Chapter 10: Emissions from Livestock and Manure Management

CHAPTER 10

EMISSIONS FROM LIVESTOCK AND
MANURE MANAGEMENT

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors
Hongmin Dong (China), Joe Mangino (USA), and Tim A. McAllister (Canada)
Jerry L. Hatfield (USA), Donald E. Johnson (USA), Keith R. Lassey (New Zealand), Magda Aparecida de Lima
(Brazil), and Anna Romanovskaya (Russian Federation)

Contributing Authors
Deborah Bartram (USA), Darryl Gibb (Canada), and John H. Martin, Jr. (USA)10.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Contents
10 Emissions from Livestock and Manure Management
10.1

Introduction ......................................................................................................................................... 10.7

10.2

Livestock Population and Feed Characterisation ................................................................................ 10.7

10.2.1

Steps to define categories and subcategories of livestock ........................................................... 10.7

10.2.2

Choice of method ........................................................................................................................ 10.8

10.2.3

Uncertainty assessment ............................................................................................................. 10.23

10.2.4Characterisation for livestock without species: Specific emission estimation methods ............ 10.23

10.3

Methane Emissions from Enteric Fermentation ................................................................................ 10.24

10.3.1

Choice of method ...................................................................................................................... 10.24

10.3.2

Choice of emission factors ........................................................................................................ 10.26

10.3.3

Choice of activity data ............................................................................................................... 10.33

10.3.4

Uncertainty assessment ............................................................................................................. 10.33

10.3.5Completeness, Time series, Quality Assurance/Quality Control and Reporting ....................... 10.33

10.4

Methane Emissions from Manure Management ................................................................................ 10.35

10.4.1

Choice of method ...................................................................................................................... 10.35

10.4.2

Choice of emission factors ........................................................................................................ 10.37

10.4.3

Choice of activity data ............................................................................................................... 10.48

10.4.4

Uncertainty assessment ............................................................................................................. 10.48

10.4.5Completeness, Time series, Quality assurance / Quality control and Reporting ....................... 10.50

10.5

N2O Emissions from Manure Management....................................................................................... 10.52

10.5.1

Choice of method ...................................................................................................................... 10.53

10.5.2

Choice of emission factors ........................................................................................................ 10.57

10.5.3

Choice of activity data ............................................................................................................... 10.61

10.5.4

Coordination with reporting for N2O emissions from managed soils ........................................ 10.64

10.5.5

Uncertainty assessment ............................................................................................................. 10.66

10.5.6

Completeness, Time series, Quality assurance/Quality control and Reporting ......................... 10.68

10.5.7

Use of worksheets ..................................................................................................................... 10.69

Annex 10A.1 Data underlying methane default emission factors for enteric fermentation ............................. 10.71
Annex 10A.2 Data underlying methane default emission factors for Manure Management ........................... 10.74
References................................................................................................................................................... 10.84

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Equations
Equation 10.1 Annual average population .................................................................................................. 10.8
Equation 10.2 Coefficient for calculating net energy for maintenance ..................................................... 10.13
Equation 10.3 Net energy for maintenance .............................................................................................. 10.15
Equation 10.4 Net energy for activity (for cattle and buffalo) .................................................................. 10.16
Equation 10.5 Net energy for activity (for sheep)..................................................................................... 10.16
Equation 10.6 Net energy for growth (for cattle and buffalo) .................................................................. 10.17
Equation 10.7 Net energy for growth (for sheep) ..................................................................................... 10.17
Equation 10.8 Net energy for lactation (for beef cattle, dairy cattle and buffalo)..................................... 10.18
Equation 10.9 Net energy for lactation for sheep (milk production known)............................................. 10.18
Equation 10.10 Net energy for lactation for sheep (milk production unknown)......................................... 10.19
Equation 10.11 Net energy for work (for cattle and buffalo)...................................................................... 10.19
Equation 10.12 Net energy to produce wool (for sheep) ............................................................................ 10.19
Equation 10.13 Net energy for pregnancy (for cattle/buffalo and sheep) ................................................... 10.20
Equation 10.14 Ratio of net energy available in a diet for maintenance to digestible energy consumed ... 10.20
Equation 10.15 Ratio of net energy available for growth in a diet to digestible energy consumed ............ 10.21
Equation 10.16 Gross energy for cattle/buffalo and sheep ......................................................................... 10.21
Equation 10.17 Estimation of dry matter intake for growing and finishing cattle ...................................... 10.22
Equation 10.18a Estimation of dry matter intake for mature beef cattle ..................................................... 10.22
Equation 10.18b Estimation of dry matter intake for mature dairy cows .................................................... 10.22
Equation 10.19 Enteric fermentation emissions from a livestock category ................................................ 10.28
Equation 10.20 Total emissions from livestock enteric fermentation ......................................................... 10.28
Equation 10.21 CH4 emission factors for enteric fermentation from a livestock category ......................... 10.31
Equation 10.22 CH4 emissions from manure management......................................................................... 10.37
Equation 10.23 CH4 emission factor from manure management ................................................................ 10.41
Equation 10.24 Volatile solid excretion rates ............................................................................................. 10.42
Equation 10.25 Direct N2O emissions from manure management ............................................................. 10.54
Equation 10.26 N losses due to volatilisation from manure management .................................................. 10.54
Equation 10.27 Indirect N2O emissions due to volatilisation of N from manure management .................. 10.56
Equation 10.28 N losses due to leaching from manure management systems ............................................ 10.56
Equation 10.29 Indirect N2O emissions due to leaching from manure management .................................. 10.57
Equation 10.30 Annual N excretion rates ................................................................................................... 10.57
Equation 10.31 Annual N excretion rates (Tier 2) ...................................................................................... 10.58
Equation 10.32 N intake rates for cattle...................................................................................................... 10.58
Equation 10.33 N retained rates for cattle................................................................................................... 10.60
Equation 10.34 Managed manure N available for application to managed soils, feed, fuel orconstruction uses ............................................................................................................... 10.65

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Chapter 10: Emissions from Livestock and Manure Management

FiguresFigure 10.1

Decision tree for livestock population characterisation ....................................................... 10.9

Figure 10.2

Decision Tree for CH4 Emissions from Enteric Fermentation .......................................... 10.25

Figure 10.3

Decision tree for CH4 emissions from Manure Management ............................................ 10.36

Figure 10.4

Decision tree for N2O emissions from Manure Management (Note 1) ............................. 10.55

TablesTable 10.1

Representative livestock categories ................................................................................... 10.11

Table 10.2

Representative feed digestibility for various livestock categories ................................. 10.14

Table 10.3

Summary of the equations used to estimate daily gross energy intake
for Cattle, Buffalo and Sheep ........................................................................................... 10.15

Table 10.4

Coefficients for calculating net energy for maintenance ( NEm )....................................... 10.16

Table 10.5

Activity coefficients corresponding to animal’s feeding situation .................................... 10.17

Table 10.6

Constants for use in calculating NEg for Sheep ................................................................. 10.18

Table 10.7

Constants for use in calculating NEp in Equation 10.13 .................................................... 10.20

Table 10.8

Examples of NEma content of typical diets fed to Cattle for estimation ofdry matter intake in Equations 10.17 and 10.18 ............................................................... 10.23

Table 10.9

Suggested emissions inventory methods for enteric fermentation..................................... 10.27

Table 10.10

Enteric fermentation emission factors for Tier 1 method .................................................. 10.28

Table 10.11

Tier 1 enteric fermentation emission factors for Cattle ..................................................... 10.29

Table 10.12

Cattle/buffalo CH4 conversion factors (Ym ) ..................................................................... 10.30

Table 10.13

Sheep CH4 conversion factors (Ym) ................................................................................... 10.31

Table 10.14

Manure management methane emission factors by temperature for Cattle, Swine,
and Buffalo ........................................................................................................................ 10.38

Table 10.15

Manure management methane emission factors by temperature for Sheep, Goats,
Camels, Horses, Mules and Asses, and Poultry ................................................................. 10.40

Table 10.16

Manure management methane emission factors for Deer, Reindeer, Rabbits,
and fur-bearing animals ..................................................................................................... 10.41

Table 10.17

MCF values by temperature for manure management systems ......................................... 10.44

Table 10.18

Definitions of manure management systems ..................................................................... 10.49

Table 10.19

Default values for nitrogen excretion rate ........................................................................ 10.59

Table 10.20

Default values for the fraction of nitrogen in feed intake of livestock that is
retained by the different livestock species/categories (fraction N-intake
retained by the animal) ...................................................................................................... 10.60

Table 10.21

Default emission factors for direct N2O emissions from manure management ................. 10.62

Table 10.22

Default values for nitrogen loss due to volatilisation of NH3 and NOx from manure
management....................................................................................................................... 10.65

Table 10.23

Default values for total nitrogen loss from manure management ...................................... 10.67

Table 10A.1

Data for estimating Tier 1 enteric fermentation CH4 emission factors for
Dairy Cows in Table 10.11 ................................................................................................ 10.72

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Table 10A.2

Data for estimating Tier 1 enteric fermentation CH4 emission factors for
Other Cattle in Table 10.11 ............................................................................................... 10.73

Table 10A.3

Data for estimating Tier 1 enteric fermentation CH4 emission factors for Buffalo .......... 10.75

Table 10A-4

Manure management methane emission factor derivation for Dairy Cows ....................... 10.77

Table 10A-5

Manure management methane emission factor derivation for Other Cattle ...................... 10.78

Table 10A-6

Manure management methane emission factor derivation for Buffalo .............................. 10.79

Table 10A-7

Manure management methane emission factor derivation for Market Swine ................... 10.80

Table 10A-8

Manure management methane emission factor derivation for Breeding Swine ................ 10.81

Table 10A-9

Manure management methane emission factor derivation for Other Animals ................. 10.82

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Chapter 10: Emissions from Livestock and Manure Management

10 EMISSIONS FROM LIVESTOCK AND
MANURE MANAGEMENT
10.1

INTRODUCTION

This chapter provides guidance on methods to estimate emissions of methane from Enteric Fermentation inlivestock, and methane and nitrous oxide emissions from Manure Management. CO2 emissions from livestock
are not estimated because annual net CO2 emissions are assumed to be zero – the CO2 photosynthesized byplants is returned to the atmosphere as respired CO2. A portion of the C is returned as CH4 and for this reason
CH4 requires separate consideration.
Livestock production can result in methane (CH4) emissions from enteric fermentation and both CH4 and nitrous
oxide (N2O) emissions from livestock manure management systems. Cattle are an important source of CH4 inmany countries because of their large population and high CH4 emission rate due to their ruminant digestivesystem. Methane emissions from manure management tend to be smaller than enteric emissions, with the mostsubstantial emissions associated with confined animal management operations where manure is handled inliquid-based systems. Nitrous oxide emissions from manure management vary significantly between the types ofmanagement system used and can also result in indirect emissions due to other forms of nitrogen loss from thesystem. The calculation of the nitrogen loss from manure management systems is also an important step indetermining the amount of nitrogen that will ultimately be available in manure applied to managed soils, or used
for feed, fuel, or construction purposes – emissions that are calculated in Chapter 11, Section 11.2 (N2O
emissions from managed soils).
The methods for estimating CH4 and N2O emissions from livestock require definitions of livestock subcategories,
annual populations and, for higher Tier methods, feed intake and characterisation. The procedures employed todefine livestock subcategories, develop population data, and characterize feed are described in Section 10.2
(Livestock Population and Feed Characterisation). Suggested feed digestibility coefficients for various livestockcategories have been provided to help estimation of feed intake for use in calculation of emissions from entericand manure sources. A coordinated livestock characterisation as described in Section 10.2 should be used toensure consistency across the following source categories:•

Section 10.3 - CH4 emissions from Enteric Fermentation;•

Section 10.4 - CH4 emissions from Manure Management;•

Section 10.5 - N2O emissions from Manure Management (direct and indirect);•

Chapter 11, Section 11.2 - N2O emissions from Managed Soils (direct and indirect).

10.2

LIVESTOCK POPULATION AND FEEDCHARACTERISATION

10.2.1

Steps to define categories and subcategories of
livestock

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 used by a country will probably undergo iterations as the needs of each source category areassessed during the emissions estimation process (see Figure 10.1, Decision Tree for Livestock Population
Characterisation). The steps are:•Identify livestock species applicable to each emission source category: The livestock species that
contribute to more than one emission source category 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 relevant source category: For the source categories ofEnteric Fermentation and Manure Management, identify the emission estimating method for each species
for that source category. For example, enteric fermentation emissions from cattle, buffalo, and sheep shouldeach be examined to assess whether the trend or level of emissions warrant a Tier 2 or Tier 3 emissions
estimate. Similarly, manure management methane emissions from cattle, buffalo, swine, and poultry should

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be examined to determine whether the Tier 2 or Tier 3 emissions estimate is appropriate. Existing inventory
estimates can be used to conduct this assessment. If no inventory has been developed to date, Tier 1
emission estimates should be calculated to provide initial estimates for conducting this assessment. See
Volume 1, Chapter 4 (Methodological Choice and Identification of Key Categories) for guidance on thegeneral 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.

10.2.2

Choice of method

TIER 1: BASIC CHARACTERISATION FOR LIVESTOCK POPULATIONSBasic characterisation for Tier 1 is likely to be sufficient for most animal species in most countries. For thisapproach 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 livestock populations that have default emission factor
values must be developed (e.g., dairy cows, other cattle, buffalo, sheep, goats, camels, llamas, alpacas, deer,horses, rabbits, mules and asses, swine, and poultry) if these categories are relevant to the country. Moredetailed categories should be used if the data are available. For example, more accurate emission estimates canbe made if poultry populations are further subdivided (e.g., layers, broilers, turkeys, ducks, and other poultry), as
the waste characteristics among these different populations varies significantly.
Annual population: If possible, inventory compilers 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, including any adjustments to the original form of the population
data as it was received from national statistical agencies or from other sources.
Annual average populations are estimated in various ways, depending on the available data and the nature of theanimal population. In the case of static animal populations (e.g., dairy cows, breeding swine, layers), estimating
the annual average population may be as simple as obtaining data related to one-time animal inventory data.
However, estimating annual average populations for a growing population (e.g., meat animals, such as broilers,
turkeys, beef cattle, and market swine) requires more evaluation. Most animals in these growing populations arealive for only part of a complete year. Animals should be included in the populations regardless if they wereslaughtered for human consumption or die of natural causes. Equation 10.1 estimates the annual average of
livestock population.
EQUATION 10.1
ANNUAL AVERAGE POPULATION NAPA 
AAP = Days _alive •  365 Where:
AAP = annual average population
NAPA = number of animals produced annually
Broiler chickens are typically grown approximately 60 days before slaughter. Estimating the average annual
population as the number of birds grown and slaughtered over the course of a year would greatly overestimate
the population, as it would assume each bird lived the equivalent of 365 days. Instead, one should estimate theaverage annual population as the number of animals grown divided by the number of growing cycles per year.
For example, if broiler chickens are typically grown in flocks for 60 days, an operation could turn overapproximately 6 flocks of chickens over the period of one year. Therefore, if the operation grew 60,000
chickens in a year, their average annual population would be 9,863 chickens. For this example the equation
would be:Annual average population = 60 days ● 60,000 / 365 days / yr = 9,863 chickens

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10.1

Decision tree for livestock population characterisation

Start

Identify livestock species
applicable to each category.

Review the emission
estimation methods for eachof the categories1.

Identify whether a basic or
enhanced characterisation isrequired for each livestock
species based on key
category analyses2.

Ask foreach livestock
species: "Are data
available to support the level ofdetail required for thecharacterisation?"Candata be collected tosupport the level ofcharacterisation?

No

No

Set the level of thecharacterisation
to the available data.
Box 1

Yes
Yes
Perform thecharacterisation at therequired level of detail.

Collect the data required tosupport the characterisation.
Yes

Box 2
Note:
1: These categories include: CH4 Emission from Enteric Fermentation, CH4 Emission from Manure Management, and N2O Emission fromManure Management.
2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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Dairy cows and milk production: The dairy cow population is estimated separately from other cattle (seeTable 10.1). Dairy cows are defined in this method as mature cows that are producing milk in commercial
quantities for human consumption. This definition corresponds to the dairy cow population reported in the FAO
Production Yearbook. In some countries the dairy cow population is comprised of two well-defined segments: (i)high-producing (also called improved) breeds in commercial operations; and (ii) low-producing cows managedwith traditional methods. These two segments can be combined, or can be evaluated separately by defining two
dairy cow categories. However, the dairy cow category does not include cows kept principally to produce calves
for meat or to provide draft power. Low productivity multi-purpose cows should be considered as other cattle.
Dairy buffalo may be categorized in a similar manner to dairy cows.
Data on the average milk production of dairy cows are also required. Milk production data are used in estimating
an emission factor for enteric fermentation using the Tier 2 method. Country-specific data sources are preferred,
but FAO data may also be used. These data are expressed in terms of kilograms of whole fresh milk produced
per year per dairy cow. If two or more dairy cow categories are defined, the average milk production per cow isrequired for each category.

TIER 2: ENHANCED CHARACTERISATION FOR LIVESTOCKPOPULATIONSThe Tier 2 livestock characterisation requires detailed information on:•

Definitions for livestock subcategories;•

Livestock population by subcategory, with consideration for estimation of annual population as per Tier 1;
and•Feed intake estimates for the typical animal in each subcategory.

The livestock population subcategories are defined to create relatively homogenous sub-groupings of animals.
By dividing the population into these subcategories, country-specific variations in age structure and animal
performance within the overall livestock population can be reflected.
The Tier 2 characterisation methodology seeks to define animals, animal productivity, diet quality andmanagement circumstances to support a more accurate estimate of feed intake for use in estimating methane
production from enteric fermentation. The same feed intake estimates should be used to provide harmonised
estimates of manure and nitrogen excretion rates to improve the accuracy and consistency of CH4 and N2Oemissions from manure management.

Definitions for livestock subcategories
It is good practice to classify livestock populations into subcategories for each species according to age, type of
production, and sex. Representative livestock categories for doing this are shown in Table 10.1. Further
subcategories are also possible:•

Cattle and buffalo populations should be classified into at least three main subcategories: mature dairy, othermature, and growing cattle. Depending on the level of detail in the emissions estimation method,subcategories can be further classified based on animal or feed characteristics. For example, growing /fattening cattle could be further subdivided into those cattle that are fed a high-grain diet and housed in dry
lot vs. those cattle that are grown and finished solely on pasture.

•

Subdivisions similar to those used for cattle and buffalo can be used to further segregate the sheep
population in order to create subcategories with relatively homogenous characteristics. For example,
growing lambs could be further segregated into lambs finished on pasture vs. lambs finished in a feedlot.
The same approach applies to national goat herds.

•

Subcategories of swine could be further segregated based on production conditions. For example, growing
swine could be further subdivided into growing swine housed in intensive production facilities vs. swine thatare grown under free-range conditions.

•

Subcategories of poultry could be further segregated based on production conditions. For example, poultry
could be divided on the basis of production under confined or free-range conditions.

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 may be used to represent differences inclimate, feeding systems, diet, and manure management. However, this further segregation is only useful if
correspondingly detailed data are available on feeding and manure management system usage by these livestockcategories.

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.1
REPRESENTATIVE LIVESTOCK CATEGORIES1,2
Main categories
Mature Dairy Cow or Mature Dairy
Buffalo

Subcategories••

Other Mature Cattle or Mature Non-dairy
BuffaloHigh-producing cows that have calved at least once and are used principally formilk production
Low-producing cows that have calved at least once and are used principally for milk
production

Females:••

Cows used to produce offspring for meat
Cows used for more than one production purpose: milk, meat, draft

Males:••

Bulls used principally for breeding purposes
Bullocks used principally for draft power

Growing Cattle or Growing Buffalo

•
•
•
•

Calves pre-weaning
Replacement dairy heifers
Growing / fattening cattle or buffalo post-weaning
Feedlot-fed cattle on diets containing > 90 % concentrates

Mature Ewes••

Breeding ewes for production of offspring and wool production
Milking ewes where commercial milk production is the primary purpose

Other Mature Sheep (>1 year)•

No further sub-categorisation recommended

Growing Lambs•••

Intact males
Castrates
FemalesMature Swine•••

Sows in gestation
Sows which have farrowed and are nursing young
Boars that are used for breeding purposes

Growing Swine

•
•
•
•

Nursery
Finishing
Gilts that will be used for breeding purposes
Growing boars that will be used for breeding purposes

Chickens•••

Broiler chickens grown for producing meat
Layer chickens for producing eggs, where manure is managed in dry systems (e.g.,high-rise houses)
Layer chickens for producing eggs, where manure is managed in wet systems (e.g.,lagoons)
Chickens under free-range conditions for egg or meat production

Turkeys•••Breeding turkeys in confinement systems
Turkeys grown for producing meat in confinement systems
Turkeys under free-range conditions for meat production

Ducks

•
•

Breeding ducks
Ducks grown for producing meat

Others (for example)

•
•
•
•
•
•
•
•
•

CamelsMules and Asses
Llamas, Alpacas
Fur bearing animals
Rabbits
Horses
Deer
Ostrich
Geese

•

1

Source IPCC Expert Group

2

Emissions should only be considered for livestock species used to produce food, fodder or raw materials used for industrial processes.

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For each of the representative animal categories defined, the following information is required:•annual average population (number of livestock or poultry as per calculations for Tier 1);•

average daily feed intake (megajoules (MJ) per day and / or kg per day of dry matter); and•methane conversion factor (percentage of feed energy converted to methane).

Generally, data on average daily feed intake are not available, particularly for grazing livestock. Consequently,
the following general data should be collected for estimating the feed intake for each representative animal
category:•

weight (kg);•average weight gain per day (kg) 1;•

feeding situation: confined, grazing, pasture conditions;•milk production per day (kg/day) and fat content (%) 2;•

average amount of work performed per day (hours day-1);•

percentage of females that give birth in a year 3;•wool growth;•

number of offspring; and•feed digestibility (%).

Feed intake estimates
Tier 2 emissions estimates require feed intakes for a representative animal in each subcategory. Feed intake istypically measured in terms of gross energy (e.g., megajoules (MJ) per day) or dry matter (e.g., kilograms (kg)
per day). Dry matter is the amount of feed consumed (kg) after it has been corrected for the water content in thecomplete diet. For example, consumption of 10 kg of a diet that contains 70% dry matter would result in a dry
matter intake of 7 kg. To support the enteric fermentation Tier 2 method for cattle, buffalo, and sheep (seeSection 10.3), detailed data requirements and equations to estimate feed intake are included in guidance below.
Constants in the equations have been combined to simplify overall equation formats. The remainder of thissubsection presents the typical data requirements and equations used to estimate feed intake for cattle, buffalo,and sheep. Feed intake for other species can be estimated using similar country-specific methods appropriate for
each.
For all estimates of feed intake, good practice is to:•Collect data to describe the animal’s typical diet and performance in each subcategory;•

Estimate feed intake from the animal performance and diet data for each subcategory.

In some cases, the equations may be applied on a seasonal basis, for example under conditions in which
livestock gain weight in one season and lose weight in another. This approach may require a more refined
variation of Tier 2 or more complex Tier 3 type methodology.
The following animal performance data are required for each animal subcategory to estimate feed intake for thesubcategory:•

Weight (W), kg: Live-weight data should be collected for each animal subcategory. It is unrealistic toperform a complete census of live-weights, so live-weight data should be obtained from representative
sample studies or statistical databases if these already exist. Comparing live-weight data with slaughterweight 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 as it fails toaccount for the complete weight of the animal. Additionally, it should be noted that the relationshipbetween live-weight and slaughter-weight varies with breed and body condition. For cattle, buffalo and

1This may be assumed to be zero for mature animals.

2

Milk production data are required for dairy animals. These can be estimated for non-dairy animals providing milk toyoung, where data are available.

3

This is only relevant for mature females.

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Chapter 10: Emissions from Livestock and Manure Management

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 or at slaughter if slaughter occurs within
the year.
•

Average weight gain per day (WG), kg day-1: Data on average weight gain are generally collected forfeedlot animals and young growing animals. Mature animals are generally assumed to have no net weightgain or loss over an entire year. Mature animals frequently lose weight during the dry season or during
temperature extremes and gain weight during the following season. However, increased emissions
associated with this weight change are likely to be small. Reduced intakes and emissions associated withweight loss are largely balanced by increased intakes and emissions during the periods of gain in bodyweight.

•Mature weight (MW), kg: The mature weight of the adult animal of the inventoried group is required todefine a growth pattern, including the feed and energy required for growth. For example, mature weight of abreed or category of cattle or buffalo is generally considered to be the body weight at which skeletal
development is complete. The mature weight will vary among breeds and should reflect the animal’s
weight when in moderate body condition. This is termed ‘reference weight’ (ACC, 1990) or ‘final shrunk
body weight’ (NRC, 1996). 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 subcategory must be
determined using the definitions shown below (Table 10.5). If the feeding situation lies between thedefinitions, the feeding 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 to assign the most appropriate coefficient. Table 10.5 defines the feeding situations for
cattle, buffalo, and sheep. For poultry and swine, the feeding situation is assumed to be under confinement
conditions and consequently the activity coefficient (Ca )is assumed to be zero as under these conditions
very little energy is expended in acquiring feed. Activity coefficients have not been developed for freeranging swine or poultry, but in most instances these livestock subcategories are likely to represent a small
proportion of the national inventory.

•

Mean winter temperature (ºC): Detailed feed intake models consider ambient temperature, wind speed,
hair and tissue insulation and the heat of fermentation (NRC, 2001; AAC, 1990) and are likely more
appropriate in Tier 3 applications. A more general relationship adapted from North America data suggest
adjusting the Cfi of Equation 10.3 for maintenance requirements of open-lot fed cattle in colder climatesaccording to the following equation (Johnson, 1986):
EQUATION 10.2
COEFFICIENT FOR CALCULATING NET ENERGY FOR MAINTENANCE
Cf i (in _ cold ) = Cf i + 0.0048 • (20 − °C )Where:
Cfi = a coefficient which varies for each animal category as shown in Table 10.4 (Coefficients forcalculating NEm), MJ day-1 kg-1
°C = mean daily temperature during winter season
Considering the average temperature during winter months, net energy for maintenance (NEm) requirements
may increase by as much as 30% in northern North America. This increase in feed use for maintenance isalso likely associated with greater methane emissions.
•Average daily milk production (kg day-1): These data are for milking ewes, dairy cows and buffalo. The
average daily production should be calculated by dividing the total annual production by 365, or reported as
average daily production along with days of lactation per year, or estimated using seasonal production
divided by number of days per season. 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 lactating cows, buffalo, and sheep producing
milk for human consumption.

•

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

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Number of off spring produced per year: This is relevant to female livestock that have multiple births per
year (e.g., ewes).

•

Feed digestibility (DE%): The portion of gross energy (GE) in the feed not excreted in the faeces is known
as digestible feed. The feed digestibility is commonly expressed as a percentage (%) of GE or TDN (total
digestible nutrients). That percentage of feed that is not digested represents the % of dry matter intake thatwill be excreted as faeces. Typical digestibility values for a range of livestock classes and diet types arepresented in Table 10.2 as a guideline. For ruminants, common ranges of feed digestibility are 45-55% forcrop by-products and range lands; 55-75% for good pastures, good preserved forages, and grain
supplemented forage-based diets; and 75-85% for grain-based diets fed in feedlots. Variations in dietdigestibility results in major variations in the estimate of feed needed to meet animal requirements andconsequently associated methane emissions and amounts of manure excreted. It is also important to notethat digestibility, intake, and growth are co-dependent phenomena. For example, a low digestibility willlead to lower feed intake and consequently reduced growth. Conversely, feeds with high digestibility willoften result in a higher feed intake and increased growth. A 10% error in estimating DE will be magnified to
12 to 20% when estimating methane emissions and even more (20 to 45%) for manure excretion (volatilesolids).
Digestibility data should be based on measured values for the dominant feeds or forages being consumed bylivestock with consideration for seasonal variation. In general, the digestibility of forages decreases withincreasing maturity and is typically lowest during the dry season. Due to significant variation, digestibility
coefficients should be obtained from local scientific data wherever possible. Although a complete census ofdigestibility is considered unrealistic, at a minimum digestibility data from research studies should be
consulted. While developing the digestibility data, associated feed characteristic data should also berecorded when available, such as measured values for Neutral Detergent Fiber (NDF), Acid Detergent Fiber(ADF), crude protein, and the presence of anti-nutritional factors (e.g., alkaloids, phenolics, % ash). NDF
and ADF are feed characteristics measured in the laboratory that are used to indicate the nutritive value ofthe feed for ruminant livestock. Determination of these values can enable DE to be predicted as defined in
the recent dairy NRC (2001). The concentration of crude protein in the feed can be used in the process of
estimating nitrogen excretion (Section 10.5.2).

•

Average annual wool production per sheep (kg yr-1): The amount of wool produced in kilograms (after
drying out but before scouring) is needed to estimate the amount of energy allocated for wool production.
TABLE 10.2
REPRESENTATIVE FEED DIGESTIBILITY FOR VARIOUS LIVESTOCK CATEGORIESMain categories

10.14

Digestibility (DE%)

Swine

•
•
•

Mature Swine – confinement
Growing Swine - confinement
Swine – free range•••

70 - 80%
80 - 90%
50 - 70% 1

Cattle and otherruminants••

75 - 85%••

Feedlot animals fed with > 90%
concentrate diet;
Pasture fed animals;
Animals fed – low quality forage••

55 - 75%
45 - 55%

•
•
•
•
•

Broiler Chickens –confinement
Layer Hens – confinement
Poultry – free range
Turkeys – confinement
Geese – confinement

•
•
•
•
•

85 - 93%
70 - 80%
55 - 90% 1
85 - 93%
80 - 90%

Poultry

1

Class

The range in digestibility of feed consumed by free-range swine and poultry is extremely variable due to the selective
nature of these diets. Often it is likely that the amount of manure produced in these classes will be limited by the amountof feed available for consumption as opposed to its degree of digestibility. In instances where feed is not limiting and highquality feed sources are readily accessible for consumption, digestibility may approach values that are similar to thosemeasured under confinement conditions.

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Chapter 10: Emissions from Livestock and Manure Management

Gross energy calculations
Animal performance and diet data are used to estimate feed intake, which is the amount of energy (MJ/day) an
animal needs for maintenance and for activities such as growth, lactation, and pregnancy. For inventory
compilers who have well-documented and recognised country-specific methods for estimating intake based onanimal performance data, it is good practice to use the country-specific methods. The following section provides
methods for estimating gross energy intake for the key ruminant categories of cattle, buffalo and sheep. The
equations listed in Table 10.3 are used to derive this estimate. If no country-specific methods are available,
intake should be calculated using the equations listed in Table 10.3. As shown in the table, separate equations areused to estimate net energy requirements for sheep as compared with cattle and buffalo. The equations used to
calculate GE are as follows:
TABLE 10.3
SUMMARY OF THE EQUATIONS USED TO ESTIMATE DAILY GROSS ENERGY INTAKE FOR CATTLE,BUFFALO AND SHEEPMetabolic functions andother estimates

Equations for cattle andbuffaloEquations for sheep

Maintenance (NEm)

Equation 10.3

Equation 10.3

Activity (NEa)

Equation 10.4

Equation 10.5

Growth (NEg)

Equation 10.6

Equation 10.7

Lactation (NEl)*

Equation 10.8

Equations 10.9 and 10.10

Draft Power (NEwork)

Equation 10.11

NANA

Equation 10.12

Pregnancy (NEp)*

Equation 10.13

Equation 10.13

Ratio of net energy available
in diet for maintenance todigestible energy consumed(REM)

Equation 10.14

Equation 10.14

Ratio of net energy available
for growth in a diet todigestible energy consumed(REG)

Equation 10.15

Equation 10.15

Gross Energy

Equation 10.16

Equation 10.16

Wool Production (NEwool)

Source: Cattle and buffalo 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.

Net energy for maintenance: (NEm ) is the net energy required for maintenance, which is the amount of energy
needed to keep the animal in equilibrium where body energy is neither gained nor lost (Jurgen, 1988).
EQUATION 10.3
NET ENERGY FOR MAINTENANCE
NEm = Cf i • (Weight )0.75

Where:
NEm = net energy required by the animal for maintenance, MJ day-1
Cfi = a coefficient which varies for each animal category as shown in Table 10.4 (Coefficients forcalculating NEm), MJ day-1 kg-1
Weight = live-weight of animal, kg
Net energy for activity: (NEa) is the net energy for activity, or the energy needed for animals to obtain their food,water and shelter. It is based on its feeding situation rather than characteristics of the feed itself. As presented inTable 10.3, the equation for estimating NEa for cattle and buffalo is different from the equation used for sheep.
Both equations are empirical with different definitions for the coefficient Ca.

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EQUATION 10.4
NET ENERGY FOR ACTIVITY (FOR CATTLE AND BUFFALO)
NEa = Ca • NE m
Where:
NEa = net energy for animal activity, MJ day-1
Ca = coefficient corresponding to animal’s feeding situation (Table 10.5, Activity coefficients)
NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1

EQUATION 10.5
NET ENERGY FOR ACTIVITY (FOR SHEEP)
NEa = Ca • (weight )Where:
NEa = net energy for animal activity, MJ day-1
Ca = coefficient corresponding to animal’s feeding situation (Table 10.5), MJ day-1 kg-1
weight = live-weight of animal, kg
For Equations 10.4 and 10.5, the coefficient Ca corresponds to a representative animal’s feeding situation as
described earlier. Values for Ca are shown in Table 10.5. If a mixture of these feeding situations occurs during
the year, NEa must be weighted accordingly.
TABLE 10.4
COEFFICIENTS FOR CALCULATING NET ENERGY FOR MAINTENANCE ( NEM )Cfi (MJ d-1 kg-1)

Comments

Cattle/Buffalo (non-lactating cows, steers
and juveniles)

0.322

Non-lactating dairy, beef and multipurpose cows, steers and juveniles

Cattle/Buffalo (lactating cows)

0.386

Maintenance energy requirementsare 20% higher during lactation

Cattle/Buffalo (bulls)

0.370

Maintenance energy requirements
are 15% higher for intact males

Sheep (lamb to 1 year)

0.236This value can be increased by 15%
for intact males

Sheep (older than 1 year)

0.217

This value can be increased by 15%
for intact males.

Animal category

Source: NRC (1996) and AFRC (1993).

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TABLE 10.5
ACTIVITY COEFFICIENTS CORRESPONDING TO ANIMAL’S FEEDING SITUATION
Situation

Definition

Ca

Cattle and Buffalo (unit for Ca is dimensionless)
Stall

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

0.00

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

Sheep (unit for Ca = MJ d-1 kg-1)
Housed ewes

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

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.

0.0240Housed fattening lambs

Animals are housed for fattening.

0.0067

Source: NRC (1996) and AFRC (1993).

Net energy for growth: (NEg) is the net energy needed for growth (i.e., weight gain). Equation 10.6 is based on
NRC (1996). Equation 10.7 is based on Gibbs et al. (2002). Constants for conversion from calories to joules andlive to shrunk and empty body weight have been incorporated into the equation.
EQUATION 10.6
NET ENERGY FOR GROWTH (FOR CATTLE AND BUFFALO) BW 
NE g = 22.02 •  C • MW 

0.75

• WG1.097

Where:
NEg = net energy needed for growth, MJ day-1
BW = the average live body weight (BW) of the animals in the population, 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 live body weight of an adult animal in moderate body condition, kg
WG = the average daily weight gain of the animals in the population, kg day-1

EQUATION 10.7
NET ENERGY FOR GROWTH (FOR SHEEP)
WGlamb • a + 0.5b BWi + BW f
NE g =
365

(

(

))

Where:
NEg = net energy needed for growth, MJ day-1
WGlamb = the weight gain (BWf – BWi), kg yr-1
BWi = the live bodyweight at weaning, kg

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BWf = the live bodyweight at 1-year old or at slaughter (live-weight) if slaughtered prior to 1 year of age,kg
a, b = constants as described in Table 10.6.
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 empirical constants (a and b) that vary by animal species/category(Table 10.6).
TABLE 10.6
CONSTANTS FOR USE IN CALCULATING NEG FOR SHEEPa(MJ kg-1)b(MJ kg-2)

Intact males

2.5

0.35

Castrates

4.4

0.32

Females

2.1

0.45

Animal species/category

Source: AFRC (1993).

Net energy for lactation: (NEl ) is the net energy for lactation. For cattle and buffalo the net energy for lactation
is expressed as a function of the amount of milk produced and its fat content expressed as a percentage (e.g., 4%)(NRC, 1989):
EQUATION 10.8
NET ENERGY FOR LACTATION (FOR BEEF CATTLE, DAIRY CATTLE AND BUFFALO)
NE1 = Milk • (1.47 + 0.40 • Fat )Where:
NEl = net energy for lactation, MJ day-1
Milk = amount of milk produced, kg of milk day-1
Fat = fat content of milk, % by weight.
Two methods for estimating the net energy required for lactation (NEl) are presented for sheep. The first method(Equation 10.9) is used when the amount of milk produced is known, and the second method (Equation 10.8) 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 10.9). 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. For multiple births, the total annual
milk production can be estimated as five times the combined weight gain of all lambs birthed by a single ewe.
The daily average milk production is estimated by dividing the resulting estimate by 365 days as shown inEquation 10.10.
EQUATION 10.9
NET ENERGY FOR LACTATION FOR SHEEP (MILK PRODUCTION KNOWN)
NE1 = Milk • EVmilk
Where:
NEl = net energy for lactation, MJ day-1
Milk = amount of milk produced, kg of milk day-1

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EVmilk = the net energy required to produce 1 kg of milk. A default value of 4.6 MJ/kg (AFRC, 1993) canbe used which corresponds to a milk fat content of 7% by weight

EQUATION 10.10
NET ENERGY FOR LACTATION FOR SHEEP (MILK PRODUCTION UNKNOWN)
 (5 • WGwean ) 
NE1 =  • EVmilk
365

Where:
NEl = net energy for lactation, MJ day-1
WG wean = the weight gain of the lamb between birth and weaning, kg
EVmilk = the energy required to produce 1 kg of milk, MJ kg-1. A default value of 4.6 MJ kg-1 (AFRC,
1993) can be used.
Net energy for work: (NEwork ) is the net energy for work. It is used to estimate the energy required for draft
power for cattle and buffalo. 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 thework performed by the animal influences the energy requirements, and consequently a wide range of energy
requirements have been 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 10.11
NET ENERGY FOR WORK (FOR CATTLE AND BUFFALO)
NE work = 0.10 • NEm • Hours
Where:
NEwork = net energy for work, MJ day-1
NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1
Hours = number of hours of work per day
Net energy for wool production: (NEwool ) is the average daily net energy required for sheep to produce a year ofwool. The NEwool is calculated as follows:
EQUATION 10.12
NET ENERGY TO PRODUCE WOOL (FOR SHEEP)• Productionwool  EV
NE wool =  wool

365


Where:
NEwool = net energy required to produce wool, MJ day-1
EVwool = the energy value of each kg of wool produced (weighed after drying but before scouring), MJ
kg-1. A default value of 24 MJ kg-1 (AFRC, 1993) can be used for this estimate.
Productionwool = annual wool production per sheep, kg yr-1
Net energy for 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% ofNEm. For sheep, the NEp requirement is similarly estimated for the 147-day gestation period, although thepercentage varies with the number of lambs born (Table 10.7, Constant for Use in Calculating NEp in Equation
10.13). Equation 10.13 shows how these estimates are applied.

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EQUATION 10.13
NET ENERGY FOR PREGNANCY (FOR CATTLE/BUFFALO AND SHEEP)
NE p = C pregnancy • NEm
Where:
NEp = net energy required for pregnancy, MJ day-1
Cpregnancy = pregnancy coefficient (see Table 10.7)
NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1
TABLE 10.7
CONSTANTS FOR USE IN CALCULATING NEP IN EQUATION 10.13
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), taking into account the
inefficiency of energy conversion.

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.
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]

Ratio of net energy available in diet for maintenance to digestible energy consumed (REM): For cattle, buffalo
and sheep, the ratio of net energy available in a diet for maintenance to digestible energy consumed (REM ) is
estimated using the following equation (Gibbs and Johnson, 1993):
EQUATION 10.14
RATIO OF NET ENERGY AVAILABLE IN A DIET FOR MAINTENANCE TO DIGESTIBLE ENERGYCONSUMED

(

) [

]


 25.4 
REM = 1.123 − 4.092 • 10 −3 • DE % + 1.126 • 10 −5 • (DE % )2 −  DE % Where:

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REM = ratio of net energy available in a diet for maintenance to digestible energy consumed
DE% = digestible energy expressed as a percentage of gross energy
Ratio of net energy available for growth in a diet to digestible energy consumed (REG): For cattle, buffalo andsheep the ratio of net energy available for growth (including wool growth) in a diet to digestible energy
consumed (REG ) is estimated using the following equation (Gibbs and Johnson, 1993):
EQUATION 10.15
RATIO OF NET ENERGY AVAILABLE FOR GROWTH IN A DIET TO DIGESTIBLE ENERGY CONSUMED

(

) [

]


 37.4 
REG = 1.164 − 5.160 • 10 −3 • DE % + 1.308 • 10 −5 • (DE % )2 −  DE % Where:REG = ratio of net energy available for growth in a diet to digestible energy consumed
DE% = digestible energy expressed as a percentage of gross energy
Gross energy, GE: As shown in Equation 10.16, GE requirement is derived based on the summed net energy
requirements and the energy availability characteristics of the feed(s). Equation 10.16 represents good practicefor calculating GE requirements for cattle and sheep using the results of the equations presented above.
In using Equation 10.16, only those terms relevant to each animal category are used (see Table 10.3).
EQUATION 10.16
GROSS ENERGY FOR CATTLE/BUFFALO AND SHEEP  NEm + NEa + NE1 + NE work + NE p   NE g + NE wool  

 
 

+
REG
REM





GE =


DE %


100



Where:
GE = gross energy, MJ day-1
NEm = net energy required by the animal for maintenance (Equation 10.3), MJ day-1
NEa = net energy for animal activity (Equations 10.4 and 10.5), MJ day-1
NEl = net energy for lactation (Equations 10.8, 10.9, and 10.10), MJ day-1
NEwork = net energy for work (Equation 10.11), MJ day-1
NEp = net energy required for pregnancy (Equation 10.13), MJ day-1
REM = ratio of net energy available in a diet for maintenance to digestible energy consumed (Equation
10.14)
NEg = net energy needed for growth (Equations 10.6 and 10.7), MJ day-1
NEwool = net energy required to produce a year of wool (Equation 10.12), MJ day-1
REG = ratio of net energy available for growth in a diet to digestible energy consumed (Equation 10.15)
DE%= digestible energy expressed as a percentage of gross energy
Once the values for GE are calculated for each animal subcategory, the feed intake in units of kilograms of dry
matter per day (kg day-1) should also be calculated. To convert from GE in energy units to dry matter intake(DMI), divide GE by the energy density of the feed. A default value of 18.45 MJ kg-1 of dry matter can be used
if feed-specific information is not available. The resulting daily dry matter intake should be in the order of 2% to
3% of the body weight of the mature or growing animals. In high producing milk cows, intakes may exceed 4%of body weight.

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Feed intake estimates using a simplified Tier 2 method
Prediction of DMI for cattle based on body weight and estimated dietary net energy concentration (NEma) ordigestible energy values (DE%): It is also possible to predict dry matter intake for mature and growing cattle
based on body weight of the animal and either the NEma concentration of the feed (NRC, 1996) or DE%.
Dietary NEma concentration can range from 3.0 to 9.0 MJ kg-1 of dry matter. Typical values for high, moderate
and low quality diets are presented in Table 10.8. These figures can also be used to estimate NEma values formixed diets based on estimate of diet quality. For example, a mixed forage-grain diet could be assumed to havea NEma value similar to that of a high-quality forage diet. A mixed grain-straw diet could be assumed to have a
NEma value similar to that of a moderate quality forage. Nutritionists within specific geographical areas shouldbe able to provide advice with regard to the selection of NEma values that are more representative of locally fed
diets.
Dry matter intake for growing and finishing cattle is estimated using the following equation:
EQUATION 10.17
ESTIMATION OF DRY MATTER INTAKE FOR GROWING AND FINISHING CATTLE)(

 0.2444 • NE ma − 0.0111 • NE ma 2 − 0.472 
DMI = BW 0.75 • 

NE ma



Where:DMI = dry matter intake, kg day-1
BW = live body weight, kg
NEma = estimated dietary net energy concentration of diet or default values in Table 10.8, MJ kg-1
Dry matter intake for mature beef cattle is estimated using the following equation:
EQUATION 10.18a
ESTIMATION OF DRY MATTER INTAKE FOR MATURE BEEF CATTLE()

 0.0119 • NE ma 2 + 0.1938 
DMI = BW 0.75 • 

NE ma



Where:DMI = dry matter intake, kg day-1
BW = live body weight, kg
NEma = estimated dietary net energy concentration of diet or default values given in Table 10.8, MJ kg-1
For mature dairy cows consuming low quality, often tropical forages, the following alternative equation for
estimating dry matter intake based on DE% can be used (NRC, 1989):
EQUATION 10.18b
ESTIMATION OF DRY MATTER INTAKE FOR MATURE DAIRY COWS  (5.4 • BW )  
 
 
500

 

DMI =
(
)
−
100
%
DE



100



Where:DMI = dry matter intake, kg day-1
BW = live body weight, kg
DE%= digestible energy expressed as a percentage of gross energy (typically 45-55% for low quality
forages)

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Equations 10.17, 10.18a, and 10.18b provide a good check to the main Tier 2 method to predict feed intake.
They can be viewed as asking ‘what is an expected intake for a given diet quality?’ and used to independently
predict DMI from BW and diet quality (NEma or DE%). In contrast, the main Tier 2 method predicts DMI basedon how much feed must be consumed to meet estimated requirements (i.e., NEm and NEg) and does not consider
the biological capacity of the animal to in fact consume the predicted quantity of feed. Consequently, thesimplified Tier 2 method can be used to confirm that DMI values derived from the main Tier 2 method are
biologically realistic. These estimates are also subject to the cross check that dry matter intake should be in theorder of 2% to 3% of the bodyweight of the mature or growing animals.
TABLE 10.8
EXAMPLES OF NEMA CONTENT OF TYPICAL DIETS FED TO CATTLE FOR ESTIMATION OF
DRY MATTER INTAKE IN EQUATIONS 10.17 AND 10.18
Diet typeNEma (MJ (kg dry matter)-1)

High grain diet > 90%

7.5 - 8.5

High quality forage (e.g., vegetative
legumes & grasses )6.5 - 7.5

Moderate quality forage (e.g., mid season
legume & grasses)

5.5 - 6.5

Low quality forage (e.g., straws, mature
grasses)

3.5 - 5.5

Source: Estimates obtained from predictive models in NRC (1996), NEma can also be estimated using
the equation: NEma = REM x 18.45 x DE% / 100.

10.2.3

Uncertainty assessment

The first step in collecting data should be to investigate existing national statistics, industry sources, researchstudies and FAO statistics. The uncertainty associated with populations will vary widely depending on source,
but should be known within +20%. Often, national livestock population statistics already have associateduncertainty estimates in which case these should be used. If published data are not available from these sources,interviews of key industry and academic experts can be undertaken. Estimates of digestibility are also
particularly important in Tier 2 estimates of gross energy intake. Uncertainty estimates for digestibility
estimates may be as high as +20%. Volume 1, Chapter 3 (Uncertainties) 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.

10.2.4Characterisation for livestock without species:
Specific emission estimation methods

Some countries may have domesticated livestock 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 livestock is to first assess whether their emissions are likely to be significant enough to warrant
characterising them and developing country-specific emission factors. Volume 1, Chapter 4 (Methodological
Choice and Identification of Key Categories) presents guidance for assessing the significance of individual
source categories within the national inventory. Similar approaches can be used to assess the importance of subsource categories (i.e. species) within a source category. 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. Research into the estimation ofemission levels from these non-characterized species should be encouraged. The data and methods used tocharacterise 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
the animals raised to the 0.75 power. The Tier 1 emission factors can be classified by digestive system as follows:

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Ruminant animals: Cattle, Buffalo, Sheep, Goats, Camels•

Non-ruminant herbivores: Horses, Mules/Asses•

Poultry: Chickens, Ducks, Turkeys, Geese•

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) / (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.

10.3

METHANE EMISSIONS FROM ENTERICFERMENTATION

Methane is produced in herbivores as a by-product of enteric fermentation, a digestive process by whichcarbohydrates are broken down by micro-organisms into simple molecules for absorption into the bloodstream.
The amount of methane that is released depends on the type of digestive tract, age, and weight of the animal, andthe quality and quantity of the feed consumed. Ruminant livestock (e.g., cattle, sheep) are major sources ofmethane with moderate amounts produced from non-ruminant livestock (e.g., pigs, horses). The ruminant gut
structure fosters extensive enteric fermentation of their diet.
Digestive system
The type of digestive system has a significant influence on the rate of methane emission. Ruminant livestock
have an expansive chamber, the rumen, at the fore-part of their digestive tract that supports intensive microbial
fermentation of their diet which yields several nutritional advantages including the capacity to digest cellulose in
their diet. The main ruminant livestock are cattle, buffalo, goats, sheep, deer and camelids. Non-ruminant
livestock (horses, mules, asses) and monogastric livestock (swine) have relatively lower methane emissions
because much less methane-producing fermentation takes place in their digestive systems.
Feed intake
Methane is produced by the fermentation of feed within the animal's digestive system. Generally, the higher the
feed intake, the higher the methane emission. Although, the extent of methane production may also be affectedby the composition of the diet. Feed intake is positively related to animal size, growth rate, and production (e.g.,
milk production, wool growth, or pregnancy).
To reflect the variation in emission rates among animal species, the population of animals should be divided into
subgroups, and an emission rate per animal is estimated for each subgroup. Types of population subgroups areprovided in Section 10.2 (Livestock and Feed Characterisation). The amount of methane emitted by apopulation subgroup is calculated by multiplying the emission rate per animal by the number of animals within
the subgroup.
Natural wild ruminants are not considered in the derivation of a country’s emission estimate. Emissions should
only be considered from animals under domestic management (e.g., farmed deer, elk, and buffalo).

10.3.1

Choice of method

It is good practice to choose the method for estimating methane emissions from enteric fermentation according
to the decision tree in Figure 10.2. The method for estimating methane emission from enteric fermentation
requires three basic steps:

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10.2

Decision Tree for CH 4 Emissions from Enteric Fermentation

Start

Do you have acountry-specific Tier 3
methodology?

Yes

Estimate emissions for the
species using Tier 3
approach.
Box 3: Tier 3

No

Is enhanced
livestock characterization
available?

No

Is entericfermentationa key category1 and is the
species significant2?

No

Estimate emissions
for the species
using Tier 1 approach.
Box 1: Tier 1

Yes
Yes
Collect enhanced
species characterization
data for Tier 2 approach.

Estimate emissions forthe species using Tier 2
approach.
Box 2: Tier 2
Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.
2: As a rule of thumb, a livestock species would be significant if it accounts for 25-30% or more of emissions from the source category.

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Step 1: Divide the livestock population into subgroups and characterize each subgroup as described in Section
10.2. It is recommended that national experts use annual averages estimated with consideration for the impact of
production cycles and seasonal influences on population numbers.
Step 2: Estimate emission factors for each subgroup in terms of kilograms of methane per animal per year.
Step 3: Multiply the subgroup emission factors by the subgroup populations to estimate subgroup emission, andsum across the subgroups to estimate total emission.
These three steps can be performed at varying levels of detail and complexity. This chapter presents thefollowing three approaches:
Tie r 1
A simplified approach that relies on default emission factors either drawn from the literature or calculated using
the more detailed Tier 2 methodology. The Tier 1 method is likely to be suitable for most animal species incountries where enteric fermentation is not a key source category, or where enhanced characterization data are
not available. When approximate enteric emissions are derived by extrapolation from main livestock categories
they should be considered to be a Tier 1 method.
Tie r 2A more complex approach that requires detailed country-specific data on gross energy intake and methane
conversion factors for specific livestock categories. The Tier 2 method should be used if enteric fermentation isa key source category for the animal category that represents a large portion of the country’s total emissions.
Tie r 3
Some countries for which livestock emissions are particularly important may wish to go beyond the Tier 2
method and incorporate additional country-specific information in their estimates. This approach could employ
the development of sophisticated models that consider diet composition in detail, concentration of products
arising from ruminant fermentation, seasonal variation in animal population or feed quality and availability, andpossible mitigation strategies. Many of these estimates would be derived from direct experimental measurements.
Although countries are encouraged to go beyond the Tier 2 method presented below when data are available,
these more complex analyses are only briefly discussed here. A Tier 3 method should be subjected to a wide
degree of international peer review such as that which occurs in peer-reviewed publications to ensure that they
improve the accuracy and / or precision of estimates.
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
method and are based on well-documented research (if it is determined that emissions from these livestock aresignificant). The approach is described in Section 10.2.4 under the heading ‘Characterisation for livestock
without species-specific emission estimation methods’ for more information.
Table 10.9 summarises the suggested approaches for the livestock emissions included in this inventory.

10.3.2

Choice of emission factors

Tier 1 Approach for methane emissions from Enteric Fermentation

This Tier 1 method is simplified so that only readily-available animal population data are needed to estimate
emissions. Default emission factors are presented for each of the recommended population subgroups. Eachstep is discussed in turn.
Step 1: Animal population
The animal population data should be obtained using the approach described in Section 10.2.
Step 2: Emission factors
The purpose of this step is to select emission factors that are most appropriate for the country's livestock
characteristics. Default emission factors for enteric fermentation have been drawn from previous studies, and areorganised by region for ease of use.
The data used to estimate the default emission factors for enteric fermentation are presented in Annex 10A.1 atthe end of this section.

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.9
SUGGESTED EMISSIONS INVENTORY METHODS FOR ENTERIC FERMENTATION
Livestock

Suggested emissions inventory methods

Dairy Cow

Tier 2a/Tier 3

Other Cattle

Tier 2a/Tier 3

Buffalo

Tier 1/Tier 2

SheepTier 1/Tier 2

Goats

Tier 1

CamelsTier 1

HorsesTier 1

Mules and Asses

Tier 1

Swine

Tier 1

Poultry

Not developed

Other (e.g., Llamas,
Alpacas, Deer)aTier 1The Tier 2 method is recommended for countries with large livestock populations. Implementing
the Tier 2 method for additional livestock subgroups may be desirable when the category emissions
are a large portion of total methane emissions for the country.

Table 10.10 shows the enteric fermentation emission factors for each of the animal species except cattle. As
shown in the table, emission factors for sheep and swine vary for developed and developing countries. The
differences in the emission factors are driven by differences in feed intake and feed characteristic assumptions(see Annex 10A.1). Table 10.11 presents the enteric fermentation emission factors for cattle. A range ofemission factors is shown for typical regional conditions. As shown in the table, the emission factors vary byover a factor of four on a per head basis.
While the default emission factors shown in Table 10.11 are broadly representative of the emission rates withineach of the regions described, emission factors vary within each region. Animal size and milk production areimportant determinants of emission rates for dairy cows. Relatively smaller dairy cows with low levels of
production are found in Asia, Africa, and the Indian subcontinent. Relatively larger dairy cows with high levelsof production are found in North America and Western Europe.
Animal size and population structure are important determinants of emission rates for other cattle. Relatively
smaller other cattle are found in Asia, Africa, and the Indian subcontinent. Also, many of the other cattle inthese regions are young. Other cattle in North America, Western Europe and Oceania are larger, and young
cattle constitute a smaller portion of the population.
To select emission factors from Tables 10.10 and 10.11, identify the region most applicable to the country being
evaluated. Scrutinise the tabulations in Annex 10A.1 to ensure that the underlying animal characteristics such as
weight, growth rate and milk production used to develop the emission factors are similar to the conditions in the
country. The data collected on the average annual milk production by dairy cows should be used to help select adairy cow emission factor. If necessary, interpolate between dairy cow emission factors shown in the table using
the data collected on average annual milk production per head.
Note that using the same Tier 1 emission factors for the inventories of successive years means that no allowance
is being made for changing livestock productivity, such as increasing milk productivity or trend in live weight. If
it is important to capture the trend in methane emission that results from a trend in livestock productivity, thenlivestock emissions can become a key source category based on trend and a Tier 2 calculation should be used.

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TABLE 10.10
ENTERIC FERMENTATION EMISSION FACTORS FOR TIER 1 METHOD1(KG CH4 HEAD-1 YR-1)
Livestock

Developed countries

Developing
countries

Liveweight

Buffalo55

55

300 kg

Sheep

8

5

65 kg - developed countries;
45 kg - developing countries

Goats

5

5

40 kg

Camels46

46

570 kgHorses18

18

550 kgMules and Asses

1010245 kg

Deer

20

20

120 kgAlpacas

8

8

65 kg

Swine

1.5

1.0

Poultry

Insufficient data for
calculation

Insufficient data for
calculation

Other (e.g., Llamas)

To be determined1

To be determined1

All estimates have an uncertainty of +30-50%.
Sources: Emission factors for buffalo and camels from Gibbs and Johnson (1993). Emission factors for other livestock from Crutzen etal., (1986), Alpacas from Pinares-Patino et al., 2003; Deer from Clark et al., 2003 .
1

One approach for developing the approximate emission factors is to use the Tier 1 emissions factor for an animal with a similardigestive system and to scale the emissions factor using the ratio of the weights of the animals raised to the 0.75 power. Liveweightvalues have been included for this purpose. Emission factors should be derived on the basis of characteristics of the livestock and feedof interest and should not be restricted solely to within regional characteristics.

Step 3: Total emission
To estimate total emission, the selected emission factors are multiplied by the associated animal population(Equation 10.19) and summed (Equation 10.20):
EQUATION 10.19
ENTERIC FERMENTATION EMISSIONS FROM A LIVESTOCK CATEGORY N (T ) 
Emissions = EF(T ) •  6 
 10 

Where:
Emissions = methane emissions from Enteric Fermentation, Gg CH4 yr-1
EF(T) = emission factor for the defined livestock population, kg CH4 head-1 yr-1
N(T) = the number of head of livestock species / category T in the country
T = species/category of livestock

EQUATION 10.20
TOTAL EMISSIONS FROM LIVESTOCK ENTERIC FERMENTATION

Total CH 4 Enteric = ∑ Ei
i

Where:
Total CH4Enteric = total methane emissions from Enteric Fermentation, Gg CH4 yr-1
Ei = is the emissions for the ith livestock categories and subcategories

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.11
TIER 1 ENTERIC FERMENTATION EMISSION FACTORS FOR CATTLE1
Regional characteristics
North America: Highly productive commercialized
dairy sector feeding high quality forage and grain.
Separate beef cow herd, primarily grazing with feed
supplements seasonally. Fast-growing beefsteers/heifers finished in feedlots on grain. Dairy
cows are a small part of the population.
Western Europe: Highly productive commercialised
dairy sector feeding high quality forage and grain.
Dairy cows also used for beef calf production. Verysmall dedicated beef cow herd. Minor amount offeedlot feeding with grains.
Eastern Europe: Commercialised dairy sector
feeding mostly forages. Separate beef cow herd,
primarily grazing. Minor amount of feedlot feedingwith grains.
Oceania: Commercialised dairy sector based on
grazing. Separate beef cow herd, primarily grazing
rangelands of widely varying quality. Growing
amount of feedlot feeding with grains. Dairy cows
are a small part of the population.
Latin America: Commercialised dairy sector basedon grazing. Separate beef cow herd grazing pasturesand rangelands. Minor amount of feedlot feedingwith grains. Growing non-dairy cattle comprise alarge portion of the population.
Asia: Small commercialised dairy sector. Most cattle
are multi-purpose, providing draft power and somemilk within farming regions. Small grazing
population. Cattle of all types are smaller than thosefound in most other regions.
Africa and Middle East: Commercialised dairy
sector based on grazing with low production per cow.
Most cattle are multi-purpose, providing draft powerand some milk within farming regions. Some cattlegraze over very large areas. Cattle are smaller thanthose found in most other regions.
Indian Subcontinent: Commercialised dairy sector
based on crop by-product feeding with low
production per cow. Most bullocks provide draft
power and cows provide some milk in farming
regions. Small grazing population. Cattle in thisregion are the smallest compared to cattle found in allother regions.

Cattlecategory

Emissionfactor 2,3(kg CH4
head-1 yr-1)

Dairy

128

Average milk production of
8,400 kg head-1 yr-1.

Other
Cattle53

Includes beef cows, bulls, calves,
growing steers/heifers, and feedlot
cattle.

Dairy

117

Average milk production of 6,000
kg head-1 yr-1.

Other
Cattle57

Includes bulls, calves, and growing
steers/heifers.

Dairy

99

Average milk production of
2,550 kg head-1 yr-1.

Other
Cattle58

Includes beef cows, bulls, andyoung.

Dairy

100

Average milk production of 2,200kg head-1 yr-1.

Other
Cattle60

Includes beef cows, bulls, andyoung.

Dairy

72

Average milk production of 800 kg
head-1 yr-1

Other
Cattle56

Includes beef cows, bulls, andyoung.

Dairy

68

Average milk production of
1,650 kg head-1 yr-1

Other
Cattle47

Includes multi-purpose cows, bulls,
and young

Dairy

46

Average milk production of 475 kg
head-1 yr-1

Other
Cattle31

Includes multi-purpose cows, bulls,
and young

Dairy

58

Average milk production of 900 kg
head-1 yr-1

Other
Cattle27

Includes cows, bulls, and young.
Young comprise a large portion of
the population

Comments

1

Emission factors should be derived on the basis of the characteristics of the cattle and feed of interest and need not be restricted solely towithin regional characteristics.

2

IPCC Expert Group, values represent averages within region, where applicable the use of more specific regional milk production data is
encouraged. Existing values were derived using Tier 2 method and the data in Tables 10 A.1 and 10A. 2.

3

The following assumptions have been made in deriving these values: i) mature weights of animals have been used; ii) cows have been
assumed to be non-lactating as lactation levels were low and, iii) the mix of bulls and castrates among "males" was undetermined as Cfi
value for castrates was not specified.

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Tier 2 Approach for methane emissions from Enteric Fermentation

The Tier 2 method is applied to more disaggregated livestock population categories and used to calculate
emission factors, as opposed to default values. The key considerations for the Tier 2 method are the developmentof emission factors and the collection of detailed activity data.
Step 1: Livestock population
The animal population data and related activity data should be obtained following the approach described in
Section 10.2.
Step 2: Emission factors
When the Tier 2 method is used, emission factors are estimated for each animal category using the detailed data
developed in Step 1.
The emission factors for each category of livestock are estimated based on the gross energy intake and methane
conversion factor for the category. The gross energy intake data should be obtained using the approach describedin Section 10.2. The following two sub-steps need to be completed to calculate the emission factor under the
Tier 2 method:
1. Obtaining the methane conversion factor (Ym)The extent to which feed energy is converted to CH4 depends on several interacting feed and animal factors. If
CH4 conversion factors are unavailable from country-specific research, the values provided in Table 10.12,
Cattle/Buffalo CH4 conversion factors, 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 factor 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 livestock and feed combinations. Such improvement is most needed for animals fed on
tropical pastures as the available data are sparse. For example, a recent study (Kurihara et al., 1999) observed
Ym values outside the ranges described in Table 10.12.
TABLE 10.12
CATTLE/BUFFALO CH4 CONVERSION FACTORS (YM )Livestock category

Ym b

Feedlot fed Cattle a

3.0% + 1.0%

Dairy Cows (Cattle and Buffalo) and their young

6.5% + 1.0%

Other Cattle and Buffaloes that are primarily fed low quality crop residues and byproducts

6.5% + 1.0%

Other Cattle or Buffalo – grazing

6.5% + 1.0%aWhen fed diets contain 90 percent or more concentrates.

b

The ± values represent the range.

Source: IPCC Expert Group.

Regional, national and global estimates of enteric methane generation rely on small scale determinations both of
Ym and of the influence of feed and animal properties upon Ym. Traditional methods for measuring Ym include
the use of respiration calorimeters for housing individual animals (Johnson and Johnson, 1995). A tracer
technique using SF6 enables methane emissions from individual animals to be estimated under both housed or
grazing conditions (Johnson et al., 1994). The results of recent measurements have been surveyed by Lassey( 2006) who also examines the "upscaling" of such measurements to national and global inventories.
It is also important to examine the influences of feed properties and animal attributes on Ym. Such influences areimportant to better understand the microbiological mechanisms involved in methanogenesis with a view todesigning emission abatement strategies, as well as to identify different values for Ym according to animalhusbandry practices. To date, the search for such influences is equivocal, and consequently there is little
variability evident both in the values reported in Table 10.12 as supported by the recent survey of Ym
measurements in the literature (Lassey, 2006).

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Table 10.13 proposes a common Ym value for all mature sheep irrespective of feed quality, but with different
values for mature and juvenile sheep with demarcation at 1 year of age. These values are based on data by
Lassey et al. (1997), Judd et al. (1999) and Ulyatt et al. (2002a, 2002b, 2005) and while consistent withmeasurements by other researchers (Murray et al., 1978; Leuning et al., 1999), may not span the full range of
pastures to be found. The median value is appropriate for most applications, but for poor quality feed the upper
limits may be more appropriate, and for high-digestibility high-energy feeds the lower limits may be used.
TABLE 10.13
SHEEP CH4 CONVERSION FACTORS (YM)
Categorya

Ym a

Lambs (<1 year old)

4.5% + 1.0%

Mature Sheep

6.5% + 1.0%

The + values represent the range.

Note that in some cases, CH4 conversion factors may not exist for specific livestock types. In these instances,
CH4 conversion factors from the reported livestock that most closely resembles those livestock types can bereported. For examples, CH4 conversion factors for other cattle or buffalo could be applied to estimate anemission factor for camels.
2. Emission factor development
An emission factor for each animal category should be developed following Equation 10.21:
EQUATION 10.21
CH4 EMISSION FACTORS FOR ENTERIC FERMENTATION FROM A LIVESTOCK CATEGORY Ym 
 • 365  GE • 100EF = 
55.65







Where:
EF = emission factor, kg CH4 head-1 yr-1
GE = gross energy intake, MJ head-1 day-1
Ym = methane conversion factor, per cent of gross energy in feed converted to methane
The factor 55.65 (MJ/kg CH4) is the energy content of 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 in Section 10.2.
Step 3: Total emissions
To estimate total emissions, the selected emission factors are multiplied by the associated animal population andsummed. As described above under Tier 1, the emissions estimates should be reported in gigagrams (Gg).

Potential for refinement of Tier 2 or development of a Tier 3 method to entericmethane emission inventories

Increased accuracy and identification of causes of variation in emissions are at the heart of inventory purpose.
Improvements in country methodology, whether as components of current Tier 1 or 2 or if additional refinements
are implemented (Tier 3), are encouraged.
Current Tier 1 and Tier 2 enteric methane emissions factors and estimation procedures are driven by firstestimating daily and annual gross energy consumption by individual animals within an inventory class which arethen multiplied by an estimate of CH4 loss per unit of feed (Ym). There is considerable room for improvement in2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Tier 2 prediction of both feed intake and in Ym. Factors potentially impacting feed requirements and/or
consumption that are not considered include:•

breed or genotype variation in maintenance requirement;•

heat and cold stress effects on intake and maintenance requirements; and•depression in digestibility with increasing levels of consumption, or diet composition limits to diet intake.

Likewise, a host of interacting factors that control variations in Ym are not included in Tier 2 methodology,
including:•effects of digestibility (DE%);•

diet dry matter intake as it relates to live body weight;•

diet chemical composition;•

particle passage and digestion kinetics, or plant microbial defensive compounds; and•variation in the microbial populations within the digestive tract.

Accurate estimation of diet DE% is singularly important in the estimation of feed intake and thus emissions, as
previously emphasized. A 10% error in the average diet DE% or TDN% will result in CH4 errors ranging from12 to 20% depending on beginning circumstance. The depression in DE% with increasing daily amounts of dietconsumed is not considered. This will underestimate feed intakes of high producing dairy cows consuming
mixtures of concentrates and forages, e.g., as is common in the North America and Europe, although some of theresulting error in methane emission estimate will be compensated by reductions in Ym as intake per day increases.
Methods to estimate digestibility depressions have been described (NRC, 1996; NRC, 2001).
There have been many attempts to refine estimates of Ym. Several researchers have developed models which
relate the chemical composition of the diet consumed, or in more detail, the composition of digested
carbohydrate and other chemical components to Ym. These models typically predict diet particle and chemicalcomponent rates of passage and digestion in each enteric compartment at varying intake and the resulting H2balance, volatile fatty acids, and microbial and CH4 yields. These approaches have generated Ym values that areconsistent with direct measurements using chamber and SF6 techniques.
The literature contains many examples of the positive relationship of plant cell wall digestion to high acetic topropionic end-product ratios, and to high CH4 yields. While fibrous carbohydrate digestion is undeniably thestrongest single indicator of CH4 yield, the CH4 per digested fiber is not constant, e.g., when soyhulls or beet
pulp are fed as single feed at varying levels of intake, Ym will vary from 8 to 11% when measured at restricted
feed intakes and from 5 to 6% when measure at ad libitum intakes (Kujawa, 1994; Diarra, 1994). Thus, entericfermentation of the same fibrous substrate can result in quite different Ym values. Perhaps the most severe
limitation to development of more complex prediction models lies in the difficulty of applying them to broadcountry inventories. The difficulty is to provide the data needed to drive these more complex models of feedintake or Ym. It is often difficult to define animal characteristics, productivity, and %DE accurately for a class of
livestock in a region of the country, let alone detailed carbohydrate fraction, rates of passage and digestion, etc.
The amount of global research on mitigation strategies currently going on, such as vaccines, ionophores,
polyunsaturated vegetable oils, condensed tannins etc, suggests a need to address how they should be reflected ininventory compilation at Tier 2 or Tier 3. First, the inventory should reflect only those technologies that conform
to QA/QC principles and have attracted a wide degree of international acceptance such as through peer-reviewed
articles that include a description of the technology, its efficacy and its validation under field conditions. Second,
the inventory should be accompanied by evidence of the take-up of the technology, and apply it only toemissions by those livestock where take-up can be validated. Third, for a newly implemented technology (suchas an administered dose of a mitigating agent), the inventory could also present an accompanying calculation of
the emissions in the absence of a mitigation measure in order to make transparent the magnitude of the emission
reductions that are being claimed. Mitigation measures should be supported by peer-reviewed publications.
Approaches to improve estimates of feed intake and Ym and to consider mitigation approaches are to be
encouraged, given due care on limitations of scope, production circumstance, etc. to which the predictive
relationships apply.

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10.3.3

Choice of activity data

Livestock population data should be obtained using the approach described in Section 10.2. If using default
enteric emission factors for livestock (Tables 10.10, 10.11) to estimate enteric emissions, a basic (Tier 1)
livestock population characterisation is sufficient. To estimate enteric emissions from livestock using estimationof Gross Energy Intake (Equations 10.16, 10.17 or 10.18), a Tier 2 characterisation is needed. As noted in
Section 10.2, good practice in characterising livestock populations is to conduct a single characterisation thatwill provide the activity data for all emissions sources that depend on livestock population data.

10.3.4

Uncertainty assessment

Emission factors
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%. The uncertainty under the Tier 2 method 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 thecoefficients in the various relationships that make up the net energy approach correspond to national
circumstances. Emission factor estimates using the Tier 2 method are likely to be in the order of +20%.
Inventory compilers using the Tier 2 method should undertake an analysis of uncertainties reflecting their
particular situation, and in the absence of this analysis the uncertainty under the Tier 2 method should beassumed similar to the uncertainty under the Tier 1 method.
Although a Tier 3 method has the potential to improve the accuracy of emission estimates, a substantial body ofscientific data is required to develop a viable Tier 3 method. The use of unreliable and unsubstantiated data in a
Tier 3 method could result in estimates that are inferior to Tier 2 or even Tier 1 methods. In many instances,direct measurements of methane emissions from livestock are lacking or have been conducted using a limited
number of diet types. A considerable amount of research on potential mitigation strategies is ongoing, but few
of these have been validated to the point that they can be extrapolated to non-research conditions. As thefoundational research on emission related science continues to expand, Tier 3 method should theoretically resultin the lowest degree of uncertainty.

Activity data
There will be an added uncertainty associated with the livestock and feed characterisation. Improving the
livestock and feed characterisation will often be the priority in reducing overall uncertainty. Accurate estimatesof feed digestibility (DE%) are also critical for reducing the degree of uncertainty. Uncertainty estimates can bederived from the good practice approach to agricultural census data outlined in the uncertainty section forlivestock and feed characterisation (see Section 10.2).
General information on the procedures to assess uncertainty is presented in Volume 1, Chapter 3 (Uncertainties).

10.3.5Completeness, Time series, Quality
Assurance/Quality Control and ReportingTo achieve completeness, all the major animal categories managed in the country should be considered. In theevent that animals are included in the inventory for which default data are not available and for which no
guidelines are provided, the emissions estimate should be developed using the same general principles presented
in the discussion in Section 10.2.
Care must be taken to use a consistent set of estimates for the CH4 conversion factors over time. In some cases,
there may be reasons to modify methane conversion factors over time. These changes may be due to theimplementation 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 driverof change, the data and methane conversion factors used to estimate emissions must reflect the change in farm
practices. If methane conversion factors over a time series are affected by a change in management practice
and/or the implementation of GHG mitigation measures, the inventory compiler should ensure that the inventory
data reflect these practices. The inventory text should thoroughly explain how the changes in management
practice and/or implementation of mitigation measures has affected the time series of methane conversion factors.
For general good practice guidance on developing a consistent time series, see Volume 1, Chapter 5 (TimeSeries Consistency).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

It is good practice to implement quality control checks as outlined in Volume 1, Chapter 6 (Quality
Assurance/Quality Control and Verification). In addition to the guidance in Volume 1, specific procedures of
relevance to this source category are outlined below:
A ct iv ity data ch e ck
• The inventory compiler should review livestock data collection methods, in particular checking thatlivestock subspecies data were collected and aggregated correctly. The data should be cross-checked withprevious years to ensure the data are reasonable and consistent with the expected trend. Inventory compilers
should document data collection methods, identify potential areas of bias, and evaluate therepresentativeness of the data. Population modeling can be used to support this approach.
R ev ie w of em is sio n fa cto rs• If using the Tier 2/Tier 3 method, the inventory compiler should cross-check country-specific factors against
the IPCC defaults. Significant differences between country-specific factors and default factors should be
explained and documented.
Ext er na l rev ie w
• If Tier 2/Tier 3 method is used, the inventory compiler is encouraged to conduct national and international
expert review, including from industry, academic institutions, and extension expertise.
•

It is important to maintain internal documentation on review results.

To improve transparency, emission estimates from this source category should be reported along with theactivity data and emission factors used to determine the estimates.
The following information should be documented:•

All activity data including 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; and(iii)

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

•

If Tier 1 method is used, all default emission factors used in the estimation of emissions for the specificanimal categories.

•

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

DE values estimated or taken from other studies; and(iii)

Full documentation of the data used including their references.

For inventories in which country- or region-specific emission factors are used or in which new methods,
such as Tier 3 are used, the scientific basis of these emission factors and the principles of the new methodshould be thoroughly documented. Documentation should include definitions of input parameters and a
description of the principle and process by which these emission factors and methods are derived, as well as
describing sources and magnitudes of uncertainties.

10.34

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

10.4

METHANE EMISSIONS FROM MANUREMANAGEMENT

This section describes how to estimate CH4 produced during the storage and treatment of manure, and frommanure deposited on pasture. The term ‘manure’ is used here collectively to include both dung and urine (i.e.,the solids and the liquids) produced by livestock. The emissions associated with the burning of dung for fuel are
to be reported under Volume 2 (Energy), or under Volume 5 (Waste) if burned without energy recovery. The
decomposition of manure under anaerobic conditions (i.e., in the absence of oxygen), during storage andtreatment, produces CH4. These conditions occur most readily when large numbers of animals are managed in aconfined area (e.g., dairy farms, beef feedlots, and swine and poultry farms), and where manure is disposed of inliquid-based systems. Emissions of CH4 related to manure handling and storage are reported under ‘Manure
Management.’
The main factors affecting CH4 emissions are the amount of manure produced and the portion of the manure that
decomposes anaerobically. The former depends on the rate of waste production per animal and the number ofanimals, and the latter on how the manure is managed. When manure is stored or treated as a liquid (e.g., inlagoons, ponds, tanks, or pits), it decomposes anaerobically and can produce a significant quantity of CH4. The
temperature and the retention time of the storage unit greatly affect the amount of methane produced. Whenmanure is handled as a solid (e.g., in stacks or piles) or when it is deposited on pastures and rangelands, it tends
to decompose under more aerobic conditions and less CH4 is produced.

10.4.1

Choice of method

There are three tiers to estimate CH4 emissions from livestock manure. Guidance for determining which tier to
use is shown in Figure 10.3 decision tree.
Tie r 1
A simplified method that only requires livestock population data by animal species/category and climate regionor temperature, in combination with IPCC default emission factors, to estimate emissions. Because someemissions from manure management systems are highly temperature dependent, it is good practice to estimate
the average annual temperature associated with the locations where manure is managed.
Tie r 2A more complex method for estimating CH4 emissions from manure management should be used where aparticular livestock species/category represents a significant share of a country’s emissions. This method
requires detailed information on animal characteristics and manure management practices, which is used todevelop emission factors specific to the conditions of the country.
Tie r 3
Some countries for which livestock emissions are particularly important may wish to go beyond the Tier 2
method and develop models for country-specific methodologies or use measurement–based approaches toquantify emission factors.
The method chosen will depend on data availability and national circumstances. Good practice in estimating
CH4 emissions from manure management systems entails making every effort to use the Tier 2 method,
including calculating emission factors using country-specific information. The Tier 1 method should only be
used if all possible avenues to use the Tier 2 method have been exhausted and/or it is determined that the source
is not a key category or subcategory.
Regardless of the method chosen, the animal population must first be divided into categories as described in
Section 10.2 that reflect the varying amounts of manure produced per animal.
The following four steps are used to estimate CH4 emissions from manure management:
Step 1: Collect population data from the Livestock Population Characterisation (see Section 10.2).
Step 2: Use default values or develop country-specific emission factors for each livestock subcategory in termsof kilograms of methane per animal per year.
Step 3: Multiply the livestock subcategory emission factors by the subcategory populations to estimate
subcategory emissions, and sum across the subcategories to estimate total emissions by primary livestock species.
Step 4: Sum emissions from all defined livestock species to determine national emissions.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 10.3

Decision tree for CH 4 emissions from Manure Management

Start

Do youhave a country-specificTier 3 methodology?

Yes

Estimate emissions
using Tier 3 method.
Box 3: Tier 3

No

Is a Tier 2
livestock population
characterization, available and do
you have country-specific VS rates,
MCF values, Bo values, andmanagement system
usage data?

No

Collect data forTier 2 method.

Is CH4 frommanure management a
key source category1 and is the
species a significant shareof emissions2?

Yes

No

Yes (all or some)

Estimate CH4 emissions
using Tier 2 methodwith available countryspecific inputs.
Box 2: Tier 2

Estimate CH4 emissions
using Tier 1 method
and IPCC defaults.
Box 1: Tier 1

Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.
2: As a rule of thumb, a livestock species would be significant if it accounts for 25-30% or more of emissions from the source category.

10.36

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Equation 10.22 shows how to calculate CH4 emissions from manure management:
EQUATION 10.22
CH4 EMISSIONS FROM MANURE MANAGEMENT
EF(T ) • N (T )CH 4 Manure = ∑
10 6
(T )

(

)

Where:
CH4Manure = CH4 emissions from manure management, for a defined population, Gg CH4 yr-1
EF(T) = emission factor for the defined livestock population, kg CH4 head-1 yr-1
N(T) = the number of head of livestock species/category T in the country
T = species/category of livestock

10.4.2

Choice of emission factors

The best way to determine emission factors is to conduct non-invasive or non-disturbing measurements ofemissions in actual systems representative of those in use in the country. These field results can be used todevelop models to estimate emission factors (Tier 3). Such measurements are difficult to conduct, and requiresignificant resources and expertise, and equipment that may not be available. Thus, while such an approach isrecommended to improve accuracy, it is not required for good practice. This section provides two alternatives
for developing emission factors, with the selection of emission factors depending on the method (i.e., Tier 1 orTier 2) chosen for estimating emissions.
Tie r 1
When using the Tier 1 method, methane emission factors by livestock category or subcategory are used. Default
emission factors by average annual temperature are presented in Table 10.14, Table 10.15, and Table 10.16 foreach of the recommended population subcategories. These emission factors represent the range in manure
volatile solids content and in manure management practices used in each region, as well as the difference inemissions due to temperature. Tables 10A-4 through 10A-9 located in Annex 10A.2 present the underlying
assumptions used for each region. Countries using a Tier 1 method to estimate methane emissions from manure
management should review the regional variables in these tables to identify the region that most closely matches
their animal operations, and use the default emission factors for that region.
Table 10.14 shows the default emission factors for cattle, swine, and buffalo for each region and temperatureclassification. Emission factors are listed by the annual average temperature for the climate zone where thelivestock manure is managed. The temperature data should be based on national meteorological statistics whereavailable. Countries should estimate the percentage of animal populations in different temperature zones andcompute a weighted average emission factor. Where this is not possible, the annual average temperature for theentire country could be utilized; however, this may not give an accurate estimate of emissions that are highly
sensitive to temperature variations (e.g., liquid/slurry systems).
Tables 10.15 and 10.16 present the default manure management emission factors for other animal species.
Separate emission factors are shown for developed and developing countries in Table 10.15, reflecting thegeneral differences in feed intake and feed characteristics of the animals in the two regions. Except for poultry“layers (wet),” these emission factors reflect the fact that virtually all the manure from these animals is managed
in ‘dry’ manure management systems, including pastures and ranges, drylots, and daily spreading on fields(Woodbury and Hashimoto, 1993).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10.14
MANURE MANAGEMENT METHANE EMISSION FACTORS BY TEMPERATURE FOR CATTLE, SWINE, AND BUFFALOa(KG CH4 HEAD-1 YR-1)
CH4 emission factors by average annual temperature (°C)b
Regional characteristics

North America: Liquid-based systems are commonlyused for dairy cows and swine manure. Other cattle
manure is usually managed as a solid and deposited on
pastures or ranges.

Western Europe: Liquid/slurry and pit storage systems
are commonly used for cattle and swine manure. Limited
cropland is available for spreading manure.

Eastern Europe: Solid based systems are used for the
majority of manure. About one-third of livestock manure
is managed in liquid-based systems.

Oceania: Most cattle manure is managed as a solid on
pastures and ranges, except dairy cows where there issome usage of lagoons. About half of the swine manure ismanaged in anaerobic lagoons.

Latin America: Almost all livestock manure is managed
as a solid on pastures and ranges. Buffalo manure isdeposited on pastures and ranges.

Livestock species

Cool

Temperate

Warm

≤ 10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

≥ 28

Dairy Cows

48

50

53

55

58

63

65

68

71

74

78

81

85

89

93

98

105

110

112

Other Cattle

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

Market Swine

10

11

11

12

12

13

13

14

15

15

16

17

18

18

19

20

22

23

23

Breeding Swine

19

20

21

22

23

24

26

27

28

29

31

32

34

35

37

39

41

44

45

Dairy Cows

21

23

25

27

29

34

37

40

43

47

51

55

59

64

70

75

83

90

92

Other Cattle

6

7

7

8

8

10

11

12

13

14

15

16

17

18

20

21

24

25

26

Market Swine

6

6

7

7

8

9

9

10

11

11

12

13

14

15

16

18

19

21

21

Breeding Swine

9

10

10

11

12

13

14

15

16

17

19

20

22

23

25

27

29

32

33

Buffalo

4

4

5

5

5

6

7

7

8

9

9

10

11

12

13

14

15

16

17

Dairy Cows

11

12

13

14

15

20

21

22

23

25

27

28

30

33

35

37

42

45

46

Other Cattle

6

6

7

7

8

9

10

11

11

12

13

14

15

16

18

19

21

23

23

Market Swine

3

3

3

3

3

4

4

4

4

5

5

5

6

6

6

7

10

10

10

Breeding Swine

4

5

5

5

5

6

7

7

7

8

8

9

9

10

11

12

16

17

17

Buffalo

5

5

5

6

6

7

8

8

9

10

11

11

12

13

15

16

17

19

19

Dairy Cows

23

24

25

26

26

27

28

28

28

29

29

29

29

29

30

30

31

31

31

Other Cattle

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

Market Swine

11

11

12

12

12

13

13

13

13

13

13

13

13

13

13

13

13

13

13

Breeding Swine

20

20

21

21

22

22

23

23

23

23

23

24

24

24

24

24

24

24

24

Dairy Cows

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

Other Cattle

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Swine

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

Buffalo

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Chapter 10: Emissions from Livestock and Manure Management
TABLE 10.14
MANURE MANAGEMENT METHANE EMISSION FACTORS BY TEMPERATURE FOR CATTLE, SWINE, AND BUFFALOa(KG CH4 HEAD-1 YR-1)
CH4 emission factors by average annual temperature (°C)b
Regional characteristics

Africa: Most livestock manure is managed as a solid on
pastures and ranges. A smaller, but significant fraction isburned as fuel.

Middle East: Over two-thirds of cattle manure isdeposited on pastures and ranges. About one-third ofswine manure is managed in liquid-based systems. Buffalo
manure is burned for fuel or managed as a solid.

Asia: About half of cattle manure is used for fuel with theremainder managed in dry systems. Almost 40% of swine
manure is managed as a liquid. Buffalo manure ismanaged in drylots and deposited in pastures and ranges.

Indian Subcontinent: About half of cattle and buffalo
manure is used for fuel with the remainder managed in dry
systems. About one-third of swine manure is managed as aliquid.

Livestock species

Cool

Temperate

Warm

≤ 10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

≥ 28

Dairy Cows

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Other Cattle

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Swine

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

Dairy Cows

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

3

3

Other Cattle

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Swine

1

1

1

2

2

2

2

2

3

3

3

3

4

4

4

5

5

5

6

Buffalo

4

4

4

4

4

5

5

5

5

5

5

5

5

5

5

5

5

5

5

Dairy Cows

9

10

10

11

12

13

14

15

16

17

18

20

21

23

24

26

28

31

31

Other Cattle

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Swine

2

2

2

2

2

3

3

3

3

4

4

4

5

5

5

6

6

7

7

Buffalo

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

Dairy Cows

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

6

6

Other Cattle

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

Swine

2

2

3

3

3

3

3

3

4

4

4

4

4

5

5

5

6

6

6

Buffalo

4

4

4

4

4

5

5

5

5

5

5

5

5

5

5

5

5

5

5

Source: See Annex 10A.2 , Tables 10A-4 to 10A-8 for derivation of these emission factors,
The uncertainty in these emission factors is +30 %.
aWhen selecting a default emission factor, be sure to consult the supporting tables in Annex 10A.2 for the distribution of manure management systems and animal waste characteristics used to estimate emissions. Selectan emission factor for a region that most closely matches your own in these characteristics.

b

All temperatures are not necessarily represented within every region. For example, there are no significant warm areas in Eastern or Western Europe. Similarly, there are no significant cool areas in Africa and the Middle
East.
Note: Significant buffalo populations do not exist in North America, Oceania, or Africa.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10.15
MANURE MANAGEMENT METHANE EMISSION FACTORS BY TEMPERATURE FOR SHEEP, GOATS, CAMELS, HORSES, MULES
AND ASSES, AND POULTRYa (KG CH4 HEAD-1 YR-1)
CH4 emission factor by average annual temperature (°C)

Livestock

Cool (<15˚C)

Temperate (15 to 25˚C)

Warm (>25˚C)

Developed countries

0.19

0.28

0.37

Developing countries

0.10

0.15

0.20

Developed countries

0.13

0.20

0.26

Developing countries

0.11

0.17

0.22

Developed countries

1.58

2.37

3.17

Developing countries

1.28

1.92

2.56

Developed countries

1.56

2.34

3.13

Developing countries

1.09

1.64

2.19

Developed countries

0.76

1.10

1.52

Developing countries

0.60

0.90

1.20

Layers (dry)b

0.03

0.03

0.03

Layers (wet)c

1.2

1.4

1.4

Broilers

0.02

0.02

0.02

Turkeys

0.09

0.09

0.09

Ducks

0.02

0.03

0.03

0.01

0.02

0.02

Sheep

Goats

CamelsHorsesMules and Asses

Poultry
Developed countries

Developing countries

The uncertainty in these emission factors is +30 %.
Sources: Emission factors developed from: feed intake values and feed digestibilities used to develop the enteric fermentation emission
factors (see Annex 10A.1); Except for poultry in developed countries, methane conversion factor (MCF), and maximum methane
producing capacity (Bo) values reported in Woodbury and Hashimoto (1993). Poultry for developed countries was subdivided into fivecategories. Layers (dry) represent layers in a "without bedding" waste management system; layers (wet) represent layers in an anaerobic
lagoon waste management system. For layers, volatile solids (VS) are values reported in USDA (1996); typical animal mass values arefrom ASAE (1999); and Bo values for Layers are values reported by Hill (1982). For broilers and turkeys, Bo values are from Hill (1984);
typical animal mass values are from ASAE (1999); and VS values are those reported in USDA (1996). Bo values for ducks weretransferred from broilers and turkeys; typical animal mass values are from MWPS-18; and VS values are from USDA, AWMFH.
Typical mass of sheep, goats and horses, and VS and Bo values of goats and horses for developed countries updated according to theanalysis of GHG inventories of Annex I countries. All manure, with the exception of Layers (wet), is assumed to be managed in dry
systems, which is consistent with the manure management system usage reported in Woodbury and Hashimoto (1993).
aWhen selecting a default emission factor, be sure to consult the supporting tables in Annex 10A.2 for the distribution of manure
management systems and animal waste characteristics used to estimate emissions. Select an emission factor for a region that mostclosely matches your own in these characteristics.

b

Layer operations that manage dry manure.

c

Layer operations that manage manure as a liquid, such as stored in an anaerobic lagoon.

10.40

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.16
MANURE MANAGEMENT METHANE EMISSION FACTORS FOR DEER, REINDEER, RABBITS, AND FUR-BEARING ANIMALS
CH4 emission factor(kg CH4 head-1 yr-1)

Livestock
Deera

0.22

Reindeerb

0.36

Rabbitsc

0.08

Fur-bearing animals (e.g., fox, mink)b

0.68

The uncertainty in these emission factors is +30 %.
aSneath et al. (1997)b

Estimations of Agricultural University of Norway, Institute of Chemistry and Biotechnology, Section for Microbiology.

cJudgement of the IPCC Expert GroupTie r 2The Tier 2 method is applicable when Manure Management is a key source or when the data used to develop thedefault values do not correspond well with the country's livestock and manure management conditions. Because
cattle, buffalo and swine characteristics and manure management systems can vary significantly by country,
countries with large populations of these animals should consider using the Tier 2 method for estimating
methane emissions. The Tier 2 method relies on two primary types of inputs that affect the calculation of
methane emission factors from manure:
Manure characteristics: Includes the amount of volatile solids (VS) produced in the manure and the maximum
amount of methane able to be produced from that manure (Bo). Production of manure VS can be estimated basedon feed intake and digestibility, which are the variables also used to develop the Tier 2 enteric fermentation
emission factors. Alternatively, VS production rates can be based on laboratory measurements of livestock
manure. Bo varies by animal species and feed regimen and is a theoretical methane yield based on the amount of
VS in the manure. Bedding materials (straw, sawdust, chippings, etc.) are not included in the VS modelled
under the Tier 2 method. The type and use of these materials is highly variable from country to country. Since
they typically are associated with solid storage systems, their contribution would not add significantly to overall
methane production.
Manure management system characteristics: Includes the types of systems used to manage manure and asystem-specific methane conversion factor (MCF) that reflects the portion of Bo that is achieved. Regional
assessments of manure management systems are used to estimate the portion of the manure that is handled witheach manure management technique. A description of manure management systems is included in Table 10.18.
The system MCF varies with the manner in which the manure is managed and the climate, and can theoreticallyrange from 0 to 100%. Both temperature and retention time play an important role in the calculation of the MCF.
Manure that is managed as a liquid under warm conditions for an extended period of time promotes methane
formation. These manure management conditions can have high MCFs, of 65 to 80%. Manure managed as dry
material in cold climates does not readily produce methane, and consequently has an MCF of about 1%.
Development of Tier 2 emission factors involves determining a weighted average MCF using the estimates ofthe manure managed by each waste system within each climate region. The average MCF is then multiplied by
the VS excretion rate and the Bo for the livestock categories. In equation form, the estimate is as follows:
EQUATION 10.23
CH4 EMISSION FACTOR FROM MANURE MANAGEMENTMCFS , kEF(T ) = VS (T ) • 365 •  Bo (T ) • 0.67 kg / m 3 • ∑• MS(T , S , k ) 
100
S ,k



(

)

Where:
EF(T) = annual CH4 emission factor for livestock category T, kg CH4 animal-1 yr-1
VS(T) = daily volatile solid excreted for livestock category T, kg dry matter animal-1 day-1
365 = basis for calculating annual VS production, days yr-1
Bo(T) = maximum methane producing capacity for manure produced by livestock category T, m3 CH4 kg-1
of VS excreted

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0.67 = conversion factor of m3 CH4 to kilograms CH4
MCF(S,k) = methane conversion factors for each manure management system S by climate region k, %MS(T,S,k) = fraction of livestock category T's manure handled using manure management system S inclimate region k, dimensionless
Even when the level of detail presented in the Tier 2 method is not possible in some countries, country-specificdata elements such as animal mass, VS excretion, and others can be used to improve emission estimates. If
country-specific data are available for only a portion of these variables, countries are encouraged to calculate
country-specific emission factors, using the data in Tables 10A-4 through 10A-9 to fill gaps.
Measurement programs can be used to improve the basis for making the estimates. In particular, measurementsof emissions from manure management systems under field conditions are useful to verify MCFs. Also,
measurements of Bo from livestock in tropical regions and for varying diet regimens are needed to expand therepresentativeness of the default factors.
As emissions can vary significantly by region and livestock species/category, emission estimates should reflect
as much as 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 updated periodically to account for changes in manure characteristics andmanagement practices. These revisions should be based on reliable scientifically reviewed data. Frequentmonitoring is desirable to verify key model parameters and to track changing trends in the livestock industry.

VS excretion rates

Volatile solids (VS) are the organic material in livestock manure and consist of both biodegradable and nonbiodegradable fractions. The value needed for the Equation 10.23 is the total VS (both degradable and nonbiodegradable fractions) as excreted by each animal species since the Bo values are based on total VS entering
the systems. The best way to obtain average daily VS excretion rates is to use data from nationally publishedsources. If average daily VS excretion rates are not available, country-specific VS excretion rates can be
estimated from feed intake levels. Feed intake for cattle and buffalo can be estimated using the ‘Enhanced’
characterisation method described in Section 10.2. This will also ensure consistency in the data underlying theemissions estimates. For swine, country-specific swine production data may be required to estimate feed intake.
The VS content of manure equals the fraction of the diet consumed that is not digested and thus excreted as fecal
material which, when combined with urinary excretions, constitutes manure. Countries should estimate grossenergy (GE) intake (Section 10.2, Equation 10.16) and its fractional digestibility, DE, in the process of
estimating enteric methane emissions.
Once these are estimated, the VS excretion rate is estimated as:
EQUATION 10.24
VOLATILE SOLID EXCRETION RATES  1 − ASH  DE % 
VS = GE • 1 −
 + (UE • GE ) • 

100 


  18.45 

Where:
VS = volatile solid excretion per day on a dry-organic matter basis, kg VS day-1
GE = gross energy intake, MJ day-1
DE% = digestibility of the feed in percent (e.g. 60%)
(UE • GE) = urinary energy expressed as fraction of GE. Typically 0.04GE can be considered urinaryenergy excretion by most ruminants (reduce to 0.02 for ruminants fed with 85% or more grain in thediet or for swine). Use country-specific values where available.
ASH = the ash content of manure calculated as a fraction of the dry matter feed intake (e.g., 0.08 forcattle). Use country-specific values where available.
18.45 = conversion factor for dietary GE per kg of dry matter (MJ kg-1). This value is relatively constantacross a wide range of forage and grain-based feeds commonly consumed by livestock.
Representative DE% values for various livestock categories are provided in Section 10.2, Table 10.2 of thisreport. The value for ash content fraction can range substantially between livestock types and should reflectnational circumstances.

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Chapter 10: Emissions from Livestock and Manure Management

B o values

The maximum methane-producing capacity of the manure (Bo) varies by species and diet. The preferred method
to obtain Bo measurement values is to use data from country-specific published sources, measured with astandardised method. It is important to standardise the Bo measurement, including the method of sampling, andto confirm if the value is based on total as-excreted VS or biodegradable VS, since the Tier 2 calculation is based
on total as-excreted VS. If country-specific Bo measurement values are not available, default values are provided
in Tables 10A-4 through 10A-9.

MCFs

Default methane conversion factors (MCFs) are provided in Table 10.17 for different manure management
systems and by annual average temperatures. MCFs are determined for a specific manure management system
and represent the degree to which Bo is achieved. The amount of methane generated by a specific manure
management system is affected by the extent of anaerobic conditions present, the temperature of the system, andthe retention time of organic material in the system. Default MCF values for lagoons presented in Table 10.17
include the effect of longer retention times, and as a result, are higher than other systems under mostcircumstances.
Since liquid-based systems are very sensitive to temperature effects, where possible default MCF values forthese systems have been presented in Table 10.17 for specific annual average temperatures in each climate range.
While these temperature ranges should cover most climate conditions, areas that have extreme high or low
annual average temperatures outside the 10 to 28 degree Celsius range should utilize the end-of-range (i.e., 10 or
28 degree) values or investigate developing country-specific values.
These default values may not encompass the potentially wide variation within the defined categories ofmanagement systems. Therefore, country-specific MCFs that reflect the specific management systems used inparticular countries or regions should be developed if possible. This is particularly important for countries withlarge animal populations or with multiple climate regions. In such cases, and if possible, field measurements
should be conducted for each climate region to replace the default MCF values. Measurements should include
the following factors:•

Timing of storage/application;•

Feed and animal characteristics at the measurement site (see Section 10.2 for the type of data that would be
pertinent);•

Length of storage;•

Manure characteristics (e.g., VS influent and effluent concentrations for liquid systems);•

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; and•Seasonal temperature variation.

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TABLE 10.17
MCF VALUES BY TEMPERATURE FOR MANURE MANAGEMENT SYSTEMSMCFs by average annual temperature (°C)
Systema

Cool
≤ 10

11

12

Temperate
13

14

15

16

17

18

19

20

21

Warm
22

23

24

25

26

27

Source and comments≥ 28
Judgement of IPCC Expert Group in
combination with Hashimoto and Steed(1994).

Pasture/Range/Paddock

1.0%

1.5%

2.0%

Daily spread

0.1%

0.5%

1.0%

Hashimoto and Steed (1993).

Solid storage

2.0%

4.0%

5.0%

Judgement of IPCC Expert Group in
combination with Amon et al. (2001),
which shows emissions of approximately
2% in winter and 4% in summer. Warmclimate is based on judgement of IPCC
Expert Group and Amon et al. (1998).

Dry lot

1.0%

1.5%

2.0%

Judgement of IPCC Expert Group in
combination with Hashimoto and Steed(1994).

With
natural10%
crust cover

11% 13% 14% 15% 17%

18%

20%

22%

24%

26%

29%

31%

34%

37%

41%

44%

48%

50%

Liquid/Slurry

Judgement of IPCC Expert Group in
combination with Mangino et al. (2001)
and Sommer (2000).
The estimatedreduction due to the crust cover (40%) isan annual average value based on a limited
data set and can be highly variable
dependent on temperature, rainfall, andcomposition.
When slurry tanks are used as fed-batch
storage/digesters,
MCF
shouldbecalculated according to Formula 1.

Without
natural17%crust cover

19% 20% 22% 25% 27%

2006 IPCC Guidelines for National Greenhouse Gas Inventories

29%

32%

35%

39%

42%

46%

50%

55%

60%

65%

71%

78%

80%

Judgement of IPCC Expert Group in
combination with Mangino et al. (2001).
When slurry tanks are used as fed-batch
storage/digesters,
MCF
shouldbecalculated according to Formula 1.

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Chapter 10: Emissions from Livestock and Manure Management
TABLE 10.17 (CONTINUED)MCF VALUES BY TEMPERATURE FOR MANURE MANAGEMENT SYSTEMSMCFs by average annual temperature (°C)
SystemaCool≤ 10

Uncovered anaerobic lagoon

66%

11

12

Temperate
13

14

15

68% 70% 71% 73% 74%

16

75%

17

76%

18

77%

19

77%

20

78%

21

78%

Warm
22

78%

23

79%

24

79%

25

79%

26

79%

27

80%

Source and comments
≥ 28

80%

Judgement of IPCC Expert Group in
combination with Mangino et al. (2001).
Uncovered lagoon MCFs vary based on
several factors, including temperature,
retention time, and loss of volatile solids
from the system (through removal of
lagoon effluent and/or solids).
Judgement of IPCC Expert Group in
combination with Moller et al. (2004) and
Zeeman (1994).

< 1 month3%3%Note that the ambient temperature, not thestable temperature is to be used for
determining the climatic conditions. Whenpits used as fed-batch storage/digesters,
MCF should be calculated according to
Formula 1.

30%

Pit storage below animal
confinements> 1 month17%

19% 20% 22% 25% 27%

2006 IPCC Guidelines for National Greenhouse Gas Inventories

29%

32%

35%

39%

42%

46%

50%

55%

60%

65%

71%

78%

80%

Judgement of IPCC Expert Group in
combination with Mangino et al. (2001).
Note that the ambient temperature, not thestable temperature is to be used for
determining the climatic conditions. Whenpits used as fed-batch storage/digesters,
MCF should be calculated according to
Formula 1.

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TABLE 10.17 (CONTINUED)MCF VALUES BY TEMPERATURE FOR MANURE MANAGEMENT SYSTEMSMCFs by average annual temperature (°C)
Systema

Cool
≤ 10

11

12

Temperate
13

14

15

16

17

18

19

20

21

Warm
22

23

24

25

26

27

Source and comments≥ 28

Anaerobic digester

0-100%

0-100%

0-100%

Should be subdivided in different
categories, considering amount of recoveryof the biogas, flaring of the biogas andstorage after digestion. Calculation with
Formula 1.

Burned for fuel

10%

10%

10%

Judgement of IPCC Expert Group in
combination with Safley et al. (1992).

30%

Judgement of IPCC Expert Group in
combination with Moller et al. (2004).
Expect emissions to be similar, andpossibly greater, than pit storage,
depending on organic content and moisture
content.

Cattle and Swine deep
bedding< 1 monthCattle and Swine deep
bedding (cont.)> 1 month

3%

17%

19%

20%

3%

22%

25%

27%

29%

32%

35%

39%

42%

46%

50%

55%

60%

65%

71%

78%

80%

Judgement of IPCC Expert Group
combination with Mangino et al. (2001).

inComposting - In-vesselb

0.5%

0.5%

0.5%

Judgement of IPCC Expert Group and Amon etal. (1998). MCFs are less than half of solid
storage. Not temperature dependant.

Composting - Static pileb

0.5%

0.5%

0.5%

Judgement of IPCC Expert Group and Amon etal. (1998). MCFs are less than half of solid
storage. Not temperature dependant.

Composting - Intensive windrowb

0.5%

1.0%

1.5%

Judgement of IPCC Expert Group and Amon etal. (1998). MCFs are slightly less than solid
storage. Less temperature dependant.

Composting – Passive windrowb

0.5%

1.0%

1.5%

Judgement of IPCC Expert Group and Amon etal. (1998). MCFs are slightly less than solid
storage. Less temperature dependant.

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Chapter 10: Emissions from Livestock and Manure Management
TABLE 10.17 (CONTINUED)MCF VALUES BY TEMPERATURE FOR MANURE MANAGEMENT SYSTEMSMCFs by average annual temperature (°C)
Systema

Cool
≤ 10

11

12

Temperate
13

14

15

16

17

18

19

20

21

Warm
22

23

24

25

26

27

Source and comments≥ 28

Poultry manure with litter

1.5%

1.5%

1.5%

Judgement of IPCC Expert Group. MCFs are
similar to sol id storage but with generally
constant warm temperatures.

Poultry manure without litter

1.5%

1.5%

1.5%

Judgement of IPCC Expert Group. MCFs are
similar to dry lot at a warm climate.

0%

MCFs are near zero. Aerobic treatment can resultin the accumulation of sludge which may betreated in other systems. Sludge requires removal
and has large VS values. It is important to
identify the next management process for thesludge and estimate the emissions from thatmanagement process if significant.

Aerobic treatment

0%0%Formula 1 (Timeframe for inputs should reflect operating period of digester):
MCF = [{CH4 prod - CH4 used - CH4 flared + (MCFstorage /100 * Bo * VSstorage * 0.67 )}/ (Bo* VSstorage * 0.67)] *100
Where:
CH4 prod = methane production in digester , (kg CH4) . 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, (kg CH4)CH4 flared = amount of methane flared, (kg CH4)
MCFstorage = MCF for CH4 emitted during storage of digested manure (%)
VSstorage = amount of VS excreted that goes to storage prior to digestion (kg VS)
When a gas tight storage is included: MCFstorage = 0 ; otherwise MCFstorage = MCF value for liquid storagea Definitions for manure management systems are provided in Table 10. 18.
b Composting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source typically at thermophilic temperatures produced by microbial heat production.

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10.4.3

Choice of activity 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 Section 10.2. As noted in
Section 10.2, it is good practice to conduct a single livestock 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 ofdisaggregation in the livestock population data required to estimate emissions from manure management, maydiffer from those used 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 entericfermentation estimate could be aggregated to broader categories that are sufficient for this source category. For
other livestock species, such as swine, it may be preferable to have more disaggregation of weight categories formanure management calculations than for enteric fermentation. However, consistency in livestock categories
should be retained throughout the inventory.
Inventory agencies in countries with varied climatic conditions are encouraged to obtain population data for eachmajor climatic zone. In addition, where possible, the associated annual average temperature for locations where
livestock manure is managed in liquid-based systems (e.g., pits, tanks, and lagoons) should be obtained. This
will allow more specific selection of default factors or MCF values for those systems more sensitive totemperature changes. Ideally, the regional population breakdown can be obtained from published national
livestock statistics, and the temperature data from national meteorological statistics. If regional data are notavailable, 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.
To implement the Tier 2 method, the portion of manure managed in each manure management system must also
be collected for each representative animal species. Table 10.18 summarizes the main types of manure
management systems. 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. Notethat in some cases, manure may be managed in several types of manure management systems. For example,
manure flushed from a dairy freestall barn to an anaerobic lagoon may first pass through a solids separation unit
where some of the manure solids are removed and managed as a solid. Therefore, it is important to carefully
consider the fraction of manure that is managed in each type of system.
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. Volume 1, Chapter 2 Approaches to Data Collection
describes how to elicit expert judgement. Similar expert elicitation protocols can be used to obtain manure
management system distribution data.

10.4.4

Uncertainty assessment

EMISSION FACTORS
There are large uncertainties associated with the default emission factors for Tier 1 (see Tables 10.14 to 10.16).
The uncertainty range for the default factors is estimated to be +30%. Improvements achieved by Tier 2
methodologies are estimated to reduce uncertainty ranges in the emission factors to +20%. Accurate and welldesigned emission measurements from well characterised types of manure and manure management systems canhelp reduce these uncertainties further. These measurements must account for temperature, moisture conditions,aeration, VS content, duration of storage, and other aspects of treatment.
The default values may have a large uncertainty for an individual country because they may not reflect the
specific manure management conditions present within the country. Uncertainties can be reduced by developing
and using MCF, Bo, and VS values that reflect country/region specific conditions.

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TABLE 10.18
DEFINITIONS OF MANURE MANAGEMENT SYSTEMS
SystemDefinition

Pasture/Range/Paddock

The manure from pasture and range grazing animals is allowed to lie as deposited, and is not
managed.

Daily spread

Manure is routinely removed from a confinement facility and is applied to cropland or pasture
within 24 hours of excretion.

Solid storage

The storage of manure, typically for a period of several months, in unconfined piles or stacks.
Manure is able to be stacked due to the presence of a sufficient amount of bedding material orloss of moisture by evaporation.

Dry lot

A paved or unpaved open confinement area without any significant vegetative cover where
accumulating manure may be removed periodically.

Liquid/Slurry

Manure is stored as excreted or with some minimal addition of water in either tanks or earthen
ponds outside the animal housing, usually for periods less than one year.

Uncovered anaerobic
lagoonA type of liquid storage system designed and operated to combine waste stabilization andstorage. Lagoon supernatant is usually used to remove manure from the associated confinement
facilities to the lagoon. Anaerobic lagoons are designed with varying lengths of storage (up to ayear or greater), depending on the climate region, the volatile solids loading rate, and otheroperational factors. The water from the lagoon may be recycled as flush water or used to irrigate
and fertilise fields.

Pit storage below
animal confinementsCollection and storage of manure usually with little or no added water typically below a slatted
floor in an enclosed animal confinement facility, usually for periods less than one year.

Anaerobic digester

Animal excreta with or without straw are collected and anaerobically digested in a large
containment vessel or covered lagoon. Digesters are designed and operated for waste
stabilization by the microbial reduction of complex organic compounds to CO2 and CH4, whichis captured and flared or used as a fuel.

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

Cattle and Swine deep
bedding

As manure accumulates, bedding is continually added to absorb moisture over a production cycle
and possibly for as long as 6 to 12 months. This manure management system also is known as abedded pack manure management system and may be combined with a dry lot or pasture.

Composting - invessela

Composting, typically in an enclosed channel, with forced aeration and continuous mixing.

Composting - Static
pilea

Composting in piles with forced aeration but no mixing.

Composting - Intensive
Composting in windrows with regular (at least daily) turning for mixing and aeration.
windrowa
Composting - Passive
windrowa

Composting in windrows with infrequent turning for mixing and aeration.

Poultry manure withlitterSimilar to cattle and swine deep bedding except usually not combined with a dry lot or pasture.
Typically used for all poultry breeder flocks and for the production of meat type chickens(broilers) and other fowl.

May be similar to open pits in enclosed animal confinement facilities or may be designed andPoultry manure without operated to dry the manure as it accumulates. The latter is known as a high-rise manure
littermanagement system and is a form of passive windrow composting when designed and operated
properly.
Aerobic treatmenta

The biological oxidation of manure collected as a liquid with either forced or natural aeration.
Natural aeration is limited to aerobic and facultative ponds and wetland systems and is due
primarily to photosynthesis. Hence, these systems typically become anoxic during periods
without sunlight.

Composting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source typicallyat thermophilic temperatures produced by microbial heat production.

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ACTIVITY DATA – LIVESTOCK POPULATIONS
See Section 10.2 Livestock and Feed Characterisation for discussion on uncertainty of animal population andcharacterisation data.

ACTIVITY DATA – MANURE MANAGEMENT SYSTEM USAGE
The uncertainty of the manure management system usage data will depend on the characteristics of eachcountry's livestock industry and how information on manure management is collected. For example, forcountries that rely almost exclusively on one type of management system, such as pasture and range, theuncertainty associated with management system usage data can be 10% or less. However, for countries where
there is a wide variety of management systems used with locally different operating practices, the uncertainty
range in management system usage data can be much higher, in the range of 25% to 50%, depending on theavailability of reliable and representative survey data that differentiates animal populations by system usage.
Preferably, each country should estimate the uncertainty associated with their management system usage data byusing the methods described in Volume 1, Chapter 3.

10.4.5Completeness, Time series, Quality assurance /Quality control and Reporting

A complete inventory should estimate CH4 emissions from all systems of manure management for all livestock
species/categories identified in Section 10.2. Countries are encouraged to use manure management systemdefinitions that are consistent with those presented in Table 10.18 to ensure that all types of systems are being
accounted for. Population data should be cross-checked between main reporting mechanisms (such as FAO andnational agricultural statistics databases) to ensure that information used in the inventory is complete andconsistent. Because of the widespread availability of the FAO database of livestock information, most countries
should be able to prepare, at a minimum, Tier 1 estimates for the major livestock categories. For more
information regarding the completeness of livestock characterisation, see Section 10.2.
Developing a consistent time series of emission estimates for this source category requires, at a minimum,collection of an internally consistent time series of livestock population statistics. General guidance on thedevelopment of a consistent time series is addressed in Volume 1, Chapter 5 (Time Series Consistency).
If significant changes in manure management practices have occurred over time, the Tier 1 method will notprovide an accurate time series of emissions (since the Tier 1 default factors are based on a historical set ofparameters), and the Tier 2 method should be considered. When developing a time series for the Tier 2 method it
is also necessary to collect country-specific manure management system data. In cases when manure
management system data are not available for some period during the time series, trends can be used to
extrapolate data from a sample area or region to the entire country, if climatic conditions are similar (i.e.,
temperature and rainfall). National livestock experts from government, industry, or universities should be
consulted where possible to develop trends in management system usage and characteristics.
If the emission estimation method has changed, historical data that are required by the current method should becollected and used to recalculate emissions for that period. If such data are not available, it may be appropriate tocreate a trend with recent data and use the trend to back-estimate management practices for the time series. For
example, it may be known that certain livestock industries are converting to more intensive management systems
in lieu of grazing. Historically, this changeover should be captured in the time series of emissions, throughmodifications to the manure management system allocation. It may be necessary to base this allocation on expert
judgment from national experts where extensive survey data are not available. Volume 1, Chapter 5 provides
additional guidance on how to address recalculation issues. Also, Section 10.2 suggests approaches for the
animal population aspects. The inventory text should thoroughly explain how the change in farm practices orimplementation of mitigation measures has affected the time series of activity data or emission factors.
It is good practice to implement general quality control checks as outlined in Volume 1, Chapter 6, Quality
Assurance/Quality Control and Verification, and expert review of the emission estimates. Additional quality
control checks and quality assurance procedures may also be applicable, particularly if higher tier methods areused to determine emissions from this source. The general QA/QC related to data processing, handling, andreporting should be supplemented with procedures discussed below.

ACTIVITY DATA CHECK•The inventory agency should review livestock data collection methods, in particular checking that livestocksubspecies data were collected and aggregated correctly. The data should be cross-checked with previous
years to ensure the data are reasonable and consistent with the expected trend. Inventory agencies should
document data collection methods, identify potential areas of bias (e.g., systematic under-reporting of

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Chapter 10: Emissions from Livestock and Manure Management

animal populations to statistical agencies by individual livestock owners), and evaluate therepresentativeness of the data.
•

Manure management system allocation should be reviewed on a regular basis to determine if changes in the
livestock industry are being captured. Conversion from one type of management system to another, andtechnical modifications to system configuration and performance, should be captured in the systemmodeling for the affected livestock.

•

National agricultural policy and regulations may have an effect on parameters that are used to calculate
manure emissions, and should be reviewed regularly to determine what impact they may have. For example,
guidelines to reduce manure runoff into water bodies may cause a change in management practices, and thus
affect the MCF value for a particular livestock category. Consistency should be maintained between theinventory and ongoing changes in agricultural practices.

REVIEW OF EMISSION FACTORS•

If using the Tier 1 method (using default IPCC emission factors), the inventory agency should evaluate how
well the default VS excretion rates, Bo values, and manure management practices represent the defined
animal population and manure characteristics of the country. This should be done by reviewing thebackground information from Tables 10A-4 to 10A-9 to see how well the default input parameters match theinventory area. If there is not a good match, substitution of more appropriate country-specific parameters
can be used to develop an improved emission factor.

•

If using the Tier 2 method, the inventory agency should cross-check the country-specific parameters (e.g.,
VS excretion rates, Bo, and MCF) against the IPCC defaults. Significant differences between countryspecific parameters and default parameters should be explained and documented.

•

If using the Tier 2 method, derivation of VS rates should be compared to background assumptions used forthe enteric fermentation Tier 2 inventory where applicable. For example, the gross energy and digestible
energy components used in the enteric fermentation inventory can be used to cross-check independentlyderived VS rates. Application of Equation 10.24 (Volatile solid excretion rates) can be used in this case forsuch a cross-comparison on ruminants. For all animals, on a gross basis, VS rates should be consistent withthe feed intake of the animal (i.e., waste energy should not exceed intake energy) and be consistent with therange of DE% values reported in Section 10.2, Table 10.2 of this report.

•

Whenever possible, available measurement data, even if they represent only a small sample of systems,
should be reviewed relative to assumptions for MCF values and CH4 production estimates. Representative
measurement data may provide insights into how well current assumptions predict CH4 production frommanure management systems in the inventory area, and how certain factors (e.g., temperature, system
configuration, retention time) are affecting emissions. Because of the relatively small amount of
measurement data available for these systems worldwide, any new results can improve the understanding ofthese emissions and possibly their prediction.

EXTERNAL REVIEW•The inventory agency should utilise experts in manure management and animal nutrition to conduct expert
peer review of the methods and data used. While these experts may not be familiar with greenhouse gas
emissions, their knowledge of key input parameters to the emission calculation can aid in the overall
verification of the emissions. For example, animal nutritionists can evaluate VS production rates to see ifthey are consistent with feed utilization research for certain livestock species. Practicing farmers can provide
insights into actual manure management techniques, such as storage times and mixed-system usage.
Wherever possible, these experts should be completely independent of the inventory process in order toallow a true external review.

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Volume 1, Chapter 6 (Quality assurance/Quality control and Verification). Whencountry-specific data (e.g., emission factors, manure management practices, and manure characteristics such as
VS and Bo) have been used, the derivation of or references for these data should be clearly documented andreported along with the inventory results under the appropriate IPCC source category. To improve transparency,
emission estimates from this source category should be reported along with the activity data and emission factors
used to determine the estimates.
The following information should be documented:•

All activity data (e.g., livestock population data by species/category and by region), including sources used,complete citations for the statistical database from which data were collected, and (in cases where activity

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Volume 4: Agriculture, Forestry and Other Land Usedata were not available directly from databases) the information and assumptions that were used to derive
the activity data.
•

Climatic conditions (e.g., average temperature during manure storage) in regions if applicable.

•

Manure management system data, by livestock species/category and by region, if applicable. If manure
management systems different than those defined in this chapter are used, these should be described.

•

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

•

Emission factors documentation, including:(i)

References for the emission factors that were used (IPCC default or otherwise); and(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 here wereused).

•

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

•

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

VS and Bo values for all livestock population species/category in inventory, whether countryspecific, region-specific, or IPCC default; and(ii)

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

10.5

N 2 O EMISSIONS FROM MANUREMANAGEMENT

The section describes how to estimate the N2O produced, directly and indirectly, during the storage andtreatment of manure before it is applied to land or otherwise used for feed, fuel, or construction purposes. The
term ‘manure’ is used here collectively to include both dung and urine (i.e., the solids and the liquids) producedby livestock. The N2O emissions generated by manure in the system ‘pasture, range, and paddock’ occur directly
and indirectly from the soil, and are therefore reported under the category ‘N2O Emissions from Managed Soils’(see Chapter 11, Section 11.2). The emissions associated with the burning of dung for fuel are to be reported
under ‘Fuel Combustion’ (see Volume 2: Energy), or under ‘Waste Combustion’ (see Volume 5: Waste) if
burned without energy recovery.
Direct N2O emissions occur via combined nitrification and denitrification of nitrogen contained in the manure.
The emission of N2O from manure during storage and treatment depends on the nitrogen and carbon content ofmanure, and on the duration of the storage and type of treatment. Nitrification (the oxidation of ammonia
nitrogen to nitrate nitrogen) is a necessary prerequisite for the emission of N2O from stored animal manures.
Nitrification is likely to occur in stored animal manures provided there is a sufficient supply of oxygen.
Nitrification does not occur under anaerobic conditions. Nitrites and nitrates are transformed to N2O anddinitrogen (N2) during the naturally occurring process of denitrification, an anaerobic process. There is generalagreement in the scientific literature that the ratio of N2O to N2 increases with increasing acidity, nitrateconcentration, and reduced moisture. In summary, the production and emission of N2O from managed manures
requires the presence of either nitrites or nitrates in an anaerobic environment preceded by aerobic conditions
necessary for the formation of these oxidized forms of nitrogen. In addition, conditions preventing reduction of
N2O to N2, such as a low pH or limited moisture, must be present.
Indirect emissions result from volatile nitrogen losses that occur primarily in the forms of ammonia and NOx.
The fraction of excreted organic nitrogen that is mineralized to ammonia nitrogen during manure collection andstorage depends primarily on time, and to a lesser degree temperature. Simple forms of organic nitrogen such asurea (mammals) and uric acid (poultry) are rapidly mineralized to ammonia nitrogen, which is highly volatileand easily diffused into the surrounding air (Asman et al., 1998; Monteny and Erisman, 1998). Nitrogen losses
begin at the point of excretion in houses and other animal production areas (e.g., milk parlors) and continue
through on-site management in storage and treatment systems (i.e., manure management systems). Nitrogen isalso lost through runoff and leaching into soils from the solid storage of manure at outdoor areas, in feedlots andwhere animals are grazing in pastures. Pasture losses are considered separately in Chapter 11, Section 11.2, N2O
Emissions from Managed Soils, as are emissions of nitrogen compounds from grazing livestock.

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Chapter 10: Emissions from Livestock and Manure Management

Due to significant direct and indirect losses of manure nitrogen in management systems it is important toestimate the remaining amount of animal manure nitrogen available for application to soils or for use in feed,
fuel, or construction purposes. This value is used for calculation N2O emissions from managed soils (see Chapter
11, Section 11.2). The methodology to estimate manure nitrogen that is directly applied to soils, or available foruse in feed, fuel, or construction purposes is described in this chapter under Section 10.5.4 “Coordination with
reporting for N2O emissions from managed soils".

10.5.1

Choice of method

The level of detail and methods chosen for estimating N2O emissions from manure management systems will
depend upon national circumstances and the decision tree in Figure 10.4 describes good practice in choosing amethod accordingly. The following sections describe the different tiers referenced in the decision tree forcalculating direct and indirect N2O emissions from manure management systems.

Direct N 2 O emissions from Manure Management
Tie r 1The Tier 1 method entails multiplying the total amount of N excretion (from all livestock species/categories) ineach type of manure management system by an emission factor for that type of manure management system (seeEquation 10.25). Emissions are then summed over all manure management systems. The Tier 1 method isapplied using IPCC default N2O emission factors, default nitrogen excretion data, and default manure
management system data (see Annex 10A.2, Tables 10A-4 to 10A-8 for default management system allocations).
Tie r 2
A Tier 2 method follows the same calculation equation as Tier 1 but would include the use of country-specificdata for some or all of these variables. For example, the use of country-specific nitrogen excretion rates forlivestock categories would constitute a Tier 2 methodology.
Tie r 3
A Tier 3 method utilizes alternative estimation procedures based on a country-specific methodology. For
example, a process-based, mass balance approach which tracks nitrogen throughout the system starting with feed
input through final use/disposal could be utilized as a Tier 3 procedure. Tier 3 methods should be well
documented to clearly describe estimation procedures.
To estimate emissions from manure management systems, the livestock population must first be divided into
categories that reflect the varying amounts of manure produced per animal as well as the manner in which themanure is handled. This division of manure by type of system should be the same as that used to characterizemethane emissions from manure management (see Section 10.4). For example, if Tier 1 default emission factors
are used for calculating CH4 emissions, then the manure management systems usage data from Tables 10A-4 to
10A-8 should be applied. Detailed information on how to characterise the livestock population for this source isprovided in Section 10.2.
The following five steps are used to estimate direct N2O emissions from Manure Management:
Step 1: Collect population data from the Livestock Population Characterisation;
Step 2: Use default values or develop the annual average nitrogen excretion rate per head (Nex(T)) for eachdefined livestock species/category T;
Step 3: Use default values or determine the fraction of total annual nitrogen excretion for each livestock
species/category T that is managed in each manure management system S (MS(T,S));
Step 4: Use default values or develop N2O emission factors for each manure management system S (EF3(S));
andStep 5: For each manure management system type S, multiply its emission factor (EF3(S)) by the total amount of
nitrogen managed (from all livestock species/categories) in that system, to estimate N2O emissions from thatmanure management system. Then sum over all manure management systems.
In some cases, manure nitrogen may be managed in several types of manure management systems. For example,
manure flushed from a dairy freestall barn to an anaerobic lagoon may first pass through a solids separation unit
where some of the manure nitrogen is removed and managed as a solid. Therefore, it is important to consider
carefully the fraction of manure nitrogen that is managed in each type of system.
The calculation of direct N2O emissions from manure management is based on the following equation:

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Volume 4: Agriculture, Forestry and Other Land Use

EQUATION 10.25
DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT
 
 44

N 2OD ( mm) = ∑ ∑ N (T ) • Nex(T ) • MS (T , S )  • EF3( S )  • S T 28()

Where:N2OD(mm) = direct N2O emissions from Manure Management in the country, kg N2O yr-1
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-1 yr-1
MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed
in manure management system S in the country, dimensionless
EF3(S) = emission factor for direct N2O emissions from 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
44/28 = conversion of (N2O-N)(mm) emissions to N2O(mm) emissions
There may be losses of nitrogen in other forms (e.g., ammonia and NOx) as manure is managed on site. Nitrogenin the volatilized form of ammonia may be deposited at sites downwind from manure handling areas andcontribute to indirect N2O emissions (see below). Countries are encouraged to consider using a mass balance
approach (Tier 3) to track the manure nitrogen excreted, managed on site in manure management systems, andultimately applied to managed soils. The estimation of the amount of manure nitrogen which is directly applied
to managed soils or otherwise available for use as feed, fuel or construction purposes is described in the Section
10.5.4, Coordination with reporting for N2O emissions from managed soils. See Chapter 11, Section 11.2 forprocedures to calculate N2O emissions from managed manure nitrogen applied to soils.

Indirect N 2 O emissions from Manure Management
Tie r 1The Tier 1 calculation of N volatilisation in forms of NH3 and NOx from manure management systems is basedon multiplication of the amount of nitrogen excreted (from all livestock categories) and managed in each manure
management system by a fraction of volatilised nitrogen (see Equation 10.26). N losses are then summed over all
manure management systems. The Tier 1 method is applied using default nitrogen excretion data, default
manure management system data (see Annex 10A.2, Tables 10A-4 to 10A-8) and default fractions of N losses
from manure management systems due to volatilisation (see Table 10.22):
EQUATION 10.26
N LOSSES DUE TO VOLATILISATION FROM MANURE MANAGEMENT  FracGasMS 
N volatilization−MMS = ∑ ∑  N (T ) • Nex(T ) • MS (T ,S ) • 


100

 (T ,S )  S T  ()

Where:
Nvolatilization-MMS = amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx, kg N yr-1
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-1 yr-1
MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed
in manure management system S in the country, dimensionless
FracGasMS = percent of managed manure nitrogen for livestock category T that volatilises as NH3 and NOx
in the manure management system S, %10.54

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Figure 10.4

Decision tree for N 2 O emissions from Manure Management (Note 1)Start

Do youhave a country-specificTier 3 methodology?

Yes

Estimate emissions
using Tier 3 method.
Box 3: Tier 3

No

Is a
Tier 2 livestock
population characterization,
available and do you have country-specific
N excretion rates, fractions of N
losses, EFs and management
system usage
data?

No

Collect data forTier 2 method.

Is N2O frommanure management a
key category2 and is the
species a significant
share ofemissions3?

Yes

Yes (all or some)

Estimate direct andindirect N2O emissions
using Tier 2 method withavailable country-specificinputs.
Box 2: Tier 2

No

Estimate direct andindirect N2O emissions
using Tier 1 method and
IPCC defaults.
Box 1: Tier 1

Note:
1: N2O emissions from manure management systems include both direct and indirect sources
2: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.
3: As a rule of thumb, a livestock species would be significant if it accounts for 25-30% or more of emissions from the source category.

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Volume 4: Agriculture, Forestry and Other Land Use

The indirect N2O emissions from volatilisation of N in forms of NH3 and NOx (N2OG(mm)) are estimated using
Equation 10.27:
EQUATION 10.27
INDIRECT N2O EMISSIONS DUE TO VOLATILISATION OF N FROM MANURE MANAGEMENT
44
N 2OG ( mm) = (N volatilization − MMS • EF4 ) •28
Where:
N2OG(mm) = indirect N2O emissions due to volatilization of N from Manure Management in the country,
kg N2O yr-1
EF4 = emission factor for N2O emissions from atmospheric deposition of nitrogen on soils and water
surfaces, kg N2O-N (kg NH3-N + NOx-N volatilised)-1 ; default value is 0.01 kg N2O-N (kg NH3-N +NOx-N volatilised)-1 , given in Chapter 11, Table 11.3
Tie r 2
Countries may wish to develop a Tier 2 methodology for better consideration of national circumstances and to
reduce uncertainty of estimates as much as possible. As for direct N2O emission from manure management, aTier 2 method would follow the same calculation equation as Tier 1 but include the use of country-specific data
for some or all of these variables. For example, the use of country-specific nitrogen excretion rates for livestockcategories would constitute a Tier 2 method. National NH3 emission inventories developed by some countries
could be used for Tier 2 estimation of nitrogen volatilisation from manure management systems. A Tier 2
method would require more detailed characterisation of the flow of nitrogen throughout the animal housing andmanure management systems used in the country. Double counting of emissions associated with the applicationof managed manure should be avoided, as well as manure associated with pasture and grazing operations, whichshould be calculated and reported under Chapter 11, Section 11.2 (N2O emissions from managed soils).
There are extremely limited measurement data on leaching and runoff losses from various manure management
systems. The greatest N losses due to runoff and leaching typically occur where animals are on a drylot. In drier
climates, runoff losses are smaller than in high rainfall areas and have been estimated in the range from 3 to 6%
of N excreted (Eghball and Power, 1994). Studies by Bierman et al. (1999) found nitrogen lost in runoff was 5
to 19% of N excreted and 10 to 16% leached into soil, while other data show relatively low loss of nitrogenthrough leaching in solid storage (less than 5% of N excreted) but greater loss could also occur (Rotz, 2004).
Further research is needed in this area to improve the estimated losses and the conditions and practices underwhich such losses occur. Equation 10.28 should only be used where there is country-specific information on thefraction of nitrogen loss due to leaching and runoff from manure management systems available. Therefore,
estimation of N losses from leaching and runoff from manure management should be considered part of a Tier 2
or Tier 3method.
Nitrogen that leaches into soil and/or runs off during solid storage of manure at outdoor areas or in feedlots isderived as follows:
EQUATION 10.28
N LOSSES DUE TO LEACHING FROM MANURE MANAGEMENT SYSTEMS  FracleachMS 
N leaching −MMS = ∑ ∑  N (T ) • Nex(T ) • MS (T ,S ) • 


100

 (T ,S )  S T  ()

Where:
Nleaching-MMS = amount of manure nitrogen that leached from manure management systems, kg N yr-1
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-1 yr-1
MS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed
in manure management system S in the country, dimensionless
FracleachMS = percent of managed manure nitrogen losses for livestock category T due to runoff andleaching during solid and liquid storage of manure

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Chapter 10: Emissions from Livestock and Manure Management

The indirect N2O emissions from leaching and runoff of nitrogen from manure management systems (N2OL(mm))
are estimated using Equation 10.29:
EQUATION 10.29
INDIRECT N2O EMISSIONS DUE TO LEACHING FROM MANURE MANAGEMENT
44
N 2OL ( mm) = N leaching − MMS • EF5 •
28

(

)

Where:
N2OL(mm) = indirect N2O emissions due to leaching and runoff from Manure Management in the country,
kg N2O yr-1
EF5 = emission factor for N2O emissions from nitrogen leaching and runoff, kg N2O-N/kg N leached andrunoff (default value 0.0075 kg N2O-N (kg N leaching/runoff)-1, given in Chapter 11, Table 11.3
Tie r 3To reduce uncertainty of the estimates, a Tier 3 method could be developed with country-specific emission
factors for volatilisation and nitrogen leaching and runoff based on actual measurements.
All losses of N through manure management systems (both direct and indirect) need to be excluded from theamount of manure N that is available for application to soils and which is reported in Chapter 11, Section 11.2
N2O Emissions from Managed Soils. Refer to Section 10.5.4, Coordination with reporting for N2O emissions
from managed soils, for guidance on calculating total N losses from manure management systems.

10.5.2

Choice of emission factors

Annual average nitrogen excretion rates, Nex ( T )Tie r 1
Annual nitrogen excretion rates should be determined for each livestock category defined by the livestock
population characterisation. Country-specific rates may either be taken directly from documents or reports suchas agricultural industry and scientific literature, or derived from information on animal nitrogen intake andretention (as explained below). In some situations, it may be appropriate to use excretion rates developed byother 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 nitrogen excretion rates presented in Table 10.19 can be used. These rates are presented in unitsof nitrogen excreted per 1000 kg of animal per day. These rates can be applied to livestock sub-categories ofvarying ages and growth stages using a typical average animal mass (TAM) for that population sub-category, as
shown in Equation 10.30.
EQUATION 10.30
ANNUAL N EXCRETION RATES
TAM
• 365
Nex(T ) = N rate (T ) •1000
Where:
Nex(T) = annual N excretion for livestock category T, kg N animal-1 yr-1
Nrate(T) = default N excretion rate, kg N (1000 kg animal mass)-1 day-1 (see Table 10.19)
TAM(T) = typical animal mass for livestock category T, kg animal-1
Default TAM values are provided in Tables 10A-4 to 10A-9 in Annex 10A.2. However, it is preferable tocollect country-specific TAM values due to the sensitivity of nitrogen excretion rates to different weightcategories. For example, market swine may vary from nursery pigs weighing less than 30 kilograms to finishedpigs that weigh over 90 kilograms. By constructing animal population groups that reflect the various growthstages of market pigs, countries will be better able to estimate the total nitrogen excreted by their swine
population.

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When estimating the Nex(T) for animals whose manure is classified in the manure management system burned
for fuel (Table 10.21, Default emission factors for direct N2O emissions from Manure Management), it should be
kept in mind that the dung is burned and the urine stays in the field. As a rule of thumb, 50% of the nitrogenexcreted is in the dung and 50% is in the urine. If the burned dung is used as fuel, then emissions are reported
under the IPCC category Fuel Combustion (Volume 2: Energy), whereas if the dung is burned without energy
recovery the emissions should be reported under the IPCC category Waste Incineration (Volume 5: Waste).
Tie r 2
The annual amount of N excreted by each livestock species/category depends on the total annual N intake andtotal 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 offeed digested by the animal, and the protein content of that feed. Total feed intake depends on the productionlevel of the animal (e.g., growth rate, milk production, draft power). Annual N retention (i.e., the fraction of N
intake that is retained by the animal for the production of meat, milk, or wool) is a measure of the animal's
efficiency of production of animal protein from feed protein. Nitrogen intake and retention data for specificlivestock 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 Section 10.2.
Default N retention values are provided in Table 10.20, Default values for the fraction of nitrogen in feed taken
in by animals that is retained by the different animal species/categories. Rates of annual N excretion for each
livestock species/category (Nex(T)) are derived as follows:

EQUATION 10.31
ANNUAL N EXCRETION RATES (TIER 2)
Nex(T) = Nintake(T) ● (1 – Nretention_frac(T)) ● 365
Where:
Nex(T) = annual N excretion rates, kg N animal-1 yr-1
Nintake(T) = the daily N intake per head of animal of species/category T , kg N animal-1 day-1
Nretention_frac(T) = fraction of N intake that is retained by animal of species/category T, dimensionlessExam p le of Tier 2 metho d fo r est ima t ing n itrog en ex cr etion for ca ttle
Nitrogen excretion may be calculated based on the same dietary assumptions used in modelling entericfermentation emissions (see Section 10.2). The amount of nitrogen excreted by cattle can be estimated as thedifference between the total nitrogen taken in by the animal and the total nitrogen retained for growth and milk
production. Equations 10.32 and 10.33 can be used to calculate the variables for nitrogen intake and nitrogenretained for use in Equation 10.31. The total nitrogen intake rate is derived as follows:
EQUATION 10.32
N INTAKE RATES FOR CATTLE
 CP % 
GE  100 •
N intake(T ) =18.45  6.25 Where:
Nintake(T) = daily N consumed per animal of category T, kg N animal-1 day-1
GE = gross energy intake of the animal, in enteric model, based on digestible energy, milk production,
pregnancy, current weight, mature weight, rate of weight gain, and IPCC constants, MJ animal-1 day-1
18.45 = conversion factor for dietary GE per kg of dry matter, MJ kg-1. This value is relatively constantacross a wide range of forage and grain-based feeds commonly consumed by livestock.
CP% = percent crude protein in diet, input
6.25 = conversion from kg of dietary protein to kg of dietary N, kg feed protein (kg N)-1

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Chapter 10: Emissions from Livestock and Manure Management
TABLE 10.19
DEFAULT VALUES FOR NITROGEN EXCRETION RATE a (KG N (1000 KG ANIMAL MASS)-1 DAY-1)
RegionCategory of animal

North America

Western Europe

Eastern Europe

Oceania

Latin America

AfricaMiddle East

Asia

Dairy Cattle

0.44

0.48

0.35

0.44

0.48

0.60

0.70

0.47

Other Cattle

0.31

0.33

0.35

0.50

0.36

0.63

0.79

0.34

Swineb

0.40

0.50

0.54

0.52

1.47

1.47

1.47

0.40

Market

0.42

0.51

0.55

0.53

1.57

1.57

1.57

0.42

Breeding

0.24

0.42

0.46

0.46

0.55

0.55

0.55

0.24

0.83

0.83

0.82

0.82

0.82

0.82

0.82

0.82

Hens >/= 1 yr

0.83

0.96

0.82

0.82

0.82

0.82

0.82

0.82

Pullets

0.62

0.55

0.60

0.60

0.60

0.60

0.60

0.60

Other Chickens

0.83

0.83

0.82

0.82

0.82

0.82

0.82

0.82

Broilers

1.10

1.10

1.10

1.10

1.10

1.10

1.10

1.10

Turkeys

0.74

0.74

0.74

0.74

0.74

0.74

0.74

0.74

Ducks

Poultry

0.83

0.83

0.83

0.83

0.83

0.83

0.83

0.83

Sheep

0.42

0.85

0.90

1.13

1.17

1.17

1.17

1.17

Goats

0.45

1.28

1.28

1.42

1.37

1.37

1.37

1.37

Horses (and mules, asses)

0.30

0.26

0.30

0.30

0.46

0.46

0.46

0.46

Camelsc

0.38

0.38

0.38

0.38

0.46

0.46

0.46

0.46

0.32

0.32

0.32

0.32

0.32

0.32

0.32

0.32

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

Rabbits (kg N head-1 yr-1)

8.10

8.10

8.10

8.10

8.10

8.10

8.10

8.10

Fox and Racoon (kg N head-1 yr-1)d

12.09

12.09

12.09

12.09

12.09

12.09

12.09

12.09

Buffaloc
Mink and Polecat1)d(kg N head-1 yr-

The uncertainty in these estimates is +50%.
a
Summarized from 1996 IPCC Guidelines, 1997; European Environmental Agency, 2002; USA EPA National NH3 Inventory Draft Report, 2004; and data of GHG inventories of Annex I Parties submitted to theSecretariat UNFCCC in 2004.
b
Nitrogen excretion for swine are based on an estimated country population of 90% market swine and 10% breeding swine.
cModified from European Environmental Agency, 2002.
d
Data of Hutchings et al., 2001.

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TABLE 10.20
DEFAULT VALUES FOR THE FRACTION OF NITROGEN IN FEED INTAKE OF LIVESTOCK THAT IS RETAINED BY
THE DIFFERENT LIVESTOCK SPECIES/CATEGORIES (FRACTION N-INTAKE RETAINED BY THE ANIMAL)
Nretention_frac(T)(kg N retained/animal/day) (kg N intake/animal/day)-1

Livestock category
Dairy Cows

0.20

Other Cattle

0.07

Buffalo

0.07

Sheep

0.10

Goats

0.10

Camels

0.07

Swine

0.30

Horses0.07

Poultry

0.30

The uncertainty in these estimates is +50%.
Source: Judgement of IPCC Expert Group (see Co-chairs, Editors and Experts; N2O emissions from Manure Management).

The total nitrogen retained is derived as follows:
EQUATION 10.33
N RETAINED RATES FOR CATTLE

 7.03 • NE g  
 Milk PR %      WG • 268 − 
 Milk • 
100   
 WG
 



+
N retention (T ) =



6.38
1000 • 6.25


 

 

Where:
Nretention(T) = daily N retained per animal of category T, kg N animal-1 day-1
Milk = milk production, kg animal-1 day-1 (applicable to dairy cows only)
Milk PR% = percent of protein in milk, calculated as [1.9 + 0.4 ● %Fat], where %Fat is an input,
assumed to be 4% (applicable to dairy cows only)
6.38 = conversion from milk protein to milk N, kg Protein (kg N)-1
WG = weight gain, input for each livestock category, kg day-1
268 = constant derived from Equation 3-8 in NRC (1996), g Protein kg-1 animal-1
7.03 = constant derived from Equation 3-8 in NRC (1996), g Protein MJ-1 animal-1
NEg = net energy for growth, calculated in livestock characterisation, based on current weight, mature
weight, rate of weight gain, and IPCC constants, MJ day-1
6.25 = conversion from kg dietary protein to kg dietary N, kg Protein (kg N)-1
Annual nitrogen excretion data are also used for the calculation of direct and indirect N2O emissions frommanaged soils (see Chapter 11, Section 11.2, N2O emissions from managed soils). The same rates of N excretion,and methods of derivation, that are used to estimate N2O emissions from Manure Management should be used toestimate N2O emissions from managed soils.

Emission factors for direct N 2 O emissions from Manure Management
The best estimate will be obtained using country-specific emission factors that have been fully documented inpeer reviewed publications. It is good practice to use country-specific emission factors that reflect the actual
duration of storage and type of treatment of animal manure in each management system that is used. Good

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Chapter 10: Emissions from Livestock and Manure Management

practice in the derivation of country-specific emission factors involves the measurement of emissions (per unitof manure N) from different management systems, taking into account variability in duration of storage andtypes of treatment. When defining types of treatment, conditions such as aeration and temperature should betaken into account. If inventory agencies use country-specific emission factors, they are encouraged to provide
justification for these values via peer-reviewed documentation.
If appropriate country-specific emission factors are unavailable, inventory agencies are encouraged to use thedefault emission factors presented in Table 10.21, Default emission factors for direct N2O emissions fromManure Management. This table contains default emission factors by manure management system. Note thatemissions from liquid/slurry systems without a natural crust cover, anaerobic lagoons, and anaerobic digesters
are considered negligible based on the absence of oxidized forms of nitrogen entering these systems combinedwith the low potential for nitrification and denitrification to occur in the system.

Emission factors for indirect N 2 O emissions from Manure Management
In order to estimate indirect N2O emissions from Manure Management, two fractions of nitrogen losses (due tovolatilization and leaching/runoff), and two indirect N2O emissions factors associated with these losses (EF4 andEF5) are needed. Default values for volatilization N losses are presented in the Table 10.22. Values represent
average rates for N loss in the forms of NH3 and NOx, with most of the loss in the form of NH3. Ranges reflectvalues that appear in the literature. The values represent conditions without any significant nitrogen control
measures in place. Countries are encouraged to develop country-specific values, particularly related to ammonia
losses where component emissions may be well characterized as part of larger air quality assessments and whereemissions may be affected by nitrogen reduction strategies. For example, detailed methodologies for estimating
NH3 and other nitrogen losses using mass balance/mass flow procedures are described in the EMEP/CORINAIR
Atmospheric Inventory Guidebook, Chapter 1009 (European Environmental Agency, 2002).
The fraction of manure nitrogen that leaches from manure management systems (FracleachMS) is highly uncertain
and should be developed as a country-specific value applied in Tier 2 method.
Default values for EF4 (N volatilisation and re-deposition) and EF5 (N leaching/runoff) are given in Chapter 11,
Table 11.3 (Default emission, volatilisation and leaching factors for indirect soil N2O emissions).

10.5.3

Choice of activity data

There are two main types of activity data for estimating N2O emissions from manure management systems: (1)livestock population data, and (2) manure management system usage data.

Livestock population data, N ( T )The animal population data should be obtained using the approach described in Section 10.2. If using default
nitrogen excretion rates to estimate N2O emissions from manure management systems, a Tier 1 livestock
population characterisation is sufficient. To estimate N2O emissions from Manure Management using calculated
nitrogen excretion rates, a Tier 2 characterisation must be performed. As noted in Section 10.2, good practice incharacterising livestock populations is to conduct a single characterisation that will provide the activity data forall emissions sources that depend on livestock population data.

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 Table 10.18 for asummary of the main types of manure management systems). The portion of manure managed in each manure
management system must be collected for each representative livestock category. Note that in some cases,
manure may be managed in several types of manure management systems. For example, manure flushed from adairy freestall barn to an anaerobic lagoon may first pass through a solids separation unit where some of the
manure solids are removed and managed as a solid. Therefore, it is important to carefully consider the fraction ofmanure that is managed in each type of system.
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. If country-specific manure management system usage
data are not available, default values should be used. The IPCC default values for dairy cows, other cattle,
buffalo, swine (market and breeding swine), and poultry should be taken from Tables 10A-4 through 10A-8 ofAnnex 10A.2. Manure from other animal categories is typically managed in pastures and grazing operations.

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TABLE 10.21
DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT
SystemEF3[kg N2O-N(kg Nitrogenexcreted)-1]

Definition

Uncertainty
ranges of EF3

Sourcea

Pasture/Range/
Paddock

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

Direct and indirect N2O emissions associated with the manure deposited on
agricultural soils and pasture, range, paddock systems are treated in
Chapter 11, Section 11.2, N2O emissions from managed soils.

Daily spread

Manure is routinely removed from a
confinement facility and is applied tocropland or pasture within 24 hours of
excretion. N2O emissions duringstorage and treatment are assumed to
be zero. N2O emissions from land
application are covered under the
Agricultural Soils category.

Not applicable

Judgement by IPCC Expert Group(see Co-chairs, Editors andExperts; N2O emissions fromManure Management).

Solid storageb

The storage of manure, typically for aperiod of several months, in
unconfined piles or stacks. Manure isable to be stacked due to the presenceof a sufficient amount of bedding
material or loss of moisture byevaporation.

0.005

Factor of 2

Judgement of IPCC Expert Groupin combination with Amon et al.
(2001), which shows emissions
ranging from 0.0027 to 0.01 kg
N2O-N (kg N)-1.

Dry lot

A paved or unpaved open
confinement area without any
significant vegetative cover where
accumulating manure may beremoved periodically. Dry lots aremost typically found in dry climates
but also are used in humid climates.

0.02

Factor of 2

Judgement of IPCC Expert Groupin combination with Kulling(2003).

0.005

Factor of 2

Judgement of IPCC Expert Groupin combination with Sommer etal. (2000).

Not applicable

Judgement of IPCC Expert Groupin combination with the following
studies: Harper et al. (2000),
Lague et al. (2004), Monteny etal. (2001), and Wagner-Riddle
and Marinier (2003). Emissions
are believed negligible based on
the absence of oxidized forms ofnitrogen entering systems in
combination with low potential
for nitrification and denitrificationin the system.

Not applicable

Judgement of IPCC Expert Groupin combination with the following
studies: Harper et al. (2000),
Lague et al. (2004), Monteny etal. (2001), and Wagner-Riddle
and Marinier (2003). Emissions
are believed negligible based on
the absence of oxidized forms ofnitrogen entering systems in
combination with low potential
for nitrification and denitrificationin the system.

Withnaturalcrust cover

Liquid/Slurry

Manure is stored as
excreted or with someminimal addition ofwater to facilitate
handling and is storedin either tanks orearthen ponds.

Without
naturalcrust cover

Uncoveredanaerobic
lagoonAnaerobic lagoons are designed andoperated to combine waste
stabilization and storage. Lagoonsupernatant is usually used to remove
manure from the associatedconfinement facilities to the lagoon.
Anaerobic lagoons are designed withvarying lengths of storage (up to ayear or greater), depending on the
climate region, the volatile solids
loading rate, and other operational
factors. The water from the lagoon
may be recycled as flush water orused to irrigate and fertilise fields.

Pit storage
below animal
confinementsCollection and storage of manure
usually with little or no added water
typically below a slatted floor in an
enclosed animal confinement facility.

10.62

0

0

0

0.002

Factor of 2

Judgement of IPCC Expert Groupin combination with the following
studies: Amon et al. (2001),
Kulling (2003), and Sneath et al.
(1997).

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.21 (CONTINUED)DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT
SystemAnaerobic
digesterBurned for fuelor as waste

Cattle andswine deep
bedding

EF3[kg N2O-N(kg Nitrogenexcreted)-1]

Definition

Anaerobic digesters are designed andoperated for waste stabilization by the
microbial reduction of complex
organic compounds to CH4 and CO2,
which is captured and flared or used
as a fuel.

0

Uncertainty
ranges of EF3

Not applicable

Sourcea
Judgement of IPCC Expert Groupin combination with the following
studies: Harper et al. (2000),
Lague et al. (2004) Monteny et al.
(2001), and Wagner-Riddle andMarinier (2003). Emissions are
believed negligible based on theabsence of oxidized forms ofnitrogen entering systems in
combination with low potential
for nitrification and denitrificationin the system.

The dung is excreted on fields. The
sun dried dung cakes are burned forfuel.

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 fuel andunder the IPCC category 'Waste Incineration' if the dung is burned without
energy recovery.

Urine N deposited on pasture andpaddock

Direct and indirect N2O emissions associated with the urine deposited on
agricultural soils and pasture, range, paddock systems are treated in
Chapter 11, Section 11.2, N2O emissions from managed soils.

As manureaccumulates, bedding
is continually added toabsorb moisture over a
production cycle andpossibly for as long as
6 to 12 months. This
manure management
system also is known
as a bedded packmanure management
system and may be
combined with a dry
lot or pasture.

No mixing

ActivemixingFactor of 2

Average value based on Sommer
and Moller (2000), Sommer(2000), Amon et al. (1998), andNicks et al. (2003).

0.07

Factor of 2

Average value based on Nicks etal. (2003) and Moller et al.
(2000). Some literature cites
higher values to 20% for wellmaintained, active mixing, but
those systems included treatment
for ammonia which is not typical.

0.01

Composting In-Vesselc

Composting, typically in an enclosed
channel, with forced aeration andcontinuous mixing.

0.006

Factor of 2

Judgement of IPCC Expert Group.
Expected to be similar to static
piles.

Composting Static Pilec

Composting in piles with forced
aeration but no mixing.

0.006

Factor of 2

Hao et al. (2001).

Composting Intensive
Windrowc

Composting in windrows with regularturning for mixing and aeration.

0.1

Factor of 2

Judgement of IPCC Expert Group.
Expected to be greater thanpassive windrows and intensive
composting operations, as
emissions are a function of theturning frequency.

Composting Passive
Windrowc

Composting in windrows with
infrequent turning for mixing andaeration.

0.01

Factor of 2

Hao et al. (2001).

Poultry manurewith litter

Similar to deep bedding systems.
Typically used for all poultry breeder
flocks and for the production of meat
type chickens (broilers) and other
fowl.

Factor of 2

Judgement of IPCC Expert Group
based on the high loss of ammonia
from these systems, which limits
the availability of nitrogen for
nitrification/denitrification.

Poultry manure
without litter

May be similar to open pits in
enclosed animal confinement facilities
or may be designed and operated to
dry the manure as it accumulates. The
latter is known as a high-rise manure
management system and is a form of
passive windrow composting when
designed and operated properly.

Factor of 2

Judgement of IPCC Expert Group
based on the high loss of ammonia
from these systems, which limits
the availability of nitrogen for
nitrification/denitrification.

0.001

0.001

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TABLE 10.21 (CONTINUED)DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT
SystemAerobic
treatmentaEF3[kg N2O-N(kg Nitrogenexcreted)-1]

Definition

The biologicaloxidation of manurecollected as a liquidwith either forced or
natural aeration.
Natural aeration islimited to aerobic andfacultative ponds andwetland systems and isdue primarily tophotosynthesis. Hence,these systems typically
become anoxic during
periods without
sunlight.

Naturalaeration
systems

Forcedaeration
systems

0.01

0.005

Uncertainty
ranges of EF3

Sourcea

Factor of 2

Judgement of IPCC Expert Group.
Nitrification-denitrification isused widely for the removal of
nitrogen in the biologicaltreatment of municipal andindustrial wastewaters withnegligible N2O emissions. Limitedoxidation may increase emissions
compared to forced aeration
systems.

Factor of 2

Judgement of IPCC Expert Group.
Nitrification-denitrification isused widely for the removal of
nitrogen in the biologicaltreatment of municipal andindustrial wastewaters withnegligible N2O emissions.

Also see Dustan (2002), which compiled information from some of the original references cited.

b

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.
cComposting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source
typically at thermophilic temperatures produced by microbial heat production.

10.5.4

Coordination with reporting for N2 O emissions frommanaged soils

Following storage or treatment in any system of manure management, nearly all the manure will be applied toland. The emissions that subsequently arise from the application of the manure to soil are to be reported under
the category N2O emissions from managed soils. The methods for estimating these emissions are discussed in
Chapter 11, Section 11.2. In estimating N2O emissions from managed soils, the amount of animal manure
nitrogen that is directly applied to soils, or available for use in feed, fuel, or construction purposes, areconsidered.
A significant proportion of the total nitrogen excreted by animals in managed systems (i.e., all livestock except
those in pasture and grazing conditions) is lost prior to final application to managed soils or for use as feed, fuel,or for construction purposes. In order to estimate the amount of animal manure nitrogen that is directly applied
to soils, or available for use in feed, fuel, or construction purposes (i.e., the value which is used in Chapter 11,
Equation 11.1 or 11.2), it is necessary to reduce the total amount of nitrogen excreted by animals in managed
systems by the losses of N through volatilisation (i.e., NH3, N2 and NOx), conversion to N2O and losses throughleaching and runoff.
Where organic forms of bedding material (straw, sawdust, chippings, etc.) are used, the additional nitrogen fromthe bedding material should also be considered as part of the managed manure N applied to soils. Bedding istypically collected with the remaining manure and applied to soils. It should be noted, however, that sincemineralization of nitrogen compounds in beddings occurs more slowly compared to manure and theconcentration of ammonia fraction in organic beddings is negligible, both volatilization and leaching losses
during storage of bedding are assumed to be zero (European Environmental Agency, 2002).

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TABLE 10.22
DEFAULT VALUES FOR NITROGEN LOSS DUE TO VOLATILISATION OF NH3 AND NOX FROM MANUREMANAGEMENT

N loss from MMS due to volatilisation of
N-NH3 and N-NOx (%) b
FracGasMS (Range of FracGasMS)

Animal type

Manure management system (MMS) a

Swine

Anaerobic lagoon

40% (25 – 75)

Pit storage

25% (15 – 30)

Deep bedding

40% (10 – 60)

Liquid/slurry

48% (15 – 60)

Solid storage

45% (10 – 65)

Anaerobic lagoon

35% (20 – 80)

Liquid/Slurry

40% (15 – 45)

Pit storage

28% (10 – 40)

Dry lot

20% (10 – 35)

Solid storage

30% (10 – 40)

Daily spread

7% (5 – 60)

Poultry without litter

55% (40 – 70)

Anaerobic lagoon

40% (25 – 75)

Poultry with litter

40% (10 – 60)

Dry lot

30% (20 – 50)

Solid storage

45% (10 – 65)

Deep bedding

30% (20 – 40)

Deep bedding

25% (10 – 30)

Solid storage

12% (5 – 20)

Dairy Cow

Poultry

Other Cattle

Other ca

Manure Management System here includes associated N losses at housing and final storage system.

bVolatilization rates based on judgement of IPCC Expert Group and following sources: Rotz ( 2003), Hutchings et al.
(2001), and U.S EPA (2004).

cOther includes sheep, horses, and fur-bearing animals.

The estimate of managed manure nitrogen available for application to managed soils, or available for use in feed,
fuel, or construction purposes is based on the following equation:
EQUATION 10.34
MANAGED MANURE N AVAILABLE FOR APPLICATION TO MANAGED SOILS, FEED, FUEL OR
CONSTRUCTION USES 
 FracLossMS    +     N (T ) • Nex(T ) • MS(T , S ) • 1 −100
N MMS _ Avb = ∑  ∑    
S (T ) 
N (T ) • MS(T , S ) • N beddingMS

 

(

)

[

]

Where:NMMS_Avb = amount of managed manure nitrogen available for application to managed soils or for feed,
fuel, or construction purposes, kg N yr-1
N(T) = number of head of livestock species/category T in the country
Nex(T) = annual average N excretion per animal of species/category T in the country, kg N animal-1 yr-1

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Volume 4: Agriculture, Forestry and Other Land UseMS(T,S) = fraction of total annual nitrogen excretion for each livestock species/category T that is managed
in manure management system S in the country, dimensionless
FracLossMS = amount of managed manure nitrogen for livestock category T that is lost in the manure
management system S, % (see Table 10.23)
NbeddingMS = amount of nitrogen from bedding (to be applied for solid storage and deep bedding MMS if
known organic bedding usage), kg N animal-1 yr-1
S = manure management system
T = species/category of livestock
Bedding materials vary greatly and inventory compilers should develop values for NbeddingMS based on thecharacteristics of bedding material used in their livestock industries. Limited data from scientific literature
indicates the amount of nitrogen contained in organic bedding material applied for dairy cows and heifers is
usually around 7 kg N animal-1 yr-1, for other cattle is 4 kg N animal-1 yr-1, for market and breeding swine isaround 0.8 and 5.5 kg N animal-1 yr-1, respectively. For deep bedding systems, the amount of N in litter isapproximately double these amounts (Webb, 2001; Döhler et al., 2002).
Table 10.23 presents default values for total nitrogen losses from manure management systems. These default
values include losses that occur from the point of excretion, including animal housing losses, manure storage
losses, and losses from leaching and runoff at the manure storage system where applicable. For example, values
provided for dairy anaerobic lagoon systems include nitrogen losses that occur in the dairy barn and milking
parlour prior to the collection and treatment of manure, as well as those that occur from the lagoon.
There is a high level of variability in the range of total nitrogen losses from managed manure systems. As shownin Table 10.23, the majority of these are due to volatilization losses, primarily ammonia losses that occur rapidly
following the excretion of the manure. However, losses also occur in the form of NO3, N2O, and N2 as well fromleaching and runoff that occurs where manure is stored in piles. The values in Table 10.23 reflect average values
for typical housing/storage combinations for each animal category. Countries are encouraged to develop countryspecific values, particularly related to ammonia losses where component emissions may be well characterised for
local practices as part of larger air quality assessments and where emissions may be affected by nitrogen
reduction strategies.
Countries may wish to develop an alternative approach for better consideration of national circumstances and to
reduce uncertainty of estimates as much as possible. This approach would entail more detailed characterisationof the flow of nitrogen through the components of the animal housing and manure management systems used in
the country, accounting for any mitigation activity (e.g., the use of covers over slurry tanks), and considerationof local practices, such as type of bedding material used.

10.5.5

Uncertainty assessment

EMISSION FACTORS – NITROGEN EXCRETION RATES
Uncertainty ranges for the default N excretion rates are estimated at about +50% (Source: Judgement by IPCC
Expert Group). The uncertainty ranges for the default N retention values provided here are also +50% (see Table
10.20). 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 reduced substantially. The degree of
uncertainty may be further reduced by using direct emission measurements of nitrogen losses from specificmanure management systems.

EMISSION FACTORS – DIRECT N 2 O EMISSIONS
There are large uncertainties associated with the default emission factors for this source category (–50% to+100%). Accurate and well-designed emission measurements from well characterised types of manure andmanure management systems can help reduce these uncertainties. These measurements must account for
temperature, moisture conditions, aeration, manure N content, metabolisable carbon, duration of storage, andother aspects of treatment.

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TABLE 10.23
DEFAULT VALUES FOR TOTAL NITROGEN LOSS FROM MANURE MANAGEMENT
Animalcategory

Manure management system a

Total N loss from MMS b
FracLossMS (Range of FracLossMS)

Anaerobic lagoon
Swine

Dairy Cow

Poultry

Other Cattle

Other c78% (55 – 99)

Pit storage

25% (15 – 30)

Deep bedding

50% (10 – 60)

Liquid/Slurry

48% (15 – 60)

Solid storage

50% (20 – 70)

Anaerobic lagoon

77% (55 – 99)

Liquid/Slurry

40% (15 – 45)

Pit storage

28% (10 – 40)

Dry lot

30% (10 – 35)

Solid storage

40% (10 – 65)

Daily spread

22% (15 – 60)

Poultry without litter

55% (40 – 70)

Anaerobic lagoon

77% (50 – 99)

Poultry with litter

50% (20 – 80)

Dry lot

40% (20 – 50)

Solid storage

50% (20 – 70)

Deep bedding

40% (10 – 50)

Deep bedding

35% (15 – 40)

Solid storage

15% (5 – 20)aManure Management System here includes associated N losses at housing and final storage system.

b

Total N loss rates based on judgement of IPCC Expert Group and following sources: Rotz ( 2003), Hutchings et al.
(2001), and U.S EPA (2004). Rates include losses in forms of NH3, NOx, N2O, and N2 as well from leaching and runoff
from solid storage and dry lots. Values represent average rates for typical housing and storage components without any
significant nitrogen control measures in place. Ranges reflect values that appear in the literature. Where measures tocontrol nitrogen losses are in place, alternative rates should be developed to reflect those measures.

c

Other includes sheep, horses, and fur-bearing animals.

EMISSION FACTORS – INDIRECT N 2 O EMISSIONS
Uncertainty ranges for default N losses due to volatilisation of NH3 and NOx and total N losses from manure
management systems are presented in the Tables 10.22 and 10.23, respectively. The uncertainty associated withdefault emission factor for nitrogen volatilisation and re-deposition (EF4) is given in Table 11.3 of Chapter 11.
The uncertainty range for the default emission factor for leaching and runoff (EF5) is also provided in Table 11.3.
Caution should be taken when developing country-specific emission factors for volatilisation and re-depositionof nitrogen, since direct measurements could include transboundary atmospheric transport.

ACTIVITY DATA – LIVESTOCK POPULATIONS
See Section 10.2 (Livestock Population and Feed Characterisation) for discussion on uncertainty of animal
population and feed characterisation data.

ACTIVITY DATA – MANURE MANAGEMENT SYSTEM USAGE
The uncertainty of the manure management system usage data will depend on the characteristics of eachcountry's livestock industry and how information on manure management is collected. For example, forcountries that rely almost exclusively on one type of management system, such as dry lot, the uncertainty
associated with management system usage data can be 10% or less. However, for countries where there is awide variety of management systems used with locally different operating practices, the uncertainty inmanagement system usage data can be much higher, in the range of 25% to 50%, depending on the availabilityof reliable and representative survey data that differentiates animal populations by system usage. Preferably,
each country should estimate the uncertainty associated with their management system usage data by using themethods described in Volume 1, Chapter 3.

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10.5.6

Completeness, Time series, Quality assurance/Quality
control and Reporting

A complete inventory should estimate N2O emissions from all systems of manure management for all livestock
species/categories. Countries are encouraged to use manure management system definitions that are consistent
with those presented in Table 10.18. Population data should be cross-checked between main reporting
mechanisms (such as FAO and national agricultural statistics databases) to ensure that information used in theinventory is complete and consistent. Because of the widespread availability of the FAO database of livestock
information, most countries should be able to prepare, at a minimum, Tier 1 estimates for the major livestockcategories. For more information regarding the completeness of livestock characterisation, see Section 10.2.
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. General guidance on thedevelopment of a consistent time series is addressed in Volume 1, Chapter 5 of this report. In most countries, theother two activity data sets required for this source 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 timeseries. However, in some cases, there may be reasons to modify these values over time. For example, farmers
may alter livestock feeding practices which could affect nitrogen excretion rates. A particular system of manure
management may change due to operational practices or new technologies such that a revised emission factor iswarranted. 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 thedriver of change, the parameters and emission factors used to estimate emissions must reflect the change. The
inventory text should thoroughly explain how the change in farm practices or implementation of mitigation
measures has affected the time series of activity data or emission factors.
It is good practice to implement general quality control checks as outlined in Volume 1, Chapter 6, Quality
Assurance/Quality Control and Verification, and expert review of the emission estimates. Additional quality
control checks and quality assurance procedures may also be applicable, particularly if higher tier methods areused to determine emissions from this source. The general QA/QC related to data processing, handling, andreporting should be supplemented with procedures discussed below:

Activity data check•

The inventory agency should review livestock data collection methods, in particular checking that livestocksubspecies data were collected and aggregated correctly with consideration for the duration of production
cycles. The data should be cross-checked with previous years to ensure the data are reasonable andconsistent with the expected trend. Inventory agencies should document data collection methods, identify
potential areas of bias, and evaluate the representativeness of the data.

•

Manure management system allocation should be reviewed on a regular basis to determine if changes in the
livestock industry are being captured. Conversion from one type of management system to another, andtechnical modifications to system configuration and performance, should be captured in the systemmodelling for the affected livestock.

•

National agricultural policy and regulations may have an effect on parameters that are used to calculate
manure emissions, and should be reviewed regularly to determine what impact they may have. For example,
guidelines to reduce manure runoff into water bodies may cause a change in management practices, andthus affect the N distribution for a particular livestock category. Consistency should be maintained between
the inventory and ongoing changes in agricultural practices.

•

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.

•

The nitrogen excretion rates, whether default or country-specific values, should be consistent with feedintake data as determined through animal nutrition analyses.

Review of emission factors•

The inventory agency should evaluate how well the implied N2O emission factors and nitrogen excretion
rates compare with alternative national data sources and with data from other countries with similar
livestock practices. Significant differences should be investigated.

•

If using country-specific emission factors, the inventory agency should compare them to the default factors
and note differences. The development of country-specific emission factors should be explained anddocumented, and the results peer-reviewed by independent experts.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management•

Whenever possible, available measurement data, even if they represent only a small sample of systems,
should be reviewed relative to assumptions for N2O emission estimates. Representative measurement data
may provide insights into how well current assumptions predict N2O production from manure management
systems in the inventory area, and how certain factors (e.g., feed intake, system configuration, retention time)
are affecting emissions. Because of the relatively small amount of measurement data available for thesesystems worldwide, any new results can improve the understanding of these emissions and possibly theirprediction.

External review•

The inventory agency should utilise experts in manure management and animal nutrition to conduct expert
peer review of the methods and data used. While these experts may not be familiar with greenhouse gas
emissions, their knowledge of key input parameters to the emission calculation can aid in the overall
verification of the emissions. For example, animal nutritionists can evaluate N production rates to see ifthey are consistent with feed utilization research for certain livestock species. Practicing farmers canprovide insights into actual manure management techniques, such as storage times and mixed-system usage.
Wherever possible, these experts should be completely independent of the inventory process in order toallow a true external review.

It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Volume 1, Chapter 6, Quality Assurance/Quality Control and Verification. Whencountry-specific emission factors, fractions of N losses, N excretion rates, or manure management system usage
data have been used, the derivation of or references for these data should be clearly 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 categories
in Table 10.18. N2O emissions from all types of manure management systems are to 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 N2O emissions from managed 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, and paddock animals.

10.5.7

Use of worksheets

Use the worksheets for Livestock N2O contained in Annex 1 (AFOLU Worksheets) to calculate and reportinventory information for default methodologies described in Section 10.5 N2O emission from manure
management. The following is a summary of the step-by-step instructions to follow when completing theworksheets. Note that columns are referred to using the symbols of the variables that both appear in theequations, as well as in column headings of the worksheets.
Step 1: Calculation of N excretion from manure management systems (see worksheet for category Manure
Management: Direct N2O emissions from Manure Management, Category code 3A2, Sheet 1 of 1). Make extra
copies of the worksheet and complete one for each manure management systems (MMS).
Step 1A:
Collect population data from the Livestock Population Characterisation and enter
corresponding values in column N(T);
Step 1B:
Use default values for Nrate and TAM (Equation 10.30 and using data from Table 10.19 andTables 10A-4 to 10A-9) or develop the annual average nitrogen excretion rate per head (Nex(T)) for eachdefined livestock species/category T and enter these values in columns Nrate and TAM, or Nex(T), respectively;
Step 1C:
Enter in column MS(T,S) default values (see Tables 10A-4 through 10A-8 of Annex 10A.2) or
determine the fraction of total annual nitrogen excretion for each livestock species/category T that is
managed in each manure management system S (MS(T,S));
Step 1D:
Multiply the number of heads (column N(T)) by the value of N excretion rate per head (Nex(T))
for each livestock species/category T (column Nex(T)) and by the fraction of manure nitrogen per MMS(column MS(T,S)) in order to estimate total nitrogen excretion for each MMS in kilograms per year (column
NEMMS). Enter the results in column NEMMS of this sheet, and in column NEMMS of Sheet 1of 2 and Sheet 2
of 2 for worksheets under category Indirect N2O emissions from Manure Management, Category code 3C6.

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Volume 4: Agriculture, Forestry and Other Land Use

Step 2: Calculation of direct N2O emissions from manure management systems (see worksheet for category
Manure Management: Direct N2O emissions from Manure Management, Category code 3A2, Sheet 1 of 1).
Step 2A:
Use default values (see Table 10.21) or develop direct N2O emission factors for each manure
management system S (EF3(S)) and enter corresponding emission factor in the column EF3(S);Step 2B:
For each manure management system type S, multiply its emission factor (column EF3(S)) by
the amount of nitrogen managed (column NEMMS) in that system, to estimate direct N2O emissions per MMS.
Note that emissions estimates should be reported in kg of N2O. Enter the results in the column N2OD(mm) of
this sheet.
Step 3: Calculation of indirect N2O emissions from manure management systems (see worksheet for category
Indirect N2O emissions from Manure Management, Category code 3C6, Sheet 1 of 2). Make extra copies of theworksheet using one for each MMS).
Step 3A:
Enter in column FracGasMS default values (see Table 10.22) or determine country-specificfraction of managed livestock manure nitrogen that volatilises as NH3 and NOx for each defined livestock
species/category T per each MMS (FracGasMS);
Step 3B:
Multiply the fraction of manure nitrogen that volatilises as NH3 and NOx (column FracGasMS)by the total amount of nitrogen excreted in each MMS per livestock categories (column NEMMS) to estimate
amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx (Nvolatilizations-MMS);
Step 3C: Use default value (see Table 11.3, Chapter 11, Section 11.2 N2O emissions from managed soils)
or develop country-specific emission factor for indirect N2O emission from atmospheric deposition of
nitrogen on soils and water surfaces and enter the emission factor in the column EF4;
Step 3D:
Multiply the amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx(column Nvolatilizations-MMS) by the emission factor (column EF4), to calculate annual indirect N2O emissions
per MMS. Note that emissions estimates should be reported in kg of N2O. Enter the results in the column
N2OG(mm) of this sheet.
Step 4: Calculation of manure N that is available for application to soils or for use in feed, fuel or construction
purposes from manure management systems (see worksheet for category Indirect N2O emissions from Manure
Management, Category code 3C6, Sheet 2 of 2). Make extra copies of the worksheet using one for each MMS).
Step 4A:
Enter in column FraclossMS default values (see Table 10.23) or develop country-specific fractionof total nitrogen loss from manure managed in each MMS for each livestock species/category T (FraclossMS);
Step 4B:
If country-specific values for organic bedding usage are available for solid storage or deep
bedding MMS, calculate the amount of N from bedding by multiplying the number of animals associatedwith these two systems by the N content in bedding per animal. Enter results obtained in the column
NbeddingMS.
Step 4C:
Calculate managed manure N available for application to managed soils, feed, fuel orconstruction using Equation 10.34 and enter obtained results in column NMMS_Avb. Then sum over all
manure management systems. This value is used for calculation of N2O emissions from managed soils (seeworksheets in Annex 1).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Annex 10A.1 Data underlying methane default emission factors
for Enteric Fermentation
This annex presents the data used to develop the default emission factors for methane emissions from EntericFermentation. The Tier 2 method was implemented with these data to estimate the default emission factors for
cattle and buffalo.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.1
DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR DAIRY COWS IN TABLE 10.11
Weight,kg

Weight gain,kg day-1

Feeding Situation

Milk,
kg day-1Work,
hr day-1%PregnantDigestibility of
feed (DE%)

CH4 conversion
factor (Ym)North Americaa

600

0

Stall fed

23.0

0

90%

75%

6.5%

Western Europe

600

0Stall fed

16.4

0

90%

70%

6.5%

Eastern Europeb

550

0Stall fed

7.0

0

80%

60%

6.5%

Oceaniac

500

0Pasture/Range

6.0

0

80%

60%

6.5%

Latin Americad

400

0

Pasture/Range

2.2

0

80%

60%

6.5%

Asiae

350

0

Stall fed

4.5

0

80%

60%

6.5%

Africa & Middle East

275

0

Stall fed

1.3

0

67%

60%

6.5%

Indian Subcontinentf

275

0

Stall fed

2.5

0

50%

55%

6.5%

RegionsaBased on estimates for the United States.

b

Based on estimates for the former USSR.

c

Based on average estimate for region.

d

Based on estimates for Brazil.

eBased on estimates for China.

fBased on estimates for India.

Source: Gibbs and Johnson (1993).

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Chapter 10: Emissions from Livestock and Manure Management
TABLE 10A.2
DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR OTHER CATTLE IN TABLE 10.11
Subcategory

Weight,kg

Weight gain,kg day-1

Feeding situation

Milk,
kg day-1Work,
hr day-1

CH4 conversion
factor (Ym)Day weighted
population mix %Emission factors,
kg CH4 head-1 yr-1

60%

6.5%

36%

76

60%

6.5%

2%

81

%PregnantDigestibility of
feed (DE%)

80%

North Americaa
Mature females
Mature males

500

0.0

800

0.0

Pasture/Range

3.3

Pasture/Range

0.0

0.0
0.0

0%

Calves on milk

100

0.9

Pasture/Range

0.0

0.0

0%

NA

0.0%

16%

0

Calves on forage

185

0.9

Pasture/Range

0.0

0.0

0%

65%

6.5%

8%

48

Growing heifers/steers

265

0.7

Pasture/Range

0.0

0.0

0%

65%

6.5%

17%

55

Replacement/growing

375

0.4

Pasture/Range

0.0

0.0

0%

60%

6.5%

11%

66

Feedlot cattle

415

1.3

Stall fed

0.0

0.0

0%

75%

3.0%

11%

33

Western Europe
Mature males

600

0.0

Pasture/Range

0.0

0.0

0%

60%

6.5%

22%

66

Replacement/growing

400

0.4

Pasture/Range

0.0

0.0

0%

60%

6.5%

54%

73

Calves on milk

230

0.3

Pasture/Range

0.0

0.0

0%

65%

0.0%

15%

0

Calves on forage

230

0.3

Pasture/Range

0.0

0.0

0%

65%

6.5%

8%

35

Eastern Europeb
Mature females

500

0.0

Pasture/Range

3.3

0.0

67%

60%

6.5%

30%

75

Mature males

600

0.0

Pasture/Range

0.0

0.0

0%

60%

6.5%

22%

66

Young

230

0.4

Pasture/Range

0.0

0.0

0%

60%

6.5%

48%

45

OceaniacaMature females

400

0.0

Pasture/Range

2.4

0.0

67%

55%

6.5 %

51%

71

Mature males

450

0.0

Pasture/Range

0.0

0.0

0%

55%

6.5%

11%

61

Young

200

0.3

Pasture/Range

0.0

0.0

0%

55%

6.5%

38%

46

Based on estimates for the United States; . Based on estimates for the former USSR;b

2006 IPCC Guidelines for National Greenhouse Gas InventoriescBased on average estimate for region.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.2 (CONTINUED)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR OTHER CATTLE IN TABLE 10.11
Subcategory

Weight,kg

Weight gain,kg day-1

Feeding situation

Milk,
kg day-1Work,
hr day-1%PregnantDigestibilityof feed(DE%)

CH4conversion
factor (Ym)Day weighted
populationmix %Emissionfactors,kg CH4 head-1
yr-1

Latin Americad
Mature females

400

0.0

Large areas

1.1

0.0

67%

60%

6.5%

37%

64

Mature males

450

0.0

Large areas

0.0

0.0

0%

60%

6.5%

6%

61

Young

230

0.3

Large areas

0.0

0.0

0%

60%

6.5%

58%

49

Asiae
Mature females- Farming

325

0.0

Stall fed

1.1

0.55

33%

55%

6.5%

27%

50

Mature females- Grazing

300

0.0

Pasture/Range

1.1

0.00

50%

60%

6.5%

9%

46

Mature males-Farming

450

0.0

Stall fed

0.0

1.37

0%

55%

6.5%

24%

59

Mature males-Grazing

400

0.0

Pasture/Range

0.0

0.00

0%

60%

6.5%

8%

48

Young

200

0.2

Pasture/Range

0.0

0.00

0%

60%

6.5%

32%

36

AfricaMature females

200

0.0

Stall fed

0.3

0.55

33%

55%

6.5%

13%

32

Draft bullocks

275

0.0

Stall fed

0.0

1.37

0%

55%

6.5%

13%

41

Mature females- Grazing

200

0.0

Large areas

0.3

0.00

33%

55%

6.5%

6%

41

Bulls- Grazing

275

0.0

Large areas

0.0

0.00

0%

55%

6.5%

25%

49

Young

75

0.1

Pasture/Range

0.0

0.00

0%

60%

6.5%

44%

16

33%

50%

6.5%

40%

28

Indian SubcontinentfdMature females

125

0.0

Stall fed

0.6

0.00

Mature males

200

0.0

Stall fed

0.0

2.74

0%

50%

6.5%

10%

42

Young

80

0.1

Stall fed

0.0

0.00

0%

50%

6.5%

50%

23

Based on estimates for the Brazil.; e Based on estimates for the China.; f Based on estimates for India; Source: Gibbs and Johnson (1993)2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.74

Chapter 10: Emissions from Livestock and Manure Management
TABLE 10A.3
DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS FOR BUFFALO
Subcategory

Weight,Weight gain,kg

kg day-1

Feeding situation

Milk,

Work,

kg day-1

hr day-1%PregnantDigestibilityof feed(DE%)

CH4conversion
factor (Ym)Day weighed
population mix %

55%

6.5%

14%

Emissionsfactors,kg CH4 head-1 yr-1

Indian Subcontinenta
Adult males

350 - 550

0.00

Stall fed

0.00

1.37

0%

55 - 77

Adult females

250 - 450

0.00

Stall fed

2.70

0.55

33%

55%

6.5%

40%

57 - 80Young

100 - 300

0.15

Stall fed

0.00

0.00

0%

55%

6.5%

46%

23 - 50

Other Countriesb
Adult males

350 - 550

0.00

Stall fed

0.00

1.37

0%

55%

6.5%

45%

55 - 77

Adult females

250 - 450

0.00

Stall fed

0.65

0.55

25%

55%

6.5%

45%

45 - 67Young

100 - 300

0.15

Stall fed

0.00

0.00

0%

55%

6.5%

10%

23 - 50aBased on estimates for India.

b

Based on estimates for China.

Source: Gibbs and Johnson (1993).

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Volume 4: Agriculture, Forestry and Other Land Use

Annex 10A.2 Data underlying methane default emission factors
for Manure Management
This annex presents the data used to develop the default emission factors for methane emissions from Manure
Management. The Tier 2 method was implemented with these data to estimate the default emission factors foreach livestock category.

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Chapter 10: Emissions from Livestock and Manure Management
Table 10A-4
Manure Management Methane Emission Factor Derivation for Dairy Cows
Annual Average Temperature (°C)
Cool

Temp

Warm

Region

Massa
kg

d

Lagoon1
66%
68%
70%
71%
73%
74%
75%
76%
77%
77%
78%
78%
78%
79%
79%
79%
79%
80%
80%

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Dairy Cow CharacteristicsbBo

m3CH4/kg VS

Liquid/Slurry1
17%
19%
20%
22%
25%
27%
29%
32%
35%
39%
42%
46%
50%
55%
60%
65%
71%
78%
80%

VSc

Manure Management System MCFs
Pasture/Range/
Daily
BurnedDrylotPaddock Spread Digester
for Fuel
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.5%
1.5%
0.5%
10.0%
10.0%
2.0%
2.0%
1.0%
10.0%
10.0%
2.0%
2.0%
1.0%
10.0%
10.0%
2.0%
2.0%
1.0%
10.0%
10.0%

Solid
Storage
2.0%
2.0%
2.0%
2.0%
2.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
5.0%
5.0%
5.0%

Other
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%

Manure Management System Usage (MS%)

Cool

kg/hd/day

North America
604
0.24
5.4
Western Europe
600
0.24
5.1
Eastern Europe
550
0.24
4.5
Oceania
500
0.24
3.5
Latin America
400
0.13
2.9
Africa275
0.13
1.9
Middle East
275
0.13
1.9
Asia
350
0.13
2.8
Indian Subcontinent
275
0.13
2.6
a
Average dairy cow mass for each region (default estimates are ±10%)b
Bo estimates are ±15%

Emission Factors
kg CH4 per head per year

15.0%
0.0%
0.0%
16.0%
0.0%
0.0%
0.0%
4.0%
0.0%

27.0%
35.7%
17.5%
1.0%
1.0%
0.0%
1.0%
38.0%
1.0%

26.3%
36.8%
60.0%
0.0%
1.0%
1.0%
2.0%
0.0%
0.0%

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

10.8%
20.0%
18.0%
76.0%
36.0%
83.0%
80.0%
20.0%
27.0%

18.4%
7.0%
2.5%
8.0%
62.0%
5.0%
2.0%
29.0%
19.0%

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
2.0%
1.0%

0.0%
0.0%
0.0%
0.0%
0.0%
6.0%
17.0%
7.0%
51.0%

2.6%
0.5%
2.0%
0.0%
0.0%
4.0%
0.0%
0.0%
0.0%

Temperate

Warm

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

48
21
11
23
1
1
2
9
5

50
23
12
24
1
1
2
10
5

53
25
13
25
1
1
2
10
5

55
27
14
26
1
1
2
11
5

58
29
15
26
1
1
2
12
5

63
34
20
27
1
1
2
13
5

65
37
21
28
1
1
2
14
5

68
40
22
28
1
1
2
15
5

71
43
23
28
1
1
2
16
5

74
47
25
29
1
1
2
17
5

78
51
27
29
1
1
2
18
5

81
55
28
29
1
1
2
20
5

85
59
30
29
1
1
2
21
5

89
64
33
29
1
1
2
23
5

93
70
35
30
1
1
2
24
5

98 105 110 112
75
83
90
92
37
42
45
46
30
31
31
31
1
2
2
2
1
1
1
1
2
2
3
3
26
28
31
31
5
5
6
6

27

28

1
Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius
Average VS production per head per day for the average dairy cow (default estimates are equation relating temperature to biological activity. Lagoon MCFs are also calculated
based on longer (up to a year) retention times. [Mangino, et. al (2001)]
±20%)
c

d

For North America, "Other" manure management system MCFs represent deep pits, whichhave the same MCF values as Liquid/Slurry.
Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, dairy cow mass values are from Safley (2000) and VS values are estimated based on an analysis of feed data from Petersen et.al (2003). North American manure management
system usage values are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. Bo values are from Morris (1976) andBryant, et.al. (1976). For Western and Eastern Europe manure management system usage, mass and VS values based on the analysis of national GHG inventories of Annex I countires submitted to thesecretariat UNFCCC in 2004. For the rest of the world, the detailed information for dairy cows are developed in Gibbs and Johnson (1993), and manure management system usage and Bo estimates arefrom Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of thesesystems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.77

Volume 4: Agriculture, Forestry and Other Land Use

Table 10A-5
Manure Management Methane Emission Factor Derivation for Other Cattle
Manure Management System MCFs
Pasture/Annual Average Temperature (°C)
Liquid/ Solid
Range/
Daily
Burned11LagoonSlurryStorage
DrylotPaddock
Spread Digester for Fuel Other
Cool
10
66%
17%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
11
68%
19%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
12
70%
20%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
13
71%
22%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
14
73%
25%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
Temp
15
74%
27%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
16
75%
29%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
17
76%
32%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
18
77%
35%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
19
77%
39%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
20
78%
42%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
21
78%
46%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
22
78%
50%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
23
79%
55%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
24
79%
60%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
25
79%
65%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
Warm
26
79%
71%
5.0%
2.0%
2.0%
1.0%
10.0%
10.0%
1.0%
27
80%
78%
5.0%
2.0%
2.0%
1.0%
10.0%
10.0%
1.0%
28
80%
80%
5.0%
2.0%
2.0%
1.0%
10.0%
10.0%
1.0%
Other Cattle Characteristics

Region

Massa

Bob

VSc

kg
m3CH4/kg VS
kg/hd/day
North America
389
0.19
2.4
Western Europe
420
0.18
2.6
Eastern Europe
391
0.17
2.7
Oceania
330
0.17
3.0
Latin America
305
0.1
2.5
Africa173
0.1
1.5
Middle East
173
0.1
1.5
Asia
319
0.1
2.3
Indian Subcontinent
110
0.1
1.4
a
Average other cattle mass for each region (default estimates are ±25%)b
Bo estimates are ±15%

Manure Management System Usage (MS%)
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

0.2%
25.2%
22.5%
0.0%
0.0%
0.0%
0.0%
0.0%
1.0%

0.0%
39.0%
44.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

18.4%
0.0%
0.0%
9.0%
0.0%
1.0%
1.0%
46.0%
4.0%

81.5%
32.0%
20.0%
91.0%
99.0%
95.0%
79.0%
50.0%
22.0%

0.0%
1.8%
0.0%
0.0%
0.0%
1.0%
2.0%
2.0%
20.0%

Emission Factors
kg CH4 per head per year
Cool

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.0%

0.0%
0.0%
0.0%
0.0%
0.0%
3.0%
17.0%
2.0%
53.0%

0.0%
2.0%
13.5%
0.0%
1.0%
0.0%
2.0%
0.0%
0.0%

Temperate

Warm

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
6
7
7
8
8 10 11 12 13 14 15 16 17 18 20 21 24 25 26
6
6
7
7
8
9 10 11 11 12 13 14 15 16 18 19 21 23 23
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2

1
Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius
Average VS production per head per day for the average non-dairy cow (default estimates equation relating temperature to biological activity. Lagoon MCFs are also calculated
based on longer (up to a year) retention times. [Mangino, et. al (2001)]
are ±35%)c

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, other cattle mass are from Safley (2000) and USDA's Agricultural Waste Management Field Handbook and VS values are estimated based on an analysis of feed data fromPetersen, et.al (2003). North American manure management system usage values are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health
Monitoring System Reports. Bo data are values reported in Hashimoto (1981). For Western and Eastern Europe manure management system usage, average mass, Bo, and VS values based on theanalysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the rest of the world, the detailed information for cattle are developed in Gibbs andJohnson (1993), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury and Hashimoto (1993). MCFs for lagoons
and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.78

Chapter 10: Emissions from Livestock and Manure Management
Table 10A-6
Manure Management Methane Emission Factor Derivation for Buffalo
Manure Management System MCFs
Pasture/Annual Average Temperature (°C)
Liquid/Solid
Range/
Daily
Burned11LagoonSlurryStorage Drylot
Paddock
Spread Digester for Fuel Other
Cool
10
66%
17%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
11
68%
19%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
12
70%
20%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
13
71%
22%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
14
73%
25%
2.0%
1.0%
1.0%
0.1%
10.0%
10.0%
1.0%
Temp
15
74%
27%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
16
75%
29%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
17
76%
32%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
18
77%
35%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
19
77%
39%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
20
78%
42%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
21
78%
46%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
22
78%
50%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
23
79%
55%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
24
79%
60%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
25
79%
65%
4.0%
1.5%
1.5%
0.5%
10.0%
10.0%
1.0%
Warm
26
79%
71%
5.0%
2.0%
2.0%
1.0%
10.0%
10.0%
1.0%
27
80%
78%
5.0%
2.0%
2.0%
1.0%
10.0%
10.0%
1.0%
28
80%
80%
5.0%
2.0%
2.0%
1.0%
10.0%
10.0%
1.0%

Regiona

Masskg

Buffalo Characteristics
VSBo

m3CH4/kg VS(not applicable)
0.1
0.1(not applicable)
0.1(not applicable)
0.1
0.1
0.1

b

Manure Management System Usage (MS%)

Cool

Emission Factors (EF) for each region are calculated based on eq.10.23.

3.9
3.9

0%
0%

20%
24%

0%
0%

3.9

0%

0%

0%

3.9
3.9
3.1

0%
0%
0%

0%
0%
0%

0%
0%
0%

(not applicable)
79%
0%
0%
29%
(not applicable)
0%99%(not applicable)
0%
20%
41%
50%
4%
19%

0%
0%

Temperate

10

11

12

13

14

15

16

0%
47%

4
5

4
5

5
5

5
6

5
6

6
7

7
8

kg/hd/day

North America
Western Europe
380
Eastern Europe
380
Oceania
Latin America
380
AfricaMiddle East
380
Asia
380
Indian Subcontinent
295
aAverage buffalo mass for each regionb
Average VS production per head per day for the average buffalo

Emission Factors
kg CH4 per head per year

0%
0%

0%
0%

0%

0%

0%

1%

1

1

1

1

1

1

1

19%
4%
21%

0%
0%
1%

42%
5%
55%

19%
0%
0%

4
1
4

4
1
4

4
1
4

4
1
4

4
1
4

5
2
5

5
2
5

Warm

17

18 19 20 21 22 23 24 25 26 27 28
Not Applicable7899 10 11 12 13 14 15 16 178
9 10 11 11 12 13 15 16 17 19 19
Not Applicable
1
1
1
1
1
1
1
1
1
2
2
2
Not Applicable
5
5
5
5
5
5
5
5
5
5
5
5
2
2
2
2
2
2
2
2
2
2
2
2
5
5
5
5
5
5
5
5
5
5
5
5

1

Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius
equation relating temperature to biological activity. Lagoon MCFs are also calculated
based on longer (up to a year) retention times. [Mangino, et. al (2001)]

Sources: The detailed information for buffalo are developed in Gibbs and Johnson (1993),and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data
are from Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.79

Volume 4: Agriculture, Forestry and Other Land Use

Table 10A-7
Manure Management Methane Emission Factor Derivation for Market Swine
Manure Management System MCFs
Annual Average Temperature (°C)
Cool

Temp

Warm

Market Swine Characteristics

Region

Massa

Liquid/ Solid
Lagoon1 Slurry1 Storage Drylot
66%
17%
2.0%
1.0%
68%
19%
2.0%
1.0%
70%
20%
2.0%
1.0%
71%
22%
2.0%
1.0%
73%
25%
2.0%
1.0%
74%
27%
4.0%
1.5%
75%
29%
4.0%
1.5%
76%
32%
4.0%
1.5%
77%
35%
4.0%
1.5%
77%
39%
4.0%
1.5%
78%
42%
4.0%
1.5%
78%
46%
4.0%
1.5%
78%
50%
4.0%
1.5%
79%
55%
4.0%
1.5%
79%
60%
4.0%
1.5%
79%
65%
4.0%
1.5%
79%
71%
5.0%
2.0%
80%
78%
5.0%
2.0%
80%
80%
5.0%
2.0%

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

Bob

VSc

kg/hd/daykg
m3CH4/kg VS
46
0.48
0.27
North America
Western Europe
50
0.45
0.3
Eastern Europe
50
0.45
0.3
Oceania
45
0.45
0.28
Latin America
28
0.29
0.3
Africa28
0.29
0.3
Middle East
28
0.29
0.3
Asia
28
0.29
0.3
Indian Subcontinent
28
0.29
0.3
a
Average marker swine mass for each region (default estimates are ±20%)b
Bo estimates are ±15%
c

Pit
Pit
<1 month>1 month
3.0%
17%
3.0%
19%
3.0%
20%
3.0%
22%
3.0%
25%
3.0%
27%
3.0%
29%
3.0%
32%
3.0%
35%
3.0%
39%
3.0%
42%
3.0%
46%
3.0%
50%
3.0%
55%
3.0%
60%
3.0%
65%
30.0%
71%
30.0%
78%
30.0%
80%

Daily
SpreadDigester
0.1%
10.0%
0.1%
10.0%
0.1%
10.0%
0.1%
10.0%
0.1%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
1.0%
10.0%
1.0%
10.0%
1.0%
10.0%

Other
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%

Manure Management System Usage (MS%)
32.8%
8.7%
3.0%
54.0%
0.0%
0.0%
0.0%
0.0%
9.0%

Average VS production per head per day for the average market swine (default estimates
are ±25%)

18.5%
0.0%
0.0%
0.0%
8.0%
6.0%
14.0%
40.0%
22.0%

4.2%
13.7%
42.0%
3.0%
10.0%
6.0%
0.0%
0.0%
16.0%

4.0%
0.0%
0.0%
15.0%
41.0%
87.0%
69.0%
54.0%
30.0%

0.0%
2.8%
24.7%
0.0%
0.0%
1.0%
0.0%
0.0%
3.0%

40.6%
69.8%
24.7%
0.0%
0.0%
0.0%
17.0%
0.0%
0.0%

Emission Factors
kg CH4 per head per year
Cool

0.0%
2.0%
0.0%
0.0%
2.0%
0.0%
0.0%
0.0%
9.0%

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
7.0%
8.0%

0.0%
3.0%
5.7%
28.0%
40.0%
0.0%
0.0%
0.0%
3.0%

Temperate

Warm

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
10 11 11 12 12 13 13 14 15 15 16 17 18 18 19 20 22 23 23
6
6
7
7
8
9
9 10 11 11 12 13 14 15 16 18 19 21 21
3
3
3
3
3
4
4
4
4
5
5
5
6
6
6
7 10 10 10
11 11 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
2
2
2
2
2
3
3
3
3
4
4
4
5
5
5
6
2
2
2
2
2
3
3
3
3
4
4
4
5
5
5
6
6
7
7
2
2
3
3
3
3
3
3
4
4
4
4
4
5
5
5
6
6
6

1

Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius
equation relating temperature to biological activity. Lagoon MCFs are also calculated
based on longer (up to a year) retention times. [Mangino, et. al (2001)]

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, mass, VS, and Bo values are from Safley (2000), USDA's Agricultural Waste Management Field Handbook, and Hashimoto (1984), respectively. North American manure
management system usage data are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. For Western and Eastern Europe
manure management system usage, mass of animals, Bo and VS values based on the analysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the rest of
the world, swine feed intake data are from Crutzen et. al (1986), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from Woodbury
and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.80

Chapter 10: Emissions from Livestock and Manure Management
Table 10A-8
Manure Management Methane Emission Factor Derivation for Breeding Swine
Manure Management System MCFs
Annual Average Temperature (°C)
Cool

Temp

Warm

Liquid/ Solid
Pit
Pit
Lagoon1 Slurry1 Storage Drylot <1 month >1 month
66%
17%
2.0%
1.0%
3.0%
17%
68%
19%
2.0%
1.0%
3.0%
19%
70%
20%
2.0%
1.0%
3.0%
20%
71%
22%
2.0%
1.0%
3.0%
22%
73%
25%
2.0%
1.0%
3.0%
25%
74%
27%
4.0%
1.5%
3.0%
27%
75%
29%
4.0%
1.5%
3.0%
29%
76%
32%
4.0%
1.5%
3.0%
32%
77%
35%
4.0%
1.5%
3.0%
35%
77%
39%
4.0%
1.5%
3.0%
39%
78%
42%
4.0%
1.5%
3.0%
42%
78%
46%
4.0%
1.5%
3.0%
46%
78%
50%
4.0%
1.5%
3.0%
50%
79%
55%
4.0%
1.5%
3.0%
55%
79%
60%
4.0%
1.5%
3.0%
60%
79%
65%
4.0%
1.5%
3.0%
65%
79%
71%
5.0%
2.0%
30.0%
71%
80%
78%
5.0%
2.0%
30.0%
78%
80%
80%
5.0%
2.0%
30.0%
80%

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

Breeding Swine Characteristics
Region

a

Mass

Bo

b

VS

c

kg
m3CH4/kg VS
kg/hd/day
North America
198
0.48
0.5
Western Europe
198
0.45
0.46
Eastern Europe
180
0.45
0.5
Oceania
180
0.45
0.5
Latin America
28
0.29
0.3
Africa28
0.29
0.3
Middle East
28
0.29
0.3
Asia
28
0.29
0.3
Indian Subcontinent
28
0.29
0.3
a
Average breed swine mass for each region (default estimates are ±20%)b
Bo estimates are ±15%c

Daily
SpreadDigester
0.1%
10.0%
0.1%
10.0%
0.1%
10.0%
0.1%
10.0%
0.1%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
0.5%
10.0%
1.0%
10.0%
1.0%
10.0%
1.0%
10.0%

Other
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%

Manure Management System Usage (MS%)
32.8%
8.7%
3.0%
54.0%
0.0%
0.0%
0.0%
0.0%
9.0%

Average VS production per head per day for the average breed swine (default estimates
are ±25%)

18.5%
0.0%
0.0%
0.0%
8.0%
6.0%
14.0%
40.0%
22.0%

4.2%
13.7%
42.0%
3.0%
10.0%
6.0%
0.0%
0.0%
16.0%

4.0%
0.0%
0.0%
15.0%
41.0%
87.0%
69.0%
54.0%
30.0%

0.0%
2.8%
24.7%
0.0%
0.0%
1.0%
0.0%
0.0%
3.0%

40.6%
69.8%
24.7%
0.0%
0.0%
0.0%
17.0%
0.0%
0.0%

Emission Factors
kg CH4 per head per year
Cool

0.0%
2.0%
0.0%
0.0%
2.0%
0.0%
0.0%
0.0%
9.0%

0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
7.0%
8.0%

0.0%
3.0%
5.7%
28.0%
40.0%
0.0%
0.0%
0.0%
3.0%

Temperate

Warm

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
19 20 21 22 23 24 26 27 28 29 31 32 34 35 37 39 41 44 45
9 10 10 11 12 13 14 15 16 17 19 20 22 23 25 27 29 32 33
4
5
5
5
5
6
7
7
7
8
8
9
9 10 11 12 16 17 17
20 20 21 21 22 22 23 23 23 23 23 24 24 24 24 24 24 24 24
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
2
2
2
2
2
3
3
3
3
4
4
4
5
5
5
6
2
2
2
2
2
3
3
3
3
4
4
4
5
5
5
6
6
7
7
2
2
3
3
3
3
3
3
4
4
4
4
4
5
5
5
6
6
6

1

Lagoon and Liquid/Slurry MCFs are calculated based on the van't Hoff-Arrhenius
equation relating temperature to biological activity. Lagoon MCFs are also calculated
based on longer (up to a year) retention times. [Mangino, et. al (2001)]

Emission Factors (EF) for each region are calculated based on eq.10.23.

Sources: For North America, mass, VS, and Bo values are from Safley (2000), USDA's Agricultural Waste Management Field Handbook, and Hashimoto (1984), respectively. North American manure
management system usage data are estimated using data from the 1992 and 1997 USDA's Census of Agriculture and National Animal Health Monitoring System Reports. For Western and Eastern Europe
manure management system usage, mass of animals, Bo and VS values based on the analysis of national GHG inventories of Annex I countires submitted to the secretariat UNFCCC in 2004. For the restof the world, swine feed intake data are from Crutzen et. al (1986), and manure management system usage and Bo estimates are from Safley et. al (1992). Methane conversion factor data are from
Woodbury and Hashimoto (1993). MCFs for lagoons and liquid/slurry systems are based on data obtained from an analysis of these systems in the United States.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.81

Volume 4: Agriculture, Forestry and Other Land Use

0.10
0.10
0.10
0.10
0.10
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.20
0.20
0.20

Temperate

Annual Average
Temperature (°C)

Warm

Developing

0.19
0.19
0.19
0.19
0.19
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.37
0.37
0.37

Developing

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

Ducks

Cool

Turkeys

Warm

Broilers

Annual Average
Temperature (°C)

Layers (wet)Temperate

Poultry
Developed

238
0.7
5.96
4
1.72
0.26

130
0.7
3.25
4
0.94
0.33

130
0.7
3.25
4
0.94
0.26

1.8
NR
NR
NR
0.02
0.39

1.8
NR
NR
NR
0.02
0.39

0.9
NR
NR
NR
0.01
0.36

6.8
NR
NR
NR
0.07
0.36

2.7
NR
NR
NR
0.02
0.36

NR
NR
NR
NR
0.02
0.24

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%

65%
68%
70%
73%
74%
75%
76%
76%
77%
78%
78%
78%
78%
79%
79%
80%
80%
80%
80%

1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%

1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

1.09
1.09
1.09
1.09
1.09
1.64
1.64
1.64
1.64
1.64
1.64
1.64
1.64
1.64
1.64
1.64
2.19
2.19
2.19

0.76
0.76
0.76
0.76
0.76
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.14
1.52
1.52
1.52

0.60
0.60
0.60
0.60
0.60
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
1.20
1.20
1.20

0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03

1.13
1.18
1.21
1.26
1.28
1.30
1.31
1.32
1.33
1.35
1.35
1.36
1.36
1.37
1.38
1.38
1.38
1.39
1.39

0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02

0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09

0.02
0.02
0.02
0.02
0.02
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03

0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02

Developed

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

Developing

1.0%
1.0%
1.0%
1.0%
1.0%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
2.0%
2.0%
2.0%

Developed

10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

377
217
217
38.5
30
0.7
0.5
0.5
0.6
0.5
5.42
5.96
5.42
0.76
0.76
4
8
8
8
8
2.13
2.49
2.49
0.3
0.35
0.21
0.3
0.26
0.18
0.13
Manure Management System MCFs
1.0%
1.0%
1.0%
1.0% 1.0%
1.0%
1.0%
1.0% 1.0%
1.0%
1.0% 1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0% 1.0%
1.0%
1.0%
1.0%
1.0% 1.0%
1.0%
1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5%
1.5% 1.5%
1.5%
1.5% 1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
1.5% 1.5%
2.0%
2.0%
2.0%
2.0% 2.0%
2.0%
2.0% 2.0%
2.0%
2.0%
2.0%
2.0%
2.0% 2.0%
2.0%
Emission Factors (kg CH4 per head per year)
1.58
1.28
1.56
0.13
0.11
1.56
0.13
0.11
1.58
1.28
1.28
1.56
0.13
0.11
1.58
1.58
1.28
1.56
0.13
0.11
1.58
1.28
1.56
0.13
0.11
0.20
0.17
2.37
1.92
2.34
2.37
1.92
2.34
0.20
0.17
2.37
1.92
2.34
0.20
0.17
2.37
1.92
2.34
0.20
0.17
2.37
1.92
2.34
0.20
0.17
0.20
0.17
2.37
1.92
2.34
1.92
2.34
0.20
0.17
2.37
2.37
1.92
2.34
0.20
0.17
0.20
0.17
2.37
1.92
2.34
0.17
2.37
1.92
2.34
0.20
0.20
0.17
2.37
1.92
2.34
0.22
3.17
2.56
3.13
0.26
0.22
3.17
2.56
3.13
0.26
2.56
3.13
0.26
0.22
3.17

Mule/Asses
Layers (dry)CoolDeveloping

Developed

28
0.5
0.7
8
0.32
0.13

Mass (kg)
Digest (%)
Intake/d (kg Feed)% Ash (Dry Basis)
VS/day (kg VS)
Bo (m3/kg VS)

Developed

Developing

48.5
0.60
1.08
8.00
0.40
0.19

Country

Developed

Animal Characteristics

Developing

Table 10A-9
Manure Management Methane Emission Factor Derivation
Sheep
Goats
HorsesCamelsAnimalNR = Not reported.
Emission factors, except for poultry, were developed from feed intake values and feed digestibilities used to develop the enteric fermentation emission factors (see Appendix 10A.1). MCFs and Bo values are reported in Woodbury
and Hashimoto (1993). All manure except for Layers (wet) is assumed to be managed in dry systems, which is consistent with the manure management system usage reported in Woodbury and Hashimoto (1993). Poultry fordeveloped countries was subdivided into five categories. Layers (dry) represent layers in a "without bedding" waste management system; Layers (wet) represent layers in an anaerobic lagoon waste managemnet system.
Estimates of animal mass are ±30%, VS values are ±50% and Bo values are ±15%2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.82

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A-9 (CONTINUED)
MANURE MANAGEMENT METHANE EMISSION FACTOR DERIVATION FOR OTHER ANIMALS
Animal Characteristics

Manuremanagement
system MCF

Emissionfactors(kg CH4 head-1
yr-1)

Animal

Mass
(kg)

VS
(kg VS day-1)Bo(m3 kg VS)

Deer a

NR

NR

NR

NR

0.22

Reindeer b

NR

0.39

0.19

2.0%

0.36

Rabbits c

1.60

0.10

0.32

1.0%

0.08

Fur-bearing
animals b

NR

0.14

0.25

8.0%

0.68

Ostrich b

NR

1.16

0.25

8.0%

5.67

a

Sneath (1997) cited in the GHG inventory of United Kingdom.

b

Estimations of Agricultural University of Norway, Institute of Chemistry and Biotechnology, Section for Microbiology.

cData obtained from GHG inventory of Italy, 2004.

NR = not reported

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

References
SECTION 10.2 LIVESTOCK POPULATION AND FEED CHARACTERISATION
AAC (Australian Agricultural Council) (1990). Feed Standards for Australian Livestock Ruminants.
Commonwealth Scientific and Industrial Research Organization (CSIRO) Publications, East Melbourne,
Victoria, Australia.
AFRC Technical Committee on Responses to Nutrients (1990). Nutritive Requirements of Ruminant Animals:
Energy. Rep. 5, CAB International, Wallingford, U.K.
Agricultural and Food Research Council (AFRC) Technical Committee on Responses to Nutrients (1993).
Energy and Protein Requirements of Ruminants. 24-159, CAB International, Wallingford, U.K.
Bamualim, A. and Kartiarso (1985). ‘Nutrition of draught animals with special reference to Indonesia.’ In:
Draught Animal Power for Production. J.W. Copland (ed.). Australian Centre for International
agricultural Research (ACIAR), Proceedings Series No. 10. ACIAR, Canberra, A.C.T., Australia.
Food and Agriculture Organisation (FAO) (1999). Statistical Database.
Gibbs, M.J. and Johnson, D.E. (1993). "Livestock Emissions." In: International Methane Emissions, US
Environmental Protection Agency, Climate Change Division, Washington, D.C., U.S.A.
Gibbs, M.J., Conneely, D., Johnson, D., Lassey, K.R. and Ulyatt, M.J. (2002). CH4 emissions from entericfermentation. In: Background Papers: IPCC Expert Meetings on Good Practice Guidance and Uncertainty
Management in National Greenhouse Gas Inventories, p 297–320. IPCC-NGGIP, Institute for Global
Environmental Strategies (IGES), Hayama, Kanagawa, Japan.
Ibrahim, M.N.M. (1985). ‘Nutritional status of draught animals in Sri Lanka.’ In: Draught Animal Power for
Production, J.W. Copland (ed.). ACIAR (Australian Centre for International Agricultural Research)
Proceedings Series No. 10. ACIAR, Canberra, A.C.T., Australia.
Jurgen, M.H. (1988). Animal Feeding and Nutrition, Sixth Edition, Kendall/Hunt Publishing Company,
Dubuque, Iowa, U.S.A.
Lawrence, P.R. (1985). ‘A review of nutrient requirements of draught oxen.’ In: Draught Animal Power for
Production. J.W. Copland (ed.). ACIAR (Australian Centre for International Agricultural Research)
Proceedings Series No. 10. ACIAR, Canberra, A.C.T., Australia.
National Research Council (NRC) (1984). Nutrient Requirements of Beef Cattle, National Academy Press,
Washington, D.C. U.S.A.
NRC (1989). Nutrient Requirements of Dairy Cattle, 6th, National Academy Press, Washington, D.C. U.S.A.
NRC (1996). Nutrient Requirements of Beef Cattle, National Academy Press, Washington, D.C. U.S.A.
NRC (2001). Nutrient Requirements of Dairy Cattle, 7th Ed., Nat. Acad. Press, Washington, DC.

SECTION 10.3 METHANE EMISSIONS FROM DOMESTIC LIVESTOCK ENTERICFERMENTATION
Clark, H., Brookes, I. and Walcroft, A. (2003). Enteric methane emissions from New Zealand ruminants 19992001 calculated using an IPCC Tier 2 approach. http://www.climatechange.govt.nz/resources/reports/nirapr03/.
Crutzen, P.J., Aselmann, I. and Seiler, W. (1986). "Methane Production by Domestic Animals, Wild Ruminants,
Other Herbivorous Fauna, and Humans," Tellus 38B:271-284.
Diarra, B. (1994). Net energy value of soybean hulls as feed for sheep. Dissertation. Colorado State University,
Ft Collins, CO.
Donovan, K. and Baldwin, L. (1999). Results of the AAMOLLY model runs for the Enteric Fermentation
Model. University of California, Davis.
Hindrichsen, I., Kreuzer, M., Machmuller, A., Knudsen, K. E., Madsen, J. and Wettstein, H.R. (2003). Methane
release and energy expenditure of dairy cows fed concentrates characterized by different carbohydrates.
In: Prog. in Res. En. & Prot. Metabol. (Souffrant, W.B, and CC. Metges, eds.) Wageningen Acad. Pub,
The Netherlands, EAAP Publ. 109:413-416.
Johnson, K., Huyler, M., Westberg, H., Lamb, B. and Zimmerman, P. (1994). Measurement of methane
emissions from ruminant livestock using a SF6 tracer technique. Environmental. Sci. Tech., 28: 359-362.

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Johnson, K.A. and Johnson, D.E. (1995). Methane emissions from cattle. J. Anim. Sci., 73: 2483-2492
Judd, M.J., Kelliher, F.M., Ulyatt, M.J., Lassey, K.R., Tate, K.R., Shelton, I.D., Harvey, M.J. and Walker, C.F.
(1999). Net methane emissions from grazing sheep, Global Change Biol., 5, pp. 647–657.
Kujawa, M. (1994). Energy partitioning in steers fed cottonseed hulls or sugar beet pulp. Dissertation, Colorado
State University, Ft Collins, CO.
Kurihara, M., Magner, T., Hunter, R.A. and McCrabb, G.J. (1999). Methane production and energy partition of
cattle in the tropics. British Journal of Nutrition, 81, pp. 227-234.
Lassey, K.R. (2006). Livestock methane emission: from the individual grazing animal through national
inventories to the global methane cycle. Agric. For. Meteorol. (in press).
Lassey, K.R., Ulyatt, M.J., Martin, R.J., Walker, C.F. and Shelton, I.D. (1997). Methane emissions measured
directly from grazing livestock in New Zealand, Atmos. Environ., 31, pp. 2905-2914.
Leuning, R., Baker, S.K., Jamie, I.M., Hsu, C.H., Klein, L., Denmead, O.T. and Griffith, D.W.T. (1999).
Methane emission from free-ranging sheep: a comparison of two measurement methods, Atmos. Environ.,33, pp. 1357–1365.
Murray, B.R., Bryant, A.M. and Leng, R.A. (1978). Methane production in the rumen and lower gut of sheep
given lucerne chaff: effect of level of intake, Br. J. Nutr., 39, pp. 337-345.
National Research Council (NRC) (1989). Nutrient Requirements of Dairy Cattle, 6th Ed., Nat. Acad. Press,
Washington, DC.
National Research Council (NRC) (1996). Nutrient Requirements of Beef Cattle, 7th Edit., Nat. Acad. Press,
Washington, DC.
National Research Council (NRC) (2001). Nutrient Requirements of Dairy Cattle, 7th Ed., Nat. Acad. Press,
Washington, DC.
Pinares-Patino, C.S., Ulyatt, M.J., Waghorn, G.C., Lassey, K.R., Barry, T.N., Holmes, C.W. and Johnson, D.E.
(2003). Methane emission by alpaca and sheep fed on Lucerne hay or grazed on pastures of perennial
ryegrass/white clover or birds foot trefoil. J. Agric. Sci. 140:215-226.
Ulyatt, M.J., Lassey, K.R., Shelton, I.D. and Walker, C.F. (2002a). “Seasonal variation in methane emission
from dairy cows and breeding ewes grazing ryegrass/white clover pasture in New Zealand.” New Zealand
Journal of Agricultural Research 45:217–226.
Ulyatt, M.J., Lassey, K.R., Shelton, I.D. and Walker, C.F. (2002b). “Methane emission from dairy cows andwether sheep fed subtropical grass-dominant pastures in midsummer in New Zealand.” New Zealand
Journal of Agricultural Research 45:227–234.
Ulyatt, M.J., Lassey, K.R., Shelton, I.D. and Walker, C.F. (2005). Methane emission from sheep grazing four
pastures in late summer in New Zealand. New Zealand Journal Agricultural Research 48: 385-390.

SECTION 10.4 METHANE EMISSIONS FROM MANURE MANAGEMENT
Amon, B., Amon, Th., Boxberger, J. and Pollinger, A. (1998). Emissions of NH3, N2O, and CH4 from composted
and anaerobically stored farmyard manure. Pages 209-216 in Martinez J, Maudet M-N (eds) Ramiran 98,
Proc. 8th Int. Conf. on the FAO ESCORENA Network on Recycling of Agricultural, Municipal andIndustrial Residues in Agriculture. Rennes, France.
Amon, B., Amon, Th., Boxberger, J. and Alt, Ch. (2001). Emissions of NH3, N2O, and CH4 from dairy cows
housed in a farmyard manure tying stall (Housing, Manure Storage, Manure Spreading). Nutrient Cyclingin Agroecosystems, 60: pp. 103-113.
ASAE (1999). ASAE Standards 1999, 46th Edition. American Society of Agricultural Engineers, St. Joseph, MI.
Hashimoto, A. and Steed, J. (1993). Methane emissions from typical U.S. livestock manure management systems.
Draft report prepared for ICF Incorporated under contract to the Global Change Division of the Office ofAir and Radiation, US Environmental Protection Agency, Washington, D.C.
Hill, D.T. (1982). Design of digestion systems for maximum methane production. Transactions of the ASAE,
25(1): pp. 226-230.
Hill, D.T. (1984). Methane productivity of the major animal types. Transactions of the ASAE 27(2): pp. 530-540.
IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho
L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental
Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.

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Mangino, J., Bartram, D. and Brazy, A. (2001). Development of a methane conversion factor to estimate
emissions from animal waste lagoons. Presented at U.S. EPA’s 17th Annual Emission Inventory
Conference, Atlanta GA, April 16-18, 2002.
Moller, H.B., Sommer, S.G. and Ahring, B. (2004). Biological degradation and greenhouse gas emissions during
pre-storage of liquid animal manure. Journal of Environmental Quality, 33: pp. 27-36.
Peterson, K. and Jacobs, H. (2003). 1990-2002 Volatile solids and Nitrogen excretion rates deliverable under
EPA Contract No. GS-10F-0124J, Task Order 004-02. Memorandum to EPA from ICF Consulting.
August 28, 2003.
Safley, L.M., Casada, M.E., Woodbury, J.W. and Roos, K.F. (1992). Global Methane Emissions from Livestockand Poultry Manure. US Environmental Protection Agency, Global Change Division, Washington, D.C.,
February 1992, EPA/400/1091/048.
Sneath, R.W., Phillips, V.R., Demmers, G.M., Burgess, L.R. and Short, J.L. (1997). Long Term Measurementsof Greenhouse Gas Emissions from UK Livestock Buildings. Bio-Engineering Division, Silsoe ResearchInstitute, Wrest Park, Silsoe, Bedford, MK45 4HS. Livestock Environment: Proceedings of the Fifth
International Symposium. Bloomington MN. May 29-31, 1997.
Sommer, S.G., Petersen, S.O. and Sogaard, H.T. (2000). Greenhouse gas emissions from stored livestock slurry.
Journal of Environmental Quality, 29: pp. 744-751.
Steed Jr, J. and Hashimoto, A.G. (1994). Methane emissions from typical manure management systems.
Bioresource Technology 50: pp. 123-130.
USDA (1996). Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH). Part
651, U.S. Department of Agriculture, Natural Resources Conservation Service. July.
Woodbury, J.W. and Hashimoto, A. (1993). Methane Emissions from Livestock Manure. In International
Methane Emissions, US Environmental Protection Agency, Climate Change Division, Washington, D.C.,
U.S.A.
Zeeman, G. (1994). Methane production/emission in storages for animal manure. Fertilizer Research 37: 207211, 1994. Kluwer Academic Publishers, Netherlands.

SECTION 10.5 NITROUS OXIDE EMISSIONS FROM MANURE MANAGEMENT
Amon, B., Amon, Th., Boxberger, J. and Pollinger, A. (1998). Emissions of NH3, N2O, and CH4 from composted
and anaerobically stored farmyard manure. Pages 209-216 in Martinez J, Maudet M-N (eds) Ramiran 98,
Proc. 8th Int. Conf. on the FAO ESCORENA Network on Recycling of Agricultural, Municipal andIndustrial Residues in Agriculture. Rennes, France.
Amon, B., Amon, Th., Boxberger, J. and Alt, Ch. (2001). Emissions of NH3, N2O, and CH4 from dairy cows
housed in a farmyard manure tying stall (Housing, Manure Storage, Manure Spreading). Nutrient Cyclingin Agroecosystems, 60: pp. 103-113.
Asman, W.A.H., Sutton, M.A. and Schjoerring, J.K. (1998). Ammonia: emission, atmospheric transport anddeposition. New Phytol., 139, p. 27-48
Bierman, S., Erickson, G.E., Klopfenstein, T.J., Stock, R.A. and Shain, D.H. (1999). Evaluation of nitrogen andorganic matter balance in the feedlot as affected by level and source of dietary fiber. J. Anim. Sci.
77:1645-1653.
Döhler, H., Eurich-Menden, B., Dämmgen, U., Osterburg, B., Lüttich, M., Bergschmidt, A., Berg, W., Brunsch,R. (2002). BMVEL/UBA-Ammoniak-Emissionsinventar der deutschen Landwirtschaft und
Minderungsszenarien bis zum Jahre 2010. Texte 05/02. Umweltbundesamt, Berlin.
Dustan, A. (2002). Review of methane and nitrous oxide emission factors in cold climates. Institutet forjordbruks-och miljoteknik, JTI-rapport, Lantbruk & Industri, 299.
Eghball, B. and Power, J.F. (1994). Beef cattle feedlot manure management. J. Soil Water Cons. 49:113-122.
European Environmental Agency (2002). Joint EMEP/CORINAIR Atmospheric Emission Inventory Guidebook,
3rd ed., July 2002, Copenhagen.
Groot Koerkamp, P.W.G., Speelman, L. and Metz, J.H.M. (1998). Effect of type of aviary, manure and litter
handling on the emission kinetics of ammonia from layer houses. Br. Poult. Sci., 39, p. 379-392.
Hao, X., Chang, C., Larney, F.J. and Travis, G.R. (2001). Greenhouse gas emissions during cattle feedlot manurecomposting. Journal Environmental Quality 30: pp. 376-386.

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Harper, L.A., Sharpe, R.R. and Parkin, T.B. (2000). Gaseous emissions from anaerobic swine lagoons:Ammonia, Nitrous Oxide, and Dinitrogen Gas. Journal of Environmental Quality 29: pp. 1356-1365.
Hutchings, N.J., Sommer, S.G., Andersen, J.M. and Asman, W.A.H. (2001). A detailed ammonia emission
inventory for Denmark. Atmospheric Environment, 35, p. 1959-1968.
Külling, D.R., Menzi, H., Sutter, F., Lischer, P. and Kreuzer, M. (2003). Ammonia, nitrous oxide and methane
emissions from differently stored dairy manure derived from grass- and hay-based rations. Nutrient
Cycling in Agroecosystems, 65: pp. 13-22.
Lague, C., Fonstad, T. A., Marquis, A., Lemay, S.P., Godbout, S. and Joncas, R. (2004). Greenhouse Gas
Emissions from Swine Operations in Québec and Saskatchewan: Benchmark Assessments. Climate
Change Funding Initiative in Agriculture (CCFIA), Canadian Agricultural Research Council, Ottawa, ON.
Meisinger, J.J. and Jokela, W.E. (2000). Ammonia Volatilization from Dairy and Poultry Manure. In: Managing
Nutrients and Pathogens from Animal Agriculture. Natural Resource, Agriculture, and Engineering
Service, Ithaca, NY. March 28-30, 2000. NRAES-130, p.334-354.
Moller, H.B., Sommer, S.G. and Anderson, B.H. (2000). Nitrogen mass balance in deep litter during the pig
fattening cycle and during composting. Journal of Agricultural Science, Cambridge 137:235-250.
Monteny G. J., Groesetein C. M. and Hilhorst M. A. (2001). Interactions and coupling between emissions ofmethane and nitrous oxide from animal husbandry. Nutrient Cycling in Agroecosystems, 60: pp. 123-132.
Monteny, G.J. and Erisman, J.W. (1998). Ammonia emissions from dairy cow buildings: A review ofmeasurement techniques, influencing factors and possibilities for reduction. Neth. J. Agric. Sci., 46, p.
225-247.
Moreira, V.R. and Satter, L.D. (2004). Estimating nitrogen loss from dairy farms. Pedology.
National Research Council (NRC) (1996). Nutrient Requirements of Beef Cattle, 7th Revised Ed., Nat. Acad.
Press, Washington., DC
Nicks, B., Laitat, M., Vandenheede, M., Desiron, A., Verhaege, C. and Canart, B. (2003). Emissions ofAmmonia, Nitrous Oxide, Methane, Carbon Dioxide, and Water Vapor in the Raising of Weaned Pigs on
Straw-Based and Sawdust-Based Deep Litters. Animal Research Journal, 52: pp. 299-308.
Rotz, C.A. (2004). Management to reduce nitrogen losses in animal production. J. Anim. Sci. 82(E.
Suppl.):E119-E137.
Sneath, R.W., Phillips, V.R., Demmers, G.M., Burgess, L.R. and Short, J.L. (1997). Long Term Measurementsof Greenhouse Gas Emissions from UK Livestock Buildings. Bio-Engineering Division, Silsoe ResearchInstitute, Wrest Park, Silsoe, Bedford, MK45 4HS. Livestock Environment: Proceedings of the Fifth
International Symposium. Bloomington MN. May 29-31, 1997.
Sommer, S.G. and Moller, H.B. (2000). Emission of greenhouse gases during composting of deep litter from pig
production – effect of straw content. Journal of Agricultural Science, Cambridge 134:327-335.
Sommer, S.G., Petersen, S.O. and Søgaard, H.T. (2000). Greenhouse gas emission from stored livestock slurry.
Journal of Environmental Quality 29: pp. 744-751.
US EPA (2004). National Emission Inventory – Ammonia Emissions from Animal Husbandry Operations, Draft
Report. January 30, 2004.
Wagner-Riddle, C. and Marinier, M. (2003). Improved Greenhouse Gas Emission Estimates from Manure
Storage Systems. Prepared for Climate Change Funding Initiative in Agriculture, Final Project Report,
Component 2-3 Projects, Climate Change Science and Technology.
Webb, J. (2001). Estimating the potential for ammonia emissions from livestock excreta and manures. Environ.
Pollut. 111, p. 395-406.

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