Land Use, Land-Use Change, and Forestry 6-1

6. Land Use, Land-Use Change, and 

Forestry

This chapter provides an assessment of the net greenhouse gas flux resulting from the uses and changes in land types 

and forests in the United States.1 The Intergovernmental Panel on Climate Change 2006 Guidelines for National 

Greenhouse Gas Inventories (IPCC 2006) recommends reporting fluxes according to changes within and 

conversions between certain land-use types termed: Forest Land, Cropland, Grassland, Settlements, Wetlands (as 

well as Other Land). The greenhouse gas flux from Forest Land Remaining Forest Land is reported using estimates 

of changes in forest carbon (C) stocks, non-carbon dioxide (non-CO2) emissions from forest fires, and the 

application of synthetic fertilizers to forest soils. The greenhouse gas flux from agricultural lands (i.e., Cropland and 

Grassland) that is reported in this chapter includes changes in organic C stocks in mineral and organic soils due to 

land use and management, and emissions of CO2 due to the application of crushed limestone and dolomite to 

managed land (i.e., soil liming) and urea fertilization. Fluxes are reported for four agricultural land use/land-use 

change categories: Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, 

and Land Converted to Grassland. Fluxes resulting from Settlements Remaining Settlements include those from 

urban trees and soil fertilization. Landfilled yard trimmings and food scraps are accounted for separately under 

Other.

The estimates in this chapter, with the exception of CO2 removals from harvested wood products and urban trees, 

and CO2 emissions from liming and urea fertilization, are based on activity data collected at multiple-year intervals, 

which are in the form of forest, land use, and municipal solid waste surveys. Carbon dioxide fluxes from forest C 

stocks (except the harvested wood product components) and from agricultural soils (except the liming component) 

are calculated on an average annual basis from data collected in intervals ranging from one to 10 years. The 

resulting annual averages are applied to years between surveys. Calculations of non-CO2 emissions from forest fires 

are based on forest CO2 flux data. For the landfilled yard trimmings and food scraps source, historical annual solid 

waste survey data were interpolated where annual data were missing so that annual storage estimates could be 

derived. This flux has been applied to the entire time series, and periodic U.S. census data on changes in urban area 

have been used to develop annual estimates of CO2 flux.

Land use, land-use change, and forestry activities in 2013 resulted in a C sequestration (i.e., total sinks) of 881.7 

MMT CO2 Eq.2 (240.5 MMT C).3 This represents an offset of approximately 13.2 percent of total (i.e., gross) 

 

1 The term “flux” is used to describe the net emissions of greenhouse gases to the atmosphere accounting for both the emissions

of CO2 to and the removals of CO2 from the atmosphere. Removal of CO2 from the atmosphere is also referred to as “carbon 

sequestration”.

2 Following the revised reporting requirements under the UNFCCC, this Inventory report presents CO2 equivalent values based 

on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.

3 The total sinks value includes the positive C sequestration reported for Forest Land Remaining Forest Land, Cropland 

Remaining Cropland, Land Converted to Grassland, Settlements Remaining Settlements, and Other Land plus the loss in C 

sequestration reported for Land Converted to Cropland and Grassland Remaining Grassland.
6-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

greenhouse gas emissions in 2013. Emissions from land use, land-use change, and forestry activities in 2013 

represent 0.3 percent of total greenhouse gas emissions.4

Total land use, land-use change, and forestry C sequestration increased by approximately 13.6 percent between 1990 

and 2013. This increase was primarily due to an increase in the rate of net C accumulation in forest C stocks.5 Net 

C accumulation in Forest Land Remaining Forest Land, Land Converted to Grassland, and Settlements Remaining 

Settlements increased, while net C accumulation in Cropland Remaining Cropland, Grassland Remaining 

Grassland, and Landfilled Yard Trimmings and Food Scraps slowed over this period. Emissions from Land 

Converted to Cropland and Wetlands Remaining Wetlands decreased. Emissions and removals for Land Use, LandUse Change, and Forestry are summarized in Table 6-1 by land-use and source category.

Table 6-1: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry by 

Land-Use Change Category (MMT CO2 Eq.)

Land-Use/Source Category 1990 2005 2009 2010 2011 2012 2013

Forest Land Remaining Forest Land (635.2) (792.9) (754.7) (757.1) (749.2) (746.7) (765.5)

Changes in Forest Carbon Stocka(639.4) (807.1) (764.9) (765.4) (773.8) (773.1) (775.7)

Forest Fires 4.2 13.8 9.7 7.9 24.2 26.0 9.7 

Forest Soilsb 0.1 0.5 0.5 0.5 0.5 0.5 0.5 

Cropland Remaining Cropland (58.1) (20.2) (20.2) (17.3) (17.8) (15.0) (13.5)

Changes in Agricultural Soil Carbon Stock (65.2) (28.0) (27.5) (25.9) (25.8) (25.0) (23.4)

Liming of Agricultural Soils 4.7 4.3 3.7 4.8 3.9 5.8 5.9 

Urea Fertilization 2.4 3.5 3.6 3.8 4.1 4.2 4.0 

Land Converted to Cropland 24.5 19.8 16.2 16.2 16.2 16.1 16.1 

Changes in Agricultural Soil Carbon Stock 24.5 19.8 16.2 16.2 16.2 16.1 16.1 

Grassland Remaining Grassland (1.9) 4.2 11.7 11.7 11.7 11.5 12.1 

Changes in Agricultural Soil Carbon Stock (1.9) 4.2 11.7 11.7 11.7 11.5 12.1 

Land Converted to Grassland (7.4) (9.0) (8.9) (8.9) (8.9) (8.8) (8.8)

Changes in Agricultural Soil Carbon Stock (7.4) (9.0) (8.9) (8.9) (8.9) (8.8) (8.8)

Settlements Remaining Settlements (59.0) (78.2) (82.8) (83.8) (84.8) (85.8) (87.1)

Changes in Urban Tree Carbon Stockc(60.4) (80.5) (85.0) (86.1) (87.3) (88.4) (89.5)

Settlement Soilsd 1.4 2.3 2.2 2.4 2.5 2.5 2.4 

Wetlands Remaining Wetlands 1.1 1.1 1.0 1.0 0.9 0.8 0.8 

Peatlands Remaining Peatlands 1.1 1.1 1.0 1.0 0.9 0.8 0.8 

Other (26.0) (11.4) (12.5) (13.2) (13.2) (12.8) (12.6)

Landfilled Yard Trimmings and Food 

Scraps (26.0) (11.4) (12.5) (13.2) (13.2) (12.8) (12.6)

Total Fluxe(762.1) (886.4) (850.2) (851.3) (844.9) (840.6) (858.5)

Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.

a Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.

b Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land, and Land Converted to 

Forest Land, but not from land-use conversion.

c Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.

d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to 

Settlements, but not from land-use conversion.

e

“Total Flux” is defined as the sum of positive emissions (i.e., sources) of greenhouse gases to the atmosphere plus removals of 

CO2 (i.e., sinks or negative emissions) from the atmosphere.

Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.

CO2 removals are presented in Table 6-2 along with CO2, CH4, and N2O emissions from Land use, Land-Use 

Change, and Forestry source categories. Liming of agricultural soils and urea fertilization in 2013 resulted in CO2

emissions of 9.9 MMT CO2 Eq. (9,936 kt). Lands undergoing peat extraction (i.e., Peatlands Remaining Peatlands) 

 

4 The emissions value includes the CO2, CH4, and N2O emissions reported for Forest Fires, Forest Soils, Liming of Agricultural 

Soils, Urea Fertilization, Settlement Soils, and Peatlands Remaining Peatlands.

5 Carbon sequestration estimates are net figures. The C stock in a given pool fluctuates due to both gains and losses. When 

losses exceed gains, the C stock decreases, and the pool acts as a source. When gains exceed losses, the C stock increases, and 

the pool acts as a sink; also referred to as net C sequestration or removal.
Land Use, Land-Use Change, and Forestry 6-3

resulted in CO2 emissions of 0.8 MMT CO2 Eq. (770 kt), methane (CH4) emissions of less than 0.05 MMT CO2 Eq., 

and nitrous oxide (N2O) emissions of less than 0.05 MMT CO2 Eq. The application of synthetic fertilizers to forest 

soils in 2013 resulted in N2O emissions of 0.5 MMT CO2 Eq. (2 kt). N2O emissions from fertilizer application to 

forest soils have increased by 455 percent since 1990, but still account for a relatively small portion of overall 

emissions. Additionally, N2O emissions from fertilizer application to settlement soils in 2013 accounted for 2.4 

MMT CO2 Eq. (8 kt). This represents an increase of 77 percent since 1990. Forest fires in 2013 resulted in CH4

emissions of 5.8 MMT CO2 Eq. (233 kt), and in N2O emissions of 3.8 MMT CO2 Eq. (13 kt). Emissions and 

removals for Land Use, Land-Use Change, and Forestry are shown in Table 6-2 and Table 6-3.

Table 6-2: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry 

(MMT CO2 Eq.)

Gas/Land-Use Category 1990 2005 2009 2010 2011 2012 2013

CO2 (767.7) (903.0) (862.6) (862.0) (872.1) (869.6) (871.0)

Forest Land Remaining Forest Land:

Changes in Forest Carbon Stocka(639.4) (807.1) (764.9) (765.4) (773.8) (773.1) (775.7)

Cropland Remaining Cropland:

Changes in Agricultural Soil Carbon 

Stock (65.2) (28.0) (27.5) (25.9) (25.8) (25.0) (23.4)

Cropland Remaining Cropland:

Liming of Agricultural Soils 4.7 4.3 3.7 4.8 3.9 5.8 5.9 

Cropland Remaining Cropland:

Urea Fertilization 2.4 3.5 3.6 3.8 4.1 4.2 4.0 

Land Converted to Cropland 24.5 19.8 16.2 16.2 16.2 16.1 16.1 

Grassland Remaining Grassland (1.9) 4.2 11.7 11.7 11.7 11.5 12.1 

Land Converted to Grassland (7.4) (9.0) (8.9) (8.9) (8.9) (8.8) (8.8)

Settlements Remaining Settlements: 

Changes in Urban Tree Carbon Stockb(60.4) (80.5) (85.0) (86.1) (87.3) (88.4) (89.5)

Wetlands Remaining Wetlands:

Peatlands Remaining Peatlands 1.1 1.1 1.0 1.0 0.9 0.8 0.8 

Other:

Landfilled Yard Trimmings and Food 

Scraps (26.0) (11.4) (12.5) (13.2) (13.2) (12.8) (12.6)

CH4 2.5 8.3 5.8 4.8 14.6 15.7 5.8 

Forest Land Remaining Forest Land:

Forest Fires 2.5 8.3 5.8 4.7 14.6 15.7 5.8 

Wetlands Remaining Wetlands:

Peatlands Remaining Peatlands + + + + + + + 

N2O 3.1 8.3 6.5 6.0 12.6 13.3 6.7 

Forest Land Remaining Forest Land:

Forest Fires 1.7 5.5 3.8 3.1 9.6 10.3 3.8 

Forest Land Remaining Forest Land:

Forest Soilsc 0.1 0.5 0.5 0.5 0.5 0.5 0.5 

Settlements Remaining Settlements:

Settlement Soilsd 1.4 2.3 2.2 2.4 2.5 2.5 2.4 

Wetlands Remaining Wetlands:

Peatlands Remaining Peatlands + + + + + + + 

Total Fluxe(762.1) (886.4) (850.2) (851.3) (844.9) (840.6) (858.5)

Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.

+ Less than 0.05 MMT CO2 Eq.

a Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.

b Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.

c Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land, and Land Converted 

to Forest Land, but not from land-use conversion.

d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to 

Settlements, but not from land-use conversion

e

“Total Flux” is defined as the sum of positive emissions (i.e., sources) of greenhouse gases to the atmosphere plus removals

of CO2 (i.e., sinks or negative emissions) from the atmosphere.

Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
6-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

Table 6-3: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry 

(kt) 

Gas/Land-Use Category 1990 2005 2009 2010 2011 2012 2013

CO2 (767,697) (902,974) (862,631) (862,025) (872,103) (869,580) (871,026)

Forest Land Remaining Forest Land:

Changes in Forest Carbon Stocka(639,432) (807,075) (764,871) (765,410) (773,843) (773,110) (775,677)

Cropland Remaining Cropland:

Changes in Agricultural Soil 

Carbon Stock (65,196) (28,035) (27,473) (25,867) (25,752) (24,990) (23,432)

Cropland Remaining Cropland:

Liming of Agricultural Soils 4,667 4,349 3,669 4,784 3,871 5,776 5,925 

Cropland Remaining Cropland:

Urea Fertilization 2,417 3,504 3,555 3,778 4,099 4,225 4,011 

Land Converted to Cropland 24,498 19,830 16,194 16,194 16,194 16,095 16,125 

Grassland Remaining Grassland (1,913) 4,230 11,704 11,694 11,680 11,532 12,083 

Land Converted to Grassland (7,410) (8,995) (8,917) (8,894) (8,871) (8,783) (8,757)

Settlements Remaining Settlements: 

Changes in Urban Tree Carbon 

Stockb(60,408) (80,523) (85,008) (86,129) (87,250) (88,372) (89,493)

Wetlands Remaining Wetlands:

Peatlands Remaining Peatlands 1,055 1,101 1,024 1,022 926 812 770 

Other:

Landfilled Yard Trimmings and 

Food Scraps (25,975) (11,360) (12,508) (13,197) (13,156) (12,766) (12,581)

CH4 101 332 234 190 584 627 233 

Forest Land Remaining Forest Land:

Forest Fires 101 332 233 190 584 626 233 

Wetlands Remaining Wetlands:

Peatlands Remaining Peatlands + + + + + + + 

N2O 10 28 22 20 42 45 23 

Forest Land Remaining Forest Land:

Forest Fires 6 18 13 11 32 35 13 

Forest Land Remaining Forest Land:

Forest Soilsc + 2 2 2 2 2 2 

Settlements Remaining Settlements:

Settlement Soilsd 5 8 8 8 8 8 8 

Wetlands Remaining Wetlands:

Peatlands Remaining Peatlands + + + + + + + 

+ Emissions are less than 0.5 kt

a Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.

b Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.

c Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land, and Land Converted to 

Forest Land, but not from land-use conversion.

d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to 

Settlements, but not from land-use conversion.

Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.

Box 6-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks

In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions 

inventories, the emissions and sinks presented in this report are organized by source and sink categories and 

calculated using internationally-accepted methods provided by the Intergovernmental Panel on Climate Change 
Land Use, Land-Use Change, and Forestry 6-5 

(IPCC).6 Additionally, the calculated emissions and sinks in a given year for the United States are presented in a 

common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under this 

international agreement.7 The use of consistent methods to calculate emissions and sinks by all nations providing 

their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions and sinks 

reported in this Inventory report are comparable to emissions and sinks reported by other countries. The manner that 

emissions and sinks are provided in this Inventory is one of many ways U.S. emissions and sinks could be 

examined; this Inventory report presents emissions and sinks in a common format consistent with how countries are 

to report inventories under the UNFCCC. The report itself follows this standardized format, and provides an 

explanation of the IPCC methods used to calculate emissions and sinks, and the manner in which those calculations 

are conducted. 

6.1 Representation of the U.S. Land Base 

A national land-use categorization system that is consistent and complete, both temporally and spatially, is needed in 

order to assess land use and land-use change status and the associated greenhouse gas (GHG) fluxes over the 

Inventory time series. This system should be consistent with IPCC (2006), such that all countries reporting on 

national GHG fluxes to the UNFCCC should: (1) Describe the methods and definitions used to determine areas of 

managed and unmanaged lands in the country, (2) describe and apply a consistent set of definitions for land-use 

categories over the entire national land base and time series (i.e., such that increases in the land areas within 

particular land-use categories are balanced by decreases in the land areas of other categories unless the national land 

base is changing), and (3) account for GHG fluxes on all managed lands. The IPCC (2006, Vol. IV, Chapter 1) 

considers all anthropogenic GHG emissions and removals associated with land use and management to occur on 

managed land, and all emissions and removals on managed land should be reported based on this guidance (see 

IPCC 2010 for further discussion). Consequently, managed land serves as a proxy for anthropogenic emissions and 

removals. This proxy is intended to provide a practical framework for conducting an inventory, even though some 

of the GHG emissions and removals on managed land are influenced by natural processes that may or may not be 

interacting with the anthropogenic drivers. Guidelines for factoring out natural emissions and removals may be 

developed in the future, but currently the managed land proxy is considered the most practical approach for 

conducting an inventory in this sector (IPCC 2010). The implementation of such a system helps to ensure that 

estimates of GHG fluxes are as accurate as possible, and does allow for potentially subjective decisions in regards to 

subdividing natural and anthropogenic driven emissions. This section of the Inventory has been developed in order 

to comply with this guidance. 

Three databases are used to track land management in the United States and are used as the basis to classify U.S. 

land area into the thirty-six IPCC land-use and land-use change categories (Table 6-5) (IPCC 2006). The primary 

databases are the U.S. Department of Agriculture (USDA) National Resources Inventory (NRI)8 and the USDA 

Forest Service (USFS) Forest Inventory and Analysis (FIA)9 Database. The Multi-Resolution Land Characteristics 

Consortium (MRLC) National Land Cover Dataset (NLCD)10 is also used to identify land uses in regions that were 

not included in the NRI or FIA. 

The total land area included in the U.S. Inventory is 936 million hectares across the 50 states.11 Approximately 890 

million hectares of this land base is considered managed, which has not changed by much over the time series of the 

 

6 See <http://www.ipcc-nggip.iges.or.jp/public/index.html>. 

7 See <http://unfccc.int/resource/docs/2013/cop19/eng/10a03.pdf>.

8 NRI data is available at <http://www.nrcs.usda.gov/wps/portal/nrcs/site/national/home>. 

9 FIA data is available at <http://www.fia.fs.fed.us/tools-data/default.asp>. 

10 NLCD data is available at <http://www.mrlc.gov/> and MRLC is a consortium of several U.S. government agencies. 

11 The current land representation does not include areas from U.S. territories, but there are planned improvements to include 

these regions in future reports. 
6-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Inventory (Table 6-5). In 2013, the United States had a total of 293 million hectares of managed Forest Land (1.3 

percent increase since 1990), 159 million hectares of Cropland (6.6 percent decrease since 1990), 321 million 

hectares of managed Grassland (1.1 percent decrease since 1990), 43 million hectares of managed Wetlands (3 

percent decrease since 1990), 51 million hectares of Settlements (31 percent increase since 1990), and 24 million 

hectares of managed Other Land (Table 6-5). Wetlands are not differentiated between managed and unmanaged, 

and are reported solely as managed. Some wetlands would be considered unmanaged, and a future planned 

improvement will include a differentiation between managed and unmanaged wetlands using guidance in the 2013 

Supplement to the 2006 Guidelines for National Greenhouse Gas Inventories: Wetlands. In addition, C stock 

changes are not currently estimated for the entire land base, which leads to discrepancies between the managed land 

area data presented here and in the subsequent sections of the Inventory (e.g., Grassland Remaining Grassland).12,13 

Planned improvements are under development to account for C stock changes on all managed land (e.g., federal 

grasslands) and ensure consistency between the total area of managed land in the land-representation description and 

the remainder of the Inventory.

Dominant land uses vary by region, largely due to climate patterns, soil types, geology, proximity to coastal regions, 

and historical settlement patterns, although all land uses occur within each of the 50 states (Table 6-4). Forest Land 

tends to be more common in the eastern states, mountainous regions of the western United States, and Alaska. 

Cropland is concentrated in the mid-continent region of the United States, and Grassland is more common in the 

western United States and Alaska. Wetlands are fairly ubiquitous throughout the United States, though they are 

more common in the upper Midwest and eastern portions of the country. Settlements are more concentrated along 

the coastal margins and in the eastern states.

Table 6-4: Managed and Unmanaged Land Area by Land-Use Categories for All 50 States 

(Thousands of Hectares)

Land-Use Categories 1990 2005 2009 2010 2011 2012 2013

Managed Lands 890,018 890,016 890,016 890,017 890,017 890,017 890,017

Forest Land 288,964 291,213 292,263 292,399 292,516 292,634 292,751

Croplands 170,448 160,107 159,248 159,243 159,238 159,234 159,230

Grasslands 324,327 321,360 320,666 320,657 320,655 320,652 320,648

Settlements 38,602 49,676 50,628 50,624 50,621 50,617 50,614

Wetlands 44,453 44,060 43,441 43,330 43,228 43,126 43,025

Other Land 23,225 23,600 23,770 23,765 23,759 23,754 23,748

Unmanaged Lands 46,212 46,214 46,214 46,213 46,213 46,214 46,214

Forest Land 9,634 9,634 9,634 9,634 9,634 9,634 9,634

Croplands 0 0 0 0 0 0 0

Grasslands 25,782 25,782 25,782 25,782 25,782 25,782 25,782

Settlements 0 0 0 0 0 0 0

Wetlands 0 0 0 0 0 0 0

Other Land 10,796 10,798 10,798 10,797 10,797 10,797 10,797

Total Land Areas 936,230 936,230 936,230 936,230 936,230 936,230 936,230

Forest Land 298,598 300,848 301,898 302,033 302,151 302,268 302,386

Croplands 170,448 160,107 159,248 159,243 159,238 159,234 159,230

Grasslands 350,109 347,142 346,448 346,439 346,437 346,434 346,430

Settlements 38,602 49,676 50,628 50,624 50,621 50,617 50,614

Wetlands 44,453 44,060 43,441 43,330 43,228 43,126 43,025

Other Land 34,021 34,397 34,568 34,562 34,556 34,551 34,545

 

12 C stock changes are not estimated for approximately 75 million hectares of Grassland Remaining Grassland. See specific 

land-use sections for further discussion on gaps in the inventory of C stock changes, and discussion about planned improvements 

to address the gaps in the near future.

13 These “managed area” discrepancies also occur in the Common Reporting Format (CRF) tables submitted to the UNFCCC.
Land Use, Land-Use Change, and Forestry 6-7

Table 6-5: Land Use and Land-Use Change for the U.S. Managed Land Base for All 50 States 

(Thousands of Hectares)

Land-Use & LandUse Change 

Categoriesa

1990 2005 2009 2010 2011 2012 2013

Total Forest Land 288,964 291,213 292,263 292,399 292,516 292,634 292,751

FF 283,860 278,979 280,844 280,977 281,092 281,207 281,322

CF 1,119 2,656 2,449 2,450 2,450 2,450 2,450

GF 3,434 7,805 7,279 7,280 7,280 7,281 7,281

WF 64 250 257 257 258 258 259

SF 103 362 376 376 376 377 377

OF 383 1,161 1,057 1,059 1,060 1,062 1,063

Total Cropland 170,448 160,107 159,248 159,243 159,238 159,234 159,230

CC 154,527 143,050 143,933 143,928 143,924 143,920 143,916

FC 1,148 688 577 576 576 576 576

GC 13,988 15,216 13,655 13,655 13,655 13,655 13,655

WC 161 199 176 176 176 175 175

SC 438 692 672 672 672 672 672

OC 185 262 236 236 236 236 236

Total Grassland 324,327 321,360 320,666 320,657 320,655 320,652 320,648

GG 313,914 301,823 302,566 302,594 302,627 302,660 302,692

FG 1,615 3,022 2,757 2,755 2,753 2,752 2,750

CG 8,099 14,986 13,912 13,878 13,844 13,810 13,776

WG 238 409 330 329 329 329 329

SG 112 274 267 267 267 267 267

OG 350 846 834 834 834 834 834

Total Wetlands 44,453 44,060 43,441 43,330 43,228 43,126 43,025

WW 43,802 42,545 42,002 41,892 41,792 41,691 41,592

FW 143 397 382 381 380 379 378

CW 132 365 345 345 344 344 344

GW 343 698 664 664 664 664 664

SW 0 10 10 10 10 10 10

OW 32 44 39 39 38 38 38

Total Settlements 38,602 49,676 50,628 50,624 50,621 50,617 50,614

SS 34,060 35,269 36,340 36,337 36,334 36,330 36,328

FS 1,787 6,112 6,090 6,090 6,090 6,090 6,089

CS 1,344 3,633 3,526 3,526 3,526 3,526 3,526

GS 1,353 4,433 4,439 4,439 4,439 4,439 4,439

WS 3 31 30 30 30 30 30

OS 55 200 202 202 202 202 202

Total Other Land 23,225 23,600 23,770 23,765 23,759 23,754 23,748

OO 22,175 21,372 21,470 21,466 21,460 21,455 21,450

FO 182 538 569 569 569 570 570

CO 345 645 703 703 703 703 703

GO 454 903 902 902 902 901 901

WO 67 121 104 104 104 104 104

SO 2 21 20 20 20 20 20

Grand Total 890,018 890,016 890,016 890,017 890,017 890,017 890,017
6-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

a The abbreviations are “F” for Forest Land, “C” for Cropland, “G” for Grassland, “W” for Wetlands, “S” for Settlements, 

and “O” for Other Lands. Lands remaining in the same land-use category are identified with the land-use abbreviation given 

twice (e.g., “FF” is Forest Land Remaining Forest Land), and land-use change categories are identified with the previous land 

use abbreviation followed by the new land-use abbreviation (e.g., “CF” is Cropland Converted to Forest Land).

Note: All land areas reported in this table are considered managed. A planned improvement is underway to deal with an 

exception for wetlands, which based on the definitions for the current U.S. Land Representation Assessment includes both 

managed and unmanaged lands. U.S. Territories have not been classified into land uses and are not included in the U.S. Land 

Representation Assessment. See the Planned Improvements section for discussion on plans to include territories in future 

inventories. In addition, C stock changes are not currently estimated for the entire land base, which leads to discrepancies 

between the managed land area data presented here and in the subsequent sections of the Inventory.
Land Use, Land-Use Change, and Forestry 6-9

Figure 6-1: Percent of Total Land Area for Each State in the General Land-Use Categories for 

2013
6-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Methodology

IPCC Approaches for Representing Land Areas

IPCC (2006) describes three approaches for representing land areas. Approach 1 provides data on the total area for 

each individual land-use category, but does not provide detailed information on changes of area between categories 

and is not spatially explicit other than at the national or regional level. With Approach 1, total net conversions 

between categories can be detected, but not the individual changes (i.e., additions and/or losses) between the landuse categories that led to those net changes. Approach 2 introduces tracking of individual land-use changes between 

the categories (e.g., Forest Land to Cropland, Cropland to Forest Land, and Grassland to Cropland), using survey 

samples or other forms of data, but does not provide location data on all parcels of land. Approach 3 extends 

Approach 2 by providing location data on all parcels of land, such as maps, along with the land-use history. The 

three approaches are not presented as hierarchical tiers and are not mutually exclusive.

According to IPCC (2006), the approach or mix of approaches selected by an inventory agency should reflect 

calculation needs and national circumstances. For this analysis, the NRI, FIA, and the NLCD have been combined 

to provide a complete representation of land use for managed lands. These data sources are described in more detail 

later in this section. NRI and FIA are Approach 2 data sources that do not provide spatially-explicit representations 

of land use and land-use conversions, even though land use and land-use conversions are tracked explicitly at the 

survey locations. NRI and FIA data can only be aggregated and used to develop a land-use conversion matrix for a 

political or ecologically-defined region. NLCD is a spatially-explicit time series of land-cover data that is used to 

inform the classification of land use, and is therefore Approach 3 data. Lands are treated as remaining in the same 

category (e.g., Cropland Remaining Cropland) if a land-use change has not occurred in the last 20 years. Otherwise, 

the land is classified in a land-use change category based on the current use and most recent use before conversion 

to the current use (e.g., Cropland Converted to Forest Land).

Definitions of Land Use in the United States

Managed and Unmanaged Land

The United States definition of managed land is similar to the basic IPCC (2006) definition of managed land, but

with some additional elaboration to reflect national circumstances. Based on the following definitions, most lands in 

the United States are classified as managed:

 Managed Land: Land is considered managed if direct human intervention has influenced its condition. 

Direct intervention occurs mostly in areas accessible to human activity and includes altering or maintaining 

the condition of the land to produce commercial or non-commercial products or services; to serve as 

transportation corridors or locations for buildings, landfills, or other developed areas for commercial or 

non-commercial purposes; to extract resources or facilitate acquisition of resources; or to provide social 

functions for personal, community, or societal objectives where these areas are readily accessible to 

society.14

 Unmanaged Land: All other land is considered unmanaged. Unmanaged land is largely comprised of areas 

inaccessible to society due to the remoteness of the locations. Though these lands may be influenced 

 

14 Wetlands are an exception to this general definition, because these lands, as specified by IPCC (2006), are only considered 

managed if they are created through human activity, such as dam construction, or the water level is artificially altered by human 

activity. Distinguishing between managed and unmanaged wetlands is difficult due to limited data availability. Wetlands are not 

characterized by use within the NRI. Therefore, unless wetlands are managed for cropland or grassland, it is not possible to

know if they are artificially created or if the water table is managed based on the use of NRI data. As a result, all wetlands are 

reported as managed. See the Planned Improvements section of the Inventory for work being done to refine the Wetland area 

estimates.
Land Use, Land-Use Change, and Forestry 6-11

indirectly by human actions such as atmospheric deposition of chemical species produced in industry or 

CO2 fertilization, they are not influenced by a direct human intervention.15

In addition, land that is previously managed remains in the managed land base for 20 years before re-classifying the 

land as unmanaged in order to account for legacy effects of management on C stocks.

Land-Use Categories

As with the definition of managed lands, IPCC (2006) provides general non-prescriptive definitions for the six main 

land-use categories: Forest Land, Cropland, Grassland, Wetlands, Settlements and Other Land. In order to reflect 

national circumstances, country-specific definitions have been developed, based predominantly on criteria used in 

the land-use surveys for the United States. Specifically, the definition of Forest Land is based on the FIA definition 

of forest,16 while definitions of Cropland, Grassland, and Settlements are based on the NRI.17 The definitions for 

Other Land and Wetlands are based on the IPCC (2006) definitions for these categories.

 Forest Land: A land-use category that includes areas at least 120 feet (36.6 meters) wide and at least one

acre (0.4 hectare) in size with at least 10 percent cover (or equivalent stocking) by live trees including land 

that formerly had such tree cover and that will be naturally or artificially regenerated. Trees are woody 

plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches (7.6 cm) in 

diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4 feet (5 meters) 

at maturity in situ. Forest Land includes all areas recently having such conditions and currently 

regenerating or capable of attaining such condition in the near future. Forest Land also includes transition 

zones, such as areas between forest and non-forest lands that have at least 10 percent cover (or equivalent 

stocking) with live trees and forest areas adjacent to urban and built-up lands. Unimproved roads and trails, 

streams, and clearings in forest areas are classified as forest if they are less than 120 feet (36.6 meters) wide 

or an acre (0.4 hectare) in size. Forest Land does not include land that is predominantly under agricultural 

or urban land use (Oswalt et al. 2014).

 Cropland: A land-use category that includes areas used for the production of adapted crops for harvest; 

this category includes both cultivated and non-cultivated lands.18 Cultivated crops include row crops or 

close-grown crops and also hay or pasture in rotation with cultivated crops. Non-cultivated cropland 

includes continuous hay, perennial crops (e.g., orchards) and horticultural cropland. Cropland also includes 

land with agroforestry, such as alley cropping and windbreaks,19 if the dominant use is crop production. 

Lands in temporary fallow or enrolled in conservation reserve programs (i.e., set-asides20) are also 

classified as Cropland, as long as these areas do not meet the Forest Land criteria. Roads through 

Cropland, including interstate highways, state highways, other paved roads, gravel roads, dirt roads, and 

railroads are excluded from Cropland area estimates and are, instead, classified as Settlements.

 Grassland: A land-use category on which the plant cover is composed principally of grasses, grass-like 

plants (i.e., sedges and rushes), forbs, or shrubs suitable for grazing and browsing, and includes both 

pastures and native rangelands.21 This includes areas where practices such as clearing, burning, chaining, 

and/or chemicals are applied to maintain the grass vegetation. Savannas, some wetlands and deserts, in 

 

15 There are some areas, such as Forest Land and Grassland in Alaska that are classified as unmanaged land due to the 

remoteness of their location.

16 See <http://socrates.lv-hrc.nevada.edu/fia/ab/issues/pending/glossary/Glossary_5_30_06.pdf>.

17 See <http://www.nrcs.usda.gov/wps/portal/nrcs/site/national/home>.

18 A minor portion of Cropland occurs on federal lands, and is not currently included in the C stock change inventory. A planned 

improvement is underway to include these areas in future C inventories.

19 Currently, there is no data source to account for biomass C stock change associated with woody plant growth and losses in 

alley cropping systems and windbreaks in cropping systems, although these areas are included in the cropland land base.

20 A set-aside is cropland that has been taken out of active cropping and converted to some type of vegetative cover, including, 

for example, native grasses or trees.

21 Grasslands on federal lands are included in the managed land base, but C stock changes are not estimated on these lands. 

Federal grassland areas have been assumed to have negligible changes in C due to limited land-use and management change, but 

planned improvements are underway to further investigate this issue and include these areas in future C inventories.
6-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

addition to tundra are considered Grassland.22 Woody plant communities of low forbs and shrubs, such as 

mesquite, chaparral, mountain shrub, and pinyon-juniper, are also classified as Grassland if they do not 

meet the criteria for Forest Land. Grassland includes land managed with agroforestry practices, such as 

silvipasture and windbreaks, if the land is principally grasses, grass-like plants, forbs, and shrubs suitable 

for grazing and browsing, and assuming the stand or woodlot does not meet the criteria for Forest Land. 

Roads through Grassland, including interstate highways, state highways, other paved roads, gravel roads, 

dirt roads, and railroads are excluded from Grassland and are, instead, classified as Settlements.

 Wetlands: A land-use category that includes land covered or saturated by water for all or part of the year, 

in addition to the areas of lakes, reservoirs, and rivers. Managed Wetlands are those where the water level 

is artificially changed, or were created by human activity. Certain areas that fall under the managed 

Wetlands definition are included in other land uses based on the IPCC guidance, including Cropland 

(drained wetlands for crop production and also systems that are flooded for most or just part of the year, 

such as rice cultivation and cranberry production), Grassland (drained wetlands dominated by grass cover), 

and Forest Land (including drained or un-drained forested wetlands).

 Settlements: A land-use category representing developed areas consisting of units of 0.25 acres (0.1 ha) or 

more that includes residential, industrial, commercial, and institutional land; construction sites; public 

administrative sites; railroad yards; cemeteries; airports; golf courses; sanitary landfills; sewage treatment 

plants; water control structures and spillways; parks within urban and built-up areas; and highways, 

railroads, and other transportation facilities. Also included are tracts of less than 10 acres (4.05 ha) that 

may meet the definitions for Forest Land, Cropland, Grassland, or Other Land but are completely 

surrounded by urban or built-up land, and so are included in the Settlements category. Rural transportation 

corridors located within other land uses (e.g., Forest Land, Cropland, and Grassland) are also included in 

Settlements.

 Other Land: A land-use category that includes bare soil, rock, ice, and all land areas that do not fall into 

any of the other five land-use categories, which allows the total of identified land areas to match the

managed land base. Following the guidance provided by the IPCC (2006), C stock changes are not 

estimated for Other Lands because these areas are largely devoid of biomass, litter and soil C pools.

Land-Use Data Sources: Description and Application to U.S.

Land Area Classification

U.S. Land-Use Data Sources

The three main sources for land-use data in the United States are the NRI, FIA, and the NLCD (Table 6-6). These 

data sources are combined to account for land use in all 50 states. FIA and NRI data are used when available for an 

area because the surveys contain additional information on management, site conditions, crop types, biometric 

measurements, and other data from which to estimate C stock changes on those lands. If NRI and FIA data are not 

available for an area, however, then the NLCD product is used to represent the land use.

Table 6-6: Data Sources Used to Determine Land Use and Land Area for the Conterminous 

United States, Hawaii, and Alaska

NRI FIA NLCD

Forest Land

Conterminous United 

States

Non-Federal •

Federal •

 

22 IPCC (2006) guidelines do not include provisions to separate desert and tundra as land categories.
Land Use, Land-Use Change, and Forestry 6-13

Hawaii

Non-Federal •

Federal •

Alaska

Non-Federal •

Federal •

Croplands, Grasslands, Other Lands, Settlements, and Wetlands

Conterminous United 

States

Non-Federal •

Federal •

Hawaii

Non-Federal •

Federal •

Alaska

Non-Federal •

Federal •

National Resources Inventory

For the Inventory, the NRI is the official source of data on all land uses on non-federal lands in the conterminous 

United States and Hawaii (except Forest Land), and is also used as the resource to determine the total land base for 

the conterminous United States and Hawaii. The NRI is a statistically-based survey conducted by the USDA 

Natural Resources Conservation Service and is designed to assess soil, water, and related environmental resources 

on non-federal lands. 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 United States Public Land Survey (Nusser 

and Goebel 1997). Within a primary sample unit (typically a 160 acre [64.75 hectare] square quarter-section), three 

sample points are selected according to a restricted randomization procedure. Each point in the survey is assigned 

an area weight (expansion factor) based on other known areas and land-use information (Nusser and Goebel 1997). 

The NRI survey utilizes data derived from remote sensing imagery and site visits in order to provide detailed 

information on land use and management, particularly for croplands and grasslands, and is used as the basis to 

account for C stock changes in agricultural lands (except federal Grasslands). The NRI survey was conducted every 

5 years between 1982 and 1997, but shifted to annualized data collection in 1998. The land use between five-year 

periods from 1982 and 1997 are assumed to be the same for a five-year time period if the land use is the same at the 

beginning and end of the five-year period. (Note: most of the data has the same land use at the beginning and end of 

the five-year periods.) If the land use had changed during a five-year period, then the change is assigned at random 

to one of the five years. For crop histories, years with missing data are estimated based on the sequence of crops 

grown during years preceding and succeeding a missing year in the NRI history. This gap-filling approach allows 

for development of a full time series of land-use data for non-federal lands in the conterminous United States and 

Hawaii. This Inventory incorporates data through 2007 from the NRI.

Forest Inventory and Analysis

The FIA program, conducted by the USFS, is another statistically-based survey for the conterminous United States, 

and the official source of data on Forest Land area and management data for the Inventory in this region of the 

country. FIA engages in a hierarchical system of sampling, with sampling categorized as Phases 1 through 3, in 

which sample points for phases are subsets of the previous phase. Phase 1 refers to collection of remotely-sensed 

data (either aerial photographs or satellite imagery) primarily to classify land into forest or non-forest and to identify 

landscape patterns like fragmentation and urbanization. Phase 2 is the collection of field data on a network of 

ground plots that enable classification and summarization of area, tree, and other attributes associated with forestland uses. Phase 3 plots are a subset of Phase 2 plots where data on indicators of forest health are measured. Data 

from all three phases are also used to estimate C stock changes for Forest Land. Historically, FIA inventory surveys 

have been conducted periodically, with all plots in a state being measured at a frequency of every five to 14 years. 

A new national plot design and annual sampling design was introduced by FIA about ten years ago. Most states, 

though, have only recently been brought into this system. Annualized sampling means that a portion of plots 

throughout each state is sampled each year, with the goal of measuring all plots once every five years. See Annex 
6-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

3.13 to see the specific survey data available by state. The most recent year of available data varies state by state 

(range of most recent data is from 2012 through 2013; see Table A-246).

National Land Cover Dataset

Though NRI provides land-area data for both federal and non-federal lands in the conterminous United States and 

Hawaii, it only includes land-use data on non-federal lands, and FIA only records data for forest land.23 

Consequently, major gaps exist when the datasets are combined, such as federal grassland operated by Bureau of 

Land Management (BLM), USDA, and National Park Service, as well as Alaska.24 The NLCD is used as a 

supplementary database to account for land use on federal lands that are not included in the NRI and FIA databases. 

The NLCD land-cover classification scheme, available for 1992, 2001, 2006, and 2011 has been applied over the 

conterminous United States (Homer et al. 2007), and also for Alaska and Hawaii in 2001. For the conterminous 

United States, the NLCD Land Cover Change Products for 2001, 2006, and 2011 were used in order to represent 

both land use and land-use change for federal lands (Fry et al. 2011, Homer et al. 2007, Jin et al. 2013). The NLCD 

products are based primarily on Landsat Thematic Mapper imagery. The NLCD contains 21 categories of landcover information, which have been aggregated into the IPCC land-use categories, and the data are available at a 

spatial resolution of 30 meters. The federal land portion of the NLCD was extracted from the dataset using the 

federal land area boundary map from the National Atlas (U.S. Department of Interior 2005). This map represents 

federal land boundaries in 2005, so as part of the analysis, the federal land area was adjusted annually based on the 

NRI federal land area estimates (i.e., land is periodically transferred between federal and non-federal ownership). 

Consequently, the portion of the land base categorized with NLCD data varied from year to year, corresponding to 

an increase or decrease in the federal land base. The NLCD is strictly a source of land-cover information, however, 

and does not provide the necessary site conditions, crop types, and management information from which to estimate 

C stock changes on those lands.

As part of Quality Assurance and Quality Control (QA/QC), the land base derived from the NRI, FIA, and NLCD 

was compared to the Topologically Integrated Geographic Encoding and Referencing (TIGER) survey (U.S. Census 

Bureau 2010). The U.S. Census Bureau gathers data on the U.S. population and economy, and has a database of 

land areas for the country. The land area estimates from the U.S. Census Bureau differ from those provided by the 

land-use surveys used in the Inventory because of discrepancies in the reporting approach for the Census and the 

methods used in the NRI, FIA, and NLCD. The area estimates of land-use categories, based on NRI, FIA, and 

NLCD, are derived from remote sensing data instead of the land survey approach used by the U.S. Census Survey. 

More importantly, the U.S. Census Survey does not provide a time series of land-use change data or land 

management information. Consequently, the U.S. Census Survey was not adopted as the official land area estimate 

for the Inventory. Rather, the NRI, FIA, and NLCD datasets were adopted because this database provides full 

coverage of land area and land use for the conterminous United States, Alaska, and Hawaii, in addition to 

management and other data relevant for the Inventory. Regardless, the total difference between the U.S. Census 

Survey and the combined NRI, FIA, and NLCD data is about 22 million hectares for the total U.S. land base of 

about 936 million hectares currently included in the Inventory, or a 2.4 percent difference. Much of this difference 

is associated with open waters in coastal regions and the Great Lakes, which is included in the Census.

Managed Land Designation

Lands are designated as managed in the United States based on the definitions provided earlier in this section. In 

order to apply the definitions in an analysis of managed land, the following criteria are used:

 All Croplands and Settlements are designated as managed so only Grassland, Forest Land or Other 

Lands may be designated as unmanaged land;25

 All Forest Land with active fire protection are considered managed;

 

23 FIA does collect some data on non-forest land use, but these are held in regional databases versus the national database. The 

status of these data is being investigated.

24 The FIA and NRI survey programs also do not include U.S. Territories with the exception of non-federal lands in Puerto Rico, 

which are included in the NRI survey. Furthermore, NLCD does not include coverage for all U.S. Territories.

25 A planned improvement is underway to deal with an exception for Wetlands which includes both managed and unmanaged 

lands based on the definitions for the current U.S. Land Representation Assessment.
Land Use, Land-Use Change, and Forestry 6-15

 All Grassland is considered managed at a county scale if there are livestock in the county;26 other areas 

are considered managed if accessible based on the proximity to roads and other transportation corridors, 

and/or infrastructure;

 Protected lands maintained for recreational and conservation purposes are considered managed (managed 

by public and private organizations);

 Lands with active and/or past resource extraction are considered managed; and

 Lands that were previously managed but subsequently classified as unmanaged remain in the managed 

land base for 20 years following the conversion to account for legacy effects of management on C 

stocks.

The analysis of managed lands is conducted using a geographic information system. Lands that are used for crop 

production or settlements are determined from the NLCD (Fry et al. 2011, Homer et al. 2007, Jin et al. 2013). Lands 

with active fire management are determined from maps of federal and state management plans from the National 

Atlas (U.S. Department of Interior 2005) and Alaska Interagency Fire Management Council (1998). It is noteworthy 

that all forest lands in the conterminous United States have active fire protection, and are therefore designated as 

managed regardless of accessibility or other criteria. The designation of grasslands as managed is determined based 

on USDA National Agricultural Statistics Service livestock population data at the county scale (U.S. Department of 

Agriculture 2011). Accessibility is evaluated based on a 10-km buffer surrounding road and train transportation 

networks using the ESRI Data and Maps product (ESRI 2008), and a 10-km buffer surrounding settlements using 

NLCD. Lands maintained for recreational purposes are determined from analysis of the Protected Areas Database 

(U.S. Geological Survey 2012). However, protected areas that are not accessible to human intervention, including 

no suppression of disturbances or extraction of resources, are not included in the managed land base. Multiple data 

sources are used to determine lands with active resource extraction: Alaska Oil and Gas Information System 

(Alaska Oil and Gas Conservation Commission 2009), Alaska Resource Data File (U.S. Geological Survey 2012), 

Active Mines and Mineral Processing Plants (U.S. Geological Survey 2005), and Coal Production and Preparation 

Report (U.S. Energy Information Administration 2011). A buffer of 3,300 and 4.000 meters is assumed around 

petroleum extraction and mine locations, respectively, to account for the footprint of operation and impacts of 

activities on the surrounding landscape. The resulting managed land area is overlaid on the NLCD to estimate the 

area of managed land by land use for both federal and non-federal lands. The remaining land represents the 

unmanaged land base.

Approach for Combining Data Sources

The managed land base in the United States has been classified into the thirty-six IPCC land-use categories using 

definitions developed to meet national circumstances, while adhering to IPCC (2006). 27 In practice, the land was 

initially classified into a variety of land-use categories within the NRI, FIA, and NLCD datasets, and then 

aggregated into the thirty-six broad land use and land-use-change categories identified in IPCC (2006). All three 

datasets provide information on forest land areas in the conterminous United States, but the area data from FIA serve 

as the official dataset for estimating Forest Land use areas in the conterminous United States.

Therefore, another step in the analysis is to address the inconsistencies in the representation of the forest land among 

the three databases. NRI and FIA have different criteria for classifying forest land in addition to different sampling 

designs, leading to discrepancies in the resulting estimates of Forest Land area on non-federal land in the 

conterminous United States. Similarly, there are discrepancies between the NLCD and FIA data for defining and 

classifying Forest Land on federal lands. In addition, dependence exists between the Forest Land area and the 

amount of land designated as other land uses in both the NRI and the NLCD, such as the amount of Grassland, 

Cropland, and Wetlands, relative to the Forest Land area. This results in inconsistencies among the three databases 

for estimated Forest Land area, as well as for the area estimates for other land-use categories. FIA is the main 

database for forest statistics, and consequently, the NRI and NLCD were adjusted to achieve consistency with FIA 

estimates of Forest Land in the conterminous United States. The adjustments were made at a state-scale, and it was 

assumed that the majority of the discrepancy in forest area was associated with an under- or over-prediction of 

 

26 Assuming all grasslands are grazed in a county with livestock is a conservation assumption about human impacts on 

grasslands. Currently, detailed information on grazing at sub-county scales is not available for the United States to make a finer 

delineation of managed land.

27 Definitions are provided in the previous section.
6-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Grassland and Wetland area in the NRI and NLCD due to differences in forest land definitions. Specifically, the 

forest land area for a given state according to the NRI and NLCD was adjusted to match the FIA estimates of Forest 

Land for non-federal and federal land in Forest Lands Remaining Forest Lands, respectively. In a second step, 

corresponding increases or decreases were made in the area estimates of Grassland and Wetland from the NRI and 

NLCD, Grasslands Remaining Grasslands and Wetlands Remaining Wetlands, in order to balance the change in 

forest area, and therefore not change the overall amount of managed land within an individual state. The 

adjustments were based on the proportion of land within each of these land-use categories at the state level. (i.e., a 

higher proportion of Grassland led to a larger adjustment in Grassland area).

The modified NRI data are then aggregated to provide the land-use and land-use change data for non-federal lands 

in the conterminous United States, and the modified NLCD data are aggregated to provide the land use and land-use 

change data for federal lands. Data for all land uses in Hawaii are based on NRI for non-federal lands and on NLCD 

for federal lands. Land use data in Alaska are based solely on the NLCD data (Table 6-6). The result is land use 

and land-use change data for the conterminous United States, Hawaii, and Alaska.28

A summary of the details on the approach used to combine data sources for each land use are described below. 

 Forest Land: Both non-federal and federal forest lands in both the continental United States and coastal 

Alaska are covered by FIA. FIA is used as the basis for both Forest Land area data as well as to estimate C 

stocks and fluxes on Forest Land. Interior Alaska is not currently surveyed by FIA so forest land in Alaska 

is evaluated with 2001 NLCD. NRI is being used in the current report to provide Forest Land areas on nonfederal lands in Hawaii, but FIA data will be collected in Hawaii in the future. 

 Cropland: Cropland is classified using the NRI, which covers all non-federal lands within 49 states 

(excluding Alaska), including state and local government-owned land as well as tribal lands. NRI is used 

as the basis for both Cropland area data as well as to estimate soil C stocks and fluxes on Cropland. NLCD 

2001 is used to determine Cropland area in Alaska.

 Grassland: Grassland on non-federal lands is classified using the NRI within 49 states (excluding Alaska), 

including state and local government-owned land as well as tribal lands. NRI is used as the basis for both 

Grassland area data as well as to estimate soil C stocks and fluxes on Grassland. Grassland on federal 

Bureau of Land Management lands, Department of Defense lands, National Parks, and within USFS lands 

are covered by the NLCD. NLCD is used to estimate the areas of federal and non-federal grasslands in 

Alaska.

 Wetlands: NRI captures wetlands on non-federal lands within 49 states (excluding Alaska), while federal 

wetlands and wetlands in Alaska are covered by the NLCD. This currently includes both managed and 

unmanaged wetlands as no database has yet been applied to make this distinction. See the Planned 

Improvements section for details.

 Settlements: NRI captures non-federal settlement area in 49 states (excluding Alaska). If areas of Forest 

Land or Grassland under 10 acres (4.05 ha) are contained within settlements or urban areas, they are 

classified as Settlements (urban) in the NRI database. If these parcels exceed the 10 acre (4.05 ha) 

threshold and are Grassland, they will be classified as such by NRI. Regardless of size, a forested area is 

classified as non-forest by FIA if it is located within an urban area. Settlements on federal lands and in 

Alaska are covered by NLCD.

 Other Land: Any land not falling into the other five land-use categories and, therefore, categorized as 

Other Land is classified using the NRI for non-federal areas in the 49 states (excluding Alaska) and NLCD 

for the federal lands and Alaska.

Some lands can be classified into one or more categories due to multiple uses that meet the criteria of more than one 

definition. However, a ranking has been developed for assignment priority in these cases. The ranking process is 

from highest to lowest priority, in the following manner:

Settlements > Cropland > Forest Land > Grassland > Wetlands > Other Land

 

28 Only one year of data are currently available for Alaska so there is no information on land-use change for this state.
Land Use, Land-Use Change, and Forestry 6-17

Settlements are given the highest assignment priority because they are extremely heterogeneous with a mosaic of 

patches that include buildings, infrastructure, and travel corridors, but also open grass areas, forest patches, riparian 

areas, and gardens. The latter examples could be classified as Grassland, Forest Land, Wetlands, and Cropland, 

respectively, but when located in close proximity to settlement areas they tend to be managed in a unique manner 

compared to non-settlement areas. Consequently, these areas are assigned to the Settlements land-use category. 

Cropland is given the second assignment priority, because cropping practices tend to dominate management 

activities on areas used to produce food, forage, or fiber. The consequence of this ranking is that crops in rotation 

with pasture will be classified as Cropland, and land with woody plant cover that is used to produce crops (e.g., 

orchards) is classified as Cropland, even though these areas may meet the definitions of Grassland or Forest Land, 

respectively. Similarly, Wetlands are considered Croplands if they are used for crop production, such as rice or 

cranberries. Forest Land occurs next in the priority assignment because traditional forestry practices tend to be the 

focus of the management activity in areas with woody plant cover that are not croplands (e.g., orchards) or 

settlements (e.g., housing subdivisions with significant tree cover). Grassland occurs next in the ranking, while 

Wetlands then Other Land complete the list.

The assignment priority does not reflect the level of importance for reporting GHG emissions and removals on 

managed land, but is intended to classify all areas into a discrete land use. Currently, the IPCC does not make 

provisions in the guidelines for assigning land to multiple uses. For example, a wetland is classified as Forest Land 

if the area has sufficient tree cover to meet the stocking and stand size requirements. Similarly, wetlands are 

classified as Cropland if they are used for crop production, such as rice or cranberries, or as Grassland if they are 

composed principally of grasses, grass-like plants (i.e., sedges and rushes), forbs, or shrubs suitable for grazing and 

browsing. Regardless of the classification, emissions from these areas are included in the Inventory if the land is 

considered managed and presumably impacted by anthropogenic activity in accordance with the guidance provided 

in IPCC (2006).

Recalculations Discussion

Relative to the previous Inventory, new data were incorporated from FIA on forestland areas, which were used to 

make minor adjustments to the time series. The managed land base was further refined this year with the new 

implementation criteria incorporating lands protected for recreation in addition to lands with mineral and petroleum 

extraction. This change increased the managed land base in Alaska, but had limited impact on the managed land 

base in the conterminous United States.

Planned Improvements

A key planned improvement is to fully incorporate area data by land-use type for U.S. Territories into the Inventory. 

Fortunately, most of the managed land in the United States is included in the current land-use statistics, but a 

complete accounting is a key goal for the near future. Preliminary land-use area data by land-use category are 

provided in Box 6-2: Preliminary Estimates of Land Use in U.S. Territories for the U.S. Territories.

Box 6-2: Preliminary Estimates of Land Use in U.S. Territories

Several programs have developed land cover maps for U.S. Territories using remote sensing imagery, including the 

Gap Analysis program, Caribbean Land Cover project, National Land Cover dataset, USFS Pacific Islands Imagery 

Project, and the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program. 

Land-cover data can be used to inform a land-use classification if there is a time series to evaluate the dominate 

practices. For example, land that is principally used for timber production with tree cover over most of the time 

series is classified as forest land even if there are a few years of grass dominance following timber harvest. These 

products were reviewed and evaluated for use in the national Inventory as a step towards implementing a planned 

improvement to include U.S. Territories in the land representation for the Inventory. Recommendations are to use 

the NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database for the smaller island 

Territories (U.S. Virgin Islands, Guam, Northern Marianas Islands, and American Samoa) because this program is 

an ongoing and therefore will be continually updated. The C-CAP product does not cover the entire territory of 

Puerto Rico so the NLCD was used for this area. The final selection of a land-cover product for these Territories is 

still under discussion. Results are presented below (in hectares). The total land area of all U.S. Territories is 1.05 

million hectares, representing 0.1 percent of the total land base for the United States.
6-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

Table 6-7: Total Land Area (Hectares) by Land-Use Category for U.S. Territories. 

Puerto Rico

U.S. Virgin 

Islands Guam

Northern 

Marianas 

Islands

American 

Samoa Total

Cropland 19,712 138 236 289 389 20,764

Forest Land 404,004 13,107 24,650 25,761 15,440 482,962

Grasslands 299,714 12,148 15,449 13,636 1,830 342,777

Other Land 5,502 1,006 1,141 5,186 298 13,133

Settlements 130,330 7,650 11,146 3,637 1,734 154,496

Wetlands 24,525 4,748 1,633 260 87 31,252

Total 883,788 38,796 54,255 48,769 19,777 1,045,385

Additional work will be conducted to reconcile differences in Forest Land estimates between the NRI and FIA, 

evaluating the assumption that the majority of discrepancies in Forest Land areas are associated with an over- or 

under-estimation of Grassland and Wetland area. In some regions of the United States, a discrepancy in Forest Land 

areas between NRI and FIA may be associated with an over- or under-prediction of other land uses. This 

improvement would include an analysis designed to develop region-specific adjustments. 

There are also other databases that may need to be reconciled with the NRI and NLCD datasets, particularly for 

Settlements. Urban area estimates, used to produce C stock and flux estimates from urban trees, are currently based 

on population data (1990, 2000, and 2010 U.S. Census data). Using the population statistics, “urban clusters” are 

defined as areas with more than 500 people per square mile. The USFS is currently moving ahead with an urban 

forest inventory program so that urban forest area estimates will be consistent with FIA forest area estimates outside 

of urban areas, which would be expected to reduce omissions and overlap of forest area estimates along urban 

boundary areas. 

As adopted by the UNFCCC, new guidance in the 2013 Supplement to the 2006 Guidelines for National Greenhouse 

Gas Inventories: Wetlands will be implemented in the Inventory. This will likely have implications for the 

classification of managed and unmanaged wetlands in the Inventory report. More detailed wetlands datasets will 

also be evaluated and integrated into the analysis in order to implement the new guidance. 

6.2 Forest Land Remaining Forest Land 

Changes in Forest Carbon Stocks (IPCC Source Category 4A1) 

For estimating carbon (C) stocks or stock change (flux), C in forest ecosystems can be divided into the following 

five storage pools (IPCC 2006): 

 Aboveground biomass, which includes all living biomass above the soil including stem, stump, branches, 

bark, seeds, and foliage. This category includes 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 C (SOC), including all organic material in soil to a depth of 1 meter but excluding the coarse 

roots of the aboveground pools. 

In addition, there are two harvested wood pools to account for when estimating C flux: 

 Harvested wood products (HWP) in use. 
Land Use, Land-Use Change, and Forestry 6-19

 HWP in solid waste disposal sites (SWDS).

Carbon is continuously cycled among these storage pools and between forest ecosystems and the atmosphere as a 

result of biological processes in forests (e.g., photosynthesis, respiration, decomposition, and disturbances such as 

fires or pest outbreaks) and anthropogenic activities (e.g., harvesting, thinning, and replanting). As trees 

photosynthesize and grow, C is removed from the atmosphere and stored in living tree biomass. As trees die and 

otherwise deposit litter and debris on the forest floor, C is released to the atmosphere and also is transferred to the 

soil by organisms that facilitate decomposition.

The net change in forest C is not equivalent to the net flux between forests and the atmosphere because timber 

harvests do not cause an immediate flux of all harvested biomass C to the atmosphere. Instead, harvesting transfers 

a portion of the C stored in wood to a "product pool." Once in a product pool, the C is emitted over time as CO2

when the wood product combusts or decays. The rate of emission varies considerably among different product 

pools. For example, if timber is harvested to produce energy, combustion releases C immediately, and these 

emissions are reported for information purposes in the Energy Sector with the harvest (i.e., the associated reduction 

in forest carbon stocks) and subsequent combustion implicitly accounted for under the Land Use, Land-Use Change 

(LULUCF) Sector (i.e., the harvested timber does not enter the HWP pools). Conversely, if timber is harvested and 

used as lumber in a house, it may be many decades or even centuries before the lumber decays and C is released to 

the atmosphere. If wood products are disposed of in SWDS, the C contained in the wood may be released many 

years or decades later, or may be stored almost permanently in the SWDS. These latter fluxes are also accounted for 

under the LULUCF Sector.

This section quantifies the net changes in C stocks in the five forest C pools and two harvested wood pools. The 

basic methodology for determining C stock and stock-change relies on data from the extensive inventories of U.S. 

forest lands, and improvements in these inventories over time are reflected in the estimates (Heath et al. 2011, Heath 

2012). The net change in stocks for each pool is estimated, and then the changes in stocks are summed for all pools 

to estimate total net flux. The focus on C implies that all C-based greenhouse gases are included, and the focus on 

stock change suggests that specific ecosystem fluxes do not need to be separately itemized in this report. Changes in 

C stocks from disturbances, such as forest fires, are implicitly included in the net changes. For instance, an 

inventory conducted after fire counts only the trees that are left. Therefore, changes in C stocks from natural 

disturbances, such as wildfires, pest outbreaks, and storms, are implicitly accounted for in the forest inventory 

approach; however, they are highly variable from year to year. Wildfire events are typically the most severe but 

other natural disturbance events can result in large C stock losses that are time- and location- specific. The IPCC

(2006) recommends reporting changes in C stocks from forest lands according to several land-use types and 

conversions, specifically Forest Land Remaining Forest Land and Land Converted to Forest Land. Research is 

ongoing to track C across a matrix of land-uses and land-use changes. Until such time that reliable and 

comprehensive estimates of C across the land-use matrix can be produced, net changes in all forest-related land, 

including non-forest land converted to forest and forests converted to non-forest, are reported here in the Forest 

Land Remaining Forest Land Sector (see the Planned Improvements section for more details).

Forest C storage pools, and the flows between them via emissions, sequestration, and transfers, are shown in Figure 

6-2. In the figure, boxes represent forest C storage pools and arrows represent flows between storage pools or 

between storage pools and the atmosphere. Note that the boxes are not identical to the five storage pools identified 

in the 2006 IPCC Guidelines. Instead, the storage pools identified have been refined in this graphic to better 

illustrate the processes that result in transfers of C from one pool to another, and emissions to as well as uptake from 

the atmosphere.
6-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Figure 6-2: Forest Sector C Pools and Flows

Approximately 34 percent of the U.S. land area is estimated to be forested (Oswalt et al. 2014). The most-recent 

forest inventories from each of the conterminous 48 states (USDA Forest Service 2014a, 2014b, and see Annex 

Table A-246) include an estimated 264 million hectares of forest land that are considered managed and are included 

in this inventory. An additional 6 million hectares of southeast and south central Alaskan forest are inventoried and 

are included here. Some differences exist in forest land defined in Oswalt et al. (2014) and the forest land included 

in this report, which is based on the USDA Forest Service (2014b) forest inventory. Survey data are not yet 

available for Hawaii and interior Alaska, but estimates of these areas are included in Oswalt et al. (2014). Updated 

survey data for central and western forest land in both Oklahoma and Texas have only recently become available, 

and these forests contribute to overall C stocks reported below. While Hawaii and U.S. territories have relatively 

small areas of forest land and thus may not influence the overall C budget substantially, these regions will be added 

to the C budget as sufficient data become available. Agroforestry systems are also not currently accounted for in the 

inventory, since they are not explicitly inventoried by either the FIA program of the USDA Forest Service or the 

NRI of the USDA Natural Resources Conservation Service (Perry et al. 2005).

An estimated 68 percent (211 million hectares) of U.S. forests in Alaska and the conterminous United States are 

classified as timberland, meaning they meet minimum levels of productivity and have not been removed from 

production. Ten percent of Alaskan forests and 80 percent of forests in the conterminous United States are classified 

as timberlands. Of the remaining non-timberland forests, 30 million hectares are reserved forest lands (withdrawn 

by law from management for production of wood products) and 69 million hectares are lower productivity forest 

lands (Oswalt et al. 2014). Historically, the timberlands in the conterminous 48 states have been more frequently or 

intensively surveyed than other forest lands.

Estimates of forest land area declined by approximately 8 million hectares over the period from the early 1960s to 

the late 1980s. Since then, forest area has increased by about 14 million hectares (Oswalt et al. 2014). Current 

trends in the managed forest area represented here increased by an average annual rate of 0.1 percent (see Annex 

Table A-248). In addition to the increase in forest area, the major influences on the current net C flux from forest 

land are management activities and the ongoing impacts of previous land-use changes. These activities affect the 

net flux of C by altering the amount of C stored in forest ecosystems. For example, intensified management of 
Land Use, Land-Use Change, and Forestry 6-21

forests that leads to an increased rate of growth may increase the eventual biomass density of the forest, thereby 

increasing the uptake and storage of C.29 Though harvesting forests removes much of the aboveground C, on 

average the estimated volume of annual net growth nationwide is about double the volume of annual removals on 

timberlands (Oswalt et al. 2014). The reversion of cropland or grassland to forest land increases C storage in 

biomass, forest floor, and soils. Emerging research into forest ecosystem C stock change for forest remaining forest 

versus land-use change transfers to the forest land use suggest that forest ecosystem C accretion continues at steady 

rates in most regions of the United States (Figure 6-3) due to the aforementioned drivers. In concert with this trend, 

conversion of croplands and grasslands to forest lands continues to facilitate net increases in forest C stocks over 

time especially in northern and southern regions. The net effects of forest management and the effects of land-use 

change involving forest land are captured in the estimates of C stocks and fluxes presented in this chapter.

Figure 6-3: Forest Ecosystem Carbon (All Pools) Stocks and Stock Change (1990-2013)

Forest ecosystem C (all pools) stocks and stock change (1990–2013) analysis attributable to forest remaining forest 

and land-use change transfers to forests: (a) Resource planning act assessment regions, (b) forest ecosystem stocks 

by region, (c) annual stock change in forest ecosystem C by region decomposed into net transfers into the forest C 

pool through land-use change and the net C accumulation in forests remaining forest (including disturbance related 

mortality and growth) (for analytical techniques see Coulston et al. in review and Wear and Coulston 2014).

In the United States, improved forest management practices, the regeneration of previously cleared forest areas, and 

timber harvesting and use have resulted in net uptake (i.e., net sequestration) of C each year from 1990 through 

2013. The rate of forest clearing in the 17th century following European settlement had slowed by the late 19th

century. Through the later part of the 20th century many areas of previously forested land in the United States were 

allowed to revert to forests or were actively reforested. The impacts of these land-use changes still influence C 

fluxes from these forest lands. More recently, the 1970s and 1980s saw a resurgence of federally-sponsored forest 

management programs (e.g., the Forestry Incentive Program) and soil conservation programs (e.g., the Conservation 

Reserve Program), which have focused on tree planting, improving timber management activities, combating soil 

erosion, and converting marginal cropland to forests. In addition to forest regeneration and management, forest 

 

T

29T The term “biomass density” refers to the mass of live vegetation per unit area. It is usually measured on a dry-weight basis. 

Dry biomass is 50 percent C by weight.
6-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

harvests have also affected net C fluxes. Because most of the timber harvested from U.S. forests is used in wood 

products, and many discarded wood products are disposed of in SWDS rather than by incineration, significant 

quantities of C in harvested wood are transferred to long-term storage pools rather than being released rapidly to the 

atmosphere (Skog 2008). The size of these long-term C storage pools has increased during the last century with the 

question arising as to how long the U.S. forests can remain a net C sink (Woodall et al. 2013).

Changes in C stocks in U.S. forests and harvested wood were estimated to account for net sequestration of 775.7 

MMT CO2 Eq. (211.5 MMT C) in 2013 (Table 6-8, Table 6-9, and Table 6-10). In addition to the net accumulation 

of C in harvested wood pools, sequestration is a reflection of net forest growth and increasing forest area over this 

period. Overall, estimates of average C in forest ecosystem biomass (aboveground and belowground) increased 

from 55 to 66 T C/ha between 1990 and 2014 (see Annex 3.13 for estimated average C densities by specific regions 

and forest types). Continuous, regular annual surveys are not available over the period for each state; therefore, 

estimates for non-survey years were derived by interpolation between known data points. Survey years vary from 

state to state, and national estimates are a composite of individual state surveys. Therefore, changes in sequestration 

over the interval 1990 to 2013 are the result of the sequences of new inventories for each state. Carbon in forest 

ecosystem biomass had the greatest effect on total change through increases in C density and total forest land. 

Management practices that increase C stocks on forest land, as well as afforestation and reforestation efforts, 

influence the trends of increased C densities in forests and increased forest land in the United States.

Estimated annual net additions to HWP C stock increased slightly between 2012 and 2013. Estimated net additions 

to solid-wood products in use increased a little with further recovery of the housing market, but additions to paper 

products in use declined. Estimated net additions to products in use for 2013 is about 20 percent of the level of net 

additions to products in use in 2007—prior to the recession. Estimated additions to landfills have been relatively 

stable over time.

Table 6-8: Estimated Net Annual Changes in C Stocks (MMT CO2/yr) in Forest and Harvested 

Wood Pools

Carbon Pool 1990 2005 2009 2010 2011 2012 2013

Forest (507.7) (704.4) (710.6) (704.9) (704.9) (704.9) (704.9)

Aboveground 

Biomass

(324.6) (402.8) (433.8) (433.7) (433.7) (433.7) (433.7)

Belowground 

Biomass

(63.2) (79.3) (87.3) (87.4) (87.4) (87.4) (87.4)

Dead Wood (45.9) (66.8) (94.2) (95.0) (95.0) (95.0) (95.0)

Litter (26.8) (11.8) (11.2) (10.9) (10.9) (10.9) (10.9)

Soil Organic C (47.2) (143.8) (84.1) (77.9) (77.9) (77.9) (77.9)

Harvested Wood (131.8) (102.7) (54.3) (60.5) (68.9) (68.2) (70.8)

Products in Use (64.8) (42.9) 6.6 0.4 (7.3) (6.2) (8.4)

SWDS (67.0) (59.8) (60.9) (60.9) (61.6) (62.0) (62.3)

Total Net Flux (639.4) (807.1) (764.9) (765.4) (773.8) (773.1) (775.7)

Note: Forest C stocks do not include forest stocks in U.S. territories, Hawaii, a portion of managed forests in 

Alaska, or trees on non-forest land (e.g., urban trees, agroforestry systems). Parentheses indicate net C 

sequestration (i.e., a net removal of C from the atmosphere). Total net flux is an estimate of the actual net flux 

between the total forest C pool and the atmosphere. Forest area estimates are based on interpolation and 

extrapolation of Inventory data as described in the text and in Annex 3.13. Harvested wood estimates are based 

on results from annual surveys and models. Totals may not sum due to independent rounding.

Table 6-9: Estimated Net Annual Changes in C Stocks (MMT C/yr) in Forest and Harvested 

Wood Pools

Carbon Pool 1990 2005 2009 2010 2011 2012 2013

Forest (138.5) (192.1) (193.8) (192.2) (192.2) (192.2) (192.2)

Aboveground Biomass (88.5) (109.9) (118.3) (118.3) (118.3) (118.3) (118.3)

Belowground Biomass (17.2) (21.6) (23.8) (23.8) (23.8) (23.8) (23.8)

Dead Wood (12.5) (18.2) (25.7) (25.9) (25.9) (25.9) (25.9)

Litter (7.3) (3.2) (3.1) (3.0) (3.0) (3.0) (3.0)

Soil Organic C (12.9) (39.2) (22.9) (21.2) (21.2) (21.2) (21.2)

Harvested Wood (35.9) (28.0) (14.8) (16.5) (18.8) (18.6) (19.3)

Products in Use (17.7) (11.7) 1.8 0.1 (2.0) (1.7) (2.3)

SWDS (18.3) (16.3) (16.6) (16.6) (16.8) (16.9) (17.0)
Land Use, Land-Use Change, and Forestry 6-23

Total Net Flux (174.4) (220.1) (208.6) (208.7) (211.0) (210.8) (211.5)

Note: Forest C stocks do not include forest stocks in U.S. territories, Hawaii, a portion of managed lands in 

Alaska, or trees on non-forest land (e.g., urban trees, agroforestry systems). Parentheses indicate net C 

sequestration (i.e., a net removal of C from the atmosphere). Total net flux is an estimate of the actual net flux 

between the total forest C pool and the atmosphere. Harvested wood estimates are based on results from annual 

surveys and models. Totals may not sum due to independent rounding.

Stock estimates for forest and harvested wood C storage pools are presented in Table 6-10. Together, the estimated 

aboveground live and forest soil pools account for a large proportion of total forest C stocks. The estimated C 

stocks summed for non-soil pools increased over time. Therefore, the estimated C sequestration was greater than C 

emissions from forests, as discussed above. Although not using the same pool delineations as this inventory 

submission, recent research into imputing FIA plot data across the coterminous United States allows spatial 

interpretation of forest C pools (Wilson et al. 2013). The imputed C density of individual forest ecosystem pools is 

highly variable across the diverse ecosystems of the United States (see Figure 6-5) highlighting the technical hurdles 

in refining C accounting across the matrix of changing land uses and ecosystem dynamics (e.g., temperate versus 

subtropical forests).

Table 6-10: Estimated Forest area (1,000 ha) and C Stocks (MMT C) in Forest and Harvested 

Wood Pools

1990 2005 2009 2010 2011 2012 2013 2014

Forest Area (1000 ha) 265,938 268,334 269,396 269,536 269,661 269,786 269,911 270,035

Carbon Pools (MMT C)

Forest 36,309 38,429 39,214 39,408 39,600 39,792 39,985 40,177

Aboveground Biomass 12,266 13,727 14,188 14,306 14,425 14,543 14,661 14,780

Belowground Biomass 2,430 2,717 2,809 2,833 2,857 2,881 2,904 2,928

Dead Wood 2,138 2,384 2,470 2,496 2,522 2,548 2,574 2,600

Litter 2,749 2,803 2,816 2,819 2,822 2,825 2,828 2,831

Soil Organic C 16,726 16,798 16,931 16,954 16,975 16,996 17,017 17,038

Harvested Wood 1,859 2,325 2,431 2,446 2,462 2,481 2,500 2,520

Products in Use 1,231 1,435 1,473 1,472 1,471 1,473 1,475 1,478

SWDS 628 890 958 974 991 1,008 1,025 1,042

Total C Stock 38,168 40,754 41,645 41,854 42,062 42,273 42,485 42,697

Note: Forest area and carbon stock estimates include all forest land in the conterminous 48 states plus managed forests in coastal

Alaska (Figure 6-6), which is the current area encompassed by FIA survey data. A recent methodological change implemented to 

address missing forest area data in coastal Alaska resulted in discrepancies between the coastal Alaska managed forest area of 1990 

through 2014, as contributes to this table, and the areas presented in Section 6.1 “Representation of the United S Land 

Base”. Coastal Alaska managed forest lands contributing to this table changed linearly from 5.77 million hectares in 1990 to 5.86 

million hectares in 2014. The estimates used for Section 6 changed linearly from 5.48 million hectares in 1990 to 5.95 million 

hectares in 2014. This represents a change of 5.3 and -1.5 percent for 1990 and 2014 in coastal Alaska, respectively. This 

discrepancy will be corrected in the 2016 submission. Forest C stocks do not include forest stocks in U.S. territories, Hawaii, a 

large portion of Alaska, or trees on non-forest land (e.g., urban trees, agroforestry systems). Wood product stocks include exports, 

even if the logs are processed in other countries, and exclude imports. Forest area estimates are based on interpolation and 

extrapolation of Inventory data as described in Smith et al. (2010) and in Annex 3.13. Harvested wood estimates are based on 

results from annual surveys and models. Totals may not sum due to independent rounding. Inventories are assumed to represent 

stocks as of January 1 of the Inventory year. Flux is the net annual change in stock. Thus, an estimate of flux for 2013 requires 

estimates of C stocks for 2013 and 2014.
6-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Figure 6-4: Estimates of Net Annual Changes in C Stocks for Major C Pools
Land Use, Land-Use Change, and Forestry 6-25

Figure 6-5: Forest Ecosystem C Density Imputed from Forest Inventory Plots, Conterminous 

United States, 2001–2009

Figure 6-5 shows: (A) Total forest ecosystem C, (B) aboveground live trees, (C) standing dead trees, (D) litter, and 

(E) soil organic C (Wilson et al. 2013).
6-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Box 6-3: CO2 Emissions from Forest Fires

As stated previously, the forest inventory approach implicitly accounts for emissions due to disturbances such as 

forest fires, because only C remaining in the forest is estimated. Net C stock change is estimated by subtracting 

consecutive C stock estimates. A forest fire disturbance removes C from the forest. The inventory data on which 

net C stock estimates are based already reflect this C loss. Therefore, estimates of net annual changes in C stocks 

for U.S. forest land already account for CO2 emissions from forest fires occurring in the lower 48 states as well as in 

the proportion of Alaska’s managed forest land captured in this Inventory. Because it is of interest to quantify the 

magnitude of CO2 emissions from fire disturbance, these estimates are highlighted here, using the full extent of 

available data. Non-CO2 greenhouse gas emissions from forest fires are also quantified in a separate section below.

The IPCC (2003) methodology and IPCC (2006) default combustion factor for wildfire were employed to estimate 

CO2 emissions from forest fires. See the explanation in Annex 3.13 for more details on the methodology used to 

estimate CO2 emissions from forest fires. Carbon dioxide emissions for wildfires and prescribed fires in the lower 

48 states and wildfires in Alaska in 2013 were estimated to be 77.9 MMT CO2/yr. This amount is masked in the 

estimate of net annual forest C stock change for 2013 because this net estimate accounts for the amount sequestered 

minus any emissions.

Table 6-11: Estimates of CO2 (MMT/yr) Emissions from Forest Fires for the Lower 48 States 

and Alaska

Year

CO2 emitted from 

Wildfires in Lower 48 

States (MMT/yr)

CO2 emitted from 

Prescribed Fires in Lower 

48 States (MMT/yr)

CO2 emitted from 

Wildfires in Alaska 

(MMT/yr)

Total CO2

emitted 

(MMT/yr)

1990 28.8 4.9 + 33.7

2005 95.8 14.8 + 110.7

2009 63.5 14.5 + 77.9

2010 49.5 13.9 + 63.4

2011 182.7 12.2 + 194.9

2012 197.7 11.5 + 209.1

2013 66.2 11.7 + 77.9

+ Does not exceed 0.05 MMT CO2 Eq.

Note: These emissions have already been accounted for in the estimates of net annual changes in C stocks, which 

account for the amount sequestered minus any emissions.

Methodology and Data Sources

The methodology described herein is consistent with IPCC (2006). Forest ecosystem C stocks and net annual C 

stock change were determined according to stock-difference methods, which involved applying C estimation factors 

to forest inventory data and interpolating between successive inventory-based estimates of C stocks. Harvested 

wood C estimates were based on factors such as the allocation of wood to various primary and end-use products as 

well as half-life (the time at which half of the amount placed in use will have been discarded from use) and expected 

disposition (e.g., product pool, SWDS, combustion). An overview of the different methodologies and data sources 

used to estimate the C in forest ecosystems or harvested wood products is provided here. See Annex 3.13 for details 

and additional information related to the methods and data.

Forest Ecosystem Carbon from Forest Inventory

Forest ecosystem stock and flux estimates are based on the stock-difference method and calculations for all 

estimates are in units of C. Separate estimates were made for the five IPCC C storage pools described above. All 

estimates were based on data collected from the extensive array of permanent forest inventory plots and associated 

models (e.g., live tree belowground biomass) in the United States (USDA Forest Service 2013b, 2013c). Carbon 

conversion factors were applied at the disaggregated level of each inventory plot and then appropriately expanded to 
Land Use, Land-Use Change, and Forestry 6-27

population estimates. A combination of tiers as outlined by IPCC (2006) were used. The Tier 3 biomass C 

estimates were calculated from forest inventory tree-level data. The Tier 2 dead organic and soil C estimates were 

obtained from empirical or theoretical models using the inventory data. All C conversion factors are specific to 

regions or individual states within the United States, which were further classified according to characteristic forest 

types within each region.

The first step in developing forest ecosystem estimates is to identify useful inventory data and resolve any 

inconsistencies among datasets. Forest inventory data were obtained from the FIA program (Frayer and Furnival 

1999, USDA Forest Service 2014b). Inventories include data collected on permanent inventory plots on forest lands 

and were organized as separate datasets, each representing a complete inventory, or survey, of an individual state at 

a specified time. Many of the more recent annual inventories reported for states are represented as “moving 

window” averages, which means that a portion—but not all—of the previous year’s inventory is updated each year 

(USDA Forest Service 2014d). Forest C calculations are organized according to these state surveys, and the 

frequency of surveys varies by state. All available datasets are identified for each state starting with pre-1990 data, 

and all unique surveys are identified for stock and change calculations. Since C stock change is based on 

differences between successive surveys within each state, accurate estimates of net C flux thus depend on consistent 

representation of forest land between these successive inventories. In order to achieve this consistency from 1990 to 

the present, states are sometimes subdivided into sub-state areas where the sum of sub-state inventories produces the 

best whole-state representation of C change as discussed in Smith et al. (2010).

The principal FIA datasets employed are freely available for download at USDA Forest Service (2014b) as the 

Forest Inventory and Analysis Database (FIADB) Version 6.0 (USDA Forest Service 2014c). However, to achieve 

consistent representation (spatial and temporal), three other general sources of past FIA data were included as 

necessary. First, older FIA plot- and tree-level data—not in the current FIADB format—are used if available. 

Second, Resources Planning Act Assessment (RPA) databases, which are periodic, plot-level only, summaries of 

state inventories, are used to provide the data at or before 1990. Finally, the Integrated Database (IDB), which is a 

compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington is used 

(Waddell and Hiserote 2005). These IDB data were identified by Heath et al. (2011) as the most appropriate nonFIADB sources for these states and are included in this Inventory. See USDA Forest Service (2014a) for 

information on current and older data as well as additional FIA Program features. A detailed list of the specific 

forest inventory data used in this Inventory is included in Annex 3.13.

Modifications to the use of some of the FIADB surveys or subsequent C conversions were initiated for this report. 

First, the most-recent FIA population summary (known as an evaluation within the FIADB) was incorporated into 

all states’ stock-change calculations which stands in contrast to the approach in previous years where most of the 

newest evaluations were already in use, but if the majority of the underlying plots in the most recent population were 

also a part of the previous population (i.e., over 50 percent redundant plots) then the recent population was 

considered insufficiently unique and not used for calculation. Second, modifications were conducted in coastal 

Alaska for developing net annual change estimates (see Annex 3.13) and separating managed versus unmanaged 

forest lands in order to exclude C stock and stock-change on unmanaged forest land (IPCC 2006, Ogle et al. in 

preparation). This reduced the plots contributing to the Alaska forest C estimates by about 5 percent. A third 

modification to the use of the FIADB-defined forest land, introduced this year, was applied to identify plots on 

woodland forest types that do not meet the height requirement within the definition of forest land (Oswalt et al. 

2014, Coulston et al. in preparation). These plots were identified as “other wooded lands” (i.e., not “forest” within 

the FIA forest inventory) and provided as C density information to the grasslands land-use category as the plots 

were not a complete inventory of the grassland land-use category in the United States. Finally, a new model 

estimating plot level C density of litter was developed and incorporated into the C budget (Domke et al. in 

preparation).

Forest C stocks were estimated from inventory data by a collection of conversion factors and models (Birdsey and 

Heath 1995, Birdsey and Heath 2001, Heath et al. 2003, Smith et al. 2004, Smith et al. 2006, Woodall et al. 2011a, 

Domke et al. 2011, Domke et al. 2012, Domke et al. in preparation), which have been formalized in an FIADB-to-C 

calculator (Smith et al. 2010). The conversion factors and model coefficients were categorized by region and forest 

type, and forest C stock estimates were calculated from application of these factors at the scale of FIA inventory 

plots. The results were estimates of C density (T C per hectare) for six forest ecosystem pools: Live trees, standing 

dead trees, understory vegetation, downed dead wood, forest floor, and soil organic matter. The six C pools used in 

the FIADB-to-C calculator were aggregated to the five C pools defined by IPCC (2006): Aboveground biomass, 

belowground biomass, dead wood, litter, and soil organic matter. The live-tree and understory C were pooled as 
6-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

biomass, and standing dead trees and downed dead wood were pooled as dead wood, in accordance with IPCC 

(2006).

Once plot-level C stocks were calculated as C densities on Forest Land Remaining Forest Land for the five IPCC 

(2006) reporting pools, the stocks were expanded to population estimates according to methods appropriate to the 

respective inventory data (for example, see Bechtold and Patterson (2005)). These expanded C stock estimates were 

summed to state or sub-state total C stocks. Annualized estimates of C stocks were developed by using available 

FIA inventory data and interpolating or extrapolating to assign a C stock to each year in the 1990 through 2014 time 

series. Flux, or net annual stock change, was estimated by calculating the difference in stocks between two 

successive years and applying the appropriate sign convention; net increases in ecosystem C were identified as 

negative flux. By convention, inventories were assigned to represent stocks as of January 1 of the inventory year; an 

estimate of flux for 1996 required estimates of C stocks for 1996 and 1997, for example. Additional discussion of 

the use of FIA inventory data and the C conversion process is in Annex 3.13.

Carbon in Biomass

Live tree C 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. If inventory plots included data on individual trees, tree C was 

based on Woodall et al. (2011a), 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. (2011a), was added to each tree following the same CRM method. Some of the older forest inventory data in use 

for these estimates did not provide measurements of individual trees. Examples of these data include plots with 

incomplete or missing tree data or the RPA plot-level summaries. The C estimates for these plots were based on 

average densities (T 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-C conversions within the 214 statelevel surveys utilized here.

Understory vegetation is a minor component of biomass, which is defined as all biomass of undergrowth plants in a 

forest, including woody shrubs and trees less than 2.54 cm dbh. In the current Inventory, it was assumed that 10 

percent of total understory C mass is belowground. Estimates of C density were based on information in Birdsey 

(1996) and biomass estimates from Jenkins et al. (2003). Understory frequently represented over 1 percent of C in 

biomass, but its contribution rarely exceeded 2 percent of the total.

Carbon in Dead Organic Matter

Dead organic matter was initially calculated as three separate pools—standing dead trees, downed dead wood, and 

litter—with C stocks estimated from sample data or from models. The standing dead tree C pools include 

aboveground and belowground (coarse root) mass and include trees of at least 12.7 cm dbh. Calculations followed 

the basic method applied to live trees (Woodall et al. 2011a) 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 population-level 

estimates. The C estimates for these plots were based on average densities (T C per hectare) obtained from plots of 

more recent surveys with similar stand characteristics and location. This applied to less than 20 percent of the forest 

land inventory-plot-to-C conversions within the 214 state-level surveys utilized here. Downed dead wood estimates 

are based on measurement of a subset of FIA plots for downed dead wood (Domke et al. 2013, Woodall and 

Monleon 2008, Woodall et al. 2013). 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. To facilitate the downscaling of downed dead wood C estimates from the state-wide population 

estimates to individual plots, downed dead wood models specific to regions and forest types within each region are 

used. Litter C is the pool of organic C (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 Domke et al. (in preparation).

Carbon in Forest Soil

Soil organic C 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 C determination was based on the general approach 
Land Use, Land-Use Change, and Forestry 6-29

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 C map. 

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 through the present, but it does not reflect the effects of past land use.

Harvested Wood Carbon

Estimates of the HWP contribution to forest C sinks and emissions (hereafter called “HWP Contribution”) were 

based on methods described in Skog (2008) using the WOODCARB II model. These methods are based on IPCC 

(2006) guidance for estimating HWP C. IPCC (2006) provides methods that allow for reporting of 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 C stocks (see Annex 3.13 for more details about 

each approach). The United States used the production accounting approach to report HWP Contribution. Under 

the production approach, C in exported wood was estimated as if it remains in the United States, and C in imported 

wood was not included in inventory estimates. Though reported U.S. HWP estimates are based on the production 

approach, estimates resulting from use of the two alternative approaches, the stock change and atmospheric flow 

approaches, are also presented for comparison (see Annex 3.13). 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 sites (SWDS). Emissions from HWP 

associated with wood biomass energy are not included in this accounting—a net of zero sequestration and emissions 

as they are a part of energy accounting (see Chapter 3).

Solidwood products added to pools include lumber and panels. End-use categories for solidwood include single and 

multifamily housing, alteration and repair of housing, and other end-uses. There is one product category and one 

end-use category for paper. Additions to and removals from pools were tracked beginning in 1900, with the 

exception that additions of softwood lumber to housing began in 1800. Solidwood and paper product production 

and trade data were taken from USDA Forest Service and other sources (Hair and Ulrich 1963; Hair 1958; USDC 

Bureau of Census; 1976; Ulrich, 1985, 1989; Steer 1948; AF&PA 2006a 2006b; Howard 2003, 2007, forthcoming). 

Estimates for disposal of products reflected the change over time in the fraction of products discarded to SWDS (as 

opposed to burning or recycling) and the fraction of SWDS that were in sanitary landfills versus dumps.

There are five annual HWP variables that were used in varying combinations to estimate HWP Contribution using 

any one of the three main approaches listed above. These are:

(1A) annual change of C in wood and paper products in use in the United States,

(1B) annual change of C in wood and paper products in SWDS in the United States,

(2A) annual change of C in wood and paper products in use in the United States and other countries where 

the wood came from trees harvested in the United States,

(2B) annual change of C in wood and paper products in SWDS in the United States and other countries 

where the wood came from trees harvested in the United States,

(3) C in imports of wood, pulp, and paper to the United States,

(4) C in exports of wood, pulp and paper from the United States, and

(5) C in annual harvest of wood from forests in the United States.

The sum of variables 2A and 2B yielded the estimate for HWP Contribution under the production accounting 

approach. A key assumption for estimating these variables was that products exported from the United States and 

held in pools in other countries have the same half-lives for products in use, the same percentage of discarded 

products going to SWDS, and the same decay rates in SWDS as they would in the United States.
6-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Uncertainty and Time Series Consistency

A quantitative uncertainty analysis placed bounds on current flux for forest ecosystems as well as C in harvested 

wood products through Monte Carlo Stochastic Simulation of the Methods described above and probabilistic 

sampling of C conversion factors and inventory data. See Annex 3.13 for additional information. The 2013 net 

annual change for forest C stocks was estimated to be between -972.9 and -575.9 MMT CO2 Eq. at a 95 percent 

confidence level. This includes a range of -900.7 to -505.9 MMT CO2 Eq. for forest ecosystems and -89.9 to -54.0

MMT CO2 Eq. for HWP.

Table 6-12: Approach 2 Quantitative Uncertainty Estimates for Net CO2 Flux from Forest 

Land Remaining Forest Land: Changes in Forest C Stocks (MMT CO2 Eq. and Percent)

Source Gas 2013 Flux Estimate Uncertainty Range Relative to Flux Estimatea

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Lower

Bound

Upper

Bound

Lower

Bound

Upper

Bound

Forest Ecosystem CO2 (704.9) (900.7) (505.9) −27.8 28.2

Harvested Wood Products CO2 (70.8) (89.9) (54.0) −27.0 23.7

Total Forest CO2 (775.7) (972.9) (575.9) −25.4 25.8

Note: Parentheses indicate negative values or net sequestration.

a Range of flux estimates predicted by Monte Carlo stochastic simulation for a 95 percent confidence interval.

Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

As discussed above, the FIA program has conducted consistent forest surveys based on extensive statistically-based 

sampling of most of the forest land in the conterminous United States, dating back to 1952. The FIA program 

includes numerous quality assurance and quality control (QA/QC) procedures, including calibration among field 

crews, duplicate surveys of some plots, and systematic checking of recorded data. Because of the statistically-based 

sampling, the large number of survey plots, and the quality of the data, the survey databases developed by the FIA 

program form a strong foundation for C stock estimates. Field sampling protocols, summary data, and detailed 

inventory databases are archived and are publicly available on the Internet (USDA Forest Service 2014d).

Many key calculations for estimating current forest C stocks based on FIA data were developed to fill data gaps in 

assessing forest C and have been in use for many years to produce national assessments of forest C stocks and stock 

changes (see additional discussion and citations in the Methodology section above and in Annex 3.13). General 

quality control procedures were used in performing calculations to estimate C stocks based on survey data. For 

example, the derived C datasets, which include inventory variables such as areas and volumes, were compared to 

standard inventory summaries such as the forest resource statistics of Smith et al. (2009) or selected population 

estimates generated from FIADB 6.0, which are available at an FIA internet site (USDA Forest Service 2014b). 

Agreement between the C datasets and the original inventories is important to verify accuracy of the data used. 

Finally, C stock estimates were compared with previous Inventory report estimates to ensure that any differences 

could be explained by either new data or revised calculation methods (see the “Recalculations” discussion, below).

Estimates of the HWP variables and the HWP contribution under the production accounting approach use data from 

U.S. Census and USDA Forest Service surveys of production and trade. Factors to convert wood and paper to units 

of C are based on estimates by industry and Forest Service published sources. The WOODCARB II model uses 

estimation methods suggested by IPCC (2006). Estimates of annual C change in solid wood and paper products in 

use were calibrated to meet two independent criteria. The first criterion is that the WOODCARB II model estimate 

of C in houses standing in 2001 needs to match an independent estimate of C in housing based on U.S. Census and 

USDA Forest Service survey data. Meeting the first criterion resulted in an estimated half-life of about 80 years for 

single family housing built in the 1920s, which is confirmed by other U.S. Census data on housing. The second 

criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needs to match 

EPA estimates of discards each year over the period 1990 to 2000 (EPA 2006). These criteria help reduce 
Land Use, Land-Use Change, and Forestry 6-31

uncertainty in estimates of annual change in C in products in use in the United States and, to a lesser degree, reduce 

uncertainty in estimates of annual change in C in products made from wood harvested in the United States. In 

addition, WOODCARB II landfill decay rates have been validated by ensuring that estimates of CH4 emissions from 

landfills based on EPA (2006) data are reasonable in comparison to CH4 estimates based on WOODCARB II 

landfill decay rates.

Recalculations Discussion

Forest ecosystem stock and stock-change estimates differ from the previous Inventory (EPA 2014) principally due to 

some changes in data and methods (see discussion above in Methodology and in Annex 3.13). The net effect of the 

modifications was to slightly reduce net C uptake (i.e., lower sequestration) and C stocks from 1990 to the present. 

The influence of the individual modifications on stock and stock-change varied considerably; these were evaluated 

to identify the relative sensitivity of totals to each. That is, the analysis identified where the estimates (as in Tables 

Table 6-8 through Table 6-10) were most affected by the revised methods incorporated with this report. First, the 

collective effects of selecting FIA population estimates and updates to the annual forest inventories for many states 

had the effect of decreasing sequestration in early years while increasing after 2005 and had the greatest effect on 

determining overall stock-change estimates for 2006 and 2007, but otherwise this modification was a minor 

influence. Second, the application of a new managed land definition as part of the land representation analysis (see 

Section 6.1) and the subsequent decrease in managed forest lands along coastal Alaska affected that individual 

state’s estimates but had minimal effect on C stock estimates for the United States as a whole. Third, the 

reallocation of selected woodlands from forest land (i.e., these “other wooded lands” were then classified as 

grasslands) had the greatest effect on annualized estimates of forest area throughout the time series. In addition, the 

removal of these lands from forest had the greatest effect on total forest stock-change through the early 1990s, yet 

the reclassification did tend to decrease sequestration throughout the entire time series. Finally, the revised litter C 

estimates generally had a lower influence on stock-change relative to the woodland modification. However, the 

revised litter estimates increased sequestration through the 1990s but decreased sequestration over more recent 

years. In addition, the change in estimated litter C had the greatest effect on forest ecosystem stocks throughout the 

time period.

The estimate of net annual change in HWP C stock and total C stock in HWP were revised upward by small 

amounts. The increase in total net annual additions compared to estimates published in 2013 was 2 to 3 percent for 

2010 through 2012. This increase was mostly due to changes in the amount of pulpwood used for paper and 

composite panel products back to 2003. All the adjustments were made as a result of corrections in the database of 

forest products statistics used to prepare the estimates (Howard forthcoming).

Planned Improvements

Reliable estimates of forest C across the diverse ecosystems/industries of the United States require a high level of 

investment in both annual monitoring and associated analytical techniques. Development of improved 

monitoring/reporting techniques is a continuous process that occurs simultaneously with annual Inventory 

submissions. Planned improvements can be broadly assigned to the following categories: Pool estimation 

techniques, land use and land-use change, and field inventories.

In an effort to reduce the uncertainty associated with the estimation of individual forest C pools, the empirical data 

and associated models for each pool are being evaluated for potential improvement (Woodall 2012). In the 1990 

through 2010 Inventory report, the approach to tree volume/biomass estimation was evaluated and refined (Domke 

et al. 2012). In the 1990 through 2011 Inventory report, the standing dead tree C model was replaced with a 

nationwide inventory and associated empirical estimation techniques (Woodall et al. 2012, Domke et al. 2011, 

Harmon et al. 2011). In the 1990 through 2012 Inventory report the downed dead tree C model was refined by 

incorporation of a national field inventory of downed dead wood (Woodall et al. 2013, Domke et al. 2013). In the 

current Inventory report, the litter C density model was refined with a nearly nationwide field inventory (Domke et 

al. in preparation). The exact timing of future pool estimation refinements is dependent on the completion of current 

research efforts. Research is underway to use a national inventory of SOC (Woodall et al. 2011b) to refine the 

estimation of this pool. It is expected that improvements to SOC estimation will be incorporated into the 1990 

through 2015 Inventory report. Components of other pools, such as C in belowground biomass (Russell et al. in 

preparation) and understory vegetation (Russell et al. in press), are being explored but may require additional 

investment in field inventories before improvements can be realized with Inventory submissions.
6-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Despite the continuing accumulation of new data within the consistent nationwide field inventory of forests that is 

measured annually, 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. Lines of research have been 

initiated to more fully examine land-use change within the FIA inventory system (see Figure 6-3; Coulston et al. in 

review, Wear and Coulston 2014) and bring together disparate sets of land-use information (e.g., forest versus 

croplands) that rely on remotely sensed imagery from the 1980s to the present (NASA CMS 2013). These lines of 

research are expected to require at least a few years for completion with subsequent time needed for application to 

future Inventory submissions.

The foundation of forest C accounting is the annual forest inventory system. The ongoing annual surveys by the 

FIA Program are expected to improve the accuracy and precision of forest C estimates as new state surveys become 

available (USDA Forest Service 2013b), particularly in western states. Hawaii and U.S. territories will be included 

when appropriate forest C data are available (as of July 21, 2014, Hawaii is not yet reporting any data from the 

annualized sampling design). In addition, the more intensive sampling of fine woody debris, litter, and SOC on a 

subset of FIA plots continues and will substantially improve resolution of C pools (i.e., greater sample intensity; 

Westfall et al. 2013) this information becomes available (Woodall et al. 2011b). Increased sample intensity of some 

C pools and using annualized sampling data as it becomes available for those states currently not reporting are 

planned for future submissions. The USDA Forest Service FIA Program’s forest and wooded land inventories 

extend beyond the forest land-use (e.g., woodlands and urban areas), and Inventory-relevant information for these 

lands will likely become increasingly available in coming years.

Towards an Accounting of Managed Forest Carbon in Interior Alaska

Given the remote nature and vast expanse of forest across the state of Alaska, consistent inventories of all Alaskan 

forest land have never been conducted. Figure 6-6 compares the vast expanse of Alaska to countries in Europe, 

which in large part explains the lack of a consistent forest inventory and provides an indication of the extent of any 

effort to include an area of this magnitude using the existing forest inventories for the United States. Starting in the 

1990s, a forest inventory of south central and southeastern coastal (SCSE) Alaska was initiated following the same 

approach applied in the conterminous United States (see Figure 6-7).
Land Use, Land-Use Change, and Forestry 6-33

Figure 6-6: The Size of Alaska Compared to European Countries
6-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Figure 6-7: Delineations between Forest, Non-forest, Managed Land, and Inventoried Areas 

of Alaska

Establishment and data collection on these plots began in 1995 with the current inventory nearing completion of a 

full re-measurement (i.e., one cycle of periodic inventory represented by the 2003 data and 90 percent of an annual 

inventory cycle represented by the 2012 data). Forest C estimates for SCSE Alaska were first included in the 

Inventory in 2008. The managed forest land in SCSE Alaska has been the only contribution to the Inventory since 

2008 owing to the lack of a consistent inventory across the much larger interior portion of Alaska that generally 

includes less productive forest lands.

Recognizing the need to inventory interior Alaskan forests for the Inventory and resource management, research is 

being conducted towards these ends:

 A spatial model delineating managed and unmanaged lands for Alaska was developed in part to better align 

greenhouse gas reporting with managed lands for Alaskan forests (Ogle et al. in preparation). In contrast to 

Alaska, all forest lands in the conterminous 48 states are considered managed for purposes of greenhouse 

gas reporting. The spatial model of managed lands for Alaska is applied to both the preliminary assessment 

of interior Alaskan forest C provided here and the reported C of SCSE Alaska in order to align with the 

practice of reporting of forest C on managed lands per IPCC (2006) Good Practice Guidelines.

 Research continues to better appraise the forest C stocks and their associated dynamics across the Alaskan 

landscape that rely on remotely sensed imagery and limited in situ measurements. Based on this emerging 

work the amount of managed forest land and ranges of C stocks will be estimated. This current work 

(McGuire et al. in preparation, Genet et al. in preparation, Saatchi et al. in preparation) has identified 46–49 

million hectares of managed forestland in interior Alaska. This represents 68 percent of total interior forest 

land. Live biomass (e.g., vegetation) C stocks are estimated to range between 1,600 and 2,100 MMT C and 

non-live biomass (e.g., soils, deadwood, litter) is estimated to range between 6,100 and 13,000 MMT C), 

all with concomitant high levels of uncertainty.
Land Use, Land-Use Change, and Forestry 6-35

 A joint USDA Forest Service-National Aeronautics and Space Administration research effort was 

conducted in interior Alaska during the summer of 2014 where high-resolution airborne scanning laser, 

hyperspectral, and thermal imagery were collected in a sampling mode over the entire Tanana valley 

(135,000 km2). These remotely-sensed data will be combined with a limited number of in situ plot 

measurements (100 FIA plots collected within the Tanana Valley State Forest and Tetlin National Wildlife 

Refuge) to explore potential application across interior Alaska (NASA CMS 2014). Results from this 

research study are expected within a few years.

As preliminary research results suggest that the managed forest C stock may be upwards of 15,000 MMT C or 37 

percent of the United States’ managed forest C stock in the current Inventory, care must be given to vet all emerging 

research especially in regards to stock change. It is hoped that the managed forest land base in interior Alaska might 

be included in future Inventories if: (a) adequate funding resources become available, and (b) research into 

combining remotely sensed technologies with in situ measurements (especially of non-vegetation pools) is a success.

Non-CO2 Emissions from Forest Fires

Emissions of non-CO2 gases from forest fires were estimated using the default IPCC (2003) methodology 

incorporating default IPCC (2006) emissions factors and combustion factor for wildfires. Emissions from this 

source in 2013 were estimated to be 5.8 MMT CO2 Eq. of CH4 and 3.8 MMT CO2 Eq. of N2O, as shown in Table 

6-13 and Table 6-14. The estimates of non-CO2 emissions from forest fires account for wildfires in the lower 48 

states and Alaska as well as prescribed fires in the lower 48 states.

Table 6-13: Estimated Non-CO2 Emissions from Forest Fires (MMT CO2 Eq.) for U.S. Forests

Gas 1990 2005 2009 2010 2011 2012 2013

CH4 2.5 8.3 5.8 4.7 14.6 15.7 5.8

N2O 1.7 5.5 3.8 3.1 9.6 10.3 3.8

Total 4.2 13.8 9.7 7.9 24.2 26.0 9.7

Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP 

values.

Note: Calculated based on C emission estimates in Changes in Forest Carbon Stocks and 

default factors in IPCC (2006).

Table 6-14: Estimated Non-CO2 Emissions from Forest Fires (kt) for U.S. Forests

Gas 1990 2005 2009 2010 2011 2012 2013

CH4 101 332 233 190 584 626 233

N2O 6 18 13 11 32 35 13

Note: Calculated based on C emission estimates in Changes in Forest Carbon Stocks and default 

factors in IPCC (2006).

Methodology

The IPCC (2003) Tier 2 default methodology was used to calculate C and CO2 emissions from forest fires. 

However, more up-to-date default emission factors from IPCC (2006) were converted into gas-specific emission 

ratios and incorporated into the methodology to calculate non-CO2 emissions from C emissions. Estimates of CH4

and N2O emissions were calculated by multiplying the total estimated CO2 emitted from forest burned by the gasspecific emissions ratios. CO2 emissions were estimated by multiplying total C emitted (Table 6-15) by the C to 

CO2 conversion factor of 44/12 and by 92.8 percent, which is the estimated proportion of C emitted as CO2 (Smith 

2008a). The equations used to calculate CH4 and N2O emissions were:

CH4 Emissions = (C released) × 92.8% × (44/12) × (CH4 to CO2 emission ratio)

N2O Emissions = (C released) × 92.8% × (44/12) × (N2O to CO2 emission ratio)

Where CH4 to CO2 emission ratio is 0.003 and N2O to CO2 emission ratio is 0.0002. See the explanation in Annex 

3.13 for more details on the CH4 and N2O to CO2 emission ratios.
6-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Estimates for C emitted from forest fires are the same estimates used to generate estimates of CO2 presented earlier 

in Box 6-3. Estimates for C emitted include emissions from wildfires in both Alaska and the lower 48 states as well 

as emissions from prescribed fires in the lower 48 states only (based on expert judgment that prescribed fires only 

occur in the lower 48 states) (Smith 2008a). The IPCC (2006) default combustion factor of 0.45 for “all ‘other’ 

temperate forests” was applied in estimating C emitted from both wildfires and prescribed fires. See the explanation 

in Annex 3.13 for more details on the methodology used to estimate C emitted from forest fires.

Table 6-15: Estimated C Released from Forest Fires for U.S. Forests (MMT/yr)

Year C Emitted (MMT/yr)

1990 9.9

2005 32.5

2009 22.9

2010 18.6

2011 57.3

2012 61.5

2013 22.9

Uncertainty and Time-Series Consistency

Non-CO2 gases emitted from forest fires depend on several variables, including: forest area for Alaska and the lower 

48 states; average C densities for wildfires in Alaska, wildfires in the lower 48 states, and prescribed fires in the 

lower 48 states; emission ratios; and combustion factor values (proportion of biomass consumed by fire). To 

quantify the uncertainties for emissions from forest fires, a Monte Carlo (Approach 2) uncertainty analysis was 

performed using information about the uncertainty surrounding each of these variables. The results of the Approach 

2 quantitative uncertainty analysis are summarized in Table 6-16.

Table 6-16: Approach 2 Quantitative Uncertainty Estimates of Non-CO2 Emissions from 

Forest Fires in Forest Land Remaining Forest Land (MMT CO2 Eq. and Percent)

Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimatea

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Lower

Bound

Upper

Bound

Lower

Bound

Upper

Bound

Non-CO2 Emissions from 

Forest Fires CH4 5.8 1.1 15.2 −80% +161%

Non-CO2 Emissions from 

Forest Fires N2O 3.8 1.1 9.2 −71% +139%

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality 

control measures for forest fires included checking input data, documentation, and calculations to ensure data were 

properly handled through the inventory process. The QA/QC analysis did not reveal any inaccuracies or incorrect 

input values.

Recalculations Discussion

The current Inventory estimates for 1990 through 2013 were developed according to the methodology used in the 

previous Inventory report. However, the FIADB updates discussed in Changes in Forest Carbon Stocks affected 

forest C stocks, C density of litter, and total forest area, including the forest area estimates for coastal Alaska, all of 
Land Use, Land-Use Change, and Forestry 6-37

which are used to calculate emissions estimates from forest fires. As a result of the FIADB updates, total non-CO2

emissions from forest fires decreased by an average of 14 percent relative to emission estimates in the previous 

Inventory report.

For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth 

Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second 

Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series recalculations 

for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to 

report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each 

greenhouse gas. The GWP of CH4 has increased, leading to an overall increase in CO2-equivalent emissions from 

CH4. The GWP of N2O has decreased, leading to a decrease in CO2-equivalent emissions for N2O. The AR4 GWPs 

have been applied across the entire time series for consistency. For more information please see the Recalculations 

and Improvements Chapter.

The combined effect of the FIADB updates and AR4 GWP values resulted in an average 7 percent decrease in total 

non-CO2 emissions from wildfires and prescribed fires over the 1990 to 2012 time series.

Planned Improvements

The default combustion factor of 0.45 from IPCC (2006) was applied in estimating C emitted from both wildfires 

and prescribed fires. Additional research into the availability of a combustion factor specific to prescribed fires is 

being conducted.

Another area of improvement is to evaluate other methods of obtaining data on forest area burned by replacing ratios 

of forest land to land under wildland protection with Monitoring Trends in Burn Severity (MTBS) burn area data. 

MTBS data is available from 1984 through a portion of 2013. MTBS burn area data could be used to develop the 

national area burned and resulting CO2 and non-CO2 emissions. Additional research is required to determine 

appropriate uncertainty inputs for national area burned data derived from MTBS data.

N2O Fluxes from Forest Soils (IPCC Source Category 4A1) 

Of the synthetic nitrogen (N) fertilizers applied to soils in the United States, no more than one percent is applied to 

forest soils. Application rates are similar to those occurring on cropland soils, but in any given year, only a small 

proportion of total forested land receives N fertilizer. This is because forests are typically fertilized only twice 

during their approximately 40-year growth cycle (once at planting and once midway through their life cycle). Thus, 

while the rate of N fertilizer application for the area of forests that receives N fertilizer in any given year is relatively 

high, the annual application rate is quite low over the entire forestland area. 

N additions to soils result in direct and indirect N2O emissions. Direct emissions occur on-site due to the N 

additions. Indirect emissions result from fertilizer N that is transformed and transported to another location in a form 

other than N2O (NH3 and NOx volatilization, NO3 leaching and runoff), and later converted into N2O at the off-site 

location. The indirect emissions are assigned to forest land because the management activity leading to the 

emissions occurred in forest land. 

Direct N2O emissions from forest soils in 2013 were 0.3 MMT CO2 Eq. (1 kt), and the indirect emission were 0.1 

MMT CO2 Eq. (0.4 kt). Total emissions for 2013 were 0.5 MMT CO2 Eq. (2 kt) and have increased by 455 percent 

from 1990 to 2013. Increasing emissions over the time series is a result of greater area of N fertilized pine 

plantations in the southeastern United States and Douglas-fir timberland in western Washington and Oregon. Total 

forest soil N2O emissions are summarized in Table 6-17.

Table 6-17: N2O Fluxes from Soils in Forest Land Remaining Forest Land (MMT CO2 Eq. and 

kt N2O)

1990 2005 2009 2010 2011 2012 2013

Direct N2O Fluxes from Soils

MMT CO2 Eq. 0.1 0.3 0.3 0.3 0.3 0.3 0.3

kt N2O + 1 1 1 1 1 1

Indirect N2O Fluxes from Soils

MMT CO2 Eq. + 0.1 0.1 0.1 0.1 0.1 0.1
6-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

kt N2O + + + + + + +

Total

MMT CO2 Eq. 0.1 0.5 0.5 0.5 0.5 0.5 0.5

kt N2O + 2 2 2 2 2 2

Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP 

values.

+ Does not exceed 0.05 MMT CO2 Eq. or 0.5 kt.

Methodology

The IPCC Tier 1 approach was used to estimate N2O from soils within Forest Land Remaining Forest Land. 

According to U.S. Forest Service statistics for 1996 (USDA Forest Service 2001), approximately 75 percent of trees 

planted were for timber, and about 60 percent of national total harvested forest area is in the southeastern United 

States. Although southeastern pine plantations represent the majority of fertilized forests in the United States, this 

Inventory also accounted for N fertilizer application to commercial Douglas-fir stands in western Oregon and 

Washington. For the Southeast, estimates of direct N2O emissions from fertilizer applications to forests were based 

on the area of pine plantations receiving fertilizer in the southeastern United States and estimated application rates 

(Albaugh et al. 2007; Fox et al. 2007). Not accounting for fertilizer applied to non-pine plantations is justified

because fertilization is routine for pine forests but rare for hardwoods (Binkley et al. 1995). For each year, the area 

of pine receiving N fertilizer was multiplied by the weighted average of the reported range of N fertilization rates 

(121 lbs. N per acre). Area data for pine plantations receiving fertilizer in the Southeast were not available for 2005-

2013, so data from 2004 were used for these years. For commercial forests in Oregon and Washington, only 

fertilizer applied to Douglas-fir was accounted for, because the vast majority (approximately 95 percent) of the total 

fertilizer applied to forests in this region is applied to Douglas-fir (Briggs 2007). Estimates of total Douglas-fir area 

and the portion of fertilized area were multiplied to obtain annual area estimates of fertilized Douglas-fir stands. 

Similar to the Southeast, data were not available for 2005 through 2013, so data from 2004 were used for these 

years. The annual area estimates were multiplied by the typical rate used in this region (200 lbs. N per acre) to 

estimate total N applied (Briggs 2007), and the total N applied to forests was multiplied by the IPCC (2006) default 

emission factor of 1 percent to estimate direct N2O emissions. 

For indirect emissions, the volatilization and leaching/runoff N fractions for forest land were calculated using the 

IPCC default factors of 10 percent and 30 percent, respectively. The amount of N volatilized was multiplied by the 

IPCC default factor of 1 percent for the portion of volatilized N that is converted to N2O off-site. The amount of N 

leached/runoff was multiplied by the IPCC default factor of 0.075 percent for the portion of leached/runoff N that is 

converted to N2O off-site The resulting estimates were summed to obtain total indirect emissions. 

Uncertainty and Time-Series Consistency

The amount of N2O emitted from forests depends not only on N inputs and fertilized area, but also on a large 

number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH, 

temperature, and tree planting/harvesting cycles. The effect of the combined interaction of these variables on N2O 

flux is complex and highly uncertain. IPCC (2006) does not incorporate any of these variables into the default 

methodology, except variation in estimated fertilizer application rates and estimated areas of forested land receiving 

N fertilizer. All forest soils are treated equivalently under this methodology. Furthermore, only synthetic N 

fertilizers are captured, so applications of organic N fertilizers are not estimated. However, the total quantity of 

organic N inputs to soils is included in the Agricultural Soil Management and Settlements Remaining Settlements

sections. 

Uncertainties exist in the fertilization rates, annual area of forest lands receiving fertilizer, and the emission factors. 

Fertilization rates were assigned a default level30 of uncertainty at ±50 percent, and area receiving fertilizer was 

assigned a ±20 percent according to expert knowledge (Binkley 2004). The uncertainty ranges around the 2005 

activity data and emission factor input variables were directly applied to the 2013 emissions estimates. IPCC (2006) 

provided estimates for the uncertainty associated with direct and indirect N2O emission factor for synthetic N 

fertilizer application to soils. 

 

30 Uncertainty is unknown for the fertilization rates so a conservative value of ±50 percent was used in the analysis.
Land Use, Land-Use Change, and Forestry 6-39 

Quantitative uncertainty of this source category was estimated using simple error propagation methods (IPCC 2006). 

The results of the quantitative uncertainty analysis are summarized in Table 6-18. Direct N2O fluxes from soils 

were estimated to be between 0.1 and 1.1 MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 

59 percent below and 211 percent above the 2013 emission estimate of 0.3 MMT CO2 Eq. Indirect N2O emissions in 

2013 were between 0.02 and 0.4 MMT CO2 Eq., ranging from 86 percent below to 238 percent above the 2013 

emission estimate of 0.11 MMT CO2 Eq. 

Table 6-18: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land 

Remaining Forest Land (MMT CO2 Eq. and Percent) 

Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimate

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Forest Land Remaining Forest 

Land

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Direct N2O Fluxes from Soils N2O 0.3 0.1 1.1 -59% +211%

Indirect N2O Fluxes from Soils N2O 0.1 0.0 0.4 -86% +238%

Note: These estimates include direct and indirect N2O emissions from N fertilizer additions to both Forest Land 

Remaining Forest Land and Land Converted to Forest Land.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above. 

QA/QC and Verification 

The spreadsheet tab containing fertilizer applied to forests and calculations for N2O and uncertainty ranges were 

checked and corrected. Linkage errors in the uncertainty calculation for 2013 were found and corrected. The 

reported emissions in the NIR were also adjusted accordingly. 

Recalculations Discussion 

Indirect emissions from forest land were previously reported in Agricultural Soil Management, but are now included 

in this source category. Including indirect emissions resulted in a 27 percent increase.

Planned Improvements 

Additional data will be compiled to update estimates of forest areas receiving N fertilizer as new reports are made 

available. Another improvement is to further disaggregate emissions by state for southeastern pine plantations and 

northwestern Douglas-fir forests to estimate soil N2O emission. This improvement is contingent on the availability 

of state-level N fertilization data for forest land. 

6.3 Land Converted to Forest Land (IPCC 

Source Category 4A2)

Land-use change is constantly occurring, and areas under a number of differing land-use types are converted to 

forest each year, just as forest land is converted to other uses. While the magnitude of these changes is known (see 

Table 6-5), research is ongoing to track C across Forest Land Remaining Forest Land and Land Converted to Forest 

Land areas. Until such time that reliable and comprehensive estimates of C across these land use and land-use 

change categories can be produced, it is not possible to separate CO2 or N2O fluxes on Land Converted to Forest 

Land from fluxes on Forest Land Remaining Forest Land at this time. 
6-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

6.4 Cropland Remaining Cropland (IPCC Source 

Category 4B1)

Mineral and Organic Soil Carbon Stock Changes 

Carbon (C) in cropland ecosystems occurs in biomass, dead biomass, and soils. However, C storage in biomass and 

dead organic matter is relatively ephemeral, with the exception of C stored in perennial woody crop biomass, such 

as citrus groves and apple orchards. Within soils, C is found in organic and inorganic forms of C, but soil organic C 

(SOC) is the main source and sink for atmospheric CO2 in most soils. IPCC (2006) recommends reporting changes 

in SOC stocks due to agricultural land-use and management activities on both mineral and organic soils.31

Well-drained mineral soils typically contain from 1 to 6 percent organic C by weight, whereas mineral soils with 

high water tables for substantial periods during the year may contain significantly more C (NRCS 1999). 

Conversion of mineral soils from their native state to agricultural land uses can cause up to half of the SOC to be 

lost to the atmosphere due to enhanced microbial decomposition. The rate and ultimate magnitude of C loss 

depends on subsequent management practices, climate and soil type (Ogle et al. 2005). Agricultural practices, such 

as clearing, drainage, tillage, planting, grazing, crop residue management, fertilization, and flooding, can modify 

both organic matter inputs and decomposition, and thereby result in a net flux of C to or from the soil C pool (Parton 

et al. 1987, Paustian et al. 1997a, Conant et al. 2001, Ogle et al. 2005). Eventually, the soil can reach a new 

equilibrium that reflects a balance between C inputs (e.g., decayed plant matter, roots, and organic amendments such 

as manure and crop residues) and C loss through microbial decomposition of organic matter (Paustian et al. 1997b). 

Organic soils, also referred to as histosols, include all soils with more than 12 to 20 percent organic C by weight, 

depending on clay content (NRCS 1999, Brady and Weil 1999). The organic layer of these soils can be very deep 

(i.e., several meters), and form under inundated conditions that results in minimal decomposition of plant residues. 

When organic soils are prepared for crop production, they are drained and tilled, leading to aeration of the soil that 

accelerates both the decomposition rate and CO2 emissions. Due to the depth and richness of the organic layers, C 

loss from drained organic soils can continue over long periods of time, which varies depending on climate and 

composition (i.e., decomposability) of the organic matter (Armentano and Menges 1986). Due to deeper drainage 

and more intensive management practices, the use of organic soils for annual crop production leads to higher C loss 

rates than drainage of organic soils in grassland or forests (IPCC 2006). 

Cropland Remaining Cropland includes all cropland in an Inventory year that has been used as cropland for the 

previous 20 years according to the 2007 USDA National Resources Inventory (NRI) land-use survey (USDA-NRCS 

2009).32 The inventory includes all privately-owned croplands in the conterminous United States and Hawaii, but 

does not include the 1 to 1.5 million hectares of Cropland Remaining Cropland (less than 1 percent of the total 

cropland area in the United States) on federal lands between 1990 and 2013. In addition, approximately 28,700 

hectares of cropland in Alaska are not included in this Inventory. This leads to a discrepancy between the total 

amount of managed area in Cropland Remaining Cropland (see Section 6.1) and the cropland area included in the 

Inventory. Improvements are underway to include croplands in Alaska and federal lands as part of future C 

inventories. 

CO2 emissions and removals33 due to changes in mineral soil C stocks are estimated using a Tier 3 approach for the 

majority of annual crops (Ogle et al. 2010). A Tier 2 IPCC method is used for the remaining crops not included in 

the Tier 3 method (i.e., vegetables, tobacco, perennial/horticultural crops, and rice) (Ogle et al. 2003, 2006). In 

addition, a Tier 2 method is used for very gravelly, cobbly, or shaley soils (i.e., classified as soils that have greater 

than 35 percent of soil volume comprised of gravel, cobbles, or shale) and for additional changes in mineral soil C 

 

31 CO2 emissions associated with liming are also estimated but are included in a separate section of the report. 

32 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and 

consequently the classifications were based on less than 20 years from 1990 to 2001. 

33 Note that removals occur through uptake of CO2 into crop and forage biomass that is later incorporated into soil C pools. 
Land Use, Land-Use Change, and Forestry 6-41

stocks that were not addressed with the Tier 3 approach (i.e., change in C stocks after 2007 due to Conservation 

Reserve Program enrollment). Emissions from organic soils are estimated using a Tier 2 IPCC method. 

Land-use and land management of mineral soils was the largest contributor to total net C stock change, especially in 

the early part of the time series (see Table 6-19 and Table 6-20). (Note: Estimates after 2007 are based on NRI data 

from 2007 and therefore do not fully reflect changes occurring in the latter part of the time series). In 2013, mineral 

soils were estimated to remove 45.6 MMT CO2 Eq. (12.4 MMT C). This rate of C storage in mineral soils 

represented about a 49 percent decrease in the rate since the initial reporting year of 1990. Emissions from organic 

soils were 22.1 MMT CO2 Eq. (6.0 MMT C) in 2013, which is an 8 percent decrease compared to 1990. In total, 

United States agricultural soils in Cropland Remaining Cropland sequestered approximately 23.4 MMT CO2 Eq. 

(6.4 MMT C) in 2013.

Table 6-19: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT 

CO2 Eq.)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Mineral Soils (89.2) (50.4) (49.6) (48.0) (47.9) (47.1) (45.6)

Organic Soils 24.0 22.4 22.1 22.1 22.1 22.1 22.1

Total Net Flux (65.2) (28.0) (27.5) (25.9) (25.8) (25.0) (23.4)

Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.

Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect 

changes occurring in the latter part of the time series

Table 6-20: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT 

C)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Mineral Soils (24.3) (13.8) (13.5) (13.1) (13.1) (12.9) (12.4)

Organic Soils 6.5 6.1 6.0 6.0 6.0 6.0 6.0

Total Net Flux (17.8) (7.6) (7.5) (7.1) (7.0) (6.8) (6.4)

Note: Totals may not sum due to independent rounding. Parentheses indicate net 

sequestration.

Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not 

fully reflect changes occurring in the latter part of the time series

The major cause of the reduction in soil C accumulation over the time series (i.e., 2013 is 49 percent less than 1990) 

is the decline in annual cropland enrolled in the Conservation Reserve Program (CRP)34 which was initiated in 1985 

(Jones et al., in prep). For example, over 2 million hectares of land in the CRP were returned to agricultural 

production, during the last 5 years resulting in a loss of soil C. However, positive increases in C stocks continue on 

the nearly 11 million hectares of land currently enrolled in the CRP, as well as from intensification of crop 

production by limiting the use of bare-summer fallow in semi-arid regions, increased hay production, and adoption 

of conservation tillage (i.e., reduced- and no-till practices). 

The spatial variability in the 2013 annual CO2 flux is displayed in Figure 6-8 and Figure 6-9 for C stock changes in 

mineral and organic soils, respectively. The highest rates of net C accumulation in mineral soils occurred in the 

Midwest, which is the region with the largest amounts of conservation tillage, with the next highest rates of 

accumulation in the South-central and Northwest regions of the United States. The regions with the highest rates of 

emissions from organic soils occur in the Southeastern Coastal Region (particularly Florida), upper Midwest and 

 

34 The Conservation Reserve Program (CRP) is a land conservation program administered by the Farm Service Agency (FSA). 

In exchange for a yearly rental payment, farmers enrolled in the program agree to remove environmentally sensitive land from 

agricultural production and plant species that will improve environmental health and quality. Contracts for land enrolled in CRP 

are 10-15 years in length. The long-term goal of the program is to re-establish valuable land cover to help improve water quality, 

prevent soil erosion, and reduce loss of wildlife habitat.
6-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Northeast surrounding the Great Lakes, and the Pacific Coast (particularly California), which coincides with largest 

concentrations of organic soils in the United States that are used for agricultural production.

Figure 6-8: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management 

within States, 2013, Cropland Remaining Cropland
Land Use, Land-Use Change, and Forestry 6-43

Figure 6-9: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management 

within States, 2013, Cropland Remaining Cropland

Methodology

The following section includes a description of the methodology used to estimate changes in soil C stocks for 

Cropland Remaining Cropland, including (1) agricultural land-use and management activities on mineral soils; and 

(2) agricultural land-use and management activities on organic soils.

Soil C stock changes were estimated for Cropland Remaining Cropland (as well as agricultural land falling into the 

IPCC categories Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland) 

according to land-use histories recorded in the USDA NRI survey (USDA-NRCS 2009). The NRI is a statisticallybased sample of all non-federal land, and includes approximately 529,558 points in agricultural land for the 

conterminous United States and Hawaii.35 Each point is associated with an “expansion factor” that allows scaling of 

C stock changes from NRI points to the entire country (i.e., each expansion factor represents the amount of area with 

the same 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, 

 

T

35T NRI points were classified as agricultural if under grassland or cropland management between 1990 and 2007. 
6-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

and 1994-1997). In 1998, the NRI program began collecting annual data, and data are currently available through 

2010 (USDA-NRCS, 2013) although this Inventory only uses NRI data through 2007 because newer data were not 

made available in time to incorporate the additional years into this Inventory. NRI points were classified as 

Cropland Remaining Cropland in a given year between 1990 and 2007 if the land use had been cropland for 20 

years.36 Cropland includes all land used to produce food and fiber, or forage that is harvested and used as feed (e.g., 

hay and silage), in addition to cropland that has been enrolled in the CRP (i.e., considered reserve cropland). 

Mineral Soil Carbon Stock Changes

An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for mineral soils 

on the majority of land that is used to produce annual crops in the United States. These crops include alfalfa hay, 

barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans, 

sugar beets, sunflowers, tomatoes, and wheat. The model-based approach uses the DAYCENT biogeochemical 

model (Parton et al. 1998; Del Grosso et al. 2001, 2011) to estimate soil C stock changes and soil nitrous oxide 

emissions from agricultural soil management. Carbon and N dynamics are linked in plant-soil systems through the 

biogeochemical processes of microbial decomposition and plant production (McGill and Cole 1981). Coupling the 

two source categories (i.e., agricultural soil C and N2O) in a single inventory analysis ensures that there is a 

consistent treatment of the processes and interactions between C and N cycling in soils. 

The remaining crops on mineral soils were estimated using an IPCC Tier 2 method (Ogle et al. 2003), including 

some vegetables, tobacco, perennial/horticultural crops, and crops that are rotated with these crops. The Tier 2 

method was also used for very gravelly, cobbly, or shaley soils (greater than 35 percent by volume). Mineral SOC 

stocks were estimated using a Tier 2 method for these areas because the DAYCENT model, which is used for the 

Tier 3 method, has not been fully tested for estimating C stock changes associated with these crops and rotations, as 

well as cobbly, gravelly, or shaley soils. An additional stock change calculation was estimated for mineral soils 

using Tier 2 emission factors to account for enrollment patterns in the CRP after 2007, which was not addressed by 

the Tier 3 method. 

Further elaboration on the methodology and data used to estimate stock changes from mineral soils are described 

below and in Annex 3.12. 

Tier 3 Approach

Mineral SOC stocks and stock changes were estimated using the DAYCENT biogeochemical37 model (Parton et al. 

1998; Del Grosso et al. 2001, 2011), which simulates cycling of C, N and other nutrients in cropland, grassland, 

forest, and savanna ecosystems. The DAYCENT model utilizes the soil C modeling framework developed in the 

Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but has been refined to simulate dynamics at a 

daily time-step. Crop production is simulated with NASA-CASA production algorithm (Potter et al.1993, Potter et 

al. 2007) using the MODIS Enhanced Vegetation Index (EVI) products, MOD13Q1 and MYD13Q1, with a pixel 

resolution of 250 m. A prediction algorithm was developed to estimate EVI (Gurung et al. 2009) for gap-filling 

during years over the inventory time series when EVI data were not available (e.g., data from the MODIS sensor 

were only available after 2000 following the launch of the Aqua and Terra Satellites). The modeling approach uses 

daily weather data as an input, along with information about soil physical properties. Input data on land use and 

management are specified at a daily resolution and include land-use type, crop/forage type, and management 

activities (e.g., planting, harvesting, fertilization, manure amendments, tillage, irrigation, residue removal, grazing, 

and fire). The model simulates net primary productivity and C additions to soil, soil temperature, and water 

dynamics, in addition to turnover, stabilization, and mineralization of soil organic matter C and nutrients (N, P, K, 

S). This method is more accurate than the Tier 1 and 2 approaches provided by the IPCC (2006) because the 

simulation model treats changes as continuous over time as opposed to the simplified discrete changes represented 

in the default method (see Box 6-4X for additional information). 

 

36 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began. Therefore, the 

classification prior to 2002 was based on less than 20 years of recorded land-use history for the time series.

37 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical 

environment
Land Use, Land-Use Change, and Forestry 6-45

Box 6-4: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches

A Tier 3 model-based approach is used to estimate soil C stock changes on the majority of agricultural land on 

mineral soils. This approach results in a more complete accounting of soil C stock changes and entails several 

fundamental differences from the IPCC Tier 1 or 2 methods, as described below. 

(1) The IPCC Tier 1 and 2 methods are simplified and classify land areas into discrete categories based on 

highly aggregated information about climate (six regions), soil (seven types), and management (eleven 

management systems) in the United States. In contrast, in the Tier 3 model, the same variables (i.e. 

climate, soils, and management systems) are represented in considerably more detail both temporally and 

spatially, and exhibit multi-dimensional interactions through the more complex model structure.

(2) The IPCC Tier 1 and 2 methods have a simplified spatial resolution, where, in the United States, data is 

aggregated to climate and soil regions. In contrast, the Tier 3 model uses more than 300,000 individual NRI 

point locations in individual fields.

(3) The IPCC Tier 1 and 2 methods use simplified equilibrium step changes for changes in carbon emissions. 

In contrast, the Tier 3 approach simulates a continuous time period. More specifically, the DAYCENT 

model (i.e., daily time-step version of the Century model) simulates soil C dynamics (and CO2 emissions 

and uptake) on a daily time step based on C emissions and removals from plant production and 

decomposition processes. These changes in soil C stocks are influenced by multiple sources that affect 

primary production and decomposition, including changes in land use and management, weather variability 

and secondary feedbacks between management activities, climate, and soils. 

Historical land-use patterns are simulated with DAYCENT based on the 2007 USDA NRI survey, in addition to 

information on irrigation (USDA-NRCS 2009). Additional sources of activity data were used to supplement the 

land-use information from NRI. The Conservation Technology Information Center (CTIC 2004) provided annual 

data on tillage activity at the county level since 1989, with adjustments for long-term adoption of no-till agriculture 

(Towery 2001). Information on fertilizer use and rates by crop type for different regions of the United States were 

obtained primarily from the USDA Economic Research Service Cropping Practices Survey (USDA-ERS 1997, 

2011) with additional data from other sources, including the National Agricultural Statistics Service (NASS 1992, 

1999, 2004). Frequency and rates of manure application to cropland during 1997 were estimated from data 

compiled by the USDA Natural Resources Conservation Service (Edmonds et al. 2003), and then adjusted using 

county-level estimates of manure available for application in other years. Specifically, county-scale ratios of 

manure available for application to soils in other years relative to 1997 were used to adjust the area amended with 

manure (see Annex 3.12 for further details). Greater availability of managed manure N relative to 1997 was, thus, 

assumed to increase the area amended with manure, while reduced availability of manure N relative to 1997 was 

assumed to reduce the amended area. Data on the county-level N available for application were estimated for 

managed systems based on the total amount of N excreted in manure minus N losses during storage and transport, 

and including the addition of N from bedding materials. Nitrogen losses include direct N2O emissions, volatilization 

of ammonia and NOx, runoff and leaching, and poultry manure used as a feed supplement. For unmanaged systems, 

it is assumed that no N losses or additions occur prior to the application of manure to the soil. More information on 

livestock manure production is available in the Manure Management, Section 5.2, and Annex 3.11.

Daily weather data were used as an input in the model simulations based on gridded data at a 32 km scale from the 

North America Regional Reanalysis Product (NARR) (Mesinger et al. 2006). Soil attributes were obtained from the 

Soil Survey Geographic Database (SSURGO) (Soil Survey Staff 2005). The C dynamics at each NRI point was 

simulated 100 times as part of the uncertainty analysis, yielding a total of over 18 million simulation runs for the 

analysis. Uncertainty in the C stock estimates from DAYCENT associated with parameterization and model 

algorithms were adjusted using a structural uncertainty estimator accounting for uncertainty in model algorithms and 

parameter values (Ogle et al. 2007, 2010). Carbon stocks and 95 percent confidence intervals were estimated for 

each year between 1990 and 2007, but C stock changes from 2008 to 2013 were assumed to be similar to 2007 for 

this Inventory due to a lack of activity data for these years. (Future Inventories will be updated with new activity 

data and the time series will be recalculated; see Planned Improvements section).
6-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Tier 2 Approach

In the IPCC Tier 2 method, data on climate, soil types, land-use, and land management activity were used to classify 

land area and apply appropriate stock change factors (Ogle et al. 2003, 2006). Major Land Resource Areas 

(MLRAs) formed the base spatial unit for conducting the Tier 2 analysis. MLRAs represent a geographic unit with 

relatively similar soils, climate, water resources, and land uses (NRCS 1981). MLRAs were classified into climate 

regions according to the IPCC categories using the PRISM climate database of Daly et al. (1994), and the factors 

were assigned based on the land management systems in the MLRA in addition to the climate and soil types. 

Reference C stocks were estimated using the National Soil Survey Characterization Database (NRCS 1997) with 

cultivated cropland as the reference condition, rather than native vegetation as used in IPCC (2006). Soil 

measurements under agricultural management are much more common and easily identified in the National Soil 

Survey Characterization Database (NRCS 1997) than are soils under a native condition, and therefore cultivated 

cropland provided a more robust sample for estimating the reference condition. 

U.S.-specific stock change factors were derived from published literature to determine the impact of management 

practices on SOC storage (Ogle et al. 2003, Ogle et al. 2006). The factors include changes in tillage, cropping 

rotations, intensification, and land-use change between cultivated and uncultivated conditions. U.S. factors 

associated with organic matter amendments were not estimated due to an insufficient number of studies in the 

United States to analyze the impacts. Instead, factors from IPCC (2003) were used to estimate the effect of those 

activities. 

Activity data were primarily based on the historical land-use/management patterns recorded in the 2007 NRI 

(USDA-NRCS 2009). Each NRI point was classified by land use, soil type, climate region (using PRISM data, Daly 

et al. 1994) and management condition. Classification of cropland area by tillage practice was based on data from 

the Conservation Technology Information Center (CTIC 2004, Towery 2001) as described above. Activity data on 

wetland restoration of Conservation Reserve Program land were obtained from Euliss and Gleason (2002). Manure 

N amendments over the inventory time period were based on application rates and areas amended with manure N 

from Edmonds et al. (2003), in addition to the managed manure production data discussed in the methodology

subsection for the Tier 3 analysis. 

Combining information from these data sources, SOC stocks for mineral soils were estimated 50,000 times for 1982, 

1992, 1997, 2002 and 2007, using a Monte Carlo stochastic simulation approach and probability distribution 

functions for U.S.-specific stock change factors, reference C stocks, and land-use activity data (Ogle et al. 2002, 

Ogle et al. 2003, Ogle et al. 2006). The annual C flux for 1990 through 1992 was determined by calculating the 

average annual change in stocks between 1982 and 1992; annual C flux for 1993 through 1997 was determined by 

calculating the average annual change in stocks between 1992 and 1997; annual C flux for 1998 through 2002 was 

determined by calculating the average annual change in stocks between 1998 and 2002; and annual C flux from 

2003 through 2013 was determined by calculating the average annual change in stocks between 2003 and 2007. 

Additional Mineral C Stock Change

Annual C flux estimates for mineral soils between 2008 and 2013 were adjusted to account for additional C stock 

changes associated with gains or losses in soil C after 2007 due to changes in CRP enrollment (USDA-FSA 2013). 

The change in enrollment relative to 2007 was based on data from USDA-FSA (2013) for 2008 through 2013. The 

differences in mineral soil areas were multiplied by 0.5 metric tons C per hectare per year to estimate the net effect 

on soil C stocks. The stock change rate is based on country-specific factors and the IPCC default method (see 

Annex 3.12 for further discussion). 

Organic Soil Carbon Stock Changes

Annual C emissions from drained organic soils in Cropland Remaining Cropland were estimated using the Tier 2

method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) rather than default IPCC rates. 

The final estimates included a measure of uncertainty as determined from the Monte Carlo Stochastic Simulation 

with 50,000 iterations. Emissions were based on the annual data from 1990 to 2007 for Cropland Remaining 

Cropland areas in the 2007 NRI (USDA-NRCS 2009). The annual emissions estimated for 2007 were applied to 
Land Use, Land-Use Change, and Forestry 6-47

2007 through 2013. (Future inventories will be updated with new activity data and the time series will be 

recalculated; see Planned Improvements section).

Uncertainty and Time-Series Consistency

Uncertainty associated with the Cropland Remaining Cropland land-use category was addressed for changes in 

agricultural soil C stocks (including both mineral and organic soils). Uncertainty estimates are presented in Table

6-21 for each subsource (mineral soil C stocks and organic soil C stocks) and the method that was used in the 

inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty for the portions of the Inventory estimated with Tier 2 and 3 

approaches was derived using a Monte Carlo approach (see Annex 3.12 for further discussion). Uncertainty 

estimates from each approach were combined using the error propagation equation in accordance with IPCC (2006). 

The combined uncertainty was calculated by taking the square root of the sum of the squares of the standard 

deviations of the uncertain quantities. The combined uncertainty for soil C stocks in Cropland Remaining Cropland

ranged from 152 percent below to 154 percent above the 2013 stock change estimate of -23.4 MMT CO2 Eq.

Table 6-21: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes 

occurring within Cropland Remaining Cropland (MMT CO2 Eq. and Percent)

Source

2013 Flux Estimate

(MMT CO2 Eq.)

Uncertainty Range Relative to Flux Estimatea

(MMT CO2 Eq.) (%)

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Mineral Soil C Stocks: Cropland Remaining 

Cropland, Tier 3 Inventory Methodology (49.3) (83.7) (14.9) -70% 70%

Mineral Soil C Stocks: Cropland Remaining 

Cropland, Tier 2 Inventory Methodology (2.8) (5.1) (0.9) -80% 68%

Mineral Soil C Stocks: Cropland Remaining 

Cropland (Change in CRP enrollment relative 

to 2003)

6.6 3.3 9.9 -50% 50%

Organic Soil C Stocks: Cropland Remaining 

Cropland, Tier 2 Inventory Methodology 22.1 14.0 32.5 -37% 47%

Combined Uncertainty for Flux associated 

with Agricultural Soil Carbon Stock 

Change in Cropland Remaining Cropland

(23.4) (59.0) 12.7 -152% 154%

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Note: Parentheses indicate net sequestration.

Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes. Biomass C 

stock changes are likely minor in perennial crops, such as orchards and nut plantations, given the small amount of 

change in land used to produce these commodities in the United States. In contrast, agroforestry practices, such as 

shelterbelts, riparian forests and intercropping with trees, may have led to significant changes in biomass C stocks, 

at least in some regions of the United States, but there are currently no datasets to evaluate the trends. Changes in 

litter C stocks are also assumed to be negligible in croplands over annual time frames, although there are certainly 

significant changes at sub-annual time scales across seasons. However, this trend may change in the future, 

particularly if crop residue becomes a viable feedstock for bioenergy production.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

Quality control measures included checking input data, model scripts, and results to ensure data were properly 

handled throughout the inventory process. Inventory reporting forms and text were reviewed and revised as needed 

to correct transcription errors. As discussed in the uncertainty section, results were compared to field measurements, 

and a statistical relationship was developed to assess uncertainties in the model’s predictive capability. The 
6-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

comparisons included over 45 long-term experiments, representing about 800 combinations of management 

treatments across all of the sites (Ogle et al. 2007) (See Annex 3.12 for more information). 

Recalculations Discussion

Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining 

parameters associated with simulating crop production and carbon inputs to the soil in the DAYCENT 

biogeochemical model; 2) improving the model simulation of snow melt and water infiltration in soils; and 3) 

driving the DAYCENT simulations with updated input data for managed manure based on national livestock 

population. The change in SOC stocks increased by an average of 4.3 MMT CO2 Eq. over the time series as a result 

of the improvements to the Inventory. 

Planned Improvements 

Two major planned improvements are underway. The first is to update the time series of land use and management 

data from the USDA NRI so that it is extended from 2008 through 2010 for both the Tier 2 and 3 methods (USDANRCS 2013). Fertilization and tillage activity data will also be updated as part of this improvement. The remotesensing based data on the Enhanced Vegetation Index will be extended through 2010 in order to use the EVI data to 

drive crop production in DAYCENT. Overall, this improvement will extend the time series of activity data for the 

Tier 2 and 3 analyses through 2010. 

The second major planned improvement is to analyze C stock changes on federal lands and Alaska for cropland and 

managed grassland, using the Tier 2 method for mineral and organic soils that is described earlier in this section. 

This analysis will initially focus on land use change, which typically has a larger impact on soil C stock changes, but 

will be further refined over time to incorporate more of the management data. 

Other improvements are planned for the DAYCENT biogeochemical model. Specifically, senescence events 

following grain filling in crops, such as wheat, will also be further evaluated and refined as needed. 

An improvement is also underway to simulate crop residue burning in the DAYCENT based on the amount of crop 

residues burned according to the data that is used in the Field Burning of Agricultural Residues source category 

(Section 5.5). This improvement will more accurately represent the C inputs to the soil that are associated with 

residue burning.

All of these improvements are expected to be completed for the 1990 through 2014 Inventory. However, the time 

line may be extended if there are insufficient resources to fund all or part of these planned improvements.

CO2 Emissions from Agricultural Liming

IPCC (2006) recommends reporting CO2 emissions from lime additions (in the form of crushed limestone (CaCO3) 

and dolomite (CaMg(CO3)2) to agricultural soils. Limestone and dolomite are added by land managers to increase 

soil pH (i.e., to reduce acidification). When these compounds come in contact with acid soils, they degrade, thereby 

generating CO2. The rate and ultimate magnitude of degradation of applied limestone and dolomite depends on the 

soil conditions, soil type, climate regime, and the type of mineral applied. Emissions from liming of agricultural 

soils have fluctuated over the past 23 years, ranging from 3.7 MMT CO2 Eq. to 5.9 MMT CO2 Eq. In 2013, liming 

of agricultural soils in the United States resulted in emissions of 5.9 MMT CO2 Eq. (1.6 MMT C), representing 

about a 27 percent increase in emissions since 1990 (see Table 6-22 and Table 6-23). The trend is driven entirely by 

the amount of lime and dolomite estimated to have been applied to soils over the time period. 

Table 6-22: Emissions from Liming of Agricultural Soils (MMT CO2 Eq.)

Source 1990 2005 2009 2010 2011 2012 2013

Limestone 4.1 3.9 3.4 4.3 3.4 4.3 4.4

Dolomite 0.6 0.4 0.3 0.5 0.4 1.5 1.5

Totala 4.7 4.3 3.7 4.8 3.9 5.8 5.9
Land Use, Land-Use Change, and Forestry 6-49

a Also includes emissions from liming on Land Converted to Cropland, Grassland 

Remaining Grassland, Land Converted to Grassland, and Settlements Remaining 

Settlements as it is not currently possible to apportion the data by land-use category.

Note: Totals may not sum due to independent rounding. 

Table 6-23: Emissions from Liming of Agricultural Soils (MMT C)

Source 1990 2005 2009 2010 2011 2012 2013

Limestone 1.1 1.1 0.9 1.2 0.9 1.2 1.2

Dolomite 0.2 0.1 0.1 0.1 0.1 0.4 0.4

Totala 1.3 1.2 1.0 1.3 1.1 1.6 1.6

a Also includes emissions from liming on Land Converted to Cropland, Grassland 

Remaining Grassland, Land Converted to Grassland, and Settlements Remaining Settlements 

as it is not currently possible to apportion the data by land-use category.

Note: Totals may not sum due to independent rounding.

Methodology

CO2 emissions from degradation of limestone and dolomite applied to agricultural soils were estimated using a Tier 

2 methodology consistent with IPCC (2006). The annual amounts of limestone and dolomite applied (see Table 

6-24) were multiplied by CO2 emission factors from West and McBride (2005). These emission factors (0.059 

metric ton C/metric ton limestone, 0.064 metric ton C/metric ton dolomite) are lower than the IPCC default emission 

factors because they account for the portion of agricultural lime that may leach through the soil and travel by rivers 

to the ocean (West and McBride 2005). This analysis of lime dissolution is based on liming occurring in the 

Mississippi River basin, where the vast majority of all U.S. liming takes place (West 2008). U.S. liming that does 

not occur in the Mississippi River basin tends to occur under similar soil and rainfall regimes, and, thus, the 

emission factor is appropriate for use across the United States (West 2008). The annual application rates of 

limestone and dolomite were derived from estimates and industry statistics provided in the Minerals Yearbook and 

Mineral Industry Surveys (Tepordei 1993 through 2006; Willett 2007a, 2007b, 2009, 2010, 2011a, 2011b, 2013a and 

2014; USGS 2008 through 2014). To develop these data, the U.S. Geological Survey (USGS; U.S. Bureau of Mines 

prior to 1997) obtained production and use information by surveying crushed stone manufacturers. Because some 

manufacturers were reluctant to provide information, the estimates of total crushed limestone and dolomite 

production and use were divided into three components: (1) production by end-use, as reported by manufacturers 

(i.e., “specified” production); (2) production reported by manufacturers without end-uses specified (i.e., 

“unspecified” production); and (3) estimated additional production by manufacturers who did not respond to the 

survey (i.e., “estimated” production).

Box 6-5: Comparison of the Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach

Emissions from liming of agricultural soils were estimated using a Tier 2 methodology based on liming emission 

factors specific to the United States that are lower than the IPCC (2006) emission default factors, and are specific to 

U.S. soil conditions under which liming occurs. For example, as described previously, most liming in the United 

States occurs in the Mississippi River basin, or in areas that have similar soil and rainfall regimes as the Mississippi 

River basin. Under such soil conditions, a significant portion of dissolved agricultural lime is predicted to leach 

through the soil and travels by rivers to the ocean, the majority of which is then predicted to precipitate in the ocean 

as CaCO3 (West and McBride 2005). Therefore, the U.S. specific emissions factors (0.059 metric ton C/metric ton 

limestone and 0.064 metric ton C/metric ton dolomite) are about half of the IPCC (2006) emission factors (0.12 

metric ton C/metric ton limestone and 0.13 metric ton C/metric ton dolomite). For comparison, the 2013 U.S. 

emissions from liming of agricultural soils are 5.9 MMT CO2 Eq. using the U.S.-specific, West and McBride (2005) 

emission factors and 12.0 MMT CO2 Eq. using the IPCC (2006) emission factors.
6-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

The “unspecified” and “estimated” amounts of crushed limestone and dolomite applied to agricultural soils were 

calculated by multiplying the percentage of total “specified” limestone and dolomite production applied to 

agricultural soils by the total amounts of “unspecified” and “estimated” limestone and dolomite production. In other 

words, the proportion of total “unspecified” and “estimated” crushed limestone and dolomite that was applied to 

agricultural soils (as opposed to other uses of the stone) was assumed to be proportionate to the amount of 

“specified” crushed limestone and dolomite that was applied to agricultural soils. In addition, data were not 

available for 1990, 1992, and 2013 on the fractions of total crushed stone production that were limestone and 

dolomite, and on the fractions of limestone and dolomite production that were applied to soils. To estimate the 1990 

and 1992 data, a set of average fractions were calculated using the 1991 and 1993 data. These average fractions 

were applied to the quantity of "total crushed stone produced or used" reported for 1990 and 1992 in the 1994 

Minerals Yearbook (Tepordei 1996). To estimate 2013 data, 2012 fractions were applied to a 2013 estimate of total 

crushed stone presented in the USGS Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First 

Quarter of 2014 (USGS 2014).

The primary source for limestone and dolomite activity data is the Minerals Yearbook, published by the Bureau of 

Mines through 1994 and by the USGS from 1995 to the present. In 1994, the “Crushed Stone” chapter in the 

Minerals Yearbook began rounding (to the nearest thousand metric tons) quantities for total crushed stone produced 

or used. It then reported revised (rounded) quantities for each of the years from 1990 to 1993. In order to minimize 

the inconsistencies in the activity data, these revised production numbers have been used in all of the subsequent 

calculations. Since limestone and dolomite activity data are also available at the state level, the national-level 

estimates reported here were broken out by state, although state-level estimates are not reported here. Also, it is 

important to note that all emissions from liming are accounted for under Cropland Remaining Cropland because it is 

not currently possible to apportion the data to each agricultural land-use category (i.e., Cropland Remaining 

Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland, and 

Settlements Remaining Settlements). The majority of liming in the United States occurs on Cropland Remaining 

Cropland.

Table 6-24: Applied Minerals (MMT)

Mineral 1990 2005 2009 2010 2011 2012 2013

Limestonea 19.0 18.1 15.7 20.0 15.9 19.9 20.4

Dolomitea 2.4 1.9 1.2 1.9 1.9 6.3 6.4

a Data represent amounts applied to Cropland Remaining Cropland, Land Converted to Cropland, Grassland 

Remaining Grassland, Land Converted to Grassland, and Settlements Remaining Settlements as it is not 

currently possible to apportion the data by land-use category.

Uncertainty and Time-Series Consistency

Uncertainty regarding limestone and dolomite activity data inputs was estimated at ±15 percent and assumed to be 

uniformly distributed around the inventory estimate (Tepordei 2003, Willett 2013b). Analysis of the uncertainty 

associated with the emission factors included the following: the fraction of agricultural lime dissolved by nitric acid 

versus the fraction that reacts with carbonic acid, and the portion of bicarbonate that leaches through the soil and is 

transported to the ocean. Uncertainty regarding the time associated with leaching and transport was not accounted 

for, but should not change the uncertainty associated with CO2 emissions (West 2005). The uncertainties associated 

with the fraction of agricultural lime dissolved by nitric acid and the portion of bicarbonate that leaches through the 

soil were each modeled as a smoothed triangular distribution between ranges of zero percent to 100 percent. The 

uncertainty surrounding these two components largely drives the overall uncertainty estimates reported below. 

More information on the uncertainty estimates for Liming of Agricultural Soils is contained within the Uncertainty 

Annex.

A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the uncertainty of CO2 emissions from 

liming of agricultural soils. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table

6-25. CO2 emissions from Liming of Agricultural Soils in 2013 were estimated to be between 0.7 and 12.1 MMT 

CO2 Eq. at the 95 percent confidence level. This indicates a range of 88 percent below to 103 percent above the 

2013 emission estimate of 5.9 MMT CO2 Eq.
Land Use, Land-Use Change, and Forestry 6-51

Table 6-25: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming of 

Agricultural Soils (MMT CO2 Eq. and Percent)

Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimatea

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Liming of Agricultural Soilsb CO2 5.9 0.7 12.1 -88% 103%

a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

b Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to 

Grassland, and Settlements Remaining Settlements as it is not currently possible to apportion the data by land-use category.

Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

A source-specific QA/QC plan for Liming was developed and implemented. This effort included a Tier 1 analysis, 

as well as portions of a Tier 2 analysis. The Tier 2 procedures focused on comparing the magnitude of emission 

factors historically to attempt to identify any outliers or inconsistencies. No problems were found.

Recalculations Discussion

Several adjustments were made in the current Inventory to improve the results. In the previous Inventory, to 

estimate 2012 data, 2011 fractions were applied to a 2012 estimate of total crushed stone presented in the USGS 

Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2013 (USGS 2013). Since 

publication of the previous Inventory, the Minerals Yearbook has published actual quantities of crushed stone sold 

or used by producers in the United States in 2012. These values have replaced those used in the previous Inventory 

to calculate the quantity of minerals applied to soil and the emissions from liming of agricultural soils. Compared to 

the estimates used in the previous Inventory for 2012, the updated activity data for 2012 are approximately 3.8 

MMT greater for limestone, and approximately 4.4 MMT greater for dolomite. As a result, the reported emissions 

from liming of agricultural soils for 2012 increased by about 47 percent.

CO2 Emissions from Urea Fertilization

The use of urea (CO(NH2)2) as a fertilizer leads to CO2 emissions through the release of CO2 that was fixed during 

the industrial production process. In the presence of water and urease enzymes, urea is converted into ammonium 

(NH4

+

), hydroxyl ion (OH), and bicarbonate (HCO3

-

). The bicarbonate then evolves into CO2 and water. Emissions 

from urea fertilization in the United States totaled 4.0 MMT CO2 Eq. (1.1 MMT C) in 2013 (Table 6-26 and Table 

6-27). Due to an increase in the use of urea as a fertilizer, emissions from urea have increased 66 percent between 

1990 and 2013.

Table 6-26: CO2 Emissions from Urea Fertilization (MMT CO2 Eq.)

Source 1990 2005 2009 2010 2011 2012 2013

Urea Fertilizationa 2.4 3.5 3.6 3.8 4.1 4.2 4.0

a Also includes emissions from urea fertilization on Land Converted to Cropland, Grassland 

Remaining Grassland, Land Converted to Grassland, Settlements Remaining Settlements, and 

Forest Land Remaining Forest Land because it is not currently possible to apportion the data by 

land-use category. 

Table 6-27: CO2 Emissions from Urea Fertilization (MMT C)

Source 1990 2005 2009 2010 2011 2012 2013

Urea Fertilizationa 0.7 1.0 1.0 1.0 1.1 1.2 1.1
6-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

a Also includes emissions from urea fertilization on Land Converted to Cropland, Grassland 

Remaining Grassland, Land Converted to Grassland, Settlements Remaining Settlements, and Forest 

Land Remaining Forest Land because it is not currently possible to apportion the data by land-use 

category.

Methodology

CO2 emissions from the application of urea to agricultural soils were estimated using the IPCC (2006) Tier 1 

methodology. The annual amounts of urea applied to croplands (see Table 6-28) were derived from the state-level 

fertilizer sales data provided in Commercial Fertilizers (TVA 1991, 1992, 1993, 1994; AAPFCO 1995 through 

2014). These amounts were multiplied by the default IPCC (2006) emission factor (0.20 metric tons of C per metric 

ton of urea), which is equal to the C content of urea on an atomic weight basis. Because fertilizer sales data are 

reported in fertilizer years (July previous year through June current year), a calculation was performed to convert the 

data to calendar years (January through December). According to monthly fertilizer use data (TVA 1992b), 35 

percent of total fertilizer used in any fertilizer year is applied between July and December of the previous calendar 

year, and 65 percent is applied between January and June of the current calendar year. For example, for the 2000 

fertilizer year, 35 percent of the fertilizer was applied in July through December 1999, and 65 percent was applied in 

January through June 2000. Fertilizer sales data for the 2013 fertilizer year (i.e., July 2012 through June 2013) were 

not available in time for publication. Accordingly, urea application in the 2013 fertilizer year was estimated using a 

linear, least squares trend of consumption over the previous five years (2008 through 2012). A trend of five years 

was chosen as opposed to a longer trend as it best captures the current inter-state and inter-annual variability in 

consumption. First, January through June 2013 urea consumption was estimated using the approach described 

above, after which the percentage change in use from the previous year (i.e., January through June 2012) was 

determined. Next, the July through December 2012 data was multiplied by the same percent change to estimate the 

July through December 2013 urea consumption (assuming a constant percentage change between 2012 and 2013).

State-level estimates of CO2 emissions from the application of urea to agricultural soils were summed to estimate 

total emissions for the entire United States. Since urea activity data are also available at the state level, the nationallevel estimates reported here were broken out by state, although state-level estimates are not reported here. Also, it 

is important to note that all emissions from urea fertilization are accounted for under Cropland Remaining Cropland 

because it is not currently possible to apportion the data to each agricultural land-use category (i.e., Cropland 

Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to 

Grassland, and Settlements Remaining Settlements). The majority of urea fertilization in the United States occurs on 

Cropland Remaining Cropland.

Table 6-28: Applied Urea (MMT)

1990 2005 2009 2010 2011 2012 2013

Urea Fertilizera 3.3 4.8 4.8 5.2 5.6 5.8 5.5

a These numbers represent amounts applied to all agricultural land, including Land Converted to 

Cropland, Grassland Remaining Grassland, Land Converted to Grassland, Settlements 

Remaining Settlements, and Forest Land Remaining Forest Land because it is not currently 

possible to apportion the data by land-use category.

Uncertainty and Time-Series Consistency

Uncertainty estimates are presented in Table 6-29 for Urea Fertilization. An Approach 2 Monte Carlo analysis was 

completed. The largest source of uncertainty was the default emission factor, which assumes that 100 percent of the 

C in CO(NH2)2 applied to soils is ultimately emitted into the environment as CO2. This factor does not incorporate 

the possibility that some of the C may be retained in the soil. The emission estimate is, therefore, likely to be an 

overestimate. In addition, each urea consumption data point has an associated uncertainty. Urea for non-fertilizer 

use, such as aircraft deicing, may be included in consumption totals; it was determined through personal 

communication with Fertilizer Regulatory Program Coordinator David L. Terry (2007), however, that this amount is 

most likely very small. Research into aircraft deicing practices also confirmed that urea is used minimally in the 

industry; a 1992 survey found a known annual usage of approximately 2,000 tons of urea for deicing; this would 
Land Use, Land-Use Change, and Forestry 6-53

constitute 0.06 percent of the 1992 consumption of urea (EPA 2000). Similarly, surveys conducted from 2002 to 

2005 indicate that total urea use for deicing at U.S. airports is estimated to be 3,740 metric tons per year, or less than 

0.07 percent of the fertilizer total for 2007 (Itle 2009). Lastly, there is uncertainty surrounding the assumptions 

behind the calculation that converts fertilizer years to calendar years. CO2 emissions from urea fertilization of 

agricultural soils in 2013 were estimated to be between 2.3 and 4.1 MMT CO2 Eq. at the 95 percent confidence 

level. This indicates a range of 42 percent below to 3 percent above the 2013 emission estimate of 4.0 MMT CO2

Eq.

Table 6-29: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization 

(MMT CO2 Eq. and Percent)

Source Gas 2013 Emission Estimate Uncertainty Range Relative to Emission Estimatea

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Urea Fertilization CO2 4.0 2.3 4.1 -42% 3%

a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

A source-specific QA/QC plan for Urea was developed and implemented. This effort included a Tier 1 analysis, as 

well as portions of a Tier 2 analysis. The Tier 2 procedures focused on comparing the magnitude of emission factors 

historically to attempt to identify any outliers or inconsistencies. No problems were found.

Recalculations Discussion

In the current Inventory, the 2011 and 2012 emissions estimates were updated to reflect the urea application reported 

in the Commercial Fertilizers Report for the 2012 fertilizer year (July through December 2011, January through 

June, 2012). Specifically, the 2011 emissions estimates were revised to reflect the July to December 2011 urea 

application data. This recalculation resulted in actual emissions that are 3 percent higher than the previously 

estimated 2011 emissions. For 2012, the January through June, 2012 actual urea application rates were used to 

replace the estimates from the previous year, and the July through December rates of application were estimated 

using the methodology described above (i.e., the July through December, 2011 urea rates were multiplied by the 

percentage change in rates from January through June, 2011 to January through June, 2012). The updated activity 

data for 2012 are approximately 1,068 kt greater than the amount estimated for 2012 in the previous Inventory. As a 

result, the reported emissions from urea for 2012 in the current Inventory are 23 percent higher than the estimated 

emission reported for 2012 in the previous Inventory.

Planned Improvements

The primary planned improvement is to investigate using a Tier 2 or Tier 3 approach, which would utilize countryspecific information to estimate a more precise emission factor. This possibility was investigated for the current 

Inventory, but no options were identified for updating to a Tier 2 or Tier 3 approach. 
6-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

6.5 Land Converted to Cropland (IPCC Source 

Category 4B2)

Land Converted to Cropland includes all cropland in an Inventory year that had been in another land use(s) during 

the previous 20 years38 (USDA-NRCS 2009). For example, grassland or forestland converted to cropland during the 

past 20 years would be reported in this category. Recently-converted lands are retained in this category for 20 years 

as recommended in the IPCC guidelines (IPCC 2006). This Inventory includes all privately-owned croplands in the 

conterminous United States and Hawaii, but does not include the approximately 100,000 hectares of Land Converted 

to Cropland on federal lands and a minor amount of Land Converted to Cropland in Alaska. Consequently there is 

a discrepancy between the total amount of managed area in Land Converted to Cropland (see Section 6.1) and the 

cropland area included in the Inventory. Improvements are underway to include federal croplands in future C 

inventories. 

Background on agricultural carbon (C) stock changes is provided in section 6.4 Cropland Remaining Cropland and 

therefore will only be briefly summarized here. Soils are the largest pool of C in agricultural land, and also have the 

greatest potential for long-term storage or release of C, because biomass and dead organic matter C pools are 

relatively small and ephemeral compared with soils, with the exception of C stored in perennial woody crop 

biomass. The IPCC (2006) guidelines recommend reporting changes in soil organic carbon (SOC) stocks due to (1) 

agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and management 

activities on organic soils.39 

Land use and management of mineral soils in Land Converted to Cropland was the largest contributor to C loss 

throughout the time series, accounting for approximately 70 percent of the emissions in the category (Table 6-30 and 

Table 6-31). The conversion of grassland to cropland was the largest source of soil C loss (accounting for 

approximately 65 percent of the emissions in the category), though losses declined over the time series. The net flux 

of C from soil stock changes in 2013 was 16.1 MMT CO2 Eq. (4.4 MMT C) in 2013, including 11.3 MMT CO2 Eq. 

(3.1 MMT C) from mineral soils and 4.8 MMT CO2 Eq. (1.3 MMT C) from drainage and cultivation of organic 

soils. 

Table 6-30: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland by Land 

Use Change Category (MMT CO2 Eq.) 

Soil Type 1990 2005 2009 2010 2011 2012 2013

Grassland Converted to Cropland

Mineral 20.0 14.0 10.6 10.6 10.6 10.5 10.6 

Organic 2.5 4.3 4.0 4.0 4.0 4.0 4.0 

Forest Converted to Cropland

Mineral 1.5 0.3 0.3 0.3 0.3 0.3 0.3 

Organic (0.2) 0.3 0.2 0.2 0.2 0.2 0.2 

Other Lands Converted Cropland

Mineral 0.3 0.1 0.1 0.1 0.1 0.1 0.1 

Organic + + + + + + +

Settlements Converted Cropland

Mineral 0.6 0.3 0.3 0.3 0.3 0.3 0.3 

Organic + 0.2 0.2 0.2 0.2 0.2 0.2 

Wetlands Converted Cropland

Mineral 0.2 0.1 0.1 0.1 0.1 0.1 0.1 

Organic (0.2) 0.3 0.4 0.4 0.4 0.4 0.4 

Total Mineral Soil Flux 22.4 14.8 11.4 11.4 11.4 11.3 11.3 

 

38 The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history 

records starting in 1982 when the NRI survey began. Consequently the classifications from 1990 to 2001 were based on less than 

20 years. 

39 CO2 emissions associated with liming urea fertilization are also estimated but included in 7.4 Cropland Remaining Cropland. 
Land Use, Land-Use Change, and Forestry 6-55

Total Organic Soil Flux 2.1 5.1 4.8 4.8 4.8 4.8 4.8 

Total Net Flux 24.5 19.8 16.2 16.2 16.2 16.1 16.1 

Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes occurring 

in the latter part of the time series.

+ Does not exceed 0.05 MMT CO2 Eq.

Table 6-31: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (MMT C)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Grassland Converted to Cropland

Mineral 5.4 3.8 2.9 2.9 2.9 2.9 2.9 

Organic 0.7 1.2 1.1 1.1 1.1 1.1 1.1 

Forest Converted to Cropland

Mineral 0.4 0.1 0.1 0.1 0.1 0.1 0.1 

Organic (0.1) 0.1 0.1 0.1 0.1 0.1 0.1 

Other Lands Converted Cropland

Mineral 0.1 + + + + + +

Organic + + + + + + +

Settlements Converted Cropland

Mineral 0.2 0.1 0.1 0.1 0.1 0.1 0.1 

Organic + 0.1 + + + + +

Wetlands Converted Cropland

Mineral 0.1 + + + + + +

Organic (0.1) 0.1 0.1 0.1 0.1 0.1 0.1 

Total Mineral Soil Flux 6.1 4.0 3.1 3.1 3.1 3.1 3.1 

Total Organic Soil Flux 0.6 1.4 1.3 1.3 1.3 1.3 1.3 

Total Net Flux 6.7 5.4 4.4 4.4 4.4 4.4 4.4 

Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes 

occurring in the latter part of the time series.

+ Does not exceed 0.05 MMT C 

Parentheses indicate net sequestration.

The spatial variability in the 2013 annual flux in CO2 from mineral soils is displayed in Figure 6-10 and from 

organic soils in Figure 6-11. Losses occurred in most regions of the United States. In particular, conversion of 

grassland and forestland to cropland led to enhanced decomposition of soil organic matter and a net loss of C from 

the soil pool. The regions with the highest rates of emissions from organic soils coincide with the largest 

concentrations of organic soils used for agricultural production, including Southeastern Coastal Region (particularly 

Florida), upper Midwest and Northeast surrounding the Great Lakes, and the Pacific Coast (particularly California).
6-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Figure 6-10: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management 

within States, 2013, Land Converted to Cropland
Land Use, Land-Use Change, and Forestry 6-57

Figure 6-11: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management 

within States, 2013, Land Converted to Cropland

Methodology 

The following section includes a description of the methodology used to estimate changes in soil C stocks for Land 

Converted to Cropland, including (1) agricultural land-use and management activities on mineral soils; and (2) 

agricultural land-use and management activities on organic soils Biomass and litter C stock changes associated with 

conversion of forest to cropland are not explicitly included in this category, but are included in the Forest Land 

Remaining Forest Land section. Further elaboration on the methodologies and data used to estimate stock changes 

for mineral and organic soils are provided in the Cropland Remaining Cropland section and Annex 3.12.

Soil C stock changes were estimated for Land Converted to Cropland according to land-use histories recorded in the 

2007 USDA NRI survey (USDA-NRCS 2009). 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. In 1998, 

the NRI program began collecting annual data, and data are currently available through 2010 (USDA-NRCS 2013). 

However, this Inventory only uses NRI data through 2007 because newer data were not made available in time to 

incorporate the additional years into this Inventory. NRI points were classified as Land Converted to Cropland in a 

given year between 1990 and 2007 if the land use was cropland but had been another use during the previous 20 

years. Cropland includes all land used to produce food or fiber, or forage that is harvested and used as feed (e.g., 

hay and silage). 
6-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Mineral Soil Carbon Stock Changes

An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for mineral soils 

on the majority of land that is used to produce annual crops in the United States. These crops include alfalfa hay, 

barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans, 

sugar beets, sunflowers, tomatoes, and wheat. Soil C stock changes on the remaining soils were estimated with the 

IPCC Tier 2 method (Ogle et al. 2003), including land used to produce some vegetables, tobacco, 

perennial/horticultural crops and crops rotated with these crops; land on very gravelly, cobbly, or shaley soils 

(greater than 35 percent by volume); and land converted from forest or federal ownership.40 

Tier 3 Approach

For the Tier 3 method, mineral SOC stocks and stock changes were estimated using the DAYCENT 

biogeochemical41 model (Parton et al. 1998; Del Grosso et al. 2001, 2011). The DAYCENT model utilizes the soil 

C modeling framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but 

has been refined to simulate dynamics at a daily time-step. National estimates were obtained by using the model to 

simulate historical land-use change patterns as recorded in the USDA NRI (USDA-NRCS 2009). C stocks and 95 

percent confidence intervals were estimated for each year between 1990 and 2007, but C stock changes from 2008 to 

2013 were assumed to be similar to 2007 due to a lack of activity data for these years. (Future inventories will be 

updated with new activity data and the time series will be recalculated; See Planned Improvements section in 

Cropland Remaining Cropland). The methods used for Land Converted to Cropland are the same as those described 

in the Tier 3 portion of Cropland Remaining Cropland section for mineral soils.

Tier 2 Approach

For the mineral soils not included in the Tier 3 analysis, SOC stock changes were estimated using a Tier 2 Approach 

for Land Converted to Cropland as described in the Tier 2 portion of the Cropland Remaining Cropland section for 

mineral soils.

Organic Soil Carbon Stock Changes

Annual C emissions from drained organic soils in Land Converted to Cropland were estimated using the Tier 2

method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the Cropland 

Remaining Cropland section for organic soils. 

Uncertainty and Time-Series Consistency

Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 methodologies were based on the 

same method described for Cropland Remaining Cropland. The uncertainty for annual C emission estimates from 

drained organic soils in Land Converted to Cropland was estimated using Tier 2, as described in the Cropland 

Remaining Cropland section.

Uncertainty estimates are presented in Table 6-32 for each subsource (i.e., mineral soil C stocks and organic soil C 

stocks) and method that was used in the Inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty for the portions of 

the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see Annex 3.12 

for further discussion). Uncertainty estimates from each approach were combined using the error propagation

equation in accordance with IPCC (2006), i.e., by taking the square root of the sum of the squares of the standard 

deviations of the uncertain quantities. The combined uncertainty for soil C stocks in Land Converted to Cropland

ranged from 72 percent below to 81 percent above the 2013 stock change estimate of 16.1 MMT CO2 Eq. 

 

40Federal land is not a land use, but rather an ownership designation that is treated as forest or nominal grassland for purposes of 

these calculations. The specific use for federal lands is not identified in the NRI survey (USDA-NRCS 2009).

41 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical 

environment.
Land Use, Land-Use Change, and Forestry 6-59

Table 6-32: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes 

occurring within Land Converted to Cropland (MMT CO2 Eq. and Percent)

Source

2013 Flux Estimate

(MMT CO2 Eq.)

Uncertainty Range Relative to Flux Estimatea

(MMT CO2 Eq.) (%)

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Grassland Converted to Cropland 14.6 3.0 27.7 -80% 90%

Mineral Soil C Stocks: Tier 3 9.8 (1.3) 20.9 -114% 114%

Mineral Soil C Stocks: Tier 2 0.8 0.4 1.2 -49% 54%

Organic Soil C Stocks: Tier 2 4.0 0.7 10.9 -83% 172%

Forests Converted to Cropland 0.5 0.2 1.1 -53% 123%

Mineral Soil C Stocks: Tier 2 0.3 0.1 0.4 -49% 54%

Organic Soil C Stocks: Tier 2 0.2 0.0 0.8 -100% 258%

Other Lands Converted to Cropland 0.1 0.1 0.2 -49% 54%

Mineral Soil C Stocks: Tier 2 0.1 0.1 0.2 -49% 54%

Organic Soil C Stocks: Tier 2 NA NA NA NA NA

Settlements Converted to Cropland 0.5 0.3 0.7 -36% 41%

Mineral Soil C Stocks: Tier 2 0.3 0.2 0.5 -49% 54%

Organic Soil C Stocks: Tier 2 0.2 0.1 0.3 -46% 63%

Wetlands Converted to Croplands 0.4 0.2 0.7 -45% 57%

Mineral Soil C Stocks: Tier 2 0.1 0.04 0.1 -49% 54%

Organic Soil C Stocks: Tier 2 0.4 0.2 0.6 -53% 68%

Total: Land Converted to Cropland 16.1 4.5 29.2 -72% 81%

Mineral Soil C Stocks: Tier 3 9.8 (1.3) 20.9 -114% 114%

Mineral Soil C Stocks: Tier 2 1.6 1.1 2.0 -28% 31%

Organic Soil C Stocks: Tier 2 4.8 1.4 11.7 -70% 145%

Note: Parentheses indicate negative values or net sequestration.

NA: Other land by definition does not include organic soil (see Section 6.1—Representation of the U.S. Land Base). 

Consequently, no land areas, C stock changes, or uncertainty results are estimated for land use conversions from Other lands to 

Croplands and Other lands to Grasslands on organic soils.

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes other than the 

loss of forest biomass and litter, which is reported in the Forest Land Remaining Forest Land section of this report. 

Biomass C stock changes are likely minor in perennial crops, such as orchards and nut plantations, given the small 

amount of change in land used to produce these commodities in the United States. In contrast, agroforestry 

practices, such as shelterbelts, riparian forests and intercropping with trees, may have led to significant changes in 

biomass C stocks, at least in some regions of the United States, but there are currently no datasets to evaluate the 

trends. Changes in litter C stocks are also assumed to be negligible in croplands over annual time frames, although 

there are certainly significant changes at sub-annual time scales across seasons. However, this trend may change in 

the future, particularly if crop residue becomes a viable feedstock for bioenergy production.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

Recalculations Discussion

Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining 

parameters associated with simulating crop production and carbon inputs to the soil in the DAYCENT 

biogeochemical model; 2) improving the model simulation of snow melt and water infiltration in soils; and 3) 

driving the DAYCENT simulations with updated input data for the excretion of C and N onto 

Pasture/Range/Paddock and N additions from managed manure based on national livestock population. Change in 

SOC stocks declined by an average of 0.9 MMT CO2 Eq. over the time series as a result of these improvements to 

the Inventory. 
6-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

QA/QC and Verification 

See QA/QC and Verification section under Cropland Remaining Cropland. 

Planned Improvements 

Soil C stock changes with land use conversion from forest land to cropland are undergoing further evaluation to 

ensure consistency in the 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. This planned improvement may 

not be fully implemented for two more years, depending on resource availability. Additional planned improvements 

are discussed in the Cropland Remaining Cropland section. 

6.6 Grassland Remaining Grassland (IPCC 

Source Category 4C1) 

Grassland Remaining Grassland includes all grassland in an Inventory year that had been classified as grassland for 

the previous 20 years42 (USDA-NRCS 2009). Grassland includes pasture and rangeland that are primarily used for 

livestock grazing. Rangelands are typically extensive areas of native grassland that are not intensively managed, 

while pastures are typically seeded grassland (possibly following tree removal) that may also have additional 

management, such as irrigation or interseeding of legumes. This Inventory includes all privately-owned grasslands 

in the conterminous United States and Hawaii, but does not include the 75 million hectares of Grassland Remaining 

Grassland on federal lands or the 36 million hectares of Grassland Remaining Grassland in Alaska. This leads to a 

discrepancy with the total amount of managed area in Grassland Remaining Grassland (see Section 6.1 —

Representation of the U.S. Land Base) and the grassland area included in the Grassland Remaining Grassland

(IPCC Source Category 4C1—Section 6.6). 

Background on agricultural carbon (C) stock changes is provided in the section 6.4, Cropland Remaining Cropland, 

and will only be summarized here. Soils are the largest pool of C in agricultural land, and also have the greatest 

potential for longer-term storage or release of C, because biomass and dead organic matter C pools are relatively 

small and ephemeral compared to the soil C pool, with the exception of C stored in tree and shrub biomass that 

occurs in grasslands. The IPCC (2006) guidelines recommend reporting changes in soil organic C (SOC) stocks due 

to (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and 

management activities on organic soils.43 

In Grassland Remaining Grassland, there has been considerable variation in soil C flux between 1990 and 2013. 

These changes are driven by variability in weather patterns and associated interaction with land management 

activity. Even in the years with larger total changes in stocks, changes remain small on a per hectare rate. Land use 

and management increased soil C in mineral soils of Grassland Remaining Grassland between 1990 and 2006, after 

which the trend was reversed to small declines in soil C. In contrast, organic soils have lost relatively small amounts 

of C annually from 1990 through 2013. While the overall trend was a gain in soil C in Grassland Remaining 

Grassland from 1990 to 2003, the last decade has seen small losses in soil C during most years (Table 6-33 and 

Table 6-34). Overall, from 1990 to 2013, the net change in soil C flux increased by 14.0 MMT CO2 Eq. (3.8 MMT 

C). Current estimates for flux from soil C stock changes in 2013 are estimated at a total of 12.1 MMT CO2 Eq. (3.3 

 

42The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history 

records starting in 1982 when the NRI survey began. Consequently the classifications from 1990 to 2001 were based on less than 

20 years 

43 CO2 emissions associated with liming and urea fertilization are also estimated but included in 6.4 Cropland Remaining 

Cropland. 
Land Use, Land-Use Change, and Forestry 6-61

MMT C), with 9.1 MMT CO2 Eq. (2.5 MMT C) from mineral soils and 3.0 MMT CO2 Eq. (0.8 MMT C) from 

organic soils. 

Table 6-33: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT 

CO2 Eq.)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Mineral Soils (6.5) 1.2 8.7 8.7 8.7 8.5 9.1 

Organic Soils 4.6 3.1 3.0 3.0 3.0 3.0 3.0

Total Net Flux (1.9) 4.2 11.7 11.7 11.7 11.5 12.1 

Note: Totals may not sum due to independent rounding. Estimates after 2007 are based on NRI data 

from 2007 and therefore may not fully reflect changes occurring in the latter part of the time series. 

Parentheses indicate net sequestration.

Table 6-34: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT 

C)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Mineral Soils (1.8) 0.3 2.4 2.4 2.4 2.3 2.5 

Organic Soils 1.3 0.8 0.8 0.8 0.8 0.8 0.8

Total Net Flux (0.5) 1.2 3.2 3.2 3.2 3.1 3.3 

Note: Totals may not sum due to independent rounding. Estimates after 2007 are based on NRI data 

from 2007 and therefore may not fully reflect changes occurring in the latter part of the time series. 

Parentheses indicate net sequestration.

The spatial variability in the 2013 annual flux in CO2 from mineral is displayed in Figure 6-12 and organic soils in

Figure 6-13. Although relatively small on a per-hectare basis, grassland gained soil C in several regions during 

2013, including the Northeast, Southeast, portions of the Midwest, and Pacific Coastal Region. The regions with the 

highest rates of emissions from organic soils coincide with the largest concentrations of organic soils used for 

managed grassland, including the Southeastern Coastal Region (particularly Florida), upper Midwest and Northeast 

surrounding the Great Lakes, and the Pacific Coast (particularly California). 
6-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Figure 6-12: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management 

within States, 2013, Grassland Remaining Grassland
Land Use, Land-Use Change, and Forestry 6-63

Figure 6-13: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management 

within States, 2013, Grassland Remaining Grassland 

Methodology 

The following section includes a brief description of the methodology used to estimate changes in soil C stocks for 

Grassland Remaining Grassland, including (1) agricultural land-use and management activities on mineral soils; 

and (2) agricultural land-use and management activities on organic soils. Further elaboration on the methodologies 

and data used to estimate stock changes from mineral and organic soils are provided in the Cropland Remaining 

Cropland section and Annex 3.12.

Soil C stock changes were estimated for Grassland Remaining Grassland according to land use histories recorded in 

the 2007 USDA NRI survey (USDA-NRCS 2009). 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. In 

1998, the NRI program initiated annual data collection, and the annual data are currently available through 2010 

(USDA-NRCS 2013). However, this Inventory only uses NRI data through 2007 because newer data were not made 

available in time to incorporate the additional years into this Inventory. NRI points were classified as Grassland 

Remaining Grassland in a given year between 1990 and 2007 if the land use had been grassland for 20 years. 
6-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Mineral Soil Carbon Stock Changes 

An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for most mineral 

soils in Grassland Remaining Grassland. The C stock changes for the remaining soils were estimated with an IPCC 

Tier 2 method (Ogle et al. 2003), including gravelly, cobbly, or shaley soils (greater than 35 percent by volume) and 

additional stock changes associated with sewage sludge amendments. 

Tier 3 Approach

Mineral SOC stocks and stock changes for Grassland Remaining Grassland were estimated using the DAYCENT 

biogeochemical44 model (Parton et al. 1998; Del Grosso et al. 2001, 2011), as described in Cropland Remaining 

Cropland. The DAYCENT model utilizes the soil C modeling framework developed in the Century model (Parton 

et al. 1987, 1988, 1994; Metherell et al. 1993), but has been refined to simulate dynamics at a daily time-step. 

Historical land-use and management patterns were used in the DAYCENT simulations as recorded in the USDA 

NRI survey, with supplemental information on fertilizer use and rates from the USDA Economic Research Service 

Cropping Practices Survey (USDA-ERS 1997, 2011) and National Agricultural Statistics Service (NASS 1992, 

1999, 2004). Frequency and rates of manure application to grassland during 1997 were estimated from data 

compiled by the USDA Natural Resources Conservation Service (Edmonds, et al. 2003), and then adjusted using 

county-level estimates of manure available for application in other years. Specifically, county-scale ratios of 

manure available for application to soils in other years relative to 1997 were used to adjust the area amended with 

manure (see Cropland Remaining Cropland for further details). Greater availability of managed manure nitrogen

(N) relative to 1997 was, thus, assumed to increase the area amended with manure, while reduced availability of 

manure N relative to 1997 was assumed to reduce the amended area. 

The amount of manure produced by each livestock type was calculated for managed and unmanaged waste 

management systems based on methods described in Manure Management, Section 5.2, and Annex 3.11. Manure N

deposition from grazing animals (i.e., PRP manure) was an input to the DAYCENT model (see Annex 3.11), and 

included approximately 91 percent of total PRP manure (the remainder is deposited on federal lands, which are not 

included in this Inventory). C stocks and 95 percent confidence intervals were estimated for each year between 

1990 and 2007, but C stock changes from 2008 to 2013 were assumed to be similar to 2007 due to a lack of activity 

data for these years. (Future inventories will be updated with new activity data and the time series will be 

recalculated; See Planned Improvements section in Cropland Remaining Cropland). The methods used for 

Grassland remaining Grassland are the same as those described in the Tier 3 portion of Cropland Remaining 

Cropland section for mineral soils.

Tier 2 Approach

The Tier 2 approach is based on the same methods described in the Tier 2 portion of Cropland Remaining Cropland

section for mineral soils.

Additional Mineral C Stock Change Calculations

A Tier 2 method was used to adjust annual C flux estimates for mineral soils between 1990 and 2013 to account for 

additional C stock changes associated with sewage sludge amendments. Estimates of the amounts of sewage sludge 

N applied to agricultural land were derived from national data on sewage sludge generation, disposition, and N 

content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from EPA 

(1999) and estimates for 2004 were obtained from an independent national biosolids survey (NEBRA 2007). These 

values were linearly interpolated to estimate values for the intervening years, and linearly extrapolated to estimate 

values for years since 2004. N application rates from Kellogg et al. (2000) were used to determine the amount of 

area receiving sludge amendments. Although sewage sludge can be added to land managed for other land uses, it 

was assumed that agricultural amendments occur in grassland. Cropland is not likely to be amended with sewage 

sludge due to the high metal content and other pollutants in human waste. The soil C storage rate was estimated at 

 

44 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical 

environment.
Land Use, Land-Use Change, and Forestry 6-65

0.38 metric tons C per hectare per year for sewage sludge amendments to grassland. The stock change rate is based 

on country-specific factors and the IPCC default method (see Annex 3.12 for further discussion).

Organic Soil Carbon Stock Changes

Annual C emissions from drained organic soils in Grassland Remaining Grassland were estimated using the Tier 2 

method provided in IPCC (2006), which utilizes U.S.-specific C loss rates (Ogle et al. 2003) rather than default 

IPCC rates. For more information, see the Cropland Remaining Cropland section for organic soils.

Uncertainty and Time-Series Consistency

Uncertainty estimates are presented in Table 6-35 for each subsource (i.e., mineral soil C stocks and organic soil C 

stocks) disaggregated to the level of the inventory methodology employed (i.e., Tier 2 and Tier 3). Uncertainty for 

the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see 

Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using the error 

propagation equation in accordance with IPCC (2006), i.e., by taking the square root of the sum of the squares of the 

standard deviations of the uncertain quantities. The combined uncertainty for soil C stocks in Grassland Remaining 

Grassland ranged from 297 percent below to 297 percent above the 2013 stock change estimate of 12.1 MMT CO2

Eq. The large relative uncertainty is due to the small net flux estimate in 2013.

Table 6-35: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring 

Within Grassland Remaining Grassland (MMT CO2 Eq. and Percent)

Source

2013 Flux Estimate

(MMT CO2 Eq.)

Uncertainty Range Relative to Flux Estimatea

(MMT CO2 Eq.) (%)

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Mineral Soil C Stocks Grassland Remaining 

Grassland, Tier 3 Methodology 10.3 (25.5) 46.2 -347% 347%

Mineral Soil C Stocks: Grassland Remaining 

Grassland, Tier 2 Methodology 0.1 0.0 0.2 -86% 109%

Mineral Soil C Stocks: Grassland Remaining 

Grassland, Tier 2 Methodology (Change in 

Soil C due to Sewage Sludge Amendments)

(1.4) (2.1) (0.7) -50% 50%

Organic Soil C Stocks: Grassland Remaining 

Grassland, Tier 2 Methodology 3.0 1.6 4.9 -46% 63%

Combined Uncertainty for Flux Associated 

with Agricultural Soil Carbon Stock 

Change in Grassland Remaining Grassland

12.1 (23.8) 48.0 -297% 297%

Note: Parentheses indicate negative values.

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Uncertainty is also associated with a lack of reporting on agricultural biomass and litter C stock changes and nonCO2 greenhouse gas emissions from burning. Biomass C stock changes may be significant for managed grasslands 

with woody encroachment that has not attained enough tree cover to be considered forest lands. Grassland burning 

is not as common in the United States as in other regions of the world, but fires do occur through both natural 

ignition sources and prescribed burning. Changes in litter C stocks are assumed to be negligible in grasslands over 

annual time frames, although there are certainly significant changes at sub-annual time scales across seasons. 

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.
6-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

QA/QC and Verification 

Quality control measures included checking input data, model scripts, and results to ensure data were properly 

handled through the inventory process. In the previous Inventory, DAYCENT was used to simulate the PRP manure 

N input with automated routines, but errors occurred leading to a mismatch between the amount of manure N 

excreted according to the Manure Management data, relative to the amount simulated in DAYCENT. This error 

appears to be corrected based on internal checks, and should provide internal consistency between the Manure 

Management data and the Agricultural Soil Management and LULUCF inventories. 

Inventory reporting forms and text were reviewed and revised as needed to correct transcription errors. Modeled 

results were compared to measurements from several long-term grazing experiments (see Annex 3.12 for more 

information). 

Recalculations Discussion 

Methodological recalculations in the current Inventory were associated with the following improvements, including 

1) improving the model simulation of snow melt and water infiltration in soils; and 2) driving the DAYCENT 

simulations with updated input data for the excretion of C and N onto Pasture/Range/Paddock and N additions from 

managed manure based on national livestock population. As a result of these improvements to the Inventory, 

changes in SOC stocks declined by an average of 1.76 MMT CO2 eq. annually over the time series. 

Planned Improvements 

One of the key planned improvements for Grassland Remaining Grassland is to develop an inventory of carbon 

stock changes for the 75 million hectares of federal grasslands in the western United States. While federal grasslands 

likely have minimal changes in land management and C stocks, improvements are underway to include these 

grasslands in future C Inventories. Grasslands in Alaska will also be further evaluated in the future. This is a 

significant improvement and estimates are expected to be available for the 1990-2014 Inventory. Another key 

planned improvement is to estimate non-CO2 greenhouse gas emissions from burning of grasslands. For 

information about other improvements, see the Planned Improvements section in Cropland Remaining Cropland. 

6.7 Land Converted to Grassland (IPCC Source 

Category 4C2)

Land Converted to Grassland includes all grassland in an Inventory year that had been in another land use(s) during 

the previous 20 years45 (USDA-NRCS 2009). For example, cropland or forestland converted to grassland during 

the past 20 years would be reported in this category. Recently-converted lands are retained in this category for 20 

years as recommended by IPCC (2006). Grassland includes pasture and rangeland that are used primarily for 

livestock grazing. Rangelands are typically extensive areas of native grassland that are not intensively managed, 

while pastures are typically seeded grassland (possibly following tree removal) that may also have additional 

management, such as irrigation or interseeding of legumes. This Inventory includes all privately-owned grasslands 

in the conterminous United States and Hawaii, but does not but does not include the 800,000 to 850,000 hectares of 

Land Converted to Grassland on federal lands or Land Converted to Grassland in Alaska. Consequently there is a 

discrepancy between the total amount of managed area for Land Converted to Grassland (see Section 6.1—

Representation of the U.S. Land Base) and the grassland area included in Land Converted to Grassland (IPCC 

Source Category 4C2—Section 6.7). 

 

45 The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history 

records starting in 1982 when the NRI survey began. Consequently the classifications from 1990 to 2001 were based on less than 

20 years. 
Land Use, Land-Use Change, and Forestry 6-67

Background on agricultural carbon (C) stock changes is provided in Cropland Remaining Cropland and therefore 

will only be briefly summarized here. Soils are the largest pool of C in agricultural land, and also have the greatest 

potential for long-term storage or release of C, because biomass and dead organic matter C pools are relatively small 

and ephemeral compared with soils, with the exception of C stored in tree and shrub biomass that occurs in 

grasslands. IPCC (2006) recommend reporting changes in soil organic C (SOC) stocks due to (1) agricultural landuse and management activities on mineral soils, and (2) agricultural land-use and management activities on organic 

soils.46 

Land use and management of mineral soils in Land Converted to Grassland led to an increase in soil C stocks 

between 1990 and 2013 (see Table 6-36 and Table 6-37). The net C flux from soil C stock changes for mineral soils 

between 1990 and 2013 led to a decrease of 1.7 MMT CO2 Eq. (0.5 MMT C) in the atmosphere. In contrast, over 

the same period, drainage of organic soils for grassland management led to an increase in C emissions to the 

atmosphere of 0.3 MMT CO2 Eq. (0.1 MMT C). The flux associated with soil C stock changes in 2013 is estimated 

at a net uptake of 8.8 MMT CO2 Eq. (-2.4 MMT C) from the atmosphere.

Table 6-36: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (MMT 

CO2 Eq.)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Cropland Converted to Grassland

Mineral (6.4) (9.0) (8.8) (8.8) (8.7) (8.6) (8.6)

Organic 0.5 1.0 0.9 0.9 0.9 0.9 0.9 

Forest Converted to Grassland

Mineral (1.1) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4)

Organic 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

Other Lands Converted Grassland

Mineral (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)

Organic + + + + + + +

Settlements Converted Grassland

Mineral (0.4) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5)

Organic + + + + + + +

Wetlands Converted Grassland

Mineral (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Organic 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

Total Mineral Soil Flux (8.2) (10.3) (10.0) (10.0) (10.0) (9.9) (9.9)

Total Organic Soil Flux 0.8 1.3 1.1 1.1 1.1 1.1 1.1 

Total Net Flux (7.4) (9.0) (8.9) (8.9) (8.9) (8.8) (8.8)

Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes 

occurring in the latter part of the time series. Parentheses indicate net sequestration. 

+ Does not exceed 0.05 MMT CO2 Eq.

Table 6-37: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (MMT 

C)

Soil Type 1990 2005 2009 2010 2011 2012 2013

Cropland Converted to Grassland

Mineral (1.7) (2.5) (2.4) (2.4) (2.4) (2.4) (2.3)

Organic 0.1 0.3 0.2 0.2 0.2 0.2 0.2 

Forest Converted to Grassland

Mineral (0.3) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Organic + + + + + + +

Other Lands Converted Grassland

Mineral (0.1) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0)

Organic + + + + + + +

Settlements Converted Grassland

Mineral (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

 

46 CO2 emissions associated with liming are also estimated but included in 6.4 Cropland Remaining Cropland.
6-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Organic + + + + + + +

Wetlands Converted Grassland

Mineral + + + + + + +

Organic + + + + + + +

Total Mineral Soil Flux (2.2) (2.8) (2.7) (2.7) (2.7) (2.7) (2.7)

Total Organic Soil Flux 0.2 0.3 0.3 0.3 0.3 0.3 0.3 

Total Net Flux (2.0) (2.5) (2.4) (2.4) (2.4) (2.4) (2.4)

Note: Estimates after 2007 are based on NRI data from 2007 and therefore may not fully reflect changes 

occurring in the latter part of the time series. 

Parentheses indicate net sequestration.

+ Does not exceed 0.05 MMT CO2 Eq.

The spatial variability in the 2013 annual flux in CO2 from mineral soils is displayed in Figure 6-14 and from 

organic soils in Figure 6-15. The soil C stock increased in most states for Land Converted to Grassland, which was 

driven by conversion of annual cropland into continuous pasture. The largest gains were in the Southeastern region, 

Northeast, South-Central, Midwest, and northern Great Plains. The regions with the highest rates of emissions from 

organic soils coincide with the largest concentrations of organic soils used for managed grasslands, including 

Southeastern Coastal Region (particularly Florida), upper Midwest and Northeast surrounding the Great Lakes, and 

the Pacific Coast (particularly California).
Land Use, Land-Use Change, and Forestry 6-69

Figure 6-14: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management 

within States, 2013, Land Converted to Grassland 
6-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Figure 6-15: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management 

within States, 2013, Land Converted to Grassland

Methodology 

The following section includes a description of the methodology used to estimate changes in soil C stocks for Land 

Converted to Grassland, including (1) agricultural land-use and management activities on mineral soils; and (2) 

agricultural land-use and management activities on organic soils. Biomass and litter C stock changes associated 

with conversion of forest to grassland are not explicitly included in this category, but are included in the Forest 

Land Remaining Forest Land section. Further elaboration on the methodologies and data used to estimate stock 

changes for mineral and organic soils are provided in the Cropland Remaining Cropland section and Annex 3.12.

Soil C stock changes were estimated for Land Converted to Grassland according to land-use histories recorded in 

the 2009 USDA NRI survey (USDA-NRCS 2009). 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. In 

1998, the NRI program initiated annual data collection, and the annual and data are currently available through 2010 

(USDA-NRCS 2013). However, this Inventory only uses NRI data through 2007 because newer data were not made 

available in time to incorporate the additional years into this Inventory. NRI points were classified as Land 

Converted to Grassland in a given year between 1990 and 2007 if the land use was grassland but had been classified 

as another use during the previous 20 years. 
Land Use, Land-Use Change, and Forestry 6-71

Mineral Soil Carbon Stock Changes

An IPCC Tier 3 model-based approach (Ogle et al. 2010) was applied to estimate C stock changes for Land 

Converted to Grassland on most mineral soils. C stock changes on the remaining soils were estimated with an IPCC 

Tier 2 approach (Ogle et al. 2003), including prior cropland used to produce vegetables, tobacco, and 

perennial/horticultural crops; land areas with very gravelly, cobbly, or shaley soils (greater than 35 percent by 

volume); and land converted from forest.47 

Tier 3 Approach

Mineral SOC stocks and stock changes were estimated using the DAYCENT biogeochemical48 model (Parton et al. 

1998; Del Grosso et al. 2001, 2011) as described for Grassland Remaining Grassland. The DAYCENT model 

utilizes the soil C modeling framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et 

al. 1993), but has been refined to simulate dynamics at a daily time-step. Historical land-use and management 

patterns were used in the DAYCENT simulations as recorded in the NRI survey (USDA-NCRS 2009), with 

supplemental information on fertilizer use and rates from the USDA Economic Research Service Cropping Practices 

Survey (USDA-ERS 1997, 2011) and the National Agricultural Statistics Service (NASS 1992, 1999, 2004). See the 

Cropland Remaining Cropland section for additional discussion of the Tier 3 methodology for mineral soils.

Tier 2 Approach

For the mineral soils not included in the Tier 3 analysis, SOC stock changes were estimated using a Tier 2 Approach 

for Land Converted to Grassland as described in the Tier 2 portion of the Cropland Remaining Cropland section for 

mineral soils.

Organic Soil Carbon Stock Changes

Annual C emissions from drained organic soils in Land Converted to Grassland were estimated using the Tier 2 

method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the Cropland 

Remaining Cropland section for organic soils. 

Uncertainty and Time-Series Consistency

Uncertainty estimates are presented in Table 6-38 for each subsource (i.e., mineral soil C stocks and organic soil C 

stocks), disaggregated to the level of the inventory methodology employed (i.e., Tier 2 and Tier 3). Uncertainty for 

the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see 

Annex 3.12 for further discussion). Uncertainty estimates from each approach were combined using the error 

propagation equation in accordance with IPCC (2006) (i.e., by taking the square root of the sum of the squares of the 

standard deviations of the uncertain quantities). The combined uncertainty for soil C stocks in Land Converted to 

Grassland ranged from 107 percent below to 107 percent above the 2013 stock change estimate of -8.8 MMT CO2

Eq. The large relative uncertainty is due to the small net flux estimate in 2013.

 

47 Federal land is converted into private land in some cases due to changes in ownership. The specific use for federal lands is not 

identified in the NRI survey (USDA-NRCS 2009), and so the land is assumed to be forest or nominal grassland for purposes of 

these calculations. 

48 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical 

environment.
6-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Table 6-38: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes 

occurring within Land Converted to Grassland (MMT CO2 Eq. and Percent)

Source

2013 Flux Estimate

(MMT CO2 Eq.)

Uncertainty Range Relative to Flux Estimatea

(MMT CO2 Eq.) (%)

Lower

Bound

Upper

Bound

Lower

Bound

Upper

Bound

Cropland Converted to Grassland (7.7) (17.1) 1.7 -122% 123%

Mineral Soil C Stocks: Tier 3 (7.3) (16.7) 2.0 -127% 127%

Mineral Soil C Stocks: Tier 2 (1.3) (1.9) (0.7) -45% 45%

Organic Soil C Stocks: Tier 2 0.9 0.3 1.8 -63% 98%

Forests Converted to Grassland (0.3) (0.6) (0.1) -62% 72%

Mineral Soil C Stocks: Tier 2 (0.4) (0.6) (0.2) -48% 44%

Organic Soil C Stocks: Tier 2 0.1 0.0 0.2 -100% 231%

Other Lands Converted to Grassland (0.2) (0.3) (0.1) -48% 44%

Mineral Soil C Stocks: Tier 2 (0.2) (0.3) (0.1) -48% 44%

Organic Soil C Stocks: Tier 2 NA NA NA NA NA

Settlements Converted to Grassland (0.5) (0.7) (0.3) -51% 47%

Mineral Soil C Stocks: Tier 2 (0.5) (0.8) (0.3) -48% 44%

Organic Soil C Stocks: Tier 2 0.0 0.0 0.1 -86% 160%

Wetlands Converted to Grasslands (8.5) (17.7) 0.7 -108% 108%

Mineral Soil C Stocks: Tier 2 (0.1) (0.2) (0.1) -48% 44%

Organic Soil C Stocks: Tier 2 0.1 0.0 0.2 -58% 81%

Total: Land Converted to Grassland (8.8) (18.1) 0.7 -107% 107%

Mineral Soil C Stocks: Tier 3 (7.3) (16.7) 2.0 -127% 127%

Mineral Soil C Stocks: Tier 2 (2.5) (3.2) (1.9) -27% 26%

Organic Soil C Stocks: Tier 2 1.1 0.5 2.0 -52% 81%

Note: Parentheses indicate negative values. 

NA: Other land by definition does not include organic soil (see Section 6.1— of the U.S. Land Base). Consequently, no 

land areas, C stock changes, or uncertainty results are estimated for land use conversions from Other lands to Croplands and 

Other lands to Grasslands on organic soils.

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Uncertainty is also associated with lack of reporting of agricultural biomass and litter C stock changes, other than 

the loss of forest biomass and litter, which is reported in the Forest Land Remaining Forest Land section of the 

report. Biomass C stock changes may be significant for managed grasslands with woody encroachment that has not 

attained enough tree cover to be considered forest lands. Changes in litter C stocks are assumed to be negligible in 

grasslands over annual time frames, although there are likely significant changes at sub-annual time scales across 

seasons.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the above Methodology 

section.

QA/QC and Verification

See the QA/QC and Verification section in Grassland Remaining Grassland.

Recalculations Discussion

Methodological recalculations in the current Inventory were associated with the following improvements: 1) refining 

parameters associated with simulating crop production and carbon inputs to the soil in the DAYCENT 

biogeochemical model; 2) improving the model simulation of snow melt and water infiltration in soils; and 3) 

driving the DAYCENT simulations with updated input data for the excretion of C and nitrogen (N) onto 

Pasture/Range/Paddock and N additions from managed manure based on national livestock population. As a result 

of these improvements to the Inventory, changes in SOC stocks increased by an average of 0.2 MMT CO2 eq. 

annually over the time series. 
Land Use, Land-Use Change, and Forestry 6-73

Planned Improvements 

Soil C stock changes with land use conversion from forest land to grassland are undergoing further evaluation to 

ensure consistency in the time series. Different methods are used to estimate soil C stock changes in forest land and 

grasslands, 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 grassland. This planned improvement may 

not be fully implemented for two more years, depending on resource availability. Another key planned 

improvement for the Land Converted to Grassland category is to develop an inventory of carbon stock changes for 

the 800,000 to 850,000 hectares of Federal grasslands in the western United States. Grasslands in Alaska will also be 

evaluated. For information about other improvements, see the Planned Improvements section in Cropland 

Remaining Cropland and Grassland Remaining Grassland.

6.8 Wetlands Remaining Wetlands (IPCC 

Source Category 4D1)

Peatlands Remaining Peatlands 

Emissions from Managed Peatlands 

Managed peatlands are peatlands which have been cleared and drained for the production of peat. The production 

cycle of a managed peatland has three phases: land conversion in preparation for peat extraction (e.g., clearing 

surface biomass, draining), extraction (which results in the emissions reported under Peatlands Remaining 

Peatlands), and abandonment, restoration, or conversion of the land to another use. 

CO2 emissions from the removal of biomass and the decay of drained peat constitute the major GHG flux from 

managed peatlands. Managed peatlands may also emit CH4 and N2O. The natural production of CH4 is largely 

reduced but not entirely shut down when peatlands are drained in preparation for peat extraction (Strack et al. 2004 

as cited in the 2006 IPCC Guidelines). Drained land surface and ditch networks contribute to the CH4 flux in 

peatlands managed for peat extraction. CH4 emissions were considered insignificant under IPCC Tier 1 

methodology (IPCC 2006), but are included in the emissions estimates for Peatlands Remaining Peatlands

consistent with the 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: 

Wetlands (IPCC 2013). N2O emissions from managed peatlands depend on site fertility. In addition, abandoned 

and restored peatlands continue to release GHG emissions, and at present no methodology is provided by IPCC 

(2006) to estimate greenhouse gas emissions or removals from restored peatlands; although methodologies are 

provided for rewetted organic soils (which includes rewetted/restored peatlands) in IPCC (2013) guidelines. This 

Inventory estimates CO2, N2O, and CH4 emissions from peatlands managed for peat extraction in accordance with 

IPCC (2006 and 2013) guidelines. 

CO2, N2O, and CH4 Emissions from Peatlands Remaining Peatlands

IPCC (2013) recommends reporting CO2, N2O, and CH4 emissions from lands undergoing active peat extraction 

(i.e., Peatlands Remaining Peatlands) as part of the estimate for emissions from managed wetlands. Peatlands occur 

where plant biomass has sunk to the bottom of water bodies and water-logged areas and exhausted the oxygen 

supply below the water surface during the course of decay. Due to these anaerobic conditions, much of the plant 

matter does not decompose but instead forms layers of peat over decades and centuries. In the United States, peat is 

extracted for horticulture and landscaping growing media, and for a wide variety of industrial, personal care, and 

other products. It has not been used for fuel in the United States for many decades. Peat is harvested from two 

types of peat deposits in the United States: sphagnum bogs in northern states (e.g., Minnesota) and wetlands in states 

further south (e.g., Florida). The peat from sphagnum bogs in northern states, which is nutrient poor, is generally 

corrected for acidity and mixed with fertilizer. Production from more southerly states is relatively coarse (i.e., 

fibrous) but nutrient rich. 
6-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

IPCC (2006 and 2013) recommend considering both on-site and off-site emissions when estimating CO2 emissions 

from Peatlands Remaining Peatlands using the Tier 1 approach. Current methodologies estimate only on-site N2O 

and CH4 emissions, since off-site N2O estimates are complicated by the risk of double-counting emissions from 

nitrogen fertilizers added to horticultural peat, and off-site CH4 emissions are not relevant given the non-energy uses 

of peat, so methodologies are not provided in IPCC (2013) guidelines. On-site emissions from managed peatlands 

occur as the land is cleared of vegetation and the underlying peat is exposed to sun and weather. As this occurs, 

some peat deposit is lost and CO2 is emitted from the oxidation of the peat. Since N2O emissions from saturated 

ecosystems tend to be low unless there is an exogenous source of nitrogen, N2O emissions from drained peatlands 

are dependent on nitrogen mineralization and therefore on soil fertility. Peatlands located on highly fertile soils 

contain significant amounts of organic nitrogen in inactive form. Draining land in preparation for peat extraction 

allows bacteria to convert the nitrogen into nitrates which leach to the surface where they are reduced to N2O, and 

contributes to the activity of methanogens and methanotrophs (Blodau 2002; Treat et al. 2007 as cited in IPCC 

2013). Drainage ditches, which are constructed as land is drained in preparation for peat extraction, also contribute 

to the flux of CH4 through in situ production and lateral transfer of CH4 from the organic soil matrix (IPCC 2013).

Off-site CO2 emissions from managed peatlands occur from waterborne carbon losses and the horticultural and 

landscaping use of peat. As drainage waters in peatlands accumulate, dissolved organic carbon reacts within aquatic 

ecosystems and is converted to CO2, then emitted to the atmosphere (Billet et al. 2004 as cited in IPCC 2013). 

During the horticultural and landscaping use of peat, nutrient-poor (but fertilizer-enriched) peat tends to be used in 

bedding plants and in greenhouse and plant nursery production, whereas nutrient-rich (but relatively coarse) peat is 

used directly in landscaping, athletic fields, golf courses, and plant nurseries. Most (nearly 98 percent) of the CO2

emissions from peat occur off-site, as the peat is processed and sold to firms which, in the United States, use it 

predominantly for the aforementioned horticultural and landscaping purposes.

Total emissions from Peatlands Remaining Peatlands were estimated to be 0.8 MMT CO2 Eq. in 2013 (see Table 

6-39) comprising 0.8 MMT CO2 Eq. (770 kt) of CO2, 0.001 MMT CO2 Eq. (0.002 kt) of N2O, and 0.004 MMT CO2

Eq. (0.16 kt) of CH4. Total emissions in 2013 were about 5 percent smaller than total emissions in 2012. Peat 

production in Alaska in 2013 was not reported in Alaska’s Mineral Industry 2013 report. However, peat production 

reported in the lower 48 states in 2013 was 5 percent lower than in 2012, resulting in smaller total 48 states plus 

Alaska emissions from Peatlands Remaining Peatlands in 2013 compared to 2012.

Total emissions from Peatlands Remaining Peatlands have fluctuated between 0.8 and 1.3 MMT CO2 Eq. across the 

time series with a decreasing trend from 1990 until 1993 followed by an increasing trend through 2000. After 2000, 

emissions generally decreased until 2006 and then increased until 2009, when the trend reversed. Emissions in 2013

represent a decline from emissions in 2012. CO2 emissions from Peatlands Remaining Peatlands have fluctuated 

between 0.8 and 1.3 MMT CO2 across the time series, and these emissions drive the trends in total emissions. CH4

and N2O emissions remained close to zero across the time series. N2O emissions showed a decreasing trend from 

1990 until 1995, followed by an increasing trend through 2001. N2O emissions decreased between 2001 and 2006, 

followed by a leveling off between 2008 and 2010, and a decline between 2011 and 2013. CH4 emissions decreased 

from 1990 until 1995, followed by an increasing trend through 2000, a period of fluctuation through 2010, then a 

decline between 2011 and 2013.

Table 6-39: Emissions from Peatlands Remaining Peatlands (MMT CO2 Eq.)

Gas 1990 2005 2009 2010 2011 2012 2013

CO2 1.1 1.1 1.0 1.0 0.9 0.8 0.8

 Off-site 1.0 1.0 1.0 1.0 0.9 0.8 0.7

 On-site 0.1 0.1 0.1 0.1 0.1 0.1 +

N2O (On-site) + + + + + + +

CH4 (On-site) + + + + + + +

Total 1.1 1.1 1.0 1.0 0.9 0.8 0.8

Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.

+ Less than 0.05 MMT CO2 Eq.

Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which does 

not take into account imports, exports, and stockpiles (i.e., apparent consumption). Off-site N2O emissions 

are not estimated to avoid double-counting N2O emitted from the fertilizer that the peat is mixed with prior 

to horticultural use (see IPCC 2006). Guidance for estimating off-site CH4 emissions is not included in 

IPCC (2013). Totals may not sum due to independent rounding.
Land Use, Land-Use Change, and Forestry 6-75

Table 6-40: Emissions from Peatlands Remaining Peatlands (kt)

Gas 1990 2005 2009 2010 2011 2012 2013

CO2 1,055 1,101 1,024 1,022 926 812 770

 Off-site 985 1,030 957 956 866 760 720

 On-site 70 71 67 66 60 53 50

N2O (On-site) + + + + + + +

CH4 (On-site) + + + + + + +

+ Less than 0.5 kt

Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which does not 

take into account imports, exports, and stockpiles (i.e., apparent consumption). Off-site N2O emissions are not 

estimated to avoid double-counting N2O emitted from the fertilizer that the peat is mixed with prior to 

horticultural use (see IPCC 2006). Guidance for estimating off-site CH4 emissions is not included in IPCC 

(2013). Totals may not sum due to independent rounding.

Methodology

Off-site CO2 Emissions

CO2 emissions from domestic peat production were estimated using a Tier 1 methodology consistent with IPCC 

(2006). Off-site CO2 emissions from Peatlands Remaining Peatlands were calculated by apportioning the annual 

weight of peat produced in the United States (Table 6-41) into peat extracted from nutrient-rich deposits and peat 

extracted from nutrient-poor deposits using annual percentage-by-weight figures. These nutrient-rich and nutrientpoor production values were then multiplied by the appropriate default C fraction conversion factor taken from 

IPCC (2006) in order to obtain off-site emission estimates. For the lower 48 states, both annual percentages of peat 

type by weight and domestic peat production data were sourced from estimates and industry statistics provided in 

the Minerals Yearbook and Mineral Commodity Summaries from the U.S. Geological Survey (USGS 1995–2014a; 

USGS 2014b). To develop these data, the U.S. Geological Survey (USGS; U.S. Bureau of Mines prior to 1997) 

obtained production and use information by surveying domestic peat producers. On average, about 75 percent of the 

peat operations respond to the survey; and USGS estimates data for non-respondents on the basis of prior-year 

production levels (Apodaca 2011).

The Alaska estimates rely on reported peat production from the annual Alaska’s Mineral Industry reports (DGGS 

1993–2014). Similar to the U.S. Geological Survey, the Alaska Department of Natural Resources, Division of 

Geological & Geophysical Surveys (DGGS) solicits voluntary reporting of peat production from producers for the 

Alaska’s Mineral Industry report. However, the report does not estimate production for the non-reporting producers, 

resulting in larger inter-annual variation in reported peat production from Alaska depending on the number of 

producers who report in a given year (Szumigala 2011). In addition, in both the lower 48 states and Alaska, large 

variations in peat production can also result from variations in precipitation and the subsequent changes in moisture 

conditions, since unusually wet years can hamper peat production. The methodology estimates Alaska emissions 

separately from lower 48 emissions because the state conducts its own mineral survey and reports peat production 

by volume, rather than by weight (Table 6-42). However, volume production data were used to calculate off-site 

CO2 emissions from Alaska applying the same methodology but with volume-specific C fraction conversion factors 

from IPCC (2006).49 Peat production was not reported for 2013 in Alaska’s Mineral Industry 2013 report (DGGS

2014); therefore Alaska’s peat production in 2013 (reported in cubic yards) was assumed to be equal to its peat 

production in 2012.

Consistent with IPCC (2013) guidelines, off-site CO2 emissions from dissolved organic carbon were estimated based 

on the total area of peatlands managed for peat extraction, which is calculated from production data using the 

methodology described in the On-Site CO2 Emissions section below. CO2 emissions from dissolved organic C were 

 

49 Peat produced from Alaska was assumed to be nutrient poor; as is the case in Canada, “where deposits of high-quality [but 

nutrient poor] sphagnum moss are extensive” (USGS 2008).
6-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

estimated by multiplying the area of peatlands by the default emissions factor for dissolved organic C provided in 

IPCC (2013).

The apparent consumption of peat, which includes production plus imports minus exports plus the decrease in 

stockpiles, in the United States is over two-and-a-half times the amount of domestic peat production. However, 

consistent with the Tier 1 method whereby only domestic peat production is accounted for when estimating off-site 

emissions, off-site CO2 emissions from the use of peat not produced within the United States are not included in the 

Inventory. The United States has largely imported peat from Canada for horticultural purposes; from 2010 to 2013, 

imports of sphagnum moss (nutrient-poor) peat from Canada represented 63 percent of total U.S. peat imports 

(USGS 2015). Most peat produced in the United States is reed-sedge peat, generally from southern states, which is 

classified as nutrient rich by IPCC (2006). Higher-tier calculations of CO2 emissions from apparent consumption 

would involve consideration of the percentages of peat types stockpiled (nutrient rich versus nutrient poor) as well 

as the percentages of peat types imported and exported.

Table 6-41: Peat Production of Lower 48 States (kt)

Type of Deposit 1990 2005 2009 2010 2011 2012 2013

Nutrient-Rich 595.1 657.6 560.3 558.9 511.2 409.9 418.5

Nutrient-Poor 55.4 27.4 48.7 69.1 56.8 78.1 46.5

Total Production 692.0 685.0 609.0 628.0 568.0 488.0 465.0

Sources: United States Geological Survey (USGS) (1991–2014a) Minerals Yearbook: Peat (1994–2013); 

United States Geological Survey (USGS) (2014b) Mineral Commodity Summaries: Peat (2013).

Table 6-42: Peat Production of Alaska (Thousand Cubic Meters)

1990 2005 2009 2010 2011 2012 2013

Total Production 49.7 47.8 183.9 59.8 61.5 93.1 93.1

Sources: Division of Geological & Geophysical Surveys (DGGS), Alaska Department of Natural Resources 

(1997–2014) Alaska’s Mineral Industry Report (1997–2013).

On-site CO2 Emissions

IPCC (2006) suggests basing the calculation of on-site emission estimates on the area of peatlands managed for peat 

extraction differentiated by the nutrient type of the deposit (rich versus poor). Information on the area of land 

managed for peat extraction is currently not available for the United States, but in accordance with IPCC (2006), an 

average production rate for the industry was applied to derive an area estimate. In a mature industrialized peat 

industry, such as exists in the United States and Canada, the vacuum method can extract up to 100 metric tons per 

hectare per year (Cleary et al. 2005 as cited in IPCC 2006).50 The area of land managed for peat extraction in the 

United States was estimated using nutrient-rich and nutrient-poor production data and the assumption that 100 

metric tons of peat are extracted from a single hectare in a single year. The annual land area estimates were then 

multiplied by the IPCC (2013) default emission factor in order to calculate on-site CO2 emission estimates. 

Production data are not available by weight for Alaska. In order to calculate on-site emissions resulting from 

Peatlands Remaining Peatlands in Alaska, the production data by volume were converted to weight using annual 

average bulk peat density values, and then converted to land area estimates using the same assumption that a single 

hectare yields 100 metric tons. The IPCC (2006) on-site emissions equation also includes a term which accounts for 

emissions resulting from the change in C stocks that occurs during the clearing of vegetation prior to peat extraction. 

Area data on land undergoing conversion to peatlands for peat extraction is also unavailable for the United States. 

However, USGS records show that the number of active operations in the United States has been declining since 

1990; therefore, it seems reasonable to assume that no new areas are being cleared of vegetation for managed peat 

 

50 The vacuum method is one type of extraction that annually “mills” or breaks up the surface of the peat into particles, which 

then dry during the summer months. The air-dried peat particles are then collected by vacuum harvesters and transported from 

the area to stockpiles (IPCC 2006).
Land Use, Land-Use Change, and Forestry 6-77

extraction. Other changes in C stocks in living biomass on managed peatlands are also assumed to be zero under the 

Tier 1 methodology (IPCC 2006 and 2013).

On-site N2O Emissions

IPCC (2006) suggests basing the calculation of on-site N2O emission estimates on the area of nutrient-rich peatlands 

managed for peat extraction. These area data are not available directly for the United States, but the on-site CO2

emissions methodology above details the calculation of area data from production data. In order to estimate N2O 

emissions, the area of nutrient rich Peatlands Remaining Peatlands was multiplied by the appropriate default 

emission factor taken from IPCC (2013).

On-site CH4 Emissions

IPCC (2013) also suggests basing the calculation of on-site CH4 emission estimates on the total area of peatlands 

managed for peat extraction. Area data is derived using the calculation from production data described in the Onsite CO2 Emissions section above. In order to estimate CH4 emissions from drained land surface, the area of 

Peatlands Remaining Peatlands was multiplied by the emission factor for direct CH4 emissions taken from IPCC 

(2013). In order to estimate CH4 emissions from drainage ditches, the total area of peatland was multiplied by the 

default fraction of peatland area that contains drainage ditches, and the appropriate emission factor taken from IPCC 

(2013).

Uncertainty and Time-Series Consistency

The uncertainty associated with peat production data was estimated to be ± 25 percent (Apodaca 2008) and assumed 

to be normally distributed. The uncertainty associated with peat production data stems from the fact that the USGS 

receives data from the smaller peat producers but estimates production from some larger peat distributors. The peat 

type production percentages were assumed to have the same uncertainty values and distribution as the peat 

production data (i.e., ± 25 percent with a normal distribution). The uncertainty associated with the reported 

production data for Alaska was assumed to be the same as for the lower 48 states, or ± 25 percent with a normal 

distribution. It should be noted that the DGGS estimates that around half of producers do not respond to their survey 

with peat production data; therefore, the production numbers reported are likely to underestimate Alaska peat 

production (Szumigala 2008). The uncertainty associated with the average bulk density values was estimated to be 

± 25 percent with a normal distribution (Apodaca 2008). IPCC (2006 and 2013) gives uncertainty values for the 

emissions factors for the area of peat deposits managed for peat extraction based on the range of underlying data 

used to determine the emission factors. The uncertainty associated with the emission factors was assumed to be 

triangularly distributed. The uncertainty values surrounding the C fractions were based on IPCC (2006) and the 

uncertainty was assumed to be uniformly distributed. The uncertainty values associated with the fraction of peatland 

covered by ditches was assumed to be ± 100 percent with a normal distribution based on the assumption that greater 

than 10 percent coverage, the upper uncertainty bound, is not typical of drained organic soils outside of The 

Netherlands (IPCC 2013). Based on these values and distributions, a Monte Carlo (Approach 2) uncertainty 

analysis was applied to estimate the uncertainty of CO2, CH4, and N2O emissions from Peatlands Remaining 

Peatlands. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 6-43. CO2

emissions from Peatlands Remaining Peatlands in 2013 were estimated to be between 0.5 and 1.0 MMT CO2 Eq. at 

the 95 percent confidence level. This indicates a range of 29 percent below to 32 percent above the 2013 emission 

estimate of 0.8 MMT CO2 Eq. N2O emissions from Peatlands Remaining Peatlands in 2013 were estimated to be 

between 0.0003 and 0.0010 MMT CO2 Eq. at the 95 percent confidence level. This indicates a range of 55 percent 

below to 62 percent above the 2013 emission estimate of 0.0006 MMT CO2 Eq. CH4 emissions from Peatlands 

Remaining Peatlands in 2013 were estimated to be between 0.002 and 0.007 MMT CO2 Eq. This indicates a range 

of 60 percent below to 85 percent above the 2013 emission estimate of 0.004 MMT CO2 Eq.

Table 6-43: Approach 2 Quantitative Uncertainty Estimates for CO2, CH4, and N2O Emissions 

from Peatlands Remaining Peatlands (MMT CO2 Eq. and Percent)

Source Gas

2013 Emission 

Estimate Uncertainty Range Relative to Emission Estimatea

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)
6-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Peatlands Remaining Peatlands CO2 0.8 0.5 1.0 −29% 32%

Peatlands Remaining Peatlands CH4 + + + −60% 85%

Peatlands Remaining Peatlands N2O + + + −55% 62%

a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

+ Does not exceed 0.05 MMT CO2 eq.

Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

A QA/QC analysis was performed for data gathering and input, documentation, and calculation. The QA/QC 

analysis revealed an incorrect emission factor for off-site CO2 emissions from dissolved organic carbon. The 

emission factor for a boreal climate zone was replaced with the emission factor for a temperate climate zone, which 

is more representative of the climate zone for the majority of peat producing areas in the United States. 

The QA/QC analysis also revealed that revised production estimates for peat were published in the 2013 Minerals 

Yearbook: Peat (USGS 2014a). The estimates for the U.S. production of peat and the percentage of sphagnum moss 

(nutrient-poor peat) reported in the 2013 Mineral Commodity Summaries: Peat (USGS 2014b) were replaced with 

the estimates reported in the 2013 Minerals Yearbook: Peat (USGS 2014a). As a result, the estimate for peat 

production decreased by 3 percent and the percentage of sphagnum moss decreased by 6 percent. 

Recalculations Discussion

The emissions estimates for Peatlands Remaining Peatlands were updated to reflect the 2013 Supplement to the 

2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (IPCC 2013). IPCC (2013) 

methodologies include off-site CO2 emissions from dissolved organic carbon, on-site CH4 emissions from drainage 

ditches and drained land surface, and updated emissions factors for off-site CO2, on-site CO2, and on-site N2O 

emissions estimates. As a result of the methodological changes listed above, CO2 emissions over the entire time 

series increased by an average of approximately 1 percent and N2O emissions over the entire time series decreased 

by an average of approximately 500 percent. Total emissions from Peatlands Remaining Peatlands increased by an 

average of approximately 1 percent over the entire time series relative to the previous emissions estimates using the 

IPCC (2006) guidelines.

The current Inventory estimates for 2011 and 2012 were also updated to incorporate information on the volume of 

peat production in Alaska from Alaska’s Mineral Industry 2012 report (DGGS 2013); and the historical estimate for 

2004 was updated to incorporate more recent information on the volume of peat product in Alaska in 2004 from 

Alaska’s Mineral Industry 2006 report (DGGS 2007). In the previous Inventory report, peat production in Alaska in 

2011 and 2012 was assumed to equal the values reported for 2011 and 2012 in the 2012 Minerals Yearbook: Peat

(USGS 2013). As a result of the updated production estimates, emissions decreased by 0.005 percent in 2011, 

increased by 0.001 percent in 2012, and increased by 10 percent in 2004. Since no peat production was reported in 

Alaska’s Mineral Industry 2013 report, peat production in Alaska in 2013 was assumed to equal the value reported 

for 2012 in Alaska’s Mineral Industry 2012 report; this will result in a recalculation in the next Inventory report if 

the production value is updated.

In addition, for the current Inventory, emission estimates have been revised to reflect the GWPs provided in the 

IPCC Fourth Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the 

IPCC Second Assessment Report (SAR) (IPCC 1996) (used in the previous inventories) which results in time-series 

recalculations for most inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries 

are required to report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of 

each greenhouse gas. The GWP of CH4 has increased, leading to an overall increase in CO2-equivalent emissions 

from CH4. The GWP of N2O has decreased, leading to a decrease in CO2-equivalent emissions for N2O. The AR4 

GWPs have been applied across the entire time series for consistency. For more information please see the 

Recalculations and Improvements Chapter. As a result of the updated GWP value for N2O, N2O emissions estimates 
Land Use, Land-Use Change, and Forestry 6-79

for each year from 1990 to 2012 decreased by 4 percent relative to the N2O emissions estimates in previous 

Inventory reports. 

Planned Improvements

In order to further improve estimates of CO2, N2O, and CH4 emissions from Peatlands Remaining Peatlands, future 

efforts will consider options for obtaining better data on the quantity of peat harvested per hectare and the total area 

undergoing peat extraction. 

6.9 Settlements Remaining Settlements 

Changes in Carbon Stocks in Urban Trees (IPCC Source 

Category 4E1)

Urban forests constitute a significant portion of the total U.S. tree canopy cover (Dwyer et al. 2000). Urban areas 

(cities, towns, and villages) are estimated to cover over 3 percent of the United States (U.S. Census Bureau 2012). 

With an average tree canopy cover of 35 percent, urban areas account for approximately 5 percent of total tree cover 

in the continental United States (Nowak and Greenfield 2012). Trees in urban areas of the United States were 

estimated to account for an average annual net sequestration of 75.8 MMT CO2 Eq. (20.7 MMT C) over the period 

from 1990 through 2013. Net C flux from urban trees in 2013 was estimated to be −89.5 MMT CO2 Eq. (−24.4 

MMT C). Annual estimates of CO2 flux (Table 6-44) were developed based on periodic (1990, 2000, and 2010) 

U.S. Census data on urbanized area. The estimate of urbanized area is smaller than the area categorized as 

Settlements in the Representation of the U.S. Land Base developed for this report, by an average of 48 percent over 

the 1990 through 2013 time series—i.e., the Census urban area is a subset of the Settlements area. 

In 2013, urban area was about 44 percent smaller than the total area defined as Settlements. Census area data are 

preferentially used to develop C flux estimates for this source category since these data are more applicable for use 

with the available peer-reviewed data on urban tree canopy cover and urban tree C sequestration. Annual 

sequestration increased by 48 percent between 1990 and 2013 due to increases in urban land area. Data on C storage 

and urban tree coverage were collected since the early 1990s and have been applied to the entire time series in this 

report. As a result, the estimates presented in this chapter are not truly representative of changes in C stocks in 

urban trees for Settlements areas, but are representative of changes in C stocks in urban trees for Census urban area. 

The method used in this report does not attempt to scale these estimates to the Settlements area. Therefore, the 

estimates presented in this chapter are likely an underestimate of the true changes in C stocks in urban trees in all 

Settlements areas—i.e., the changes in C stocks in urban trees presented in this chapter are a subset of the changes in 

C stocks in urban trees in all Settlements areas. 

Urban trees often grow faster than forest trees because of the relatively open structure of the urban forest (Nowak 

and Crane 2002). However, areas in each case are accounted for differently. Because urban areas contain less tree 

coverage than forest areas, the C storage per hectare of land is in fact smaller for urban areas. However, urban tree 

reporting occurs on a basis of C sequestered per unit area of tree cover, rather than C sequestered per total land area. 

Expressed per unit of tree cover, areas covered by urban trees have a greater C density than do forested areas 

(Nowak and Crane 2002). Expressed per unit of land area, however, the situation is the opposite: Urban areas have 

a smaller C density than forest areas. 

Table 6-44: Net C Flux from Urban Trees (MMT CO2 Eq. and MMT C) 

Year MMT CO2 Eq. MMT C

1990 (60.4) (16.5)

2005 (80.5) (22.0)

2009 (85.0) (23.2)

2010 (86.1) (23.5)
6-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

2011 (87.3) (23.8)

2012 (88.4) (24.1)

2013 (89.5) (24.4)

Note: Parentheses indicate net 

sequestration.

Methodology

Methods for quantifying urban tree biomass, C sequestration, and C emissions from tree mortality and 

decomposition were taken directly from Nowak et al. (2013), Nowak and Crane (2002), and Nowak (1994). In 

general, the methodology used by Nowak et al. (2013) to estimate net C sequestration in urban trees followed three 

steps. First, field data from cities and states were used to generate allometric estimates of biomass from measured 

tree dimensions. Second, estimates of annual tree growth and biomass increment were generated from published 

literature and adjusted for tree condition, land-use class, and growing season to generate estimates of gross C 

sequestration in urban trees for all 50 states and the District of Columbia. Third, estimates of C emissions due to 

mortality and decomposition were subtracted from gross C sequestration values to derive estimates of net C 

sequestration. Finally, sequestration estimates for all 50 states and the District of Columbia, in units of C 

sequestered per unit area of tree cover, were used to estimate urban forest C sequestration in the United States by 

using urban area estimates from U.S. Census data and urban tree cover percentage estimates for each state and the 

District of Columbia from remote sensing data, an approach consistent with Nowak et al. (2013).

This approach is also consistent with the default IPCC methodology in IPCC (2006), although sufficient data are not 

yet available to separately determine interannual gains and losses in C stocks in the living biomass of urban trees. 

In order to generate the allometric relationships between tree dimensions and tree biomass for cities and states, 

Nowak et al. (2013) and previously published research (Nowak and Crane 2002; and Nowak 1994, 2007b, and 

2009) collected field measurements in a number of U.S. cities between 1989 and 2012. For a sample of trees in each 

of the cities in Table 6-45, data including tree measurements of stem diameter, tree height, crown height and crown 

width, and information on location, species, and canopy condition were collected. The data for each tree were 

converted into C storage by applying allometric equations to estimate aboveground biomass, a root-to-shoot ratio to 

convert aboveground biomass estimates to whole tree biomass, moisture content, a C content of 50 percent (dry 

weight basis), and an adjustment factor of 0.8 to account for urban trees having less aboveground biomass for a 

given stem diameter than predicted by allometric equations based on forest trees (Nowak 1994). C storage estimates 

for deciduous trees include only C stored in wood. These calculations were then used to develop an allometric 

equation relating tree dimensions to C storage for each species of tree, encompassing a range of diameters.

Tree growth was estimated using annual height growth and diameter growth rates for specific land uses and diameter 

classes. Growth calculations were adjusted by a factor to account for tree condition (fair to excellent, poor, critical, 

dying, or dead). For each tree, the difference in C storage estimates between year 1 and year (x + 1) represents the 

gross amount of C sequestered. These annual gross C sequestration rates for each species (or genus), diameter class, 

and land-use condition (e.g., parks, transportation, vacant, golf courses) were then scaled up to city estimates using 

tree population information. The area of assessment for each city or state was defined by its political boundaries; 

parks and other forested urban areas were thus included in sequestration estimates (Nowak 2011).

Most of the field data used to develop the methodology of Nowak et al. (2013) were analyzed using the U.S. Forest 

Service’s Urban Forest Effects (UFORE) model. UFORE is a computer model that uses standardized field data 

from random plots in each city and local air pollution and meteorological data to quantify urban forest structure, 

values of the urban forest, and environmental effects, including total C stored and annual C sequestration. UFORE 

was used with field data from a stratified random sample of plots in each city to quantify the characteristics of the 

urban forest (Nowak et al. 2007).

Where gross C sequestration accounts for all carbon sequestered, net C sequestration takes into account carbon 

emissions associated with urban trees. Net C emissions include tree death and removals. Estimates of net C 

emissions from urban trees were derived by applying estimates of annual mortality and condition, and assumptions 

about whether dead trees were removed from the site to the total C stock estimate for each city. Estimates of annual 

mortality rates by diameter class and condition class were derived from a study of street-tree mortality (Nowak 

1986). Different decomposition rates were applied to dead trees left standing compared with those removed from 
Land Use, Land-Use Change, and Forestry 6-81

the site. For removed trees, different rates were applied to the removed/aboveground biomass in contrast to the 

belowground biomass. The estimated annual gross C emission rates for each species (or genus), diameter class, and 

condition class were then scaled up to city estimates using tree population information.

The data for all 50 states and the District of Columbia are described in Nowak et al. (2013), which builds upon 

previous research, including: Nowak and Crane (2002), Nowak et al. (2007), and references cited therein. The 

allometric equations applied to the field data for each tree were taken from the scientific literature (see Nowak 1994, 

Nowak et al. 2002), but if no allometric equation could be found for the particular species, the average result for the 

genus was used. The adjustment (0.8) to account for less live tree biomass in urban trees was based on information 

in Nowak (1994). Measured tree growth rates for street (Frelich 1992; Fleming 1988; Nowak 1994), park (deVries 

1987), and forest (Smith and Shifley 1984) trees were standardized to an average length of growing season (153 

frost free days) and adjusted for site competition and tree condition. Standardized growth rates of trees of the same 

species or genus were then compared to determine the average difference between standardized street tree growth 

and standardized park and forest growth rates. Crown light exposure (CLE) measurements (number of sides and/or 

top of tree exposed to sunlight) were used to represent forest, park, and open (street) tree growth conditions. Local 

tree base growth rates (BG) were then calculated as the average standardized growth rate for open-grown trees 

multiplied by the number of frost free days divided by 153. Growth rates were then adjusted for CLE. The CLE 

adjusted growth rate was then adjusted based on tree health and tree condition to determine the final growth rate. 

Assumptions for which dead trees would be removed versus left standing were developed specific to each land use 

and were based on expert judgment of the authors. Decomposition rates were based on literature estimates (Nowak 

et al. 2013).

Estimates of gross and net sequestration rates for each of the 50 states and the District of Columbia (Table 6-45) 

were compiled in units of C sequestration per unit area of tree canopy cover. These rates were used in conjunction 

with estimates of state urban area and urban tree cover data to calculate each state’s annual net C sequestration by 

urban trees. This method was described in Nowak et al. (2013) and has been modified to incorporate U.S. Census 

data.

Specifically, urban area estimates were based on 1990, 2000, and 2010 U.S. Census data. The 1990 U.S. Census 

defined urban land as “urbanized areas,” which included land with a population density greater than 1,000 people 

per square mile, and adjacent “urban places,” which had predefined political boundaries and a population total 

greater than 2,500. In 2000, the U.S. Census replaced the “urban places” category with a new category of urban 

land called an “urban cluster,” which included areas with more than 500 people per square mile. In 2010, the 

Census updated its definitions to have “urban areas” encompassing Census tract delineated cities with 50,000 or 

more people, and “urban clusters” containing Census tract delineated locations with between 2,500 and 50,000 

people. Urban land area increased by approximately 23 percent from 1990 to 2000 and 14 percent from 2000 to 

2010; Nowak et al. (2005) estimate that the changes in the definition of urban land are responsible for approximately 

20 percent of the total reported increase in urban land area from 1990 to 2000. Under all Census (i.e., 1990, 2000, 

and 2010) definitions, the urban category encompasses most cities, towns, and villages (i.e., it includes both urban 

and suburban areas). Settlements area, as assessed in the Representation of the U.S. Land Base developed for this 

report, encompassed all developed parcels greater than 0.1 hectares in size, including rural transportation corridors, 

and as previously mentioned represents a larger area than the Census-derived urban area estimates. However, the 

smaller, Census-derived urban area estimates were deemed to be more suitable for estimating national urban tree 

cover given the data available in the peer-reviewed literature (i.e., the data set available is consistent with Census 

urban rather than Settlements areas), and the recognized overlap in the changes in C stocks between urban forest and 

non-urban forest (see Planned Improvements below). U.S. Census urban area data is reported as a series of 

continuous blocks of urban area in each state. The blocks or urban area were summed to create each state’s urban 

area estimate.

Net annual C sequestration estimates were derived for all 50 states and the District of Columbia by multiplying the 

gross annual emission estimates by 0.74, the standard ratio for net/gross sequestration set out in Table 3 of Nowak et 

al. (2013) (unless data existed for both gross and net sequestration for the state in Table 2 of Nowak et. al. (2013), in 

which case they were divided to get a state-specific ratio). The gross and net annual C sequestration values for each 

state were multiplied by each state’s area of tree cover, which was the product of the state’s urban/community area 

as defined in the U.S. Census (2012) and the state’s urban/community tree cover percentage. The urban/community 

tree cover percentage estimates for all 50 states were obtained from Nowak and Greenfield (2012), which compiled 

ten years of research including Dwyer et al. (2000), Nowak et al. (2002), Nowak (2007a), and Nowak (2009). The 

urban/community tree cover percentage estimate for the District of Columbia was obtained from Nowak et al. 
6-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

(2013). The urban area estimates were taken from the 2010 U.S. Census (2012). The equation, used to calculate the 

summed carbon sequestration amounts, can be written as follows: 

Net annual C sequestration = Gross sequestration rate × Net to Gross sequestration ratio × Urban Area ×

% Tree Cover

Table 6-45: Annual C Sequestration (Metric Tons C/yr), Tree Cover (Percent), and Annual C 

Sequestration per Area of Tree Cover (kg C/m2-yr) for 50 states plus the District of Columbia

State

Gross Annual 

Sequestration

Net Annual 

Sequestration

Tree 

Cover

Gross Annual 

Sequestration 

per Area of 

Tree Cover

Net Annual 

Sequestration 

per Area of 

Tree Cover

Net: Gross 

Annual 

Sequestration 

Ratio

Alabama 1,123,944 831,718 55.2 0.343 0.254 0.74 

Alaska 44,895 33,223 39.8 0.168 0.124 0.74 

Arizona 369,243 273,239 17.6 0.354 0.262 0.74 

Arkansas 411,363 304,409 42.3 0.331 0.245 0.74 

California 2,092,278 1,548,286 25.1 0.389 0.288 0.74 

Colorado 149,005 110,264 18.5 0.197 0.146 0.74 

Connecticut 766,512 567,219 67.4 0.239 0.177 0.74 

Delaware 129,813 96,062 35.0 0.335 0.248 0.74 

DC 14,557 11,568 35.0 0.263 0.209 0.79 

Florida 3,331,471 2,465,288 35.5 0.475 0.352 0.74 

Georgia 2,476,627 1,832,704 54.1 0.353 0.261 0.74 

Hawaii 241,105 178,417 39.9 0.581 0.430 0.74 

Idaho 24,658 18,247 10.0 0.184 0.136 0.74 

Illinois 747,411 553,084 25.4 0.283 0.209 0.74 

Indiana 396,776 366,882 23.7 0.250 0.231 0.92 

Iowa 115,796 85,689 19.0 0.240 0.178 0.74 

Kansas 182,154 141,747 25.0 0.283 0.220 0.78 

Kentucky 237,287 175,592 22.1 0.286 0.212 0.74 

Louisiana 727,949 538,683 34.9 0.397 0.294 0.74 

Maine 107,875 79,827 52.3 0.221 0.164 0.74 

Maryland 586,554 434,050 34.3 0.323 0.239 0.74 

Massachusetts 1,294,359 957,826 65.1 0.254 0.188 0.74 

Michigan 731,314 541,172 35.0 0.220 0.163 0.74 

Minnesota 349,007 258,265 34.0 0.229 0.169 0.74 

Mississippi 480,298 355,421 47.3 0.344 0.255 0.74 

Missouri 488,287 361,332 31.5 0.285 0.211 0.74 

Montana 52,675 38,980 36.3 0.184 0.136 0.74 

Nebraska 49,685 41,927 15.0 0.238 0.201 0.84 

Nevada 41,797 30,929 9.6 0.207 0.153 0.74 

New Hampshire 244,715 181,089 66.0 0.217 0.161 0.74 

New Jersey 1,192,996 882,817 53.3 0.294 0.218 0.74 

New Mexico 68,789 50,904 12.0 0.263 0.195 0.74 

New York 1,090,092 806,668 42.6 0.240 0.178 0.74 

North Carolina 1,989,946 1,472,560 51.1 0.312 0.231 0.74 

North Dakota 14,372 6,829 13.0 0.223 0.106 0.48 

Ohio 910,839 674,021 31.5 0.248 0.184 0.74 

Oklahoma 358,363 265,189 31.2 0.332 0.246 0.74 

Oregon 257,480 190,535 36.6 0.242 0.179 0.74 

Pennsylvania 1,241,922 919,022 41.0 0.244 0.181 0.74 

Rhode Island 136,841 101,262 51.0 0.258 0.191 0.74 

South Carolina 1,063,705 787,141 48.9 0.338 0.250 0.74 

South Dakota 20,356 17,653 14.0 0.236 0.205 0.87 

Tennessee 1,030,972 921,810 43.8 0.303 0.271 0.89 

Texas 2,712,954 2,007,586 31.4 0.368 0.272 0.74 

Utah 87,623 64,841 16.4 0.215 0.159 0.74 

Vermont 46,111 34,122 53.0 0.213 0.158 0.74 

Virginia 822,286 608,492 39.8 0.293 0.217 0.74 

Washington 560,055 414,440 34.6 0.258 0.191 0.74 

West Virginia 249,592 184,698 61.0 0.241 0.178 0.74 
Land Use, Land-Use Change, and Forestry 6-83

Wisconsin 356,405 263,739 31.8 0.225 0.167 0.74 

Wyoming 18,726 13,857 19.9 0.182 0.135 0.74 

Uncertainty and Time-Series Consistency

Uncertainty associated with changes in C stocks in urban trees includes the uncertainty associated with urban area, 

percent urban tree coverage, and estimates of gross and net C sequestration for each of the 50 states and the District 

of Columbia. A 10 percent uncertainty was associated with urban area estimates based on expert judgment. 

Uncertainty associated with estimates of percent urban tree coverage for each of the 50 states was based on standard 

error estimates reported by Nowak and Greenfield (2012). Uncertainty associated with estimate of percent urban 

tree coverage for the District of Columbia was based on the standard error estimate reported by Nowak et al. (2013). 

Uncertainty associated with estimates of gross and net C sequestration for each of the 50 states and the District of 

Columbia was based on standard error estimates for each of the state-level sequestration estimates reported by 

Nowak et al. (2013). These estimates are based on field data collected in each of the 50 states and the District of 

Columbia, and uncertainty in these estimates increases as they are scaled up to the national level.

Additional uncertainty is associated with the biomass equations, conversion factors, and decomposition assumptions 

used to calculate C sequestration and emission estimates (Nowak et al. 2002). These results also exclude changes in 

soil C stocks, and there may be some overlap between the urban tree C estimates and the forest tree C estimates. 

Due to data limitations, urban soil flux is not quantified as part of this analysis, while reconciliation of urban tree 

and forest tree estimates will be addressed through the land-representation effort described in the Planned 

Improvements section of this chapter.

A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the overall uncertainty of the 

sequestration estimate. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 

6-46. The net C flux from changes in C stocks in urban trees in 2013 was estimated to be between −133.1 and −47.0

MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 49 percent more sequestration to 48

percent less sequestration than the 2013 flux estimate of −89.5 MMT CO2 Eq.

Table 6-46: Approach 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C 

Stocks in Urban Trees (MMT CO2 Eq. and Percent)

2013 Flux Estimate Uncertainty Range Relative to Flux Estimatea

Source Gas (MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Changes in C Stocks in 

Urban Trees CO2 (89.5) (133.1) (47.0) 49% −48%

Note: Parentheses indicate negative values or net sequestration.

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

QA/QC and Verification

Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality 

control measures for urban trees included checking input data, documentation, and calculations to ensure data were 

properly handled through the inventory process. Errors that were found during this process were corrected as 

necessary. The net C flux resulting from urban trees was predominately calculated using state-specific estimates of 

gross and net C sequestration estimates for urban trees and urban tree coverage area published in the literature.

Planned Improvements

A consistent representation of the managed land base in the United States is discussed at the beginning of the Land 

Use, Land-Use Change, and Forestry chapter, and discusses a planned improvement by the USDA Forest Service to 
6-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

reconcile the overlap between urban forest and non-urban forest greenhouse gas inventories. Urban forest 

inventories are including areas also defined as forest land under the Forest Inventory and Analysis (FIA) program of 

the USDA Forest Service, resulting in “double-counting” of these land areas in estimates of C stocks and fluxes for 

this report. For example, Nowak et al. (2013) estimates that 13.7 percent of urban land is measured by the forest 

inventory plots, and could be responsible for up to 87 MMT C of overlap.

Future research may also enable more complete coverage of changes in the C stock in urban trees for all Settlements

land. To provide estimates for all Settlements, research would need to establish the extent of overlap between 

Settlements and Census-defined urban areas, and would have to characterize sequestration on non-urban Settlements

land.

N2O Fluxes from Settlement Soils (IPCC Source Category 4E1)

Of the synthetic N fertilizers applied to soils in the United States, approximately 2.4 percent are currently applied to 

lawns, golf courses, and other landscaping occurring within settlement areas. Application rates are lower than those 

occurring on cropped soils, and, therefore, account for a smaller proportion of total U.S. soil N2O emissions per unit 

area. In addition to synthetic N fertilizers, a portion of surface applied sewage sludge is applied to settlement areas.

N additions to soils result in direct and indirect N2O emissions. Direct emissions occur on-site due to the N 

additions. Indirect emissions result from fertilizer and sludge N that is transformed and transported to another 

location in a form other than N2O (NH3 and NOx volatilization, NO3 leaching and runoff), and later converted into 

N2O at the off-site location. The indirect emissions are assigned to settlements because the management activity 

leading to the emissions occurred in settlements. 

In 2013, total N2O emissions from settlement soils were 2.4 MMT CO2 Eq. (8 kt). There was an overall increase of 

77 percent over the period from 1990 through 2013 due to a general increase in the application of synthetic N 

fertilizers on an expanding settlement area. Interannual variability in these emissions is directly attributable to 

interannual variability in total synthetic fertilizer consumption and sewage sludge applications in the United States. 

Emissions from this source are summarized in Table 6-47.

Table 6-47: N2O Fluxes from Soils in Settlements Remaining Settlements (MMT CO2 Eq. and 

kt N2O)

1990 2005 2009 2010 2011 2012 2013

Direct N2O Fluxes from Soils

MMT CO2 Eq. 1.0 1.8 1.7 1.8 1.9 1.9 1.8

kt N2O 3 6 6 6 6 6 6

Indirect N2O Fluxes from Soils

MMT CO2 Eq. 0.4 0.6 0.6 0.6 0.6 0.6 0.6

kt N2O 1 2 2 2 2 2 2

Total

MMT CO2 Eq. 1.4 2.3 2.2 2.4 2.5 2.5 2.4

kt N2O 5 8 8 8 8 8 8

Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values.

Methodology

For soils within Settlements Remaining Settlements, the IPCC Tier 1 approach was used to estimate soil N2O 

emissions from synthetic N fertilizer and sewage sludge additions. Estimates of direct N2O emissions from soils in 

settlements were based on the amount of N in synthetic commercial fertilizers applied to settlement soils, and the 

amount of N in sewage sludge applied to non-agricultural land and surface disposal (see Annex 3.12 for a detailed 

discussion of the methodology for estimating sewage sludge application). 

Nitrogen applications to settlement soils are estimated using data compiled by the USGS (Ruddy et al. 2006). The 

USGS estimated on-farm and non-farm fertilizer use is based on sales records at the county level from 1982 through 

2001 (Ruddy et al. 2006). Non-farm N fertilizer was assumed to be applied to settlements and forest lands; values 

for 2002 through 2013 were based on 2001 values adjusted for annual total N fertilizer sales in the United States 

because there is no new activity data on application after 2001. Settlement application was calculated by subtracting 
Land Use, Land-Use Change, and Forestry 6-85

forest application from total non-farm fertilizer use. Sewage sludge applications were derived from national data on 

sewage sludge generation, disposition, and N content (see Annex 3.12 for further detail). The total amount of N 

resulting from these sources was multiplied by the IPCC default emission factor for applied N (1 percent) to 

estimate direct N2O emissions (IPCC 2006). 

For indirect emissions, the total N applied from fertilizer and sludge was multiplied by the IPCC default factors of 

10 percent for volatilization and 30 percent for leaching/runoff to calculate the amount of N volatilized and the 

amount of N leached/runoff. The amount of N volatilized was multiplied by the IPCC default factor of 1 percent for 

the portion of volatilized N that is converted to N2O off-site and the amount of N leached/runoff was multiplied by 

the IPCC default factor of 0.075 percent for the portion of leached/runoff N that is converted to N2O off-site. The

resulting estimates were summed to obtain total indirect emissions. 

Uncertainty and Time-Series Consistency 

The amount of N2O emitted from settlements depends not only on N inputs and fertilized area, but also on a large 

number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH, 

temperature, and irrigation/watering practices. The effect of the combined interaction of these variables on N2O flux 

is complex and highly uncertain. The IPCC default methodology does not explicitly incorporate any of these 

variables, except variations in fertilizer N and sewage sludge application rates. All settlement soils are treated 

equivalently under this methodology. 

Uncertainties exist in both the fertilizer N and sewage sludge application rates in addition to the emission factors. 

Uncertainty in fertilizer N application was assigned a default level of ±50 percent.51 Uncertainty in the amounts of 

sewage sludge applied to non-agricultural lands and used in surface disposal was derived from variability in several 

factors, including: (1) N content of sewage sludge; (2) total sludge applied in 2000; (3) wastewater existing flow in 

1996 and 2000; and (4) the sewage sludge disposal practice distributions to non-agricultural land application and 

surface disposal. The uncertainty ranges around 2005 activity data and emission factor input variables were directly 

applied to the 2013 emission estimates. Uncertainty in the direct and indirect emission factors was provided by the 

IPCC (2006).

Quantitative uncertainty of this source category was estimated using simple error propagation methods (IPCC 2006). 

The results of the quantitative uncertainty analysis are summarized in Table 6-48. Direct N2O emissions from soils 

in Settlements Remaining Settlements in 2013 were estimated to be between 0.9 and 4.8 MMT CO2 Eq. at a 95 

percent confidence level. This indicates a range of 49 percent below to 163 percent above the 2013 emission 

estimate of 1.8 MMT CO2 Eq. Indirect N2O emissions in 2013 were between 0.1 and 1.9 MMT CO2 Eq., ranging 

from a -85 percent to 212 percent around the estimate of 0.6 MMT CO2 Eq.

Table 6-48: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements 

Remaining Settlements (MMT CO2 Eq. and Percent)

Source Gas 2013 Emissions Uncertainty Range Relative to Emission Estimate

(MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Settlements Remaining 

Settlements:

Lower 

Bound

Upper 

Bound

Lower 

Bound

Upper 

Bound

Direct N2O Fluxes from Soils N2O 1.8 0.9 4.8 -49% 163%

Indirect N2O Fluxes from Soils N2O 0.6 0.1 1.9 -85% 212%

Note: These estimates include direct and indirect N2O emissions from N fertilizer additions to both Settlements Remaining 

Settlements and from Land Converted to Settlements.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.

 

51 No uncertainty is provided with the USGS fertilizer consumption data (Ruddy et al. 2006) so a conservative ±50 percent was 

used in the analysis.
6-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 

QA/QC and Verification 

The spreadsheet containing fertilizer and sewage sludge applied to settlements and calculations for N2O and 

uncertainty ranges were checked and corrections were made. Linkage errors in the uncertainty calculation for 2013 

were found and corrected. The reported emissions in the Inventory were also adjusted accordingly. 

Recalculations Discussion 

Indirect emissions from settlements were previously reported in Agricultural Soil Management, but are now 

included in this source category. Including indirect emissions resulted in a 66 percent increase. 

For the current Inventory, emission estimates have been revised to reflect the GWPs provided in the IPCC Fourth 

Assessment Report (AR4) (IPCC 2007). AR4 GWP values differ slightly from those presented in the IPCC Second 

Assessment Report (SAR) (IPCC 1996) (used in the previous Inventories) which results in time-series recalculations 

for most Inventory sources. Under the most recent reporting guidelines (UNFCCC 2014), countries are required to 

report using the AR4 GWPs, which reflect an updated understanding of the atmospheric properties of each 

greenhouse gas. The GWP of N2O decreased, leading to a decrease in CO2-equivalent emissions for N2O. The AR4 

GWPs have been applied across the entire time series for consistency. For more information please see the 

Recalculations and Improvements Chapter. 

Planned Improvements 

A minor improvement is planned to update the uncertainty analysis for direct emissions from settlements to be 

consistent with the most recent activity data for this source. 

6.10 Land Converted to Settlements (IPCC 

Source Category 4E2) 

Land-use change is constantly occurring, and land under a number of uses undergoes urbanization in the United 

States each year. However, data on the amount of land converted to settlements is currently lacking. Given the lack 

of available information relevant to this particular IPCC source category, it is not possible to separate CO2 or N2O 

fluxes on Land Converted to Settlements from fluxes on Settlements Remaining Settlements at this time. 

6.11 Other (IPCC Source Category 4H)

Changes in Yard Trimming and Food Scrap Carbon Stocks in 

Landfills 

In the United States, yard trimmings (i.e., grass clippings, leaves, and branches) and food scraps account for a 

significant portion of the municipal waste stream, and a large fraction of the collected yard trimmings and food 

scraps are discarded in landfills. Carbon (C) contained in landfilled yard trimmings and food scraps can be stored 

for very long periods. 

Carbon-storage estimates are associated with particular land uses. For example, harvested wood products are 

accounted for under Forest Land Remaining Forest Land because these wood products are considered a component 

of the forest ecosystem. The wood products serve as reservoirs to which C resulting from photosynthesis in trees is 

transferred, but the removals in this case occur in the forest. Carbon stock changes in yard trimmings and food 

scraps are associated with settlements, but removals in this case do not occur within settlements. To address this 

complexity, yard trimming and food scrap C storage is reported under the “Other” source category.
Land Use, Land-Use Change, and Forestry 6-87

Both the amount of yard trimmings collected annually and the fraction that is landfilled have declined over the last 

decade. In 1990, over 53 million metric tons (wet weight) of yard trimmings and food scraps were generated (i.e., 

put at the curb for collection to be taken to disposal sites or to composting facilities) (EPA 2014a). Since then, 

programs banning or discouraging yard trimmings disposal have led to an increase in backyard composting and the 

use of mulching mowers, and a consequent 3 percent decrease in the tonnage of yard trimmings generated (i.e., 

collected for composting or disposal). At the same time, an increase in the number of municipal composting 

facilities has reduced the proportion of collected yard trimmings that are discarded in landfills—from 72 percent in 

1990 to 35 percent in 2013. The net effect of the reduction in generation and the increase in composting is a 53 

percent decrease in the quantity of yard trimmings disposed of in landfills since 1990.

Food scrap generation has grown by 53 percent since 1990, and though the proportion of food scraps discarded in 

landfills has decreased slightly from 82 percent in 1990 to 78 percent in 2013, the tonnage disposed of in landfills 

has increased considerably (by 46 percent). Overall, the decrease in the landfill disposal rate of yard trimmings has 

more than compensated for the increase in food scrap disposal in landfills, and the net result is a decrease in annual 

landfill C storage from 26.0 MMT CO2 Eq. (7.1 MMT C) in 1990 to 12.6 MMT CO2 Eq. (3.4 MMT C) in 2013 

(Table 6-49 and Table 6-50X).

Table 6-49: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills 

(MMT CO2 Eq.)

Carbon Pool 1990 2005 2009 2010 2011 2012 2013

Yard Trimmings (21.0) (7.4) (8.5) (9.3) (9.4) (9.3) (9.3)

Grass (1.8) (0.6) (0.8) (0.9) (0.9) (0.9) (0.9)

Leaves (9.0) (3.4) (3.9) (4.2) (4.3) (4.3) (4.3)

Branches (10.2) (3.4) (3.8) (4.1) (4.2) (4.2) (4.2)

Food Scraps (5.0) (4.0) (4.0) (3.9) (3.8) (3.4) (3.3)

Total Net Flux (26.0) (11.4) (12.5) (13.2) (13.2) (12.8) (12.6)

Note: Parentheses indicate net sequestration.

Table 6-50: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills 

(MMT C)

Carbon Pool 1990 2005 2009 2010 2011 2012 2013

Yard Trimmings (5.7) (2.0) (2.3) (2.5) (2.6) (2.5) (2.5)

Grass (0.5) (0.2) (0.2) (0.3) (0.3) (0.2) (0.2)

Leaves (2.5) (0.9) (1.1) (1.1) (1.2) (1.2) (1.2)

Branches (2.8) (0.9) (1.0) (1.1) (1.1) (1.1) (1.1)

Food Scraps (1.4) (1.1) (1.1) (1.1) (1.0) (0.9) (0.9)

Total Net Flux (7.1) (3.1) (3.4) (3.6) (3.6) (3.5) (3.4)

Note: Parentheses indicate net sequestration.

Methodology

When wastes of biogenic origin (such as yard trimmings and food scraps) are landfilled and do not completely 

decompose, the C that remains is effectively removed from the global C cycle. Empirical evidence indicates that 

yard trimmings and food scraps do not completely decompose in landfills (Barlaz 1998, 2005, 2008; De la Cruz and 

Barlaz 2010), and thus the stock of C in landfills can increase, with the net effect being a net atmospheric removal of 

C. Estimates of net C flux resulting from landfilled yard trimmings and food scraps were developed by estimating 

the change in landfilled C stocks between inventory years, based on methodologies presented for the Land Use, 

Land-Use Change, and Forestry sector in IPCC (2003). Carbon stock estimates were calculated by determining the 

mass of landfilled C resulting from yard trimmings or food scraps discarded in a given year; adding the accumulated 

landfilled C from previous years; and subtracting the mass of C that was landfilled in previous years that 

decomposed.

To determine the total landfilled C stocks for a given year, the following were estimated: (1) The composition of the 

yard trimmings; (2) the mass of yard trimmings and food scraps discarded in landfills; (3) the C storage factor of the 
6-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

landfilled yard trimmings and food scraps; and (4) the rate of decomposition of the degradable C. The composition 

of yard trimmings was assumed to be 30 percent grass clippings, 40 percent leaves, and 30 percent branches on a 

wet weight basis (Oshins and Block 2000). The yard trimmings were subdivided, because each component has its 

own unique adjusted C storage factor (i.e., moisture content and C content) and rate of decomposition. The mass of 

yard trimmings and food scraps disposed of in landfills was estimated by multiplying the quantity of yard trimmings 

and food scraps discarded by the proportion of discards managed in landfills. Data on discards (i.e., the amount 

generated minus the amount diverted to centralized composting facilities) for both yard trimmings and food scraps 

were taken primarily from Municipal Solid Waste Generation, Recycling, and Disposal in the United States: 2012 

Facts and Figures (EPA 2014a), which provides data for 1960, 1970, 1980, 1990, 2000, 2005, 2008 and 2010 

through 2012. To provide data for some of the missing years, detailed backup data were obtained from historical 

data tables that EPA developed for 1960 through 2012 (EPA 2014b). Remaining years in the time series for which 

data were not provided were estimated using linear interpolation. Data for 2013 are not yet available, so they were 

set equal to 2012 values. The EPA (2014a) report and historical data tables (EPA 2014b) do not subdivide the 

discards (i.e., total generated minus composted) of individual materials into masses landfilled and combusted, 

although it provides a mass of overall waste stream discards managed in landfills52 and combustors with energy 

recovery (i.e., ranging from 67 percent and 33 percent, respectively, in 1960 to 92 percent and 8 percent, 

respectively, in 1985); it is assumed that the proportion of each individual material (food scraps, grass, leaves, 

branches) that is landfilled is the same as the proportion across the overall waste stream.

The amount of C disposed of in landfills each year, starting in 1960, was estimated by converting the discarded 

landfilled yard trimmings and food scraps from a wet weight to a dry weight basis, and then multiplying by the 

initial (i.e., pre-decomposition) C content (as a fraction of dry weight). The dry weight of landfilled material was 

calculated using dry weight to wet weight ratios (Tchobanoglous et al. 1993, cited by Barlaz 1998) and the initial C 

contents and the C storage factors were determined by Barlaz (1998, 2005, 2008) (Table 6-51).

The amount of C remaining in the landfill for each subsequent year was tracked based on a simple model of C fate. 

As demonstrated by Barlaz (1998, 2005, 2008), a portion of the initial C resists decomposition and is essentially 

persistent in the landfill environment. Barlaz (1998, 2005, 2008) conducted a series of experiments designed to 

measure biodegradation of yard trimmings, food scraps, and other materials, in conditions designed to promote 

decomposition (i.e., by providing ample moisture and nutrients). After measuring the initial C content, the materials 

were placed in sealed containers along with methanogenic microbes from a landfill. Once decomposition was 

complete, the yard trimmings and food scraps were re-analyzed for C content; the C remaining in the solid sample 

can be expressed as a proportion of initial C (shown in the row labeled “C Storage Factor, Proportion of Initial C 

Stored (%)” in Table 6-51).

The modeling approach applied to simulate U.S. landfill C flows builds on the findings of Barlaz (1998, 2005, 

2008). The proportion of C stored is assumed to persist in landfills. The remaining portion is assumed to degrade 

over time, resulting in emissions of CH4 and CO2. (The CH4 emissions resulting from decomposition of yard 

trimmings and food scraps are accounted for in the Waste chapter.) The degradable portion of the C is assumed to 

decay according to first-order kinetics. The decay rates for each of the materials are shown in Table 6-51.

The first-order decay rates, k, for each component were derived from De la Cruz and Barlaz (2010). De la Cruz and 

Barlaz (2010) calculate first-order decay rates using laboratory data published in Eleazer et al. (1997), and a 

correction factor, f, is found so that the weighted average decay rate for all components is equal to the AP-42 default 

decay rate (0.04) for mixed MSW for regions that receive more than 25 inches of rain annually. Because AP-42 

values were developed using landfill data from approximately 1990, 1990 waste composition for the United States 

from EPA’s Characterization of Municipal Solid Waste in the United States: 1990 Update was used to calculate f. 

This correction factor is then multiplied by the Eleazer et al. (1997) decay rates of each waste component to develop 

field-scale first-order decay rates.

 

52 EPA (2014) reports discards in two categories: “combustion with energy recovery” and “landfill, other disposal,” which 

includes combustion without energy recovery. For years in which there is data from previous EPA reports on combustion 

without energy recovery, EPA assumes these estimates are still applicable. For 2000 to present, EPA assumes that any 

combustion of MSW that occurs includes energy recovery, so all discards to “landfill, other disposal” are assumed to go to 

landfills.
Land Use, Land-Use Change, and Forestry 6-89

De la Cruz and Barlaz (2010) also use other assumed initial decay rates for mixed MSW in place of the AP-42 

default value based on different types of environments in which landfills in the United States are found, including 

dry conditions (less than 25 inches of rain annually, k=0.02) and bioreactor landfill conditions (moisture is 

controlled for rapid decomposition, k=0.12). The Landfills section of the Inventory (which estimates CH4

emissions) estimates the overall MSW decay rate by partitioning the U.S. landfill population into three categories, 

based on annual precipitation ranges of: (1) Less than 20 inches of rain per year, (2) 20 to 40 inches of rain per year, 

and (3) greater than 40 inches of rain per year. These correspond to overall MSW decay rates of 0.020, 0.038, and 

0.057 year−1, respectively.

De la Cruz and Barlaz (2010) calculate component-specific decay rates corresponding to the first value (0.020 

year−1), but not for the other two overall MSW decay rates. To maintain consistency between landfill methodologies 

across the Inventory, the correction factors (f) were developed for decay rates of 0.038 and 0.057 year−1through 

linear interpolation. A weighted national average component-specific decay rate was calculated by assuming that 

waste generation is proportional to population (the same assumption used in the landfill methane emission estimate), 

based on population data from the 2000 U.S. Census. The component-specific decay rates are shown in Table 6-51.

For each of the four materials (grass, leaves, branches, food scraps), the stock of C in landfills for any given year is 

calculated according to the following formula:

 t

LFCi,t = Σ Wi,n × (1 − MCi) × ICCi × {[CSi × ICCi] + [(1 − (CSi × ICCi)) × e−k(t − n)]}

 

n

where,

t = Year for which C stocks are being estimated (year),

i = Waste type for which C stocks are being estimated (grass, leaves, branches, food scraps),

LFCi,t = Stock of C in landfills in year t, for waste i (metric tons),

Wi,n = Mass of waste i disposed of in landfills in year n (metric tons, wet weight),

n = Year in which the waste was disposed of (year, where 1960 < n < t),

MCi = Moisture content of waste i (percent of water),

CSi = Proportion of initial C that is stored for waste i (percent),

ICCi = Initial C content of waste i (percent),

e = Natural logarithm, and

k = First-order decay rate for waste i, (year−1).

For a given year t, the total stock of C in landfills (TLFCt) is the sum of stocks across all four materials (grass, 

leaves, branches, food scraps). The annual flux of C in landfills (Ft) for year t is calculated as the change in stock 

compared to the preceding year:

Ft = TLFCt − TLFC(t − 1)

Thus, the C placed in a landfill in year n is tracked for each year t through the end of the inventory period (2013). 

For example, disposal of food scraps in 1960 resulted in depositing about 1,135,000 metric tons of C. Of this 

amount, 16 percent (179,000 metric tons) is persistent; the remaining 84 percent (956,000 metric tons) is degradable. 

By 1965, more than half of the degradable portion (518,000 metric tons) decomposes, leaving a total of 617,000 

metric tons (the persistent portion, plus the remainder of the degradable portion).

Continuing the example, by 2013, the total food scraps C originally disposed of in 1960 had declined to 179,000 

metric tons (i.e., virtually all degradable C had decomposed). By summing the C remaining from 1960 with the C 

remaining from food scraps disposed of in subsequent years (1961 through 2013), the total landfill C from food 

scraps in 2013 was 40.8 million metric tons. This value is then added to the C stock from grass, leaves, and 

branches to calculate the total landfill C stock in 2013, yielding a value of 262.0 million metric tons (as shown in 

Table 6-52). In exactly the same way total net flux is calculated for forest C and harvested wood products, the total 

net flux of landfill C for yard trimmings and food scraps for a given year (Table 6-50) is the difference in the landfill 

C stock for that year and the stock in the preceding year. For example, the net change in 2013 shown in Table 6-50

(3.4 MMT C) is equal to the stock in 2013 (262.1 MMT C) minus the stock in 2012 (258.6 MMT C).

The C stocks calculated through this procedure are shown in Table 6-52.
6-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013

Table 6-51: Moisture Contents, C Storage Factors (Proportions of Initial C Sequestered), 

Initial C Contents, and Decay Rates for Yard Trimmings and Food Scraps in Landfills

Variable Yard Trimmings Food Scraps Grass Leaves Branches

Moisture Content (% H2O) 70 30 10 70

C Storage Factor, Proportion of Initial C 

Stored (%) 53 85 77 16

Initial C Content (%) 45 46 49 51

Decay Rate (year−1) 0.323 0.185 0.016 0.156

Table 6-52: C Stocks in Yard Trimmings and Food Scraps in Landfills (MMT C)

Carbon Pool 1990 2005 2009 2010 2011 2012 2013

Yard Trimmings 155.8 202.9 211.0 213.6 216.1 218.7 221.2

Branches 14.5 18.1 18.8 19.0 19.3 19.5 19.8

Leaves 66.7 87.3 91.1 92.2 93.4 94.5 95.7

Grass 74.6 97.5 101.2 102.3 103.5 104.6 105.7

Food Scraps 17.6 32.8 36.9 38.0 39.0 39.9 40.8

Total Carbon Stocks 173.5 235.6 248.0 251.6 255.1 258.6 262.1

Uncertainty and Time-Series Consistency

The uncertainty analysis for landfilled yard trimmings and food scraps includes an evaluation of the effects of 

uncertainty for the following data and factors: disposal in landfills per year (tons of C), initial C content, moisture 

content, decay rate, and proportion of C stored. The C storage landfill estimates are also a function of the 

composition of the yard trimmings (i.e., the proportions of grass, leaves and branches in the yard trimmings 

mixture). There are respective uncertainties associated with each of these factors.

A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the overall uncertainty of the 

sequestration estimate. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 

6-53. Total yard trimmings and food scraps CO2 flux in 2013 was estimated to be between -19.3 and -4.9 MMT

CO2 Eq. at a 95 percent confidence level (or 19 of 20 Monte Carlo stochastic simulations). This indicates a range of 

53 percent below to 61 percent above the 2013 flux estimate of -12.6 MMT CO2 Eq. More information on the 

uncertainty estimates for Yard Trimmings and Food Scraps in Landfills is contained within the Uncertainty Annex.

Table 6-53: Approach 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard 

Trimmings and Food Scraps in Landfills (MMT CO2 Eq. and Percent)

2013 Flux 

Estimate Uncertainty Range Relative to Flux Estimatea

Source Gas (MMT CO2 Eq.) (MMT CO2 Eq.) (%)

Lower

Bound

Upper

Bound

Lower

Bound

Upper

Bound

Yard Trimmings and Food 

Scraps CO2 (12.6) (19.3) (4.9) -53% +61%

a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.

Note: Parentheses indicate negative values or net C sequestration.

Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990 

through 2013. Details on the emission trends through time are described in more detail in the Methodology section, 

above.
Land Use, Land-Use Change, and Forestry 6-91

QA/QC and Verification

A QA/QC analysis was performed for data gathering and input, documentation, and calculation. The QA/QC 

analysis did not reveal any inaccuracies or incorrect input values.

Recalculations Discussion

The current Inventory has been revised relative to the previous report. Generation and recovery data for yard 

trimmings and food scraps was not previously provided for every year from 1960 in the Municipal Solid Waste 

Generation, Recycling, and Disposal in the United States: Facts and Figures report. EPA has since released 

historical data, which included data for each year from 1960 through 2012. The recalculations based on these 

historical data resulted in changes ranging from a 17 percent increase in sequestration in 1996 to a 5 percent 

decrease in sequestration in 2005, and an average 4 percent increase in sequestration across the 1990–2012 time 

series compared to the previous Inventory.

Planned Improvements

Future work is planned to evaluate the consistency between the estimates of C storage described in this chapter and 

the estimates of landfill CH4 emissions described in the Waste chapter. For example, the Waste chapter does not 

distinguish landfill CH4 emissions from yard trimmings and food scraps separately from landfill CH4 emissions from 

total bulk (i.e., municipal solid) waste, which includes yard trimmings and food scraps.