February 4, 2009
TECHNICAL SUPPORT DOCUMENT FOR
BIOLOGIC PROCESS SOURCES EXCLUDED
FROM THIS RULE
Office of Air and Radiation
U.S. Environmental Protection Agency
February 4, 2009
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CONTENTS
Introduction 4
Biological Process Excluded Sources Summary 4
Total Emissions 4
Review of existing relevant reporting programs/ methodologies 4
Monitoring Methods 4
Threshold Analysis 5
Enteric Fermentation 6
Monitoring Emissions 6
Information to be Collected 6
Uncertainty 6
Reporters and Thresholds 7
Existing Federal Data Collection Systems 9
References 9
Rice Cultivation 10
Monitoring Emissions 10
Information to be Collected 10
Uncertainty 11
Reporters and Thresholds 11
Existing Federal Collection Systems 12
References 12
Field Burning of Agricultural Residues 13
Monitoring Emissions 13
Information to be Collected 13
Uncertainty 13
Reporters and Thresholds 13
Existing Federal Data Collection Systems 14
References 14
Composting 16
Monitoring Emissions 16
Information to be Collected 16
Uncertainty 16
Reporters and Thresholds 17
Existing Federal Data Collection Systems 17
References 17
Agricultural Soil Carbon Sequestration 19
Monitoring 19
Information to be Collected 20
Uncertainty 20
Reporters and Thresholds 20
Existing Federal Data Collection Systems 21
References 22
Agricultural Soil NiO Emissions (including fertilizer use) 23
Monitoring 23
Information to be Collected 23
Uncertainty 24
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Reporters and Thresholds 24
Existing Federal Data Collection Systems 25
Source Category 26
Monitoring Emissions 26
Information to be Collected 26
Uncertainty 26
Identification of Reporters 26
Existing Federal Data Collection Systems 27
References 27
Forest Land NiO and CEL; (including fertilizer use and forest fires) 28
Monitoring Emissions 28
Information to be Collected 29
Uncertainties 29
Reporters and Thresholds 29
Existing Federal Data Collection Systems 31
References 31
Other Land Use, Land-Use Change, and Forestry C Emissions and Sinks 33
Monitoring 33
Information to be Collected 34
Uncertainties 34
Reporters and Thresholds 34
Existing Federal Data Collection Systems 35
References 35
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Introduction
The proposed rule does not require reporting of GHG emissions from enteric fermentation, rice
cultivation, field burning of agricultural residues, composting, agricultural soils (including C
sequestration and N2O emissions), settlements (including N2O emissions), forestland (including
CH4 and N2O emissions) or other land uses and land-use changes, such as emissions associated
with deforestation, and carbon storage in living biomass or harvested wood products. The
challenges to including these source categories in the rule are that available methods to estimate
facility-level emissions for these sources yield uncertain results, and that these sources are
characterized by a large number of small emitters. In light of these challenges, we have
determined that it is impractical to require entity-level reporting of emissions from these sources
in the proposed rule for the reasons explained below.
For more information on these sources and sinks of greenhouse gases, please see page 6 of this
TSD for enteric fermentation, page 10 for rice cultivation, page 13 for field burning of
agricultural residues, page 16 for composting, page 19 for agricultural C sequestration, page 23
for agricultural N2O emissions (including fertilizer use), page 26 for settlement N2O emissions
(including fertilizer use), page 28 for forestland CFLi and N2O emissions (including fires and
fertilizer use), and page 33 for other land use, and land-use change, and forestry emissions and
sinks.
Biological Process Excluded Sources Summary
Total Emissions
EPA reports on the greenhouse gas emissions and sinks associated with the biological process
sources excluded from this rule in the Inventory of U.S. Greenhouse Gas Emissions and Sinks.
In the agriculture sector, the U.S. GHG Inventory estimates that agricultural soil management
contributed emissions of 265 MMTCC^e and enteric fermentation contributed emissions of 126
MMTCO26 in 2006. Rice cultivation, agricultural field burning, and composting contributed
emissions of 5.9, 1.2, and 3.3 MMTCC^e, respectively, in 2006. Total carbon fluxes for U.S.
forestlands and other land uses and land-use changes were also reported in the U.S. GHG
Inventory, rather than specific emissions from deforestation. Land use, land-use change, and
forestry activities in 2006 resulted in a net C sequestration of 883.7 MMTCC^e.
Review of existing relevant reporting programs/ methodologies
Several protocols and programs contain methods for estimating greenhouse gases from these
sources, including the 2006 IPCC GL and the U.S. GHG Inventory. These methods are used to
estimate national-level emissions and sinks.
Monitoring Methods
For these sources, there are no direct greenhouse gas emission measurement methods available
except for research methods that are prohibitively expensive and require sophisticated
equipment. Instead, limited modeling-based methods have been developed for voluntary GHG
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reporting protocols, which use general emission factors, or large-scale models that are used for
comprehensive national-level emissions estimates.
To calculate the emissions resulting from these sources at a reporting entity-level using emission
factor or carbon stock change approaches, it would be necessary for landowners to report on a
number of parameters such as management practices and a variety of data inputs. While some
input data can be collected with reasonable certainty, the emissions estimates would have a high
degree of uncertainty because the factors available for individual reporters do not reflect the
variety of conditions that need to be considered for accurate estimates. At the scale of individual
reporters, these estimates can be complex and costly to generate.
Without accurate facility-level emissions factors and the ability to accurately measure all facility-
level calculation variables, estimates of national-level emissions from these sources are more
suitably calculated on a broad regional basis using models and data available from national
databases. While a systematic measurement program of these sources could improve
understanding of the environmental factors and management practices that influence emissions,
this type of measurement program would be very difficult to implement through a landowner-
based reporting program due to the difficulty and expense in establishing and maintaining
rigorous measurements over time.
Threshold Analysis
Despite these issues, threshold analyses were conducted for several of these sources as part of
their consideration for inclusion in this rule. The resulting analyses showed that for most of
these sources no facilities would meet thresholds consistent with those proposed in this rule.
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Enteric Fermentation
Ruminant agriculture (cattle -beef and dairy, sheep, goats and buffalo) is the primary source of enteric
CH4. Since feed quality and quantity affects enteric CH4 emissions, approaches for estimating QrU
emissions focus on gross energy intake from feed and CH4 yield (portion of gross energy that is converted
to CFL^n the rumen). For example, dairy cows in California on a total mixed ration (a blend of all
feedstuffs provided to dairy cows) diet emit between 100 to 160 kg CHVcow/yr (2,100 to 3,360 kg
CO2e/cow/yr). At these emission rates, only the largest facilities (over 3,000-5,000 cows) would have to
report enteric emissions under a mandatory reporting threshold of 10,000 mtCO2e/yr.
Monitoring Emissions
In general, there are two approaches for monitoring enteric CH4 emissions: direct measurement and
modeling. Since direct measurement using tracers is prohibitively expensive and overly burdensome for
reporters, modeling enteric emissions with emission factors is the only reasonable alternative. The 2006
IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories
Volume 4, Chapter 10, Equation 10.21 provides the following emission factor equations most suitable for
monitoring:
EFEntenc_cH4 = [GE x Ym ] *365 / [55.65 MJ/kg CHJ
Where:
EFEnteric-cH4 = emission factor (kg CHVhead/year)
GE = gross energy intake (MJ/head/day)
Ym = CFLt conversion rate which is the fraction of gross energy in feed converted to
CFLt (percent)
Most livestock producers have a good understanding of their diet regimes. However, they would need to
calculate the gross energy intake based on the amount and type of feed.
Information to be Collected
The following information would need to be collected to monitor emissions using the IPCC methodology:
number of animals by livestock type on farm (track seasonal changes), gross energy intake (derived from
diet) by livestock type, and estimate of methane conversion rate (could be estimated based on feed
efficiency, but requires chamber measurements to estimate accurately).
Uncertainty
In addition to the uncertainty in estimating gross energy intake, a large source of uncertainty in estimating
enteric emissions is due to the large variability in the CFLjconversion rates (Ym). Tables 10.12 and 10.13
from Volume 4, Chapter 10 of 2006 IPCC Guidelines for National Greenhouse Gas Inventories provide
ranges of Ym based on livestock category. For example, feedlot cattle have Ym range of 3 ±1%,
indicating that using the 3% value can result in overestimation by 50% or an underestimation up to 33%.
Research by Benchaar et al (1998) has shown an even greater range of Ym values from less than 3% to
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greater than 10%. Although the use of feed additives (e.g., ionophores, probiotics, propionate precursors,
and growth hormones), which can improve feed efficiency by suppressing methanogenesis, is becoming
more widespread, quantification of their effectiveness in reducing enteric emissions is not well
understood.
Reporters and Thresholds
Individual livestock operations would be the reporters as they have information on their livestock
numbers and general feeding regimes. According to the 2002 NASS Agricultural Census there are over 1
million farms with cattle and approximately 2,450 of these farms have over 2,500 cattle. Tables 1 and 2
present the number of farms by size class for beef and dairy cattle, respectively.
Table 1. Beef Farm Sizes
Beef Farm Size (2002)
Less than 1,000 head
1,000-2,499
2,500-4,999 head
5,000 - 9,999 head
Greater than 10,000 head
Number of Farms
918,184
5,728
1553
655
250
% of Total
Population
70%
8.6%
6.0%
8.5%
6.5%
Note: Given the lack of data on farms larger than 10,000 head and the observed decrease in number of
beef farms with increasing size, we estimate that there are less than 96 farms with greater than 20,000
head of beef cattle. There are a few very large beef feedlots (e.g., A ranch in California has over 100,000
head of cattle).
Table 2. Large Dairy Farms Size Distribution
Dairy Farm Size (2002)
1,000-1,999 cows
2,000-2,999 cows
3,000-3,999 cows
4,000-4,999 cows
5,000-9,999 cows
10,000 or more
Number of Farms
795
249
115
48
39
8
% of Total
Population
13%
6.8%
4.4%
2.4%
3.2%
1.1%
Table 3 presents the size thresholds for beef and dairy livestock operations to exceed the 3 reporting
thresholds. Given, the large amount of uncertainty in estimating enteric emissions, two sets of
calculations are provided with average and high end emission factors. The average emission factors were
derived dividing total enteric emissions (Table A-157 EPA 2008) by population (Table A-159 EPA
2008). The high factors were estimated at 50% greater than the average emission factors.
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Table 3. Threshold Populations for Beef and Dairy Farms
BEEF FARM: AVERAGE (Emission factor: 1,016 kg
CO2e/head/yr)
BEEF FARM: HIGH (Average plus 50%: 1,524 kg
CO2e/head/yr)
DAIRY FARM (Average emission factor: 2,305kg
CO2e/head/yr)
DAIRY FARM (Average plus 50%: 3,458 kg
CO2e/head/yr)
Threshold Levels (mtCO2e)
1,000
10,000
25,000
100,000
Total number of head to meet threshold
984
656
434
289
9,843
6,562
4,338
2,892
24,606
16,404
10,846
7,230
98,425
65,617
43,384
28,918
Note: Estimates presented have not been adjusted to account for significant figures.
Table 4 presents the maximum number of potential reporters by threshold level. The number of reporters
was estimated based on the number of livestock needed to exceed each threshold level (Table 3) and a
rough estimate of the number of livestock facilities that have the corresponding number of cattle (see note
on interpolation assumptions within each size category in Tables 1 and 2.
Table 4. Maximum Number of Beef and Dairy Farms (reporters) that Exceed Threshold Levels
BEEF FARM: AVERAGE (Emission factor: 1,016 kg
CO2e/head/yr)
BEEF FARM: HIGH (Average plus 50%: 1,524 kg
CO2e/head/yr)
DAIRY FARM (Average emission factor: 2,305kg
CO2e/head/yr)
DAIRY FARM (Average plus 50%: 3, 45 8 kg
CO2e/head/yr)
Threshold Levels (mtCO2e)
1,000
10,000
25,000
100,000
Maximum number of farms to exceed
threshold
8,186*
197,410
3129
5,175
25 0+
460
65
222
<96
150
8
17
<13
21
<8
<8
Note: Estimates assumed the following inter-censal distribution of farms within farm size ranges with
50%, 30%, 15% and 5% in each of the quartiles. For example, there are 3,000 beef farms with 2,000
to 4,999 cattle. We assume 1,500 have 2,000 to 2,750 head, 900 have 2,750 to 3,500 head, 450 have
3,500 to 4,250 head, and 150 have 4,250 to 4,999 head.
* is the number of farms with 1,000 or more cattle, thus is a conservative estimate of maximum
number of farms that could exceed the 1,000 mtCO2e threshold).
+ is the number of farms with 10,000 or more head, thus is a conservative estimate for the number of
farms with 9,843 or more head.
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Existing Federal Data Collection Systems
There are currently no federal data collection systems that collect the information required to estimate
these emissions at the entity-level. However, with the EPA 2005 Air Quality Compliance Agreement,
animal feeding operations will be required to report any qualifying releases of ammonia (NH3), hydrogen
sulfide (H2S) and volatile organic compounds (VOCs: CH/tis a VOC, but this agreement includes non-
methane VOCs) as required by section 103 of CERCLA and section 304 of EPCRA. However, since the
content and mechanisms of these reporting requirements have not been set, it is difficult to gauge how the
data collection systems could be used to report enteric emissions of CH4.
References
Benchaar, C., J. Rivest, C. Pomar, and J. Chiquette, Prediction of methane production from dairy cows
using existing mechanistic models and regression equations, Journal of Animal Science, 76, 617-627,
1998.
EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks: 1990-2006 (April 2008) USEPA #430-
R-08-005.
IPCC (2007) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas
Inventories, Intergovernmental Panel on Climate Change.
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Rice Cultivation
Rice cultivation can produce QrU through the biological reduction of CO2 or organic carbon under
anaerobic conditions in flooded rice fields, and N2O through the processes of nitrification (microbial
oxidation of ammonium) and denitrification (microbial reduction of nitrate) [Note: N2O is discussed
under the Agricultural soils source category]. Emission rates of CF^are a function of water management
practice (flooding and draining), soil type (texture, organic carbon content, pH, and bulk density), climate
(temperature and precipitation), rice cultivar, and other cultivation practices (e.g., fertilizers, organic
amendments, tillage, herbicide use). Methane emissions from rice cultivation in the United States are
highly variable, with emissions ranging from 22 to 1,490 kg CHVhectare/season, and double cropped rice
systems yielding higher emissions (EPA 2008 Chapter 6.3).
Monitoring Emissions
There are three general approaches for monitoring CH4 emissions from rice cultivation: direct
measurement (using automated flux chambers and/or eddy correlation techniques), use of emission
factors, and process modeling. Direct measurement is prohibitively expensive, over burdensome, and not
suitable for producer reporting. Use of emissions factors is difficult unless there are a sufficient number of
factors to capture the range in management practices and local environmental conditions. Use of process
models (e.g., DNDC model) could be considered but requires systematic validation coupled with
statistical modeling to quantify accuracy and precision of model estimates. Other approaches include
using simple equations that would require measurement of soil conditions (e.g., soil carbon content,
texture) and tracking of management activities (e.g. number of days flooded) for estimating CFU
emissions (see Chapter 9, Willey and Chameides 2007). The 2006IPCC Good Practice Guidance and
Uncertainty Management in National Greenhouse Gas Inventories, Volume 4, Equation 4.41 provides the
following emission factor equations:
EFnce_cH4 (kg/yr) = Zi Zj Zk (EFljk * Aljk )
Where:
EFljk = a seasonally integrated emission factor for i,j, and k conditions, in kg CFiyha
Aljk = annual harvested area for i,j, and k conditions, in ha/yr
i, j, and k = represent different ecosystems, water management regimes, and other
conditions under which CFi4 emissions from rice may vary (e.g. addition of organic
amendments).
The i, j, and k indices are used to adjust the EF based on a scaling factor for water management regime,
organic amendments, and soil type.
Information to be Collected
The following information would need to be collected to monitor emissions using the IPCC methodology:
water management practices (continuous flooding vs. intermittent drainage, number of drain events), type
and amount of organic amendments, number of rice crops grown annually, and soil type.
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Uncertainty
The uncertainties in the scaling factors, and hence emissions, is quite high, with uncertainty ranges more
than double the default values (source 2006IPCC Good Practice Guidance and Uncertainty Management
in National Greenhouse Gas Inventories, Volume 4, Table 4.22). With uncertainties greater than 100%,
current emission factor approaches do not provide emission estimates suitable for mandatory reporting.
Approaches for reducing uncertainties include use of statistical modeling and biogeochemical process
modeling.
Reporters and Thresholds
There are approximately 485,000 hectares of rice grown on over 8,000 rice farms in the United States
(USDA 2006, 2002). Table 1 presents the distribution of farms and harvested acreage of rice. Assuming
the EPA average per hectare emission factors of 210 kg CHVhectare/season (1,785 kg CO2e/acre) and 780
kg CHVhectare/season (6,632 kg CO2e/acre) for single and double (ratoon) cropped rice (EPA 2008),
respectively, Table 2 presents the size of harvested acreage required to meet the reporting thresholds of
1,000 mtCO2e, 10,000 mtCO2e, 25,000 mtCO2e, and 100,000 mtCO2e levels.
Table 1. Rice Farm Size Distribution.
Harvested Rice Farm Size (1997) - Source U.S. Census of
Agriculture
1-99 acres
100 -249 acres
250 - 499 acres
500 - 999 acres
1,000 or more acres
Number of Farms
1,747
2,885
2,812
1,433
414
Table 2. Acreage requirements to exceed reporting thresholds.
Single Rice with Emission factor: 1,785 kg CO2e/acre
Ratoon Rice with Emission Factor 6,632 kg CO2e/acre
Threshold Levels (mtCO2e)
1,000
10,000
25,000
100,000
Total number of acres to meet threshold
560
151
5,602
1,508
14,006
3,770
56,022
15,078
In 2005, total ratoon rice acreage was 53,144 acres. Florida, Louisiana and Texas were the only states that
had ratoon rice with total harvested acres greater than 1,508 acres. However, only Texas had farms (64)
that harvested more than 500 acres. Given the total area of ratoon rice in Texas was 21,963 acres, it is
likely that only a few may harvest sufficient areas to trigger the 10,000 mtCO2e and 25,000 mtCO2e
thresholds. Given the high acreage requirements for single rice, it is also unlikely that there are many
farms that reach the threshold levels.
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Existing Federal Collection Systems
There are no current systems that collect information on water management, organic amendments, rice
cultivars, and soil property information.
References
EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks: 1990-2006 (April 2008) USEPA #430-
R-08-005.
USD A, 2002, NASS Agricultural Census, http://www.agcensus.usda.gov/Publications/2002/index.asp
USDA, 2006 USDA Crop Production Summary,
http://usda.mannlib.Cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1046
Willey, Z. and Chameides, B, 2007, Harnessing Farms and Forests in the Low-Carbon Economy: How to
Create, Measure and Verify Greenhouse Gas Offsets, Duke University Press, Durham and London.
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Field Burning of Agricultural Residues
Agricultural field crop residues include stalks and stubble or stems, leaves, and seed pods. Farmers often
choose to remove crop residue from their fields by directly burning the material. However, open field
burning of residue results in a complex mix of aerosols and GHG emissions to the atmosphere that
include PM, CFLt, CO, NOX, and N2O (Guoliang et al. 2007; Gupta et al. 2004; and others).
Monitoring Emissions
Techniques for calculating emissions from residue burning on field crops (including rice, wheat,
sugarcane, barley, corn, soybeans, and peanuts) are discussed in EPA 2007, and are based on the Revised
1996IPCC Guidelines. Emissions are calculated using a series of step calculations and crop-specific
statistics. There is no direct measurement technique suitable for capturing emissions from this disperse
source category. There are, however, hybrid-type approaches that combine satellite-derived data with
ground-report databases, such as SMARTFIRE (http:www. getbluesky.org/smartfire).
Information to be Collected
Specific data needed for determining emissions by crop include annual crop production (Ibs), residue/crop
ratio, proportion of crop produced in fields where residue is to be burned (%), dry matter content of the
residue (%), crop burn efficiency (%), crop combustion efficiency (%), and the carbon/nitrogen content of
the residue to be burned (Ibs of C and N/ Ibs of dry matter).
Uncertainty
Emission estimation techniques are subject to a large amount of uncertainty (EPA 1999) and would
require extensive effort on the part of farmers to consistently record the needed crop statistics. Emission
ratios also vary significantly between the flaming and smoldering phases of a fire. CO2 and N2O are
mainly emitted during the flaming stage, while CFLt is mainly emitted during the smoldering stage. The
relative importance of these two stages will vary between fires in different ecosystems and under different
climatic conditions. Since simple emission factors are not available without direct monitoring of emission
during burning, growers will be unable to estimate emissions from burning of agricultural residue.
Reporters and Thresholds
Reporters would be the entity that controls how crops are grown or grassland is managed on the land (e.g.
lessee for leased lands). In 2007, there were 2.08 million farms in the United States with a total land in
farms of 930.9 million acres with an average farm size of 449 acres (USDA Agricultural Statistics Board
2008). In 2002, there were 14,644 farms that harvested over 5,000 acres (USDA 2002 Agricultural
census, Volume 1, Table 9). Figure 1 provides the size class distribution of farm.
For demonstration purposes, we calculated the acreage requirements for burning of corn residues to meet
the reporting thresholds under consideration. We assumed an average of 75 Ibs of corn residue per bushel
production of corn (source from biomass energy study in Wyoming,
http://www.wyomingbusiness.org/pdf/energy/Biomass_CropResidue.pdf). With averaged corn yields of
140 bushels per acre, we estimate average corn residue of 10,500 Ibs/acre. Based on greenhouse gas
emission ratios and crop residue characteristics from Tables 6-23 and 6-24 in EPA 2008, burning corn
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residue produces a total of 324 kg CO2e/acre (78 kg CO2e/acre from nitrous oxide and 246 kg CO2e/acre
from methane). EPA (2008) estimates that approximately 3% of crop residues are burned each year
(excluding rice where a much higher percentage of residue is burned annually).
Ditribution of US Farm Sizes
Source: 2002 USDA Agricultural Census
•s
*
200000 -
179346|
1
1
i~O
[
1
• All Farms
• Farms with Harvested Croplands
CO
ro
P
PI R r
• | o ^ 1
CO 0> Jg | 1
CM i- |5i O
n-i istl l oH ""*!
1 1 1 fl 1 ft
1
fS
CM
^ SI
CD ^|
1 S|l 1 a
to 9 1 0 to 50 to 70 to 1 00 to 1 40 to 1 80 to 220 to 260 to 500 to 1 , 000 2, 000
5,000
acres 49 acres 69 acres 99 acres 139 179 219 259 499 999 to 1,999 to 4,999 acres or
acres acres acres acres acres acres acres acres more
Figure 1. Size class distribution of United States farms based on acreage of cultivated lands
[Note: Data are presented for all farms and those farms that have harvested cropland.]
Table 1. Acreage requirements (corn example) to exceed reporting thresholds.
Corn Residue Example: 324 kg CO2e/acre
Threshold Levels (mtCO2e)
1,000
10,000
25,000
100,000
Total number of acres to meet threshold
3,086
30,864
77,160
308,642
Existing Federal Data Collection Systems
There are no existing federal collection systems that collect the information needed to estimate
greenhouse gas emissions from agricultural residue burning at the entity-level.
References
Andrews, SS, 2006. Crop residue removal for biomass energy production: Effects on soils and
recommendations. United States Department of Agriculture (USDA), Natural Resource Conservation
Service, White paper, 25 pp.
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EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks: 1990-2006 (April 2008) USEPA #430-
R-08-005.
Guoliang et al., 2008. Investigation on emission factors of particulate matter and gaseous pollutants from
crop residue burning. Journal of Environmental Sciences (20) 50-55.
Gupta et al., 2004. Residue burning in rice-wheat cropping system: Causes and implications. Current
Science (87) 1713-1717.
Pathak, H and R Wassmann, 2007. Introducing greenhouse gas mitigation as a development objective in
rice-based agriculture: I. Generation of technical coefficients. Agricultural Systems (94) 807-825
U.S. Environmental Protection Agency (EPA), Office of Research and Development. Emissions of
organic air toxics from open burning, EPA-600/R-02-076, 62 pp.
U.S. Environmental Protection Agency (EPA), Emission Inventory Improvement Program (EIIP) Volume
VIII, 1999. Methods for estimating greenhouse gas emission from burning of agricultural crop wastes.
Chapter 11 - Agricultural crop wastes, 25 pp.
Wang, WJ and RC Dalai, 2006. Carbon inventory for a cereal cropping system under contrasting tillage,
nitrogen fertilisation and stubble management practices. Soil & Tillage Research (91) 68-74.
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Composting
Both N2O and CH^ can be emitted during the composting process. While CH4 is produced only under
anaerobic conditions, the compost pile itself tends to be heterogeneous, such that N2O is produced in
aerobic sections of the compost and QrU is produced in anaerobic sections of compost that are created
due to excessive moisture or inadequate mixing. It is estimated that the CH4 emissions total <1% to a few
percent of the C present in the waste material, while N2O emissions total 0.5% to 5% of the initial N
present in the waste material (EPA 2008). Nitrous oxide emissions from compost generally decrease over
time, unless the organic material is composed at least partially of manure (He et al. 2000, Morand et al.
2005). The mass of material composted has jumped nearly 400% between 1990 and 2006, due to steady
growth in population as well as state and local regulations discouraging landfilling of yard trimmings, and
includes primarily yard trimmings (grass, leaves, and tree and brush trimmings) and food scraps from
residences and commercial establishments (such as grocery stores, restaurants, and school and factory
cafeterias) (EPA 2008).
Monitoring Emissions
Methods for measuring N2O and QrU emissions from compost usually involve closed compost systems
(Morand et al. 2005) or equipment such as dynamic chambers (Osada and Fukumoto 2001).
Development of decay curves for typical compost materials and compost operation sizes will be useful for
ongoing measurement and monitoring of emissions from compost.
To apply the IPCC default methodology for estimating N2O and CH4 emissions from composting
operations, the mass of wet waste composted (M) is multiplied by an emission factor (EFi) (typically 4 g
t per kg of wet organic waste and 0.3 g N2O per kg of wet organic waste). The relevant equation is:
Ei = M x EFi. (EPA 2008)
Information to be Collected
The following information would need to be collected to monitor emissions using the IPCC methodology:
mass of material composted, and associated emission factors.
Uncertainty
In 2006, compost was included in the U.S. GHG Inventory for the first time. That report estimated
annual emissions of N2O and CFLt from composting operations, not including backyard composting
operations, at 3.3 MMTCO2e, with a quantitative estimate of uncertainty (with 95% confidence) between
1.7 and 5.0 MMTCO2e annually (EPA 2008). While uncertainty is held constant at +/-50% in the IPCC
Tier 1 methodology, additional uncertainty in N2O and CFLt emissions from composting can be attributed
to the scale of the operation (Fukumoto et al. 2003), the turning schedule of the compost, and the
composition of the inputs.
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Reporters and Thresholds
The U.S. Economic Census estimates that there are 17 establishments that produce compost as fertilizer in
the United States, with annual shipments of roughly $57.0 million. Clearly most composting operations
are small-scale endeavors, conducted on farms or in backyards nationwide.
Using the equation above and assuming a global warming potential (GWP) of 21 for CH4, in order to
meet the 1,000 mtCO2e/ year emission threshold for 10,000 mtCO2e/ year emission threshold for CUt
alone, a composting operation would need to compost 11,905 tons of waste annually (Table 1). To meet
the 10,000 mtCO2e/ year, 25,000 mtCO2e/ year and 100,000 mtCO2e/ year thresholds for CH4 emissions,
an entity would need to compost 119,048 tons, 297,619 tons, and 1.2 million tons of wet waste annually,
respectively (Table 1). Assuming a GWP of 310 for N2O, the mass of waste composted would be
somewhat lower in order to meet the reporting threshold based on N2O emissions alone. Specifically, a
facility would need to compost 10,753 tons, 107,527 tons, 268,817 tons, and 1.08 million tons of wet
waste annually to meet the threshold reporting targets based on N2O (Table 1). Practically, a compost
operation would emit both gases simultaneously, thus reducing the volume of waste composted to meet
the same emission threshold. Assuming the same emission factors, the waste needed to meet the
threshold for reporting would be 5,650 tons for the 1,000 mtCO2e threshold, 56,497 tons for the 10,000
mtCO2e threshold, 141,243 tons for the 25,000 mtCO2e threshold and 564,972 tons for the 100,000
mtCO2e threshold (Table 1).
Table 1. Mass of wet organic waste (in tons) needed to meet threshold reporting targets for annual
CH4and N2O emissions, separately and in combination, from composting operations.
N2O
CH4
combined
10,753
11,905
5,650
107,527
119,048
56,497
268,817
297,619
141,243
1,075,269
1,190,476
564,972
N2O
CH4
combined
10,753
11,905
5,650
107,527
119,048
56,497
268,817
297,619
141,243
1,075,269
1,190,476
564,972
Existing Federal Data Collection Systems
There are no current systems that collect the data needed for entity reporting, though existing systems for
managing waste could be adapted to track the amount of organic waste directed to compost operations.
References
EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks:
R-08-005.
1990-2006 (April 2008) USEPA #430-
Fukumoto Y, Osada T, Hanajima D, Haga K. 2003. Patterns and quantities of NH3, N2O and CH4
emissions during swine manure composting without forced aeration—effect of compost pile scale.
Bioresource Technology 89: 109-114.
17
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He Y, InamoribY, Mizuochib M, Kongb H, Imawib N, Suna T. 2000. Measurements of N2O and CH4
from the aerated composting of food waste. The Science of the Total Environment 254: 65-74.
Morand P, Peres G, Robin P, Yulipriyanto H, Baron S. 2005. Gaseous emissions from composting
bark/manure mixtures. Compost Science and Utilization 13: 14-26.
Osada T, Fukumoto Y. 2001. Development of a new dynamic chamber system for measuring harmful
gas emissions from composting livestock waste. Water Science and Technology 44: 79-86.
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Agricultural Soil Carbon Sequestration
The top one meter of soil is estimated to have 1,502 billion metric tons of soil organic carbon
(Schlesinger, 1997, Jobbagy and Jackson 2000), which is approximately 3 times the size of other
terrestrial carbon pools (i.e. biomass and dead organic matter). The top one meter of agricultural soils
contains approximately 170 billion metric tons of C (Cole et al, 1996). Soil organic carbon (SOC) pools
in agricultural soils are highly dynamic as agricultural processes, such as tillage, change the temperature
and moisture regimes in soils and rate, quantity and quality of organic inputs. Thus, rates of SOC
sequestration and oxidation (release) vary based on SOC pools, soil type, climate, and agricultural
management.
Monitoring
In general, there are three approaches for monitoring changes in SOC from cultivation of agricultural
soils: direct measurement, use of activity-based emission factors, and process modeling. Accuracy of
direct measurement of SOC pools in agricultural soils vary with the scale of the measurements from ±0.1
MT/hectare at plot scale and ±1 MT/hectare at farm scale (Kimble et al. 2002). However, the cost of
direct measurements can be expensive and overly burdensome for mandatory reporting due to the
sampling design requirements to meet desired accuracy (e.g., sample depth, # of soil samples, frequency
of sampling) and costs of analyzing soil samples (Willey and Chameides 2007). Performance or activity-
based approaches (e.g. CCX, IPCC) use regionally-based emission factor approaches for monitoring SOC
changes of time. While these approaches may not capture the influence of different soils or climate
conditions within the region, they are thought to capture average regional changes in SOC, as opposed to
farm-specific SOC changes. Process models (e.g., CENTURY, EPIC, DNDC) simulate the
biogeochemical processes that drive crop growth and SOC dynamics. An advantage of process models is
that they can be used for full GHG accounting to look at the relationship between SOC sequestration and
subsequent emissions of N2O (see Li et al. 2005 and Six et al. 2004). Process models have been used to
generate data for web-based modeling tools (e.g., COMET-VR and C-LOCK) to enable growers to
estimate changes in SOC based on local soils and climate and their specific management practices.
The IPCC methodology accounts for net C emissions (sinks and sources) for three categories of
agricultural soils: (i) changes in C stocks of mineral soils due to cropland management practices;
(ii)changes in C stocks from organic soils that are drained; and (iii) liming of agricultural soils. For
mineral soils, changes in soil carbon stocks are estimated based on reference carbon stocks and stock
change factors related to land use (long-term cultivated, paddy rice and set aside), tillage practices (full
till, reduced till or no-till) and organic matter inputs (low, medium, high without manure, and high with
manure). For drained organic soils, the IPCC Tier 1 method for estimating changes in soil carbon content:
ACorgamc (t C/yr) = EC (A*EF)C
Where:
EF = emission factor for climate region c, in tC/ha/yr
A = land area of drained organic soils in climate region c, in ha
19
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Information to be Collected
The following information is required to apply the IPCC methods for estimating change in soil organic
carbon stocks: crop type (characterized by amount of crop residue), local climate, soil type, tillage
practices and use of organic amendments.
Uncertainty
A range of techniques are used to estimate uncertainty in process model estimates, including standard
error propagation and simple empirical models, to more computationally-intensive Monte Carlo
numerical approaches (Ogle et al. 2007). The IPCC approach for estimating changes in SOC stocks uses a
set of stock change factors that are adjusted based on climate, soil type, tillage practices, and organic
carbon inputs. IPCC estimates that errors in using their stock change factor approach for SOC
sequestration over a 20-year period ranges from ±4% (for low C input systems) to ±90% (for high C input
systems, like rice residue incorporation). Since process models offer the best opportunity for reducing
uncertainty in SOC sequestration, rigorous uncertainty analyses, such as the current efforts to improve the
uncertainty estimator in COMET-VR, are needed.
In summary, the required data collection for accurate reporting and subsequent measurement of changes
in organic carbon stocks in agricultural soils is subject to large uncertainties and burdensome calculations,
whether it is for reporting loss or sequestration of soil carbon.
Reporters and Thresholds
In 2007, there were 2.08 million farms in the United States with a total land in farms of 930.9 million
acres with an average farm size of 449 acres (USDA Agricultural Statistics Board 2008). In 2002, there
were 14,644 farms that harvested over 5,000 acres (USDA Agricultural census, Volume 1, Table 9). Rates
of carbon loss or gain in agricultural soils are highly variable and can be difficult to estimate. Using IPCC
stock change factors for cool temperate dry region emissions of carbon, and agricultural lands with low
biomass inputs and full conventional tillage, average emission rates can be as high as 0.93
mtCO2e/acre/yr. Table 1 presents the acreage required to meet the reporting thresholds at this emission
rate.
Table 1. Example acreage requirement for reporting thresholds
Mineral Soils - Cold Temperate Region, low inputs,
full tillage
Threshold Levels (mtCO2e)
1,000
10,000
25,000
100,000
Total number of acres to meet threshold
1,075
10,753
26,881
107,527
Note: Since the uncertainties (see discussion below) are high, this is meant to be an illustrative example.
To put SOC sequestration in agricultural soils in perspective with possible reporting thresholds for
emissions, assuming the upper range of SOC sequestration in the Kimble et al. (2002) summary
estimates, a farmer would need to switch 12,200 acres from plow-till to no-till or shift 5,900 acres to
Conservation Reserve Program (CRP) to sequester 10,000 mtCO2e/yr.
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Drainage of organics soils can lead to high rates of soil carbon loss. On average from 1993 to 2006
drainage of organic soils in the United States released 27.7 MMTCO2e/yr (EPA 2008). Approximately
640,000 hectare of organic soils were drained during this time period. Thus the average carbon flux was
43,281 kg CO2e/ha. Table 2 present a summary analysis of acreage required to meet candidate reporting
thresholds for drainage of organic soils.
Table 2. Acreage requirements for drainage of organic soils to exceed reporting thresholds.
Organic Soils - Avg emission rate 43,281 kg CO2e/ha
Threshold Levels (mtCO2e)
1,000
10,000
25,000
100,000
Total number of acres to meet threshold
57
571
1,427
5,707
Figure 1 present the size distribution of U.S. farms. Approximately 7% of all farms have over 1,000
acres. The total area of cropland on organics soils is 720,000 ha which represents less than 0.5% of the
total cropland area. Assuming an even distribution of farm size on mineral and organic soils, we expect
approximately 0.035% of all farms are cultivating more than 1,000 acres of organic soils. Thus, while the
emission rates can be high for drained organic soils, the likely number of reporters would be small.
Ditribution of US Farm Sizes
Source: 2002 USDA Agricultural Census
600000 1
500000
DAI I Farms
• Farms with Harvested Croplands
1to9 10 to 50 to 70 to
acres 49 acres 69 acres 99 acres
Figure 2. Size class distribution of US farms based on acreage of cultivated lands. Data are
presented for all farms and those farms that have harvested cropland.
Existing Federal Data Collection Systems
There are no current systems that collect all the necessary data and information for accurate reporting of
changes in organic carbon stocks in agricultural soils.
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References
Cole, V., C. Cerri, K. Minami, A. Mosier, N. Rosenberg, D. Sauerbeck, J. Dumanski, J. Duxbury, J.
Freney, R. Gupta, O. Heinemeyer, T., Kolchugina, J. Lee, K. Paustian, D. Powlson, N. Sampson, H.,
Tiessen, M.Van Noordwijk, andQ. Zhao. 1996. Agricultural options for mitigation of greenhouse gas
emissions, p. 745-771. In R.T. Watson et al. (ed.) Climate Change 1995. Impacts, adaptations and
mitigation of climate change: Scientific-technical analyses. IPCC Working Group II. Cambridge
Univ. Press, Cambridge.
EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks: 1990-2006 (April 2008) USEPA #430-
R-08-005.
IPCC, 2003, Good Practice Guidance for Land Use, Land-Use Change and Forestry, IPCC National
Greenhouse Gas Inventories Programme.
Jabbagy, E.G., and R.B. Jackson, 2000, Below-ground processes and global changes, Ecol. Appl., 10:423-
436.
Kimble, J.M., Lai, R., and R.R. Follett, 2002, Agricultural Practices and Policy Options for Carbon
Sequestration in Soil, Lewis Publishers, 495-502.
Lai, R., 1999, Soil management and restoration for carbon sequestration to mitigate the accelerated
greenhouse effect, Prog. Env. Sci., 1:307-326.
Li, C., Frolking, S., Butterbach-Bahl, K., 2005, Carbon Sequestration in Arable Soils is Likely to Increase
Nitrous Oxide Emissions, Offsetting Reductions in Climate Radiative Forcing, Climate Change,
72(3):1573-1480.
Schlesinger, W.H. 1997. Biogeochemistry: An analysis of global change. Academic Press, San Diego,
CA.
Six, J., Ogle, S.M., Jay breidt, F., Conant, R.T., Mosier, A. R, and K. Paustian, 2004, The potential to
mitigate global warming with no-tillage management is only realized when practised in the long term
Global Change Biology 10 (2), 155-160 doi:10.1111/j.l529-8817.2003.00730.x
Smith, P., D. Martino, Z. Cai, D. Gwary, H.H. Janzen, P. Kumar, B. McCarl, S. Ogle, F. O'Mara, C. Rice,
R.J. Scholes, O. Sirotenko, M. Howden, T. McAllister, G. Pan, V. Romanenkov, U. Schneider, S.
Towprayoon, M. Wattenbach, and J.U. Smith, 2007, Greenhouse gas mitigation in agriculture.
Philosophical Transactions of the Royal Society, B., 363. doi:10.1098/rstb.2007.2184.
USD A Agricultural Census, 2002, Volume 1, Table 9. Land in Farms, Harvested Cropland, and Irrigated
Land, by Size of Farm: 2002 and 1997
(http://www.nass.usda.gov/census/census02/volume l/us/st99_l_009_010.pdf).
Willey, Z. and Chameides, B, 2007, Harnessing Farms and Forests in the Low-Carbon Economy: How to
Create, Measure and Verify Greenhouse Gas Offsets, Duke University Press, Durham and London.
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Agricultural Soil N2O Emissions (including fertilizer use)
Nitrous oxide is produced naturally in soils through the microbial processes of nitrification and
denitrification both through anthropogenic and natural causes.
The IPCC considers all emissions of N2O from managed lands to be anthropogenic. The U.S. GHG
Inventory conforms to IPCC guidance, and accounts for all emissions from managed lands, which
includes natural background N2O emissions. In 2006, N2O emissions from agricultural soil management
were 265.0 MMTCO2e, which is 72% of all U.S. N2O emissions, and 3.8% of all U.S. GHG emissions.
Anthropogenic emissions of N2O from agricultural soils consist of both direct and indirect emissions that
result from inputs of N, and management practices that lead to a greater release of mineral N to the soil on
managed lands. Direct emissions result from a variety of management practices, including: fertilization;
application of managed livestock manure and other organic materials such as sewage sludge; deposition
of manure by grazing animals; production of N-fixing crops and forages; retention of crop residues; and
drainage and cultivation of organic cropland soils (i.e., soils with a high organic matter content, otherwise
known as histosols). Other agricultural soil management activities, including irrigation, drainage, tillage
practices, and fallowing of land, can influence N mineralization in soils and thereby affect direct
emissions. Indirect emissions of N2O occur through two pathways: (1) volatilization and subsequent
atmospheric deposition of applied N, and (2) surface runoff and leaching of applied N into groundwater
and surface water. (See attached figure of N flows resulting in emissions of N2O.)
Monitoring
In general, there are three approaches for monitoring N2O emissions from management of agricultural
soils: (1) direct measurement (using automated flux chambers and/or eddy correlation techniques), (2) use
of emission factors, and (3) process modeling. Direct measurement is prohibitively expensive due to the
cost of equipment and need for continuous measurements to capture episodic emission events. Use of a
single emissions factor, like the IPCC factor of 1%, based on amount of applied nitrogen can result in
large uncertainty at the farm level as field data have shown that actual emission rates can range from
0.1% to almost 10% of applied fertilizer. Use of process models (e.g., DAYCENT, DNDC model) is
promising but can be data intensive and requires systematic validation coupled with statistical modeling
to quantify accuracy and precision of model estimates. A hybrid option that combines the IPCC emission
factor and modeling approaches is the use of a model such as the one under development for NRCS using
preset DAYCENT runs. This model could be used in combination with the COMET-VR soil carbon
model to estimate soil N2O emissions, utilizing activity data similar to that required by the IPCC
methodology, but would be an improvement in accuracy over the standard IPCC approach while keeping
the data requirements at a reasonable level.
Information to be Collected
Application of N at a farm results in direct emissions onsite and also offsite through volatilization,
leaching/runoff of N and later deposition where the N is made available for nitrification/denitrification,
(i.e., indirect emissions). Accounting for these indirect emissions is extremely uncertain, as it is rarely
known where the N is eventually emitted as N2O. For emissions estimates, it is only practical to include
direct emissions resulting from inputs of N by the landowner. Indirect emissions (those resulting from N
that was not directly applied to the land) are not under the control of the landowner and very difficult to
23
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quantify. Reporting N2O emissions onsite and not reporting N2O offsite would, however, result in
incomplete estimates.
In order to capture all of the direct N2O emissions resulting from application of N to soils, it would be
necessary for farmers to report on a number of different N inputs. Synthetic N and organic N inputs (e.g.,
synthetic fertilizer, manure, sewage sludge) are the only inputs that be measured with reasonable accuracy
and minimal burden by a landowner. N resulting from mineralization of organic matter (plant residue or
soil organic matter) would be very uncertain. This leaves synthetic and organic inputs of N as the only
potentially reportable inputs.
Table 4. Activity data for calculation N2O emissions
Activity data for N inputs
Synthetic N application (at farm level)
Urine and Dung (from grazing
animals) N input to land
Organic Amendments (including
sewage sludge, manure, compost)
Crop residue N contribution
Other (Mineralization of soil organic
matter, asymbiotic fixation of N from
atmosphere)
Feasibility of data collection
High
Medium/Low
Medium/Low
Low
Very Low
Share of N2O
Emissions from
Agricultural Soils
26%
9%
5%
10%
50%
Uncertainty
While some input data can be collected with reasonable certainty, the estimation of N2O emission from
these inputs varies greatly spatially and temporally. Until the available modeling-based approaches can
be implemented in a routine manner, efforts for reporting N2O emissions from agricultural soils will be
hampered with emission factor approaches that suffer from large uncertainties.
Reporters and Thresholds
All land-use types occurring in the United States (cropland, grassland, forestland, settlements and
wetlands) emit N2O. Thus all landowners could potentially be reporting entities. In 2007, there were
2.08 million farms in the United States with a total land area of 930.9 million acres, and an average farm
size of 449 acres (USDA Agricultural Statistics Board 2008).
Analysis for the GHG reporting rulemaking is focusing on thresholds of 1,000 mtCO2e, 10,000 mtCO2e,
25,000 mtCO2e, and 100,000 mtCO2e. Using average fertilizer application rates and IPCC emission
factor N2O estimation methodologies, it becomes apparent that even at the highest N fertilization rate of
180 Ibs N/acre, it would take a farm of over 25,000 acres to equal the 10,000 mtCO2e threshold. Given
that the USDA Farm Census from 2002 reports as its largest farm size 5000+ acres (see Figure 1), there is
a very low probability that any farm in the United States would meet even the 10,000 mtCO2e threshold.
(See Table 1 below.)
24
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600000
500000
400000
300000
200000
100000 - -
DAN Farms
Farms with Harvested Croplands
is
-i
1to9 10 to 50 to 70 to 100 to 140 to 180 to 220 to 260 to 500 to 1,000 2,000 5,000
acres 49 acres 69 acres 99 acres 139 179 219 259 499 999 to 1,999 to 4,999 acres or
acres acres acres acres acres acres acres acres more
Figure 1. Distribution of US Farm Sizes.
Source: 2002 USD A Agricultural Census
Table 1, Threshold analysis with IPCC Factors
N Fertilizer Rates In US
Wheat: 68 Ibs/acre
Cotton: 92 Ibs/acre
Corn (Avg. rate) 137 Ibs/acre
Corn (High value) 180 Ibs/acre
Number of acres to reach 10,000 mtCO2e threshold
68,488
49,019
33,112
25,062
Another way of performing this analysis is to use data from the U.S. GHG Inventory (EPA 2008) and
estimate area-based emission factors for direct N2O emissions from all N inputs to cropland as well as
isolating just synthetic N inputs (See Table 2 below).
Table 2. Threshold analysis with US GHG Inventory Factors
Category
Cropland: Synthetic N Additions
Cropland: All N Inputs
Grassland: All N Inputs
N2O Emission Rate (kg
CO2e/acre)
134
347
104
Number of Acres to reach
10,000 mtCO2e Threshold
74,626
28,818
96,000
It becomes apparent after performing these analyses and reviewing farm size data that it is very unlikely
that any farm in the United States would meet a 10,000 mtCO2e threshold.
Existing Federal Data Collection Systems
There are no current systems that collect the data needed for entity reporting.
25
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Settlement NiO Emissions (including fertilizer use)
Source Category
N2O is emitted from soils in settlements due to nitrification and denitrification. While typical nitrification
and denitrification rates in natural systems vary primarily with moisture and temperature, in settlements
lawn fertilization and irrigation can increase rates of N2O release by as much as 15 times during the days
immediately following fertilization (Bremer 2006, Hall et al. 2008). Significant release of CH4 has not
been measured from urban soil, and - overall - soils in urban settlements are probably a net sink for QrU
(Kaye et al. 2004).
Monitoring Emissions
Application rates of fertilizer are quite heterogeneous and can vary by homeowner, but do correlate with
socioeconomic characteristics, neighborhood, and lawn size (Law et al. 2004, Zhou et al. 2007). Despite
these correlations, empirically predictive methods for understanding fertilizer application rates do not yet
exist. Monitoring of N2O emissions from settlements are also complicated by the rapid change in
settlement land area, as the overall land area devoted to settlements increased by 32.2% between 1990 and
2006, resulting in an increase in N2O flux by 48% over the same period (EPA 2008). Current
methodology for estimating N2O flux from settlements remaining settlements is based on aggregate
fertilizer applications rather than on per-unit-area estimates, thus there are no region- or area-specific
emission factors appropriate for settlements. Since lawn areas tend to be fairly homogeneous, however,
one can estimate the per-unit-area emissions by dividing the total N2O flux from fertilizer application (1.5
MMTCO2e) by the total area of turfgrass in the United States (32 million acres [Milesi et al. 2005]) for an
overall nationwide average of 0.05 mtCO2e per acre of turfgrass per year.
Information to be Collected
The following information would need to be collected to monitor emissions: area subject to fertilizer
application, type of fertilizer and application rate. Also needed are accurate estimates of emission factors
for settlements remaining settlements.
Uncertainty
N2O flux from settlements depends on a large number of variables in addition to N inputs, including
organic C availability, O2 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 only accounts for variations in fertilizer N and sewage sludge
application rates, such that all settlement soils are treated equivalently. A quantitative uncertainty
analysis of N2O flux from settlements remaining settlements found that the 95% confidence interval
ranged from -59% to +163% of the estimated 2006 emission estimate of 1.5 MMTCO2e (EPA 2008).
Identification of Reporters
Estimates of the turfgrass area covered by home lawns in the United States range from 17.7 million (EPA
2007) to 21 million acres (Bormann et al. 2001). A 2005 remote sensing study estimated the total land
area covered by turfgrass in the United States (including home lawns as well as recreational fields,
commercial and industrial parks, golf courses, etc.) to be roughly 32 million acres, corresponding to 1.9%
of total U.S. land area (Milesi et al. 2005). The 2000 U.S. Census reports 105.5 million households in the
United States, roughly 80% of which maintain a private lawn (Tempelton et al. 1998). While the average
26
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lawn size in the United States varies with region, the national mean lawn size is 0.3 acres (Vinlove and
Torla 1995).
At the nationwide average N2O emission rate of 0.05 mtCO2e per acre per year, an entity would need to
reach 20,000 acres of fertilized turfgrass in order to be eligible for reporting under the 1,000 mtCO2e/
year threshold and 200,000 acres of fertilized turfgrass in order to be eligible for reporting under the
10,000 mtCO2e/ year threshold. Entities larger than 533,000 acres would be eligible under the 25,000
mtCO2e/ year threshold, and entities larger than 2.1 million acres would be required to report under the
100,000 mtCO2e/ year threshold. For reference, an 18-hole golf course can be built on as little as 100
acres, and few courses are larger than 1000 acres.
Existing Federal Data Collection Systems
There are no current systems that collect the data needed for entity reporting.
References
Bremer, DJ. 2006. Nitrous oxide fluxes in turfgrass: effects of nitrogen fertilization rates and types.
Journal of Environmental Quality 35: 1678-1685.
Hall SJ, Huber D, Grimm NB. 2008. Soil N2O and NO emissions from an arid, urban ecosystem.
Journal of Geophysical Research 113: doi: 10.1029/2007JG000523.
Kaye JP, Burke 1C, Mosier AR, Guerschman JP. 2004. Methane and nitrous oxide fluxes from urban
soils to the atmosphere. Ecological Applications 14: 975-981.
Law, N.L., L.E. Band, and J.M. Grove. 2004. Nitrogen input from residential lawn care practices in
suburban watersheds in Baltimore County, MD. Journal of Environmental Planning and Management
47(5): 737-755.
Zhou, W., A. Troy, and M. Grove. 2007. Modeling Residential Lawn Fertilization Practices: Integrating
High Resolution Remote Sensing with Socioeconomic Data. Environmental Management, DOI
10.1007/s00267-007-9032-z.
EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks: 1990-2006 (April 2008) USEPA #430-
R-08-005.
Milesi C, Running SW, Elvidge CD, Dietz JB, Turtle BT, Nemani RR. 2005. Mapping and modeling the
biogeochemical cycling of turf grasses in the United States. Environmental Management 36: 426-438.
EPA, 2007, EPA Pesticide Environmental Stewardship Program,
http ://www. epa. gov/pesp/strategies/2007/planet07 .htm
Bormann, F.H., Balmori, D., Geballe, G.T. (2001). Redesigning the American Lawn: A Search for
Environmental Harmony (2nd edition). Yale University Press, 192 pp.
Templeton, S.R., Zilberman, D., & Yoo, S.J. (1998). An economic perspective on outdoor residential
pesticide use. Environmental Science & Technology, 2, 416A - 423A.
Vinlove, F.K., Torla, R. (1995) Comprehensive Estimation of U.S. Home Lawn Area. Journal of
Turfgrass Management. l(l):83-97.
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Forest Land N2O and CH4 (including fertilizer use and forest
fires)
Nitrous oxide and CFL, are emitted in this source category primarily via emissions from soils and wildfire.
N2O is emitted from forest soils via nitrification and denitrification (Carnol and Ineson 1999, Davidson et
al. 1993, Kester et al. 1997, Wolf and Brumme 2002). Dry upland forest soils are sinks for CH4 (Castaldi
et al. 2006, DelGrosso et al. 2000, Hein et al. 1997, Jang et al. 2006, Wuebbles and Hayhoe 2002), though
some studies have suggested that CH4 may be emitted from wet forest soil under natural conditions due to
anaerobic decomposition (Megonigal and Guenther 2008, Ullah et al. 2008). Wildfire emissions of N2O
and CH4 from forests depend on the amount of biomass burned, together with the expected emission
factors for the biomass involved in the fire.
In the United States, forest fires caused the release of 24.6 MMTCO2e as CH4 and 2.5 MMTCO2e as N2O
in 2006 (EPA 2008). These fire-related emissions totaled 27.1 MMTCO2e, or 73% of the non-CO2
emissions from the LULUCF sector in that year. This 2006 emissions total was a five-fold increase from
the 5.0 MMTCO2e (0.5 MMTCO2e as N2O, 4.5 MMTCO2e as CFL,) attributable to fire in 1990, when the
forest-fire-related non-CO2 emissions totaled only 38% of the non-CC>2 emissions from the LULUCF
sector. While recent research has yielded important information about dry upland soils as sinks for CFL,,
this phenomenon has not yet been quantified at the national scale. Globally, a sink of roughly 30
MMTCFL, per year (630 MMTCO2e per year) in upland soils has been estimated. In the United States,
this sink would partially offset the emissions from wildfire, as the ratio of upland soils to wetland soils is
large.
Monitoring Emissions
Direct measurement of trace gas fluxes such as N2O and CFL, typically involve chamber-based
instrumentation that is quite costly and time-consuming to install and maintain. The measurements
collected tend to be quite variable over space and time, and can depend substantially on microclimatic
variables such as temperature and moisture. Thus collection of direct measurements of CFL, and N2O
fluxes, and even interpolation of existing trace gas measurements, is difficult for large scales. While
fertilizer application could be used as a proxy for N2O emissions from soils, substantial uncertainty exists
related to fertilization rates, area of land receiving fertilizer, and emission factors. Non-CCh gases
emitted from forest fires depend on several variables, including forest area and C density, emission ratios,
and combustion factor values (proportion of biomass consumed by fire). In the IPCC default
methodology (IPCC 2006), CFL, and N2O emissions from fire are calculated by multiplying the total
estimated C emitted from forest burned by gas-specific emissions ratios and conversion factors. The
relevant equation is:
Lfire=A-MB -Cf-Gef-W3
Where
L = total emissions from fire (in tonnes of GHG emitted)
A = spatial extent of fire (area burnt, ha)
M = mass available for combustion (tonnes per ha)
Cf = combustion factor (the proportion of biomass that is consumed by fire) (dimensionless)
Gef = emission factor (tonnes GHG emitted per kg biomass combusted)
At large scales, the extent of wildfires can be measured using satellite based monitoring programs such as
those spearheaded by the Fire and Environmental Research Applications Team
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(http://www.fs.fed.us/pnw/fera/fccs/index.shtmn and the MODIS Active Fire Mapping Program at the
USDA Forest Service (http://activefiremaps.fs.fed.us/'). These can be paired with information about forest
inventory developed from the USDA Forest Service Forest Inventory and Analysis Program, but there is
likely a minimum area below which this approach would not be feasible. This minimum area is
determined by the spatial accuracy of the spatial input layers ~ specifically, the pixel size of the satellite
imagery being used as well as the relative accuracy of the classification. Small ownerships would not be
well represented by a satellite monitoring approach. A modeling approach can also be used, at small
scales or together with maps of the spatial extent of fires. Process models such as Consume 2. 1 (and 3.0)
can be used to predict trace gas emission from wildfire, but these models must still be parameterized with
field data about the biomass involved in the fire and the fire severity
(http://www.fs. fed.us/pnw/fera/research/smoke/consume/consume_download.shtmn. In addition, there
are also hybrid-type approaches that combine satellite-derived data with ground-report databases, such as
SMARTFIRE (http : www. getbluesky.org/smartfire) .
Information to be Collected
The following information would need to be collected to monitor emissions using the IPCC methodology.
For non-fire N2O losses, fertilizer application rate, and type of fertilizer used would be needed. For fire-
related N2O and CFLt losses, the required information includes spatial extent of fire, severity of fire (i.e.
proportion of biomass consumed by fire), and C density of burned forest.
Uncertainties
For N2O emissions from forest soils, uncertainties relate to variability in human-induced parameters such
as fertilizer inputs and tree planting/ harvesting cycles, as well as biogeochemical processes including
organic C availability, O2 partial pressure, soil moisture content, pH, and temperature (EPA 2008).
Quantitative analysis suggests uncertainties in inventory-based estimates of N2O flux between +211% and
-59% (EPA 2008). Uncertainty also exists "due to lack of sufficient field data, sampling conditions with
a tendency to over-represent one mode of combustion over the other, and differences in the types of
measurements (tower vs. ground-based vs. aircraft measurements). Furthermore, emission factors vary as
the fire season progresses due to changing moisture conditions (Hayhoe, pers. comm..). These
uncertainties result in quantitative uncertainty estimates of between +71% (CUt)/ +75% (N2O) and -69%
and N2O) around existing estimates of wildfire emissions (EPA 2008).
Reporters and Thresholds
There are 620 million acres of forest land in the United States, of which 393 million acres (roughly two-
thirds) are in private ownership, including a combination of family forestland owners and land held by
partnerships and corporations (Butler and Leatherberry 2004). An estimated 10.3 million family forest
owners in the United States collectively control 42% of forested land in the United States (family forest
land is owned by individuals not incorporated as a legal entity). Most of these (88%) family forestland
owners are in the Eastern United States; the remaining 12% own land dispersed across the Western states.
Owners with 50+ acres hold 69% of family forestland across the United States, but account for 1 1% of
family forest owners. Public forestland is predominantly owned by the Federal Government in the West,
and by State and county governments in the East. Public land accounts for 69% of the forest land in the
West, and 17% of the forest land in the East (USDA 2001).
The IPCC methodology for quantifying non-CO2 GHG emissions from wildfires and prescribed fires
describes a range of emission factors from 0.06 g N2O per kg biomass burned (for biofuel burning) to
0.26 g N2O per kg of biomass burned (for extratropical forests). For CFLj, the low emission factor
estimate is 2.3 g CFLt per kg biomass burned (for savanna and grassland), and the high emission factor is
6.8 g CFLt per kg biomass burned (for tropical forest). Clearly there is biome-specific variation in these
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factors, but emission factors with finer spatial or biome-specific resolution are not available. Similarly, a
range of pre-burn biomass (M) and proportion burned (Cf) are available. Assuming the IPCC default of
0.45 for Cf in "temperate forests," we can identify a threshold reporter size for wildfire extent to be
eligible for entity-level reporting.
N2O: Assuming the highest emission factor (0.26 g N2O per kg burned) and a high forest C density of
300 tons C per ha, a wildfire of 227 acres would be eligible for entity reporting if the threshold were
1,000 mtCO2e and a wildfire of 2,270 acres would be eligible if the threshold were 10,000 mtCO2e. A
wildfire of 5,675 acres would be required if the threshold for reporting were 25,000 mtCO2e, and a
wildfire of 22,700 acres would meet the 100,000 mtCO2e threshold (Table 1). Assuming the lowest
emission factor (0.06 g N2O per kg of biomass burned) and an average forest density of 150 tons C per ha,
a wildfire of 1,967 acres would be eligible for entity reporting at an emissions threshold of 1,000 mtCO2e
and a wildfire of 19,673 acres would be eligible at an emissions threshold of 10,000 mtCO2e. A wildfire
of 49,184 acres would be required at an emissions threshold of 25,000 mtCO2e, and a wildfire of 196,734
acres would be eligible for reporting under an emissions threshold of 100,000 mtCO2e (Table 1).
Assuming the highest emission factor (6.8 g QrU per kg burned) and a high forest C density of 300
tons C per ha, a wildfire of 128 acres would be eligible for entity reporting if the threshold were 1,000
mtCO2e and a wildfire of 1,281 acres would be eligible if the threshold were 10,000 mtCO2e. A wildfire
of 3,203 acres would be required if the threshold for reporting were 25,000 mtCO2e, and a wildfire of
12,813 acres would meet the 100,000 mtCO2e threshold (Table 2). Assuming the lowest emission factor
(2.3 g CH4 per kg of biomass burned) and an average forest density of 150 tons C per ha, a wildfire of 758
acres would be eligible for entity reporting at an emissions threshold of 1,000 mtCO2e and a wildfire of
7,576 acres would be eligible at an emissions threshold of 10,000 mtCO2e. A wildfire of 18,940 acres
would be required at an emissions threshold of 25,000 mtCO2e, and a wildfire of 75,761 acres would be
eligible for reporting under an emissions threshold of 100,000 mtCO2e (Table 2).
N2O and CH/jCombined: Assuming that both Or^and N2O are released simultaneously during fire and a
high forest C density of 300 tons C per ha, and using the highest emission factors reported in the
literature, a wildfire of 82 acres would be eligible for entity reporting at the 1,000 mtCO2e threshold level
and a wildfire of 8 1 9 acres would be eligible at the 1 0,000 mtCO2e threshold level. A wildfire of 2,047
acres would trigger the reporting requirement at the 25,000 mtCO2e level, and a fire size of 8, 190 acres
would be reported under the 100,000 mtCO2e threshold. Assuming the lowest emission factors for both
N2O and Or^and an average forest C density of 150 tons C per ha, the threshold sizes are much larger:
547 acres for the 1,000 mtCO2e reporting threshold, 5,470 acres for the 10,000 mtCO2e reporting
threshold, 13,674 acres for the 25,000 mtCO2e threshold and 54,697 acres for the 100,000 mtCO2e
threshold (Table 3).
Table 1. Threshold wildfire sizes under
Highest emission factor, high forest C
density
Lowest emission factor, average forest C
density
various reporting thresholds for N2O emissions (acres).
1,000
mtCO2e
227
1,967
10,000 25,000
mtCO2e mtCO2e
2,270 5,675
19,673 49,184
100,000
mtCO2e
22,700
196,734
Table 2. Threshold wildfire sizes under various reporting thresholds for CH4 emissions (acres).
1,000
mtCO2e
10,000
mtCO2e
25,000
mtCO2e
100,000
mtCO2e
Highest emission factor, high forest C
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density
Lowest emission factor, average forest C
density
758
7,576 18,940
75,761
Table 3. Threshold wildfire sizes under various reporting thresholds for combined N2O and CH4
emissions (acres).
Highest emission factor, high C density
Lowest emission factor, average C
density
1,000
82
547
10,000
819
5,470
25,000
2,047
13,674
100,000
8,190
54,697
While fires larger than 400 ha (about 1000 acres) have historically been fairly infrequent in the United
States, Westerling et al. (2006) reported a dramatic increase in these large fires over the last several
decades. Between 2000 and 2003, between 50 and 100 such large wildfires burned annually in the
Western states.
Existing Federal Data Collection Systems
There are no Federal monitoring programs for N2O and CFL, emissions from soils and vegetation in
forests remaining forests. Data is available on a national level for "wildland area burned." To complete
national emissions estimates for this source, the forest proportion of wildland area must be approximated
and extracted from this area data. There are no current systems that collect the data needed at the entity
level for reporting of N2O and CFL, emissions from fire (fire severity, proportion of biomass burned per
fire, aerial extent of fire).
References
Carnol M and Ineson P. 1999. Environmental factors controlling NO3 leaching, N2O emissions and
numbers of NH4 oxidisers in a coniferous forest soil. Soil Biology and Biochemistry 31: 979-990.
Castaldi, S., A. Ermice, & S. Strumia. 2006. Fluxes of N2O and CH4 from soils of savannas and
seasonally-dry ecosystems. J. of Biogeogmphy 33(3): 401-415.
Davidson EA, Matson PA, Vitousek PM, Riley R, Dunkin K, Garcia-Mendez G, Maass, JM. 1993.
Processes regulating soil emissions of NO and N2O in a seasonally dry tropical forest. Ecology 74:
130-139.
Del Grosso, S. J., W. J. Parton, A. R. Mosier, D. S. Ojima, C. S. Potter, W. Borken, R. Brumme, K.
Butterbach-Bahl, K. D. P.M. Crill, & K. A. Smith. 2000. General CH4 oxidation model and
comparisons of CH4 oxidation in natural and managed systems. Global Biogeochem. Cycles 14(4):
999-1019.
EPA, 2008, Inventory of US Greenhouse Gas Sources and Sinks: 1990-2006 (April 2008) USEPA #430-
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Hein, R., P. J. Crutzen, & M. Heimann. 1997. An inverse modeling approach to investigate the global
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and Land Use. Chapter 2: Generic methodologies applicable to multiple land-use categories.
Available at: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html.
Jang, I., S. Lee, & J. Hong. 2006. Methane oxidation rates in forest soils and their controlling variables: A
review and a case study in Korea. Ecol. Res. 21(6): 849-854.
Keppler F, Hamilton JTG, Brass M, Rockmann T. 2006. Methane emissions from terrestrial plants under
aerobic conditions. Nature 439: 187-191.
Kester RA, Meijer ME, Libochant JA, De Boer W, Laanbroek HJ. 1997. Contribution of nitrification and
denitrification to the NO and N2O emissions of an acid forest soil, a river sediment and a fertilized
grassland soil. Soil Biology and Biochemistry 29: 1655-1664.
Megonigal JP and Guenther AB. 2008. Methane emissions from upland forest soils and vegetation. Tree
Physiology 28: 491-498.
Ullah S, Frasier R, King L, Picotte-Anderson N, Moore TR. 2008. Potential fluxes of N2O and CH4
from soils of three forest types in Eastern Canada. Soil Biology and Biochemistry 40: 986-994.
Wolf I and Brumme R. 2002. Contribution of nitrification and denitrification sources for seasonal N2O
emissions in an acid German forest soil. Soil Biology and Biochemistry 34: 741-744.
Wuebbles, D. J., & K. Hayhoe. 2002. Atmospheric methane and global change. Earth Sci. Rev. 57(3-4):
177-210.
Butler, B. J. and E.G. Leatherberry 2004. America's Family Forest Owners. Journal of Forestry 102(7): 4-
9.
Westerling, A.L., H.G. Hidalgo, D.R. Cayan, T.W. Swetnam. 2006. Warming and earlier spring increase
western US forest wildfire activity. Science 303: 940-943.
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Trends, FS-696.
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Other Land Use, Land-Use Change, and Forestry C
Emissions and Sinks
In the Inventory of U.S. Greenhouse Gas Emissions and Sinks, the United States reports net greenhouse
gas fluxes associated with IPCC designated land-use categories according to UNFCCC reporting
guidelines and IPCC guidance. The carbon flux estimates included in the national GHG Inventory
represent total net carbon stock changes on United States land areas. This net carbon stock change
approach accounts for both gains and losses of forest carbon in the aboveground and belowground
biomass, dead organic matter, and soil, as well as in durable wood products in use and in landfills. The
net carbon stock changes reflect growth, mortality, harvesting, and other management activities, as well
as increases and decreases in forest area. The approach used for the national GHG Inventory, therefore,
implicitly accounts for carbon dioxide emissions due to disturbances such as forest fires. For more
information on the magnitude of CO2 emissions from forest fires in the United States, the Inventory of
U.S. Greenhouse Gas Emissions and Sinks. Net carbon CO2 flux reported in the GHG Inventory also
includes C fluxes from croplands, grasslands, and settlements, and changes from one land use type to
another. In the United States, the total net CO2 flux from C stock changes in Land Use, Land-Use
Change, and Forestry was 883.7 MMTCO2 in 2006.
Monitoring
Land use-based accounting methods for quantifying CO2 sources and sinks typically involve average C
density and accumulation values for land use types (emission factors) applied to land areas categorized by
type (activity data). Emission factors are developed at multiple scales and involve different levels of
resolution depending on the datasets used to develop them. Often, average emission factors by region or
vegetation type are developed and used (e.g. Smith et al. 2006).
At the national scale, the U.S. Forest Service Forest Inventory and Analysis (FIA) program collects data
on forest area and management. Forest carbon stocks and net carbon stock changes are estimated by
applying a collection of conversion factors and models, referred to as FORCARB2, to the tree and plot-
level forest survey data collected through the FIA program.
For forest C accounting at the project and entity scales, the USDA Forest Service has developed look-up
tables based on FIA data, which is available in a consistent format at the national scale. These look-up
tables 1) quantify C stocks by age in "average" forest for a given region, stratified by forest type; and 2)
directly estimate biomass using allometric approaches but indirectly estimate pools such as soil C, forest
floor C, coarse woody debris, and understory C. Inventory datasets can provide useful activity data,
though their data are most robust at the county scale, so there are limitations in tracking emissions from
smaller-scale land conversion.
In the IPCC default methodology for land converted to settlements, the biomass in vegetation after land
conversion is set to zero (IPCC 2006). Thus, for these forests the emission factor is essentially the C
density in the standing forest prior to conversion. These default methods assume that 20% of soil C is
also lost during forest conversion (IPCC 2006).
Land use conversions to cropland typically result in a net loss of C and N2O from biomass and soils,
though conversion of sparsely vegetated or highly degraded land to cropland may lead to a net C increase.
As with settlements, in the IPCC default methodology for land converted to croplands the biomass in
vegetation after conversion is set to zero (IPCC 2006). Year-to-year increases in woody biomass on
cropland (orchards, vineyards, etc.) can be estimated using default emission factors, though no change in
vegetation biomass occurs for annual crops (IPCC 2006). C stock changes on the majority of cropland
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are typically a result of soil C gain or loss from the soil pool. The emission factors describing change in
the soil C pool before and after conversion to cropland can indicate a net loss or a net increase of soil C,
depending on the intensity of cultivation, the types of inputs used, and the climate regime in the area of
interest (IPCC 2006).
Because grasslands "vary greatly in their degree and intensity of management, from extensively managed
rangelands and savannahs - where animal stocking rates and fire regimes are the main management
variables - to intensively managed (e.g., with fertilization, irrigation, species changes) continuous pasture
and hay land" (IPCC 2006), it is even more difficult to generalize about the impacts of land conversion to
this type. Depending on the land use prior to conversion, C may be gained or lost from the vegetation and
soils. Prescribed fire may also contribute to the emissions due to land conversion to grassland (IPCC
2006).
Information to be Collected
The following information would need to be collected to monitor emissions using the IPCC methodology:
land area converted, and forest and soil C density prior to land conversion.
Uncertainties
When large land areas are involved in land use change-based emissions monitoring, coarse estimates may
be appropriate and even desirable. At the scale of individual reporters, accurate reporting of C gains and
losses due to land use change could require reporters to report the amount of land use change along with
estimates of emissions associated with the change. These estimates may be quite uncertain at the scale of
individual reporters, especially if the land areas being considered are small or if there are deviations from
standard management regimes. For individual reporters, the emissions factors and look-up tables that are
readily available for assessment of forest C storage are not likely to reflect the variety of conditions that
exist for a specific portion of the landscape. Similarly, for croplands, grasslands, and settlements, the
management regimes before and after conversion are the main drivers of changes in C stocks for
particular ownerships. Characterization of these changes requires site-specific information that is
typically not available at the scale of individual reporters. Even for cases where such information is
available, emissions factors are not expressed at a resolution fine enough to account for this site-level
variability.
Reporters and Thresholds
Reporters could be real estate developers or investors, individuals with private land, land conservation
organizations, governments, or other entities. Complicating the identification of reporters, and the
reporting of emissions, is that a plot of land that exceeds an emissions threshold level one year may be a
sink of emissions the next and vice versa.
The emission or storage of greenhouse gases in a land area is determined by the C density of the original
forests or soils, management practices, any land conversion that occurs on that land, and the fate of the C
and N in any cleared soil and vegetation. Developed land areas are quite heterogeneous and this will
greatly impact the change in soil C and biomass C stocks as well as the mineralization of soil organic
carbon and resulting N2O emissions. As an example, there could be a hectare of grassland converted to a
hectare of settlement area, which could include trees, a building, turf grass, a parking lot, ornamental
plants, maybe even a pond. Soil C and biomass C could increase or decrease depending on the actual
conversion implemented. This makes a threshold analysis for emissions from land use very difficult as
there is great variation between land types and land use changes in the United States, and land areas can
be either emissions sources or sinks.
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To conduct a threshold analysis for this source, it would be necessary to estimate the emissions per unit
land area in the United States. An estimate based on carbon flux nationally would result in net
sequestration per acre and no land area would therefore meet or exceed the threshold. An estimate that
uses IPCC default values for forest C density and the default assumption that forest clearing for
development results in a complete loss of aboveground biomass due to decomposition would not provide
information that could be used to assess the number of reporters or emission covered because it may
overestimate carbon loss per unit of land. Either of these approaches is also complicated by the fact that
management practices, vegetation, soils, etc., in any specific land area can vary greatly from year to year.
Existing Federal Data Collection Systems
Detailed, spatially-explicit activity data are available from a variety of sources at numerous spatial
resolutions, including the National Land Cover Dataset (coarse resolution), the National Resource
Inventory dataset (fine resolution), satellite imagery purchase by federal/state/local governments and
organizations (varying resolution), or the National Agricultural Imagery Program (fine resolution). Many
of these sources provide raw data that must be classified in order to be useful, yet classification is
expensive, time-consuming, and often inaccurate.
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