Chapter 4

Agriculture

4

AGRICULTURE

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Agriculture

Chapter 4

CO-CHAIRS, EDITORS AND EXPERTS
Co-chairs of the Expert Meeting on Emissions f rom Agriculture
Arvin Mosier (USA) and Carolien Kroeze (Netherlands)

REVIEW EDITORSTaka Hiraishi (Japan) and Wang Minxing (China)

Joint Expert Group: Livestock Population Characterisation and CH 4
Emissions from Enteric Fermentation in Domestic Livestock
CO-CHAIRS
Michael Gibbs (USA) and Luis Ruiz-Suarez (Mexico)

AUTHORS OF BACKGROUND PAPERS
Michael Gibbs (USA), Don Johnson (USA), Keith Lassey (New Zealand), M. Ulyatt (New Zealand), Paul Jun(USA), Kathryn Gaffney (USA), and David Conneely (USA)CONTRIBUTORSDavid Beever (UK), Guillermo Berra (Argentina), Budg Bujidmaa (Mongolia), Ian Galbally(Australia),
Hongmin Dong (China), Robert Hoppaus (IPCC/OECD), Jean Koch (Israel), Cecilia Ramos-Mane (Uruguay),
Michael Strogies (Germany), and Pravee Vijchulata (Thailand)Expert Group: CH 4 Emissions from Manure Management
CO-CHAIRS
Grietje Zeeman (Netherlands) and Bart Mupeta (Zimbabwe)

AUTHORS OF BACKGROUND PAPERS
Kathryn Gaffney (USA), Sybren Gerbens (Netherlands), Michael Gibbs (USA), Paul Jun (USA), and Grietje
Zeeman (Netherlands)

CONTRIBUTORS
Sybren Gerbens (Netherlands), Lowry Harper (USA), Paul Jun (USA), Erik Lyck (Denmark), Thomas Martinsen(IPCC/OECD), and Kenneth Olsen (Canada)

Expert Group: N 2 O Emissions from Manure Management Systems
CO-CHAIRS
Oene Oenema (Netherlands) and Lambert Gnapelet (Central African Republic)

AUTHORS OF BACKGROUND PAPERS
Oene Oenema (Netherlands) and Otto Heinemeyer (Germany)

CONTRIBUTORSJohn van Aardenne (Netherlands), Barbara Amon (Austria), Andre van Amstel (Netherlands), Karin Groenestein(Netherlands), and Otto Heinemeyer (Germany)

Joint Expert Group: CH 4 and N 2 O Emissions from Savanna Burning andAgricultural Waste Burning
CO-CHAIRS
Wei Min Hao (USA) and Joseph Kwasi Adu (Ghana)

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Agriculture

AUTHOR OF BACKGROUND PAPER
Wei Min Hao (USA)CONTRIBUTORS
Kay Abel (Australia), Jean Brennan (USA), and Yahaiya Mohamed (Comores)

Expert Group: Direct N 2 O Emissions from Agricultural Soils
CO-CHAIRS
Keith Smith (UK) and Bernard Siska (Slovak Republic)

AUTHORS OF BACKGROUND PAPERS
Lex Bouwman (Netherlands), Barbara Braatz (USA), and Keith Smith (UK)CONTRIBUTORS
Sue Armstrong-Brown (UK), Lex Bouwman (Netherlands), Barbara Braatz (USA), Martti Esala (Finland), JeanClaude Germon (France), Niels Kilde (Denmark), Katarina Mareckova (IPCC/OECD), Paul Ruyssenaars(Netherlands), Haruo Tsuruta (Japan), and Tom Wirth (USA)Expert Group: Indirect N 2 O Emissions from Nitrogen Used in Agriculture
CO-CHAIRS
Cindy Nevison (USA) and Michael Gytarsky (Russia)

AUTHOR OF BACKGROUND PAPER
Cindy Nevison (USA)CONTRIBUTORS
Jochen Harnish (Germany), Steve Jarvis (UK), Carolien Kroeze (Netherlands), Riitta Pipatti (Finland), Erik
Rasmussen (Denmark), Kristin Rypdal (Norway), Martin Schmid (Switzerland), Jeff Smith (USA), and Kiyoto
Tanabe (Japan)

Expert Group: CH 4 Emissions from Rice Production
CO-CHAIRS
Ron Sass (USA) and Kazuyuki Yagi (Japan)

AUTHOR OF BACKGROUND PAPER
Ron Sass (USA)CONTRIBUTORS
Hugo Denier van der Gon (Netherlands), Bill Irving (USA), Leon Janssen (Netherlands), and Rhoda Lantin(Philippines)

IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

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Agriculture

Chapter 4

Contents
4 AGRICULTURE
4.1 LIVESTOCK POPULATION CHARACTERISATION ......................................................................4.8
4.1.1

Methodological issues ...............................................................................................................4.8

4.1.2

Reporting and documentation..................................................................................................4.21

4.1.3

Inventory quality assurance/quality control (QA/QC).............................................................4.22

4.2 CH4 EMISSIONS FROM ENTERIC FERMENTATION IN DOMESTIC LIVESTOCK.................4.23
4.2.1

Methodological issues .............................................................................................................4.23

4.2.2

Reporting and documentation..................................................................................................4.28

4.2.3

Inventory quality assurance/quality control (QA/QC).............................................................4.28

4.3 CH4 EMISSIONS FROM MANURE MANAGEMENT ....................................................................4.30
4.3.1

Methodological issues .............................................................................................................4.30

4.3.2

Reporting and documentation..................................................................................................4.38

4.3.3

Inventory quality assurance/quality control (QA/QC).............................................................4.38

4.4 N2O EMISSIONS FROM MANURE MANAGEMENT ...................................................................4.40
4.4.1

Methodological issues .............................................................................................................4.40

4.4.2

Reporting and documentation..................................................................................................4.47

4.4.3

Inventory quality assurance/quality control (QA/QC).............................................................4.47

4.5 CH4 AND N2O EMISSIONS FROM SAVANNA BURNING...........................................................4.49
4.6 CH4 AND N2O EMISSIONS FROM AGRICULTURAL RESIDUE BURNING..............................4.51
4.7 DIRECT N2O EMISSIONS FROM AGRICULTURAL SOILS.........................................................4.53
4.7.1

Methodological issues .............................................................................................................4.53

4.7.2

Reporting and documentation..................................................................................................4.65

4.7.3

Inventory quality assurance/quality control (QA/QC).............................................................4.65

4.8 INDIRECT N2O EMISSIONS FROM NITROGEN USED IN AGRICULTURE .............................4.67
4.8.1

Methodological issues .............................................................................................................4.67

4.8.2

Reporting and documentation..................................................................................................4.75

4.8.3

Inventory quality assurance/quality control (QA/QC).............................................................4.76

4.9 CH4 EMISSIONS FROM RICE PRODUCTION ...............................................................................4.77
4.9.1

Methodological issues .............................................................................................................4.77

4.9.2

Reporting and documentation..................................................................................................4.82

4.9.3

Inventory quality assessment/quality control (QA/QC)...........................................................4.83

APPENDIX 4A.1

CH4 AND N2O EMISSIONS FROM SAVANNA BURNING:
BASIS FOR FUTURE METHODOLOGICAL DEVELOPMENT ..........................4.84

4A.1.1 Methodological issues .............................................................................................................4.84
4A.1.2 Reporting and documentation..................................................................................................4.87
4A.1.3 Inventory quality assurance/quality control (QA/QC).............................................................4.88

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APPENDIX 4A.2

Agriculture

CH4 AND N2O EMISSIONS FROM AGRICULTURAL RESIDUE BURNING:
BASIS FOR FUTURE METHODOLOGICAL DEVELOPMENT ..........................4.89

4A.2.1 Methodological issues .............................................................................................................4.89
4A.2.2 Reporting and documentation..................................................................................................4.90
4A.2.3 Inventory quality assurance/quality control (QA/QC).............................................................4.90
APPENDIX 4A.3

CH4 EMISSIONS FROM RICE PRODUCTION: MEASUREMENT,REPORTING, AND QA/QC OF FIELD DATA.......................................................4.91

REFERENCES ............................................................................................................................................4.92

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Agriculture

Chapter 4

Figures

4.6

Figure 4.1

Decision Tree for Livestock Population Characterisation ...............................................4.9

Figure 4.2

Decision Tree for CH4 Emissions from Enteric Fermentation.......................................4.24

Figure 4.3

Decision Tree for CH4 Emissions from Manure Management ......................................4.33

Figure 4.4

Decision Tree for N2O Emissions from Manure Management ......................................4.41

Figure 4.5

Decision Tree for CH4 and N2O Emissions from Savanna Burning ..............................4.50

Figure 4.6

Decision Tree for CH4 and N2O Emissions from Agricultural Residue Burning ..........4.52

Figure 4.7

Decision Tree for Direct N2O Emissions from Agricultural Soils.................................4.55

Figure 4.8

Decision Tree for Indirect N2O Emissions from Nitrogen Used in Agriculture ............4.69

Figure 4.9

Decision Tree for CH4 Emission from Rice Production ................................................4.79

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Agriculture

TablesTable 4.1

Representative Cattle and Buffalo Categories ...............................................................4.11

Table 4.2

Representative Sheep Categories...................................................................................4.11

Table 4.3

Summary of the Equations Used to Estimate Gross Energy Intake for Cattle
and Buffalo and for Sheep .............................................................................................4.14

Table 4.4

Coefficients for Calculating NEm...................................................................................4.15

Table 4.5

Activity Coefficients corresponding to Animal's Feeding Situation .............................4.15

Table 4.6

Constants for Use in Calculating NEg for Sheep ...........................................................4.16

Table 4.7

Constants for Use in Calculating NEp in Equation 4.8 ..................................................4.19

Table 4.8

Cattle/Buffalo CH4 Conversion Rates (Ym) ...................................................................4.26

Table 4.9

Sheep CH4 Conversion Rates (Ym) ................................................................................4.27

Table 4.10

MCF Values for Manure Management Systems Defined in the IPCC Guidelines ........4.36

Table 4.11

MCF Values for Manure Management Systems not Specified
in the IPCC Guidelines .................................................................................................4.37

Table 4.12

Default Emission Factors for N2O from Manure Management .....................................4.43

Table 4.13

Default Emission Factors for N2O from Manure Management Systems
not Specified in the IPCC Guidelines ...........................................................................4.44

Table 4.14

Default Adjustment Factors for Table 4-20 in the IPCC Guidelines whenestimating N excretion rates for young animals ............................................................4.45

Table 4.15

Default Values for the Fraction of Nitrogen in Feed Taken in by Animals
that is Retained by the Different Animal Species/Categories .......................................4.46

Table 4.16

Selected Crop Residue Statistics....................................................................................4.58

Table 4.17

Updated Default Emission Factors to Estimate Direct N2O Emissions
from Agriculural Soils ...................................................................................................4.60

Table 4.18

Default Emission Factors for Estimating Indirect N2O Emissions from N used inAgriculture.....................................................................................................................4.73

Table 4.19

Data for Estimating Indirect N2O ..................................................................................4.74

Table 4.20

IPCC Default CH4 Emission Scaling Factors for Rice Ecosystems
and Water Management Regimes Relative to Continuously Flooded Fields.................4.80

Table 4.21

Dose-Response Table for Non-Fermented Organic Amendments.................................4.81

Table 4.22

Default Emission Factor, Default Scaling Factors, and Ranges for CH4Emissions from Rice Fields ...........................................................................................4.82

Table 4.A1

Amount of Aboveground Biomass Burned....................................................................4.85

Table 4.A2

Combustion Efficiency and Corresponding CH4 Emission Factor ................................4.86

Table 4.A3

Emission Factors of N2O in Various Savanna Ecosystems............................................4.87

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Agriculture

Chapter 44 AGR ICU L TUR E
4.1

LIVESTOCK POPULATIONCHARACTERISATION

4.1.1

Methodological issues

The methods for estimating methane (CH4) and nitrous oxide (N2O) emissions from livestock-related source
categories all require information such as the definitions of livestock sub-categories, annual populations and feedintake estimates. To ensure that these definitions and data are used consistently across the source categories asingle ‘characterisation’ should be developed for each species. A coordinated livestock characterisation ensuresconsistency across the following source categories:•

Section 4.2 - CH4 emissions from enteric fermentation in domestic livestock;•

Section 4.3 - CH4 emissions from manure management;•

Section 4.4 - N2O emissions from manure management;•

Section 4.7 - Direct N2O emissions from agricultural soils;•

Section 4.8 - Indirect N2O emissions from nitrogen used in agriculture.

4.1.1.1

C HOICE OF CHARACTERI SATION DETAI L

Good practice is to identify the appropriate method for estimating emissions for each source category, and thenbase the characterisation on the most detailed requirements identified for each livestock species. The livestock
characterisation ultimately developed will likely undergo multiple iterations as the needs of each source category
are assessed during the emissions estimation process (see Figure 4.1, Decision Tree for Livestock Population
Characterisation). The steps required are as follows:•

Identify the Species Contributing to Multiple Emission Source Categories. The livestock species that
contribute to multiple emission source categories should first be listed. These species are typically: cattle,
buffalo, sheep, goats, swine, horses, camels, mules/asses, and poultry.

•

Review the Emission Estimation Method for each of the Pertinent Source Category. For the source
categories of enteric fermentation, CH4 and N2O from manure management, as well as direct and indirect
N2O emissions, identify the emission estimating method for that species for that source category. For
example, enteric fermentation emissions from cattle, buffalo, and sheep should each be examined to assess
whether emissions are large enough to warrant the Tier 2 emissions estimate for each of these species.
Similarly, manure management methane emissions from cattle, buffalo, swine, and poultry should be
examined to determine whether the Tier 2 emissions estimate is appropriate. Existing inventory estimates
can be used to conduct this assessment. If no inventory has been developed to date, Tier 1 emissions
estimates should be calculated to provide initial estimates for conducting this assessment. See Chapter 7,
Methodological Choice and Recalculation, for guidance on the general issues of methodological choice.

•Identify the Most Detailed Characterisation Required for each Livestock Species. Based on theassessments for each species under each source category, identify the most detailed characterisation required
to support each emissions estimate for each species. Typically, the ‘Basic’ characterisation can be used
across all relevant source categories if the enteric fermentation and manure sources are both estimated with
their Tier 1 methods. An ‘Enhanced’ characterisation should be used to estimate emissions across all the
relevant sources if the Tier 2 method is used for either enteric fermentation or manure.

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Agriculture

Figure 4.1 Decision Tree for Livestock Population Characterisation

Identify livestock species
common to multiple
emission sources

Review the emission
estimation methods foreach of the pertinent source
categories*

Identify the most detailed
characterisation required foreach livestock species(see Chapter 7, Methodological
Choice and Recalculation)

Ask for each species:
Are data available tosupport the level of detailrequired for thecharacterisation?

No

Can data
be collected tosupport the level ofdatail required for thecharacterisation?

No

Yes
Yes
Collect the datarequired to support
the characterisation?
Box 2
Box 2

Perform thecharacterisation at therequired level of detail

Box 1
Reduce the level of detailof the characterisation
to the level that can besupported by
the available data

*These sources include: CH4 from Enteric Fermentation, CH4 from Manure Management, N2O from Manure Management, Direct N2O from
Agricultural Soils, and Indirect N2O from Nitrogen used in Agriculture

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Chapter 4

BASIC CHARACTERISATION
For the ‘Basic’ Characterisation it is good practice to collect the following livestock characterisation data to
support the emissions estimates:
Livestock Species and Categories: A complete list of all significant livestock populations that have default
emission factor values provided in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories(IPCC Guidelines) must be developed (i.e. dairy cows, other cattle, buffalo, sheep, goats, camels, horses, mules
and asses, swine, and poultry).1 More detailed categories can (and should) be used if the data are available.
Annual Population: If possible, inventory agencies should use population data from official national statistics
or industry sources. Food and Agriculture Organisation (FAO) data can be used if national data are unavailable.
Seasonal births or slaughters may cause the population size to expand or contract at different times of the year,
which will require the population numbers to be adjusted accordingly. It is important to fully document themethod used to estimate the annual population, particularly if adjustments to the original data are required.
Milk Production: Average annual milk production for dairy cows is required. Milk production data are used inestimating an emission factor for enteric fermentation using the Tier 1 method. Country-specific data sources are
preferred, but FAO data may also be used.
Climate: For some large countries, livestock may be managed in regions with different climates. For each
livestock category, the percentage of animals in each climate region should be estimated. In the IPCC
Guidelines, Reference Manual, Table 4-1, three climate regions are defined in terms of annual averagetemperature: cool (<15°C), temperate (15°C - 25°C), and warm (>25°C). Livestock population data by region
can be developed from country-specific climate maps.

ENHANCED CHARACTERISATIONThe ‘Enhanced’ livestock characterisation provides detailed information on:•

Definitions for livestock sub-categories;•

Livestock population by sub-category;•Feed intake estimates for the typical animal in each sub-category.

The livestock population sub-categories should be defined to create relatively homogenous sub-groupings ofanimals. By dividing the population into these sub-categories, country-specific variations in age structure andanimal performance within the overall livestock population can be reflected.
The feed intake estimates developed through the ‘Enhanced’ characterisation are used in the Tier 2 entericfermentation emissions estimate for cattle, buffalo, and sheep. Additionally, these same feed intake estimatesshould be used to harmonise the estimated manure and nitrogen excretion rates used to estimate CH4 and N2Oemissions from manure management and direct and indirect N2O emissions.
Define Livestock Sub-categories: It is good practice to classify cattle and buffalo populations into a minimum of
three main sub-categories for each species:•

Cattle: Mature Dairy Cows, Mature Non-Dairy Cattle, and Young Cattle.

•

Buffalo: Mature Dairy Buffalo (females only), Mature Non-Dairy Buffalo, and Young Buffalo.

Depending on the level of detail in the implementation of the emissions estimation method, these maincategories can be further classified into sub-categories based on animal or feed characteristics. The mostcommon sub-categories for cattle and buffalo are shown in Table 4.1, Representative Cattle and Buffalo
Categories, although other sub-categories could be developed in particular countries.
For sheep, the national flock can be disaggregated into categories according to animal and management class aspresented in Table 4.2, Representative Sheep Categories. Subdivisions similar to those used for cattle andbuffalo can be used to further disaggregate the sheep population with the goal of creating sub-categories withrelatively homogenous characteristics.
When completing the Tier 2 manure management methane estimate for swine, it is preferable to classify theswine population into the following sub-categories: sows, boars, and growing animals. Sows could be further
1 The IPCC Guidelines uses the term ‘dairy cattle’ to refer to cows that have calved at least once and are being kept to
produce milk. For good practice, the term ‘dairy cattle’ has been changed to ‘dairy cows’ to avoid possible confusion withother cattle (e.g. replacement dairy heifers) connected with the dairy industry. The term ‘other cattle’ is used to refer to cattle
that are not in other defined categories.

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Chapter 4

Agricultureclassified into farrowing and gestation sows, and growing animals further divided into nursery, growing andfinisher pigs. It should be noted, however, that this disaggregation is only necessary if detailed data are available
on manure management system usage by these animal species/categories.
For large countries or for countries with distinct regional differences, it may be useful to designate regions andthen define categories within those regions. Regional subdivisions are generally defined to represent differencesin feeding systems and diet.
TABLE 4.1
REPRESENTATIVE CATTLE AND BUFFALO CATEGORIESMain Categories
Mature Dairy Cows or Mature Dairy
Buffalo

Other Mature Cattle or Mature Nondairy Buffalo

Sub-categories•

High-producing dairy cows or dairy buffalo that have calved at least
once and are used principally for milk production;•

Low-producing dairy cows or dairy buffalo that have calved at least
once and are used principally for milk production.

Females:•

Cows used principally for producing meat;•

Cows used for more than one production purpose: milk, meat, draft.

Males:

Young Cattle or Young Buffaloes•

Bulls used principally for breeding purposes;•

Bullocks used principally for draft power;•

Steers used principally for producing meat.

•

Pre-Weaned Calves;•

Growing cattle or buffaloes;•

Feedlot-fed cattle or buffalo on high-grain diets.

Source: IPCC Guidelines, Reference Manual, Table 4-7.

TABLE 4.2
REPRESENTATIVE SHEEP CATEGORIESMain Categories

Sub-categories•

Breeding ewes where either meat or wool production or both is theprimary purpose;•

Milking ewes where commercial milk production is the primary
purpose.

Other Mature Sheep (>1 year)•

No further sub-categorisation recommended

Young Sheep•

Intact males;•

Castrates;•

Females.

Mature Ewes

Source: Lassey and Ulyatt (1999).

Livestock Population by Sub-category: For each livestock sub-category, the average annual population should be
estimated in terms of the number of head per year, although in some cases a period of less than a year may be
used. Regardless of the length of time chosen, it is important to ensure temporal consistency between the activity
data and the emission factor. As far as possible inventory agencies are encouraged to use their own population
data from official national statistics or industry sources, but FAO data can be used if necessary. Seasonal births
and slaughters may cause the population to expand or contract at different times of the year, which will require
the population numbers to be adjusted accordingly. It is important to fully document the method used to estimate
the average annual population, particularly if adjustments to the original data are required.
Feed Intake Estimates: The feed intake of a representative animal in each sub-category is estimated to support
the Tier 2 emissions estimates. Feed intake is typically measured in terms of energy (e.g. Mega Joules (MJ) perday) or dry matter (e.g. kilograms (kg) per day). To support the enteric fermentation Tier 2 method (seeSection 4.2), detailed data requirements and equations are included in the IPCC Guidelines to estimate feed

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Chapter 4

intake. The good practice guidance presented below updates the IPCC Guidelines for cattle and buffalo to makethe equations more applicable to a wider range of animal species/categories and management conditions. In
addition, an enhanced characterisation to support the Tier 2 method for sheep is presented, recognising that forsome countries sheep are a significant source of emissions. Feed intake for other species can be estimated using
similar country-specific methods appropriate for each. The remainder of this sub-section presents the datarequirements and equations used to estimate feed intake for cattle, buffalo, and sheep. For all estimates of feedintake, good practice is to:•Collect data to describe the performance of the typical animal in each sub-category;•

Estimate feed intake from the animal performance data for each sub-category.

In some cases, the equations should be applied on a seasonal basis, for example under conditions in which
livestock gain weight in one season and lose weight in another.
The following animal performance data are required for each animal sub-category to estimate feed intake for the
sub-category:•

Weight (W), kg: Live-weight data should be collected for each animal sub-category, and the data should bebased on weight measurements of live animals. As it is unrealistic to perform a complete census of liveweights, live-weight data could be obtained from research studies, expert assessments or statistical
databases. Live-weight data should be checked to ensure that it is representative of country conditions.
Comparing the live-weight data with slaughter-weight data is a useful cross-check to assess whether the
live-weight data are representative of country conditions. However, slaughter-weight data should not be
used in place of live-weight data. Additionally it should be noted that the relationship between live-weightand slaughter-weight varies between countries. For cattle, buffalo and mature sheep, the yearly average
weight for each animal category (e.g. mature beef cows) is needed. For young sheep, weights are needed at:birth, weaning, one year of age, and at slaughter if slaughter occurs prior to one year.

•

Average weight gain (or loss) per day (WG), kg/d (for cattle and buffalo): Data on average weight gain are
generally collected for feedlot animals and young growing animals. Mature animals are generally assumed
to have no net weight gain or loss over an entire year. However, collecting data on weight gain and loss formature animals may be appropriate for countries with wet and dry seasons or extreme temperatures. Mature
animals lose weight during the dry season and under extreme temperatures gain weight during the wet
season. In this circumstance, the feed intake would be estimated separately for the wet and dry seasons andhot and cold seasons.

•

Mature weight (MW), kg (for cattle and buffalo): The mature weight is the potential body weight of an adultanimal were it to reach 28% body fat (NRC 1996). The mature weight will vary among breeds. Mature bodyweight may be similar to ‘reference weight’ or ‘final shrunk body weight’ values as used in different
countries. Estimates of mature weight are typically available from livestock specialists and producers.

•

Average number of hours worked per day: For draft animals, the average number of hours worked per day
must be determined.

•

Feeding situation: The feeding situation that most accurately represents the animal sub-category must be
determined using the definitions shown below. If the feeding situation falls between the definitions, thefeeding situation should be described in detail. This detailed information may be needed when calculating
the enteric fermentation emissions, because interpolation between the feeding situations may be necessary toassign the most appropriate coefficient. For cattle and buffalo the feeding situations are:(i)

Stall or housed – animals are confined to a small area (i.e. tethered, pen, barn) with the result thatthey expend very little energy to acquire feed;(ii)

Pasture – animals are confined in areas with sufficient forage requiring modest energy expense toacquire feed;(iii)

Grazing large areas – animals graze open range land or hilly terrain and expend significant energy
to acquire feed.

For sheep, the feeding situations are:

4.12

(i)

Housed ewes – animals are confined due to pregnancy in final trimester (50 days);(ii)

Grazing flat pasture – animals walk up to 1000 meters per day and expend very little energy toacquire feed;(iii)

Grazing hilly pasture – animals walk up to 5,000 meters per day and expend significant energy toacquire feed;

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Chapter 4(iv)

Agriculture

Housed fattening lambs – animals are housed for fattening.

•

Average milk production per day, kg/d: These data are for milking ewes, dairy cows and buffalo and othercows or non-dairy buffalo nursing calves. The average daily production should be calculated by dividing thetotal annual production by 365, or reported as average daily production along with days of lactation peryear, or estimated using seasonal production divided by number of days per season. (Note: If using seasonal
production data, the emission factor must be developed for that seasonal period).

•Fat content, %: Average fat content of milk is required for all lactating cows and buffalo.

•

Percent of females that give birth in a year: This is collected only for mature cattle, buffalo, and sheep.

•

Feed digestibility, (DE): The proportion of energy in the feed not excreted in the feces is known as feed
digestibility. The feed digestibility is commonly expressed as a percentage (%). Common ranges of feed
digestibility are 50-60% for crop by-products and range lands; 60-75% for good pastures, good preserved
forages, and grain supplemented forage-based diets; and 75-85% for grain-based diets fed in feedlots.
Digestibility data should be based on measured values for the dominant feeds or forages being consumed,
considering seasonal variations. Although a complete census of digestibility is considered unrealistic, at aminimum digestibility data from research studies should be consulted. While developing the digestibility
data, associated feed characteristic data should also be recorded when available, such as measured values for
Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF) and crude protein. NDF and ADF are feedcharacteristics measured in the laboratory that are used to indicate the nutritive value of the feed forruminant animals. The concentration of crude protein in the feed can be used to estimate nitrogen excretion.

•

Average annual wool production per sheep (kg/yr): The amount of wool produced in kilograms (after dryingout but before scouring) is needed to estimate the amount of energy allocated for wool production.

The first step in collecting these data should be to research national statistics, industry sources, research studies
and FAO statistics. If published data are not available from these sources, interviews of key industry andacademic experts can be undertaken. Section 6.2.5 of Chapter 6, Quantifying Uncertainties in Practice, describes
how to elicit expert judgement for uncertainty ranges. Similar expert elicitation protocols can be used to obtain
the information required for the livestock characterisation if published data and statistics are not available.
The animal performance data are used to estimate gross energy (GE) intake, which is the amount of energy(MJ/day) an animal needs to perform activities such as growth, lactation, and pregnancy. For inventory agencies
that have well-documented and recognised country-specific methods for estimating GE intake based on animal
performance data, it is good practice to use the country-specific methods. All the metabolic functions listed inTable 4.3, Summary of the Equations Used to Estimate Gross Energy Intake for Cattle and Buffalo and for
Sheep, should be included in the GE intake estimate. If no country-specific methods are available, GE intake
should be calculated using the equations listed in Table 4.3. As shown in the table, separate equations are used toestimate net energy requirements for sheep as compared with cattle and buffalo. The equations used to calculate
GE are as follows:
Maintenance: NEm is the net energy required for maintenance, which is the amount of energy needed to keep
the animal in equilibrium where body tissue is neither gained nor lost (Jurgen, 1988).
EQUATION 4.1
NET ENERGY FOR MAINTENANCE
NEm = Cfi • (Weight)0.75
Where:
NEm = net energy required by the animal for maintenance, MJ/day
Cfi = a coefficient which varies for each animal category as shown in Table 4.4 (Coefficients for
Calculating NEm)
Weight = live-weight of animal, kg

Activity: NEa is the net energy for activity, that is the energy needed for animals to obtain their food. The net
energy for activity was previously termed NEfeed in the IPCC Guidelines. NEfeed is now called NEa because thenet energy refers to the amount of energy the animal expends to acquire its feed and is based on its feeding
situation rather than characteristics of the feed itself. As presented in Table 4.3, the equation for estimating NEa
for cattle and buffalo is different from the equation used for sheep.

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Agriculture

Chapter 4

EQUATION 4.2a
NET ENERGY FOR ACTIVITY (FOR CATTLE AND BUFFALO)
NEa = Ca • NEm
Where:
NEa = net energy for animal activity, MJ/day
Ca = coefficient corresponding to animal’s feeding situation (Table 4.5, Activity Coefficients)
NEm = net energy required by the animal for maintenance (Equation 4.1), MJ/day

EQUATION 4.2b
NET ENERGY FOR ACTIVITY (FOR SHEEP)
NEa = Ca • (weight)
Where:
NEa = net energy for animal activity, MJ/day
Ca = coefficient corresponding to animal’s feeding situation (Table 4.5)
weight = live-weight of animal, kg

For Equations 4.2a and 4.2b, the coefficient Ca corresponds to a representative animal’s feeding situation as
described earlier. Values for Ca are shown in Table 4.5. If a feeding situation falls between the definitions
provided or occurs for only part of the year, NEa must be weighted accordingly.
TABLE 4.3
SUMMARY OF THE EQUATIONS USED TO ESTIMATE GROSS ENERGY INTAKE FOR CATTLE AND
BUFFALO AND FOR SHEEPMetabolic Functions andOther Estimates

Equations for Cattle andBuffaloEquations for Sheep

Maintenance (NEm)

Equation 4.1

Equation 4.1

Activity (NEa)

Equation 4.2aEquation 4.2b

Growth (NEg)

Equation 4.3a

Equation 4.3b

Weight Loss (NEmobilized).

Equations 4.4a and 4.4b

NA

Lactation (NEl)*

Equation 4.5a

Equations 4.5b and 4.5c

Draft Power (NEw)

Equation 4.6

NA

Wool Production (NEwool)

NA

Equation 4.7

Pregnancy (NEp)*

Equation 4.8

Equation 4.8{NEma/DE}

Equation 4.9

Equation 4.9{NEga/DE}

Equation 4.10

Equation 4.10

Gross Energy

Equation 4.11

Equation 4.11

Source: Beef equations based on NRC (1996) and sheep based on AFRC (1993).
NA means ‘not applicable’.
* Applies only to the proportion of females that give birth.

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Chapter 4

Agriculture

TABLE 4.4
COEFFICIENTS FOR CALCULATING NEm
Animal Category

Cfi

Comments

Cattle/Buffalo (non-lactating)

0.322

Cattle/Buffalo (lactating)

0.335

NRC (1989) provides a higher
maintenance allowance for lactation

Sheep (lamb to 1 year)

0.236

15% higher for intact males

Sheep (older than 1 year)

0.217

15% higher for intact males

Source: NRC (1984) and AFRC (1993).

TABLE 4.5
ACTIVITY COEFFICIENTS CORRESPONDING TO ANIMAL’S FEEDING SITUATION
Situation

DefinitionCa

CATTLE AND BUFFALO
StallAnimals are confined to a small area (i.e. tethered, pen,barn) with the result that they expend very little or noenergy to acquire feed.

0

PastureAnimals are confined in areas with sufficient forage
requiring modest energy expense to acquire feed.

0.17

Grazing large areas

Animals graze in open range land or hilly terrain and
expend significant energy to acquire feed.

0.36

Housed ewes

Animals are confined due to pregnancy in final trimester (50days).

0.0090Grazing flat pasture

Animals walk up to 1000 meters per day and expend very
little energy to acquire feed.

0.0107

Grazing hilly pasture

Animals walk up to 5,000 meters per day and expend
significant energy to acquire feed.

0.024

Housed fattening lambs

Animals are housed for fattening.

0.0067

SHEEP

Source: IPCC Guidelines.

Growth: NEg is the net energy needed for growth (i.e. weight gain). The current NEg equation based on NRC(NRC, 1996) is different from the NEg equation in the IPCC Guidelines. The main difference is that the current
NEg equation for cattle and buffalo (shown in Equation 4.3a) includes a mature weight-scaling factor. Whencharacterising an animal category that has a net weight loss for a period of time (e.g. cattle during the dry
season), do not use Equation 4.3a, go directly to Equation 4.4a or 4.4b. For sheep, NEg is estimated using
Equation 4.3b.
EQUATION 4.3a
NET ENERGY FOR GROWTH (FOR CATTLE AND BUFFALO)
NEg = 4.18 • {0.0635 • [0.891 • (BW • 0.96) • (478/(C • MW))] 0.75 • (WG • 0.92) 1.097}

Where:
NEg = net energy needed for growth, MJ/day
BW = the live body weight (BW) of the animal, kg
C = a coefficient with a value of 0.8 for females, 1.0 for castrates and 1.2 for bulls (NRC, 1996)
MW = the mature body weight of an adult animal, kg
WG = the daily weight gain, kg/day

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Agriculture

Chapter 4

EQUATION 4.3b
NET ENERGY FOR GROWTH (FOR SHEEP)
NEg = {WGlamb • [a + 0.5b (BWi + BWf)]} / (365 days/year)
Where:
NEg = net energy needed for growth, MJ/day
WGlamb = the corresponding weight gain (BWf – BWi), kg
BWi = the bodyweight at weaning, kg
BWf = the bodyweight at 1-year old or at slaughter (live-weight) if slaughtered prior to 1 year of age, kg

Note that lambs will be weaned over a period of weeks as they supplement a milk diet with pasture feed or
supplied feed. The time of weaning should be taken as the time at which they are dependent on milk for half
their energy supply.
The NEg equation used for sheep includes two constants that vary by animal species/category, and are presented
in Table 4.6, Constant for Use in Calculating NEg for Sheep:
TABLE 4.6
CONSTANTS FOR USE IN CALCULATING NEg FOR SHEEP
Animalspecies/categoryabIntact Males

2.5

0.35

Castrates

4.4

0.32

Females

2.1

0.45

Source: AFRC (1993).

Weight Loss for Cattle and Buffalo: When an animal loses weight, NEmobilised represents the energy in theweight loss that can be used by the animal for maintenance. Weight loss is typically not observed when
performing an inventory because data are generally collected to describe the change in weight for a year, andmature cattle and buffalo typically have no net change in weight from one year to the next. However, animals
sometimes lose weight during part of the year and gain weight during part of the year. For example, in somecountries animals lose weight during the dry season and gain weight during the wet season. Additionally, a highproducing dairy cow typically loses weight early in lactation, as body tissues are used to supply energy for milk
production. This weight is typically gained back later in the year.
Equations 4.4a and 4.4b are provided for estimating NEmobilised for high-producing lactating dairy cows and forother cattle and buffalo. These equations would typically only be used if feed intake is being estimated for
portions of a year during which weight loss is observed.
For lactating dairy cows, approximately 19.7 MJ of NE is mobilised per kilogram of weight loss. Therefore, theNEmobilised is calculated as follows (NRC, 1989):
EQUATION 4.4a
NET ENERGY DUE TO WEIGHT LOSS (FOR LACTATING DAIRY COWS)
NEmobilised = 19.7 • Weight Loss
Where:
NEmobilised = net energy due to weight loss (mobilised), MJ/day
Weight Loss = animal weight lost per day, kg/day

Note that weight loss is taken as a negative quantity in Equation 4.4a, such that the estimated NEmobilised is anegative number.

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Chapter 4

Agriculture

For other cattle and buffalo, the amount of energy mobilised through weight loss is calculated by: (1) inserting
the amount of weight lost (kg/day) as a positive number into Equation 4.3a as WG to calculate NEg; and(2) calculating NEmobilised as negative 0.8 times this NEg value (NRC, 1996).
EQUATION 4.4b
NET ENERGY DUE TO WEIGHT LOSS (FOR BUFFALO AND OTHER CATTLE)
NEmobilised = NEg • (−0.8)
Where:
NEmobilised = net energy due to weight loss (mobilised), MJ/day
NEg = net energy needed for growth, MJ/day

The result from Equation 4.4b is also a negative number.
Lactation: NEl is the net energy for lactation. For cattle and buffalo the net energy for lactation is expressed as afunction of the amount of milk produced and its fat content expressed as a percentage (e.g. 4%) (NRC, 1989):
EQUATION 4.5a
NET ENERGY FOR LACTATION (FOR CATTLE AND BUFFALO)
NEl = kg of milk per day • (1.47 + 0.40 • Fat)
Where:
NEl = net energy for lactation, MJ/day
Fat = fat content of milk, %Two methods for estimating the net energy required for lactation (NEl) are presented for sheep. The first method(Equation 4.5b) is used when the amount of milk produced is known, and the second method (Equation 4.5c) isused when the amount of milk produced is not known. Generally, milk production is known for ewes kept forcommercial milk production, but it is not known for ewes that suckle their young to weaning. With a known
amount of milk production, the total annual milk production is divided by 365 days to estimate the average daily
milk production in kg/day (Equation 4.5b). When milk production is not known, AFRC (1990) indicates that fora single birth, the milk yield is about 5 times the weight gain of the lamb. Consequently, total annual milk
production can be estimated as five times the increase in lamb weight prior to weaning. The daily average milk
production is estimated by dividing the resulting estimate by 365 days as shown in Equation 4.5c.
EQUATION 4.5b
NET ENERGY FOR LACTATION FOR SHEEP (MILK PRODUCTION KNOWN)
NEl = kg of milk/day • EVmilk
Where:
NEl = net energy for lactation, MJ/day
EVmilk = the energy value for milk. A default value of 4.6 MJ/kg (AFRC, 1993) can be used

EQUATION 4.5C
NET ENERGY FOR LACTATION FOR SHEEP (MILK PRODUCTION UNKNOWN)
NEl = ((5 • WGlamb)/365 days/year) • EVmilk
Where:
NEl = net energy for lactation, MJ/day
WGlamb = the weight gain of the lamb between birth and weaning in kg/day
EVmilk = the energy value for milk. A default value of 4.6 MJ/kg (AFRC, 1993) can be used

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Agriculture

Chapter 4

Equations 4.5b and 4.5c assume that the characterisation is being developed for a full year (365 days). If ashorter period is being characterised (e.g. a wet season), then the number of days must be adjusted accordingly.
Work: NEw is the net energy for work. It is used to estimate the energy required for draft power for cattle andbuffalo. Various authors have summarised the energy intake requirements for providing draft power (e.g.
Lawrence, 1985; Bamualim and Kartiarso, 1985; and Ibrahim, 1985). The strenuousness of the work performedby the animal influences the energy requirements, and consequently a wide range of energy requirements havebeen estimated. The values by Bamualim and Kartiarso show that about 10 percent of a day’s NEm requirements
are required per hour for typical work for draft animals. This value is used as follows:
EQUATION 4.6
NET ENERGY FOR WORK (FOR CATTLE AND BUFFALO)
NEw = 0.10 • NEm • hours of work per day
Where:
NEw = net energy for work, MJ/day
NEm = net energy required by the animal for maintenance (Equation 4.1), MJ/day

Wool Production: NEwool is the net energy required for sheep to produce a year of wool. The NEwool is calculated
as follows:
EQUATION 4.7
NET ENERGY TO PRODUCE WOOL (FOR SHEEP)
NEwool = (EVwool • annual wool production per sheep, kg/year)/(365 days/year)
Where:
NEwool = net energy required to produce a year of wool, MJ/day
EVwool = the energy value of each kg of wool produced (weighed after drying but before scouring)
AFRC provides for EVwool the value 24 MJ/kg. At a typical wool production of about 4
kg/sheep/year, the energy demand will normally be quite small.

Pregnancy: NEp is the energy required for pregnancy. For cattle and buffalo, the total energy requirement for
pregnancy for a 281-day gestation period averaged over an entire year is calculated as 10% of NEm. For sheep,
the NEp requirement is similarly estimated for the 147-day gestation period, although the percentage varies with
the number of lambs born (Table 4.7, Constant for Use in Calculating NEp in Equation 4.8). Equation 4.8 shows
how these estimates are applied.
EQUATION 4.8
NET ENERGY FOR PREGNANCY (FOR CATTLE/BUFFALO AND SHEEP)
NEp = Cpregnancy • NEm
Where:
NEp = net energy required for pregnancy, MJ/day
Cpregnancy = pregnancy coefficient (see Table 4.7)
NEm = net energy required by the animal for maintenance (Equation 4.1), MJ/day

When using NEp to calculate GE for cattle and sheep, the NEp estimate must be weighted by the portion of themature females that actually go through gestation in a year. For example, if 80% of the mature females in the
animal category give birth in a year, then 80% of the NEp value would be used in the GE equation below.

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Chapter 4

Agriculture

TABLE 4.7
CONSTANTS FOR USE IN CALCULATING NEP IN EQUATION 4.8
Animal Category

Cpregnancy

Cattle and Buffalo

0.10

Sheep
Single birth

0.077

Double birth (twins)

0.126

Triple birth or more (triplets)

0.150

Source: Estimate for cattle and buffalo developed from data in NRC (1996). Estimates for sheep developed from data in AFRC (1993).

To determine the proper coefficient for sheep, the portion of ewes that have single births, double births, andtriple births is needed to estimate an average value for Cpregnancy. If these data are not available, the coefficient
can be calculated as follows:•

If the number of lambs born in a year divided by the number of ewes that are pregnant in a year is less thanor equal to 1.0, then the coefficient for single births can be used.

•

If the number of lambs born in a year divided by the number of ewes that are pregnant in a year exceeds 1.0
and is less than 2.0, calculate the coefficient as follows:
Cpregnancy = [(0.126 • Double Birth Fraction) + (0.077 • Single Birth Fraction)]
Where:
Double Birth Fraction = [(lambs born) / (pregnant ewes)] – 1
Single Birth Fraction = 1 – Double Birth Fraction•

If the number of lambs born in a year divided by the number of ewes that are pregnant in a year exceeds 2,
then expert judgement should be sought on how to estimate NEp.

NEma/DE: For cattle, buffalo and sheep, the ratio of net energy available in a diet for maintenance to digestible
energy consumed NEma/DE is estimated using the following equation:
EQUATION 4.9
RATIO OF NET ENERGY AVAILABLE IN A DIET FOR MAINTENANCE TO DIGESTIBLE ENERGYCONSUMED
NEma/DE = 1.123 – (4.092 • 10-3 • DE) + [1.126 • 10-5 • (DE)2] – (25.4/DE)
Where:
NEma/DE = ratio of net energy available in a diet for maintenance to digestible energy consumed
DE = digestible energy expressed as a percentage of gross energy

NEga/DE: For cattle, buffalo and sheep the ratio of net energy available for growth (including wool growth) in adiet to digestible energy consumed NEga/DE is estimated using the following equation:
EQUATION 4.10
RATIO OF NET ENERGY AVAILABLE FOR GROWTH IN A DIET TO DIGESTIBLE ENERGYCONSUMED

NEga/DE = 1.164 – (5.160 • 10

-3• DE) + (1.308 • 10-5 • (DE)2) – (37.4/DE)

Where:
NEga/DE = ratio of net energy available for growth in a diet to digestible energy consumed
DE = digestible energy expressed as a percentage of gross energy

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Agriculture

Chapter 4Gross Energy, GE: As shown in Equation 4.11, GE is derived based on the net energy estimates and the feedcharacteristics. Equation 4.11 is similar to Equation 4.13 from the IPCC Guidelines, but it corrects a typesetting
error and changes the subscripts on some of the terms to distinguish between the net energy available in the feed
to meet a net energy requirement (i.e. NEga ) and the net energy requirement of the animal (i.e. NEg). It is goodpractice to use the corrected equation presented as Equation 4.11 below. Although the IPCC Guidelines do notpresent an equation specifically for sheep, Equation 4.11 represents good practice for calculating GE
requirements for sheep using the results of the equations presented above.
In using Equation 4.11, only those terms relevant to each animal category are used (see Table 4.3).
EQUATION 4.11
GROSS ENERGY FOR CATTLE/BUFFALO AND SHEEPGE = {[(NEm + NEmobilized + NEa + NEl + NEw + NEp)/(NEma/DE)] +[(NEg + NEwool ) / (NEga/DE)]} / (DE/100)
Where:
GE = gross energy, MJ/day
NEm = net energy required by the animal for maintenance (Equation 4.1), MJ/day
NEmobilised = net energy due to weight loss (mobilised) (Equations 4.4a and 4.4b), MJ/day
NEa = net energy for animal activity (Equations 4.2a and 4.2b), MJ/day
NEl = net energy for lactation (Equations 4.5a, 4.5b, and 4.5c), MJ/day
NEw = net energy for work (Equation 4.6), MJ/day
NEp = net energy required for pregnancy (Equation 4.8), MJ/day
NEma/DE = ratio of net energy available in a diet for maintenance to digestible energy consumed(Equation 4.9)
NEg = net energy needed for growth (Equations 4.3a and 4.3b), MJ/day
NEwool = net energy required to produce a year of wool (Equation 4.7), MJ/day
NEga/DE = ratio of net energy available for growth in a diet to digestible energy consumed (Equation
4.10)
DE = digestible energy expressed as a percentage of gross energy

Once the values for GE are calculated for each animal sub-category, the feed intake in units of kilograms of dry
matter per day (kg/day) should also be calculated and compared to the weight of the typical animal in the subcategory. To convert from GE in energy units to dry matter intake, divide by the energy density of the feed. A
default value of 18.45 MJ/kg can be used if feed-specific information is not available. The resulting daily dry
matter intake should be on the order of 1% to 3% of the body weight of the animal.

CHARACTERISATION FOR ANIMALS WITHOUT EMISSION ESTIMATION METHODS
Some countries may have domesticated animals for which there are currently no Tier 1 or Tier 2 emissions
estimating methods (e.g. llamas, alpacas, wapiti, emus, and ostriches). Good practice in estimating emissions
from these animals is to first assess whether their emissions are likely to be significant enough to warrant
characterising them and developing country-specific emission factors. Chapter 7, Methodological Choice and
Recalculation, presents guidance for assessing the significance of individual source categories within thenational inventory. Similar approaches can be used to assess the importance of sub-source categories (i.e.
species) within a source category such as enteric fermentation. If the emissions from a particular sub-species are
determined to be significant, then country-specific emission factors should be developed, and a characterisation
should be performed to support the development of the emission factors. The characterisation used to support theTier 2 emissions estimate for enteric fermentation from cattle is one example of how to develop an emission
factor. The data and methods used to characterise the animals should be well documented.
As emissions estimation methods are not available for these animals, approximate emission factors based on‘order of magnitude calculations’ are appropriate for conducting the assessment of the significance of theiremissions. One approach for developing the approximate emission factors is to use the Tier 1 emissions factor
for an animal with a similar digestive system and to scale the emissions factor using the ratio of the weights of

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Chapter 4

Agriculture

the animals raised to the 0.75 power. The Tier 1 emission factors can be classified by digestive system asfollows:•

Ruminant animals: cattle, buffalo, sheep, goats, camels•

Non-ruminant herbivores: horses, mules/asses•

Poultry: chickens, ducks, turkeys•

Non-poultry monogastric animals: swine

For example, an approximate enteric fermentation methane emissions factor for alpacas could be estimated fromthe emissions factor for sheep (also a ruminant animal) as follows:
Approximate emissions factor = [(alpaca weight)0.75 / (sheep weight)0.75 ] • sheep emissions factor.
Similarly, an approximate manure methane emissions factor for ostriches could be estimated using the Tier 1
emission factor for chickens. Approximate emission factors developed using this method can only be used toassess the significance of the emissions from the animals, and are not considered sufficiently accurate for
estimating emissions as part of a national inventory.

4.1.1.2

D EVELOPING A CONSISTENT TIME SERIES

Developing a consistent time series may require estimating past livestock population characteristics. Typically,
livestock population, milk production, and meat production data are available from national statistics for thecomplete time series. The other key attributes, which may not be as easily obtained through a review of past
production data records, do not change rapidly, so back-estimating on the basis of ongoing trends (e.g. trends inlive-weights) should be reliable. It should be noted, however, that some countries are experiencing rapid changes
in livestock populations as a result of economic restructuring and changing market conditions. Additional
investigation will be warranted in these circumstances to ensure that an adequate time series is developed. For
general good practice guidance related to ensuring a consistent time series, see Chapter 7, Methodological
Choice and Recalculation.

4.1.1.3

U NCERTAINTY ASSESSMENT

Each data element in the livestock characterisation has associated uncertainty that depends on how data wereobtained. The factors that contribute most to the sensitivity of the feed intake estimates should be identified sothat efforts are focused on estimating the uncertainties in these factors. The uncertainty of these factors should
then be propagated through to the final estimates of feed intake to estimate the total uncertainty of the feed intake
estimate.
The uncertainty in livestock population data is larger than typically recognised. There may well be systematic
biases in the reporting of the livestock population to national census takers (positive and negative). The
migration of livestock within or between countries may lead to double counting or under counting of someanimals. Seasonal changes in populations may not be adequately reflected in annual census data. The population
data should be examined in cooperation with the national statistical agencies with these factors in mind.

4.1.2

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. The current IPCC reporting tables do not provide a mechanism for reporting
detailed livestock characteristics. It is good practice to provide additional tables for reporting detailed livestock
characterisation. The detailed livestock characteristics could be reported in a summary table, such as shown in
Table A-1 (p. 4.31) and Table A-2 (pp. 4.32-4.33) in Section 4 of the IPCC Guidelines, Reference Manual. The
sources for the data in the summary table should be identified and cited clearly.

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Agriculture

4.1.3

Chapter 4

Inventory quality assurance/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in the Tier 2 procedures in Chapter 8, QA/QC, and quality
assurance procedures may also be applicable, particularly if higher tier methods are used to determine emissions
from this source. The general check of data processing, handling, and reporting could be supplemented withsource specific procedures discussed below:
Activ ity da ta c he c k• The inventory agency should check for consistency in the livestock characterisation data that are used in theemission estimates for each of the pertinent source categories. Standard QC checks should verify that there
is consistency in the data used across source categories.
•

If data are available, the inventory agency should compute the change in total population over time using the
population, birth and death rates, slaughter rates, and imports/exports for each of the animal categories or
sub-categories and compare this to statistics on total population to ensure consistency. The inventory agency
should make this calculation across years (e.g. 1990 to 1991 to 1992, and so on) as well as across seasons
within individual years. The analysis across seasons is particularly important in countries with seasonal
production conditions that create large variations in livestock populations during the year.

•

The inventory agency should compare total production (e.g. meat, milk and wool) for the animal categories
and sub-categories with the statistics on total production to ensure consistency.

•

Feed intake estimates developed to support the Tier 2 enteric fermentation emissions estimates should bechecked for reasonableness. For ruminant animals, the feed intake in dry matter (kg/day) should be on theorder of 1% to 3% of the weight of the animals.

•

The inventory agency should review QA/QC associated with secondary data sources (e.g. national food andagriculture agencies, agricultural trade associations, agricultural research organisations). Many of theorganisations preparing the livestock-related data will have their own procedures for assessing the quality ofthe data, independent of what the end use of the data may be. If the QA/QC satisfies the minimum activities
listed in the QA/QC plan, reference the QC activity conducted by the statistical organisation. If it isinadequate, establish independent QC checks on the secondary data, re-assess the uncertainty of theemissions estimates derived from the data, or reconsider how the data is used.

•

The inventory agency should cross-check activity data against other available reference sources. For
example, country-specific data should be compared to FAO statistics for livestock population data and milk
production data. Investigate large discrepancies.

E xte rna l r eview• The inventory agency should conduct expert peer review on the livestock characterisation data, involving
agricultural experts and specialists.

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Chapter 4

Agriculture

4.2

CH4 EMISSIONS FROM ENTERICFERMENTATION IN DOMESTICLIVESTOCK

4.2.1

Methodological issues

Livestock are produced throughout the world and are a significant source of global methane (CH4) emissions.
The amount of enteric methane emitted is driven primarily by the number of animals, the type of digestivesystem, and the type and amount of feed consumed. Cattle, buffalo and sheep are the largest sources of entericmethane emissions.

4.2.1.1

C HOICE OF METHOD

To estimate CH4 emissions from enteric fermentation, the IPCC Guidelines recommend multiplying the numberof animals for each animal category by an appropriate emissions factor. Emissions from all animal categories arethen summed to get total emissions. In order to maintain consistency in underlying data, it is good practice to
use a single livestock population characterisation as a framework for estimating CH4 emissions from entericfermentation as well as CH4 and N2O emissions from manure management. The Livestock Population
Characterisation section (see Section 4.1) provides guidance on preparing the characterisation.
The IPCC Guidelines describe two general methods for estimating emissions from enteric fermentation (seeFigure 4.2, Decision Tree for CH4 Emissions from Enteric Fermentation):•

The Tier 1 method is a simplified approach that relies on default emission factors drawn from previous
studies. The Tier 1 approach is likely to be sufficient for many countries and can be used to estimate
emissions for the following animals: dairy cows, other cattle, buffalo, sheep, goats, camels, horses, mules,
asses and swine.

•

The Tier 2 method is a more complex approach that requires detailed country-specific data on nutrient
requirements, feed intake and CH4 conversion rates for specific feed types, which are used to develop
emission factors for country-defined livestock categories. The Tier 2 approach should be used if entericfermentation is a key source category (as defined in Chapter 7, Methodological Choice and Recalculation)
for the animal categories that represent a large portion of the country’s total emissions.2

Tier 1 Method
Under the Tier 1 method, data on livestock categories and milk production should be used to select default
emission factors. Tables 4.3 and 4.4 in the Reference Manual of the IPCC Guidelines provide default emission
factors for each livestock category. As shown in Equation 4.12, the emission factor is multiplied by the numberof animals to determine total emissions for each livestock category. Total emissions for this source category are
the sum of all livestock categories as shown in Equation 4.13. It is good practice to review the Tier 1 emission
factors to ensure that the underlying animal characteristics such as weight, growth rate and milk production used
to develop them are similar to the conditions in the country. The IPCC Guidelines currently provide detailed
information for cattle and buffalo. These data should be reviewed by livestock experts in the country and if theunderlying characteristics are significantly different, the emission factors should be adjusted accordingly.

2 Countries, with large populations of domesticated animal species for which there are no IPCC default emission factors (e.g.

llamas and alpacas), are encouraged to develop national methods that are similar to the Tier 2 approach and are based onwell-documented research (if it is determined that emissions from these animals are significant). See Section 4.1 under theheading ‘Characterisation for Animals Without Emission Estimation Methods’ for more information.

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Chapter 4

Figure 4.2

Decision Tree for CH4 Emissions from EntericFermentationDoes
the country manage
cattle, buffalo, sheep, goats,camels, mules/asses,
swine, or otherlivestock?

No

Report‘Not Occurring’Yes

Is entericfermentation a key
source category?
(Note 1)

No

Yes

Ask
for each species:
Is this sub-source
category significant?
(Note 2)No

Askfor each species:
Are data available withwhich to perform a
Tier 2 estimate?

No

Yes
Yes
Box 2
Estimate emissions
for the species
using Tier 2

Box 1
Estimate emissions
for the species
using Tier 1

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)
Note 2: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source
category.

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EQUATION 4.12
EMISSIONS FROM A LIVESTOCK CATEGORY
Emissions = EF • population/(106 kg/Gg)
Where:
Emissions = methane emissions from enteric fermentation, Gg CH4/year
EF = emission factor for the specific population, kg/head/year
Population = the number of animals, head

EQUATION 4.13
TOTAL EMISSIONS FROM LIVESTOCK
Total CH4 Emissions =Σi Ei

Where:
Total Emissions = total methane emissions from enteric fermentation, Gg CH4/year
index i = sums all livestock categories and sub-categories
Ei = is the emissions for the ith livestock categories and sub-categories

Tier 2 Method
The Tier 2 method also uses Equation 4.12 to calculate emissions, but applies it to more disaggregated livestock
population categories and uses calculated emission factors, as opposed to default values. Equation 4.13 should be
used to sum the emissions from the disaggregated categories of livestock species for all livestock species toobtain the total emissions for a country. The key issues for the Tier 2 method are the development of emission
factors and the collection of detailed activity data. The development of emission factors is described in the next
section. Issues related to the collection of activity data are covered in Section 4.1, Livestock Population
Characterisation.

4.2.1.2

C HOICE OF EMISSIO N FACTORS

When the Tier 1 method is used, default emission factors should be taken from the IPCC Guidelines Tables 4-3
and 4-4, unless documented country-specific factors are available. When Tier 2 methods are used, in contrast,
emission factors specific to the country and its animal species/categories need to be developed. As described in
Chapter 7, Methodological Choice and Recalculation, inventory agencies are encouraged to determine whatsource sub-categories are significant, as some species are likely to represent the major share of entericfermentation emissions. It is considered good practice to develop disaggregated emission factors for those subcategories that are most significant in terms of emissions.
When the Tier 2 method is used, emission factors are estimated for each animal category using the detailed data
developed through the livestock characterisation as set out in the Livestock Population Characterisation section(see Section 4.1). The IPCC Guidelines discuss how to develop emission factors for cattle. Good practice indeveloping these factors is discussed below. In the absence of data for buffalo, the approach described for cattle
can be applied to buffalo, given the similarities between these bovine species. In addition, good practice fordeveloping sheep emission factors is described below, since this is an important animal species in many
countries.
An emission factor for each animal category should be developed using Equation 4.14:

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Chapter 4

EQUATION 4.14
EMISSION FACTOR DEVELOPMENT
EF = (GE • Ym • 365 days/yr) / (55.65 MJ/kg CH4)
Where:
EF = emission factor, kg CH4/head/yr
GE = gross energy intake, MJ/head/day
Ym = methane conversion rate which is the fraction of gross energy in feed converted to methaneThis emission factor equation assumes that the emission factors are being developed for an animal category foran entire year (365 days). While a full year emission factor is typically used, in some circumstances the animal
category may be defined for a shorter period (e.g. for the wet season of the year or for a 150-day feedlot feeding
period). In this case, the emission factor would be estimated for the specific period (e.g. the wet season) and the
365 days would be replaced by the number of days in the period. The definition of the period to which theemission factor applies is described as part of the livestock characterisation.
The gross energy intake value (GE) for each animal category is taken from the livestock characterisation
presented in Section 4.1.

Obtaining the Methane Conversion Rate (Y m )The extent to which feed energy is converted to CH4 depends on several interacting feed and animal factors. If
CH4 conversion rates are unavailable from country-specific research, the values provided in Table 4.8,
Cattle/Buffalo CH4 Conversion Rates, can be used for cattle and buffalo. These general estimates are a roughguide based on the general feed characteristics and production practices found in many developed anddeveloping countries. When good feed is available (i.e. high digestibility and high energy value) the lower
bounds should be used. When poorer feed is available, the higher bounds are more appropriate. A CH4
conversion rate of zero is assumed for all juveniles consuming only milk (i.e. milk-fed lambs as well as calves).
Due to the importance of Ym in driving emissions, substantial ongoing research is aimed at improving estimatesof Ym for different animals and feed combinations. Such improvement is most needed for animals fed on tropical
pastures as the available data is sparse. For example, a recent study (Kurihara et al., 1999) observed Ym values
outside the ranges described in Table 4.8.
TABLE 4.8
CATTLE/BUFFALO CH4 CONVERSION RATES (Ym)Countries
Developed Countries

Developing Countries

Livestock type

Ym b

Feedlot fed cattle a

0.04 + 0.005

All other cattle

0.06 + 0.005

Dairy cows (cattle and buffalo) and their young

0.06 + 0.005

Other cattle and buffaloes that are primarily fed low quality crop
residues and by-products

0.07 + 0.005

Other cattle or buffalo in Africa - grazing

0.07 + 0.005

Other cattle or buffalo in developing countries other than Africagrazing

0.06 + 0.005

aWhen fed diets contain 90 percent or more concentrates.

b

The ± values represent the range.

Source: IPCC Guidelines.

The Ym value for sheep may not be the same as for cattle. Lassey et al. (1997) suggest that the Ym for 8-monthold lambs is less (0.045) than for lactating dairy cows (0.062) fed near-identical high quality pasture. Sheepshould not be viewed as merely small cattle as far as nutritional performance is concerned, as they differ
behaviourally (feed selection) and may also differ in their rumen microbiology. Using Table 4.9, Sheep CH4
Conversion Rates, Ym values are selected according to feed quality (as measured by digestibility) and sheep
maturity. They are based on data by Lassey et al. (1997), Judd et al. (1999) and on unpublished data from thesame research group [K.R. Lassey and M.J. Ulyatt, personal communication]. The median of each range may be

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adopted, including 0.07 for mature sheep on all pastures. These values are consistent with measurements byother researchers (Murray et al., 1998; Leuning et al., 1999) but may not span the full range of pastures that may
be found.

TABLE 4.9
SHEEP CH4 CONVERSION RATES (Ym)
Category

Diets less than 65% digestible

Diets greater than 65%
digestible

0.06 + 0.005

0.05 + 0.005

0.07

0.07

Lambs (<1 year old)
Mature sheep
Note: The ± values represent the range.
Source: Lassey et al. (1997); Lassey and Ulyatt (1999).

4.2.1.3

C HOICE OF ACTIVI TY DATA

The activity data should be collected following the guidance from the Livestock Population Characterisation
section (see Section 4.1). This approach will ensure consistency with the other related source categories.

4.2.1.4

C OMPLETENESS

It is likely that all the major animals managed in the country are known. Consequently, completeness should beachievable. In the event that animals are included in the inventory for which default data are not available andfor which no guidelines are provided, the emissions estimate should be developed using the same generalprinciples presented in the discussion of how to develop Tier 2 emission factors.

4.2.1.5

D EVELOPING A CONSISTENT TIME SERIESThe key issues associated with developing a consistent time series are discussed in the Livestock Population
Characterisation section (Section 4.1). Care must be taken to use a consistent set of estimates for the CH4
conversion rate over time. In some cases, there may be reasons to modify these values of methane conversion
rates over time. These changes may be due to the implementation of explicit greenhouse gas (GHG) mitigation
measures, or may be due to changing agricultural practices such as feed conditions or other management factors
without regard to GHGs. Regardless of the driver of change, the data and methane conversion rates used toestimate emissions must reflect the change in data and methods, and the results must be thoroughly documented.
If methane conversion rates over a time series are affected by a change in farm practices and/or the
implementation of GHG mitigation measures, the inventory agency is encouraged to ensure that the inventory
data reflect these practices and that the inventory text thoroughly explains how the change in farm practices
and/or implementation of mitigation measures has affected the time series of methane conversion rates. For
general good practice guidance on developing a consistent time series, see Chapter 7, Methodological Choice
and Recalculation, Section 7.3.2.2.

4.2.1.6

U NCERTAINTY ASSESSMENT

Below is a description of the major uncertainty issues for the Tier 1 and Tier 2 methods.

Tier 1 Method
As the emission factors for the Tier 1 method are not based on country-specific data, they may not accurately
represent a country’s livestock characteristics, and may be highly uncertain as a result. Emission factors
estimated using the Tier 1 method are unlikely to be known more accurately than ± 30% and may be uncertain to± 50%.
There will be an added uncertainty associated with the livestock population characterisation (see Section 4.1)
which can be minimised provided the good practice approach to agricultural census data outlined in the section
on livestock population characterisation is followed.

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Chapter 4

Tier 2 Method
The uncertainty under the Tier 2 approach will depend on the accuracy of the livestock characterisation (e.g.
homogeneity of livestock categories), and also on the extent to which the methods for defining the coefficients inthe various relationships that make up the net energy approach correspond to national circumstances. Improving
the livestock characterisation will often be the priority in reducing overall uncertainty. Emission factor estimatesusing the Tier 2 method are likely to be in the order of ± 20%. Inventory agencies using the Tier 2 method areencouraged to undertake an analysis of uncertainties reflecting their particular situation, and in the absence of
this analysis the uncertainty under the Tier 2 method should be assumed similar to the uncertainty under the Tier
1 method.

4.2.2

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. To improve transparency, emission estimates from this source category should bereported along with the activity data and emission factors used to determine the estimates.
The following information should be documented:•

All activity data, including :(i)•Animal population data by category and region.

Activity data documentation including:(i)

The sources of all activity data used in the calculations (i.e. complete citation for the statistical
database from which data were collected);(ii)

The information and assumptions that were used to develop the activity data, in cases where
activity data were not directly available from databases;(iii)

The frequency of data collection, and estimates of accuracy and precision.

•

If the Tier 1 method is used, all default emission factors used in the emissions estimations for the specificanimal categories•

If the Tier 2 method is used(i)Values for Ym;(ii)

GE values estimated or taken from other studies;(iii)

Documentation of the data used, including their references.

In inventories in which country- or region-specific emission factors were used or in which new methods (otherthan those described in the IPCC Guidelines) were used, the scientific basis of these emission factors andmethods should be documented. Documentation should include definitions of input parameters providing adescription of the process by which these emission factors and methods are derived, as well as describing
sources and magnitudes of uncertainties.

4.2.3

Inventory quality assurance/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, and quality assurance
procedures may also be applicable, particularly if higher tier methods are used to determine emissions from thissource category. In addition to the guidance in Chapter 8, QA/QC, specific procedures of relevance to this source
category are outlined below:
Rev i e w o f e mi s sio n f a c t o r s• If using the Tier 2 method, the inventory agency should cross-check country-specific factors against the
IPCC defaults. Significant differences between country-specific factors and default factors should be
explained and documented.

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E xte rna l r eview• If the Tier 2 method is being used, the inventory agency should conduct expert peer review, including fromindustry, academic institutions, and extension expertise.
•

It is important to maintain internal documentation on review results.

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Chapter 4

4.3

CH4 EMISSIONS FROM MANUREMANAGEMENT

4.3.1

Methodological issues

Livestock manure is principally composed of organic material. When this organic material decomposes in ananaerobic environment, methanogenic bacteria produce methane (CH4). These conditions often occur when large
numbers of animals are managed in confined areas (e.g. dairy, swine and poultry farms, and beef feedlots, where
manure is typically stored in large piles or disposed of in storage tanks or lagoons).

4.3.1.1

C HOICE OF METHOD

To estimate emissions of CH4 from manure management systems, the animal population must first be divided
into the appropriate species and categories to reflect the varying amounts of manure produced per animal and themanner in which the manure is handled. Detailed information on how to characterise the livestock population forthis source is provided in the section on Livestock Population Characterisation (see Section 4.1).
As described in the IPCC Guidelines, the four main steps used to estimate CH4 emissions from livestock manure
are as follows:(i)Collect population data from Livestock Population Characterisation;(ii)

Use default IPCC emission factors or develop emission factors on the basis of manurecharacteristics (Bo, VS, MCF) for each relevant livestock population (species, category or
subcategory) and manure management system;(iii)

Multiply each emission factor by the defined livestock population to obtain the CH4 emission
estimate for that livestock population;(iv)

Sum emissions from all defined livestock population to determine national emissions.

Emission estimates should be reported in gigagrams (Gg). As the emission factors are to be reported in
kilograms per head per year, the emissions are divided by 106. Equation 4.15 shows how to calculate emissions
for a defined population:
EQUATION 4.15
CH4 EMISSION FROM MANURE MANAGEMENT
CH4 Emissions(mm) = Emission Factor • Population / (106 kg/Gg)
Where:
CH4 Emissions(mm) = CH4 emissions from manure management, for a defined population Gg/year
Emission Factor = emission factor for the defined livestock population, kg/head/year
Population = the number of head in the defined livestock population

The IPCC Guidelines include two tiers to estimate CH4 emissions from livestock manure. The Tier 1 approach isa simplified method that only requires livestock population data by animal species/category and climate region(cool, temperate, warm), in order to estimate emissions.
The Tier 2 approach provides a detailed method for estimating CH4 emissions from manure management
systems, and is encouraged to be used for countries where a particular livestock species/category represents asignificant share of emissions. This method requires detailed information on animal characteristics and themanner in which manure is managed. Using this information, emission factors are developed that are specific tothe conditions of the country.
The method chosen will depend on data availability and natural circumstances. Good practice in estimating CH4emissions from manure management systems entails making every effort to use the Tier 2 method, including
calculating emission factors using country-specific factors. The Tier 1 approach should only be used if all

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possible avenues to use the Tier 2 approach have been exhausted. The process for determining which tier to use
is shown in the decision tree (see Figure 4.3).

4.3.1.2

C HOICE OF EMISSIO N FACTORS

The ideal means of determining emission factors is to conduct non-invasive or non-disturbing measurements ofemissions in actual production systems (feedlot, pasture). These field results can be used to develop models toestimate emission factors. Such measurements are difficult to conduct, however, and require significant
resources, unique expertise, and equipment that may not be available. Thus, while such an approach isrecommended to improve accuracy, it is not necessarily required for good practice depending on national
circumstances.
When using the Tier 1 method, default emission factors are used. Default emission factors are presented in Table
4-6 of the IPCC Guidelines, Reference Manual for each of the recommended population subgroups.3
If region-specific or country-specific measurement data are not available, Tier 2 emission factors should be
developed using the method described in the IPCC Guidelines. The process of developing Tier 2 emission
factors involves determining the mass of volatile solids excreted by the animals (VS, in kg) along with the
maximum CH4 producing capacity for the manure (Bo, in m3/kg of VS). In addition, a CH4 conversion factor(MCF) that accounts for the influence of climate on CH4 production must be obtained for each manure
management system.
As emissions can vary significantly by region and animal species/category, emission estimates should reflect tothe maximum extent possible the diversity and range of animal populations and manure management practices
between different regions within a country. This may require separate estimates to be developed for each region.
Emission factors should be periodically updated to account for changes in manure management practices, animal
characteristics, and technologies. These revisions should be based on the most reliable scientifically reviewed
data available. Frequent monitoring is desirable to verify key model parameters, but this may not be feasible.
VS Excretion Rates: The best way to obtain average daily VS excretion rates is to use data from country-specificpublished sources. If average daily VS excretion rates are not available, country-specific VS excretion rates canbe estimated from feed intake levels. Feed intake for cattle and buffalo can be estimated using the ‘Enhanced’
characterisation method described in the Livestock Population Characterisation section (see Section 4.1). This
will also assure consistency in the data underlying the emissions estimates. For swine, country-specific swine
production data may be required to estimate feed intake. Once feed intake is estimated, the VS excretion rate isestimated as:
EQUATION 4.16
VOLATILE SOLID EXCRETION RATESVS = GE • (1 kg-dm/18.45 MJ) • (1 – DE/100) • (1 – ASH/100)
Where:
VS = volatile solid excretion per day on a dry-matter weight basis, kg-dm/day
GE = Estimated daily average feed intake in MJ/day
DE = Digestible energy of the feed in percent (e.g. 60%)
ASH = Ash content of the manure in percent (e.g. 8%)
Note: The value 18.45 is the energy density of feed expressed in MJ per kg dry matter. This value isrelatively constant across a wide range of forage and grain-based feeds commonly consumed bylivestock.

For cattle, the DE value used should be the value used in the ‘Enhanced’ characterisation method described in
the Livestock Population Characterisation (see Section 4.1). The ash content of cattle and buffalo manure is
generally around 8% (IPCC,1996). For swine, default values for digestibility are 75% and 50% for developed

3 It should be noted, however, that there is an error in Table 4-6 of the IPCC Guidelines. The error is the default CH
4emission factor for non-dairy cattle in temperate regions in Latin America. The value should be 1 instead of 2, as shown
correctly in Appendix B of the IPCC Guidelines, Vol. 3.

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Chapter 4

and developing countries, respectively. As to ash content, values of 2% and 4% can be used for developed anddeveloping countries, respectively (IPCC, 1996).
If country-specific VS values cannot be developed, the default VS production rates presented in the IPCC
Guidelines Reference Manual (Tables B1-B7) can be used. These default factors were developed based on
average feed intake and feed digestibility data, and are considered reasonably reliable.
Bo values: The preferred method to obtain Bo measurement values is to use data from country-specific publishedsources, measured with a standardised method. It is important to standardise the Bo measurement, including theway of sampling. If country-specific Bo measurement values are not available, default values are provided inAppendix B of the IPCC Guidelines, Reference Manual.4
MCF Values: Default MCF values are provided in the IPCC Guidelines for different manure management
systems and climate zones. These default values may not, however, encompass the potentially wide variation
within the defined categories of management systems. Therefore, country-specific MCFs that reflect the specificmanagement systems used in particular countries or regions should be developed as far as possible. This isparticularly important for countries with large animal populations or with multiple climate regions. In suchcases, and if possible, field measurements should be conducted for each climate region to replace the laboratorybased default MCF values. Measurements should include the following factors:•

Timing of storage/application;•

Length of storage;•

Manure characteristics;•

Determination of the amount of manure left in the storage facility (methanogenic inoculum);•

Time and temperature distribution between indoor and outdoor storage;•

Daily temperature fluctuation;•

Seasonal temperature variation.

If country-specific MCF measurements are not available, default MCF values are presented in the IPCC
Guidelines Reference Manual (Table 4-8). Some of these default values are revised as shown in Table 4.10,
MCF Values for Manure Management Systems Defined in the IPCC Guidelines, (revisions are in italics). The
revisions in Table 4.10 present an approach for subdividing digester and anaerobic lagoon systems to account for
the recovery, flaring and use of biogas. Such subdivision is important in order to account for policy measuresthat encourage CH4 recovery from these systems. Table 4.11, MCF Values for Manure Management Systems notSpecified in the Guidelines, presents MCF values for some additional manure management systems currently in
use in various countries that were not specifically addressed in the IPCC Guidelines. In countries where thesesystems are in use, disaggregation into these categories is encouraged. The default MCF values presented inTable 4.11 can be used if country-specific values are unavailable.

4 When choosing default B values, if the production practices in the developing country are similar to those in developedocountries, then the value for developed countries should be chosen.

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Figure 4.3

Agriculture

Decision Tree for CH4 Emissions from Manure
Management

Does the country
manage populations of cattle,
buffalo, swine, sheep, goats, horses,
mules/asses, poultry or otherspecies?

No

Report‘Not Occurring’

Yes
Divide livestock
species into species for‘basic’ and ‘enhanced’
characterisation
Cattle, buffalo, swine
or other species without default
emission factor values

Sheep, goats,horses, mules/asses
or poultry

Preparea ‘basic’ Livestock
Population
CharacterisationNo

Is thisa key source
category, and is thisspecies a significant
share of emissions?
(Note 1andNote 2)No

Is thedata available
to do an ‘enhanced’
Livestock Population
Characterisation?
Yes

Yes
Do youhave country-specificemission factors(EF)?

Obtain the data

Yes

No
Box 1
Estimateemissions using
Tier 1 & IPCC
default EF

Box 2
Estimateemissions using
Tier 1 & countryspecific EF

Box 3

Yes
(all or some)Do youhave country-specific
MCF, Bo, VS or manure
management system
usage data?
Box 4

Estimate emissions using
Tier 2 available countryspecific EF and default
values where necessary

No (none)

Estimateemissions using
Tier 2 default
values

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)
Note 2: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source
category.

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Chapter 4

Emission Factor Equation: Equation 4.17 shows how to calculate the emission factor for CH4 from manure
management:
EQUATION 4.17
EMISSION FACTOR FROM MANURE MANAGEMENT
EFi = VSi • 365 days/year • Boi • 0.67 kg/m3 •Σ(jk) MCFjk • MS ijk

Where:
EFi = annual emission factor for defined livestock population i, in kg
VSi = daily VS excreted for an animal within defined population i, in kg
Boi = maximum CH4 producing capacity for manure produced by an animal within defined
population i, m3/kg of VS
MCFjk = CH4 conversion factors for each manure management system j by climate region k
MSijk = fraction of animal species/category i’s manure handled using manure system j in climate
region k

4.3.1.3

C HOICE OF ACTIVI TY DATA

There are two main types of activity data for estimating CH4 emissions from manure management: (1) animal
population data and (2) manure management system usage data.
The animal population data should be obtained using the approach described in the Livestock Population
Characterisation section (see Section 4.1). As noted in the section, the good practice method for characterising
livestock populations is to conduct a single characterisation that will provide the activity data for all emissions
sources relying on livestock population data. It is important to note, however, that the level of disaggregation in
the livestock population data required to estimate emissions from this source category may differ from thoseused for other sources, such as enteric fermentation. For example, for some livestock population
species/categories, such as cattle, the ‘Enhanced’ characterisation required for the Tier 2 enteric fermentation
estimate could be aggregated to broader categories that are sufficient for this source category.
Inventory agencies in countries with varied climatic conditions are encouraged to obtain population data for eachmajor climatic zone. Such an effort will improve accuracy because CH4 emissions from manure management
systems can vary considerably depending on the climate. Ideally, the regional breakdown can be obtained frompublished national statistics. If regional data are not available, experts should be consulted regarding regional
production (e.g. milk, meat, and wool) patterns or land distribution, which may provide the required information
to estimate the regional animal distributions.
The best means of obtaining manure management system distribution data is to consult regularly publishednational statistics. If such statistics are unavailable, the preferred alternative is to conduct an independent surveyof manure management system usage. If the resources are not available to conduct a survey, experts should be
consulted to obtain an opinion of the system distribution. Chapter 6, Quantifying Uncertainties in Practice,
Section 6.2.5, describes how to elicit expert judgement for uncertainty ranges. Similar expert elicitation
protocols can be used to obtain manure management system distribution data.
For a regional emissions analysis, it is important that regional data for both population and manure management
system usage is used. Additionally, information on climatic differences among regions within a country must be
obtained so that the proper MCFs can be applied. If all of these data are not available at a regional level, a
regional analysis will not be more accurate than a national-level emissions study.

4.3.1.4

C OMPLETENESS

A complete inventory will include emissions estimates from all domesticated animal population manure sourcesin a country, regardless of the tier that is applied. The listed IPCC animal population categories are distinct andpopulation data are generally available from national references or the FAO. Thus, inventory agencies should be
able to develop an emissions estimate that encompasses all of the required animal population species.

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4.3.1.5

Agriculture

D EVELOPING A CONSISTENT TIME SERIES

Developing a consistent time series for the Tier 1 method requires collecting and compiling animal populationand manure management data during the time period. For the Tier 1 method, difficulties arise when:•

Animal population data are not available for the entire period;•

Animal population data over the entire period are not broken down into the animal species/categories
recommended by IPCC;•

Changes in manure management practices over time affect CH4 emissions.

Animal population data can be obtained by collecting aggregate historical data from FAO and using current datato break out historical population data into the animal groups. If significant changes in manure management
practices have occurred over time, the Tier 1 method will not provide an accurate time series of emissions, andthe Tier 2 method should be considered.
In addition to the data issues described for the Tier 1 method, developing a time series for the Tier 2 method
requires the collection and compilation of country-specific manure management system data. Difficulties arise in
the Tier 2 method when:•

Manure management system data are not available for some period during the time series;•

Manure management system data are not broken down into the systems recommended by IPCC;•The Tier 2 method was not used throughout the time series.

The lack of reliable manure management system data can be addressed by extrapolating manure management
system trends from a sample area or region to the entire country, if climatic conditions are similar (i.e.
temperature and rainfall). If the emission estimation method has changed, historical data that are required by thecurrent method should be collected and used to recalculate emissions for that period. If such data are notavailable, it may be appropriate to create a trend with recent data and use the trend to back-estimate management
practices for the time series. Among other sources, publications and industry and university experts can be used
to develop trends for the animal population and manure characteristics. Chapter 7, Methodological Choice and
Recalculation, provides guidance on how to address these issues. Section 4.1 suggests approaches for the animal
population aspects.

4.3.1.6

U NCERTAINTY ASSESSMENT

Expert judgement will, in the probable absence of extensive empirical data, be required to assess uncertainties
for this source. Chapter 6, Quantifying Uncertainties in Practice, provides advice for obtaining expert
judgements and combining them with other uncertainties.
Experts can estimate uncertainty by evaluating the various components of the emission estimate. The major
sources of uncertainty are the accuracy of emission factors, manure management system distribution, andactivity data. The default values (either Tier 1 or 2 method) may have a large uncertainty for an individual
country, because they may not reflect the actual conditions within the country. Uncertainties can be reduced bydeveloping and using a model that relates MCF and Bo values to different country/region specific factors.

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TABLE 4.10
MCF VALUES FOR MANURE MANAGEMENT SYSTEMS DEFINED IN THE IPCC GUIDELINES (REVISIONS ARE NOTED IN ITALICS)
MCFS BY CLIMATESYSTEM

DEFINITION

Cool

Temperate

Warm

Pasture/Range/
Paddock

The manure from pasture and range grazing animals is allowed tolie as is, and is not managed.

1%

1.5%

2%

Daily Spread

Dung and urine are collected by some means such as scraping.
The collected waste is applied to fields.

0.1%

0.5%

1%

Dung and urine are excreted in a stall. The solids (with orwithout litter) are collected and stored in bulk for a long time(months) before disposal, with or without liquid runoff into a pit
system.

1%

1.5%

2%

In dry climates animals may be kept on unpaved feedlots where
the manure is allowed to dry until it is periodically removed.
Upon removal the manure may be spread on fields.

1%

1.5%

5%

Dung and urine are collected and transported in liquid state totanks for storage. Liquid may be stored for a long time (months).
To facilitate handling water may be added.

39%

45%

72%

Solid Storage

Dry lot

Liquid/Slurry

Anaerobic Lagoon

Pit Storage below
animalconfinements

Characterised by flush systems that use water to transport manure
to lagoons. The manure resides in the lagoon for period from 30
days to over 200 days. The water from the lagoon may berecycled as flush water or used to irrigate and fertilise fields.

COMMENTS

When slurry tanks are used as fed-batch storage/digesters, MCF should
be calculated according to formula 1.
Should be subdivided in different categories, considering % recoveryof the biogas and flaring of the biogas .

0-100%

0-100%

0-100%

0

0

30%

39%

45%

72%

0-100%

0-100%

0-100%

10%

10%

10%

Calculation with formula 1.

Combined storage of dung and urine below animal confinements:<1 month>1 month

Anaerobic Digester

The dung and urine in liquid/slurry are collected andanaerobically digested. CH4 may be burned flared or vented.

Burned for Fuel

The dung and urine are excreted on fields. The sun dried dung
cakes are burned for fuel.

When pits used as fed-batch storage/digesters, MCF should be
calculated according to formula 1.
Note that the ambient temperature, not the stable temperature is to be
used for determining the climatic conditions.
Should be subdivided in different categories, considering amount ofrecovery of the biogas, flaring of the biogas and storage after digestion.

Source: IPCC Guidelines and Judgement by Expert Group (see Co-chairs, Editors and Experts; CH4 Emissions from Manure Management).

Formula 1:
MCF = [{CH4 prod - CH4 used - CH4 flared + MCFstorage * (Bo - CH4 prod)}/ Bo] *100%
Where:
CH4 prod = methane production in digester , (l CH4/gVS added). Note: When a gas tight coverage of the storage for digested manure is used, the gas production of the storage should be included.
CH4 used = amount of methane gas used for energy, (l CH4/gVS added)
CH4 flared = amount of methane flared, (l CH4/gVS added)
MCFstorage = CH4 emitted during storage of digested manure (%)
When a gas tight storage is included: MCFstorage = 0 ; otherwise MCFstorage = MCF value for liquid storage

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TABLE 4.11
MCF VALUES FOR MANURE MANAGEMENT SYSTEMS NOT SPECIFIED IN THE IPCC GUIDELINES (JUDGEMENT BY EXPERT GROUP)MCFS BY CLIMATE
Additional Systems

Definition

Comments

Cool

Temperate

Warm<1 month

0

0

30%

>1 month

39%

45%

72%

Composting - Intensive

Dung and urine are collected and placed in a
vessel or tunnel, there is forced aeration of thewaste.

0.5%

0.5%

0.5%

MCF’s are less than half of solid storage. Not temperaturedependant.

Composting - Extensive

Dung and urine collected, stacked andregularly turned for aeration.

0.5%

1%

1.5%

MCF’s are slightly less than solid storage. Less
temperature dependant.

Poultry manure with bedding

Manure is excreted on floor with bedding.
Birds walk on waste.

1.5%

1.5%

1.5%

MCF’s are similar to solid storage but with generally
constant warm temperatures.

Poultry manure without bedding

Manure is excreted on floor without bedding.
Birds do not walk on waste.

1.5%

1.5%

1.5%

MCF’s are similar to dry lot at a warm climate.

Aerobic Treatment

Dung and urine are collected as a liquid. The
waste undergoes forced aeration, or treated inaerobic pond or wetland systems to provide
nitrification and denitrification.

0.1%

0.1%

0.1%

MCF’s are near zero.

Cattle and Swine
Deep Litter

Cattle/swine dung and urine are excreted onstall floor. The accumulated waste is removed
after a long time.
MCF’s are similar to liquid/slurry; temperature dependant.

Aerobic treatment results in large accumulations ofsludge. Sludge requires removal and has large VS values.
It is important to identify the next management process
for the sludge and estimate the emissions from thatmanagement process if significant.

Source: Judgement by Expert Group (see Co-chairs, Editors and Experts; CH4 Emissions from Manure Management).

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Agriculture

4.3.2

Chapter 4

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. To improve transparency, emission estimates from this source category should bereported along with the activity data and emission factors used to determine the estimates.
The following information should be documented:••

All activity data, including:(i)

Animal population data by species/category and by region if applicable;(ii)

Climatic conditions in regions if applicable;5(iii)

Manure management system data, by animal species/category and by region, if applicable.

Activity data documentation, including:(i)

The sources of all activity data used in the calculations (i.e. complete citations for the statistical
database from which data were collected), and in cases where activity data were not available
directly from databases, the information and assumptions that were used to derive the activity data;(ii)

The frequency of data collection, and estimates of accuracy and precision.

•

If the Tier 1 method is used, all default emission factors used in the emissions estimation for the specificanimal population species/category.

•

If the Tier 2 method is used, emission factor calculation components, including:•(i)

VS and Bo values for all animal population types in inventory, whether country-specific, regionspecific, or IPCC default.

(ii)

MCF values for all manure management systems used, whether country-specific or IPCC default.

Emission factors documentation, including:(i)

References for the emission factors that were used (IPCC default or otherwise).

(ii)

The scientific basis of these emission factors and methods, including definition of input parametersand description of the process by which these emission factors and methods are derived, as well as
describing sources and magnitudes of uncertainties. (In inventories, in which country- or regionspecific emission factors were used or in which new methods other than those described in the
IPCC Guidelines were used).

4.3.3

Inventory quality assurance/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, and quality assurance
procedures may also be applicable, particularly if higher tier methods are used to determine emissions from thissource. The general QA/QC related to data processing, handling, and reporting, as outlined in Chapter 8,
QA/QC, could be supplemented with procedures discussed below:
Activ ity da ta c he c k• The inventory agency should review data collection methods, checking the data to ensure they werecollected and aggregated correctly. The data should be cross-checked with previous years to ensure the data
are reasonable. Inventory agencies should document data collection methods, identify potential areas of bias,
and evaluate the representativeness of the data.

5 e.g. average temperature during manure storage.

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Rev i e w o f e mi s sio n f a c t o r s• If using defaults, the inventory agency should review the available default emission factor values anddocument the rationale for selecting specific values.
•

If using the Tier 2 method (i.e. where country-specific emission factors by animal and manure management
type are used to calculate emissions), the inventory agency should cross-check the country-specific factor
parameters (i.e. VS excretion rates, Bo, and MCF) against the IPCC defaults. Significant differences between
country-specific parameters and default parameters should be explained and documented.

•

If using the Tier 1 method (using default IPCC emission factors), the inventory agency should evaluate how
well the default VS excretion rates and Bo values represent the defined animal population and manurecharacteristics of the country.

•

Any available country-specific data should be used to verify relevant default components.

•

Inventory agency should review the method used to determine the country- or region-specific VS and Bo
values, particularly in terms of the standardised procedures previously described. A detailed description of
the equations used to estimate emission factors should be reviewed as well, including the numbers used ineach calculation and the source of any data collected.

E xte rna l r eview• If using the Tier 2 method, the inventory agency should conduct an expert peer review of the manure
management practice assumptions by involving individuals with specific knowledge in disciplines
associated with the parameters used to calculate factors (e.g. manure management practices and animal
nutrition).
•If using the Tier 2 method, the inventory agency should provide a proper justification for country-specificemission factors via peer-reviewed documentation.

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4.4

N2O EMISSIONS FROM MANUREMANAGEMENT

4.4.1

Methodological issues

The nitrous oxide (N2O) estimated in this section is the N2O produced during the storage and treatment ofmanure before it is applied to land. The term ‘manure’ is used here collectively to include both dung and urine(i.e. the solids and the liquids) produced by livestock. The emission of N2O from manure during storage andtreatment depends on the nitrogen and carbon content of manure, and on the duration of the storage and type oftreatment. The term ‘manure management’6 is used as a collective noun for all types of storage and treatment ofmanure. This chapter describes good practice for estimating N2O emissions from manure management systems(MMS) using the method in the IPCC Guidelines. In the case of animals whose manure is unmanaged (i.e.
animals grazing on pasture or grassland, animals that forage or are fed in paddocks, animals kept in pens aroundhomes) the manure is not stored or treated but is deposited directly on land. This system of ‘manure
management’ is referred to in the IPCC Guidelines as ‘pasture, range, and paddock’. The N2O emissions
generated by manure in the system ‘pasture, range, and paddock’ occur directly and indirectly from the soil, andare therefore reported under the IPCC category ‘agricultural soils’. However, because the estimation method for
pasture, range, and paddock N2O emissions is the same as that for other systems of manure management,
pasture, range, and paddock is discussed in this section of the good practice guidance.

4.4.1.1

C HOICE OF METHODThe IPCC Guidelines method for estimating N2O emissions from manure management entails multiplying thetotal amount of N excretion (from all animal species/categories) in each type of manure management system byan emission factor for that type of manure management system. Emissions are then summed over all manure
management systems. The level of detail being applied to the good practice method for estimating N2Oemissions from manure management systems will depend upon national circumstances. The decision tree in
Figure 4.4, Decision Tree for N2O Emissions from Manure Management, describes good practice in adapting themethods in the IPCC Guidelines to country-specific circumstances.
To estimate emissions from manure management systems, the animal population must first be divided into
species/categories that reflect the varying amounts of manure produced per animal as well as the manner inwhich the manure is handled. Detailed information on how to characterise the livestock population for thissource is provided in Section 4.1.
The following five steps are required to estimate N2O emissions from manure management systems:(i)Collect population data from livestock population characterisation;(ii)

Determine the annual average nitrogen excretion rate per head (Nex(T)) for each defined livestock
species/category T;(iii)

Determine the fraction of total annual excretion for each livestock species/category T that is
managed in each manure management system S (MS(T,S));(iv)

Determine the N2O emission factors for each manure management system S (EF3(S));(v)

For each manure management system type S, multiply its emission factor (EF3(S)) by the total
amount of nitrogen excretion (from all animal species/categories) in that system, to estimate N2O
emissions from that manure management system. Then sum over all manure management systems.

6 Both the term ‘manure management’ and the term ‘animal waste management’ are used in the IPCC Guidelines to refer toanimal manure that produces nitrous oxide. In this guidance, the term ‘manure management’ is used, so as to be consistent
with Section 4.3 on CH4 emissions from manure management.

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Figure 4.4

Agriculture

Decision Tree for N2O Emissions from Manure
Management

Does the country
manage populations of cattle,
buffalo, swine, sheep, goats, horses,
mules/asses, poultry or otherspecies?

No

Report‘Not Occurring’

Yes
Divide livestock
species into species for‘basic’ and ‘enhanced’
characterisation
Cattle, buffalo,swine or otherspecies

Sheep, goats,horses, mules/asses
or poultry

Preparea ‘basic’ Livestock
Population
CharacterisationNo

Is thisa key source
category, and is thisspecies a significant
share of emissions?
(Note 1andNote 2)No

Is thedata available
to do an ‘enhanced’
Livestock Population
Characterisation?
Yes

Yes
Obtain the data
Do youhave countryspecific N-excretion
rates, manure
management usage
data or EF?

Box 2
Yes

Estimate emissions
using country-specificfactors and default
values where necessaryNo(none)

Yes(all or some)Box 1
Estimateemissions using
IPCC default
valuesBox 3

Do youhave country-specificN-excretion/retention/intake
values or manure
management system
usage data?
Box 4

Estimate emissions using
available country-specific
EF and default values where
necessary

No (none)

Estimateemissions using
IPCC default
values

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)
Note 2: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source
category.

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Chapter 4

The calculation of N2O emissions from manure management is based on the following equation according to the
IPCC Guidelines:
EQUATION 4.18
N2O EMISSIONS FROM MANURE MANAGEMENT(N2O-N)(mm) =Σ(S) {[Σ (T) (N(T) • Nex(T) • MS(T,S) )] • EF3(S)}Where:(N2O-N)(mm) = N2O-N emissions from manure management in the country (kg N2O-N/yr)
N(T) = Number of head of livestock species/category T in the country
Nex(T) = Annual average N excretion per head of species/category T in the country (kg N/animal/yr)
MS(T,S) = Fraction of total annual excretion for each livestock species/category T that is managed inmanure management system S in the country
EF3(S) = N2O emission factor for manure management system S in the country (kg N2O-N/kg N in manure
management system S)
S = Manure management system
T = Species/category of livestock
Conversion of (N2O-N)(mm) emissions to N2O(mm) emissions for reporting purposes is performed by using thefollowing equation:
N2O(mm) = (N2O-N)(mm) • 44/28

4.4.1.2

C HOICE OF EMISSIO N FACTORSThe most accurate estimate will be obtained using country-specific emission factors that have been fully
documented in peer reviewed publications. It is good practice to use country-specific emission factors thatreflect the actual duration of storage and type of treatment of animal manure in each management system that isused. Good practice in the derivation of country-specific emission factors involves the measurement ofemissions (per unit of manure N) from different management systems, taking into account variability in durationof storage and types of treatment. When defining types of treatment, conditions such as aeration and temperature
should be taken into account. If inventory agencies use country-specific emission factors, they are encouraged toprovide justification for these values via peer-reviewed documentation. If appropriate country-specific emission
factors are unavailable, inventory agencies are encouraged to use the default emission factors. The IPCC goodpractice emission factors are presented in Table 4.12, Default Emission Factors for N2O from Manure
Management, and Table 4.13, Default Emission Factors for N2O from Manure Management Systems notSpecified in the IPCC Guidelines. These tables contain default emission factors, along with descriptions of the
management systems, for several manure management systems that are not included in Table 4-22 of theReference Manual of the IPCC Guidelines.

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TABLE 4.12
DEFAULT EMISSION FACTORS FOR N2O FROM MANURE MANAGEMENT(ADDITIONAL SYSTEMS AND CHANGES TO THE IPCC GUIDELINES ARE NOTED IN ITALICS.)
EF3
SystemDescription

Pasture/range/
paddockThis manure is deposited directly on soils by livestock, i.e. it isunmanaged.

Daily Spread

There is little or no storage or treatment of manure before it isapplied to soils, so emissions during storage and treatment are
assumed to be zero.
Dung and urine (with or without litter) is collected but is stored
in bulk for a long time (months) before disposal, with or withoutliquid runoff into a pit system.

Solid storagea

Dry lot

Liquid/Slurry

Anaerobic Lagoon

Open pits below
animalconfinements
Anaerobic Digester
Burned for fuelb

In dry climates animals may be kept on unpaved feedlots where
the manure is allowed to dry until it is periodically removed.
Upon removal the manure may be spread on fields.
These systems are characterised by combined storage of dungand urine in tanks.
To facilitate handling as a liquid, water may be added to the dungand urine.
Anaerobic lagoon systems are characterised by flush
systems that use water to transport manure to lagoons. The
manure resides in the lagoon for periods from 30 days to over
200 days. The water from the lagoon may be recycled as flush
water or used to irrigate and fertilise fields.
Combined storage of dung and urine below animal confinements.

Dung and urine is anaerobically digested to produce CH4 gas forenergy.
Dung is collected and dried in cakes and burned for heating or
cooking.
The urine N is deposited on pasture and paddock and must betreated in that category.

(kg N2O-N/kg
Nitrogenexcreted)

Uncertainty
ranges of EF3
[%]

0.02

-50%/ +100%

0.0

Not Applicable

0.02-50%/ +100%

0.02-50%/ +100%-50%/ +100%
0.001

0.001

-50%/ +100%

0.001-50%/ +100%

0.001-50%/ +100%

0.007-50%/ +100%

0.02

a

Quantitative data should be used to distinguish whether the system is judged to be a solid storage or liquid/slurry. The borderline between dry
and liquid can be drawn at 20% dry matter content.
b

The emissions associated with the burning of the dung are to be reported under the IPCC category ‘fuel combustion’ if the dung is used as fueland under the IPCC category ‘waste incineration,’ if the dung is burned without energy recovery. Direct and indirect N2O emissions associatedwith the urine deposited on agricultural soils are treated in Sections 4.7 and 4.8, respectively.
Source: IPCC Guidelines and Judgement by Expert Group (see Co-chairs, Editors and Experts; N2O Emissions from Manure Management).

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TABLE 4.13
DEFAULT EMISSION FACTORS FOR N2O FROM MANURE MANAGEMENT SYSTEMS NOT SPECIFIED IN THE IPCC
GUIDELINES (JUDGEMENT BY EXPERT GROUP)
EF3
Additional Systems

Cattle and Swine Deep Litter

Definition

Uncertainty
Ranges of EF3(kg N2O-N/kg
nitrogenexcreted)(%)

Cattle/swine dung and urine are excreted onstall floor. The accumulated waste is removed
after a long time.
<1 month

0.005-50%/+100%>1 month

0.02-50%/+100%

Dung and urine are collected and placed in a
vessel or tunnel, there is forced aeration of the
waste

0.02

-50%/+100%

Composting - Extensive

Dung and urine collected, stacked andregularly turned for aeration

0.02-50%/+100%

Poultry manure with bedding

Manure is excreted on floor with bedding.
Birds walk on waste.

0.02-50%/+100%

Poultry manure without bedding

Manure is excreted on floor without bedding.
Birds do not walk on waste

0.005-50%/+100%

Aerobic Treatment

Dung and manure is collected as a liquid. The
waste undergoes forced aeration, or is treatedin aerobic ponds or wetland systems toprovide nitrification and denitrification.

0.02-50%/+100%

Composting - Intensive

Source: Judgement by Expert Group (see Co-chairs, Editors and Experts; N2O Emissions from Manure Management).

4.4.1.3

C HOICE OF ACTIVI TY DATA

There are three main types of activity data for estimating N2O emissions from manure management systems: (1)livestock population data, (2) nitrogen excretion data for each animal species/category, and (3) manure
management system usage data.

Livestock population data ( N ( T ) )The livestock population data should be obtained using the approach described in the Livestock Population
Characterisation (see Section 4.1). If using default nitrogen excretion rates to estimate N2O emissions frommanure management systems, a ‘Basic’ livestock population characterisation is sufficient. To estimate N2O
emissions from manure management using calculated nitrogen excretion rates, an ‘Enhanced’ characterisationmust be performed. As noted in Section 4.1, good practice in characterising livestock populations is to conduct asingle characterisation that will provide the activity data for all emissions sources that depend on livestock
population data.

Annual average nitrogen excretion rates (Nex(T))
Accurate annual nitrogen excretion rates should be determined for each animal species/category defined by the
livestock population characterisation. Country-specific rates may either be taken directly from documents orreports such as from the agricultural industry and scientific literature, or derived from information on animal
nitrogen intake and retention (as explained below). In some situations, it may be appropriate to utilise excretion
rates developed by other countries that have livestock with similar characteristics. If country-specific data cannot
be collected or derived, or appropriate data are not available from another country, the IPCC default excretion
rates should be used (see Table 4-20 in the Reference Manual of the IPCC Guidelines). In order to adjust thevalues for young animals, it is good practice to multiply the N excretion rates in Table 4-20 by the default
adjustment factors shown in Table 4.14, Default Adjustment Factors for Table 4-20 in the IPCC Guidelines.
When estimating the Nex(T) for animals whose manure is classified in the manure management system burned
for fuel (Table 4.12, Default emission factors for N2O from Manure Management), it should be kept in mind thatthe dung is burned and the urine stays in the field. As a rule of thumb, 50% of the nitrogen excreted is in thedung and 50% is in the urine. Therefore, these proportions of Nex(T) should be multiplied by the appropriate

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emission factors in Table 4.12 to obtain N2O-N emissions for these sub-source categories. If the burned dung isused as fuel, then emissions are reported under the IPCC category fuel combustion, whereas if the dung is burned
without energy recovery the emissions should be reported under the IPCC category waste incineration.
TABLE 4.14
DEFAULT ADJUSTMENT FACTORS FOR TABLE 4-20 IN THE IPCC GUIDELINES (REFERENCE MANUAL)
WHEN ESTIMATING N EXCRETION RATES FOR YOUNG ANIMALS

Animal Species/Categorya

Age Range (years)

Adjustment Factor

Non-Dairy Cattle

0-1

0.3

Non-Dairy Cattle

1-2

0.6

Dairy Cattle

0-1

0.3

Dairy Cattle

1-2

0.6

Poultry0 - 0.25

0.5

Sheep

0-1

0.5

Swine

0 - 0.5

0.5a

The adjustment factor is 1 when the age of the animals exceeds the indicated age range.

Note: The category termed Other Animals in Table 4-20 of the IPCC Guidelines, Reference Manual, is not provided with adjustment
factors.
Source: Judgement by Expert Group (see Co-chairs, Editors and Experts; N2O Emissions from Manure Management).

The annual amount of N excreted by each animal species/category depends on the total annual N intake and total
annual N retention of the animal. Therefore, N excretion rates can be derived from N intake and N retention data.
Annual N intake (i.e. the amount of N consumed by the animal annually) depends on the annual amount of feed
digested by the animal, and the protein content of that feed. Total feed intake depends on the production level ofthe animal (e.g. growth rate, milk production, draft power). Annual N retention (i.e. the fraction of N intake thatis retained by the animal for the production of meat, milk, and wool) is a measure of the animal's efficiency of
production of animal protein from feed protein. Nitrogen intake and retention data for specific animal
species/categories may be available from national statistics or from animal nutrition specialists. Nitrogen intake
can also be calculated from data on feed and crude protein intake developed in the Livestock Population
Characterisation Section (see Section 4.1). Default N retention values are provided in Table 4.15, Default Values
for the Fraction of Nitrogen in Feed Taken in by Animals that is Retained by the Different AnimalSpecies/Categories. Rates of annual N excretion for each animal species/category (Nex(T)) are derived as follows:
EQUATION 4.19
ANNUAL N EXCRETION RATES
Nex(T) = Nintake(T) • (1 – Nretention(T))
Where:(Nex(T)) = annual N excretion rates, kg N/animal-year
Nintake(T) = The annual N intake per head of animal of species/category T , kg N/animal-year
Nretention(T) = Fraction of annual N intake that is retained by animal of species/category T kg N
retained/animal/year per kg N intake/animal/year

Note that annual nitrogen excretion data are also used for the calculation of direct and indirect N2O emissions
from agricultural soils (see Sections 4.7 and 4.8). The same rates of N excretion, and methods of derivation, thatare used to estimate N2O emissions from manure management should be used to estimate N2O emissions fromagricultural soils.

Manure management system usage data (MS ( T , S ) )The manure management system usage data used to estimate N2O emissions from manure management shouldbe the same as those that are used to estimate CH4 emissions from manure management (see Section 4.3). If
country-specific manure management system usage data are not available, default values from the IPCC
Guidelines should be used. The IPCC default values for dairy cattle, non-dairy cattle, buffalo, and swine should

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Chapter 4

be taken from Tables B-3 through B-6 of Appendix B of Section 4.2 (livestock) of the Agriculture Chapter of theReference Manual. The IPCC default values for all other animal species/categories should be taken from Table
4-21 of the Agriculture Chapter of the Reference Manual.

TABLE 4.15
DEFAULT VALUES FOR THE FRACTION OF NITROGEN IN FEED TAKEN IN BY ANIMALS THAT IS RETAINED BY THE
DIFFERENT ANIMAL SPECIES/CATEGORIES (FRACTION N-INTAKE RETAINED BY THE ANIMAL)
Animal category

Nretention(T)Uncertainty range(kg N retained/animal/year per kg
N intake/animal/year)[%]

Dairy cattle

0.2

+/-50

Non dairy cattle

0.07

+/-50

Buffalo

0.07

+/-50

Sheep

0.1

+/-50

Goats

0.1

+/-50

Camels

0.07

+/-50

Swine

0.3

+/-50

Horses

0.07

+/-50

Poultry

0.3

+/-50

Source: Judgement by Expert Group (see Co-chairs, Editors and Experts; N2O Emissions from Manure Management).

4.4.1.4

U NCERTAINTY ASSESSMENT

Emission f actors
There are large uncertainties associated with the default emission factors for this source category (see Tables
4.12 and 4.13). Accurate and well-designed emission measurements from well characterised types of manure andmanure management systems can help to reduce these uncertainties. These measurements must account for
temperature, moisture conditions, aeration, manure N content, metabolisable carbon, duration of storage, andother aspects of treatment.

Activity data – Livestock populations
See Section 4.1- Livestock Population Characterisation

Activity data - Nitrogen excretion rates
Uncertainty ranges for the default N excretion rates (see Table 4-20 in the IPCC Guidelines, Reference Manual),
which are not provided in the IPCC Guidelines, are estimated at about +/−50% (Source: Judgement by Expert
Group. See Co-chairs, Editors and Experts; N2O Emissions from Manure Management). The uncertainty ranges
for the default N retention values provided here are also +/−50% (see Table 4.15). If inventory agencies derive N
excretion rates using accurate in-country statistics on N intake and N retention, the uncertainties associated with
the N excretion rates may be as low as +/−25%.

Activity data – Manure management system usage
For some countries, the uncertainties associated with manure management system usage data are high. Althougha well-defined classification scheme has been developed (see Tables 4.12 and 4.13), many inventory agencies
only have limited, if any, quantitative data on the amounts of manure managed in different systems, beyond what
is presented in Table 4-21 in the IPCC Guidelines, Reference Manual.

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Chapter 4

4.4.1.5

Agriculture

C OMPLETENESS

A complete inventory should estimate N2O emissions from all systems of manure management for all livestock
species/categories. Countries are encouraged to utilise manure management definitions that are consistent withthose in Tables 4.12 and 4.13. For more information regarding the completeness of livestock characterisation,see Section 4.1.

4.4.1.6

D EVELOPING A CONSISTENT TIME SERIES

Developing a consistent time series of emission estimates for this source category requires, at a minimum, thecollection of an internally consistent time series of livestock population statistics. Guidance on the developmentof this time series is addressed in Section 4.1. In most countries, the other two activity data sets required for thissource category (i.e. N excretion rates and manure management system usage data), as well as the manure
management emission factors, will be kept constant for the entire time series. However, in some cases, there may
be reasons to modify these values over time. For example, farmers may alter livestock feeding practices, or theentire livestock sector may undergo a change such that a greater fraction of manure from a certain livestock
species/category is managed in wet systems rather than in dry systems, or a particular system of manure
management may change such that a revised emission factor is warranted. These changes in practices may be
due to the implementation of explicit greenhouse gas mitigation measures, or may be due to changing
agricultural practices without regard to greenhouse gases. Regardless of the driver of change, the data andemission factors used to estimate emissions must reflect the change, and the data, methods, and results must be
thoroughly documented. If activity data over a time series are affected by a change in farm practices or the
implementation of greenhouse gas mitigation measures (e.g. annual N excretion rates decline due to policy
measures implemented to decrease N2O emissions through reductions in annual N intake), the inventory agency
is encouraged to ensure that the activity data reflect these practices and that the inventory text thoroughly
explains how the change in farm practices or implementation of mitigation measures has affected the time seriesof activity data or emission factors.

4.4.2

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. When country-specific emission factors, N excretion rates or manure
management system usage data or both have been used, the derivation of or references for these data should beclearly documented and reported along with the inventory results under the appropriate IPCC source category.
N2O emissions from different types of manure management systems have to be reported according to the IPCC
Guidelines. Referring to the IPCC Guidelines, N2O emissions from all types of manure management systems areto be reported under manure management, with two exceptions:•

Emissions from the manure management system for pasture, range, and paddock are to be reported underthe IPCC source category agricultural soils because this manure is deposited directly on soils by the
livestock.

•

Emission from the manure management system burned for fuel, are to be reported under the IPCC category
fuel combustion if the dung is used as fuel and under the IPCC category waste incineration if the dung isburned without energy recovery. It should be noted, however, if the urine nitrogen is not collected forburning it must be reported under N2O emissions from pasture, range, paddock animals.

It must be kept in mind that after storage or treatment in any system of manure management, nearly all the
manure will be applied to land. The emissions that subsequently arise from the application of the manure to soil
are to be reported under agricultural soils. The methods for estimating these emissions are discussed in Sections
4.7 and 4.8.

4.4.3

Inventory quality assurance/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.

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Chapter 4

Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, and quality assurance
procedures may also be applicable, particularly if higher tier methods are used to determine emissions from thissource. The general QA/QC related to data processing, handling, and reporting, as outlined in Chapter 8,
QA/QC, could be supplemented with procedures discussed below:
Rev i e w o f e mi s sio n f a c t o r s•

If using country-specific emission factors, the inventory agency should compare them to the default factors,
and differences noted. The development of country-specific emission factors should be explained anddocumented, and inventory agencies are encouraged to ensure that good practice methods have been used
and the results have been peer-reviewed.

Activ ity da ta c he c k• If using country-specific data for Nex(T) and MS(T,S), the inventory agency should compare these values tothe IPCC default values. Significant differences, data sources, and methods of data derivation, should be
documented.
E xte rna l r eview• The inventory agency should utilise experts in manure management, animal nutrition, and GHG emissions
to conduct expert peer review of the methods and data used.

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Chapter 4

4.5

Agriculture

CH4 AND N2O EMISSIONS FROMSAVANNA BURNINGAt present, ‘good practice’ for this source category is the application of the IPCC Guidelines following thesuggested approach as set out in the decision tree in Figure 4.5, Decision Tree for CH4 and N2O Emissions fromSavanna Burning. There is potential for further refinement of the method as indicated in Appendix 4A.1 at theend of this chapter. The appendix describes some of the details of a possible procedure for future revision of themethodology. At this time, the paucity of the data and size of uncertainties in many of the key parameters do notjustify adoption of the material discussed in Appendix 4A.1 as good practice.

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Agriculture

Chapter 4

Figure 4.5

Decision Tree for CH4 and N2O Emissions fromSavanna Burning

Does
the country burnsavannas?

No

Report‘Not Occurring’Yes

Is thisa key source
category?
(Note 1)

No

No

Yes

Do you havea country-specific activity dataon the fraction of area burned,
aboveground biomass density, abovegroundbiomass burned, aboveground biomass
that is living or combustion
efficiency?

Do you have acountry-specificemission factor?

No

Yes

No

Yes

Do you have acountry-specificemission factor?

Yes

Box 1

Box 2

Box 3

Box 4

Estimate emissions
using
IPCC default values

Estimate emissions
using
country-specific EF
and IPCC default
valuesEstimate emissions
using
country-specificactivity data and
IPCC default values

Estimate emissions
using
country-specificactivity data and EF

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)

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Chapter 4

4.6

Agriculture

CH4 AND N2O EMISSIONS FROMAGRICULTURAL RESIDUE BURNINGAt present, ‘good practice’ for this source category is the application of the IPCC Guidelines following thesuggested approach as set out in the decision tree in Figure 4.6, Decision Tree for CH4 and N2O Emissions from
Agricultural Residue Burning. There is potential for further refinement of the method as indicated in Appendix
4A.2 at the end of this chapter. The appendix describes some of the details of a possible procedure for futurerevision of the methodology. At this time, the paucity of the data and size of uncertainties in many of the key
parameters do not justify adoption of the material discussed in Appendix 4A.2 as good practice.

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Chapter 4

Figure 4.6

Decision Tree for CH4 and N2O Emissions from
Agricultural Residue Burning

Does
the country burnagriculturalresidue?

No

Report‘Not Occurring’Yes

Is thisa key source
category?
(Note 1)

No

No

Yes

Do you havea country-specific activity dataon the fraction of area burned,
aboveground biomass density, abovegroundbiomass burned, aboveground biomass
that is living or combustion
efficiency?

Do you have acountry-specificemission factor?

No

Yes

No

Yes

Do you have acountry-specificemission factor?

Yes

Box 1

Box 2

Box 3

Box 4

Estimate emissions
using
IPCC default values

Estimate emissions
using
country-specific EF
and IPCC default
valuesEstimate emissions
using
country-specificactivity data and
IPCC default values

Estimate emissions
using
country-specificactivity data and EF

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)

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Agriculture

4.7

DIRECT N2O EMISSIONS FROMAGRICULTURAL SOILS

4.7.1

Methodological issues

Nitrous oxide (N2O) is produced naturally in soils through the microbial processes of nitrification anddenitrification. A number of agricultural activities add nitrogen to soils, increasing the amount of nitrogen (N)available for nitrification and denitrification, and ultimately the amount of N2O emitted. The emissions of N2O
that result from anthropogenic N inputs occur through both a direct pathway (i.e. directly from the soils to which
the N is added), and through two indirect pathways (i.e. through volatilisation as NH3 and NOx and subsequent
redeposition, and through leaching and runoff). In the IPCC Guidelines, direct and indirect emissions of N2O
from agricultural soils are estimated separately.
The IPCC Guidelines method for estimating direct N2O emissions from agricultural soils has two parts: (i)estimation of direct N2O emissions due to N-inputs to soils (excluding N-inputs from animals on pasture, range,and paddock); and (ii) estimation of direct N2O emissions from unmanaged animal manure (i.e. manuredeposited by animals on pasture, range, and paddock).7 This section discusses the first part of this method. The
second part, estimation of direct N2O emissions from pasture, range, and paddock manure, is covered in Section
4.4: N2O Emissions from Manure Management.8 Note, however, that direct N2O emissions from pasture, range
and paddock manure are to be reported in the agricultural soil category.

4.7.1.1

C HOICE OF METHOD

The approach described in the IPCC Guidelines for estimating direct N2O emissions from agricultural soils due
to applications of N and other cropping practices accounts for anthropogenic nitrogen (N) inputs from theapplication of: synthetic fertilisers (FSN) and animal manure (FAM); the cultivation of N-fixing crops (FBN);
incorporation of crop residues into soils (FCR); and soil nitrogen mineralisation due to cultivation of organicsoils9 (i.e. histosols) (FOS).10 As the IPCC Guidelines treat indirect and direct emissions separately, the portionof applied synthetic fertiliser and animal manure N that volatilises after application is subtracted from theamounts applied, and the N2O that is eventually emitted from this volatilised N is included as part of the indirectemissions (see Section 4.8).
The terms Tier 1a and Tier 1b have been used throughout the IPCC Report on Good Practice Guidance andUncertainty Management in National Greenhouse Gas Inventories (Good Practice Report), Subsections 4.7 and
4.8, to differentiate between the equations in the IPCC Guidelines (Tier 1a) and new equations (Tier 1b)
presented here. The Tier 1b equations represent increased precision due to expansion of the terms in theequations. However, while Tier 1b equations may be preferred, the activity data needed to use them may not be
available. In these cases, use of Tier 1a equations is appropriate. Estimating emissions using a combination ofTier 1a and Tier 1b equations for different sub-source categories, depending upon availability of activity data, isalso acceptable. In some cases, there is no Tier 1b alternative because no refinement of the equation in the IPCC
Guidelines was considered necessary.
The decision tree, Figure 4.7, Decision Tree for Direct N2O Emissions from Agricultural Soils describes goodpractice in adapting the methods in the IPCC Guidelines to country-specific circumstances. The decision tree
7 As in Section 4.4, the term ‘manure’ is used here collectively to include both dung and urine.
8 Even though animal manure deposited on pasture, range, and paddock is not managed, it is addressed in Section 4.4,

because the method for estimating emissions from pasture, range, and paddock manure is the same as the method for
estimating emissions from manure management systems.
9 Organic soils are soils described as Histosols which are defined as: ‘Organic soils that have organic soil materials in morethan half of the upper 80 cm, or that are of any thickness of overlying rock or fragmented materials that have interstices filledwith organic soil materials.’ An organic soil material is defined as: ‘soil materials that are saturated with water and have 174
g kg-1 or more organic carbon if the mineral fraction has 500 g kg-1 or more clay, or 116 g kg-1 organic carbon if the mineral
fraction has no clay, or has proportional intermediate contents, or if never saturated with water, have 203 g kg-1 or moreorganic carbon (SSSA, 1996).
10 Histosols are soils containing an organic-rich surface layer at least 40 cm in thickness, with a minimum of 20% organic
matter if the clay content is low, and a minimum of 30% organic matter where the clay content exceeds 50%.

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Chapter 4

describes how to choose the method of estimation. Both Tier 1a and Tier 1b are consistent with good practice,provided the emission factors and activity data are developed according to the guidance presented below.
In its most basic form, direct N2O emissions from agricultural soils are estimated as follows:
EQUATION 4.20
DIRECT N2O EMISSIONS FROM AGRICULTURAL SOILS (TIER 1a)
N2ODirect -N = [(FSN + FAM + FBN + FCR ) • EF1 ] + (FOS • EF2)
Where:
N2ODirect -N = Emission of N2O in units of Nitrogen
FSN = Annual amount of synthetic fertiliser nitrogen applied to soils adjusted to account for the amountthat volatilises as NH3 and NOx
FAM = Annual amount of animal manure nitrogen intentionally applied to soils adjusted to account for theamount that volatilises as NH3 and NOx
FBN = Amount of nitrogen fixed by N-fixing crops cultivated annually
FCR = Amount of nitrogen in crop residues returned to soils annually
FOS = Area of organic soils cultivated annually
EF1 = Emission factor for emissions from N inputs (kg N2O-N/kg N input)
EF2 = Emission factor for emissions from organic soil cultivation (kg N2O-N/ha-yr)
Conversion of N2O-N emissions to N2O emissions for reporting purposes is performed by using the following
equation:
N2O = N2O-N • 44/28

The use of Equation 4.20 is considered Tier 1a. If more detailed emission factors are available to a country,
further disaggregation of the terms in the equation can be undertaken, as shown in Equation 4.21 which is theTier 1b equation. For example, if emission factors are available for the application of synthetic fertilisers andanimal manure (FSN and FAM) under different conditions i, Equation 4.20 would be expanded as:
EQUATION 4.21
DIRECT N2O EMISSIONS FROM AGRICULTURAL SOILS (TIER 1b)
N2ODirect -N =

Σi {[(FSN + FAM )i • EFi] + [(FBN + FCR ) • EF1 ] + [FOS • EF2]}

Where:
EFi = Emission factors developed for N2O emissions from synthetic fertiliser and animal manure
application under different conditions i.
Conversion of N2O-N emissions to N2O emissions for reporting purposes is performed by using the following
equation:
N2O = N2O-N • 44/28

The Tier 1a approach can also be expanded to include other forms of N applied to all types of soils. For example,
sewage sludge, an additional form of organic N, is often applied to soils as a soil amendment or dispose of thesludge. Sewage sludge nitrogen (NSEWSLUDGE) can be included in this calculation if sufficient information isavailable. The sludge input should be measured in units of N and multiplied by EF1 (i.e. in Equation 4.20,
NSEWSLUDGE should be added to the first set of parentheses in Equation 4.21, it should be added inside the second
set of parentheses).

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Chapter 4

Agriculture

Figure 4.7

Decision Tree for Direct N2O Emissions from
Agricultural Soils
Does
the country apply N
to the soil or cultivateorganic soil?

No

Report‘Not Occurring’YesForeach N sourceask:
Do you have countryspecific activity
data?

Yes

No

Is this a
key source category
and is this N source
significant?
(Note 1 andNote 2)
Box 1

Yes
Obtain countryspecific data

Estimate emissions using
Tier 1a equations, FAO
activity data, default FracGASP/FracGASM values and default
emission factors

Addition ofsynthetic fertiliser, animal
manure, and sewage
sludgeYes

Do youhave rigorously
documented country-specific
FracGASP /FracGASM value(s)and/or emission
factors?

No

Increase of available
soil N through biological fixation,
crop residue incorporation andcultivation of organic soils

Do youhave rigorously
documented
country-specific
EF values?

No

Box 5

Box 4

Estimate emissions
using Tier 1a or 1b,
and available
country-specific
FracGASP /FracGASM
value(s) andcountry-specific EF

Estimate emissions
using Tier 1a or 1b,
default
FracGASP/FracGASM
value(s), and default
EF

Yes

Box 3
Estimate emissions
using Tier 1a or 1b
and country-specific
EF

No

Box 2

Estimate emissions
using Tier 1a or 1b
and default EF

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)
Note 2: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source category.

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Agriculture

Chapter 4Note that there are no default data for the new parameter NSEWSLUDGE, or guidance on collecting such data.
Therefore, this refinement should only be used if reliable country-specific data are available. The sewage sludge
activity data used to estimate direct N2O emissions should be the same as those used to estimate indirect N2Oemissions (see Section 4.8, Indirect N2O Emissions from Nitrogen Used in Agriculture).
In order to apply either Equation 4.20 or 4.21, the amounts of various N inputs (FSN, FAM, FBN, FCR, FOS) must be
estimated. The IPCC Guidelines describe methods for how such calculations are to be made. In some cases,
refinements in these methods are suggested for good practice in order to correct errors, ensure consistency
between this source category and other agricultural source categories, and incorporate new information that has
become available since the IPCC Guidelines were written. In addition, for some N inputs, detailed equations that
describe how to implement the more disaggregated approaches are presented. Using a mix of aggregated anddisaggregated equations to calculate the various N inputs is consistent with good practice in the derivation ofeach term in Equations 4.20 and 4.21 as described below.
Synthetic Fertiliser Nitrogen, Adjusted for Volatilisation (FSN): The term FSN refers to the annual amount ofsynthetic fertiliser nitrogen applied to soils after adjusting to account for the amount that volatilises. It isestimated by determining the total amount of synthetic fertiliser consumed annually (NFERT), and then adjusting
this amount by the fraction that volatilises as NH3 and NOx (FracGASF). The equation is thus:
EQUATION 4.22
N FROM SYNTHETIC FERTILISER APPLICATIONFSN = NFERT • (1 – FracGASF)

Animal Manure Nitrogen Used as Fertiliser, Adjusted for Volatilisation (FAM): The term FAM refers to theamount of animal manure nitrogen intentionally applied to soils after adjusting to account for the amount thatvolatilises. It is estimated by determining the total amount of animal manure nitrogen produced annually(ΣT(N(T) • Nex(T))) 11, and then adjusting this amount to account for the animal manure that is burned for fuel(FracFUEL-AM)12, deposited onto soils by grazing livestock (FracPRP) and volatilised as NH3 and NOx (FracGASM).
For this calculation, the equation presented in the IPCC Guidelines is replaced by:
EQUATION 4.23
N FROM ANIMAL MANURE APPLICATION
FAM =ΣT(N(T) • Nex(T) ) • (1 – FracGASM )[1 – (FracFUEL-AM + FracPRP )]Equation 4.23, however, may not be complete for all countries because animal manure may be used in ways
other than as fuel. Since some countries use some of their animal manure for animal feed and for construction, acomplete assessment should also determine the fractions of the animal manure (if any) that are used in this way(FracFEED-AM and FracCNST-AM, respectively). Tier 1b can account for these uses and avoid overestimating
emissions. It is assumed that all animal manure not used for another purpose will be applied to soils. The
suggested good practice Tier 1b equation is thus:
EQUATION 4.24
N FROM ANIMAL MANURE APPLICATION (EXPANDED)

FAM =ΣT(N(T) • Nex(T) ) • (1 – FracGASM) • [1 – (FracFUEL-AM + FracPRP + FracFEED-AM +FracCNST-AM)]

Note, however, that if the term FracPRP includes fractions of animal manure used as fuel, feed, or construction,then whichever fractions are included in FracPRP should not be included in Equation 4.24.

11 In this part of the IPCC Guidelines, the variable Nex is used for the total amount of animal manure produced. To beconsistent with good practice in Section 4.4, this variable name has been revised to ΣT(N(T) • Nex(T)).

12 In equations 4.23 and 4.24, the term used in the IPCC Guidelines (Frac
FUEL), has been renamed FracFUEL-AM, so as todistinguish it from the fraction of crops used as fuel (FracFUEL-CR) in Equation 4.29.

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Agriculture

N fixed by Crops (FBN): The approach presented in the IPCC Guidelines for estimating the amount of nitrogen
fixed by N-fixing crops cultivated annually (FBN) is based on the assumption that the amount of N contained in
the aboveground plant material (crop product plus residues) is a reasonable proxy for the total amount of N fixedby the crop. The IPCC Guidelines also assumes that the mass ratio of residue to product is 1 (i.e. the total
aboveground plant biomass is 2 times the crop product). Therefore, the amount of fixed N is estimated bymultiplying the seed yield of pulses and soybeans (CropBF) by a default value of 2 and then by the fraction ofcrop biomass that is nitrogen (FracNCRBF). The Tier 1a equation presented in the IPCC Guidelines is thus:
EQUATION 4.25
N FIXED BY CROPS (TIER 1A)
FBN = 2 • CropBF • FracNCRBF

The approach suggested in the IPCC Guidelines can be modified in several ways to estimate more accurately the
total mass of aboveground crop residue and product nitrogen. For example, Equation 4.25 uses a default value of
2 to convert CropBF to total aboveground crop residue and product. This factor is too low for some pulses andsoybeans, and may result in underestimating the total aboveground crop residue and product (see Table 4.16,
Selected Crop Residue Statistics). As the ratio of aboveground biomass to crop product mass varies among crop
types, more accurate estimates can be developed if crop specific values are used. Dry matter fractions also need
to be incorporated into the equation so that adjustments are made for moisture contents. In addition, CropBF
should be defined so that it is representative of the products of all N-fixing crops, not just the seed yield of pulses
and soybeans. In particular, N-fixing forage crops such as alfalfa should be included in the calculations. The
approach is shown in Equation 4.26:
EQUATION 4.26
N FIXED BY CROPS (TIER 1b)
FBN =Σi [CropBFi • (1 + ResBFi/CropBFi) • FracDMi • FracNCRBFi]

Equation 4.26 introduces two new terms. The first, ResBFi/CropBFi, represents the residue to crop product mass
ratio specific to each crop type i (see Table 4.16). The second, FracDMi, is the fraction of dry matter in theaboveground biomass of each crop type i. The term [(1 + ResBFi/CropBFi) • FracDMi] replaces the default value of‘2’ presented in the IPCC Guidelines. Note that it is assumed that the dry matter content of the residue andproduct are equal so only one dry matter variable is included in the equation. Countries may have dry matter
contents specific to the product and the residue – these should be used if the additional effort is warranted byincreased accuracy. Additionally, the variable CropBF as currently defined in the IPCC Guidelines is the seedyield of pulses + soybeans in a country. However, this does not take into account crops such as alfalfa where theentire plant is harvested as product. Therefore, as mentioned above, CropBF should be defined as the ‘productionof N-fixing crops.’ In the case of N-fixing forage crops such as alfalfa, ResBFi/CropBFi will equal 0, and theequation 4.26 becomes:
EQUATION 4.27
N FIXED BY N-FIXING FORAGE CROPSFBN =Σi (CropBFi • FracDMi • FracNCRBFi)

Note that if inventory agencies use Equation 4.26 to estimate the amount of N fixed by N-fixing crops, and if anyof the residues of these crops are burned in the field, they should use the same values for CropBF, ResBFi/CropBFi,
and FracDMi that are used in estimating emissions from agricultural residue burning. The values used for
FracNCRBFi should also be consistent with N/C ratios used in estimating emissions from agricultural residue
burning. Good practice default values for ResBFi/CropBFi, FracDMi, and FracNCRBFi, for some crop types, arepresented in Table 4.16. Inventory agencies may use these values if country-specific data are not available. If a
default residue nitrogen content is needed for a crop type for which a value is not provided in Table 4.16, thenon-crop specific default value listed in Table 4-19 of the Reference Manual of the IPCC Guidelines (0.03 kg
N/kg dry matter) can be used.

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TABLE 4.16
SELECTED CROP RESIDUE STATISTICS
Product

Residue/Crop Product
Ratio

Dry Matter Fraction

Carbon Fraction

Nitrogen Fraction

Wheat

1.3

0.82-0.88

0.48530.0028

Barley1.2

0.82-0.88

0.45670.0043Maize

1.0

0.70-0.86

0.47090.0081

Oats

1.3

0.92

0.0070

Rye

1.6

0.90

0.0048Rice

1.4

0.82-0.88

Millet1.4

0.85-0.92

0.0070

Sorghum

1.4

0.91

0.0108

Peas

1.5

0.87

0.0142Beans

2.1

0.82-0.89

Soybeans

2.1

0.84-0.89

Potatoes

0.4

0.42260.01100.3

a0.0228aFeedbeet

0.41440.00670.02300.4072

Sugarcane tops

0.32

0.42350.0040

Sugarcane leaves

0.83

0.42350.0040Jerusalem artichoke

0.8

Peanuts

1.0

a

0.86

0.0106

These figures are for beet leaves.

Source: All data from Strehler and Stützle (1987), except sugarcane data (Turn et al., 1997), dry matter and nitrogen fraction data for oats,rye, sorghum, peas, and peanuts (Cornell, 1994), and nitrogen fraction data for millet and soybeans (Barnard and Kristoferson, 1985).

N in Crop Residues Returned to Soils (FCR): In the IPCC Guidelines, the amount of nitrogen returned to soils
annually through incorporation of crop residues (FCR) is estimated by determining the total amount of crop
residue N produced (from both non-nitrogen-fixing crops and N-fixing crops), and adjusting it for the fractionthat is burned in the field when residues are burned during or after harvest. The annual production of residue N is
estimated by multiplying annual crop production of N-fixing crops (CropBF) and other crops (CropO) by theirrespective N contents (FracNCRBF and FracNCRO), summing these two nitrogen values, multiplying by a default
value of 2 (to yield total aboveground crop biomass), and then adjusting for the amount of total abovegroundcrop biomass that is removed from the field as product (FracR)13 and burned (FracBURN). The Tier 1a equation
presented in the IPCC Guidelines is thus:
EQUATION 4.28
N IN CROP RESIDUE RETURNED TO SOILS (TIER 1a)
FCR = 2 • (CropO • FracNCRO + CropBF • FracNCRBF) • (1 – FracR) • (1 – FracBURN)

The Tier 1a approach can be modified in several ways to estimate more accurately the amount of crop residue
nitrogen that is incorporated into soils:•

First, Equation 4.28 uses a default value of 2 to convert CropO and CropBF to total aboveground crop residue
and product. As previously mentioned with FBN, this factor is too low for some pulses and soybeans, and

13 The IPCC Guidelines define Frac as the ‘fraction of crop residue that is removed from the field as crop.’ However, thisRvariable, as it is currently used, is instead the ‘fraction of total aboveground crop biomass that is removed from the field as
crop.’

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may result in underestimating the total aboveground crop residue and product. In addition, this factor of 2 isinconsistent with the default value for FracR presented in the IPCC Guidelines. 14•

Second, CropBF should be defined so that it is representative of the products of all N-fixing crops, not justseed yield of pulses and soybeans.

•Third, dry matter fractions need to be incorporated into the equation so that adjustments are made formoisture contents.

•

Fourth, the equation should be modified to account for additional uses of crop residues, specifically as fuel,construction material, and fodder. These modifications are shown in Equation 4.29:
EQUATION 4.29
N IN CROP RESIDUE RETURNED TO SOILS (TIER 1b)

Σi [(CropOi • ResOi /CropOi • FracDMi • FracNCROi ) • (1 – FracBURNi – FracFUEL-CRi –FracCNST-CRi – FracFODi)] + Σj [(CropBFj • ResBFj/CropBFj • FracDMj • FracNCRBFj) •FCR =(1 – FracBURNj – FracFUEL-CRj – FracCNST-CRj – FracFODj)]

Equation 4.29 allows for available crop-specific values to be used for the following variables (i.e. each othercrop type i and each nitrogen-fixing crop type j): (i) the residue to crop product mass ratio (ResOi/CropOi and
ResBFj/CropBFj); (ii) the dry matter content of the aboveground biomass (FracDMi and FracDMj); (iii) the nitrogencontent of the aboveground biomass (FracNCROi and FracNCRBFj); (iv) the fraction of residue burned in the field
before and after harvest (FracBURNi and FracBURNj); (v) the fraction of residue used as fuel (FracFUEL-CRi and
FracFUEL-CRj); (vi) the fraction of residue used for construction (FracCNST-CRi and FracCNST-CRj); and (vii) the
fraction of residue used as fodder (FracFODi and FracFODj). Good practice default values for ResOi/CropOi, FracDMi,
and FracNCROi, for some crop types, are presented in Table 4.16. Inventory agencies may use these values if
country-specific data are not available. If a default residue nitrogen content is needed for a crop type for which avalue is not provided in Table 4.16, the non-crop specific default values for N-fixing and Non-N-fixing cropsthat are listed in Table 4-19 of the Reference Manual of the IPCC Guidelines can be used (0.03 and 0.015 kg
N/kg dry matter, respectively).
Area of organic soils harvested (FOS): The IPCC Guidelines defines FOS as the area (in hectares) of organicsoils cultivated annually. This definition is applicable for both the Tier 1a and Tier 1b methods.

4.7.1.2

C HOICE OF EMISSIO N FACTORS

Two emission factors are needed to estimate direct N2O emissions from agricultural soils. The first (EF1)indicates the amount of N2O emitted from the various nitrogen additions to soils, and the second (EF2) estimates
the amount of N2O emitted from cultivation of organic soils.
Country-specific emission factors should be used where possible, in order to reflect the specific conditions of acountry and the agricultural practices involved. Such emission factors should be based on measurements that are
conducted frequently enough and over a long enough time period to reflect the variability of the underlying
biogeochemical processes, given the selected measurement technique, and documented in refereed publications.
Good practice in the derivation of country-specific emission factors is described in Box 4.1.
If country-specific emission factors are not available, emission factors from other countries with comparable
management and climatic conditions are good alternatives. If this is not a key source category (see Chapter 7,
Methodological Choice and Recalculation) or if the necessary resources are not available for deriving country- or
region-specific emission factors, default emission factors may be used. It is anticipated that some inventory
agencies will use a mix of default values and country-specific emission factors when the latter do not cover the

14 The IPCC Guidelines present a default value for Frac of 0.45 that is not consistent with the default value presented forRaboveground crop residue and product. If FracR = 0.45, then 55% of the residue plus crop product mass equals residue.
However, if residue plus crop product mass equals 2 times the crop product, then 50% of the residue plus crop product massequals residue.

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full range of environmental and management conditions. If country-specific or other emission factors are usedinstead of defaults, their derivation must be clearly documented.
The good practice default emission factors are summarised in Table 4.17, Updated Default Emission Factors to
Estimate Direct N2O Emissions from Agricultural Soils. The default value for EF1 in the IPCC Guidelines is
1.25% of the nitrogen applied to soils. In many cases, this factor will be adequate. However, if synthetic
fertilisers are applied to fields that are already receiving applications of organic manure, recent data indicate thathigher N2O losses may occur (Clayton et al., 1997). At this time no recommendation to change the default ismade because of the need for further corroborating evidence. Where this correction is needed, good practicerequires use of a more detailed form of the basic equation presented in the IPCC Guidelines to ensure that theappropriate emission factors are applied to the various nitrogen inputs.
The default value for EF2 presented in the IPCC Guidelines should be updated based on the results of more
recent measurements. These measurements indicate that the emission factors for organic soils in mid-latitudes
are higher than previously estimated (Klemedtsson et al., 1999). These data suggest that a value of 8 rather than
5 is appropriate for EF2 in mid-latitudes. Consistent with the approach taken in the IPCC Guidelines, in whichmineralisation rates are assumed to be about 2 times greater in tropical climates than in temperate climates, theemission factor EF2 for tropical climates should be 16.

TABLE 4.17
UPDATED DEFAULT EMISSION FACTORS TO ESTIMATE DIRECT N2O EMISSIONS FROM AGRICULTURAL SOILSIPCC Default Value

Updated Default Value(EF1 in kg N2O-N/kg N)(EF2 in kg N2O-N/ha-yr )(EF1 in kg N2O-N/kg N)(EF2 in kg N2O-N/ha-yr)

EF1 for FSN

1.25%

No Change

EF1 for FSN when applied to fields already receiving organic
fertiliser/animal manure (applied or grazing)

1.25%

No Change

EF1 for FAM

1.25%

No Change

EF1 for FBN

1.25%

No Change

EF1 for FCR

1.25%

No Change

EF2 for Mid-Latitude Organic Soils

5

8

EF2 for Tropical Organic Soils

10

16

Emission Factor

Source: IPCC Guidelines, Klemedtsson et al. (1999), Clayton et al. (1997).

4.7.1.3

C HOICE OF ACTIVI TY DATA

Several types of activity data are required to estimate direct N2O emissions from soils. For the anthropogenic N
inputs from application of synthetic fertilisers (FSN) and animal manure (FAM), as well as biological N-fixation bycrops (FBN), mineralisation of crop residues returned to soils (FCR), and soil nitrogen mineralisation due tocultivation of organic soils (FOS), the types and sources of the activity data and key considerations related to theapplication of more detailed country-and potentially crop-specific methods (now or in the future) are described
below. Even if inventory agencies cannot currently prepare estimates based on country- or crop-specificemission factors , it is good practice to collect detailed activity data as far as possible. This will allow for a moreaccurate future revision of previously constructed inventories should country- or crop-specific emission factors
become available.
FSN: The inputs required for calculation of FSN are NFERT and FracGASF.
•

4.60

Synthetic fertiliser consumption (NFERT) data should be collected from official statistics (e.g. national
bureaux of statistics) using yearly census data. Most inventory agencies may be able to readily obtain suchdata. If country-specific data are not available, data from the International Fertiliser Industry Association(IFA, Paris; www.fertiliser.org/stats.htm) on total fertiliser use by type and by crop, or from the Food andAgriculture Organisation of the United Nations (FAO; www.apps.fao.org) on synthetic fertiliser
consumption can be used. It may be useful to compare national statistics to international databases such as

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those of the IFA and FAO. If possible, NFERT data should be disaggregated by fertiliser type, crop type andclimatic regime for major crops, if sufficient data are available.
•

For the fraction of nitrogen that volatilises as NH3 and NOx from applied synthetic fertilisers (FracGASF), a
fixed loss rate of 10% can be used in the (IPCC Guidelines, Table 4-19, Reference Manual). However, theloss rate can be highly variable, and depends on the type of synthetic fertiliser applied, the mode ofapplication, and climate. The use of appropriately documented country-specific loss rates is encouraged.

FAM: Good practice in developing the inputs for the calculation of FAM using either the Tier 1a or Tier 1bequation has been summarised above. Regardless of how FAM is estimated, it is suggested that the amount ofanimal manure applied and areas covered be disaggregated by crop type and climatic region if possible. Thisdata may be useful in developing revised emission estimates if inventory methods are improved in the future.
•

The total amount of nitrogen excreted by a country’s animal population (ΣT(N(T) • Nex(T))) is calculated bydetermining the number of animals within a country by animal species/category (N(T)) and multiplying by N
excretion rates for each animal species/category (Nex(T)). For good practice, the livestock population data
should be developed following the approach described in Section 4.1, Livestock Population
Characterisation, and must be consistent with the livestock characterisations used for other emission source
categories. The N excretion rates for each animal species/category must also be consistent across source
categories. The good practice approach for developing country-specific nitrogen excretion rates is describedin Section 4.4: Estimating N2O Emissions from Manure Management. If country-specific ΣT(N(T) • Nex(T))
rates are not available, default values from Table 4-20 in the IPCC Guidelines Reference Manual should be
used.

•

For the fraction of nitrogen that volatilises as NH3 and NOx from animal manure (FracGASM), a fixed loss rateof 20% is reported in the IPCC Guidelines Reference Manual Table 4-19. These losses are highly variableand depend on the type of animal manure, its storage, mode of application, and climate. Country-specific
FracGASM factors are encouraged for use if appropriately documented.

•

The amounts of animal manure used for purposes other than fertiliser (represented by FracFUEL-AM, FracPRP,
and if using the Tier 1b equation, FracCNST-AM FracFEED-AM) can be obtained from official statistics or asurvey of experts. The FracPRP value used in this calculation must be consistent with the value used in
calculating the N2O emissions from grazing animals in the manure management section.

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BOX 4.1
GOOD PRACTICE IN DERIVATION OF COUNTRY-SPECIFIC EMISSION FACTORSIn general, good practice requires the measurement of emissions by individual sub-source category(i.e. synthetic fertiliser (FSN), animal manure (FAM), biological N-fixation (FBN), crop residue
mineralisation (FCR) and cultivation of organic soils (FOS)). For emission factors to be
representative of environmental and management conditions within the country, measurements
should be made in the major crop growing regions within a country, in all seasons, and if relevant,in different geographic and soil regions and under different management regimes. Appropriate
selection of regions or regimes may enable a reduction in the number of sites that must be sampled
to derive a reliable flux estimate. Maps or remote sensing data can provide a useful basis fordelineation by utilising the variability of a system or landscape. Aggregation errors may occur if
available measurements do not cover the actual range of environmental and soil management
conditions, and inter-annual climatic variability. Validated, calibrated, and well-documented
simulation models may be a useful tool to develop area-average emission factors on the basis ofmeasurement data (Smith et al., 1999).
Regarding measurement period and frequency, emission measurements should be taken over anentire year (including fallow periods), and preferably over a series of years, in order to reflect
differences in weather conditions and inter-annual climatic variability. Measurements should betaken at least once per day following major disturbances that would cause emissions to increase
above background levels (e.g. during and after rainfall events, ploughing, or fertiliser application).
Less frequent measurements (once per day or less) are acceptable during periods when emissions
are close to background levels. A good description of the measurement techniques that areavailable can be found in IAEA (1992).
To ensure accurate emission factors, it is good practice to monitor on representative sites thosefactors that may influence inter-annual variability of N2O emissions. Such factors include fertiliser
application, the previous crop, soil texture and drainage condition, soil temperature, and soil
moisture. A complete list of factors that are involved in the regulation of N2O formation,
consumption, and exchange between soil and air can be found in Firestone and Davidson (1989).
For N2O emissions from organic soil cultivation, it can be assumed that the frequency ofmeasurement need not be more detailed than that for mineral soils. The frequency of measurement
should be consistent with the frequency of the disturbance event. Emissions are likely to bevariable among geographic regions, especially among different cropping systems.
It is possible that N deposition from industrial sources might result in unrepresentative emission
factors, but this is probably not a significant problem. In general, emission factors are determinedby subtracting the emissions of a control plot (zero fertiliser) from the emissions of a fertilised
plot. Since N deposition affects both plots, one would expect that N deposition is not included in
the derived emission factor. Hence, the current default emission factor is most likely correct.
Note that the emission factors derived for both synthetic fertiliser and animal manure application
should include corrections for volatilisation. In other words, the emission factor for these two subsources should represent the following: kg N2O-N emitted / (kg N input- kg N volatilised)15.

15 In words: kg N O (as N) emitted divided by (kg N input minus kg N volatilised).
2

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FBN and FCR: The factors required for calculation of FBN and FCR using the Tier 1a method are CropBF, CropO,
FracNCRBF, FracNCRO, FracR, and FracBURN:•

CropBF and CropO, FracNCRBF, FracNCRO, FracR, and FracBURN: Data on the production of N-fixing crops(CropBF), as well as non-N-fixing crops (CropO), can generally be obtained from national statistics. If suchdata are not available, FAO publishes data on crop production (see the website: www.apps.fao.org). As
previously mentioned, the definition of the term CropBF should be modified from the definition provided in
the IPCC Guidelines. It should be defined so that it represents the products of all N-fixing crops, not just theseed yield of pulses and soybeans. For the fraction of nitrogen in N-fixing crops (FracNCRBF), non-N-fixing
crops (FracNCRO), and the fraction of residues burned in the field (FracBURN) some crop-specific default
values are provided in the Good Practice Report Table 4.16, and non-crop specific values are provided inTable 4-19, Reference Manual of the IPCC Guidelines. The IPCC Guidelines definition of the term FracR
should be modified to the fraction of total aboveground biomass that is removed from the field as crop
product. Also, as already discussed, the default value for FracR provided in Table 4-19 in the Reference
Manual of the IPCC Guidelines is inconsistent with the default value ‘2’ in Equation 4.28. If Equation 4.28
is used, a value of 0.50 should be used for FracR. For the fractions of residues burned, the same values thatare used in the agricultural residue burning calculations should be used here.

•

Some additional inputs are required for calculation of FBN and FCR using the Tier 1b method. These are
ResBF/CropBF, ResO/CropO, FracDM , FracFUEL , FracCNST , FracFOD. The data needed to determine the residue
to crop product mass ratio for N-fixing (ResBF/CropBF) and non-N-fixing (ResO/CropO) crops can generally
be obtained from national statistics. If possible, crop-specific values should be used because of thevariability among crops. If such data are not available nationally, default ResBF/CropBF and ResO/CropO
values from Good Practice Report Table 4.16 can be used. If available, the dry matter content of theaboveground biomass for both N-fixing and non-N-fixing crops (FracDM) should also be obtained fromnational statistics and should be specific to specific crop types. Alternatively, default values for dry matter
residue in Table 4.16 can be used. For the fractions of residue used as fuel (FracFUEL), used in construction(FracCNST), and used as fodder (FracFOD), country-specific values should be used. The values used for
FracFUEL must be consistent with those used in the energy calculations.
It should also be noted that in the IPCC Guidelines’ method for incorporation of crop residues, thecontribution from root biomass from the harvested crop is not accounted for. Ideally, both the abovegroundand the root biomass should be accounted for to include nitrogen from the total plant, but the root biomass
cannot readily be estimated. For N-fixing crops, the IPCC Guidelines method does not include root biomass
because it is assumed that the N contained in the aboveground part of the plant (crop product + shoots) is a
proxy for the N2O emissions associated with the processes of nitrogen fixation in the roots and movement
aboveground.

FOS: The area (in hectares) of organic soils cultivated annually (FOS) should be collected from official national
statistics. If this source is not available, data from FAO can be used.

4.7.1.4

C OMPLETENESS

Complete coverage for this source category requires estimation of emissions for all of the anthropogenic inputs
and activities (FSN, FAM, FBN, FCR, and FOS, FSEWSLUDGE), if they occur. Experience has shown that none of these
sub-categories are likely to be missed in inventories, although countries may have difficulty obtaining accurate
statistics for all sub-categories, particularly the amounts of crop residues (by crop type) that are typicallyincorporated into soils, and the area of cultivated organic soils.
Currently, the IPCC method does not explicitly address several activities that may enhance N2O emissions,
including:•

Consumption of commercial and non-commercial organic fertilisers other than animal manure and crop
residues and sewage sludge;•

Production of N-fixing forage crops such as alfalfa;•

Production of mixed grass and N-fixing forage;•

Use of cover crops (catch crops) sown as green manure to reduce leaching of N in post-harvest periods;•

Ploughing of pasture lands;•

Use of plastic sheeting on horticultural soils;

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Chapter 4

N deposition onto agricultural land from industrial sources (see Box 1: Good Practice in Derivation ofCountry-specific Emission Factors).

These additional activities can be considered, if appropriate, and if national activity data for these activities arecollected. Some of these activities can be readily included in national inventories based on available information.
For the additional commercial and non-commercial organic fertilisers, the default emission factor used for
applied N and default fraction of animal manure N volatilised may be used. For N-fixing forage crops, use of thegood practice method for biological nitrogen fixation is suggested, using harvested crop dry matter as themeasure of total aboveground biomass. For cover (catch) crops, the good practice method for crop residues issuggested. Further research will be required to develop the flux data that is needed to develop emission factors
for mixed grass and legume pastures, ploughing of grasslands, and use of plastic sheeting on horticultural areas.

4.7.1.5

D EVELOPING A CONSISTENT TIME SERIES

Ideally, the same method should be used throughout the entire time series. However, it is likely that the detailand disaggregation of emissions estimates from this source category will improve over time. In cases wheresome historical data are missing, it may be necessary to derive the data using other references or data sets. For
example, annual data of areas for cultivated organic soils may need to be derived by interpolation from a longer
time series based upon long-term trends (e.g. from decadal statistics over a 20- or 30-year period). Estimates of
the amounts of crop residue incorporated annually may also need to be derived based on expert judgement. For
general good practice guidance on ensuring time series consistency (see Chapter 7, Methodological Choice and
Recalculation, Section 7.3.2.2).
It is important that the methods used reflect the results of action taken to reduce emissions and the methods andresults are thoroughly documented. If policy measures are implemented such that activity data are affected
directly (e.g. increased efficiency of fertiliser use resulting in a decrease in fertiliser consumption), the effect ofthe policy measures on emissions will be transparent, assuming the activity data are carefully documented. In
cases where policy measures have an indirect effect on activity data or emission factors (e.g. a change in animal
population feed practices to improve animal productivity that results in a change in animal excretion per head),inventory input data should reflect these effects. The inventory text should thoroughly explain the effect of thepolicies on the input data.

4.7.1.6

U NCERTAINTY ASSESSMENT

Uncertainties in estimates of direct emissions of N2O from agricultural soils are caused by uncertainties related
to the emission factors and activity data, lack of coverage of measurements, spatial aggregation, and lack of
information on specific on-farm practices. Additional uncertainty will be introduced in an inventory whenemission measurements that are not representative of all conditions in a country are used. For good practicemeasurements of direct N2O emissions from soils for a specific sub-category (Smith et al., 1999), the associateduncertainty is expected to be about 25%. In general, the reliability of activity data will be higher than that of theemission factors. As an example, further uncertainties may be caused by missing information on observance oflaws and regulations related to handling and application of fertiliser and manure, and changing management
practices in farming. Generally it is difficult to obtain information on the actual observance of laws and possible
emission reductions achieved as well as information on farming practices.
Recent data (Smith et al, 1999; Mosier and Kroeze, 1999) indicate that measured emission factors for N2O fromapplied nitrogen have a skewed distribution which is nearer to log-normal than normal, with a range from theorder of 0.1% to the order of 10%. The best estimate of the 95% confidence limit ranges from one-fifth to 5
times the default emission factor of 1.25%, i.e. from about 0.25% to 6%.
For histosols, the uncertainty range is 1 to 80 kg N2O-N ha-1yr-1 for soils in mid-latitudes and 5 to >100 kg
N2O-N ha-1yr-1 in tropical histosols.
As uncertainties for this source category are caused by many different factors, the uncertainty needs to be
estimated using expert judgement based on knowledge of various error components. Chapter 6, Quantifying
Uncertainties in Practice, provides advice on quantifying uncertainties in practice, including application ofMonte Carlo methods.

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4.7.2

Agriculture

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. N2O emissions from agricultural soils (direct-soils, direct-grazing animals, andindirect) are reported in aggregate under the IPCC category ‘Agriculture’. These three source categories should
be listed separately in inventory reports. In addition, to improve the transparency of reporting, estimates ofemissions from this source category should be reported by the following components:•

Synthetic fertiliser consumption;•

Animal manure applied to soils (other than that used as commercial fertiliser);•

Production of leguminous (N-fixing) crops;•

Crop residue incorporation;•

Organic soil cultivation.

If other components are included, such as commercial organic fertiliser, these should be reported separately as
well. In addition to completing the reporting formats, the following additional information is necessary to
document the estimate:•

Activity data: Sources of all activity data used in the calculations (i.e. complete citations for the statistical
databases from which data were collected), and in cases when activity data were not available directly fromdatabases, the information and assumptions that were used to derive the activity data. This documentation
should include the frequency of data collection and estimation, and estimates of accuracy and precision.

•

Emission factors: The sources of the emission factors that were used (specific IPCC default values orotherwise). In inventories in which country- or region-specific emission factors were used, or in which new
methods (other than the default IPCC methods) were used, the scientific basis of these emission factors andmethods should be completely described and documented. This includes defining the input parameters anddescribing the process by which these emission factors and methods are derived, as well as describing
sources and magnitudes of uncertainties.

•

Emission results: Significant fluctuations in emissions between years should be explained. A distinction
should be made between changes in activity levels and changes in emission factors from year to year, andthe reasons for these changes documented. If different emission factors are used for different years, thereasons for this should be explained and documented.

4.7.3

Inventory quality assurance/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, and quality assurance
procedures may also be applicable, particularly if higher tier methods are used to determine emissions from thissource category.
It is good practice to supplement the general QA/QC related to data processing, handling, and reporting, as
outlined in Chapter 8, QA/QC, with source-specific category procedures discussed below. The persons whocollect data are responsible for reviewing the data collection methods, checking the data to ensure that they arecollected and aggregated or disaggregated correctly, and cross-checking the data with previous years to ensurethat the data are reasonable. The basis for the estimates, whether statistical surveys or ‘desk estimates’, must be
reviewed and described as part of the QC effort. Documentation is a crucial component of the review process
because it enables reviewers to identify mistakes and suggest improvements.
Rev i e w o f e mi s sio n f a c t o r s• The inventory agency should review the default emission factors and document the rationale for setting
specific values.
•

If using country-specific factors, the inventory agency should compare them to the IPCC default emission
factors, and, if accessible, the country-specific emission factors used by other countries with comparable

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Chapter 4circumstances. Differences between country-specific factors and default or other country factors should be
explained and documented.
Rev i e w o f a ny d ir e c t mea su r e me nt s
• If using factors based on direct measurements, the inventory agency should review the measurements toensure that they are representative of the actual range of environmental and soil management conditions, andinter-annual climatic variability, and were developed according to recognised standards (IAEA, 1992).
•

The QA/QC protocol in effect at the sites should also be reviewed and the resulting estimates compared
between sites and with default-based estimates.

Activ ity da ta c he c k• The inventory agency should compare country-specific data on synthetic fertiliser consumption withfertiliser usage data from the IFA and synthetic fertiliser consumption estimates from the FAO.
•

The inventory agency should ensure that N excretion data are consistent with those used for the manure
management systems source category.

•

National crop production statistics should be compared to FAO crop production statistics.

•

The inventory agency should ensure that the QA/QC described in Section 4.1 for livestock population
characterisation has been implemented and that a consistent livestock population characterisation is used
across sources.

•

Country-specific values for various parameters should be compared to IPCC defaults.

E xte rna l r eview• The inventory agency should conduct expert (peer) review when first adopting or revising the method.
Given the complexity and uniqueness of the parameters used in calculating country-specific factors for thesecategories, involve specialists in the field should be involved in such reviews.

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4.8

Agriculture

INDIRECT N2O EMISSIONS FROMNITROGEN USED IN AGRICULTURE

Nitrous oxide (N2O) is produced naturally in soils and aquatic systems through the microbial processes ofnitrification and denitrification. A number of agricultural and other anthropogenic activities add nitrogen (N) tosoils and aquatic systems, increasing the amount of N available for nitrification and denitrification, andultimately the amount of N2O emitted. The emissions of N2O that result from anthropogenic N inputs occur
through a direct pathway (i.e. directly from the soils to which N is applied), and through a number of indirect
pathways, including the leaching and runoff of applied N in aquatic systems, and the volatilisation of applied N
as ammonia (NH3) and oxides of nitrogen (NOx) followed by deposition as ammonium (NH4) and NOx on soils
and water.

4.8.1

Methodological issues

The IPCC Guidelines provide methods to estimate N2O emissions from both direct and indirect pathways. Thissection provides good practice guidance on how to estimate indirect emissions of N2O, while the direct pathway
is covered in Section 4.7. Indirect emissions from both aquatic systems and agricultural soils are covered in
Section 4.5.4 of the Reference Manual of the IPCC Guidelines. The method for estimating indirect N2Oemissions from human sewage, that is discharged into rivers or estuaries, is also presented in this section,although these emissions are reported under the Waste Sector.

4.8.1.1

C HOICE OF METHOD

The method in the IPCC Guidelines for estimating indirect N2O emissions from N used in agriculture describes
five separate pathways by which anthropogenic inputs of N become available for formation of N2O:•

Atmospheric deposition on soils of NOx and ammonium (NH4)16 with the sources of N including
volatilisation of N inputs to soils, as well as combustion and industrial process sources;•

Leaching and runoff of N that is applied to, or deposited on, soils;•

Disposal of sewage N;•

Formation of N2O in the atmosphere from NH3 emissions originating from anthropogenic activities;•

Disposal of processing effluents from food processing and other operations.

Of these five sources, the IPCC Guidelines describe how to estimate emissions from: (i) that portion of theatmospheric deposition of NOx and ammonium (NH4) associated with the N from synthetic fertilisers and animal
manure that have been applied to soils; (ii) that portion of the N from applied synthetic fertilisers and animal
manure lost as leaching and runoff; and (iii) the discharge of sewage N into rivers or estuaries. However, there isno current method for estimating conversion of NH3 to N2O in the atmosphere. The basic equation shown in the
IPCC Guidelines for estimating a country’s indirect N2O emissions (N2Oindirect) (kg N/year) is:
EQUATION 4.30
INDIRECT N2O EMISSIONSN2Oindirect-N = N2O(G) + N2O(L) + N2O(S)Where:
N2Oindirect-N = Emissions of N2O in units of nitrogen

16 The IPCC Guidelines refer to ‘atmospheric deposition of NO and NH ’, but the process actually entails the volatilisationx

3

of applied N (or direct gaseous emissions of N) as oxides of nitrogen (NOx) and ammonia (NH3), transformations of thesegases within the atmosphere (or upon deposition) and subsequent deposition as NOx, nitric acid (HNO3), and particulateammonium (NH4). NOx is often hydrolysed in the atmosphere or upon deposition to form HNO3, while NH3 gas generally
combines with atmospheric nitric acid or sulphuric acid (H2SO4) to form ammonium nitrate and ammonium sulphateaerosols, and hence is transformed to particulate ammonium form (NH4).

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N2O(G) = N2O produced from volatilisation of applied synthetic fertiliser and animal manure N, and its
subsequent atmospheric deposition as NOx and NH4 (kg N/yr)
N2O(L) = N2O produced from leaching and runoff of applied fertiliser and animal manure N (kg N/yr)
N2O(S) = N2O produced from discharge of human sewage N into rivers or estuaries (kg N/yr) 17
Conversion of N2O-N emissions to N2O emissions for reporting purposes is performed by using the following
equation:
N2O = N2O-N • 44/28

To apply the estimation method, the amount of N2O produced from each of these indirect pathways must be
determined. Good practice guidance on how to apply the IPCC Guidelines is provided below in order to clarify
the method, and ensure consistency and completeness between source categories. The choice of good practicemethod is illustrated by the decision tree in Figure 4.8, Decision Tree for Indirect N2O Emissions from Nitrogenused in Agriculture.
The terms Tier 1a and Tier 1b are used throughout Good Practice Report, Subsections 4.7 and 4.8, todifferentiate between the equations in the IPCC Guidelines (Tier 1a) and new equations (Tier 1b) presented here.
The Tier 1b equations represent increased precision due to expansion of the terms in the equations. However,while Tier 1b equations may be preferred, the activity data needed to use them may not be available. In thesecases, use of the Tier 1a equations is appropriate. Estimating emissions using a combination of Tier 1a and Tier
1b equations for different sub-source categories, depending upon availability of activity data, is also acceptable.
In some cases, there is no Tier 1b alternative because no refinement of the equation in the IPCC Guidelines wasconsidered necessary.
Atmospheric deposition of NOx and NH4 (N2O(G)): Atmospheric deposition of nitrogen compounds such as
nitrogen oxides (NOx) and ammonium (NH4) fertilises soils and surface waters, which results in enhanced
biogenic N2O formation. According to the IPCC Guidelines, the amount of applied agricultural N that volatilises
and subsequently deposits on nearby soils is equal to the total amount of synthetic fertiliser nitrogen applied tosoils (NFERT) plus the total amount of animal manure nitrogen excreted in the country (ΣT(N(T) • Nex(T)))
multiplied by appropriate volatilisation factors.18 The volatilised N is then multiplied by an emission factor foratmospheric deposition (EF4) to estimate N2O(G) emissions.
The equation in the IPCC Guidelines is thus:
EQUATION 4.31
N2O FROM ATMOSPHERIC DEPOSITION OF N (TIER 1a)
N2O(G)-N = [(NFERT • FracGASF ) + (ΣT(N(T) • Nex(T)) • FracGASM)] • EF4

Where19:
N2O(G) = N2O produced from atmospheric deposition of N, kg N/yr
NFERT = total amount of synthetic nitrogen fertiliser applied to soils, kg N/yr 20

ΣT(N(T) • Nex(T)) = total amount of animal manure nitrogen excreted in a country, kg N/yr
FracGASF = fraction of synthetic N fertiliser that volatilises as NH3 and NOx, kg NH3-N and NOx-N/kg of
N input
FracGASM = fraction of animal manure N that volatilises as NH3 and NOx, kg NH3-N and NOx-N/kg of N
excreted

17 Nitrous Oxide produced from human sewage (N O ) is reported under the Waste sector.
2 (S)18 In this part of the IPCC Guidelines, the variable Nex is used for the total amount of animal manure produced. To beconsistent with Good Practice in Section 4.4, this variable name has been revised to ΣT(N(T) • Nex(T)).
19 Refer to Section 4.7 for more information on all of these terms except EF .
4

20 The definition of N

FERT as total synthetic N fertiliser applied would cover application to forest soils.

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Figure 4.8

Decision Tree for Indirect N2O Emissions from
Nitrogen Used in AgricultureDoes
the country apply
or deposit N
on soils?

No

Report‘Not Occurring’YesForeach N sourceask:
Do you have countryspecific activity
data?

Yes

If
indirect N2Oemission are a key
source category, does this N
sub-source represent a significant
portion of total emissions?
(Note 1 andNote 2)

No

No

Yes
Obtain countryspecific data

Yes

For eachsource of N, do
you have rigorously
documented country-specificemission factor (EF4 or EF5) and, if
relevant, rigorously documented countryspecific partitioning fraction(FracGASF, FracGASM,
FracLEACH)
values?

No

Box 4

Box 3

Estimate emissions
using country-specificactivity data andavailable countryspecific EF andpartitioning fraction
valuesEstimate emissions
using Tier 1a or 1b,
country-specific
FracGASP/FracGASM
value(s), and defaultEF

Do youhave rigorously
documented
country-specific
EF values?

Yes

No

Box 2

Box 1

Estimate emissions
using available
activity data anddefault EF

Estimate emissions
using mix ofcountry-specific andother available data
and default EF

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)
Note 2: As a rule of thumb, a sub-source category would be significant if it accounts for 25-30% of emissions from the source category.

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Chapter 4EF4 = emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces,kg N2O-N/kg NH3-N and NOx-N emitted

Use of Equation 4.31 is consistent with good practice. If more detailed data are available, however, a more
complete estimate can be prepared.
First, the activity data used to estimate N2O(G) can be expanded to include other forms of N applied to all soils,
rather than just synthetic fertilisers and animal manure applied to agricultural soils. For example, sewage sludge,
an additional form of organic N, is often applied to soils as a soil amendment or to dispose of the sludge. Sewage
sludge nitrogen (NSEWSLUDGE) can be included in this calculation if sufficient information is available.21 The
sludge input should be measured in units of N and multiplied by the volatilization factor that is used for animal
manure N, FracGASM. The resulting equation for estimating the amount of N2O produced from atmosphericdeposition, renamed N2O(G-SOIL), is:
EQUATION 4.32
N2O FROM ATMOSPHERIC DEPOSITION OF N (TIER 1b)
N2O(G-SOIL)-N = {(NFERT • FracGASF) + [ΣT(N(T) • Nex(T)) + NSEWSLUDGE ] • FracGASM} • EF4

This equation will ensure a more complete accounting of the N2O emissions from the volatilisation and redeposition of N applied to soils. These emissions should be reported within the Agriculture Sector.
Second, other sources of N deposited on soils N2O (G-i) can be accounted for. The estimation of N2O(G-i), can beundertaken to the extent that data allow the inclusion of deposited N from other anthropogenic activities
associated with agriculture that release NOx and NH3. This would include emissions of NOx and NH3 (in units ofN) from prescribed burning of savannas and field burning of agricultural residues.22
Equation 4.33 shows the good practice approach for estimating N2O emissions from these additional indirect
sub-categories associated with agriculture. For each sub-category ‘i,’ (i.e. prescribed burning of savannas andfield burning of agricultural residues) the amount of N emitted as NOx and NH3 is multiplied by EF4.
EQUATION 4.33
N2O FROM ADDITIONAL INDIRECT SUB-SOURCES
N2O(G-i)-N = (NOx-i + NH3-i) • EF4

Although the method for estimating these additional sub-categories of indirect emissions of N2O is presented
here, the estimates should be reported under the sector in which the originating activity is reported.
Leaching/runoff of applied or deposited nitrogen (N2O(L)): A large proportion of nitrogen is lost fromagricultural soils through leaching and runoff. This nitrogen enters the groundwater, riparian areas and wetlands,
rivers, and eventually the ocean, where it enhances biogenic production of N2O. To estimate the amount ofapplied N that leaches or runs off (NLEACH) using the method in the IPCC Guidelines, the total amount ofsynthetic fertiliser nitrogen (NFERT) applied to the soils and the total amount of animal N excretion in the country(ΣT(N(T) • Nex(T))) are summed and then multiplied by the fraction of N input that is lost through leaching andrunoff (FracLEACH). NLEACH is then multiplied by the emission factor for leaching/runoff (EF5) to obtain emissionsof N2O in units of N, N2O(L). The equation in the IPCC Guidelines is thus:

21 Since there are no default data for the new parameter N

SEWSLUDGE, or guidance on collecting such data, this refinement
should only be used if reliable country-specific data are available. Note that the sewage sludge activity data used to estimate
indirect N2O emissions should be the same as those used to estimate direct N2O emissions (see Section 4.7).

22 A complication in the estimation of N O emissions resulting from atmospheric deposition is that a significant fraction of2
NOx and NH3 may be deposited on the ocean, where EF4 is probably not applicable and for which little information exists todefine a more appropriate emission factor. This is particularly problematic for NOx, which has a longer atmospheric lifetime
than NH3 and therefore is more likely to be transported far from its source (Smil, 1999). For the present, it is assumed that all
NOx and NH3 are deposited on land.

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EQUATION 4.34
DEPOSITED N FROM LEACHING/RUNOFF 23
N2O(L)-N = [NFERT +ΣT(N(T) • Nex(T))] • FracLEACH • EF5

For good practice, this basic approach should be corrected so that it accounts for only the portion of animal
manure N which is applied to soils (see Section 4.7).24 As currently defined, the equation will overestimate N2O
emissions from this source because it does not reduce the total amount of animal manure N generated in acountry (ΣT(N(T) • Nex(T))) by the amounts not applied to soil (i.e. the fractions used as fuel (FracFUEL-AM), feed
(FracFEED-AM), and construction material (FracCNST-AM)). 25 The corrected equation is shown in Equation 4.35:
EQUATION 4.35
DEPOSITED N FROM LEACHING/RUNOFF (EXPANDED AS TO ANIMAL MANURE)
N2O(L)-N = NFERT + {ΣT(N(T) • Nex(T)) • [1 – (FracFUEL-AM + Frac FEED-AM + FracCNST-AM)]}• FracLEACH • EF5

As with the estimation of N2OG-SOIL, if data are available the indirect emissions associated with the application of
sewage sludge to soils should be included in the estimate (Tier 1b). In this case, the term N2O(L) is renamed
N2OL-SOIL and the equation for estimating indirect N2O emissions from leaching and runoff of N applied to soils
is:
EQUATION 4.36
DEPOSITED N FROM LEACHING/RUNOFF (EXPANDED AS TO SEWAGE SLUDGE)
N2O(L-SOIL)-N = (NFERT + {ΣT(N(T) • Nex(T)) • [1 – (FracFUEL-AM + FracFEED-AM +FracCNST-AM)]} + NSEWSLUDGE ) • FracLEACH • EF5

Note that when estimating the animal manure N applied to soils, the calculation may need to be undertaken foreach major animal species/category ‘i’ because the fractions of animal manure used for fuel, feed, andconstruction may not be constant across all animal species/categories. In this case, Equation 4.36 should be
rewritten as:
EQUATION 4.37
DEPOSITED N FROM LEACHING/RUNOFF (EXPANDED FOR MAJOR ANIMALSPECIES/CATEGORIES)N2O(L-SOIL)-N = {NFERT +Σi (N(EX)i • [1 – (Frac (FUEL-AM)i + Frac(FEED-AM)i + Frac(CNST-AM)i )]+ NSEWSLUDGE} • FracLEACH • EF5

The estimates derived from Equations 4.35, 4.36, and 4.37 should be reported as part of Agricultural Soilemissions within the Agriculture sector.
The term N2O(L) can also be expanded to include other sources of N deposited on soils N2O(L-i). If data allow, thisshould be undertaken to the extent that data allow the inclusion of deposition from other anthropogenic activities

23 Equation 4.34 combines the equations for N

LEACH and N2O(L) from the IPCC Guidelines.

24 This correction ensures that the estimates prepared for this source are consistent with those prepared for direct N O
2emissions from agricultural soils, as described in Section 4.7.
25 Note that in Equation 4.35, the fraction of N volatilised from fertiliser and animal manure is not accounted for. This is not
an oversight but rather reflects the method’s assumption that such N is subject to leaching after it redeposits on soil.

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Chapter 4

associated with agriculture that release NOx and NH3. This would include emissions of NOx and NH3 (in units ofN) from prescribed burning of savannas and field burning of agricultural residues.
Equation 4.38 shows the good practice approach for estimating N2O emissions from these additional indirect
sub-source categories. For each source ‘i’ (i.e. prescribed burning of savannas and field burning of agriculturalresidues), the amount of N emitted as NOx and NH3 is multiplied by FracLEACH and EF5.
EQUATION 4.38
DEPOSITED N FROM LEACHING/RUNOFF (EXPANDED AS TO ADDITIONAL INDIRECT SUBSOURCES)N2O(L-i)-N = (NOx-i + NH3-i) • FracLEACH • EF5

Although the method for estimating these additional sources of indirect emissions of N2O is presented here, theestimates should be reported under the source category in which the originating activity is reported.
Human consumption followed by municipal sewage treatment (N2O(S)): Human consumption of food resultsin the production of sewage, that can be processed in septic systems or wastewater treatment facilities, and thenmay seep into groundwater systems, be disposed of directly on land, or be discharged into a water source (e.g.
rivers and estuaries). N2O can be produced during all of these processes through nitrification and denitrificationof sewage nitrogen. The IPCC Guidelines assume that N2O emissions associated with sewage treatment and landdisposal are negligible, so that all sewage nitrogen enters rivers and estuaries, where it is available fornitrification and denitrification. The method also recognises that some sewage N may be applied to soil as
sludge. To estimate total sewage nitrogen (NSEWAGE) using the method in the IPCC Guidelines, 26 the annual per
capita protein consumption (PROTEIN, in kg protein/person-year) is multiplied by the national population(NrPEOPLE) and the fraction of protein that is nitrogen (FracNPR). NSEWAGE is then multiplied by the emission factor
for indirect emissions from sewage treatment (EF6) to obtain N2O emissions (in units of N) from discharge ofsewage (N2O(S)). The two equations presented in the IPCC Guidelines to calculate N2O emissions from dischargeof sewage are combined in the single good practice equation below:
EQUATION 4.39
N2O EMISSIONS FROM DISCHARGE OF SEWAGE27
N2O(S)-N = PROTEIN • NrPEOPLE • FracNPR • EF6

It is good practice to use this basic approach, if a basic approach has also been used for estimating indirectemissions from the atmospheric deposition and leaching/runoff pathways (i.e. if Equations 4.31 and 4.35 havebeen used). If a more detailed estimate has been prepared for these other pathways, however, a more detailed
approach should also be used for this sub-category. To avoid double-counting of sewage N in this case, NSEWAGE
should be decreased by the amount of sewage N that is applied to soils in the form of sewage sludge(NSEWSLUDGE), and that has already been accounted for in estimating both N2O(G-SOIL) and N2O(L-SOIL). Therefore,
the more detailed equation for estimating N2O(S), is :EQUATION 4.40
N2O EMISSIONS FROM DISCHARGE OF SEWAGE (EXPANDED AS TO SEWAGE SLUDGE)N2O(S)-N = [(PROTEIN • NPEOPLE • FracNPR) – NSEWSLUDGE] • EF6

These emissions should be reported under Domestic and Commercial Wastewater in Chapter 5, Waste (Section
5.2).

26 General guidance for estimating N O emissions from human sewage is provided in Section 6.4, Nitrous Oxide from2

Human Sewage, IPCC Guidelines, Vol. 3. For a detailed description of the proposed method, the reader is referred to Section
4.5.4 of the IPCC Guidelines, Reference Manual.
27 Equation 4.39 combines the equations for N

SEWAGE and N2O(S) from the IPCC Guidelines.

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4.8.1.2

Agriculture

C HOICE OF EMISSIO N FACTORSThe method for estimating indirect N2O emissions includes three emission factors: one associated with deposited
nitrogen (EF4), the second associated with nitrogen lost through leaching and runoff (EF5), and the third
associated with nitrogen in discharged sewage (EF6).
Very little information exists, even on a global scale, for specifying EF4, EF5, and EF6. Therefore, although the
IPCC Guidelines normally encourages inventory agencies to substitute country-specific data for default emission
factors, for this source category the default values should be used unless rigorously documented and peerreviewed country-specific values have been developed. The following discussion summarises the default values
and describes some refinements of them. For good practice, the IPCC default emission factors are presented inTable 4.18, Default Emission Factors for Estimating Indirect N2O Emissions from N used in Agriculture.
•

Emission factor for deposited nitrogen (EF4): The default value for EF4 is 0.01 kg N2O-N/kg NH4-N and
NOx-N deposited. Country-specific values for EF4 should be used with great caution because of the special
complexity of transboundary atmospheric transport. Although inventory agencies may have specificmeasurements of N deposition and associated N2O flux, in many cases the deposited N may not haveoriginated in their country. Similarly, some of the N that volatilises in their country may be transported toand deposited in another country, where different conditions that affect the fraction emitted as N2O may
prevail.

•Emission factor for leaching and runoff (EF5): This value should be updated based on a recent reexamination of one of the factors from which it was derived. However, more research will be required
before a new default value can be established.

•

Emission factor for discharged sewage effluent: The default value for EF6 is 0.01 kg N2O-N/kg N. Thisvalue was derived by adding estimates of emission factors for rivers (EF5-r = 0.0075) and estuaries (EF5-e =0.0025). Country-specific values of EF6 must be used with great caution because of the complexity of thisemission pathway.
TABLE 4.18
DEFAULT EMISSION FACTORS FOR ESTIMATING INDIRECT N2O EMISSIONS FROM N USED IN AGRICULTURE
Emission Factor

IPCC Default Value

EF4 (kg N2O-N/kg NH4-N & NOx-N deposited)

0.01

EF5 (kg N2O-N/kg N leached & runoff)

0.025

EF6 (kg N2O-N/kg sewage N discharged sewage effluent)

0.01

Source: IPCC Guidelines, Reference Manual, Table 4-23.

4.8.1.3

C HOICE OF ACTIVI TY DATA

Much of the activity data required to estimate indirect N2O emissions, such as fertiliser consumption andlivestock nitrogen excretion, will have been previously developed for estimating emissions from other source
categories. Table 4.19, Data for Estimating Indirect N2O, summarises the key activity data required, anddescribes where to obtain them. It is essential that the same data sets be used across source categories to ensure
consistency in emission estimates.
As Table 4.19 shows, most of the activity data will be data developed for other source category estimates. Good
practice in obtaining that data is described in the appropriate sections. The discussion below summarises goodpractice for developing the activity data:•

Estimating NOx and NH3 emissions from new source categories included for good practice: Emissionsof NOx and NH3 resulting from savanna burning and agricultural residue burning are required to estimate
indirect N2O emissions from these activities. Estimation methods and default emission factors (or emission
ratios) for estimating NOx emissions are included for these sub-categories in the IPCC Guidelines under
their respective sectors or sub-sectors. The same methods used to estimate NOx emissions for each subcategory should be used to estimate NH3 emissions, but the NOx emission factors should be replaced with
NH3 emission factors. A default emission factor of 0.038 Gg NH3-N/Gg fuel N (Crutzen and Andreae,

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Chapter 41990)28 may be used to estimate NH3 emissions from savanna burning and agricultural residue burning if
country-specific emission factors are not available.
TABLE 4.19
DATA FOR ESTIMATING INDIRECT N2O
Activity Data

How to Obtain

NFERT

From estimate of NFERT value collected for Direct N2O Emissions from Agricultural Soils

ΣT(N(T) • Nex(T))

From estimate of ΣT(N(T) • Nex(T)) value collected for Direct N2O Emissions from Agricultural Soils

NSEWSLUDGE

From estimate of NSEWSLUDGE value collected for Direct N2O Emissions from Agricultural Soils

PROTEIN

Food and Agricultural Organisation (FAO)

NrPEOPLE

Food and Agricultural Organisation (FAO)

FracNPR

See Table 4-24 in the IPCC Guidelines Reference Manual

FracLEACH

See Table 4-24 in the IPCC Guidelines Reference Manual

FracGASF

See Table 4-19 in the IPCC Guidelines Reference Manual

FracGASM

See Table 4-19 in the IPCC Guidelines Reference Manual

FracFUEL-AM

From estimate of FracFUEL-AM value collected for Direct N2O Emissions from Agricultural Soils

FracFEED-AM

From estimate of FracFEED-AM value collected for Direct N2O Emissions from Agricultural Soils

FracCNST-AM

From estimate of FracCNST-AM value collected for Direct N2O Emissions from Agricultural Soils•

Partitioning fractions for volatilisation (FracGASF, FracGASM): For the fraction of nitrogen that volatilises
as NH3 and NOx from applied synthetic fertilisers (FracGASF) and animal manure and sewage sludge(FracGASM), default values of 10% and 20%, respectively, are presented in the IPCC Guidelines. Countryspecific volatilisation fractions can be used with reasonable documentation.

•

Partitioning fraction for leaching (FracLEACH): A default value of 30% is presented in the IPCC
Guidelines for FracLEACH. Note, however, that this default value was largely based on mass balance studies
comparing agricultural N inputs to N recovered in rivers. Agricultural practices (e.g. irrigation, frequentploughing, and drainage tiles) can promote heavy leaching losses of N applied to agricultural soils.
However, for N that is deposited away from agricultural land, a lower value of FracLEACH may be more
appropriate. Future revisions of the method may reflect this consideration. Due to difficulties in developinga reliable factor for this source category, inventory agencies should use caution and provide rigorous
documentation if using a country-specific factor.

•

Partitioning fraction for nitrogen in protein (FracNPR): A default values of 16% is presented in the IPCC
Guidelines for the fraction of animal and plant protein that is nitrogen (FracNPR). This term is not highlyvariable, and therefore country-specific values are unnecessary.

4.8.1.4

C OMPLETENESS

Complete coverage for indirect N2O emissions from nitrogen used in agriculture requires estimation of emissions

from all of the agricultural input activities (i.e. NFERT, ΣT(N(T) • Nex(T)), and NSEWSLUDGE). If data are available,
NSEWSLUDGE application (on all soils) can also be included. Complete coverage for indirect N2O emissions fromhuman sewage requires estimation of emissions from the discharge of sewage N (i.e. NSEWAGE, NSEWAGE minus
NSEWSLUDGE).

28 Table 2 of Andreae and Crutzen (1990) is the basis for the NO and NH emission factors associated with biomassx
3

burning. Note that this table also lists an emission factor of 0.034 mole RCN per mole total N in biomass, on par with the
NH3 emission factor. RCN is a form of nitrogen which is biologically available and therefore subject to microbial
nitrification, denitrification, and N2O production. Furthermore, Table 2 of Andreae and Crutzen (1990) only accounts forabout 70% of biomass N, implying that combustion may yield additional, as yet unidentified forms of biologically available
nitrogen. Thus by only accounting for NOx and NH3 emissions, the method likely underestimates the total amount of
biologically available nitrogen released by biomass burning.

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If data are available, the inventory should also include indirect N2O emissions from savanna burning andagricultural residue burning. These emissions are based upon direct emissions of NOx and NH3 from these
activities.

4.8.1.5

D EVELOPING A CONSISTENT TIME SERIESEmission estimates over a time series should be made using the same method (in terms of level of detail). Interannual changes in FracGASF, FracGASM, FracLEACH, FracNPR, EF4, EF5, and EF6 are not expected unless mitigation
measures are undertaken. These factors should be changed only with the proper justification and documentation.
If updated defaults for any of these variables become available through future research, inventory agencies may
recalculate their historical emissions. For general good practice guidance on ensuring consistency in a timeseries, see Chapter 7, Methodological Choice and Recalculation, Section 7.3.2.2.

4.8.1.6

U NCERTAINTY ASSESSMENT

Information about emission factors (EF4, EF5, and EF6), leaching and volatilisation fractions are sparse andhighly variable. Expert judgement indicates that emission factor uncertainties are at least in order of magnitudeand volatilisation fractions of about +/−50%. Uncertainties in activity data estimates should be taken from thecorresponding direct emissions source categories. Chapter 6, Quantifying Uncertainties in Practice, provides
advice on quantifying uncertainties in practice including combining expert judgements and empirical data into
overall uncertainty estimates.

4.8.2

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. The worksheets in the IPCC Guidelines (Workbook) for calculating indirect N2O
from agricultural soils provide for transparent documentation of the default method of the IPCC Guidelines, andthe data used to implement the method. However, to implement good practice, these worksheets should be
expanded to incorporate the new variables that have been added to the deposition and leaching calculations (i.e.
NSEWSLUDGE, FracFUEL-AM, FracFEED-AM, and FracCNST-AM) and should be revised to reflect Equations 4.31 and 4.35
or 4.36.
The worksheets in the Workbook of the IPCC Guidelines used for calculating indirect N2O from human sewage
also provide for transparent documentation of the default method and the data used to implement the method.
However, to implement the good practice approach, these worksheets must be expanded to incorporate the newvariable that has been added to the calculation (i.e. NSEWSLUDGE) and must be revised to reflect Equation 4.40.
To implement good practice for indirect N2O emissions from savanna burning and agricultural residue burning,
new worksheets must be developed for each of these sub-categories. The worksheet for indirect N2O emissions
from savanna burning and agricultural residue burning should reflect Equations 4.33 and 4.38.
The reporting tables in the Reporting Instructions are inadequate. Direct and indirect agricultural N2O sources
are reported together as one entry entitled ‘agricultural soils’ rather than separately. Furthermore, the title is amisnomer for indirect emissions, since a large fraction of these emissions occurs from aquatic systems. To
improve the transparency of reporting, estimates of emissions from deposition and leaching should be reported
separately. An explicit entry for indirect emissions from human sewage should be added in the Waste section.
Entries for the new indirect N2O sources (savanna burning and agricultural residue burning) should also be
added to the reporting tables.
In addition to completing the reporting formats, the following additional information is necessary to document
indirect N2O emission estimates:•

Activity data: References for all activity data used in the calculations (i.e. complete citations for thestatistical database from which data were collected), and in cases when activity data were not available
directly from databases, the information and assumptions that were used to derive the activity data. This
documentation should include the frequency of data collection and estimation, and estimates of accuracy andprecision.

•

Emission factors: References for the emission factors that were used (specific IPCC default values orotherwise). In inventories in which country- or region-specific emission factors were used, or in which new
methods (other than the default IPCC methods) were used, the scientific basis of these emission factors or

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methods should be completely described and documented. This includes defining the input parameters anddescribing the process by which these emission factors or methods are derived, as well as describing sources
and magnitudes of uncertainties.
•

Emission results: Significant fluctuations in emissions between years should be explained. A distinction
should be made between changes in activity levels and changes in emission factors from year to year and thereasons for these changes documented. If different emission factors are used for different years, the reasonsfor this should be explained and documented.

4.8.3

Inventory quality assurance/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, and quality assurance
procedures may also be applicable, particularly if higher tier methods are used to determine emissions from thissource.
It is good practice to supplement the general QA/QC related to data processing, handling and reporting, as
outlined in Chapter 8, QA/QC, with source-specific procedures discussed below. The persons who collect data
are responsible for reviewing the data collection methods, checking the data to ensure that they are collected andaggregated or disaggregated correctly, and cross-checking the data with previous years to ensure that the data arereasonable. The basis for the estimates, whether statistical surveys or ‘desk estimates’ must be reviewed anddescribed as part of the QC effort. Documentation is a crucial component of the review process because it
enables reviewers to identify mistakes and suggest improvements.
Rev i e w o f e mi s sio n f a c t o r s• The inventory agency should review the parameters, equations and calculations used to develop theemission factors. These QC steps are particularly important for subcategories in this source category becauseof the number of parameters that are used to construct the emission factors.
•

If using country-specific factors, the inventory agency should compare them to the IPCC default factors.
This is particularly important for the emission factors for deposited N and for discharged sewage, where
caution should be used in developing country-specific factors.

Activ ity da ta c he c k• Since many of the activity parameters used for this source category are also used for other agricultural
sources, it is critical to ensure that consistent values are being used.
•

If using country-specific values for various parameters, (i.e. FracLEACH), the inventory agency should
compare them to the IPCC defaults. Rigorous documentation of the development of country-specific values
should also be maintained.

E xte rna l r eview• Agricultural specialists (particularly nitrogen cycle specialists) as well as agricultural industry and otherstakeholders, should peer review the inventory estimates and all important parameters and emission factors.

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4.9

CH4 EMISSIONS FROM RICEPRODUCTION

4.9.1

Methodological issues

Anaerobic decomposition of organic material in flooded rice fields produces methane (CH4), which escapes tothe atmosphere primarily by transport through the rice plants. The annual amount emitted from an area of riceacreage is a function of rice cultivar, number and duration of crops grown, soil type and temperature, water
management practices, and the use of fertilisers and other organic and inorganic amendments.

4.9.1.1

C HOICE OF METHODThe IPCC Guidelines outline one method for estimating emissions from rice production, that uses annual
harvested areas29 and area-based seasonally integrated emission factors.30 In its most simple form, the IPCC
method can be implemented using national activity data (i.e. national total area harvested) and a single emission
factor. However, the conditions under which rice is grown (e.g. water management practices, organic fertiliseruse, soil type) may be highly variable within a country, and these conditions can affect seasonal CH4 emissions
significantly. The method can be modified to account for this variability in growing conditions by disaggregating
national total harvested area into sub-units (e.g. harvested areas under different water management regimes), andmultiplying the harvested area for each sub-unit by an emission factor that is representative of the conditions thatdefine the sub-unit. With this disaggregated approach, total annual emissions are equal to the sum of emissions
from each sub-unit of harvested area. Thus, the basic equation is as follows:
EQUATION 4.41
CH4 EMISSIONS FROM RICE PRODUCTION
Emissions from Rice Production (Tg/yr) =Σi Σj Σk (EFijk • Aijk • 10-12)

Where:
EFijk = a seasonally integrated emission factor for i, j, and k conditions, in g CH4/m2
Aijk = annual harvested area for i, j, and k conditions, in m2/yr
i, j, and k = represent different ecosystems, water management regimes, and other conditions underwhich CH4 emissions from rice may vary (e.g. addition of organic amendments)

The different conditions that should be considered include rice ecosystem type, water management regime, typeand amount of organic amendments, and soil type. The primary rice ecosystem types, and water management
regimes in each ecosystem type, are listed in Table 4.20, IPCC Default CH4 Emission Scaling Factors for Rice
Ecosystems and Water Management Regimes Relative to Continuously Flooded Fields. If rice is produced in
distinct regions within the country (e.g. district, province), the equation above should be applied to each region.
National emissions are equal to the sum of the regional estimates. In addition, if more than one crop is harvested
in a particular region during the year, and the conditions of cultivation (e.g. use of organic amendments) vary
among cropping seasons, then for that region, emissions should be estimated for each cropping season, and thensummed over all cropping seasons. In this case, the activity data are cultivated area, rather than harvested area.
If rice is a key source category (as defined in Chapter 7, Methodological Choice and Recalculation), inventory
agencies are encouraged to:•

Implement the IPCC method at the most disaggregated level possible;

29 In case of multiple cropping during the same year, ‘harvested area’ is equal to the sum of the area cultivated for each
cropping.
30 An emission factor represents the total missions over an entire cropping season (from land preparation until harvest or post

season drainage) per unit area. As in Appendix 4A.3, emission factors should be based on measurements over the entire
period of flooding, and should account for fluxes of soil-entrapped methane that typically occur upon drainage.

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Incorporate as many of the characteristics (i, j, k, etc.,) that influence CH4 emissions as possible;•

Develop country-specific emission factors to reflect the local impacts of these characteristics, preferably
through collection of field data;•

Use emission factors and activity data at the same level of aggregation.

The decision tree in Figure 4.9, Decision Tree for CH4 Emissions from Rice Production, guides inventory
agencies through the process of applying the good practice IPCC approach. Implicit in this decision tree is ahierarchy of disaggregation in implementing the IPCC method. Within this hierarchy, the level of disaggregation
utilised by an inventory agency will depend upon the availability of activity and emission factor data, as well as
the importance of rice as a contributor to national greenhouse gas emissions. The specific steps and variables inthis decision tree, and the logic behind it, are discussed in the text that follows the decision tree.

4.9.1.2

C HOICE OF EMISSIO N FACTORS

Ideally, inventory agencies will have seasonally integrated emission factors for each commonly occurring set ofrice production conditions in the country developed from standardised field measurements. These local,
measurement-based emission factors account for the specific mix of different conditions that influence CH4
emissions in one area implicitly. The most important conditions that influence rice emissions are summarised inBox 4.2:

BOX 4.2
CONSIDERATIONS FOR RICE PRODUCTION EMISSION FACTOR DEVELOPMENT

The following rice production characteristics should be considered in developing emission factors:
Regional differences in rice cropping practices: If the country is large and has distinct agricultural
regions, a separate set of measurements should be performed for each region.
Multiple crops: If more than one crop is harvested on a given area of land during the year, and thegrowing conditions vary among cropping seasons, emissions should be measured for each season.
Ecosystem type: At a minimum, separate measurements should be undertaken for each ecosystem(i.e. irrigated, rainfed, and deep water rice production).
Water management regime: Each ecosystem should be broken down further to account fordifferent water management practices (e.g. continuously flooded vs. intermittently flooded).
Addition of organic amendments: Measurements should be designed so that the effect of organic
amendments (e.g. green manure, rice straw, animal manure, compost, weeds and other aquaticbiomass, etc.) on CH4 emissions can be quantified.
Soil type: Inventory agencies are encouraged to make every effort to undertake measurements on
all major soil types under rice cultivation because of the significant influence that soil type canhave on CH4 emissions. Up to now the soil factor has not been taken into account in the IPCC
Guidelines because data on harvested area by (major) soil type are not available from the standard
activity data sources. However, with the recent developments of models to simulate CH4 emissions
from rice fields, deriving scaling factors for major soil types grown to rice will be feasible in thenear future (e.g. Ding et al., 1996, and Huang et al., 1998). Combining measured or modelsimulated soil type-specific scaling factors and a breakdown of rice acreage by soil type would
further improve inventory accuracy if available.

Since some countries grow rice under a wide diversity of conditions, a complete set of local measurement-based
emission factors may not be possible. In this case, inventory agencies are encouraged to first obtain a seasonallyintegrated emission factor for continuously flooded fields without organic amendments (EFc), which is to be
used as a starting point, and use scaling factors to adjust it to account for different conditions. The adjusted
emission factors can then be determined using the following equation:

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Figure 4.9

Decision Tree for CH4 Emission from Rice Production

Is there any
rice production in the
country?

No

Report‘Not Occurring’YesAre
harvested area data
available for all ecosystems
and water
regimes?

Collect data from theInternational Rice ResearchInstitute, or the Food andAgriculture Organization

No

Yes
Box 1

Are
country-specificseasonally integrated
emission factors available
for each major rice
cropping
region?

No

Isrice production a
key source category?
(Note 1)

No

Estimate emissions using
default EF, and where data are
available, using scaling factors
for organic amendments
and other factors

Yes
Yes

Are there
multiple croppings
during the same
year?

Determine seasonally-integrated
emission factors for each major rice
cropping region through a goodpractice measurement programme
Determine the appropriate
emission factor for each cropping(i.e. dry-, wet-, early-,
single-, late-cropping)

Yes

No
Box 2
Are
activity data
available for the typeof water management practices,
type and amount of organic
amendments and/or
soil type?
Yes

No

Estimate emissions
for each cropping in eachregion using countryspecific emission factors
Box 3

Estimate emissions for each cropping ineach region, using country-specificemission factors and using scaling
factors for water management, organic
amendments, and soil type

Note 1: A key source category is one that is prioritised within the national inventory system because its estimate has a significant
influence on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, orboth. (See Chapter 7, Methodological Choice and Recalculation, Section 7.2, Determining National Key Source Categories.)

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EQUATION 4.42
ADJUSTED SEASONALLY INTEGRATED EMISSION FACTOR
EFi = EFc • SFw • SFo • SFs
Where:
EFi = Adjusted seasonally integrated emission factor for a particular harvested area
EFc = Seasonally integrated emission factor for continuously flooded fields without organic amendments
SFw = Scaling factor to account for the differences in ecosystem and water management regime (fromTable 4.20)
SFo = Scaling factors should vary for both types and amount of amendment applied. (from Table 4.21,
Dose-Response Table for Non-Fermented Organic Amendments)
SFs = Scaling factor for soil type, if available

The seasonally integrated emission factor for continuously flooded fields of major soil types without organic
amendments should be determined through field measurements according to good practice procedures, as
discussed in Appendix 4A.3. If data to determine EFc are not yet available, the IPCC default of 20 g/m2 may be
used.
Scaling factors can be used to adjust the seasonally integrated emission factor for continuously flooded fields(EFc) to account for the various conditions discussed in Box 4.2. In order, the three most important scaling
factors are rice ecosystem/water management regime, organic amendments, and soil type. Country-specificscaling factors should only be used if they are based on well-researched and documented measurement data. If
data to determine scaling factors are not yet available, IPCC defaults can be used.
Water management system: The main types of methane-emitting rice ecosystems are irrigated, rainfed anddeep water. Within each ecosystem are water management systems, which affect the amount of CH4 emitted
during a cropping season. Table 4.20 provides IPCC default scaling factors for SFw that can be used whencountry-specific data are unavailable. Scaling factors for additional ecosystem types and water management
regimes can be applied only if country-specific data are available.
TABLE 4.20
IPCC DEFAULT CH4 EMISSION SCALING FACTORS FOR RICE ECOSYSTEMS AND WATER MANAGEMENT REGIMES RELATIVETO CONTINUOUSLY FLOODED FIELDS (WITHOUT ORGANIC AMENDMENTS)
Category

Water Management Regime

UplandNone

Lowland

Irrigated

RainfedDeep water

Scaling Factor (SFw)0

Continuous Flooded

1.0

Intermittently Flooded – Single Aeration

0.5 (0.2-0.7)

Intermittently Flooded – Multiple
Aeration
Flood prone

0.2 (0.1-0.3)
0.8 (0.5-1.0)

Drought prone

0.4 (0-0.5)

Water depth 50-100 cm

0.8 (0.6-1.0)

Water depth > 100 cm

0.6 (0.5-0.8)

Source: IPCC Guidelines, Reference Manual, Table 4-12.

Organic amendments: Good practice is to develop a scaling factor (SFo) that incorporates information on thetype and amount of organic amendment applied (rice straw, animal manure, green manure, compost, andagricultural wastes). On an equal mass basis, more CH4 is emitted from amendments containing higher amountsof easily decomposable carbon, and emissions also increase as more of each organic amendment is applied.
Table 4.21 presents an approach to vary the scaling factor according to the amount of amendment applied.
Theoretically, the different amendments should be ranked according to the carbon content per unit of weight, but
most often only information on the amount applied is available. In this case, the inventory agency should
distinguish between fermented and non-fermented organic amendments. CH4 emissions from fermented
amendments (e.g. compost, residue of biogas pits) are significantly lower than non-fermented amendments

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because they contain much less easily decomposable carbon. Denier van der Gon and Neue (1995) empiricallydetermined a reduction factor of six implying that the increase in CH4 emission upon application of 12 t/ha
compost is comparable to the increase upon application of 2 t/ha non-fermented organic amendment.
TABLE 4.21
DOSE-RESPONSE TABLE FOR NON-FERMENTED ORGANIC AMENDMENTS
Amount applied as dry matter(t/ha)

Scaling factor
(SFo)

Range

1-2

1.5

1-2

2-4

1.8

1.5-2.5

4-8

2.5

1.5-3.5

8-15

3.5

2-4.5

15+

4

3-5

Note: To use the table for fermented organic amendments, divide the amount applied by six.
Source: Derived from Denier van der Gon and Neue, 1995.

Soil types: In some cases emission data for different soil types are available and can be used to derive SFs. The
major motivation to incorporate soil type as a scaling factor is that both experiments and mechanistic knowledge
confirm its importance. It is anticipated that in the near future simulation models will be capable of producing
soil-specific scaling factors.

4.9.1.3

C HOICE OF ACTIVI TY DATA

Activity data consist of rice production and harvested area statistics, which should be available from a national
statistics agency. The activity data should be broken down by rice ecosystem or water management type. If thesedata are not available in-country, they can be downloaded from an FAO website:(http://www.fao.org/ag/agp/agpc/doc) or can be obtained from IRRI's World Rice Statistics (e.g. IRRI, 1995).
Most likely, the accuracy of activity data will be high compared to the accuracy of the emission factor. However,for various reasons the area statistics may be biased and a check of the harvested area statistics for (parts of) the
country with remotely sensed data is encouraged.
In addition to the essential activity data requested above, it is good practice to match data on organic
amendments and soil types to the same level of disaggregation as the activity data. It may be necessary tocomplete a survey of cropping practices to obtain data on the type and amount of organic amendments applied.

4.9.1.4

C OMPLETENESS

Complete coverage for this source category requires estimation of emissions from the following activities, where
present:•

If soil submergence is not limited to the actual rice growing season, emissions outside of the rice growing
season should be included (e.g. from a flooded fallow period);•

Other rice ecosystem categories, like swamp, inland-saline or tidal rice fields may be discriminated withineach sub-category according to local emission measurements;•

If more than one rice crop is grown annually, these rice crops should be reported independently according to
the local definition (e.g. early rice, late rice, wet season rice, dry season rice). The rice crops may fall into
different categories with a different seasonally integrated emission factor and different correction factors forother modifiers like organic amendments.

4.9.1.5

D EVELOPING A CONSISTENT TIME SERIESThe emission estimation method should be applied consistently to every year in the time series, at the same levelof disaggregation. If detailed activity level data are unavailable for earlier years, emissions for these years should
be re-calculated according to the guidance provided in Chapter 7, Methodological Choice and Recalculation,
Section 7.3. If there have been significant changes in agricultural practices affecting CH4 emissions over the

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Chapter 4times series, the rice estimation method should be implemented at a level of disaggregation which is sufficient todiscern the effects of these changes. For example, various trends in (Asian) rice agriculture such as the adoptionof new rice varieties, increasing use of inorganic fertiliser, improved water management, changing use of organic
amendments, and direct seeding may lead to increases or decreases in overall emissions. To weigh the impact ofthese changes, it may be necessary to use model studies.

4.9.1.6

U NCERTAINTY ASSESSMENT

Table 4.22 presents a default emission factor, default scaling factors, and ranges for the default values. The rangeof emission factor, defined as the standard deviation about the mean, indicates the uncertainty associated withthis default value for this source category. The uncertainty may be influenced by the following:
Natural Variability: The natural variability is a result of variations in natural controlling variables, such as
annual climate variability, and variability within units that are assumed to be homogenous, such as spatial
variability in a field or soil unit. For this source category, good practice should permit determination of
uncertainties using standard statistical methods when enough experimental data are available. Studies to quantify
some of this uncertainty are rare but available (e.g. for soil type induced variability). The variability found insuch studies is assumed to be generally valid. For more detail, see Sass (1999).
Lack of activity data and documentation: Important activity data necessary to apply scaling factors (i.e. dataon cultural practices and organic amendments) may not be available in current databases/statistics. Estimates ofthe fraction of rice farmers using a particular practice or amendment must then be based on expert judgement,
and the range in the estimated fraction should also be based on expert judgement. As a default value for theuncertainty in the fraction estimate, ± 0.2 is proposed (e.g. the fraction of farmers using organic amendment
estimated at 0.4, the uncertainty range being 0.2-0.6). Chapter 6, Quantifying Uncertainties in Practice, provides
advice on quantifying uncertainties in practice including combining expert judgements and empirical data into
overall uncertainty estimates.
TABLE 4.22
DEFAULT EMISSION FACTOR, DEFAULT SCALING FACTORS, AND RANGES FOR CH4 EMISSIONS FROM RICE FIELD
Emission component

Default value

Ranges20 g CH4 m season

12-28 g CH4 m-2 season-1

Scaling factor water management SFw

See Table 4.20

Table 4.20

Scaling factor organic amendments SFo

2

1.5-5

Scaling factor soil types SFs

1

0.1-2

Standard emission factor (EF)-2

-1

Source: IPCC Guidelines and Judgement by Expert Group (see Co-chairs, Editors and Experts; CH4 Emissions from Rice Production).

4.9.2

Reporting and documentation

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. It is good practice to document the emission estimate by reporting theinformation required to fill out the rice worksheet in the Workbook of the IPCC Guidelines. Inventory agencies
that do not use the worksheets should provide comparable information. If the emission estimate is disaggregatedby region, information on each region should be reported.
The following additional information should be reported, if available, to ensure transparency:•

Water management practices;•

The types and amounts of organic amendments used. (Incorporation of rice straw or residues of the previous(non-rice) crop should be considered an organic amendment, although it may be a normal production
practice and not aimed at increasing nutrient levels as is the case with manure additions);•

Soil types used for rice agriculture;•

Number of rice crops grown annually;•

Most important rice cultivars grown.

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When simple default emission factors are used to estimate CH4 emissions, uncertainty can increase dramatically.
Inventory agencies using country-specific emission factors should provide information on the origin and basis of
the factor, compare it to other published emission factors, explain any significant differences, and attempt toplace bounds on the uncertainty.

4.9.3

Inventory quality assessment/quality control(QA/QC)

It is good practice to implement quality control checks as outlined in Chapter 8, Quality Assurance and Quality
Control, Table 8.1, Tier 1 General Inventory Level QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, and quality assurance
procedures may also be applicable, particularly if higher tier methods are used to determine emissions from thissource.
A detailed treatment on inventory QA/QC for field measurement is given by Sass (1999) and in Appendix 4A.3.
Some important issues are highlighted and summarised below.
Compiling national emissions: It is, at present, not possible to cross-check emissions estimates from this source
category through external measurements. However, the inventory agency should ensure that emission estimates
undergo quality control by:•

Cross-referencing aggregated crop yield and reported field area statistics with national totals or othersources of crop yield/area data;•

Back-calculating national emission factors from aggregated emissions and other data;•

Cross-referencing reported national totals with default values and data from other countries.

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APPENDIX 4A.1
CH4 AND N2O EMISSIONS FROM SAVANNABURNING: BASIS FOR FUTURE
METHODOLOGICAL DEVELOPMENT
4A.1.1 Methodological issues
In savanna regions burning is carried out every one to several years. The burning results in instantaneousemissions of carbon dioxide (CO2). As the vegetation regenerates between burning cycles, however, the CO2
released into the atmosphere is reabsorbed during the next vegetation growth period. For this reason, net CO2
emissions from savanna burning are assumed to be zero. Savanna burning also releases other trace gases,
including CH4, CO, NMVOCs, N2O and NOx. In this chapter, only emissions of the direct greenhouse gases CH4
and N2O are discussed.

4A.1.1.1 C HOICE OF METHOD
The choice of method depends upon the availability of activity data and emission factors for CH4 and N2O. If an
inventory agency does not have activity data and emission factors, the default values in the IPCC Guidelines
may be used.
The current method requires a value for the living fraction of aboveground biomass in Table 4-12 of the
Workbook of the IPCC Guidelines. In addition, Table 4-13 of the IPCC Guidelines requires values for oxidised
fraction and carbon fraction in living and dead biomass for calculating the amount of carbon and nitrogen
released from savanna burning. These parameters are difficult to measure in the field. Combustion efficiency canbe used to depict the vegetation and combustion conditions, which ultimately determine the emission factors of
CH4 and N2O. The combustion efficiency is defined as the molar ratio of emitted CO2 concentrations to the sumof emitted CO and CO2 concentrations from savanna fires. A column for combustion efficiency is included inTable 4.A1 of this document. The compiled combustion efficiency data are derived from the results of biomassburning experiments in different savanna ecosystems in tropical America and Africa. Therefore, in the proposed
method the revised equation for computing the amount of CH4 or N2O emitted per year would be:

EQUATION 4.A1
CH4 OR N2O RELEASED FROM SAVANNA BURNING
Amount of CH4 or N2O released = Amount of biomass burned (t dm) • Emission factor of CH4
or N2O (kg/t dm)

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TABLE 4.A1
AMOUNT OF ABOVEGROUND BIOMASS BURNED
RegionFraction of Total
Savanna Burned
Annually

Aboveground
Biomass Density(t dm/ha)Fraction of Biomass
Actually Burned

Combustion
Efficiency

Tropical America

0.50

6.6±1.8

0.85

0.95

Campo limpoa.b

0.3-1.0

7.1±0.5

1.0

0.96

Campo sujoa.b

0.3-1.0

7.3±0.5

0.97

0.3-1.0

8.6±0.8

0.72

0.94

0.3-1.0

10.0±0.5

0.84

0.94

0.75

6.6±1.6

0.86

0.94

Sahel zone

0.05-0.15

0.5-2.5

0.95

North Sudan zone

0.25-0.50

2-4

0.85

South Sudan zone

0.25-0.50

3-6

0.85

Guinea zone

0.60-0.80

4-8

0.90-1.0

0.5-1.0

8.9±2.7

0.74±0.04

0.92

0.5-1.0

5.1±0.4

0.88±0.02

0.91

0.5-1.0

3.0±0.5

0.99±0.01

0.95

0.1

7.3±0.7

0.71±0.05

0.96

0.25-0.5

4.6±2.8

0.85±0.11

0.93

Campo cerradoa.b
Cerrado sensu stricto
Tropical Africa

Moist Miombo

c.d,eSemiarid Miombo
Moist Dambo

c,e

c,d,e
c,e

Fallow Chitemene

a.b

Semiarid Woodland(South Africa) c,ea

Kauffman et al. (1994), bWard et al. (1992), cShea et al. (1996), dHoffa et al.(1999), eWard et al. (1996).

For regions not specifically listed, data are contained in Table 4-14 of the IPCC Guidelines, Reference Manual(same as Table 4-12 of the Workbook of the IPCC Guidelines.) This table provides the basic ecological zones
according to the available savanna statistics. Table 4.A1 above contains additional savanna data for four
ecological zones in tropical America and five ecological zones in tropical Africa, based on the results of field
experiments in Brazil, Zambia, and South Africa.
If an inventory agency has the necessary data for the fraction of savanna area burned annually, the abovegroundbiomass density, and the fraction of biomass actually burned in each ecological zone, the amount of biomass
burned can be calculated at a disaggregated level.
It is desirable to develop the seasonal-dependent activity data and the emission factors of CH4 and N2O fromsavanna burning in various savanna ecosystems in each country if data are available. Fewer savanna areas and asmaller percentage of aboveground biomass are burned in the early dry season than in the late dry season.
Therefore, as the dry season progresses in different savanna ecosystems, it is critical to monitor (i) the fraction ofburned savanna area; (ii) the aboveground biomass density; (iii) the percentage of the aboveground biomass
burned; and (iv) combustion efficiency.

4A.1.1.2 C HOICE OF EMISSIO N FACTORS
For savanna fires, there is a linear negative correlation between the CH4 emission factor and the combustion
efficiency. The emission factor is high for a fire of low combustion efficiency. The relationship is similar
regardless of the climatic zone, the herbaceous species, or the amount of aboveground biomass.
Table 4.A2 lists different combustion efficiencies and associated CH4 emission factors. Once the combustion
efficiency of a savanna fire is determined according to the ecological zone and the burning period, the

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corresponding emission CH4 factor should be used to calculate the amount of CH4 released per year fromsavanna burning.
TABLE 4.A2
COMBUSTION EFFICIENCY AND CORRESPONDING
CH4 EMISSION FACTOR
Combustion Efficiency

CH4 Emission Factor (kg/t dm)

0.88

4.2

0.90

3.4

0.91

3.0

0.92

2.6

0.93

2.3

0.94

1.9

0.95

1.5

0.96

1.1

Source: Ward et al. (1996).

The emission of N2O from biomass burning is linearly correlated with the emission of CO2 and is dependent on
the nitrogen content of the vegetation. The emission factor of N2O is calculated by the equation:
EQUATION 4.A2
N2O EMISSION FACTOR
Emission factor of N2O (kg/t dm) = Emission factor of CO2 (kg/t dm) • 1/Molecular weight of
CO2 • Molar emission ratio of N2O to CO2 • Molecular weight of N2O
Equation 4.A2 is simplified to:
EQUATION 4.A3
N2O EMISSION FACTOR
Emission factor of N2O (kg/t dm) = Emission factor of CO2 (kg/t dm) • Molar emission ratio of
N2O to CO2Since N2O is not stable during storage of smoke samples, the molar emission ratio of N2O to CO2 has been
derived from laboratory experiments in which different types of vegetation were burned (Hao et al., 1991) andcan be expressed by:
EQUATION 4.A4
MOLAR EMISSION RATIO OF N2O TO CO2
Molar emission ratio of N2O to CO2 = 1.2 • 10-5 + [3.3 • 10-5 • Molar ratio of nitrogen tocarbon (N/C) in the biomass]
Emission factors for N2O in several savanna ecosystems have been tabulated in Table 4.A3 on the basis of theresults of field measurements of CO2 emissions and the N/C ratios of the biomass. The default emission factors
for N2O in tropical America and Africa are calculated by averaging the emission factors for the continent. If an
inventory agency has data on the N/C ratio in the biomass and assumes the emission factor for CO2 to be 1700
kg/t dm, the emission factor for N2O can be calculated by the two Equations 4.A3 and 4.A4 above.

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TABLE 4.A3
EMISSION FACTORS OF N2O IN VARIOUS SAVANNA ECOSYSTEMSRegionEmission Factor of CO2(kg/t dm)

N/C Ratio in Biomass(%)

Emission Factor of N2O(kg/t dm)

Tropical America

-

-

0.065

Campo limpoa, b, c

1745

0.60

0.055

1700

0.56

0.052

1698

0.95

0.074

1722

1.02

0.079

-

-

0.070

1680

1.42

0.099

1649

0.94

0.071

1732

0.33

0.040

1761

0.77

0.066

0.98 ± 0.11

0.075

Campo sujo

a, b, cCampo cerrado

a, b, cCerrado sensu strictoa, b, c
Tropical Africa
Moist Miombob, c, dSemiarid Miombob, c, d
Moist Dambob, c, dFallow Chitemeneb, c, dSemiarid Woodland
a

1699
b

c

d

Source: Ward et al. (1992), Susott et al. (1996), Hao et al. (1991), Ward et al. (1996).

4A.1.1.3 C HOICE OF ACTIVI TY DATA
The activity statistics for each savanna ecosystem include the following values: the savanna area; the fraction ofsavanna area burned; the aboveground biomass density; the fraction of aboveground biomass burned; and thecarbon and nitrogen content in the biomass. Other parameters (i.e. the fraction of living and dead biomass burned
and the carbon/nitrogen fraction of living and dead biomass) have been removed here because of thecomplexities of collecting these data in the field. Since the emission factor for CH4 can decrease by 50-75% as
the burning season progresses, it is strongly suggested that each inventory agency collect seasonal data on thefraction of savanna area burned, the aboveground biomass density, and the fraction of aboveground biomass
burned in each savanna ecosystem from the early dry season to the late dry season.

4A.1.1.4 D EVELOPING A CONSISTENT TIME SERIES
Since there is a large degree of uncertainty in determining the burned area in each savanna ecosystem, it may be
useful to take an average of at least three years to provide a base year estimate for identification of any trend in
the emissions of CH4 and N2O from savanna burning. The methods for ensuring a consistent time series aredescribed in Chapter 7, Methodological Choice and Recalculation.

4A.1.1.5 U NCERTAINTY ASSESSMENT
The uncertainty of the emission factor for CH4 is about ±20%, based on the results of extensive field experiments
in tropical America and Africa. The uncertainty of the N2O emission factor is also about ±20%, based on
extensive laboratory experiments. The uncertainty of the aboveground biomass density in a savanna ecosystem
ranges from ±2% to ±60%. The larger uncertainty is probably due to the variation of the composition ofaboveground biomass at different sites. The uncertainty of the fraction of biomass actually burned is less than
±10%. Presently, it is difficult to estimate the uncertainty for the fraction of savanna area burned each year, or
the amount of burning in, for example, the early and late season.

4A.1.2 Reporting and documentation
It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. The reporting in the worksheets contained in the IPCC Guidelines is transparent,
however, the most critical issue in reporting and documentation is that majority of the activity data (e.g. thepercentage of savanna area burned, the aboveground biomass density, and the fraction of biomass actually

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Chapter 4burned) are not available or are difficult to collect in the field. There are also no standard methods of collecting
the information on area burned and fraction of biomass actually burned, resulting in inconsistency among thereported data.

4A.1.3 Inventory quality assurance/quality control(QA/QC)As mentioned above, there are large degrees of uncertainty in the activity data to compute the amount of biomass
burned in savanna. Very limited data are available on the seasonal trends of the savanna areas burned, theaboveground biomass densities, and the fractions of aboveground biomass burned. The monitoring of thelocations of active savanna fires and the mapping of burned areas in each country can be improved by using thesatellite imagery available from various national and international agencies. In addition, standard methods haveto be developed to measure the aboveground biomass density, the fraction of biomass burned, and the
combustion efficiency in order to ensure the quality and consistency of the data.

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APPENDIX 4A.2
CH4 AND N2O EMISSIONS FROMAGRICULTURAL RESIDUE BURNING: BASISFOR FUTURE METHODOLOGICALDEVELOPMENT
Although the burning of agricultural residues is not considered a net source of carbon dioxide because the carbon
released to the atmosphere is reabsorbed during the next growing season, this burning is a source of net
emissions of many trace gases including CH4, CO2, N2O, and NOx. It is important to note that some agriculturalresidues are removed from the fields and burned as a source of energy, especially in developing countries. NonCO2 emissions from this type of burning are dealt with in the Energy sector of the IPCC Guidelines. Crop
residue burning must be properly allocated to these two components in order to avoid double counting. The
following discussions are focused only on the direct greenhouse gases CH4 and N2O.

4A.2.1 Methodological issues
4A.2.1.1 C HOICE OF METHOD
The choice of method will depend on the availability of activity data and emission factors for CH4 and N2O ineach country. Where available, country-specific activity data and emission factors for CH4 and N2O should be
used. If a country does not have its own activity data and emission factors, the default values in the IPCC
Guidelines may be used instead.
The largest degree of uncertainty in estimating the emission inventories of CH4 and N2O from agriculturalresidue burning is the fraction of agricultural residue burned in the field. The percentage of residue burned onsite must be based on a complete mass balance accounting of the residue. For substantial improvement in theemission estimates of CH4 and N2O, inventory agencies are encouraged to estimate local and regional practices
that reflect: (i) the fraction of residue burned in the field; (ii) the fraction transported off the field and burned
elsewhere (associated with processing); (iii) the fraction consumed by animals in the field; (iv) the fraction
decayed in the field; and (v) the fraction used by other sectors (e.g. biofuel, domestic livestock feed, building
materials, etc.). Currently, it is estimated that 10% of the total agricultural residue is burned in the field indeveloped countries and 25% in developing countries. These figures may be too high. Good practice suggeststhat an estimate of 10% may be more appropriate for developing countries.

4A.2.1.2 C HOICE OF EMISSIO N FACTORSThe CH4 and N2O emission factors in Table 4.16 of the Workbook of the IPCC Guidelines are generally
reasonable. There are also insufficient data to update these emission factors as few field experiments have been
conducted in the past five years that measure emissions produced by burning agricultural residue in the field.
Emission factors, however, are probably dependent on weather conditions in the burning periods, as the emission
factor of CH4 from savanna burning decreases from the early dry season to the late dry season. If an inventory
agency is conducting experiments to measure the CH4 and N2O emission factors from burning agriculturalresidue, the experiments should be carried out in the dry season and rainy season when crop residue is burned.

4A.2.1.3 C HOICE OF ACTIVI TY DATA
The activity data for crop production can be obtained from either the country’s data or the FAO Production
Yearbook (U.N. Food and Agriculture Organisation). These statistical data are reasonably accurate. There arefew data available to update residue/crop ratios, dry matter fractions, carbon fractions, and nitrogen to carbon
ratios for different crop residue. When an inventory agency is compiling its activity data, it is necessary tocollect monthly weather data and data on the amount of each crop residue burned after harvest. Weather
conditions would influence the combustion efficiency (see Appendix 4A.1 of this chapter) and the CH4 and N2Oemission factors.

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Chapter 4

4A.2.1.4 C OMPLETENESS
The current method incorporates all the factors necessary to estimate the CH4 and N2O emissions from burning
agricultural residue. Several crops are missing in Table 4.15 of the Workbook of the IPCC Guidelines (e.g.
sugarcane and root crops such as cassava and yam). The ratio of residue to crop is 0.16 for sugarcane and 0.4 forroot crops. It is important to account for the entire disposition of agricultural residue in the mass balance.
Residue not being burned in the field will become a source of CH4 or N2O from microbial decomposition,
domestic energy consumption, and domestic waste. These sources will have to be incorporated into thecomputation of CH4 and N2O emissions from other activities.

4A.2.1.5 D EVELOPING A CONSISTENT TIME SERIES
There are good prospects for developing the trend of CH4 and N2O emissions from agricultural residue burning
because the statistics of agricultural production are compiled with reasonable accuracy. The weakness in thecomputation is estimating the percentage of residue burned in the field. Each inventory agency has to collectactivity data on disposition of each crop residue, especially the percentage of residue burned on-site, after
harvest.

4A.2.1.6 U NCERTAINTY ASSESSMENT
Crop production data, including cash crops and subsistence farming, are reasonably accurate, although it isdifficult to determine the uncertainty. The uncertainties in CH4 and N2O emission factors for burning agriculturalresidue in the dry season are about ±20%. It is not known, however, about the emission factors in the rainyseason. The fraction of agricultural residue burned in the field is probably the variable with the largest degree of
uncertainty in estimating the amount of CH4 and N2O emitted from agricultural residue burning. Statistical data
have to be compiled to account for the use of agricultural residue after harvest.

4A.2.2 Reporting and documentation
It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving. Agricultural production data are easily accessible from each country or the FAO
Production Yearbook. Weather conditions and the amount of each crop burned in the field during the dry season
and rainy season have to be reported. It is necessary to measure and report the dry matter fraction, the carbon
fraction, and the nitrogen to carbon ratio for each crop residue. It is also important to conduct field experimentsthat measure the CH4 and N2O emission factors in the dry and rainy season.

4A.2.3Inventory quality assurance/quality control(QA/QC)The quality of CH4 and N2O emissions estimates from agricultural residue burning will vary considerably fromcountry to country, depending largely on the quality of the data on the percentage of the residue burned in thefield. The qualities of other activity data and emission factors are reasonable and can be improved by collecting
the data of the amount of residue burned during the dry season and rainy season. Crop production data can beverified by using commodity trade statistics.

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APPENDIX 4A.3
CH4 EMISSIONS FROM RICE PRODUCTION:
MEASUREMENT, REPORTING, AND QA/QC OF
FIELD DATA
Conducting Field Measurement: A standardised control rice plot with at least three replicate fields should be
used to obtain standard regional and country emission factors. Plots are to be kept flooded from shortly before
transplanting until maturity. The experimental plots should not have a recent history (i.e. five years) of addedorganic amendments to the soil other than recycled roots and perhaps short stubble. CH4 flux measurements
should be recorded at least twice per week over an entire flooded season. In areas where double or triple rice
cropping is practised, data should be collected for all growing seasons. For a guideline for good practicestandardised measurements for the irrigated rice ecosystem, see IGAC (1994). The nature of the instrumentationand the frequency of measurement will determine the associated uncertainty. For typical measurements, theassociated uncertainty is expected to be at least 20%.
The accuracy and precision of CH4 emission estimates increases with both the number of sites tested and thefrequency and number of measurements at each site.
Other data, such as the location and extent of area that the measurement represents, soil data, and climate
information, should be collected. Agronomic data such as rice yield and other crop production data are also
important because these data can be used to determine if measurements are representative of typical agronomic
conditions. In general, the various predictive models that have been recently published (e.g. Huang et al., 1998)
may aid in reporting CH4 emission values. Good practice is to provide as much country- or region-specific detailas is feasible.
Reporting of Field Measurements: The minimum data set that should accompany flux measurements for (i)scaling factor determination, (ii) verification of inventory using models and, (iii) QA/QC consists of:•Geographic data including site country and province, latitude and longitude, average elevation, and a short
description of the location;•

A data log of agricultural events (e.g. time of organic input application, water management, weeding, etc.),
method of crop establishment and dates of important plant events (e.g. transplanting, heading, harvest date);•

Air and soil temperature at 5 cm depth taken at the time of each flux measurement;•

Fertiliser types, application rates (including chemical amendments), and timing and mode of application;•

Soil types classified according to USDA Soil Taxonomy or FAO/UNESCO Soil Classification, at least on
subgroup levels. General soil characteristics, including texture, should be measured;•

Water management (number of flooding days, drainage/drought events);•

Impact of organic amendment on emissions (type and amount of amendment should be documented);•

Rice cultivar used (name, crop duration, height, traditional or modern variety, specific traits);•

Plant parameters preferably for different growth stages (e.g. leaf area index, above ground biomass (straw
and stubble), yield, harvest index).

Field Measurement QA/QC: Country scientists will usually determine field-level QA/QC procedures toestablish country-specific emission factors. To ensure the comparability and inter-calibration of extended data
sets used to establish country-specific emission factors, there are certain internationally determined procedures toobtain ‘standard emission factors’ that should be common to all monitoring programs (see IGAC (1994), Sass
(1999)):(i)

CH4 flux measurements should be recorded at least twice per week over an entire flooded season.

(ii)

In areas where double rice cropping (or 5 rice crops in 2 years) is practised, data should be collected for
all growing seasons.

(iii)

Manual sampling of flux chambers may miss the large fluxes of soil-entrapped CH4 upon drainage. In
such cases, a correction should be made. If no specific data are available, an estimated 10-20% increaseof seasonal emission can be applied.

(iv)

Significance of pre-planting emissions should be discussed and, if appropriate, estimated or measured.

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Chapter 4

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Greenhouse Gas Inventories, J.T. Houghton et al., IPCC/OECD/IEA, Paris, France.
Judd, M.J., F.M. Kelliher, M.J. Ulyatt, K.R. Lassey, K.R. Tate, I.D. Shelton, M.J. Harvey, and C.F. Walker(1999) Net methane emissions from grazing sheep, Global Change Biol., 5, pp. 647–657.
Kurihara , M., T. Magner, R.A. Hunter, and G.J. McCrabb (1999) Methane production and energy partition of
cattle in the tropics. British Journal of Nutrition, 81, pp. 227-234.
Lassey, K.R., and M.J. Ulyatt (1999) Enterically fermented methane, with emphasis on sheep emissions. Report
WLG99/5, National Institute of Water and Atmospheric Research, Wellington, New Zealand.
Lassey, K.R., M.J. Ulyatt, R.J. Martin, C.F. Walker, and I.D. Shelton (1997) Methane emissions measured
directly from grazing livestock in New Zealand, Atmos. Environ., 31, pp. 2905-2914.
Leuning, R., S.K. Baker, I.M. Jamie, C.H. Hsu, L. Klein, O.T. Denmead, and D.W.T. Griffith (1999) Methane
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N2O EMISSIONS FROM MANURE MANAGEMENT
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Intergovernmental Panel on Climate Change (IPCC) (1997) Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories, J.T. Houghton et al., IPCC/OECD/IEA, Paris, France.
Oenema, O., O. Heinemeyer, L. Erda, R. Sherlock (1999) Nitrous oxide from Animal Waste Management
Systems. Background paper for IPCC expert meeting on Good Practice in Inventory Preparation: Agricultural
Sources of Methane and Nitrous Oxide, 24-26 February 1999, Wageningen, The Netherlands.

DIRECT N2O EMISSIONS FROM AGRICULTURAL SOILS
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INDIRECT N2O EMISSIONS FROM NITROGEN USED IN AGRICULTURE
Crutzen, P.J. and M.O. Andreae (1990) Biomass burning in the tropics: Impact on Atmospheric chemistry andbiogeochemical cycles, Science 250: pp.1669-1678.
Intergovernmental Panel on Climate Change (IPCC) (1997) Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories, J.T. Houghton et al., IPCC/OECD/IEA, Paris, France.
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pp.647-662.

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Chapter 4CH4 EMISSIONS FROM RICE PRODUCTION
Denier van der Gon, H.A.C. and H.U. Neue (1995) Influence of organic matter incorporation on the methane
emission from a wetland rice field. Global Biogeochemical Cycles, 9: pp. 11-22.
Ding Aijiu and Wang Mingxing (1996) A model for methane emission from rice field and its application insouthern China, Advances in Atmospheric Sciences, Vol.13 pp.`159-168.
Huang, Y., R.L. Sass, and F.M. Fisher Jr. (1998) A semi-empirical model of methane emission from flooded rice
paddy soils. Global Change Biology 4:247-268.
IGAC (1994) Global Measurement Standardisation of Methane Emissions from Irrigated Rice Cultivation, A
Report of the Rice Cultivation and Trace Gas Exchange Activity (RICE) of the International Global
Atmospheric Chemistry (IGAC) Project, IGAC Core Office, Cambridge, MA.
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Greenhouse Gas Inventories, J.T. Houghton et al., IPCC/OECD/IEA, Paris, France.
International Rice Research Institute (1995) World Rice Statistics 1993-94. The International Rice ResearchInstitute, Manila, Philippines.
Sass, R.L. (1999) Methane from Rice Agriculture, Background paper presented at IPCC/OECD/IEA programmeon national greenhouse gas inventories, Expert Group Meeting on Good Practice in Inventory Preparation.
Agricultural Sources of Methane and Nitrous Oxide, 24-26 February 1999, Wageningen, The Netherlands.

APPENDICES 4A.1 AND 4A.2: CH4 AND N2O EMISSIONS FROM SAVANNA AND
AGRICULTURAL RESIDUE BURNING
Hao, Wei Min, D. Scharffe, J.M. Lobert and P.J. Crutzen, Emissions of N2O from the burning of biomass in anexperimental system, Geophys. Res. Lett., 18, pp. 999-1002, 1991.
Hao, Wei Min, D.E. Ward, G. Olbu and S.P. Baker, Emissions of CO2, CO, and hydrocarbons from fires indiverse African savanna ecosystems, J. Geophys. Res., 101, pp. 23577-23584, 1996.
Hoffa, E.A., D.E. Ward, W.M. Hao, R.A. Susott and R.H. Wakimoto, Seasonality of carbon emissions frombiomass burning in a Zambian savanna, J. Geophys. Res., 104, pp. 13841-13853, 1999.
Kauffman, J.B., D.L. Cummings and D.E. Ward, Relationships of fire, biomass and nutrient dynamics along avegetation gradient in the Brazilian cerrado, J. of Ecology, 82, pp. 519-531, 1994.
Shea, R.W., B.W. Shea, J.B. Kauffman, D.E. Ward, C.I. Haskins and M.C. Scholes, Fuel biomass andcombustion factors associated with fires in savanna ecosystems of South Africa and Zambia, J. Geophys.
Res., 101, pp. 23551-23568, 1996.
Susott, R.A., G.J. Olbu, S.P. Baker, D.E. Ward, J.B. Kauffman and R.W. Shea, Carbon, hydrogen, nitrogen andthermogravimetric analysis of tropical ecosystem biomass, in Biomass Burning and Global Change, edited
by J.S. Levine, pp. 249-259, MIT Press, Cambridge, Mass., 1996.
Ward, D.E., R.A. Susott, J.B. Kauffman, R.E. Babbitt, D.L. Cummings, B. Dias, B.N. Holben, Y.J. Kaufman,
R.A. Rasmussen and A.W. Setzer (1992) Smoke and fire characteristics for cerrado and deforestation burnsin Brazil: BASE-B Experiment, J. Geophys. Res., 97, pp. 14601-14619, 1992.
Ward, D.E., W.M. Hao, R.A. Susott, R.E. Babbitt, R.W. Shea, J.B. Kauffman and C.O. Justice (1996) Effect offuel composition on combustion efficiency and emission factors for African savanna ecosystems, J. Geophys.
Res., 101, pp. 23569-23576.

4.94

IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories