesota 0.8083 0.0433 0.0412 1.8686 0.0002 0.0034 0.0016 2.7665

Mississippi 0.0147 0.0455 0.2567 0.2284 0.0002 0.0047 0.0002 0.5503

Missouri 0.1437 0.1075 0.0845 0.7209 0.0007 0.0061 0.0009 1.0643

Montana 0.0445 0.1131 0.0103 0.0381 0.0001 0.0053 0.0028 0.2141

Nebraska 0.1890 0.2538 0.0199 0.8344 0.0002 0.0035 0.0009 1.3017

Nevada 0.1654 0.0166 0.0012 0.0005 0.0001 0.0013 0.0009 0.1860

New Hampshire 0.0162 0.0003 0.0024 0.0004 0.0000 0.0005 0.0001 0.0199

New Jersey 0.0066 0.0006 0.0025 0.0020 0.0000 0.0015 0.0001 0.0134

New Mexico 1.9197 0.0302 0.0170 0.0000 0.0002 0.0027 0.0012 1.9710

New York 0.7868 0.0088 0.0175 0.0151 0.0002 0.0050 0.0008 0.8342

North Carolina 0.0640 0.0232 0.3806 4.1306 0.0004 0.0035 0.0003 4.6026

North Dakota 0.0319 0.0611 0.0017 0.0340 0.0000 0.0025 0.0009 0.1320

Ohio 0.5041 0.0264 0.0336 0.5345 0.0003 0.0062 0.0014 1.1066

Oklahoma 0.2018 0.1344 0.1036 1.1355 0.0005 0.0086 0.0009 1.5852

Oregon 0.4455 0.0428 0.0246 0.0013 0.0002 0.0036 0.0025 0.5205

Pennsylvania 0.4334 0.0171 0.0392 0.3099 0.0003 0.0066 0.0010 0.8076

Rhode Island 0.0008 0.0001 0.0025 0.0002 0.0000 0.0001 0.0001 0.0037

South Carolina 0.0285 0.0165 0.1411 0.1240 0.0004 0.0045 0.0002 0.3152

South Dakota 0.3005 0.1242 0.0066 0.3150 0.0001 0.0038 0.0032 0.7534

Tennessee 0.0339 0.0566 0.0221 0.0694 0.0005 0.0048 0.0004 0.1877

Texas 2.4767 0.5980 0.1691 0.3246 0.0077 0.0318 0.0123 3.6203

Utah 0.4225 0.0244 0.0826 0.1800 0.0001 0.0032 0.0035 0.7163

Vermont 0.1501 0.0011 0.0009 0.0003 0.0001 0.0006 0.0001 0.1531

Virginia 0.0721 0.0451 0.0409 0.1133 0.0003 0.0047 0.0010 0.2775

Washington 1.1650 0.0313 0.0369 0.0061 0.0002 0.0033 0.0006 1.2434

West Virginia 0.0087 0.0131 0.0150 0.0007 0.0001 0.0013 0.0004 0.0394

Wisconsin 2.3711 0.0308 0.0137 0.0733 0.0004 0.0055 0.0010 2.4957

Wyoming 0.0214 0.0544 0.0008 0.0156 0.0001 0.0039 0.0044 0.1005

Total 31.7743 3.0036 3.2233 23.0582 0.0199 0.2240 0.0715 61.3748

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. Managed manure includes emissions from grazed lands. Bison were not 

portioned at the State level because emissions were minimal.

Source: EPA 2015
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

41

Appendix Table A-17 Nitrous Oxide Emissions from Manure Management by State and Animal in 2013 Appendix Table A-17 Nitrous Oxide Emissions from Manure Management by State and 

Animal in 2013

Dairy cattle Beef cattle Poultry Swine Total

State MMT CO2 eq.

Alabama 0.0022 0.0050 0.1201 0.0027 0.1299

Alaska 0.0002 0.0000 0.0018 0.0000 0.0021

Arizona 0.1144 0.1543 0.0019 0.0045 0.2751

Arkansas 0.0022 0.0000 0.1421 0.0037 0.1481

California 1.1133 0.2755 0.0394 0.0026 1.4308

Colorado 0.1128 0.5975 0.0081 0.0207 0.7392

Connecticut 0.0076 0.0002 0.0040 0.0000 0.0118

Delaware 0.0021 0.0002 0.0224 0.0002 0.0248

Florida 0.0455 0.0027 0.0192 0.0001 0.0675

Georgia 0.0232 0.0039 0.1612 0.0035 0.1919

Hawaii 0.0020 0.0007 0.0018 0.0003 0.0048

Idaho 0.4462 0.1332 0.0019 0.0011 0.5824

Illinois 0.0707 0.0906 0.0115 0.1370 0.3098

Indiana 0.1030 0.0574 0.0585 0.1077 0.3268

Iowa 0.1578 0.7224 0.0766 0.5824 1.5392

Kansas 0.1209 1.2832 0.0018 0.0597 1.4656

Kentucky 0.0170 0.0080 0.0379 0.0090 0.0719

Louisiana 0.0025 0.0024 0.0096 0.0000 0.0145

Maine 0.0125 0.0004 0.0047 0.0000 0.0177

Maryland 0.0217 0.0057 0.0339 0.0006 0.0619

Massachusetts 0.0048 0.0002 0.0010 0.0001 0.0060

Michigan 0.2720 0.0871 0.0235 0.0317 0.4145

Minnesota 0.3562 0.1755 0.0582 0.2178 0.8076

Mississippi 0.0030 0.0048 0.0829 0.0132 0.1040

Missouri 0.0534 0.0289 0.0974 0.0772 0.2570

Montana 0.0108 0.0207 0.0015 0.0049 0.0379

Nebraska 0.0366 1.4551 0.0184 0.0903 1.6004

Nevada 0.0177 0.0048 0.0018 0.0000 0.0243

New Hampshire 0.0054 0.0001 0.0018 0.0000 0.0074

New Jersey 0.0029 0.0002 0.0018 0.0002 0.0050

New Mexico 0.1969 0.0107 0.0019 0.0000 0.2095

New York 0.2555 0.0142 0.0124 0.0018 0.2839

North Carolina 0.0130 0.0035 0.1299 0.2466 0.3929

North Dakota 0.0143 0.0272 0.0018 0.0039 0.0472

Ohio 0.1756 0.0929 0.0509 0.0601 0.3796

Oklahoma 0.0294 0.1987 0.0258 0.0636 0.3176

Oregon 0.0790 0.0377 0.0091 0.0001 0.1259

Pennsylvania 0.2138 0.0427 0.0546 0.0341 0.3453

Rhode Island 0.0004 0.0000 0.0018 0.0000 0.0022

South Carolina 0.0038 0.0014 0.0391 0.0074 0.0518

South Dakota 0.0730 0.1811 0.0071 0.0347 0.2959

Tennessee 0.0106 0.0024 0.0203 0.0047 0.0380

Texas 0.2850 1.5660 0.0868 0.0206 1.9583

Utah 0.0704 0.0156 0.0092 0.0210 0.1162

Vermont 0.0511 0.0006 0.0010 0.0000 0.0527

Virginia 0.0195 0.0131 0.0417 0.0075 0.0819

Washington 0.1620 0.1396 0.0149 0.0007 0.3173

West Virginia 0.0037 0.0024 0.0142 0.0001 0.0204

Wisconsin 0.9368 0.1358 0.0132 0.0082 1.0941

Wyoming 0.0045 0.0401 0.0008 0.0028 0.0482

Total 5.739 7.647 1.583 1.890 16.8586

Note: Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. Other animal types were not portioned at the State 

level because emissions were minimal.

Source: EPA 2015
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

42

Appendix Table A-18 Waste Characteristics Data Appendix Table A-18 Waste Characteristics Data

Animal Group Average 

TAM1 (kg) 

Nitrogen, Nex2 

(kg/day per 1,000 kg mass)

Max Methane Generation 

Potential, Bo 

(m3 CH4/kg VS added)

Volatile Solids, 

VS

(kg/day per 

1,000 kg mass) 

Dairy Cows 680 0.62 0.24 10.99 

Dairy Heifers 406-408 0.50 0.17 10.08 

Feedlot Steers 419-457 0.34 0.33 3.97 

Feedlot Heifers 384-430 0.35 0.33 4.34 

Bulls NOF3 831-917 0.21 0.17 5.03 

Calves NOF 118 0.45 0.17 7.70 

Heifers NOF 296-407 0.32 0.17 4.59 

Steers NOF 314-335 0.31 0.17 8.16 

Cows NOF 554-611 0.31 0.17 7.66 

American Bison 579 0.70 0.17 12.10 

Market Swine <50 lbs. 13 0.54 0.48 8.80

Market Swine 50-119 lbs. 39 0.54 0.48 5.40 

Market Swine 120-179 lbs. 68 0.54 0.48 5.40 

Market Swine >180 lbs. 91 0.20 0.48 5.40 

Breeding Swine 198 0.45 0.48 2.70 

Sheep 80 0.45 0.19 8.30 

Goats 64 0.79 0.17 9.50 

Horses 450 0.30 0.33 6.10 

Mules and Asses 130 0.54 0.33 7.20 

Hens ≥ 1 yr 1.8 0.79 0.39 10.20 

Pullets 1.8 1.10 0.39 10.20 

Other Chickens 1.8 0.96 0.39 11.00

Broilers 0.9 0.63 0.36 17.00 

Turkeys 6.8 0.25 0.36 8.50

Source: EPA 2015.

1(TAM) Typical animal mass. 2(Nex) Nitrogen excretion. 3(NOF) Not on feed.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

43

Appendix Table A-19 State Volatile Solids Production Rates in 2013 Appendix Table A-19 State Volatile Solids Production Rates in 2013

Dairy Cow Dairy Heifer Beef Cow 

NOF1

Beef Heifer 

NOF

Beef Steer 

NOF

Beef Heifer 

OF2

Beef Steer 

OF

State kg/day/1,000 kg mass

Alabama 8.66 8.51 7.46 7.41 7.49 4.40 4.03

Alaska 7.64 8.51 8.48 8.59 8.61 4.40 4.02

Arizona 11.54 8.45 8.48 8.28 8.61 4.36 3.98

Arkansas 7.96 8.51 7.46 7.37 7.49 NA NA

California 11.33 8.51 8.48 8.16 8.61 4.40 4.02

Colorado 11.73 8.45 8.48 8.03 8.61 4.36 3.98

Connecticut 10.63 8.49 7.51 7.47 7.54 4.39 4.02

Delaware 10.30 8.49 7.51 7.31 7.54 4.39 4.02

Florida 10.48 8.51 7.46 7.43 7.49 4.40 4.03

Georgia 10.52 8.51 7.46 7.38 7.49 4.40 4.02

Hawaii 8.46 8.51 8.48 8.46 8.61 4.40 4.03

Idaho 11.48 8.45 8.48 8.19 8.61 4.36 3.98

Illinois 10.26 8.46 7.12 6.83 7.12 4.37 3.99

Indiana 10.98 8.46 7.12 6.86 7.12 4.37 3.99

Iowa 11.09 8.46 7.12 6.63 7.12 4.37 3.99

Kansas 11.01 8.46 7.12 6.59 7.12 4.37 3.99

Kentucky 9.21 8.49 7.46 7.22 7.49 4.39 4.00

Louisiana 8.32 8.51 7.46 7.41 7.49 4.40 4.03

Maine 10.31 8.49 7.51 7.32 7.54 4.39 4.02

Maryland 10.28 8.49 7.51 7.28 7.54 4.39 4.01

Massachusetts 9.75 8.49 7.51 7.39 7.54 4.39 4.01

Michigan 11.69 8.46 7.12 6.79 7.12 4.37 3.99

Minnesota 10.36 8.46 7.12 6.78 7.12 4.37 3.99

Mississippi 8.63 8.51 7.46 7.36 7.49 4.40 4.03

Missouri 8.84 8.46 7.12 6.94 7.12 4.37 3.99

Montana 10.84 8.45 8.48 8.45 8.61 4.36 3.98

Nebraska 10.92 8.46 7.12 6.65 7.12 4.37 3.99

Nevada 11.11 8.45 8.48 8.34 8.61 4.36 3.98

New Hampshire 10.70 8.49 7.51 7.35 7.54 4.39 4.01

New Jersey 9.89 8.49 7.51 7.39 7.54 4.39 4.02

New Mexico 11.94 8.45 8.48 8.25 8.61 4.36 3.99

New York 11.07 8.49 7.51 7.27 7.54 4.39 4.01

North Carolina 10.77 8.49 7.46 7.39 7.49 4.39 4.01

North Dakota 10.15 8.46 7.12 6.88 7.12 4.37 4.00

Ohio 10.50 8.46 7.12 6.85 7.12 4.37 3.99

Oklahoma 9.73 8.45 7.46 7.17 7.49 4.36 3.98

Oregon 10.58 8.51 8.48 8.38 8.61 4.40 4.02

Pennsylvania 10.39 8.49 7.51 7.25 7.54 4.39 4.01

Rhode Island 10.15 8.49 7.51 7.47 7.54 4.39 4.01

South Carolina 9.61 8.51 7.46 7.39 7.49 4.40 4.03

South Dakota 10.91 8.46 7.12 6.79 7.12 4.37 3.99

Tennessee 9.46 8.49 7.46 7.35 7.49 4.39 4.03

Texas 11.07 8.45 7.46 7.06 7.49 4.36 3.98

Utah 11.09 8.45 8.48 8.31 8.61 4.36 3.98

Vermont 10.28 8.49 7.51 7.21 7.54 4.39 4.01

Virginia 10.17 8.49 7.46 7.31 7.49 4.39 4.01

Washington 11.60 8.51 8.48 8.09 8.61 4.40 4.02

West Virginia 9.00 8.49 7.51 7.34 7.54 4.39 4.01

Wisconsin 10.96 8.46 7.12 6.93 7.12 4.37 3.99

Wyoming 10.86 8.45 8.48 8.38 8.61 4.36 3.98

Source: EPA 2015.

1(NOF) Not on feed.

2(OF) On feed.
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

44

Appendix Table A-20 State-Based Methane Conversion Factors1 for Liquid Waste Management Systems in 2013 Appendix Table A-20 State-Based Methane Conversion Factors1 for Liquid Waste 

Management Systems in 2013

Dairy Swine Beef Poultry

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit Liquid/Slurry Anaerobic Lagoon

State percent

Alabama 75 37 75 36 38 75

Alaska 47 15 47 15 15 47

Arizona 78 57 77 47 52 74

Arkansas 75 34 76 37 35 75

California 73 32 72 31 41 74

Colorado 65 22 68 24 24 65

Connecticut 69 25 69 25 26 69

Delaware 73 31 73 31 31 73

Florida 79 55 79 53 53 79

Georgia 76 39 75 38 37 75

Hawaii 76 57 76 57 57 76

Idaho 69 25 66 22 22 68

Illinois 72 29 72 28 27 72

Indiana 70 27 71 27 27 71

Iowa 70 25 70 26 26 70

Kansas 74 32 74 32 32 74

Kentucky 73 31 73 31 30 73

Louisiana 77 45 77 46 46 77

Maine 63 21 63 21 21 64

Maryland 72 30 72 30 31 73

Massachusetts 67 24 68 25 25 68

Michigan 67 23 67 24 24 67

Minnesota 68 24 69 24 24 67

Mississippi 76 40 76 39 41 76

Missouri 73 30 73 30 30 74

Montana 61 19 64 21 21 64

Nebraska 72 27 72 27 27 72

Nevada 70 26 71 27 25 70

New Hampshire 64 22 65 22 22 65

New Jersey 71 28 71 29 28 71

New Mexico 73 31 71 28 30 70

New York 65 23 66 23 23 66

North Carolina 73 31 75 36 30 73

North Dakota 66 22 66 22 22 66

Ohio 69 26 70 27 27 70

Oklahoma 76 37 76 35 36 76

Oregon 64 21 63 21 22 63

Pennsylvania 69 26 70 27 27 70

Rhode Island 69 26 69 26 26 69

South Carolina 75 37 75 38 36 75

South Dakota 69 24 70 25 25 70

Tennessee 73 31 74 32 31 73

Texas 76 41 76 44 38 77

Utah 65 22 68 24 24 65

Vermont 63 21 63 21 21 63

Virginia 71 28 72 31 29 71

Washington 64 21 66 22 23 65

West Virginia 69 26 70 26 26 69

Wisconsin 66 23 68 24 23 67

Wyoming 63 20 64 21 22 64

Source: EPA 2015, IPCC 2006. 1(MCF) Methane conversion factors represent weighted average of multiple animal types.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

45

Appendix Table A-21 Maximum Methane Generation Potential, B Appendix Table A 0 -21 Maximum Methane Generation Potential, B0

Animal Group m3 CH4/kg VS added1 Source 

Dairy Cows 0.24 Morris 1976

Dairy Heifers 0.17 Bryant et al. 1976

Feedlot Steers/Heifers 0.33 Hashimoto 1981

NOF Beef 0.17 Hashimoto 1981

American Bison 0.17 Based on the beef NOF bull B0

Swine 0.48 Hashimoto 1984

Sheep* 0.34 EPA 1992

Goats 0.17 EPA 1992

Horses 0.33 EPA 1992

Mules 0.33 Based on the horse B0

Broilers 0.36 Hill 1984

Other Chickens 0.39 Hill 1982

Turkeys 0.36 Hill 1984

Dairy Cows 0.24 Morris 1976

Source: EPA 2015, IPCC 2006.

1 m3 CH4/kg VS added is cubic meter methane per kilogram of volatile solids.

Appendix Table A-22 Methane Conversion Factors for Dry Systems

Cool Climate 

MCF1

Temperate 

Climate MCF

Warm Climate 

MCF

Waste Management System percent

Aerobic Treatment 0 0 0

Anaerobic Digester 0 0 0

Cattle Deep Litter (<1 month) 3 3 30

Cattle Deep Litter (>1 month) 21 44 76

Composting - In Vessel 0.5 0.5 0.5

Composting - Static Pile 0.5 0.5 0.5

Composting-Extensive/Passive 0.5 1 1.5

Composting-Intensive 0.5 1 1.5

Daily Spread 0.1 0.5 1

Dry Lot 1 1.5 5

Fuel 10 10 10

Pasture 1 1.5 2

Poultry with bedding 1.5 1.5 1.5

Poultry without bedding 1.5 1.5 1.5

Solid Storage 2 4 5

Source: EPA 2015, IPCC 2006. 1 MCF is methane conversion factor.

Appendix Table A-22 Methane Conversion Factors for Dry Systems

Appendix Table A-21 Maximum Methane Generation Potential, B0

Animal Group m3 CH4/kg VS added1 Source

Dairy Cows 0.24 Morris 1976

Dairy Heifers 0.17 Bryant et al. 1976

Feedlot Steers/Heifers 0.33 Hashimoto 1981

NOF Beef 0.17 Hashimoto 1981

American Bison 0.17 Based on the beef NOF bull B0

Swine 0.48 Hashimoto 1984

Sheep* 0.34 EPA 1992

Goats 0.17 EPA 1992

Horses 0.33 EPA 1992

Mules 0.33 Based on the horse B0

Broilers 0.36 Hill 1984

Other Chickens 0.39 Hill 1982

Turkeys 0.36 Hill 1984

Dairy Cows 0.24 Morris 1976

Source: EPA 2015, IPCC 2006.

1 m3 CH4/kg VS added is cubic meter methane per kilogram of volatile solids.

Appendix Table A-22 Methane Conversion Factors for Dry Systems

Cool Climate 

MCF1

Temperate 

Climate MCF 

Warm Climate 

MCF

Waste Management System percent

Aerobic Treatment 0 0 0

Anaerobic Digester 0 0 0

Cattle Deep Litter (<1 month) 3 3 30

Cattle Deep Litter (>1 month) 21 44 76

Composting - In Vessel 0.5 0.5 0.5

Composting - Static Pile 0.5 0.5 0.5

Composting-Extensive/Passive 0.5 1 1.5

Composting-Intensive 0.5 1 1.5

Daily Spread 0.1 0.5 1

Dry Lot 1 1.5 5

Fuel 10 10 10

Pasture 1 1.5 2

Poultry with bedding 1.5 1.5 1.5

Poultry without bedding 1.5 1.5 1.5

Solid Storage 2 4 5

Source: EPA 2015, IPCC 2006. 1 MCF is methane conversion factor.
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

46

Appendix Table A-23 Methane Conversion Factors for Livestock Waste Emissions in 2013 Appendix Table A-23 Methane Conversion Factors for Livestock Waste Emissions in 2013

Beef 

Feedlot 

Heifer

Beef 

Feedlot 

Steer

Dairy 

Cow 

Dairy 

Heifer 

Swine 

Market 

Swine 

Breeding Layer Broiler Turkey Sheep Goats Horses 

State percent

Alabama 2.0 2.0 16.9 1.9 54.2 53.9 32.3 1.5 1.5 1.5 1.5 1.5

Alaska 1.2 1.2 16.4 1.1 8.1 8.1 12.9 1.5 1.5 1.0 1.0 1.0

Arizona 21.0 21.0 79.5 21.9 77.3 75.5 61.0 3.0 3.0 3.0 3.0 3.0

Arkansas 1.4 1.4 9.7 1.3 50.1 50.0 1.5 1.5 1.5 1.5 1.5 1.5

California 2.0 2.0 49.8 1.8 47.7 47.7 10.2 1.5 1.5 1.5 1.5 1.5

Colorado 1.1 1.1 47.2 1.1 29.2 29.0 39.6 1.5 1.5 1.0 1.0 1.0

Connecticut 1.3 1.3 13.2 1.2 8.3 8.3 4.9 1.5 1.5 1.0 1.0 1.0

Delaware 1.3 1.3 14.3 1.3 34.7 34.7 5.1 1.5 1.5 1.0 1.0 1.0

Florida 2.2 2.2 42.4 2.0 17.0 16.8 34.0 1.5 1.5 1.5 1.5 1.5

Georgia 2.0 2.0 22.3 1.9 54.8 54.3 32.1 1.5 1.5 1.5 1.5 1.5

Hawaii 2.2 2.2 57.7 2.1 40.7 40.7 20.2 1.5 1.5 1.5 1.5 1.5

Idaho 1.1 1.1 48.0 1.1 25.3 25.3 41.3 1.5 1.5 1.0 1.0 1.0

Illinois 1.2 1.2 21.1 1.1 32.8 33.0 2.9 1.5 1.5 1.0 1.0 1.0

Indiana 1.2 1.2 18.6 1.1 31.7 31.8 1.5 1.5 1.5 1.0 1.0 1.0

Iowa 1.2 1.2 23.2 1.1 48.5 48.7 1.5 1.5 1.5 1.0 1.0 1.0

Kansas 1.2 1.2 39.4 1.2 35.2 35.2 3.0 1.5 1.5 1.0 1.0 1.0

Kentucky 1.3 1.3 5.6 1.2 48.7 48.7 5.1 1.5 1.5 1.0 1.0 1.0

Louisiana 2.1 2.1 11.8 2.0 7.4 7.4 46.9 1.5 1.5 1.5 1.5 1.5

Maine 1.2 1.2 10.8 1.2 10.8 10.8 4.6 1.5 1.5 1.0 1.0 1.0

Maryland 1.3 1.3 12.0 1.2 33.5 33.9 5.1 1.5 1.5 1.0 1.0 1.0

Massachusetts 1.3 1.3 10.4 1.2 14.9 14.8 4.8 1.5 1.5 1.0 1.0 1.0

Michigan 1.1 1.1 27.1 1.1 28.6 28.3 2.8 1.5 1.5 1.0 1.0 1.0

Minnesota 1.1 1.1 16.7 1.1 30.6 30.5 1.5 1.5 1.5 1.0 1.0 1.0

Mississippi 2.0 2.0 11.8 1.9 57.0 58.2 46.4 1.5 1.5 1.5 1.5 1.5

Missouri 1.2 1.2 17.5 1.2 32.0 32.2 1.5 1.5 1.5 1.0 1.0 1.0

Montana 1.1 1.1 29.5 1.1 26.2 26.2 38.7 1.5 1.5 1.0 1.0 1.0

Nebraska 1.2 1.2 31.2 1.1 31.8 31.8 2.9 1.5 1.5 1.0 1.0 1.0

Nevada 13.1 13.1 64.4 12.9 35.6 35.2 13.5 2.5 2.5 2.0 2.0 2.0

New Hampshire 12.8 12.8 22.2 12.8 23.0 22.9 16.2 2.5 2.5 2.0 2.0 2.0

New Jersey 14.7 14.7 22.3 14.8 37.5 37.5 18.5 2.5 2.5 2.0 2.0 2.0

New Mexico 15.1 15.1 66.6 15.4 14.4 14.6 55.4 2.5 2.5 2.0 2.0 2.0

New York 1.2 1.2 11.2 1.2 25.8 25.9 4.7 1.5 1.5 1.0 1.0 1.0

North Carolina 1.3 1.3 12.9 1.2 56.4 56.2 31.5 1.5 1.5 1.0 1.0 1.0

North Dakota 1.1 1.1 17.4 1.1 27.8 27.4 2.8 1.5 1.5 1.0 1.0 1.0

Ohio 1.2 1.2 17.8 1.1 31.0 31.0 1.5 1.5 1.5 1.0 1.0 1.0

Oklahoma 1.1 1.1 45.4 1.6 56.7 57.1 46.0 1.5 1.5 1.0 1.0 1.0

Oregon 1.3 1.3 34.5 1.2 15.5 15.5 16.8 1.5 1.5 1.0 1.0 1.0

Pennsylvania 1.3 1.3 7.6 1.2 31.3 30.9 1.5 1.5 1.5 1.0 1.0 1.0

Rhode Island 1.3 1.3 8.0 1.2 10.2 10.2 4.9 1.5 1.5 1.0 1.0 1.0

South Carolina 2.0 2.0 18.4 1.9 56.4 56.1 45.8 1.5 1.5 1.5 1.5 1.5

South Dakota 1.1 1.1 30.1 1.1 31.1 31.1 2.9 1.5 1.5 1.0 1.0 1.0

Tennessee 1.3 1.3 7.3 1.2 43.9 43.6 5.1 1.5 1.5 1.0 1.0 1.0

Texas 1.7 1.7 51.9 1.6 50.3 50.4 10.5 1.5 1.5 1.5 1.5 1.5

Utah 1.1 1.1 42.7 1.1 30.7 27.6 39.6 1.5 1.5 1.0 1.0 1.0

Vermont 1.2 1.2 10.8 1.2 11.4 11.5 4.6 1.5 1.5 1.0 1.0 1.0

Virginia 1.3 1.3 7.4 1.2 50.5 50.3 5.0 1.5 1.5 1.0 1.0 1.0

Washington 1.3 1.3 38.4 1.2 17.1 16.7 9.1 1.5 1.5 1.0 1.0 1.0

West Virginia 14.2 14.2 22.4 14.2 27.1 27.0 17.6 2.5 2.5 2.0 2.0 2.0

Wisconsin 1.1 1.1 16.3 1.1 27.0 26.9 2.8 1.5 1.5 1.0 1.0 1.0

Wyoming 1.1 1.1 33.1 1.1 16.7 16.7 38.7 1.5 1.5 1.0 1.0 1.0

Note: Methane conversion factors are weighted by the distribution of waste management systems for each animal type within a State.

Source: EPA 2015
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

47

Appendix Table A-24 Direct Nitrous Oxide Emission Factors for 2013 Appendix Table A-24 Direct Nitrous Oxide Emission Factors for 2013

Waste Management System Direct N2O Emission Factor

kg N2O-N/kg Kjdl N1

Aerobic Treatment (forced aeration) 0.005

Aerobic Treatment (natural aeration) 0.01

Anaerobic Digester 0

Anaerobic Lagoon 0

Cattle Deep Bed (active mix) 0.07

Cattle Deep Bed (no mix) 0.01

Composting in vessel 0.006

Composting intensive 0.1

Composting passive 0.01

Composting static 0.006

Daily Spread 0

Deep Pit 0.002

Dry Lot 0.02

Fuel 0

Liquid/Slurry 0.005

Pasture2 0

Poultry with bedding 0.001

Poultry without bedding 0.001

Solid Storage 0.005

Note: N2O is nitrous oxide.

Source: EPA 2015, IPCC 2006.

1 kg N2O-N/kg Kjdl N is kilograms nitrogen in nitrous oxide per kilograms 

kjeldahl nitrogen.

2 Calculated using Tier 3 DayCent Model simulations. 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

48

Appendix Table A-25 Nitrogen in Livestock Waste on Grazed Lands Appendix Table A-27 Nitrogen in Livestock Waste on Grazed Lands

Year MMT N

1990 4.1

1991 4.1

1992 4.3

1993 4.3

1994 4.4

1995 4.5

1996 4.5

1997 4.4

1998 4.3

1999 4.2

2000 4.1

2001 4.1

2002 4.1

2003 4.1

2004 4.1

2005 4.1

2006 4.2

2007 4.0

2008 4.0

2009 4.0

2010 3.9

2011 3.8

2012 3.7

2013 3.7

Note: MMT N is million metric 

tons nitrogen.

Source: EPA 2015
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

49

Appendix Table A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes from Non-Federal 

Grasslands, 2003-2007 Appendix Table A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes 

from Non-Federal Grasslands, 2003-2007

MLRA1 Area Total dSOC

ha Gg CO2 eq.2

1 54,597 -52.8

2 243,632 -333.8

3 17,773 -86.5

5 263,473 -345.0

6 179,315 -35.1

7 409,089 -4.4

8 2,379,429 -4.3

9 796,574 41.0

10 2,071,584 142.9

11 722,976 73.4

12 145,949 -1.5

13 625,994 24.5

14 140,555 -33.4

15 2,036,110 -174.5

17 1,025,755 -95.4

18 850,862 -14.4

19 167,401 -51.9

20 710,349 -71.3

21 578,811 -89.1

23 987,306 6.0

24 531,483 162.7

25 1,567,731 133.9

26 310,276 6.3

27 799,461 154.2

29 333,839 -28.3

30 1,193,928 -39.1

31 146,278 -22.0

32 807,861 -90.1

35 8,939,750 568.5

36 1,339,729 35.6

38 1,678,101 -57.5

39 300,000 36.2

40 2,644,850 113.4

41 2,032,033 -11.1

42 7,117,114 231.7

44 1,386,170 155.0

46 2,334,195 473.0

47 1,299,930 198.1

49 1,451,736 143.7

51 576,854 -128.6

52 2,037,706 825.3

54 4,052,914 272.8

56 272,722 -92.5

57 217,910 -49.5

61 237,646 17.1

62 133,877 54.6

MLRA1 Area Total dSOC

ha Gg CO2 eq.2

64 1,962,501 -58.9

65 4,703,281 -109.5

66 970,886 -45.6

69 2,245,770 71.1

71 1,012,309 167.5

72 2,419,203 70.8

73 2,267,127 248.5

74 607,367 -61.8

75 323,527 -31.5

76 1,510,595 -188.1

79 411,928 -27.4

85 1,369,323 -235.2

88 63,503 -81.8

89 31,158 -18.9

92 36,473 -22.0

96 51,706 -16.5

97 47,882 -12.3

98 301,989 -130.2

99 63,472 -18.7

101 154,267 -35.1

103 328,349 -218.8

74 607,367 -61.8

75 323,527 -31.5

76 1,510,595 -188.1

79 411,928 -27.4

85 1,369,323 -235.2

88 63,503 -81.8

89 31,158 -18.9

92 36,473 -22.0

96 51,706 -16.5

97 47,882 -12.3

98 301,989 -130.2

99 63,472 -18.7

101 154,267 -35.1

103 328,349 -218.8

104 140,345 -166.1

105 626,326 -425.5

106 572,613 -144.4

109 926,578 -483.5

110 66,940 -41.0

112 2,230,417 -661.1

113 277,306 -143.2

117 240,831 -88.5

119 341,603 -70.6

121 632,837 -315.7

122 940,743 -464.0

123 339,212 -176.0

124 250,471 -89.7

125 201,304 -77.0

126 385,164 -163.5

127 189,799 -46.5

128 861,793 -490.2

129 204,725 -115.4

134 737,073 -603.6

136 1,117,885 -865.2

137 46,206 -43.2

138 72,997 -37.6

139 144,352 -81.1

140 371,164 -130.4

141 8,165 -2.4

142 126,775 -68.8

143 39,121 -15.6

MLRA1 Area Total dSOC

ha Gg CO2 eq.2

145 10,074 -5.0

147 404,552 -180.0

148 338,595 -126.7

151 78,578 1.8

154 260,436 -82.6

155 1,209,929 -356.8

102A 802,261 -627.7

102B 56,522 -67.9

102C 451,346 -247.1

107A 62,367 -213.7

107B 341,131 -1011.1

108A 42,462 -27.2

108B 106,015 -92.6

108C 143,023 -321.7

108D 230,418 -572.3

111A 144,848 -93.2

111B 119,404 -42.9

111C 41,763 -12.2

111D 59,226 -6.3

111E 27,900 -22.7

114A 104,534 -42.2

114B 121,249 -71.0

115A 53,780 -36.7

115B 305,634 -137.4

115C 345,948 -216.1

116A 2,126,540 -821.3

116B 507,825 -294.8

116C 66,379 -28.6

118A 616,870 -162.0

118B 291,406 -82.2

120A 265,303 -171.6

120B 63,592 -25.8

120C 11,235 -1.7

130A 12,035 -8.3

130B 203,692 -114.0

131A 241,071 -158.0

131B 42,168 -18.0
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

50

Continued - Appendix Table A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes from NonFederal Grasslands, 2003-2007

MLRA1 Area Total dSOC

ha Gg CO2 eq.2

150B 249,079 -13.3

152A 38,047 -21.8

152B 79,521 -57.7

153A 86,206 -44.0

153B 9,909 -11.4

153C 15,220 -11.8

153D 14,083 -13.5

156A 67,768 -8.4

156B 104,655 -17.2

22A 82,010 6.8

28A 1,330,232 262.1

28B 305,998 31.5

34A 3,017,610 140.3

34B 731,884 133.5

43A 364,330 -26.6

43B 2,443,145 375.3

43C 233,023 -35.6

48A 1,536,456 379.2

48B 317,086 -37.9

4A 58,168 -8.3

4B 85,756 -6.2

53A 958,782 103.0

53B 1,768,162 -4.4

53C 422,626 69.4

55A 419,991 0.5

55B 590,906 -39.5

55C 863,882 28.7

58A 6,188,798 968.9

58B 3,783,888 632.0

58C 199,531 30.8

58D 528,343 44.2

60A 1,792,553 10.6

60B 668,113 106.7

63A 1,647,732 -78.1

63B 658,307 -8.5

67A 1,454,712 344.7

67B 2,202,593 186.4

70A 1,974,891 311.2

70B 1,854,571 90.9

70C 2,043,147 164.1

70D 279,833 11.7

77A 702,904 -86.2

77B 640,671 -27.9

77C 1,205,012 -52.8

77D 1,405,153 266.8

77E 1,664,797 91.9

78A 639,230 40.8

78B 2,491,438 123.8

115B 305,634 -137.4

115C 345,948 -216.1

116A 2,126,540 -821.3

116B 507,825 -294.8

116C 66,379 -28.6

118A 616,870 -162.0

118B 291,406 -82.2

120A 265,303 -171.6

120B 63,592 -25.8

120C 11,235 -1.7

130A 12,035 -8.3

130B 203,692 -114.0

131A 241,071 -158.0

131B 42,168 -18.0

131C 140,088 -71.6

131D 29,985 -12.4

133A 1,441,544 -1125.0

133B 1,187,959 -354.9

135A 381,841 -304.5

135B 309,250 -82.7

144A 119,969 -55.9

144B 79,998 -46.5

149A 31,531 -6.8

149B 3,112 -0.9

150A 1,165,952 -111.6

MLRA1 Area Total dSOC

ha Gg CO2 eq.2

78C 2,445,635 -63.2

80A 1,963,077 -347.1

80B 968,445 53.3

81A 2,866,367 -2.8

81B 1,940,970 97.6

81C 1,236,724 120.7

81D 516,702 -37.7

82A 401,734 13.3

82B 57,923 -1.0

83A 1,706,897 123.5

83B 1,463,751 1.2

83C 755,825 55.5

83D 129,375 -7.5

83E 659,343 35.3

84A 875,391 -131.7

84B 745,691 -134.3

84C 113,856 -30.2

86A 1,453,945 -373.1

86B 351,296 5.5

87A 1,544,144 -23.5

87B 410,138 -149.7

90A 265,868 -168.5

90B 248,178 -208.1

91A 161,283 -85.5

91B 52,108 -43.2

94A 130,138 -102.6

94B 61,815 -50.0

94C 25,098 -15.9

95A 91,989 -91.8

95B 223,059 -195.9

Total 180,415,846 -10889.0

Note: dSOC is dissolved soil organic carbon. 1 MLRA = Major Land Resource Area 2 Gg CO2 eq. = Gigagrams carbon dioxide equivalent
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

51

Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from Non-Federal 

Grasslands, 2003-2007

Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O 

Emissions From Non-Federal Grasslands, 2003-2007 

MLRA1 Area Direct Soil N2O Indirect N2O from 

NO3 Leached/Runoff 

Indirect N2O from 

NH3/NOx

Volitilization 

ha Gg CO2 eq.2 

1 54,597 81.5 11.3 2.3 

2 243,632 466.8 49.1 14.0 

3 17,773 68.2 3.0 1.1 

5 263,473 674.3 44.9 16.3 

6 179,315 265.0 1.6 2.8 

7 409,089 105.7 0.0 4.1 

8 2,379,429 1030.9 2.5 27.1 

9 796,574 567.3 3.3 11.6 

10 2,071,584 978.2 2.8 27.6 

11 722,976 242.5 0.0 8.3 

12 145,949 84.6 0.1 1.8 

13 625,994 287.2 0.4 7.4 

14 140,555 15.1 2.5 1.7 

15 2,036,110 133.0 12.2 18.7 

17 1,025,755 69.8 3.5 9.7 

18 850,862 96.5 7.5 8.7 

19 167,401 7.4 0.4 1.4 

20 710,349 28.5 0.3 5.1 

21 578,811 891.9 7.2 12.2 

23 987,306 391.0 0.0 12.8 

24 531,483 167.0 0.0 5.3 

25 1,567,731 563.7 0.1 20.0 

26 310,276 170.3 0.3 3.3 

27 799,461 88.6 0.0 7.2 

29 333,839 33.4 0.1 2.1 

30 1,193,928 44.7 0.0 7.9 

31 146,278 4.9 0.0 1.0 

32 807,861 193.7 0.0 12.1 

35 8,939,750 986.7 0.0 102.5 

36 1,339,729 406.0 0.2 19.8 

38 1,678,101 90.4 0.0 12.4 

39 300,000 30.6 0.0 4.1 

40 2,644,850 76.8 0.0 14.4 

41 2,032,033 64.4 0.0 11.0 

42 7,117,114 1321.3 0.0 145.8 

44 1,386,170 1239.4 6.8 22.1 

46 2,334,195 1115.9 2.2 38.1 

47 1,299,930 634.0 1.7 17.7 

49 1,451,736 432.1 0.0 27.6 

51 576,854 197.2 0.1 8.9 

52 2,037,706 510.9 0.0 29.4 

54 4,052,914 884.0 0.0 64.7 

56 272,722 112.3 0.4 7.9 

57 217,910 142.1 4.2 8.1 

61 237,646 56.3 0.2 3.9 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

52

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1 Area Direct Soil N2O Indirect N2O from 

NO3 Leached/Runoff 

Indirect N2O from 

NH3/NOx

Volitilization 

ha Gg CO2 eq.2 

62 133,877 31.4 0.3 2.3 

64 1,962,501 386.5 0.0 38.3 

65 4,703,281 1194.1 0.6 117.9 

66 970,886 229.9 0.0 22.3 

69 2,245,770 674.5 0.0 43.1 

71 1,012,309 343.5 0.5 22.0 

72 2,419,203 774.3 0.0 58.8 

73 2,267,127 1022.8 0.2 56.9 

74 607,367 277.6 5.1 16.0 

75 323,527 134.5 1.1 7.9 

76 1,510,595 871.1 18.8 39.1 

79 411,928 109.0 0.9 11.6 

85 1,369,323 1174.6 24.5 63.1 

88 63,503 91.5 1.5 3.6 

89 31,158 53.2 2.9 1.6 

92 36,473 94.5 1.1 2.1 

96 51,706 68.9 3.3 1.9 

97 47,882 80.2 3.3 2.2 

98 301,989 353.5 17.1 13.4 

99 63,472 50.3 2.2 2.2 

101 154,267 349.7 9.0 6.5 

103 328,349 283.5 9.5 10.6 

104 140,345 208.8 6.5 5.7 

105 626,326 789.1 22.3 22.6 

106 572,613 298.1 3.0 13.9 

109 926,578 949.5 52.2 32.1 

110 66,940 92.3 3.2 2.4 

112 2,230,417 1228.1 37.7 56.8 

113 277,306 248.7 9.6 9.3 

117 240,831 115.6 14.6 8.3 

119 341,603 151.3 20.9 10.6 

121 632,837 852.0 42.0 22.0 

122 940,743 658.1 63.5 39.1 

123 339,212 218.9 22.2 12.1 

124 250,471 342.1 16.5 9.5 

125 201,304 164.7 19.4 7.8 

126 385,164 483.7 22.3 13.9 

127 189,799 287.8 12.8 8.3 

128 861,793 603.4 55.4 36.3 

129 204,725 66.3 13.0 9.6 

134 737,073 344.1 26.8 32.4 

136 1,117,885 827.3 81.1 77.7 

137 46,206 22.9 5.3 3.6 

138 72,997 20.7 5.2 5.2 

139 144,352 241.8 6.5 6.3 

140 371,164 799.8 23.1 18.6 

141 8,165 19.6 0.4 0.4 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

53

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1 Area Direct Soil N2O Indirect N2O from 

NO3 Leached/Runoff 

Indirect N2O from 

NH3/NOx

Volitilization 

ha Gg CO2 eq.2 

142 126,775 252.3 5.6 5.0 

143 39,121 73.9 2.6 1.5 

145 10,074 17.9 0.6 0.4 

147 404,552 472.2 28.4 22.3 

148 338,595 308.7 19.2 16.0 

151 78,578 44.0 0.8 1.8 

154 260,436 91.1 25.0 14.2 

155 1,209,929 335.6 76.4 45.4 

102A 802,261 311.0 3.0 18.5 

102B 56,522 15.0 0.0 1.1 

102C 451,346 153.5 1.4 9.7 

107A 62,367 77.9 1.1 2.7 

107B 341,131 287.0 3.5 11.8 

108A 42,462 55.1 1.3 1.5 

108B 106,015 127.4 2.7 3.7 

108C 143,023 245.9 9.8 6.5 

108D 230,418 285.6 12.0 9.4 

111A 144,848 147.4 8.2 4.7 

111B 119,404 89.6 4.0 3.9 

111C 41,763 33.1 2.5 1.6 

111D 59,226 73.5 3.8 2.0 

111E 27,900 34.2 1.5 1.1 

114A 104,534 113.7 5.0 3.8 

114B 121,249 146.4 6.6 4.4 

115A 53,780 56.5 2.5 2.1 

115B 305,634 282.0 8.7 8.8 

115C 345,948 353.1 8.2 11.8 

116A 2,126,540 1554.0 113.0 74.3 

116B 507,825 367.5 20.0 18.9 

116C 66,379 71.4 3.0 1.9 

118A 616,870 248.2 33.2 17.6 

118B 291,406 116.3 8.5 7.5 

120A 265,303 226.2 13.5 10.6 

120B 63,592 76.7 4.0 2.5 

120C 11,235 14.5 0.8 0.4 

130A 12,035 12.2 0.8 0.5 

130B 203,692 161.1 13.1 8.6 

131A 241,071 149.7 6.1 9.0 

131B 42,168 30.8 1.3 1.3 

131C 140,088 101.5 3.3 5.5 

131D 29,985 14.1 1.5 1.0 

133A 1,441,544 567.7 108.2 81.9 

133B 1,187,959 413.2 59.1 41.4 

135A 381,841 315.0 18.7 17.6 

135B 309,250 194.0 15.3 10.6 

144A 119,969 202.9 8.0 5.0 

144B 79,998 148.5 4.7 3.0 
Chapter 2 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

54

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1 Area Direct Soil N2O Indirect N2O from 

NO3 Leached/Runoff 

Indirect N2O from 

NH3/NOx

Volitilization 

ha Gg CO2 eq.2 

149A 31,531 20.7 2.6 1.5 

149B 3,112 1.7 0.2 0.1 

150A 1,165,952 380.2 16.6 35.9 

150B 249,079 53.9 2.4 5.3 

152A 38,047 11.3 1.9 2.0 

152B 79,521 23.1 2.9 3.2 

153A 86,206 30.3 6.8 5.8 

153B 9,909 3.7 0.4 0.6 

153C 15,220 10.3 1.3 1.0 

153D 14,083 9.1 1.2 0.9 

156A 67,768 12.3 2.3 1.8 

156B 104,655 27.2 5.4 3.4 

22A 82,010 71.0 0.9 1.0 

28A 1,330,232 476.9 0.1 17.6 

28B 305,998 77.7 0.0 3.2 

34A 3,017,610 963.9 0.4 39.0 

34B 731,884 217.6 0.3 9.4 

43A 364,330 516.3 2.2 6.7 

43B 2,443,145 1403.7 5.7 36.3 

43C 233,023 203.7 1.4 3.3 

48A 1,536,456 866.2 3.4 22.1 

48B 317,086 168.4 0.7 4.1 

4A 58,168 51.4 11.2 1.8 

4B 85,756 16.0 5.1 1.5 

53A 958,782 194.8 0.0 14.3 

53B 1,768,162 349.2 0.0 27.5 

53C 422,626 102.9 0.0 7.6 

55A 419,991 84.5 0.0 6.9 

55B 590,906 148.2 0.0 9.7 

55C 863,882 226.8 0.0 17.0 

58A 6,188,798 2457.9 0.1 107.6 

58B 3,783,888 943.3 0.4 57.8 

58C 199,531 41.0 0.0 3.0 

58D 528,343 97.6 0.0 9.7 

60A 1,792,553 521.0 0.0 22.6 

60B 668,113 336.3 0.0 10.0 

63A 1,647,732 656.5 0.0 18.2 

63B 658,307 291.9 0.0 9.4 

67A 1,454,712 323.9 0.0 31.8 

67B 2,202,593 549.2 0.0 48.0 

70A 1,974,891 432.4 0.0 43.7 

70B 1,854,571 315.7 0.0 39.5 

70C 2,043,147 314.1 0.0 41.0 

70D 279,833 29.7 0.0 4.3 

77A 702,904 228.2 0.0 18.1 

77B 640,671 126.6 0.0 14.0 

77C 1,205,012 352.9 0.0 27.2 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 2

55

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1 Area Direct Soil N2O Indirect N2O from 

NO3 Leached/Runoff 

Indirect N2O from 

NH3/NOx

Volitilization 

ha Gg CO2 eq.2 

77D 1,405,153 179.5 0.0 26.7 

77E 1,664,797 511.3 0.0 44.9 

78A 639,230 211.2 0.0 15.3 

78B 2,491,438 766.2 0.0 58.9 

78C 2,445,635 835.2 0.0 60.2 

80A 1,963,077 741.6 17.3 53.0 

80B 968,445 346.7 0.0 28.3 

81A 2,866,367 525.9 0.0 68.4 

81B 1,940,970 444.0 0.0 50.3 

81C 1,236,724 320.4 0.0 32.5 

81D 516,702 53.5 0.0 11.1 

82A 401,734 54.2 0.0 10.1 

82B 57,923 25.2 0.0 1.7 

83A 1,706,897 359.0 0.0 38.9 

83B 1,463,751 504.3 0.0 31.7 

83C 755,825 122.0 0.0 17.5 

83D 129,375 18.5 0.0 3.0 

83E 659,343 76.4 0.1 15.1 

84A 875,391 313.1 26.9 24.4 

84B 745,691 585.5 14.5 35.5 

84C 113,856 78.3 3.8 5.7 

86A 1,453,945 1742.6 34.9 53.4 

86B 351,296 220.6 7.7 10.7 

87A 1,544,144 444.7 17.6 42.9 

87B 410,138 182.0 17.6 13.2 

90A 265,868 440.0 13.3 12.8 

90B 248,178 422.6 11.8 11.9 

91A 161,283 135.5 6.8 7.4 

91B 52,108 87.0 6.5 2.6 

94A 130,138 223.3 8.9 5.6 

94B 61,815 152.5 4.0 3.0 

94C 25,098 56.6 1.7 1.1 

95A 91,989 174.6 8.2 5.2 

95B 223,059 379.0 15.3 11.3 

Total 180,415,846 70678.7 1828.9 3944.0 

Note: N2O is nitrous oxide. NO3 is nitric oxide. 1 MLRA = Major Land Resource Area 

2 Gg CO2 eq. = Gigagrams carbon dioxide equivalents 

 

57

3.1 Summary of U.S. Greenhouse Gas 

Emissions From Cropland Agriculture

Based on IPCC Tier 1 (default emission factors) 

and Tier 3 (DayCent model simulations) methods, 

cropland agriculture resulted in approximately 209 

MMT CO2 eq. total emissions of greenhouse gases 

(GHG) in 2013 (Table 3-1). Cropland agriculture 

is responsible for almost half (46 percent) of all 

emissions from the agricultural sector (EPA 2015). 

Nitrous oxide (N2

O), carbon dioxide (CO2), and 

methane (CH4) emissions from cropped soils totaled 

168, 33, and 9 MMT CO2 eq. in 2013. However, 

that amount was offset by a storage, or carbon 

sequestration, of 34 MMT CO2 eq. in cropped 

mineral soils in 2013. When carbon sequestration 

is taken into account, net emissions of GHG from 

cropland agriculture amount to approximately 175 

MMT CO2 eq. The 95-percent confidence interval for 

net emissions in 2013 is estimated to lie between 129 

and 249 MMT CO2 eq. (Table 3-1).

Annual fluctuations in CO2 sequestration are 

primarily a result of changes in land use and 

variability in weather patterns. In 2013, net emissions 

from cropland agriculture were about 50 percent 

higher than the baseline year (1990), mainly from an 

increase in N2O emissions associated with increased 

cropping and a simultaneous reduction in the CO2

sink in cropland mineral soils. Greenhouse gas 

emissions from agricultural soils fluctuated between 

1990 and 2013, with CH4

 and N2O reaching their 

highest levels in 2010 and 2012 respectively (Table 

3-2). Net CO2 flux showed substantial inter-annual 

variability, mainly due to fluctuations in the size of 

the mineral soil CO2 sink.

Greenhouse gas emissions from agricultural soils, 

primarily N2O, were responsible for the majority 

of total emissions (80 percent), while CH4 and N2O 

from residue burning and rice cultivation caused 

about 4 percent of emissions in 2013 (Tables 3-1, 

3-2). Soil CO2

 emissions from cultivation of organic 

soils (13 percent) and from liming (3 percent) are 

the remaining sources. Nitrous oxide emissions 

from soils are the largest source in the United States 

because N2O is a potent greenhouse gas (see Chapter 

1 Box 1-1). Large amounts of nitrogen are added 

to crops from fertilizer amendments and legume 

cropping, which both stimulate N2

O production. 

Emissions from residue burning are minor because 

only ~3 percent of crop residue is assumed to be 

burned in the United States (EPA 2015). Cropped 

mineral soils in the United States are a net CO2

sink for various reasons, including improved crop 

varieties and better management leading to increased 

carbon inputs from residues and reduced tillage 

intensity that has become more popular in recent 

years, reducing carbon losses from decomposition. 

In addition, lands used for perennial hay cropping, 

as well as idle cropland enrolled in the Conservation 

Reserve Program (CRP), continue to store carbon. 

However, the magnitude of this sequestration in 

recent years is not as great as it was during the 1990s, 

partially due to land conversion from CRP back to 

cropping and lands that have been in CRP for about 

10 years or more, storing less carbon than they did 

initially or even becoming carbon neutral.

Nitrous oxide emissions are largest in areas where a 

large portion of land is used for intensive agriculture 

(Map 3-1a, Figures 3-1a, 3-1b). For example, more 

than 50 percent of the land area in some Major 

Land Resource Areas (MLRAs) that lie within the 

Corn Belt is intensively cropped. Row crops such 

as corn, soybeans, and sorghum make up close to 

40 percent of total cropland and have the highest 

N2

O emissions, followed by small grain crops such 

as wheat, barley and rye, other cropland, and hay 

Chapter 3

Cropland Agriculture

Table 3-1 Estimates and Uncertainties for Cropland Greenhouse Gas 

Emissions, 2013 Table Error! No text of specified style in document.-9 Estimates and Uncertainties for 

Cropland Greenhouse Gas Emissions, 2013

GHG Emissions Lower Bound Upper Bound

Source MMT CO2 eq.

N2O 168 142 230 

Soils Direct 136 189 282 

Soils Indirect1 32 21 102 

Residue Burning 0.1 0.1 0.1 

CH4 9 4 16 

Residue Burning 0.3 0.2 0.4 

Rice Cultivation 8 4 14 

CO2

2 (1) (39) 38 

Mineral Soils (34) (71) 2 

Organic Soils 27 18 39 

Liming of Soils 6 0 8 

Total Emissions 209 165 294 

Net Emissions3 175 129 249

Note: Parentheses indicate a net sequestration. MMT CO2 eq. is million metric tons carbon dioxide equivalent. CH4 is methane; N2O is nitrous 

oxide; CO2 is carbon dioxide. 1 SoilsIndirect N2O emissions account for both volatilization and leaching/runoff. 2 Does not include CO2 emissions from urea fertilization. 3 Includes sources and sinks.

Download data: http://dx.doi.org/10.15482/USDA.ADC/1264151
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

58

cropping (Table 3-3). Unit area emissions were 

highest in the Northeast (Map 3-1b) largely because 

of N2O pulses during spring when snow cover and 

soil surface layers melt while subsoil remains frozen, 

thus causing water ponding and associated emissions. 

Changes in emissions through time are driven 

largely by land conversion (e.g., land previously 

left fallow or used for small grain cropping that has 

been converted to row cropping). Similar to Figure 

3-1a, Map 3-1 and Table 3-3 only include areas and 

emissions from Tier 3 cropped land, which covers 

~87 percent of total cropped land. Appendix Table 

B-1 provides recent MLRA-level land area estimates 

for the same major crop rotations presented in Figure 

3-1a.

Cropland agriculture results in GHG emissions from 

multiple sources, with 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 lead 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, 

MLRA 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 2011a), with some improvements in model 

algorithms and model input data. 

In contrast to previous editions of the inventory that 

reported emissions from individual crops at the State 

level, emissions are now partitioned by crop rotations 

and reported at the MLRA level. Partitioning was 

performed for rotations because emissions are 

thought to be better correlated to farming systems as 

opposed to individual crops, because the emissions 

in a given year reflect management history. For 

example, wheat might be growing during a particular 

year, but the emissions for that year are partly 

(and sometimes largely) due to what happened the 

previous year(s). Emissions were partitioned into 

nine major cropping rotations (Figure 3-1a) by 

generating queries for each MLRA. That is, for each 

MLRA, the emissions and land area for a particular 

rotation were extracted from the databases. The 

queries were performed in a particular order (top to 

bottom in Figure 3-1a, Table 3-3) and were mutually 

exclusive. For example, land area used predominately 

for production of row crops that was also irrigated 

Map 3-1a Total Nitrous Oxide (Direct and Indirect) for 

Major Land Resource Areas, Tier 3 Crops, Annual Means 

2003–2007 (Gg CO2 eq. is gigagrams carbon dioxide equivalent.)

Table 3-2 Summary of Greenhouse Gas Emissions from Cropland Agriculture, 

1990, 1995, 2000, 2005-2013 Table Error! No text of specified style in document.-10 Summary of Greenhouse Gas Emissions 

From Cropland Agriculture, 1990, 1995, 2000, 2005-2013

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013

Source MMT CO2 eq.

N2O 143.6 158.3 141.9 158.7 156.9 164.6 169.8 167.9 168.2 169.9 170.6 167.9

Soils Direct 117.1 127.3 115.7 130.6 129.1 134.2 137.4 136.0 136.2 137.2 137.6 135.7

Soils Indirect1 26.4 30.9 26.1 28.1 27.7 30.3 32.3 31.8 31.9 32.6 32.9 32.1

Residue Burning 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

CH4 9.5 10.1 9.9 9.2 8.0 8.3 9.6 9.7 11.4 8.8 9.6 8.6

Residue Burning 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Rice Cultivation 9.2 9.8 9.6 8.9 7.7 8.0 9.3 9.4 11.1 8.5 9.3 8.3

CO2

2 (36.0) (6.9) (18.8) (3.9) (7.5) (9.4) (6.8) (7.6) (4.9) (5.7) (3.1) (1.4)

Mineral Soils (66.7) (38.6) (49.4) (35.7) (38.9) (40.8) (38.8) (38.2) (36.6) (36.5) (35.8) (34.2)

Organic Soils 26.0 27.3 26.4 27.5 27.2 26.9 26.9 26.9 26.9 26.9 26.9 26.9

Liming of Soils 4.7 4.4 4.3 4.3 4.2 4.5 5.0 3.7 4.8 3.9 5.8 5.9

Total Emissions 183.8 200.1 182.5 199.7 196.3 204.3 211.3 208.2 211.3 209.5 212.9 209.3

Net Emissions3 117.0 161.5 133.1 164.0 157.3 163.5 172.5 170.0 174.7 173.0 177.1 175.1

Note: Parentheses indicate a net sequestration. MMT CO2 eq. is million metric tons carbon dioxide equivalent. CH4 is methane; 

N2O is nitrous oxide; CO2 is carbon dioxide. 1 SoilsIndirect N2O emissions account for both volatilization and leaching/runoff. 2 Does not include CO2 emissions from urea fertilization. 3 Includes sources and sinks.

Map 3-1b Unit Area Nitrous Oxide (Direct and Indirect) 

for Major Land Resource Areas, Tier 3 Crops, Annual 

Means 2003–2007 (Mg CO2 eq. ha-1 yr-1 is megagrams carbon 

dioxide equivalent per hectare per year.)
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

59

would appear in the irrigated category and not be 

included in the row crops category. If queries were 

not mutually exclusive, then there would be double 

accounting because the land areas of some rotations 

partially overlap. 

The data reported represent 5-year means (except 

from years 1990-1992) to reduce interannual 

variation due to weather and other factors. Rotations 

were defined using a general majority rule. For 

example, if a land area was fallow at least 3 out 

of 5 years it was classified as fallow, if land was 

in rice production at least 3 out of 5 years, it was 

classified as rice, and so on. Based on availability of 

land use data, we considered four time periods and 

reported emissions for the median years. These were 

1990–1992, 1993–1997, 1998–2002, and 2003–2007. 

Figure 3-1a does not include years beyond 2007 

because that was the most recent year for which land 

use data were available and subsequent years were 

assumed to have identical land use. In addition to 

rotations, areas are also shown for individual crops 

(Figure 3-1b). In contrast to Figure 3-1a, which 

includes only Tier 3 cropland areas up to 2007, 

Figure 3-1b represents total areas up to 2013. Tier 

3 cropped lands were simulated using the DayCent 

model while Tier 1 emission factors were used to 

estimate emissions for remaining cropped land, see 

section 3.3 for details.

Figure 3-1a U.S. Planted Cropland Area by Rotation Category, 1990-2007 

(CRP is USDA Conservation Reserve Program)

0

10

20

30

40

50

60

1990 1992 1994 1996 1998 2000 2002 2004 2006

Million hectares

Fallow

Rice

Irrigated

Hay

SmallGrain

RowCrop

LowResidue

CRP

OtherCropland

Small Grain

Row Crop

Low Residue

Other Cropland

Figure 3-1b U.S. Planted Cropland Area by Crop Type, 1995-2013

Figure 3-1(b)

U.S. Planted Cropland Area by Crop Type, 1995-2013

0

10

20

30

40

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

Million hectares

Small Grains (Barley, Rye, Millet,

Oats)

Corn (Total Grain + Silage)

Hay

Rice

Sorghum (Total Grain + Silage)

Wheat

Oilseeds (Incl Soybeans)

Table 3-3 Tier 3 Cropland Area by Management Practice, 2013

Table 3-3 Tier 3 Cropland Area by Management Practice, 2013 

 Area Total Tier 3 Cropland 

Current Management million ha % 

Fallow 10.3 7.2 

Rice 1.9 1.3 

Irrigated 17.4 12.1 

Hay 16.2 11.3 

Small Grain 18.5 12.9 

Row Crop 57.4 40.0 

Low Residue 4.4 3.0 

USDA Conservation 

Reserve Program 12.5 8.7 

Other Cropland 4.9 3.4 

 
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

60

3.2 Sources and Sinks of Greenhouse 

Gas Emissions in Cropland Agriculture

3.2.1 Cropped Soils

Agricultural soils act as both a source of GHGs 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 denitrification in soils (Firestone 

& Davidson 1989). In nitrification, soil microorganisms (“microbes”) convert ammonium (NH4) to 

nitrate (NO3) through aerobic oxidation (IPCC 2006). 

In denitrification, microbes convert nitrate to nitrogen 

oxides (NOx) and nitrogen gas (N2) by anaerobic 

reduction. During nitrification and denitrification, 

N2O is created as a byproduct, which can diffuse 

from the soil and enter the earth’s atmosphere (IPCC 

2006). Cropland soil amendments that add nitrogen 

to soils drive the production of N2O by providing 

additional substrate, which enhances nitrification and 

denitrification. Synthetic fertilizer, livestock manure, 

sewage sludge, cultivation of N-fixing crops, and 

incorporation of crop residues all add various forms 

of 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 2015). 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 United States 

due to the small land area cropped with paddy 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 United States. 

Nitrous oxide is the major GHG emitted from 

cropland agriculture in the United States. 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). 

Cropped soils can be a source or sink of CO2. Net 

CO2 flux is related to changes soil organic carbon 

(SOC) stocks (IPCC 2006). Changes in SOC content 

are controlled by the balance between C inputs (e.g., 

atmospheric CO2

 fixed as carbon in plants through 

photosynthesis) and losses from plant (autotrophic) 

respiration and decomposition of soil organic matter 

and plant litter (IPCC 2006). 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 2006). 

Changes in agricultural practices such as vegetation 

clearing, water 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 percent 

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. Historically, 

conversion of native ecosystems to agricultural 

uses resulted in large soil carbon losses, as much 

as 30 to 50 percent 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 

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 percent 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 inhibited. 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. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

61

In addition, lime is often added to mineral and 

organic agricultural soils to reduce acidic conditions. 

Lime contains carbonate compounds (e.g., limestone 

and dolomite) that when added to soils release CO2

through the bicarbonate equilibrium reaction to 

increase alkalinity (IPCC 2006).

3.2.2 Rice Cultivation

Rice is usually cultivated on flooded fields and 

is almost always grown in flooded fields in the 

United States (EPA 2015). 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 2006, Le Mer & Roger 2001). Methane from 

paddy 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 2006). Diffusion 

through plants is considered the primary pathway, 

with diffusion losses from surface water being 

the least important process (IPCC 2006). 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 2006). Since U.S. paddy rice acreage 

is relatively small compared to other crops, CH4

emissions from rice cultivation are small compared 

to other domestic cropland agriculture sources (EPA 

2015).

3.2.3 Residue Burning

Crop residues are sometimes burned in fields 

to prepare for cultivation and control for pests, 

although this is no longer a common practice in the 

United States (EPA 2015). 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 2006). 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 United States (EPA 2015).

3.3 Nitrous Oxide Emissions from 

Cropped Soils

In 2013, 65 percent of total cropland soil emissions 

were direct soil N2O emissions (Table 3-2). Of the 

19 percent of total emissions from indirect N2O, 

53 percent are from NO3 leaching/runoff and the 

remainder are associated with volatilization (Table 

3-4). Emissions are highest from row cops (mostly 

corn and soybean) because row crops cover the 

largest land area (Map 3-2) and nitrogen inputs 

from fertilizer and biological fixation in legumes 

are high (Figure 3-2). Other factors contributing 

to high emissions for these crops are that 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. Emissions from the small 

grain rotation category, or cereals, were the second 

highest, followed closely by irrigated cropland and 

hay. 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 paddy rice are low, as the 

cropland areas for this crop are small compared to 

the other major crops in the United States. Emissions 

from histosol cultivation are small (~2 percent of 

total direct emissions) because histosols represent 

only ~1 million ha, which is less than 1 percent of 

U.S. cropped land. As explained in Section 3-1, 

partitioning was performed for rotations (Table 3-4) 

because emissions are thought to be better correlated 

to farming systems as opposed to individual crops. 

Appendix Tables B-2, B-3 and B-4 report direct 

and indirect N2

O emissions data at a finer spatial 

resolution (i.e., MLRA level) for the same cropping 

rotations presented in Table 3-4. Years beyond 2007 

are not included in Table 3-4 and Figure 3-2 because 

that was the most recent year for which land use data 

were available and subsequent years were assumed to 

have identical land use.

Figure 3-2 Annual Nitrogen Inputs to Cropland Soil, 1990-2007

Figure 3 (Tg N is teragrams nitrogen) -2 Annual Nitrogen Inputs to Cropland Soil, 1990-2007

0

2

4

6

8

10

12

14

16

1990 1992 1994 1996 1998 2000 2002 2004 2006

Tg N

Synthetic Fertilizer

Biological Fixation

N from Residue

Manure

Total
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

62

Table 3-4 Nitrous Oxide Emissions from Differently 

Cropped Soils, 5-Year Means

Table Error! No text of specified style in document.-12 Nitrous Oxide Emissions From 

Differently Cropped Soils, 5-year Means

1992 1997 2002 2007

Rotations1 MMT CO2 eq.

USDA Conservation 

Reserve Program 2.6 3.3 2.8 2.8

Direct 2.2 2.8 2.4 2.3

Volatilization 0.4 0.5 0.4 0.4

Leaching & Runoff 0.1 0.1 0.1 0.0

Fallow 6.6 6.3 4.6 4.5

Direct 5.8 5.6 3.8 3.9

Volatilization 0.5 0.5 0.4 0.3

Leaching & Runoff 0.3 0.3 0.4 0.3

Hay 15.4 17.8 16.1 16.5

Direct 13.6 15.9 13.9 14.6

Volatilization 0.9 0.9 1.0 1.0

Leaching & Runoff 1.0 1.0 1.2 1.0

Irrigated 19.3 22.6 22.2 21.3

Direct 14.1 15.7 15.2 15.3

Volatilization 1.5 1.5 1.5 1.6

Leaching & Runoff 3.7 5.4 5.5 4.5

Low Residue 2.6 3.0 3.3 3.4

Direct 2.0 2.3 2.6 2.7

Volatilization 0.2 0.2 0.2 0.3

Leaching & Runoff 0.4 0.5 0.5 0.5

Other Cropland 5.4 4.9 3.6 3.3

Direct 4.5 4.0 3.1 2.8

Volatilization 0.5 0.4 0.3 0.3

Leaching & Runoff 0.4 0.5 0.2 0.2

Rice 4.0 4.0 4.4 4.2

Direct 3.5 3.5 4.0 3.8

Volatilization 0.2 0.2 0.2 0.2

Leaching & Runoff 0.3 0.3 0.3 0.2

Row Crop 52.4 57.2 57.5 60.8

Direct 42.9 47.5 46.0 50.5

Volatilization 5.5 5.9 6.5 6.9

Leaching & Runoff 4.0 3.7 5.0 3.4

Small Grain 13.3 12.7 10.5 10.5

Direct 11.7 11.2 9.1 9.1

Volatilization 1.1 0.8 0.9 0.9

Leaching & Runoff 0.6 0.7 0.4 0.4

Tier 1 cropped land 24.5 27.7 26.9 28.0

Direct 18.8 21.1 20.5 21.3

Volatilization 2.3 2.6 2.5 2.6

Leaching & Runoff 3.4 4.0 3.9 4.1

Histosol Cultivation2 2.7 2.6 2.5 2.6

All Direct 119.1 129.7 120.4 126.2

All Volatilization 13.0 13.5 14.0 14.6

All Leaching & 

Runoff 14.0 16.3 17.5 14.5

Total 148.8 162.1 154.5 157.9

1992 1997 2002 2007

Rotations1 MMT CO2 eq.

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent 1 Emissions from residue burning are not included.

2 Direct emissions.

3-2), while N2O emissions varied between 141 and 

172 MMT CO2 eq. However, variation in N inputs 

explained less than 5 percent 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 estimated N2O emissions. 

Specifically, amount and timing of precipitation, 

temperature patterns, and soil carbon and nitrogen 

availability interact to influence N2O emissions. 

Because the responses of N2O emissions to the 

controlling variables are often non-linear and the 

interactions complex, the correlations between any 

single variable (or even groups of variables) and 

measured emissions are typically weak (Stehfest and 

Bouwman 2006, Nishina et al. 2012, Philibert 2012).

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 2015), with some exceptions. The 

U.S. GHG Inventory (EPA 2015) includes 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. 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 most land area 

covered by major crop types and many specialty 

crops. Default Tier 1 emission factors from IPCC 

(2006) were 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, such 

as DayCent, 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 

summarized below shows how the Tier 1 and Tier 

3 approaches can be combined to derive overall 

emission estimates. Refer to EPA (2015) for a 

complete description of the methodologies used to 

estimate N2

O emissions.

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 ~17 and 

20 MMT N per year between 1990 and 2007 (Fig. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

63

3.3.2.1 IPCC Tier 3 DayCent Simulations for Most 

Cropped Soils

The DayCent ecosystem model (Del Grosso et al. 

2001, Parton et al. 1998) was used to estimate direct 

N2O emissions from most mineral soils producing 

most commodity and specialty crops, including 

alfalfa hay, barley, corn, cotton, dry beans, grass hay, 

grass-clover hay, oats, onions, peanuts, potatoes, 

rice, sorghum, soybeans, sugar beets, sunflowers, 

tomatoes, wheat, and other crops) which represent 

approximately 87 percent of total cropland in the 

United States. DayCent simulates crop growth, 

soil organic matter decomposition, greenhouse gas 

fluxes, and key biogeochemical processes affecting 

N2O emissions. The simulations are driven by model 

input data generated from daily weather records, land 

management, and soil physical properties determined 

in national soil surveys.

DayCent simulates carbon and nitrogen dynamics, 

soil water content and temperature, and other 

ecosystem variables (Parton et al.1994). Key sub 

models 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 20-30 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 United States 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). 

DayCent has been parameterized to represent the 

major commodity crops, as well as many specialty 

crops, grown in the United States. In addition to not 

being parameterized to simulate all crops, the 

model also does not simulate any crops grown on 

organic soils.

Map 3-2 U.S. Cropped Land

Data obtained from the 2011 National Land Cover Database at http://www.mrlc.gov
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

64

DayCent simulations were conducted at the National 

Resources Inventory (NRI) point resolution. The NRI 

has information on cropping and land-use histories 

(USDA 2009). The NRI is a statistically based 

sample of all non-Federal land, and includes 380,956 

points in agricultural land for the conterminous 

United States that are included in the Tier 3 methods. 

Each point is associated with an expansion factor that 

allows scaling of N2O emissions from NRI points 

to the entire country (i.e., each expansion factor 

represents the amount of area with similar land-use/

management history as the sample point). Land 

use and some management information (e.g., crop 

type, soil attributes, and irrigation) were originally 

collected for each NRI point on a 5-year cycle 

beginning in 1982. For cropland, data were collected 

for 4 out of 5 years in the cycle (i.e., 1979–1982, 

1984–1987, 1989–1992, and 1994–1997). In 1998, 

the NRI program began collecting annual data, and at 

the time of this report’s analysis, data were currently 

available through 2007. 

The simulations reported here assumed conventional 

tillage cultivation, gradual improvement of cultivars, 

and gradual increases in fertilizer application until 

1978. We accounted for improvements of cultivars 

(cultivated varieties) because, for example, it is 

unrealistic to assume that modern corn is identical, 

in terms of yield potential, nitrogen demand, etc., 

to 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 N2

O emissions. 

In addition to simulating historical crop management, 

the model also represented at least 1,000 years of 

native vegetation before land was initially plowed.

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 N2

O 

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 

Tier 1 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 Tier 1 methodology.

3.3.2.2 Sources of Uncertainty for DayCent 

Simulations

The DayCent model results imbed three types 

of uncertainty: model input uncertainty, model 

structural uncertainty, and land-area scaling 

uncertainty. Uncertainty in three types of model 

inputs (N additions from synthetic fertilizer, N and 

C additions from manure, and tillage intensity) 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. Probability distribution functions (PDFs) 

were derived from surveys at the county scale for 

the inputs in most cases. A Monte Carlo analysis 

was used with 100 iterations for each NRI point; 

random draws were made from PDFs for fertilizer, 

manure application, and tillage. An adjustment factor 

was also selected from PDFs with normal densities 

to represent the dependence between manure 

amendments and N fertilizer application rates. 

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. This component is the largest source of 

uncertainty in the Tier 3 model-based inventory 

analysis, accounting for more than 80 percent of 

the overall uncertainty in the final estimates (Ogle 

et al. 2009, Del Grosso et al. 2010). To quantify 

model structural error, N2

O emissions generated by 

DayCent were compared with emissions measured in 

24 field plots at various locations around the world, 

but mostly from the United States. Specifically, an 

empirically based procedure was applied to develop a 

structural uncertainty estimator from the relationship 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

65

between modeled results and field measurements 

(Ogle et al. 2007). Model inputs are assumed to be 

precisely known for the experiments so structural 

uncertainty can be isolated.

The third element is the uncertainty associated with 

scaling the DayCent results for each NRI point to 

the entire land base by using the expansion factors 

provided with the NRI survey dataset. The expansion 

factors represent the number of hectares associated 

with the land use and management history for a 

particular point. This uncertainty is determined by 

computing the variances from a set of replicated 

weights for the expansion factor.

3.3.2.3 Activity Data for DayCent Simulations

The National Resources Inventory provided land 

use information for the DayCent simulations. The 

NRI has a stratified multi-stage sampling design, 

where primary sample units are stratified on the basis 

of county and township boundaries defined by the 

U.S. Public Land Survey (Nusser and Goebel 1997). 

Within a primary sample unit, typically a 160-acre 

(64.75 ha) square quarter-section, three sample points 

are selected according to a restricted randomization 

procedure. Each point in the survey is assigned an 

expansion factor based on other known areas and 

land-use information (Nusser and Goebel 1997). In 

principle, the expansion factors represent the amount 

of area with the land use and land-use change history 

that is the same as the point location. It is important 

to note that the NRI uses a sampling approach, and 

therefore there is some uncertainty associated with 

scaling the point data to a region or the country using 

the expansion factors. In general, those uncertainties 

decline at courser scales, such as States, compared 

to smaller county units, because of a larger sample 

size. An extensive amount of soils, land use, and 

land management data have been collected through 

the survey (Nusser et al. 1998). Primary sources 

for data include aerial photography and remote 

sensing imagery as well as field visits and county 

office records. In addition to providing land cover 

information, NRI differentiates between irrigated 

and non-irrigated land, but does not provide more 

detailed information on the type and intensity of 

irrigation. Hence, irrigation is modeled by assuming 

that applied water to field capacity with intervals 

between irrigation events where the soils drain to 

about 60 percent of field capacity.

The annual NRI data product provides crop data 

for most years between 1979 and 2007, with the 

exception of 1983, 1988, and 1993. These years are 

gap-filled using an automated set of rules so that 

cropping sequences are filled with the most likely 

crop type given the historical cropping pattern at 

each NRI point location. NRI points are included in 

the land base for the agricultural soil N2

O emissions 

inventory if they were identified as cropland or 

grassland between 1990 and 2007. Land use for 2008 

to 2013 is assumed to be the same as 2007, but will 

be updated with newer NRI as it becomes available 

(i.e., USDA 2013). Note that the NRI includes only 

non-Federal lands because Federal lands are not 

classified into land uses as part of the NRI survey 

(i.e., they are only designated as Federal lands). 

Data on N fertilizer rates were based primarily on 

the USDA Agricultural Resource Management 

Survey (USDA 1997a, 2011b). In these surveys, 

data on inorganic N fertilization rates are collected 

for most of the crops simulated by DayCent in the 

high-production States and for a subset of lowproduction States. These data are used to build a time 

series of fertilizer application rates for specific crops 

and States for 1990–2013. Mean fertilizer rates and 

standard deviations for irrigated and rainfed crops 

are produced for each State. If a State is not surveyed 

for a particular crop or if there are not enough data 

to produce a State-level estimate, then data are 

aggregated to USDA Farm Production Regions in 

order to estimate a mean and standard deviation for 

fertilization rates (Farm Production Regions are 

groups of States in the United States with similar 

agricultural commodities) (USDA 2014). If Farm 

Production Region data are not available, crop data 

are aggregated to the entire United States to estimate 

a mean and standard deviation. Standard deviations 

for fertilizer rates are used to construct PDFs with 

log-normal densities in order to address uncertainties 

in application rates. The survey summaries also 

present estimates for fraction of crop acres receiving 

fertilizer, and these fractions are used to determine if 

a crop is receiving fertilizer. Alfalfa hay and grassclover hay are assumed to not be fertilized, but grass 

hay is fertilized according to rates from published 

farm enterprise budgets (NRIAI 2003).

Manure N addition rates were based on data 

developed by the USDA Natural Resources 

Conservation Service (NRCS) (Edmonds et al. 

2003). USDA-NRCS has coupled estimates of 

manure N produced with estimates of manure 

N recoverability by animal waste management 

system to produce county-level rates of manure N 

application to cropland and pasture. Edmonds et al. 

(2003) estimated the area amended with manure and 

application rates in 1997 for both manure-producing 

farms and manure-receiving farms within a county 

for two scenarios, one before implementation 

of Comprehensive Nutrient Management Plans 

(baseline) and one after implementation (Edmonds et 

al. 2003). 
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

66

For DayCent simulations, the rates for manureproducing farms and manure-receiving farms have 

been area weighted and combined to produce a single 

county-level estimate for the amount of land amended 

with manure and the manure N application rate for 

each crop in each county. The estimates were based 

on the assumption that Comprehensive Nutrient 

Management Plans have not been fully implemented. 

This is a conservative assumption because it allows 

for higher leaching rates due to some over application 

of manure to soils. In order to address uncertainty 

in these data, uniform probability distributions 

are constructed based on the proportion of land 

receiving manure versus the amount not receiving 

manure for each crop type and pasture. For example, 

if 20 percent of land producing corn in a county is 

amended with manure, randomly drawing a value 

equal to or greater than 0 and less than 20 would 

lead to a simulation with a manure amendment, 

while drawing a value greater than or equal to 20 

and less than 100 would lead to no amendment in the 

simulation.

Edmonds et al. (2003) only provides manure 

application rate data for 1997, but the amount of 

managed manure available for soil application 

changes annually, so the area amended with manure 

is adjusted relative to 1997 to account for all the 

manure available for application in other years. 

Specifically, the manure N available for application 

in other years is divided by the manure N available 

in 1997. If the ratio is greater than 1, there is more 

manure N available in that county relative to the 

amount in 1997, and so it is assumed a larger area 

is amended with manure. In contrast, ratios less 

than 1 imply less area is amended with manure 

because there is a lower amount available in the 

year compared to 1997. The amendment area in 

each county for 1997 is multiplied by the ratio to 

reflect the impact of manure N availability on the 

area amended. The amount of managed manure N 

available for application to soils is calculated by 

determining the populations of livestock on feedlots 

or otherwise housed, requiring collection and 

management of the manure. To estimate C inputs 

(associated with manure N application rates derived 

from Edmonds et al. (2003), carbon-nitrogen (C:N) 

ratios for livestock-specific manure types are adapted 

from the Agricultural Waste Management Field 

Handbook (USDA 1996), On-Farm Composting 

Handbook (NRAES 1992), and recoverability factors 

provided by Edmonds et al (2003). The C:N ratios 

are applied to county-level estimates of manure N 

excreted by animal type and management system 

to produce a weighted county average C:N ratio 

for manure amendments. The average C:N ratio is 

used to determine the associated C input for crop 

amendments derived from Edmonds et al. (2003). 

To account for the common practice of reducing 

inorganic N fertilizer inputs when manure is added 

to a cropland soil, crop-specific reduction factors 

are derived from mineral fertilization data for land 

amended with manure versus land not amended 

with manure in the ERS 1995 Cropping Practices 

Survey (USDA 1997a). Mineral N fertilization rates 

are reduced for crops receiving manure N based 

on a fraction of the amount of manure N applied, 

depending on the crop and whether it is irrigated or 

rainfed. The reduction factors are randomly selected 

from PDFs with normal densities in order to address 

uncertainties in the dependence between manure 

amendments and mineral fertilizer application.

Tillage practices are estimated for each cropping 

system based on data from the Conservation 

Technology Information Center (CTIC 2004). CTIC 

compiles data on cropland area under five tillage 

classes by major crop species and year for each 

county in the United States. Because the surveys 

involve county-level aggregate area, they do not 

fully characterize tillage practices as they are applied 

within a management sequence (e.g., crop rotation). 

This is particularly true for area estimates of cropland 

under no-till, which include a relatively high 

proportion of “intermittent” no-till, where no-till in 

one year may be followed by tillage in a subsequent 

year. For example, a common practice in maizesoybean rotations is to use tillage in the maize crop 

while no-till is used for soybean, such that no-till 

practices are not continuous in time. Estimates of the 

area under continuous no-till are provided by experts 

at CTIC to account for intermittent tillage activity 

and its impact on soil C (Towery 2001). 

Tillage practices are grouped into three categories: 

full, reduced, and no-tillage. Full tillage is defined 

as multiple tillage operations every year, including 

significant soil inversion (e.g., plowing, deep 

disking) and low surface-residue coverage. This 

definition corresponds to the intensive tillage and 

“reduced” tillage systems as defined by CTIC (2004). 

No-till is defined as not disturbing the soil except 

through the use of fertilizer and seed drills and where 

no-till is applied to all crops in the rotation. Reduced 

tillage made up the remainder of the cultivated area, 

including mulch tillage and ridge tillage as defined 

by CTIC and intermittent no-till. The specific tillage 

implements and applications used for different crops, 

rotations, and regions to represent the three tillage 

classes are derived from the 1995 Cropping Practices 

Survey by the Economic Research Service (USDA 

1997a).
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

67

Daily maximum/minimum temperature and 

precipitation data are based on gridded weather data 

from the North America Regional Reanalysis Product 

(NARR) (Mesinger et al. 2006). It is necessary to use 

computer-generated weather data because weather 

station data do not exist near all NRI points and, 

moreover, weather station data are for a point in 

space. The NARR product uses this information with 

interpolation algorithms to derive weather patterns 

for areas between these stations. NARR weather 

data are available for the United States from 1980 

through 2007 at a 32 km resolution. Each NRI point 

is assigned the NARR weather data for the grid cell 

containing the point. 

Soil texture and natural drainage capacity (i.e., hydric 

versus non-hydric soil characterization) are the main 

soil variables used as input to the DayCent model. 

Texture is one of the main controls on soil processes 

in the DayCent model, which uses particle-size 

fractions of sand (50-2,000 μm), silt (2-50 μm), and 

clay (< 2 μm) as inputs. Hydric soils are poorlydrained and hence prone to have a high water table 

for part of the year in their native (pre-cultivation) 

condition. Non-hydric soils are moderately to well 

drained.2

 Poorly drained soils can be subject to 

anaerobic (lack of oxygen) conditions if water inputs 

(precipitation and irrigation) exceed water losses 

from drainage and evapotranspiration. Depending 

on moisture conditions, hydric soils can range from 

being fully aerobic to completely anaerobic, varying 

over the year. Other soil characteristics needed for 

simulations, such as field capacity and wilting-point 

water contents, are estimated from soil texture data 

using a standardized hydraulic properties calculator 

(Saxton et al. 1986). Soil input data are derived 

from Soil Survey Geographic Database (SSURGO) 

(Soil Survey Staff 2011). The data are based on field 

measurements collected as part of soil survey and 

mapping. Each NRI point is assigned the dominant 

soil component in the polygon containing the point 

from the SSURGO data product.

3.3.2 IPCC Tier 1 Methodology for Cropped 

Land Not Simulated by DayCent

3.3.2.1 Mineral Soils

For mineral agricultural soils not simulated by 

DayCent, the Tier 1 IPCC methodology was used to 

estimate direct N2

O emissions. Estimates of direct 

N2

O 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) and nonmanure organic fertilizers. 

Annual synthetic fertilizer nitrogen additions 

to cropped land not simulated by DayCent are 

calculated by process of elimination. For each 

year, fertilizer applied to cropped and grazed lands 

simulated by DayCent was subtracted from total 

fertilizer used on farms in the United States. The 

difference was assumed to be applied to cropped 

land not simulated by DayCent. Residue nitrogen 

for these crops was derived from information 

on crop production yields, residue management 

(retained versus 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 (2014), 

Schueneman (1997, 1999a- 2001), Deren (2002), 

Kirstein (2003- 2004, 2006), Gonzalez (2007- 

2014), Cantens (2004- 2005), Lee (2003 -2007), 

Slaton (1999- 2001), Wilson (2002- 2007, 2009- 

2012), Hardke (2013, 2014), Linscombe (1999, 

2001-2014), Anderson (2008- 2014), Klosterboer 

(1997, 1999- 2003), Stansel (2004- 2005), 

Texas Agricultural Experiment Station (2006, 

2007-2014). 

2

 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a 

management variable. 
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

68

• Crop residue N was derived by combining 

amounts of above- and below-ground biomass, 

which were determined based on crop 

production yield statistics (USDA 2014), dry 

matter fractions (IPCC 2006), linear equations 

to estimate above-ground biomass given dry 

matter crop yields (IPCC 2006), ratios of belowto-above-ground biomass (IPCC 2006), and N 

contents of the residues (IPCC 2006). 

Estimates of total national annual N additions from 

land application of other organic fertilizers were 

derived from organic fertilizer statistics (TVA 1991-

1994, AAPFCO 1995- 2014). 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-toyear (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 

MMT N.

IPCC Tier 1 methodology for emissions from mineral 

soils is based on nitrogen inputs. Nitrogen inputs 

from synthetic and organic fertilizer and above- and 

below-ground crop residues were added together. 

This sum was multiplied by the default Tier 1 

emission factor (1.0 percent) to derive an estimate of 

cropland direct N2

O 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 Tier 1 default 

factors (30 percent and 10 percent, respectively).

3.3.2.2 Cultivation of Histosols 

The IPCC Tier 1 method was 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 NRI 

(USDA 2000, 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; IPCC 2006), and the subtropical histosol area is multiplied by the average of 

the temperate and tropical IPCC default emission 

factors (12 kg N2

O-N/ha cultivated; IPCC 2006).

3.3.2.3 Total N2O Emissions

Total direct emissions were obtained by summing 

DayCent-generated emissions from most crops on 

mineral soils, Tier 1-generated estimates for crops 

on mineral soils not simulated by DayCent, and 

Tier 1 estimates of emissions from organic soils. 

Total indirect emissions from NO3

 leaching or 

runoff in landscapes where annual water inputs 

from precipitation and irrigation exceed potential 

evaporation rates were obtained by adding DayCent 

estimates for most crops on mineral soils to Tier 

1 default estimates for crops on mineral soils not 

simulated by DayCent and multiplying by the default 

emission factor (0.75 percent of N leached/runoff ). 

Total indirect emissions from nitrogen volatilization 

were obtained by adding DayCent estimates for 

most crops on mineral soils to Tier 1 estimates for 

crops on mineral soils not simulated by DayCent and 

multiplying by the default emission factor (1 percent 

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 N2

O emissions from cropped soils.

3.3.3 Uncertainty in N2O Emissions

Uncertainty was combined for direct emissions from 

crop rotations simulated by DayCent, croplands 

not calculated by DayCent, and indirect emissions 

from all cropped lands. Section 3.3.2.2 describes 

uncertainty for direct emissions calculated using 

DayCent. Uncertainty for direct emissions from 

cropped lands not simulated by DayCent was 

estimated using simple error propagation (IPCC 

2006). Uncertainty in indirect emissions for most 

crops combined uncertainty in DayCent estimates 

of nitrate leaching and N gas volatilization based 

on 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 crops not simulated by 

DayCent 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 N2

O 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-percent confidence interval in N2O emissions 

was estimated to lie between 153 and 281 MMT CO2

eq. (Table 3-1).

3.3.4 Changes Compared to the 3rd edition of 

the USDA GHG Report

There were several changes compared to the previous 

edition of the inventory. The most important was 

using NRI for land use information. In previous 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

69

inventories, land cover was based on USDA-NASS 

statistics for areas of major crops (corn, soybeans, 

wheat, alfalfa hay, other hay, sorghum, and cotton) 

at the county level and region-specific assumptions 

regarding common cropping practices. For example, 

in the north central United States, corn and soybean 

were assumed to alternate every other year in a 

2-year rotation cycle and were not irrigated while 

corn grown in Western States was assumed to be 

irrigated and grown continuously instead of being 

rotated with other crops. In contrast to these regionspecific assumptions for land use, NRI data represent 

actual land use during any particular year. For 

example, a given NRI point could have irrigated corn 

grown for 3 years, followed by 2 years of irrigated 

soybean, followed by a year of non-irrigated wheat. 

Another improvement relates to land area considered 

eligible to contribute to indirect N2O from NO3

leached or runoff from cropped fields. Instead of 

assuming that nitrate leaching and runoff can occur 

anywhere, a criterion was used to designate lands 

where nitrate is susceptible to be leached or runoff 

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 are related to improvements in the 

DayCent model and uncertainty estimation. The most 

noteworthy of these changes relates to expanding 

the number of study sites used to quantify model 

uncertainty for direct N2O emissions and bias 

correction. There were also various changes to the 

DayCent model, including modifying algorithms to 

more realistically represent plant and soil processes 

and modifying parameters to improve model outputs. 

For example, the temperature algorithm used to 

simulate crop production as well as soil carbon inputs 

was modified. These changes resulted in an increase 

in N2O emissions of approximately 4 percent, relative 

to the previous inventory. 

3.3.5 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, NH3 emissions 

and NO3

 leaching. Using enhanced efficiency 

fertilizers designed to release N slowly or formulated 

with nitrification inhibitors and applying fertilizer in 

multiple applications should improve the synchrony 

between nitrogen supply and plant nitrogen demand. 

However, multiple applications of fertilizer require 

increased time and equipment usage by farmers and 

enhanced efficiency fertilizers are more expensive 

than conventional fertilizers. Use of nitrification 

inhibitors and slow-release fertilizers has been 

shown to decrease N2

O emissions in some systems 

(Migliorati et al. 2015, Halvorson et al. 2014, 

Akiyama et al. 2010, Weiske et al. 2001, McTaggert 

et al. 1997). However, use of these improved 

fertilizers does not always result in N2O mitigation 

(Parkin and Hatfield 2014, Dell et al. 2014, Sistani 

et al. 2011), and there is some evidence that these 

fertilizers are more effective in irrigated systems and 

when rainfed systems receive consistent precipitation 

(Hatfield and Venterea 2014). Climate-specific 

scaling factors have been developed to represent 

the expected direct N2O reduction for enhanced 

efficiency fertilizers and are reported in a recent 

USDA publication (Ogle et al. 2014). Ogle et al. 

(2014) also includes scaling factors for the expected 

reductions in NO3 leaching (which contributes to 

indirect N2O emissions) for leguminous and nonleguminous cover crops.

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 2015). Methane emissions from primary 

and ratoon crops are accounted for separately 

because emissions from ratoon crops tend to be 

higher (EPA 2015). Overall, rice cultivation is a small 

source of CH4 in the United States. In 2013, CH4

emissions totaled 8.3 MMT CO2 eq., of which 5.8 

MMT CO2 eq. were from primary crops in all seven 

States and 2.5 MMT CO2 was from ratoon crops in 

four States (Table 3-5).

Figure 3-3 Methane from Rice Cultivation by State, 1990 & 2013

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

Figure 3-3

Methane from Rice Cultivation by State, 1990 & 2013

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

AR CA FL LA MS MO OK TX

MMT CO2 eq.

1990

2013

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.
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

70

Arkansas and California had the highest CH4

emissions (2.6 MMT CO2 eq. and 1.2 MMT CO2 eq. 

respectively) from rice cultivation in 2013, followed 

by Louisiana and Missouri. Mississippi, Texas, and 

Florida each had emissions less than or equal to 0.4 

MMT CO2 eq. (Table 3-5). State-level shifts in CH4

emissions are positively correlated with changes 

in area of rice cultivation (Appendix Table B-5). 

For example, since 1990, CH4 emissions from rice 

cultivation have decreased by nearly 10 percent, 

while total area of rice cultivation has decreased by 

11 percent. The State of Texas accounts for most of 

the overall reduction, with a decline of 43 percent 

(Table 3-6). Appendix Table B-5 provides a complete 

time series of areas harvested for rice by State with 

primary versus ratoon crops from 1990-2013.

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 2015). 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 (EPA 2015). 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 237 kg CH4

/ha per season, and the 

Table 3-5 Methane from Rice Cultivation from Primary and Ratoon Operations by State, 

1990, 1995, 2000, 2005-2013

Table Error! No text of specified style in document.-13 Methane From Rice Cultivation From 

Primary and Ratoon Operations by State, 1990, 1995, 2000, 2005-2013

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013

Source MMT CO2 eq.

Primary 6.7 7.4 7.2 6.7 5.6 5.5 5.9 6.2 7.2 5.2 5.3 5.8

Arkansas 2.9 3.2 3.4 3.3 2.8 2.7 2.8 3.0 3.6 2.3 2.6 2.6

California 0.8 1.0 1.2 0.9 0.9 1.0 0.9 1.0 1.0 1.0 1.0 1.2

Florida 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Louisiana 1.3 1.4 1.2 1.1 0.7 0.8 0.9 0.9 1.1 0.8 0.8 1.0

Mississippi 0.6 0.7 0.5 0.5 0.4 0.4 0.5 0.5 0.6 0.3 0.3 0.3

Missouri 0.2 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.3 0.4 0.4

Texas 0.8 0.8 0.5 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.3 0.3

Ratoon 2.5 2.4 2.4 0.8 0.9 1.3 1.9 1.8 2.1 1.9 2.1 2.5

Arkansas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.4

Florida 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Louisiana 1.3 1.3 1.5 0.5 0.5 0.9 1.2 1.1 1.4 1.0 1.1 1.2

Texas 1.1 1.0 0.8 0.4 0.4 0.3 0.6 0.7 0.7 0.9 0.5 0.8

Total 9.2 9.8 9.6 7.5 6.5 6.7 7.8 7.9 9.3 7.1 7.4 8.3

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent.

Table 3-6 Change in Methane Emissions from Rice 

Cultivation, 1990-2013

Table Error! No text of specified style in document.-14 Change in Methane Emissions From 

Rice Cultivation, 1990-2013

1990 2013 1990-2013

State MMT CO2 eq. % Change

Arkansas 2.88 2.99 4

California 0.85 1.21 42

Florida 0.08 0.08 5

Louisiana 2.60 2.23 -14

Mississippi 0.60 0.30 -50

Missouri 0.19 0.37 95

Texas 1.96 1.12 -43

Total 9.16 8.30 -9

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

71

resultant emission factor for the ratoon crop is 780 kg 

CH4/ha per season (EPA 2015).

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 2015). 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 61 

to 500 kg CH4

/ha per season and ratoon emissions 

ranged from 481 to 1,490 kg CH4/ha per season 

(EPA 2015). Other sources of uncertainty include the 

primary rice-cropped area for each State, percent of 

rice-cropped area that is ratooned, the length of the 

growing season, and the extent to which flooding 

outside of the normal rice season is practiced. 

Uncertainties in primary and ratooned areas were 

based on expert judgement and estimates of the 

portion of ratooned areas by State. Uncertainty 

regarding flooding outside the normal growing 

season was estimated for California (+/- 20 percent), 

but insufficient data were available to estimate this 

uncertainty source for other States.

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 2013 were 

estimated to be between 4 and 16 MMT CO2 eq. at a 

95-percent confidence level, which indicates a range 

of 50 percent below to 91 percent above the actual 

2013 emission estimate of 8 MMT 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 United States, crop 

residues burned include wheat, rice, sugarcane, 

corn, cotton, soybeans, and lentils and often occur 

in the Southeastern States, the Great Plains, and the 

Pacific Northwest (EPA 2015). For most crops, a 

small portion of residues are burned each year, but a 

higher portion of rice residues are burned annually 

(EPA 2015). Consequently, emissions from residue 

burning are a small source of overall crop-related 

emissions in the United States. One-fourth of GHG 

emissions from residue burning, across all crop types, 

consisted of CH4 in 2013; the remaining emissions 

were N2O (Table 3-7, Figure 3-4). The highest GHG 

Table 3-7 Greenhouse Gas Emissions from Agriculture Burning by Crop, 1990, 1995, 2000, 2005–2013

Table Error! No text of specified style in document.-15 Greenhouse Gas Emissions From 

Agriculture Burning by Crop, 1990, 1995, 2000, 2005–2013

1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013

Source MMT CO2 eq.

CH4 0.32 0.28 0.31 0.22 0.28 0.28 0.32 0.29 0.29 0.30 0.30 0.31

Wheat 0.16 0.13 0.14 0.10 0.10 0.12 0.16 0.12 0.12 0.14 0.13 0.13

Rice 0.05 0.05 0.05 0.04 0.05 0.07 0.05 0.06 0.06 0.05 0.05 0.05

Sugarcane 0.07 0.06 0.06 0.03 0.07 0.03 0.04 0.05 0.04 0.05 0.05 0.05

Corn 0.02 0.02 0.03 0.02 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.05

Cotton 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Soybeans 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Lentils 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

N2O 0.10 0.09 0.10 0.08 0.09 0.10 0.11 0.10 0.10 0.10 0.10 0.10

Wheat 0.04 0.04 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.04 0.04 0.04

Rice 0.02 0.02 0.02 0.01 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.02

Sugarcane 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Corn 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Cotton 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Soybeans 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Lentils 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total 0.42 0.37 0.41 0.30 0.37 0.38 0.43 0.39 0.38 0.40 0.40 0.42

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

72

emissions were from burning of wheat crop residues, 

at 42 percent. Burning of rice, sugarcane, corn, and 

soybean crop residues each contributed 20 percent 

or less to overall GHG emissions. Burning of lentil 

crop residues contributed almost nothing to this 

source of GHG due to the relatively small amount 

of land area planted with this crop. This is also why 

a small increase in land area (Figure 3-5) for lentil 

crops from 1990 to 2013 exhibits such a dramatic 

proportional increase (Figure 3-6).

Total GHG emissions from residue burning decreased 

8 percent from 1990 to 2013. Trends in relative 

GHG emissions were similar across crop types in 

1990 compared to 2013, with a few exceptions. 

In both 1990 and 2013, burning of wheat residues 

contributed the most to GHG emissions from residue 

burning, while rice burning was the second-largest 

source. Between 1990 and 2013, soybean and corn 

for grain production (excluding corn for silage) both 

increased in absolute amounts, while GHG emissions 

from burning decreased in wheat (Figure 3-5). 

Proportionally, soybean production increased slightly 

more than corn but still not near the level of increase 

for lentil production (Figure 3-6). Despite the higher 

nitrogen content in soybeans relative to corn, corn 

production was still greater than soybean production 

in 2013 (Table 3-8), thus resulting in higher GHG 

emissions from corn residue burning.

Appendix Table B-6 provides the complete time 

series of crop production from 1990 to 2013 for 

crop types that contribute to GHG emissions from 

burning. Appendix Table B-7 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. Wheat production 

has declined since the mid-1990s. The State-level 

rice harvest estimates were provided directly by EPA 

based on State production data. 

3.5.1 Methods for Estimating CH4

 and N2O 

Emissions from Residue Burning

A Tier 2 method (EPA 2015) was used to estimate 

greenhouse gas emissions from field burning of 

agricultural residues. The methodology described 

below is summarized with permission from EPA.

Figure 3-4 Greenhouse Gas Emissions from Field Burning by Crop Type, 2013

(CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. MMT CO2 eq. is million metric tons 

of carbon dioxide equivalent.)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Wheat Rice Sugarcane Corn Soybeans Cotton Lentils

MMT CO2 eq.

Figure 3-4 

Greenhouse Gas Emissions from Field Burning by Crop Type, 2013

CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

N20 = 0.10 Tg CO2 eq.

CH4 = 0.31 Tg CO2 eq.

Total Emissions from Burning 

= 0.41 MMT CO2 eq.

Figure 3-5 Change in Commodity Production, 1990-2013

Figure 3-5

Change in Commodity Production, 1990-2013

-20

-10

0

10

20

30

40

50

60

70

80

Corn Soybeans Sugarcane Rice Cotton Lentils Wheat

Million metric tons

Figure 3-6 Percent Change in Commodity Production, 

Figure 3 1990-2013 -6

Change in Commodity Production, 1990-2013

-100

0

100

200

300

400

500

600

Lentils Soybean Corn Rice Sugarcane Cotton Wheat

Percent
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

73

The equation below was used to estimate the amounts 

of carbon and nitrogen released during burning. 

C or N released = Σ for all crop types and State:

 AB/(CAH x CP x RCR x DMF x BE x CE x (FC or 

FN) )

where, Area Burned (AB) = Total area of crop 

burned, by State; Crop Area Harvested (CAH) 

= Total area of crop harvested, by State; Crop 

Production (CP) = Annual production of crop in Gg, 

by State; Residue/Crop Ratio (RCR) = Amount of 

residue produced per unit of crop production, by 

State; Dry Matter Fraction (DMF) = Amount of dry 

matter per unit of biomass for a crop; Fraction of C 

or N (FC or FN) = Amount of C or N per unit of dry 

matter for a crop; Burning Efficiency (BE) = The 

proportion of pre-fire fuel biomass consumed; and 

Combustion Efficiency (CE) = The proportion of C 

or N released with respect to the total amount of C or 

N available in the burned material, respectively.

Crop production and area harvested were available 

by State and year from USDA (2014) for all crops 

(except rice in Florida and Oklahoma, as detailed 

below). The amount C or N released was used in 

the following equation to determine the CH4

 and 

N2O emissions from the field burning of agricultural 

residues:

CH4 or N2O Emissions from Field Burning of 

Agricultural Residues = C or N Released × ER for C 

or N × CF 

where, Emissions Ratio (ER) = g CH4-C released, or 

g N2O-N /g N released, and Conversion Factor (CF) 

= conversion, by molecular weight ratio, of CH4-C to 

C (16/12), or N2O-N to N (44/28).

National and State-level crop production statistics are 

provided in Appendix Table B-6 and Appendix Table 

B-7. The sources for developing these input data are 

described for each parameter below. Values used 

in the equation above to estimate emissions from 

residue burning are summarized in Appendix Tables 

B-8(a-c).

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 2005-2014). 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 

acreages (Schueneman 1999b, 2001; Deren 2002; 

Kirstein 2003, 2004; Cantens 2004, 2005; Gonzalez 

2007-2014), and crop yields for Arkansas (USDA 

1994, 1998, 2003, 2005- 2009) were applied to 

Oklahoma acreages (Lee 2003- 2006; Anderson 

2008, 2009). 

Residue-to-Crop Product Mass Ratios:

All residue:crop product mass ratios except 

sugarcane and cotton were obtained from Strehler 

and Stützle (1987). The ratio for sugarcane is from 

Kinoshita (1988) and the ratio for cotton is from 

Huang et al. (2007). The residue:crop ratio for lentils 

was assumed to be equal to the average of the values 

for peas and beans. Residue dry matter fractions for 

all crops except soybeans, lentils, and cotton were 

obtained from Turn et al. (1997). Soybean and lentil 

dry-matter fractions were obtained from Strehler 

and Stützle (1987); the value for lentil residue was 

assumed to equal the value for bean straw. The cotton 

dry-matter fraction was taken from Huang et al. 

(2007). The residue C contents and N contents for all 

crops except soybeans and cotton are from Turn et 

al. (1997). The residue C content for soybeans is the 

IPCC default (IPCC/UNEP/OECD/IEA 1997). The N 

content of soybeans is from Barnard and Kristoferson 

(1985). The C and N contents of lentils were assumed 

to equal those of soybeans. The C and N contents 

Table 3-8 Agricultural Crop Production Table Error! No text of specified style in document.-16 Agricultural Crop Production 

Crop 1990 2005 2006 2007 2008 2009 2010 2011 2012 2013

MMT of product

Corn1 222.2 311.1 294.9 365.1 338.6 366.6 348.5 346.1 301.8 351.3

Cotton 3.7 5.7 5.2 4.6 3.1 2.9 4.3 3.7 4.2 2.8

Legumes2 0.0 0.3 0.2 0.2 0.1 0.3 0.4 0.2 0.3 2.1

Rice 7.8 11.2 9.7 10.0 10.2 11.0 12.2 9.2 10.0 8.6

Soybeans 57.8 92.1 95.9 80.3 89.0 100.8 99.9 92.8 90.4 91.4

Sugarcane 28.1 26.6 29.6 30.0 27.6 30.4 27.4 29.2 32.2 27.9

Wheat 81.9 63.1 54.3 61.5 75.0 66.5 66.2 60.0 68.1 58.1

Note: MMT is million metric tons.

Source: USDA, NASS Crop Production 2014 Summary 1Corn for grain (i.e., excludes corn for silage).

2 Legumes are dry beans, peas, and lentils
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

74

of cotton are from Lachnicht et al. (2004). These 

data are listed in Table 5-27. The burning efficiency 

was assumed to be 93 percent, and the combustion 

efficiency was assumed to be 88 percent for all crop 

types except sugarcane (EPA 1994). For sugarcane, 

the burning efficiency was assumed to be 81 percent 

(Kinoshita 1988) and the combustion efficiency 

was assumed to be 68 percent (Turn et al. 1997). 

Emission ratios and conversion factors for all gases 

(see Table 5-28) were taken from the Revised 1996 

IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997).

Fraction of Residues Burned: 

The fraction of crop area burned was calculated 

using data on area burned by crop type and State 

from McCarty (2010) for corn, cotton, lentils, rice, 

soybeans, sugarcane, and wheat. McCarty (2010) 

used remote sensing data from Moderate Resolution 

Imaging Spectroradiometer (MODIS) to estimate 

area burned by crop. State-level area burned data 

were divided by State-level crop-area-harvested data 

to estimate the percent of crop area burned by crop 

type for each State. As described above, all croparea-harvested data were from USDA (2014) except 

for rice acreage in Florida and Oklahoma, which is 

not measured by USDA (Schueneman 1999, 2000, 

2001; Deren 2002; Kirstein 2003, 2004; Cantens 

2004, 2005; Gonzalez 2007-2014; Lee 2003- 2007; 

Anderson 2008- 2014). Data on crop area burned 

were only available from McCarty (2010) for the 

years 2003 through 2007. For other years in the time 

series, the percent area burned was set equal to the 

average 5-year percent area burned, based on data 

availability and interannual variability. This average 

was taken at the crop and State level. Table 5-26 

shows these percent-area estimates aggregated for the 

United States as a whole, at the crop level. State-level 

estimates based on State-level crop-area-harvested 

and area burned data were also prepared, but are not 

presented here. 

3.5.2 Uncertainty in Estimating Methane and 

Nitrous Oxide Emissions from Residue Burning

Calculations for crop-specific burned areas, 

residue-to-crop harvest ratios, burning/combustion 

efficiencies, and other factors contribute to overall 

uncertainty. A Monte Carlo analysis was performed 

to quantify these uncertainties. The calculated 

95-percent confidence interval was 0.07 to 0.14 

MMT CO2

 eq. for N2O emissions from residue 

burning, or 30 percent below and 32 percent above 

the estimate of 0.1 MMT CO2 eq. and 0.15 to 0.36 

MMT CO2 eq. for CH4 emissions from residue 

burning, or 41 percent below and 42 percent above 

the estimate of 0.31 MMT CO2 eq. (Table 3-1). 

3.5.3 Changes Compared to the 3rd edition of 

the USDA GHG Report

The methodology was revised relative to the previous 

inventory to incorporate more recent State- and croplevel data on area burned from McCarty (2010). 

Cotton and lentils were added as crops, and peanuts 

and barley were removed because McCarty (2009) 

found that their residues are not burned in significant 

quantities in the United States. Fraction of residue 

burned was calculated at the State and crop level 

based on McCarty (2010) and USDA (2010) data, 

rather than assuming a 3-percent burn rate for all 

crops except rice and sugarcane, as was used in the 

previous inventory. Because the percent area burned 

was lower than previously assumed for almost all 

crops, these changes resulted in an average decrease 

in CH4

 emissions of about 66 percent and an average 

decrease in N2O emissions of about 80 percent across 

the time series, compared to the previous inventory.

Map 3-3a Soil Carbon Changes for Major Land Resource 

Areas, Tier 3 Crops, Annual Means 2003-2007 (Gg CO2 eq. 

is gigagrams carbon dioxide equivalent.)

Map 3-3b Unit Area Soil Carbon Changes for Major Land 

Resource Areas, Tier 3 Crops, Annual Means 2003–2007 

(Mg CO2 eq. ha-1 yr-1 is megagrams carbon dioxide equivalent per 

hectare per year.)
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

75

3.6 Carbon Stock Changes in 

Cropped Soils

Except for cultivated organic soils and liming 

practices, cropped soils in the United States were 

estimated to accumulate about 34 MMT CO2 eq. 

in 2013 (Table 3-1)4. Much of the carbon change is 

attributable to the land enrolled in the CRP and land 

used to grow hay (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.

In contrast, the small area of cultivated organic soils 

(less than 1 million hectares) 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 

(1990-2013). In 2013, about 27 MMT CO2 eq. was 

emitted from cultivation of these soils (Table 3-1). 

Liming of agricultural soils resulted in emissions 

of about 6 MMT CO2 eq. per year. Total net carbon 

sequestration in 2013 equaled ~1 MMT CO2 eq. 

when all of the above components were taken into 

consideration. Carbon uptake on agricultural soils 

varied between 1990 and 2013 (Table 3-2), driven 

largely by land use changes and weather fluctuations.

Many regions in the Corn Belt, Great Plains, and 

Eastern United States are storing C in cropped mineral 

soils due to adoption of reduced tillage and other 

practices (see Map 3-3a for total emissions and Maps 

3-3b and 3-4 for emissions per unit area). On average, 

conventional till soils used for annual cropping were 

a source of about 0.25 MT CO2 eq. ha-1 yr-1, reduced 

till soils were roughly carbon neutral, and no-till soils 

stored about 0.68 MT CO2 eq. ha-1 yr-1. Note that 

the maps in this chapter only show C stock changes 

for mineral soils and, as stated above, emissions from 

cropped organic soils are significant in some regions.

Map 3-4a Soil Carbon Changes for Major Land Resource 

Areas, Tier 3 Crops Conventional Till, Annual Means 

2003-2007 (Mg CO2 eq. ha-1 yr-1 is megagrams carbon dioxide 

equivalent per hectare per year.)

Map 3-4b Soil Carbon Changes for Major Land Resource 

Areas, Tier 3 Crops Reduced Till, Annual Means 2003-

2007 (Mg CO2 eq. ha-1 yr-1 is megagrams carbon dioxide equivalent 

per hectare per year.)

Map 3-4c Soil Carbon Changes for Major Land Resource 

Areas, Tier 3 Crops No Till, Annual Means 2003-2007 

(Mg CO2 eq. ha-1 yr-1 is megagrams carbon dioxide equivalent per 

hectare per year.)

Figure 3-7 CO2 Emissions and Sequestration Sources 

from Cropland Soils, 2003-2007 (MMT CO2 eq. is million 

metric tons of carbon dioxide equivalent. CRP is USDA Conservation 

Reserve Program)

Figure 3-7

CO2 Emissions and Sequestration Sources from Cropland Soils, 2003-2007

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

-16

-14

-12

-10

-8

-6

-4

-2

0

2

4

6

MMT CO2 eq.

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.
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

76

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 (DayCent 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 NRI 

(USDA 2009). The NRI represents a robust statistical 

sampling of land use and management on all nonFederal land in the United States, and more 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 2015).

3.6.2 Tier 3 DayCent Model Simulations for 

Most Cropped Mineral Soils

In this section, we highlight aspects of the DayCent 

model relevant to soil C stocks because the 

simulations described in detail in section 3.3.2 apply 

here except for the quantification of model structural 

uncertainty. Namely, soil C stock changes generated 

by DayCent were compared with measurements 

from 84 long-term field plots to quantify structural 

uncertainty for this GHG source. Soil C stock change 

estimates from DayCent reflect the balance between 

C additions from plant residues that are not removed 

during harvest operations and manure amendments 

and C losses from decomposition of plant residues 

and soil organic matter. Note that the model does 

not account for C losses from erosion nor gains from 

deposition of soil or organic matter.

3.6.3 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 crop rotations not simulated by 

the DayCent 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 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. 2003, 2006b). Cultivated histosol areas 

are listed in Appendix Table B-9, carbon loss rates 

from organic soils under agricultural management in 

the United States are listed in Appendix Table B-10, 

MLRA-level estimates of annual soil carbon stock 

changes by major land use and management type 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

77

are listed in Appendix Table B-11, and State-level 

estimates of mineral soil carbon changes on cropland 

by major activity are listed in Appendix Table B-12.

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 longterm monthly climate data set (Daly et al. 1998) 

was used to classify each of the 180 MLRAs in 

the United States into climate zones. 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 

1997b).

Based on the NRI, crop management systems 

were aggregated into 22 different categories. 

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. Data 

for wetland restoration under CRP were obtained 

from Euliss and Gleason (2002). 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 (1997, 2006) methodology except that 

U.S.-specific carbon loss rates were used in the 

calculations instead of the default IPCC rates (Ogle et 

al. 2003). Manure N amendments over the inventory 

time period were based on application rates and areas 

amended with manure N from Edmonds 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 2015). 

3.6.4 Uncertainty in Estimating Carbon Stock 

Changes in Agricultural Soils

Uncertainty was calculated separately for the Tier 

3 and Tier 2 approaches used to estimate soil CO2

fluxes. The methodologies summarized below are 

described in detail in Chapter 7 and Annex 3.13 of 

the U.S. GHG Inventory (EPA 2015). Uncertainty 

was combined for soil C stock changes on mineral 

soils for crop rotations simulated by DayCent, 

mineral soils for crop rotations not calculated by 

DayCent, cropped organic soils, and emissions from 

liming. Section 3.3.2.2 describes uncertainty for crop 

rotations calculated using DayCent. Uncertainty for 

the remaining sources was estimated using simple 

error propagation (IPCC 2006). 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 combined 95-percent 

confidence interval for C stock change in cropped 

soils in 2013 ranged from -39 to 38 MMT CO2 eq. 

around the estimate of -1 MMT CO2 eq. (Table 3-1). 

Because the estimate (-1 MMT CO2 eq.) is close to 0 

(i.e., C neutral) the uncertainty bounds in Table 3-1 

stated as percentages are very wide.

There were important changes in land classification 

data that affected C stock change estimates. More 

recent annual data from the USDA NRI were used 

to classify land use and management practices in 

this edition. 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 2007. In addition, annual C flux estimates 

for mineral soils between 1990 and 20013 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. Overall, 

these methodological changes resulted in an average 

decline in mineral soil C sequestration of about 14 

percent during 1990-2008. The smaller average C 

sequestration estimated with the current methodology 

results mainly from smaller estimates during the 

latter part of the time series. This is due to using 

updated NRI data instead of assuming that land use 

was constant after 1997.
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

78

3.7 Mitigation of CO2 Emissions

Currently, cropped mineral soils in the United 

States are estimated to be storing carbon at a 

rate of approximately 34 MMT CO2 per year, 

but this is largely nullified when emissions from 

cropped organic soils and liming are accounted. 

Taking organic soils out of production provides 

an opportunity to mitigate emissions because they 

make up less than 1 percent of total cropped land in 

the United States, but are a source of 27 MMT CO2

per year (Table 3-1). Other strategies to increase 

carbon storage and decrease net C emissions include 

increasing cropping intensity, conversion to CRP, 

reducing tillage intensity, and amending soils with 

organic matter. Increasing cropping intensity by 

growing cover crops and minimizing fallow periods 

can sequester C because carbon inputs to soil are 

increased. When soils are fallow, particularly during 

summer, carbon levels tend to decrease because 

plants are not present to provide carbon inputs but 

decomposition of soil carbon by microbes continues. 

Growing-season length limits where fall-spring cover 

crops can be grown, while soil moisture availability 

precludes growing summer crops every year in 

some arid areas of the United States. Cropped land 

converted to CRP stores carbon because the land 

is not cultivated and trees or grasses are planted to 

provide carbon inputs that typically exceed those 

of annual crops. However, increases in demand, 

particular for grains supplied by row crops, have 

led to conversion of CRP back to cropping in recent 

years. 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. Further reductions in 

tillage intensity should also store C, but this is not 

feasible in all regions. Additions of organic matter 

(manure and compost) and biochar also typically 

promote C sequestration in soil, but transportation 

and other costs associated with these amendments 

limit their widespread use. 

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 United 

States. 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.

3.8 Planned Improvements

There are many updates currently being made to 

the methodology to calculate GHG emissions from 

croplands. Land cover/use activity data are being 

improved by accounting for USDA NRI time series 

and land use/management data through 2010. 

Improvements to the DayCent crop phenology 

sub-model are anticipated to better represent 

senescence, particularly following grain filling in 

crops. In addition, the effects of temperature on plant 

production will be improved by continued calibration 

of DayCent. The number of experimental study sites 

used for testing will be expanded to more accurately 

assess model structural uncertainty, and studies 

measuring daily N2O fluxes frequently will be given 

higher priority because they provide more robust 

estimates of annual emissions than do studies that 

measure emissions less frequently. Another planned 

improvement is to account for the use of slowrelease fertilizers and nitrification inhibitors. Field 

investigations suggest that the use of these types of 

N sources often contribute to reductions in the rate of 

N2O emissions, and although the DayCent model is 

capable of simulating use of nitrification inhibitors, 

validation requires that simulated data be compared 

with data from a sufficient number of in situ studies. 

Currently there is a mismatch between the amount 

of residue DayCent simulates for burning and the 

amount of residue burned according to the Field 

Burning of Agricultural Residues source category 

(EPA 2015). Significant updates have been made to 

this source category based on new spatial data, and 

ideally, future DayCent simulations will account for 

the same amount of residue available for burning. 

Hawaii and Alaska are not currently included in 

the inventory for agricultural soil management, 

except for N2O emissions from drained organic soils 

(croplands and grasslands) in Hawaii. In addition 

to more fully including Alaska and Hawaii in the 

subsequent inventory, it is also expected that more 

crop types will be incorporated to the DayCent model 

simulations and removed from the Tier 1 analyses. 

Soil C stock changes with land use conversion 

from forest land to cropland are undergoing further 

evaluation to ensure consistency in the landrepresentation time series. Different methods are used 

to estimate soil C stock changes in forest land and 

croplands, and while the areas have been reconciled 

between these land uses, there has been limited 

evaluation of the consistency in C stock changes with 

conversion from forest land to cropland.

SUGGESTED CITATION

Del Grosso, S.J., S.M. 

Ogle, M. Reyes-Fox, K.L. 

Nichols, E. Marx, and A. 

Swan, 2016. Chapter 3: 

Cropland Agriculture. 

In U.S. Agriculture and 

Forestry Greenhouse Gas 

Inventory: 1990-2013, 

Technical Bulletin No. 1943, 

United States Department 

of Agriculture, Office 

of the Chief Economist, 

Washington, DC. 137 pp. 

September 2016. Del Grosso 

S.J. and M. Baranski, Eds. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

79

3.9 References

AAPFCO (1995-2000a, 2002-2007). Commercial Fertilizers. 

Association of American Plant Food Control Officials. University 

of Kentucky. Lexington, KY.

AAPFCO (2008-2014). Commercial Fertilizers. Association of 

American Plant Food Control Officials. University of Missouri. 

Columbia, MO. 

Akiyama, Hiroko, Xiaoyuan Yan, and Kazuyuki Yagi (2010). 

Evaluation of effectiveness of enhanced-efficiency fertilizers as 

mitigation options for N2

O and NO emissions from agricultural 

soils: meta-analysis. Global Change Biology, 16.6: 1837-1846.

Allmaras, R.R., H.H. Schomberg, C.L. Douglas, and T.H. Dao 

(2000). Soil organic carbon sequestration potential of adopting 

conservation tillage in U.S. croplands. Journal of Soil and Water 

Conservation, 55:365-373.

Anderson, M. (2008-2014). Email correspondence. Monte 

Anderson, Oklahoma Farm Service Agency and ICF International.

Barnard, G. and L. Kristoferson (1985). Agricultural residues as 

fuel in the Third World. Earthscan Energy Information Programme 

and the Beijer Institute of the Royal Swedish Academy of 

Sciences, London, England.

Bossio, D.A., W. Horwath, R.G. Mutters, and C. Van Kessel 

(1999). Methane pool and flux dynamics in a rice field 

following straw incorporation. Soil Biology and Biochemistry, 

31:1313-1322.

Brady, N. and R. Weil (1999). The nature and properties of soils: 

twelfth edition. Prentice Hall, Upper Saddle River, NJ.

Bricklemyer, R.S., P.R. Miller, P.J. Turk, K. Paustian, T. Keck and 

G.A. Nielsen (2007). Sensitivity of the century model to scalerelated soil texture variability. Soil Science Society of America 

Journal, 71:784-792.

Cantens, G. (2004-2005). Telephone conversation between Lauren 

Flinn of ICF Consulting and Janet Lewis, Assistant to Gaston 

Cantens, Vice President of Corporate Relations, Florida Crystals 

Company.

Cerri, C.E.P., M. Easter, K. Paustian, K. Killian, K. Coleman, 

M. Bernoux, P. Falloon, D.S. Powlson, N. Batjes, E. Milne, and 

C.C. Cerri (2007). Simulating soil organic carbon changes in 11 

land use change chronosequences from the Brazilian Amazon 

with RothC and Century models. Agriculture, Ecosystem, and 

Environment, 122:46-57. 

Cicerone, R.J., C.C. Delwiche, S.C. Tyler, and P.R. Zimmerman 

(1992). Methane emissions from California rice paddies with 

varied treatments. Global Biogeochemical Cycles, 6:233-248. 

CTIC (1998). 1998 crop residue management executive summary. 

Conservation Technology Information Center. Available online at 

<http://www.ctic.purdue.edu/Core4/CT/CT.html>. 

CTIC (2004). 2004 Crop Residue Management Survey. 

Conservation Technology Information Center. Available online at 

<http://www.ctic.purdue.edu/CRM/>.

Daly, C., R.P. Neilson, and D.L. Phillips (1994). A statisticaltopographic model for mapping climatological precipitation over 

mountainous terrain. Journal of Applied Meteorology, 33:140-158.

Daly, C., G.H. Taylor, W.P. Gibson, T. Parzybok, G.L. Johnson, 

and P.A. Pasteris (1998). Development of high quality spatial 

datasets for the United States. Pp. I 512-I 519 in Proceedings, 1st 

international conference on geospatial information in agriculture 

and forestry. Lake Buena Vista, FL, June 1-3.

Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, 

J. Brenner, D.S. Ojima, and D.S. Schimel (2001). Simulated 

interaction of carbon dynamics and nitrogen trace gas fluxes 

using the DAYCENT model. Pp 303-332 in Modeling carbon and 

nitrogen dynamics for soil management. M. Schaffer, L. Ma, and 

S. Hansen, editors. CRC Press, Boca Raton, FL. 

Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.K. Walsh, D.S. 

Ojima, and P.E. Thornton (2006). DAYCENT national-scale 

simulations of N2

O emissions from cropped soils in the USA. 

Journal of Environmental Quality, 35:1451-1460. doi: 10.2134/

jeq2005.0160.

Del Grosso, S.J., S.M. Ogle, W.J. Parton, F.J. Breidt 

(2010). Estimating Uncertainty in N2

O Emissions from US 

Cropland Soils, Global Biogeochemical Cycles, 24, GB1009, 

doi:10.1029/2009GB003544.

Dell, Curtis J., K. Han, R.B. Bryant, J.P. Schmidt (2014). Nitrous 

oxide emissions with enhanced efficiency nitrogen fertilizers in a 

rainfed system. Agronomy Journal 106.2:723-731.

Deren, C. (2002). Telephone conversation between Caren Mintz 

of ICF Consulting and Chris Deren, Everglades Research and 

Education Centre at the University of Florida.

Dixon, R.K. (1995). Agroforestry systems: sources or sinks of 

greenhouse gases? Agroforestry Systems, 31:99-116.

Edmonds, L., N. Gollehon, R.L. Kellogg, B. Kintzer, L. Knight, 

C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J. Schaeffer 

(2003). Costs associated with development and implementation 

of comprehensive nutrient management plans, part 1: nutrient 

management, land treatment, manure and wastewater handling 

and storage, and recordkeeping. Natural Resource Conservation 

Service, United States Department of Agriculture, Government 

Printing Office, Washington, D.C. 

EPA (1994). International anthropogenic methane emissions: 

estimates for 1990. Report to Congress. EPA 230R-93-010. Office 

of Policy Planning and Evaluation, U.S. Environmental Protection 

Agency, Washington, D.C.

EPA (2015). U.S. Greenhouse Gas Inventory Report: Inventory 

of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013. 

Environmental Protection Agency, Office of Atmospheric 

Programs, Washington D.C. April, 2015. Available online 

at <http://www.epa.gov/climatechange/ghgemissions/

usinventoryreport.html>

Euliss, N. and R. Gleason (2002). Wetland restoration factor 

estimates and restoration activity data. Compiled by N. Euliss 

and R. Gleason, USGS, Jamestown, ND, for S. Ogle, National 

Resource Ecology Laboratory, Fort Collins, CO. 

Eve, M., D. Pape, M. Flugge, R. Steele, D. Man, M. Riley-Gilbert, 

and S. Biggar, Eds. (2014). Quantifying Greenhouse Gas Fluxes 

in Agriculture and Forestry: Methods for Entity-Scale Inventory. 

Technical Bulletin Number 1939, Office of the Chief Economist, 

United States Department of Agriculture, Washington, DC. 606 

pages. July 2014.
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

80

Firestone, M.K., and E.A. Davidson (1989). Microbial basis of 

NO and N2

O production and consumption in soils. Pp. 7-21 in 

Exchange of trace gases between terrestrial ecosystems and the 

atmosphere. M.O. Andreae and D.S. Schimel, editors. John Wiley, 

New York, NY.

Follett, R.L., J.M. Kimble, and R. Lal (2001). The potential of U.S. 

grazing lands to sequester soil carbon. In The potential of U.S. 

grazing lands to sequester soil carbon and mitigate the greenhouse 

effect. R.L. Follett and J.M. Kimble, editors. CRC Press, Boca 

Raton, FL.

Gonzalez, R. (2007-2014). Email correspondence. Rene Gonzalez, 

Plant Manager, Sem-Chi Rice Company and Sarah Menassian, ICF 

International. 

Guo, L.B. and R.M. Gifford (2002). Soil carbon stocks and land 

use change: a meta analysis. Global Change Biology, 4(8):345-

360. doi:10.1046/j.1354-1013.2002.00486.x.

Haas, H.J., C.E. Evans, and E.F. Miles (1957) Nitrogen and 

carbon changes in Great Plains soils as influenced by cropping 

and soil treatments. Technical bulletin number 1164. United 

States Department of Agriculture, Government Printing Office, 

Washington, D.C.

Halvorson, A.D., C.S. Snyder, A.D. Blaylock, and S.J. Del Grosso 

(2014). Enhanced Efficiency Nitrogen Fertilizers: Potential Role in 

Nitrous Oxide Emission Mitigation. Agronomy Journal, 106: 715-

722, doi:10.2134/agronj2013.0081.

Hardke, J. (2013). Email correspondence. Jarrod Hardke, Rice 

Extension Agronomist at the University of Arkansas Rice Research 

and Extension Center and Cassandra Snow, ICF International. July 

15, 2013.

Hardke, J. (2014). Personal Communication. Jarrod Hardke, Rice 

Extension Agronomist at the University of Arkansas Rice Research 

and Extension Center and Kirsten Jaglo, ICF International. 

September 11, 2014.

Hatfield, Jerry L., and Rodney T. Venterea (2014). Enhanced 

efficiency fertilizers: a multi-site comparison of the effects on 

nitrous oxide emissions and agronomic performance. Agronomy 

Journal 106.2: 679-680.

Huang, Y., W. Zhang, W. Sun, and X. Zheng (2007) Net Primary 

Production of Chinese Croplands from 1950 to 1999. Ecological 

Applications, 17(3):692-701.

IPCC (2003). Good practice guidance for land use, land-use 

change, and forestry. J. Penman, M. Gytarsky, T. Hiraishi, T. Krug, 

D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe 

and F. Wagner, editors. Intergovernmental Panel on Climate 

Change, National Greenhouse Gas Inventories Programme, 

Technical Support Unit, Kanagawa, Japan. Available online at 

<http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.

htm>.

IPCC (2006). IPCC guidelines for national greenhouse gas 

inventories, vol. 4: agriculture, forestry and other land use. S. 

Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, editors. 

Intergovernmental Panel on Climate Change, National Greenhouse 

Gas Inventories Programme, Technical Support Unit, Kanagawa, 

Japan. Available online at <http://www.ipcc-nggip.iges.or.jp>.

IPCC (2007). Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, 

M. Tignor and H.L. Miller (eds.). Cambridge University Press. 

Cambridge, United Kingdom 996 pp. 

IPCC (2010). Revisiting the use of managed land as a 

proxy for estimating national anthropogenic emissions and 

removals. Eggleston HS, Srivastava N, Tanabe K, Baasansuren 

J, (eds.).Institute for Global Environmental Studies, 

Intergovernmental Panel on Climate Change, Hayama, Kanagawa, 

Japan.

IPCC (2013). Climate Change 2013: The Physical Science Basis. 

Contribution of Working Group I to the Fifth Assessment Report 

of the Intergovernmental Panel on Climate Change. [Stocker, 

T.F., D. Qin, G.-K., Plattner, M. Tignor, S.K. Allen, J. Boschung, 

A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge 

University Press, Cambridge, United Kingdom and New York, NY, 

USA, 1535 pp. 

IPCC/UNEP/OECD/IEA (1997). Revised 1996 IPCC Guidelines 

for National Greenhouse Gas Inventories. Intergovernmental Panel 

on Climate Change, United Nations Environment Programme, 

Organization for Economic Co-Operation and Development, 

International Energy Agency, Paris, France.

Kelly, R.H., W.J. Parton, G.J. Crocker, P.R. Grace, J. Klir, M. 

Korschens, P.R. Poulton, and D.D. Richter (1997) Simulating 

trends in soil organic carbon in long-term experiments using the 

Century model. Geoderma, 81:75-90.

Kinoshita, C.M. (1988). Composition and processing of burned 

and unburned cane in Hawaii. International Sugar Journal 90:1070, 

34-37. 

Kirstein, A. (2003-2004, 2006). Personal communication. Arthur 

Kirstein, Coordinator, Agricultural Economic Development 

Program, Palm Beach County Cooperative Extension Service, 

Florida and Caren Mintz, ICF International.

Klosterboer, A. (1997, 1999-2003). Personal Communication. 

Arlen Klosterboer, retired Extension Agronomist, Texas A&M 

University and Caren Mintz, ICF International. July 7, 2003. 

Kort, J. and R.Turlock (1999). Carbon reservoir and biomass in 

Canadian prairie shelterbelts. Agroforestry Systems, 44:175-189.

Lachnicht, S.L., P.F. Hendrix, R.L. Potter, D.C. Coleman, and 

D.A. Crossley Jr. (2004). Winter decomposition of transgenic 

cotton residue in conventional-till and no-till systems. Applied Soil 

Ecology, 27:135-142.

Lal, R. (2004). Soil carbon sequestration impacts on global climate 

change and food security. Science, 304(5677):1623-1627.

Le Mer, J. and P. Roger (2001). Production, oxidation, emission 

and consumption of methane by soils: a review. European Journal 

of Soil Biology 37(1):25-50.

Lee, D. (2003-2007). Email correspondence. Danny Lee, OK Farm 

Service Agency and Victoria Thompson, ICF International.

Lindau, C.W. and P.K. Bollich (1993). Methane emissions from 

Louisiana first and ratoon crop rice. Soil Science, 156:42-48. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

81

Lindau, C.W., P.K. Bollich, and R.D. DeLaune (1995). Effect of 

rice variety on methane emission from Louisiana rice. Agriculture, 

Ecosystems and Environment 54:109-114. 

Linscombe, S. (1999, 2001- 2014). Email correspondence. Steve 

Linscombe, Professor with the Rice Research Station at Louisiana 

State University Agriculture Center and ICF International.

Lugato, E., K. Paustian and L. Giardini (2007). Modeling soil 

organic carbon dynamics in two long-term experiments of 

north-eastern Italy. Agriculture, Ecosystem and Environment 

120:423-432.

McCarty, J.L. (2009). Seasonal and Interannual Variability of 

Emissions from Crop Residue Burning in the Contiguous United 

States. Dissertation. University of Maryland, College Park. 

McCarty, J.L. (2010). Agricultural Residue Burning in the 

Contiguous United States by Crop Type and State. Geographic 

Information Systems (GIS) Data provided to the EPA Climate 

Change Division by George Pouliot, Atmospheric Modeling and 

Analysis Division, EPA. 

McTaggart, I.P., H. Clayton, J. Parker, L. Swan, and K.A. Smith 

(1997.) Nitrous oxide emissions from grassland and spring 

barley, following nitrogen fertilizer application with and without 

nitrification inhibitors. Biology and Fertility of Soils 25:261-268.

Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P. C. Shafran, W. 

Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, E. H. Berbery, M. B. 

Ek, Y. Fan, R. Grumbine, W. Higgins, H. Li, Y. Lin, G. Manikin, 

D. Parrish, and W. Shi (2006). North American regional reanalysis. 

Bulletin of the American Meteorological Society 87:343-360.

Migliorati, M.D.A., W.J. Parton, S.J. Del Grosso, P.R. Grace, M.J. 

Bell, D.W. Rowlings, C. Scheer. (2015). Legumes or nitrification 

inhibitors to reduce N2

O emissions in subtropical cereal cropping 

systems? A simulation study. Agriculture, Ecosystems and 

Environment 213:228-240,

Minerals Yearbook (2014). Published by the Bureau of Mines 

and by the U.S. Geological Survey from 1994-Present. Available 

online at <http://minerals.usgs.gov/minerals/pubs/myb.html>

Nair, P.K.R. and V.D. Nair (2003). Carbon storage in North 

American agroforestry systems. Pp. 333-346 in The potential of 

U.S. forest soils to sequester carbon and mitigate the greenhouse 

effect. J. Kimble, L.S. Heath, R.A. Birdsey, and R. Lal, editors. 

CRC Press, Boca Raton, FL.

Nishina, K., Akiyama, H., Nishimura, S., Sudo, S., & Yagi, K. 

(2012). Evaluation of uncertainties in N2O and NO fluxes from 

agricultural soil using a hierarchical Bayesian model. Journal of 

Geophysical Research: Biogeosciences, 117(G4).

NRAES (1992). On-Farm Composting Handbook (NRAES-54). 

Natural Resource, Agriculture, and Engineering Service. Available 

online at <http://compost.css.cornell.edu/OnFarmHandbook/

onfarm_TOC.html>.

NRIAI (2003). Regional Budget and Cost Information. United 

States Department of Agriculture, Natural Resources Conservation 

Service, Natural Resources Inventory and Analysis Institute. 

Available online at <http://www.economics.nrcs.usda.gov/care/

budgets/index.html>

Nusser S.M., Breidt F.J., Fuller W.A. (1998). Design and 

estimation for investigating the dynamics of natural resources. 

Ecological Applications 8, 234–245.

Nusser S.M., Goebel J.J. (1997). The national resources inventory: 

a long term monitoring programme. Environmental and Ecological 

Statistics 4, 181–204.

Ogle, S.M., M.D. Eve, F.J. Breidt, and K. Paustian (2003). 

Uncertainty in estimating land use and management impacts on 

soil organic carbon storage for U.S. agroecosystems between 1982 

and 1997. Global Change Biology 9:1521-1542. 

Ogle, S.M., F.J. Breidt, and K. Paustian. (2006b). Bias and 

variance in model results due to spatial scaling of measurements 

for parameterization in regional assessments. Global Change 

Biology 12:516-523.

Ogle, S.M., F.J. Breidt, M. Easter, S.Williams and K. Paustian 

(2007). An empirically based approach for estimating uncertainty 

associated with modeling carbon sequestration in soils. Ecological 

Modeling 205:453-463.

Ogle, S. M., F. J. Breidt, M. Easter, S. Williams, K. Killian, and 

K. Paustian (2009). Scale and uncertainty in modeled soil organic 

carbon stock changes for US croplands using a process-based 

model, Global Change Biology 16, 810–822.

Ogle, S.M., P.R. Adler, F.J. Breidt, S. Del Grosso, J. Derner, 

A. Franzluebbers, M. Liebig, B. Linquist, G.P. Robertson, M. 

Schoeneberger, J. Six, C. van Kessel, R. Venterea, T. West (2014). 

Chapter 3: Quantifying greenhouse gas sources and sinks in 

cropland and grazing land systems. In Quantifying Greenhouse 

Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale 

Inventory. Technical Bulletin Number 1939, Office of the Chief 

Economist, United States Department of Agriculture, Washington, 

DC. 606 pages. Eve, M., D. Pape, M. Flugge, R. Steele, D. Man, 

M. Riley-Gilbert, and S. Biggar, Eds.

Parkin, Timothy B. and Jerry L. Hatfield (2014). Enhanced 

efficiency fertilizers: Effect on nitrous oxide emissions in Iowa. 

Agronomy Journal 106.2: 694-702.

Parton W.J., J.M. Scurlock, D.S. Ojima, T.G. Gilmanov, R.J. 

Scholes, D.S. Schimel, T. Kirchner, J.C. Menaut, T. Seastedt, E. 

Garcia Moya, A. Kamnalrut, J.L. Kinyamario (1993). Observations 

and modeling of biomass and soil organic matter dynamics for the 

grassland biome worldwide. Global Biogeochemical Cycles 7: 

785-809.

Parton, W.J., D.S. Ojima, C.V. Cole, and D.S. Schimel (1994). A 

general model for soil organic matter dynamics: sensitivity to litter 

chemistry, texture and management. Pp. 147-167 in Quantitative 

modeling of soil forming processes. R.B. Bryant and R.W. Arnold, 

editors. Soil Sciences Society of America, Madison, WI.

Parton, W.J., M.D. Hartman, D.S. Ojima, and D.S. Schimel (1998). 

DAYCENT: its land surface submodel: description and testing. 

Global and Planetary Change 19: 35-48. 

Paustian, K., H.P. Collins, and E.A. Paul (1997). Management 

controls on soil carbon. Pp. 15-49 in Soil organic matter in 

temperate agroecosystems: long-term experiments in North 

America. E.A. Paul, K. Paustian, E.T. Elliott, and C.V. Cole, 

editors. CRC Press, Boca Raton, FL. 
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

82

Philibert, A., Loyce, C., & Makowski, D. (2012). Quantifying 

uncertainties in N2

O emission due to N fertilizer application in 

cultivated areas. PLoS One, 7(11), e50950.

Sass, R.L., F.M Fisher, P.A. Harcombe, and F.T. Turner (1991a). 

Mitigation of methane emissions from rice fields: possible adverse 

effects of incorporated rice straw. Global Biogeochemical Cycles 

5:275-287. 

Sass, R.L., F.M. Fisher, F.T. Turner, and M.F. Jund (1991b). 

Methane emissions from rice fields as influenced by solar 

radiation, temperature, and straw incorporation. Global 

Biogeochemical Cycles 5:335-350. 

Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick 

(1986). Estimating generalized soil-water characteristics from 

texture. Soil Sciences Society of America Journal 50:1031-1036. 

Schlesinger, W. H. (1986). Changes in soil carbon storage and 

associated properties with disturbance and recovery. Pp. 194-220 

in The changing carbon cycle: a global analysis. J.R. Trabalka and 

D.E. Reichle, editors. Springer-Verlag, New York, NY.

Schroeder, P. (1994). Carbon storage benefits of agroforestry 

systems. Agroforestry Systems 27:89-97.

Schueneman, T. (1997, 1999-2001). Personal Communication. 

Tom Schueneman, Agricultural Extension Agent, Palm Beach 

County, FL and ICF International. 

Schueneman, T. and C. Deren (2002). An overview of the Florida 

rice industry. Publication SS-AGR-77. Agronomy Department, 

Florida Cooperative Extension Service, Institute of Food and 

Agricultural Sciences, University of Florida, Gainesville, FL.

Sistani, K. R., Jn-Baptiste, M., Lovanh, N.C., Cook, K.L. 

(2011). Atmospheric emissions of nitrous oxide, methane, and 

carbon dioxide from different nitrogen fertilizers. Journal of 

Environmental Quality 40.6: 1797-1805.

Slaton, N. (1999-2001) Personal Communication. Nathan Slaton, 

Extension Agronomist - Rice, University of Arkansas Division of 

Agriculture Cooperative Extension Service and ICF International

Soil Survey Staff (2011). State Soil Geographic (STATSGO) 

Database for State. Natural Resources Conservation Service, 

United States Department of Agriculture. Available online at 

<http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/index.

html>.

Stansel, J. (2004-2005) Email correspondence. Jim Stansel, 

Resident Director and Professor Emeritus, Texas A&M University 

Agricultural Research and Extension Center and ICF International.

Stehfest, E., and Bouwman, L. (2006). N2

O and NO emission from 

agricultural fields and soils under natural vegetation: summarizing 

available measurement data and modeling of global annual 

emissions. Nutrient Cycling in Agroecosystems, 74(3), 207-228.

Strehler, A. and W. Stützle (1987). Biomass residues. Pp. 75-102 in 

Biomass, vol. 4. D.O. Hall and R.P. Overend, editors. John Wiley 

and Sons, Chichester, UK.

Texas Agricultural Experiment Station (2007-2014). Texas Rice 

Acreage by Variety. Agricultural Research and Extension Center, 

Texas Agricultural Experiment Station, Texas A&M University 

System. Available online at <http://beaumont.tamu.edu/

CropSurvey/CropSurveyReport.aspx>.

Texas Agricultural Experiment Station (2005-2006). Texas Rice 

Crop Statistics Report. Agricultural Research and Extension 

Center, Texas Agricultural Experiment Station, Texas A&M 

University System, p. 8. Available online at <http://beaumont.

tamu.edu/eLibrary/TRRFReport_default.htm>

Towery, D. (2001). Personal Communication. Dan Towery 

regarding adjustments to the CTIC (1998) tillage data to reflect 

long-term trends, Conservation Technology Information Center, 

West Lafayette, IN, and Marlen Eve, National Resource Ecology 

Laboratory, Fort Collins, CO.

Turn, S.Q., B.M. Jenkins, J.C. Chow, L.C. Pritchett, D. Campbell, 

T. Cahill, and S.A. Whalen (1997). Elemental characterization 

of particulate matter emitted from biomass burning: wind tunnel 

derived source profiles for herbaceous and wood fuels. Journal of 

Geophysical Research 102(D3):3683-3699.

TVA (1991-1994). Commercial fertilizers 1992. Tennessee Valley 

Authority, Muscle Shoals, AL. 

USDA (1994). Field crops: final estimates, 1987-1992. Statistical 

bulletin number 896. National Agriculture Statistics Service, 

United States Department of Agriculture, Government Printing 

Office, Washington, D.C. Available online at <http://usda.mannlib.

cornell.edu/data-sets/crops/94896/sb896.txt>. 

USDA (1996). Agricultural Waste Management Field Handbook, 

National Engineering Handbook (NEH), Part 651. Natural 

Resources Conservation Service, United States Department of 

Agriculture.

USDA (1997a). Cropping practices survey data 1995. 

Economic Research Service, United States Department of 

Agriculture. Available online at <http://www.ers.usda.gov/data/

archive/93018/>. 

USDA (1997b). National soil survey laboratory characterization 

data, digital data. United States Department of Agriculture, Natural 

Resources Conservation Service, National Soil Survey Center, 

Lincoln, NE.

USDA (1998). Field Crops Final Estimates 1992-1997. Statistical 

Bulletin Number 947a. National Agricultural Statistics Service, 

United States Department of Agriculture. Washington, DC. 

Available online at <http://usda.mannlib.cornell.edu>. 

USDA (1999). Soil Taxonomy: A basic system of soil classification 

for making and interpreting soil surveys, 2nd Edition. Agricultural 

Handbook Number 436, Natural Resources Conservation Service, 

United States Department of Agriculture. Washington, DC.

USDA (2000). 1997 National Resources Inventory. United States 

Department of Agriculture, Natural Resources Conservation 

Service. Washington, D.C. Available online at <http://www.nrcs.

usda.gov/technical/NRI/>.

USDA (2003). Field Crops Final Estimates 1998-2002. Statistical 

Bulletin Number 982. National Agricultural Statistics Service, 

United States Department of Agriculture. Washington, DC. 

Available online at <http://usda.mannlib.cornell.edu>.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

83

USDA (2005-2014). Crop Production Summary. National 

Agricultural Statistics Service, Agricultural Statistics Board, 

United States Department of Agriculture. Washington, D.C. 

Available online at <http://usda.mannlib.cornell.edu>

USDA (2009). Summary Report: 2007 National Resources 

Inventory, Natural Resources Conservation Service, Washington, 

D.C, and Center for Survey Statistics and Methodology, Iowa State 

University, Ames, Iowa. Available online at <http://www.nrcs.

usda.gov/technical/NRI/2007/2007_NRI_Summary.pdf>.

USDA (2011a). U.S. Agriculture and Forestry Greenhouse Gas 

Inventory: 1990-2008. Del Grosso, S.J. and M.K. Walsh (Eds.) 

Technical bulletin 1930. Office of the Chief Economist, United 

States Department of Agriculture. Washington, D.C. Available 

online at <http://www.usda.gov/oce/climate_change/AFGG_

Inventory/USDA_GHG_Inv_1990-2008_June2011.pdf>

USDA (2011b). Agricultural Resource Management Survey. 

Economic Research Service, United States Department of 

Agriculture. Available online at <http://www.ers.usda.gov/>. 

USDA (2013). Summary Report: 2010 National Resources 

Inventory. Natural Resources Conservation Service, Washington, 

D.C, and Center for Survey Statistics and Methodology, Iowa State 

University, Ames, Iowa. Available online at <http://www.nrcs.

usda.gov/Internet/FSE_DOCUMENTS/stelprdb1167354.pdf>

USDA (2014). Agricultural Statistics. National Agricultural 

Statistics Service, United States Department of Agriculture. 

Available online at <http://www.nass.usda.gov/Publications/

Ag_Statistics/index.asp> and <http://www.nass.usda.gov/

Data_and_Statistics/Quick_Stats/>.

USDA (2014). Conservation Reserve Program Monthly 

Summary – September 2014. Farm Service Agency, United States 

Department of Agriculture. Washington, DC. Available online at 

<https://www.fsa.usda.gov/Assets/USDA-FSA-Public/usdafiles/

Conservation/PDF/summarysept2014.pdf>

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. (2002-2007, 2009-2012). Personal Communication. 

Chuck Wilson, Rice Specialist at the University of Arkansas 

Cooperative Extension Service, ICF International.

3.10 Appendix B

B-1 MLRA-Level Area Estimates by Major Crop Rotation, 

2003-2007

B-2 MLRA-Level Estimates of Total Annual Direct N2O 

Emissions by Major Crop Rotation, 2003-2007

B-3 MLRA-Level Estimates of Total Annual Indirect N2O 

Emissions from Ammonia, Nitric Oxide and Nitrogen Dioxide 

Volatilization by Major Crop Rotation, 2003-2007

B-4 MLRA-Level Estimates of Total Annual Indirect N2O 

Emissions for Nitrate Leaching by Major Crop Rotation, 

2003-2007

B-5 Rice Harvested Area, 1990, 1995, 2000-2013

B-6 Total U.S. Production of Crops Managed With Burning, 1990, 

1995, 2000-2013

B-7 Production of Crops Managed With Burning

B-8 Information Used in Estimating Methane and Nitrous Oxide 

Emissions from Crop Residue Burning

B-9 Cultivated Histosol (Organic Soils) Area

B-10 Carbon Loss Rates from Organic Soils Under Agricultural 

Management in the United States

B-11 MLRA-Level Estimates of Annual Soil Carbon Stock 

Changes by Major Crop Rotation, 2003-2007

B-12 State-Level Estimates of Mineral Soil Carbon Changes on 

Cropland by Major Activity, 2013
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

84

Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007 Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 hectares

2 - - 23,715 21,480 25,374 91,821 - - - - 189,942

5 - - - - - 20,334 - - - - -

7 32,780 68,351 - - - 370,458 - - - - -

8 516,582 1,187,268 - - - 147,229 - 28,449 - - 220,109

9 135,571 231,521 - - 29,866 48,548 - - - - 584,542

10 - 15,781 - - 16,511 139,697 - - - - -

11 - 44,733 - - - 959,877 - - - - -

12 - - - - - 95,053 - - - - -

13 222,575 63,126 - 16,026 17,442 132,510 - - - - 76,018

14 - - 16,026 - - 63,813 - - - - -

15 - - - - - 46,607 - 34,115 - - -

16 - - - - - 34,217 - - - - -

17 - 24,079 - - - 721,579 - 77,255 205,024 - -

21 - - - - - 174,120 - - - - -

23 - - - - - 113,689 - - - - -

24 - - - - - 82,208 - - - - -

25 - - - - - 42,909 - - - - -

26 - - - - - 7,917 - - - - -

27 - - - - - 66,271 - 2,266 - - -

29 - - - - - 9,264 - - - - -

30 - - - - - 23,254 - - - - -

31 - 21,408 - - - 179,357 - 10,158 - - -

32 - - - - - 131,672 - - - - -

35 - - - - - 35,565 - - - - -

36 19,830 34,317 - - - 70,638 - - - - 19,708

40 - - - - - 121,208 - 13,152 - - -

41 - - - - - 20,437 - - - - -

42 - - - - - 171,981 - 52,569 - - -

44 - 112,083 7,608 - 49,412 361,826 - - - - 37,879

46 70,092 269,302 - - 94,292 158,441 - - - - 143,906

47 - - - - - 67,989 - - - - -

49 - 25,778 - - - 11,935 - - - - -

51 - - - - - 149,262 - - - - -

52 542,239 1,582,282 - - 20,760 87,974 - - - - 295,926

54 244,268 193,278 131,159 106,262 264,705 42,735 - 34,722 - 110,965 1,317,166

56 318,245 - 34,075 41,278 42,168 - 112,907 484,490 - 1,181,429 629,530

57 - - - 61,269 80,087 - - - - 154,125 -

61 - - - - 20,315 - - - - - -

64 30,311 193,480 - - 46,498 107,067 - - - 35,491 56,575

65 - - - - 11,938 124,481 - - - - -

66 - - 16,592 38,838 72,884 113,548 - - - 99,553 32,618

69 150,098 58,194 - - - 115,950 - - - - -

71 12,424 - 15,095 - 50,586 683,436 - - - 145,606 -

72 695,557 2,113,497 - - 18,494 1,074,751 - 369,681 - 381,133 551,870

73 288,130 959,834 20,275 67,973 91,135 356,913 - 458,752 - 493,029 865,745

74 82,632 18,899 21,974 41,157 47,227 56,211 - 28,692 - 326,501 577,123
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

85

Continued - Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 hectares

75 19,263 31,727 - - 16,349 877,138 - - - 479,715 69,646

76 13,193 - 56,251 20,720 19,708 - - - - 145,197 105,623

79 161,385 91,945 - - 18,170 208,535 - 37,636 - 103,896 571,766

85 - 18,049 - - - - - - - 43,625 169,760

89 - - - 19,263 15,257 34,398 - - - 40,469 -

92 - - - - 12,667 - - - - - -

96 - - 6,232 - 8,620 - - - - - -

97 - - - - 24,322 15,124 - - - 138,160 -

98 39,455 - 62,524 58,027 142,126 164,778 - 45,730 - 1,264,292 16,875

99 26,588 - - 20,286 46,417 - 12,141 52,043 - 1,268,131 10,805

101 - - 79,238 140,361 201,615 - - 50,869 - 249,624 18,899

103 108,530 - 21,367 55,482 68,028 - - 19,951 - 5,022,610 -

104 52,417 - 13,233 48,382 33,670 - - 35,572 - 2,233,534 -

105 250,318 - 56,089 330,595 208,413 22,619 - 105,583 - 1,409,838 -

106 136,258 - 55,118 34,830 43,666 88,141 - 15,661 - 1,200,584 55,280

109 480,654 - 177,010 78,132 195,140 - - 15,014 - 1,143,162 -

110 - - - 7,972 - - - 9,874 - 1,217,948 -

112 82,663 - 222,173 35,702 99,634 37,696 - 38,567 - 950,411 362,369

113 159,562 - 60,460 55,740 64,183 11,614 - 17,928 - 1,722,294 25,576

121 - - 114,405 35,254 156,937 - - - - 102,335 -

122 33,076 - 170,858 52,208 190,809 - 23,310 - - 425,580 23,917

123 - - 37,433 - 46,296 - - - - 41,235 -

124 - - 77,983 47,987 98,055 - - 13,152 - 162,039 -

125 - - 26,750 - 17,240 - - - - 21,003 -

126 - - 183,606 40,478 72,317 - - - - 72,603 -

127 - - 95,465 46,590 54,187 - - 12,748 - 57,004 -

128 - - 107,525 30,191 100,119 - 68,716 13,314 - 116,409 -

129 - - 21,772 - - - - - - 42,439 -

134 231,640 26,993 113,838 - - 196,075 320,269 82,961 217,516 755,810 50,707

136 36,098 - 308,614 63,694 29,097 - 19,223 42,856 - 209,545 46,440

137 - - 22,501 - - - 26,386 12,626 - 27,216 -

138 - - - - - 12,096 - - - - -

139 - - 53,702 83,733 156,168 - - 26,305 - 369,536 -

140 - - 243,904 165,009 236,782 - - 24,443 - 142,569 -

142 - - 84,134 62,367 140,912 - - - - 53,336 -

143 - - 34,520 - 20,639 - - - - - -

145 - - 15,459 - - - - - - 13,993 -

146 - - - - - - 19,546 - - - -

147 - - 161,672 131,700 119,585 - - 40,307 - 353,730 21,570

148 - - 83,163 86,885 77,133 - - 34,277 - 302,066 21,655

155 - - - - - 110,784 - - - - -

102A 231,606 - 41,318 71,225 94,575 50,748 - 54,268 - 1,880,376 135,813

102B - - - - 23,674 - - - - 373,886 -

102C 88,339 - 23,593 60,662 71,994 507,355 - - - 1,359,543 -

107A - - - - - - - 15,014 - 926,590 -

107B 86,927 - 52,650 26,752 40,307 109,994 - - - 2,213,355 14,261

108A - - - - 14,528 - - - - 1,916,475 -
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

86

Continued - Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 hectares

108B 16,514 - - - 26,628 27,715 - - - 1,904,706 -

108C 118,856 - 19,830 37,960 30,392 - - 28,045 - 1,177,787 -

108D 111,469 - 21,813 44,973 66,692 - - - - 683,944 -

111A - - 15,459 - 32,011 - - - - 1,610,775 -

111B 100,628 - 17,806 40,745 67,947 9,712 - 17,604 - 1,904,787 12,383

111C - - - - - - - - - 575,028 -

111D - - - - 11,979 - - - - 828,783 -

111E - - - 16,147 16,633 - - 9,348 - 339,457 -

114A - - 23,148 15,321 34,075 - - - - 384,282 -

114B 21,246 - 15,216 27,999 19,546 - - - - 812,550 16,592

115A - - - - 14,973 17,968 - - - 627,496 24,888

115B 22,743 - 64,143 - 30,999 - - 20,639 - 375,613 16,552

115C 97,497 - 48,805 44,070 66,045 61,431 - 22,986 - 1,545,842 -

116A - - 178,426 - 65,802 - - - - 83,047 -

116B - - 104,854 - 29,380 - - - - 22,161 15,621

118A - - 14,285 - - - - - - 21,974 27,814

120A 80,472 - 37,110 27,511 68,554 - - - - 385,636 -

120B - - 13,112 - - - - - - 97,961 -

120C - - - - - - - - - 19,061 -

130A - - - - - - - - - 5,382 -

130B - - 25,131 - 22,541 - - - - - -

131A 68,554 49,169 17,685 - - 1,118,667 532,162 122,660 722,280 1,316,008 81,301

131B - 20,315 - - - 306,643 27,761 - 205,338 60,946 -

131C - - - - - 25,455 31,039 - 30,149 115,740 -

131D - - - - - 94,697 - - 158,354 - -

133A 376,047 74,278 180,895 58,728 16,997 301,787 880,685 216,467 - 536,764 81,423

133B - - 46,134 - - - - - - 47,429 20,826

135A 145,627 - 60,784 - - - 70,780 10,360 - 149,410 -

144A - - 118,533 40,647 61,108 - 6,596 9,955 - 49,940 -

144B - - 92,997 10,687 32,253 - - - - 13,476 -

149A - - 7,608 - - 14,812 - - - 74,336 -

150A - - - - - 242,060 173,408 258,554 280,439 319,500 -

150B - - - - - - - - 10,279 - -

152B - - - - - - - 20,922 16,673 - -

153A - - - - - 18,345 133,936 34,924 - 181,828 -

153B - - - - - 8,158 35,208 11,048 - 139,549 5,666

153C - - - - - 11,007 - - - 165,759 -

153D - - - - - 44,904 - - - 122,932 -

156A - - - - - 23,512 - - - - -

28A 93,685 42,613 - - - 391,900 - - - - 15,174

28B - - - - - 15,083 - - - - -

34A - 15,580 - - - 193,445 - - - - -

34B - - - - - 128,932 - - - - -

43A 26,871 - - - 16,754 - - - - - 72,803

43B 30,473 - - - 24,848 100,708 - - - - -

48A - - - - - 65,288 - - - - -

48B - - - - - 24,442 - - - - -
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

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Continued - Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 hectares

53A 299,063 297,727 17,563 - 16,754 15,459 - 54,956 - - 742,396

53B 412,172 106,999 74,017 83,350 115,133 - - 107,646 - 604,601 1,199,004

53C 19,627 - - - 15,297 16,552 - - - 205,742 172,801

55A 250,501 - 25,333 - 19,668 - 26,628 176,484 - 128,488 1,502,113

55B 378,783 - 57,465 71,920 63,495 31,889 21,610 219,664 - 1,458,407 666,545

55C 57,101 - 21,772 64,426 103,316 46,370 - 47,955 - 1,178,071 75,312

58A 360,171 488,335 85,389 123,762 164,667 171,904 - 60,339 - - 191,174

58B - - - - 15,540 56,996 - - - - -

60A - 61,147 - - 38,728 27,761 - - - - 53,540

63A 66,166 62,160 29,259 - 20,518 - - - - 89,274 241,719

63B - - 9,105 16,511 53,661 - - 17,847 - 89,881 39,700

67A 141,964 89,593 - - 17,037 235,975 - - - 11,048 17,078

67B 578,903 873,526 - - - 302,986 - 81,544 - 195,221 122,863

70A - - - - - 10,141 - - - - -

70B - - - - - 30,311 - - - - -

70C - - - - - 7,163 - - - - -

77A 226,988 120,354 - - - 498,043 - 21,610 - 113,393 392,019

77B 23,472 - - - - 128,680 - - - - -

77C 1,121,627 137,985 - - - 1,353,623 810,106 49,776 - 57,749 463,285

77D 163,898 - - - - 62,259 - - - - 35,929

77E 229,295 25,171 - - - 36,870 - - - - 114,966

78A - - - - - - - - - - 119,802

78B 302,019 - - - - 52,189 289,818 - - - 407,644

78C 195,359 37,231 - - 20,679 103,737 184,438 31,768 - 18,696 1,203,236

80A 36,058 - 25,900 - 65,519 56,101 - - - 38,000 1,711,492

80B - - - - - - - - - - 121,120

81A - - - - - 89,193 51,759 69,201 - - 36,426

81B - - - - - - - - - - 40,766

81C - - - - - - - - - - 26,669

82B - - - - - - - - - - 37,396

83A - - - - - 87,203 - 17,280 - 97,853 69,140

83C - - - - - - - - - 23,836 -

83D - - - - - 167,773 43,504 - - 129,095 -

83E - - - - - - - - - 39,295 -

84A - - 25,252 - - - - - - - 74,299

84B - - - - - 32,495 - - - - 91,358

86A - - 24,403 - - - 32,577 76,769 - 411,602 323,225

87A - - - - - - - - - 55,887 22,674

87B - - - - - - - - - - 32,529

90A - - 67,218 107,074 114,202 - - - - 129,816 -

90B 13,881 - 51,719 158,888 95,304 - - 41,723 - 351,542 -

91A 44,371 - - 23,259 35,734 91,861 - - - 120,634 -

91B - - - - 13,152 - - - - 38,526 -

94A - - - - 67,987 - - - - 55,578 -

94B - - - - 25,212 - - - - - -

95A 29,137 - 15,864 163,384 75,514 - - 37,717 - 353,169 12,060

95B 37,838 - 21,125 141,795 73,936 23,876 - 45,122 - 959,343 -

CRP1 Fallow Hay 

Grass

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop

Small 

Grain

MLRA2 hectares 1 CRP = Conservation Reserve Program 2 MLRA = Major Land Resource Area
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

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Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 2003-2007

Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by 

Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq.3

2 - - 34.28 26.08 22.21 78.26 - - - - 140.14

5 - - - - - 25.13 - - - - -

7 7.09 29.75 - - - 304.47 - - - - -

8 111.01 482.06 - - - 167.32 - 15.61 - - 112.93

9 40.84 124.56 - - 27.31 55.69 - - - - 448.83

10 - 10.55 - - 16.82 159.65 - - - - -

11 - 30.61 - - - 963.09 - - - - -

12 - - - - - 94.63 - - - - -

13 63.71 36.29 - 11.83 11.54 150.35 - - - - 71.76

14 - - 11.91 - - 32.47 - - - - -

15 - - - - - 22.84 - 8.52 - - -

16 - - - - - 27.38 - - - - -

17 - 26.57 - - - 759.82 - 21.68 623.93 - -

21 - - - - - 204.76 - - - - -

23 - - - - - 103.71 - - - - -

24 - - - - - 67.19 - - - - -

25 - - - - - 54.51 - - - - -

26 - - - - - 10.59 - - - - -

27 - - - - - 51.26 - 0.67 - - -

29 - - - - - 5.04 - - - - -

30 - - - - - 13.46 - - - - -

31 - 15.86 - - - 277.41 - 4.97 - - -

32 - - - - - 98.71 - - - - -

35 - - - - - 33.96 - - - - -

36 5.21 16.35 - - - 67.88 - - - - 7.99

40 - - - - - 106.61 - 5.96 - - -

41 - - - - - 22.90 - - - - -

42 - - - - - 159.16 - 25.78 - - -

44 - 63.09 12.04 - 49.57 402.75 - - - - 33.74

46 8.90 99.43 - - 45.33 123.50 - - - - 72.69

47 - - - - - 63.70 - - - - -

49 - 7.49 - - - 11.06 - - - - -

51 - - - - - 130.99 - - - - -

52 64.40 420.76 - - 6.70 73.58 - - - - 94.43

54 25.78 57.71 59.20 32.64 63.65 31.47 - 14.39 - 70.42 521.07

56 58.36 - 20.37 37.41 20.84 - 82.47 331.07 - 923.89 400.35

57 - - - 38.17 45.24 - - - - 119.39 -

61 - - - - 7.45 - - - - - -

64 2.37 55.79 - - 13.86 81.29 - - - 19.94 19.71

65 - - - - 3.15 103.41 - - - - -

66 - - 14.36 23.24 25.86 93.34 - - - 92.20 18.35

69 24.40 32.45 - - - 134.79 - - - - -

71 2.15 - 15.90 - 15.75 659.43 - - - 128.31 -

72 117.05 744.00 - - 4.11 984.92 - 154.54 - 268.80 197.26

73 39.33 326.59 17.19 41.28 23.84 346.89 - 182.38 - 275.36 344.05
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

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Continued - Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 

2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq.3

74 19.79 6.80 28.62 26.34 18.19 37.57 - 11.73 - 197.40 288.44

75 3.98 18.26 - - 6.06 821.47 - - - 365.71 41.01

76 3.19 - 67.69 19.00 8.33 - - - - 102.74 58.03

79 21.66 29.22 - - 4.98 133.57 - 15.03 - 56.69 216.01

85 - 8.50 - - - - - - - 46.49 100.41

89 - - - 16.46 11.23 28.79 - - - 36.52 -

92 - - - - 13.07 - - - - - -

96 - - 8.89 - 9.11 - - - - - -

97 - - - - 22.76 14.94 - - - 152.68 -

98 12.74 - 74.90 56.73 128.06 154.18 - 33.93 - 1,190.01 14.66

99 8.05 - - 16.40 37.86 - 12.77 42.56 - 1,199.86 9.53

101 - - 127.69 195.30 220.33 - - 58.03 - 366.69 22.19

103 27.08 - 16.25 42.79 41.78 - - 16.12 - 4,494.84 -

104 14.42 - 11.94 40.73 25.28 - - 33.48 - 2,221.85 -

105 78.41 - 54.58 289.00 156.02 22.63 - 97.11 - 1,462.65 -

106 32.31 - 40.70 21.92 24.45 78.69 - 8.86 - 1,005.43 38.23

109 131.20 - 137.93 60.31 133.51 - - 11.04 - 994.19 -

110 - - - 7.26 - - - 6.74 - 1,303.22 -

112 19.81 - 144.26 25.57 67.21 29.02 - 25.18 - 707.90 223.41

113 42.24 - 45.85 41.75 49.40 11.44 - 12.92 - 1,416.53 17.98

121 - - 100.79 32.27 149.06 - - - - 94.45 -

122 8.86 - 111.56 34.03 152.57 - 12.22 - - 326.93 16.15

123 - - 22.87 - 32.37 - - - - 26.13 -

124 - - 72.46 50.06 96.91 - - 12.92 - 176.59 -

125 - - 16.30 - 12.56 - - - - 14.14 -

126 - - 155.09 37.61 69.71 - - - - 77.60 -

127 - - 109.92 53.21 58.89 - - 15.28 - 79.38 -

128 - - 63.81 19.86 77.98 - 35.65 4.29 - 72.44 -

129 - - 9.70 - - - - - - 23.55 -

134 44.73 12.74 67.03 - - 128.30 177.00 36.58 391.30 449.92 32.12

136 6.95 - 230.41 37.87 16.52 - 9.05 15.20 - 133.18 24.56

137 - - 12.80 - - - 11.50 3.42 - 13.90 -

138 - - - - - 5.43 - - - - -

139 - - 101.13 102.47 168.49 - - 23.22 - 417.03 -

140 - - 653.17 343.96 304.35 - - 39.81 - 259.51 -

142 - - 323.98 163.95 236.50 - - - - 134.96 -

143 - - 113.19 - 29.51 - - - - - -

145 - - 43.81 - - - - - - 21.79 -

146 - - - - - - 25.40 - - - -

147 - - 270.67 175.96 119.70 - - 49.14 - 474.71 24.30

148 - - 121.08 95.64 71.58 - - 37.57 - 332.97 22.10

155 - - - - - 48.92 - - - - -

102A 38.93 - 28.37 40.04 41.29 37.13 - 29.27 - 1,396.38 76.37

102B - - - - 11.19 - - - - 298.10 -

102C 17.56 - 14.82 40.57 35.49 479.53 - - - 1,173.53 -

107A - - - - - - - 14.29 - 1,077.88 -

107B 21.96 - 35.20 18.50 24.13 162.56 - - - 2,016.77 11.97
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

90

Continued - Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 

2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq.3

108A - - - - 10.82 - - - - 1,926.14 -

108B 6.05 - - - 19.34 28.08 - - - 1,925.11 -

108C 34.32 - 17.90 31.59 23.02 - - 26.37 - 1,183.03 -

108D 29.43 - 15.66 36.55 44.32 - - - - 647.46 -

111A - - 10.95 - 25.44 - - - - 1,502.43 -

111B 32.75 - 16.34 34.62 55.64 9.37 - 12.96 - 1,813.91 11.44

111C - - - - - - - - - 539.66 -

111D - - - - 9.44 - - - - 788.44 -

111E - - - 13.82 13.57 - - 6.53 - 328.99 -

114A - - 19.48 13.26 29.14 - - - - 353.10 -

114B 5.75 - 12.02 25.19 16.75 - - - - 719.42 12.85

115A - - - - 12.07 14.52 - - - 551.41 19.04

115B 6.25 - 44.61 - 24.00 - - 16.07 - 333.44 12.66

115C 28.59 - 36.45 35.42 50.20 54.60 - 18.81 - 1,470.60 -

116A - - 135.18 - 55.01 - - - - 73.06 -

116B - - 75.59 - 24.20 - - - - 17.34 11.46

118A - - 7.91 - - - - - - 12.53 12.77

120A 25.44 - 28.70 18.84 57.00 - - - - 333.39 -

120B - - 11.10 - - - - - - 95.81 -

120C - - - - - - - - - 18.60 -

130A - - - - - - - - - 5.55 -

130B - - 17.92 - 18.10 - - - - - -

131A 17.06 36.74 12.59 - - 1,024.69 496.79 83.95 1,311.68 1,064.17 55.37

131B - 15.76 - - - 324.37 23.68 - 351.43 50.33 -

131C - - - - - 37.53 26.17 - 59.85 95.38 -

131D - - - - - 69.05 - - 223.46 - -

133A 56.63 26.53 94.44 25.42 8.30 99.75 361.19 67.91 - 275.66 37.07

133B - - 28.41 - - - - - - 44.41 13.20

135A 31.74 - 41.93 - - - 50.15 3.48 - 95.91 -

144A - - 303.48 78.20 80.06 - 9.55 14.50 - 80.04 -

144B - - 321.08 21.72 46.73 - - - - 31.57 -

149A - - 9.04 - - 11.47 - - - 59.65 -

150A - - - - - 193.82 179.67 98.79 676.85 311.64 -

150B - - - - - - - - 30.40 - -

152B - - - - - - - 8.38 40.11 - -

153A - - - - - 8.30 59.70 12.59 - 83.13 -

153B - - - - - 3.96 17.76 4.84 - 72.82 3.01

153C - - - - - 9.63 - - - 139.87 -

153D - - - - - 40.27 - - - 102.98 -

156A - - - - - 4.84 - - - - -

28A 22.89 27.14 - - - 334.09 - - - - 9.18

28B - - - - - 13.19 - - - - -

34A - 8.58 - - - 219.45 - - - - -

34B - - - - - 144.76 - - - - -

43A 10.75 - - - 18.49 - - - - - 66.56

43B 5.94 - - - 20.31 122.52 - - - - -

48A - - - - - 78.56 - - - - -
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

91

Continued - Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 

2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq.3

48B - - - - - 41.38 - - - - -

53A 21.07 73.90 9.41 - 4.83 8.71 - 15.65 - - 236.38

53B 48.76 33.21 36.98 32.67 31.14 - - 47.75 - 360.58 496.51

53C 3.40 - - - 5.36 9.60 - - - 135.37 86.03

55A 33.47 - 13.26 - 6.90 - 15.38 81.75 - 74.47 674.62

55B 55.37 - 27.40 33.02 19.63 20.35 11.23 95.06 - 898.33 284.56

55C 9.71 - 12.59 31.06 37.59 33.13 - 28.82 - 880.07 43.87

58A 70.80 180.91 56.94 48.28 42.39 151.71 - 26.64 - - 69.75

58B - - - - 5.41 44.87 - - - - -

60A - 32.50 - - 15.03 62.91 - - - - 25.51

63A 12.31 54.94 50.67 - 10.07 - - - - 72.50 145.53

63B - - 12.66 12.10 20.87 - - 14.28 - 95.52 26.14

67A 18.93 27.10 - - 3.76 198.75 - - - 8.26 6.09

67B 82.12 290.78 - - - 299.07 - 37.68 - 136.21 45.93

70A - - - - - 9.42 - - - - -

70B - - - - - 13.32 - - - - -

70C - - - - - 5.74 - - - - -

77A 39.01 53.05 - - - 440.68 - 12.21 - 84.68 205.48

77B 3.79 - - - - 101.07 - - - - -

77C 179.25 63.43 - - - 728.78 423.78 26.59 - 39.00 249.87

77D 24.07 - - - - 24.14 - - - - 17.00

77E 40.99 8.75 - - - 25.83 - - - - 64.81

78A - - - - - - - - - - 67.64

78B 42.10 - - - - 18.19 145.65 - - - 218.02

78C 33.32 14.42 - - 5.96 43.64 100.43 16.25 - 13.68 603.55

80A 8.40 - 27.05 - 22.64 22.27 - - - 22.56 765.81

80B - - - - - - - - - - 52.24

81A - - - - - 45.92 27.56 37.41 - - 22.54

81B - - - - - - - - - - 20.74

81C - - - - - - - - - - 14.29

82B - - - - - - - - - - 19.51

83A - - - - - 56.87 - 10.66 - 77.71 34.58

83C - - - - - - - - - 14.75 -

83D - - - - - 134.23 27.78 - - 84.73 -

83E - - - - - - - - - 23.22 -

84A - - 27.81 - - - - - - - 36.13

84B - - - - - 8.17 - - - - 38.00

86A - - 49.22 - - - 33.58 42.83 - 488.92 190.60

87A - - - - - - - - - 64.79 25.28

87B - - - - - - - - - - 16.14

90A - - 66.98 87.67 90.75 - - - - 121.72 -

90B 3.69 - 49.11 126.69 69.12 - - 33.79 - 304.98 -

91A 8.35 - - 15.48 17.82 74.98 - - - 95.00 -

91B - - - - 10.10 - - - - 31.10 -

94A - - - - 59.97 - - - - 64.25 -

94B - - - - 28.80 - - - - - -

95A 9.07 - 17.26 152.13 64.10 - - 36.21 - 378.44 11.26

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq.3

95B 11.11 - 20.33 125.89 59.13 20.72 - 40.08 - 924.96 - 1 CRP = Conservation Reserve Program 

2 MLRA = Major Land Resource Area 

3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent 

 
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

92

Appendix Table B-3 MLRA-Level Estimates of Total Annual Indirect N2O Emissions from Ammonia, Nitric Oxide, and 

Nitrogen Dioxide Volatilization, by Major Crop Rotation, 2003-2007

Appendix Table B-3 MLRA-Level Estimates of Total Annual Indirect N2O Emissions From 

Ammonia, Nitric Oxide, and Nitrogen Dioxide Volatilization, by Major Crop Rotation, 

2003-2007

CRP1 Fallow Hay Grass Hay In 

Rotation 

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

 MLRA2 Gg CO2 eq3

2 - - 2.87 2.39 1.51 7.68 - - - - 10.00

5 - - - - - 2.44 - - - - -

7 0.43 1.81 - - - 23.14 - - - - -

8 7.53 18.96 - - - 8.51 - 0.83 - - 6.04

9 2.37 5.69 - - 0.93 2.09 - - - - 20.10

10 - 0.76 - - 0.45 10.05 - - - - -

11 - 1.66 - - - 57.62 - - - - -

12 - - - - - 5.04 - - - - -

13 3.63 1.26 - 0.35 0.23 6.50 - - - - 2.62

14 - - 0.53 - - 3.70 - - - - -

15 - - - - - 3.75 - 0.93 - - -

16 - - - - - 1.74 - - - - -

17 - 1.54 - - - 53.44 - 2.85 19.26 - -

21 - - - - - 12.04 - - - - -

23 - - - - - 7.25 - - - - -

24 - - - - - 6.75 - - - - -

25 - - - - - 5.29 - - - - -

26 - - - - - 0.65 - - - - -

27 - - - - - 3.78 - 0.05 - - -

29 - - - - - 0.28 - - - - -

30 - - - - - 1.81 - - - - -

31 - 1.16 - - - 12.57 - 0.38 - - -

32 - - - - - 8.41 - - - - -

35 - - - - - 2.69 - - - - -

36 0.50 1.36 - - - 6.95 - - - - 1.01

40 - - - - - 11.86 - 0.65 - - -

41 - - - - - 2.59 - - - - -

42 - - - - - 12.57 - 1.84 - - -

44 - 4.13 0.68 - 1.21 22.16 - - - - 1.41

46 1.08 8.06 - - 1.59 9.82 - - - - 6.45

47 - - - - - 3.48 - - - - -

49 - 0.78 - - - 1.56 - - - - -

51 - - - - - 14.66 - - - - -

52 11.18 42.08 - - 0.38 4.71 - - - - 10.53

54 4.36 4.78 5.46 2.90 3.75 2.67 - 1.33 - 6.50 47.60

56 9.47 - 1.74 1.76 0.83 - 3.22 29.41 - 114.43 36.79

57 - - - 3.10 2.44 - - - - 16.24 -

61 - - - - 0.53 - - - - - -

64 0.50 5.67 - - 0.96 7.83 - - - 2.01 2.14

65 - - - - 0.28 14.68 - - - - -

66 - - 1.76 2.37 1.86 14.13 - - - 8.26 1.86

69 3.78 2.29 - - - 9.17 - - - - -

71 0.48 - 0.88 - 0.88 78.04 - - - 14.83 -

72 25.48 74.04 - - 0.28 105.14 - 17.35 - 27.60 22.36
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

93

Continued - Appendix Table B-3 MLRA-Level Estimates of Total Annual Indirect N2O Emissions from Ammonia, Nitric 

Oxide, and Nitrogen Dioxide Volatilization, by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay Grass Hay In 

Rotation 

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

 MLRA2 Gg CO2 eq3

73 10.05 33.59 1.41 3.32 1.26 34.42 - 22.92 - 35.28 40.54

74 3.78 0.55 1.26 1.31 0.63 4.48 - 1.56 - 31.38 31.35

75 0.83 1.99 - - 0.23 110.83 - - - 49.71 4.48

76 0.58 - 3.37 0.63 0.28 - - - - 16.17 6.77

79 6.02 3.73 - - 0.33 19.49 - 2.14 - 8.59 28.53

85 - 0.65 - - - - - - - 2.87 9.04

89 - - - 0.78 0.43 2.54 - - - 4.16 -

92 - - - - 0.43 - - - - - -

96 - - 0.48 - 0.40 - - - - - -

97 - - - - 0.83 1.36 - - - 16.32 -

98 1.59 - 4.56 3.10 4.84 19.42 - 3.07 - 146.79 1.26

99 0.98 - - 0.86 1.21 - 0.65 3.35 - 139.84 0.83

101 - - 5.79 7.86 6.14 - - 3.05 - 21.96 1.31

103 4.53 - 0.86 2.52 1.39 - - 1.44 - 594.64 -

104 2.22 - 0.43 2.19 0.63 - - 2.14 - 271.62 -

105 11.66 - 1.99 14.15 3.85 2.27 - 6.30 - 151.17 -

106 5.39 - 2.69 1.54 0.83 10.85 - 1.08 - 135.26 4.03

109 19.87 - 9.85 3.70 5.31 - - 1.11 - 137.17 -

110 - - - 0.38 - - - 0.65 - 148.15 -

112 3.48 - 11.71 2.17 2.49 4.10 - 2.64 - 115.64 23.82

113 6.35 - 3.27 3.32 1.71 1.41 - 1.46 - 209.67 2.01

121 - - 9.97 3.05 9.09 - - - - 12.67 -

122 1.59 - 16.12 5.52 12.82 - 1.41 - - 55.30 1.66

123 - - 3.65 - 2.47 - - - - 5.82 -

124 - - 6.32 3.88 4.99 - - 0.88 - 19.54 -

125 - - 2.57 - 1.01 - - - - 2.87 -

126 - - 16.07 3.25 4.41 - - - - 7.50 -

127 - - 8.31 3.45 3.27 - - 0.88 - 5.57 -

128 - - 10.85 2.39 7.83 - 4.41 0.76 - 14.20 -

129 - - 2.19 - - - - - - 5.06 -

134 9.27 1.36 8.34 - - 20.35 18.79 5.84 27.20 87.74 3.35

136 1.54 - 39.18 6.75 1.56 - 1.51 3.10 - 29.79 3.35

137 - - 3.00 - - - 1.99 0.76 - 4.33 -

138 - - - - - 0.98 - - - - -

139 - - 4.21 4.84 5.99 - - 1.86 - 41.00 -

140 - - 26.32 14.23 13.57 - - 2.19 - 13.30 -

142 - - 9.34 4.13 6.22 - - - - 3.42 -

143 - - 3.80 - 1.13 - - - - - -

145 - - 1.99 - - - - - - 0.55 -

146 - - - - - - 1.13 - - - -

147 - - 19.67 11.13 7.08 - - 3.53 - 38.53 1.84

148 - - 9.49 6.85 3.93 - - 3.32 - 35.08 2.12

155 - - - - - 9.22 - - - - -

102A 7.61 - 1.51 2.97 1.94 6.35 - 3.50 - 196.43 10.02

102B - - - - 0.40 - - - - 38.81 -

102C 3.27 - 1.11 2.82 1.28 62.05 - - - 156.76 -

107A - - - - - - - 1.11 - 115.34 -
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

94

Continued - Appendix Table B-3 MLRA-Level Estimates of Total Annual Indirect N2O Emissions from Ammonia, Nitric 

Oxide, and Nitrogen Dioxide Volatilization, by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay Grass Hay In 

Rotation 

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

 MLRA2 Gg CO2 eq3

107B 4.18 - 2.44 1.54 0.96 13.40 - - - 273.66 1.06

108A - - - - 0.35 - - - - 239.36 -

108B 0.93 - - - 0.53 3.48 - - - 232.71 -

108C 5.62 - 0.68 1.71 0.55 - - 1.71 - 152.03 -

108D 5.39 - 0.98 2.04 1.26 - - - - 84.89 -

111A - - 0.63 - 0.63 - - - - 200.00 -

111B 4.26 - 0.76 1.99 1.59 1.11 - 1.46 - 228.33 1.13

111C - - - - - - - - - 73.06 -

111D - - - - 0.33 - - - - 105.21 -

111E - - - 0.88 0.38 - - 0.73 - 41.45 -

114A - - 1.46 1.16 1.28 - - - - 49.99 -

114B 0.86 - 0.91 1.91 0.58 - - - - 98.54 1.46

115A - - - - 0.63 2.44 - - - 77.06 1.71

115B 1.03 - 3.45 - 0.88 - - 1.84 - 47.27 1.28

115C 4.41 - 2.37 1.81 1.46 7.68 - 1.74 - 190.46 -

116A - - 18.18 - 5.19 - - - - 9.42 -

116B - - 11.11 - 2.42 - - - - 2.52 1.18

118A - - 1.54 - - - - - - 2.90 1.31

120A 4.16 - 3.00 2.24 2.97 - - - - 48.05 -

120B - - 1.21 - - - - - - 12.92 -

120C - - - - - - - - - 2.44 -

130A - - - - - - - - - 0.76 -

130B - - 2.72 - 1.99 - - - - - -

131A 2.59 2.32 1.36 - - 114.35 35.10 9.59 86.53 153.74 5.64

131B - 1.06 - - - 30.77 1.94 - 24.60 7.15 -

131C - - - - - 2.44 2.82 - 3.10 13.60 -

131D - - - - - 11.26 - - 20.83 - -

133A 15.19 4.36 20.15 6.90 0.55 18.53 54.57 13.75 - 68.42 4.66

133B - - 6.12 - - - - - - 4.16 1.44

135A 5.69 - 5.31 - - - 4.63 0.50 - 20.57 -

144A - - 13.72 2.80 2.82 - 0.38 0.86 - 3.27 -

144B - - 10.68 0.86 1.74 - - - - 0.73 -

149A - - 0.86 - - 1.26 - - - 8.97 -

150A - - - - - 21.93 11.58 15.26 31.23 24.68 -

150B - - - - - - - - 1.21 - -

152B - - - - - - - 1.28 1.84 - -

153A - - - - - 1.79 9.70 2.49 - 25.74 -

153B - - - - - 0.68 2.47 0.83 - 16.75 0.35

153C - - - - - 1.08 - - - 18.79 -

153D - - - - - 4.68 - - - 15.34 -

156A - - - - - 0.55 - - - - -

28A 1.54 1.28 - - - 18.11 - - - - 0.53

28B - - - - - 1.74 - - - - -

34A - 0.53 - - - 14.35 - - - - -

34B - - - - - 12.69 - - - - -

43A 0.55 - - - 0.33 - - - - - 2.57

43B 0.45 - - - 0.43 6.37 - - - - -
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

95

Continued - Appendix Table B-3 MLRA-Level Estimates of Total Annual Indirect N2O Emissions from Ammonia, Nitric 

Oxide, and Nitrogen Dioxide Volatilization, by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay Grass Hay In 

Rotation 

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop 

Small 

Grain 

 MLRA2 Gg CO2 eq3

48A - - - - - 6.09 - - - - -

48B - - - - - 2.64 - - - - -

53A 4.91 7.81 0.65 - 0.23 0.88 - 1.59 - - 25.36

53B 8.84 3.20 3.32 3.30 2.12 - - 4.99 - 47.55 51.93

53C 0.53 - - - 0.40 1.03 - - - 13.85 7.96

55A 5.59 - 1.16 - 0.38 - 0.78 9.02 - 10.83 67.72

55B 9.09 - 2.39 3.37 1.26 3.10 0.81 13.37 - 135.94 36.31

55C 1.79 - 1.26 2.77 2.34 5.21 - 3.22 - 115.16 4.31

58A 9.44 13.32 5.29 3.60 2.34 11.28 - 1.99 - - 6.77

58B - - - - 0.33 3.70 - - - - -

60A - 2.14 - - 1.03 1.49 - - - - 2.22

63A 1.11 1.79 1.44 - 0.33 - - - - 3.02 7.50

63B - - 0.60 0.58 0.55 - - 0.55 - 4.26 1.69

67A 3.37 2.80 - - 0.38 18.79 - - - 0.86 0.71

67B 15.08 28.96 - - - 24.93 - 4.00 - 12.47 5.24

70A - - - - - 1.11 - - - - -

70B - - - - - 2.39 - - - - -

70C - - - - - 0.55 - - - - -

77A 8.49 5.59 - - - 42.86 - 1.13 - 9.34 16.90

77B 0.86 - - - - 14.00 - - - - -

77C 40.52 6.40 - - - 94.64 54.85 2.69 - 3.42 20.62

77D 6.27 - - - - 3.83 - - - - 1.94

77E 10.27 0.91 - - - 3.25 - - - - 5.74

78A - - - - - - - - - - 6.42

78B 11.48 - - - - 2.97 18.71 - - - 20.55

78C 8.41 1.33 - - 0.38 6.19 10.78 1.76 - 1.74 58.40

80A 1.61 - 1.81 - 1.33 2.74 - - - 2.54 80.38

80B - - - - - - - - - - 6.77

81A - - - - - 5.94 3.10 3.27 - - 1.66

81B - - - - - - - - - - 1.74

81C - - - - - - - - - - 1.11

82B - - - - - - - - - - 1.86

83A - - - - - 6.57 - 1.11 - 8.41 3.58

83C - - - - - - - - - 2.12 -

83D - - - - - 14.08 3.10 - - 10.45 -

83E - - - - - - - - - 3.60 -

84A - - 2.24 - - - - - - - 3.73

84B - - - - - 1.61 - - - - 5.36

86A - - 2.06 - - - 1.69 3.70 - 29.41 16.24

87A - - - - - - - - - 4.58 1.08

87B - - - - - - - - - - 1.99

90A - - 2.62 4.46 2.92 - - - - 11.76 -

90B 0.60 - 1.69 7.00 2.14 - - 2.34 - 34.45 -

91A 1.64 - - 1.18 0.88 9.95 - - - 12.77 -

91B - - - - 0.43 - - - - 3.98 -

94A - - - - 2.37 - - - - 5.36 -

94B - - - - 0.93 - - - - - -

CRP1 Fallow Hay Grass Hay In 

Rotation

Hay 

Legume Irrigated Low Residue

Other 

Cropland Rice Row Crop

Small 

Grain

 MLRA2 Gg CO2 eq3

95A 1.13 - 0.96 7.98 2.39 - - 2.37 - 31.73 0.88

95B 1.54 - 1.13 6.98 1.79 2.19 - 2.95 - 100.28 -

Note: N2O is nitrous oxide. 1 CRP = Conservation Reserve Program

2 MLRA = Major Land Resource Area 

3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

96

Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching by Major Crop 

Rotation, 2003-2007

Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for 

Nitrate Leaching by Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue 

Other 

Cropland Rice Row 

Crop 

Small 

Grain 

MLRA2 Gg CO2 eq3

2 - - 4.14 5.33 6.61 26.68 - - - - 61.8

5 - - - - - 3.97 - - - - -

7 0 3.58 - - - 58.46 - - - - -

8 0.12 13.07 - - - 30.04 - 0 - - 1.09

9 0.27 5.78 - - 0.38 2.25 - - - - 35.99

10 - 2.21 - - 0.16 7.15 - - - - -

11 - 15.27 - - - 205.46 - - - - -

12 - - - - - 4.46 - - - - -

13 0.01 2.05 - 0.17 0.15 24.77 - - - - 7.4

14 - - 3.4 - - 9.47 - - - - -

15 - - - - - 7.23 - 1.39 - - -

16 - - - - - 2.91 - - - - -

17 - 4.52 - - - 97.01 - 0.51 13.81 - -

21 - - - - - 11.92 - - - - -

23 - - - - - 6.28 - - - - -

24 - - - - - 7.61 - - - - -

25 - - - - - 0.9 - - - - -

26 - - - - - 1.43 - - - - -

27 - - - - - 2.83 - 0 - - -

29 - - - - - 0.22 - - - - -

30 - - - - - 1.72 - - - - -

31 - 0 - - - 0.56 - 0 - - -

32 - - - - - 25.29 - - - - -

35 - - - - - 29.4 - - - - -

36 0 0.46 - - - 12.18 - - - - 0

40 - - - - - 34.67 - 0.01 - - -

41 - - - - - 2.13 - - - - -

42 - - - - - 247.13 - 0 - - -

44 - 4.95 0.98 - 1.02 11.73 - - - - 2.3

46 0.01 1.57 - - 0.27 3.45 - - - - 1.7

47 - - - - - 3.15 - - - - -

49 - 0.3 - - - 0.74 - - - - -

51 - - - - - 74.83 - - - - -

52 0 1.11 - - 0 12.3 - - - - 0

54 0 0 0 0 0 3.2 - 0 - 0 0

56 0.47 - 0.58 0.47 0.07 - 0.03 3.25 - 8.23 6.78

57 - - - 2.65 2.25 - - - - 16.08 -

61 - - - - 0.01 - - - - - -

64 0 2.14 - - 0 7.12 - - - 0 0

65 - - - - 0 37.67 - - - - -

66 - - 0 0 0 23.85 - - - 0.06 0

69 0 17.03 - - - 37.29 - - - - -

71 0.02 - 0.17 - 0.07 100.12 - - - 3.71 -

72 0 26.78 - - 0 197.64 - 0 - 0 0

73 0 7.93 0 0 0 48.88 - 0.05 - 0.75 2.7
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

97

Continued - Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching by 

Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue 

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq3

74 0.41 0.34 7.67 2.13 0.13 4.92 - 0.47 - 13.12 14.76

75 0.1 1.35 - - 0.04 69.38 - - - 13.65 1.63

76 0.07 - 21.66 2.74 0.09 - - - - 10 5.82

79 0.1 1.39 - - 0.01 37.21 - 0.26 - 0.76 5.96

85 - 0 - - - - - - - 0.14 1.64

89 - - - 1.19 0.79 7.67 - - - 4.71 -

92 - - - - 0.26 - - - - - -

96 - - 0.22 - 0.5 - - - - - -

97 - - - - 1.6 2.67 - - - 19.93 -

98 0.42 - 2.02 4.22 7.81 29.04 - 3.26 - 144.3 1.65

99 0.24 - - 1 2.21 - 1.35 4.01 - 93.76 1.18

101 - - 3.66 17.43 10.24 - - 7.48 - 65.01 3.5

103 0.43 - 0.37 1.83 1.07 - - 1.31 - 411.02 -

104 0.25 - 0.16 2.23 0.86 - - 2.36 - 206.26 -

105 1.16 - 1.66 15.61 5.89 4.87 - 6.02 - 119.12 -

106 0.18 - 0.73 0.27 0.23 5.15 - 0.39 - 39.85 2.36

109 3.54 - 4.28 2.98 6.51 - - 1.18 - 97.46 -

110 - - - 0.32 - - - 0.4 - 103.26 -

112 0.41 - 3.3 2.03 3.31 3.98 - 3.08 - 78.98 29.11

113 1.28 - 1.1 2.48 2.86 0.86 - 0.81 - 144.67 1.91

121 - - 5.61 3.26 20.21 - - - - 14.82 -

122 0.48 - 8.75 4.11 25.61 - 3.64 - - 54.14 3.17

123 - - 1.75 - 5.28 - - - - 5.5 -

124 - - 3.9 3.75 10.43 - - 1.47 - 24.22 -

125 - - 1.67 - 2.4 - - - - 3.3 -

126 - - 8.38 3.59 7.43 - - - - 10.49 -

127 - - 5.17 4.39 5.94 - - 1.56 - 15.79 -

128 - - 5.5 2.59 11.33 - 9.98 0.95 - 16.53 -

129 - - 1.58 - - - - - - 7.68 -

134 3.57 5.93 7.18 - - 28.4 63.09 8.5 35.93 105.34 10.07

136 0.75 - 33.14 7.01 3.57 - 3.62 3.12 - 31.9 7.6

137 - - 4.04 - - - 5.79 1.03 - 5.26 -

138 - - - - - 2.65 - - - - -

139 - - 9.46 13.09 14.3 - - 3.18 - 60.85 -

140 - - 39.48 33.36 22.69 - - 8.92 - 57.48 -

142 - - 17.54 18.47 12.99 - - - - 25.8 -

143 - - 11.11 - 2.97 - - - - - -

145 - - 5.16 - - - - - - 4.33 -

146 - - - - - - 4.91 - - - -

147 - - 33.68 25.13 15.9 - - 10.42 - 103.97 5.24

148 - - 17.18 15.57 9.57 - - 8.09 - 63.33 5.66

155 - - - - - 35.28 - - - - -

102A 0.2 - 0.13 0.75 0.44 4.2 - 1.15 - 43.26 4.4

102B - - - - 0 - - - - 0.83 -

102C 0.17 - 0.22 0.76 0.31 60.73 - - - 37.66 -

107A - - - - - - - 0.32 - 43.14 -

107B 0.25 - 0.26 0.29 0.46 4.8 - - - 84.04 0.52
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

98

Continued - Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching by 

Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue 

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq3

108A - - - - 0.66 - - - - 109.02 -

108B 0.15 - - - 0.56 3.34 - - - 87.05 -

108C 0.51 - 0.45 0.63 0.34 - - 0.83 - 48.65 -

108D 0.53 - 0.26 1.52 1.13 - - - - 43.68 -

111A - - 0.41 - 2.18 - - - - 175.63 -

111B 0.94 - 0.64 2.75 3.62 1.57 - 1.61 - 219.36 1.53

111C - - - - - - - - - 83.03 -

111D - - - - 0.7 - - - - 81.58 -

111E - - - 1.18 0.94 - - 0.56 - 36.07 -

114A - - 1.1 1.16 2.85 - - - - 39.97 -

114B 0.17 - 0.62 1.86 1.71 - - - - 72.44 1.46

115A - - - - 1.45 2.53 - - - 62.06 2.14

115B 0.18 - 1.08 - 1.5 - - 1.44 - 28.76 1.81

115C 0.68 - 0.45 0.94 1.3 7.98 - 0.83 - 70.74 -

116A - - 7.42 - 7.24 - - - - 11.49 -

116B - - 3.94 - 3.61 - - - - 3.02 1.95

118A - - 0.97 - - - - - - 2.95 3.15

120A 1.6 - 2.06 2.37 8.37 - - - - 51.06 -

120B - - 0.83 - - - - - - 14.58 -

120C - - - - - - - - - 2.19 -

130A - - - - - - - - - 0.95 -

130B - - 2.15 - 3.01 - - - - - -

131A 0.75 9.28 0.79 - - 152.94 80.35 14.83 110.32 116.32 12.72

131B - 3.25 - - - 28.1 4.12 - 11.66 4.16 -

131C - - - - - 2.28 6.37 - 2.87 8.86 -

131D - - - - - 14.36 - - 21.64 - -

133A 7.91 14.54 24.16 6.83 1.81 40.25 165.85 22.55 - 86.12 15.22

133B - - 6.01 - - - - - - 3.97 2

135A 2.04 - 4.1 - - - 14.76 0.61 - 17.05 -

144A - - 30.65 10.21 6.92 - 3.56 3.23 - 16.66 -

144B - - 28.47 3.21 5.6 - - - - 8.94 -

149A - - 2.5 - - 3.15 - - - 11.11 -

150A - - - - - 25.43 10.42 5.69 26.23 12.64 -

150B - - - - - - - - 0.36 - -

152B - - - - - - - 0.44 1.58 - -

153A - - - - - 3.75 28.31 4.09 - 30.28 -

153B - - - - - 1.72 8.21 1.61 - 19.26 0.68

153C - - - - - 2.97 - - - 29.18 -

153D - - - - - 15.11 - - - 29.99 -

156A - - - - - 3.35 - - - - -

28A 0.01 2.13 - - - 22.49 - - - - 0

28B - - - - - 0.57 - - - - -

34A - 0.73 - - - 41.81 - - - - -

34B - - - - - 36.51 - - - - -

43A 0.37 - - - 0.41 - - - - - 6.66

43B 0.02 - - - 0.27 2.1 - - - - -

48A - - - - - 8.88 - - - - -
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

99

Continued - Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching by 

Major Crop Rotation, 2003-2007

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue 

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq3

48B - - - - - 4.16 - - - - -

53A 0 0.01 0 - 0 0.91 - 0 - - 0

53B 0 0.2 0 0 0 - - 0 - 0 0

53C 0 - - - 0 0.85 - - - 0 0

55A 0 - 0 - 0 - 0 0 - 0 0

55B 0 - 0 0 0 3.06 0 0.06 - 0.25 0.01

55C 0 - 0 0.07 0 2.47 - 0 - 0.16 0

58A 0 6.32 0 0 0 121.36 - 0 - - 0

58B - - - - 0.01 2.36 - - - - -

60A - 2.02 - - 0 0.08 - - - - 0

63A 0 0 0 - 0 - - - - 0 0

63B - - 0 0.01 0 - - 0 - 0 0

67A 0 15.64 - - 0 81.01 - - - 0 0

67B 0 68.02 - - - 137.28 - 6.95 - 0 0

70A - - - - - 7.18 - - - - -

70B - - - - - 2.17 - - - - -

70C - - - - - 2.09 - - - - -

77A 0 5.07 - - - 68.25 - 0.06 - 0 0.01

77B 0 - - - - 11.32 - - - - -

77C 0 4.4 - - - 123.35 0.01 0.15 - 0 0.18

77D 0 - - - - 5.09 - - - - 0

77E 0 0.08 - - - 5.63 - - - - 0.03

78A - - - - - - - - - - 0

78B 0 - - - - 1.96 0 - - - 0

78C 0 0.48 - - 0 5.31 0.37 0 - 0 0.05

80A 0.02 - 9.49 - 0.23 2.82 - - - 3.7 47.01

80B - - - - - - - - - - 0

81A - - - - - 51.98 0 0 - - 0

81B - - - - - - - - - - 0

81C - - - - - - - - - - 0

82B - - - - - - - - - - 0

83A - - - - - 8.33 - 0 - 0 0

83C - - - - - - - - - 0 -

83D - - - - - 8.62 0.75 - - 1.71 -

83E - - - - - - - - - 0 -

84A - - 7.77 - - - - - - - 4.06

84B - - - - - 1.85 - - - - 1.51

86A - - 7.4 - - - 2.21 1.4 - 3.76 8.98

87A - - - - - - - - - 4.43 0

87B - - - - - - - - - - 1.94

90A - - 1.43 4.68 3.54 - - - - 13.67 -

90B 0.04 - 1.17 5.78 2.89 - - 1.72 - 21.84 -

91A 0.18 - - 1.5 0.96 14.08 - - - 15.06 -

91B - - - - 0.74 - - - - 5.79 -

94A - - - - 4.13 - - - - 8.51 -

94B - - - - 1.07 - - - - - -

95A 0.13 - 0.64 13.18 3.69 - - 2.91 - 43.96 1.53

CRP1 Fallow Hay 

Grass 

Hay In 

Rotation

Hay 

Legume Irrigated Low Residue 

Other 

Cropland Rice Row Crop 

Small 

Grain 

MLRA2 Gg CO2 eq3

95B 0.25 - 0.6 9.76 3.11 3.18 - 2.92 - 62.1 -

Note: N2O is nitrous oxide. 1 CRP = Conservation Reserve Program 

2 MLRA = Major Land Resource Area 

3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent

 
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

100

Appendix Table B-5 Rice Harvested Area, 1990, 1995, 2000-2013 Appendix Table B-5 Rice Harvested Area, 1990, 1995, 2000-2013

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

State and 

Crop 1,000 hectares

Arkansas 1,200 1,340 1,410 1,621 608 589 629 1637 1400 1325 1395 1470 1785 1154 1414 1124

Primary 1,200 1,340 1,410 1,621 1,503 1,455 1,555 1,635 1,400 1,325 1,395 1,470 1,785 1,154 1,285 1,070

Ratoon 0 0 0 0 0 0 0 2 0 0 0 0 0 0 129 54

California 395 465 548 471 528 507 590 526 523 533 517 556 553 580 557 561

Florida 18 36 27 18 19 12 16 11 15 20 18 20 19 26 22 22

Primary 12 24 19 11 13 6 9 11 11 15 13 14 13 20 15 17

Ratoon 6 12 8 7 7 6 7 0 3 5 4 6 6 6 7 5

Louisiana 709 741 672 710 615 608 693 593 414 510 650 626 749 564 556 570

Primary 545 570 480 546 535 450 533 525 345 378 464 464 535 418 397 413

Ratoon 164 171 192 164 80 158 160 68 69 132 186 162 214 146 159 157

Mississippi 250 288 218 253 253 234 234 263 189 189 229 243 303 157 129 124

Missouri 80 112 169 207 182 171 195 214 214 178 199 200 251 128 177 156

Texas 494 445 321 302 282 248 294 255 209 197 263 269 290 319 216 242

Primary 353 318 214 216 206 180 218 201 150 145 172 170 188 180 134 144

Ratoon 141 127 107 86 76 68 76 54 59 52 91 99 102 139 82 98

Total 3,146 3,427 3,365 3,582 2,488 2,368 2,652 3,499 2,963 2,953 3,270 3,384 3,949 2,928 3,070 2,798

Appendix Table B-6 Total U.S. Production of Crops Managed with Burning, 1990, 1995, 2000-2013

Appendix Table B-6 Total U.S. Production of Crops Managed with Burning, 1990, 1995, 

2000-2013

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Crop 1,000 Metric tons

Wheat 2,200 1,788 1,949 1,666 1,425 1,615 1,482 1,405 1,316 1,598 2,210 1,664 1,607 1,865 1,807 1,841

Rice 723 783 830 844 857 751 904 607 744 1,097 813 889 922 823 825 804

Sugarcane 15,040 12,971 13,017 12,190 13,068 16,631 10,638 6,234 14,951 7,153 9,776 10,207 9,428 10,631 10,914 10,481

Corn 412 406 554 514 488 552 465 361 691 630 661 703 693 710 693 875

Cotton 43 50 48 58 47 63 74 70 43 35 39 35 49 51 53 41

Soybeans 129 128 146 154 147 93 128 192 182 189 187 217 198 180 187 210

Lentil 1 2 2 2 2 0 0 1 2 2 1 2 2 1 1 2

Total 18,548 16,128 16,547 15,428 16,034 19,705 13,692 8,870 17,929 10,703 13,688 13,717 12,899 14,261 14,481 14,253
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

101

Appendix Table B-7 Production of Crops Managed with Burning Appendix Table B-7 Production of Crops Managed With Burning

Corn Soybeans Cotton Wheat Lentils Rice Sugarcane

Year 1,000 bushels 1,000 cwt 1,000 tons

1990 16,227 4,725 195 80,847 23 15,937 16,578

1991 15,867 4,779 229 53,075 41 16,488 16,185

1992 19,388 5,552 214 68,820 0 17,740 15,414

1993 13,867 4,523 207 71,653 47 16,088 15,703

1994 20,809 5,943 250 67,555 39 19,773 15,354

1995 15,987 4,689 230 65,713 44 17,259 14,298

1996 21,705 5,519 250 79,415 27 17,472 13,434

1997 21,489 6,211 248 72,633 45 17,613 13,983

1998 21,801 5,701 174 74,146 34 16,771 14,965

1999 20,587 5,515 224 68,424 40 18,234 14,118

2000 21,810 5,379 219 71,629 51 18,290 14,348

2001 20,222 5,676 264 61,199 50 18,609 13,437

2002 19,230 5,398 217 52,342 45 18,902 14,404

2003 21,747 3,402 291 59,323 0 16,555 18,333

2004 18,308 4,716 341 54,453 10 19,928 11,726

2005 14,213 7,048 320 51,631 20 13,390 6,872

2006 27,211 6,688 199 48,348 49 16,394 16,481

2007 24,791 6,942 160 58,702 45 24,194 7,884

2008 26,033 6,867 180 81,195 24 17,933 10,776

2009 27,662 7,975 162 61,150 41 19,601 11,251

2010 27,292 7,266 227 59,057 34 20,320 10,393

2011 27,944 6,601 232 68,532 32 18,150 11,718

2012 27,291 6,882 241 66,412 32 18,179 12,031

2013 34,442 7,733 186 67,653 35 17,716 11,553

Appendix Table B-8(a) Crop Assumptions and Coefficients Appendix Table B-8(a) Crop Assumptions and Coefficients

Assumption/Coefficient Corn Cotton Lentils Rice Soybean Sugarcane Wheat

Residue/Crop Ratio 1.0 1.6 2.0 1.4 2.1 0.2 1.3 

Fraction Residue Burned 0.00 0.01 0.01 0.09 0.00 0.37 0.03 

Fraction Dry Matter 0.91 0.90 0.85 0.91 0.45 0.62 0.93 

Burning Efficiency 0.93 0.93 0.93 0.93 0.93 0.81 0.93 

Combustion Efficiency 0.88 0.88 0.88 0.88 0.88 0.68 0.88 

Fraction Carbon 0.45 0.45 0.45 0.38 0.45 0.42 0.44 

Fraction Nitrogen 0.006 0.012 0.023 0.007 0.023 0.004 0.006 

Appendix Table B-8(b)

Emissions Factors and Global Warming Potentials

Appendix Table B-8(b) Emissions 

Factors and Global Warming Potentials

GHG Factor & GWP

Emissions Factor

Methane 0.005

Nitrous Oxide 0.007

Global Warming Potential

Methane 25

Nitrous Oxide 298

Appendix Table B-8(c)

Rice Area Burned by State

Appendix Table B-8(c) Rice Area 

Burned by State

State % Burned

Arkansas 6

California 16

Florida 84

Louisiana 2

Mississippi 2

Missouri 3

Oklahoma 100

Texas 26
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

102

Appendix Table B-9 Cultivated Histosol (Organic Soils) Area Appendix Table B-9 Cultivated Histosol (Organic Soils) Area

Cold Temperate Warm Temperate Sub-Tropical

Year Million hectares

1990 0.72 0.17 0.30

1991 0.72 0.17 0.30

1992 0.71 0.17 0.30

1993 0.70 0.16 0.30

1994 0.70 0.17 0.30

1995 0.69 0.17 0.29

1996 0.69 0.17 0.29

1997 0.68 0.16 0.28

1998 0.68 0.17 0.28

1999 0.67 0.17 0.28

2000 0.67 0.17 0.28

2001 0.65 0.16 0.28

2002 0.64 0.16 0.28

2003 0.63 0.16 0.26

2004 0.63 0.17 0.26

2005 0.63 0.17 0.26

2006 0.62 0.16 0.26

2007 0.62 0.16 0.26

2008 0.62 0.16 0.26

2009 0.62 0.16 0.26

2010 0.62 0.16 0.26

2011 0.62 0.16 0.26

2012 0.62 0.16 0.26

2013 0.62 0.16 0.26

Note: Data from EPA 2015

Appendix Table B-10 Carbon Loss Rates from Organic Soils Under Agricultural Management in the United States

Appendix Table B-10 Carbon Loss Rates from Organic Soils Under Agricultural 

Management in the United States

Cropland Grassland1

Climate Regions Metric Tons C/ha-yr2

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 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).

2 Metric Tons C/ha-yr is metric tons carbon per hectare per year

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: 1990–2013 Chapter 3

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Appendix Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by Major Crop Rotation, 2003-2007

Appendix Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by 

Major Crop Rotation, 2003-2007

CRP1 Fallow

Hay 

Grass

Hay in 

Rotation

Hay 

Legume Irrigated

Low 

Residue

Other 

Cropland Rice

Row 

Crop

Small 

Grain

MLRA2 Gg CO2 eq3

2 - - -70.12 -51.24 -8.29 -26.48 - - - - 138.63

5 - - - - - -20.86 - - - - -

7 -28.27 22.35 - - - -108.18 - - - - -

8 -475.31 451.85 - - - 26.86 - -8.69 - - 39.80

9 -114.61 45.01 - - -30.13 -27.99 - - - - -5.09

10 - 34.36 - - 2.26 -104.39 - - - - -

11 - 68.57 - - - -301.58 - - - - -

12 - - - - - -26.00 - - - - -

13 -229.48 25.07 - -28.04 -32.45 -32.14 - - - - 20.15

14 - - 6.07 - - -8.43 - - - - -

15 - - - - - 45.11 - 11.81 - - -

16 - - - - - 3.40 - - - - -

17 - -36.65 - - - -204.68 - -83.22 -45.45 - -

21 - - - - - -151.58 - - - - -

23 - - - - - -52.73 - - - - -

24 - - - - - 28.77 - - - - -

25 - - - - - -62.43 - - - - -

26 - - - - - 2.79 - - - - -

27 - - - - - 15.22 - 1.52 - - -

29 - - - - - -1.22 - - - - -

30 - - - - - 42.72 - - - - -

31 - 9.77 - - - -178.61 - -11.16 - - -

32 - - - - - 9.25 - - - - -

35 - - - - - 21.75 - - - - -

36 -21.25 45.04 - - - -67.94 - - - - 11.49

40 - - - - - -68.82 - -12.77 - - -

41 - - - - - 3.16 - - - - -

42 - - - - - -59.03 - -33.71 - - -

44 - 393.32 -12.05 - -49.58 -258.94 - - - - 6.42

46 -111.35 125.28 - - -132.05 -136.05 - - - - 101.95

47 - - - - - -10.42 - - - - -

49 - -3.14 - - - -9.77 - - - - -

51 - - - - - -61.89 - - - - -

52 -779.05 605.59 - - -6.52 -91.50 - - - - 8.81

54 -301.28 62.66 -68.94 -72.72 -182.95 -10.54 - -19.34 - 156.46 624.19

56 -393.54 - -50.30 -31.54 -66.27 - 59.18 -91.94 - 52.14 188.52

57 - - - -27.91 -45.85 - - - - 139.63 -

61 - - - - -16.91 - - - - - -

64 -33.96 54.68 - - 15.97 46.81 - - - 8.35 29.18

65 - - - - 0.96 -36.55 - - - - -

66 - - -25.82 -36.18 -45.11 -8.35 - - - 73.04 73.46

69 -29.23 67.57 - - - -11.02 - - - - -

71 -20.62 - -43.96 - 2.83 -202.00 - - - 35.88 -

72 -836.33 94.11 - - 2.42 -397.37 - -157.74 - -102.42 -38.35

73 -374.53 85.94 -36.66 -51.24 -105.80 -190.42 - -139.99 - -287.90 102.64
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

104

Continued - Appendix Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by Major Crop 

Rotation, 2003-2007

CRP1 Fallow

Hay 

Grass

Hay in 

Rotation

Hay 

Legume Irrigated

Low 

Residue

Other 

Cropland Rice

Row 

Crop

Small 

Grain

MLRA2 Gg CO2 eq3

74 -106.62 3.42 -34.08 -43.80 -69.67 -34.28 - -1.35 - -135.07 -50.70

75 -24.67 27.78 - - -26.06 -292.06 - - - -105.09 31.51

76 -11.55 - -90.58 -43.21 0.76 - - - - -68.35 -17.90

79 -158.58 21.20 - - -13.71 -89.44 - -36.66 - 11.47 36.63

85 - -7.59 - - - - - - - -6.13 41.48

89 - - - -7.26 -17.43 -4.25 - - - 34.16 -

92 - - - - -5.64 - - - - - -

96 - - -10.17 - -5.02 - - - - - -

97 - - - - -25.50 0.43 - - - 33.05 -

98 -25.60 - -149.12 -71.61 -117.86 -29.92 - -43.76 - 254.51 -0.34

99 -59.30 - - -8.47 -69.85 - 8.93 -42.31 - -341.12 0.66

101 - - -208.72 -151.89 -132.42 - - -12.25 - 564.32 31.34

103 -261.57 - -79.35 -141.55 -204.04 - - -46.74 - -1330.07 -

104 -48.86 - -26.46 -70.52 -52.20 - - -57.57 - -148.27 -

105 -76.07 - -145.52 -251.91 -218.28 -5.18 - -77.80 - 1765.92 -

106 -166.86 - -142.90 -70.08 -36.34 -37.82 - -24.39 - 107.31 34.50

109 -381.42 - -398.82 -109.29 -92.63 - - -2.28 - 381.66 -

110 - - - -25.35 - - - -17.17 - -170.01 -

112 -76.11 - -509.05 -77.42 -62.64 -29.33 - -56.58 - -5.98 162.67

113 -197.14 - -176.22 -109.78 -36.61 -7.48 - -45.43 - -371.09 -5.06

121 - - -254.48 -25.46 -7.84 - - - - 161.04 -

122 0.97 - -365.21 -76.30 -52.51 - 16.13 - - 74.76 10.64

123 - - -97.60 - -9.77 - - - - 16.04 -

124 - - -201.73 -116.53 -62.73 - - -8.10 - 224.54 -

125 - - -44.28 - -14.88 - - - - 3.57 -

126 - - -382.31 -54.04 -34.99 - - - - 89.01 -

127 - - -223.34 -25.43 -4.00 - - -2.89 - 123.64 -

128 - - -237.27 -53.72 -1.28 - -27.85 -15.25 - -52.55 -

129 - - -71.76 - - - - - - -28.63 -

134 -166.05 3.18 -400.19 - - -43.20 112.47 -56.42 -63.30 97.66 57.89

136 -2.21 - -598.39 -95.46 -4.84 - -5.88 -32.00 - 26.17 -17.77

137 - - -43.19 - - - 11.00 -9.23 - 0.06 -

138 - - - - - 16.47 - - - - -

139 - - -170.99 25.09 -85.79 - - -4.14 - 452.66 -

140 - - -797.59 -141.40 -100.87 - - 41.43 - 389.66 -

142 - - -263.65 43.18 -23.10 - - - - 190.71 -

143 - - -60.88 - 3.91 - - - - - -

145 - - -28.93 - - - - - - 6.09 -

146 - - - - - - 10.22 - - - -

147 - - -473.16 -117.76 -99.80 - - -20.37 - 211.03 12.86

148 - - -246.28 -115.53 -73.11 - - -16.10 - 157.62 12.23

155 - - - - - 257.16 - - - - -

102A -271.08 - -157.73 -113.15 -145.54 -11.37 - -43.49 - 585.51 86.65

102B - - - - -53.30 - - - - 19.51 -

102C -96.36 - -60.82 -264.58 -134.97 -161.97 - - - 263.53 -

107A - - - - - - - -59.66 - -588.16 -

107B -112.86 - -194.55 -73.20 11.66 -70.96 - - - 81.59 10.08
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

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Continued - Appendix Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by Major Crop 

Rotation, 2003-2007

CRP1 Fallow

Hay 

Grass

Hay in 

Rotation

Hay 

Legume Irrigated

Low 

Residue

Other 

Cropland Rice

Row 

Crop

Small 

Grain

MLRA2 Gg CO2 eq3

108A - - - - -27.37 - - - - -684.44 -

108B -22.83 - - - -48.00 -14.05 - - - -664.44 -

108C -108.02 - -82.35 -109.41 -60.60 - - -64.58 - -76.91 -

108D -135.78 - -54.02 -63.29 -78.07 - - - - 160.50 -

111A - - -49.79 - -38.49 - - - - -604.97 -

111B -139.76 - -41.53 -95.91 -115.07 -3.48 - -23.36 - -337.17 2.26

111C - - - - - - - - - -166.24 -

111D - - - - -11.34 - - - - -305.93 -

111E - - - -28.22 -12.70 - - -15.78 - -19.53 -

114A - - -45.41 -29.87 -40.78 - - - - -5.10 -

114B -28.18 - -50.38 -64.39 -23.50 - - - - -93.95 8.67

115A - - - - -25.24 -10.19 - - - -150.93 -1.53

115B -5.73 - -137.00 - -8.37 - - -17.45 - 188.33 2.78

115C -141.68 - -161.42 -124.31 -63.79 -33.52 - -29.00 - -236.21 -

116A - - -389.87 - -3.71 - - - - 72.85 -

116B - - -204.67 - -2.14 - - - - -14.52 10.69

118A - - -34.22 - - - - - - 13.67 25.30

120A -77.78 - -88.49 -15.90 -61.84 - - - - 86.66 -

120B - - -28.33 - - - - - - 74.72 -

120C - - - - - - - - - 20.07 -

130A - - - - - - - - - 7.02 -

130B - - -36.60 - -2.47 - - - - - -

131A -143.64 31.50 -50.70 - - -169.09 -51.44 -94.58 -147.92 -157.84 2.52

131B - 24.63 - - - -74.56 0.84 - 8.74 -4.21 -

131C - - - - - -1.63 20.45 - -24.87 -31.29 -

131D - - - - - -20.51 - - -32.12 - -

133A -255.83 3.07 -381.70 -19.18 -5.18 -61.68 511.97 -116.96 - -220.33 34.95

133B - - -84.70 - - - - - - 6.71 -8.40

135A -113.56 - -152.23 - - - 64.28 -8.68 - 37.88 -

144A - - -286.27 -18.80 -9.27 - 22.36 17.96 - 66.03 -

144B - - -209.49 -6.96 13.30 - - - - 57.60 -

149A - - -20.38 - - 3.22 - - - -14.48 -

150A - - - - - -74.64 40.85 -230.29 -274.07 8.79 -

150B - - - - - - - - -13.89 - -

152B - - - - - - - -9.24 -18.58 - -

153A - - - - - 6.39 46.57 -18.92 - -57.92 -

153B - - - - - -2.75 19.33 -2.79 - -51.59 -2.21

153C - - - - - -5.00 - - - -103.18 -

153D - - - - - -16.18 - - - -89.81 -

156A - - - - - -4.73 - - - - -

28A -144.74 68.02 - - - 86.86 - - - - -3.94

28B - - - - - -24.73 - - - - -

34A - 6.98 - - - -44.60 - - - - -

34B - - - - - -166.44 - - - - -

43A -32.45 - - - -18.89 - - - - - -7.99

43B -44.82 - - - -33.34 -84.18 - - - - -

48A - - - - - -42.60 - - - - -
Chapter 3 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

106

Continued - Appendix Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by Major Crop 

Rotation, 2003-2007

CRP1 Fallow

Hay 

Grass

Hay in 

Rotation

Hay 

Legume Irrigated

Low 

Residue

Other 

Cropland Rice

Row 

Crop

Small 

Grain

MLRA2 Gg CO2 eq3

48B - - - - - -30.46 - - - - -

53A -261.68 48.12 -22.89 - -9.27 10.27 - -54.07 - - 35.87

53B -490.83 48.86 -57.97 -51.31 -58.63 - - -24.26 - -48.40 306.94

53C -42.62 - - - -8.54 -12.67 - - - 0.80 4.48

55A -395.12 - -28.37 - -12.58 - 10.60 -109.10 - -36.38 -238.89

55B -555.38 - -81.86 -121.22 -68.73 -2.91 11.72 -118.28 - 139.71 124.65

55C -67.77 - -38.23 -126.31 -131.55 5.32 - 12.07 - 332.56 -13.41

58A -636.19 253.83 -77.55 -103.82 -145.15 -117.02 - 17.25 - - 27.75

58B - - - - -1.80 -12.88 - - - - -

60A - 104.77 - - -10.78 -0.51 - - - - 25.50

63A -111.53 250.41 -65.13 - -15.81 - - - - 33.68 80.83

63B - - -39.74 -29.38 -102.88 - - 24.92 - -11.30 -5.73

67A -84.50 19.56 - - -6.43 -133.51 - - - 2.66 1.54

67B -471.14 167.05 - - - -165.70 - -8.13 - -59.18 -5.61

70A - - - - - 4.50 - - - - -

70B - - - - - -24.81 - - - - -

70C - - - - - -6.25 - - - - -

77A -285.26 -36.94 - - - -94.78 - -7.82 - -62.84 87.54

77B -4.20 - - - - 65.74 - - - - -

77C -1117.36 -8.72 - - - -226.05 -146.43 -14.83 - -12.06 69.76

77D -120.01 - - - - 14.44 - - - - 12.30

77E -342.86 8.80 - - - -0.45 - - - - 19.19

78A - - - - - - - - - - -18.50

78B -366.82 - - - - 4.45 -26.34 - - - 91.79

78C -168.58 -24.60 - - 0.74 -36.84 -4.65 15.37 - -13.46 44.24

80A -37.84 - -47.28 - -65.41 -38.49 - - - -16.85 6.57

80B - - - - - - - - - - 28.13

81A - - - - - 14.19 -9.94 -47.43 - - 1.23

81B - - - - - - - - - - 5.65

81C - - - - - - - - - - 4.64

82B - - - - - - - - - - 11.69

83A - - - - - -21.46 - -3.68 - -12.17 8.67

83C - - - - - - - - - 2.13 -

83D - - - - - -55.22 8.36 - - -7.22 -

83E - - - - - - - - - -6.38 -

84A - - -23.20 - - - - - - - 13.40

84B - - - - - -45.17 - - - - -12.01

86A - - -18.69 - - - 25.09 -79.26 - -60.68 27.65

87A - - - - - - - - - -8.27 4.49

87B - - - - - - - - - - -7.61

90A - - -142.03 -101.24 -109.63 - - - - 96.73 -

90B -8.03 - -123.42 -28.69 -88.23 - - -16.46 - 381.13 -

91A -21.23 - - -19.96 -8.14 20.55 - - - 74.26 -

91B - - - - -11.73 - - - - 12.44 -

94A - - - - -76.98 - - - - 33.63 -

94B - - - - -6.22 - - - - - -

95A -8.95 - -27.50 -75.70 -78.03 - - -23.08 - 348.63 4.39

CRP1 Fallow

Hay 

Grass

Hay in 

Rotation

Hay 

Legume Irrigated

Low 

Residue

Other 

Cropland Rice

Row 

Crop

Small 

Grain

MLRA2 Gg CO2 eq3

95B -33.80 - -64.40 -166.83 -106.40 7.94 - -46.76 - 694.39 - 1 CRP = Conservation Reserve Program

2 MLRA = Major Land Resource Area 

3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 3

107

Appendix Table B-12 State-Level Estimates of Mineral Soil Carbon Changes on Cropland1 by Major Activity, 2013 Appendix Table B-12 State-Level Estimates of Mineral Soil Carbon Changes on Cropland1

by Major Activity, 2013

Cropland 

Remaining 

Cropland

Land Converted to 

Cropland2

Grassland 

Remaining 

Grassland

Land Converted to 

Grassland

Net Total

State Tg CO2 eq. 

Alabama (0.63) (0.08) (0.33) (1.38) (2.41)

Alaska ND ND ND ND ND

Arizona (0.05) (0.01) (0.65) (0.73) (1.43)

Arkansas (0.69) 0.14 (0.23) (0.84) (1.61)

California (0.46) 0.28 (0.57) (0.93) (1.67)

Colorado (0.43) 0.17 1.08 (0.31) 0.50 

Connecticut (0.01) (0.01) (0.02) (0.00) (0.03)

Delaware (0.09) 0.00 (0.00) (0.01) (0.11)

Florida 0.04 0.73 (0.27) (0.09) 0.41 

Georgia (0.31) (0.00) (0.40) (0.19) (0.90)

Hawaii 0.00 0.00 0.00 0.00 0.00 

Idaho (5.35) 0.98 (1.90) (0.18) (6.45)

Illinois (1.00) 0.14 0.19 (0.12) (0.80)

Indiana (6.15) 0.76 (0.46) (0.26) (6.12)

Iowa (2.52) 0.34 (0.17) (0.12) (2.46)

Kansas (4.20) 0.48 0.83 (0.28) (3.17)

Kentucky (0.76) (0.06) (0.67) (0.26) (1.75)

Louisiana (0.86) (0.05) (0.14) (0.28) (1.33)

Maine (0.06) 0.00 (0.01) 0.00 (0.07)

Maryland (0.35) 0.03 (0.02) (0.04) (0.37)

Massachusetts (0.12) (0.02) 0.01 (0.00) (0.14)

Michigan (0.82) 0.03 (0.15) (0.13) (1.07)

Minnesota (2.91) 0.62 (1.14) (0.46) (3.89)

Mississippi (2.55) 0.80 (1.06) (0.33) (3.14)

Missouri (0.57) 0.14 (0.42) (0.28) (1.13)

Montana (2.26) 0.44 5.63 (0.34) 3.46 

Nebraska (0.78) 0.01 (0.24) (0.14) (1.15)

Nevada (0.90) 0.71 0.36 0.05 0.22 

New Hampshire (2.39) 0.58 1.37 (0.11) (0.54)

New Jersey (0.05) (0.01) 0.00 (0.00) (0.06)

New Mexico (0.13) 0.00 (0.01) (0.03) (0.18)

New York (0.28) (0.05) 1.19 (0.05) 0.81 

North Carolina (0.02) (0.00) 0.08 (0.01) 0.05 

North Dakota (1.05) 0.08 (0.13) (0.07) (1.18)

Ohio (2.00) 0.12 (0.14) (0.17) (2.19)

Oklahoma (1.46) 0.19 1.01 (0.33) (0.58)

Oregon (0.26) (0.15) 0.25 (0.22) (0.39)

Pennsylvania (0.54) (0.03) (0.17) (0.08) (0.82)

Rhode Island (0.00) 0.01 (0.01) 0.00 (0.00)

South Carolina (0.39) (0.05) (0.19) (0.05) (0.68)

South Dakota (1.02) 1.33 0.30 (0.10) 0.50 

Tennessee (0.98) 0.08 (0.79) (0.32) (2.01)

Texas (2.97) 0.50 4.67 (0.72) 1.48 

Utah (0.01) 0.07 2.38 (0.08) 2.36 

Cropland 

Remaining 

Cropland

Land Converted to 

Cropland2

Grassland 

Remaining 

Grassland

Land Converted to 

Grassland

Net Total

State Tg CO2 eq.

Vermont (0.91) (0.10) (0.43) (0.11) (1.55)

Virginia 0.00 0.00 0.02 (0.01) 0.02 

Washington (0.04) 0.15 (0.29) (0.10) (0.28)

West Virginia 0.74 0.31 (0.36) (0.32) 0.36 

Wisconsin (0.34) (0.06) (0.07) (0.02) (0.49)

Wyoming (0.44) 0.23 2.44 (0.04) 2.19 

Total (49.33) 9.79 10.34 (10.60) (39.80)

Note: Parentheses indicate a net sequestration. Tg CO2 eq is teragrams carbon dioxide equivalent. ND= No data. 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, set-aside lands, and perennial/horticultural cropland.

109

Chapter 4

Carbon Stocks and 

Stock Changes in U.S. Forests

4.1 Summary 

This chapter includes summary updates of 

inventories and carbon estimations relative to 

the national forest carbon budgets reported in the 

previous edition of the USDA Greenhouse Gas 

Inventory, Chapter 4 (Smith and Heath 2011). We 

present estimates of stocks and net annual stock 

change for carbon on forest lands and in harvested 

wood products for the United States that correspond 

to values reported for forest lands in the recent U.S. 

GHG Inventory, specifically Chapter 6: Land Use, 

Land-Use Change, and Forestry of EPA (2015). 

Results are generally 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).

Chapter 6 (Land Use, Land-Use Change, and 

Forestry) of the U.S. GHG Inventory reported 

that carbon sequestered, or stored, in U.S. forest 

ecosystems and harvested wood products offset 

approximately 11.6 percent of total U.S. greenhouse 

gas emissions in 2013 (EPA 2015). The U.S. GHG 

Inventory also found that forests in the United States 

stored an estimated 705 and 71 MMT CO2

 eq. in 

2013 (MMT ≡ million metric tons, where 1 metric 

ton = 106g) for forest ecosystems and harvested 

wood products, respectively. These numbers 

represent the amount of carbon sequestered in 2013 

alone, adding to carbon stocks built up over past 

years. Total sequestration in 2013 was estimated to 

be 776 MMT CO2

 eq. with a 95-percent confidence 

interval from 973 to 576 MMT CO2 eq. (Table 4-1). 

Forest ecosystems plus harvested wood products 

sequestered about 21 percent more CO2 eq. in 2013 

than in 1990 (Table 4-2). The forest ecosystems 

included in the report are in the conterminous United 

States and south central and southeastern coastal 

Alaska (Map 4-1). Estimated total carbon stocks of 

forest ecosystems are 146,600 MMT CO2

 eq.

Forest lands of the United States constitute 

approximately one-third of total land area (Oswalt 

et al. 2014). Recently summarized data indicate that 

forest land area in the conterminous United States has 

increased by 5 percent over the interval from 1987 

to 2012, increasing from 243 to 257 million hectares 

(Oswalt et al. 2014). Table 4-2 shows the overall 

increase in forest land since 1990, based on the U.S. 

GHG Inventory. Carbon stocks in forest ecosystems 

and harvested wood products have also increased 

since 1990. Overall, the increased forest carbon 

sequestration between 1990 and 2013 is due to both 

increased forest area and increased carbon density 

(MT C per hectare of forest, where MT ≡ metric 

ton). The apparent increased carbon density from 

Table 4-2 is based on dividing total carbon stock by 

forest area, and this national-scale effect is influenced 

by more localized factors including management, 

disturbances, climate, and land use. The general trend 

of increased forest area and carbon stocks of Table 

4-2 does not hold for all regions and ownerships 

(Tables 4-4 and 4-5); both area and carbon stocks 

have decreased in privately owned forest lands in 

the Rocky Mountains. In contrast, privately owned 

forests in the South generally decreased in forest 

area since the year 2000, while total carbon stocks 

increased over that same time interval.

Stock change sequences as calculated for the carbon 

pools are sometimes large and variable over time; 

this is particularly apparent with the larger pools such 

as aboveground biomass and soil organic carbon – 

as in Table 4-2 between 2000 and 2005. Because 

change over an interval here is based on all forestland 

at time one relative to all forestland at time two, 

carbon pools on land entering or leaving “forest land” 

relative to other sectors is retained in this change 

Table 4-1 Forest Carbon Stock Change Annualized Estimates and 

Uncertainty Intervals, 2013 Table Error! No text of specified style in document.-17 Forest Carbon 

Stock Change Annualized Estimates and Uncertainty Intervals, 2013

Estimate 95% Confidence Interval

Source MMT CO2 eq.

Forest (705) (901) to (506)

Harvested Wood (71) (90) to (54)

Total (776) (973) to (576)

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent.

Forest ecosystem carbon stock change is based on annualized estimates for 2013 from the shaded area in Map 4-1.

Parentheses (i.e., negative net annual change) indicate net forest ecosystem or wood products sequestration, by convention.

Source: EPA 2015

Download data: http://dx.doi.org/10.15482/USDA.ADC/1264247
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

110

accounting as stock gains or losses, respectively. 

These apparent highly variable change estimates 

can be partitioned to individual States and specific 

inventories within those States (Smith and Heath 

2015); however, such an extension of the analysis is 

beyond the scope of this chapter. 

Tables 4-1 and 4-2 do not include woody biomass 

burned for energy production and carbon sequestered 

by trees in urban areas, though these affect net GHG 

emissions. An additional 209 MMT CO2 eq. was 

harvested and burned to produce energy in 2013. 

This quantity of emitted CO2 eq. is not included in 

this chapter (or the Land Use, Land-Use Change, 

and Forestry portion of the national GHG inventory) 

because it is a part of energy accounting; see Chapter 

3 (Energy) of EPA (2015). Trees in urban areas also 

sequestered about 90 MMT CO2

 eq. in 2013. This 

quantity is reported in Chapter 6, Land Use, LandUse Change, and Forestry of EPA (2015) but is 

reported separately from forest land because urban 

lands fall within the settlements land use category.

4.2 Background Concepts and 

Conventions for Reporting Forest 

Carbon

This chapter summarizes carbon stocks and stock 

changes on the approximately 270 million hectares 

located in the conterminous 48 States and coastal 

Alaska that are considered managed (EPA 2015). 

Land designated as managed aligns with IPCC 

guidance for greenhouse gas inventories. The IPCC 

defines managed forests as those under human 

influence and with a potential to affect anthropogenic 

carbon emissions. All forest land of the conterminous 

United States is considered managed under IPCC 

guidance due to explicit timber and fire management 

(e.g., fire suppression in wilderness areas). A 

large proportion of conterminous U.S. forests, 

80 percent, are classified as timberland, meaning 

they meet minimum levels of productivity and are 

administratively available for timber harvest. We do 

not distinguish between the effects of management 

and land use change, such as afforestation, increased 

Table 4-2 Forest Carbon Stock/Stock Change and Area Annualized Estimates, 1990, 1995, 2000, 2005, 

2010, and 2013

Table 4-2 Forest Carbon Stock/Stock Change and Area Annualized Estimates, 1990, 1995, 

2000, 2005, 2010, and 2013 

 1990 1995 2000 2005 2010 2013

Annual Change MMT CO2 eq. yr-1

Forest (507.7) (542.5) (376.4) (704.4) (704.9) (704.9)

Aboveground Biomass (324.6) (372.5) (329.9) (402.8) (433.7) (433.7) 

Belowground Biomass (63.2) (73.2) (65.0) (79.3) (87.4) (87.4) 

Dead Wood (45.9) (47.3) (70.2) (66.8) (95.0) (95.0) 

Litter (26.8) (18.2) 0.7 (11.8) (10.9) (10.9) 

Soil Organic Carbon (47.2) (31.2) 88.0 (143.8) (77.9) (77.9) 

Harvested Wood (131.8) (118.4) (113.0) (102.7) (60.5) (70.8)

Wood Products (64.8) (55.2) (47.1) (44.0) 3.7 (11.0) 

SWDS (67.0) (63.2) (65.9) (58.7) (62.3) (62.3) 

Total (639.4) (660.9) (489.4) (807.1) (765.4) (775.7)

Carbon Stock MMT CO2 eq. 

Forest 133,134 135,686 138,082 140,905 144,496 146,611

Aboveground Biomass 44,974 46,661 48,470 50,331 52,457 53,758 

Belowground Biomass 8,911 9,241 9,597 9,963 10,387 10,650 

Dead Wood 7,838 8,077 8,380 8,743 9,153 9,438 

Litter 10,080 10,204 10,254 10,276 10,336 10,369 

Soil Organic Carbon 61,330 61,503 61,380 61,592 62,163 62,397 

Harvested Wood 6,817 7,440 8,021 8,525 8,969 9,167

Wood Products 4,514 4,807 5,069 5,262 5,397 5,408 

SWDS 2,303 2,633 2,952 3,263 3,571 3,758 

Total 139,951 143,125 146,103 149,430 153,465 155,777

 1,000 ha 

Forest Area 265,938 267,565 267,987 268,334 269,536 269,911

Notes: Forest ecosystem carbon stocks and stock changes as well as forest area are based on annualized estimates for the shaded 

area in Map 4-1. Parentheses (i.e., negative net annual change) indicate net forest ecosystem or wood products sequestration, by 

convention. SWDS is Solid Waste Disposal Site. MMT CO2 eq. is million metric tons carbon dioxide equivalent. MMT CO2 eq. 

yr-1 is million metric tons carbon dioxide equivalent per year. 

Source: EPA 2015 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

111

productivity, reduced conversion to non-forest uses, 

lengthened rotations, and increased proportion and 

retention of carbon in harvested wood products in 

this chapter, but the effects are implicitly a part of 

the inventory and are thus reflected in estimates of 

carbon stocks and stock changes. 

For reporting purposes (e.g., as in Table 4-2), we 

classify carbon estimates in forest ecosystems into 

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 also 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), which includes all 

organic material, including fine roots, in soil to a 

depth of 1 meter but excluding the coarse roots 

of the belowground pools. 

Table 4-3 Carbon Stocks by Ownership and Forest Type and Groups by Region, 2013 Table 4-3 Carbon Stocks by Ownership and Forest Type and Groups by Region, 2013 

 

Region: Pacific Coast Rocky Mountain North South 

Federal 

Other 

Public Private Federal

Other 

Public Private Federal

Other 

Public Private Federal

Other 

Public Private

Forest Type Group MMT CO2 eq. yr-1

White/Red/Jack Pine 407 636 1,602 43 6 72

Spruce/Fir 111 34 26 793 1,833 3,013 6 5 3

Longleaf/Slash Pine 526 359 2,326

Loblolly/Shortleaf Pine 47 126 207 1,040 309 9,784

Other Eastern Softwoods 3 5 138 12 8 192

Pinyon/Juniper 94 4 10 1,874 160 880 11 2 10 15 21 517

Douglas-fir 4,663 869 3,145 2,499 142 648 0 1 

Ponderosa Pine 1,091 60 636 969 83 553 128 10 63 

Western White Pine 43 4 1 5 

Fir/Spruce/Mountain 

Hemlock 4,238 127 393 4,312 126 346 9 

Lodgepole Pine 659 21 115 1,855 28 133 

Hemlock/Sitka Spruce 3,683 653 1,046 234 26 68 

Western Larch 114 9 22 178 25 41 

Redwood 53 87 263 

Other Western Softwoods 511 18 200 301 14 29 

California Mixed Conifer 2,005 32 553 

Exotic Softwoods 26 124 

Other Softwoods 1 

Oak/Pine 123 229 984 470 193 3,425

Oak/Hickory 1 4 7 881 2,140 11,871 1,903 618 13,463

Oak/Gum/Cypress 18 57 143 801 734 5,480

Elm/Ash/Cottonwood 53 46 66 22 10 66 253 705 3,386 134 144 1,814

Maple/Beech/Birch 1,132 2,282 9,289 90 17 320

Aspen/Birch 74 16 55 1,077 43 281 682 1,370 2,630 1 2

Alder/Maple 156 217 707 2 1 1 

Western Oak 615 71 885 

Tanoak/Laurel 349 57 442 

Other Hardwoods 97 24 131 1 0 0 39 109 241 59 11 159

Woodland Hardwoods 49 3 20 432 35 227 9 38 1,455

Tropical Hardwoods 43 107 97

Exotic Hardwoods 0 0 0 1 0 6 42 3 17 171

Nonstocked 266 21 147 539 32 164 36 77 254 31 46 665

Notes: 

See USDA Forest Service (2015a) for additional details on how classifications are defined. 

Carbon densities are based on the most recent inventory per state for shaded area in Map 4-1. 

Blank indicates that the type group does not appear within the inventory for that region and ownership, zeros are the result of

rounding a small quantity. 

MMT CO2 eq. is million metric tons carbon dioxide equivalent. 
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

112

Within the carbon pool of biomass, we further 

separated initial carbon estimates into the categories 

of live trees (diameter greater than 2.5 cm) and 

understory (smaller live vegetation). Similarly, we 

separated the dead wood pool into standing dead 

wood and down dead wood. 

The two carbon pools reported for harvested wood 

products are: 

• Harvested wood products in use. 

• Harvested wood products in solid waste disposal 

sites. 

The U.S. GHG Inventory estimates of carbon in 

harvested wood products are reported at the national 

scale in Tables 4-1 and 4-2, and are not disaggregated 

to the State level. 

The U.S. GHG Inventory relies on annualized 

estimates of forest carbon stocks within each U.S. 

State from 1990 to present. Many of the carbon 

stock summaries presented here (and some in 

EPA 2015) are based on the most recent per-State 

forest inventory data; the year of these newest data 

varies by State. Thus, some of our results reflect 

the annualized State data (EPA 2015, Smith et al. 

2010), and other results are based on the most recent 

available forest inventory data per State. Specifically, 

we used the annualized model for stock and stock 

change as the basis for Tables 4-1, 4-2, 4-4, 4-7, 4-8, 

and C-2 and Figures 4-1 and 4-2. The most recent 

surveys per State are summarized in Tables 4-3, 4-6, 

C-1, C-3, and C-4.

The estimates in this chapter focus on carbon mass, 

but we report results as the equivalent mass of carbon 

dioxide by multiplying by 44/12, by convention. Also 

following reporting conventions, GHG inventory 

reporting records net ecosystem carbon gain as a 

negative value (i.e., CO2

 loss from the atmosphere). 

Therefore, numbers in parentheses (negative values) 

represent a net annual gain in carbon accumulated 

within forests or harvested wood pools (i.e., forest 

carbon gain as a negative net change, or flux, of 

carbon stocks). For example, Table 4-2 lists (706) 

MMT CO2 eq. as the net amount sequestered by 

forest ecosystems in 2013, which from an atmosphere 

perspective represents CO2 removed from the 

atmosphere.

The carbon stocks estimated in this chapter reflect 

lands identified as forest at the time field data were 

collected. Thus, the stock change estimates include 

net change in forest land area and do not separately 

account for land use change. Net gains or losses 

within the carbon pools could result in either a 

CO2

 exchange with the atmosphere or movement 

of carbon to or from non-forest lands. Future 

improvements in the forest carbon estimates will 

directly address these issues. Most live tree and dead 

wood carbon changes are very likely the result of 

forest growth, removals, or mortality rather than land 

use changes. However, soil organic carbon, while 

generally much higher in forests as compared to 

other ecosystems, is a relatively large pool and slow 

to change. 

4.3 Carbon Stocks and Stock 

Changes by Forest Type, Region, and 

Ownership

Some of the results in this chapter are reprinted 

from EPA 2015; specifically Tables 4-1 and 4-2. 

The remaining tables are based on the same 

underlying inventory-based forest carbon data 

(developed by the authors and provided to EPA 

2015) but are summarized according to additional 

classification details not included in EPA (2015) 

such as ownership, regions, forest types, or stand 

characteristics. Thus, the forest carbon estimates 

reported here expand on the information provided in 

the U.S. GHG Inventory (EPA 2015).

Table 4-3 lists total forest ecosystem carbon 

stocks according to forest type group, region, and 

ownership. Forest type groups are partitioned 

according to those in the forest inventory database 

(FIADB, USDA FS 2015a). Regions are identified 

in Map 4-1. There are three broad classes of land 

ownership. Publicly owned forest lands are divided 

into Federally owned lands and “other public” (i.e., 

those under State, city, or other local government). 

All privately owned forest lands are combined into 

the third ownership classification of “private.” Table 

4-3 is based on the most recent survey data per State. 

Map 4-1 Geographic Regions Used for Carbon Stock and 

Stock Change Summaries (The shaded area represents the 

extent of the forest inventories used for forest carbon estimates.)

Map	
  4-­‐1	
  

Geographic	
  Regions	
  Used	
  for	
  Carbon	
  Stock	
  and	
  Stock	
  Change	
  Summaries

(The	
  shaded	
  area	
  represents	
  the	
  extent	
  of	
  the	
  forest	
  inventories	
  used	
  for	
  forest	
  carbon	
  estimates.)
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

113

The majority of forest carbon in the Western United 

States is on public lands while the majority of forest 

carbon in the Eastern United States is on privately 

owned forest lands (Table 4-3). There are some 

trends apparent between public and private lands. For 

example, carbon stocks in the ponderosa pine and 

fir/spruce/mountain hemlock group tend to occur on 

publicly owned land, which corresponds to the type 

of forest. As seen in Table 4-3, the oak/hickory type 

group contains the largest stock of forest carbon. 

Appendix tables C-1a and C-1b provide forest area 

and carbon stocks of live trees, respectively, in the 

same format as Table 4-3.

The same classifications for region and ownership 

were applied to disaggregated annualized stock 

and stock change estimates. Tables 4-4 and 4-5 

show the total annualized carbon stock change and 

annualized forest area by region. These tables also 

show uncertainty around the 2013 estimates using 

a 95-percent confidence interval. In general, the 

gains in total carbon stocks (negative values in Table 

4-4) were accompanied by increases in forest area 

(Table 4-5). The trend toward continuous increase 

in stocks and area does not hold for all regions and 

ownerships; both area and carbon stocks decreased 

for privately owned forest lands in the Pacific Coast 

Table 4-4 Total Annualized Carbon Stock Change 1990-2013, With Uncertainty Interval for 2013

Table Error! No text of specified style in document.-20 Total Annualized Carbon Stock Change 

1990-2013, With Uncertainty Interval for 2013

1990 1995 2000 2005 2010 2013 2013 2013

Region Ownership group Forest ecosystem total carbon stock change Uncertainty

LB UB

MMT CO2 eq. yr-1 MMT CO2 eq. yr-1

Pacific Coast Federal (60) (60) (51) (47) (101) (101) (185) (15)

Pacific Coast Other Public (26) (26) (16) (16) (12) (12) (65) 41 

Pacific Coast Private (28) (28) 12 12 (11) (11) (89) 70 

Rocky Mountain Federal (59) (58) (22) (22) (34) (35) (93) 21 

Rocky Mountain Other Public (4) (4) (3) (1) (2) (2) (14) 8 

Rocky Mountain Private 22 22 19 26 27 27 (1) 54 

North Federal (13) (6) (13) (38) (32) (32) (64) (0)

North Other Public (48) (65) (81) (95) (99) (99) (162) (37)

North Private (99) (68) (33) (258) (170) (170) (254) (86)

South Federal (61) (96) (101) (47) (42) (42) (110) 35 

South Other Public (51) (76) (76) (59) (44) (44) (96) 12 

South Private (69) (66) (12) (161) (191) (191) (304) (79)

Notes: MMT CO2 eq. yr-1 is million metric tons carbon dioxide equivalent per year. Parentheses (i.e., negative net annual change) 

indicate net forest ecosystem or wood products sequestration, by convention.

Table 4-5 Total Annualized Forest Land 1990-2013, with Uncertainty Interval for 2013

Table Error! No text of specified style in document.-21 Total Annualized Forest Land 1990-

2013, With Uncertainty Interval for 2013

1990 1995 2000 2005 2010 2013 2013 2013

Region Ownership 

group

Forest land

1,000 ha

Uncertainty

LB UB 

1,000 ha

Pacific Coast Federal 23,610 23,663 23,672 23,492 23,250 23,096 22,629 23,553

Pacific Coast Other Public 2,318 2,409 2,485 2,549 2,588 2,599 2,342 2,856

Pacific Coast Private 14,596 14,583 14,497 14,215 13,861 13,638 13,130 14,142

Rocky Mountain Federal 39,256 39,714 39,656 39,054 39,110 39,229 38,376 40,063

Rocky Mountain Other Public 2,452 2,481 2,510 2,517 2,546 2,568 2,311 2,829

Rocky Mountain Private 14,716 14,216 13,726 13,252 12,845 12,631 12,062 13,196

North Federal 6,009 6,072 6,134 6,249 6,382 6,452 6,320 6,587

North Other Public 10,987 11,383 11,919 12,485 13,050 13,405 13,153 13,650

North Private 53,346 53,262 53,070 53,641 54,355 54,564 54,147 54,989

South Federal 7,172 7,615 8,230 8,676 8,889 9,000 8,558 9,408

South Other Public 2,474 2,918 3,514 4,108 4,537 4,746 4,433 5,029

South Private 89,085 89,293 88,586 88,083 88,117 87,994 87,237 88,752

Notes:

See USDA Forest Service (2014a) for additional details on how classifications are defined.

Uncertainty bounds (LB=Lower Bounds; UB=Upper Bounds) are the 2.5th and 97.5th percentiles of the results of the 

Monte Carlo simulation.

Carbon stock change and forest area are based on annualized estimates for the shaded area in Map 4-1.

Parentheses (i.e., negative net annual change) indicate net forest ecosystem sequestration, by convention.
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

114

and Rocky Mountain regions for at least a portion of 

the interval. In Federally owned forest lands in the 

Pacific Coast region and privately owned forests in 

the South, forest area generally decreased since the 

year 2000 while total carbon stocks increased over 

that same interval. 

Estimates of current average stocks and stock change 

according to ecosystem carbon pools are illustrated 

in Figures 4-1 and 4-2. Table 4-6 shows plot-level 

carbon densities for the six ecosystem pools: live 

trees, understory, standing dead trees, down dead 

wood, forest floor, and soil organic carbon by 

region and ownership. The densities—measured in 

MT CO2 eq. per hectare—were based on the most 

recent survey data per State. Note that despite the 

sometimes much greater carbon stock per hectare in 

some western forests, especially along the Pacific 

Coast, the generally larger total area of forest land 

in the East places those forests as the major portion 

of stock and change as illustrated in Figures 4-1 and 

4-2. Tables 4-7 and 4-8 disaggregate the ecosystem 

pools for the annualized data for 2013 for carbon 

stocks (MMT CO2 eq.) and net stock change (MMT 

CO2 eq. per year). As discussed above, these stock 

change estimates are not separately allocated 

according to land use change, and corresponding 

stock gains or losses are retained in the net annual 

changes provided in Tables 4-2 and 4-8, for example. 

See Smith and Heath (2015) for additional discussion 

on how aggregate change at regional or national 

levels can be attributed to individual State-level 

forest inventories.

Additional summaries are provided in the appendix 

tables. Annualized stock and net stock change 

estimates for 2013 are provided for the 49 States 

included in the inventory in Table C-2. In addition 

Figure 4-1 Forest Ecosystem Carbon Stocks

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

Figure 4-1

Forest Ecosystem Carbon Stocks

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

10,000

20,000

30,000

40,000

50,000

60,000

Pacific

Coast

Rocky

Mountain

North South

MMT CO2 eq.

Live tree

Understory

Standing dead tree

Down dead wood

Forest floor

Soil organic carbon

Figure 4-2 Net Annual Forest Carbon Stock Change

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent) Figure 4-2

Net Annual Forest Carbon Stock Change

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

(350)

(300)

(250)

(200)

(150)

(100)

(50)

0

50

100

Pacific

Coast

Rocky

Mountain

North South

MMT CO2 eq. per year

Live tree

Understory

Standing dead tree

Down dead wood

Forest floor

Soil organic carbon

Table 4-6 Mean Plot-Level Carbon Densities According to Region and Ownership for Six Carbon Pools Based on 

the Most Recent Inventory Per State

Table Error! No text of specified style in document.-22 Mean Plot-level Carbon Densities 

According to Region and Ownership for Six Carbon Pools Based on the Most Recent 

Inventory Per State

Region Ownership 

group

Live 

tree Understory Standing dead tree

Down dead 

wood

Forest 

floor

Soil organic 

carbon

Forest 

area

MT CO2 eq. per ha

1,000

ha

Pacific Coast Federal 400.7 11.1 37.0 47.5 66.1 247.5 23,370

Pacific Coast Other Public 451.8 12.1 23.3 57.6 67.4 304.4 2,582

Pacific Coast Private 272.1 12.8 11.1 40.1 48.9 251.5 14,011

Rocky 

Mountain Federal 151.4 9.7 28.8 19.6 42.7 116.6 38,784

Rocky 

Mountain Other Public 112.8 10.7 9.0 15.6 33.4 108.5 2,508

Rocky 

Mountain Private 95.1 10.8 7.2 14.7 32.5 107.3 12,888

North Federal 242.8 6.6 9.5 16.4 43.3 391.7 6,409

North Other Public 244.8 6.7 8.4 16.5 45.5 409.2 13,150

North Private 246.4 6.6 6.6 16.0 39.7 309.3 54,443

South Federal 286.6 10.3 7.2 21.7 34.0 222.1 8,911

South Other Public 232.6 10.4 3.9 21.9 34.2 272.9 4,570

South Private 189.6 11.8 3.0 18.4 25.9 204.8 88,076

Note: MT CO2 eq. per ha is metric tons carbon dioxide equivalent per hectare.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

115

to the annualized forest area for 2013, Table C-2 

allocates total forest carbon stocks into three pools: 

live trees, total non-soil (including live trees), 

and soil organic carbon. Net annualized stock 

change summed for each State for 2013 is also 

included for the live tree and total non-soil carbon 

classifications. The two remaining appendix tables 

were compiled from the most recent forest inventory 

data per State and organized about the four regions, 

but the ownership classifications were modified 

slightly because the emphasis in these tables is 

on productivity and reserved status (and multiple 

ownership classifications are superfluous). First, 

all forest lands classified as reserved (see USDA 

FS 2015a) were pooled, and the remaining, nonreserved, forest land was sorted according to public 

versus private ownership. We also disaggregated 

carbon density by the three pools from Table C-2, 

land area, and stand age class (Table C-3). Table C-3 

reports the range of plot-level carbon densities from 

the 5th to 95th percentiles for the three pools. Similar 

classifications and summary values were compiled 

according to stand size class for Table C-4. 

Table 4-7 Total Forest Ecosystem Carbon Stocks According to Region and Ownership for Six Carbon Pools Based 

on Annualized Estimates for 2013

Table Error! No text of specified style in document.-23 Total Forest Ecosystem Carbon Stocks 

According to Region and Ownership for Six Carbon Pools Based on Annualized Estimates 

for 2013

Region Ownership 

group

Live tree Understory Standing 

dead tree

Down dead 

wood Forest floor Soil organic carbon

MMT CO2 eq.

Pacific Coast Federal 9,840 252 905 1,128 1,553 5,739

Pacific Coast Other Public 1,235 31 58 150 174 780

Pacific Coast Private 3,876 173 158 570 674 3,462

Rocky Mountain Federal 5,813 386 1,301 765 1,660 4,584

Rocky Mountain Other Public 289 27 25 40 86 278

Rocky Mountain Private 1,173 137 90 185 407 1,348

North Federal 1,590 42 66 107 280 2,525

North Other Public 3,329 90 119 224 609 5,456

North Private 13,644 360 380 884 2,166 16,903

South Federal 2,613 92 69 197 306 2,010

South Other Public 1,109 49 17 103 163 1,296

South Private 17,247 1,034 255 1,641 2,291 18,015

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent.

Table 4-8 Net Annual Forest Ecosystem Carbon Stock Change According to Region and Ownership for Six Carbon 

Pools Based on Annualized Estimates for 2013

Table Error! No text of specified style in document.-24 Net Annual Forest Ecosystem Carbon 

Stock Change According to Region and Ownership for Six Carbon Pools Based on 

Annualized Estimates for 2013

Region Ownership 

group

Live tree Understory Standing 

dead tree

Down dead 

wood Forest floor Soil organic carbon

MMT CO2 eq. yr-1

Pacific Coast Federal (96) 1 (9) (4) (2) 8 

Pacific Coast Other Public (13) (0) 0 (0) (0) 1 

Pacific Coast Private (25) 1 (1) (2) 2 12 

Rocky Mountain Federal 21 (2) (45) (0) 0 (10)

Rocky Mountain Other Public (1) (0) (0) (0) (0) (0)

Rocky Mountain Private 13 1 0 1 3 9 

North Federal (19) (0) (3) (1) (1) (8)

North Other Public (50) (1) (4) (3) (5) (36)

North Private (115) (0) (11) (7) (2) (36)

South Federal (24) (0) (1) (1) (2) (14)

South Other Public (20) (1) 0 (1) (3) (20)

South Private (201) 2 3 (6) (3) 14 

Notes:

MMT CO2 eq. yr-1 is million metric tons carbon dioxide equivalent per year. 

See USDA Forest Service (2015a) for additional details on how classifications are defined. 

Summaries are based on forest inventories for the shaded area in Map 4-1. 

Parentheses (i.e., negative net annual change) indicate net forest ecosystem sequestration, by convention.
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

116

4.4 Mechanisms of Carbon Transfer

Forest management can be defined as activities 

involving the regeneration, tending, protection, 

harvest, and utilization of forest resources to meet 

goals defined by the forest land owner. Forest 

management affects carbon stocks and stock changes 

through the control of mechanisms associated 

with carbon gain and loss. For example, increased 

tree volume per area of forest generally indicates 

increased carbon stocks.

Carbon sequestration results from the continuous 

exchange of carbon dioxide between forest 

ecosystems/products and the atmosphere (Figure 

4-3). Note that comprehensive greenhouse gas 

reporting for forests would also include some nonCO2

 emissions such as methane and non-carbon 

emissions such as nitrous oxide, for example. 

However, the vast majority of exchange is in terms 

of CO2, which is the focus of this chapter. Trees 

accumulate carbon as they grow and remove it from 

the atmosphere, whereas other processes such as 

respiration, decomposition, or combustion remove 

CO2 from forests. Forests convert much of the 

accumulated organic carbon to wood, which stores 

carbon and energy. Plant death and subsequent 

decomposition as well as external influences such as 

harvest and utilization of wood play significant roles 

in emissions of CO2

 from forests to the atmosphere. 

Mortality and disturbance emit some CO2 (e.g., from 

fire) and also add to the pools of down dead wood 

and forest floor, which decay over time. Carbon can 

also be removed from forest ecosystems through 

runoff or leaching through soil. 

Wood products that are removed from the forest 

sequester carbon until it is eventually released. 

Harvested wood carbon pools can lengthen the 

time before that carbon returns to the atmosphere; 

however, expected life-spans of wood products vary 

considerably. Wood products emit CO2 through 

either burning or decay (Figure 4-3). Net release of 

carbon from wood products depends on the product, 

its end use, and the means of disposal (Smith et al. 

2006, Skog 2008). Wood can be burned for energy 

or without energy capture (Figure 4-3). Because 

of its role as an energy source, wood can displace 

other fuel sources. Improved management of wood 

products in their use and in landfills provides a 

number of opportunities to reduce emissions and 

increase sequestration, such as substituting for 

nonrenewable materials, for example (Perez-Garcia 

et al. 2005).

Figure 4Figure 4-3 Summary Diagram of Forest Carbon Pools and Carbon Transfer Among Pools -3. Summary diagram of forest carbon pools and carbon transfer among pools.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

117

4.5 Methods

We based estimates of forest ecosystem carbon on 

the stock change method, using collected forest 

data to produce a series of successive carbon stock 

estimates for an individual State (Penman et al. 

2003, Smith et al. 2010). The USDA Forest Service’s 

Forest Inventory and Analysis (FIA) Program 

conducts a series of partial surveys per State each 

year with re-measurements at 5- to 10-year intervals, 

depending on the State (USDA FS 2015b). The 

term “survey” is used here to describe a complete 

inventory for a State for 1 year, which is repeated at 

regular intervals. The FIA Program defines the extent 

of forest land within each State (USDA FS 2014a,c), 

and limited adjustments on what to include in the 

greenhouse gas inventory to reflect United Nations 

Framework Convention on Climate Change reporting 

guidelines. Specifically, some of the forest area of 

southern coastal Alaska (which is the only portion of 

Alaska forests currently included, see Annex 3.13 of 

EPA 2015) is identified as unmanaged and excluded 

from these estimates (Ogle et al. In Prep). In addition, 

some stands of the woodland forest type groups are 

also excluded because they are on sites very unlikely 

to support trees meeting the minimum height defined 

for “forest” (Coulston et al. In Prep). 

Current forest survey data for the United States are 

available from the FIA Database (FIADB) version 

6.0.1 (USDA FS 2015c). All FIADB surveys used 

for carbon stock estimates were obtained from the 

FIADB data download Web site (http://apps.fs.fed.

us/fiadb-downloads/datamart.html) on July 21, 

2014. Surveys from the FIADB are supplemented 

with some older surveys; see Annex 3.13 of EPA 

(2015) for a list of the specific surveys used for the 

estimates. Carbon estimation factors (EPA 2015, 

Smith et al. 2010) were applied to the plot-level 

inventory data and summed to calculate carbon 

stocks for each survey of each State. Carbon stocks 

for each State or sub-State classification were 

assigned to survey years with net stock change 

based on the interval (in years) between the stocks 

(i.e., difference in successive stocks divided by the 

interval in years). In this way, State-wide annualized 

estimates of ecosystem stock and stock change can 

be calculated and summed to U.S. totals as presented 

in EPA (2015). A similar approach was taken to 

produce the estimates according to the additional 

classifications as provided here. Note that these 

stock change calculations are based on total forest 

land in each successive inventory, and an effect of 

land use change on these estimates is to increase 

apparent sequestration or emission in proportion to 

the land moved between sectors. Carbon estimates 

for harvested wood products are based on a separate 

stock change method (EPA 2015) and are not 

available for more detailed classifications other than 

national totals in the tables provided here. Methods 

are described below with additional details in EPA 

(2015), Smith et al. (2010), and Smith et al. (2013); 

in particular, see Annex 3.13 of EPA (2015). 

4.5.1 Live Trees

Live tree carbon pools include aboveground and 

belowground (coarse root) biomass of live trees with 

diameter at diameter breast height (dbh) of at least 

2.54 cm at 1.37 m above the forest floor. Separate 

estimates were made for above- and below-ground 

biomass components. When inventory plots included 

data on individual trees, tree carbon was estimated 

using approaches defined by Woodall et al. (2011), 

which is also known as the component ratio method 

(CRM) and is a function of volume, species, and 

diameter. An additional component of foliage, which 

was not explicitly included in Woodall et al. (2011), 

was added to each tree following the CRM method 

and component proportions. Some of the older 

forest inventory data did not provide measurements 

of individual trees. The carbon estimates for those 

plots were based on average densities (MT C per 

hectare) obtained from plots of more recent surveys 

with similar stand characteristics and location. This 

applies to less than 5 percent of the forest land 

inventory-plot-to-carbon conversions utilized for the 

1990-2013 stock change estimates of Table 4-2.

4.5.2 Understory Vegetation

Understory vegetation is defined as all biomass of 

undergrowth plants in a forest, including woody 

shrubs and trees less than 2.54 cm dbh. We assumed 

that 10 percent 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. See Annex 3.13 of EPA (2015) for 

calculation details.

4.5.3 Dead Organic Matter

Dead organic matter was calculated as three separate 

pools: standing dead trees, down dead wood, and 

litter. Sample data or models were used to estimate 

carbon stocks. The standing-dead-tree carbon pools 

include aboveground and belowground (coarse root) 

mass and include dead trees of at least 12.7 cm dbh. 

Calculations followed the basic method applied 
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

118

to live trees (Woodall et al. 2011) with additional 

modifications to account for decay and structural loss 

(Domke et al. 2011, Harmon et al. 2011). Similar to 

the situation with live-tree data, some of the older 

forest inventory data did not provide sufficient data 

on standing dead trees to make accurate populationlevel estimates. The carbon estimates for these 

plots were based on average densities (MT C per 

hectare) obtained from plots of more recent surveys 

with similar stand characteristics and location. 

This applied to about 20 percent of the forest land 

inventory-plot-to-carbon conversions utilized for the 

1990-2013 stock change estimates. Downed 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. This includes 

stumps and roots of harvested trees. Downeddead-wood estimates were a two-step calculation 

process detailed in Annex 3.13 of EPA (2015). Initial 

estimates based on live-tree carbon were modified 

according to measurements of a limited subset of 

FIA plots for downed dead wood (Domke et al. 2013, 

Woodall and Monleon 2008, Woodall et al. 2013). 

To facilitate the downscaling of downed-dead-wood 

carbon estimates from the State-wide population 

estimates to individual plots, downed-dead-wood 

models specific to regions and forest types within 

each region were used. Litter carbon is the pool of 

organic carbon (also known as 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 a model developed around 

measurements of a subset of FIA plots (Domke et al. 

2016).

4.5.4 Soil Organic Carbon

Soil organic carbon (SOC) includes all organic 

material in soil to a depth of 1 meter but excludes 

the coarse roots of the biomass or dead wood pools. 

Estimates of SOC were based on the national 

STATSGO spatial database (USDA 1991), which 

includes region and soil type information. Soil 

organic carbon determination was based on the 

general approach described by Amichev and 

Galbraith (2004). Links to FIA inventory data 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 

(see Annex 3.13 of EPA 2015 and Smith et al. 2013 

for additional details about this approach). This 

method produced mean SOC densities stratified 

by region and forest type group. It did not provide 

separate estimates for mineral or organic soils but 

instead weighted their contribution to the overall 

average based on the relative amount of each within 

forest land. Thus, forest SOC is a function of species 

and location, and net change also depends on these 

two factors as total forest area changes. In this 

respect, SOC provides a country-specific reference 

stock for 1990-present, but it does not reflect effects 

of past land use.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

119

4.5.5 Harvested Wood Products

Calculations for carbon in harvested wood products 

(HWP) are separate from the ecosystem estimates 

because the underlying datasets and methods are 

compiled separately. These methods are based on 

Eggleston et al. (2006) guidance for estimating 

HWP carbon (Skog 2008). Eggleston et al. (2006) 

provide methods that estimate HWP contribution 

using one of several different accounting approaches: 

production, stock change, and atmospheric flow, as 

well as a default method that assumes there is no 

change in HWP carbon stocks (see Annex 3.13 of 

EPA 2015 for more details about each approach). The 

U.S. GHG Inventory used the production accounting 

approach to report HWP contribution. Under the 

production approach, carbon in exported wood was 

estimated as if it remained in the United States, 

and carbon in imported wood was not included in 

inventory estimates. Annual estimates of change were 

calculated by tracking the additions to and removals 

from the pool of products held in end uses (i.e., 

products in use such as housing or publications) and 

the pool of products held in solid waste disposal.

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 (2010) and EPA 

(2015) in terms of net stock change since 1990. 

New annual inventory data for most States and 

adjustments to the identification of land area 

classified as forests included in the inventories 

have affected stock totals and changes. In addition, 

major changes in carbon conversion factors as 

applied to live and standing dead trees as well 

as the down dead wood and litter pools affected 

estimates as each update was implemented. When 

reviewing estimates provided for the 1990-to-present 

interval, it is important to note that data updates 

and methodological changes can affect stock and 

stock change estimates throughout the interval, as 

can be seen when comparing Table 4-2 with past 

versions of the same in USDA or EPA reports. See 

the methods (above) for general descriptions of new 

approaches, and compare EPA 2010 and 2015 for 

additional details and citations related to changes in 

the methods. The estimates for down dead wood have 

also been slightly modified—see the citations above 

and in the respective EPA annexes for additional 

information. 

4.7 Uncertainty

Uncertainty estimates in this chapter are consistent 

with the IPCC-recommended methodology 

(Eggleston et al. 2006). Separate analyses were 

produced for forest ecosystem and HWP flux. 

The uncertainty estimates are from Monte Carlo 

simulations of the respective models and input 

data. Methods generally follow those described in 

Heath and Smith (2000), Smith and Heath (2001), 

Skog et al. (2004), and Skog (2008). Uncertainties 

surrounding input data or model processes were 

quantified as probability distribution functions, so 

that a series of sample values could be selected 

from the distributions. The separate results from the 

ecosystem and HWP simulations were pooled for 

total uncertainty.

Carbon stocks were based on forest plot-level 

calculations, and the Monte Carlo simulations for 

uncertainty estimates include probabilistic sampling 

at the plot level. That is, the deterministic stock 

change calculations of Smith et al. (2010) were 

repeated many times following the probabilistic 

sampling of input starting conditions. Uncertainty 

surrounding carbon density was defined for each 

of six pools for each inventory plot. Live and 

standing dead trees were generally assigned normal 

probability distributions, which vary according to 

species, number of trees, and area representation. 

Error estimates for volume and the CRM for 

estimating biomass are not available, so an assumed 

10-percent error on biomass from volume was 

applied to the volume portion of the estimate; error 

information in Jenkins et al. (2003) was applied 

to uncertainty about the additional components 

(e.g., tops, leaves, and roots). Uniform probability 

distributions with a range of ±90 percent of the 

average were used for those plots that used carbon 

densities from similarly classified forest stands.

Probability distributions for the remaining C pools 

are triangular or uniform, which partly reflects the 

lower level of information available about these 

estimates. The functions defined for these four pools 

were sampled as marginal distributions. Downed 

dead wood, understory, and litter were assigned 

triangular distributions with the mean at the expected 

value for each plot and the minimum and mode at 

10 percent of the expected value. In this method, 

we assumed that a small proportion of plots would 

have relatively high carbon densities. Soil organic 

carbon was defined as a uniform distribution at ±50 

percent of the mean. Sub-State or State total carbon 

stocks associated with each survey are the cumulative 

sum of random samples from the plot-level of the 
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

120

functions, which were then appropriately expanded 

to population estimates. These expected values for 

each carbon pool include uncertainty associated with 

sampling, which was also incorporated in the Monte 

Carlo simulation. Sampling errors were determined 

according to methods described for the FIADB 

(Bechtold and Patterson 2005), were assumed 

to be normally distributed, and were assigned a 

slight positive correlation between successive 

surveys for Monte Carlo sampling. More recent 

annual inventories were assigned higher sampling 

correlation between successive surveys based on 

the proportion of plot data jointly included in each. 

Errors for older inventory data are not available, and 

these surveys were assigned values consistent with 

those obtained from the FIADB.

Uncertainty about net carbon flux in HWP is based 

on Skog et al. (2004) and Skog (2008). Estimates 

of the HWP variables and HWP contribution 

under the production approach are subject to many 

sources of uncertainty. The uncertainty estimate for 

HWP resulted from our evaluation of the effect of 

uncertainty in 13 sources, including production and 

trade data and parameters used to make the estimate. 

Uncertain data and parameters include: (a) data on 

production and trade and factors to convert them to 

carbon, (b) the census-based estimate of carbon in 

housing in 2001, (c) the EPA estimate of wood and 

paper discarded to solid waste disposal sites (SWDS) 

for 1990 to 2000, (d) the limits on decay of wood and 

paper in SWDS, (e) the decay rate (half-life) of wood 

and paper in SWDS, (f) the proportion of products 

produced in the United States made with wood 

harvested in the United States, and (g) the rate of 

storage of wood and paper carbon in other countries 

that came from United States harvest, compared to 

storage in the United States.

4.8 Planned Improvements 

Developing improved monitoring and reporting 

techniques is a continuous process that occurs 

simultaneously with annual U.S. GHG Inventory 

submissions. Only forest carbon monitoring 

techniques that are reviewed and published are 

adopted as part of the forest carbon contribution to 

the U.S. GHG Inventory. Planned improvements can 

be broadly assigned to the following categories: pool 

estimation techniques, land use and land use change, 

and field inventories.

In an effort to reduce the uncertainty associated with 

the estimation of individual forest C pools, we are 

evaluating the empirical data and associated models 

for each pool for potential improvement (Woodall 

2012). The exact timing of future pool estimation 

refinements is dependent on the vetting of current 

research outcomes. Research is underway to use a 

national inventory of SOC (Domke et al. in review) 

to refine estimates of these pools following the 

methods applied for litter (Domke et al. 2016). We 

expect that improvements to SOC estimates will 

be incorporated into the 2016 U.S. GHG Inventory 

submission. Despite a consistent nationwide, annual 

field survey of forests, additional research advances 

are needed to attain a complete, consistent, and 

accurate time series of annual land use and land-use 

change matrices from 1990 to the present report year. 

The stock change estimates in the 2016 submission 

will address changes in forest land use classifications. 

Researchers are exploring techniques for bringing 

together disparate sets of land use information (e.g., 

forest versus croplands) that rely on remotely sensed 

imagery from the 1980s to the present.

The ongoing annual surveys by the FIA Program are 

expected to improve the precision of forest carbon 

estimates as new State surveys become available 

(USDA Forest Service 2015c), particularly in 

Western States. As of July 21, 2014, Hawaii was the 

only State not yet reporting data from the annualized 

sampling design of FIA. The annual surveys will 

eventually include Hawaii. In addition, data from 

more intensive sampling of fine woody debris, litter, 

and SOC on some of the permanent FIA plots will 

substantially improve resolution of carbon pools (i.e., 

greater sample intensity) (Westfall et al. 2013) at the 

plot level for all U.S. forest land.

SUGGESTED CITATION

Smith, J.E., C.W. Woodall, 

and G. Domke, 2016. Chapter 

4: Carbon Stocks and Stock 

Changes in U.S. Forests. In 

U.S. Agriculture and Forestry 

Greenhouse Gas Inventory: 

1990-2013,Technical Bulletin 

No. 1943, United States 

Department of Agriculture, 

Office of the Chief Economist, 

Washington, DC. 137 pp. 

September 2016. Del Grosso 

S.J. and M. Baranski, Eds. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

121

4.9 References

Amichev, B.Y. and J.M. Galbraith (2004). A revised methodology 

for estimation of forest soil carbon from spatial soils and forest 

inventory data sets. Environmental Management, 33(S1):S74-S86. 

Bechtold, William A.; Patterson, Paul L., Editors. (2005). The 

enhanced Forest Inventory and Analysis program—national 

sampling design and estimation procedures. Gen. Tech. Rep. SRS80. Asheville, NC: United States Department of Agriculture, Forest 

Service, Southern Research Station. 85 p.

Coulston, J.W., Woodall, C.W., Domke, G.M., and Walters, B.F. In 

Review. Refined forest land use classification with implications for 

United States national carbon accounting. Intended outlet: Land 

Use Policy.

Domke, G.M., J.E. Smith, and C.W. Woodall (2011). Accounting 

for density reduction and structural loss in standing dead trees: 

Implications for forest biomass and carbon stock estimates in the 

United States. Carbon Balance and Management. 6:14.

Domke, G.M., Woodall, C.W., Walters, B.F., Smith, J.E. (2013). 

From models to measurements: comparing down dead wood 

carbon stock estimates in the U.S. forest inventory. PLoS ONE 

8(3): e59949.

Domke, G.M., Walters, B.F., Perry, C.H., Woodall, C.W., Russell, 

M.B., Smith, J.E. 2016. Estimating litter carbon stocks on forest 

land in the United States. Science of the Total Environment 557-

558: 469-478. 

Domke, G.M., Perry, C.H., Walters, B.F., Woodall, C.W., Nave, 

L., Swanston, C. In review. Toward inventory-based estimates of 

soil organic carbon in forests of the United States Intended outlet: 

Ecological Applications.

Eggleston, S., L. Buendia, K. Miwa, T. Ngara, and K. Tanabe 

(Ed.) (2006). 2006 IPCC guidelines for national greenhouse 

gas inventories, vol. 4: agriculture, forestry and other land use. 

Intergovernmental Panel on Climate Change, National Greenhouse 

Gas Inventories Programme, Technical Support Unit, Kanagawa, 

Japan. 

EPA (2010). Inventory of U.S. Greenhouse Gas Emissions 

and Sinks: 1990-2008. EPA 430-R-10-006. United States 

Environmental Protection Agency, Office of Atmospheric 

Programs, Washington, DC. Available at: http://www.epa.gov/

climatechange/ghgemissions/usinventoryreport/archive.html 

Accessed 08 April 2015.

EPA (2015). Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990–2013. EPA 430-R-15-004. U. S. Environmental 

Protection Agency, Office of Atmospheric Programs, Washington, 

DC. Available at: http://www.epa.gov/climatechange/

ghgemissions/usinventoryreport.html Accessed 15 April 2015. 

Harmon, M.E., C.W. Woodall, B. Fasth, J. Sexton, M. Yatkov 

(2011). Differences between standing and downed dead tree wood 

density reduction factors: A comparison across decay classes and 

tree species. Res. Paper. NRS-15. Newtown Square, PA: United 

States Department of Agriculture, Forest Service, Northern 

Research Station. 40 p.

Heath, L.S. and J.E. Smith (2000). An assessment of uncertainty 

in forest carbon budget projections. Environmental Science and 

Policy, 3:73-82. 

Heath, L.S., J.E. Smith, and R.A. Birdsey (2003). Carbon trends 

in U.S. forest lands: A context for the role of soils in forest carbon 

sequestration. P. 35–45 in The potential of US forest soils to 

sequester carbon and mitigate the greenhouse effect, Kimble, J.M., 

L.S. Heath, R.A. Birdsey, and R. Lal (eds.). CRC Press, New York.

Ogle, S.M., Woodall, C.W., Swan, A., Smith, J., and Wirth. T. (in 

preparation). Determining the Managed Land Base for Delineating 

Carbon Sources and Sinks in the United States. Environmental 

Science and Policy.

Oswalt, S.N.; Smith, W.B; Miles, P.D.; Pugh, S.A. (2014). Forest 

Resources of the United States, 2012: a technical document 

supporting the Forest Service 2015 update of the RPA Assessment. 

Gen. Tech. Rep. WO-91. Washington, DC: United States 

Department of Agriculture, Forest Service, Washington Office. 

218 p.

Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., 

Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, T., Wagner, 

F., (Ed.) (2003). Good practice guidance for land use, land use 

change, and forestry. Institute for Global Environmental Strategies 

for the Intergovernmental Panel on Climate Change. Hayama, 

Kanagawa, Japan, 502 p. 

Perez-Garcia, J., B. Lippke, J. Comnick, and C. Manriquez (2005). 

An assessment of carbon pools, storage, and wood products market 

substitution using life-cycle analysis results. Wood and Fiber 

Science, 37:140-148. 
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

122

Skog, K.E., K. Pingoud, and J.E. Smith (2004). A method 

countries can use to estimate changes in carbon stored in 

harvested wood products and the uncertainty of such estimates. 

Environmental Management, 33(S1): S65-S73. 

Skog, K.E. (2008). Sequestration of carbon in harvested wood 

products for the United States. In preparation. 

Smith, J.E. and L.S. Heath (2001). Identifying influences on model 

uncertainty: an application using a forest carbon budget model. 

Environmental Management, 27:253-267. 

Smith, J.E., L.S. Heath, K.E. Skog, and R.A. Birdsey (2006). 

Methods for calculating forest ecosystem and harvested carbon, 

with standard estimates for forest types of the United States. 

General technical report NE-343. United States Department 

of Agriculture, Forest Service, Northeastern Research Station, 

Newtown Square, PA. 

Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. forest 

carbon calculation tool: forest-land carbon stocks and net annual 

stock change. Revised. Gen. Tech. Rep. NRS-13. Newtown 

Square, PA: United States Department of Agriculture, Forest 

Service, Northern Research Station. 34 p. [DVD-ROM].

Smith, J.E., and L.S. Heath (2011). Carbon stocks & stock changes 

in U.S. forests. pp. 68-82 in The US Agriculture and Forestry 

Greenhouse Gas Inventory: 1990-2008. Technical Bulletin No. 

1930, Washington DC: US Department of Agriculture, Office of 

the Chief Economist, Global Change Program Office. 159 p.

Smith, J.E., L.S. Heath, and C.M. Hoover (2013). Carbon factors 

and models for forest carbon estimates for the 2005–2011 National 

Greenhouse Gas Inventories of the United States. Forest Ecology 

and Management 307:7–19.

Smith, J.E., and L.S. Heath (2015). Measuring and modeling 

carbon stock change estimates for US forests and uncertainties 

from apparent inter-annual variability. P. 111-127 in Synthesis 

and modeling of greenhouse gas emissions and carbon storage in 

agricultural and forest systems to guide mitigation and adaptation, 

Del Grosso, S., W. Parton, L. Ahuja (eds.). Madison, WI: 

American Society of Agronomy, Crop Science Society of America, 

Inc., and Soil Science Society of America.

USDA Forest Service (2015a). Forest Inventory and Analysis 

National Program, FIA library: Database Documentation. United 

States Department of Agriculture, Forest Service, Washington 

Office. Available online at < http://www.fia.fs.fed.us/library/

database-documentation/ >. Accessed 08 April 2015.

USDA Forest Service (2015b). Forest Inventory and Analysis 

National Program: Program Features. United States Department of 

Agriculture Forest Service. Washington, DC. Available online at < 

http://fia.fs.fed.us/program-features/>. Accessed 08 April 2015.

USDA Forest Service (2015c). Forest Inventory and Analysis 

National Program: FIA Data Mart. United States Department of 

Agriculture Forest Service. Washington, DC. Available online at 

<http://apps.fs.fed.us/fiadb-downloads/datamart.html>. Accessed 

08 April 2015.

USDA (1991). State Soil Geographic (STATSGO) Data Base 

Data use information. Miscellaneous Publication Number 1492, 

National Soil Survey Center, Natural Resources Conservation 

Service, United States Department of Agriculture, Fort Worth, TX.

Woodall, C.W. (2012). Where did the U.S. forest biomass/carbon 

go? Journal of Forestry. 110: 113-114.

Woodall, C.W., and V.J. Monleon (2008). Sampling protocol, 

estimation, and analysis procedures for the down woody materials 

indicator of the FIA program. Gen. Tech. Rep. NRS-22. Newtown 

Square, PA: United States Department of Agriculture, Forest 

Service, Northern Research Station. 68 p.

Woodall, C.W., L.S. Heath, G.M. Domke, and M.C. Nichols 

(2011). Methods and equations for estimating aboveground 

volume, biomass, and carbon for trees in the U.S. forest inventory, 

2010. Gen. Tech. Rep. NRS-88. Newtown Square, PA: United 

States Department of Agriculture, Forest Service, Northern 

Research Station. 30 p.

Woodall, C.W., Walters, B.F., Oswalt, S.N., Domke, G.M., Toney, 

C., Gray, A.N. (2013). Biomass and carbon attributes of downed 

woody materials in forests of the United States. Forest Ecology 

and Management 305: 48-59. 

Westfall, J.A., Woodall, C.W., Hatfield, M.A. (2013). A statistical 

power analysis of woody carbon flux from forest inventory data. 

Climatic Change. 118: 919-931.

Wilson, B.T., Woodall, C.W., Griffith, D. (2013). Imputing forest 

carbon stock estimates from inventory plots to a nationally 

continuous coverage. Carbon Balance and Management. 8: 1.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

123

4.10 Appendix C

Appendix Table C-1. Summary total from most recent survey 

data according to region, ownership, and forest type group for (a) 

current forest land area and (b) total stocks of carbon in live trees.

Appendix Table C-2. State-level annualized estimates for 2013 for: 

forest area, live tree stocks, non-soil stocks, soil organic carbon 

stocks, net annual stock change for live trees, and net annual stock 

change for total non-soil stocks.

Appendix Table C-3. Forest ecosystem carbon density based on 

most recent forest inventories according to stand age class, region, 

and ownership for three carbon pools – live tree, total non-soil, 

and soil organic carbon – as well as forest area. Note that the 

ownership classification is somewhat different; all reserved forest 

lands are combined in “reserved,” and the balance are classified 

according to private versus public ownership.

Appendix Table C-4. Forest ecosystem carbon density based on 

most recent forest inventories according to stand size class, region, 

and ownership for three carbon pools – live tree, total non-soil, 

and soil organic carbon – as well as forest area. Note that the 

ownership classification is somewhat different; all reserved forest 

lands are combined in “reserved,” and the balance are classified 

according to private versus public ownership.
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

124

Appendix Table C-1a Current Forest Land Area According to Region, Ownership, and Forest Type Group, 2013

Appendix Table C-1a Current Forest Land Area According to Region, Ownership, and 

Forest Type Group, 2013

Region: Pacific Coast Rocky Mountain North South 

Ownership group: Federal 

Other 

Public Private Federal 

Other 

Public Private Federal 

Other 

Public Private Federal 

Other 

Public Private 

Forest Type Group 1,000 ha

White/Red/Jack Pine 545 889 2,258 63 8 126 

Spruce/Fir 226 76 55 746 1,758 3,798 5 5 3 

Longleaf/Slash Pine 829 573 3,883 

Loblolly/Shortleaf Pine 93 215 360 1,767 562 21,028 

Other Eastern Softwoods 9 16 417 32 24 681 

Pinyon/Juniper 485 20 56 10,486 934 5,129 35 5 32 51 92 2,159 

Douglas-Fir 3,820 796 3,796 5,186 327 1,617 0 2 

Ponderosa Pine 2,204 138 1,523 2,813 270 1,865 333 26 184 

Western White Pine 83 10 3 13 

Fir/Spruce/Mountain Hemlock 5,205 168 692 7,996 241 790 10 

Lodgepole Pine 1,360 47 264 4,159 74 349 

Hemlock/Sitka Spruce 2,792 525 1,037 272 44 127 

Western Larch 168 17 44 318 44 104 

Redwood 20 34 240 

Other Western Softwoods 1,395 49 560 892 62 120 

California Mixed Conifer 2,311 33 881 

Exotic Softwoods 44 227 

Other Softwoods 2 

Oak/Pine 232 387 1,789 902 399 7,821 

Oak/Hickory 0 14 30 1,553 3,525 21,464 3,503 1,267 31,835 

Oak/Gum/Cypress 27 75 196 1,077 887 7,935 

Elm/Ash/Cottonwood 67 63 113 62 31 184 333 897 4,647 300 309 4,279 

Maple/Beech/Birch 1,422 3,049 14,034 129 28 610 

Aspen/Birch 182 37 138 2,307 93 660 906 1,914 3,870 2 4 

Alder/Maple 176 222 851 4 3 2 

Western Oak 1,439 195 2,490 

Tanoak/Laurel 391 66 584 

Other Hardwoods 198 37 254 6 1 3 86 216 552 67 16 335 

Woodland Hardwoods 180 10 56 1,964 184 1,026 30 121 4,655 

Tropical Hardwoods 57 129 118 

Exotic Hardwoods 1 1 2 6 0 15 92 9 26 395 

Nonstocked 667 47 376 2,295 196 863 90 116 510 88 125 2,207 

 

Notes: 

See USDA Forest Service (2015a) for additional details on how classifications are defined. 

Carbon densities are based on the most recent inventory per state for shaded area in Map 4-1. 

Blank indicates that the type group does not appear within the inventory for that region and ownership, zeros are the result of

rounding a small quantity. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

125

Appendix Table C-1b Current Forest Carbon Stocks in Live Trees According to Region, Ownership, and Forest Type 

Group, 2013

Appendix Table C-1b Current Forest Carbon Stocks in Live Trees According to Region, 

Ownership, and Forest Type Group, 2013 

Region: Pacific Coast Rocky Mountain North South 

Ownership group: Federal 

Other 

Public Private Federal 

Other 

Public Private Federal 

Other 

Public Private Federal 

Other 

Public Private 

Forest Type Group 1,000 ha

White/Red/Jack Pine 130 220 638 26 4 39 

Spruce/Fir 14 2 3 117 230 593 2 1 1 

Longleaf/Slash Pine 192 94 674 

Loblolly/Shortleaf Pine 25 48 83 572 145 4,269 

Other Eastern Softwoods 1 1 34 6 3 61 

Pinyon/Juniper 24 1 2 683 57 296 2 0 2 4 4 113 

Douglas-Fir 2,620 455 1,324 1,191 64 272 0 1 

Ponderosa Pine 483 22 234 472 36 234 47 3 22 

Western White Pine 18 1 1 2 

Fir/Spruce/Mountain 

Hemlock 2,092 57 145 1,913 58 139 1 

Lodgepole Pine 260 6 41 802 11 52 

Hemlock/Sitka Spruce 1,812 310 404 142 13 33 

Western Larch 58 4 9 90 14 17 

Redwood 44 73 182 

Other Western Softwoods 85 2 27 109 3 8 

California Mixed Conifer 1,179 20 275 

Exotic Softwoods 11 44 

Other Softwoods 0 

Oak/Pine 50 93 430 240 84 1,504 

Oak/Hickory 1 0 1 463 1,087 5,947 1,097 330 6,757 

Oak/Gum/Cypress 7 25 60 287 303 2,145 

Elm/Ash/Cottonwood 23 18 20 9 3 25 72 184 1,001 56 59 714 

Maple/Beech/Birch 486 1,028 3,896 49 10 164 

Aspen/Birch 21 4 16 336 14 79 145 246 567 0 1 

Alder/Maple 66 97 268 0 0 0 

Western Oak 334 38 452 

Tanoak/Laurel 175 44 287 

Other Hardwoods 42 14 58 0 0 0 11 40 82 18 3 32 

Woodland Hardwoods 9 1 4 110 8 64 1 5 175 

Tropical Hardwoods 3 16 14 

Exotic Hardwoods 0 0 0 0 0 2 12 1 3 31 

Nonstocked 6 1 4 12 1 5 1 1 5 1 1 9 

Notes: 

See USDA Forest Service (2015a) for additional details on how classifications are defined. 

Carbon densities are based on the most recent inventory per state for shaded area in Map 4-1. 

Blank indicates that the type group does not appear within the inventory for that region and ownership, zeros are the result of rounding a small 

quantity. 

MMT CO2 eq. is million metric tons carbon dioxide equivalent. 

 
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

126

Appendix Table C-2 Annualized Carbon Stock Estimates per State, 2013 Appendix Table C-2 Annualized Carbon Stock Estimates per State, 2013

State

Forest area Live tree stock Total non-soil 

stock

Soil organic 

carbon stock

Live tree net 

stock change

Total non-soil net 

stock change

1,000 ha MMT CO2 eq. MMT CO2 eq. MMT CO2 eq. MMT CO2 eq. yr-1 MMT CO2 eq. yr-1

Alabama 9,272 1,927 2,495 1,457 (28.8) (30.1)

Alaska (Coastal) 5,841 2,049 3,165 1,865 (3.5) (2.5)

Arizona 6,234 543 864 510 5.3 5.8 

Arkansas 7,675 1,670 2,128 1,187 (21.7) (22.6)

California 13,022 4,212 5,660 1,864 (36.2) (38.8)

Colorado 8,435 1,319 2,119 966 6.6 (1.9)

Connecticut 702 259 324 159 (4.1) (4.3)

Delaware 141 49 62 36 (0.4) (0.2)

Florida 6,990 1,215 1,787 2,657 (15.4) (15.5)

Georgia 10,017 2,210 2,690 3,047 (21.1) (21.7)

Idaho 8,626 1,816 2,868 1,295 (1.1) (5.3)

Illinois 1,984 504 600 412 (8.2) (9.5)

Indiana 1,973 548 663 383 (7.0) (8.3)

Iowa 1,201 248 318 246 (3.7) (4.5)

Kansas 1,045 177 228 308 (4.0) (5.2)

Kentucky 5,063 1,359 1,650 754 (14.5) (15.3)

Louisiana 6,018 1,239 1,584 1,026 (13.3) (15.4)

Maine 7,137 1,455 2,093 2,152 (10.7) (11.7)

Maryland 990 368 447 231 (3.0) (3.0)

Massachusetts 1,225 434 534 308 (4.6) (4.7)

Michigan 8,238 1,784 2,360 4,463 (26.9) (33.6)

Minnesota 7,033 1,004 1,508 4,290 (10.4) (15.0)

Mississippi 7,879 1,689 2,100 1,265 (33.4) (33.0)

Missouri 6,253 1,305 1,645 1,116 (12.5) (16.3)

Montana 10,251 1,712 2,962 1,486 (0.9) (18.7)

Nebraska 623 95 126 158 (1.5) (2.2)

Nevada 3,547 206 371 284 (0.5) 0.3 

New Hampshire 1,956 589 760 526 (4.6) (4.8)

New Jersey 796 232 295 199 (2.6) (2.3)

New Mexico 7,115 626 1,033 606 0.9 0.1 

New York 7,691 2,294 2,941 2,026 (19.3) (23.8)

North Carolina 7,536 2,008 2,493 1,974 (27.6) (28.4)

North Dakota 309 39 57 86 (0.4) (0.4)

Ohio 3,297 974 1,156 774 (10.5) (12.2)

Oklahoma 4,913 574 839 763 (2.5) (2.7)

Oregon 12,061 4,288 5,855 3,516 (51.7) (52.7)

Pennsylvania 6,778 2,139 2,680 1,540 (19.8) (23.4)

Rhode Island 147 50 62 34 (1.2) (1.2)

South Carolina 5,279 1,294 1,582 1,570 (21.8) (21.6)

South Dakota 781 94 139 161 (0.1) (0.8)

Tennessee 5,633 1,581 2,095 836 (8.2) (10.4)

Texas 18,856 1,695 2,730 3,373 (1.2) (1.2)

Utah 5,962 572 967 586 5.2 1.1 

Vermont 1,860 583 759 500 (4.7) (4.9)

Virginia 6,428 1,857 2,329 1,352 (18.8) (18.6)

Washington 9,039 3,740 5,379 2,849 (24.8) (33.7)

West Virginia 4,921 1,651 1,967 1,051 (20.5) (23.3)

Wisconsin 6,921 1,317 1,725 3,569 (15.1) (19.1)

Wyoming 4,010 583 1,115 445 12.6 8.3 

Notes:

Carbon stocks, stock changes, and forest areas are based on annualized estimates for 2013 for shaded area in Map 4-1.

Parentheses (i.e., negative net annual change) indicate net forest ecosystem sequestration, by convention.

Note that total non-soil stock and stock change also includes live trees.

MMT CO2 eq. is million metric tons carbon dioxide equivalent. MMT CO2 eq. yr-1 is million metric tons carbon dioxide equivalent per year.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

127

Appendix Table C-3a Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Publicly Owned 

Forestland (non-reserved) by Region and Stand-Age Class, 2013

Appendix Table C-3a Mean Carbon Density, Range of Plot-Level Densities, and Forest Area 

on Publicly Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 

Region

Stand age 

class

Live tree 

carbon density

Live tree 5th

and 95th

percentiles

Total non-soil 

carbon density

Total non-soil 

5th and 95th

percentiles

Soil organic 

carbon density

Forest 

area

Years MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha

1,000 

ha 

Pacific Coast <20 36.7 0 – 161 178.3 69 – 427 253 1,363

Pacific Coast 20-40 194.3 5 – 558 317.3 81 – 745 291 1,692

Pacific Coast 40-60 339.1 8 – 942 464.8 66 – 1134 271 1,541

Pacific Coast 60-80 350.0 11 – 1119 478.3 69 – 1339 244 2,513

Pacific Coast 80-100 357.4 22 – 1047 490.6 80 – 1281 237 2,538

Pacific Coast 100-150 460.8 19 – 1301 614.5 83 – 1530 241 3,649

Pacific Coast 150-200 496.9 22 – 1338 675.4 90 – 1610 263 1,906

Pacific Coast 200+ 646.5 49 – 1544 864.6 127 – 1880 304 2,587

Pacific Coast unknown 375.6 3 – 1272 517.3 48 – 1579 217 1,269

Rocky Mountain <20 23.6 0 – 95 124.4 37 – 303 127 4,652

Rocky Mountain 20-40 62.2 6 – 150 153.8 48 – 311 138 1,177

Rocky Mountain 40-60 103.9 11 – 283 178.5 43 – 406 125 1,357

Rocky Mountain 60-80 158.3 16 – 447 243.4 45 – 611 126 3,411

Rocky Mountain 80-100 179.0 20 – 468 270.7 52 – 631 120 5,398

Rocky Mountain 100-150 192.6 24 – 512 292.2 55 – 704 113 10,060

Rocky Mountain 150-200 168.5 24 – 493 259.2 53 – 695 102 4,733

Rocky Mountain 200+ 152.1 25 – 488 234.6 55 – 660 93 1,973

Rocky Mountain unknown 151.2 28 – 404 240.9 59 – 570 94 1,076

North <20 43.1 0 – 140 106.0 45 – 219 471 1,615

North 20-40 122.6 16 – 279 182.8 60 – 353 443 2,002

North 40-60 201.3 25 – 435 268.6 74 – 519 434 2,905

North 60-80 270.9 59 – 532 347.9 115 – 626 401 4,741

North 80-100 322.3 73 – 612 403.6 129 – 709 368 3,674

North 100-150 312.9 41 – 626 393.4 95 – 743 416 1,633

North 150-200 237.9 43 – 534 314.7 100 – 642 582 78

North 200+ 211.2 211 – 211 257.2 257 – 257 178 1

North unknown 429.3 133 – 757 524.9 179 – 867 334 19

South <20 62.2 0 – 223 120.6 38 – 297 249 1,406

South 20-40 200.0 17 – 443 264.9 66 – 520 247 1,991

South 40-60 264.0 30 – 581 330.2 72 – 667 229 2,493

South 60-80 327.8 85 – 625 401.0 146 – 719 207 3,571

South 80-100 367.9 114 – 707 448.5 172 – 808 228 1,871

South 100-150 391.7 120 – 721 479.7 195 – 825 225 474

South 150-200 679.3 679 – 679 786.2 786 – 786 154 2

Note: MT CO2 eq/ha is metric tons carbon dioxide equivalent per hectare.
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

128

Appendix Table C-3b Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Privately Owned 

Forestland (non-reserved) by Region and Stand-Age Class, 2013 

Appendix Table C-3b Mean Carbon Density, Range of Plot-Level Densities, and Forest 

Area on Privately Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 

Region

Stand age class Live tree carbon 

density

Live tree 5th 

and 95th 

percentiles

Total non-soil 

carbon density

Total non-soil 

5th and 95th 

percentiles

Soil organic 

carbon density

Forest 

area

Years MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha 1,000 ha 

Pacific Coast <20 56.2 0 - 230 185.2 84 - 377 317 2,361

Pacific Coast 20-40 298.4 21 - 698 430.8 98 - 877 318 2,096

Pacific Coast 40-60 383.5 25 - 992 498.2 81 - 1171 263 2,310

Pacific Coast 60-80 307.4 18 - 958 410.0 66 - 1113 230 2,375

Pacific Coast 80-100 331.4 24 - 882 440.7 75 - 1074 221 1,737

Pacific Coast 100-150 317.0 19 - 1043 426.6 66 - 1231 216 1,312

Pacific Coast 150-200 403.3 21 - 1288 542.2 64 - 1374 223 290

Pacific Coast 200+ 433.2 29 - 1363 611.6 96 - 1693 302 203

Pacific Coast unknown 182.6 7 - 498 243.4 44 - 583 120 1,328

Rocky Mountain <20 22.4 0 - 82 98.7 39 - 188 125 2,270

Rocky Mountain 20-40 56.0 6 - 171 124.2 37 - 275 127 573

Rocky Mountain 40-60 88.3 11 - 271 151.2 40 - 385 125 882

Rocky Mountain 60-80 110.3 16 - 319 175.5 43 - 435 116 1,622

Rocky Mountain 80-100 131.4 18 - 350 200.0 42 - 472 110 2,113

Rocky Mountain 100-150 123.6 17 - 379 186.7 41 - 508 98 3,179

Rocky Mountain 150-200 93.8 19 - 261 146.0 45 - 393 83 1,282

Rocky Mountain 200+ 92.4 21 - 207 139.9 50 - 274 76 570

Rocky Mountain unknown 85.1 15 - 203 132.6 41 - 264 77 290

North <20 45.1 0 - 172 104.3 40 - 239 367 3,574

North 20-40 138.4 14 - 327 199.8 57 - 401 332 6,992

North 40-60 229.4 48 - 474 293.7 96 - 559 308 13,898

North 60-80 289.8 88 - 541 361.4 142 - 632 298 17,226

North 80-100 319.4 104 - 587 396.0 159 - 678 294 9,545

North 100-150 328.5 107 - 599 407.0 160 - 699 302 3,102

North 150-200 333.8 127 - 599 424.6 209 - 678 439 89

North 200+ 562.1 373 - 670 634.8 457 - 735 290 3

North unknown 402.1 105 - 900 491.2 167 - 998 262 14

South <20 77.6 0 - 246 132.6 40 - 317 213 24,831

South 20-40 184.2 14 - 418 240.5 47 - 493 212 21,996

South 40-60 219.1 17 - 510 277.4 50 - 585 194 20,516

South 60-80 294.3 40 - 608 361.3 81 - 695 193 15,222

South 80-100 319.4 30 - 679 391.0 64 - 764 207 4,338

South 100-150 319.0 26 - 730 390.1 65 - 810 220 1,101

South 150-200 113.9 19 - 429 171.3 47 - 481 173 68

South unknown 24.2 19 - 31 54.3 50 - 60 183 5

Note: MT CO2 eq/ha is metric tons carbon dioxide equivalent per hectare.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

129

Appendix Table C-3c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Reserved Forestland 

(both public and private ownerships) by Region and Stand-Age Class, 2013

Appendix Table C-3c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area 

on Reserved Forestland (both public and private ownerships) by Region and Stand-Age 

Class, 2013

Region

Stand age 

class

Live tree carbon 

density

Live tree 5th 

and 95th 

percentiles

Total non-soil 

carbon density

Total non-soil 

5th and 95th 

percentiles

Soil organic 

carbon 

density

Forest 

area

Years MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha

MT CO2

eq/ha 1,000 ha 

Pacific Coast <20 25.4 0 - 104 235.8 75 - 545 234 340

Pacific Coast 20-40 98.6 0 - 455 233.8 75 - 582 228 187

Pacific Coast 40-60 214.9 0 - 860 356.0 96 - 964 236 314

Pacific Coast 60-80 269.6 1 - 958 408.6 96 - 1197 227 564

Pacific Coast 80-100 357.5 10 - 993 509.5 91 - 1161 219 611

Pacific Coast 100-150 437.2 24 - 1197 616.0 116 - 1482 236 1,331

Pacific Coast 150-200 502.5 35 - 1238 695.0 111 - 1550 234 952

Pacific Coast 200+ 646.5 88 - 1523 878.9 197 - 1981 277 1,811

Pacific Coast unknown 470.2 2 - 1333 643.4 54 - 1666 203 783

Rocky Mountain <20 16.9 0 - 78 175.8 56 - 382 134 1,379

Rocky Mountain 20-40 52.9 4 - 145 144.1 59 - 298 139 339

Rocky Mountain 40-60 92.4 7 - 255 174.4 37 - 377 126 189

Rocky Mountain 60-80 136.1 22 - 384 227.7 54 - 513 123 530

Rocky Mountain 80-100 178.0 24 - 422 286.0 53 - 578 123 824

Rocky Mountain 100-150 190.7 22 - 451 318.4 53 - 649 117 1,962

Rocky Mountain 150-200 202.6 33 - 486 329.3 67 - 712 111 1,369

Rocky Mountain 200+ 203.6 30 - 549 314.3 61 - 694 105 625

Rocky Mountain unknown 253.1 35 - 793 380.5 62 - 1008 109 343

North <20 31.9 0 - 122 130.3 50 - 230 504 93

North 20-40 142.4 20 - 384 214.1 69 - 448 400 118

North 40-60 198.5 35 - 386 274.9 84 - 484 375 359

North 60-80 287.4 90 - 542 384.8 159 - 636 354 774

North 80-100 347.9 91 - 620 449.6 183 - 734 321 952

North 100-150 375.8 98 - 612 477.6 170 - 748 317 557

North 150-200 348.8 131 - 574 474.4 188 - 723 329 30

North unknown 510.1 452 - 535 652.5 576 - 685 410 9

South <20 27.3 0 - 124 101.1 52 - 193 419 174

South 20-40 111.5 3 - 355 173.3 53 - 424 419 235

South 40-60 187.1 20 - 479 262.1 49 - 586 371 337

South 60-80 327.7 87 - 608 419.7 148 - 746 298 445

South 80-100 389.7 83 - 711 485.9 159 - 840 226 333

South 100-150 382.1 196 - 640 505.8 263 - 867 238 147

South 150-200 576.5 576 - 576 666.5 666 - 666 166 2

Notes:

See USDA Forest Service (2015a) for additional details on how classifications are defined.

Carbon densities and forest areas are based on the most recent inventory per state for shaded area in Map 4-1.

Note that total non-soil stock also includes live trees.

MT CO2 eq/ha is metric tons carbon dioxide equivalent per hectare.
Chapter 4 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

130

Appendix Table C-4a Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Publicly Owned 

Forestland (non-reserved) by Region and Stand-Age Class, 2013

Appendix Table C-4a Mean Carbon Density, Range of Plot-Level Densities, and Forest Area 

on Publicly Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013

Region

Stand size class Live tree 

carbon density

Live tree 5th

and 95th 

percentiles

Total non-soil 

carbon density

Total non-soil 

5th and 95th 

percentiles

Soil organic 

carbon 

density

Forest 

area

MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha

MT CO2

eq/ha MT CO2 eq/ha 1,000 ha

Pacific Coast large diameter 486.9 32 - 1324 651.0 90 - 1604 265 14,545

Pacific Coast medium diameter 167.8 9 - 460 274.8 67 - 588 229 1,637

Pacific Coast small diameter 43.7 0 - 143 154.1 54 - 321 242 2,342

Pacific Coast nonstocked 11.4 0 - 51 160.8 67 - 437 229 533

Rocky Mountain large diameter 177.4 22 - 507 266.6 52 - 692 107 23,199

Rocky Mountain medium diameter 149.5 18 - 352 253.3 54 - 520 137 4,867

Rocky Mountain small diameter 46.1 0 - 136 151.4 59 - 326 139 3,826

Rocky Mountain nonstocked 4.9 0 - 28 95.1 36 - 270 111 1,946

North large diameter 333.8 113 - 608 414.7 177 - 709 326 8,541

North medium diameter 187.5 67 - 341 257.5 118 - 432 469 4,557

North small diameter 58.9 0 - 166 117.4 48 - 238 557 3,383

North nonstocked 8.4 0 - 33 74.5 44 - 119 466 187

South large diameter 345.9 102 - 652 421.1 160 - 748 220 7,821

South medium diameter 183.5 47 - 362 248.7 95 - 446 230 2,245

South small diameter 45.1 0 - 137 98.9 33 - 204 257 1,570

South nonstocked 6.5 0 - 29 75.0 43 - 122 278 171

Note: MT CO2 eq/ha is metric tons carbon dioxide equivalent per hectare.

Table C-4b Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Privately Owned Forestland 

(non-reserved) by Region and Stand-Age Class, 2013

Table C-4b Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on 

Privately Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 

Region

Stand size class Live tree 

carbon density

Live tree 5th 

and 95th 

percentiles

Total non-soil 

carbon density

Total non-soil 

5th and 95th 

percentiles

Soil organic 

carbon 

density

Forest 

area

MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha

MT CO2

eq/ha MT CO2 eq/ha 1,000 ha 

Pacific Coast large diameter 384.1 36 - 990 505.0 89 - 1171 248 8,500

Pacific Coast medium diameter 170.0 21 - 433 259.2 67 - 563 233 2,560

Pacific Coast small diameter 41.9 0 - 145 150.0 61 - 275 281 2,574

Pacific Coast nonstocked 12.0 0 - 54 137.1 79 - 224 254 376

Rocky Mountain large diameter 115.7 16 - 336 175.9 42 - 468 97 8,351

Rocky Mountain medium diameter 97.7 13 - 301 168.4 41 - 425 128 1,760

Rocky Mountain small diameter 35.7 0 - 101 114.4 45 - 207 128 1,813

Rocky Mountain nonstocked 5.5 0 - 26 72.7 38 - 121 117 858

North large diameter 324.2 120 - 583 397.0 173 - 672 278 30,673

North medium diameter 194.2 66 - 357 260.3 113 - 444 337 14,982

North small diameter 67.3 0 - 189 127.4 42 - 274 369 8,278

North nonstocked 9.9 0 - 37 70.8 42 - 120 427 510

South large diameter 286.1 44 - 589 351.8 83 - 672 203 42,729

South medium diameter 157.1 28 - 322 214.6 66 - 397 206 23,052

South small diameter 41.9 0 - 141 89.3 33 - 200 204 20,088

South nonstocked 4.1 0 - 16 60.8 42 - 102 241 2,207

Note: MT CO2 eq/ha is metric tons carbon dioxide equivalent per hectare.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 4

131

Table C-4c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on Reserved Forestland (both public 

and private ownerships) by Region and Stand-Age Class, 2013 

Table C-4c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on 

Reserved Forestland (both public and private ownerships) by Region and Stand-Age Class, 

2013 

Region 

Stand size class Live tree 

carbon density

Live tree 5th 

and 95th 

percentiles 

Total non-soil 

carbon density

Total non-soil 

5th and 95th 

percentiles 

Soil organic 

carbon density

Forest 

area 

 MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha 1,000 ha 

Pacific Coast large diameter 555.6 67 - 1381 755.8 146 - 1687 249 5,279

Pacific Coast medium diameter 180.1 3 - 504 302.4 59 - 670 191 495

Pacific Coast small diameter 43.8 0 - 145 187.6 69 - 362 217 938

Pacific Coast nonstocked 6.9 0 - 58 215.3 72 - 503 222 181

Rocky Mountain large diameter 197.9 26 - 499 316.8 56 - 691 112 4,795

Rocky Mountain medium diameter 151.9 22 - 351 268.8 54 - 515 130 966

Rocky Mountain small diameter 36.1 0 - 141 162.9 60 - 358 137 1,251

Rocky Mountain nonstocked 5.1 0 - 36 184.2 47 - 428 128 551

North large diameter 370.2 148 - 617 470.2 236 - 734 297 1,900

North medium diameter 215.4 79 - 391 308.8 147 - 493 376 672

North small diameter 70.0 0 - 189 151.0 57 - 364 566 302

North nonstocked 4.8 0 - 25 105.7 40 - 153 537 19

South large diameter 352.3 86 - 660 449.1 167 - 816 268 1,054

South medium diameter 165.8 30 - 350 245.5 87 - 455 331 246

South small diameter 43.2 0 - 142 102.3 40 - 243 490 332

South nonstocked 13.3 0 - 43 81.5 49 - 132 310 41

Notes: 

See USDA Forest Service (2015a) for additional details on how classifications are defined. 

Carbon densities and forest areas are based on the most recent inventory per State for shaded area in Map 4-1. 

Note that total non-soil stock also includes live trees. 

MT CO2 eq/ha is metric tons carbon dioxide equivalent per hectare. 

 

133

Chapter 5

Energy Use in Agriculture

5.1 Summary of Greenhouse 

Gas Emissions From Energy Use in 

Agriculture 

Approximately 0.83 quadrillion BTU of direct 

energy were used in agricultural production in 2013, 

resulting in more than 74 MMT of CO2 emissions 

(Table 5-1). The total energy consumption for all 

sectors in the United States, including agriculture, 

resulted in 5,331.5 MMT of CO2

 emissions 

(EPA 2015). Production agriculture contributed 

approximately 1.4 percent of those total emissions. 

Within production agriculture, diesel fuel accounted 

for 41.9 percent of CO2 emissions and electricity 

contributed 37.4 percent of CO2 emissions. Gasoline 

consumption accounted for 9.6 percent of CO2

emissions, while liquefied petroleum (LP) gas and 

natural gas accounted for 6.8 percent and 4.1 percent 

respectively.

5.2 Spatial and Temporal Trends in 

Greenhouse Gas Emissions From Energy 

Use in Agriculture

The highest emissions from agricultural energy 

use in 2013 were in the Corn Belt and Northern 

Plains (Figure 5-1), followed by the Mountain, 

Southern Plains, Lake States, and the Pacific, which 

had the lowest emissions in this group. Relatively 

small emissions were estimated for the Southeast, 

Northeast, Delta, and Appalachian States (regions are 

defined in Table 5-2). There is a strong correlation 

between production and energy use/emissions. 

Generally, the States with the most agricultural 

production use the most energy and therefore 

have the highest CO2

 emissions from agricultural 

production (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 only sixth in CO2

 emissions due to 

its reliance on hydroelectric power (Figure 5-1). 

Agricultural energy use and the resulting CO2

emissions grew throughout the 1960s and 1970s, 

peaking in the late 1970s (Figure 5-2). High energy 

prices, stemming from the oil crises of the 1970s 

and early 1980s, drove farmers to be more energy 

efficient, resulting in a decline in total energy use 

and CO2 emissions throughout most of the 1980s 

(Miranowski 2005). This decline is attributed to 

switching from gasoline-powered to more fuelefficient diesel-powered engines, adopting energyconserving tillage practices, shifting to larger 

multifunction machines, and adopting energy-saving 

methods for crop drying and irrigation (Uri and 

Day 1991; Sandretto and Payne 2006; Lin et al. 

1995). Furthermore, policies such as the Energy 

Policy and Conservation Act of 1975 resulted in 

greater average fuel economy standards, and both 

gasoline- and diesel-powered equipment became 

Table 5-1 Energy Use and Carbon Dioxide Emissions by Fuel Source on U.S. Farms, 2013

Table Error! No text of specified style in document.-25 Energy Use and Carbon Dioxide 

Emissions by Fuel Source on U.S. Farms, 2013

Fuels Energy consumed Carbon content Fraction oxidized CO2 emissions 

QBTU MMT C/QBTU MMT CO2 eq.

Diesel 0.422 20.17 1 31.20 

Gasoline 0.101 19.46 1 7.21

LP1 gas 0.082 16.83 1 5.08 

Natural gas 0.058 14.46 1 3.07

Electricity 0.165 ** ** 27.86 

Total 0.828 74.42

Notes: QBTU is quadrillion British thermal units. MMT C/QBTU is million metric tons carbon per quadrillion British thermal units. MMT CO2 eq. is 

million metric tons carbon dioxide equivalent.

1 LP gas = liquefied petroleum gas

** Varies dependent on fuel source used to generate electricity and heat rate of power generating plants. 

Download data: http://dx.doi.org/10.15482/USDA.ADC/1264249 
Chapter 5 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

134

increasingly energy efficient throughout the 1980s 

and 1990s. Declines in farm energy use leveled off 

in the late 1980s as energy prices dropped (Figure 

5-2). Total energy use increased throughout most of 

the 1990s but, since 2000, yearly changes in total 

energy use have been annually variable with a slight 

average decreasing trend (-4.6 trillion BTU per year). 

However, energy productivity (i.e., output per unit 

of energy input) has increased significantly over 

that time, due to higher crop yields and more energy 

efficient input use. The spikes in diesel and gasoline 

use in 2009 reflect record-breaking U.S. corn and 

soybean production that year.

5.3 Sources of Greenhouse Gas 

Emissions From Energy Use on 

Agricultural Operations

Agricultural operations—including crop and 

livestock farms, dairies, nurseries, orchards, and 

greenhouses—require a variety of energy sources. 

Energy use varies by commodity produced, 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. For example, 

estimated diesel use spiked in 2009 when corn and 

soybean production reached all-time highs (Figure 

5-2). The demand for diesel fuel in 2009 may have 

also been boosted by low bulk diesel prices, which 

fell to their lowest level in 5 years, dropping to 

$1.68 compared to $3.62 the year before (USDA/

NASS 2008-09). In 2012, when corn production was 

down because of a drought, the energy-use estimates 

for diesel fuel, LP gas, and natural gas all moved 

downward (USDA/NASS 2014a). 

Energy used on farms is typically categorized as 

direct or indirect energy (Maranowski 2005). Direct 

energy is energy used on the farm, 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 because it can be used in vehicles and 

stationary equipment. Crop production uses large 

amounts of diesel fuel, gasoline, and 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. As farms have 

grown larger over time, overall gasoline consumption 

has declined (Figure 5-2). 

Farmers use a significant amount of energy to dry 

crops such as grain, tobacco, and peanuts. LP gas, 

electricity, diesel fuel, or natural gas can be used for 

crop drying. Annual rainfall can have a significant 

effect on the amount of energy used to dry crops 

from year to year. Above average rainfall, especially 

just prior to harvest time, increases the moisture level 

of grain, and more energy may be required to dry 

the grain to meet quality standards. The 2009 corn 

crop, for example, had high moisture content due to 

unusually wet weather that that fall (USDA/WAOB, 

2009). Because 2009 was also a record year for corn 

and soybean production, energy requirements for 

drying were extremely high and the estimated LP use 

was a record high that year.

Weather can also affect the energy used in livestock 

facilities, greenhouses, and other farm buildings. 

Natural gas and electricity are commonly used for 

controlling indoor temperatures. A significant amount 

of electricity is also used for lighting, air circulation, 

and powering electric motors with various functions. 

For example, dairies rely heavily on electricity 

to power milking machines. The applications of 

electric-powered farm equipment have increased over 

time, contributing to higher on-farm electricity use. 

There were about 55 million irrigated acres in 

2013, about 200,000 less than reported in 2008. 

While some irrigation systems are gravity-flow 

systems that require relatively little energy for water 

distribution, irrigation systems that use pumps are 

energy intensive. Based on the 2013 USDA Farm 

and Ranch Irrigation Survey, about 52 million acres 

of U.S. farmland were irrigated with pumps powered 

by liquid fuels, natural gas, LP gas, and electricity, 

costing a total of $2.67 billion (USDA/NASS 2014b). 

Electricity was the principle power source for these 

Figure 5-1 CO2 Emissions from Energy Use in Agriculture, by Region, 2013 

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent) Figure 5-1

CO2 emissions from energy use in agriculture, by region, 2013

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

2

4

6

8

10

12

14

16

Corn BeltNorthern

Plains

MountainSouthern

Plains

Lake StatesPacificAppalachianDelta StatesSoutheastNortheast

MMT CO2 eq
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 5

135

pumps, costing about $1.85 billion to irrigate over 33 

million acres. Diesel fuel was used to power pumps 

on about 13 million acres, costing over $500 million, 

and natural gas was used on about 4 million acres, 

costing around $222 million (USDA/NASS 2014b). 

The remaining irrigation acreage was powered by LP 

gas, butane, and gasoline.

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 agricultural inputs, these indirect 

emissions are not detailed in this inventory. For 

information on the GHG emissions associated with 

manufacturing commercial fertilizers, see Inventory 

of U.S. Greenhouse Gas Emissions and Sinks: 1990–

2013 (EPA 2015).

5.4 Methods for Estimating Carbon 

Dioxide Emissions From Energy Use in 

Agriculture

CO2 emission estimates for energy use are 

constructed from fuel consumption data using 

standardized methods published in the U.S. GHG 

Inventory. Emission estimates for fuel use in 

agriculture are not separately published in the U.S. 

GHG Inventory; 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. GHG 

Inventory. 

Estimates of CO2

 from agricultural operations 

are based on annual energy expense data from 

the Agricultural Resource Management Survey 

(ARMS) conducted by the National Agricultural 

Statistics Service (NASS) of the USDA. NASS 

collects information on farm production expenditures 

including expenditures on diesel fuel, gasoline, 

LP gas, natural gas, and electricity use on the 

farm (USDA/NASS 2014c). NASS also collects 

data on price per gallon paid by farmers for 

gasoline, diesel, and LP gas (USDA/NASS 2013). 

Energy expenditures are divided by fuel prices to 

approximate gallons of fuel consumed on the farm. 

Gallons of gasoline, diesel, and LP gas are then 

Figure 5-2 Energy Use in Agriculture, by Source, 1965-2013

(BTU - British thermal unit)

Figure 5-2

Energy use in agriculture, by source, 1965-2013

(BTU - British thermal unit)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Quadrillion btu

Gasoline Diesel LP gas Natural gas Electricity

Table 5-2 Definition of Regions Used in Figure 5-1

Table Error! No text of specified style in document.-26 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
Chapter 5 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

136

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 grouped into 10 

production regions, allowing fuel consumption to be 

estimated at the national and regional levels (Table 

5-2). Farm consumption estimates for electricity 

and natural gas are also approximated by dividing 

prices into expenditures. Because the prices farmers 

pay for electricity and natural gas are not collected 

by NASS, we use data from the Energy Information 

Administration (EIA), which reports average prices 

by State (EIA 2015a; EIA 2015b). The EIA State data 

are grouped into the NASS production regions. 

Following the method outlined in Annex 2 of 

the U.S. GHG Inventory, consumption of diesel 

fuel, gasoline, LP gas, and natural gas used on 

the farm was converted to CO2 emissions using 

the coefficients for carbon content of fuels and 

fraction of carbon oxidized during combustion 

(Table 5-1). 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 million 

metric tons (MMT) 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 atmosphere if all carbon were oxidized (EPA 

2015). To convert from carbon content to CO2, it 

was assumed that 100 percent of the carbon became 

oxidized.

A different approach was used to estimate emissions 

from electricity that includes on-farm electricity 

use, as well as the energy required to generate the 

electricity off the farm. A number of fuel sources 

can be used to generate electricity, therefore the mix 

of fuel sources used by power plants in a region 

can vary significantly. Some regions of the country 

rely more on coal for electricity generation, while 

other regions use more natural gas to generate 

electricity. To account for this variation, the CO2

emission estimates from electricity generation in 

this chapter are derived from State data available 

from EIA. In response to a special request from 

USDA, EIA tabulated State emission factors for 

the States in the NASS production regions. The 

regional electricity emission factors represent 

average CO2 emissions generated by utility and nonutility electric generators for the 1998 through 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 on agricultural operations. 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 fourth edition of the U.S. 

Agriculture and Forestry Greenhouse Inventory, 

which estimates GHG emissions for the year 2013. 

Figure 5-3 compares the 2013 results with the three 

previous study periods, 2008, 2005 and 2001. As 

discussed in Section 5.3, annual GHG emissions 

from energy use in the agricultural sector will vary 

with changes in crop and livestock production levels 

and with changes in annual weather conditions. Total 

emissions in 2001 are slightly greater than the other 

3 years, with most of the difference coming from a 

higher use of diesel fuel (Figure 5-3). It appears that 

changes in GHG emissions generally follow longterm energy trends as shown in Figure 5.2. When a 

short term fluctuation in GHG emissions occurred, 

it probably was related to a major weather event 

or other factors significantly affecting agricultural 

production.

Figure 5-3 CO2 Emissions from Energy Use in Agriculture, by Fuel Source, 

2001, 2005, 2008, and 2013 

(MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

0

10

20

30

40

50

60

70

80

Diesel Gasoline LP gas Natural gas Electricity Total

MMT CO2 eq

2001 2005 2008 2013

*

LP* = liquid petroleum gas

SUGGESTED CITATION

Duffield, J., 2016. Chapter 

5: Energy Use in Agriculture. 

In U.S. Agriculture and 

Forestry Greenhouse Gas 

Inventory: 1990-2013, 

Technical Bulletin No. 1943, 

United States Department 

of Agriculture, Office 

of the Chief Economist, 

Washington, DC. 137 pp. 

September 2016. Del Grosso 

S.J. and M. Baranski, Eds. 
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Chapter 5

137

5.6 References

AEIA (2015a). Electric Power Monthly. Energy Information 

Administration, U.S. Department of Energy. February, 2015. 

Available online at http://www.eia.gov/electricity/monthly/index.

cfm?src=Electricity-f2

EIA (2015b). Natural Gas Monthly. Energy Information 

Administration, U.S. Department of Energy. May, 2015. Available 

online at http://www.eia.gov/naturalgas/monthly/?src=Natural-f1

EPA (2015). Inventory of U.S. Greenhouse Gas Emissions 

and Sinks: 1990-2013. ANNEX 2 Methodology and Data 

for Estimating CO2 Emissions from Fossil Fuel Combustion. 

Environmental Protection Agency, Office of Atmospheric 

Programs, Washington DC. April, 2015. Available at http://www.

epa.gov/climatechange/ghgemissions/usinventoryreport.html

Lin, B.H., M. Padgitt, L. Bull, H. Delvo, D. Shank, and H. 

Taylor (1995). Pesticide and Fertilizer Use and Trends in U.S. 

Agriculture. AER-717, Economic Research Service, United States 

Department of Agriculture, Washington DC.

Miranowski, J. A. (2005). Energy Consumption in US Agriculture, 

in Agriculture as a Producer and Consumer of Energy, J. Outlaw, 

K. Collins, and J. Duffield, eds., pages 68-95, CABI Publishing, 

Cambridge, MA, 2005.

Sandretto and Payne (2006). Chapter 4: Soil Management 

and Conservation. Agricultural Resources and Envirnomental 

Indicators, 2006 edition. Economic Research Service, United 

States Department of Agriculture. Available online at http://www.

ers.usda.gov/publications/eib-economic-information-bulletin/

eib16.aspx 

Uri, N.D. and K. Day (1991). Energy Efficiency, Technological 

Change and the Dieselization of Agriculture in the United States. 

Transportation Planning and Technology, 16: 221-231.

USDA NASS (2014a). Crop Production Historical Track Records, 

April 2014. National Agricultural Statistics Service, United States 

Department of Agriculture, Washington DC.

USDA NASS (2014b). Farm and Ranch Irrigation Survey 

(2013), Volume 3, Special Studies, Part 1. AC-12-SS-1, National 

Agricultural Statistics Service, United States Department of 

Agriculture, Washington, DC, November 2014. Available 

online at http://www.agcensus.usda.gov/Publications/2012/

Online_Resources/Farm_and_Ranch_Irrigation_Survey/ 

USDA NASS (2014c). Farm Production Expenditures 2013 

Summary. National Agricultural Statistics Service, United States 

Department of Agriculture, Washington DC. Available online at 

http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.

do?documentID=1066

USDA NASS Agricultural Prices (2008-2009). National 

Agricultural Statistics Service, Agricultural Statistics Board, 

United States Department of Agriculture, Washington, DC, April, 

2008-09. Available online at http://usda.mannlib.cornell.edu/

MannUsda/viewDocumentInfo.do?documentID=1002

USDA NASS Agricultural Prices (2013). National Agricultural 

Statistics Service, Agricultural Statistics Board, United States 

Department of Agriculture, Washington, DC, April, 2013. 

Available online at http://usda.mannlib.cornell.edu/MannUsda/

viewDocumentInfo.do?documentID=1002

USDA WAOB (2009) Weekly Weather and Crop Bulletin. 

World Agricultural Outlook Board, United States Department 

of Agriculture, Washington, DC. Available online at http://

usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.

do?documentID=1393.
U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013

United States Department of Agriculture, Office of the Chief Economist, Climate Change Program Office. 

Technical Bulletin No. 1943. 137 pp. September 2016. 

This publication supersedes TB-1930, U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2008.

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