### 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 f

ertilizer application, then follows the general organization of the previous memorandum (ICF 2008): I. Summary of Changes to N 2 O Emission Estimates from Fertilizer Application II. Fertilizer and Pesticide Consumption Projections III. N 2 O Emissions from Fertilizer Co nsumption and Crop Residues IV. GHG Emission Rates for Agricultural Energy Use V. C H 4 Emission Factors for Rice Cultivation VI. Additional Research Each section details updates as well as resulting changes in emissions, if applicable. A revised a ppendi x incorporating expert reviewer comments is also included. I. Summary of Changes to N 2 O Emission Estimates from Fertilizer Application Overall emissions related to fertilizer application have been revised downward from 1.90 billion kg N2 O in the ICF 2008 memo, to 3. 36 mi llion kg N2 O (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 Sectio n 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 e rror . The increases in direct and indirect N2 O 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 20 06/07 3 and 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). Int ernational 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 dis cussed 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 : N 2 O emissions from fertilizer application and crop residues (kg) Emissions Source ICF 2008 Analysis Revised ICF 2008

Analysis ICF 2009

Analysis Direct N 2 O Emissions from Synthetic Fertilizer Application 1,149,886,629 1,149,887 1,995,628 Indirect N 2 O Emissions from Synthetic Fertilizer Application 373,713,154 37 3,713 648,579 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 N 2 O

. 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 (P 2 O 5 ), and potassium (K2 O) were updated by usi ng 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 year s 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.Notable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 3
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. 7 IF A data cover roughly 92% of world fertilizer consumption for the periods 2006

- 2006/07 and 2007

- 2007/08. IFA fertilizer consump tion 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 cli mates. 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 Europe 10 Argentina Chile Overall, incorporating IFA data increased total fertilizer related emissions by 973,919 kg N2 O, with 735,034 kg N 2 O attributable to direct emissions from synthetic fertilizer application and 238,886 kg N 2 O 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 us ed the most recent version. Overall, total fertilizer consumption has decreased by 3.3 4 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 “o ther 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/Home

Page/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 s eed cotton , maize , s unflower seed , groundnuts, in shell , and rice, paddy equal c otton ; c orn ; s unflower ; p eanut ; 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.Notable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 4
2.2 2 million tonnes for P 2 O 5 , and 0. 81 million tonnes for K2 O. 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” sin ce 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) P 2 O 5

(‘000 tonnes) K2 O

(‘000 tonnes) IFA 14 Avg 2006

- 2006/07 &

2007

- 2007/08 97 1,426 1,313 FAO 15 2004 148 1,21 7 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 f ungicides and b actericides, h erbicides, and i nsecticides 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 me morandum. 16 Pesticide consumption data was added for Belgium.

12 Fertistat data for the EU is EU

- 27 might not match EU defi nition 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 (200 4). 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 se ries 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 indi cation. http://faostat.fao.org/site/291/default.aspxNotable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 5 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 Agricultu re’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 dis tributed 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. N 2 O Emissions from Fertilizer Consumption and Crop Residues In response to a request from EPA, research was conducted into N2 O emissions from the app lication 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 E PA’s manure management emissions calculation. With this information, this analysis estimates the manure N available and subtract s the amount typically lost before application, according to the IPCC methodology (see IPCC 2006 Guidelines Section 10.5.4 and T able 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 use s IPCC equation 10.34, which calculates managed manure N avai lable 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:

[N T × Nex T × MS T,S × (1

– Frac Loss MS/100)] + N T × MS T,S × NbeddingMS Where: N T = number of hea d of livestock species/category T in the country Nex T = 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%.Notable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 6
MS T,S = fraction of to tal annual nitrogen excretion for each livestock species/category T that is

manage d in manure management system S in the country, dimensionless Frac Loss 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 Frac Loss 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 ex ample, in the manure management calculation, some non

- dairy cattle manure was managed by daily spread. However, there is no Frac Loss MS value for “other cattle” for daily spread. Therefore, in ICF’s calculation, the dairy cow value for daily spread was used. For other livesto ck/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 f romManure Managem ent
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 versusNotable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 7
indirect emissions from s heep 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 N 2 O, or approximately 5. 7 percent of the current estimated total emissions from synthetic fertilizers and crop residues. Itis 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 soi ls. The original text from the memo is shown below, with typo corrections in red text and yellow highlight

. Direct Emissions: 1. Direct N 2 O emissions from synthetic fertilizers: (1) Emissions = F SN × EF 1 × 44/28 Where: F SN = the annual amount of synthet ic fertilizer N applied to soils (kg N)EF 1 = emission factor, (equal to 0. 0 1 kg N 2 O
- N /kg N input) 44/28 = conversion of N 2 O
- N to N 2 O 2. Direct N 2 O emissions from crop residues: (2) Emissions = F CR × EF 1 × 44/28 Where F CR = the annual amount of N in crop re sidues and forage/pasture renewal (kg N) EF 1 = emission factor,(equal to 0. 0 1 kg N 2 O
- N/kg N input) 44/28 = conversion of N 2 O
- N to N 2 O N additions to soils from crop residues depend on the crop type and yield, since different crop types have different N c ontents and different amounts of residue typically left in the soil. The equation for F CR is: (3) F CR = ∑ (Yield Fresh T × DRY T × S T + I T ) × Area T × (N ag(T) + Rbg
- BIO (T) × N bg(T) ) Where:Notable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 8
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 = S lope for above
- ground residue dry matter I = Intercept for above
- ground residue dry matter Area = total annual area harvested (ha) N ag = N content of above
- ground residues (kg N/kg dry matter) Rbg
- BIO = ratio of belowground residues to above ground bio mass N bg = 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. O n 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 appear s 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. Itis 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 N 2 O. The change results in a decrease in emissions becau se 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 N 2 O from crop residue N inputs. Order -of - Ma gnitude Error in Fertilizer and Crop Residue Equations In reviewing the N2 O emissions calculations during the revision process, we uncovered an order

- of

ma gnitude error that affected f ertilizer (direct and indirect emissions) and crop r esidue (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 N2 O given in billions of kg of N 2 O should have been in millions of kg of N2 O.Notable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 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 G g (7.59 Tg) of synthetic fertilizer N were added to U.S. croplands. The resulting direct emissions were 32.2 Tg CO 2 Eq., which equals a rate of 4.24 units CO 2 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 N 2 O (1,973 Mg N 2 O, or 611,594 Mg CO 2 Eq.). The resulting rate of emissions was 4.87 units CO 2 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 s tandard 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 wi th the relevant proportion of U.S. N2 O 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 CO 2 Eq./unit synthetic N 4.24 4.87 4,871 Total N 2 O emissions (Tg CO 2 Eq.) 80.3* 1.1** 1032 Hectares of cropland 125.3 million 1.9 million 1.9 million Emissions rate (Tg CO 2 Eq./ha) 0.64 0.58 543 * Includes only direct emissions from synthetic ferti lizer, crop residues, and organic amendments; plus a proportional fraction of indirect emissions. Total U.S. ag. soil management N 2 O emissions were 207.9 million Tg CO 2 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 MTCO 2 e/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 f rom the analysis in the ICF 2008 memorandum. The fuel types excluded were: biogas, charcoal, gas w orks g as, g eothermal, o ther liquid biofuels, p rimary Solid Biomass, s olar thermal (note that these are IEA’s fuel type categories). These fuel types were exc luded because they are primarily biogenic fuel types , which are not associated with fossil fuel CO 2 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.Notable Revisions and Recalculations for the Memorandum: International Agriculture GHG Emissions and GHG Metrics 10
V. CH 4 Emission Factors for Rice Cultivation T he e mission f

actor (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 numb er 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 CH 4/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 CH 4/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 orig inally 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, t otal area under survey, o r 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” cat egories. 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.