Greenhouse Gas Inventory - USDA

Greenhouse Gas Inventory - USDA

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 1 9 9 0 – 2 0 1 3 TECHNICAL BUL...

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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 1 9 9 0 – 2 0 1 3

TECHNICAL BULLETIN NUMBER 1943. SEPTEMBER 2016. Office of the Chief Economist | Climate Change Program Office

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

1 9 9 0 – 2 0 1 3

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 (N2O) 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 N2O 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.

ii

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

iii

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

iv

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

v

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

vi

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

vii

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

viii

Table B-1

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

Table B-2

MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 2003-2007.............................................88

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........................................................................................................................................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

ix

Acknowledgments

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.

x

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 QBTU EIA LP gas

British thermal unit Quadrillion British thermal units Energy Information Administration Liquid petroleum gas

Other EF GHG GWP NRI

Emission factor Greenhouse gas Global warming potential U.S. National Resources Inventory xi

Chapter 1 Download data: http://dx.doi.org/10.15482/USDA.ADC/1260729

Introduction 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.

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.

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).

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.

Table 1-1 Agriculture and Forestry Greenhouse Gas Emission Estimates Table x-1 Agriculture and Forestry Greenhouse Gas Emission Estimates and Uncertainty Intervals, 2013

Intervals, 2013

Estimate Source Livestock Crops1 Grassland1 Energy Use2 Forestry Urban Trees

Net Emissions

Observed increases in atmospheric GHG concentrations are primarily a result of fossil fuel combustion for power generation, transportation,

243 175 102 74 (776) (90) (270)

Lower Bound Upper Bound MMT CO2 eq. 222 129 32

276 249 190

(973) (133) (486)

(576) (47) (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.

1

and U

Chapter 1

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

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 b CO2 1 CH4a 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

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). 2

(CH4, N2O) 79 MMT CO2 eq. 13%

(CH4) 165 MMT CO2 28%

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

Chapter 1

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

Rice cultivation & residue burning (CH4, N2O) 9 MMT CO2 eq. 1%

Energy use (CO2) 74 MMT CO2 eq. 13%

Urban trees (90) MMT CO2 eq. 10%

Cropland soils (N2O) 168 MMT CO2 eq. 28%

Harvested wood (71) MMT CO2 eq. 8%

Grazed lands (CH4, N2O) 99 MMT CO2 eq. 17%

Managed livestock waste (CH4, N2O) 79 MMT CO2 eq. 13%

Forests (705) MMT CO2 eq. 81%

Enteric fermentation (CH4) 165 MMT CO2 28%

Figure 1-2 Agricultural and forest sinks of carbon dioxide in 2013

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

Figure 1-1 Agricultural Sources of Greenhouse Gas Emissions 2013 (CHCO is methane; N2O is nitrous oxide; CO2 Urbanin trees (90) MMT 4 2

Figure 1-2 Agricultural and Forest Sinks of Carbon Dioxide in 2013 (MMT CO2 eq. is million metric tons of carbon

eq. 10% is carbon dioxide. MMT CO2 eq. is million metric tons of carbon Harvested equivalent) wood dioxide

dioxide equivalent)

(71) MMT CO2 eq. 8%

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

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

1990

1995

Source GHG Forests Livestock (705)215.1 MMT CO2 eq. 236.9 81% Enteric CH4 164.2 178.7 Fermentation CH Figure 1-2Managed Waste 4 37.2 43.3 Agricultural and forest sinks of carbon dioxide in 2013 N2O Managed Waste 13.8 15.0 Grassland 73.9 93.6 CH4 Grassland 2.7 2.9 N2O Grassland 80.5 90.3 CO2 Grassland (9.3) 0.3 Crops 117.0 161.5 N2O Cropland Soils1 143.5 158.2 2 CO2 Cropland Soils (36.0) (6.9) CH4 Rice Cultivation 9.2 9.8 CH 4 Residue Burning 0.3 0.3 N2O Residue Burning 0.1 0.1 3 CO 2 Energy Use 73.9 73.9 Forestry (699.8) (728.0) CO2 Forests4 (508) (542) 4 CO2 Harvested Wood (132) (118) CO2 Urban Trees5 (60.4) (67.1)

Net Emissions

All GHGs

(219.8)

(162.0)

2000 236.9

2005

2010

MMT CO2 eq. 241.6 249.1

2011

2012

2013

247.4

247.4

243.2

170.6

168.9

171.1

168.7

166.3

164.5

50.0 16.3 33.0 2.7 70.8 (40.5) 133.1 141.8 (18.8) 9.6 0.3 0.1 73.9

56.3 16.4 82.9 2.7 85.0 (4.8) 164.0 158.6 (3.9) 8.9 0.2 0.1 69.9

60.9 17.1 101.5 2.6 96.1 2.8 174.7 168.1 (4.9) 11.1 0.3 0.1 72.7

61.4 17.3 101.4 2.6 96.0 2.8 173.0 169.8 (5.7) 8.5 0.3 0.1 73.3

63.7 17.3 100.7 2.5 95.5 2.7 177.1 170.5 (3.1) 9.3 0.3 0.1 73.9

61.4 17.3 102.0 2.8 95.9 3.3 175.1 167.8 (1.4) 8.3 0.3 0.1 74.4

(563.2) (376) (113) (73.8)

(887.6) (704) (103) (80.5)

(851.5) (705) (60.5) (86.1)

(856.1) (705) (63.9) (87.3)

(860.7) (705) (67.3) (88.4)

(865.2) (705) (70.8) (89.5)

(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.

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

800 600

Rice/Residue Burning Managed Waste Grazed Lands Enteric Fermentation Cropland Soils Agricultural Soils Urban Trees Harvested Wood Forests

MMT CO2 eq.

400 200 0 (200) (400) (600) (800) (1000)

Figure 1-3 Agriculture and Forestry Emissions and Offsets for 1990, 1995, 2000-2013 Figure 1-3 Agriculture and Forestry Emissions Offsets for 1990, 1995, 2000-2013 (MMT CO2 eq. is million metric tons of carbonand dioxide equivalent) (MMT CO2 eq. is million metric tons of carbon dioxide equivalent) microbes, resulting in methane emissions. Finally, burning of residues in agricultural fields produces CH4 and N2O as combustion byproducts.

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.

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.

1.2 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

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 4

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

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).

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.

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).

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.

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.

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 5

Chapter 1

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

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.

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:

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.



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

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

6

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

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.

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 N2O 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.

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 N2O emissions from grazed soils which are generated primarily by DayCent model simulations.

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 N2O and CO2 emissions (see chapter 3 for details). The most important changes 7

Chapter 1

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

decline in estimated C sequestration in mineral soils of 14 percent, relative to the previous inventory.

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

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.

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1.7

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.

References

Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005). DAYCENT model analysis of past and contemporary soil N2O 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.

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.

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.

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.

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 .

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.

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.

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.

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.

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 . 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.

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.

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

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>.

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).

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.

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Chapter 1

Chapter 2 Download data: http://dx.doi.org/10.15482/USDA.ADC/1264149

Livestock and Grazed Land Emissions 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).

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 Table 2-1 Greenhouse Gas Emission Estimates and Table Error! No text of specified Emission Estimates and are responsible for most emissions in Uncertainty Intervals in 2013style in document.-3 Greenhouse Gasemissions Uncertainty Intervals in 2013 California. Emissions were also relatively high in Lower Upper Estimate Bound Bound Idaho, Montana, South Dakota, Nebraska, Colorado, Source MMT CO2 eq. Kansas, Oklahoma, Wisconsin, Iowa, and Missouri.

Total

165 61 17 96 12 (9) 342

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

146 50 15 72 (24) (18) 293

194 74 21 138 48 1 407

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

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

140

Total 240 MMT CO2 eq.

Enteric CH4

120

Manure CH4

7%

N2O

100 MMT CO2 eq.

CH4 enteric fermentation CH4 managed waste + grazed land N2O managed waste N2O grazed land CO2 grazed land remaining grazed land CO2 land converted to grazed land

24%

80 68%

60 40 20 0

Beef Cattle

Dairy Cattle

Sheep

Poultry

Swine

Horses

Goats

Bison

Mules

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.

11CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.

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

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

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

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.

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 Livestock contribute GHGs to the atmosphere both whether C inputs to the soil—from plant residues directly and indirectly. Livestock emit CH4 directly and manure—exceed C losses from decomposition of as a byproduct of digestion through a process called soil organic matter. Soils that have been historically enteric fermentation. In addition, livestock manure cropped using conventional tillage are often depleted and urine (waste) cause CH4 and N2O emissions to of C because tillage disturbs soil aggregates and warms soil, which increases decomposition rates. Table 2-2 Greenhouse Gas Emissions by Livestock Category and Source Carbon-depleted soils can act as CO2 sinks when Table Error! No text of specified style in document.-4 Greenhouse Gas Emissions by Livestock in 2013 converted to grazing land, because grazed soils Category and Source in 2013 Enteric Managed Livestock are typically not plowed. Factors such as grazing Grazed Land Total Fermentation Waste intensity and weather patterns also influence net CO2 CH4 CH4 N2O N2O1 CH4 CO2 fluxes, so a particular parcel of grazed land may be a Animal Type MMT CO2 eq. Beef Cattle 117.10 0.62 7.65 85.16 2.38 2.95 215.87 net source or sink of C during any given year. Dairy Cattle Swine Horses Poultry Sheep Goats American Bison Mules and Asses

Total

41.59 2.47 1.59 NA 1.07 0.31 0.32 0.07 164.53

31.66 23.05 0.02 3.22 0.03 0.00 NA 0.00 58.61

5.74 1.89 0.12 1.58 0.31 0.02 NA 0.00 17.3

5.06 0.24 3.44 0.17 0.80 0.64 0.32 0.10 95.93

0.11 0.01 0.21 0.01 0.04 0.02 0.01 0.01 2.78

0.18 0.01 0.12 0.01 0.03 0.02 0.01 0.00 3.33

84.34 27.66 5.49 4.98 2.28 1.02 0.66 0.19 342.49

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

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.

12

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

amendment are discussed in the next chapter along with GHG emissions from other nutrient amendments for crop production.

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.

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.

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

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.

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

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 13

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

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

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 14

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

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.

2.3

U.S. Livestock Populations

2.2.3

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.

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.

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 N2O 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.

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.

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.

2.4

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 15

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

Map 2-2 Methane Emissions from Enteric Fermentation in 2013 (CH4 is methane. Tg CO2 eq. is teragrams of carbon dioxide equivalent)

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.

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

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

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 Table 2-4 U.S. Methane Emissions from Enteric Fermentation in methods detailed Table Error! No text of specified style in document.-6 U.S. Methane Emissions From Entericbelow correspond to both Tier 1 1990, 1995, 2000, 2005, 2010-2013 Fermentation in 1990, 1995, 2000, 2005, 2010-2013 and Tier 2 approaches. With the permission of EPA, Animal Type MMT CO2 eq. Annex 3.10 from the official U.S. GHG Inventory 1990 1995 2000 2005 2010 2011 2012 2013 is summarized below. Methane emissions from Beef Cattle 119.1 135.5 126.7 125.2 124.4 121.7 118.7 117.1 enteric fermentation were estimated for seven Dairy Cattle 39.4 37.5 38.0 37.6 40.7 41.1 41.7 41.6 livestock categories: cattle, horses, sheep, swine, Sheep 2.3 1.8 1.4 1.2 1.1 1.1 1.1 1.1 goats, American bison, and mules. Emissions from 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 cattle represent the majority of U.S. emissions; Goats 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 consequently, the more detailed IPCC Tier 2 American Bison 0.1 0.2 0.4 0.4 0.4 0.3 0.3 0.3 methodology was used to estimate emissions from Mules and Asses 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 cattle and the IPCC Tier 1 methodology was used to Total 164.2 178.7 170.6 168.9 171.1 168.7 166.3 164.5 Note: MMT CO eq. is million metric tons carbon dioxide equivalent. estimate emissions from the other types of livestock. 2

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

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).

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.

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: • • • • • • • • •

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).



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.

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

1

17

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

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).

2.4.1.2 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.

2.5

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.

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).

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

2.4.3 Changes Compared to the 3rd edition of the USDA GHG Report

Table 2-5 Greenhouse Gas Emissions from Managed Table Error!Waste No textin of 1990, specified1995, style in2000, document.-7 Livestock 2005, Greenhouse 2010-2013Gas Emissions Fro Managed Livestock Waste in 1990, 1995, 2000, 2005, 2010-2013

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

GHG Type

1990

1995

2000

2005

2010

2011

2012

2013 17.3

MMT CO2 eq. Nitrous Oxide1

13.8

15.0

16.3

16.4

17.1

17.3

17.3

Methane 2

37.2

43.3

50.0

56.3

60.9

61.4

63.7

61.4

51.0

58.2

66.4

72.8

78.0

78.7

81.0

78.7

Total

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 CH from managed sources and from grazed grasslands. Manure deposited on grasslands 4 produces little CH4 due to predominantly aerobic conditions.

18

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

40

Total 78 MMT CO2 eq. N2O

30

24%

CH4

25

MMT CO2 eq.

MMT CO2 eq.

35

76%

20 15 10 5 0

Dairy cattle

Swine

Beef cattle

Poultry

Horses

Sheep

Goats

90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 1990

Chapter 2

Total CH4 N2O

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

MMT CO2 eq. is million metric tons carbon dioxide equivalent

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

Figure Figure 2-3 2-3 Greenhouse Gas Emissions from Managed Greenhouse Gas Waste, Emissions from Managed Livestock Waste, 1990-2013 Livestock 1990-2013 CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. Figure 2-2 (CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide. (CH4CO is 2methane; N2O is nitrous oxide; is carbon dioxide. (MMT eq. is million metric tons of carbon dioxideCO equivalent) 2 Greenhouse Gas Emissions from Managed Livestock Waste by Livestock Type in 2013 MMT CO2 eq. N is2O million metric dioxide equivalent) CH is nitrous oxide;tons CO2ofis carbon carbon dioxide. 4 is methane; (MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

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

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).

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

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

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

Data required to calculate CH4 emissions from livestock waste: • • • • • •

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).

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).

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.

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. 20

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

Direct N2O emissions were calculated by multiplying the amount of Nex (kg per year) in each WMS by the N2O direct emission factor for that WMS (EFWMS, in kg N2O-N per kg N, 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).

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).

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 N2O per kg N), and the conversion factor of N2O-N to N2O. The indirect N2O emissions from volatilization and runoff and leaching were summed to determine the total indirect N2O emissions.

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).

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).

To estimate N2O emissions, first, the amount of N excreted (kg per year) in manure in each WMS for each animal type, State, and year was calculated. The population (head) for each State and animal was multiplied by TAM (kg animal mass per head) divided by 1,000, the N excretion rate (Nex, in kg N per 1,000 kg animal mass per day), WMS distribution (percent), and the number of days per year. 21

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

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 N2O 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.

2.5.3 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.

2.6.1 Methods for Estimating Nitrous Oxide Emissions From Grazed Lands

Table 2-6 Greenhouse Gas Emissions from Grazed Lands in 1990, 1995, Table 2-6 Greenhouse Gas Emissions From Grazed Lands in 1990, 1995, 2000, 2005, 20102000, 2013 2005, 2010-2013 1990

1995

2000

80.5 73.7 4.2 2.7 2.7 (9.3) (1.9) (7.4) 73.9

90.3 83.4 4.3 2.6 2.9 0.3 8.1 (7.7) 93.6

70.8 64.8 4.0 2.0 2.7 (40.5) (30.1) (10.4) 33.0

GHG Type Nitrous Oxide1 Direct Indirect Volatilization Indirect Leaching & Runoff Methane2 Carbon Dioxide Grazed Lands Remaining Grazed Land Converted to Grazed Land

Total

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

2005 2010 MMT CO2 eq.

2011

2012

2013

85.0 78.1 4.5 2.4 2.7 (4.8) 4.2 (9.0) 82.9

96.0 89.1 4.5 2.5 2.6 2.8 11.7 (8.9) 101.4

95.5 88.5 4.5 2.5 2.5 2.7 11.5 (8.8) 100.7

95.9 89.0 4.4 2.5 2.8 3.3 12.1 (8.8) 102.0

96.1 89.2 4.5 2.5 2.6 2.8 11.7 (8.9) 101.5

22

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.

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

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

(N2O is nitrous oxide. Tg CO2 eq. is teragrams of carbon dioxide equivalent)

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).

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 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., 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.

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 N2O 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 N20-N/kg N to estimate direct N2O-N emissions, as opposed to the 0.01 kg N2O-N/kg N used to estimate N additions from

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 23

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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).

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 N2O 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.

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, ). 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).

2.6.3 Methodology To Estimate Methane Emissions From Grazed Lands

Indirect N2O 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.

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

Total grazed land N2O emissions were partitioned among different animal types by assuming that emissions are linearly proportional to waste N production.

24

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

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).

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.

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 N2O emissions, CO2 fluxes were partitioned among different animal types by assuming that fluxes are linearly proportional to waste N production.

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 N2O 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.

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

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 25

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

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.

2.7.1

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):

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



• • •



Direct Indirect Total

2 MMT CO2 eq.

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

Figure 2-4 Estimated Reductions in Methane Emissions from Anaerobic

Figure 2-4 Digesters, 2000-2013 Estimated Reductions in Methane Emissions from Anaerobic Digesters, 2000-2013 (MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

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 waste) that compromise the efficacy of general

1

0

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:

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).

3

Enteric Fermentation

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

Chapter 2

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.

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).

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.

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.

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. 27

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

2.9

References

EPA (2005). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2003. Office of Atmospheric Programs, Washington, D.C.

AAPFCO (1995-2000a, 2002 -2007). Commercial Fertilizers. Association of American Plant Food Control Officials. University of Kentucky. Lexington, KY.

EPA (2015). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013. Office of Atmospheric Programs, Washington, D.C.

AAPFCO (2008- 2014). Commercial Fertilizers. Association of American Plant Food Control Officials. University of Missouri. Columbia, MO.

ERG (2000). Calculations: percent distribution of manure for waste management systems. Eastern Research Group, Inc., Morrisville, NC.

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.

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.

ASAE (2003). Manure production and characteristics. American Society of Agricultural Engineers, St. Joseph, MI.

ERG (2010b). Updating Current Inventory Manure Characteristics: New USDA Agricultural Waste Management Field Handbook Values. Memorandum to EPA from ERG. August 13, 2010.

Del Grosso, S.J., S.M. Ogle, W.J. Parton, F.J. Breidt. (2010). Estimating uncertainty in N2O emissions from US cropland soils, Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.

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.

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.

Garrett, W.N. and D.E. Johnson (1983). Nutritional energetics of ruminants. Journal of Animal Science, 57(suppl.2): 478-497.

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.

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.

Enns, M. (2008). Personal Communication. Mark Enns, Colorado State University and staff at ICF International.

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.

Holstein Association (2010). History of the Holstein Breed (website). Available online at .

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.

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.

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.

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.

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.

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.

EPA (2003). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2001. Office of Atmospheric Programs, Washington, D.C.

28

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

USDA (2000a). Layers ’99—Part II: References of 1999 Table Egg Layer Management in the U.S. USDA-APHIS-VS. Fort Collins, CO. . USDA (2000b). 1997 National Resources Inventory. United States Department of Agriculture, Natural Resources Conservation Service, Washington, D.C. Available online at .

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.

USDA (2008). Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651. Natural Resources Conservation Service, United States Department of Agriculture.

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 .

USDA (2009). Agricultural Statistics Annual. National Agricultural Statistics Service. Available online at . 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.

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.

USDA (2013a). Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, United States Department of Agriculture. Washington, DC. Available online at . 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.

Ott, S.L. (2000). Dairy ’96 Study. Stephen L. Ott, Animal and Plant Health Inspection Service, United States Department of Agriculture. June 19, 2000.

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.

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 . 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.

29

Chapter 2

Chapter 2

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

2.10 Appendix A

A-14 IPCC Emission Factors for Livestock

A-1 Population of Animals by State in 2013

A-15 Summary of Greenhouse Gas Emissions From Managed Waste by State in 2013

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

A-16 Methane Emissions From Manure Management by State and Animal in 2013

A-3 State-Level Methane Emissions From Enteric Fermentation by Livestock Category in 2013

A-17 Nitrous Oxide Emissions From Manure Management by State and Animal in 2013

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

A-18 Waste Characteristics Data

A-5 Cattle Population Categories Used for Estimating Methane Emissions

A-19 State Volatile Solids Production Rates in 2013

A-6 Dairy Lactation by Region

A-20 State-Based Methane Conversion Factors for Liquid Waste Management Systems in 2013

A-7 Typical Livestock Weights

A-21 Maximum Methane Generation Potential, B0

A-8 U.S. Feedlot Placement in 2013

A-22 Methane Conversion Factors for Dry Systems

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

A-23 Methane Conversion Factors for Livestock Waste Emissions in 2013

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

A-24 Direct Nitrous Oxide Emission Factors for 2013 A-25 Nitrogen in Livestock Waste on Grazed Lands

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

A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes From Non-Federal Grasslands, 2003-2007

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

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

A-13 Methane Emissions From Cattle Enteric Fermentation, 1990-2013

30

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Appendix Table A-1 Population of AnimalsofbyAnimals State in 2013 Appendix Table A-1 Population by State

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Total

Beef Cattle 1,164,323 11,681 716,787 1,529,163 1,948,078 2,905,849 12,609 8,354 1,544,585 864,002 123,956 1,368,084 928,878 505,098 3,739,688 6,075,096 1,946,299 800,046 24,191 94,328 14,149 461,980 1,292,988 879,933 3,298,804 2,958,072 7,339,137 435,537 7,792 17,937 789,115 250,477 673,494 1,885,554 780,309 3,942,639 1,109,398 488,239 2,916 318,626 3,809,849 1,655,886 11,475,115 639,196 30,796 1,329,691 780,991 383,913 908,325 1,428,100 75,700,053

Dairy Cattle 17,605 805 360,285 20,585 3,470,786 288,910 36,203 9,796 219,544 148,980 5,168 1,186,413 196,112 319,163 429,699 299,269 158,722 29,200 63,864 106,053 24,893 726,955 982,438 28,158 180,591 29,151 103,169 52,863 26,904 14,576 608,835 1,241,783 92,519 40,177 533,110 89,538 253,845 1,118,260 1,860 31,187 193,980 97,535 857,519 185,983 261,563 177,138 508,126 20,113 2,618,905 13,061 18,481,893

Swine 85,000 1,000 175,000 115,000 95,000 705,000 3,500 6,000 15,000 141,000 11,500 38,200 4,625,000 3,625,000 20,375,000 1,812,500 315,000 8,000 4,500 22,000 8,500 1,045,000 7,787,500 500,000 2,800,000 166,000 3,037,500 2,000 3,800 9,000 1,200 66,000 8,900,000 135,000 2,140,000 2,187,500 8,500 1,127,500 1,900 245,000 1,162,500 175,000 632,500 730,000 3,200 255,000 38,200 5,000 305,000 90,000 65,746,500

Source: EPA 2015

31

Chapter 2

in 2013 Sheep Head 12,083 12,083 140,000 12,083 570,000 435,000 7,333 12,083 12,083 12,083 12,083 235,000 53,000 55,000 175,000 65,000 43,000 12,083 7,333 12,083 7,333 82,000 135,000 12,083 75,000 235,000 80,000 73,000 7,333 12,083 100,000 70,000 26,000 74,000 121,000 75,000 210,000 86,000 7,333 12,083 275,000 33,000 700,000 295,000 7,333 87,000 54,000 30,000 84,000 375,000 5,335,000

Goat

Horse

47,212 626 77,557 39,816 141,886 31,913 4,356 1,704 50,923 69,256 13,761 18,208 31,120 36,940 56,297 40,878 57,308 18,220 6,558 9,516 8,674 26,903 33,107 23,304 105,113 9,937 24,087 23,287 5,072 7,785 30,044 35,745 59,969 4,830 47,969 81,811 32,249 48,366 923 37,761 17,706 83,866 826,704 14,210 11,388 48,379 25,906 17,001 62,145 9,416 2,517,711

59,026 1,443 97,124 57,514 134,921 108,624 18,607 6,596 121,118 68,492 4,827 58,921 59,361 100,629 60,248 71,868 135,110 59,645 11,953 28,245 20,288 85,370 61,633 57,371 110,921 96,457 64,066 23,278 8,936 27,161 50,144 91,189 64,569 45,375 113,113 157,591 66,628 120,614 2,203 54,217 68,665 87,449 387,214 58,818 11,342 86,135 59,591 24,215 100,168 70,858 3,539,852

Poultry 206,577,762 1,212,966 1,212,966 211,111,970 35,115,697 6,310,216 3,292,216 40,412,966 22,346,307 270,221,762 1,212,966 1,212,966 15,352,580 51,466,697 76,182,580 1,212,966 63,290,034 13,121,580 3,912,216 59,341,125 442,216 26,567,580 37,940,121 143,196,762 150,681,212 896,216 21,772,580 1,212,966 1,212,966 1,212,966 1,212,966 16,162,580 175,307,515 1,212,966 51,455,788 42,041,125 12,974,580 63,279,242 1,212,966 50,900,818 4,586,333 34,381,398 136,119,489 6,021,333 459,216 54,576,485 18,362,580 20,672,333 16,784,762 329,216 2,177,309,818

Chapter 2

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Appendix Table A-2 U.S. Livestock Population, Population, 1990, 1995, 2000, 2005-2013 Appendix Table A-2 U.S. Livestock 1990, 1995, 2000,

Animal Type Dairy Cattle1 Dairy Cows Dairy Heifers Swine Market <60 lbs. Market 60-119 lbs. Market 120-179 lbs. Market >180 lbs. Breeding Swine Beef cattle Feedlot Steers Feedlot Heifers Bulls NOF2 Calves NOF Heifers NOF Steers NOF Cows NOF Sheep Goats Poultry Hens >1 yr. Pullets Chickens Broilers Turkeys Horses

1990

1995

2000

2005

2006

14 10 4 54 18 12 9 8 7 82 17 6 2 32 10 3 10 11 3 1,537 273 73 7 1,066 118 2

14 9 4 59 20 13 11 9 7 90 18 7 2 35 12 4 12 9 2 1,827 299 81 8 1,332 107 3

13 9 4 59 20 13 11 9 6 85 17 8 2 34 9 5 10 7 2 2,033 334 95 8 1,506 90 3

13 9 4 61 20 14 11 10 6 82 17 8 2 33 8 5 10 6 3 2,150 348 97 8 1,613 84 4

13 9 4 62 21 14 11 10 6 83 17 9 2 33 8 5 10 6 3 2,154 350 97 8 1,612 87 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.

32

2007 2008 1 million head 13 14 9 9 4 4 65 67 22 20 15 17 12 13 11 11 6 6 83 82 17 16 9 8 2 2 33 32 8 8 5 5 10 9 6 6 3 3 2,167 2,176 347 340 104 99 8 8 1,619 1,638 89 91 4 4

2005-2013

2009

2010

2011

2012

2013

14 9 4 66 19 17 13 11 6 81 16 8 2 32 9 5 9 6 3 2,089 341 102 8 1,555 82 4

14 9 5 65 19 17 12 11 6 80 16 9 2 31 8 5 9 6 3 2,104 342 106 7 1,568 81 4

14 9 5 66 19 17 12 11 6 79 16 9 2 31 8 5 9 5 3 2,096 339 102 7 1,565 83 4

14 9 5 66 19 17 13 11 6 77 15 8 2 30 7 5 9 5 3 2,169 347 104 7 1,626 84 4

14 9 5 66 19 17 13 11 6 76 15 8 2 29 8 5 9 5 3 2,177 353 105 7 1,633 80 4

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

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 State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont State Virginia Washington West Virginia Wisconsin Wyoming

Total

Dairy cattle Swine Horses MMT CO2 eq.

1.94

0.05

0.00

0.03

2.55

0.04

0.00

0.03

0.02 1.07 3.08 4.30 0.02 0.01 2.58 1.43 0.22 2.24 1.40 0.76 5.23 8.36 3.22 1.34 0.04 0.15 0.02 0.66 1.89 1.47 5.31 5.23

10.22 0.77 0.01 0.03 1.39 0.41 1.13 3.01 1.16 6.27 1.92 0.76 0.00 0.53 5.93 2.76

17.20 1.11

Beef cattle 0.05

2.18 1.20 0.64 1.32 2.48

0.00 0.86 8.22 0.60 0.09 0.02 0.56 0.38 0.01 2.73 0.40 0.72 0.96 0.56 0.36 0.07 0.14 0.24 0.05 1.62 1.97 0.06 0.33 0.06 0.22 0.13 0.06 0.03 1.54 2.93 0.23 0.07 1.09 0.22 0.56 2.53 0.00 0.07 0.37 0.24 2.11 0.42

0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.01 0.00 0.00 0.17 0.14 0.76 0.07 0.01 0.00 0.00 0.00 0.00 0.04 0.29 0.02 0.11 0.01 0.11 0.00 0.00 0.00 0.00 0.00 0.33 0.01 0.08 0.08 0.00 0.04 0.00 0.01 0.04 0.01 0.02 0.03

0.00 0.04

0.06 0.03 0.00 0.03 0.03 0.05 0.03 0.03 0.06 0.03 0.01 0.01 0.01 0.04 0.03

0.03 0.05 0.04

4.91 0.11 3.13 1.81 0.23 4.98 1.80 1.48 6.19 8.92 3.58 1.40 0.18 0.39 0.08 2.27 3.86 1.53 5.64 5.29

0.03

10.44

0.01

0.06

0.01 0.00 0.02 0.04 0.03 0.02 0.05 0.07 0.03 0.05 0.00 0.02 0.03 0.04 0.17 0.03

0.03

0.00

0.03

117.0 41.7 2.5 Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. Source: EPA 2015 *State totals include all livestock categories

2.59

0.04

0.01

0.03

0.01

1.93

0.00

0.05

0.00 0.00

0.02 11.30

1.22 5.47

1.99

0.06

Dairy cattle Swine 0.58 0.00 Horses 0.01 MMT CO 2 eq. 0.45 0.01 0.04 0.04

Total*

0.01 0.05 1.6

33

0.89 0.08

2.93 3.34 1.36 3.09 2.25 6.49 2.48 3.29 0.01 0.61 6.29 3.00

19.31 1.53

Total* 0.63

2.64 2.42 0.68 6.79 2.50

158.8

Chapter 2

Chapter 2

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

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

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

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Total

1990

1995

2000

2005

2006

2.53 0.02 1.55 2.82 8.69 4.34 0.17 0.05 3.58 2.25 0.32 2.87 2.71 1.95 6.49 7.79 3.92 1.85 0.23 0.61 0.16 2.08 4.35 2.12 6.63 4.00 8.88 0.90 0.10 0.14 2.37 3.44 1.51 2.83 2.55 7.24 2.46 3.64 0.01 0.91 5.35 3.61 20.76 1.44 0.71 2.73 2.57 0.76 7.42 2.05 158.44

2.82 0.02 1.62 3.17 9.05 4.96 0.16 0.05 3.87 2.49 0.30 3.34 2.67 1.87 6.44 9.46 4.31 1.74 0.22 0.61 0.14 2.11 4.40 2.18 7.34 4.98 9.90 0.92 0.10 0.13 2.83 3.26 1.81 3.44 2.45 8.08 2.82 3.46 0.01 0.87 6.44 4.02 24.75 1.63 0.68 2.83 2.61 0.82 6.62 2.57 173.40

2.37 0.02 1.61 2.91 9.92 5.08 0.15 0.05 3.43 2.18 0.29 3.74 2.38 1.55 5.96 9.48 3.63 1.62 0.21 0.51 0.12 1.87 4.11 1.81 6.76 4.88 10.64 0.92 0.10 0.10 3.06 3.34 1.59 3.23 2.15 7.39 2.65 3.40 0.01 0.78 6.28 3.42 22.55 1.65 0.70 2.58 2.52 0.69 6.37 2.69 165.42

2.16 0.03 1.86 3.01 10.69 4.27 0.12 0.04 3.26 2.10 0.28 4.16 2.17 1.50 5.76 9.74 3.80 1.57 0.19 0.44 0.10 1.92 3.77 1.79 6.84 4.47 10.34 0.91 0.09 0.08 3.11 3.16 1.47 3.10 2.28 7.69 2.73 3.19 0.01 0.74 6.26 3.52 22.48 1.61 0.64 2.67 2.23 0.67 6.17 2.31 163.51

2.08 0.03 1.95 2.82 10.80 4.50 0.11 0.04 3.21 2.07 0.29 4.28 2.17 1.51 6.05 9.98 3.87 1.51 0.18 0.45 0.10 1.99 3.75 1.61 7.14 4.58 10.69 0.92 0.09 0.08 3.28 3.19 1.42 3.09 2.29 7.97 2.68 3.17 0.01 0.70 6.32 3.60 23.16 1.53 0.64 2.73 2.31 0.69 6.23 2.44 166.29

2007 2008 MMT CO2 eq. 2.08 2.00 0.03 0.03 2.03 2.07 2.82 2.90 11.50 11.47 5.02 5.08 0.11 0.11 0.04 0.04 3.30 3.23 2.04 1.96 0.28 0.27 4.50 4.58 2.12 2.03 1.57 1.56 6.26 6.31 9.88 10.06 4.11 3.98 1.55 1.60 0.18 0.19 0.43 0.40 0.09 0.09 2.09 2.12 3.85 3.86 1.58 1.59 6.84 6.63 4.98 5.34 11.07 10.77 0.90 0.88 0.08 0.09 0.07 0.07 3.30 3.44 3.32 3.38 1.43 1.37 3.20 3.09 2.24 2.27 7.76 7.78 2.52 2.65 3.24 3.24 0.01 0.01 0.69 0.67 6.27 6.17 3.71 3.51 22.83 22.63 1.60 1.67 0.65 0.65 2.66 2.62 2.39 2.34 0.72 0.69 6.47 6.52 2.69 2.55 169.12 168.55

2009

2010

2011

2012

2013

2.02 0.02 2.14 2.85 11.26 4.93 0.11 0.04 3.24 1.91 0.27 4.56 1.98 1.51 6.35 9.89 3.81 1.60 0.19 0.39 0.09 2.14 3.89 1.58 6.52 5.26 10.76 0.89 0.09 0.08 3.44 3.28 1.40 3.08 2.37 7.86 2.42 3.22 0.01 0.65 6.23 3.21 23.08 1.60 0.64 2.47 2.34 0.70 6.59 2.57 167.53

2.05 0.02 1.97 2.96 11.06 4.92 0.10 0.04 3.25 1.88 0.26 4.58 1.91 1.58 6.29 9.56 3.70 1.53 0.19 0.40 0.08 2.21 3.89 1.62 6.35 5.13 10.62 0.90 0.08 0.07 3.31 3.30 1.35 3.00 2.35 8.01 2.47 3.27 0.01 0.65 6.32 3.31 22.73 1.58 0.62 2.57 2.24 0.65 6.68 2.51 166.13

2.00 0.02 1.88 2.80 11.04 5.04 0.10 0.04 3.15 1.82 0.26 4.76 1.78 1.55 6.19 9.73 3.53 1.43 0.19 0.39 0.08 2.21 3.84 1.55 5.97 5.13 10.41 0.91 0.08 0.06 3.25 3.31 1.33 2.99 2.27 7.53 2.54 3.25 0.01 0.65 6.16 3.25 22.42 1.55 0.64 2.53 2.35 0.66 6.75 2.49 163.87

1.97 0.03 1.96 2.72 11.34 5.24 0.10 0.04 3.23 1.86 0.25 4.81 1.72 1.51 6.14 9.45 3.42 1.40 0.18 0.40 0.08 2.27 3.82 1.55 5.88 5.09 10.77 0.91 0.08 0.06 3.06 3.35 1.35 2.91 2.31 6.70 2.53 3.26 0.01 0.64 6.08 3.16 20.74 1.57 0.63 2.50 2.38 0.66 6.79 2.57 161.49

1.98 0.02 1.95 2.58 11.20 5.05 0.10 0.04 3.15 1.82 0.23 5.01 1.81 1.47 6.24 9.17 3.58 1.39 0.18 0.39 0.08 2.32 3.88 1.53 5.64 5.30 10.66 0.89 0.08 0.06 2.88 3.36 1.36 3.10 2.28 6.50 2.47 3.28 0.01 0.61 6.35 2.98 19.55 1.53 0.65 2.63 2.42 0.68 6.86 2.51 159.82

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 CategoriesCategories Used for Estimating Methane EmissionsMethane Appendix Table A-5 Cattle Population Used for Estimating

Dairy Cattle

Calves (4-6 mo) Heifer Replacements Cows

Beef Cattle

Calves (4-6 mo) Heifer Replacements 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.

34

Emissions

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

Appendix Table A-6 Dairy by Region1by Appendix Table A-6 Lactation Dairy Lactation

Region1

California

West

Northern Great Plains

18,456 18,534 18,722 18,852 20,203 19,573 19,161 19,829 19,451 20,781 21,130 20,904 21,277 20,993 21,139 21,404 21,815 22,440 22,344 22,000 23,025 23,438 23,457 23,178

146,737 149,227 155,838 155,984 160,840 159,752 162,417 164,233 166,106 166,741 169,877 168,163 172,668 171,078 170,757 174,066 175,077 178,152 176,679 179,386 184,540 187,898 190,031 184,682

94,384 95,175 98,240 98,723 101,511 102,563 104,164 105,060 108,478 111,222 116,222 116,523 121,146 122,244 122,811 127,412 131,933 132,981 136,074 139,674 143,910 144,853 149,516 150,877

Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Southcentral

Northeast

Midwest

Southeast

(lbs * year)/cow 50,123 49,752 51,413 52,135 52,944 52,913 52,860 52,846 53,279 53,903 55,413 55,120 56,623 57,926 61,092 62,071 61,406 60,537 61,381 62,443 63,000 64,369 66,056 64,082

170,274 174,570 180,353 179,289 180,102 184,544 185,547 191,086 195,078 197,570 199,323 204,650 208,267 205,592 207,408 209,638 213,221 213,130 217,190 217,153 220,024 221,859 227,856 231,351

115,308 117,551 121,223 121,622 122,992 125,823 124,764 128,219 131,930 133,766 138,105 136,009 139,990 145,306 147,148 151,582 152,633 152,983 151,903 154,529 157,303 157,887 162,671 163,624

116,277 117,666 121,419 124,859 127,801 129,453 128,195 130,930 130,626 134,263 137,216 139,062 140,620 136,904 140,976 143,500 145,258 149,937 148,871 152,199 150,623 151,149 152,473 151,718

Source: EPA 2015 1 Beef lactation data developed using methodology described in EPA 2015.

Appendix Table A-7 Typical LivestockLivestock Weights forWeights 2013 Appendix Table A-7 Typical

Cattle Type Calves

Dairy Cows Dairy Replacements

lbs

270 899 1,348

Bulls

2,022

Beef Replacements

893

Steer Stockers

721

Heifer Stockers

711

Steer Feedlot Heifer Feedlot

for 2013

1,500

Beef Cows

Chapter 2

1,017 959

Source: Feedstuffs (1998), Western Dairyman (1998), Enns (2008), Johnson (2010), NRC (1999), Holstein Association 2010, USDA (2013b,) EPA (2015).

35

Chapter 2

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

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

Weight Placed

Jun

July

Aug

Sep

Oct

Nov

Dec

Total

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

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

Total

Source: USDA (2002f, 2001f, 2000f, 1999a, 1995a), EPA 2015. Note: Totals may not sum due to independent rounding.

Appendix Table A-9 Regional Digestible Energy and Methane Conversion Appendix Table A-9 Regional EstimatesEstimates of Digestibleof Energy and Methane Conversion Rates for Foraging Animals 2007-2013 Rates for Foraging Animals 2007-2013 Animal Type Beef Repl. Heif. Steer Stockers Heifer Stockers Beef Cows Beef Calves (4-6 mo) Bulls

Data

West

DE 1 Ym 2 DE Ym DE Ym DE Ym DE Ym DE Ym

Central

61.9 6.5% 61.9 6.5% 61.9 6.5% 59.9 6.5% 61.9 6.5% 59.9 6.5%

Northeast

65.6 6.5% 65.6 6.5% 65.6 6.5% 63.6 6.5% 65.6 6.5% 63.6 6.5%

64.5 6.5% 64.5 6.5% 64.5 6.5% 62.5 6.5% 64.5 6.5% 62.5 6.5%

Southeast 64.6 6.5% 64.6 6.5% 64.6 6.5% 62.6 6.5% 64.6 6.5% 62.6 6.5%

Source: EPA 2015 1 (DE) Digestible energy; in units of percent gross energy (GE) in MJ/Day. 2 (Y ) Methane conversion rate is the fraction of gross energy (GE) in feed converted to methane. m

Appendix Table A-10 Regional EstimatesEstimates of Digestibleof Energy and Methane Conversion Rates for Dairy and Feedlot Appendix Table A-10 Regional Digestible Energy and Methane Conversion Cattle for 2013

Rates for Dairy and Feedlot Cattle for 2013 Data

Dairy Repl. Heif.

DE1 63.7 63.7 63.7 63.7 Ym2 6.0% 6.0% 5.7% 6.5% DE 82.5 82.5 82.5 82.5 Ym 3.9% 3.9% 3.9% 3.9% DE 82.5 82.5 82.5 82.5 Ym 3.9% 3.9% 3.9% 3.9% DE 66.7 66.7 66.7 66.7 Ym 5.9% 5.9% 5.6% 6.4% DE 63.7 63.7 63.7 63.7 Ym 7.8% (4 mo), 8.03% (5 mo), 8.27% (6 mo) - all regions

Steer Feedlot Heifer Feedlot Dairy Cows Dairy Calves (4-6 mo)

California

West

Northern Great Plains

Animal Type

Southcentral

Source: EPA 2015 1 (DE) Digestible energy; in units of percent gross energy (GE) in megajoules (MJ) per day. 2 (Y ) Methane conversion rate is the fraction of gross energy (GE) in feed converted to methane. m

36

Northeast

Midwest

63.7 6.4% 82.5 3.9% 82.5 3.9% 66.7 6.3% 63.7

63.7 5.7% 82.5 3.9% 82.5 3.9% 66.7 5.6% 63.7

Southeast 63.7 7.0% 82.5 3.9% 82.5 3.9% 66.7 6.9% 63.7

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

Chapter 2

Appendix Table A-11 Definition Regions for Characterizing Diets of Dairy Cattle (all Appendix Table A-11 Definition of Regionsoffor Characterizing the Diets of Dairy the Cattle (all years) and Foraging Cattle 1990-2006 years) and Foraging Cattle 1990-2006 Region & State(s) West Alaska Arizona Hawaii Idaho Nevada New Mexico Oregon Utah Washington

Northern Great Plains Colorado Kansas Montana Nebraska North Dakota South Dakota Wyoming California California

Midwest Illinois Indiana Iowa Michigan Minnesota Missouri Ohio Wisconsin

Northeast Connecticut Delaware Maine Maryland Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont West Virginia

South Central Arkansas Louisiana Oklahoma Texas

Southeast Alabama Florida Georgia Kentucky Mississippi North Carolina South Carolina Tennessee Virginia

Source: EPA 2015

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 Alaska Arizona California Colorado Hawaii Idaho Montana Nevada New Mexico Oregon Utah Washington Wyoming

Central Illinois Indiana Iowa Kansas Michigan Minnesota Missouri Nebraska North Dakota Ohio South Dakota Wisconsin

Northeast Connecticut Delaware Maine Maryland Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont West Virginia

Source: EPA 2015

37

Southeast Alabama Arkansas Florida Georgia Kentucky Louisiana Mississippi North Carolina Oklahoma South Carolina Tennessee Texas Virginia

Chapter 2

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

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

Appendix Table A-13 Methane Emissions from Cattle Enteric Fermentation, 1990-2013 Animal Type Dairy Calves Cows Replacements 7-11 months Replacements 12-23 months Beef

1990

1995

2000

2005

2006

2007 2008 kt CH4

2009

2010

2011

2012

2013

1,574

1,498

1,519

1,503

1,534

1,601

1,622

1,639

1,626

1,643

1,668

1,664

62

59

59

54

55

58

58

58

57

57

58

58

1,242

1,183

1,209

1,197

1,219

1,271

1,289

1,304

1,287

1,301

1,324

1,325

58

56

55

56

57

60

60

61

62

63

62

61

212

201

196

196

203

213

216

216

221

222

224

220

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 Total Feedlot Cattle

306 375

375 416

323 502

299 488

299 521

296 556

290 554

300 552

301 559

283 570

267 559

269 560

6,338

6,917

6,589

6,510

6,615

6,725

6,700

6,661

6,602

6,510

6,416

6,348

Total

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 DAIRY Calves Cows Replacements 7-11 months Replacements 12-23 months BEEF Bulls

(kg CH4/head/year)

Calves Cows Replacements 7-11 months Replacements 12-23 months Steer Stockers Heifer Stockers Total Feedlot Sheep Horses Swine Goats American Bison Mules and Asses

11 95 60 70 58 60 43 8 18 2 5 82 10

Note: kg CH4 is kilograms methane. Source: EPA 2015, IPCC 2006.

12 144 46 69 98

38

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

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

N2O Total MMT C02 eq. 0.44 0.13 0.57 0.01 0.00 0.01 1.44 0.28 1.73 0.25 0.15 0.40 10.24 1.46 11.70 1.15 0.76 1.90 0.03 0.01 0.05 0.03 0.03 0.06 0.79 0.07 0.86 0.80 0.20 1.00 0.03 0.01 0.04 3.25 0.59 3.84 1.55 0.32 1.86 1.38 0.34 1.72 8.93 1.56 10.49 1.39 1.47 2.86 0.29 0.08 0.38 0.13 0.02 0.14 0.05 0.02 0.06 0.11 0.06 0.18 0.02 0.01 0.02 1.50 0.43 1.92 2.77 0.82 3.59 0.55 0.11 0.66 1.06 0.27 1.33 0.22 0.05 0.26 1.30 1.61 2.91 0.19 0.03 0.21 0.02 0.01 0.03 0.01 0.01 0.02 1.97 0.21 2.19 0.83 0.30 1.13 4.60 0.40 5.00 0.13 0.06 0.19 1.11 0.40 1.50 1.59 0.33 1.92 0.52 0.14 0.66 0.81 0.36 1.17 0.00 0.00 0.01 0.32 0.06 0.37 0.75 0.33 1.08 0.19 0.05 0.23 3.62 2.00 5.62 0.72 0.13 0.84 0.15 0.05 0.21 0.28 0.10 0.37 1.24 0.32 1.57 0.04 0.02 0.06 2.50 1.11 3.60 0.10 0.06 0.16 61.39 17.31 78.70

CH4 State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Total

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.

39

Chapter 2

Chapter 2

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

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

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Total

Dairy cattle

0.0133 0.0005 1.2876 0.0073 9.9547 0.7402 0.0253 0.0067 0.5376 0.1877 0.0103 3.1659 0.2116 0.3529 0.5249 0.5689 0.0391 0.0159 0.0359 0.0634 0.0129 1.1848 0.8083 0.0147 0.1437 0.0445 0.1890 0.1654 0.0162 0.0066 1.9197 0.7868 0.0640 0.0319 0.5041 0.2018 0.4455 0.4334 0.0008 0.0285 0.3005 0.0339 2.4767 0.4225 0.1501 0.0721 1.1650 0.0087 2.3711 0.0214 31.7743

Beef cattle

0.0602 0.0005 0.0414 0.0522 0.1175 0.1095 0.0004 0.0003 0.0799 0.0444 0.0072 0.0518 0.0311 0.0172 0.1286 0.2131 0.0662 0.0413 0.0008 0.0033 0.0005 0.0159 0.0433 0.0455 0.1075 0.1131 0.2538 0.0166 0.0003 0.0006 0.0302 0.0088 0.0232 0.0611 0.0264 0.1344 0.0428 0.0171 0.0001 0.0165 0.1242 0.0566 0.5980 0.0244 0.0011 0.0451 0.0313 0.0131 0.0308 0.0544 3.0036

Poultry

Swine

0.3143 0.0056 0.0181 0.1214 0.1042 0.1029 0.0068 0.0221 0.1576 0.5008 0.0084 0.0166 0.0119 0.0389 0.0482 0.0017 0.0435 0.0618 0.0077 0.0359 0.0009 0.0252 0.0412 0.2567 0.0845 0.0103 0.0199 0.0012 0.0024 0.0025 0.0170 0.0175 0.3806 0.0017 0.0336 0.1036 0.0246 0.0392 0.0025 0.1411 0.0066 0.0221 0.1691 0.0826 0.0009 0.0409 0.0369 0.0150 0.0137 0.0008 3.2233

Goats

MMT CO2 eq. 0.0460 0.0004 0.0001 0.0000 0.0858 0.0007 0.0665 0.0004 0.0399 0.0013 0.1810 0.0002 0.0003 0.0000 0.0019 0.0000 0.0024 0.0005 0.0611 0.0006 0.0046 0.0001 0.0091 0.0001 1.2863 0.0002 0.9647 0.0002 8.2221 0.0004 0.5982 0.0003 0.1371 0.0004 0.0006 0.0002 0.0004 0.0000 0.0063 0.0001 0.0011 0.0001 0.2660 0.0002 1.8686 0.0002 0.2284 0.0002 0.7209 0.0007 0.0381 0.0001 0.8344 0.0002 0.0005 0.0001 0.0004 0.0000 0.0020 0.0000 0.0000 0.0002 0.0151 0.0002 4.1306 0.0004 0.0340 0.0000 0.5345 0.0003 1.1355 0.0005 0.0013 0.0002 0.3099 0.0003 0.0002 0.0000 0.1240 0.0004 0.3150 0.0001 0.0694 0.0005 0.3246 0.0077 0.1800 0.0001 0.0003 0.0001 0.1133 0.0003 0.0061 0.0002 0.0007 0.0001 0.0733 0.0004 0.0156 0.0001 23.0582 0.0199

Horses

0.0048 0.0001 0.0080 0.0047 0.0111 0.0059 0.0010 0.0004 0.0099 0.0056 0.0004 0.0032 0.0033 0.0055 0.0033 0.0039 0.0074 0.0049 0.0007 0.0015 0.0011 0.0047 0.0034 0.0047 0.0061 0.0053 0.0035 0.0013 0.0005 0.0015 0.0027 0.0050 0.0035 0.0025 0.0062 0.0086 0.0036 0.0066 0.0001 0.0045 0.0038 0.0048 0.0318 0.0032 0.0006 0.0047 0.0033 0.0013 0.0055 0.0039 0.2240

Sheep

0.0002 0.0001 0.0025 0.0002 0.0100 0.0051 0.0001 0.0001 0.0002 0.0002 0.0002 0.0028 0.0006 0.0006 0.0021 0.0008 0.0005 0.0002 0.0001 0.0001 0.0001 0.0010 0.0016 0.0002 0.0009 0.0028 0.0009 0.0009 0.0001 0.0001 0.0012 0.0008 0.0003 0.0009 0.0014 0.0009 0.0025 0.0010 0.0001 0.0002 0.0032 0.0004 0.0123 0.0035 0.0001 0.0010 0.0006 0.0004 0.0010 0.0044 0.0715

Total

0.4392 0.0069 1.4441 0.2527 10.2387 1.1448 0.0339 0.0315 0.7881 0.8005 0.0312 3.2495 1.5450 1.3802 8.9295 1.3869 0.2943 0.1248 0.0457 0.1108 0.0166 1.4977 2.7665 0.5503 1.0643 0.2141 1.3017 0.1860 0.0199 0.0134 1.9710 0.8342 4.6026 0.1320 1.1066 1.5852 0.5205 0.8076 0.0037 0.3152 0.7534 0.1877 3.6203 0.7163 0.1531 0.2775 1.2434 0.0394 2.4957 0.1005 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

40

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

Chapter 2

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

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Total

0.0022 0.0002 0.1144 0.0022 1.1133 0.1128 0.0076 0.0021 0.0455 0.0232 0.0020 0.4462 0.0707 0.1030 0.1578 0.1209 0.0170 0.0025 0.0125 0.0217 0.0048 0.2720 0.3562 0.0030 0.0534 0.0108 0.0366 0.0177 0.0054 0.0029 0.1969 0.2555 0.0130 0.0143 0.1756 0.0294 0.0790 0.2138 0.0004 0.0038 0.0730 0.0106 0.2850 0.0704 0.0511 0.0195 0.1620 0.0037 0.9368 0.0045 5.739

Beef cattle

Poultry

0.0050 0.0000 0.1543 0.0000 0.2755 0.5975 0.0002 0.0002 0.0027 0.0039 0.0007 0.1332 0.0906 0.0574 0.7224 1.2832 0.0080 0.0024 0.0004 0.0057 0.0002 0.0871 0.1755 0.0048 0.0289 0.0207 1.4551 0.0048 0.0001 0.0002 0.0107 0.0142 0.0035 0.0272 0.0929 0.1987 0.0377 0.0427 0.0000 0.0014 0.1811 0.0024 1.5660 0.0156 0.0006 0.0131 0.1396 0.0024 0.1358 0.0401 7.647

MMT CO2 eq. 0.1201 0.0018 0.0019 0.1421 0.0394 0.0081 0.0040 0.0224 0.0192 0.1612 0.0018 0.0019 0.0115 0.0585 0.0766 0.0018 0.0379 0.0096 0.0047 0.0339 0.0010 0.0235 0.0582 0.0829 0.0974 0.0015 0.0184 0.0018 0.0018 0.0018 0.0019 0.0124 0.1299 0.0018 0.0509 0.0258 0.0091 0.0546 0.0018 0.0391 0.0071 0.0203 0.0868 0.0092 0.0010 0.0417 0.0149 0.0142 0.0132 0.0008 1.583

Swine

0.0027 0.0000 0.0045 0.0037 0.0026 0.0207 0.0000 0.0002 0.0001 0.0035 0.0003 0.0011 0.1370 0.1077 0.5824 0.0597 0.0090 0.0000 0.0000 0.0006 0.0001 0.0317 0.2178 0.0132 0.0772 0.0049 0.0903 0.0000 0.0000 0.0002 0.0000 0.0018 0.2466 0.0039 0.0601 0.0636 0.0001 0.0341 0.0000 0.0074 0.0347 0.0047 0.0206 0.0210 0.0000 0.0075 0.0007 0.0001 0.0082 0.0028 1.890

Total

0.1299 0.0021 0.2751 0.1481 1.4308 0.7392 0.0118 0.0248 0.0675 0.1919 0.0048 0.5824 0.3098 0.3268 1.5392 1.4656 0.0719 0.0145 0.0177 0.0619 0.0060 0.4145 0.8076 0.1040 0.2570 0.0379 1.6004 0.0243 0.0074 0.0050 0.2095 0.2839 0.3929 0.0472 0.3796 0.3176 0.1259 0.3453 0.0022 0.0518 0.2959 0.0380 1.9583 0.1162 0.0527 0.0819 0.3173 0.0204 1.0941 0.0482 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

41

Chapter 2

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

Appendix Table A-18 Waste Characteristics Data

Appendix Table A-18 Waste Characteristics Data Animal Group Dairy Cows Dairy Heifers

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)

680

0.62

0.24

10.99

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.

42

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

Chapter 2

Appendix Table A-19 State Volatile Solids Production Rates in 2013

Appendix Table A-19 State Volatile Solids Production Rates in 2013 State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Source: EPA 2015. 1(NOF) Not on feed. 2(OF) On feed.

Dairy Cow

Dairy Heifer

Beef Cow NOF1

8.66 7.64 11.54 7.96 11.33 11.73 10.63 10.30 10.48 10.52 8.46 11.48 10.26 10.98 11.09 11.01 9.21 8.32 10.31 10.28 9.75 11.69 10.36 8.63 8.84 10.84 10.92 11.11 10.70 9.89 11.94 11.07 10.77 10.15 10.50 9.73 10.58 10.39 10.15 9.61 10.91 9.46 11.07 11.09 10.28 10.17 11.60 9.00 10.96 10.86

8.51 8.51 8.45 8.51 8.51 8.45 8.49 8.49 8.51 8.51 8.51 8.45 8.46 8.46 8.46 8.46 8.49 8.51 8.49 8.49 8.49 8.46 8.46 8.51 8.46 8.45 8.46 8.45 8.49 8.49 8.45 8.49 8.49 8.46 8.46 8.45 8.51 8.49 8.49 8.51 8.46 8.49 8.45 8.45 8.49 8.49 8.51 8.49 8.46 8.45

7.46 8.48 8.48 7.46 8.48 8.48 7.51 7.51 7.46 7.46 8.48 8.48 7.12 7.12 7.12 7.12 7.46 7.46 7.51 7.51 7.51 7.12 7.12 7.46 7.12 8.48 7.12 8.48 7.51 7.51 8.48 7.51 7.46 7.12 7.12 7.46 8.48 7.51 7.51 7.46 7.12 7.46 7.46 8.48 7.51 7.46 8.48 7.51 7.12 8.48

Beef Heifer Beef Steer NOF NOF kg/day/1,000 kg mass 7.41 7.49 8.59 8.61 8.28 8.61 7.37 7.49 8.16 8.61 8.03 8.61 7.47 7.54 7.31 7.54 7.43 7.49 7.38 7.49 8.46 8.61 8.19 8.61 6.83 7.12 6.86 7.12 6.63 7.12 6.59 7.12 7.22 7.49 7.41 7.49 7.32 7.54 7.28 7.54 7.39 7.54 6.79 7.12 6.78 7.12 7.36 7.49 6.94 7.12 8.45 8.61 6.65 7.12 8.34 8.61 7.35 7.54 7.39 7.54 8.25 8.61 7.27 7.54 7.39 7.49 6.88 7.12 6.85 7.12 7.17 7.49 8.38 8.61 7.25 7.54 7.47 7.54 7.39 7.49 6.79 7.12 7.35 7.49 7.06 7.49 8.31 8.61 7.21 7.54 7.31 7.49 8.09 8.61 7.34 7.54 6.93 7.12 8.38 8.61

43

Beef Heifer OF2

Beef Steer OF

4.40 4.40 4.36 NA 4.40 4.36 4.39 4.39 4.40 4.40 4.40 4.36 4.37 4.37 4.37 4.37 4.39 4.40 4.39 4.39 4.39 4.37 4.37 4.40 4.37 4.36 4.37 4.36 4.39 4.39 4.36 4.39 4.39 4.37 4.37 4.36 4.40 4.39 4.39 4.40 4.37 4.39 4.36 4.36 4.39 4.39 4.40 4.39 4.37 4.36

4.03 4.02 3.98 NA 4.02 3.98 4.02 4.02 4.03 4.02 4.03 3.98 3.99 3.99 3.99 3.99 4.00 4.03 4.02 4.01 4.01 3.99 3.99 4.03 3.99 3.98 3.99 3.98 4.01 4.02 3.99 4.01 4.01 4.00 3.99 3.98 4.02 4.01 4.01 4.03 3.99 4.03 3.98 3.98 4.01 4.01 4.02 4.01 3.99 3.98

Chapter 2

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

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

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Dairy Anaerobic Liquid/Slurry Lagoon and Deep Pit 75 47 78 75 73 65 69 73 79 76 76 69 72 70 70 74 73 77 63 72 67 67 68 76 73 61 72 70 64 71 73 65 73 66 69 76 64 69 69 75 69 73 76 65 63 71 64 69 66 63

37 15 57 34 32 22 25 31 55 39 57 25 29 27 25 32 31 45 21 30 24 23 24 40 30 19 27 26 22 28 31 23 31 22 26 37 21 26 26 37 24 31 41 22 21 28 21 26 23 20

Swine Beef Anaerobic Liquid/Slurry Liquid/Slurry Lagoon and Deep Pit percent 75 36 38 47 15 15 77 47 52 76 37 35 72 31 41 68 24 24 69 25 26 73 31 31 79 53 53 75 38 37 76 57 57 66 22 22 72 28 27 71 27 27 70 26 26 74 32 32 73 31 30 77 46 46 63 21 21 72 30 31 68 25 25 67 24 24 69 24 24 76 39 41 73 30 30 64 21 21 72 27 27 71 27 25 65 22 22 71 29 28 71 28 30 66 23 23 75 36 30 66 22 22 70 27 27 76 35 36 63 21 22 70 27 27 69 26 26 75 38 36 70 25 25 74 32 31 76 44 38 68 24 24 63 21 21 72 31 29 66 22 23 70 26 26 68 24 23 64 21 22

Source: EPA 2015, IPCC 2006. 1(MCF) Methane conversion factors represent weighted average of multiple animal types.

44

Poultry Anaerobic Lagoon 75 47 74 75 74 65 69 73 79 75 76 68 72 71 70 74 73 77 64 73 68 67 67 76 74 64 72 70 65 71 70 66 73 66 70 76 63 70 69 75 70 73 77 65 63 71 65 69 67 64

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

Appendix Table A-21 Maximum Methane Generation Potential, B0

Appendix Table A-21 Maximum Methane Generation Potential, B0 Animal GroupTable Appendix

m3 CH4/kg Methane VS added1 Generation Potential, Source A-21 Maximum B0

Dairy Group Cows 0.24VS added1 Morris 1976 Animal m3 CH4/kg Source Dairy Heifers 0.17 Bryant et al. 1976 Dairy Cows 0.24 Morris 1976 Feedlot Steers/Heifers 0.33 Hashimoto 1981 Dairy Heifers 0.17 Bryant et al. 1981 1976 NOF Beef Hashimoto Feedlot Steers/Heifers 0.33 Hashimoto 1981 bull B0 American Bison 0.17 Based on the beef NOF NOF 0.17 Hashimoto 1981 Swine Beef 0.48 1984 American 0.17 Based on the beef NOF bull B0 Sheep* Bison 0.34 EPA 1992 Swine 0.48 Hashimoto 1984 Goats 0.17 EPA 1992 Sheep* 0.34 EPA 1992 Horses 0.33 Goats 0.17 1992 Mules 0.33 BasedEPA on the horse B0 Horses 0.33 EPA 1992 Broilers 0.36 Hill 1984 Mules 0.33 Based Hill on the horse B0 Other Chickens 0.39 1982 Broilers 0.36 Hill 1984 Turkeys Other Chickens 0.39 Hill 1982 Dairy Cows 0.24 Morris 1976 Turkeys 0.36 Hill 1984 Source: EPA 2015, IPCC 2006. 1 mDairy 3 CH4Cows 0.24 /kg VS added is cubic meter methane per kilogram of volatile solids. Morris 1976 Source: EPA 2015, IPCC 2006. Appendix Table A-22

Methane Conversion Factors for Dry Systems

m3 CH4/kg VS added is cubic meter methane per kilogram of volatile solids. Cool Climate Temperate Appendix Table A-22 Methane Conversion Factors for Dry Systems Appendix Table A-22 Methane MCF Conversion Factors for 1 Climate MCF Cool Climate Temperate Waste Management System percent MCF1 Climate MCF Aerobic Treatment 0 0 Waste Management percent Anaerobic Digester System 0 0 Aerobic Treatment 03 03 Cattle Deep Litter (<1 month) Anaerobic 0 0 Cattle DeepDigester Litter (>1 month) 21 44 Cattle Deep Litter (<1 month) 3 3 Composting - In Vessel 0.5 0.5 Cattle Deep Litter 21 44 Composting - Static(>1 Pilemonth) 0.5 0.5 Composting - In Vessel 0.5 0.5 Composting-Extensive/Passive 1 Composting - Static Pile 0.5 0.5 Composting-Intensive 1 Composting-Extensive/Passive 0.5 1 Daily Spread 0.1 0.5 Composting-Intensive 0.5 1 Dry Lot 1 1.5 Daily 0.1 0.5 Fuel Spread 10 10 Dry Lot 1 1.5 Pasture Fuel 10 10 Poultry with bedding 1.5 1.5 Pasture 1 1.5 Poultry without bedding 1.5 Poultry with bedding 1.5 1.5 Solid Storage 2 4 Source: EPAwithout 2015, IPCC 2006. Poultry bedding 1.5 1.5 1 MCF is methane conversion factor. Solid Storage 2 4 1

Source: EPA 2015, IPCC 2006. 1 MCF is methane conversion factor.

45

Warm Climate

Dry Systems MCF

Warm Climate MCF 0 0 0 30 0 76 30 0.5 76 0.5 0.5 1.5 0.5 1.5 1.5 1 1.5 5 1 10 52 10 1.5 2 1.5 1.5 5 1.5 5

Chapter 2

Chapter 2

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

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 State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Beef Beef Dairy Dairy Swine Swine Layer Broiler Turkey Sheep Goats Horses Feedlot Feedlot Cow Heifer Market Breeding Heifer Steer percent 2.0 2.0 16.9 1.9 54.2 53.9 32.3 1.5 1.5 1.5 1.5 1.5 1.2 1.2 16.4 1.1 8.1 8.1 12.9 1.5 1.5 1.0 1.0 1.0 21.0 21.0 79.5 21.9 77.3 75.5 61.0 3.0 3.0 3.0 3.0 3.0 1.4 1.4 9.7 1.3 50.1 50.0 1.5 1.5 1.5 1.5 1.5 1.5 2.0 2.0 49.8 1.8 47.7 47.7 10.2 1.5 1.5 1.5 1.5 1.5 1.1 1.1 47.2 1.1 29.2 29.0 39.6 1.5 1.5 1.0 1.0 1.0 1.3 1.3 13.2 1.2 8.3 8.3 4.9 1.5 1.5 1.0 1.0 1.0 1.3 1.3 14.3 1.3 34.7 34.7 5.1 1.5 1.5 1.0 1.0 1.0 2.2 2.2 42.4 2.0 17.0 16.8 34.0 1.5 1.5 1.5 1.5 1.5 2.0 2.0 22.3 1.9 54.8 54.3 32.1 1.5 1.5 1.5 1.5 1.5 2.2 2.2 57.7 2.1 40.7 40.7 20.2 1.5 1.5 1.5 1.5 1.5 1.1 1.1 48.0 1.1 25.3 25.3 41.3 1.5 1.5 1.0 1.0 1.0 1.2 1.2 21.1 1.1 32.8 33.0 2.9 1.5 1.5 1.0 1.0 1.0 1.2 1.2 18.6 1.1 31.7 31.8 1.5 1.5 1.5 1.0 1.0 1.0 1.2 1.2 23.2 1.1 48.5 48.7 1.5 1.5 1.5 1.0 1.0 1.0 1.2 1.2 39.4 1.2 35.2 35.2 3.0 1.5 1.5 1.0 1.0 1.0 1.3 1.3 5.6 1.2 48.7 48.7 5.1 1.5 1.5 1.0 1.0 1.0 2.1 2.1 11.8 2.0 7.4 7.4 46.9 1.5 1.5 1.5 1.5 1.5 1.2 1.2 10.8 1.2 10.8 10.8 4.6 1.5 1.5 1.0 1.0 1.0 1.3 1.3 12.0 1.2 33.5 33.9 5.1 1.5 1.5 1.0 1.0 1.0 1.3 1.3 10.4 1.2 14.9 14.8 4.8 1.5 1.5 1.0 1.0 1.0 1.1 1.1 27.1 1.1 28.6 28.3 2.8 1.5 1.5 1.0 1.0 1.0 1.1 1.1 16.7 1.1 30.6 30.5 1.5 1.5 1.5 1.0 1.0 1.0 2.0 2.0 11.8 1.9 57.0 58.2 46.4 1.5 1.5 1.5 1.5 1.5 1.2 1.2 17.5 1.2 32.0 32.2 1.5 1.5 1.5 1.0 1.0 1.0 1.1 1.1 29.5 1.1 26.2 26.2 38.7 1.5 1.5 1.0 1.0 1.0 1.2 1.2 31.2 1.1 31.8 31.8 2.9 1.5 1.5 1.0 1.0 1.0 13.1 13.1 64.4 12.9 35.6 35.2 13.5 2.5 2.5 2.0 2.0 2.0 12.8 12.8 22.2 12.8 23.0 22.9 16.2 2.5 2.5 2.0 2.0 2.0 14.7 14.7 22.3 14.8 37.5 37.5 18.5 2.5 2.5 2.0 2.0 2.0 15.1 15.1 66.6 15.4 14.4 14.6 55.4 2.5 2.5 2.0 2.0 2.0 1.2 1.2 11.2 1.2 25.8 25.9 4.7 1.5 1.5 1.0 1.0 1.0 1.3 1.3 12.9 1.2 56.4 56.2 31.5 1.5 1.5 1.0 1.0 1.0 1.1 1.1 17.4 1.1 27.8 27.4 2.8 1.5 1.5 1.0 1.0 1.0 1.2 1.2 17.8 1.1 31.0 31.0 1.5 1.5 1.5 1.0 1.0 1.0 1.1 1.1 45.4 1.6 56.7 57.1 46.0 1.5 1.5 1.0 1.0 1.0 1.3 1.3 34.5 1.2 15.5 15.5 16.8 1.5 1.5 1.0 1.0 1.0 1.3 1.3 7.6 1.2 31.3 30.9 1.5 1.5 1.5 1.0 1.0 1.0 1.3 1.3 8.0 1.2 10.2 10.2 4.9 1.5 1.5 1.0 1.0 1.0 2.0 2.0 18.4 1.9 56.4 56.1 45.8 1.5 1.5 1.5 1.5 1.5 1.1 1.1 30.1 1.1 31.1 31.1 2.9 1.5 1.5 1.0 1.0 1.0 1.3 1.3 7.3 1.2 43.9 43.6 5.1 1.5 1.5 1.0 1.0 1.0 1.7 1.7 51.9 1.6 50.3 50.4 10.5 1.5 1.5 1.5 1.5 1.5 1.1 1.1 42.7 1.1 30.7 27.6 39.6 1.5 1.5 1.0 1.0 1.0 1.2 1.2 10.8 1.2 11.4 11.5 4.6 1.5 1.5 1.0 1.0 1.0 1.3 1.3 7.4 1.2 50.5 50.3 5.0 1.5 1.5 1.0 1.0 1.0 1.3 1.3 38.4 1.2 17.1 16.7 9.1 1.5 1.5 1.0 1.0 1.0 14.2 14.2 22.4 14.2 27.1 27.0 17.6 2.5 2.5 2.0 2.0 2.0 1.1 1.1 16.3 1.1 27.0 26.9 2.8 1.5 1.5 1.0 1.0 1.0 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

46

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

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 Anaerobic Lagoon Cattle Deep Bed (active mix)

0 0 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 Pasture2

0.005 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.

kg N2O-N/kg Kjdl N is kilograms nitrogen in nitrous oxide per kilograms kjeldahl nitrogen. 1

2 Calculated

using Tier 3 DayCent Model simulations.

47

Chapter 2

Chapter 2

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

Appendix Table A-25 Nitrogen in Livestock Waste on Grazed Lands

Appendix Table A-27 Nitrogen in Livestock Waste on Grazed Lands Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

MMT N 4.1 4.1 4.3 4.3 4.4 4.5 4.5 4.4 4.3 4.2 4.1 4.1 4.1 4.1 4.1 4.1 4.2 4.0 4.0 4.0 3.9 3.8 3.7 3.7

Note: MMT N is million metric tons nitrogen. Source: EPA 2015

48

74 607,367 -61.8 75 323,527 -31.5 76 1,510,595 -188.1 411,928 -27.4 U.S. Agriculture and Forestry Greenhouse79Gas Inventory: 1990–2013 85 1,369,323 -235.2 88 63,503 -81.8 Appendix Table A-26 MLRA-Level Estimates of Mean Annual Soil 89 Carbon Stock Changes 31,158 from Non-Federal -18.9 Appendix Table A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes 92 36,473 -22.0 Grasslands, 2003-2007 from Non-Federal Grasslands, 2003-2007 96 51,706 -16.5 MLRA1 Area Total dSOC 97 47,882 -12.3 ha Gg CO2 eq.2 98 301,989 -130.2 1 54,597 -52.8 99 63,472 -18.7 2 243,632 -333.8 101 154,267 -35.1 3 17,773 -86.5 103 328,349 -218.8 5 263,473 -345.0 104 140,345 -166.1 6 179,315 -35.1 105 626,326 -425.5 7 409,089 -4.4 106 572,613 -144.4 8 2,379,429 -4.3 109 926,578 -483.5 9 796,574 41.0 110 66,940 -41.0 10 2,071,584 142.9 112 2,230,417 -661.1 11 722,976 73.4 113 277,306 -143.2 12 145,949 -1.5 117 240,831 -88.5 13 625,994 24.5 119 341,603 -70.6 14 140,555 -33.4 121 632,837 -315.7 15 2,036,110 -174.5 122 940,743 -464.0 17 1,025,755 -95.4 123 339,212 -176.0 18 850,862 -14.4 124 250,471 -89.7 19 167,401 -51.9 125 201,304 -77.0 20 710,349 -71.3 126 385,164 -163.5 21 578,811 -89.1 127 189,799 -46.5 23 987,306 6.0 128 861,793 -490.2 24 531,483 162.7 129 204,725 -115.4 25 1,567,731 133.9 134 737,073 -603.6 26 310,276 6.3 136 1,117,885 -865.2 27 799,461 154.2 137 46,206 -43.2 29 333,839 -28.3 138 72,997 -37.6 30 1,193,928 -39.1 139 144,352 -81.1 31 146,278 -22.0 140 371,164 -130.4 32 807,861 -90.1 141 8,165 -2.4 MLRA1 Area Total dSOC 35 8,939,750 568.5 142 126,775 -68.8 2 ha Gg CO-15.6 2 eq. 36 1,339,729 35.6 143 39,121 145 10,074 -5.0 38 1,678,101 -57.5 147 404,552 -180.0 39 300,000 36.2 148 338,595 -126.7 40 2,644,850 113.4 151 78,578 1.8 41 2,032,033 -11.1 154 260,436 -82.6 42 7,117,114 231.7 155 1,209,929 -356.8 44 1,386,170 155.0 102A 802,261 -627.7 46 2,334,195 473.0 102B 56,522 -67.9 47 1,299,930 198.1 102C 451,346 -247.1 49 1,451,736 143.7 107A 62,367 -213.7 51 576,854 -128.6 107B 341,131 -1011.1 52 2,037,706 825.3 108A 42,462 -27.2 54 4,052,914 272.8 108B 106,015 -92.6 56 272,722 -92.5 108C 143,023 -321.7 57 217,910 -49.5 MLRA1 Area Total dSOC 108D 230,418 -572.3 61 237,646 17.1 ha Gg CO254.6 eq.2 111A 144,848 -93.2 62 133,877 64 1,962,501 -58.9 111B 119,404 -42.9 65 4,703,281 -109.5 111C 41,763 -12.2 66 970,886 -45.6 111D 59,226 -6.3 69 2,245,770 71.1 111E 27,900 -22.7 71 1,012,309 167.5 114A 104,534 -42.2 72 2,419,203 70.8 114B 121,249 -71.0 73 2,267,127 248.5 115A 53,780 -36.7 74 607,367 -61.8 115B 305,634 -137.4 75 323,527 -31.5 115C 345,948 -216.1 76 1,510,595 -188.1 116A 2,126,540 -821.3 79 411,928 -27.4 116B 507,825 -294.8 85 1,369,323 -235.2 116C 66,379 -28.6 88 63,503 -81.8 118A 616,870 -162.0 89 31,158 -18.9 118B 291,406 -82.2 92 36,473 -22.0 120A 265,303 -171.6 96 51,706 -16.5 120B 63,592 -25.8 97 47,882 -12.3 120C 11,235 -1.7 49 130A 98 301,989 -130.2 12,035 -8.3 99 63,472 -18.7 130B 203,692 -114.0 101 154,267 -35.1 131A 241,071 -158.0 103 328,349 -218.8 131B 42,168 -18.0

Chapter 2

Chapter 2

115B 305,634 -137.4 115C 345,948 -216.1 116A 2,126,540 -821.3 116B 507,825 -294.8 U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 116C 66,379 -28.6 118A 616,870 -162.0 118B 291,406 -82.2 Continued - Appendix Table A-26 MLRA-Level Estimates of Mean Annual Soil Carbon Stock Changes from NonMLRA1 Area Total dSOC 120A Grasslands, 2003-2007 265,303 -171.6 Federal ha Gg CO2 eq.2 120B 63,592 -25.8 78C 2,445,635 -63.2 120C 11,235 -1.7 80A 1,963,077 -347.1 130A 12,035 -8.3 80B 968,445 53.3 130B 203,692 -114.0 81A 2,866,367 -2.8 131A 241,071 -158.0 81B 1,940,970 97.6 131B 42,168 -18.0 81C 1,236,724 120.7 131C 140,088 -71.6 81D 516,702 -37.7 131D 29,985 -12.4 82A 401,734 13.3 133A 1,441,544 -1125.0 82B 57,923 -1.0 133B 1,187,959 -354.9 83A 1,706,897 123.5 135A 381,841 -304.5 83B 1,463,751 1.2 135B 309,250 -82.7 83C 755,825 55.5 144A 119,969 -55.9 83D 129,375 -7.5 144B 79,998 -46.5 83E 659,343 35.3 149A 31,531 -6.8 MLRA1 Area Total dSOC 84A 875,391 -131.7 149B 3,112 -0.9 ha Gg CO 2 eq.2 84B 745,691 -134.3 150A 1,165,952 -111.6 150B 249,079 -13.3 84C 113,856 -30.2 152A 38,047 -21.8 86A 1,453,945 -373.1 152B 79,521 -57.7 86B 351,296 5.5 153A 86,206 -44.0 87A 1,544,144 -23.5 153B 9,909 -11.4 87B 410,138 -149.7 153C 15,220 -11.8 90A 265,868 -168.5 153D 14,083 -13.5 90B 248,178 -208.1 156A 67,768 -8.4 91A 161,283 -85.5 156B 104,655 -17.2 91B 52,108 -43.2 22A 82,010 6.8 94A 130,138 -102.6 28A 1,330,232 262.1 94B 61,815 -50.0 28B 305,998 31.5 94C 25,098 -15.9 34A 3,017,610 140.3 95A 91,989 -91.8 34B 731,884 133.5 95B 223,059 -195.9 43A 364,330 -26.6 Total 180,415,846 -10889.0 Note: dSOC is dissolved soil organic carbon. 43B 2,443,145 375.3 1 MLRA = Major Land Resource Area 43C 233,023 -35.6 2 Gg CO eq. = Gigagrams carbon dioxide equivalent 2 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

50

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

Chapter 2

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

Emissions From Non-Federal Grasslands, 2003-2007 MLRA1

1 2 3 5 6 7 8 9 10 11 12 13 14 15 17 18 19 20 21 23 24 25 26 27 29 30 31 32 35 36 38 39 40 41 42 44 46 47 49 51 52 54 56 57 61

Area ha 54,597 243,632 17,773 263,473 179,315 409,089 2,379,429 796,574 2,071,584 722,976 145,949 625,994 140,555 2,036,110 1,025,755 850,862 167,401 710,349 578,811 987,306 531,483 1,567,731 310,276 799,461 333,839 1,193,928 146,278 807,861 8,939,750 1,339,729 1,678,101 300,000 2,644,850 2,032,033 7,117,114 1,386,170 2,334,195 1,299,930 1,451,736 576,854 2,037,706 4,052,914 272,722 217,910 237,646

Indirect N2O from NO3 Leached/Runoff

Direct Soil N2O

Indirect N2O from NH3/NOx Volitilization

Gg CO2 eq.2 81.5 466.8 68.2 674.3 265.0 105.7 1030.9 567.3 978.2 242.5 84.6 287.2 15.1 133.0 69.8 96.5 7.4 28.5 891.9 391.0 167.0 563.7 170.3 88.6 33.4 44.7 4.9 193.7 986.7 406.0 90.4 30.6 76.8 64.4 1321.3 1239.4 1115.9 634.0 432.1 197.2 510.9 884.0 112.3 142.1 56.3

51

11.3 49.1 3.0 44.9 1.6 0.0 2.5 3.3 2.8 0.0 0.1 0.4 2.5 12.2 3.5 7.5 0.4 0.3 7.2 0.0 0.0 0.1 0.3 0.0 0.1 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 6.8 2.2 1.7 0.0 0.1 0.0 0.0 0.4 4.2 0.2

2.3 14.0 1.1 16.3 2.8 4.1 27.1 11.6 27.6 8.3 1.8 7.4 1.7 18.7 9.7 8.7 1.4 5.1 12.2 12.8 5.3 20.0 3.3 7.2 2.1 7.9 1.0 12.1 102.5 19.8 12.4 4.1 14.4 11.0 145.8 22.1 38.1 17.7 27.6 8.9 29.4 64.7 7.9 8.1 3.9

Chapter 2

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

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1

62 64 65 66 69 71 72 73 74 75 76 79 85 88 89 92 96 97 98 99 101 103 104 105 106 109 110 112 113 117 119 121 122 123 124 125 126 127 128 129 134 136 137 138 139 140 141

Area ha 133,877 1,962,501 4,703,281 970,886 2,245,770 1,012,309 2,419,203 2,267,127 607,367 323,527 1,510,595 411,928 1,369,323 63,503 31,158 36,473 51,706 47,882 301,989 63,472 154,267 328,349 140,345 626,326 572,613 926,578 66,940 2,230,417 277,306 240,831 341,603 632,837 940,743 339,212 250,471 201,304 385,164 189,799 861,793 204,725 737,073 1,117,885 46,206 72,997 144,352 371,164 8,165

Indirect N2O from NO3 Leached/Runoff

Direct Soil N2O

Indirect N2O from NH3/NOx Volitilization

Gg CO2 eq.2 31.4 386.5 1194.1 229.9 674.5 343.5 774.3 1022.8 277.6 134.5 871.1 109.0 1174.6 91.5 53.2 94.5 68.9 80.2 353.5 50.3 349.7 283.5 208.8 789.1 298.1 949.5 92.3 1228.1 248.7 115.6 151.3 852.0 658.1 218.9 342.1 164.7 483.7 287.8 603.4 66.3 344.1 827.3 22.9 20.7 241.8 799.8 19.6

52

0.3 0.0 0.6 0.0 0.0 0.5 0.0 0.2 5.1 1.1 18.8 0.9 24.5 1.5 2.9 1.1 3.3 3.3 17.1 2.2 9.0 9.5 6.5 22.3 3.0 52.2 3.2 37.7 9.6 14.6 20.9 42.0 63.5 22.2 16.5 19.4 22.3 12.8 55.4 13.0 26.8 81.1 5.3 5.2 6.5 23.1 0.4

2.3 38.3 117.9 22.3 43.1 22.0 58.8 56.9 16.0 7.9 39.1 11.6 63.1 3.6 1.6 2.1 1.9 2.2 13.4 2.2 6.5 10.6 5.7 22.6 13.9 32.1 2.4 56.8 9.3 8.3 10.6 22.0 39.1 12.1 9.5 7.8 13.9 8.3 36.3 9.6 32.4 77.7 3.6 5.2 6.3 18.6 0.4

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

Chapter 2

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1

142 143 145 147 148 151 154 155 102A 102B 102C 107A 107B 108A 108B 108C 108D 111A 111B 111C 111D 111E 114A 114B 115A 115B 115C 116A 116B 116C 118A 118B 120A 120B 120C 130A 130B 131A 131B 131C 131D 133A 133B 135A 135B 144A 144B

Area ha 126,775 39,121 10,074 404,552 338,595 78,578 260,436 1,209,929 802,261 56,522 451,346 62,367 341,131 42,462 106,015 143,023 230,418 144,848 119,404 41,763 59,226 27,900 104,534 121,249 53,780 305,634 345,948 2,126,540 507,825 66,379 616,870 291,406 265,303 63,592 11,235 12,035 203,692 241,071 42,168 140,088 29,985 1,441,544 1,187,959 381,841 309,250 119,969 79,998

Indirect N2O from NO3 Leached/Runoff

Direct Soil N2O

Indirect N2O from NH3/NOx Volitilization

Gg CO2 eq.2 252.3 73.9 17.9 472.2 308.7 44.0 91.1 335.6 311.0 15.0 153.5 77.9 287.0 55.1 127.4 245.9 285.6 147.4 89.6 33.1 73.5 34.2 113.7 146.4 56.5 282.0 353.1 1554.0 367.5 71.4 248.2 116.3 226.2 76.7 14.5 12.2 161.1 149.7 30.8 101.5 14.1 567.7 413.2 315.0 194.0 202.9 148.5

53

5.6 2.6 0.6 28.4 19.2 0.8 25.0 76.4 3.0 0.0 1.4 1.1 3.5 1.3 2.7 9.8 12.0 8.2 4.0 2.5 3.8 1.5 5.0 6.6 2.5 8.7 8.2 113.0 20.0 3.0 33.2 8.5 13.5 4.0 0.8 0.8 13.1 6.1 1.3 3.3 1.5 108.2 59.1 18.7 15.3 8.0 4.7

5.0 1.5 0.4 22.3 16.0 1.8 14.2 45.4 18.5 1.1 9.7 2.7 11.8 1.5 3.7 6.5 9.4 4.7 3.9 1.6 2.0 1.1 3.8 4.4 2.1 8.8 11.8 74.3 18.9 1.9 17.6 7.5 10.6 2.5 0.4 0.5 8.6 9.0 1.3 5.5 1.0 81.9 41.4 17.6 10.6 5.0 3.0

Chapter 2

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

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1

149A 149B 150A 150B 152A 152B 153A 153B 153C 153D 156A 156B 22A 28A 28B 34A 34B 43A 43B 43C 48A 48B 4A 4B 53A 53B 53C 55A 55B 55C 58A 58B 58C 58D 60A 60B 63A 63B 67A 67B 70A 70B 70C 70D 77A 77B 77C

Area ha 31,531 3,112 1,165,952 249,079 38,047 79,521 86,206 9,909 15,220 14,083 67,768 104,655 82,010 1,330,232 305,998 3,017,610 731,884 364,330 2,443,145 233,023 1,536,456 317,086 58,168 85,756 958,782 1,768,162 422,626 419,991 590,906 863,882 6,188,798 3,783,888 199,531 528,343 1,792,553 668,113 1,647,732 658,307 1,454,712 2,202,593 1,974,891 1,854,571 2,043,147 279,833 702,904 640,671 1,205,012

Indirect N2O from NO3 Leached/Runoff

Direct Soil N2O

Indirect N2O from NH3/NOx Volitilization

Gg CO2 eq.2 20.7 1.7 380.2 53.9 11.3 23.1 30.3 3.7 10.3 9.1 12.3 27.2 71.0 476.9 77.7 963.9 217.6 516.3 1403.7 203.7 866.2 168.4 51.4 16.0 194.8 349.2 102.9 84.5 148.2 226.8 2457.9 943.3 41.0 97.6 521.0 336.3 656.5 291.9 323.9 549.2 432.4 315.7 314.1 29.7 228.2 126.6 352.9

54

2.6 0.2 16.6 2.4 1.9 2.9 6.8 0.4 1.3 1.2 2.3 5.4 0.9 0.1 0.0 0.4 0.3 2.2 5.7 1.4 3.4 0.7 11.2 5.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1.5 0.1 35.9 5.3 2.0 3.2 5.8 0.6 1.0 0.9 1.8 3.4 1.0 17.6 3.2 39.0 9.4 6.7 36.3 3.3 22.1 4.1 1.8 1.5 14.3 27.5 7.6 6.9 9.7 17.0 107.6 57.8 3.0 9.7 22.6 10.0 18.2 9.4 31.8 48.0 43.7 39.5 41.0 4.3 18.1 14.0 27.2

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

Chapter 2

Continued - Appendix Table A-27 MLRA-Level Estimates of Mean Annual Direct and Indirect N2O Emissions from NonFederal Grasslands, 2003-2007

MLRA1

Area

Indirect N2O from NO3 Leached/Runoff

Direct Soil N2O

ha 1,405,153 77D 1,664,797 77E 639,230 78A 2,491,438 78B 2,445,635 78C 1,963,077 80A 968,445 80B 2,866,367 81A 1,940,970 81B 1,236,724 81C 516,702 81D 401,734 82A 57,923 82B 1,706,897 83A 1,463,751 83B 755,825 83C 129,375 83D 659,343 83E 875,391 84A 745,691 84B 113,856 84C 1,453,945 86A 351,296 86B 1,544,144 87A 410,138 87B 265,868 90A 248,178 90B 161,283 91A 52,108 91B 130,138 94A 61,815 94B 25,098 94C 91,989 95A 223,059 95B 180,415,846 Total Note: N2O is nitrous oxide. NO3 is nitric oxide. 1 MLRA = Major Land Resource Area 2 Gg CO2 eq. = Gigagrams carbon dioxide equivalents

Indirect N2O from NH3/NOx Volitilization

Gg CO2 eq.2 179.5 511.3 211.2 766.2 835.2 741.6 346.7 525.9 444.0 320.4 53.5 54.2 25.2 359.0 504.3 122.0 18.5 76.4 313.1 585.5 78.3 1742.6 220.6 444.7 182.0 440.0 422.6 135.5 87.0 223.3 152.5 56.6 174.6 379.0 70678.7

55

0.0 0.0 0.0 0.0 0.0 17.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 26.9 14.5 3.8 34.9 7.7 17.6 17.6 13.3 11.8 6.8 6.5 8.9 4.0 1.7 8.2 15.3 1828.9

26.7 44.9 15.3 58.9 60.2 53.0 28.3 68.4 50.3 32.5 11.1 10.1 1.7 38.9 31.7 17.5 3.0 15.1 24.4 35.5 5.7 53.4 10.7 42.9 13.2 12.8 11.9 7.4 2.6 5.6 3.0 1.1 5.2 11.3 3944.0

Chapter 3 Download data: http://dx.doi.org/10.15482/USDA.ADC/1264151

Cropland Agriculture 1 Box 1-1). Large amounts of nitrogen are added to crops from fertilizer amendments and legume cropping, which both stimulate N2O 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.

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 (N2O), 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).

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 N2O emissions, followed by small grain crops such as wheat, barley and rye, other cropland, and hay

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.

Table 3-1 Estimates and Uncertainties for Cropland Greenhouse Gas Table Error! No text of specified style in document.-9 Estimates and Uncertainties for Emissions, 2013 Gas Emissions, 2013 Cropland Greenhouse GHG Emissions

Source N2O Soils Direct Soils Indirect1 Residue Burning CH4 Residue Burning Rice Cultivation 2 CO2 Mineral Soils Organic Soils Liming of Soils

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

Total Emissions

168 136 32 0.1 9 0.3 8 (1) (34) 27 6 209

Net Emissions3

175

Lower Bound MMT CO2 eq. 142 189 21 0.1 4 0.2 4 (39) (71) 18 0 165 129

Upper Bound 230 282 102 0.1 16 0.4 14 38 2 39 8 294 249

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 Soils Indirect 2 Does

N2O emissions account for both volatilization and leaching/runoff. not include CO2 emissions from urea fertilization. sources and sinks.

3 Includes

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

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.)

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

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.

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.

dioxide equivalent per hectare per year.)

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

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 Table 3-2 Summary of Greenhouse Gas Emissions from Cropland Agriculture, and reported at the MLRA level. Partitioning was Table Error! No text of specified style in document.-10 Summary of Greenhouse Gas Emissions 1990, 1995, 2000, 2005-2013 performed for rotations because emissions are From Cropland Agriculture, 1990, 1995, 2000, 2005-2013 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013 thought to be better correlated to farming systems as Source MMT CO2 eq. opposed to individual crops, because the emissions 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 in a given year reflect management history. For 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 example, wheat might be growing during a particular 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 year, but the emissions for that year are partly 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 (and sometimes largely) due to what happened the 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 previous year(s). Emissions were partitioned into 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 CO22 nine major cropping rotations (Figure 3-1a) by (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) generating queries for each MLRA. That is, for each 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 MLRA, the emissions and land area for a particular 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 rotation were extracted from the databases. The 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 queries were performed in a particular order (top to Note: Parentheses indicate a net sequestration. MMT CO eq. is million metric tons carbon dioxide equivalent. CH is methane; bottom in Figure 3-1a, Table 3-3) and were mutually N O is nitrous oxide; CO is carbon dioxide. 1 Soils Indirect N2O emissions account for both volatilization and leaching/runoff. exclusive. For example, land area used predominately 2 Does not include CO2 emissions from urea fertilization. 3 Includes sources and sinks. for production of row crops that was also irrigated 2

2

4

2

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Chapter 3

60

Fallow Rice

50

Million hectares

Irrigated Hay

40

SmallGrain Small Grain Row Crop RowCrop

30

Low Residue LowResidue 20

CRP Other Cropland OtherCropland

10 0 1990

1992

1994

1996

1998

2000

2002

2004

2006

Figure 3-1a U.S. Planted Cropland Area by Rotation Category, 1990-2007 (CRP is USDA Conservation Reserve Program)

Million hectares

40 Small Grains (Barley, Rye, Millet, Oats) Corn (Total Grain + Silage)

30

Hay Rice

20

Sorghum (Total Grain + Silage) Wheat

10

Oilseeds (Incl Soybeans)

0 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 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

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.

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

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

Table 3-3 Tier 3 Cropland Area by Management Practice, 2013 Current Management Fallow Rice Irrigated Hay Small Grain Row Crop Low Residue USDA Conservation Reserve Program Other Cropland

59

Area million ha 10.3 1.9 17.4 16.2 18.5 57.4 4.4

Total Tier 3 Cropland % 7.2 1.3 12.1 11.3 12.9 40.0 3.0

12.5 4.9

8.7 3.4

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(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.

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.

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.

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

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. 60

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

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

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 N2O 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.

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

Chapter 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).

16 Synthetic Fertilizer Biological Fixation N from Residue Manure Total

14

Tg N

12 10 8 6 4 2 0 1990

1992

1994

1996

1998

2000

2002

2004

2006

Figure 3-2 Annual Nitrogen Inputs to Cropland Soil, 1990-2007 Figure Annual Nitrogen (Tg N is3-2 teragrams nitrogen) Inputs to Cropland Soil, 1990-2007

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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.

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 Table Nitrous Emissions from Differently Table3-4Error! NoOxide text of specified style in document.-12 Nitrous Oxide Emissions FromN2O emissions. Cropped Soils, 5-Year Means Specifically, amount and timing of precipitation, Differently Cropped Soils, 5-year Means temperature patterns, and soil carbon and nitrogen 1992 1997 2002 2007 availability interact to influence N O emissions. 1 Rotations MMT CO2 eq. 2 Because the responses of N2O emissions to the USDA Conservation Reserve Program 2.6 3.3 2.8 2.8 controlling variables are often non-linear and the Direct 2.2 2.8 2.4 2.3 interactions complex, the correlations between any Volatilization 0.4 0.5 0.4 0.4 single variable (or even groups of variables) and Leaching & Runoff 0.1 0.1 0.1 0.0 measured emissions are typically weak (Stehfest and Fallow 6.6 6.3 4.6 4.5 Bouwman 2006, Nishina et al. 2012, Philibert 2012). Direct Volatilization Leaching & Runoff Hay Direct Volatilization Leaching & Runoff Irrigated Direct Volatilization Leaching & Runoff Low Residue Direct Volatilization Leaching & Runoff Other Cropland Direct Volatilization Leaching & Runoff Rice Direct Volatilization Leaching & Runoff Row Crop Direct Volatilization Leaching & Runoff Small Grain Direct Volatilization Leaching & Runoff Tier 1 cropped land Direct Volatilization Leaching & Runoff Histosol Cultivation2 All Direct All Volatilization All Leaching & Runoff

Total Rotations1

5.8 0.5 0.3 15.4 13.6 0.9 1.0 19.3 14.1 1.5 3.7 2.6 2.0 0.2 0.4 5.4 4.5 0.5 0.4 4.0 3.5 0.2 0.3 52.4 42.9 5.5 4.0 13.3 11.7 1.1 0.6 24.5 18.8 2.3 3.4 2.7 119.1 13.0

14.0 1992 148.8

5.6 0.5 0.3 17.8 15.9 0.9 1.0 22.6 15.7 1.5 5.4 3.0 2.3 0.2 0.5 4.9 4.0 0.4 0.5 4.0 3.5 0.2 0.3 57.2 47.5 5.9 3.7 12.7 11.2 0.8 0.7 27.7 21.1 2.6 4.0 2.6 129.7 13.5

3.8 0.4 0.4 16.1 13.9 1.0 1.2 22.2 15.2 1.5 5.5 3.3 2.6 0.2 0.5 3.6 3.1 0.3 0.2 4.4 4.0 0.2 0.3 57.5 46.0 6.5 5.0 10.5 9.1 0.9 0.4 26.9 20.5 2.5 3.9 2.5 120.4 14.0

3.9 0.3 0.3 16.5 14.6 1.0 1.0 21.3 15.3 1.6 4.5 3.4 2.7 0.3 0.5 3.3 2.8 0.3 0.2 4.2 3.8 0.2 0.2 60.8 50.5 6.9 3.4 10.5 9.1 0.9 0.4 28.0 21.3 2.6 4.1 2.6 126.2 14.6

16.3 2002 17.5 1997 162.1 154.5 MMT CO2 eq.

14.5 2007 157.9

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 N2O emissions.

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent 1

Emissions from residue burning are not included. emissions.

2 Direct

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Map 3-2 U.S. Cropped Land

Data obtained from the 2011 National Land Cover Database at http://www.mrlc.gov

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.

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, 63

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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.

Nitrous oxide emission estimates from DayCent include the influence of N additions, crop type, irrigation, and other factors in aggregate, and therefore it is not possible to reliably partition N2O emissions by anthropogenic activity (e.g., N2O emissions from synthetic fertilizer applications cannot be distinguished from those resulting from manure applications). Consequently, emissions are not subdivided according to activity (e.g., N fertilization, manure amendments), as is suggested in the IPCC Guidelines, but the overall estimates are likely more accurate than the more simplistic 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.

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 N2O 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.

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, N2O 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 64

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

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.

each NRI point location. NRI points are included in the land base for the agricultural soil N2O 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).

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.

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).

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.

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).

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 65

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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.

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).

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

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).

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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.

(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:

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 N2O emissions. Estimates of direct N2O emissions from N applications to non-major crop types were based on the annual increase in mineral soil N from the following practices: (1) the application of synthetic commercial fertilizers, Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.

2

67

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 (20072014), Cantens (2004- 2005), Lee (2003 -2007), Slaton (1999- 2001), Wilson (2002- 2007, 20092012), Hardke (2013, 2014), Linscombe (1999, 2001-2014), Anderson (2008- 2014), Klosterboer (1997, 1999- 2003), Stansel (2004- 2005), Texas Agricultural Experiment Station (2006, 2007-2014).

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Chapter 3

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



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).

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 N2O emissions from cropped soils.

Estimates of total national annual N additions from land application of other organic fertilizers were derived from organic fertilizer statistics (TVA 19911994, 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.

3.3.3

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 N2O emissions from non-major crop types. Nitrate leached or runoff and N volatilized from non-major crop types are calculated by multiplying N fertilizer applied by the Tier 1 default factors (30 percent and 10 percent, respectively).

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 N2O emissions. Error propagation was used to combine uncertainties in the various components by taking the square root of the sum of the squares of the standard deviations of the components (IPCC 2006). The 95-percent confidence interval in N2O emissions was estimated to lie between 153 and 281 MMT CO2 eq. (Table 3-1).

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 N2O-N/ha cultivated; IPCC 2006).

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

3.3.2.3 Total N2O Emissions Total direct emissions were obtained by summing DayCent-generated emissions from most crops on 68

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

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.

enhanced efficiency fertilizers are more expensive than conventional fertilizers. Use of nitrification inhibitors and slow-release fertilizers has been shown to decrease N2O 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). 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.

3

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

MMT CO2 eq.

3.3.5

Chapter 3

3.5

1990

3.0

2013

2.5 2.0 1.5 1.0 0.5 0.0

AR

CA

FL

LA

MS

MO

Figure 3-3 Methane from Rice Cultivation by State, 1990 & 2013 (MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

69Figure 3-3

Methane from Rice Cultivation by State, 1990 & 2013 (MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

OK

TX

Chapter 3

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Table Methane Cultivation from and Ratoon Operations by State, Table3-5Error! Nofrom text Rice of specified style inPrimary document.-13 Methane From Rice 1990, 1995, 2000, 2005-2013 Primary and Ratoon Operations by State, 1990, 1995, 2000, 2005-2013

Source Primary Arkansas California Florida Louisiana Mississippi Missouri Texas Ratoon Arkansas Florida Louisiana Texas

Total

1990

1995

2000

2005

2006

6.7 2.9 0.8 0.0 1.3 0.6 0.2 0.8 2.5 0.0 0.0 1.3 1.1 9.2

7.4 3.2 1.0 0.1 1.4 0.7 0.3 0.8 2.4 0.0 0.1 1.3 1.0 9.8

7.2 3.4 1.2 0.0 1.2 0.5 0.4 0.5 2.4 0.0 0.1 1.5 0.8 9.6

6.7 3.3 0.9 0.0 1.1 0.5 0.4 0.4 0.8 0.0 0.0 0.5 0.4 7.5

5.6 2.8 0.9 0.0 0.7 0.4 0.4 0.3 0.9 0.0 0.0 0.5 0.4 6.5

2007

2008

MMT CO2 eq. 5.5 5.9 2.7 2.8 1.0 0.9 0.0 0.0 0.8 0.9 0.4 0.5 0.4 0.4 0.3 0.3 1.3 1.9 0.0 0.0 0.0 0.0 0.9 1.2 0.3 0.6 6.7 7.8

Cultivation From

2009

2010

2011

2012

2013

6.2 3.0 1.0 0.0 0.9 0.5 0.4 0.3 1.8 0.0 0.0 1.1 0.7 7.9

7.2 3.6 1.0 0.0 1.1 0.6 0.5 0.4 2.1 0.0 0.0 1.4 0.7 9.3

5.2 2.3 1.0 0.0 0.8 0.3 0.3 0.4 1.9 0.0 0.0 1.0 0.9 7.1

5.3 2.6 1.0 0.0 0.8 0.3 0.4 0.3 2.1 0.4 0.0 1.1 0.5 7.4

5.8 2.6 1.2 0.0 1.0 0.3 0.4 0.3 2.5 0.4 0.0 1.2 0.8 8.3

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

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.

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.

A review of published experiments was used to develop emissions factors for primary and ratoon crops (EPA 2015). Experiments where nitrate or Table 3-6 Change Methane Emissions from Rice Table Error! Nointext of specified style in document.-14 Change in Methane Emissions sulfate fertilizers or other substancesFrom believed Cultivation, 1990-2013 Rice Cultivation, 1990-2013 to suppress CH4 formation were applied, and 1990 2013 1990-2013 experiments where measurements were not made State MMT CO2 eq. % Change over an entire flooding season or where floodwaters Arkansas 2.88 2.99 4 were drained mid-season were excluded from the California 0.85 1.21 42 analysis. The remaining experimental results were Florida 0.08 0.08 5 then sorted by season (i.e., primary and ratoon) and Louisiana 2.60 2.23 -14 type of fertilizer amendment (i.e., no fertilizer added, Mississippi 0.60 0.30 -50 organic fertilizer added, and synthetic and organic Missouri 0.19 0.37 95 fertilizer added). The experimental results from Texas 1.96 1.12 -43 primary crops with synthetic and organic fertilizer Total 9.16 8.30 -9 added (Bossio et al. 1999, Cicerone et al. 1992, Sass Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. 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 3.4.1 Methods for Estimating CH4 Emissions fertilizer added (Lindau et al. 1995, Lindau & Bollich From Rice Cultivation 1993) were averaged to derive an emission factor for the ratoon crop. The resultant emission factor for the The EPA provided estimates for CH4 emissions primary crop is 237 kg CH4/ha per season, and the from rice cultivation for this report. Details on the 70

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

resultant emission factor for the ratoon crop is 780 kg CH4/ha per season (EPA 2015).

Chapter 3

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.

3.4.2 Uncertainty in Estimating Methane Emissions From Rice Cultivation

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.

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

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 Error! No text of specified style in document.-15 Greenhouse Gas Emissions From Table 3-7 Greenhouse from 1995, Agriculture by Crop, 1990, 1995, 2000, 2005–2013 Agriculture BurningGas byEmissions Crop, 1990, 2000,Burning 2005–2013 1990

1995

2000

2005

2006

2007 2008 2009 MMT CO2 eq.

2010

2011

2012

2013

CH4 Wheat Rice Sugarcane Corn Cotton Soybeans Lentils

0.32 0.16 0.05 0.07 0.02 0.00 0.01 0.00

0.28 0.13 0.05 0.06 0.02 0.00 0.01 0.00

0.31 0.14 0.05 0.06 0.03 0.00 0.02 0.00

0.22 0.10 0.04 0.03 0.02 0.01 0.02 0.00

0.28 0.10 0.05 0.07 0.04 0.00 0.02 0.00

0.28 0.12 0.07 0.03 0.04 0.00 0.02 0.00

0.32 0.16 0.05 0.04 0.04 0.00 0.02 0.00

0.29 0.12 0.06 0.05 0.04 0.00 0.02 0.00

0.29 0.12 0.06 0.04 0.04 0.00 0.02 0.00

0.30 0.14 0.05 0.05 0.04 0.00 0.02 0.00

0.30 0.13 0.05 0.05 0.04 0.00 0.02 0.00

0.31 0.13 0.05 0.05 0.05 0.00 0.02 0.00

N2O Wheat Rice Sugarcane Corn Cotton Soybeans Lentils

0.10 0.04 0.02 0.01 0.01 0.00 0.01 0.00 0.42

0.09 0.04 0.02 0.01 0.01 0.00 0.01 0.00 0.37

0.10 0.04 0.02 0.01 0.01 0.00 0.02 0.00 0.41

0.08 0.03 0.01 0.01 0.01 0.00 0.02 0.00 0.30

0.09 0.03 0.02 0.01 0.01 0.00 0.02 0.00 0.37

0.10 0.03 0.03 0.01 0.01 0.00 0.02 0.00 0.38

0.11 0.04 0.02 0.01 0.01 0.00 0.02 0.00 0.43

0.10 0.03 0.02 0.01 0.01 0.00 0.02 0.00 0.39

0.10 0.03 0.02 0.01 0.01 0.00 0.02 0.00 0.38

0.10 0.04 0.02 0.01 0.01 0.00 0.02 0.00 0.40

0.10 0.04 0.02 0.01 0.01 0.00 0.02 0.00 0.40

0.10 0.04 0.02 0.01 0.01 0.00 0.02 0.00 0.42

Source

Total

Note: MMT CO2 eq. is million metric tons carbon dioxide equivalent. CH4 is methane; N2O is nitrous oxide; CO2 is carbon dioxide.

71

Chapter 3

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

Total Emissions from Burning = 0.41 MMT CO2 eq.

0.20

MMT CO2 eq.

0.18 0.16

N20 = 0.10 Tg CO2 eq.

0.14

CH4 = 0.31 Tg CO2 eq.

0.12 0.10 0.08 0.06 0.04 0.02 0.00

Wheat

Rice

Sugarcane

Corn

Soybeans

Cotton

Lentils

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.)

Figure 3-4 Greenhouse Gas Emissions from Field Burning by Crop Type, 2013 CH4burning is methane; oxide; CO2 is carbon dioxide. 2O is nitrous emissions were from of N wheat crop residues, Total GHG emissions from residue burning decreased (MMTof CO 2 eq. is million metric tons of carbon dioxide equivalent) at 42 percent. Burning rice, sugarcane, corn, and 8 percent from 1990 to 2013. Trends in relative

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).

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.

80 70

Million metric tons

60 50 40 30 20 10

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.

0 -10 -20

Corn

Soybeans Sugarcane

Rice

Cotton

Lentils

Wheat

Figure 3-5 Change in Commodity Production, 1990-2013 Figure 3-5 Change in Commodity Production, 1990-2013 600 500

Percent

400 300 200

3.5.1 Methods for Estimating CH4 and N2O Emissions from Residue Burning

100 0 -100

Lentils

Soybean

Corn

Rice

Sugarcane

Cotton

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.

Wheat

Figure 3-6 Percent Change in Commodity Production,

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

72

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

Chapter 3

Table 3-8 Agricultural Crop Production

Table Error! No text of specified style in document.-16 Agricultural Crop Production Crop Corn1 Cotton Legumes2 Rice Soybeans Sugarcane Wheat

1990

2005

2006

222.2 3.7 0.0 7.8 57.8 28.1 81.9

311.1 5.7 0.3 11.2 92.1 26.6 63.1

294.9 5.2 0.2 9.7 95.9 29.6 54.3

Note: MMT is million metric tons. Source: USDA, NASS Crop Production 2014 Summary 1 Corn for grain (i.e., excludes corn for silage). 2 Legumes are dry beans, peas, and lentils

2007 2008 MMT of product 365.1 338.6 4.6 3.1 0.2 0.1 10.0 10.2 80.3 89.0 30.0 27.6 61.5 75.0

The equation below was used to estimate the amounts of carbon and nitrogen released during burning.

2009

2010

2011

2012

2013

366.6 2.9 0.3 11.0 100.8 30.4 66.5

348.5 4.3 0.4 12.2 99.9 27.4 66.2

346.1 3.7 0.2 9.2 92.8 29.2 60.0

301.8 4.2 0.3 10.0 90.4 32.2 68.1

351.3 2.8 2.1 8.6 91.4 27.9 58.1

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).

C or N released = Σ for all crop types and State:

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).

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.

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

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 73

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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).

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).

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.

3.5.3 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.

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.)

is gigagrams carbon dioxide equivalent.)

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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.

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.)

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.

Map 3-4b Soil Carbon Changes for Major Land Resource Areas, Tier 3 Crops Reduced Till, Annual Means 20032007 (Mg CO2 eq. ha-1 yr-1 is megagrams carbon dioxide equivalent

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.

per hectare per year.)

6 4 2

MMT CO2 eq.

0

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

-2 -4

(Mg CO2 eq. ha-1 yr-1 is megagrams carbon dioxide equivalent per hectare per year.)

-6 -8

-10 -12 -14 -16

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.

4

Figure Figure 3-7 3-7 CO2 Emissions and Sequestration Sources CO and Sequestration Sources from(MMT Cropland from Cropland Soils, 2003-2007 COSoils, eq.2003-2007 is million 2 Emissions 2 (MMT COtons metric tons ofequivalent. carbon dioxideCRP equivalent) 2 eq. is metric ofmillion carbon dioxide is USDA Conservation Reserve Program)

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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.

3.6.1 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).

3.6.3 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

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

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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.

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.

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).

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.

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). 77

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3.7

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.

Mitigation of CO2 Emissions

year 2025, the potential carbon sequestration of agroforestry in the United States will be 90 million metric tons of carbon per year.

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.

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.

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 78

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3.9

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3.10 Appendix B

USDA (2005-2014). Crop Production Summary. National Agricultural Statistics Service, Agricultural Statistics Board, United States Department of Agriculture. Washington, D.C. Available online at

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

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 .

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

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

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

USDA (2011b). Agricultural Resource Management Survey. Economic Research Service, United States Department of Agriculture. Available online at .

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

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

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

USDA (2014). Agricultural Statistics. National Agricultural Statistics Service, United States Department of Agriculture. Available online at and .

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

USDA (2014). Conservation Reserve Program Monthly Summary – September 2014. Farm Service Agency, United States Department of Agriculture. Washington, DC. Available online at

B-12 State-Level Estimates of Mineral Soil Carbon Changes on Cropland by Major Activity, 2013

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.

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

Appendix Table B-1 MLRA-Level Area Estimates byEstimates Major Crop Rotation, 2003-2007 Appendix Table B-1 MLRA-Level Area by Major Crop Rotation,

CRP1 MLRA2 2 5 7 8 9 10 11 12 13 14 15 16 17 21 23 24 25 26 27 29 30 31 32 35 36 40 41 42 44 46 47 49 51 52 54 56 57 61 64 65 66 69 71 72 73 74

Fallow

32,780 68,351 516,582 1,187,268 135,571 231,521 15,781 44,733 222,575 63,126 24,079 21,408 19,830 34,317 - 112,083 70,092 269,302 25,778 542,239 1,582,282 244,268 193,278 318,245 30,311 193,480 150,098 58,194 12,424 695,557 2,113,497 288,130 959,834 82,632 18,899

Hay Grass 23,715 16,026 7,608 131,159 34,075 16,592 15,095 20,275 21,974

Hay In Low Other Hay Irrigated Rotation Legume Residue Cropland 21,480 16,026 106,262 41,278 61,269 38,838 67,973 41,157

hectares 25,374 91,821 20,334 - 370,458 - 147,229 29,866 48,548 16,511 139,697 - 959,877 95,053 17,442 132,510 63,813 46,607 34,217 - 721,579 - 174,120 - 113,689 82,208 42,909 7,917 66,271 9,264 23,254 - 179,357 - 131,672 35,565 70,638 - 121,208 20,437 - 171,981 49,412 361,826 94,292 158,441 67,989 11,935 - 149,262 20,760 87,974 264,705 42,735 42,168 80,087 20,315 46,498 107,067 11,938 124,481 72,884 113,548 - 115,950 50,586 683,436 18,494 1,074,751 91,135 356,913 47,227 56,211

84

112,907 -

28,449 34,115 77,255 2,266 10,158 13,152 52,569 34,722 484,490 369,681 458,752 28,692

Rice

2003-2007 Row Crop

Small Grain

- 189,942 - 220,109 - 584,542 76,018 205,024 19,708 37,879 - 143,906 - 295,926 - 110,965 1,317,166 - 1,181,429 629,530 - 154,125 35,491 56,575 99,553 32,618 - 145,606 - 381,133 551,870 - 493,029 865,745 - 326,501 577,123

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

Chapter 3

Continued - Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1 MLRA2 75 76 79 85 89 92 96 97 98 99 101 103 104 105 106 109 110 112 113 121 122 123 124 125 126 127 128 129 134 136 137 138 139 140 142 143 145 146 147 148 155 102A 102B 102C 107A 107B 108A

19,263 13,193 161,385 39,455 26,588 108,530 52,417 250,318 136,258 480,654 82,663 159,562 33,076 231,640 36,098 231,606 88,339 86,927 -

Fallow 31,727 91,945 18,049 26,993 -

Hay Grass 56,251 6,232 62,524 79,238 21,367 13,233 56,089 55,118 177,010 222,173 60,460 114,405 170,858 37,433 77,983 26,750 183,606 95,465 107,525 21,772 113,838 308,614 22,501 53,702 243,904 84,134 34,520 15,459 161,672 83,163 41,318 23,593 52,650 -

Hay Hay In Low Other Irrigated Rotation Legume Residue Cropland 20,720 19,263 58,027 20,286 140,361 55,482 48,382 330,595 34,830 78,132 7,972 35,702 55,740 35,254 52,208 47,987 40,478 46,590 30,191 63,694 83,733 165,009 62,367 131,700 86,885 71,225 60,662 26,752 -

16,349 19,708 18,170 15,257 12,667 8,620 24,322 142,126 46,417 201,615 68,028 33,670 208,413 43,666 195,140 99,634 64,183 156,937 190,809 46,296 98,055 17,240 72,317 54,187 100,119 29,097 156,168 236,782 140,912 20,639 119,585 77,133 94,575 23,674 71,994 40,307 14,528

hectares 877,138 208,535 34,398 15,124 164,778 22,619 88,141 37,696 11,614 196,075 12,096 110,784 50,748 507,355 109,994 -

85

12,141 23,310 68,716 320,269 19,223 26,386 19,546 -

37,636 45,730 52,043 50,869 19,951 35,572 105,583 15,661 15,014 9,874 38,567 17,928 13,152 12,748 13,314 82,961 42,856 12,626 26,305 24,443 40,307 34,277 54,268 15,014 -

Rice 217,516 -

Row Crop 479,715 145,197 103,896 43,625 40,469 138,160 1,264,292 1,268,131 249,624 5,022,610 2,233,534 1,409,838 1,200,584 1,143,162 1,217,948 950,411 1,722,294 102,335 425,580 41,235 162,039 21,003 72,603 57,004 116,409 42,439 755,810 209,545 27,216 369,536 142,569 53,336 13,993 353,730 302,066 1,880,376 373,886 1,359,543 926,590 2,213,355 1,916,475

Small Grain 69,646 105,623 571,766 169,760 16,875 10,805 18,899 55,280 362,369 25,576 23,917 50,707 46,440 21,570 21,655 135,813 14,261 -

Chapter 3

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

Continued - Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1 MLRA2 108B 108C 108D 111A 111B 111C 111D 111E 114A 114B 115A 115B 115C 116A 116B 118A 120A 120B 120C 130A 130B 131A 131B 131C 131D 133A 133B 135A 144A 144B 149A 150A 150B 152B 153A 153B 153C 153D 156A 28A 28B 34A 34B 43A 43B 48A 48B

16,514 118,856 111,469 100,628 21,246 22,743 97,497 80,472 68,554 376,047 145,627 93,685 26,871 30,473 -

Fallow 49,169 20,315 74,278 42,613 15,580 -

Hay Grass 19,830 21,813 15,459 17,806 23,148 15,216 64,143 48,805 178,426 104,854 14,285 37,110 13,112 25,131 17,685 180,895 46,134 60,784 118,533 92,997 7,608 -

Hay Hay In Low Other Irrigated Rotation Legume Residue Cropland 37,960 44,973 40,745 16,147 15,321 27,999 44,070 27,511 58,728 40,647 10,687 -

hectares 26,628 27,715 30,392 66,692 32,011 67,947 9,712 11,979 16,633 34,075 19,546 14,973 17,968 30,999 66,045 61,431 65,802 29,380 68,554 22,541 - 1,118,667 - 306,643 25,455 94,697 16,997 301,787 61,108 32,253 14,812 - 242,060 18,345 8,158 11,007 44,904 23,512 - 391,900 15,083 - 193,445 - 128,932 16,754 24,848 100,708 65,288 24,442

86

532,162 27,761 31,039 880,685 70,780 6,596 173,408 133,936 35,208 -

28,045 17,604 9,348 20,639 22,986 122,660 216,467 10,360 9,955 258,554 20,922 34,924 11,048 -

Rice 722,280 205,338 30,149 158,354 280,439 10,279 16,673 -

Row Crop 1,904,706 1,177,787 683,944 1,610,775 1,904,787 575,028 828,783 339,457 384,282 812,550 627,496 375,613 1,545,842 83,047 22,161 21,974 385,636 97,961 19,061 5,382 1,316,008 60,946 115,740 536,764 47,429 149,410 49,940 13,476 74,336 319,500 181,828 139,549 165,759 122,932 -

Small Grain 12,383 16,592 24,888 16,552 15,621 27,814 81,301 81,423 20,826 5,666 15,174 72,803 -

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

Chapter 3

Continued - Appendix Table B-1 MLRA-Level Area Estimates by Major Crop Rotation, 2003-2007

CRP1

Fallow

Hay Grass

Hay Other Hay In Low Irrigated Rotation Legume Residue Cropland

MLRA2 hectares 53A 299,063 297,727 17,563 16,754 15,459 54,956 53B 412,172 106,999 74,017 83,350 115,133 107,646 53C 19,627 15,297 16,552 55A 250,501 25,333 19,668 26,628 176,484 55B 378,783 57,465 71,920 63,495 31,889 21,610 219,664 55C 57,101 21,772 64,426 103,316 46,370 47,955 58A 360,171 488,335 85,389 123,762 164,667 171,904 60,339 58B 15,540 56,996 60A 61,147 38,728 27,761 63A 66,166 62,160 29,259 20,518 63B 9,105 16,511 53,661 17,847 67A 141,964 89,593 17,037 235,975 67B 578,903 873,526 - 302,986 81,544 70A 10,141 70B 30,311 70C 7,163 77A 226,988 120,354 - 498,043 21,610 77B 23,472 - 128,680 77C 1,121,627 137,985 - 1,353,623 810,106 49,776 77D 163,898 62,259 77E 229,295 25,171 36,870 78A 78B 302,019 52,189 289,818 78C 195,359 37,231 20,679 103,737 184,438 31,768 80A 36,058 25,900 65,519 56,101 80B 81A 89,193 51,759 69,201 81B 81C 82B 83A 87,203 17,280 83C 83D - 167,773 43,504 83E 84A 25,252 84B 32,495 86A 24,403 32,577 76,769 87A 87B 90A 67,218 107,074 114,202 90B 13,881 51,719 158,888 95,304 41,723 91A 44,371 23,259 35,734 91,861 91B 13,152 94A 67,987 94B 25,212 Hay Hay In Hay Low - Other 95A 29,137 15,864 163,384 75,514 37,717 1 CRP Fallow Irrigated Grass Rotation Legume Residue Cropland 95B 37,838 21,125 141,795 73,936 23,876 45,122 2 MLRA hectares 1 CRP = Conservation Reserve Program 2 MLRA = Major Land Resource Area

87

Rice

Row Crop

Small Grain

- 742,396 - 604,601 1,199,004 - 205,742 172,801 - 128,488 1,502,113 - 1,458,407 666,545 - 1,178,071 75,312 - 191,174 53,540 89,274 241,719 89,881 39,700 11,048 17,078 - 195,221 122,863 - 113,393 392,019 57,749 463,285 35,929 - 114,966 - 119,802 - 407,644 18,696 1,203,236 38,000 1,711,492 - 121,120 36,426 40,766 26,669 37,396 97,853 69,140 23,836 - 129,095 39,295 74,299 91,358 - 411,602 323,225 55,887 22,674 32,529 - 129,816 - 351,542 - 120,634 38,526 55,578 Row Small 353,169 12,060 Rice Crop Grain - 959,343 -

Chapter 3

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

Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by

Appendix Table Rotation, B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 2003-2007 Major Crop 2003-2007

CRP1 MLRA2 2 5 7 8 9 10 11 12 13 14 15 16 17 21 23 24 25 26 27 29 30 31 32 35 36 40 41 42 44 46 47 49 51 52 54 56 57 61 64 65 66 69 71 72 73

7.09 111.01 40.84 63.71 5.21 8.90 64.40 25.78 58.36 2.37 24.40 2.15 117.05 39.33

Fallow 29.75 482.06 124.56 10.55 30.61 36.29 26.57 15.86 16.35 63.09 99.43 7.49 420.76 57.71 55.79 32.45 744.00 326.59

Hay Grass 34.28 11.91 12.04 59.20 20.37 14.36 15.90 17.19

Hay In Hay Low Other Irrigated Rotation Legume Residue Cropland 26.08 11.83 32.64 37.41 38.17 23.24 41.28

22.21 27.31 16.82 11.54 49.57 45.33 6.70 63.65 20.84 45.24 7.45 13.86 3.15 25.86 15.75 4.11 23.84

Gg CO2 eq.3 78.26 25.13 304.47 167.32 55.69 159.65 963.09 94.63 150.35 32.47 22.84 27.38 759.82 204.76 103.71 67.19 54.51 10.59 51.26 5.04 13.46 277.41 98.71 33.96 67.88 106.61 22.90 159.16 402.75 123.50 63.70 11.06 130.99 73.58 31.47 81.29 103.41 93.34 134.79 659.43 984.92 346.89

88

82.47 -

15.61 8.52 21.68 0.67 4.97 5.96 25.78 14.39 331.07 154.54 182.38

Rice 623.93 -

Row Crop 70.42 923.89 119.39 19.94 92.20 128.31 268.80 275.36

Small Grain 140.14 112.93 448.83 71.76 7.99 33.74 72.69 94.43 521.07 400.35 19.71 18.35 197.26 344.05

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

Chapter 3

Continued - Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 2003-2007

CRP1 MLRA2 74 75 76 79 85 89 92 96 97 98 99 101 103 104 105 106 109 110 112 113 121 122 123 124 125 126 127 128 129 134 136 137 138 139 140 142 143 145 146 147 148 155 102A 102B 102C 107A 107B

19.79 3.98 3.19 21.66 12.74 8.05 27.08 14.42 78.41 32.31 131.20 19.81 42.24 8.86 44.73 6.95 38.93 17.56 21.96

Fallow 6.80 18.26 29.22 8.50 12.74 -

Hay Grass 28.62 67.69 8.89 74.90 127.69 16.25 11.94 54.58 40.70 137.93 144.26 45.85 100.79 111.56 22.87 72.46 16.30 155.09 109.92 63.81 9.70 67.03 230.41 12.80 101.13 653.17 323.98 113.19 43.81 270.67 121.08 28.37 14.82 35.20

Hay In Hay Low Other Irrigated Rotation Legume Residue Cropland 26.34 19.00 16.46 56.73 16.40 195.30 42.79 40.73 289.00 21.92 60.31 7.26 25.57 41.75 32.27 34.03 50.06 37.61 53.21 19.86 37.87 102.47 343.96 163.95 175.96 95.64 40.04 40.57 18.50

18.19 6.06 8.33 4.98 11.23 13.07 9.11 22.76 128.06 37.86 220.33 41.78 25.28 156.02 24.45 133.51 67.21 49.40 149.06 152.57 32.37 96.91 12.56 69.71 58.89 77.98 16.52 168.49 304.35 236.50 29.51 119.70 71.58 41.29 11.19 35.49 24.13

Gg CO2 eq.3 37.57 821.47 133.57 28.79 14.94 154.18 22.63 78.69 29.02 11.44 128.30 5.43 48.92 37.13 479.53 162.56

89

12.77 12.22 35.65 177.00 9.05 11.50 25.40 -

11.73 15.03 33.93 42.56 58.03 16.12 33.48 97.11 8.86 11.04 6.74 25.18 12.92 12.92 15.28 4.29 36.58 15.20 3.42 23.22 39.81 49.14 37.57 29.27 14.29 -

Rice 391.30 -

Row Crop 197.40 365.71 102.74 56.69 46.49 36.52 152.68 1,190.01 1,199.86 366.69 4,494.84 2,221.85 1,462.65 1,005.43 994.19 1,303.22 707.90 1,416.53 94.45 326.93 26.13 176.59 14.14 77.60 79.38 72.44 23.55 449.92 133.18 13.90 417.03 259.51 134.96 21.79 474.71 332.97 1,396.38 298.10 1,173.53 1,077.88 2,016.77

Small Grain 288.44 41.01 58.03 216.01 100.41 14.66 9.53 22.19 38.23 223.41 17.98 16.15 32.12 24.56 24.30 22.10 76.37 11.97

Chapter 3

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

Continued - Appendix Table B-2 MLRA-Level Estimates of Total Annual Direct N2O Emissions by Major Crop Rotation, 2003-2007

CRP1 MLRA2 108A 108B 108C 108D 111A 111B 111C 111D 111E 114A 114B 115A 115B 115C 116A 116B 118A 120A 120B 120C 130A 130B 131A 131B 131C 131D 133A 133B 135A 144A 144B 149A 150A 150B 152B 153A 153B 153C 153D 156A 28A 28B 34A 34B 43A 43B 48A

6.05 34.32 29.43 32.75 5.75 6.25 28.59 25.44 17.06 56.63 31.74 22.89 10.75 5.94 -

Fallow 36.74 15.76 26.53 27.14 8.58 -

Hay Grass 17.90 15.66 10.95 16.34 19.48 12.02 44.61 36.45 135.18 75.59 7.91 28.70 11.10 17.92 12.59 94.44 28.41 41.93 303.48 321.08 9.04 -

Hay In Hay Low Other Irrigated Rotation Legume Residue Cropland 31.59 36.55 34.62 13.82 13.26 25.19 35.42 18.84 25.42 78.20 21.72 -

10.82 19.34 23.02 44.32 25.44 55.64 9.44 13.57 29.14 16.75 12.07 24.00 50.20 55.01 24.20 57.00 18.10 8.30 80.06 46.73 18.49 20.31 -

Gg CO2 eq.3 28.08 9.37 14.52 54.60 1,024.69 324.37 37.53 69.05 99.75 11.47 193.82 8.30 3.96 9.63 40.27 4.84 334.09 13.19 219.45 144.76 122.52 78.56

90

496.79 23.68 26.17 361.19 50.15 9.55 179.67 59.70 17.76 -

26.37 12.96 6.53 16.07 18.81 83.95 67.91 3.48 14.50 98.79 8.38 12.59 4.84 -

Rice

Row Crop

1,311.68 351.43 59.85 223.46 676.85 30.40 40.11 -

1,926.14 1,925.11 1,183.03 647.46 1,502.43 1,813.91 539.66 788.44 328.99 353.10 719.42 551.41 333.44 1,470.60 73.06 17.34 12.53 333.39 95.81 18.60 5.55 1,064.17 50.33 95.38 275.66 44.41 95.91 80.04 31.57 59.65 311.64 83.13 72.82 139.87 102.98 -

Small Grain 11.44 12.85 19.04 12.66 11.46 12.77 55.37 37.07 13.20 3.01 9.18 66.56 -

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

Chapter 3

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 Hay Low Other Irrigated Rotation Legume Residue Cropland

MLRA2 Gg CO2 eq.3 48B 41.38 53A 21.07 73.90 9.41 4.83 8.71 15.65 53B 48.76 33.21 36.98 32.67 31.14 47.75 53C 3.40 5.36 9.60 55A 33.47 13.26 6.90 15.38 81.75 55B 55.37 27.40 33.02 19.63 20.35 11.23 95.06 55C 9.71 12.59 31.06 37.59 33.13 28.82 58A 70.80 180.91 56.94 48.28 42.39 151.71 26.64 58B 5.41 44.87 60A 32.50 15.03 62.91 63A 12.31 54.94 50.67 10.07 63B 12.66 12.10 20.87 14.28 67A 18.93 27.10 3.76 198.75 67B 82.12 290.78 299.07 37.68 70A 9.42 70B 13.32 70C 5.74 77A 39.01 53.05 440.68 12.21 77B 3.79 101.07 77C 179.25 63.43 728.78 423.78 26.59 77D 24.07 24.14 77E 40.99 8.75 25.83 78A 78B 42.10 18.19 145.65 78C 33.32 14.42 5.96 43.64 100.43 16.25 80A 8.40 27.05 22.64 22.27 80B 81A 45.92 27.56 37.41 81B 81C 82B 83A 56.87 10.66 83C 83D 134.23 27.78 83E 84A 27.81 84B 8.17 86A 49.22 33.58 42.83 87A 87B 90A 66.98 87.67 90.75 90B 3.69 49.11 126.69 69.12 33.79 91A 8.35 15.48 17.82 74.98 91B 10.10 Hay - Hay In Hay Other94A 59.97 Irrigated- Low CRP1 - Fallow Grass Rotation Legume Residue Cropland 94B 28.80 MLRA 95A 2 9.07 17.26 152.13 64.10 Gg CO2 eq.-3 36.21 95B 11.11 20.33 125.89 59.13 20.72 40.08 1 CRP = Conservation Reserve Program 2 MLRA = Major Land Resource Area 3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent

91

Rice

Row Crop

Small Grain

Rice -

360.58 135.37 74.47 898.33 880.07 72.50 95.52 8.26 136.21 84.68 39.00 13.68 22.56 77.71 14.75 84.73 23.22 488.92 64.79 121.72 304.98 95.00 31.10 Row 64.25 Crop 378.44 924.96

236.38 496.51 86.03 674.62 284.56 43.87 69.75 25.51 145.53 26.14 6.09 45.93 205.48 249.87 17.00 64.81 67.64 218.02 603.55 765.81 52.24 22.54 20.74 14.29 19.51 34.58 36.13 38.00 190.60 25.28 16.14 Small Grain 11.26 -

Chapter 3

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

Appendix Table B-3 MLRA-Level Estimates of Total Annual Indirect N2O Emissions From

Appendix TableNitric B-3 MLRA-Level Estimates of TotalDioxide Annual Indirect N2O Emissions Ammonia, Oxide, and Ammonia, Oxide, and Nitrogen Volatilization, byfrom Major Crop Nitric Rotation, Nitrogen Dioxide Volatilization, by Major Crop Rotation, 2003-2007

2003-2007

CRP1 MLRA2 2 5 7 8 9 10 11 12 13 14 15 16 17 21 23 24 25 26 27 29 30 31 32 35 36 40 41 42 44 46 47 49 51 52 54 56 57 61 64 65 66 69 71 72

0.43 7.53 2.37 3.63 0.50 1.08 11.18 4.36 9.47 0.50 3.78 0.48 25.48

Fallow 1.81 18.96 5.69 0.76 1.66 1.26 1.54 1.16 1.36 4.13 8.06 0.78 42.08 4.78 5.67 2.29 74.04

Hay Grass 2.87 0.53 0.68 5.46 1.74 1.76 0.88 -

Hay In Rotation 2.39 0.35 2.90 1.76 3.10 2.37 -

Hay Legume

Irrigated

Gg CO2 eq3 1.51 7.68 2.44 23.14 8.51 0.93 2.09 0.45 10.05 57.62 5.04 0.23 6.50 3.70 3.75 1.74 53.44 12.04 7.25 6.75 5.29 0.65 3.78 0.28 1.81 12.57 8.41 2.69 6.95 11.86 2.59 12.57 1.21 22.16 1.59 9.82 3.48 1.56 14.66 0.38 4.71 3.75 2.67 0.83 2.44 0.53 0.96 7.83 0.28 14.68 1.86 14.13 9.17 0.88 78.04 0.28 105.14

92

Low Residue 3.22 -

Other Cropland

Rice

Row Crop

0.83 0.93 2.85 19.26 0.05 0.38 0.65 1.84 1.33 6.50 29.41 - 114.43 - 16.24 2.01 8.26 - 14.83 17.35 - 27.60

Small Grain 10.00 6.04 20.10 2.62 1.01 1.41 6.45 10.53 47.60 36.79 2.14 1.86 22.36

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

Chapter 3

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 MLRA2 73 74 75 76 79 85 89 92 96 97 98 99 101 103 104 105 106 109 110 112 113 121 122 123 124 125 126 127 128 129 134 136 137 138 139 140 142 143 145 146 147 148 155 102A 102B 102C 107A

10.05 3.78 0.83 0.58 6.02 1.59 0.98 4.53 2.22 11.66 5.39 19.87 3.48 6.35 1.59 9.27 1.54 7.61 3.27 -

Fallow 33.59 0.55 1.99 3.73 0.65 1.36 -

Hay Grass 1.41 1.26 3.37 0.48 4.56 5.79 0.86 0.43 1.99 2.69 9.85 11.71 3.27 9.97 16.12 3.65 6.32 2.57 16.07 8.31 10.85 2.19 8.34 39.18 3.00 4.21 26.32 9.34 3.80 1.99 19.67 9.49 1.51 1.11 -

Hay In Rotation 3.32 1.31 0.63 0.78 3.10 0.86 7.86 2.52 2.19 14.15 1.54 3.70 0.38 2.17 3.32 3.05 5.52 3.88 3.25 3.45 2.39 6.75 4.84 14.23 4.13 11.13 6.85 2.97 2.82 -

Hay Legume

Irrigated

Gg CO2 eq3 1.26 34.42 0.63 4.48 0.23 110.83 0.28 0.33 19.49 0.43 2.54 0.43 0.40 0.83 1.36 4.84 19.42 1.21 6.14 1.39 0.63 3.85 2.27 0.83 10.85 5.31 2.49 4.10 1.71 1.41 9.09 12.82 2.47 4.99 1.01 4.41 3.27 7.83 20.35 1.56 0.98 5.99 13.57 6.22 1.13 7.08 3.93 9.22 1.94 6.35 0.40 1.28 62.05 -

93

Low Residue 0.65 1.41 4.41 18.79 1.51 1.99 1.13 -

Other Cropland

Rice

Row Crop

22.92 - 35.28 1.56 - 31.38 - 49.71 - 16.17 2.14 8.59 2.87 4.16 - 16.32 3.07 - 146.79 3.35 - 139.84 3.05 - 21.96 1.44 - 594.64 2.14 - 271.62 6.30 - 151.17 1.08 - 135.26 1.11 - 137.17 0.65 - 148.15 2.64 - 115.64 1.46 - 209.67 - 12.67 - 55.30 5.82 0.88 - 19.54 2.87 7.50 0.88 5.57 0.76 - 14.20 5.06 5.84 27.20 87.74 3.10 - 29.79 0.76 4.33 1.86 - 41.00 2.19 - 13.30 3.42 0.55 3.53 - 38.53 3.32 - 35.08 3.50 - 196.43 - 38.81 - 156.76 1.11 - 115.34

Small Grain 40.54 31.35 4.48 6.77 28.53 9.04 1.26 0.83 1.31 4.03 23.82 2.01 1.66 3.35 3.35 1.84 2.12 10.02 -

Chapter 3

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

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 MLRA2 107B 108A 108B 108C 108D 111A 111B 111C 111D 111E 114A 114B 115A 115B 115C 116A 116B 118A 120A 120B 120C 130A 130B 131A 131B 131C 131D 133A 133B 135A 144A 144B 149A 150A 150B 152B 153A 153B 153C 153D 156A 28A 28B 34A 34B 43A 43B

4.18 0.93 5.62 5.39 4.26 0.86 1.03 4.41 4.16 2.59 15.19 5.69 1.54 0.55 0.45

Fallow 2.32 1.06 4.36 1.28 0.53 -

Hay Grass 2.44 0.68 0.98 0.63 0.76 1.46 0.91 3.45 2.37 18.18 11.11 1.54 3.00 1.21 2.72 1.36 20.15 6.12 5.31 13.72 10.68 0.86 -

Hay In Rotation 1.54 1.71 2.04 1.99 0.88 1.16 1.91 1.81 2.24 6.90 2.80 0.86 -

Hay Legume

Irrigated

Gg CO2 eq3 0.96 13.40 0.35 0.53 3.48 0.55 1.26 0.63 1.59 1.11 0.33 0.38 1.28 0.58 0.63 2.44 0.88 1.46 7.68 5.19 2.42 2.97 1.99 114.35 30.77 2.44 11.26 0.55 18.53 2.82 1.74 1.26 21.93 1.79 0.68 1.08 4.68 0.55 18.11 1.74 14.35 12.69 0.33 0.43 6.37

94

Low Residue 35.10 1.94 2.82 54.57 4.63 0.38 11.58 9.70 2.47 -

Other Cropland 1.71 1.46 0.73 1.84 1.74 9.59 13.75 0.50 0.86 15.26 1.28 2.49 0.83 -

Rice

Row Crop

86.53 24.60 3.10 20.83 31.23 1.21 1.84 -

273.66 239.36 232.71 152.03 84.89 200.00 228.33 73.06 105.21 41.45 49.99 98.54 77.06 47.27 190.46 9.42 2.52 2.90 48.05 12.92 2.44 0.76 153.74 7.15 13.60 68.42 4.16 20.57 3.27 0.73 8.97 24.68 25.74 16.75 18.79 15.34 -

Small Grain 1.06 1.13 1.46 1.71 1.28 1.18 1.31 5.64 4.66 1.44 0.35 0.53 2.57 -

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

Chapter 3

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 Hay In - Hay0.43 Low - Other Row 91B 3.98 Small CRP1 - Fallow - Hay GrassIrrigated Rice Rotation Legume Residue Cropland Crop Grain 94A 2.37 5.36 2 3 MLRA Gg CO eq 94B 0.93 2 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

95

Chapter 3

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Appendix Table B-4 MLRA-Level Estimates ofEstimates Total Annualof Indirect O Emissions for Nitrate by Major Crop Appendix Table B-4 MLRA-Level TotalN2Annual Indirect N2Leaching O Emissions for Rotation, 2003-2007

Nitrate Leaching by Major Crop Rotation, 2003-2007 CRP1

MLRA2 2 5 7 8 9 10 11 12 13 14 15 16 17 21 23 24 25 26 27 29 30 31 32 35 36 40 41 42 44 46 47 49 51 52 54 56 57 61 64 65 66 69 71 72 73

0 0.12 0.27 0.01 0 0.01 0 0 0.47 0 0 0.02 0 0

Fallow 3.58 13.07 5.78 2.21 15.27 2.05 4.52 0 0.46 4.95 1.57 0.3 1.11 0 2.14 17.03 26.78 7.93

Hay Grass

4.14 3.4 0.98 0 0.58 0 0.17 0

Hay In Hay Low Other Irrigated Rotation Legume Residue Cropland 5.33 0.17 0 0.47 2.65 0 0

6.61 0.38 0.16 0.15 1.02 0.27 0 0 0.07 2.25 0.01 0 0 0 0.07 0 0

Gg CO2 eq3 26.68 3.97 58.46 30.04 2.25 7.15 205.46 4.46 24.77 9.47 7.23 2.91 97.01 11.92 6.28 7.61 0.9 1.43 2.83 0.22 1.72 0.56 25.29 29.4 12.18 34.67 2.13 247.13 11.73 3.45 3.15 0.74 74.83 12.3 3.2 7.12 37.67 23.85 37.29 100.12 197.64 48.88

96

0.03 -

0 1.39 0.51 0 0 0.01 0 0 3.25 0 0.05

Rice 13.81 -

Row Crop 0 8.23 16.08 0 0.06 3.71 0 0.75

Small Grain 61.8 1.09 35.99 7.4 0 2.3 1.7 0 0 6.78 0 0 0 2.7

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

Chapter 3

Continued - Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching by Major Crop Rotation, 2003-2007

CRP1 MLRA2 74 75 76 79 85 89 92 96 97 98 99 101 103 104 105 106 109 110 112 113 121 122 123 124 125 126 127 128 129 134 136 137 138 139 140 142 143 145 146 147 148 155 102A 102B 102C 107A 107B

0.41 0.1 0.07 0.1 0.42 0.24 0.43 0.25 1.16 0.18 3.54 0.41 1.28 0.48 3.57 0.75 0.2 0.17 0.25

Fallow 0.34 1.35 1.39 0 5.93 -

Hay Grass 7.67 21.66 0.22 2.02 3.66 0.37 0.16 1.66 0.73 4.28 3.3 1.1 5.61 8.75 1.75 3.9 1.67 8.38 5.17 5.5 1.58 7.18 33.14 4.04 9.46 39.48 17.54 11.11 5.16 33.68 17.18 0.13 0.22 0.26

Hay In Hay Low Other Irrigated Rotation Legume Residue Cropland 2.13 2.74 1.19 4.22 1 17.43 1.83 2.23 15.61 0.27 2.98 0.32 2.03 2.48 3.26 4.11 3.75 3.59 4.39 2.59 7.01 13.09 33.36 18.47 25.13 15.57 0.75 0.76 0.29

0.13 0.04 0.09 0.01 0.79 0.26 0.5 1.6 7.81 2.21 10.24 1.07 0.86 5.89 0.23 6.51 3.31 2.86 20.21 25.61 5.28 10.43 2.4 7.43 5.94 11.33 3.57 14.3 22.69 12.99 2.97 15.9 9.57 0.44 0 0.31 0.46

Gg CO2 eq3 4.92 69.38 37.21 7.67 2.67 29.04 4.87 5.15 3.98 0.86 28.4 2.65 35.28 4.2 60.73 4.8

97

1.35 3.64 9.98 63.09 3.62 5.79 4.91 -

0.47 0.26 3.26 4.01 7.48 1.31 2.36 6.02 0.39 1.18 0.4 3.08 0.81 1.47 1.56 0.95 8.5 3.12 1.03 3.18 8.92 10.42 8.09 1.15 0.32 -

Rice 35.93 -

Row Crop 13.12 13.65 10 0.76 0.14 4.71 19.93 144.3 93.76 65.01 411.02 206.26 119.12 39.85 97.46 103.26 78.98 144.67 14.82 54.14 5.5 24.22 3.3 10.49 15.79 16.53 7.68 105.34 31.9 5.26 60.85 57.48 25.8 4.33 103.97 63.33 43.26 0.83 37.66 43.14 84.04

Small Grain 14.76 1.63 5.82 5.96 1.64 1.65 1.18 3.5 2.36 29.11 1.91 3.17 10.07 7.6 5.24 5.66 4.4 0.52

Chapter 3

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

Continued - Appendix Table B-4 MLRA-Level Estimates of Total Annual Indirect N2O Emissions for Nitrate Leaching by Major Crop Rotation, 2003-2007

CRP1 MLRA2 108A 108B 108C 108D 111A 111B 111C 111D 111E 114A 114B 115A 115B 115C 116A 116B 118A 120A 120B 120C 130A 130B 131A 131B 131C 131D 133A 133B 135A 144A 144B 149A 150A 150B 152B 153A 153B 153C 153D 156A 28A 28B 34A 34B 43A 43B 48A

0.15 0.51 0.53 0.94 0.17 0.18 0.68 1.6 0.75 7.91 2.04 0.01 0.37 0.02 -

Fallow 9.28 3.25 14.54 2.13 0.73 -

Hay Grass 0.45 0.26 0.41 0.64 1.1 0.62 1.08 0.45 7.42 3.94 0.97 2.06 0.83 2.15 0.79 24.16 6.01 4.1 30.65 28.47 2.5 -

Hay In Hay Low Other Irrigated Rotation Legume Residue Cropland 0.63 1.52 2.75 1.18 1.16 1.86 0.94 2.37 6.83 10.21 3.21 -

0.66 0.56 0.34 1.13 2.18 3.62 0.7 0.94 2.85 1.71 1.45 1.5 1.3 7.24 3.61 8.37 3.01 1.81 6.92 5.6 0.41 0.27 -

Gg CO2 eq3 3.34 1.57 2.53 7.98 152.94 28.1 2.28 14.36 40.25 3.15 25.43 3.75 1.72 2.97 15.11 3.35 22.49 0.57 41.81 36.51 2.1 8.88

98

80.35 4.12 6.37 165.85 14.76 3.56 10.42 28.31 8.21 -

0.83 1.61 0.56 1.44 0.83 14.83 22.55 0.61 3.23 5.69 0.44 4.09 1.61 -

Rice 110.32 11.66 2.87 21.64 26.23 0.36 1.58 -

Row Crop 109.02 87.05 48.65 43.68 175.63 219.36 83.03 81.58 36.07 39.97 72.44 62.06 28.76 70.74 11.49 3.02 2.95 51.06 14.58 2.19 0.95 116.32 4.16 8.86 86.12 3.97 17.05 16.66 8.94 11.11 12.64 30.28 19.26 29.18 29.99 -

Small Grain 1.53 1.46 2.14 1.81 1.95 3.15 12.72 15.22 2 0.68 0 6.66 -

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

Chapter 3

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 Hay Low Other Irrigated Rotation Legume Residue Cropland

MLRA2 Gg CO2 eq3 48B 4.16 53A 0 0.01 0 0 0.91 0 53B 0 0.2 0 0 0 0 53C 0 0 0.85 55A 0 0 0 0 0 55B 0 0 0 0 3.06 0 0.06 55C 0 0 0.07 0 2.47 0 58A 0 6.32 0 0 0 121.36 0 58B 0.01 2.36 60A 2.02 0 0.08 63A 0 0 0 0 63B 0 0.01 0 0 67A 0 15.64 0 81.01 67B 0 68.02 137.28 6.95 70A 7.18 70B 2.17 70C 2.09 77A 0 5.07 68.25 0.06 77B 0 11.32 77C 0 4.4 123.35 0.01 0.15 77D 0 5.09 77E 0 0.08 5.63 78A 78B 0 1.96 0 78C 0 0.48 0 5.31 0.37 0 80A 0.02 9.49 0.23 2.82 80B 81A 51.98 0 0 81B 81C 82B 83A 8.33 0 83C 83D 8.62 0.75 83E 84A 7.77 84B 1.85 86A 7.4 2.21 1.4 87A 87B 90A 1.43 4.68 3.54 90B 0.04 1.17 5.78 2.89 1.72 91A 0.18 1.5 0.96 14.08 91B 0.74 Hay - Hay InHay Low Other 94A 4.13 IrrigatedCRP1 - Fallow 94B - Grass - Rotation- Legume 1.07 - Residue- Cropland 2 3 MLRA Gg CO eq 2 95A 0.13 0.64 13.18 3.69 2.91 95B 0.25 0.6 9.76 3.11 3.18 2.92 Note: N2O is nitrous oxide. 1 CRP = Conservation Reserve Program 2 MLRA = Major Land Resource Area 3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent

99

Rice

Row Crop

Small Grain

Rice -

0 0 0 0.25 0.16 0 0 0 0 0 0 0 3.7 0 0 1.71 0 3.76 4.43 13.67 21.84 15.06 5.79 Row 8.51 Crop 43.96 62.1

0 0 0 0 0.01 0 0 0 0 0 0 0 0.01 0.18 0 0.03 0 0 0.05 47.01 0 0 0 0 0 0 4.06 1.51 8.98 0 1.94 Small Grain 1.53 -

Chapter 3

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

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

1990

2000

2001

2002

2003

2004

2005

2000-2013

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

1995

2006

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 Crop Wheat Rice

1995

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

1,000 Metric tons 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 1,841 723 783 830 844 857 751 904 607 744 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 10,481 412 406 554 514 488 552 465 361 691 630 661 703 693 710 693 875 Corn 43 50 48 58 47 63 74 70 43 35 39 35 49 51 53 41 Cotton 129 128 146 154 147 93 128 192 182 189 187 217 198 180 187 210 Soybeans Lentil

Total

1 2 2 2 2 0 0 1 2 2 1 2 2 1 1 2 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

100

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

Appendix Table B-7 Production of Crops Managed Burning Appendix Table B-7 Production of Cropswith Managed

Corn Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

16,227 15,867 19,388 13,867 20,809 15,987 21,705 21,489 21,801 20,587 21,810 20,222 19,230 21,747 18,308 14,213 27,211 24,791 26,033 27,662 27,292 27,944 27,291 34,442

Soybeans Cotton 1,000 bushels 4,725 195 4,779 229 5,552 214 4,523 207 5,943 250 4,689 230 5,519 250 6,211 248 5,701 174 5,515 224 5,379 219 5,676 264 5,398 217 3,402 291 4,716 341 7,048 320 6,688 199 6,942 160 6,867 180 7,975 162 7,266 227 6,601 232 6,882 241 7,733 186

Corn 1.0 0.00 0.91 0.93 0.88 0.45 0.006

With Burning

Wheat

Lentils

Rice 1,000 cwt

80,847 53,075 68,820 71,653 67,555 65,713 79,415 72,633 74,146 68,424 71,629 61,199 52,342 59,323 54,453 51,631 48,348 58,702 81,195 61,150 59,057 68,532 66,412 67,653

Appendix Table B-8(a)B-8(a) Crop Assumptions and Coefficients Appendix Table Crop Assumptions and

Assumption/Coefficient Residue/Crop Ratio Fraction Residue Burned Fraction Dry Matter Burning Efficiency Combustion Efficiency Fraction Carbon Fraction Nitrogen

23 41 0 47 39 44 27 45 34 40 51 50 45 0 10 20 49 45 24 41 34 32 32 35

15,937 16,488 17,740 16,088 19,773 17,259 17,472 17,613 16,771 18,234 18,290 18,609 18,902 16,555 19,928 13,390 16,394 24,194 17,933 19,601 20,320 18,150 18,179 17,716

Lentils 2.0 0.01 0.85 0.93 0.88 0.45 0.023

Rice 1.4 0.09 0.91 0.93 0.88 0.38 0.007

Soybean 2.1 0.00 0.45 0.93 0.88 0.45 0.023

Appendix Table B-8(b)B-8(b) Emissions Appendix Table Emissions and Global Warming Potentials Factors Factors and Global Warming Potentials

Appendix Table B-8(c)B-8(c) Appendix Table Rice Area Burned by State Burned by State

GHG Emissions Factor Methane Nitrous Oxide

State Arkansas California Florida

Global Warming Potential Methane Nitrous Oxide

0.005 0.007

Sugarcane 1,000 tons 16,578 16,185 15,414 15,703 15,354 14,298 13,434 13,983 14,965 14,118 14,348 13,437 14,404 18,333 11,726 6,872 16,481 7,884 10,776 11,251 10,393 11,718 12,031 11,553

Coefficients

Cotton 1.6 0.01 0.90 0.93 0.88 0.45 0.012

Factor & GWP

Chapter 3

Louisiana Mississippi Missouri Oklahoma Texas

25 298

101

Sugarcane 0.2 0.37 0.62 0.81 0.68 0.42 0.004

Rice Area % Burned 6 16 84 2 2 3 100 26

Wheat 1.3 0.03 0.93 0.93 0.88 0.44 0.006

Chapter 3

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

Appendix Table B-9 Cultivated Histosol (Organic Soils) Area

Appendix Table B-9 Cultivated Histosol (Organic Soils) Area Cold Temperate Year 1990 0.72 1991 0.72 1992 0.71 1993 0.70 1994 0.70 1995 0.69 1996 0.69 1997 0.68 1998 0.68 1999 0.67 2000 0.67 2001 0.65 2002 0.64 2003 0.63 2004 0.63 2005 0.63 2006 0.62 2007 0.62 2008 0.62 2009 0.62 2010 0.62 2011 0.62 2012 0.62 2013 0.62 Note: Data from EPA 2015

Warm Temperate Million hectares 0.17 0.17 0.17 0.16 0.17 0.17 0.17 0.16 0.17 0.17 0.17 0.16 0.16 0.16 0.17 0.17 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Sub-Tropical 0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26

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).

102

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

Chapter 3

Appendix Table B-11 MLRA-Level Estimates of Annual Soil Carbon Stock Changes by

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

CRP1 MLRA2 2 5 7 8 9 10 11 12 13 14 15 16 17 21 23 24 25 26 27 29 30 31 32 35 36 40 41 42 44 46 47 49 51 52 54 56 57 61 64 65 66 69 71 72 73

-28.27 -475.31 -114.61 -229.48 -21.25 -111.35 -779.05 -301.28 -393.54 -33.96 -29.23 -20.62 -836.33 -374.53

Fallow 22.35 451.85 45.01 34.36 68.57 25.07 -36.65 9.77 45.04 393.32 125.28 -3.14 605.59 62.66 54.68 67.57 94.11 85.94

Hay Grass

-70.12 6.07 -12.05 -68.94 -50.30 -25.82 -43.96 -36.66

Hay in Hay Low Other Rotation Legume Irrigated Residue Cropland Gg CO2 eq3 -51.24 -28.04 -72.72 -31.54 -27.91 -36.18 -51.24

-8.29 -30.13 2.26 -32.45 -49.58 -132.05 -6.52 -182.95 -66.27 -45.85 -16.91 15.97 0.96 -45.11 2.83 2.42 -105.80

103

-26.48 -20.86 -108.18 26.86 -27.99 -104.39 -301.58 -26.00 -32.14 -8.43 45.11 3.40 -204.68 -151.58 -52.73 28.77 -62.43 2.79 15.22 -1.22 42.72 -178.61 9.25 21.75 -67.94 -68.82 3.16 -59.03 -258.94 -136.05 -10.42 -9.77 -61.89 -91.50 -10.54 46.81 -36.55 -8.35 -11.02 -202.00 -397.37 -190.42

59.18 -

-8.69 11.81 -83.22 1.52 -11.16 -12.77 -33.71 -19.34 -91.94 -157.74 -139.99

Rice -45.45 -

Row Crop

156.46 52.14 139.63 8.35 73.04 35.88 -102.42 -287.90

Small Grain

138.63 39.80 -5.09 20.15 11.49 6.42 101.95 8.81 624.19 188.52 29.18 73.46 -38.35 102.64

Chapter 3

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

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

CRP1 MLRA2 74 75 76 79 85 89 92 96 97 98 99 101 103 104 105 106 109 110 112 113 121 122 123 124 125 126 127 128 129 134 136 137 138 139 140 142 143 145 146 147 148 155 102A 102B 102C 107A 107B

-106.62 -24.67 -11.55 -158.58 -25.60 -59.30 -261.57 -48.86 -76.07 -166.86 -381.42 -76.11 -197.14 0.97 -166.05 -2.21 -271.08 -96.36 -112.86

Fallow 3.42 27.78 21.20 -7.59 3.18 -

Hay Grass -34.08 -90.58 -10.17 -149.12 -208.72 -79.35 -26.46 -145.52 -142.90 -398.82 -509.05 -176.22 -254.48 -365.21 -97.60 -201.73 -44.28 -382.31 -223.34 -237.27 -71.76 -400.19 -598.39 -43.19 -170.99 -797.59 -263.65 -60.88 -28.93 -473.16 -246.28 -157.73 -60.82 -194.55

Hay in Hay Low Other Rotation Legume Irrigated Residue Cropland Gg CO2 eq3 -43.80 -43.21 -7.26 -71.61 -8.47 -151.89 -141.55 -70.52 -251.91 -70.08 -109.29 -25.35 -77.42 -109.78 -25.46 -76.30 -116.53 -54.04 -25.43 -53.72 -95.46 25.09 -141.40 43.18 -117.76 -115.53 -113.15 -264.58 -73.20

-69.67 -26.06 0.76 -13.71 -17.43 -5.64 -5.02 -25.50 -117.86 -69.85 -132.42 -204.04 -52.20 -218.28 -36.34 -92.63 -62.64 -36.61 -7.84 -52.51 -9.77 -62.73 -14.88 -34.99 -4.00 -1.28 -4.84 -85.79 -100.87 -23.10 3.91 -99.80 -73.11 -145.54 -53.30 -134.97 11.66

104

-34.28 -292.06 -89.44 -4.25 0.43 -29.92 -5.18 -37.82 -29.33 -7.48 -43.20 16.47 257.16 -11.37 -161.97 -70.96

8.93 16.13 -27.85 112.47 -5.88 11.00 10.22 -

-1.35 -36.66 -43.76 -42.31 -12.25 -46.74 -57.57 -77.80 -24.39 -2.28 -17.17 -56.58 -45.43 -8.10 -2.89 -15.25 -56.42 -32.00 -9.23 -4.14 41.43 -20.37 -16.10 -43.49 -59.66 -

Rice

Row Crop

- -135.07 - -105.09 -68.35 11.47 -6.13 34.16 33.05 254.51 - -341.12 564.32 - -1330.07 - -148.27 - 1765.92 107.31 381.66 - -170.01 -5.98 - -371.09 161.04 74.76 16.04 224.54 3.57 89.01 123.64 -52.55 -28.63 -63.30 97.66 26.17 0.06 452.66 389.66 190.71 6.09 211.03 157.62 585.51 19.51 263.53 - -588.16 81.59

Small Grain -50.70 31.51 -17.90 36.63 41.48 -0.34 0.66 31.34 34.50 162.67 -5.06 10.64 57.89 -17.77 12.86 12.23 86.65 10.08

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

Chapter 3

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

CRP1 MLRA2 108A 108B 108C 108D 111A 111B 111C 111D 111E 114A 114B 115A 115B 115C 116A 116B 118A 120A 120B 120C 130A 130B 131A 131B 131C 131D 133A 133B 135A 144A 144B 149A 150A 150B 152B 153A 153B 153C 153D 156A 28A 28B 34A 34B 43A 43B 48A

-22.83 -108.02 -135.78 -139.76 -28.18 -5.73 -141.68 -77.78 -143.64 -255.83 -113.56 -144.74 -32.45 -44.82 -

Fallow 31.50 24.63 3.07 68.02 6.98 -

Hay Grass -82.35 -54.02 -49.79 -41.53 -45.41 -50.38 -137.00 -161.42 -389.87 -204.67 -34.22 -88.49 -28.33 -36.60 -50.70 -381.70 -84.70 -152.23 -286.27 -209.49 -20.38 -

Hay in Hay Low Other Rotation Legume Irrigated Residue Cropland Gg CO2 eq3 -109.41 -63.29 -95.91 -28.22 -29.87 -64.39 -124.31 -15.90 -19.18 -18.80 -6.96 -

-27.37 -48.00 -60.60 -78.07 -38.49 -115.07 -11.34 -12.70 -40.78 -23.50 -25.24 -8.37 -63.79 -3.71 -2.14 -61.84 -2.47 -5.18 -9.27 13.30 -18.89 -33.34 -

105

-14.05 -3.48 -10.19 -33.52 -169.09 -74.56 -1.63 -20.51 -61.68 3.22 -74.64 6.39 -2.75 -5.00 -16.18 -4.73 86.86 -24.73 -44.60 -166.44 -84.18 -42.60

-51.44 0.84 20.45 511.97 64.28 22.36 40.85 46.57 19.33 -

-64.58 -23.36 -15.78 -17.45 -29.00 -94.58 -116.96 -8.68 17.96 -230.29 -9.24 -18.92 -2.79 -

Rice

Row Crop

-147.92 8.74 -24.87 -32.12 -274.07 -13.89 -18.58 -

-684.44 -664.44 -76.91 160.50 -604.97 -337.17 -166.24 -305.93 -19.53 -5.10 -93.95 -150.93 188.33 -236.21 72.85 -14.52 13.67 86.66 74.72 20.07 7.02 -157.84 -4.21 -31.29 -220.33 6.71 37.88 66.03 57.60 -14.48 8.79 -57.92 -51.59 -103.18 -89.81 -

Small Grain 2.26 8.67 -1.53 2.78 10.69 25.30 2.52 34.95 -8.40 -2.21 -3.94 -7.99 -

Chapter 3

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

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 Hay Low Other Rotation Legume Irrigated Residue Cropland Gg CO2 eq3

MLRA2 48B -30.46 53A -261.68 48.12 -22.89 -9.27 10.27 -54.07 53B -490.83 48.86 -57.97 -51.31 -58.63 -24.26 53C -42.62 -8.54 -12.67 55A -395.12 -28.37 -12.58 10.60 -109.10 55B -555.38 -81.86 -121.22 -68.73 -2.91 11.72 -118.28 55C -67.77 -38.23 -126.31 -131.55 5.32 12.07 58A -636.19 253.83 -77.55 -103.82 -145.15 -117.02 17.25 58B -1.80 -12.88 60A - 104.77 -10.78 -0.51 63A -111.53 250.41 -65.13 -15.81 63B -39.74 -29.38 -102.88 24.92 67A -84.50 19.56 -6.43 -133.51 67B -471.14 167.05 -165.70 -8.13 70A 4.50 70B -24.81 70C -6.25 77A -285.26 -36.94 -94.78 -7.82 77B -4.20 65.74 77C -1117.36 -8.72 -226.05 -146.43 -14.83 77D -120.01 14.44 77E -342.86 8.80 -0.45 78A 78B -366.82 4.45 -26.34 78C -168.58 -24.60 0.74 -36.84 -4.65 15.37 80A -37.84 -47.28 -65.41 -38.49 80B 81A 14.19 -9.94 -47.43 81B 81C 82B 83A -21.46 -3.68 83C 83D -55.22 8.36 83E 84A -23.20 84B -45.17 86A -18.69 25.09 -79.26 87A 87B 90A - -142.03 -101.24 -109.63 90B -8.03 - -123.42 -28.69 -88.23 -16.46 91A -21.23 -19.96 -8.14 20.55 91B -11.73 94A - Hay - Hay in - Hay -76.98 - Low - Other CRP1 Fallow Grass Rotation Legume Irrigated Residue Cropland 94B -6.22 3 2 MLRA 95A -8.95 -27.50 -75.70 -78.03Gg CO2 eq -23.08 95B -33.80 -64.40 -166.83 -106.40 7.94 -46.76 1 CRP = Conservation Reserve Program 2 MLRA = Major Land Resource Area 3 Gg CO2 eq. = Gigagrams carbon dioxide equivalent

106

Rice

Row Crop

Small Grain

Rice -

35.87 -48.40 306.94 0.80 4.48 -36.38 -238.89 139.71 124.65 332.56 -13.41 27.75 25.50 33.68 80.83 -11.30 -5.73 2.66 1.54 -59.18 -5.61 -62.84 87.54 -12.06 69.76 12.30 19.19 -18.50 91.79 -13.46 44.24 -16.85 6.57 28.13 1.23 5.65 4.64 11.69 -12.17 8.67 2.13 -7.22 -6.38 13.40 -12.01 -60.68 27.65 -8.27 4.49 -7.61 96.73 381.13 74.26 12.44 Row 33.63 Small Crop Grain 348.63 4.39 694.39 -

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

Chapter 3

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 Tg CO2 eq.

Land Converted to Grassland

Net Total

(0.63) ND (0.05) (0.69) (0.46) (0.43) (0.01) (0.09) 0.04 (0.31)

(0.08) ND (0.01) 0.14 0.28 0.17 (0.01) 0.00 0.73 (0.00)

(0.33) ND (0.65) (0.23) (0.57) 1.08 (0.02) (0.00) (0.27) (0.40)

(1.38) ND (0.73) (0.84) (0.93) (0.31) (0.00) (0.01) (0.09) (0.19)

(2.41) ND (1.43) (1.61) (1.67) 0.50 (0.03) (0.11) 0.41 (0.90)

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia

0.00 0.00 0.00 0.00 0.00 (5.35) 0.98 (1.90) (0.18) (6.45) (1.00) 0.14 0.19 (0.12) (0.80) (6.15) 0.76 (0.46) (0.26) (6.12) (2.52) 0.34 (0.17) (0.12) (2.46) (4.20) 0.48 0.83 (0.28) (3.17) (0.76) (0.06) (0.67) (0.26) (1.75) (0.86) (0.05) (0.14) (0.28) (1.33) (0.06) 0.00 (0.01) 0.00 (0.07) (0.35) 0.03 (0.02) (0.04) (0.37) (0.12) (0.02) 0.01 (0.00) (0.14) (0.82) 0.03 (0.15) (0.13) (1.07) (2.91) 0.62 (1.14) (0.46) (3.89) (2.55) 0.80 (1.06) (0.33) (3.14) (0.57) 0.14 (0.42) (0.28) (1.13) (2.26) 0.44 5.63 (0.34) 3.46 (0.78) 0.01 (0.24) (0.14) (1.15) (0.90) 0.71 0.36 0.05 0.22 (2.39) 0.58 1.37 (0.11) (0.54) (0.05) (0.01) 0.00 (0.00) (0.06) (0.13) 0.00 (0.01) (0.03) (0.18) (0.28) (0.05) 1.19 (0.05) 0.81 (0.02) (0.00) 0.08 (0.01) 0.05 (1.05) 0.08 (0.13) (0.07) (1.18) (2.00) 0.12 (0.14) (0.17) (2.19) (1.46) 0.19 1.01 (0.33) (0.58) (0.26) (0.15) 0.25 (0.22) (0.39) (0.54) (0.03) (0.17) (0.08) (0.82) (0.00) 0.01 (0.01) 0.00 (0.00) (0.39) (0.05) (0.19) (0.05) (0.68) (1.02) 1.33 0.30 (0.10) Cropland Land Converted to Grassland Land Converted to Net0.50 Total Remaining Cropland Remaining Grassland (0.98) 0.08 2 (0.79) (0.32) (2.01) Cropland Grassland (2.97) 0.50 4.67 (0.72) 1.48 Tg CO (0.01) 0.07 2.382 eq. (0.08) 2.36 (0.91) (0.10) (0.43) (0.11) (1.55) 0.00 0.00 0.02 (0.01) 0.02 (0.04) 0.15 (0.29) (0.10) (0.28) 0.74 0.31 (0.36) (0.32) 0.36 (0.34) (0.06) (0.07) (0.02) (0.49) (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. Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas State Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

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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 Stock Annualized Estimates and Table Error! No textCarbon of specified styleChange in document.-17 Forest Carbon Uncertainty Stock ChangeIntervals, Annualized2013 Estimates and Uncertainty Intervals, 2013 Source

Estimate

95% Confidence Interval MMT CO2 eq.

Forest

(705)

Harvested Wood

(71)

(90) to (54)

(776)

(973) to (576)

Total

(901) to (506)

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

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Table Forest Carbon Stock/Stock Change and Area Annualized Estimates, 1990, 1995, 2000, 2005, Table4-24-2 Forest Carbon Stock/Stock Change and Area Annualized Estimates, 1990, 1995, 2010, and 2013

2000, 2005, 2010, and 2013

1990

1995

2000

Annual Change Forest Aboveground Biomass Belowground Biomass Dead Wood Litter Soil Organic Carbon Harvested Wood Wood Products SWDS

Total

2010

2013

(704.4) (402.8) (79.3) (66.8) (11.8) (143.8) (102.7) (44.0) (58.7) (807.1)

(704.9) (433.7) (87.4) (95.0) (10.9) (77.9) (60.5) 3.7 (62.3) (765.4)

(704.9) (433.7) (87.4) (95.0) (10.9) (77.9) (70.8) (11.0) (62.3) (775.7)

140,905 50,331 9,963 8,743 10,276 61,592 8,525 5,262 3,263 149,430

144,496 52,457 10,387 9,153 10,336 62,163 8,969 5,397 3,571 153,465

146,611 53,758 10,650 9,438 10,369 62,397 9,167 5,408 3,758 155,777

MMT CO2 eq. yr (507.7) (324.6) (63.2) (45.9) (26.8) (47.2) (131.8) (64.8) (67.0) (639.4)

(542.5) (372.5) (73.2) (47.3) (18.2) (31.2) (118.4) (55.2) (63.2) (660.9)

Carbon Stock Forest Aboveground Biomass Belowground Biomass Dead Wood Litter Soil Organic Carbon Harvested Wood Wood Products SWDS

2005 -1

(376.4) (329.9) (65.0) (70.2) 0.7 88.0 (113.0) (47.1) (65.9) (489.4) MMT CO2 eq.

Total

133,134 44,974 8,911 7,838 10,080 61,330 6,817 4,514 2,303 139,951

135,686 46,661 9,241 8,077 10,204 61,503 7,440 4,807 2,633 143,125

138,082 48,470 9,597 8,380 10,254 61,380 8,021 5,069 2,952 146,103

Forest Area

265,938

267,565

267,987

1,000 ha 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

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.

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

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. 110

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

Chapter 4

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 Other Federal Public Private

Rocky Mountain Other Federal Public Private

Federal

Private

White/Red/Jack Pine

Federal

111

34

26

Loblolly/Shortleaf Pine Other Eastern Softwoods 94

4

10

1,874

160

880

Douglas-fir

4,663

869

3,145

2,499

142

648

Ponderosa Pine

1,091

60

636

969

83

553

4

1

5

Western White Pine Fir/Spruce/Mountain Hemlock Lodgepole Pine Hemlock/Sitka Spruce Western Larch Redwood Other Western Softwoods California Mixed Conifer

636

1,602

43

6

793

1,833

3,013

6

5

3

526

359

2,326

43 4,238

127

393

4,312

126

346

659

21

115

1,855

28

133

3,683

653

1,046

234

26

68

114

9

22

178

25

41

53

87

263

511

18

200

301

14

29

2,005

32

553

126

207

1,040

309

9,784

3

5

138

12

8

192

11

2

10

15

21

517

0

1

10

63

128

9

26

124

123

229

984

470

193

3,425

7

881

2,140

11,871

1,903

618

13,463

18

57

143

801

734

5,480

253

705

3,386

134

144

1,814

1,132

2,282

9,289

90

17

320

682

1,370

2,630

1

39

109

241

59

11

159

9

38

1,455

43 3 31

107 17 46

97 171 665

Other Softwoods

1

Oak/Pine Oak/Hickory

1

4

Oak/Gum/Cypress 53

46

66

22

10

66

Aspen/Birch

74

16

55

1,077

43

281

Alder/Maple

156

217

707

2

1

1

885

Maple/Beech/Birch

Western Oak

615

71

Tanoak/Laurel

349

57

442

Other Hardwoods

97

24

131

1

0

0

Woodland Hardwoods

49

3

20

432

35

227

21

0 147

539

0 32

1 164

Tropical Hardwoods Exotic Hardwoods Nonstocked

72

47

Exotic Softwoods

Elm/Ash/Cottonwood

Private

407

Longleaf/Slash Pine

Pinyon/Juniper

South Other Public

MMT CO2 eq. yr-1

Forest Type Group Spruce/Fir

North Other Public

0 266

0 36

6 77

42 254

2

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.

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.

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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.

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.

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

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

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 Geographic Regions Used for Carbon Stock and Map   4-­‐1  4-1   Geographic   Regions  USummaries sed  for  Carbon  S(The tock  ashaded nd  Stock  Carea hange   Summaries  the Stock Change represents extent of the inventories used forest carbonused   estimates.) (The   shaded   area  forest represents   the  extent   of  the  for forest   inventories   for  forest  carbon  estimates.)  

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

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.

Chapter 4

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 Error! No text of specified style in document.-20 Total Annualized Carbon Stock Change Table 4-4 Total Annualized Carbon Interval Stock Change 1990-2013, With Uncertainty Interval for 2013 1990-2013, With Uncertainty for 2013 Region Pacific Coast Pacific Coast Pacific Coast Rocky Mountain Rocky Mountain Rocky Mountain North North North South South South

Ownership group Federal Other Public Private Federal Other Public Private Federal Other Public Private Federal Other Public Private

1990

1995 2000 2005 2010 Forest ecosystem total carbon stock change

(60) (26) (28) (59) (4) 22 (13) (48) (99) (61) (51) (69)

(60) (26) (28) (58) (4) 22 (6) (65) (68) (96) (76) (66)

MMT CO2 eq. yr-1 (51) (47) (16) (16) 12 12 (22) (22) (3) (1) 19 26 (13) (38) (81) (95) (33) (258) (101) (47) (76) (59) (12) (161)

(101) (12) (11) (34) (2) 27 (32) (99) (170) (42) (44) (191)

2013

(101) (12) (11) (35) (2) 27 (32) (99) (170) (42) (44) (191)

2013 2013 Uncertainty LB UB MMT CO2 eq. yr-1 (185) (15) (65) 41 (89) 70 (93) 21 (14) 8 (1) 54 (64) (0) (162) (37) (254) (86) (110) 35 (96) 12 (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 Error! No text of specified style in document.-21 Total Annualized Forest Land 1990-

Table Total Annualized Forest Landfor 1990-2013, 2013,4-5 With Uncertainty Interval 2013 with Uncertainty Interval for 2013

Region

Ownership group

1990

1995

2000 2005 Forest land 1,000 ha

2010

2013

2013 2013 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.

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

60,000

MMT CO2 eq.

Understory

40,000

Standing dead tree

30,000

Down dead wood

20,000

Forest floor Soil organic carbon

10,000

MMT CO2 eq. per year

100 Live tree

50,000

50 Live tree

0

Understory

(50) (100)

Standing dead tree

(150)

Down dead wood Forest floor

(200)

Soil organic carbon

(250)

0

Pacific Coast

Rocky Mountain

North

South

(300) (350)

Figure 4-1 Forest Ecosystem Carbon Stocks Carbon Stocks Figure 4-1 Forest Ecosystem (MMT is is million metric tonstons of carbon dioxide equivalent) 2 eq. (MMTCO CO eq. million metric of carbon dioxide equivalent)

Pacific Coast

Rocky Mountain

North

South

Figure 4-2 Net Annual Forest Carbon Stock Change

(MMT4-2 CO2 eq. is million metric tons of carbon dioxide equivalent) Figure Net Annual Forest Carbon Stock Change (MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

2

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.

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.

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

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

Table Error! No text of specified style in document.-22 Mean Plot-level Carbon Densities

Table 4-6 Mean Carbon Densities According to RegionPools and Ownership forthe Six Carbon Pools Based on According toPlot-Level Region and Ownership for Six Carbon Based on Most Recent the Most Recent Inventory Per State

Inventory Per State Region

Ownership group

Live tree

Understory

Standing dead tree

Down dead wood

Forest floor

Soil organic carbon

MT CO2 eq. per ha

Forest area 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 Rocky Mountain Rocky Mountain Rocky Mountain

Private

272.1

12.8

11.1

40.1

48.9

251.5

14,011

Federal

151.4

9.7

28.8

19.6

42.7

116.6

38,784

Other Public

112.8

10.7

9.0

15.6

33.4

108.5

2,508

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.

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

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

Chapter 4

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 Error! No text of specified style in document.-23 Total Forest Ecosystem Carbon Stocks Table 4-7 Total Ecosystem Carbon Stocks to Region and Ownership for Six CarbonEstimates Pools Based According toForest Region and Ownership forAccording Six Carbon Pools Based on Annualized on Annualized Estimates for 2013 for 2013 Region

Ownership group

Live tree

Standing dead tree

Understory

Down dead wood

Forest floor

Soil organic carbon 5,739

MMT CO2 eq. Pacific Coast

Federal

9,840

Pacific Coast

Other Public

1,235

Pacific Coast

Private

3,876

Rocky Mountain Federal

905

1,128

1,553

31

58

150

174

780

173

158

570

674

3,462

5,813 289

386

1,301

765

1,660

4,584

27

25

40

86

278

Rocky Mountain Private North

Federal

1,173

137

90

185

407

1,348

1,590

42

66

107

280

North

Other Public

2,525

3,329

90

119

224

609

5,456

North South

Private

13,644

360

380

884

2,166

16,903

Federal

2,613

92

69

197

306

2,010

South

Other Public

South

Private

Rocky Mountain Other Public

252

1,109

49

17

103

163

1,296

17,247

1,034

255

1,641

2,291

18,015

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

Table Error! No text of specified style in document.-24 Net Annual Forest Ecosystem Carbon Table 4-8 Net Annual Forest Ecosystem Carbon Change According to RegionPools and Ownership for Six Carbon Stock Change According to Region andStock Ownership for Six Carbon Based on Pools Based on Annualized Estimates for 2013 Annualized Estimates for 2013 Region

Ownership group

Live tree

Understory

Standing dead tree

Down dead Soil organic Forest floor wood carbon

MMT CO2 eq. yr-1 Pacific Coast

Federal

(96)

1

(9)

(4)

(2)

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.

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4.4

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.

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.

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).

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

FigureSummary 4-3 Summary Diagramof of forest Forest Carbon Pools and and Carbon Transfer Among Pools Figure 4-3. diagram carbon pools carbon transfer among pools.

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

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 117

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

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. 118

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

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 119

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

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.

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.

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.

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. 120

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.

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

4.9

References

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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.

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.

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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.

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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

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.

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Appendix Table C-1a Current Forest Land Area According to Region, Ownership, and

Appendix Table C-1a Current Forest Land Area According to Region, Ownership, and Forest Type Group, 2013 Forest Type Group, 2013 Region: Ownership group:

Pacific Coast Other Federal Public Private

Rocky Mountain Other Federal Public Private

Federal

White/Red/Jack Pine

Federal

226

76

55

889

2,258

63

8

126

746

1,758

3,798

5

5

3

573

3,883

93

215

360

1,767

829

Loblolly/Shortleaf Pine Other Eastern Softwoods 485

20

56

10,486

934

5,129

Douglas-Fir

3,820

796

3,796

5,186

327

1,617

Ponderosa Pine

2,204

138

1,523

2,813

270

1,865

Western White Pine

10

3

13

Fir/Spruce/Mountain Hemlock

5,205

168

692

7,996

241

790

Lodgepole Pine

1,360

47

264

4,159

74

349

Hemlock/Sitka Spruce

2,792

525

1,037

272

44

127

168

17

44

318

44

104

892

62

120

Western Larch Redwood

83

20

34

240

Other Western Softwoods

1,395

49

560

California Mixed Conifer

2,311

33

881

9

16

417

32

24

681

5

32

51

92

2,159

0

2

333

26

184

399

7,821

10

44

227

232

387

1,789

902

30

1,553

3,525

21,464

3,503

27

75

196

1,077

887

7,935

333

897

4,647

300

309

4,279

1,422

3,049

14,034

129

28

610

906

1,914

3,870

2

86

216

552

Other Softwoods

2

Oak/Pine Oak/Hickory

0

14

Oak/Gum/Cypress 67

63

113

62

31

184

Aspen/Birch

182

37

138

2,307

93

660

Alder/Maple

176

222

851

4

3

2

Western Oak

2,490

Maple/Beech/Birch

1,439

195

Tanoak/Laurel

391

66

584

Other Hardwoods

198

37

254

6

1

3

Woodland Hardwoods

180

10

56

1,964

184

1,026

2

6

0

15

92

196

863

90

116

510

Tropical Hardwoods Exotic Hardwoods Nonstocked

562 21,028

35

Exotic Softwoods

Elm/Ash/Cottonwood

Private

545

Longleaf/Slash Pine

Pinyon/Juniper

South Other Public

1,000 ha

Forest Type Group Spruce/Fir

North Other Public Private

1 667

1 47

376

2,295

1,267 31,835

4

67

16

335

30

121

4,655

57

129

118

9

26

395

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.

124

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

Chapter 4

Appendix Table C-1b Current Forest Carbon Stocks in LiveStocks Trees According Region,According Ownership, and Forest Type Appendix Table C-1b Current Forest Carbon in LivetoTrees to Region, Group, 2013 Ownership, and Forest Type Group, 2013 Region: Ownership group:

Pacific Coast Other Federal Public Private

Rocky Mountain Other Federal Public Private

Federal

White/Red/Jack Pine

Federal

14

2

3

Loblolly/Shortleaf Pine Other Eastern Softwoods Douglas-Fir Ponderosa Pine Western White Pine Fir/Spruce/Mountain Hemlock Lodgepole Pine Hemlock/Sitka Spruce

24

1

2

683

2,620

455

1,324

1,191

64

272

483

22

234

472

36

234

1

1

2

18

57

296

2,092

57

145

1,913

58

139

260

6

41

802

11

52

1,812

310

404

142

13

33

Western Larch

58

4

9

90

14

17

Redwood

44

73

182 109

3

8

Other Western Softwoods California Mixed Conifer

220

638

26

4

117

230

593

2

1

1

192

94

674

85

2

27

1,179

20

275

4,269

48

83

572

145

1

1

34

6

3

61

2

0

2

4

4

113

0

1

47

3

22

1

11

44

50

93

430

240

84

1,504

463

1,087

5,947

1,097

330

6,757

7

25

60

287

303

2,145

72

184

1,001

56

59

714

486

1,028

3,896

49

10

164

145

246

567

0

11

40

82

18

3

32

1

5

175

3

16

14

1

3

31

Other Softwoods

0

Oak/Pine Oak/Hickory

1

0

1

Oak/Gum/Cypress 23

18

20

9

3

25

Maple/Beech/Birch Aspen/Birch

21

4

16

336

14

79

Alder/Maple

66

97

268

0

0

0

Western Oak

334

38

452

Tanoak/Laurel

175

44

287

42

14

58

0

0

0

9

1

4

110

8

64

0

0

Other Hardwoods Woodland Hardwoods Tropical Hardwoods Exotic Hardwoods

39

25

Exotic Softwoods

Elm/Ash/Cottonwood

Private

130

Longleaf/Slash Pine

Pinyon/Juniper

South Other Public

1,000 ha

Forest Type Group Spruce/Fir

North Other Public Private

0

0

0

2

12

1

Nonstocked 6 1 4 12 1 5 1 1 5 1 1 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.

125

9

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Appendix Table C-2 Annualized Carbon Stock Estimates per Estimates State, 2013 Appendix Table C-2 Annualized Carbon Stock

State

Forest area

Live tree stock

Total non-soil stock MMT CO2 eq. 2,495 3,165 864 2,128 5,660 2,119 324 62 1,787 2,690 2,868 600 663 318 228 1,650 1,584 2,093 447 534 2,360 1,508 2,100 1,645 2,962 126 371 760 295 1,033 2,941 2,493 57 1,156 839 5,855 2,680 62 1,582 139 2,095 2,730 967 759 2,329 5,379 1,967 1,725 1,115

per State, 2013

Soil organic Live tree net Total non-soil net carbon stock stock change stock change MMT CO2 eq. MMT CO2 eq. yr-1 MMT CO2 eq. yr-1 1,457 (28.8) (30.1) 1,865 (3.5) (2.5) 510 5.3 5.8 1,187 (21.7) (22.6) 1,864 (36.2) (38.8) 966 6.6 (1.9) 159 (4.1) (4.3) 36 (0.4) (0.2) 2,657 (15.4) (15.5) 3,047 (21.1) (21.7) 1,295 (1.1) (5.3) 412 (8.2) (9.5) 383 (7.0) (8.3) 246 (3.7) (4.5) 308 (4.0) (5.2) 754 (14.5) (15.3) 1,026 (13.3) (15.4) 2,152 (10.7) (11.7) 231 (3.0) (3.0) 308 (4.6) (4.7) 4,463 (26.9) (33.6) 4,290 (10.4) (15.0) 1,265 (33.4) (33.0) 1,116 (12.5) (16.3) 1,486 (0.9) (18.7) 158 (1.5) (2.2) 284 (0.5) 0.3 526 (4.6) (4.8) 199 (2.6) (2.3) 606 0.9 0.1 2,026 (19.3) (23.8) 1,974 (27.6) (28.4) 86 (0.4) (0.4) 774 (10.5) (12.2) 763 (2.5) (2.7) 3,516 (51.7) (52.7) 1,540 (19.8) (23.4) 34 (1.2) (1.2) 1,570 (21.8) (21.6) 161 (0.1) (0.8) 836 (8.2) (10.4) 3,373 (1.2) (1.2) 586 5.2 1.1 500 (4.7) (4.9) 1,352 (18.8) (18.6) 2,849 (24.8) (33.7) 1,051 (20.5) (23.3) 3,569 (15.1) (19.1) 445 12.6 8.3

1,000 ha MMT CO2 eq. Alabama 9,272 1,927 Alaska (Coastal) 5,841 2,049 Arizona 6,234 543 Arkansas 7,675 1,670 California 13,022 4,212 Colorado 8,435 1,319 Connecticut 702 259 Delaware 141 49 Florida 6,990 1,215 Georgia 10,017 2,210 Idaho 8,626 1,816 Illinois 1,984 504 Indiana 1,973 548 Iowa 1,201 248 Kansas 1,045 177 Kentucky 5,063 1,359 Louisiana 6,018 1,239 Maine 7,137 1,455 Maryland 990 368 Massachusetts 1,225 434 Michigan 8,238 1,784 Minnesota 7,033 1,004 Mississippi 7,879 1,689 Missouri 6,253 1,305 Montana 10,251 1,712 Nebraska 623 95 Nevada 3,547 206 New Hampshire 1,956 589 New Jersey 796 232 New Mexico 7,115 626 New York 7,691 2,294 North Carolina 7,536 2,008 North Dakota 309 39 Ohio 3,297 974 Oklahoma 4,913 574 Oregon 12,061 4,288 Pennsylvania 6,778 2,139 Rhode Island 147 50 South Carolina 5,279 1,294 South Dakota 781 94 Tennessee 5,633 1,581 Texas 18,856 1,695 Utah 5,962 572 Vermont 1,860 583 Virginia 6,428 1,857 Washington 9,039 3,740 West Virginia 4,921 1,651 Wisconsin 6,921 1,317 Wyoming 4,010 583 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.

126

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

Chapter 4

Appendix Table C-3a Mean Range of Plot-Level Densities, and Forest Area on Publicly Owned Appendix Table C-3aCarbon MeanDensity, Carbon Density, Range of Plot-Level Densities, and Forest Forestland (non-reserved) by Region and Stand-Age Class, 2013

on Publicly Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 Region

Stand age class

Years

Live tree carbon density

Live tree 5th and 95th percentiles

Total non-soil Total non-soil carbon density 5th and 95th percentiles

Area

Soil organic Forest carbon density 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

<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.

127

Chapter 4

U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Appendix Table C-3b Mean Range of Plot-Level Densities, and Forest Area on Privately Owned Appendix Table C-3bCarbon MeanDensity, Carbon Density, Range of Plot-Level Densities, and Forest Forestland (non-reserved) by Region and Stand-Age Class, 2013

Area on Privately Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 Region

Stand age class Live tree carbon Live tree 5th density and 95th percentiles

Total non-soil Total non-soil Soil organic carbon density 5th and 95th carbon density percentiles

Years

MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha 1,000 ha

MT CO2 eq/ha MT CO2 eq/ha

Forest area

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.

128

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

Chapter 4

Appendix Table C-3c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area

Appendix Table C-3c Mean Carbon Density, Range Plot-Levelownerships) Densities, and by Forest Area on Reserved Forestland on Reserved Forestland (both public andof private Region and Stand-Age (both public and private ownerships) by Region and Stand-Age Class, 2013

Class, 2013 Region

Stand age class Years

Pacific Coast

<20

Pacific Coast Pacific Coast

Live tree carbon density MT CO2 eq/ha

Live tree 5th and 95th percentiles MT CO2 eq/ha

Total non-soil carbon density MT CO2 eq/ha

Total non-soil 5th and 95th percentiles MT CO2 eq/ha

Soil organic carbon density MT CO2 eq/ha

Forest area 1,000 ha

25.4

0 - 104

235.8

75 - 545

234

340

20-40

98.6

0 - 455

233.8

75 - 582

228

187

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

166

2

South 150-200 576.5 576 - 576 666.5 666 - 666 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.

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U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2013 Appendix Table C-4a Mean Range of Plot-Level Densities, and Forest Area on Publicly Owned Appendix Table C-4aCarbon MeanDensity, Carbon Density, Range of Plot-Level Densities, and Forest Forestland (non-reserved) by Region and Stand-Age Class, 2013

on Publicly Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 Stand size class

Region

Live tree carbon density

Live tree 5th and 95th percentiles

MT CO2 eq/ha

MT CO2 eq/ha

Area

Total non-soil Total non-soil Soil organic Forest carbon density 5th and 95th carbon area percentiles density MT CO2 MT CO2 eq/ha 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

46.1

0 - 136

151.4

59 - 326

139

3,826

4.9

0 - 28

95.1

36 - 270

111

1,946

Rocky Mountain small diameter Rocky Mountain nonstocked 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, Density, Range of Plot-Level Densities, and Forest Area on and Privately Owned Forestland Table C-4b Mean Carbon Range of Plot-Level Densities, Forest Area on (non-reserved) by Region and Stand-Age Class, 2013

Privately Owned Forestland (non-reserved) by Region and Stand-Age Class, 2013 Stand size class

Region

Live tree carbon density

Live tree 5th and 95th percentiles

Total non-soil Total non-soil Soil organic Forest carbon density 5th and 95th carbon area percentiles density MT CO2 MT CO2 eq/ha MT CO2 eq/ha MT CO2 eq/ha 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.

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Table C-4c Mean Carbon Density, Range of Plot-Level Densities, and Forest Area on

Table C-4c Mean Carbon Density, of Plot-Level Densities, and Forestby Area on Reserved Forestland (both public Reserved Forestland (bothRange public and private ownerships) Region and Stand-Age Class, and private ownerships) by Region and Stand-Age Class, 2013

2013

Stand size class

Region

Live tree carbon density

Live tree 5th and 95th percentiles

Total non-soil Total non-soil Soil organic carbon density 5th and 95th carbon density percentiles

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

310

41

South nonstocked 13.3 0 - 43 81.5 49 - 132 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.

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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 Error! No text of specified style in document.-25 Energy Use and Carbon Dioxide Emissions by Fuel Source on U.S. Farms, 2013

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

Fuels

Diesel Gasoline LP1 gas Natural gas Electricity Total

Energy consumed QBTU

Carbon content MMT C/QBTU

Fraction oxidized

CO2 emissions MMT CO2 eq.

0.422 0.101 0.082 0.058 0.165 0.828

20.17 19.46 16.83 14.46 **

1 1 1 1 **

31.20 7.21 5.08 3.07 27.86 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.

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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.

16 14 MMT CO2 eq

12 10 8 6

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).

4 2 Northeast

Southeast

Delta States

Appalachian

Pacific

Lake States

Southern Plains

Mountain

Northern Plains

Corn Belt

0

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

Figure (MMT5-1 CO2 eq. is million metric tons of carbon dioxide equivalent) CO2 emissions from energy use in agriculture, by region, 2013 (MMT CO2 eq. is million metric tons of carbon dioxide equivalent)

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.

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.

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).

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 134

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Chapter 5

Table Error! No text of specified style in document.-26 Definition of Regions Used in Figure 51

Table 5-2 Definition of Regions Used in Figure 5-1

Region Corn Belt

States of Region Illinois Indiana Iowa Missouri Ohio Mountain Arizona Colorado Idaho Montana Nevada New Mexico Utah Wyoming Northern Plains Kansas Nebraska North Dakota South Dakota

Region Pacific

States of Region California Oregon Washington Southern Plains Oklahoma Texas Lake States Michigan Minnesota Wisconsin Appalachian Kentucky North Carolina Tennessee Virginia West Virginia Delta States Arkansas Louisiana Mississippi

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).

Region Southeast

Northeast

States of Region Alabama Florida Georgia South Carolina Connecticut Delaware Maine Maryland Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont

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 1.6 1.4

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

Quadrillion btu

1.2 1

0.8 0.6 0.4 0.2 0 1965

1970

1975

1980

1985

1990

1995

2000

2005

Figure 5-2 Energy Use in Agriculture, Source, Gasoline Diesel LP gas byNatural gas1965-2013 Electricity (BTU - British thermal unit)

135 Figure 5-2

Energy use in agriculture, by source, 1965-2013 (BTU - British thermal unit)

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

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.

80 70

MMT CO2 eq

60 50 40 30 20 10 0

Diesel

Gasoline

LP*gas

Natural gas Electricity

Total

LP* = liquid petroleum gas

2001

2005

2008

2013

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)

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.

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.

5.5 Major Changes Compared to Previous Inventories

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.

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.

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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.

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This publication supersedes TB-1930, U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990–2008. In accordance with Federal civil rights law and United States Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, gender identity (including gender expression), sexual orientation, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the responsible Agency or USDA’s TARGET Center at (202) 720-2600 (voice and TTY) or contact USDA through the Federal Relay Service at (800) 877-8339. Additionally, program information may be made available in languages other than English. To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: United States Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Washington, D.C. 202509410; (2) fax: (202) 690-7442; or (3) email: [email protected] USDA is an equal opportunity provider, employer, and lender.