MEMORANDUM

To: Vincent Camobreco and Doris Wu (EPA)

From: John Venezia, Erin Gray, Victoria Thompson, and Sarah Menassian (ICF)

Date: September 30, 2009

Re: Notable Revisions and Recalculations: International Agriculture GHG Emissions and GHG 

Metrics

This memorandum details notable revisions, recalculations, and clarifications for the ICF memorandum to 

EPA, International Agriculture GHG Emissions and GHG Metrics (revised) V.3 (ICF 2008).

1

 Updates 

are largely based on suggestions from the report, Peer Review of International Greenhouse Gas 

Emissions and Factors as provided to EPA to support its RFS2 rulemaking,

2

referred to hereafter as the 

Peer Review Report. The discussion begins with a summary of notable changes to N2O emission 

estimates from fertilizer application, then follows the general organization of the previous memorandum 

(ICF 2008): 

I. Summary of Changes to N2O Emission Estimates from Fertilizer Application

II. Fertilizer and Pesticide Consumption Projections

III. N2O Emissions from Fertilizer Consumption and Crop Residues

IV. GHG Emission Rates for Agricultural Energy Use

V. CH4 Emission Factors for Rice Cultivation

VI. Additional Research

Each section details updates as well as resulting changes in emissions, if applicable. A revised appendix 

incorporating expert reviewer comments is also included.

I. Summary of Changes to N2O Emission Estimates from Fertilizer Application

Overall emissions related to fertilizer application have been revised downward from 1.90 billion kg N2O 

in the ICF 2008 memo, to 3.36 million kg N2O (see Table 1). In performing QC checks, we uncovered a 

conversion error in the calculations that had caused the calculations to be higher by a factor of 1,000. The 

error and its correction are fully detailed in Section II.

After this correction was made, additional updates to the data and emissions methodology resulted in 

increases in emission estimates, though all emissions presented in this memo are much lower than those 

presented in ICF 2008 due to the conversion error. The increases in direct and indirect N2O emissions 

from synthetic fertilizer application are almost entirely a result of using updated fertilizer consumption 

data from the International Fertilizer Industry Association (IFA) for the periods 2006 or 2006/073and 

2007 or 2007/08. Increases in direct and indirect emissions from crop residues results from an increase in 

agricultural land use acreage and from the correction of an additional conversion error affecting the crop 

 

1

ICF International (2008). International Agriculture GHG Emissions and GHG Metrics (revised) V.3. 

2 Ross and Associates Environmental Consulting, Ltd (2009). Peer Review of International Greenhouse Gas 

Emissions and Factors as provided to EPA to support its RFS2 rulemaking.

3

In 2006 for countries with fertilizer consumption statistics in calendar years, and in 2006/07 for countries with 

statistics in fertilizer years (e.g., growing seasons).
Notable Revisions and Recalculations for the Memorandum: 

International Agriculture GHG Emissions and GHG Metrics

2

residues calculations (also discussed in Section II). Additionally, this analysis updates the ICF 2008 

analysis to include crop residue N emissions from cotton, palm oil, rapeseed, sugarbeet, sugarcane, or 

sunflower. As explained in more detail below, the ICF 2008 memo did not include these crops because 

default crop-specific IPCC factors used in the equation were not available. Proxy IPCC factors for these 

crops are employed in this analysis. 

Table 1: N2O emissions from fertilizer application and crop residues (kg)

Emissions Source ICF 2008 

Analysis 

Revised ICF 2008 

Analysis 

ICF 2009 

Analysis

Direct N2O Emissions from Synthetic Fertilizer 

Application

1,149,886,629 1,149,887 1,995,628

Indirect N2O Emissions from Synthetic Fertilizer 

Application

373,713,154 373,713 648,579

Direct Emissions from Crop Residues 307,993,297 761,241 580,553

Indirect Emissions from Crop Residues 69,298,492 171,337 133,736

TOTAL 1,900,891,572 1,901,104 3,358,496

Emission rates from agricultural energy consumption have not been revised. Additionally, the emission 

factor (EF) for rice cultivation in the United States has been revised to use the IPCC 2006 default EF for 

the applicable cropping regime. 

Finally, we have included an additional emissions source – Manure Fertilizer Use – which increases 

emissions by 188,762 kg N2O. 

II. Fertilizer and Pesticide Consumption Projections

Revisions to fertilizer and pesticide consumption projections result from the incorporation of updates 

made by the Food and Agriculture Organization (FAO) to its Fertistat and FAOStat datasets, as well as 

the incorporation of more up-to-date fertilizer consumption statistics provided by a recent IFA report, 

Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 – 2007/08 (Heffer 2009).4

Additionally, data tables provided in the Appendix have been updated to report data in metric units only. 

Updates and recalculations are described in more detail below, along with details on how these updates 

affect greenhouse gas (GHG) emission estimates. 

Edits to Fertilizer Consumption Projections

The following updates and activities were conducted: 

A. Incorporated IFA Fertilizer Consumption Data

Historical fertilizer application rates for nitrogen (N), phosphorus (P2O5), and potassium (K2O) were 

updated by using more recent data available from the International Fertilizer Industry Association (Heffer 

2009), as recommended by expert reviewers in the Peer Review Report. 

IFA data are preferred over FAO’s Fertistat data because estimates are more current and years of 

available data are consistent across all countries. Additionally, FAO has altered its survey methodology 

since 2004.5

 

4 Heffer, Patrick (2009). Assessment of Fertilizer Use by Crop at the Global Level, 2006/07 – 2007/08. International 

Fertilizer Industry Association. Paris, France. 

5 Heffer, Patrick (2009). Personal correspondence between Patrick Heffer, IFA and Erin Gray, ICF International. 

Sept. 25, 2009.
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The IFA dataset covers 23 countries (considering the European Union-27 as one country6) and 11 crop 

groups including: wheat, rice, corn, other coarse grains, soybean, palm oil, other oilseeds, cotton, sugar 

crops, fruits and vegetables, and other crops.7IFA data cover roughly 92% of world fertilizer 

consumption for the periods 2006 - 2006/07 and 2007 - 2007/08. IFA fertilizer consumption statistics are 

collected by a network of correspondents and fertilizer associations.8

IFA consumption data were averaged over the two reported time periods: 2006 or 2006/07, and 2007 or 

2007/08 to account for seasonal variations in crop production. Fertilizer application rates were calculated 

by dividing IFA total consumption values by FAOStat agricultural area harvested data from the FAOStat 

database.9 Agricultural area harvested is based on an average of 2006 and 2007 data to account for the 

fact that IFA collects data in both calendar year and fertilizer year. Previously, in the ICF 2008 memo, 

individual country research was conducted to determine national fertilizer consumption statistics for the 

following country/crop combinations: Russia/wheat, China/wheat, and India/soybean. As IFA provides 

recent data for these combinations, these independent estimates are no longer needed. 

IFA data for “sugar crops” includes both sugar beet and sugar cane. Ratios of sugarcane to sugar crop 

were developed for IFA countries using Fertistat data for sugar beet and sugar cane to determine the 

relative percentage of each sugar crop by country. Where Fertistat data were not available to match an 

IFA country, data were proxied using nearby countries with similar climates. The following proxies were 

used to estimate a sugar cane to sugar beet ratio from the Fertistat dataset:

Table 2: Fertistat countries used to approximate sugarcane ratio for IFA sugar crop data

Country Proxy Country/Region

Canada United States

Malaysia Indonesia

Iran Turkey

Russia Other Eastern Europe10

Argentina Chile

Overall, incorporating IFA data increased total fertilizer related emissions by 973,919 kg N2O, with 

735,034 kg N2O attributable to direct emissions from synthetic fertilizer application and 238,886 kg N2O 

attributable to indirect emissions from synthetic fertilizer application.

11

B. Updated FAO Fertistat Data

FAO has updated its Fertistat dataset since the ICF 2008 memo; therefore, ICF downloaded and used the 

most recent version. Overall, total fertilizer consumption has decreased by 3.34 million tonnes for N, 

 

6

Fertistat data for the region “European Union” might not match directly with IFA’s dataset as the IFA dataset 

includes 27 EU countries, whereas data availability varies by crop for the Fertistat dataset. 

7

For the purpose of this analysis, the crop categories, “other coarse grains,” “other oilseeds,” and “other crops” are 

not included since data is not available to break out these categories into individual crop components (e.g., barley, 

sorghum, rapeseed, peanut, and sunflower). 

8

IFA (2009). Frequently Asked Questions (FAQ). Retrieved from http://www.fertilizer.org/ifa/HomePage/STATISTICS/FAQ#IFADATA7 on 9/24/2009.

9

FAO (2009). FAOStat Production (Crops). Retrieved Sept. 25, 2009 from 

http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor. Assumes FAO definitions for seed cotton,

maize, sunflower seed, groundnuts, in shell, and rice, paddy equal cotton; corn; sunflower; peanut; and rice, 

respectively. 

10 “Other Eastern Europe” includes Croatia and Slovakia.

11 Emissions increase calculated after updating Fertistat data for country/crop combinations not covered by IFA. 
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4

2.22 million tonnes for P2O5, and 0.81 million tonnes for K2O. Total agricultural area has also decreased 

by 50.8 million hectares. 

Fertistat data are used for country/crop and region/crop combinations not covered by the IFA dataset. 

While IFA provides fertilizer consumption data for a “rest of world” region, Fertistat data are preferred to 

calculate fertilizer consumption for “rest of world” since the dataset provides information for a greater 

number of countries and because IFA “rest of world” values include some of the individual priority 

countries requested by EPA but not available in the IFA dataset. Additionally, while IFA provides 

fertilizer consumption data for the EU-27 region, the dataset is not complete for all priority crops. As a 

result, Fertistat data is aggregated for available EU countries for remaining priority crops, including 

barley, peanut, rapeseed, sorghum, and sunflower. 12

Fertistat fertilizer application rates (kg/ha) are calculated by dividing total Fertistat fertilizer consumption 

by Fertistat agricultural area fertilized.13

C. Researched Fertilizer and Lime Use Associated with Soybean Production

Expert reviewers requested that additional research be conducted to review fertilizer consumption for 

soybean production expansion in Brazil as well as associated lime use. Both the FAO and IFA provide 

data for Brazil soybean production and fertilizer use (see Table 3): 

Table 3: Fertilizer consumption statistics for Brazil/Soybean

Data Source Year N 

(‘000 tonnes)

P2O5 

(‘000 tonnes)

K2O 

(‘000 tonnes)

IFA14 Avg 2006 - 2006/07 & 

2007 - 2007/08 97 1,426 1,313

FAO15 2004 148 1,217 1,144

IFA data are used to project fertilizer consumption for Brazil/soybean production as this data is more 

recent. In response to an expert reviewer comment, additional research was conducted into lime use for 

expanded soybean production in Brazil. Given limited data availability however, lime use data and 

resulting emissions are not incorporated in this analysis. 

Edits to Pesticide Consumption Projections

Historical pesticide consumption estimates for fungicides and bactericides, herbicides, and insecticides 

have been updated using the most current data available from FAO’s FAOStat dataset for pesticide 

consumption. FAO has also updated its FAOStat dataset for agricultural area since the ICF 2008 

memorandum.16 Pesticide consumption data was added for Belgium. 

 

12 Fertistat data for the EU is EU-27 might not match EU definition for Fertistat, as fertilizer consumption data 

availability varies by crop for Fertistat countries. 

13 Fertistat reports that fertilizer consumption is in 1000 tons, but data is actually reported in metric tonnes.

14 Heffer (2009). Op cit. 

15 FAO (2004). Fertilizer use by crop in Brazil. FAO Land and Plant Nutrition Management Service, Land and 

Water Development Division. Rome, Italy.

16 Updates last reported in April, 2009. Pesticide consumption database limitations: 1) The country coverage and 

time series are incomplete due to a high rate of non-response; 2) Although countries have been requested to report 

data in terms of active ingredients, some countries may have reported in formulation weight (including diluents and 

adjuvants) without specific indication. http://faostat.fao.org/site/291/default.aspx
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Expert reviewers (Peer Review Report 2009) noted that FAO’s pesticide consumption dataset did not 

provide values for China pesticide consumption. To remedy this, ICF researched pesticide consumption 

in China. The U.S. Department of Agriculture’s (USDA) Economic Research Service (ERS) states that 

total pesticide consumption for China in 2007 was 1.623 million tons.17 As ERS does not break out total 

pesticide consumption into the three pesticide product types, total pesticide consumption was distributed 

using the average ratio of pesticide consumption from nearby countries with a similar climate, including 

Vietnam, Thailand, and South Korea.18

Table 4 in the data appendix has been updated to reflect instances where FAO reports zero consumption 

values versus non-responses from its data collection survey. Empty cells represent survey non-responses.

III. N2O Emissions from Fertilizer Consumption and Crop Residues

In response to a request from EPA, research was conducted into N2O emissions from the application of 

manure to agricultural soils as an organic fertilizer. Previously, only synthetic fertilizer and crop residue

emissions were estimated. EPA’s manure management calculations were used to make the estimates of 

manure applied to soils. In addition, the equation and estimates of N inputs from crop residues were 

reviewed, and estimates of crop residues from several crops that had previously been excluded due to lack 

of default IPCC factors specific to these crops were added. This section discusses these updates in more 

detail, along with how updates have altered GHG emission estimates. 

Projections of Changes in GHG Emissions from Manure Fertilizer Use

In order to estimate emissions from use of manure as organic fertilizer, this analysis uses EPA’s existing 

estimates of N excretion (kg N/animal-year) by animal type and region, the distribution of animals into 

waste management systems by type and region, and the projected change in livestock population by type 

and region, which had all been used in EPA’s manure management emissions calculation. With this 

information, this analysis estimates the manure N available and subtracts the amount typically lost before 

application, according to the IPCC methodology (see IPCC 2006 Guidelines Section 10.5.4 and Table 

10.23). However, due to lack of information, information on additional N that might be added to the 

manure from bedding materials (e.g. wood chips, straw) was not included.

The analysis uses IPCC equation 10.34, which calculates managed manure N available for application to 

managed soils, feed, fuel or construction uses. It is the sum, for each livestock species (T) and manure 

management system (S), of:

[NT × NexT × MST,S × (1 – FracLoss MS/100)] + NT × MST,S × NbeddingMS

Where:

NT = number of head of livestock species/category T in the country

NexT = annual average N excretion per animal of species/category T in the country, kg N animal-1 

yr-1

 

17 USDA Economic Research Service. China Agricultural and Economic Data: National Data Results. Retrieved 

from 

http://www.ers.usda.gov/Data/China/NationalResults.aspx?DataType=6&DataItem=160&StrDatatype=Agricultural

+inputs&ReportType=0 on Sept 15, 2009.

18 Of the total 1.623 million tons of pesticide consumption by China, this analysis estimates that fungicides and 

bactericides equal 25% of pesticide consumption; herbicides equal 33%; and insecticides equal 42%. 
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MST,S = fraction of total annual nitrogen excretion for each livestock species/category T that is 

managed in manure management system S in the country, dimensionless

FracLoss MS = amount of managed manure nitrogen for livestock category T that is lost in the manure 

management system S, % (see Table 10.23)

NbeddingMS = amount of nitrogen from bedding (to be applied for solid storage and deep bedding MMS if 

known organic bedding usage), kg N animal-1 yr-1

S = manure management system

T = species/category of livestock

As stated above, it is assumed that NbeddingMS is zero, due to lack of data. IPCC Table 10.23 (Table 4) 

gives the values for FracLoss MS. Some manure management systems used in EPA’s manure management 

calculations for certain livestock types did not correspond with an entry in Table 10.23. For example, in 

the manure management calculation, some non-dairy cattle manure was managed by daily spread. 

However, there is no FracLoss MS value for “other cattle” for daily spread. Therefore, in ICF’s calculation, 

the dairy cow value for daily spread was used. For other livestock/systems where a factor was not 

available, the corresponding dairy cow factor was similarly used. For digester systems, the dairy cattle 

and swine anaerobic lagoon values were used for all cattle and swine, respectively.

Table 4: IPCC Table 10.23, Default Values for Total Nitrogen Loss from Manure Management

Animal category Manure management system

Total N loss from MMS 

FracLossMS (Range of FracLossMS) 

Swine 

Anaerobic lagoon 78% (55 – 99)

Pit storage 25% (15 – 30)

Deep bedding 50% (10 – 60)

Liquid/Slurry 48% (15 – 60)

Solid storage 50% (20 – 70)

Dairy Cow 

Anaerobic lagoon 77% (55 – 99)

Liquid/Slurry 40% (15 – 45)

Pit storage 28% (10 – 40)

Dry lot 30% (10 – 35)

Solid storage 40% (10 – 65)

Daily spread 22% (15 – 60)

Poultry 

Poultry without litter 55% (40 – 70)

Anaerobic lagoon 77% (50 – 99)

Poultry with litter 50% (20 – 80)

Other Cattle 

Dry lot 40% (20 – 50)

Solid storage 50% (20 – 70)

Deep bedding 40% (10 – 50)

Other (sheep, horses) Deep bedding 35% (15 – 40)

Solid storage 15% (5 – 20)

In calculating the manure N available for application, the manure N that had been categorized under the 

pasture, range, and paddock (PRP) management system was subtracted because the “application” 

emissions from PRP were already accounted for in the manure management calculations. Similarly, daily 

spread manure application emissions were also accounted for in the manure management calculations and 

were subtracted out. 

However, in reviewing the methodology, some possible methodological discrepancies were detected in 

the treatment of indirect emissions from PRP and daily spread, and in the treatment of direct versus 
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7

indirect emissions from sheep manure (in the direct calculation 100% of sheep manure is assumed to be 

PRP; in the indirect calculation, it is divided between solid storage and deep bedding). 

These additions will increase emission estimates by 188,762 kg N2O, or approximately 5.7 percent of the 

current estimated total emissions from synthetic fertilizers and crop residues.

It is important to note that the need of crops for fertilizer N is unlinked in this analysis to changes in 

animal population. Therefore it could occur that a country or region has a projected increase in fertilizer 

needs (i.e., crop growth) while simultaneously projecting a decrease in livestock emissions. The amount 

of synthetic fertilizer change is directly proportional to the amount of crop area change in this analysis, so 

any possible shortfall in crop N supply due to decreasing livestock populations combined with increasing 

crop areas is not addressed. 

Edits to Projections of Changes in GHG Emissions from Synthetic Fertilizer and Crop Residues

Based on comments from one of the expert reviewers, Dr. Kenneth Cassman, typographical errors were 

corrected in the portion of the original memo describing the methodology used to calculate emissions 

from synthetic N and crop residue N additions to agricultural soils. The original text from the memo is 

shown below, with typo corrections in red text and yellow highlight.

Direct Emissions: 

1. Direct N2O emissions from synthetic fertilizers: 

(1) Emissions = FSN × EF1 × 44/28

Where: 

FSN = the annual amount of synthetic fertilizer N applied to soils 

(kg N)

EF1 = emission factor,(equal to 0. 01 kg N2O-N/kg N input)

44/28 = conversion of N2O -N to N2O

2. Direct N2O emissions from crop residues: 

(2) Emissions = FCR × EF1 × 44/28

Where

FCR = the annual amount of N in crop residues and forage/pasture 

renewal (kg N)

EF1 = emission factor,(equal to 0.01 kg N2O-N/kg N input)

44/28 = conversion of N2O -N to N2O

N additions to soils from crop residues depend on the crop type and 

yield, since different crop types have different N contents and 

different amounts of residue typically left in the soil. The equation 

for FCR is: 

(3) FCR = ∑ (Yield FreshT × DRYT × ST + IT) × AreaT × (Nag(T) + Rbg-BIO(T) × 

Nbg(T))

Where: 
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T = crop or forage type

Yield Fresh = fresh weight yield of crop (kg fresh weight/ha)

DRY = dry matter fraction of harvested crop (kg dry matter/kg 

fresh weight)

S = Slope for above-ground residue dry matter 

I = Intercept for above-ground residue dry matter

Area = total annual area harvested (ha)

Nag = N content of above-ground residues (kg N/kg dry matter)

Rbg-BIO = ratio of belowground residues to above ground biomass

Nbg = N content of below-ground residues (kg N/kg dry matter)

The crop residue N input write-up and spreadsheet were also reviewed in response Dr. Cassman’s

comment that the equations used were incorrect. On review it was concluded that with respect to the 

comments raised by Dr. Cassman, the equation used was correct and was used correctly. However, a 

conversion error in the equation was uncovered during recent QC activities. In ICF 2008, crop area 

inputs were acre-based. The crop area term appears twice in the equation (once to determine Yield Fresh, 

and once on its own). However, the conversion from acres to hectares mistakenly only appeared once. 

This problem was rectified when the area input data was converted to a hectare basis. The effect of this 

correction was an increase in emission estimates for all crops by a factor of about 2.5.

It is important to note that due to lack of data, we have been unable to account for any portion of the crop 

residue that might be removed from the field for other purposes, such as animal bedding or fuel. In some 

areas, these removals could be a significant portion of residues, and in such cases, our estimates are likely 

to be high.

In addition, adjustments were made to the estimates of crop residue N inputs. In the original analysis, crop 

residue N emissions from cotton, palm oil, rapeseed, sugarbeet, sugarcane, or sunflower were not 

included because default crop-specific IPCC factors used in the equation were not available. A 

commenter felt that this exclusion was not defensible. In response, these crops are now included using the 

“Root crops, other” category from the IPCC default factors. The equation used to calculate crop residues 

was also adjusted to account for the fact that palm oil is not an annual crop (it is assumed all other crops 

are annual). In accordance with IPCC guidelines, a term was inserted into the equation with the percent 

of the crop renewed annually. Based on ICF’s research, a replanting interval of 23 years seemed typical, 

and so the percent of annual renewal was estimated at 1/23 or 4.35 percent. For all other crops the 

renewal rate was set at 100 percent. These changes resulted in an estimated decrease in emissions of 

208,776 kg N2O. The change results in a decrease in emissions because the crops that were added 

showed a net decrease in crop area.

Together, the correction of the acreage conversion error and the inclusion of the additional crop types 

resulted in a net increase of 336,785 kg N2O from crop residue N inputs.

Order-of-Magnitude Error in Fertilizer and Crop Residue Equations

In reviewing the N2O emissions calculations during the revision process, we uncovered an order-ofmagnitude error that affected fertilizer (direct and indirect emissions) and crop residue (direct and 

indirect) equations. In both cases, the equation multiplied by one million in order to convert from Gg (of 

crop in the case of crop residues and of N in the case of fertilizer) to kg. However, in both cases the 

multiplier should have been one thousand, to convert from Mg (metric tons) to kg. As a result, the totals 

submitted in the 2008 analysis were too high by a factor of 1,000. All emissions of N2O given in billions 

of kg of N2O should have been in millions of kg of N2O.
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9

As a further check to confirm the change, we have drawn comparisons with data from the U.S. 

Greenhouse Gas Inventory and U.S. Census of Agriculture, as shown in Table 5. In 2007, 7,587 Gg (7.59 

Tg) of synthetic fertilizer N were added to U.S. croplands. The resulting direct emissions were 32.2 Tg 

CO2 Eq., which equals a rate of 4.24 units CO2 Eq./unit N added. For our analysis, the change in synthetic 

N application was 125,547.2 Mg, and the resulting emissions (according to the revised calculation) were

1,972,885 kg N2O (1,973 Mg N2O, or 611,594 Mg CO2 Eq.). The resulting rate of emissions was 4.87

units CO2 Eq./unit N added, which is very similar to the rate in the United States (the difference is 

attributable to different methodologies used). 

As another standard of comparison, the total harvested cropland in the U.S. in 2007 was 309.6 million 

acres (125.3 million hectares).19 The total change in acreage in this analysis, for all crops considered was 

1.85 million hectares. We also compared our estimates with the relevant proportion of U.S. N2O 

emissions from agricultural soil management. The result of these comparisons shows that, broadly 

speaking, the revised numbers in this analysis are “normal” in proportion to similar metrics for the United 

States. 

Table 5: Comparison of Analysis Results with Year 2007 Estimates from 1990-2007 U.S. Greenhouse Gas 

Inventory.

1990-2007 U.S. GHG 

Inventory (2007)

International Ag. 

Analysis (Corrected)

International Ag. 

Analysis (Uncorrected)

Units CO2 Eq./unit synthetic N 4.24 4.87 4,871

Total N2O emissions (Tg CO2

Eq.)

80.3* 1.1** 1032

Hectares of cropland 125.3 million 1.9 million 1.9 million

Emissions rate (Tg CO2 Eq./ha) 0.64 0.58 543

* Includes only direct emissions from synthetic fertilizer, crop residues, and organic amendments; plus a 

proportional fraction of indirect emissions. Total U.S. ag. soil management N2O emissions were 207.9 million Tg 

CO2 Eq., but this number includes emissions from sources not considered in this analysis, such as grasslands and 

organic soils.

** Includes direct and indirect emissions from synthetic fertilizer, crop residues, and application of managed manure 

to soils as fertilizer.

IV. GHG Emission Rates for Agricultural Energy Use

In response to expert reviewers’ comments, the values in Table 8 of the Appendix have been converted to 

MTCO2e/ha to ensure small values appear as non-zero numbers. Further, any zero values have been 

denoted by a dash (“-”) for clarity. This change will not affect emissions values since the values in Table 

8 have not been revised; they were only converted to different units.

For all energy data provided, any apparent data gaps exist because no data were available from IEA for 

the given parameter.

Several fuel types were excluded from the analysis in the ICF 2008 memorandum. The fuel types 

excluded were: biogas, charcoal, gas works gas, geothermal, other liquid biofuels, primary Solid Biomass, 

solar thermal (note that these are IEA’s fuel type categories). These fuel types were excluded because 

they are primarily biogenic fuel types, which are not associated with fossil fuel CO2 emissions. As for the 

non-biogenic fuel types excluded, consumption of these fuel types in the Agriculture/Forestry sector was 

minimal and thus excluded.

 

19 USDA 2007 Census of Agriculture, Table 8.
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V. CH4 Emission Factors for Rice Cultivation

The emission factor (EF) for the United States has been revised to use the IPCC 2006 default EF for the 

applicable cropping regime. In response to a comment received from an expert reviewer regarding the 

value of the U.S. EF, the conversion of the seasonal factor to the daily factor was checked. Previously,

the conversion was completed using an average season length from the U.S.’s analysis to determine the 

EF; this season length accounted for only a limited number of states. ICF recalculated the EF using a 

season length that accounted for all rice-growing states (i.e., the U.S. season length from IRRI) and 

calculated an EF of 1.246 kg CH4/ha/day. This compares well with the IPCC default factor for the 

irrigated cropping regime (the only cropping regime used in the U.S.) - 1.237 kg CH4/ha/day, thus 

confirming the validity of the default EF for the United States.

VI. Additional Research

Key Country Pasture Areas and Livestock Populations

At the request of EPA, research was conducted to review more detailed country-specific data on pasture 

areas and populations of pastured livestock for Argentina, China, India, and Indonesia. These data are 

summarized in the spreadsheet Livestock Land Area_9.30.09.xls, which was originally sent to EPA on 

September 14, 2009, and has not been updated since then.

Information is currently available mainly from national statistical agencies and agriculture departments.

For the years 2000 – 2007 (according to data availability by country), a summary of livestock population 

data by animal type was created. The analysis includes two “total” population categories, the first for

bovines, sheep, goats, and horses/donkeys/mules, i.e., animals we considered most likely to be on 

pastureland, and the second also including swine and camels. Animal population data for Argentina were 

available for 1988 and 2002 for all animal types, and for 1993 – 2001 for cattle, sheep, and goats. Data 

for China were available for 1996 – 2007. Data for India were available for 1982, 1987, 1992, 1997, and 

2003. Data for Indonesia were available for 1995 – 1999, 2003, and 2005 – 2007. 

In addition, data were summarized for area of pasture or grazing land for each country. Where possible, 

total area under survey, or total agricultural land, were included as a point of comparison. Data for 

Argentina were available for 1988 and 2002. Data for China were available for 2003 – 2007. Data for 

India were available for the first year of every decade going back to 1950, and then annually from 1990 

on through the 2005 – 2006 agricultural year. Data for Indonesia were available for 1999 – 2003.

To complete the analysis, rough estimates of animal population density on pasture were created using the 

two different “total” categories. Since data for the housing of the various animal types by country were 

not available, these density estimates are based on the unrealistic assumption that all animals listed in 

population estimates are on pasture.