Chapter 10: Emissions from Livestock and Manure Management

CHAPTER 10

EMISSIONS FROM LIVESTOCK AND
MANURE MANAGEMENT

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors:
Olga Gavrilova (Estonia), Adrian Leip (EU), Hongmin Dong (China), James Douglas MacDonald (Canada),
Carlos Alfredo Gomez Bravo (Peru), Barbara Amon (Germany), Rolando Barahona Rosales (Honduras), Agustin
del Prado (Spain), Magda Aparecida de Lima (Brazil), Walter Oyhantçabal (Uruguay), Tony John van der
Weerden (New Zealand), Yeni Widiawati (Indonesia)

Contributing Authors:
Andre Bannink (Netherlands), Karen Beauchemin (Canada), Harry Clark (New Zealand), April Leytem (USA),
Ermias Kebreab (USA), Ngwa Martin Ngwabie (Cameroon), Carolyn Imede Opio (Uganda), Andrew VanderZaag(Canada), Theunis Valentijn Vellinga (Netherlands)

10.2

2019 Refinement to the 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 ................................................................................................................................ 10.10

10.2

Livestock population and feed characterisation.......................................................................... 10.11

10.2.1

Steps to define categories and subcategories of livestock ..................................................... 10.11

10.2.2

Choice of method .................................................................................................................. 10.11

10.2.3

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

10.2.4Characterisation for livestock without species: Specific emission estimation methods........ 10.33

10.3

Methane emissions from enteric fermentation............................................................................ 10.34

10.3.1

Choice of method .................................................................................................................. 10.34

10.3.2

Choice of emission factors .................................................................................................... 10.36

10.3.3

Choice of activity data .......................................................................................................... 10.49

10.3.4

Uncertainty assessment ......................................................................................................... 10.49

10.3.5Completeness, Time series, Quality Assurance/Quality Control and Reporting .................. 10.49

10.4

Methane Emissions from Manure Management ......................................................................... 10.50

10.4.1

Choice of method .................................................................................................................. 10.50

10.4.2

Choice of emission factors .................................................................................................... 10.54

10.4.3

Choice of activity data .......................................................................................................... 10.71

10.4.4

Uncertainty assessment ......................................................................................................... 10.73

10.4.5Completeness, Time series, Quality assurance / Quality control and Reporting .................. 10.73

10.5

N2O Emissions from Manure Management ................................................................................ 10.74

10.5.1

Choice of method .................................................................................................................. 10.74

10.5.2

Choice of emission factors .................................................................................................... 10.80

10.5.3

Choice of activity data .......................................................................................................... 10.94

10.5.4

Coordination with reporting for N2O emissions from managed soils ................................... 10.94

10.5.5

Uncertainty assessment ......................................................................................................... 10.99

10.5.6

Completeness, Time series, Quality assurance/Quality control and Reporting .................... 10.99

10.5.7

Use of worksheets ............................................................................................................... 10.102

Annex 10A.1

Data underlying methane default emission factors for enteric fermentation, volatile solids andnitrogen excretion and retention fractions for Cattle and Buffalo ................................. 10.103

Annex 10A.2

Additional data and information for the calculation of methane and nitrous oxide fromManure Management..................................................................................................... 10.116

Annex 10A.3

Spreadsheet example for the calculation of a country or regions specific MCF ........... 10.131

Annex 10A.4

Calculations of Methane Conversion Factors (MCFs) for biogas systems.................... 10.139

Annex 10A.5

Equations relating all direct and indirect N2O emissions from manure along all stages inagricultural production for livestock ............................................................................. 10.144

Annex 10.B

Data and Explanatory Text for Development of New Parameters in the 2019 Refinement....................................................................................................................................... 10.149

10B.1

Raw data used to compile Annex 10A.1 enteric fermentation Tier 1 emission factors, volatilesolids and nitrogen excretion for cattle and buffalo ...................................................... 10.149

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10B.2

Estimation of Cattle/Buffalo CH4 conversion factors (Ym) ........................................... 10.153

10B.3

Estimation of Default Emission Factor(s) based on Goat Tier 2 parameters ................ 10.156

10B.4

Feed intake estimates using a simplified Tier 2 method................................................ 10.160

10B.5

Basis for Changes to MCF Calculations for Liquid/Slurry ........................................... 10.161

10B.6

Revision of methane from dung deposited onto pasture range and paddocks (Table 10.17)....................................................................................................................................... 10.164

10B.7

Estimation of default values for MCFs, EFs for direct N2O emissions, NH3 and N2 volatilized
as well as NO3 leached from solid storage and composting systems ............................ 10.165

Section 10.2

Livestock Population and Feed Characterisation .......................................................... 10.169

Section 10.3

Methane Emissions from Enteric Fermentation ............................................................ 10.170

Section 10.4

Methane Emissions from Manure Management ............................................................ 10.175

Section 10.5

N2O Emissions from Manure Management .................................................................. 10.176

Annex 10A.1

Data underlying methane default emission factors for Enteric Fermentation, Volatile solids
and nitrogen excretion and retention fractions for Cattle and Buffalo .......................... 10.177

Annex 10A.2

Additional data and information for the calculation of methane and nitrous oxide fromManure Management..................................................................................................... 10.178

Annex 10A.3

MCF Spreadsheet example for the calculation of a country or regions specific MCF .. 10.179

Annex 10A.4

Calculations of Methane Conversion Factors (MCFs) for biogas systems.................... 10.179

Annex 10A.5

Equations relating all direct and indirect N2O emissions from manure along all stages inagricultural production for livestock ............................................................................. 10.179

Annex 10.B

Data and Explanatory Text for Development of New Parameters in the 2019 Refinement....................................................................................................................................... 10.179

10B.1

Raw data used to compile Annex 10A.1 enteric fermentation Tier 1 emission factors,
volatilesolids and nitrogen excretion for cattle and buffalo .......................................... 10.179

10B.2

Estimation of Cattle/Buffalo CH4 conversion factors (Ym) .......................................... 10.200

10B.3

Estimation of Default Emission Factor(s) for Goat Tier 2 parameters .......................... 10.201

10B.4

Feed intake estimates using a simplified Tier 2 method................................................ 10.204

10B.5

Basis for Changes to MCF Calculations for Liquid/Slurry ........................................... 10.205

10B.6

Revision of MCFs from dung deposited onto pasture, range and paddocks (Table 10.17)....................................................................................................................................... 10.206

10B.7

Estimation of default emission factors for MCF CH4 values, EF for direct N2O emissions,
NH3, NO3 leaching and N2 emissions from solid storage and composting systems ...... 10.206

Reference

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Chapter 10: Emissions from Livestock and Manure Management

Equations
Equation 10.1 (Updated)

Annual average population .......................................................................10.11

Equation 10.2

Coefficient for calculating net energy for maintenance ............................10.20

Equation 10.3

Net energy for maintenance ......................................................................10.23

Equation 10.4

Net energy for activity (for cattle and buffalo) .........................................10.24

Equation 10.5

Net energy for activity (for sheep and goats) ............................................10.24

Equation 10.6

Net energy for growth (for cattle and buffalo) ..........................................10.25

Equation 10.7

Net energy for growth (for sheep and goats) (updated) ............................10.26

Equation 10.8

Net energy for lactation (for beef cattle, dairy cattle and buffalo) ............10.26

Equation 10.9 (Updated)

Net energy for lactation for sheep and goats (milk production known) ....10.27

Equation 10.10

Net energy for lactation for sheep and goats (milk production unknown) 10.27

Equation 10.11

Net energy for work (for cattle and buffalo) .............................................10.27

Equation 10.12 (Updated)

Net energy to produce wool (for sheep and goats) ...................................10.28

Equation 10.13 (Updated)

Net energy for pregnancy (for cattle/buffalo and sheep and goats) ..........10.28

Equation 10.14

Ratio of net energy available in a diet for maintenance to digestible energy..................................................................................................................10.29

Equation 10.15

Ratio of net energy available for growth in a diet to digestible energy consumed..................................................................................................................10.30

Equation 10.16 (Updated)

Gross energy for cattle/buffalo, sheep and goats ......................................10.30

Equation 10.17 (New)

Estimation of dry matter intake for calves ................................................10.31

Equation 10.18 (New)

Estimation of dry matter intake for growing cattle ...................................10.31

Equation 10.18a (Updated)

Estimation of dry matter intake for steers and bulls .................................10.31

Equation 10.18a (Updated)

Estimation of dry matter intake for heifers ...............................................10.31

Equation 10.18b (Updated)

Estimation of dry matter intake for lactating dairy cows ..........................10.32

Equation 10.19 (Updated)

Enteric fermentation emissions from a livestock category (Tier 1) ..........10.39

Equation 10.20 (Updated)

Total emissions from livestock enteric fermentation (Tier 1) ...................10.39

Equation 10.21

Methane emission factors for enteric fermentation from a livestock category..................................................................................................................10.47

Equation 10.21a (New)Methane emission factors for enteric fermentation from a livestock category..................................................................................................................10.47

Equation 10.22 (Updated)

CH4 emissions from manure management (Tier 1)...................................10.54

Equation 10.22a (New)Annual VS excretion rates (Tier 1) ...........................................................10.55

Equation 10.23

CH4 emission factor from manure management .......................................10.64

Equation 10.24 (Updated)

Volatile solid excretion rates ....................................................................10.65

Equation 10.25 (Updated)

Direct N2O emissions from manure management .....................................10.75

Equation 10.26 (Updated)

N losses due to volatilisation from manure management .........................10.76

Equation 10.27 (Updated)

N losses due to leaching from manure management .................................10.77

Equation 10.28

Indirect N2O emissions due to volatilisation of N from manure management..................................................................................................................10.78

Equation 10.29

Indirect N2O emissions due to leaching from manure management .........10.78

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Volume 4: Agriculture, Forestry and Other Land Use

Equation 10.30 (Updated)

Annual N excretion rates ..........................................................................10.80

Equation 10.31

Annual N excretion rates, Option 1 (Tier 2) .............................................10.81

Equation 10.31a (New)Annual N excretion rates, Option 2 (Tier 2) .............................................10.81

Equation 10.32

N intake rates for cattle, Sheep and Goats ................................................10.82

Equation 10.32a (New)N intake rates for swine and poultry .........................................................10.82

Equation 10.33

N retention rates for cattle ........................................................................10.85

Equation 10.33a (New)N retention rates for breeding sows ..........................................................10.86

Equation 10.33b (New)N retention rates for piglets ......................................................................10.86

Equation 10.33c (New)N retention rates for growing pigs ............................................................10.87

Equation 10.33d (New)N retention rates for layer type hens .........................................................10.88

Equation 10.33e (New)Daily N retention rates for pullets or broilers ...........................................10.89

Equation 10.34 (Updated)

Managed manure N available for application to managed soils, feed, fuel orconstruction uses .......................................................................................10.94

Equation 10.34a (New)Fraction of managed manure N lost prior to application to managed soils forthe production of feed, fuel or for construction uses ................................10.95

Equation 10.34b (New)

Estimation of FracN2ms ..............................................................................10.95

Equation 10A.1 (New)Calculation of MCF for the combination “digester + digestate storage” 10.139

Equation 10A.2 (New)Calculation of relative amount of potential off gas related to B0 ............10.140

Equation 10A.3 (New)Calculation of relative amount of residual gas related to B0 ...................10.140

Equation 10A.4 (New)Calculation of relative amount of residual gas related to CH4 production................................................................................................................10.140

Equation 10A.5 (New)Digester’s methane balance ....................................................................10.141

Equation 10A.6 (New)Calculation of methane leakage rate of digester .....................................10.141

Equation 10A.7 (New)Calculation of methane conversion factor ..............................................10.141

Equation 10A.8 (New)Calculation of methane conversion factor of residues ............................10.142

Equation 10A.9 (New)Calculation of methane conversion factor for the combination “prestorage +digester + digestate storage” ...................................................................10.142

Equation 10A.10 (New)

Total N2O emissions for animal type T ...................................................10.144

Equations 10A.11 - 10A.12 (New) Total N2O emissions from manure management for animal type T ........10.144
Equations 10A.13 - 10A.14 (New) Total, direct and indirect N2O emissions from the application of manure to
managed soils for animal type T .............................................................10.144
Equation 10A.15 (New)

Total amount of animal manure N applied to soils other than by grazing
animals for animal type T .......................................................................10.145

Equation 10A.16 (New)Fraction of total animal manure N lost in manure management systems foranimal type T ..........................................................................................10.145

Equation 10A.17 (New)Fraction of animal manure N available for application to managed soils,
applied to managed soils for animal type T ............................................10.145

Equation 10A.18 - 10A.19 (New) Total, direct and indirect N2O emissions from N in urine and dung depositedby grazing animals on pasture, range and paddock (Tier 1) for animal type T................................................................................................................10.145
Equation 10A.20 (New)

Relationship between average annual nitrogen flows associated with anindividual animal and the annual nitrogen flow for the animal population of
livestock category/species T in a country ...............................................10.145

Equation 10A.21 (New)

Total manure-N excreted ........................................................................10.145

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Chapter 10: Emissions from Livestock and Manure Management

Equation 10A.22 - 10A.23 (New) Nitrogen excretion calculated either using a default fraction of retention (Tier
1) or directly from retention data ............................................................10.146
Equation 10A.24 (New)

Total manure-N in manure management systems ...................................10.146

Equation 10A.25 (New)Manure-N managed in system S .............................................................10.146

Equation 10A.26 (New)Manure-N deposited by grazing animals, with X=CPP,SO ....................10.146

Equation 10A.27 (New)N in bedding material added to managed manure...................................10.146

FiguresFigure 10.1

Decision tree for livestock population characterisation ....................................... 10.13

Figure 10.2 (Updated)

Decision Tree for CH4 Emissions from Enteric Fermentation .............................. 10.35

Figure 10.3 (Updated)

Decision tree for CH4 emissions from Manure Management ............................... 10.53

Figure 10.4 (Updated)

Decision tree for N2O emissions from Manure Management ............................... 10.79

Figure 10.5 (New)Processes leading to the emission of gaseous N species from manure ....................10.102
Information required to determine climate zones according to Chapter 3 of Volume 4
Current Guideline ....................................................................................................10.127

Figure 10A.1 (New)

Mapping of IPCC climate zones (taken from Volume 4, Chapter 3, Annex 3A.5) .10.128

Figure 10A.2 (New)Colour code for cells in the example spreadsheet ....................................................10.131

Figure 10A.3 (New)Temperature and manure removal inputs to the model............................................10.133

Figure 10A.4 (New)Constants and other input parameters for the model................................................10.134

Figure 10A.5 (New)Monthly model inputs and outputs over a three year period ....................................10.135

Figure 10A.6 (New)Formulae used in the model .....................................................................................10.136

Figure 10A.7 (New)

Example of monthly patterns in Year 3: manure temperature, VS available (kg), VS
emptied (kg), and methane production. ...................................................................10.137

Figure 10A.8 (New)

Summary of Year 3 VS and methane production, and calculation of MCF. ...........10.137

Figure 10B.1 (New)

Relationships between mean and median neutral detergent fibre (NDF) and methane
conversion rate (Ym) from summary statistics of Niu et al. (2018). ........................10.154

Figure 10B.2 (New)Annual enteric methane output per animal expressed in mass in relation to daily dry
matter (DM) intake. .................................................................................................10.157

Figure 10B.3 (New)Daily enteric methane output per animal expressed in energy in relation to daily grossenergy (GE) intake...................................................................................................10.158

Figure 10B.4 (New)Daily N intake per animal expressed in relation to animal weight. .........................10.158

Figure 10B.5 (New)Daily N excretion output per animal expressed in relation to daily N intake. .........10.159

Figure 10B.6 (New)Comparison between ranges of CH4-C emissions observed in collected studies withestimations for those studies according to the 2006 IPCC Guidelines methodology.................................................................................................................................10.166

Figure 10B.7 (New)

Effect on cumulative NH3-N emissions of different solid storage and composting methods
compared with conventional solid storage. ..............................................................10.167

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TablesTable 10.1 (Updated)

Representative livestock categories ..................................................................10.16

Table 10.2 (Updated)

Representative feed digestibility for various livestock categories .......................10.22

Table 10.3 (Updated)

Summary of the equations used to estimate daily gross energy intake for cattle, buffalo
and sheep and goats.............................................................................................10.23

Table 10.4 (Updated)

Coefficients for calculating net energy for maintenance (NEm) ...........................10.24

Table 10.5 (Updated)

Activitycoefficients
correspondingtoanimal’s
feeding
situation.............................................................................................................................10.25

Table 10.6 (Updated)

Constants for use in calculating NEg for sheep and goats ....................................10.26

Table 10.7 (Updated)

Constants for use in calculating NEp in Equation 10.13 ......................................10.29

Table 10.8 (New)DMI Required By Mature Non Dairy Cows Based On Forage Quality ...............10.32

Table 10.8a (Updated)

Examples of NEmf content of typical diets fed to cattle for estimation of dry matterintake in equations 10.17 and 10.18 .....................................................................10.33

Table 10.9 (Updated)

Suggested emissions inventory methods for enteric fermentation .......................10.37

Table 10.10 (Updated)

Enteric fermentation emission factors for Tier 1 method (kg CH4 head-1 yr-1) ....10.38

Table 10.11 (Updated)

Tier 1 and Tier 1a Enteric fermentation emission factors for Cattle and Buffalo 10.40

Table 10.12 (Updated)

Cattle/buffalo methane conversion factors (Ym )..................................................10.46

Table 10.13 (Updated)

Sheep and goats CH4 conversion factors (Ym) .....................................................10.47

Table 10.13a (New)

Default values for volatile solid excretion rate (kg VS (1000 kg animal mass)-1 day-1).............................................................................................................................10.56

Table 10.14 (Updated)

Methane Emission Factors by animal category, manure management system andclimate zone (g CH4 kg VS-1)...............................................................................10.58

Table 10.15 (Updated)

Manure management methane emission factors for deer, reindeer, rabbits, ostrich andfur-bearing animals and derivation parameters applied .......................................10.63

Table 10.16a (Updated)

Default values for maximum methane producing capacity (B0) (m3 CH4 kg-1 VS).............................................................................................................................10.67

Table 10.17 (Updated)

Methane Conversion Factors for manure management systems ..........................10.68

Table 10.18 (Updated)

Definitions of manure management systems .......................................................10.72

Table 10.19 (Updated)

Default values for nitrogen excretion rate (kg N (1000 kg animal mass)-1 day-1) 10.83

Table 10.20 (Updated)

Default values for the fraction of nitrogen in feed intake of livestock that is retainedby the different livestock species/categories (fraction N-intake retained by the animal).............................................................................................................................10.85

Table 10.20a (New)Calculation of N retention in breeding swine from different production systems, anexample ................................................................................................................10.87

Table 10.20b (New)

Default values for Ngain by growth stage ..............................................................10.88

Table 10.21 (Updated)

Default emission factors for direct N2O emissions from manure management ...10.91

Table 10.22 (Updated)

Default values for nitrogen loss fractions due to volatilisation of NH3 and NOx andleaching of nitrogen from manure management...................................................10.97

Table 10.23 (New)

Default value for molecular nitrogen (N2) loss from manure management .........10.99

Table 10A.1 (New)Data for estimating Tier 1 and Tier 1A Enteric Fermentation CH4 Emission Factors,
Volatile solid excretion and N excretion rates, and N retention fraction rates for Dairy
Cattle ..................................................................................................................10.104

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Chapter 10: Emissions from Livestock and Manure Management

Table 10A.2 (New)Data for estimating Tier 1 Enteric Fermentation CH4 Emission Factors, Volatile solidand nitrogen excretion rates, and N retention fraction for Other cattle ..............10.106

Table 10A.3 (New)Data for estimating Tier 1A Enteric Fermentation CH4 Emission Factors, Volatile
Solid and nitrogen excretion rates and N retention fraction for Other cattle......10.110

Table 10A.4 (New)Data for estimating Tier 1 Enteric Fermentation CH4 Emission Factors, Volatile solidand nitrogen excretion rates, and N retention fraction rates for Buffalo ............10.114

Table 10A.5 (New)

Default values for Live weights for animal categories (kg) ...............................10.117

Table 10A.6 (New)Animal Waste Management System (AWMS) Regional Averages for Cattle andBuffalo ...............................................................................................................10.119

Table 10A.7 (New)Animal Waste Management System (AWMS) regional averages for swine (%)...........................................................................................................................10.121

Table 10A.8 (New)Animal Waste Management System (AWMS) regional averages for Sheep and Goats...........................................................................................................................10.123

Table 10A.9 (New)Animal Waste Management System (AWMS) regional averages for Poultry and Other
animals ...............................................................................................................10.125

Table 10A.10 (New)Comparison of manure storage type definitions used by the IPCC and by the
EMEP/EEA
airpollutantemission
inventory
guidebook2016(https://www.eea.europa.eu/publications/emep-eea-guidebook-2016) ..............10.129

Table 10A.11 (New)Methane conversion factor (MCFdg) including biogas digester and digestate storage...........................................................................................................................10.139

Table 10B.1 (New)

Summary statistics from Niu et al. (2018) database...........................................10.154

Table 10B.2 (New)Threshold calculation based on NDF correction ................................................10.155

Table 10B.3 (New)

Summary of data compiled for the compilation of Ym values for cattle and buffalo...........................................................................................................................10.155

Table 10B.4 (New)Mean, median, maximum, minimum and quartile 1 and 3 (Q1 and Q3) values for aselection feed diet composition, feed intake, body weight and milk productivity...........................................................................................................................10.156

Table 10B.5 (New)Mean, median, maximum, minimum and quartile 1 and 3 (Q1 and Q3) values for CH4
production results referred as a proportion of gross energy intake (CH4 conversion
factor: Ym), day-1, kg DM intake-1, kg of milk produced-1 and kg of body weight-1...........................................................................................................................10.157

Table 10B.6

2006 IPCC Guidelines Table of MCF values for Liquid/Slurry (Table 10.17)..10.162

Table 10B.7 (New)MCFs calculated for each retention time and climate (selected IPCC Climate regions
shown) ................................................................................................................10.162

Table 10B.8 (New)Regional distribution of data used to derive MCF and Methane EF from excretion
Volitile Solids data on PRP ................................................................................10.164

Table 10B.9 (New)Methane conversion factor (MCF) and methane emission factors (per kg faecal dry
matter (FDM)) and volatile solids (VS) for cattle and sheep .............................10.164

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10.

EMISSIONS FROM LIVESTOCKMANURE MANAGEMENT

AND

Users are expected to go to Mapping Tables in Annex 1 Volume 4 (AFOLU), before reading this chapter. This isrequired to correctly understand both the refinements made and how the elements in this chapter relate to thecorresponding chapter in the 2006 IPCC Guidelines.

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. Carbon dioxide (CO2) emissions
from livestock are not estimated because annual net CO2 emissions are assumed to be zero – the CO2
photosynthesized by plants is returned to the atmosphere as respired CO2. A portion of the C is returned as methane(CH4) and for this reason CH4 requires separate consideration.
Livestock production can result in 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 in many countries
because of their large population and high CH4 emission rate due to their ruminant digestive system. Methane
emissions from manure management tend to be smaller than enteric emissions, with the most substantial emissions
associated with confined animal management operations where manure is handled in liquid-based systems. Nitrous
oxide emissions from manure management vary significantly between the types of management system used andcan also result in indirect emissions due to other forms of nitrogen loss from the system. The calculation of thenitrogen loss from manure management systems is also an important step in determining the amount of nitrogenthat 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 to ensure
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).
In calculating agricultural emissions, it is important to establish consistency among the different emission sources.
Key drivers of emissions such as animal weight and productivity must be treated using the same parameters foremissions of enteric and manure management CH4, as well as N2O from manure management. Further, Section
10.5.4 discusses the coordination between N2O emissions from Manure Management and Managed Soils.
Emissions of N2O from nitrogen excretion should be assessed following a nitrogen mass flow approach which isfurther explained in Section 10.5.6 and illustrated in Figure 10.5.

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Chapter 10: Emissions from Livestock and Manure Management

10.2

LIVESTOCKPOPULATIONCHARACTERISATION

AND

FEED

10.2.1 Steps to define categories and subcategories of
livestock
No refinement.

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 : A complete list of all livestock populations by species 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 species are relevant to the country. Populations by species
can also be further subdivided by category. Category refers to classification inside a species by different relevant
attributes as sex, age or productive purpose in a relevant production system in any given country (e.g. in the caseof cattle: mature males and females, replacement heifers, calves, etc.). More detailed categories should be used if
the data are available. For example, more accurate emission estimates can be made if poultry populations arefurther subdivided (e.g., layers, broilers, turkeys, ducks, and other poultry), as the waste characteristics among
these different populations vary significantly.
Annual population: If possible, inventory compilers should use population data from official national statistics
or industry sources. Food and Agriculture Organisation (FAO) data, FAOSTAT and other FAO statistics, can beused 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 the method used to estimate the annual population, including any adjustments to theoriginal form of the population data as it was received from national statistical agencies or from other sources.
When population by species is subdivided by categories it is important to fully document any adjustments done in
the population to match the categories used in the inventory compilation.
Compilers could consider to communicate/share the annual population data needs with the national statisticalagency and/or the other sources from which the data was obtained, so this sources are better aware of the needs ofinventory compilers. In addition, national statistical agencies and agencies responsible for inventory compilation
can work closely together to ensure that official statistics better meet the needs of the inventory compilers.
Annual average populations are referred to as the number of head of livestock species per category within a given
country (N(T)). This can be estimated in various ways, depending on the available data and the nature of the animal
population. In the case of static animal populations (e.g. dairy cows, breeding swine, layers), estimating thenumber of head of a given livestock species in the country (N(T)) may be as simple as obtaining data related to onetime animal inventory data. However, estimating N(T) 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 are alive for only part of a complete year. Animals should be included in the populations regardlessif they were slaughtered for human consumption or die of natural causes. Equation 10.1 estimates N(T).
EQUATION 10.1 (UPDATED)ANNUAL AVERAGE POPULATION NAPA =
NT Days _alive •  365 Where:

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NT= the number of head of livestock species / category T in the country (equivalent to annual
average population)

NAPA= number of animals produced annually

Broiler chickens are typically grown approximately 60 days before slaughter. Estimating N(T) as the number ofgrown and slaughtered over the course of a year would greatly overestimate the population, as it would assume
each lived the equivalent of 365 days. Instead, one should estimate the average annual population as the numberof animals grown divided by the number of growing cycles per year. For example, if broiler chickens are typicallygrown in flocks for 60 days, an operation could turn over approximately 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 tosupport the level of detailrequired for thecharacterisation?”

Yes

No

Can
data be collected tosupport the level ofcharacterisation?

No

Set the level of thecharacterisation
to the available data

Box 1
Yes
Collect the data required tosupport the characterisation.

Perform the characterisation at
the required level of detail

Box 2
Note:
1: These categories include: CH4 from Enteric Fermentation, CH4 from Manure Management, N2O from Manure 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.

Con s id era t ion of diffe ring P rodu ctiv it y s ys tem s (Tie r 1a)
In certain countries agricultural production systems may be transitioning from low productivity local subsistencesystems to higher productivity systems aimed at fulfilling national and export markets or may simply have dual
agricultural systems, with coexistence of low and high productivity systems clearly identified. In these cases
inventory compilers may wish to use the Tier 1a approach in which they are able to better track the transitions and2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Usechanges in the productivity of their agricultural systems and related emissions over time. Tier 1a emission factors(on a per head basis) have been developed for use with basic population estimates separated by low and highproductivity systems according to the definitions below.
In this case animal populations by species may be divided by productivity systems. For each animal species highand low productivity systems may be defined according to characteristics such as: feedbase, genetics, purpose(draft, cultural reasons, self-consumption, market), production objectives (e.g. milk, meat, eggs), and level of
inputs and outputs.
D ef in it io ns of H igh a nd Low P rodu ct iv it y S yst em s

Dairy Cattle and milk production:
The dairy cow population is estimated separately from other cattle (see Table 10.1). Dairy cows are defined in thismethod as mature cows (first lactation and beyond) that are producing milk in commercial quantities for
consumption. This definition corresponds to the dairy cow population reported in FAO, IDF, IFCN (2014). Dairy
cow population should not be confused with multi-purpose cows that may be used for more than one production
purpose milk, meat or draft.
In some countries the dairy cow population is comprised of two well-defined segments:
High-productivity systems are based on high-yielding dairy cows that are concentrated in confinement
production systems or grazing on high quality pastures with supplements. The farms are 100 percent market
oriented for commercial milk production, for national markets and/or export; Purebred or crossbred cattle are
genetically improved through selective breeding for milk production (FAO, IDF, IFCN 2014). Indicative levels ofhigh milk productivity by cow corresponding to a given region are included in Table 10.11 to guide the selectionof the emission factors.
Low productivity systems are based on low-yielding dairy cows, grazing non-improved pastures, and using
locally produced roughage (e.g. crop residues), and agro-industrial by-products. Local breeds or crossbred cows
are bred locally, without intensive selection for milk productivity. Milk production is mostly for local market andlocal consumption (FAO, IDF, IFCN. 2014). Indicative levels of low milk productivity by cow corresponding toa given region are included in Table 10.11 to guide the selection of the emission factors.
Dairy buffalo may be categorized in a similar manner to dairy cows.

Other cattle:
High-productivity systems are based on animal feeding systems using forage (e.g. high-quality grass) andconcentrates in confinement production systems or grazing with supplements or on improved pastures, producing
high rates of daily weight gain. Animals can be purebred or crossbred and are genetically improved throughselective breeding for improved commercial meat production. Growing cattle may be finished young in "intensive
grazing with supplements" or feedlot systems, and meat is produced for national markets and/or export (FAO,
IDF, IFCN 2014).
Low productivity systems are based on animal feeding systems where locally produced roughage (e.g. crop
residues) or low quality rangelands represent the major source of feed utilized, producing low rates of daily weightgain. Animals can be represented by local breeds or may be crossbred and can also be used for multiple purposessuch as draft, meat and milk for self consumption and markets (FAO, IDF, IFCN 2014).

Other livestock species:
High-productivity systems, which are 100 percent market oriented with high level of capital input requirements
and high level of overall herd (flock) performance. Feed is purchased from local or international market or
intensively produced on farm. Animals are improved through breeding practices for commercial production. The
high-productivity systems are common in swine, poultry, goats and sheep production (MacLeod et al. 2017).
Low productivity systems which are mainly driven by local market or by self-consumption, with low capital
input requirements and low level of overall herd (fowl) performance typically using large areas for production or
backyards. Locally produced feed represents the major source of feed utilized or animals are kept-free range formajor part or all of their production cycle, the yield of the activity being linked to the natural fertility of the land
and the seasonal production of the pastures. The low-productivity systems are common in swine, poultry, goatsand sheep production (MacLeod et al. 2017).
I nt er nat iona l s tat ist ics so ur c es fo r a ct iv it y data , para me te r s and too ls r e late d to an ima lpo pu lat io n

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Chapter 10: Emissions from Livestock and Manure Management

FAO provides international statistical information for livestock characterization, including population andproduction. Relevant sources are: FAOSTAT Production database and FAO World Census of Agriculture 2020.
Additionally, FAO provides a free access e-learning course to support developing countries in the preparation of
the national GHG inventory for the agriculture sector. FAO also provides tools that may be useful for inventory
compilers in the agriculture sector as the FAOSTAT Emissions Analysis Tools, to identify data gaps and perform
QA/QC analysis. Another tool is the Global Livestock Environmental Accounting Model (GLEAM), which is a
GIS-based model for livestock production activities and related resource flows in all countries. The FAO-IPCCIFAD workshop report (IPCC, 2015), identifies the list of all FAOSTAT and other FAO data sources in supportof National Inventory compilation in the AFOLU sector.

TIER
2:
ENHANCED
POPULATIONSCHARACTERISATIONFORLIVESTOCKThe 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.
D ef in it io ns fo r liv esto ck s ub categor ie s
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 with 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 populationin 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.

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TABLE 10.1 (UPDATED)
Representative livestock categories1,2
Main categories
Mature Dairy Cow
or Mature Dairy
Buffalo

Production categories
Tier 1a

Subcategories

High Productivity Systems

High-producing cows that have calved at least once and areused principally for milk production

Low Productivity Systems

Low-producing cows that have calved at least once and areused principally for milk production
Females:High Productivity Systems•

Cows used to produce offspring for meat•

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

Males:

Other Mature Cattleor Mature Non-dairy
Buffalo•Bulls used principally for breeding purposes

Females:•
Low Productivity Systems

Cows that may be used for more than one production
purpose: milk, meat, draft

Males:

Growing Cattle or
Growing Buffalo•

Bulloks used principally for draft power•

Calves pre-weaning•

Replacement dairy heifers•

Growing / fattening cattle or buffalo post-weaning•

Feedlot-fed cattle on diets containing > 85% concentrates•

Calves pre-weaning•

Growing / fattening cattle or buffalo post-weaning•

Breeding ewes for production of offspring and wool
production•

Milking ewes where commercial milk production is theprimary purpose•

Other Mature Sheep (> 1 year)•

Breeding ewes for production of offspring and wool
production•

Other Mature Sheep (> 1 year)

High productivity systems•

Intact Males, Castrates and Females, concentrate-fed.

Low productivity systems•

Castrates and Females, grass-fed.

High Productivity Systems

Low Productivity Systems

High productivity systems
Mature Sheep
Low productivity systems

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.1 (UPDATED) (CONTINUED)
Representative livestock categories1,2
Main categories

Production categories
Tier 1aHigh productivity systems

Goats

Low productivity systems

High Productivity Systems
Mature Swine
Low Productivity Systems

High Productivity Systems
Growing Swine
Low Productivity Systems

Subcategories•

Dairy Does•

Mature does•

Yearlings

•

Bucks

•

Kids (<1 yr)•

Mature does•

Yearlings

•

Bucks

•

Kids (<1 yr)•

Sows in gestation•

Sows which have farrowed and are nursing young•

Boars that are used for breeding purposes•

Sows in gestation•

Sows which have farrowed and are nursing young•

Boars that are used for breeding purposes•

Nursery•

Growing/Finishing•

Gilts that will be used for breeding purposes•

Growing boars that will be used for breeding purposes•

Growing / fattening swine•

Free-range growing swine•

Gilts/boars that will be used for breeding purposes•

Broiler chickens grown for producing meat inconfinement systems
Breeder Broiler chickens grown in confinement systems•
High Productivity Systems
Chickens

Low Productivity Systems

TurkeysHigh Productivity Systems

Low Productivity Systems•

Layer chickens for producing eggs, where manure ismanaged in dry systems (e.g., high-rise houses)•

Layer chickens for producing eggs, where manure ismanaged in wet systems (e.g., lagoons)•

Chickens under free-range conditions for egg or meatproduction•

Chickens under free-range conditions for egg or meatproduction•

Breeding turkeys in confinement systems•

Turkeys grown for producing meat in confinement
systems•

Turkeys under free-range conditions for meat production•

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TABLE 10.1 (UPDATED) (CONTINUED)
Representative livestock categories1,2
Production categories
Tier 1aMain categories
Ducks

Others (for example)•

Breeding ducks•

Ducks grown for producing meat•

Camels•Mules and Asses•

Llamas, Alpacas•

Fur bearing animals•

Rabbits•

Horses•

Deer•

Ostrich•Geese

Subcategories

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.

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 in climate,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 livestock categories.
The livestock classification that is chosen should be consistent for all emission sources, enteric and manure
management methane and N2O from manure management. 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 or kg per day); and• Methane conversion factor (Ym) 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;• Average milk production per day (kg/day), fat and protein content;• Average amount of work performed per day (hours day-1);• Percentage of females that give birth in a year 2;• Wool growth;• Number of offspring;• Digestibility of feed, expressed as the percentage of digestible energy in feed gross energy (DE, percent);• Crude protein in diet (CP, percent);

1 This may be assumed to be zero for mature animals.
2 This is only relevant for mature females.

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Chapter 10: Emissions from Livestock and Manure Management• Neutral Detergent Fibre (NDF, % DMI), proportion of feed composed of insoluble fibres, hemicellulose,
cellulose, lignin and some protein fractions.
F e ed intak e es t imat es
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., mega joules (MJ) per day) or dry matter (e.g., kilograms (kg))
consumed per day. Dry matter is the amount of feed consumed (kg) after it has been corrected for the water content
in the complete diet. For example, consumption of 10 kg of a diet that contains 70 percent dry matter would resultin a dry matter intake of 7 kg. To support the enteric fermentation Tier 2 method for cattle, buffalo, and sheep(see Section 10.3), detailed data requirements and equations to estimate feed intake are included in the 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,
sheep and goats. 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 required 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 to perform acomplete 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 slaughter-weight data is a useful crosscheck to assess whether the live-weight data are representative of country conditions. However, slaughter-weightdata should not be used in place of live-weight data as it fails to account for the complete weight of the animal.
Additionally, it should be noted that the relationship between live-weight and slaughter-weight varies with breed
and body condition. For cattle, buffalo and mature sheep, the yearly average weight for each animal category (e.g.,mature beef cows) is needed. For young animals, weights are needed at birth, weaning, one year of age or atslaughter if slaughter occurs within the year. Slaughter weights can be utilized in live weight estimations if
slaughter ages, dressing percentages and growth curves are also available.
Average weight gain per day (WG), kg day-1: Data on average weight gain are generally collected for feedlot
animals and young growing animals. Mature animals are generally assumed to have no net weight gain or loss
over an entire year. 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 with weight loss are largely balanced by increased
intakes and emissions during the periods of gain in body weight.
Mature weight (MW), kg: The mature weight of the adult animal of the inventoried group is required to define agrowth pattern, including the feed and energy required for growth. For example, mature weight of a breed or
category of cattle or buffalo is generally considered to be the body weight at which skeletal development iscomplete. The mature weight will vary among breeds and should reflect the animal’s weight when in moderate
body condition. This is termed ‘reference weight’ (AAC 1990) or ‘final shrunk body weight’ (NRC 1996).
Estimates of mature weight are typically available from livestock specialists and producers. Mature weights ofbulls may be 1.5 times higher than cows in the same genotype (Doren et al. 1989).
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 is intermediate to thedefinitions given, 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 benecessary to assign the most appropriate coefficient value. Table 10.5 defines the feeding situations for cattle,
buffalo, sheep and goats. 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 free-ranging swine or
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Volume 4: Agriculture, Forestry and Other Land Usepoultry, 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 andtissue 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 during the cold months for maintenance requirements of open-lot fed cattle in colder climates according to
the following equation (Johnson, 1986):
EQUATION 10.2
COEFFICIENT FOR CALCULATING NET ENERGY FOR MAINTENANCE

Cfi (in _ cold=) Cfi + 0.0048 • (20 − °C )Where:

Cf i= a coefficient which varies for each animal category as shown in Table 10.4 (Coefficientsfor calculating 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 percent in northern North America. This increase in feed use for maintenance leads togreater methane emissions. The Nutrient Requirements of Beef Cattle, 8th Revised Edition (2016) cautions that thegeneral response to cold temperature can vary with thermal susceptibility of the animal, acclimation, and diet.
Thus, Equation 10.2 may not be applicable for adapted animals, or for those protected by wind-breaks or shelter
during cold weather. The equation should be applied to adjust the annual Cfi for unsheltered animals during theperiod in which they are first exposed to sub-zero (oC) temperatures, prior to their acclimation (a period of one totwo months depending on the region).
Average daily milk production (kg day-1): These data are for milking ewes, milking does, dairy cows andbuffalo. The average daily production should be calculated by dividing the total annual production by 365, orreported as average daily production along with days of lactation per year, or estimated seasonal production divided
by number of days per season. If using seasonal production data, the emission factor must be developed forseasonal period.
Fat content (percent): Average fat content of milk is required for lactating cows, buffalo, sheep and goatsproducing milk for human consumption.
Protein content (percent): Average protein content of milk is required for lactating cows, buffalo, sheep, andgoats producing milk for human consumption.
Percent of females that give birth in a year: This is collected for cattle, buffalo, sheep and goats.
Number of off spring produced per year: This is relevant to female livestock that have multiple births per year
(e.g., ewes).
Weaning age of calves: Prior to weaning and to the development of an active rumen, calves do not emit methane.
Since calves pre-weaning is a livestock subcategory, it will in any case be necessary for a country to determine theweaning age and the diet composition pre-weaning to choose the appropriate emission factor.
Feed digestibility (DE): The portion of gross energy (GE) in the feed not excreted in the faeces is known asdigestible energy expressed as a percentage (percent). Feed digestibility is commonly expressed as a percentage
of GE or as TDN (total digestible nutrients). The percentage of feed that is not digested represents the percent of
GE intake that will be excreted as faeces. Typical digestibility (DE) values for a range of livestock classes and diettypes are presented in Table 10.2 as a guideline. The values have been refined compared to the 2006 IPCC
Guidelines, based on more recent information (Table 10.2). For ruminants, common ranges of feed digestibility
are 45-55 percent for crop by-products and range lands 3; 55-80 percent for managed pastures, well preserved
forages, crop by-products and grain supplemented forage-based diets; and 72-85 percent for grain-based diets fed

3 Rangelands are defined as land primarily covered by natural grasslands, savannas, woodlands (not meeting the definition ofForest Land) and shrublands, including introduced plant species that are naturalised (Grice et al. 2008).

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Chapter 10: Emissions from Livestock and Manure Managementin feedlots. Variation in diet digestibility results directly in major variation in the estimated amount of feed needed
to meet animal requirements and consequently is a main cause of variation in associated methane emissions andin the amounts of manure excreted (next to variation in yield of methane per unit of digested GE as explained
further in Section 10.3).
A low digestibility of feed will lead to lower feed intake and consequently reduced growth but at the same time alarger production of associated methane per unit of growth or production. Conversely, feeds with high digestibility
will often result in higher feed intake and increased growth but at the same time a smaller amount of feed required
per unit of growth and consequently lower associated methane production per unit growth or production. A factor
directly affecting feed digestibility is the rate of passage of feed in the digestive tract, in particular in highproductivity dairy cows (NRC 2001; Nousiainen et al. 2009) with direct impact on methane production as well,though in current Tier 2 methodology this impact is resolved through the selection of appropriate methane
conversion rates instead of appropriate digestibility estimates (see Section 10.3.2).
A change of 10 percent in DE will be magnified to change in 12 to 20 percent when estimating methane emissions
and even more (20 to 45 percent) for amounts of manure excreted (volatile solids). It is important to note that feed
requirements, feed digestibility, production and growth, and yield of methane from digested GE (explained further
in Section 10.3) are co-dependent phenomena.
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 with increasing
maturity and is typically lowest during hot weather or dry season. Due to significant variation, digestibility values
should be obtained from local scientific data wherever possible. Although a complete census of digestibility is
considered unrealistic, at a minimum digestibility data from research studies should be consulted. Whiledeveloping the digestibility data, associated feed characteristic data should also be recorded when available, suchas feed content of neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein, crude fat, ash and thepresence of anti-nutritional factors (e.g., alkaloids, phenolics). NDF and ADF are feed characteristics measured in
the laboratory that are used to indicate the nutritive value of the feed for ruminant livestock. Determination ofthese values can enable DE to be predicted as defined in the last dairy National Research Council (2008)
publication. The concentration of crude protein in the feed can be used in the process of estimating nitrogenexcretion (Section 10.5.2). Accurate estimation of the crude fat content of feed is important, especially in the caseof high-fat feeds, for accurate estimation of the GE content in feed, which is needed to calculate feed intake neededto achieve GE requirements (Section 10.2.2.).
Protein content in diet (CP, percent) – the total amount of protein present in animal diet. It is determined byanalysing the nitrogen content in animal feed and multiplying by 6.25. The data on CP percent is required for the
calculation of N excretion using a Tier 2 method.
Average annual wool production per sheep and goats (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.
For goats this is only applicable if the country has relevant numbers of fibre-producing goats.

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TABLE 10.2 (UPDATED)REPRESENTATIVE FEED DIGESTIBILITY FOR VARIOUS LIVESTOCK CATEGORIESMain categories

Class

Digestibility (DE as percent)

Swine1Mature Swine – confinement
Growing Swine - confinement
Swine – free range

70 - 80
80 - 90
50 - 70

Cattle and otherruminants

Feedlot animals fed with > 85%
concentrate or high-grain diet
Pasture / mixed-diet fed animals
Animals fed – low quality forage

72 - 85
55 - 80
45 - 55

Poultry1

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

85 - 93
70 - 80
55 - 901
85 - 93
80 - 90

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

1

G ros s en e rg y ca lcu lat ion s
Animal performance and diet data are used to estimate feed intake which is the amount of energy (MJ/day) animalneeds for maintenance and for such as growth, lactation, and pregnancy. For inventory compilers who have welldocumented and recognised country-specific methods for estimating intake based on animal performance data, it
is good practice to use the country-specific methods. The following section provides methods for estimating grossenergy intake for the key ruminant categories of cattle, buffalo, sheep and goats. The equations listed in Table
10.3 are used to derive this estimate. If no country-specific methods are available, intake should be calculatedusing the equations listed in Table 10.3. As shown in the table, separate equations are used to estimate net energy
requirements for sheep and goats as compared with cattle and buffalo. The equations used to calculate GE are as
follows:

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.3 (UPDATED)SUMMARY OF THE EQUATIONS USED TO ESTIMATE DAILY GROSS ENERGY INTAKE FOR CATTLE,BUFFALO AND SHEEP AND GOATS

Metabolic functions and otherestimates

Equations for cattle andbuffaloEquations for sheep andgoats

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 and goats based on AFRC (1993; 1995).
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 (Jurgens 1988).
EQUATION 10.3
NET ENERGY FOR MAINTENANCENE=
Cf i • (Weight )m

0.75

Where:

NEm= net energy required by the animal for maintenance, MJ day-1

Cf i= a coefficient which varies for each animal category as shown in Table 10.4 (Coefficientsfor calculating NEm), MJ day-1 kg-1

Weight= live-weight of animal, kg

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TABLE 10.4 (UPDATED)COEFFICIENTS FOR CALCULATING NET ENERGY FOR MAINTENANCE (NEM)
ANIMAL CATEGORY

CFI (MJ D-1 KG-1)

COMMENTS

Cattle/Buffalo

0.322

All non-lactating cows, steers, heifers and calves

Cattle/Buffalo (lactating cows)

0.386

Maintenance energy requirements are 20% higher duringlactation

Cattle/Buffalo (bulls)

0.370

Maintenance energy requirements are 15% higher for intact
males than non lactating females

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.

Goats

0.315

Source: NRC (1996) and AFRC (1993; 1995).

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 andgoats. Both equations are empirical with different definitions for the coefficient Ca.
EQUATION 10.4
NET ENERGY FOR ACTIVITY (FOR CATTLE AND BUFFALO)NE=
Ca • NE ma
Where:

NEa= net energy for animal activity, MJ day-1

Ca= coefficient corresponding to animal’s feeding situation (Table 10.5, Activity coefficients),
dimensionless

NE m= net energy required by the animal for maintenance (Equation 10.3), MJ day-1
EQUATION 10.5
NET ENERGY FOR ACTIVITY (FOR SHEEP AND GOATS)NE=
Ca • ( weight )aWhere:

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 theyear, NEa must be weighted accordingly.

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TABLE 10.5 (UPDATED)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) withthe result that they expend very little or no energy to acquire feed.

0

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

0.17

Grazing large areas

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

0.36

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

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

0.0096

Grazing flat pasture

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

0.0107

Grazing hilly pasture

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

0.024

Housed fattening lambs

Animals are housed for fattening.

0.0067

Lowland goats

Animals walk and graze in lowland pasture.

0.019

Hill and mountaingoats

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

0.024

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 
22.02 • 
NE g =
 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, kgC= a coefficient with a value of 0.8 for females, 1.0 for castrates and 1.2 for bulls (NRC, 1996)

MW= the mature body weight of an adult animal individually, mature females, mature males andsteers) in moderate body condition 4, kg

WG= the average daily weight gain of the animals in the population, kg day-1

4 Since statistical offices may collect and report data on highly disaggregated number of cattle population (e.g., bovines lessthan one year old or bovines aged under 8 months, cattle aged between one and two years old), hence, this parameter (i.e.,mature weight) may refer to target weight related to stage of growth. Herewith, the number of days needed for animals toreach from the beginning of growing stage to target weight of this growing stage should be taken into consideration.

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EQUATION 10.7
NET ENERGY FOR GROWTH (FOR SHEEP AND GOATS) (UPDATED)

NEg =

(

WGlamb / kid • a + 0.5b ( BWi + BW f ))365

Where:
NEg= net energy needed for growth, MJ day-1

WGlamb / kid= the weight gain (BWf – BWi), kg yr-1

BWi

= the live bodyweight at weaning, kg

BW f= the live bodyweight at 1-year old or at slaughter (live-weight) if slaughtered prior to 1 yearof 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 and goats includes two empirical constants (a and b) that vary by animal
species/category (Table 10.6).
TABLE 10.6 (UPDATED)CONSTANTS FOR USE IN CALCULATING NEG FOR SHEEP AND GOATSa(MJ kg-1)b(MJ kg-1)

Intact males (Sheep)

2.5

0.35

Castrates (Sheep)

4.4

0.32

Females (Sheep)

2.1

0.45

Goats (all categories)

5.0

0.33

Animal species/category

Source: AFRC (1993; 1995).

Net energy for lactation: (NEl) is the net energy for lactation. For cattle and buffalo the net energy for lactation isexpressed as a function of the amount of milk produced and its fat content expressed as a percentage (e.g., 4
percent) (NRC 1989):
EQUATION 10.8
NET ENERGY FOR LACTATION (FOR BEEF CATTLE, DAIRY CATTLE AND BUFFALO)

NE1 = Milk • (1.47 + 0.40 • Fat )Where:

NE1= net energy for lactation, MJ day-1

Milk= amount of milk produced, kg of milk day-1

Fat= fat content of milk, percent 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 10.10) is
used when the amount of milk produced is not known. Generally, milk production is known for ewes kept for

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commercial 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 in Equation 10.10.
EQUATION 10.9 (UPDATED)NET ENERGY FOR LACTATION FOR SHEEP AND GOATS (MILK PRODUCTION KNOWN)

NE=
Milk • EVmilk
1
Where:

NE1= net energy for lactation, MJ day-1

Milk= amount of milk produced, kg of milk day-1

EVmilk= the net energy required to produce 1 kg of milk.

EQUATION 10.10
NET ENERGY FOR LACTATION FOR SHEEP AND GOATS (MILK PRODUCTION UNKNOWN)

 ( 5 • WGwean ) NE1 = • EVmilk
365

Where:

NE1= net energy for lactation, MJ day-1

WGwean= 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 EVmilk value of 4.6 MJ/kg(sheep) (AFRC 1993; AFRC 1995) and 3 MJ/kg (goats) (AFRC 1998) can be used whichcorresponds to a milk fat content of 7 percent and 3.8 percent by weight for sheep and goats,
respectively. Milk fat can vary greatly among breeds. Compilers are encouraged to use
country-specific milk fat content to derive EVmilk when available

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(Bamualim & Kartiarso 1985; Ibrahim 1985; Lawrence 1985). The strenuousness of the work 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)

NEwork = 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

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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 (UPDATED)NET ENERGY TO PRODUCE WOOL (FOR SHEEP AND GOATS) EV • Prwool NEwool =  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 can be used for sheep estimate. For goats this energy
value is not considered unless fibre-producing goat numbers are relevant for a country(AFRC 1995).

Prwool= annual wool production per sheep/goat, kg yr-1

For fibre-producing sheep NEwool can be estimated that 0.25 MJ day-1 is retained in the fibre (AFRC 1993; AFRC
1995). For fibre-producing goats NEwool can be estimated that 0.25 and 0.08 MJ/day for angora and cashmere
breeds (AFRC 1993; AFRC 1995), respectively.
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 percent
of NEm. 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.
EQUATION 10.13 (UPDATED)NET ENERGY FOR PREGNANCY (FOR CATTLE/BUFFALO AND SHEEP AND GOATS)=NE p C pregnancy • NEm
Where:

10.28

NE p

= net energy required for pregnancy, MJ day-1

C pregnancy= pregnancy coefficient (see Table 10.7)

NEm= net energy required by the animal for maintenance (Equation 10.3), MJ day-1

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TABLE 10.7 (UPDATED)CONSTANTS FOR USE IN CALCULATING NEP IN EQUATION 10.13
Animal category

Cpregnancy

Cattle and Buffalo

0.10

Sheep/Goats
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); AFRC (1995), taking into account the inefficiency of energy
conversion.

When using NEp to calculate GE for cattle, sheep and goats, the NEp estimate must be weighted by the portion of
the mature females that actually go through gestation in a year. For example, if 80 percent of the mature females
in the animal category give birth in a year, then 80 percent of the NEp value would be used in the GE equation
below.
To determine the proper coefficient for sheep/goats, the portion of ewes/does that have single births, double births,
and triple 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/kids born in a year divided by the number of ewes that are pregnant in a year is lessthan or equal to 1.0, then the coefficient for single births can be used.
• If the number of lambs/kids born in a year divided by the number of ewes/does 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,
sheep and goats, the ratio of net energy available in a diet for maintenance to digestible energy (REM) is estimatedusing the following equation (Gibbs & Johnson 1993):
EQUATION 10.14
RATIO OF NET ENERGY AVAILABLE IN A DIET FOR MAINTENANCE TO DIGESTIBLE ENERGY

(

)


2
 25.4  
REM= 1.123 − ( 4.092 • 10−3 • DE ) + 1.126 • 10−5 • ( DE ) −  DE  Where:
REM= ratio of net energy available in diet for maintenance to digestible energyDE= digestibility of feed expressed as a fraction of gross energy (digestible energy/grossenergy*100, i.e. DE%)

Ratio of net energy available for growth in a diet to digestible energy consumed (REG): For cattle, buffalo, sheep
and goats 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 & Johnson 1993):

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EQUATION 10.15
RATIO OF NET ENERGY AVAILABLE FOR GROWTH IN A DIET TO DIGESTIBLE ENERGY CONSUMED

(

)


2
 37.4  
REG= 1.164 − ( 5.16 • 10−3 • DE ) + 1.308 • 10−5 • ( DE ) −  DE  Where:
REG= ratio of net energy available for growth in a diet to digestible energy consumed

DE= digestibility of feed expressed as a fraction of gross energy (digestible energy/grossenergy*100, i.e. DE%)

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, buffalo, sheep and goats 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 (UPDATED)GROSS ENERGY FOR CATTLE/BUFFALO, SHEEP AND GOATS  NEm + NEa + NEl + NEwork + NE p   NEg + NEwool  

+

REM
REG





GE =


DE
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

NE p= 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 (Equation 10.14)

NEg= net energy needed for growth (Equations 10.6 and 10.7), MJ day-1

REG= ratio of net energy available for growth in a diet to digestible energy consumed (Equation
10.15)

NEwool= net energy required to produce a year of wool (Equation 10.12), MJ day-1

DE= digestibility of feed expressed as a fraction of gross energy (digestible energy/gross energy,
i.e. DE%/100)

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 percent
to 3 percent of the body weight of the mature or growing animals. In high producing milk cows, intakes may
exceed 4 percent of body weight.
F e ed intak e es t imat es u s ing a s imp lif ied Tie r 2 m etho d

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Prediction of DMI for cattle based on body weight and estimated dietary net energy concentration (NEmf) anddigestibility 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 dietary net energy of maintenance concentration of the feed NEmf (MJ
kg-1 DM) (National Academies of Sciences, Engineering and Medicine 2016) or DE, and for lactating dairy cows,
fat corrected milk production. Dietary NEmf concentration can range from 3.0 to 9.0 MJ kg-1 of dry matter. Typicalvalues for high, moderate and low quality diets are presented in Table 10.8a. These figures can also be used toestimate NEmf values for mixed diets based on estimate of diet quality. For example, a mixed forage-grain dietcould be assumed to have a NEmf value similar to that of a high-quality forage diet. A mixed grain-straw diet could
be assumed to have a NEmf value similar to that of a moderate quality forage. Nutritionists within specificgeographical areas should be able to provide advice with regard to the selection of NEmf values that are more
representative of locally fed diets.
Dry matter intake for calves is estimated using the following equation:
EQUATION 10.17 (NEW)ESTIMATION OF DRY MATTER INTAKE FOR CALVESDMI BW=0.75

 ( 0.0582 • NEmf − 0.00266 • NEmf 2 − 0.1128 ) •
0.239 • NEmf

Where:DMI= dry matter intake, kg day-1

BW= live body weight, kg

NEmf= estimated dietary net energy concentration of diet or default values in Table 10.8a,
MJ kg-1

Dry matter intake for growing cattle is estimated using the following equation:
EQUATION 10.18 (NEW)ESTIMATION OF DRY MATTER INTAKE FOR GROWING CATTLE

 ( 0.0582 • NEmf − 0.00266 • NEmf 2 − 0.0869 ) 
DMI BW 0.75 • =
0.239 • NEmf
Where:DMI= dry matter intake, kg day-1

BW= live body weight, kg

NEmf= estimated dietary net energy concentration of the feed or diet with default values in Table
10.8a, MJ kg-1 DM-1

Dry matter intake for feedlot cattle (on high grain diets) is estimated using the following equation:
EQUATION 10.18A (UPDATED)ESTIMATION OF DRY MATTER INTAKE FOR STEERS AND BULLSDMI = 3.83 + 0.0143  BW  0.96
ESTIMATION OF DRY MATTER INTAKE FOR HEIFERS

DMI = 3.184 + 0.01536  BW  0.96
Where:

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DMI= dry matter intake, kg day-1

BW= live body weight, kg

For mature beef cows use the following values (National Academies of Sciences, Engineering and Medicine 2016)
TABLE 10.8 (NEW)DMI REQUIRED BY MATURE NON DAIRY COWS BASED ON FORAGE QUALITY
Forage type

Digestibility (DE, %)Low quality

Forage DMI capacity (kg/day), % of BW (kg)Non-lactating

Lactating

<52

1.8

2.2

Average quality

52-59

2.2

2.5

High quality

>59

2.5

2.7

For lactating dairy cows the following equation can be used (Cornell Net Carbohydrate and Protein System(CNCPS), Fox et al. 1992) as modified by Arnerdal (2005):
EQUATION 10.18B (UPDATED)ESTIMATION OF DRY MATTER INTAKE FOR LACTATING DAIRY COWS

DMI= 0.0185 • BW + 0.305 • FCM
Where:DMI= dry matter intake, kg day-1

BW= live body weight, kg

FCM= Fat corrected milk, kg day-1, 3.5 percent * [(0.4324 * kg of milk) + (16.216 * kg of fat)].

Equations 10.17, 10.18, 10.18A and 10.18B and values in Table 10.8 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 in the case that countries do not have the data required to carry out a full estimate of gross energy use for theircatle herd, these equations could be used to independently predict DMI from BW, diet quality (NEmf or DE percent)and milk production. In contrast, the main Tier 2 method predicts DMI based on how much feed must be consumed
to meet estimated energy requirements (i.e., NEm and NEg) and does not consider the biological capacity of theanimal to in fact consume the predicted quantity of feed. While the Tier 2 estimate of gross energy is the preferred
method, the simplified 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
the order of 2 percent to 3 percent of the bodyweight of the mature or growing animals and up to 4 percent forhigh yielding lactating dairy cattle.

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TABLE 10.8A (UPDATED)EXAMPLES OF NEMF CONTENT OF TYPICAL DIETS FED TO CATTLE FOR ESTIMATION OF DRY MATTER INTAKEIN EQUATIONS 10.17 AND 10.18
Diet typeNEmf (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), NEmf can also be estimated using theequation: NEmf = REM * 18.45 * DE%/100

10.2.3 Uncertainty assessment
No refinement.

10.2.4 Characterisation for livestock without species: Specificemission estimation methods
Some countries may have domesticated livestock for which there are currently no Tier 1 or Tier 2 emissions
estimating methods (e.g., wapiti, bison or emus). 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 anddeveloping 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 asource category. If the emissions from a particular sub-species are determined to be significant, then countryspecific emission factors should be developed, and a characterisation should be performed to support thedevelopment of the emission factors. Research into the estimation of emission levels from these non-characterized
species should be encouraged. The data and methods used to characterise the animals should be well documented.
As emissions estimation methods are not available for these animals, approximate emission factors based on ‘orderof magnitude calculations’ are appropriate for conducting the assessment of the significance of their emissions.
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:• 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 wapiti could be estimated fromthe emissions factor for deer (also a ruminant animal) as follows:
Approximate emissions factor = [(wapiti weight) / (deer weight)]0.75 * deer emissions factor
Similarly, an approximate manure methane emissions factor could be estimated for emus using the Tier 1 emission
factor for ostriches. Approximate emission factors developed using this method can only be used to assess thesignificance of the emissions from the animals, and are not considered sufficiently accurate for estimating
emissions as part of a national inventory.

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10.3

METHANE
EMISSIONSFERMENTATIONFROMENTERICMethane 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 released depends on the type of digestive tract, age, and weight of the animal, and thequality and quantity of the feed consumed. Ruminant livestock (e.g., cattle, sheep) are major sources of methanewith moderate amounts produced from non-ruminant livestock (e.g., pigs, horses). The ruminant gut structure
fosters extensive enteric fermentation of their diet.
D ig est iv e sys te m
The type of digestive system has a significant influence on the rate of methane emission. Ruminant livestock
have an expansive chamber known as the rumen, located at the fore-part of their digestive tract. The rumen
supports intensive microbial fermentation of the diet, which yields several nutritional advantages including thecapacity to digest cellulose (the major component of fiber). The main ruminant livestock are cattle, buffalo, goats,
sheep, deer and camelids. Non-ruminant livestock (horses, mules, asses) and monogastric livestock (swine) haverelatively lower methane emissions because much less methane-producing fermentation takes place in theirdigestive systems.
F e ed intak eMethane 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, methane production is also affected by the compositionof 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 a population
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 (Updated) Decision Tree for CH 4 Emissions from Enteric Fermentation
Start

Do you havea country- specific Tier 3
methodology?

Estimate emissions for the
species using Tier 3 approach.

Yes

Box 4: Tier 3

No

Is enhanced livestock
characterization available?
Estimate emissions for the
species using Tier 1 approachNo
Yes

Box 1: Tier 1

No
Is enteric fermentationa key category1 and is the
species significant2?

Yes

Collect enhanced species
characterization data
for Tier 2 approach?

No

Do you have
population data by
productivity System?
Yes
Estimate emissions for the
species using Tier 1a approach.
Box 2: Tier 1a

Yes

Estimate emissions for the
species using Tier 2 approach.
Box 3: 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.

S te p 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.
S te p 2: Estimate emission factors for each subgroup in terms of kilograms of methane per animal per year.
S te p 3: Multiply the subgroup emission factors by the subgroup populations to estimate subgroup emission,and sum across the subgroups to estimate total emission.
These three steps can be performed at varying levels of detail and complexity. This chapter presents the following
three approaches:
Tie r 1
A simplified approach that relies on default emission factors established in these guidelines that were either drawn
from the literature or calculated using regional data taken from the literature and derived using the Tier 2 method.

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The Tier 1 method is likely to be suitable for most animal species in countries where enteric fermentation is not a
key source category, or where enhanced characterization data are not available. When approximate entericemissions are derived by extrapolation from main livestock categories they should be considered to be a Tier 1
method.
Tie r 1a
An advanced Tier 1 method, applicable in particular to countries that have differentiated production systems withcoexistence of low and high productivity systems, or whose agricultural production systems are transitioning from
low to high productivity. Countries can consider the split in their production systems, yet still use default emission
factors, to customize their emission estimates based on populations of high and low productivity animals andtherefore track change in their emissions related to improved productivity.
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 is a
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 thedevelopment of sophisticated models that consider diet composition in detail, concentration of products arisingfrom ruminant fermentation, seasonal variation in animal population or feed quality and availability, and possible
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, thesemore complex analyses are only briefly discussed here. A Tier 3 method should be subjected to a wide degree ofinternational peer review such as that which occurs in peer-reviewed publications to ensure that they improve theaccuracy 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 are significant).
The approach is described in Section 10.2.4 under the heading ‘Characterisation for livestock without speciesspecific 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. Each step
is discussed in turn.
S te p 1: Anima l pop u lat ion an d pr odu ct iv it y s ys te m
The animal population data should be obtained using the approach described in Section 10.2.
S te p 2: Emis s ion fac tor s
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 and by productivity system for ease of use.
The data used to estimate the default emission factors for enteric fermentation are presented in Annex 10A.1.

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.9 (UPDATED)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/Tier 2

CamelsTier 1

HorsesTier 1

Mules and Asses

Tier 1

Swine

Tier 1

Poultry

Not developed

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

aTable 10.10 shows the enteric fermentation emission factors for each of the animal species except cattle andbuffalo. As shown in the table, emission factors for sheep, goats and swine vary for low and high productivity
systems and it is important to consider that these conditions may exist within individual countries. The differencesin the emission factors are driven by differences in feed intake (as related to animal size) and feed characteristic
assumptions. Table 10.11 presents the enteric fermentation emission factors for cattle and buffaloes, accordingly.
A range of emission factors is shown for typical regional conditions.
Animal size and milk production are important 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 levels of production are found in North America, Western Europe and several countries of
Latin America.
Animal size and population structure and production systems implemented 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 in these regions are young. Other cattle in North America, Western Europe and Oceania
are larger, and young cattle constitute a smaller portion of the population.
For countries with highly differentiated agricultural systems in which there is a coexistence of low and highproductivity systems or whose agricultural systems are transitioning from local low input productivity systems tohigher productivity systems and do not have the information necessary for implementing Tier 2 method, the useof the diversification of emission factors given for an animal category provides an alternative or intermediary
option. This approach can reflect changes in activity data and productivity with time, whereas the Tier 1a approach
only takes into account changes in the number of animals in a country.
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 (UPDATED)2,3
ENTERIC FERMENTATION EMISSION FACTORS FOR TIER 1 METHOD (KG CH4 HEAD-1 YR-1)
High Productivity
Systems1

Low Productivity
Systems

Sheep

9

5

65 kg – high productivity systems
45 kg – low productivity systems

Swine61.5

1

72 kg - high productivity systems
52 kg - low productivity systems

Goats59

550 kg – high productivity systems
28 kg – low productivity systems

Livestock

Liveweight7

Horses18

550 kgCamels46

570 kgMules and Asses

10

245 kgDeer

20

120 kgOstrich4

5

120 kgPoultry

Insufficient data for calculation

Llamas and Alpacas

8

Other (e.g., bison)

65 kg
To be determined

All estimates have an uncertainty of +30-50%.
Sources:
Emission factors: camels from Gibbs & Johnson (1993); Alpacas from Pinares-Patino et al. (2003); Deer from Clark et al. (2003); Sheep(High productivity systems) derived from Swainson et al. (2016). Sources and assumptions to calculate goats EFs are detailed in Annex
10B.3. Emission factors for other livestock from Crutzen et al. (1986),
1
For the application of the simple Tier 1, for all regions other than North America, Europe and Oceania the Tier 1 default values are thelow productivity EFs.
2
One approach for developing the approximate emission factors is to use the Tier 1 emissions factor for an animal with a similar digestivesystem and to scale the emissions factor using the ratio of the weights of the animals raised to the 0.75 power. Liveweight values have been
included for this purpose. Emission factors should be derived on the basis of characteristics of the livestock and feed of the animals andcompilers should not base their decision of an emission factor entirely on regional characteristics.
3

The enteric fermentation emission factor shall be applied for the whole livestock population including non-mature animals.

4

CH4 EF for ostrich was calculated based on Frei et al. (2015) and Danish NIR (Nielsen et al. 2018).

5

Sources and assumptions to adjust weight of goats for low- and high-productivity systems are detailed in Annex 10B.3.

The values of swine weight for low and high productivity systems were obtained from FAO GLEAM databases (FAO 2017). More detailed
data on swine weight are reported in Annex 10A.2 (in Table 10A.5). Crutzen et al. (1986) did not report weights for swine. Regional Tier
2 calculations for swine have been carried out by the FAO GLEAM research group, countries could consult these values for consistency
with their production systems for refinement to their emission factors.
6

7
If a country-specific liveweight of animal category is different from those reported in Table 10.10, an inventory-compiler may use theapproach presented in section 10.2.4 of the current chapter.

It is recommended to continue to use Tier 1 emission factor uncertainty ranges as defined in Section 10.3.4 of the 2006 IPCC Guidelines.

S te p 3: Tota l e m is s ion
To estimate total emission, the selected emission factors are multiplied by the associated animal population(Equation 10.19) and summed (Equation 10.20):

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EQUATION 10.19 (UPDATED)ENTERIC FERMENTATION EMISSIONS FROM A LIVESTOCK CATEGORY (TIER 1)

 N (T , P ) 
ET = ∑( P ) EF(T , P ) •  106 
Where:

ET= methane emissions from Enteric Fermentation in animal category T, Gg CH4 yr-1

EF(T , P )= emission factor for the defined livestock population T and the productivity system P, in kg
CH4 head-1 yr-1

N (T , P )= the number of head of livestock species / category T in the country classified as
productivity system PT= species/category of livestockP= productivity system, either high or low productivity for use in advanced Tier 1a – omitted
if using Tier 1 approach
EQUATION 10.20 (UPDATED)TOTAL EMISSIONS FROM LIVESTOCK ENTERIC FERMENTATION (TIER 1)

Total CH 4 Enteric = ∑ Ei , P
i,P

Where:

Total CH 4 Enteric= total methane emissions from Enteric Fermentation, Gg CH4 yr-1

Ei , P= is the emissions for the ith livestock categories and subcategories based on
production systems (P)Tier 1 method entails multiplying the total number of livestock population and CH4 emission factor for eachcategory of livestock (Table 10.10 or Table 10.11). Tier 1a method relies on number of livestock population ineach productivity system (i.e., low-productivity systems and high-productivity systems) and CH4 emission factor
for each category of livestock developed per head of animal kept in the specified productivity system (Table 10.10
or Table 10.11).

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TABLE 10.11 (UPDATED)TIER 1 AND TIER 1A ENTERIC FERMENTATION EMISSION FACTORS FOR CATTLE AND BUFFALO1

Regional characteristics8Tier 1 and Tier 1aEmission Factor2,3(kg CH4 head-1 yr-1)

Comments7

Dairy Cattle

138

Average milk production of
10,250 kg head-1 yr-1.

Other cattle

64

Includes mature males, mature
females,
calves,growingsteers/heifers, and feedlot cattle.

Dairy Cattle

126

Average milk production of
7,410 kg head-1 yr-1.

Other cattle

52

Includes mature males, calves,and growing steers/heifers.

Buffalo78

Includes mature females, mature
males, growing animals andcalves.

Dairy cattle

93

Average milk production of
4,000 kg head-1 yr-1.

Other cattle

58

Includes mature males, mature
females,
growingandreplacement animals, and calves.

Buffalo68

Includes mature females, mature
males, growing animals andcalves.

Dairy cattle

93

Average milk production of
4,400 kg head-1 yr-1.

Other cattle

63

Includes mature males, mature
females and young.

Animal category

North America
Cattle:Highlyproductive
commercialised dairy sector feedinghigh quality forage and grain.
Separate beef cow herd, primarily
grazing with feed supplements
seasonally.
Fast-growing
beef
steers/heifers finished in feedlots on
grain. Dairy cows are a small part ofthe population. There are no buffalo
herds, but American bison may beraised.
Western Europe
Cattle:Highlyproductive
commercialised dairy sector feedinghigh quality forage and grain. Dairy
cows also used for beef calf
production. Separated beef cow
herd. Minor amount of feedlot
feeding with grains.
Buffalo: Buffalo farming system is
exclusively
intensive.
The
concentrates are largely used only
during the lactation phase. Animals
are maintained in paddocks, grazing
practices are not widespread.
Eastern Europe
Cattle: Commercialised dairy sector
feeding based on forages and grains.
Separate beef cow herd, primarily
grazing. Minor amount of feedlot
feeding with grains.
Buffalo: Commercialized buffalo
sector feeding primarily withroughages. Buffaloes are managed
according to their categories.
Animals are maintained paddock
and tied up during the winter, insummer they are allowed to graze
Oceania4
Cattle: Commercialised dairy sector
based on grazing. Separate beef cow
herd, primarily grazing rangelands5
and hill country of widely varying
forage quality. Growing amount offeedlot feeding with grains. Dairy
cows are a small part of the
population. No Buffalo herd.

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TABLE 10.11 (UPDATED) (CONTINUED)TIER 1 AND TIER 1A ENTERIC FERMENTATION EMISSION FACTORS FOR CATTLE AND BUFFALO1
Regional characteristics8Tier 1 and Tier 1aEmission Factor2,3(kg CH4 head-1 yr-1)

Comments7

Dairy Cattle

87

Average milk production of 2,050 kg
head-1 yr-1

High productivity
systems

103

Average milk production of 3,400 kg
head-1 yr-1

Low productivity
systems

78

Average milk production of 1,250 kg
head-1 yr-1

Other cattle

56

High productivity
systems55

Low productivity
systems

58

Buffalo68

Includes mature females, mature
males, growing animals and calves.

Dairy cattle

78

Average milk production of 3,200kg head-1 yr-1

High productivity
systems

96

Average milk production of 5,000kg head-1 yr-1

Low productivity
systems

71

Average milk production of 2,600
kg head-1 yr-1

Other cattle

54

High productivity
systems43

Low productivity
systems

56

Buffalo68

Animal category

Latin America

Cattle: Commercialised dairy
sector based on grazing. Separate
beef cow herd grazing pasturesand rangelands. Minor amount offeedlot feeding with grains.
Growing
non-dairy
cattlecomprise a large portion of the
population.

Buffalo: Buffalo husbandry isbased on extensive systems innative or cultivated pastures inlowlands and uplands, most often
without supply of concentrated
feed. Milk production is based on
pasturewithfrequent
supplementation of roughage(sugar cane, silage, etc.), with apredominance of one single
milking.

Includes mature females, mature
males, growing steers/heifers andcalves.

Asia
Cattle: Commercialised dairy
sector is experienced fundamental
changes due to increasing numberof large farms with intensive
production system based on grains
and forage. Cattle kept intraditional production systems are
multi-purpose, providing draft
power and some milk within
farming regions. Cattle of all types
are smaller than those found in
most other regions.
Buffalo: Buffaloes are generally
swamp type. Buffaloes are raisedby smallholder farmers as sourceof draft power. Animals arecommonly grazed in field and fed
on agriculture residual products.
Milk yield per cow is low.
Nevertheless, the dairy buffalo
breeding is rapidly developing incountryside of Asia.

Includes mature males, mature
females, growing and replacement
animals, and calves.

Includes mature females, mature
males, growing animals and calves.

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TABLE 10.11 (UPDATED) (CONTINUED)TIER 1 AND TIER 1A ENTERIC FERMENTATION EMISSION FACTORS FOR CATTLE AND BUFFALO1
Regional characteristics8Tier 1 and Tier 1aEmission Factor2,3(kg CH4 head-1 yr-1)

Comments7

Dairy cattle

76

Average milk production of
1,300 kg head-1 yr-1

High productivity
systems

86

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

Low productivity
systems

66

Average milk production of 500
kg head-1 yr-1

Other cattle

52

High productivity
systems60

Low productivity
systems48

Buffalo81

Includes breeding and working
males and females, growing
animals and calves

Dairy cattle

76

Average milk production of
2,500 kg head-1 yr-1

High productivity
systems

94

Average milk production of
3,900 kg head-1 yr-1

Low productivity
systems

62

Average milk production of
1,300 kg head-1 yr-1

Other cattle

60

High productivity
systems61

Low productivity
systems

55

Buffalo67

Animal category

Africa6Cattle: Commercialised dairy sector
based on grazing with low
production per cow. Most cattle are
multi-purpose, providing draft
power and some milk within
farming regions. Some cattle grazeover very large areas. Cattle aresmaller than those found in mostother regions.

Buffalo: Small-scale buffalo sector
well-integrated with cropland.
Animals are raised for multipurpose. Feeding primarily depends
on roughages and crop-residues.
Minor commercial dairy buffalo
farms feeding with concentrate feed
mixture.

Includes mature males, multipurpose
maturefemales,
growingandreplacement
animals, and calves.

Middle East

Cattle: Majority of cattle populationis still kept by small holders in the
traditional production systems. The
animals are fed primarily by crop
residues and are grazed. Most
animals are dual-purpose. In
contrast to the small-scale farms,
commercial dairy sector is generally
intensive,
mainlybased
oncompound feed and grains.

Buffalo: Buffalo farming system
primarily based on smallholders
rearing animals for meat, milk anddraught. Animals obtain theirfeedingbygrazing.
Minor
commercialized buffalo sector
feeding forage and concentrate
supplemented feed.

10.42

Includes mature males, multipurpose
maturefemales,
growingandreplacement
animals, and calves.

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.11 (UPDATED) (CONTINUED)TIER 1 AND TIER 1A ENTERIC FERMENTATION EMISSION FACTORS FOR CATTLE AND BUFFALO1
Regional characteristics8Tier 1 and Tier 1aEmission Factor2,3(kg CH4 head-1 yr-1)

Comments7

Dairy cattle

73

Average milk productionof 1,900 kg head-1 yr-1

High productivity
systems

70

Average milk productionof 2,600 kg head-1 yr-1

Low productivity
systems

74

Average milk productionof 1,700 kg head-1 yr-1

Other Cattle

46

High productivity
systems41

Low productivity
systems

47

Animal category

Indian Subcontinent

Cattle: Commercialised dairy sector based oncrop by-product feeding with low production
per cow. Most bullocks provide draft powerand cows provide some milk in farming
regions. Cattle in this region are the smallest
compared to cattle found in all other regions.

Buffalo: Smallholder buffalo sector feedingpoor quality roughages and crop-residues.
Buffaloes are primarily free grazing.
Concentrates are fed to dairy animals during
last months of pregnancy. Dairy and meatproduction are intimately related. Animals areused as draft power. Minor commercializedbuffalo sector providing animals withbalanced ration.

Buffalo85

Includes mature males,
multi-purpose
maturefemales, growing andreplacement animals, andcalves.

Includes breeding andworkingmales
andfemales, growing animals
and calves

Emission factors should be derived on the basis of the characteristics of the cattle and feed of the animals and compilers should not base theirdecision of an emission factor entirely on regional characteristics.
1

The values represent averages within region. Existing values were derived using Tier 2 method and the data in Tables 10A.1–10A.4. Data on
a livestock population mix corresponding to low- and high-productivity systems were used.

2

Uncertainty values from the 2006 IPCC Guidelines were validated during the development of the emission factors using a Monte Carlo analysis
in the 2019 Refinement, based on data compiled during the emission factor development process. It is recommended to continue to use Tier 1
emission factor uncertainty ranges as defined in Section 10.3.4 of the 2006 IPCC Guidelines.

3

4
All data are weighted values, representative of Australia and New Zealand. For Pacific Island nations, GHG inventory compilers may refer to
Asian values. GHG inventory compilers from the Island nations of Oceania who wish to use a Tier 1a method could use values from Asia forlow productivity systems and the high productivity Tier 1a emission factor from Oceania, whichever is more representative of their production
systems.
5
Rangelands are defined as land primarily covered by woodlands, shrublands, grasslands and savannas, as well as introduced plant species thatare naturalised (Grice et al. 2008).
6

North African countries may wish to use values derived for the Middle East if productions systems are more similar.

7

Buffalo mature females livestock sub-category includes lactating dairy mature females.

Sources: Cattle of Asia: IPCC (2006); Ma et al. (2007); Ma et al. (2012); FAO et al. (2014) . Cattle of Middle East: Kamalzadeh et al.
(2008); Karakok (2007); Yilmaz et al. (2012); Yilmaz & Wilson (2012); FAO et al. (2014). Buffalo of Western Europe: Borghese (2013);
Neglia et al. (2014); Sabia et al. (2015). Buffalo of Eastern Europe: FAO (2005). Buffalo of Latin America: Bernardes (2007). Buffalo of
Asia: Cruz (2007); Yang et al. (2007). Buffalo of Africa: Habeeb et al. (2016); Radwan (2016); Ali et al. (2009); Hassan & Abdel-Raheem(2013); Ibrahim (2012); Soliman (2009). Ali et al. (2009). Buffalo of Middle East: Azary et al. (2007); Soysal (2013); Dezfuli et al. (2011);
Hossein-zadeh et al. (2012); Soysal et al. (2007); Naserian & Saremi (2007); Ermetin (2017). Dezfuli et al. (2011). Buffalo of Indian
subcontinent: Ranjhan (2007); Anjum et al. (2012); Khan et al. (2008); Khadda et al. (2017); Ahirwar (2010); Khan et al. (2007); Chawla etal. (2009)8

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 development ofemission factors and the collection of detailed activity data.
S te p 1: Live sto ck popu lat io n
The animal population data and related activity data should be obtained following the approach described in
Section 10.2.
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S te p 2: Emis s ion fac tor s
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 (Y m )The extent to which feed energy is converted to CH4 depends on several interacting feed and animal factors andthat rate of conversion is embodied in the methane conversion factor (Ym), defined as the percentage of grossenergy intake converted to methane.
There are a wide variety of factors that influence methane conversion rates and due to national circumstances
related to breeds, genetic pools as well as particularities of feed and herd interactions, the Ym factors may varyfrom region to region. Considering interactions between feed (type and quality) and animals (breed and genetics),
it is good practice for countries to derive their own Ym values considering their herds and their typical feedcharacteristics.
Nonetheless, numerous empirical studies demonstrate the statistical significance of improved feed quality on
methane emission rates and biochemical modelling exhibits the biochemical processes that impact methane
production with the introduction of improved feeds and concentrates to ruminant diets (Mills et al. 2001; Mills
et al. 2003; Ellis et al. 2006; Ellis et al. 2007; Ellis et al. 2009; Ellis et al. 2010; Alemu et al. 2011; Bannink et
al. 2011; Ellis et al. 2014; Escobar-Bahamondes et al. 2016; Kebreab et al. 2016). When country specific Ym
factors for cattle and buffalo are unavailable, the values provided in Table 10.12 can be used. These estimates area guide based on the general feed characteristics and production practices found in many countries. It is goodpractice for compilers to justify their choice of Ym factors based on detailed feed data and research.
In Table 10.12, the Ym of dairy cows is linked to annual milk production levels and to feed quantity and quality.
The lowest Ym value is associated with highest producing dairy cattle that are fed diets of greater than 70 percent
digestibility, and that have percentage of NDF in DMI of less than 35 percent. These diets may be further
supplemented with additives or supplements that impact feed efficiency. In cases where countries are achieving
high production on high quality silage diets that have digestibility greater than 70 percent but also NDF greaterthan 35 percent of DMI, compilers should use Ym values that are midway between the high production and the
mid-range productivity Ym values (6.0 percent GEI).
Diets with digestible fractions that range from 63 to 70 percent and NDF greater than 37 percent DMI, consistingof good quality forages, silages and some grains and have associated milk production between 5000 to 8500 kg
year-1, are advised to use Ym values of 6.3 percent GEI. For low production dairy systems with feed digestibility
less than 62 percent and NDF fractions greater than 38 percent, the Ym factor from the 2006 IPCC Guidelines(6.5) has been maintained as there is a paucity of reliable globally representative data that could be used to revisethat value. In cases where dairy cattle are strictly grazed on low quality forage diets, compilers should use thenon-dairy low quality forage value of 7.0 percent GEI.
It is important for inventory compilers to base their decision to select the Ym on a thorough understanding of
national feeding systems. In the case of dairy cows, milk production is presented as a proxy for feed quality andYm values in Table 10.12 represent the relationship between feed quality and methane yield. It is possible for acountry’s national herd, or for parts of the national herd, to have production levels that are inconsistent with thefeed quality bounds that are defined by the categories in Table 10.12. In these cases, it is good practice to develop
their own country-specific Ym factors, and they should also use their information on animal diets to validate theirchoice of Ym against methane yield equations recommended in Niu et al. (2018).
With the non-dairy animal category, the non-feedlot diets can be differentiated between forage based diets forwhich the Ym value of 7.0 should be used, and mixed concentrate diets or high quality forage diets for which
compilers should use the value of 6.3. Reliable estimates for grazing cattle on very poor quality diets are notavailable, and due to the lack of data, the value of 7.0 is recommended. Countries that have large cattle herds
consuming these types of diets are encouraged to develop country-specific values and research efforts should
focus on providing more data on these cattle herds.
Emissions from feedlot animals are influenced by the type of grain fed to the animals during the finishing stage,
the lowest value of 3.0 can be used when steam-flaked corn is fed at rates greater than 90 percent of the diet incombination with ionophores. Low forage diets of less than 15 percent that incorporate other grains arerecommended to use the value of 4.0.
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A methane conversion rate of zero is assumed for all juveniles consuming only milk (i.e., milk-fed lambs andcalves). While some studies have demonstrated low level emissions from calves during the activation of theirrumens (Gerrits et al. 2014), the Ym for the addition of small quantities of emission from unweaned calves does
not significantly influence emission factors. For weaned animals the Ym values indicated for the non-dairy animal
category are recommended.
Due to the importance of Ym in driving emissions, ongoing research is aimed at improving estimates for different
livestock and feed combinations. It is important to better understand the mechanisms involved in methanogenesis
with a view to designing emission abatement strategies, as well as to identify different values for Ym according toanimal husbandry practices.
Significant improvements are needed for grazing animals in general, but in particular for low producing dairy
cattle on diverse diets and grazing animals on low quality forages particularly in tropical regions as the available
data are currently very sparse.
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 and head enclosures for housing individual animals (Johnson & Johnson 1995).
A tracer technique using SF6 enables methane emissions from individual animals to be estimated under bothhoused or grazing conditions (Johnson et al. 1994). Hammond et al. (2015) present an in-depth review of theadvantages and limitations of methane measurement techniques used to determine Ym values.

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TABLE 10.12 (UPDATED)6CATTLE/BUFFALO METHANE CONVERSION FACTORS (YM )Livestock
category

1,4Dairy cows

and Buffalo

2Non dairy andmulti-purpose
Cattle andBuffaloFeed Digestibility(DE %) and Neutral
Detergent Fibre(NDF, % DMI)MY,g CH4 kg DMI-1

Ym3 (%)

High-producing cows5(>8500 kg/head/yr-1)

DE ≥ 70
NDF ≤ 35

19.0

5.7

High-producing cows5(>8500 kg/head/yr-1)

DE ≥ 70
NDF ≥ 35

20.0

6.0

Medium producing cows
(5000 – 8500 kg yr-1)

DE 63-70
NDF > 37

21.0

6.3

Low producing cows(<5000 kg yr-1)

DE ≤ 62
NDF >38

21.4

6.5> 75 % forage

DE ≤ 62

23.3

7.0

Rations of >75% high quality
forage and/or mixed rations,
forage of between 15 and 75%
the total ration mixed withgrain, and/or silage.

DE 62–71

21.0

6.3

Feedlot (all other grains, 0-15%
forage)DE ≥ 72

13.6

4.0

Feedlot (steam-flaked corn - 010% forage)

DE ≥ 75

10.0

3.0

Description

1Expert judgement of IPCC authors in consideration of Appuhamy et al. (2016); Jayasundara et al. (2016) Hellwing et al. (2017) and Niu

et al. (2018)2
Sources: Boadi and Wittenberg (2002); Pinares-Patiño et al. (2003); Boadi et al. (2004); Beauchemin and McGinn (2005); Beaucheminand McGinn (2006a); Beauchemin and McGinn (2006b); Chaves et al. (2006); Jordan et al. (2006a); Jordan et al. (2006b); Beauchemin
et al. (2007); Hegarty et al. (2007); Hart et al. (2009); McGinn et al. (2009); Mc Geough et al. (2010a); Mc Geough et al. (2010b); Doreau
et al. (2011); Hales et al. (2012); Kennedy and Charmley (2012); Staerfl et al. (2012); Chung et al. (2013); Hünerberg et al. (2013);
Fiorentini et al. (2014); Hales et al. (2014); Hales et al. (2015); Troy et al. (2015); Nascimento et al. (2016); Vyas et al. (2016a); Vyas etal. (2016b); Baron et al. (2017); Hales et al. (2017).
3
Uncertainty values are ± 20% based on published standard deviations from Niu et al. (2018) and data compilations for non dairy cattle
as described in Annex 10B.2.
4
Ym cited for dairy cattle are for lactating dairy cows. For dairy cattle during their dry phase, in high and medium production systems, thenon-dairy high quality forage value (6.3) should be selected and for low production systems with >75% low quality forage the value of(7.0) should be selected.
5
The lowest Ym factor for high producing cows refers to feeding situations in which additives or supplements may be used in productionthat stimulate feed use efficiency and/or milk production. The Ym values given here do not yet account for any potential reducing effectof additives or supplements on Ym.
6

For details on the development of these values, refer to Annex 10B.2.

Table 10.13 proposes a common Ym value for all sheep irrespective of feed quality values. This value is based on
the mean value of raw data from New Zealand collated between 2009 and 2015 (Swainson et al. 2016). Data were
derived from respiration chamber measurements where intake was accurately measured and covered a range ofdiet qualities. These replace values in the 2006 IPCC Guidelines which were based on indirect measurements
using the sulphur hexafluoride tracer technique where dry matter intake was generally estimated in grazing
animals (Ulyatt et al. 2002a; Ulyatt et al. 2002b; Ulyatt et al. 2005). The mean value of 6.7 percent is mostappropriate for situations where average dry matter intake per day is between 0.6 and 0.8 kg day-1 with a value of
7.0 percent being more appropriate where average intake is <0.6 kg day-1, and a value of 6.5 percent being more
appropriate where average intakes are >0.8kg day-1. Table 10.13 also includes a Ym value for goats (2006 IPCC
Guidelines did not propose any specific value for goats). This value is based on the analysis of 65 studies that
calculated in-vivo enteric CH4 production from a varied sample of countries and goat breeds (sources andassumptions are explained in Annex 10B.3).

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TABLE 10.13 (UPDATED)SHEEP AND GOATS CH4 CONVERSION FACTORS (YM)
Category

Ym 1

Sheep

6.7% + 0.9

Goats5.5% + 1.0

Sources and assumptions to calculate the Ym for goats are detailed in Annex 10B.3.
1

The ± values are the standard deviation of the mean of the Ym.

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 be used. For
example, CH4 conversion factors for other cattle or buffalo could be applied to estimate an emission factor forcamels.

2. Emission factor development
Using the energy balance Tier 2 approach an emission factor for each animal category should be developed
following Equation 10.21:
EQUATION 10.21
METHANE EMISSION FACTORS FOR ENTERIC FERMENTATION FROM A LIVESTOCK CATEGORYY GE •  m  • 365
 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 methane
In cases in which the inventory compiler has used the simplified Tier 2 the emission factors should be calculated
following equation 10.21a:
EQUATION 10.21A (NEW)METHANE EMISSION FACTORS FOR ENTERIC FERMENTATION FROM A LIVESTOCK CATEGORY MY EF =DMI •  • 365
 1000 
Where:
EF= emission factor, kg CH4 head-1 yr-1

DMI

= kg DMI day-1

MY= Methane yield, g CH4 kg DMI-1 (Table 10.12)

365= days per year

1000

= conversion from g CH4 to kg CH4

These emission factor equations assume 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 the2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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365 days would be replaced by the number of days in the period. The definition of the period to which the emission
factor applies is described in Section 10.2, defined according the the enhanced livestock characterisation that isused in calculation methodology.
S te p 3: Tota l e m is s ion s
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 with Tier 3, are encouraged.
Tier 1 and Tier 2 enteric methane emissions factors and estimation procedures are driven by first estimating daily
and annual gross energy consumption by individual animals within an inventory class which are then multipliedby an estimate of CH4 loss per unit of feed (Ym). There is considerable room for improvement in Tier 2 predictionof both feed intake and in Ym. Factors potentially impacting feed requirements and/or consumption may include:• depression in digestibility with increasing levels of consumption or due to rumen acidification, feedpreparation or diet composition putting limits to feed intake;• breed or genotype variation in maintenance requirement; and• heat and cold stress effects on feed intake and maintenance requirements.
Likewise, a host of interacting factors cause variation in the rumen microbiome and its fermentation profile, andhence in hydrogen production which delivers the main substrate for methanogens. These factors lead to variationin Ym that may include:• variation in feed digestibility (DE);• breed or genotype variation;• level of feed intake;• chemical composition of feed;• kinetics of particle and fluid passage and of digestion, rumen volume, rumen fermentation profile; and• other factors (such as secondary plant compounds, additives and other products) affecting the rumen
microbiome.
The values in Table 10.12 capture some aspects of these factors as they are broadly related to feed quality andanimal productivity, however these estimates can be improved for country-specific circumstances using higher
tier methods. Accurate estimation of diet DE is singularly important in the estimation of feed intake and entericmethane emission, as previously emphasized. A change of 10 percent in DE will be magnified to a change in CH4emissions ranging from 12 to 20 percent depending on the dietary circumstances for which calculations are made.
The depression in DE with increasing daily amounts of feed consumed (increasing rates of passage) is notinherently considered with Tier 2 and this neglect could underestimate feed intakes of high producing dairy cows
consuming mixtures of concentrates and forages as is common in the North America and Europe, and hence
underestimate methane emission. The balance between both effects (i.e. a reduction of feed digestibility and ofYm) determines the net effect on methane emission which may vary with dietary circumstances. More complex
models may be developed as Tier 3 to capture the intricacies of such effects.
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 andother chemical components to Ym. These models typically predict diet particle and chemical component rates of
passage and digestion in each enteric compartment at varying intake and the resulting H2 balance, volatile fatty
acids, and microbial and CH4 yields. These approaches have generated Ym values that are consistent with direct
measurements (Bannink et al. 2011; Gregorini et al. 2013; Huhtanen et al. 2015; Dougherty et al. 2017). A
mechanistic model has been developed in the Netherlands that employs Tier 3 approach (Bannink et al. 2011) toestimate CH4 yield from dairy cattle while the US use mechanistic models (Baldwin 1995; Kebreab et al. 2008)
to refine estimates of Ym for dairy and beef in different states within the US.

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Chapter 10: Emissions from Livestock and Manure Management

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 the strongest
indicator of CH4 yield, the CH4 per digested fiber is not constant and enteric fermentation of similar fibrous feeds
can result in different Ym values. For example, grass silage made from grass cut at different stages of maturity
resulted in strongly different carbohydrate and protein composition, resulting in Ym values varying from 5.5 to
6.9 percent with increased maturity and intake (Warner et al. 2017). Exchange of carbohydrates may also lead toa lower Ym as demonstrated in studies where dietary starch content is increased through a higher proportion ofcorn silage (Hassanat et al. 2013; Benchaar et al. 2014) or through a higher proportion of starch containing
concentrates (Aguerre et al. 2011). Prerequisite for the use of more complex prediction models for broad country
inventories is that the data need to be provided to drive these more complex models of feed intake or Ym. It is
often difficult to define animal characteristics, productivity, and DE accurately for a livestock category in various
regions or various production systems in a country. Of particular importance is a good characterization ofroughages when they constitute a main part of the diet.
Ongoing global research, such as the use of direct methanogen inhibitors, 3-nitrooxypropanol (3-NOP), oxygenrich anions, fats and oils, ionophores or condensed tannins, and also genetic selection suggests a need to address
how they should be reflected in inventory compilation at Tier 2 or Tier 3. First, it is good practice that theinventory reflect only those technologies or genotypes developed through selection, 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 validation under field conditions. Second, it is good practice thatthe inventory be accompanied by evidence of the uptake of the technology in agricultural practice, and apply it
only to emissions by those livestock where uptake can be validated. Mitigation measures and their representation
in inventory compilation should be supported by peer-reviewed publications.
Concluding, approaches to improve estimates of feed intake (i.e. of diet composition, DE and dietary GE content)
and Ym, and approaches to account for specific mitigation measures are to be encouraged, given due care on
limitations of the scope and on production circumstances where mitigation measures are applied and to which
predictive models or relationships must apply as well.

10.3.3 Choice of activity data
Livestock population data should be obtained using the approach described in Section 10.2. If using default entericemission 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 estimation of Gross
Energy Intake (Equations 10.21, or 10.21A), a Tier 2 characterisation is needed. As noted in Section 10.2, goodpractice in characterising livestock populations is to conduct a single characterisation that will provide the activity
data for all emissions sources that depend on livestock population data.

10.3.4 Uncertainty assessment
Em iss io n fac tor s
No refinement.
A ct iv it y data
No refinement.

10.3.5 Completeness, Time series, Quality Assurance/Quality
Control and Reporting
No refinement.

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10.4

METHANE
EMISSIONSMANAGEMENTFROM

MANURE

This section describes how to estimate CH4 produced during the storage and treatment of manure, and from manuredeposited on pasture. The Tier 1 approach is based on default emission factors per unit volatile solid (VS) by animal
category and manure storage system. The Tier 2 is based on country-specific estimates of volatile solids and the impact
of interactions between manure management systems and animal categories on total CH4 emissions during excretionand storage, including manure treatments such as the production of biogas.
The term ‘manure’ is used here collectively to include both dung and urine (i.e., the solids and the liquids) producedby 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. When manure is used in the production of biogas, theemissions reported under the manure management category are those occurring on the farm site not resulting fromcombustion. These include, on-farm storage of the digestion input materials - pre-digestion, leakage during thedigestion process and emissions from the storage and application of digestate to agricultural fields (included in Volume
4, Chapter 11, Section 11.2, Nitrous oxide emissions from managed Soils). Emissions from biogas combustion during
the production of energy, whether on or off farm should be reported under Volume 2 “Energy”.
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 of animals,
and the latter on how the manure is managed. When manure is stored or treated as a liquid (e.g., in lagoons, ponds,
tanks, or pits), it decomposes anaerobically and can produce a significant quantity of CH4. The temperature and theretention time of the storage unit greatly affect the amount of methane produced. When manure 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 as shown in the 2006 IPCC Guidelines.
To be consistent with consideration of differing productivity classes in section of enteric fermentation, a new Tier 1
was developed. In some regions, particularly in developing countries, production systems can vary between highproductivity systems aimed at commercial food production and low productivity systems, largely serving local foodproduction. In this case countries may choose to use a Tier 1 method in which emission factors are defined for low
and high productivity systems based on the updated volatile solids and B0, and the values of volatile solids werealigned with the updated enteric fermentation section.
Guidance for determining which methods to use is shown in Figure 10.3 decision tree.
Tie r 1The Tier 1 method entails multiplying the total amount of VS excreted (from all livestock species/categories) in eachtype of manure management system by an emission factor for that type of livestock category in the specified climatezone and manure management system (see Equation 10.22). Emissions are summed over all manure management
systems and livestock categories. The Tier 1 method is applied using IPCC default VS excretion rates (See Table
10.13a), default typical animal mass (see Table 10A.5), default CH4 Emission Factors (see Table 10.14), and default
animal waste management systems (AWMS). Animal waste management system (manure management systems) data
have been collected for regions and countries by the FAO and average manure fractions treated by different
management systems are presented in Annex 10A.2, Tables 10A.6 to 10A.9. As emissions from manure management
systems are highly temperature dependent, it is good practice to consider the climate zone associated with the locations

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where manure is managed. Breakdowns of manure management systems by regional climate zone and production
system can be found in the supplemental data supplied with this chapter. Please refer to "Raw data for cattle andbuffalo spreadsheet". Further finer-scale country-specific data is also available from FAO GLEAM databases (FAO
2017).
An advanced Tier 1a method has been developed as an alternative for countries with differentiated agricultural systems
in which there is a coexistence of low and high productivity systems or whose agricultural systems are transitioning
from local low input productivity systems to higher productivity systems. In this case, where countries do not havethe information necessary for implementing Tier 2 systems, the use of the productivity based emission factors given
for an animal category provides an alternative or intermediary option. The advanced Tier 1a approach will provide anestimate of the changes in both productivity and manure management that occur when a transition from lower
productivity systems to higher productivity systems occurs.
Tie r 2A more complex method for estimating CH4 emissions from manure management should be used where a particular
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 to develop emission factors
specific to the conditions of the country.
The main differences between the Tier 1 and Tier 2 calculations is whether default information or country-specificinformation is used in the calculation of emissions from manure management system. The Tier 2 system provides amuch wider group of options for estimating emissions from different manure management systems.
Tie r 3
Some countries for which livestock emissions are particularly important may wish to go beyond the Tier 2 methodand develop models for country-specific methodologies or use measurement–based approaches to quantify emission
factors.
The method chosen will depend on data availability and national circumstances. Good practice in estimating CH4emissions from manure management systems entails making every effort to use the Tier 2 method, including
calculating emission factors using country-specific 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 steps are used to estimate CH4 emissions from manure management:
S te p 1: Collect population data based on the Livestock Population Characterization (see Section 10.2).
S te p 2: Identify default (Table 10A.5) or collect country-specific typical animal mass (TAM) values. Calculate
volatile solid excretion according to Equation 10.22a or develop country-specific volatile solid excretion rates
according to Equation 10.24.
S te p 3: Collect country-specific information on manure management system methods and develop country-specificmanure management system fractions or use default manure storage fractions presented in Annex Tables 10A.6 toTables 10A.9.
S te p 4: Identify either default emission factors Table 10.14 or build country-specific emission factors for each
livestock subcategory based on climate zones and manure management system fractions.
• Tier 1: Identify default values (Table 10.14) for emission factors for each livestock category in terms of grams of
methane per kg VS per year for the appropriate climate zone and productivity class if using advanced Tier 1a.
• Tier 2: Select local manure management specific methane conversion factors (MCF’s, Table 10.17) for different
climate zones and the animal categories specific maximum methane producing capacity (B0).
S te p 5: Calculate methane emission for each livestock subcategory.

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Volume 4: Agriculture, Forestry and Other Land Use• Tier 1: According to Equation 10.22 for each livestock category, climate zone (include production level if using
Tier 1a) multiply the livestock category population (Step 1) by quantity of volatile solid (Step 2) by the manurestorage fraction (Step 3) and the default emission factor (Step 4).
• Tier 2: According to Equation 10.23, for each livestock category and climate zone calculate the country-specificemission factor based on the country-specific or default quantity of volatile solids (Step 2), the manure
management system fraction (AWMS) and the MCF and B0 factors (Step 4); To estimate total emissions, thecountry specific emission factor is then multiplied by the population number (Step 1).
S te p 6: Sum emissions from all defined livestock categories to determine national emissions.

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10.3 (Updated) Decision tree for CH 4 emissions from Manure Management
Start

Do you have a countryspecific Tier 3 methodology ?

Yes

Estimate emissions using
Tier 3 method.
Box 4: Tier 3

No

Is a Tier 2
livestock population
characterisation available? And
do you have country-specific VS rates, MCF
values, B0 values,
and management system
usage data?
No

Yes

Is CH4 frommanure management a key
source category1 and is the species
a significant share ofemissions2?

No

Do you have
population data by highand low productivity
system?

No

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

Yes

Yes

Collect data for the Tier 2
method.

Collect data for the
Tier 1a approach.

Estimate CH4 emissions using
Tier 2 method with available
country-specific inputs.

Estimate CH4 emissions using
Tier 1a method and
IPCC defaults.

Box 3: Tier 2

Box 2: Tier 1aNote:
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 thesource category.

The calculation of CH4 emissions from manure management for Tier 1 uses Equation 10.22 for both simple Tier 1 oradvanced Tier 1a methods.

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EQUATION 10.22 (UPDATED)CH4 EMISSIONS FROM MANURE MANAGEMENT (TIER 1)
CH 4(= ∑ ( N (T , P ) • VS(T , P ) • AWMS(T , S , P ) • EFT , S , P ) / 1000 mm )T , S , P
Where:
CH 4( mm )= CH4 emissions from Manure Management in the country, kg CH4 yr-1

N (T , P )= number of head of livestock species/category T in the country, for productivity system P, whenapplicable

VS(T , P )= annual average VS excretion per head of species/category T, for productivity system P, whenapplicable in kg VS animal-1 yr-1 (Table 10.13a calculated by Equation 10.22a)

AWMS(T , S , P ) = fraction of total annual VS for each livestock species/category T that is managed in manure

management system S in the country, for productivity system P, when applicable; dimensionless,default regionally specific AWMS fractions are found in Tables 10A.6 through 10A.9 in Annex
10A.2
EF(T , S , P )= emission factor for direct CH4 emissions from manure management system S, by animal
species/category T, in manure management system S, for productivity system P, when applicable(Table 10.14), g CH4 kg VS-1

S

= manure management systemT= species/category of livestockP= high productivity system or low productivity system for use in advanced Tier 1a – omitted if
using a simple Tier 1 approach

10.4.2 Choice of emission factors
The best way to determine emission factors is to conduct non-invasive or non-disturbing measurements of emissionsin actual systems representative of those in use in the country. These field results can be used to develop models toestimate emission factors (Tier 3). Such measurements are difficult to conduct, and require significant resources andexpertise, and equipment that may not be available. Thus, while such an approach is recommended to improveaccuracy, 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 or Tier 2) chosen for estimating emissions.
Tie r 1
When using the Tier 1 method, methane emission factors per unit of VS by livestock category or subcategory are used.
Default emission factors by animal productivity class, manure management system and climate zone are presented inTable 10.14 for each of the recommended population subcategories. These emission factors represent the range inmanure management practices used in each region, as well as the difference in emissions due to climate zone.
Tables 10A.5 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 regionalvariables in these tables to identify the region that most closely matches their animal operations, and use the default
emission factors for that region.
Annual volatile solid excretion rates should be determined for each livestock category defined by the livestock
population characterization. Country-specific rates may either be taken directly from documents or reports such as
agricultural industry and scientific literature, or calculated based on estimates of dry matter intake (DMI), ash content
and urinary energy (as explained below). In some situations, it may be appropriate to use excretion rates developedby other countries that have livestock with similar characteristics.
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Chapter 10: Emissions from Livestock and Manure Management

If country-specific data cannot be collected or derived, or appropriate data are not available from another country, the
IPCC default volatile solid excretion rates presented in Table 10.13a can be used. These rates are presented in unitsof volatile solid 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 shownin Equation 10.22a for a Tier 1 calculation. The TAM should be consistent with median weight of the animal during
its production stage. Typically, for animals used in meat production systems, this is the median weight of the animal
during its growth period. Animals that are kept for the production of products (milk, eggs), draft or other uses would
use the typical live weight of the animal herd.
Volatile solids should be calculated according to Equation 10.22a, either for the simple Tier 1 or the advanced Tier
1a, where parameters are split by their productivity class in the calculaton of volatile solid excretion. Note that if
countries are mixing Tier 1 and Tier 2 methods and volatile solids are calculated through Equation 10.22A and areapplied in Equation 10.23 (Tier 2), the constant of 365 should be removed from that equation.
EQUATION 10.22A (NEW)
ANNUAL VS EXCRETION RATES (TIER 1)

TAM T ,P VS( T ,P ) = VSrate ( T ,P ) •• 365
1000 
Where:
VS(T , P )= annual VS excretion for livestock category T, for productivity system P (when applicable), kg
VS animal-1 yr-1

VS rate (T , P )= default VS excretion rate, for productivity system P (when applicable), kg VS (1000 kg animalmass)-1 day-1 (see Table 10.13a)

TAM (T , P )= typical animal mass for livestock category T, for productivity system P (when applicable), kg
animal-1

The calculation is a simple linear adjustment, so in the case of an animal that is 500 kg of weight, the VS emission
rate will be half of the rate presented per 1000 kg live weight.
Default TAM values are provided in Table 10A.5 as well as in the Annexes of Chapter 10 of the 2006 IPCC
Guidelines. However, it is preferable to collect country-specific TAM values to be able to track changes in emissions
with changes in productivity and animal size in certain animal categories. For example, market swine may vary fromnursery pigs weighing less than 30 kilograms to finished pigs that weigh over 90 kilograms. By constructing animal
population groups that reflect the various growth stages of market pigs, countries will be better able to estimate thetotal volatile solid excreted by their swine population.
Table 10.14 shows the default emission factors per kg of volatile solid excretion and year for all animal categories foreach manure management system and climate zone. Emission factors are listed for the climate zone where the livestock
manure is managed. It is good practice for countries to estimate the percentage of animal populations in different
climate zones and compute a weighted average emission factor. Where this is not possible, an estimate should be madebased on the proportion of area in each climate zone; however, this may not give an accurate estimate of emissions
that are highly sensitive to temperature variations (e.g., liquid/slurry systems).
Separate emission factors are shown for high and low productivity systems in these t*ables, reflecting the generaldifferences in feed intake and feed characteristics of the animals in regions that have highly differential production
systems existing in the same country. Emission factors result from the MCFs in Table 10.17 and the B0 in Table 10.16
and as a result, vary by animal category and manure management system, with liquid systems demonstrating higher
emissions per unit VS. Lower emission factors associated with low productivity systems are representative of thelower B0 values associated with lower quality feeds and manures with high C to N ratios.

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TABLE 10.13A (NEW)DEFAULT VALUES FOR VOLATILE SOLID EXCRETION RATE (KG VS (1000 KG ANIMAL MASS)-1 DAY-1)
RegionCategory ofanimalMeanHigh PS1

Low PS1

Mean

High PS

Low PS

Mean

High PS

Low PS

Mean

High PS

Low PS

Mean

High PS

Low PS

India subcontinent

Oceania7

Asia

Eastern Europe

Middle East6

Western Europe

Latin America

North America

Africa6

Dairy cattle4

9.2

8.4

6.7

6.0

7.9

9.0

7.1

18.2

21.7

15.2

10.7

8.4

11.8

9.0

8.1

9.2

14.1

9.1

16.1

Other cattle4

7.6

5.7

7.6

8.7

8.5

8.1

8.6

12.1

10.2

12.7

12.3

9.3

14.5

9.8

6.8

10.8

12.2

13.5

12.0

Buffalo4

NA

7.7

6.2

NA

11.2

Swine3

3.3

4.5

4.0

4.0

5.0

3.3

8.3

7.2

4.3

8.7

4.3

3.9

7.2

5.8

4.3

7.1

7.7

5.5

8.7

Finishing

3.9

5.3

4.9

5.6

6.4

4.3

10.0

8.2

5.3

9.4

4.9

4.4

7.8

6.8

5.1

8.1

8.6

6.5

9.5

Breeding

1.8

2.4

2.0

2.1

2.7

1.7

4.8

4.4

2.4

6.0

2.5

2.3

4.6

3.4

2.3

4.3

4.6

3.0

5.5

Chicken3

14.5

12.3

12.6

15.4

13.5

13.3

15.7

12.6

12.3

13.0

14.2

14.1

16.5

11.2

10.6

14.3

14.9

14.3

15.7

Hens ±1 yr

9.4

8.6

9.4

8.6

10.1

9.3

14.7

10.2

8.0

11.6

9.0

8.4

15.8

9.3

8.5

12.8

13.2

11.6

14.6

Pullets

5.9

5.3

5.9

6.2

7.6

5.7

18.5

12.0

5.8

16.5

6.8

5.6

18.5

7.5

5.4

17.7

13.2

6.8

18.9

Broilers

16.8

16.1

16.0

18.3

15.6

15.5

17.8

15.9

16.0

15.4

17.7

17.7

17.9

15.7

15.6

17.1

17.7

17.6

18.2

NE

12.9

NE

9.8

Turkeys8

10.3

Ducks8

7.4

NE

Sheep3

8.2

8.3

Goats59

10.4

Horses8

5.65

7.2

Mules/ Asses8

7.2

Camels8

11.5

10.56

13.5

NE

15.2

NE

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.13A (NEW) (CONTINUED)DEFAULT VALUES FOR VOLATILE SOLID EXCRETION RATE (KG VS (1000 KG ANIMAL MASS)-1 DAY-1)
1

High PS and Low PS refer to high and low productivity systems required for Tier 1a methodology.

NE is reported when values are not estimated, due to their not being adequate differences between high and low productivity production systems and NA refers to situations in which these animal categories do notoccur in these regions.
2

3

Values are taken from FAO GLEAM databases (FAO 2017).

4

Values are derived from diets used in the calculation of enteric fermentation Tier 1 emission factors.

5

Calculations are detailed in Annex 10B.3.

6

North African countries may wish to use values from the Middle East if their production systems are more similar.

All data are weighted values, representative of Australia and New Zealand. For Pacific Island nations, GHG inventory compilers may refer to Asian values. GHG inventory compilers from the Island nations from
Oceania may wish to use a Tier 1a approach. In this case, they could used values from Asia, or low productivity systems and high Tier 1a emission factor from Oceania, whichever is more representative to theirproduction systems.
7

8

Values are derived directly from the parameters reported in Table 10A-9 of the 2006 IPCC Guidelines.

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10.14 (UPDATED)METHANE EMISSION FACTORS BY ANIMAL CATEGORY, MANURE MANAGEMENT SYSTEM AND CLIMATE ZONE (G CH4 KG VS-1) 7
Livestock
species

Productivity
Class

High
Productivity

Manure Storage
System4

Temperate

Low
Productivity1

Warm

Cool Temp.
Moist

Cool Temp.
Dry

BorealMoist

BorealDry

Warm Temp.
Moist

Warm Temp.
Dry

TropicalMontaneTropicalWet

Tropical
Moist

TropicalDry

Uncoveredanaerobic lagoon

96.5

107.7

80.4

78.8

117.4

122.2

122.2

128.6

128.6

128.6

Liquid/Slurry, Pit
storage > 1 month 5

33.8

41.8

22.5

22.5

59.5

65.9

94.9

122.2

117.4

119.0

Solid storage

3.2

6.4

Dry lot

1.6

2.4

8.0
3.2

Daily spread

0.2

0.8

1.6

Anaerobic
Digestion -Biogas8

3.2

3.7

3.7

Burned for fuel

Dairy
Cattle16.1

Uncoveredanaerobic lagoon

52.3

58.4

43.6

42.7

63.6

66.2

66.2

69.7

69.7

69.7

Liquid/Slurry, Pit
storage > 1 month 5

18.3

22.6

12.2

12.2

32.2

35.7

51.4

66.2

63.6

64.5

Solid storage

1.7

3.5

4.4

Dry lot

0.9

1.3

1.7

Daily spread

0.1

0.4

0.9

Anaerobic
Digestion -Biogas8

9.2

9.5

9.5

Burned for fuel

10.58

Cool

8.7

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.14 (UPDATED) (CONTINUED)METHANE EMISSION FACTORS BY ANIMAL CATEGORY, MANURE MANAGEMENT SYSTEM AND CLIMATE ZONE (G CH4 KG VS-1) 7
Livestock
species

Productivity
Class

High
Productivity

Manure Storage
System4

Cool

Temperate

Cool Temp.
Moist

Cool Temp.
Dry

BorealMoist

BorealDry

Warm Temp.
Moist

Warm Temp.
Dry

TropicalMontaneTropicalWet

Tropical
Moist

TropicalDry

Uncoveredanaerobic lagoon

72.4

80.8

60.3

59.1

88.0

91.7

91.7

96.5

96.5

96.5

Liquid/Slurry, Pit
storage > 1 month5

25.3

31.4

16.9

16.9

44.6

49.4

71.2

91.7

88.0

89.2

Solid storage

2.4

4.8

6.0

Dry lot

1.2

1.8

2.4

Daily spread

0.1

0.6

1.2

Anaerobic
Digestion -Biogas8

2.4

2.7

2.8

Burned for fuelNon Dairy
CattleLow
Productivity1,6

Warm

12.1

Uncoveredanaerobic lagoon

52.3

58.4

43.6

42.7

63.6

66.2

66.2

69.7

69.7

69.7

Liquid/Slurry, Pit
storage > 1 month5

18.3

22.6

12.2

12.2

32.2

35.7

51.4

66.2

63.6

64.5

Solid storage

1.7

3.5

4.4

Dry lot

0.9

1.3

1.7

Daily spread

0.1

0.4

0.9

Anaerobic
Digestion -Biogas8

9.2

9.5

9.5

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TABLE 10.14 (UPDATED) (CONTINUED)METHANE EMISSION FACTORS BY ANIMAL CATEGORY, MANURE MANAGEMENT SYSTEM AND CLIMATE ZONE (G CH4 KG VS-1) 7
Livestock
species

Productivity
Class

High
Productivity

GrowingandBreeding
Swine

Cool
Manure Storage System4

Warm

Cool Temp.
Moist

Cool Temp.
Dry

BorealMoist

BorealDry

Warm Temp.
Moist

Warm Temp.
Dry

TropicalMontaneTropicalWet

Tropical
Moist

TropicalDry

Uncovered anaerobic
lagoon

180.9

202.0

150.8

147.7

220.1

229.1

229.1

241.2

241.2

241.2

Liquid/Slurry, and Pit
storage below animal
confinements > 1 month5

63.3

78.4

42.2

42.2

111.6

123.6

177.9

229.1

220.1

223.1

Liquid/Slurry, and Pit
storage below animal
confinements < 1 month5

18.1

24.1

12.1

12.1

39.2

45.2

75.4

114.6

108.5

126.6

Solid storage

6.0

12.1

15.1

Dry lot

3.0

4.5

6.0

Daily spread

0.3

1.5

3.0

Anaerobic Digestion Biogas8

6.0

6.8

7.0

Burned for fuelLow
Productivity1

30.2

Uncovered anaerobic
lagoon

116.6

130.2

97.2

95.2

141.8

147.7

147.7

155.4

155.4

155.4

Liquid/Slurry, and Pit
storage below animal
confinements > 1 month5

40.8

50.5

27.2

27.2

71.9

79.7

114.6

147.7

141.8

143.8

Liquid/Slurry, and Pit
storage below animal
confinements < 1 month5

11.7

15.5

7.8

7.8

25.3

29.1

48.6

73.8

69.9

81.6

Solid storage

3.9

7.8

9.7

Dry lot

1.9

2.9

3.9

Daily spread

0.2

1.0

1.9

Anaerobic Digestion Biogas8

20.6

21.1

21.2

Burned for fuel10.60

Temperate

19.4

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.14 (UPDATED) (CONTINUED)METHANE EMISSION FACTORS BY ANIMAL CATEGORY, MANURE MANAGEMENT SYSTEM AND CLIMATE ZONE (G CH4 KG VS-1) 7
Livestock
species

Poultry

Productivity
Class

High
productivity

Low
productivity1

Sheep

Goats

High
productivity
Low
productivity1High
productivity
Low
productivity1

Cool
Manure Storage System4

Temperate

Warm

Cool Temp.
Moist

Cool Temp.
Dry

BorealMoist

BorealDry

Warm Temp.
Moist

Warm Temp.
Dry

TropicalMontaneTropicalWet

Tropical
Moist

TropicalDry

Uncovered anaerobic
lagoon

156.8

175.1

130.7

128.0

190.7

198.6

198.6

209.0

209.0

209.0

Liquid/Slurry, and Pit
storage below animal
confinements > 1 month5

54.9

67.9

36.6

36.6

96.7

107.1

154.2

198.6

190.7

193.4

Solid storage

5.2

10.5

13.1

Dry lot

2.6

3.9

5.2

Anaerobic Digestion Biogas8

5.2

10.5

13.1

Burned for fuel2.6

All Systems

2.4

Solid storage

2.5

5.1

6.4

Dry lot

1.3

1.9

2.5

Solid storage

1.7

3.5

4.4

Dry lot

0.9

1.3

1.7

Solid storage

2.4

4.8

6.0

Dry lot

1.2

1.8

2.4

Solid storage

1.7

3.5

4.4

Dry lot

0.9

1.3

1.7

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TABLE 10.14 (UPDATED) (CONTINUED)METHANE EMISSION FACTORS BY ANIMAL CATEGORY, MANURE MANAGEMENT SYSTEM AND CLIMATE ZONE (G CH4 KG VS-1) 7
Livestock
speciesCamelsHorsesMules/Asses

All
Animals

Productivity
Class

Manure Storage
System4

Cool

Temperate

Warm

High
productivity

Solid storage

3.5

7.0

8.7

Dry lot

1.7

2.6

0.0

Solid storage

2.8

5.6

7.0

Dry lot

1.4

2.1

2.8

Solid storage

4.0

8.0

10.1

Dry lot

2.0

3.0

4.0

Solid storage

3.5

7.0

8.7

Dry lot

1.7

2.6

3.5

Solid storage

4.4

8.8

11.1

Dry lot

2.2

3.3

4.4

Low
productivity1High
productivity
Low
productivity1High
productivity
Low
productivity1

Solid storage

3.5

7.0

8.7

Dry lot

1.7

2.6

3.5

High and Low
Productivity

Pasture Range andPaddock

0.6

All values are calculated based on MCFs and B0s reported in Tables 10.17 and 10.16, respectively, using the equation MCF*B0*0.67.
1
For the application of Tier 1, for all regions other than North America, Europe and Oceania the Tier 1 default values are the low productivity EFs. Pasture range and paddock emission factor is based on observation in the
updated version of Cai et al. (2017) database (see Annex 10B.6). No differences were observed for animal type, region or productivity class and are therefore reported as a constant for all animal and productivity categories.
2

Temp. is an abbreviation for temperate.

3

Composting is the biological oxidation of organic material.

4

Definitions of manure management systems can be found in Table 10.18.

5Emissions for liquid systems are calculated from manure management systems with a 6 month retention time.

6

Buffalo emission factors are equivalent to low productivity non dairy animals.

7

Uncertainty is ±30% consisten with the 2006 IPCC Guidelines.

Anaerobic digestion biogas values: for high productivity are based on emission estimates from high quality gas-tight digesters and average MCFs for storage whereas, for low productivity are based on emission estimatesfrom high digester leakage rates and average MCFs for storage leakage rates. Countries should consider the type and quality of digesters used in their individual countries in evaluating what emission factors they choose toemploy as opposed to the level of productivity for anaerobic digesters only.
8

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.15 (UPDATED)
MANURE MANAGEMENT METHANE EMISSION FACTORS FOR DEER, REINDEER, RABBITS, OSTRICH AND FUR-BEARINGANIMALS AND DERIVATION PARAMETERS APPLIED

CH4 emission factor(kg CH4 head-1 yr-1)

VS (kg VS day-1)4

B0 (m3 kg VS)4

Deer1

0.22

NR

NR

Reindeer2

0.36

0.39

0.19

Rabbits3

0.08

0.10

0.32

Fur-bearing animals(e.g., fox, mink)2

0.68

0.14

0.25

Ostrich

5.67

1.16

0.25

Livestock

The uncertainty in these emission factors is ±30 %.
1

Sneath et al. (1997).

2

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

3

Judgement of the IPCC Expert Group.

4

Table 10A-9 of the 2006 IPCC Guidelines.

Tie 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 (B0). 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.
B0 varies by animal species and feed regime 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 areassociated with solid storage systems, their contribution would not add significantly to overall methane production.
CH4 emissions from co-digestion of on-farm organic resources (crop residues, energy crops) need to be reported
under the source category ‘3.A2(k) – Co-digestates’.
Animal waste management system characteristics (AWMS): Includes the types of systems used to manage
manure and a system-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 ishandled with each manure management technique. A description of manure management systems is included inTable 10.18. The system MCF varies with the manner in which the manure is managed and the climate.
Theoretically the value can range from 0 to 100 percent. Both temperature and retention time play an importantrole 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 76 to
80 percent. Manure managed as dry material in cold climates does not readily produce methane, and consequently
has an MCF of about 1 percent.
Development of Tier 2 emission factors involves determining a weighted average MCF using the estimates of the
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:2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

EQUATION 10.23
CH4 EMISSION FACTOR FROM MANURE MANAGEMENT 5=
EF(T )

(VST 365)  B0(T ) • 0.67 • ∑S ,kMCFS ,k100• AWMS(T , 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

B0(T )= maximum methane producing capacity for manure produced by livestock category T, m3
CH4 kg-1 of VS excreted

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,
percent

AWMS(T , S , k ) = fraction of livestock category T's manure handled using animal waste management system S

in climate 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 countryspecific data are available for only a portion of these variables, countries are encouraged to calculate countryspecific emission factors, using the data available in Annex 10A.1 and 10A.2 to fill gaps.There is no defined
threshold to indicate how much country-specific information is required for a Tier 2 method, but it is understoodthat increased use of country-specific information improves emission estimates, by better representing local
production characteristics.
Measurement programs can be used to improve the basis for making the estimates. In particular, measurements ofemissions from manure management systems under field conditions are useful to verify MCFs. Also,
measurements of B0 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 asmuch 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 and management practices.
These revisions should be based on reliable scientifically reviewed data. Frequent monitoring 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 10.24 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 thesystems. The best way to obtain average daily VS excretion rates is to use data from nationally published sources.
If average daily VS excretion rates are not available, country-specific VS excretion rates can be estimated fromfeed 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 the emissions 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 gross5 When biogas is produced in on-farm plants, emissions from on-farm co-digestates can be calculated separately using a similar

equation: CH4(cdg)=Ccdg *B0*0.67*(MCF/100) and added to methane totals, where Ccdg is the total kg dry matter of the codigested material and other parameters are as defined in Equation 10.23

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Chapter 10: Emissions from Livestock and Manure Management

energy (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 (UPDATED)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 percent)(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 percent ormore grain in the diet or for swine). Use country-specific values where available.

ASH= the ash content of feed calculated as a fraction of the dry matter feed intake (e.g., 0.06 forsows: Dämmgen et al. 2011). 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
constant across a wide range of forage and grain-based feeds commonly consumed bylivestock.

Representative DE percent 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.

B 0 values
The maximum methane-producing capacity of the manure (B0) varies by species and diet. The preferred method
to obtain B0 measurement values is to use data from country-specific published sources, measured with astandardised method. It is important to standardise the B0 measurement, including the method of sampling, and to
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 B0 measurement values are not available, default values are provided inTable 10.16 where data is summarized from Table 10A-4 through 10A-9 of 2006 IPCC Guidelines.

MCFs
MCFs are determined for a specific manure management system and represent the degree to which B0 is achieved.
Default methane conversion factors (MCFs) are provided in Table 10.17 for different manure management
systems. The amount of methane generated by a specific manure management system is affected by the amount ofvolatile solids, the extent of anaerobic conditions present, the temperature of the system, and the retention time oforganic material in the system. Default MCF values for liquid systems and lagoons presented in Table 10.17
include the effect of longer retention times.
Liquid-based systems are sensitive to temperature effects. Average annual MCF values for a specific system will
largely be determined by the quantity of VS in the storage system during peak temperature periods (Balde et al.
2016). Emissions increase exponentially with increasing temperatures. For this reason, monthly temperature
variations in combination with timing of storage and application times largely define annual MCFs rather than
average annual temperatures.
Climate zones are used to differentiate variations in MCFs associated with ranges and annual monthly temperature
variability. Detailed definitions of climate zones and a decision tree to determine in what climate zone a specificregion falls, can be found in Annex 10A.2, Figure 10A.1. Inventory compilers should consult long-term averages
from national meteorological statistics and evaluate the climate zones for each region of their country based on thecriteria outlined in Annex 10A.2. It is good practice to assure consistency of the definition of climate zones for all
sectors of the inventory that may be influenced by climate.
Manure removal statistics should be taken from farm practice surveys or from expert consultation. Compilers
should develop an estimate of the average number of manure removals per year and the months of the highest

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frequency of removals. If regional practices vary, compilers should develop MCFs that are representative ofregional practice by entering consistent manure removal statistics with regional temperature profiles. If regional
MCFs are calculated, the national MCF should be weighted based on the number of animals feeding into theregional manure management systems represented by the manure removal profile and the regional temperature
profile.
In cases in which countries lie in multiple climate zones, it is good practice for compilers, if possible, todisaggregate livestock populations by climate zone. However, when it is not possible, compilers should select thedominant climate zone in their country or region.
Further, in cases that countries have information available on their manure spreading practices (number of times
that manure storages are emptied per year) and have monthly temperature profiles it is good practice that theycustomize MCF calculations based on their monthly temperature profiles according to the example provided in
Annex 10A.2. If regional MCFs are calculated, the national MCF should be weighted based on the number ofanimals feeding into the regional manure management systems represented by the manure removal profile and theregional temperature profile. Global temperature data can be downloaded from a number of sites such as the
National Oceanic and Atmospheric Administration (NOAA) website, long-term monthly averages should be used
for the development of MCFs.
Likewise, for cases in which manure is maintained in the animal housing, compilers may wish to calculate the
MCF considering the temperature profile of the housing. An example of how to derive an MCF for a liquid system
is provided in Annex 10A.3 and a simple spreadsheet model is available in the supplemental data supplied withthis chapter. Please refer to "Raw data for cattle and buffalo spreadsheet".
For manure deposited by grazing animals onto pastures, ranges and paddocks, it is recommended to use a valuethat is consistent with the emission factor provided in Table 10.14. In this table, a single emission factor per unitof volatile solid excretion is provided, as an analysis of 45 data points showed that there was no significant
difference between climatic zones nor were there differences per animal category (Annex 10B.6). Therefore, theMCF reported in Table 10.17 must be used in conjunction with a single B0 value of 0.19 m3 CH4 kg-1 of VS
excreted, derived from the experimental results described in Annex 10B.6. This single emission factor was judged
by the expert panel to be more accurate than emission factors estimated from regionally based MCFs and animal
category based B0, considering the differences in processes that would result in methane production between
excretion of VS on pasture, range and paddock relative to typical manure storage systems.
Anaerobic digestion is an important manure management technology that provides renewable energy throughbiogas production. There is a wide variety of digesters available of varyinig quality and use including industrial
centralised biogas digester plants, and animal farm based biogas digesters. Some biogas digesters may co-digest
energy crops and different types of organic waste in varying combinations. The quality of the digester and the preand post-storage of digester input and output (digestate) are the main factors in determining the methane that islost to the atmosphere before, during and after digestion.
Default methane conversion factors (MCFs) of anaerobic digesters are provided in Table 10.17. Default values forbiogas digesters presented include the estimated MCF from combinations of either high and low quality anaerobic
digetsers with different types and qualities of storage systems. The approach to calculate these MCFs, based on
Haenel et al. (2018) is outlined in Annex 10A.4. The main factors considered in differentiating between digester
systems are the degree of leakage from the digester itself (varying between 1 and 10 percent of the methane
production potential B0) and the loss of CH4 from the digeste storage system.
All manure management methane emission factors are based on experimental measurements that typicallycombine the VS and bedding. Based on current scientific literature, these two sources cannot be separated. Morerefined measurements of methane from manure storage and stages of storage are for further scientific development.
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 thefollowing factors:• Duration of storage and timing of application;• Information on manure treatment and VS (including bedding) entering the storage system;• Feed and animal characteristics at the measurement site (see Section 10.2 for the type of data that would be
pertinent);• Determination of the amount of manure left in the storage facility after emptying (methanogenic inoculum);

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Chapter 10: Emissions from Livestock and Manure Management• Monthly temperature in the storage.
CH 4 e m is s ion s f rom mu lt ip le Manu r e Manag em en t s yst em s
If manure is managed in multiple systems, by default, manure emission factors should be allocated to the dominantstorage systems; but a country specific emission factor could be developed considering the emissions originatingfrom all systems used in storage prior to field applicaton.
A number of combinations are possible and it is beyond the scope of these guidelines to provide guidance for all
possibilities but common examples include: i.) manure flushed from a dairy freestall barn to an anaerobic lagoon
that first pass through a solids separation unit where some of the manure nitrogen is removed and managed as asolid. In this case, the methodology must integrate an additional fraction to the AWMS system that tranfers thosesolids to a solid storage systems; ii.) pit storage that is flushed to a larger holding tank. In this case, the methodology
must consider modifications to the B0 that result from the initial storage period based on the length of the storage
and the temperature dependant MCF. iii.) solid manure pack that is allowed to accumulate, and periodicallytransferred to heaps. Likewise in this case, the impact of the prestorage period to the B0 of the manure in thesecondary storage should be adjusted to consider the emissions that occurred during the initial storage.
In cases in which country-specific methodologies are used to estimate emissions from multiple systems rely on
B0s and MCFs as defined in Tables 10.16 and 10.17, these methodologies should assure that the total annual B0 of
the stored manure is not reduced or increased during the application of the methodology. Further, methodologies
that require additional fractionation of manure must assure that calculated VS input is not reduced or increased.
TABLE 10.16A (UPDATED)DEFAULT VALUES FOR MAXIMUM METHANE PRODUCING CAPACITY (B0) (M3 CH4 KG-1 VS)
RegionCategory ofanimal 2

Other Regions1
North
America

WesternEuropeDairy cattle
Non dairy cattle

Oceania

0.24
0.19

0.18

Buffalo
Swine

EasternEurope

0.17

0.17

0.10
0.48

0.45

0.45

0.45

High
productivity
systems

Low
productivity
systems

0.24

0.13

0.18

0.13

0.10

0.10

0.45

0.29

Chicken-Layer

0.39

0.39

0.24

All other poultry

0.36

0.36

0.24

Sheep

0.19

0.19

0.13

Goats

0.18

0.18

0.13

Horses

0.30

0.30

0.26

Mules/ Asses

0.33

0.33

0.26

Camels

0.26

0.26

0.21

All Animals PRP

0.19

Sources: All values are consistent with 2006 IPCC Guidelines values from Annex 10A.2 with the exception of PRP, taken fromthe analysis described in Annex 10B.6.
1

For other regions, low productivity is considered the default value for Tier 1 if not using the Tier 1a.

Only presenting values for manure, compilers are recommended to consult scientific literature or develop country-specific B0
values for the different codigestates that may be used in anaerobic digesters.
2

Uncertainty values are ±15 percent.

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TABLE 10.17 (UPDATED)METHANE CONVERSION FACTORS FOR MANURE MANAGEMENT SYSTEMSMCFs by climate zone
Cool

System4

Cattle and Swine deep
bedding5

Warm

Cool Temp.
Moist

Cool Temp.
Dry

BorealMoist

BorealDry

Warm Temp.
Moist

Warm Temp.
Dry

TropicalMontaneTropicalWet

Tropical
Moist

Tropical
Dry

60%

67%

50%

49%

73%

76%

76%

80%

80%

80%

1 Month

6%

8%

4%

4%

13%

15%

25%

38%

36%

42%

3 Month8

12%

16%

8%

8%

24%

28%

43%

61%

57%

62%

4 Month9

15%

19%

9%

9%

29%

32%

50%

67%

64%

68%

6 Month9

21%

26%

14%

14%

37%

41%

59%

76%

73%

74%

12 Month9

31%

42%

21%

20%

55%

64%

73%

80%

80%

80%

> 1 month10

21%

26%

14%

14%

37%

41%

59%

76%

73%

74%

Uncovered anaerobic lagoon7

Liquid/Slurry, and Pit
storage below animal
confinements1

Temperate< 1 month11

2.75%

6.50%

18%

Solid storage6,12

2.00%

4.00%

5.00%

Solid storage – Covered/compacted6,13

2.00%

4.00%

5.00%

Solid storage – Bulking agent addition6,14

0.50%

1.00%

1.50%

Solid storage – Additives6,15

1.00%

2.00%

2.50%

Dry lot16

1.00%

1.50%

2.00%

Daily spread17

0.10%

0.50%

1.00%

Composting - In-vessel4,18

0.50%

Composting - Static pile (Forced
aeration)4,6,19

1.00%

2.00%

2.50%

Composting - Intensive windrow4,20

0.50%

1.00%

1.5%

Composting – Passive windrow(Unfrequent turning)3,4,6,21

1.00%

2.00%

2.50%

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.17 (UPDATED) (CONTINUED)METHANE CONVERSION FACTORS FOR MANURE MANAGEMENT SYSTEMSMCFs by climate zone
System4

Cool
Cool Temp.
Moist

Cool Temp.
Dry

Temperate
BorealMoist

BorealDry

Warm Temp.
Moist

Warm Temp.
Dry

Pasture/Range/Paddock2

0.47%

Poultry manure with and without litter22

1.50%

Aerobic treatment23

0.00%

Burned for fuel24

10.00%

Anaerobic Digester25, Low leakage, Highquality gastight storage, best complete
industrial technology

TropicalWet

Tropical
Moist

Tropical
Dry

1.41%
3.55%

4.38%

Anaerobic Digester25, High leakage, low
quality technology, high quality gastightstorage technology

9.59%

Anaerobic Digester25, High leakage, low
quality technology, low quality gastightstorage technology

10.00%

Anaerobic Digester25, High leakage, low
quality technology, open storage

TropicalMontane1.00%

Anaerobic Digester25, Low leakage, Highquality industrial technology, low quality
gastight storage technology
Anaerobic Digester25, Low leakage, Highquality industrial technology, open storage

Warm

12.14%

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12.97%

4.59%

13.17%

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TABLE 10.17 (UPDATED) (CONTINUED)METHANE CONVERSION FACTORS FOR MANURE MANAGEMENT SYSTEMS
1
The initial judgement of IPCC Expert Group supported by additional new research. See Annex 10B.7 for additional details. Suggested default values are equivalent to liquid systems with 6 month retention time if retention
times are unknown. A reduction of 40% due to crust cover may be applied only when a thick, dry, crust is present. Thick dry crusts occur in systems in which organic bedding is used in the barn and is allowed to be flushed
into the liquid storage tank and solids are not separated from the manure stream and further the surface is not exposed to regular heavy precipitation that may disrupt the surface. Sources: Aguerre et al. (2012); Nielsen et al.
(2013); VanderZaag et al. (2008).
New information suggests that a solid cover reduces CH4 emissions by 25 to 50% (range: 0 to 90%). Sources: Amon et al. (2006), Amon et al. (2007); Clemens et al. (2006); Guarino et al. (2006), Matulaitis et al. (2015),
Misselbrook et al. (2016), VanderZaag et al. (2009), Hou et al. (2015), VanderZaag et al. (2008).
2
Pasture Range and Paddock MCFs must always be used in conjunction with a B0 value of 0.19 m3 CH4 kg-1 of VS excreted to maintain consistency with the data in the updated version of Cai et al. (2017) database (seeAnnex 10B.6).
3
Definitions for manure management systems are provided in Table 10.18.
4Composting is the biological oxidation of a solid waste including manure usually with bedding or another organic carbon source typically at thermophilic temperatures due to microbial heat production.
5
Articles from which these values were derived were for cattle and swine, but for other animal production systems that use deep bedding these values are proposed to be used as surrogates. Suggested default values areequivalent to liquid systems with 6 month retention time.
6
Sources and assumptions to calculate MCF values for Solid storage categories and composting (static pile and passive windrows) are detailed in Annex 10B.7.
7Judgement of IPCC Expert Group utilizing a 12 month retention time and the equations and parameters presented in Mangino et al. (2001). Solid-liquid separation that removes VS and diverts it to aerobic/solid management
should be considered when calculating the VS loading rate into liquid systems.
8
The avg °C for Cool Temperate Moist, Cool Temperate Dry, Warm Temperate Moist, Warm Temperate Dry, Tropical Montane, Tropical Wet, Tropical Moist, Tropical Dry were 4.6, 5.8, 13.9, 14.0, 21.5, 25.9, 25.2,
25.6 respectively.
9
Solid-liquid separation that removes VS and diverts it to aerobic/solid management should be considered when calculating the VS loading rate into liquid systems.
10
Judgement of IPCC 2006 Expert Group in combination with Mangino et al. (2001). Values are consistent with liquid systems. Values presented here are consistent with a 6 month retention time, however compilers
should take into account country-specific retention time when possible.
11
Judgement of IPCC 2006 Expert Group in combination with Moller et al. (2004). Expect emissions to be similar, and possibly greater, than pit storage, depending on organic content and moisture content.
12
Expert judgement based on IPCC (2006) and update supported by Pardo et al. (2015). Emissions in temperate climate can be double relative to a cool climate.
13
Expert judgement based on Pardo et al., (2015). Emissions in the same range than solid storage.
14
Expert judgement based on Pardo et al. (2015). Estimated reduction of 75% due to bulking agent addition.
15
Expert judgement based on Pardo et al. (2015). Estimated reduction of 50% due to additives addition.
16
Judgement of IPCC 2006 Expert Group in combination with Hashimoto & Steed (1993).
17
Hashimoto & Steed (1993).
18
Judgement of IPCC 2006 Expert Group and Amon et al. (1998a). MCFs are less than half of solid storage. Not temperature dependant.
19
Expert judgement updated based on Pardo et al. (2015). Estimated reduction of 50% compared to solid storage. Previously it was considered "Not temperature dependent" but now temperature influence has been considered.
20
Judgement of IPCC Expert Group and Amon et al. (1998a). MCFs are slightly less than solid storage. Less temperature dependant.
21
Expert judgement update based on Pardo et al. (2015). Estimated reduction of 50% compared to solid storage. Previous MCFs have been modified as they could underestimate CH4 emissions.
22
Judgement of 2006 IPCC Expert Group. MCFs are similar to solid storage or to dry lot but with generally constant warm temperatures.
23
Judgement of 2006 IPCC Expert Group. MCFs are near zero. Aerobic treatment can result in the accumulation of sludge which may be treated in other systems. Sludge requires removal and has large VS values. It is
important to identify the next management process for the sludge and estimate the emissions from that management process if significant.
24
Judgement of IPCC 2006 Expert Group in combination with Safley et al. (1992).
25
Calculations based on Haenel et al. (2018), outlined in Annex 10A.4.

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Chapter 10: Emissions from Livestock and Manure Management

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 enteric fermentation
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 for manure
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 as defined in Volume 4, Chapter 3, Annex 3A.5, Figure 3A.5.1 or the version found in Annex
10A.2 of this Chapter. This will allow more specific selection of default factors or MCF values for those systems
more sensitive to temperature changes. Ideally, the regional population breakdown can be obtained from publishednational livestock statistics, and the temperature data from national meteorological statistics. If regional data are
not available, experts should be consulted regarding regional production (e.g., milk, meat, and wool) patterns orland 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 15 percent dry matter content.
Note that 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, if manure is managed in multiple
systems, it is good practice to report the respective CH4 emissions from each system (see N2O emissions frommultiple Manure Management systems). Manure removal statistics should also be monitored where possible. It is
recommended that agencies develop an estimate of the average number of manure removals per year and themonths of the highest frequency of removals. If regional practices vary and also represent significant differencesin temperature profiles, statistics that are representative of regional practice should be tracked combining theappropriate manure removal statistics together with regional temperature profiles.
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.

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TABLE 10.18 (UPDATED)DEFINITIONS OF MANURE MANAGEMENT SYSTEMS3
SystemDefinition

Pasture/Range/Paddock(PRP)

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 orstacks. Manure is able to be stacked due to the presence of a sufficient amount of bedding
material or loss of moisture by evaporation.
Solid stores can be covered or compacted. In some cases, bulking agent or additives areadded.

Solid storageCovered/compacted

Similar to solid storage, but the manure pile is a) covered with a plastic sheet to reduce thesurface of manure exposed to air and/or b) compacted to increase the density and reduce
the free air space within the material.

Solid storage - Bulking
agent addition

Specific materials (bulking agents) are mixed with the manure to provide structural
support. This allows the natural aeration of the pile, thus enhancing decomposition (e.g.
sawdust, straw, coffee husks, maize stover).

Solid storage - Additives

The addition of specific substances to the pile in order to reduce gaseous emissions.
Addition of certain compounds such as attapulgite, dicyandiamide or mature compost haveshown to reduce N2O emissions; while phosphogypsum reduces CH4 emissions.

Dry lot

A paved or unpaved open confinement area without any significant vegetative cover. Drylots do not require the addition of bedding to control moisture. Manure may be removed
periodically and spread on fields.

Liquid/Slurry 1

Manure is stored as excreted or with some minimal addition of water or bedding material
in tanks or ponds outside the animal housing. Manure is removed and spread on fields onceor more in a calendar year. Manure is agitated before removal from the tank/ponds toensure that most of the VS are removed from the tank.

Uncovered anaerobic
lagoonA type of liquid storage system designed and operated to combine waste stabilization andstorage. Lagoons have a lower depth and a much larger surface compared to liquid slurry
stores. Anaerobic lagoons are designed with varying lengths of storage (up to a year or
greater), depending on the climate region, the volatile solids loading rate, and otheroperational factors. The supernatant water from the lagoon may be recycled as flush wateror used to irrigate and fertilise fields.

Pit storage below animal
confinementsCollection and storage of manure usually with little or no added water typically below aslatted floor in an enclosed animal confinement facility, usually for periods less than one
year. Manure may be pumped out of the storage to a secondary storage tank multiple times
in one year, or stored and applied directly to fields. It is assumed that VS removal rates ontank emptying are >90%.

Digestersof highqualityand lowleakage

Animal manure with and without straw is collected and anaerobically digested in acontainment vessel. Co-digestion with other waste or energy crops may occur.
Digesters are designed, constructed and operated according to industrial technology
standard for waste stabilization by the microbial reduction of complex organic compounds
to CO2 and CH4.
Biogas is captured and used as a fuel.
Digestate is stored either in open storage, in covered storage with no leakage control, or ingas tight storage with gas recovery or flaring.

Digesterswith highleakage

Animal manure with and without straw is collected and anaerobically digested in covered
lagoon.
Digesters are used for waste stabilization by the microbial reduction of complex organic
compounds to CO2 and CH4.
Biogas is captured and flared or used as a fuel.
After anaerobic digestion, digestate is stored either openly, covered, or gas tightly.

Anaerobic
digesterBurned for fuel10.72

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

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.18 (UPDATED) (CONTINUED)
DEFINITIONS OF MANURE MANAGEMENT SYSTEMS
SystemDefinition

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 a bedded pack manure management system and may be
combined with a dry lot or pasture. Manure may undergo periods where animals arepresent and are actively mixing the manure, or periods in which the pack is undisturbed.
In-vessel2

Static pile

Composting

Intensive
windrow2

Composting- Passive
windrow2

Composting, typically in an enclosed channel, with forced aeration and continuous
mixing.
Composting in piles with forced aeration but no mixing, with runoff/leachingcontainment.
Composting in piles with forced aeration but no mixing, without runoff/leachingcontainment.
Composting in windrows with regular (at least daily) turning for mixing and aeration,
runoff/leaching containment.
Composting in windrows with regular (at least daily) turning for mixing and aeration, no
runoff/leaching containment.
Composting in windrows with infrequent turning for mixing and aeration, withrunoff/leaching.
Composting in windrows with infrequent turning for mixing and aeration, no
runoff/leaching.

Poultry manure with litter

Similar to cattle and swine deep bedding except usually not combined with a dry lot or
pasture. Typically used for all poultry breeder flocks, for alternative systems for layers
and for the production of meat type chickens (broilers) and other fowl. Litter and manure
are left in place with added bedding during the poultry production cycle and cleaned
between poultry cycles, typically 5 to 9 weeks in productive systems and greater in lower
productivity systems.

Poultry manure without
litterMay 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-risemanure management system and is a form of passive windrow composting when
designed and operated properly. Some intensive poultry farms installed the manure belt
under the cage, where the manure is dried inside housing.

Aerobic treatment

The biological oxidation of manure collected as a liquid with either forced or naturalaeration. 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.

1
Covers on manure management systems can impact emissions of direct N2O, CH4 and NH3. With N2O and CH4 emission, the effect of
the cover depends upon character of the cover material.

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.

2

Comparative definitions with the EMEP/EEA Air Pollutant Emission Inventory 2016 Guidebook can be found in Annex 10A.2, Table
10A.10.

3

10.4.4 Uncertainty assessment
No refinement.

10.4.5 Completeness, Time series, Quality assurance / Quality
control and Reporting
No refinement.

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10.5

N2O
EMISSIONSMANAGEMENTFROM

MANURE

This section describes how to estimate the N2O produced, directly and indirectly, during the storage and treatmentof manure before it is applied to land or otherwise used for feed, fuel, or construction purposes. The approach isbased on N excretion, emission factors for N2O emissions, as well as volatilization and leaching factors. Thissection also details the principles of N flow and the connection between IPCC N2O reporting and NH3 and NOx
reporting required for UNECE countries.
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). Direct and indirect N2O emissions generated by manure managed in other systems
and following its application to soils are also 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 nitrogento 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 notoccur under anaerobic conditions. Nitrites and nitrates are transformed to N2O and dinitrogen (N2) during thenaturally occurring process of denitrification, an anaerobic process. There is general agreement in the scientific
literature that the ratio of N2O to N2 increases with increasing acidity, nitrate concentration, 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 ammonium nitrogen during manure collection andstorage depends primarily on oxygen supply, time, and on temperature. Simple forms of organic nitrogen such asurea (mammals) and uric acid (poultry) are rapidly mineralized to ammonium nitrogen, which is converted toammonia under alkaline conditions. Ammonia is highly volatile and easily diffused into the surrounding air(Asman et al. 1998; Monteny & Erisman 1998). Nitrogen losses begin at the point of excretion in houses and otheranimal production areas (e.g., milk parlors) and continue through on-site management in storage and treatment
systems (i.e., manure management systems). Nitrogen is also lost through runoff and leaching into soils from thesolid storage of manure at outdoor areas, in feedlots and where animals are grazing in pastures. Emissions of
nitrogen compounds from grazing livestock are considered separately in Chapter 11, Section 11.2, N2O Emissions
from Managed Soils.
In the case of co-digestion of animal manures with additional organic residues, energy crops, additional N enters
the system. This additional N source also emits N2O during the storage, and must be considered in the section“N2O emissions from manure management”.
Due to significant direct and indirect losses of manure nitrogen in management systems it is important to estimate
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 calculating 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 for use
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.
D ire ct N 2 O e m iss ion s fr om Ma nu re Manag eme nt

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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 is applied
using IPCC default N2O emission factors, default nitrogen excretion data, and default manure management systemdata (see Annex 10A.2, Tables 10A.5 to 10A.9 for default animal weights and manure management system
allocations). It is recommended to consult the methane and enteric fermentation sections to clarify how to
implement the Tier 1a approach, if that is the approach selected.
Tie r 2
A Tier 2 method follows the same calculation equation as Tier 1 but would 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 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 in detail 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.5 to
10A.9 should be applied. Detailed information on how to characterise the livestock population for this source isprovided in Section 10.2.
In the case of anaerobic digestion of animal manures with additional organic residues it is essential to estimate theadditional N input from these organic residues and the respective N2O emissions.
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,P)) for eachdefined livestock species/category T, and productivity system P, when applicable;
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 (AWMS(T,S,P));
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. If manure ismanaged in multiple systems, it is good practice to estimate N2O emissions from all systems.
The calculation of direct N2O emissions from manure management is based on the following equation:
EQUATION 10.25 (UPDATED)DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT

 
 44

N 2=
OD ( mm )  ∑  ∑ ( N (T , P ) • Nex(T , P ) ) • AWMS(T , S , P ) + N cdg ( s )  • EF3( S )  • S  T ,P
 28

(

)

Where:
N 2 OD ( mm )= direct N2O emissions from Manure Management in the country, kg N2O yr-1

N (T , P )= number of head of livestock species/category T in the country, for productivity system P,
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Nex(T , P )= annual average N excretion per head of species/category T in the country, for productivity
system P, when applicable in kg N animal-1 yr-1

N cdg ( s )= annual nitrogen input via co-digestate in the country, kg N yr-1, where the system (s) refers
exclusively to anaerobic digestion

AWMS(T , S , P ) = fraction of total annual nitrogen excretion for each livestock species/category T that is

managed in manure management system S in the country, dimensionless; to consider
productivity class P, if using a Tier 1a approachEF3( 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 systemT= species/category of livestockP= productivity class, high or low, to be considered if using the Tier 1a approach

44/28= conversion of N2O-N(mm) emissions to N2O(mm) emissions

As is the case in the calculation of methane emission, countries may choose to consider if they have significantly
different production systems in their country and apply a Tier 1a approach. In this case, compilers should consider
the productivity class of their animal system as included in the calculation of CH4 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 and contributeto indirect N2O emissions (see below). Countries are encouraged to consider using a mass balance approach to
track the manure nitrogen excreted, managed on site in manure management systems, and ultimately applied to
managed soils. The estimation of the amount of manure nitrogen which is directly applied to managed soils orotherwise 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 is required. See Chapter 11, Section 11.2 for procedures tocalculate N2O emissions from managed manure nitrogen applied to soils. Additional guidance on ensuringconsistency in the mass balance approach and between emissions from manure in the source category N2O
Emissions from Manure Management and N2O Emissions from Managed Soils is given in Section 10.5.6 in thesubsection Consistency of nitrogen flows.
I nd ire ct N 2 O e m is s ions f rom Ma nu r e Manag em en t
Tier 1The Tier 1 calculation of N volatilisation in forms of NH3 and NOx from manure management systems is based on
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, animal weights (see Annex 10A.2, Tables 10A.5 to 10A.9) and default fractions of N
loss from manure management systems (see Table 10.22):
EQUATION 10.26 (UPDATED)N LOSSES DUE TO VOLATILISATION FROM MANURE MANAGEMENT=
N volatilization− MMS∑ ∑ ( ( ( NS T ,P(T , P ))• Nex(T , P ) ) • AWMS (T , S , P ) + N cdg ( s ) • FracGasMS (T , S )  )Where:N volatilization − MMS= amount of manure nitrogen that is lost due to volatilisation of NH3 and NOx, kg N yr-1

N (T , P )= number of head of livestock species/category T in the country, for productivity systemP, when applicable

Nex(T , P )= annual average N excretion per head of species/category T in the country, , for
productivity system P, when applicable in kg N animal-1 yr-1

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N cdg ( s )= amount of nitrogen from co-digestates added to biogas plants such as food wastes or
purpose grown crops, kg N yr-1 where the system (s) refers exclusively to anaerobic
digestion

P= productivity class, high or low, to be considered if using the Tier 1a approach

AWMS(T , S , P )= fraction of total annual nitrogen excretion for each livestock species/category T that is
managed in manure management system S in the country, dimensionless. To consider
productivity class P, if using a Tier 1a approach

FracgasMS (T , S )= fraction of managed manure nitrogen for livestock category T that volatilises as NH3
and NOx in the manure management system S

The Tier 1 calculation of N leached and runoff from manure management systems is based on multiplication of
the amount of nitrogen excreted (from all livestock categories) and managed in each manure management system
by a fraction of nitrogen leached, in analogy to the approach to estimate nitrogen volatilsation (see Equation 10.26).
There are 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, pens, in over-wintering
areas or feeding pens used during dormant growth periods for pastured animals and manure heaps or composting
systems, uncovered and uncontained. In drier climates, runoff losses are smaller than in high rainfall areas andhave been estimated in the range from 3 to 6 percent of N excreted (Eghball & Power 1994). Studies by Bierman
et al. (1999) found nitrogen lost in runoff was 5 to 19 percent of N excreted and 10 to 16 percent leached into soil,
while other data show relatively low loss of nitrogen through leaching in solid storage (less than 5 percent of N
excreted); but greater loss could also occur (Rotz 2004). Table 10.22 contains leaching loss fractions that may be
applied under very specific circumstances. Leaching can be estimated using these fractions in cases in whichmanure is uncovered on permeable soil, or where runoff may occur to permeable soil and runoff is not collected
in a impermeable basin and redistributed to agricultural fields. Leaching losses are estimated only in cases in whichmanure nitrogen is being lost to the environment and not accounted for in any other N flows. Further research isneeded in this area to improve the estimated losses and the conditions and practices under which such losses occur
however an estimate may be provided.
EQUATION 10.27 (UPDATED)N LOSSES DUE TO LEACHING FROM MANURE MANAGEMENT• Nex(T , P ) • AWMS (T , S , P ) ) + N cdg ( s ) • FracLeachMS (T , S )  N leaching=− MMS∑ ∑ ( ( N

N leaching − MMS= amount of manure nitrogen that is lost due to leaching, kg N yr-1

N (T , P )= number of head of livestock species/category T in the country, for productivity systemS T ,P(T , P ))Where:

P, when applicable
Nex(T , P )= annual average N excretion per head of species/category T in the country, for
productivity system P, when applicable in kg N animal-1 yr-1

N cdg ( s )= amount of nitrogen from co-digestates added to biogas plants such as food wastes or
purpose grown crops, kg N yr-1 where the system (s) refers exclusively to anaerobic
digestion

P= productivity class, high or low, to be considered if using the Tier 1a approach

AWMS(T , S , P )= fraction of total annual nitrogen excretion for each livestock species/category T that is
managed in manure management system S in the country, for productivity system P, whenapplicable, dimensionless

FracLeachMS (T , S )= fraction of managed manure nitrogen for livestock category T that is leached from themanure management system S (from Table 10.22)

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The indirect N2O emissions from volatilisation of N in forms of NH3 and NOx (N2OG(mm)) are estimated using
Equation 10.28:
EQUATION 10.28
INDIRECT N2O EMISSIONS DUE TO VOLATILISATION OF N FROM MANURE MANAGEMENTN=
2OG ( mm )( N volatilization − MMS • EF4 ) •44
28

Where:
N 2 OG ( 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 andwater surfaces, kg N2O-N (kg NH3-N + NOx-N volatilised)-1 ; given in Chapter 11, Table
11.3

The indirect N2O emissions due to leaching and runoff from Manure Management (N2OL(mm)) are estimated using
Equation 10.29:
EQUATION 10.29
INDIRECT N2O EMISSIONS DUE TO LEACHING FROM MANURE MANAGEMENT( N leaching − MMS • EF5 ) •N 2O=
L ( mm )44
28

Where:
N 2 OL ( mm )= indirect N2O emissions due to leaching and runoff from Manure Management in the country,
kg N2O yr-1

N leaching − MMS = amount of manure nitrogen that is lost due to leaching, kg N yr-1

EF5

10.78

= emission factor for N2O emissions from nitrogen leaching and runoff, kg N2O-N/kg N
leached and runoff, given in Chapter 11, Table 11.3

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10.4 (Updated) Decision tree for N 2 O emissions from Manure Management (1)Start

Do you have a countryspecific Tier 3 methodology?

Estimate emissions using
Tier 3 method.

Yes

Box 4: 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, andmanagement system
usage data?

No

Yes

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

No

Do you have
population data by highand low productivity
System?

Yes

Yes

Collect data for the Tier 2
method.

Collect data for the
Tier 1a approach.

Estimate direct and indirect
N2O emissions using Tier 2
method with available
country-specific inputs.

Estimate N2O emissions using
Tier 1a method and IPCC
defaults.

Box 3: Tier 2

Box 2: Tier 1a

No

Estimate direct and indirect
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 thesource category.

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, a Tier
2 method would follow the same calculation equation as Tier 1 but include the use of country-specific data forsome or all of variables. For example, the use of country-specific nitrogen excretion rates for livestock categories
would constitute a Tier 2 method. Tier 2 method would require more detailed characterisation of the flow ofnitrogen throughout the animal housing and manure management systems used in the country. It is good practice
to check N balance in a Tier 2 approach. Double counting of emissions associated with the application of managed
manure should be avoided, as well as manure associated with pasture and grazing operations as described in
Section 10.5.6. National NH3 emission inventories developed by some countries could be used for Tier 2

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estimation of NH3 volatilisation from manure management systems. For countries reporting emissions of NH3 andNOx to the UN-ECE Convention on Long-Range Transboundary Air Pollution (UN-ECE LRTAP) using a Tier 2
approach as described in the EEA (2016) emission inventory guidebook, it is good practice to report
𝑁𝑁𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣−𝑀𝑀𝑀𝑀𝑀𝑀 in consistency to the NH3 and NOx emissions reported to the UN-ECE.
Tie r 3To reduce uncertainty of the estimates, a Tier 3 method could be developed using advanced or process-basedmodels 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 subtracted 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 frommanaged soils, for guidance on calculating total N losses from manure management systems.

10.5.2 Choice of emission factors
An nua l av er age n it roge n e xcr et ion rat e s, N ex ( T )Tie r 1
Annual nitrogen excretion rates should be determined for each livestock category defined by the livestock
population characterization. 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 and retention(as explained below). In some situations, it may be appropriate to use excretion rates developed by other countries
that have livestock with similar characteristics.
If country-specific data cannot be collected or derived, or appropriate data are not available from another country,
the IPCC default 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 (UPDATED)ANNUAL N EXCRETION RATES

Nex( T ,P ) =N rate ( T ,P ) •TAM ( T ,P )
1000

• 365

Where:
Nex(T , P )= annual N excretion for livestock category T, kg N animal-1 yr-1 (production level P if usinga Tier 1 approach

N rate (T , P )= default N excretion rate, kg N (1000 kg animal mass)-1 day-1 for animal category T (andproduction level P, if using a Tier 1a (see Table 10.19))

TAM (T , P )= typical animal mass for livestock category T, kg animal-1

P= productivity class, high or low, to be considered if using the Tier 1a approach

Default TAM values are provided in Annex 10A.1, Table 10A.5. However, it is preferable to collect countryspecific TAM values due to the sensitivity of nitrogen excretion rates to different weight categories. For example,
market swine may vary from nursery pigs weighing less than 30 kilograms to finished pigs that weigh over 90
kilograms. By constructing animal population groups that reflect the various growth stages of market pigs,
countries will be better able to estimate the total nitrogen excreted by their swine population.
When estimating the Nex(T) for animals whose manure is classified in the manure management system burned forfuel (Table 10.21), it should be kept in mind that the dung is burned, and the urine stays in the field. Generally, 50
percent of the nitrogen excreted is in the dung and 50 percent 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

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The annual amount of N excreted by each livestock species/category depends on the total annual N intake and total
annual N retention of the animal. Therefore, N excretion rates can be derived from N intake and N retention data.
Annual N intake (i.e., the amount of N consumed by the animal annually) depends on the annual amount of feed
digested by the animal, and the protein content of that feed. Total feed intake depends on the production level ofthe animal (e.g., growth rate, milk production, draft power). Annual N retention (i.e., the fraction of N intake thatis retained by the animal for the production of meat, milk, or wool) is a measure of the animal's efficiency of
production of animal protein from feed protein. Nitrogen intake and retention data for specific livestock
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, OPTION 1 (TIER 2)Nex=
N intake (T ) • (1 − N retention _ frac (T ) ) • 365(T )Where:
Nex(T )

= annual N excretion rates, kg N animal-1 yr-1

N int ake (T )= the daily N intake per head of animal of species/category T, kg N animal-1 day-1

N retention _ frac (T ) = fraction of daily N intake that is retained by animal of species/category T, dimensionless

365= Number of days in a year 6
EQUATION 10.31A (NEW)
ANNUAL N EXCRETION RATES, OPTION 2 (TIER 2)

Nex(T ) =( N intake (T ) − N retention (T ) ) • 365
Where:
Nex(T )

= annual N excretion rates, kg N animal-1 yr-1

N int ake (T )= the daily N intake per head of animal of species/category T, kg N animal-1 day-1

N retention (T )= amount of daily N intake by head of animal of species / category T, that is retained byanimal of species/category T, kg N animal-1 day-1

365= Number of days in a year6

nitrogen excretion may be calculated based on the same dietary assumptions used in modelling enteric fermentation
emissions (see Section 10.2). The amount of nitrogen excreted by cattle can be estimated as the difference between
the total nitrogen taken in by the animal and the total nitrogen retained for growth and milk production. Equations
10.32, 10.32A and 10.33, 10.33A, 10.33B, 10.33C, 10.33D and 10.33E can be used to calculate the variables fornitrogen intake and nitrogen retained for use in Equations 10.31 and 10.31a. The daily nitrogen intake rate isderived as follows:6

Consideration should be taken of periods between production cycles, particularly for animal categories that may have multiple
annual growth cycles. For livestock species with a lifetime shorter than one year, the approach suggested by Haenel et al.
(2018) (see chapters 3.1.2.2.1 to 3.1.2.2.3) and by Rösemann et al. (2017) can be considered.

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EQUATION 10.32
N INTAKE RATES FOR CATTLE, SHEEP AND GOATS

 CP % 
GE  100 
N int=•
ake (T )18.45  6.25 

EQUATION 10.32A (NEW)N INTAKE RATES FOR SWINE AND POULTRY

 CP%i 
N int ake=
DMI i •  100 (T ,i )
 6.25 Where:
N int ake (T ,i )=daily N consumed per animal of category T, kg N animal-1 day-1, per growth stage-1 “i" whenapplicableGE= 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 (used in conjunction with Tier 2 gross energy calculation for
cattle, sheep and goats)

18.45= conversion factor for dietary GE per kg of dry matter, MJ kg-1. This value is relatively
constant across a wide range of forage and grain-based feeds commonly consumed bylivestock

DMI i= dry matter intake per day during a specific growth stage “i”, (kg DMI animal day-1)

CP%i= percent crude protein in dry matter for growth stage “i”. (Table 10A.1, Table 10A.2 andTable 10A.3 present default CP% values for all regions)

6.25= conversion from kg of dietary protein to kg of dietary N, kg feed protein (kg N)-1

As an example, the intake of N for a growing pig between 32 to 60 kg of body weight with a daily intake of 1.67
kg of a diet containing 18 percent CP would be, applying the above equation, equivalent to 0.05 kg N day-1.
The daily value can be converted to a total N input per year or per growth stage by multiplying either by 365 orby the length of the growth period of interest.

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TABLE 10.19 (UPDATED)DEFAULT VALUES FOR NITROGEN EXCRETION RATE (KG N (1000 KG ANIMAL MASS)-1 DAY-1)

North America

Western Europe

Eastern Europe

Oceania

Mean

High PS1

Low PS1

Mean

High PS

Low PS

Mean

High PS

Low PS

Mean

High PS

Low PS

Mean

High PS

Low PS

RegionDairy cattle3

0.59

0.54

0.42

0.72

0.39

0.60

0.28

0.44

0.41

0.45

0.50

0.49

0.51

0.44

0.55

0.41

0.65

0.51

0.70

Other cattle3

0.40

0.42

0.47

0.46

0.31

0.36

0.29

0.45

0.42

0.44

0.56

0.51

0.59

0.38

0.36

0.39

0.44

0.64

0.41

Buffalo3

NA

0.45

0.35

NA

Swine4

0.39

0.65

0.63

0.54

0.59

0.55

0.67

0.44

0.33

0.49

0.66

0.67

0.56

0.61

0.54

0.67

0.68

0.63

0.71

Finishing

0.46

0.76

0.77

0.72

0.73

0.69

0.80

0.49

0.39

0.54

0.73

0.75

0.60

0.70

0.63

0.76

0.76

0.74

0.76

Breeding

0.24

0.38

0.36

0.31

0.35

0.32

0.43

0.29

0.21

0.35

0.40

0.41

0.37

0.37

0.32

0.43

0.43

0.37

0.47

1.45

0.99

0.96

1.42

1.20

1.13

2.14

1.29

1.16

1.44

1.29

1.27

1.79

1.10

1.00

1.62

1.62

1.48

1.83

Hens >/= 1 yr

1.13

0.87

0.81

1.04

1.17

1.02

2.01

1.20

0.99

1.34

1.11

1.06

1.70

1.00

0.89

1.50

1.65

1.60

1.70

Pullets

0.77

0.58

0.58

0.76

0.95

0.68

2.50

1.29

0.70

1.72

0.85

0.74

2.03

0.83

0.60

1.91

1.63

0.98

2.20

Broilers

1.59

1.14

1.12

1.59

1.23

1.21

2.39

1.40

1.34

1.58

1.43

1.42

1.95

1.35

1.31

1.84

1.58

1.47

2.11

Category ofanimalChicken4

Latin America

Africa0.41

Middle East

0.42

0.39

Turkeys12

0.74

Ducks12

0.83

Sheep4

0.35

0.36

0.36

0.43

0.32

Goats5

0.46

0.46

0.44

0.42

0.34

Horses and mules
and asses12

0.30

0.26

0.30

0.30

0.46

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Asia

India subcontinent

0.44

0.58

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TABLE 10.19 (UPDATED) (CONTINUED)DEFAULT VALUES FOR NITROGEN EXCRETION RATE (KG N (1000 KG ANIMAL MASS)-1 DAY-1)

Camels12

0.38
0.36

Deer8,11

0.67

Mink and Polecat (kg N head

Low PSHigh PS

Mean

India sub-continent
Low PSHigh PS

Asia
Mean

Low PSHigh PS

Mean

Low PSHigh PS

Middle East

0.46

Ostrich7,11

Reindeer9,11

AfricaMean

Low PS1High PS1

Latin America
Mean

Oceania

Eastern Europe

Western Europe

North America

Region

0.23-1yr )6,11-1

4.59

Rabbits (kg N head-1 yr-1)10,11

8.10

Fox and Racoon (kg N head-1 yr-1)6,11

12.09

High PS and Low PS refer to high- and low productivity systems required for Tier 1a methodology.
NA refers to situations in which these animal categories do not occur in these regions.
3
Values are derived from diets used in the calculation of enteric fermentation Tier 1 emission factors (Annex 10A.1).
4
Values are taken from FAO GLEAM databases (FAO 2017).
5
Calculations are detailed in Annex 10B.3.
6
Data of Hutchings et al. (2001) (as cited in 2006 IPCC Guidelines).
7
Nex rate for ostrich in kg N (1000 kg animal mass)-1 day-1 was calculated taking into account an average value on weight of bird (120 kg) and a rate of nitrogen excretion per head per year (15.6 kgN/head/year). Sources:
Velthof (2014); Reis & Oliveira (2008); du Toit et al. (2013).
8
Nex rate for deer in kg N (1000 kg animal mass)-1 day-1 was calculated taking into account an average value on weight of animal (120 kg) and a rate of nitrogen excretion per head per year (29.32 kgN/head/year). Sources:
Danish NIR (Nielsen et al. 2018), New Zealand’s NIR (Ministry for the Environment 2018).
9
Nex rate for reindeer in kg N (1000 kg animal mass)-1 day-1 was calculated taking into account an average value on weight of animal (120 kg) and a rate of nitrogen excretion per head per year (5.75 kgN/head/year).Sources:
Danish NIR (Nielsen et al. 2018); New Zealand’s NIR (Ministry for the Environment 2018).
10
Nex rate per average doe, including young reproduction stock and males. Sources: Maertens et al. (2005); Xiccato et al. (2005); Gasco et al. (2014); Velthof et al. (2015).
11
The IPCC expert group reviewed the national inventory submissions under the UNFCCC and concluded that common distribution of systems used to manage manure as follows: 80 percent of ostrich’manure is deposited in
pasture and in range and 20 percent is managed in solid based systems; deer and reindeer manure deposited mainly in pasture and in range, manure of rabbits and fur-bearing animals is managed mostly in a solid based
systems. Hence, countries may apply the same allocation of MMS in the calculation of N2O emissions from manure stored in manure management systems. However, countries are encouraged to develop a country-specificdataset on MMS used to manage manure generated by these categories of animals.
12
The values are taken from Table 10.19 of Chapter 10 of Volume 4 of the 2006 IPCC Guidelines.
1
2

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.20 (UPDATED)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) 1
Livestock category

Nretention_frac(T), (kg N retained/animal/day) (kg N intake/animal/day)-1

Cattle and Buffalo

See values in Annex 10A.1

Sheep

0.10

Goats

0.10

Camels

0.07

Swine

0.30

Horses0.07

Poultry

0.30

This N retention values apply to non-dairy sheep and goats. For dairy sheep and goats country-specific values arerecommended. Values are applied in Equation 10.31, Option 1 for the calculation of annual N excretion.
The uncertainty in these estimates is +50%.
Source: 2006 IPCC Guidelines, Table 10.21.

Tie r 2 m ethod for e st ima t ing n itrog en e xcr etion for ca ttle
The total nitrogen retained for cattle is derived as follows:
EQUATION 10.33
N RETENTION RATES FOR CATTLE 7.03 • NEg    268 −   Milk PR%   
 WG
 

•
Milk

  WG •


 100
 +
1000
=
N retention (T ) 


6.38
6.25

 
Where:
N retention (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), or the values reported in Table 10A.1, Table 10A.2
and Table 10A.3 can be used

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 and 7.03 = constants from Equation 3-8 in NRC (1996), g Protein kg-1 animal-1 and g Protein MJ-1
animal-1 respectively

1000= conversion from g protein to kg protein

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

Nitrogen excretion is calculated using Equation 10.31a, Option 2.
Tie r 2 m ethod for e st ima t ing n itrog en e xcr etion for p ig s
The nitrogen excretion rate depends on the balance between the animal’s feed N intake and its N retention in tissue.
Different categories of animals (e.g. adult females, adult males and growing pigs) can have quite different N

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requirements depending on, for example, their growth rates, lactation rates and yields (MacLeod et al. 2013).
Likewise, the N retention rates can be different among different animal categories. Thus, when following a Tier 2
approach for estimating nitrogen excretion for pigs, it is a good practice to include N excretion estimates for atleast the pig categories listed in Table 10.1.
For breeding pigs, if inventory compilers have detailed information about feed, breeding statistics piglets born andweaned and proportions of sows entering the breeding herd (optional), N retention may be calculated as follows:
EQUATION 10.33A (NEW)N RETENTION RATES FOR BREEDING SOWS

= N gain + N weaned piglets
N retention
Where:

N retention= amount of N retained by the animal (in kg N animal-1 year-1)

N gain= amount of N retained in the sow (in kg N animal-1 year-1), calculated as (0.025 *FR * Skg)
Where:
0.025 = fraction of N retained in BW, kg N·kg BW gain-1
FR= fertility rate of sows, parturitions·year-1

Skg= is the sum of live weight change of sows from parturition to parturition, kg·head-1
and can be calculated from the litter birth weight and number (LITSIZE * Ckg/0.806
Where:
LITSIZE = litter size, heads

N weaned piglets

Ckg= live weight of piglets at birth, kg·head-1

0.806= constant to correct for the higher kg N per kg BW gain-1 in piglets,
fraction)= amount of N in piglets weaned calculated as in Equation 10.33b (in kg N animal-1 year-1)
EQUATION 10.33B (NEW)N RETENTION RATES FOR PIGLETS

N weaned piglets = 0.025 • LITSIZE • FR •(Wkg − Ckg )0.98

Where:

10.86

0.025

= fraction of N retained in BW, kg N·kg BW gain-1

LITSIZE= litter size, heads

FR= fertility rate of sows, parturitions·year-1

Wkg= live weight of piglet at weaning age, kg·head-1

Ckg= live weight of piglets at birth, kg·head-1 and

0.98= protein digestibility as fraction (FAO, 2017)2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.20A (NEW)CALCULATION OF N RETENTION IN BREEDING SWINE FROM DIFFERENT PRODUCTION SYSTEMS, AN EXAMPLE
Ckg

Wkg

kg

kg

Low Productivity

0.8

6.5

High Productivity

1.2

7.0

Systema

FR

LITSIZE

Sows Ngaina

Piglets Ngain

heads

kg N animal-1 year-1

kg N animal-1 year-1

1.7

9.1

0.92

2.25

2.1

9.2

1.38

2.85

For this example, a live weight change of young sows from parturition to parturition of 12.5 kg was assumed.

The value of Skg is derived from national statistics on the average litter size and birth weight of piglets, factorsthat vary based on national production systems. Further, gilts will typically be mated at 125 – 140 kg of bodyweight, and continue to grow over several reproductive cycles to a mature weight of approximately 200 kg of bodyweight. The N excretion by breeding sows (Equation 10.33B) should also take into account that the sow will gain
between 10 to 15 kg of body weight during the first four or five reproductive cycles (Chiba 2009). Inventory
compilers are advised to: (a) use data that best describe their country production practices and/or genetic stocks
employed for estimates of litter size and birth weight; (b) estimate the proportion of the breeding animals that are
not at mature weight and consider the growth of these animals in their estimate of Skg. The weight of the placenta
and other products of conception are not included in Tier 2 calculations as they are discarded at parturition.
Countries that are able to track nitrogen in conception products may choose to do so through country specificmethods
The daily N retention can be calculated by dividing the result of equation 10.33a by the total number of days in
the gestational and weaning periods to provide a daily N retention in kg N day-1. Nitrogen excretion is calculatedusing Equation 10.31a, Option 2.
For estimating N retention by growing animals, the following approach may be followed:
EQUATION 10.33C (NEW)N RETENTION RATES FOR GROWING PIGS=
N retention∑ ( BWi

Final ( i )– BWInitial ( i ) ) • N gain ( i )Where:

N retention= amount of N retained in animal (in kg N animal-1 year-1)

BWFinal( i )= Live weight of the animal at the end of the stage (kg) per defined growth stage (i)BWInitial( i )= Live weight of the animal at the beginning of the stage (kg) per defined growth stage (i)N gain( i )= fraction of N retained at a given BW per defined growth stage (i), the fraction should be
calculated for the final BW of the phase. For example a finishing hog that weighed 109 kg
at slaughter would use a value of 0.021 kg N kg BW gain-1

These should be summed over the different production stages 7. Daily N retention is calculated by dividing the total
N retention by the length of the production period from weaning to slaughter. Nitrogen excretion is calculatedusing Equation 10.31a, Option 2.

7 It should be noted that factors other than physiological stage can affect nitrogen retention, including body weight (Pettey etal. 2015), sex and genetic line (Wiseman et al. 2007).

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TABLE 10.20B (NEW)DEFAULT VALUES FOR NGAIN BY GROWTH STAGE
Phase

Ngain
(kg N kg BW-1)

Nursery (4 to 7 kg)

0.031

Nursery (7 to 20 kg)

0.028

Grower (20 to 40 kg)

0.025

Grower (40 to 80 kg)

0.024

Finisher (80 to 120 kg)

0.021

Ngain was calculated for a given BW as Ngain = -0.004 ln(BW) + 0.0381. Based on Shields et al. (1983) and further
adjusted based on data from Poulsen & Kristensen (1998) and FAO (2017).

The approach used for estimating N excretion from growing pigs can be followed for gilts and growing boars thatwill be used for breeding purposes and for nursery, growing and finishing market pigs.
Tie r 2 m ethod for e st ima t ing n itrog en e xcr etion for poult r y
In broiler production, chicks generally cannot digest and absorb all nutrients, especially in the case of nutritional
imbalance or high concentration of nutrients in feed. Thus, the surplus nutrients are broken down, and carbon isused to produce energy whereas nitrogen is excreted in faeces (Boonsinchai et al. 2016). Different categories ofanimals (for meat or eggs) can have quite different N requirements and different N retention rates (Poulsen &Kristensen 1998; Williams 2013; Velthof et al. 2015). Thus, when following a Tier 2 approach for estimating
nitrogen excretion for poultry, it is a good practice to include N excretion estimations for at least the poultry
categories listed in Table 10.1.
In estimating nitrogen excretion, the nitrogen balance approach is also very useful, for which information on feedintake, feed N content and animal productivity (egg production, weight gain, lengths of production stages) is
required.
A suitable approach to estimate annual nitrogen retention by layer type hens is as follows (Poulsen & Kristensen
1998):
EQUATION 10.33D (NEW)N RETENTION RATES FOR LAYER TYPE HENS

N retention ,c= N EGG • EGG   N LW • WGc + 
1000



Where:
N retention , c= daily nitrogen retention by animal in cohort c, kg N·head-1day-1

N LW= average content of nitrogen in live weight, kg N·kg head-1. Default value of 0.028 is used

WGc

= average daily weight gain for cohort c, kg·head-1·day-1

N EGG= average content of nitrogen in eggs, kg N·kg egg-1. Default value of 0.0185 is used

EGG

= egg mass production, g egg·head-1 day-1

A suitable approach to estimate annual nitrogen retention by pullets and broilers is as follows (Poulsen &Kristensen 1998):

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Chapter 10: Emissions from Livestock and Manure Management

EQUATION 10.33E (NEW)DAILY N RETENTION RATES FOR PULLETS OR BROILERS

N retention =( BWFinal – BWInitial ) • N gain
production _ period

Where:

N retention= amount of N retained in animal (kg N head-1) day-1

BWFinal= Live weight of the animal at the end of the stage (kg)

BWInitial= Live weight of the animal at the beginning of the stage (kg)N gain= the fraction of N (kg) retained per kg BW gain

production _ period= length of time from chick to slaughter (days)

Default value for N gain

= 0.028 based on data from Poulsen & Kristensen (1998) and FAO (2017)

Nitrogen excretion is calculated using Equation 10.31a, Option 2.
Em iss io n fac tor s fo r d ir e ct N 2 O em is sio ns from Manu r e Ma nage men t
The best estimate will be obtained using country-specific emission factors that have been fully documented in peer
reviewed publications. It is good practice to use country-specific emission factors that reflect the actual durationof storage and type of treatment of animal manure in each management system that is used. Good practice in thederivation of country-specific emission factors involves the measurement of emissions (per unit of manure N)
from different management systems, taking into account variability in duration of storage and types of treatment.
When defining types of treatment, conditions such as aeration and temperature should be taken into account. If
inventory agencies use country-specific emission factors, they are encouraged to provide justification for thesevalues 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 from Manure
Management. This table contains default emission factors by manure management system. Note that emissions
from liquid/slurry systems without a natural crust cover and anaerobic lagoons are considered negligible based on
the absence of oxidized forms of nitrogen entering these systems combined with the low potential for nitrificationand denitrification to occur in the system.
Em iss io n fac tor s fo r ind ir ect N 2 O e m iss ion s fr om Ma nu re Manag eme nt
In order to estimate indirect N2O emissions from Manure Management, two fractions of nitrogen losses (due tovolatilization, FracGasMS, and leaching/runoff, FracLeachMS), and two indirect N2O emissions factors associated withthese losses (EF4 and EF5) are needed. Default values for volatilization N losses are presented in the Table 10.22
for single manure systems. Values represent the sum of the loss rates for N in the forms of NH3 and NOx, withmost of the loss in the form of NH3. Ranges reflect values 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 wellcharacterized as part of larger air quality assessments and where emissions may be affected by nitrogen reduction
strategies. For example, detailed methodologies for estimating NH3 and other nitrogen losses using massbalance/mass flow procedures are described in the EMEP/CORINAIR air pollutant emission inventory guidebook,
Chapter 3B (current version: EEA 2016).
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.
N 2 O e m is s ion s f rom mu lt ip le Manu r e Manag em en t s yst em s
Consistent with CH4 manure management, if manure is managed in multiple systems, by default, manure emission
factors should be allocated to the dominant storage systems; But a country specific emission factor could be
developed considering the emissions originating from all other systems used in storage prior to field applicaton.
A number of combinations are possible and as was the case with methane emissions from manure management, it
is beyond the scope of these guidelines to provide guidance for all possibilities but common examples include: i.)manure flushed from a dairy freestall barn to an anaerobic lagoon that first pass through a solids separation unit

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where some of the manure nitrogen is removed and managed as a solid; ii.) pit storage that is flushed to a larger
holding tank; iii.) solid manure pack that is allowed to accumulate, and periodically transferred to heaps.
In these cases, emissions could be calculated based on Tier 2 factors from the separate manure fractions andweighted based on the duration of storage in the different systems. Emission factors as developed and reported inthese guidelines are assumed to be for a full year of N within a given manure management system. The applicationof a country-specific, staged emission factor would require estimates of sub-annual emissions based on manure
residency times in each stage. Further, this type of approach should consider the application of a full mass balance
approach and likewise consider N loss at each stage.
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).
Consistency should be maintained for the treatment of nitrogen flows throughout all agricultural emission
calculations, including managed soils as outlined in Section 10.5.4 8.

8

As discussed in Section 10.5.4, N losses from housing and storage have to be subtracted before the calculation of N2Oemissions (direct and indirect) from agricultural soils.

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TABLE 10.21 (UPDATED)DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT24
EF3[kg N2O-N (kg nitrogen excreted)-1]
Direct and indirect N2O emissions associated with themanure deposited on agricultural soils and pasture,
range, paddock systems are treated in Chapter 11,
Section 11.2, N2O emissions from managed soils.

SystemDefinition

Pasture/Range/ Paddock

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

Daily spread5

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

0

Solid storage2,4,6

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

0.010

Solid storageCovered/compacted4,7
Solid storageBulking agent addition4,8
Solid storage –Additives4,8
Dry lot9

Liquid/Slurry

Uncovered12 anaerobic
lagoonPit storage13 below animal
confinements
Anaerobic14 digester

Similar to solid storage, but the manure pile is a) covered with a plastic sheet to reduce the surface of manure
exposed to air and/or b) compacted to increase the density and reduce the free air space within the material.
Specific materials (bulking agents) are mixed with the manure to provide structural support. This allows thenatural aeration of the pile, thus enhancing decomposition. (e.g. sawdust, straw, coffee husks, maize stover).
The addition of specific substances to the pile in order to reduce gaseous emissions. Addition of certain
compounds such as attapulgite, dicyandiamide or mature compost have shown to reduce N2O emissions;while phosphogypsum reduces CH4 emissions.
A paved or unpaved open confinement area without any significant vegetative cover where accumulating
manure may be removed periodically. Dry lots are most typically found in dry climates but also are used inhumid climates.
9
Manure is stored as excreted or with some minimal With natural crust coveraddition of water to facilitate handling and is stored ineither tanks or earthen ponds.

Without 10 natural crust cover

Cover11
Anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoonsupernatant 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 a year 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 or used to irrigate and fertilise fields.
Collection and storage of manure usually with little or no added water typically below a slatted floor in an
enclosed animal confinement facility.
Anaerobic digesters are designed and operated for waste stabilization by the microbial reduction ofcomplex organic compounds to CH4 and CO2, which is captured and flared or used as a fuel.

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0.01
0.005
0.005
0.02
0.005
0
0.005
0

0.002
0.0006

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TABLE 10.21 (UPDATED) (CONTINUED)DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT24
SystemEF3[kg N2O-N (kg nitrogen excreted)-1]

Definition

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

The emissions associated with the burning of the dung are to
be reported under the IPCC category 'Fuel Combustion' if thedung is used as fuel and under the IPCC category 'Waste
Incineration' if the dung is burned without energy recovery.

Urine N deposited on pasture and paddock.

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.

Burned for fuel or as
waste

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 a bedded pack manure
management system and may be combined with a dry
lot or pasture.

Composting - InVessel3,17Composting, typically in an enclosed channel, with forced aeration and continuous mixing.

0.006

Composting - Static Pile3(Forced aeration)4,18

Composting in piles with forced aeration but no mixing.

0.010

Composting - Intensive
Windrow3,19 (Frequent
turning)

Composting in windrows with regular turning for mixing and aeration.

0.005

Composting- Passive
windrow (infrequent
turning) 4, 20

Composting in windrows with infrequent turning for mixing and aeration.

0.005

Poultry manure withlitter21

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

0.001

Poultry manure without
litter21

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 andis a form of passive windrow composting when designed and operated properly.

0.001

10.92

No mixing15

0.01

Active mixing 16

0.07

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10.21 (UPDATED) (CONTINUED)DEFAULT EMISSION FACTORS FOR DIRECT N2O EMISSIONS FROM MANURE MANAGEMENT24
SystemDefinition

Aerobic treatment

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

EF3[kg N2O-N (kg nitrogen excreted)-1]
Natural aeration systems22

0.01

Forced aeration systems23

0.005

Also see AFRC (1995) and Dustan (2002), which compiled information from some of the original references cited.
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 15% dry matter content.
3
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.
4
Sources and assumptions to calculate N2O EF for Solid storage systems with/without treatments and composting (static pile and passive windrows) are detailed in Annex 10B.7.
52006 IPCC Guidelines, Table 10.21.
6
Expert judgement based on Pardo et al. (2015). Median of N2O emissions from farm-scale collected studies.
7
Expert judgement based on Pardo et al. (2015). Emissions in the same range than solid storage.
8
Expert judgement based on Pardo et al. (2015). Estimated reduction of 50% N2O emissions due to bulking agent addition.
9
Judgement of IPCC Expert Group in combination with Kulling et al. (2003).
10
Judgement of IPCC Expert Group in combination with the following studies: Harper et al. (2000), Lague et al. (2004), Monteny et al. (2001), and Wagner-Riddle & Marinier (2003). Emissions are believed negligible
based on the absence of oxidized forms of nitrogen entering systems in combination with low potential for nitrification and denitrification in the system.
11
A detailed literature review carried out during the 2019 Refinement revealed only few new datasets on the measurement of N2O emissions from manure stores. These datasets emcompass a large range of N2O emissions
from a 50% reduction to a 100 % increase in N2O emissions when slurry stores are covered. The 2019 Refinement therefore suggest to use the emission factor of natural crust cover.
12
Judgement of IPCC Expert Group in combination with the following studies: Harper et al. (2000), Lague et al. (2004), Monteny et al. (2001), and Wagner-Riddle & Marinier (2003). Emissions are believed negligible
based on the absence of oxidized forms of nitrogen entering systems in combination with low potential for nitrification and denitrification in the system.
13
Judgement of IPCC Expert Group in combination with the following studies: Amon et al. (2001), Kulling et al. (2003), and Sneath et al. (1997).
14
The emission mainly from storage of digestate.
15
Judgement of IPCC Expert Group in combination with the following studies: Wang et al. (2016), Rodhe et al. (2015); Wang et al. (2014b); Wang et al. (2014a); Li (2016); Amon et al. (2006); Moitzi et al. (2007);
Clemens et al. (2006). Average value based on Moller et al. (2000), Sommer & Møller (2000), Amon et al. (1998a); Amon et al. (1998b), and Nicks et al. (2003).
16
Average value based on Nicks et al. (2003) and Moller et al. (2000). Some literature cites higher values to 20% for well maintained, active mixing, but those systems included treatment for ammonia which is not
typical.
17
Judgement of IPCC Expert Group. Expected to be similar to static piles.
18
Expert judgement based on Pardo et al. (2015). Emissions in the same range than solid storage.
19
Assuming similar range to passive windrow.
20
Expert judgement based on Pardo et al. (2015). Median of N2O emissions from farm-scale collected studies and estimated reduction of 50% due to bulking agent addition.
21
Judgement of IPCC Expert Group based on the high loss of ammonia from these systems, which limits the availability of nitrogen for nitrification/denitrification.
22
Judgement of IPCC Expert Group. Nitrification-denitrification is used widely for the removal of nitrogen in the biological treatment of municipal and industrial wastewaters with negligible N2O emissions. Limitedoxidation may increase emissions compared to forced aeration systems.
23
Judgement of IPCC Expert Group. Nitrification-denitrification is used widely for the removal of nitrogen in the biological treatment of municipal and industrial wastewaters with negligible N2O emissions.
24
Uncertainties for emission factors are defined as varying by a factor of 2 (±100%).
1
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10.5.3 Choice of activity data
No refinement.

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, are considered.
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 use as feed, fuel, orfor construction purposes. In order to estimate the amount of animal manure nitrogen that is directly applied tosoils, 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 of reactive nitrogen gases (i.e., NH3 and NOx) or through leaching
and runoff (both leading to indirect emissions of N2O), direct conversion to N2O, or losses as inert molecular
nitrogen (N2).
nitrogen in manure is present both as organic nitrogen (Norg) and mineral nitrogen, of which the majority consistsof ‘Total Ammoniacal nitrogen’ (TAN). The sum of Norg and TAN gives the total nitrogen available (Ntot).
Volatilization of NH3 and other forms of gaseous N arise from the mineral fraction of nitrogen in manure, TAN.
Organic nitrogen in manure needs first to be converted to TAN before NH3 volatilization can happen. The
EMEP/EEA air pollutant emission inventory Guidebook 2016 (EEA 2016, Chapter 3B) therefore distinguishes theflow of TAN and Norg and the transitions between the two forms in agricultural systems. The values for thevolatilisation fraction FracGASMS listed in Table 10.22 attempt to account for typical TAN contents in manure for
the MMS considered. However, different excretion ratios of TAN vs. total N as a consequence of changes inlivestock diets are not reflected. Also, information on the TAN content in manure available for application,
𝑁𝑁𝑀𝑀𝑀𝑀𝑀𝑀_𝐴𝐴𝐴𝐴𝐴𝐴 , is not kept if using Equation 10.34. Farming practices that reduce the escape of NH3 from MMS but not
the amount of TAN available are likely to lead to higher NH3 volatilization rates once the manure is applied tosoils or used for feed, fuel, or for construction purposes.
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. The same applies
to additional N input from co-digestates during anaerobic digestion. Bedding is typically collected with theremaining manure and applied to soils. It should be noted, however, that since mineralization of nitrogencompounds in beddings occurs more slowly compared to manure and the concentration of ammonia fraction inorganic beddings is negligible, both volatilization and leaching losses during storage of bedding are assumed to be
zero. If bedding material comes from crop residues, the amount of nitrogen needs to be considered whencalculating N2O emissions from crop residues from managed soils by accounting for this quantity in FracRemove(T)in Equation 11.6 of Chapter 11. Further codigestates in the production of biogas may include food waste as wellas purpose grown crops. Differences in N loss that might occur with crop residue being digested or being returned
directly to the fields should be considered in this case.
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 (UPDATED)MANAGED MANURE N AVAILABLE FOR APPLICATION TO MANAGED SOILS, FEED, FUEL OR
CONSTRUCTION USES

(

)(

)

   N • Nex • AWMS
 
(T )
(T , S ) + N cdg • 1 − FracLossMS( T ,S )  +     (T )N MMS Avb = ∑ ∑ S  (T ) +  N  (T ) • AWMS(T , S ) • N beddingMS(T ,S )  Where:

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N MMS Avb

= amount of managed manure nitrogen available for application to managed soils or for feed,

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

fuel, or construction purposes, kg N yr-1

yr-1

AWMS(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(T ,S ) = total fraction of managed manure nitrogen for livestock category T that is lost in the manure
management system S in the country, dimensionless. FracLossMS is calculated according toEquation 10.34a

NbeddingMS T ,S = 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
= amount of nitrogen from co-digestates added to biogas plants such as food wastes or

N cdg

purpose grown crops, kg N yr-1
S

= manure management systemT= species/category of livestock

EQUATION 10.34A (NEW)FRACTION OF MANAGED MANURE N LOST PRIOR TO APPLICATION TO MANAGED SOILS FOR THE
PRODUCTION OF FEED, FUEL OR FOR CONSTRUCTION USESFRACLOSSMS( T ,S )=FRACGASMS( T ,S )+ FRACLEACHSMS( T ,S )+ FRAC N 2 MS( S ) + EF3(S )Where:FRACLOSSMS( T ,S )= total fraction of managed manure nitrogen for livestock category T that is lost in the
manure management system S

FRACGASMS= fraction of managed manure nitrogen for livestock category T that is lost by( T ,S )volatilisation in the manure management system S as NH3 or NOX (see Table 10.22)

FRACLEACHSMS( T ,S )= fraction of managed manure nitrogen for livestock category T that is lost in the
manure management system S by leaching or run-off (see Table 10.22)

FRAC N 2 MS( S )= fraction of managed manure nitrogen that is lost in the manure management system
S as N2 (see Equation 10.34b)

EF3

(S )

= emission factor for direct N2O emissions from manure management system S; inthis case considered dimensionless (see Table 10.21)

The amount of managed nitrogen that is lost by denitrification to N2 can be obtained as a ratio of N2:N2O emissions.
Webb & Misselbrook (2004) reviewed available data and concluded that as first approximation, emissions of N2
might be 3-times those of N2O. FracN2MS can thus be calculated according to Equation 10.34B.
EQUATION 10.34B (NEW)ESTIMATION OF FRACN2MS

FracN2 MS( S ) = RN2 ( N O ) • EF3( S )2

Where:

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FRAC N 2 MS( S ) = fraction of managed manure nitrogen for livestock category T that is lost as N2 in the
manure management system S,

EF3= emission factor for direct N2O emissions from manure management system S in the(S )country, kg N2O-N (kg N)-1 in manure management system S

RN (2 N 2O)= Ratio of N2 : N2O emissions. The default value of RN2(N2O) is 3 kg N2-N (kg N2O-N)-1 (seeTable 10.23)

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 usuallyaround 7 kg N animal-1 yr-1, for other cattle is 4 kg N animal-1 yr-1, for market and breeding swine is around 0.8
and 5.5 kg N animal-1 yr-1, respectively. For deep bedding systems, the amount of N in litter is approximately
double these amounts (Webb 2001; Döhler et al. 2002).
As regards NbeddingMS a cross check with the categories "3.C.4 - Direct N2O emissions from managed soils” (FCR volume 11, chapter 11, section 11.2.1.3), "3.C.1 - Biomass burning" (volume 4, chapter 5, section 5.2.4 Non-CO2
greenhouse gas emissions from biomass burning) and "4.C.2 - Open burning of waste" (volume 5, chapter 5,
section 5.3.2 Amount of waste open-burned) relative to the amount of agricultural residues that is removed forother purposes (i.e. bedding) other than the amount of agricultural residues returned to soils or burnt should be
done. This is important to eliminate the possibility of double counting.
nitrogen content of co-digestates should be consistent in quantity and definition with the co-digests defined
information in guidance on the use of co-digestates in “Energy” found in Volume 2, Chapter 2, Section 2.3.3.4
and “Waste”, Volume 5, Chapters 2 and 3, Sections 2.3.2 and 3.2.
Table 10.22 presents default values for nitrogen loss due to volatilisation of NH3 and NOx and N leaching and runoff of nitrogen from manure management.
Table 10.23 presents default values for total losses of N2 from manure management systems relative to emissionsof N2O. This ratio is used in combination with Equation 10.34b to calculate default N2 emission factors. Thesedefault values include losses that occur from the point of excretion, including animal housing losses, manurestorage losses, and losses from leaching and runoff at the manure storage system where applicable.
Countries may wish to develop an alternative approach for better consideration of national circumstances and to
reduce the 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 consideration of
local practices, such as type of bedding material used. For Tier 2 or Tier 3 approaches it is good practice to accountfor the TAN fraction in total manure N along the different stages of manure management. Additional details areavailable in the EMEP/EEA air pollutant emission inventory Guidebook 2016 (EEA 2016, Chapter 3B and 3.D
and Annex A1.4).

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TABLE 10.22 (UPDATED)DEFAULT VALUES FOR NITROGEN LOSS FRACTIONS DUE TO VOLATILISATION OF NH3 AND NOX AND LEACHING OF NITROGEN FROM MANURE MANAGEMENT
Applicable
SystemVariation

SystemSwine
1

FracGas_MS

Dairy Cow
2,5

Frac

leach_MS

FracGas_MS

Poultry2,5

Fracleach_MS

FracGas_MS

Other Cattle
2,5

Frac

leach_MS

FracGas_MS

Other animals
2,5

Frac

leach_MS

FracGas_MS

2,5

Frac

leach_MS

0.40(0.25 – 0.75)

00.35(0.20 – 0.80)

00.40(0.25 – 0.75)

00.35(0.20 – 0.80)

00.35(0.20 – 0.80)

0With natural
crust cover

0.30(0.09 – 0.36)

00.30(0.09 – 0.36)

0no data

00.30(0.09 – 0.36)

0

0.09

0

Without natural
crust cover

0.48(0.15 – 0.60)

00.48(0.15 – 0.60)

00.40(0.25 – 0.75)

00.48(0.15 – 0.60

0

0.15

0

With cover

0.10(0.03 – 0.12)

00.10(0.03 – 0.12)

00.08(0.05-0.15)

0

0.10(0.03 – 0.12)

0

0.03

0

Pit storage below animal
confinements

0.25(0.15 – 0.30)

00.28(0.10 – 0.40)

00.28(0.10 – 0.40)

00.25(0.15 – 0.30)

00.25(0.15 – 0.30)

0Daily spread

0.07(0.05 – 0.60)

00.07(0.05 – 0.60)

00.07(0.05 – 0.60)

00.07(0.05 – 0.60)

00.07(0.05 – 0.60)

0Covered/comp
acted

0.22
(0.04 – 0.26)

00.14(0.02 – 0.17)

0

0.20(0.04 – 0.24)

00.22(0.03 – 0.26)

0

0.05(0 – 0.07)

0Bulking agent
addition

0.58(0.11 – 0.70)

0.02

0.38
(0.06 – 0.46)

0.02

0.54
(0.10 – 0.65)

0.02

0.58
(0.08 – 0.70)

0.02

0.15
(0.06 – 0.18)

0.02

Additives

0.17(0.03 – 0.21)

0.02

0.11
(0.01 – 0.14)

0.02

0.16
(0.03 – 0.20)

0.02

0.17
(0.02 – 0.21)

0.02

0.04
(0.01 – 0.05)

0.02

0.45
(0.10 – 0.65)

0.02

0.30
(0.10 – 0.40)

0.02

0.40
(0.12 – 0.60)

0.02

0.45
(0.10 – 0.65)

0.02

0.12
(0.05 – 0.20)

0.02

4Dry lot

0.45(0.10 – 0.65)

0.035(0 – 0.07)

0.30(0.20 – 0.50)

0.035(0 – 0.07)

NA

NA

0.30
(0.20 – 0.50)

0.035(0 – 0.07)

0.30(0.20 – 0.50)

0.035

3Anaerobic digester

0.05 – 0.50

00.05 – 0.50

00.05 – 0.50

00.05 – 0.50

00.05 – 0.50

0Uncovered anaerobic lagoon

Liquid/Slurry

7Solid storage-

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TABLE 10.22 (UPDATED) (CONTINUED)DEFAULT VALUES FOR NITROGEN LOSS FRACTIONS DUE TO VOLATILISATION OF NH3 AND NOX AND LEACHING OF NITROGEN FROM MANURE MANAGEMENT
SystemApplicable System
Variation

Swine
1

FracGas_MS

Dairy Cow
2,5

Frac

FracGas_MS

leach_MS

Poultry2,5

Fracleach_MS

FracGas_MS

Burned for fuel or as waste

7Composting

Poultry
manure3Aerobictreatment

Fracleach_MS

FracGas_MS

Other animals
2,5

Frac

leach_MS

FracGas_MS

2,5

Frac

leach_MS

NA
0.40(0.10 – 0.60)

0.035

0.25
(0.10 – 0.30)

0.035

In-Vessel

0.60(0.12-0.65)

00.45(0.07-0.54)

00.60(0.12-0.65)

Static Pile

0.65
(0.14-0.70)

0.06

0.50
(0.07-0.60)

0.06

Intensive Windrow

0.65(0.14-0.70)

0.06

0.50
(0.07-0.60)

Passive Windrow

0.60(0.12-0.65)

0.04

0.45
(0.07-0.54)

Cattle and swine deep bedding

Other Cattle
2,5

0.25
(0.10 – 0.30)

0.035

0

0.60
(0.12-0.65)

00.18(0.04-0.21)

00.65(0.14-0.70)

0.06

0.65
(0.14-0.70)

0.06

0.20
(0.05-0.24)

0.06

0.06

0.65
(0.14-0.70)

0.06

0.65
(0.14-0.70)

0.06

0.20
(0.05-0.24)

0.06

0.04

0.60
(0.12-0.65)

0.04

0.60
(0.12-0.65)

0.04

0.18
(0.04-0.21)

0.04

NA

NA

with litter

NA

0.40
(0.10 – 0.60)

0NA

without litter

NA

0.48
(0.15 – 0.60)

0NANatural aeration
systems

no data60

no data60

no data60

no data60

no data60Forced aeration
systems

0.85(0.27 – 1)

0

0.85
(0.27 – 1)

0

no data600.85(0.27 – 1)

0

0.27

0

Source: The values are mainly from 2006 IPCC Guidelines but other sources and analyses are discussed in Annex B.7. Values in italics are not derived specifically from literature but are taken from the most likely
surrogate among the existing values and are for that reason prone to greater uncertainty.
1
N loss due to volatilisation of NH3+NOx fraction of total N excreted.
2
N loss due to leaching, fraction of total N excreted.
3
Nitrogen losses from digestate storage strongly depend on the digestate composition and on the storage cover. Digestate with a low dry matter content and no cover can loose up to 0.5 of nitrogen. The lower range of
0.05 losses is valid for digestate with a high dry matter content and a cover. The ranges indicated also apply to co-digestates. It is advised to use the liquid slurry without cover for uncovered digestate.
4
Uncertainty range is 0 to 0.07. Leaching values are dependant on annual rainfall. Country-specific data should be developed if leaching is observed to be a significant source based on default values and in humid
climates should use the upper bound.
5
Leaching is only included in the case of uncovered manure without confinement of runoff in which N is lost to the environment and therefore lost from the overall reactive N balance.
6
No data indicates that no literature values were found, nor was there adequate certainty in providing a surrogate value. Country specific values should be used, or a surrogate should be selected from the table andjustified based on consideration of factors controlling rates of volatilisation in the management system.
7

Sources and assumptions to calculate NH3 volatilization and N leaching/run-off for Solid storage and composting (static pile and passive windrows) systems are detailed in Annex 10B.7.

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TABLE 10.23 (NEW)DEFAULT VALUE FOR MOLECULAR NITROGEN (N2) LOSS FROM MANURE MANAGEMENT
FactorRN2(N2O)
1

Unit

Value

Range

kg N2-N (kg N2O-N)-1

31

1-10

Webb & Misselbrook (2004)

10.5.5 Uncertainty assessment
No refinement.

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. Additional N input from organic residues and/or energy crops used for co-digestion in biogas
plants must also be considered. Countries are encouraged to use manure management system definitions that areconsistent 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, the other two activity data sets required for this source category (i.e., N excretion rates andmanure management system usage data), as well as the manure management emission factors, will be kept constantfor the entire time series. However, in some cases, there may be reasons to modify these values over time. For
example, farmers may alter livestock feeding practices which could affect nitrogen excretion rates. A particularsystem of manure management may change due to operational practices or new technologies such that a revisedemission factor is warranted. These changes in practices may be due to the implementation of explicit greenhousegas mitigation measures, or may be due to changing agricultural practices without regard to greenhouse gases.
Regardless of the driver 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:
A ct iv it y data ch e ck
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 and consistent with the
expected trend. Inventory agencies should document data collection methods, identify potential areas of bias, andevaluate 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, and technicalmodifications to system configuration and performance, should be captured in the system modelling for theaffected 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 toreduce manure runoff into water bodies may cause a change in management practices, and thus affect the N
distribution for a particular livestock category. Consistency should be maintained between the inventory andongoing changes in agricultural practices.

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If using country-specific data for Nex(T) and MS(T,S), the inventory agency should compare these values to the
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 feed intake data
as determined through animal nutrition analyses.
R ev iew of em is sio n fa cto rs
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 andnote differences. The development of country-specific emission factors should be explained and documented, andthe results peer-reviewed by independent experts.
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 theinventory 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 these systems worldwide,
any new results can improve the understanding of these emissions and possibly their prediction.
Ext er na l rev iew
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, theirknowledge 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 if they 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 to allow 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. When countryspecific emission factors, fractions of N losses, N excretion rates, or manure management system usage data havebeen used, the derivation of or references for these data should be clearly documented and reported along with theinventory 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 under the
IPCC source category N2O emissions from managed soils because this manure is deposited directly on soils
by the livestock.
• Emissions 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 for burning
it must be reported under N2O emissions from pasture, range, and paddock animal if deposited by grazing
animals, or under manure management if collected in housed systems.
Con s is te ncy of n it rog en f lows :As discussed in Section 10.5.4, most of the manure excreted by livestock is finally applied to land or deposited toland by grazing animals, causing direct and indirect N2O emissions from managed soils. On its way from theanimal to uptake by crops or the release of N2O, losses of nitrogen happen at all stages and in different forms. Withanaerobic digestion, additional N might enter the system through co-digestates (e.g. organic residues, energy
crops). The equations given in Chapters 10 and 11 follow a nitrogen balance approach, but are not capturing all
effects on direct and indirect N2O emissions that might occur as a consequence of ‘upstream’ changes of nitrogenflow, such as manure covers, changes in animal feeding, or nitrogen application technique, some of which are
discussed in Section 10.5.4. It is also important to consider total N2O emissions (see Equation 10.A4-1) when
making a key source assessment.
The inventory agency should consult with experts to make sure that any potential effects on N2O emissions arereflected in the total N2O emission estimates. Annex 10A.5 lists a set of equations derived from relevant equationsin Chapter 10 and 11, allowing the calculation of all direct and indirect N2O emissions per livestock
species/category. These equations can help identifying emissions estimates that might become inaccurate when

10.1002019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

national methodologies for upstream flows are used. For example, Equation 10A.13-10A.16 and Equations 11.211.4 show that direct N2O emissions from soils depend on the amount of manure N available for application, notconsidering any NH3 losses that might change the amount of N available for N2O formation. So any application
technique that reduces or increases losses of NH3, modifies the ratio of inorganic to organic N and increases ordecreases the availability of N that can be transformed to N2O must be carefully evaluated (see also Chapter 11,
Sections 11.2.1.1 and 11.2.2.1). In this case, methodologies may want to consider, a correction factor that is
consistent with the national method for NH3 emissions and takes into account the forms of nitrogen that are stored,transferred and lost during these processes.
An illustration of N flows through animal and crop production systems is given in Figure 10.5. The figure follows
the flow of nitrogen, starting from excretion of nitrogen by animals through livestock and crop production systems
down to direct or indirect emissions of N2O. For each flow shown in Figure 10.5, reference is made to therespective equation in Chapter 10 Emissions from livestock and manure management and Chapter 11 N2Oemissions from managed soils, and CO2 emissions from lime and urea application. Losses to the environment are
shown with broken arrows and indicate the emission factor or loss fraction to be used. Nitrogen input from bedding
material and co-digestates enter the system and become part of the N available for application or for other uses.
Symbols are defined under the equations in Chapter 10 and 11 and in Annex 10A.5 of Chapter 10. In this Figure
all flows denoted with N are averaged annual N flows per head of livestock species/category [kg N animal-1 yr-1]
or annual N input via co-digestates [kg N yr-1]; symbols denoted with Frac are fractions in [kg N (kg N)-1]; symbols
denoted with EF are N2O emission factors in [kg N2O –N (kg N)-1]. X: different EF3 are used for cattle, pig andpoultry (X=CPP) and for sheep and other animals (X=SO). Y: different EF1 are used for flooded rice fields (Y=FR)and for other fields (no index Y used).
Broken arrows indicate flows that are split into an emission pathway and a flow of N in the agricultural system.
Note that for N deposited by grazing animals or N applied to managed soils, the flow of N is a sequence ofprocesses with first volatilization of NH3+NOx and only thereafter emissions of N2O and N leaching. This is notreflected in the equations proposed for Tier 1 methodology.

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10.101Volume 4: Agriculture, Forestry and Other Land Use

Figure 10.5 (New)Processes leading to the emission of gaseous N species from manure

10.5.7 Use of worksheets
No refinement.

10.1022019 Refinement to the 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, volatile solids andnitrogen excretion and retention fractions for
Cattle and Buffalo

This annex presents the data used to develop the default emission factors for methane emissions from EntericFermentation and Manure Management, Volatile solid and nitrogen excretion rates, and nitrogen retention fraction.
The Tier 2 method was implemented with these data to estimate the default Tier 1 emission factors and rates for
cattle and buffalo.
This annex also presents the data used to develop the volatile solid estimates used for methane emissions frommanure management methane and for nitrogen excretion rates for cattle and buffaloes. The Tier 2 method was
implemented with these data.
The literature source for these values are presented in Annex 10B.1 and spreadsheets with raw data for cattle andbuffalo compiled for this refinement is available as supplemental material.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.103Volume 4: Agriculture, Forestry and Other Land Use

Weight, kg

Weight gain, kg/day

Feeding situation

Milk yield1, kg/day

Fat content of milk, %Protein content of milk, %Work, hrs/day% Pregnant

Digestibility of Feed, %CP in diet, %CH4 conversion, %2

Day weighted population
mix, %

Enteric fermentation EF,

kg CH4/head/year

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

TABLE 10A.1 (NEW)DATA FOR ESTIMATING TIER 1 AND TIER 1A ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID EXCRETION AND N EXCRETION RATES, AND N RETENTION FRACTION RATES FORDAIRY CATTLE

North America

6500Stall Fed

28.0

3.7

3.2

0

90

71

16.7

5.86

100

138

9.2

0.59

0.27

Western Europe

600

0Stall Fed

20.3

4.2

3.2

0

90

71

16.1

6.3

100

126

8.4

0.54

0.24

Eastern Europe

550

0Stall Fed

10.9

3.9

3.2

0

85

70

15.1

6.5

100

93

6.7

0.42

0.19

Oceania3

488

0Pasture/Range

12.1

4.8

3.7

0

92

77

22.3

6.5

100

93

6.0

0.72

0.17

Latin America

508

0Pasture/Range

5.6

4.0

3.2

0

70

65

12.7

6.5

100

87

7.9

0.39

0.12

High productivity systems

520

0

Pasture/Range

9.3

4.0

3.1

0

72

65

17.0

6.5

38

103

9.0

0.60

0.13

Low productivity systems

500

0

Pasture/Range

3.4

4.0

3.2

0

68

65

10.0

6.5

62

78

7.1

0.28

0.11

Asia

386

0

Stall Fed

8.9

3.9

3.2

0

70

66

13.5

6.5

100

78

9.0

0.44

0.20

High productivity systems

485

0

Stall Fed

13.8

4.1

3.1

0

80

70

16.5

6.3

24

96

8.1

0.55

0.20

Low productivity systems

355

0

Stall Fed

7.3

3.9

3.2

0

67

65

12.6

6.5

76

71

9.2

0.41

0.20

Africa260

0Stall Fed5

3.5

4.3

3.6

0

54

51

8.7

6.5

100

76

18.2

0.44

0.15

High productivity systems

250

0

Stall Fed

5.8

4.3

3.6

0

57

50

7.8

6.5

49

86

21.7

0.41

0.24

Low productivity systems

270

0

Pasture/Range

1.2

4.3

3.6

0

52

51

9.6

6.5

51

66

15.2

0.45

0.05

Middle East

349

0

Stall Fed5

5.9

4.1

3.5

0

52

62

13.6

6.5

100

76

10.7

0.50

0.16

High productivity systems

510

0

Stall Fed

10.6

3.5

3.2

0

55

65

15.8

6.5

33

94

8.4

0.49

0.18

Low productivity systems

270

0

Pasture/Range

3.6

4.5

3.7

0

50

60

12.5

6.5

67

62

11.8

0.51

0.13

Regions7

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Chapter 10: Emissions from Livestock and Manure Management

Weight, kg

Weight gain, kg/day

Feedingi situation

Milk yield1, kg/day

Fat content of milk, %Protein content of milk, %Work, hrs/day% Pregnant

Digestibility of Feed, %CP in diet, %CH4 conversion, %2

Day weighted population
mix, %

Enteric fermentation EF,
kg CH4/head/year

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

TABLE 10A.1 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1 AND TIER 1A ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID EXCRETION AND N EXCRETION RATES, AND N RETENTION FRACTION RATES FORDAIRY CATTLE

Indian subcontinent

285

0Pasture/Range5

5.2

4.2

3.7

0

42

57

14.3

6.5

100

73

14.1

0.65

0.14

High productivity systems

350

0

Stall Fed

7.1

4.0

3.6

0

50

65

15.5

6.5

23

70

9.1

0.51

0.18

Low productivity systems

265

0

Pasture/Range

4.6

4.2

3.7

0

40

55

14.0

6.5

77

74

16.1

0.70

0.13

Regions7

1

The value represent milk yield in kg per day during the whole year.

2

Ym values are consist with those reported in Table 10.12.

3

All data are weighted values, representative of Australia and New Zealand. For Pacific Island nations, may refer to Asia values.

Data of Latin America, Asia, Africa, Middle East and Indian subcontinent were estimated as weighted average by taken into account parameter values related to low- and high-production systems and livestock population
structure of low and high productivity systems.

4

5

As Feeding Situation corresponding to high productivity systems is defined as Stall Fed, but for low productivity systems as Pasture/Range, a weigted activitiy coefficient was applied to estimate Net energy for activity.

6

Ym is a weighted annual value using the high productivity value of 5.7 from Table 10.12 for the lactating period of 305 days and the value of 6.3 for the dry period (60 days).

7

Scientific articles and reports consulted to derive peformance parameters of dairy and non-dairy cattle are listed in Annex 10B.1.

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10.105Volume 4: Agriculture, Forestry and Other Land Use

CP in diet, %CH4 Conversion, %2

Day weighted population
mix, %

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kgN retained/animal/day)(kg N intake/animal/day)-

98

7.7

0.35

0.07

Pasture/Range

62

12.0

7.0

2

98

5.4

0.27

0.00

1.0

Pasture/Range

95

16.0

0.0

16

0

1.9

0.35

0.42

215

1.0

Pasture/Range

65

13.0

6.3

8

59

12.8

0.63

0.16

Growing heifers/steers

300

0.9

Pasture/Range

62

12.0

6.3

17

67

11.3

0.48

0.14

Replacement/growing

400

0.5

Pasture/Range

62

12.0

7.0

11

73

8.3

0.39

0.07

Feedlot cattle

500

1.4

Stall Fed

75

14.0

3.0

11

37

5.4

0.39

0.13

Pasture/Range

60

14.7

7.0

22

81

6.5

0.38

0.00

Pasture/Range

3.0

4.0

3.5

Mature Males

820

Calves on milk

125

Calves on forage

Weight, kg

Weight gain, kg/day

580

1

Digestibility of feed, %35

Mature Females

Regions4,5

Work, hrs/day

7.0

Protein content of milk,%

12.0

Fat content of milk, %62Milk yield, kg/day1

80

Feeding Situation

Pregnant, %TABLE 10A.2 (NEW)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES, AND N RETENTION FRACTION FOR OTHER CATTLE

North America

Western Europe
Mature Males

600

Replacement/growing

400

0.4

Pasture/Range

65

16.5

6.3

55

57

6.7

0.47

0.04

Calves on milk

230

0.3

Stall fed

95

17.1

0.0

15

0

0.9

0.28

0.10

Calves on forage

230

0.3

Pasture/Range

73

16.5

6.3

8

32

5.2

0.45

0.06

70

15.1

6.3

39

67

5.5

0.39

0.08

Eastern Europe
Mature Females

500

Pasture/Range

Mature Males

600

Pasture/Range

65

14.2

6.3

9

65

5.1

0.32

0.00

Replacement/growing

350

0.4

Pasture/Range

65

14.2

6.3

27

53

7.2

0.43

0.05

Calves on forage

180

0.7

Pasture/Range

65

14.3

6.3

25

46

12.1

0.68

0.12

10.106

3.0

4.2

3.7

80

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Digestibility of feed, %CP in diet, %CH4 Conversion, %2

Day weighted population
mix, %

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

45

76

8.5

0.46

0.05

Pasture/ Range

62

14.0

7.0

25

64

6.3

0.36

0.00

Pasture/ Range

61

14.0

7.0

30

43

10.9

0.55

0.10

59

9.5

7.0

36

81

9.0

0.31

0.07

416

Pasture/ Range

1.7

4.8

3.7

Mature Males

467

Young

185

Weight, kg

Weight gain, kg/day

Mature Females

Regions4,5

Work, hrs/day

7.0

Protein content of milk, %14.0

Fat content of milk, %61

Milk yield, kg/day1

81

Feeding Situation

Pregnant, %TABLE 10A.2 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES, AND N RETENTION FRACTION FOR OTHER CATTLE

Oceania3

0.41

Latin America
Mature Females

435

Pasture/Range

2.0

4.3

3.5

63

Mature Males

582

Pasture/Range

59

9.8

7.0

2

81

6.8

0.26

0.00

Growing heifers/steers

240

0.35

Pasture/Range

61

9.8

7.0

22

47

9.3

0.33

0.11

Replacement/growing

302

0.34

Pasture/Range

60

9.6

7.0

18

57

9.0

0.32

0.08

Calves on milk

66

0.35

Pasture/Range

95

9.5

0.0

10

0

1.8

0.16

0.50

Calves on forage

160

0.35

Pasture/Range

61

10.0

7.0

10

39

11.5

0.40

0.13

Feedlot cattle

460

0.90

Stall Fed

74

14.0

4.0

1

39

4.8

0.35

0.10

50

61

10.6

7.0

27

65

8.0

0.33

0.06

65

59

10.0

7.0

9

54

10.0

0.36

0.06

57

10.1

7.0

15

72

7.3

0.27

0.00

57

10.0

7.0

6

68

8.1

0.30

0.00

Asia
Mature Females

376

Stall Fed

1.5

4.7

3.3

Mature Females - grazing

305

Pasture/Range

1.4

4.7

3.3

Mature Males

501

Stall Fed

Mature Males - grazing

430

Pasture/Range

Growing/Replacement

207

0.28

Pasture/Range

61

10.5

7.0

25

44

10.1

0.39

0.07

Calves on forage6

90

0.36

Pasture/Range

62

10.7

6.3

18

26

15.0

0.56

0.16

6

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1.1

1.1

10.107Volume 4: Agriculture, Forestry and Other Land Use

4.1

3.6

Mature Males

Weight, kg

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

1.2

kg Nex (1000 kg animalmass-1) day-1

Large Areas

kg VS (1000 kg animalmass-1) day-1

275

Enteric fermentation EF,
CH4 kg/head/yr

Mature Females - grazing

Day weighted population
mix, %

3.5

CH4 Conversion, %2

4.0

CP in diet, %2.4

Digestibility of feed, %Protein content of milk, %Pasture/Range

Pregnant, %Fat content of milk, %356

Work, hrs/day

Milk yield, kg/day1

Mature Females

Regions4,5

Weight gain, kg/day

Feeding Situation

TABLE 10A.2 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES, AND N RETENTION FRACTION FOR OTHER CATTLE

0.557

62

60

11.3

7.0

17

74

9.9

0.41

0.08

54

58

10.0

7.0

11

67

12.1

0.43

0.05

58

11.2

7.0

2

79

7.3

0.31

0.00

58

10.0

7.0

4

46

7.8

0.29

0.00

Africa540

Pasture/Range

8340

Stall Fed

Bulls - Grazing

340

Large Areas

58

10.0

7.0

8

67

9.6

0.36

0.00

Growing/Replacement

204

0.24

Pasture/Range

59

10.4

7.0

42

46

11.0

0.41

0.06

Calves on forage

82

0.33

Pasture/Range

59

10.3

7.0

18

31

18.9

0.65

0.14

61

12.5

7.0

27

71

8.8

0.42

0.07

59

12.9

7.0

9

75

7.2

0.35

0.00

Draft Bullocks

1.1

Middle East
Mature Females

372

Pasture/Range

2.4

3.7

3.2

51

Mature Males

519

Pasture/Range

Replacement/growing

250

0.33

Pasture/Range

58

12.7

7.0

42

57

11.5

0.52

0.06

Calves on forage

115

0.51

Pasture/Range

58

12.8

7.0

23

46

19.8

0.85

0.12

55

10.2

7.0

22

62

13.0

0.44

0.07

57

11.4

7.0

3

53

8.7

0.37

0.00

55

10.0

7.0

43

47

8.6

0.31

0.00

0.55

Indian subcontinent
Mature Females

253

Pasture/Range

Mature Males

309

Pasture/Range

290

Stall Fed

Draft bullocks

8

1.7

4.6

3.2

40
1.7

Replacement/growing

152

0.20

Pasture/Range

57

10.9

7.0

16

40

13.4

0.51

0.06

Calves on forage

72

0.26

Pasture/Range

57

11.2

7.0

16

29

20.2

0.75

0.11

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.2 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES, AND N RETENTION FRACTION FOR OTHER CATTLE
1

The value represent milk yield in kg per day during the whole year.

2

Ym values are consist with those reported in Table 10.12.

3

All data are weighted values, representative of Australia and New Zealand. For Pacific Island nations, may refer to Asia values.

Data of Latin America, Asia, Africa, Middle East and Indian subcontinent were estimated as weighted average by taken into account parameter values related to low production systems and high production systems
and livestock population structure of low and high productivity systems. The values were estimated based on the data reported in Table 10A.3.
4

5

Scientific articles and reports consulted to derive peformance parameters of dairy and non-dairy cattle are presented in Annex 10B.1.

As Feeding Situation corresponding to high productivity systems is defined as Stall Fed, but for low productivity systems as Pasture/Range, a weigted activitiy coefficient was applied to estimate Net energy for
activity.
6

7

It was assumed that the whole population of stall fed mature females is not used for draught on the regional scale in Africa.

8

Draft bullocks were all assumed to be castrates and CFi values were adjusted accordingly.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.109Volume 4: Agriculture, Forestry and Other Land Use

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

0.28

0.00

9.2

0.40

0.13

11.0

7.0

16

70

9.3

0.38

0.08

95

9.5

0.0

12

0

1.9

0.18

0.49

Pasture/Range

63

12.3

7.0

12

44

10.8

0.50

0.11

Stall Fed

74

14.0

4.0

4

39

4.8

0.35

0.10

Day weighted population
mix, %3

6.2

45

CH4 Conversion, %2

79

22

CP in diet, %1

6.3

Digestibility of feed, %7.0

Pregnant, %0.07

Work, hrs/day

0.35

Protein content of milk, %8.4

Fat content of milk, %89

Milk yield, kg/day1

33

Feeding Situation

Weight, kg

Regions4

Weight gain, kg/day

TABLE 10A.3 (NEW)DATA FOR ESTIMATING TIER 1A ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES AND N RETENTION FRACTION FOR OTHER CATTLE

Latin America
High productivity systems

233

Mature Females

490

Mature Males

595

Growing heifers/steers

240

0.50

Replacement/growing

350

Calves on milk

Pasture/Range

2.7

4.2

3.2

78

61

11.2

7.0

Pasture/Range

61

11.2

Pasture/Range

63

11.8

0.50

Pasture/Range

61

82

0.50

Pasture/Range

Calves on forage

200

0.50

Feedlot cattle

460

0.90

Low productivity systems

77

Mature Females

420

Pasture/Range

Mature Males

580

Growing heifers/steers

240

Replacement/growing

59

9.1

7.0

37

79

9.2

0.30

0.07

Pasture/Range

59

9.6

7.0

2

81

6.8

0.25

0.00

0.30

Pasture/Range

60

9.2

7.0

22

47

9.3

0.30

0.10

290

0.30

Pasture/Range

60

9.3

7.0

19

54

8.9

0.30

0.08

Calves on milk

60

0.30

Pasture/Range

95

9.5

0.0

10

0

1.7

0.16

0.50

Calves on forage

145

0.30

Pasture/Range

60

9.2

7.0

10

35

11.7

0.37

0.14

10.110

1.8

4.3

3.2

59

3

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Chapter 10: Emissions from Livestock and Manure Management

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

Day weighted population
mix, %3

CH4 Conversion, %2

CP in diet, %Digestibility of feed, %Pregnant, %Work, hrs/day

Protein content of milk, %Fat content of milk, %Milk yield, kg/day1

Feeding Situation

Weight, kg

Regions4

Weight gain, kg/day

TABLE 10A.3 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1A ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES AND N RETENTION FRACTION FOR OTHER CATTLE

55

5.3

0.30

0.07

Asia
High productivity systems
Mature Females

173
450

Stall Fed

1.9

4.7

3.3

80

68

12.5

6.3

41

Mature Males

550

Stall Fed

68

12.5

6.3

2

49

3.9

0.23

0.00

Growing/Replacement

285

0.40

Stall Fed

68

12.5

6.3

27

41

6.3

0.35

0.07

Calves on forage

125

0.50

Stall Fed

68

12.5

6.3

30

28

9.6

0.47

0.18

Low productivity systems

833

Mature Females-Farming

350

Stall Fed

1.4

4.7

3.3

Mature Females-Grazing

305

Pasture/Range

1.4

4.7

3.3

Mature Males-Farming

500

Stall Fed

Mature Males-Grazing

430

Growing/Replacement

190

Calves on forage

75

1.1

40

59

10.0

7.0

25

64

9.0

0.33

0.06

65

59

10.0

7.0

11

63

10.0

0.36

0.06

57

10.0

7.0

18

73

7.4

0.27

0.00

Pasture/Range

57

10.0

7.0

8

68

8.1

0.30

0.00

0.25

Pasture/Range

59

10.0

7.0

25

44

11.3

0.41

0.07

0.30

Pasture/Range

59

10.0

7.0

15

28

18.5

0.61

0.14

1.1

AfricaHigh productivity systems

303

Mature Females390

Pasture/Range

Mature Males

540

Growing/Replacement

250

Calves on forage

105

2.9

3.9

3.5

61

11.8

7.0

39

76

9.1

0.40

0.09

Pasture/Range

58

11.2

7.0

6

79

7.3

0.31

0.00

0.34

Pasture/Range

60

11.2

7.0

41

50

9.6

0.39

0.09

0.43

Pasture/Range

61

11.4

7.0

14

36

16.1

0.64

0.14

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

65

10.111Volume 4: Agriculture, Forestry and Other Land Use

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

7

60

11.0

0.39

0.06

54

58

10.0

7.0

15

67

12.1

0.43

0.05

Low productivity systems

Day weighted population
mix, %3

7.0

CH4 Conversion, %2

10.0

CP in diet, %58

Digestibility of feed, %54

Pregnant, %Work, hrs/day

Protein content of milk, %Fat content of milk, %Milk yield, kg/day1

Feeding Situation

Weight, kg

Regions4

Weight gain, kg/day

TABLE 10A.3 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1A ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES AND N RETENTION FRACTION FOR OTHER CATTLE

703

Mature Females

275

Pasture/Range

1.2

4.1

3.6

Mature Females-Grazing

275

Large Areas

1.2

4.1

3.6

Draft Bullocks5

340

Stall Fed

58

10.0

7.0

5

53

7.8

0.29

0.00

Bulls - Grazing

340

Large Areas

58

10.0

7.0

11

65

9.6

0.36

0.00

Growing/Replacement

185

0.20

Pasture/Range

58

10.0

7.0

42

42

11.2

0.40

0.06

Calves on forage

75

0.30

Pasture/Range

58

10.0

7.0

20

30

19.9

0.65

0.13

0.55

1.1

Middle EastHigh productivity systems

333

Mature Females

500

Pasture/Range

Mature Males

600

Replacement/growing

350

Calves on forage

165

2.8

3.5

3.3

55

65

14.0

6.3

20

72

6.8

0.39

0.07

Pasture/Range

63

14.0

6.3

12

68

5.6

0.33

0.00

0.50

Pasture/Range

63

14.0

6.3

42

61

8.6

0.48

0.06

0.70

Pasture/Range

63

14.0

6.3

26

47

14.2

0.74

0.13

30

67

9.7

0.43

0.08

Low productivity systems

673

Mature Females

330

Mature Males

450

Replacement/growing

200

0.25

Pasture/Range

Calves on forage

85

0.40

Pasture/Range

10.112

Pasture/Range
Pasture/Range

2.3

3.8

3.2

50
0.55

60

12.0

7.0

55

12.0

7.0

7

79

9.3

0.40

0.00

55

12.0

7.0

42

50

13.4

0.54

0.05

55

12.0

7.0

21

40

25.3

0.96

0.11

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day-1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1

13.0

7.0

11

52

7.5

0.39

0.00

0.33

Pasture/Range

60

13.0

7.0

35

45

12.0

0.58

0.07

0.33

Pasture/Range

60

13.0

7.0

45

31

16.7

0.77

0.11

Day weighted population
mix, %360

CH4 Conversion, %2

Pasture/Range

CP in diet, %0.09

Digestibility of feed, %0.48

Pregnant, %10.2

Work, hrs/day

64

Protein content of milk, %9

Fat content of milk, %7.0

Milk yield, kg/day1

13.0

Feeding Situation

60

Weight gain, kg/day

Weight, kg

Regions4

Enteric fermentation EF,
CH4 kg/head/yr

TABLE 10A.3 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1A ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES AND N RETENTION FRACTION FOR OTHER CATTLE

Indian subcontinentHigh productivity systems

143

Mature Females

300

Pasture/Range

Mature Males

330

Replacement/growing

180

Calves on forage

90

2.5

4.0

3.6

40

Low productivity systems

863

Mature Females

250

Pasture/Range

Mature Males

290

Pasture/Range

290

Stall Fed

Draft bullocks

5

1.7

4.6

3.7

40

1.7

55

10.0

7.0

24

62

13.2

0.43

0.08

55

10.0

7.0

2

54

9.9

0.35

0.00

55

10.0

7.0

50

47

8.6

0.31

0.00

Replacement/growing

140

0.15

Pasture/Range

55

10.0

7.0

13

37

13.9

0.47

0.05

Calves on forage

60

0.22

Pasture/Range

55

10.0

7.0

11

26

23.2

0.73

0.11

1

The value represent milk yield in kg per day during the whole year.

2

Ym values are consist with those reported in Table 10.12.

3

A share of low- and high-productivity animals from the total livestock population of a region.

4

Scientific articles and reports consulted to derive peformance parameters of dairy and non-dairy cattle are presented in Annex 10B.1.

5

Draft bullocks were all assumed to be castrates and CFi values were adjusted accordingly.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.113Volume 4: Agriculture, Forestry and Other Land Use

Digestibility of feed, %CP in diet, %CH4 Conversion, %2

Day weighted population
mix, %

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1% Pregnant

Work, hrs/day

Protein content of milk, %Fat content of milk, %Milk yield, kg/day1

Feeding Situation

Weight, kg

Regions3

Weight gain, kg/day

TABLE 10A.4 (NEW)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES, AND N RETENTION FRACTION RATES FOR BUFFALO

65

14.0

6.3

3

59

4.4

0.27

0.00

65

15.0

6.3

59

81

6.2

0.36

0.15

65

14.0

6.3

25

59

6.6

0.39

0.06

65

14.0

6.3

13

42

11.7

0.64

0.13

71

13.0

6.3

8

61

3.7

0.26

0.00

71

13.0

6.3

62

80

5.8

0.31

0.22

Western Europe
Mature Males

700

Stall Fed

Mature Females

615

Growing/Replacement

420

0.53

Stall Fed

Calves170

0.68

Stall Fed

Stall Fed

3.0

8.0

4.6

87

Eastern Europe
Mature Males

650

Pasture/Paddock

Mature Females

550

Pasture/Paddock

Growing/Replacement

350

0.55

Pasture/Paddock

71

13.0

6.3

14

53

6.0

0.38

0.08

Calves155

0.66

Pasture/Paddock

71

13.0

6.3

16

37

9.4

0.54

0.16

60

11.0

7.0

4

86

6.3

0.28

0.00

60

11.0

7.0

40

106

10.1

0.35

0.21

4.0

7.5

4.3

85

Latin America
Adult Males

650

Pasture/Range

Adult Females

500

Pasture/Range

Growing/Replacement

200

0.40

Pasture/Range

60

11.0

7.0

26

47

10.7

0.42

0.09

Calves90

0.28

Pasture/Range

60

11.0

7.0

30

26

13.9

0.51

0.15

55

10.0

7.0

20

88

9.5

0.34

0.00

55

10.0

7.0

40

99

12.5

0.39

0.12

4.2

7.1

4.3

62

Asia
Mature Males

490

Pasture/Paddock

Mature Females

420

Pasture/Paddock

Growing/Replacement

225

0.26

Pasture/Paddock

55

10.0

7.0

25

56

13.1

0.44

0.05

Calves90

0.32

Pasture/Paddock

55

10.0

7.0

15

37

22.0

0.69

0.11

10.114

1.1
1.6

9.1

5.2

1.1

45

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Digestibility of feed, %CP in diet, %CH4 Conversion, %2

Day weighted population
mix, %

Enteric fermentation EF,
CH4 kg/head/yr

kg VS (1000 kg animalmass-1) day-1

kg Nex (1000 kg animalmass-1) day1

Nretention fraction, (kg N
retained/animal/day) (kg N
intake/animal/day)-1% Pregnant

Work, hrs/day

Protein content of milk, %Fat content of milk, %Milk yield, kg/day1

Feeding Situation

Weight, kg

Regions3

Weight gain, kg/day

TABLE 10A.4 (NEW) (CONTINUED)DATA FOR ESTIMATING TIER 1 ENTERIC FERMENTATION CH4 EMISSION FACTORS, VOLATILE SOLID AND NITROGEN EXCRETION RATES, AND N RETENTION FRACTION RATES FOR BUFFALO

58

10.0

7.0

6

94

8.0

0.30

0.00

AfricaMature Males

590

Pasture/Paddock
Pasture/Paddock

1.37

Mature Females

440

58

10.0

7.0

42

107

12.2

0.35

0.24

Growing/Replacement

300

0.40

Pasture/Paddock

4.3

7.2

3.7

0.55

44

58

10.0

7.0

32

68

11.3

0.40

0.07

Calves115

0.45

Pasture/Paddock

58

10.0

7.0

20

43

18.7

0.61

0.14

60

11.0

7.0

5

96

7.1

0.31

0.00

65

11.0

6.3

52

83

7.5

0.30

0.17

Middle East
Mature Males

650

Pasture/Paddock

1.37

Mature Females

520

Pasture/Paddock

Growing/Replacement

255

0.39

Pasture/Paddock

61

11.0

7.0

22

54

9.9

0.40

0.08

Calves105

0.41

Pasture/Paddock

61

11.0

7.0

21

36

16.0

0.61

0.14

Pasture/Paddock

55

12.0

7.0

1

88

8.4

0.36

0.00

55

12.0

7.0

4

86

8.2

0.35

0.00

55

12.0

7.0

48

127

14.1

0.54

0.09

3.0

7.0

4.2

0.55

65

Indian subcontinent
Breeding males

560

Working males4

560

Mature Females

480

Growing/Replacement

195

0.31

Pasture/Paddock

59

12.0

7.0

21

45

11.2

0.48

0.08

Calves85

0.31

Pasture/Paddock

56

12.0

7.0

26

35

21.2

0.83

0.10

8

Pasture/Paddock
Pasture/Paddock

1.37
4.8

7.3

3.5

1

The value represent milk yield in kg per day during the whole year.

2

Ym values are consist with those reported in Table 10.12.

3

Scientific articles and reports consulted to derive pefromance parameters of buffalo are listed in Annex 10B.1.

4

Draft bullocks were all assumed to be castrates and CFi values were adjusted accordingly.

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

0.55

50

10.115Volume 4: Agriculture, Forestry and Other Land Use

Annex 10A.2

Additional data and information for the
calculation of methane and nitrous oxide fromManure Management

This annex presents the required default data to implement the Tier 1 method for estimation of methane and nitrous
oxide emissions from manure management. Data required in both methods include animal weight data required
for the calculation of average VS and N excretion per animal category as well as AWMS information for regionsin the country and improved definitional data, relating IPCC AWMS and definitions used in the EMEP/EEA air
pollutant emission inventory guidebook. The information is a combination of the consistent data collection for
cattle and buffalo that is compiled in Annex 10A.1 and data compiled by the FAO for use in their modelling system
GLEAM (FAO 2017; MacLeod et al. 2017). More specific information can be found, sometimes at the country
level at http://www.fao.org/gleam/resources/en/. Furthermore, information is supplied on IPCC climate zones.

10.1162019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.5 (NEW)DEFAULT VALUES FOR LIVE WEIGHTS FOR ANIMAL CATEGORIES (KG)
RegionMean

High PS1

Low PS1

Mean

High PS

Low PS

Mean

High PS

Low PS

Mean

High PS

Low PS

Mean

High PS

Low PS

India subcontinent

Oceania

Asia

Eastern Europe

Middle East

Western Europe

AfricaNorth America

Latin America

Dairy cattle2

650

600

550

488

508

520

500

260

250

270

349

510

270

386

485

355

285

350

265

Other cattle2

407

405

389

359

303

329

295

236

302

208

275

362

232

299

310

296

226

167

236

Buffalo2

NA

509

467

NA

Swine3

77

76

77

61

65

81

59

49

72

37

59

70

53

58

69

52

59

68

53

Finishing

61

61

59

41

51

59

47

41

54

33

52

60

48

49

56

44

51

55

48

Breeding

184

190

204

163

143

205

121

100

200

61

118

157

99

122

160

102

121

162

99

Chicken3

1.4

1.4

1.3

1.3

1.1

1.3

0.9

0.9

1

0.8

0.9

1.2

0.7

1.2

1.4

1

1.0

1.2

0.8

Hens >/= 1 yr

1.5

1.9

1.9

2

1.4

1.6

1.3

1.4

1.9

1.1

1.2

1.7

1

1.5

1.9

1.3

1.3

1.5

1.1

Pullets

1.2

1.5

1.3

1.4

0.7

1.3

0.5

0.7

1.4

0.5

0.6

1.2

0.4

0.8

1.5

0.6

0.6

1.3

0.4

Broilers

1.4

1.2

1.1

1.2

0.9

1.2

0.7

0.8

0.8

0.7

0.7

1

0.5

0.8

1

0.7

0.8

1

0.6

Category ofanimal

315

339

381

Turkeys4

6.8

Ducks4

2.7

Sheep3Goats5
Horses4

40
41

40

336

321

31
36

33

24

377

238

Mules and asses4

130

Camels4217

Ostrich5120

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.117Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.5 (NEW) (CONTINUED)DEFAULT VALUES FOR LIVE WEIGHTS FOR ANIMAL CATEGORIES (KG)

120

Reindeer5120

1High PS and Low PS refer to high- and low productivity systems required for Tier 1a methodology.

2

Values are derived from diets used in the calculation of enteric fermentation Tier 1 and Tier 1a emission factors (Table 10A.1 – Table 10A.4).

3

Values are taken from FAO GLEAM databases (FAO 2017). High and low estimates are simplified extracts from the model database.

4

Values are taken from Table 10A-9 of the 2006 IPCC Guidelines.

5

For more information see Table 10.10 and Table 10.19 of the 2019 Refinement.

10.118High PS

Mean

Low PS1High PS1

India sub-continent

Mean

High PS

Asia

Mean

Low PS1

Mean

Deer5

High PS1

Middle EastHigh PS

Mean

AfricaLow PS1High PS1

Mean

Latin America

Oceania

Eastern Europe

Western Europe

Category ofanimalNorth America

Region2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.6 (NEW)ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR CATTLE AND BUFFALOAWMS (%)
AnimalCategory

LagoonLiquid/SlurrySolidstorage

DrylotPasture/Range/
Paddock

Daily
spreadDigesterBurnedfor fuel

Other

North America

26

24

24

no data15

11

no datano datano dataWestern Europe

no data43

29

no data26

2

no datano datano data

Eastern Europe

no data5

74

no data20

1

no datano datano data5

no datano datano data94

1

no datano datano data

East Asia and South-East Asia (Asia)

no data

1

21

29

38

no datano data11

no dataSouth Asia (Indian subcontinent)no datano data

1

49

30

no datano data20

no data

Latin America and the Caribbean

no datano data

5

38

57

no datano datano datano data

Near East (Middle East) and North Africa

no datano data

14

35

46

no datano data5

no data

Sub-Saharan Africat

no datano data

20

29

45

no datano data6

no data

North America

no data

1

43

14

42

no datano datano datano dataWestern Europe

no data22

26

no data48

4no datano datano data

Eastern Europe

no data64

5

no data31

no datano datano datano data

Oceaniano datano datano datano data100

no datano datano datano data

East Asia and South-East Asia (Asia)

no datano data

29

28

36

no datano data7

no dataSouth Asia (Indian subcontinent)no datano data

1

49

30

no datano data20

no data

Latin America and the Caribbean

no datano data3

5

92

no datano datano datano data

Near East (Middle East) and North Africa

no datano data5

46

42

no datano data7

no data

Sub-Saharan Africa

no datano data

15

30

50

no datano data5

no data

Region1

Oceania
Dairy Cattle

non-Dairy
Cattle2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.119Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.6 (NEW) (CONTINUED)
ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR CATTLE AND BUFFALO
AnimalCategory

Buffalo Dairy

Buffalo
non-Dairy

10.120AWMS (%)
LagoonLiquid/SlurrySolidstorage

DrylotPasture/Range/
Paddock

Daily
spreadDigesterBurnedfor fuel

Other

North America

no data

43

40

no data17

no datano datano datano dataWestern Europe

no data

34

63

no data3

no datano datano datano data

Eastern Europe

no data18

68

no data13

1

no datano datano data

East Asia and South-East Asia (Asia)

no datano data

10

58

29

no datano data3

no dataSouth Asia (Indian subcontinent)no datano data

1

41

38

no datano data20

no data

Latin America and the Caribbean

no datano data

2

48

50

no datano datano datano data

Near East (Middle East) and North Africa

no datano data

18

35

46

no datano data1

no data

Eastern Europe (including Russia)

no data

964

no data27

no datano datano datano data

East Asia and South-East Asia (Asia)

no datano data

664

28

no datano data2

no dataSouth Asia (Indian subcontinent)no datano data

1

40

39

no datano data20

no data

Latin America and the Caribbean

no datano data2

593

no datano datano datano data

Near East (Middle East) and North Africa

no datano data

16

12

57

no datano data15

no data

Region2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.7 (NEW)ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR SWINE (%)
AnimalCategory

Productivity
class

LagoonLiquid/SlurrySolidstorage

DrylotPit < 1

Pit > 1Daily
spreadDigester

PastureNorth America

28

31

4

3

no data34

no datano datano dataWestern Europe

6

51

14

no data2

26

1

no datano data

Eastern Europe

5

31

55

1

4

4

no datano datano datano data

24

76

no datano datano datano datano datano datano datano datano datano datano datano data7

no data

RegionRussian Federation
High
Productivity
Growing
Swine

Oceaniano data1

8East Asia and South East Asia

35

21

no data2

35

no data

Indian subcontinent

12

23

13

35

2

no data7

8

no data

Latin America and the Caribbean

11

34

12

41

no datano data2

no datano data54

no datano datano data7

no datano datano datano data

Near East (Middle East) and North Africa

1029

no datano data

7

6

86

1

no data

East Asia and South East Asia

5

27

18

14

14

5

6

5

6

Indian subcontinent

5

30

15

15

15

5

5

5

5

Latin America and the Caribbean

5

30

15

15

15

5

5

5

5

Near East (Middle East) and North Africa

5

30

15

15

15

5

5

5

5

Sub-saharan Africa

5

30

15

15

15

5

5

5

5

Sub-saharan Africa

Low
Productivity

91

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.121Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.7 (NEW) (CONTINUED)
ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR SWINE (%)
AnimalCategory

Productivity
class

LagoonLiquid/SlurrySolidstorage

DrylotPit < 1

Pit > 1Daily
spreadDigester

PastureNorth America

28

31

4

3

no data34

no datano datano dataWestern Europe

6

51

15

no data2

25

1

no datano data

Eastern Europe

5

31

55

1

4

4

no datano datano datano data

24

76

no datano datano datano datano datano data

Oceania91

no data

1

8

no datano datano datano datano data

East Asia and South East Asia

35

21

no data2

35

no datano data7

no data

Indian subcontinent

12

23

14

32

3

no data8

8

no data

Latin America and the Caribbean

11

34

12

41

no datano data2

no datano data

Near East (Middle East) and North Africa

1029

no data54

no datano datano data7

no datano data

7

6

86

1

no datano datano datano data

East Asia and South East Asia

4

28

22

13

13

4

6

4

6

Indian subcontinent

5

30

15

15

15

5

5

5

5

Latin America and the Caribbean

5

30

15

15

15

5

5

5

5

Near East (Middle East) and North Africa

5

30

15

15

15

5

5

5

5

Sub-saharan Africa

5

30

15

15

15

5

5

5

5

RegionRussian Federation
High
Productivity
Breeding
Swine

Sub-saharan Africa

Low
Productivity

10.1222019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.8 (NEW)ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR SHEEP AND GOATSAWMS (%)
AnimalCategory

Sheep Meat

Sheep Dairy

LagoonLiquid/SlurrySolidstorage

DrylotPasture/Range/
Paddock

Daily
spreadDigesterBurnedfor fuel

Other

North America

no datano data54

no data46

no datano datano datano dataWestern Europe

no datano data13

no data87

no datano datano datano data

Eastern Europe

no datano data54

no data46

no datano datano datano data

Near East (Middle East) and North Africa

no datano datano data

50

50

no datano datano datano data

East Asia and South-East Asia

no datano data17

3

80

no datano datano datano data

Oceaniano datano datano datano data100

no datano datano datano dataSouth Asia (Indian subcontinent)no datano data17

3

80

no datano datano datano data

Latin America and the Caribbean

no datano data17

3

80

no datano datano datano data

Sub-Saharan Africa

no datano data17

3

80

no datano datano datano dataWestern Europe

no datano data22

no data78

no datano datano datano data

Eastern Europe

no datano data42

no data58

no datano datano datano data

Near East (Middle East) and North Africa

no datano datano data

50

50

no datano datano datano data

East Asia and South-East Asia

no datano data17

3

80

no datano datano datano dataSouth Asia (Indian subcontinent)no datano data17

3

80

no datano datano datano data

Latin America and the Caribbean

no datano data17

3

80

no datano datano datano data

Sub-Saharan Africa

no datano data17

3

80

no datano datano datano data

Region1

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.123Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.8 (NEW) (CONTINUED)
ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR SHEEP AND GOATSAWMS (%)
AnimalCategory

Goats

10.124

LagoonLiquid/SlurrySolidstorage

DrylotPasture/Range/
Paddock

Daily
spreadDigesterBurnedfor fuel

Other

North America

no datano data50

no data50

no datano datano datano data

Russian Federation

no datano data82

no data18

no datano datano datano dataWestern Europe

no datano data28

no data72

no datano datano datano data

Eastern Europe

no datano data9no data91

no datano datano datano data

Near East (Middle East) and North Africa

no datano datano data

50

50

no datano datano datano data

East Asia and South-East Asia

no datano data50

no data50

no datano datano datano data

Oceaniano datano datano datano data100

no datano datano datano dataSouth Asia (Indian subcontinent)no datano data50

no data50

no datano datano datano data

Latin America and the Caribbean

no datano data17

3

80

no datano datano datano data

Sub-Saharan Africa

no datano data17

3

80

no datano datano datano data

Region1

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.9 (NEW)ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR POULTRY AND OTHER1 ANIMALSAWMS (%)
AnimalCategory

LagoonLiquid/SlurrySolidstorage

DrylotPasture/Range/
Paddock

Pit >1month

Daily
spreadDigester

Other(Poultry manurewith litter)

1

29

70

no datano datano datano datano datano data

Russian Federation

no datano datano datano datano data100

no datano datano dataWestern Europe

no data1

20

21

no data43

1

no data

14

Eastern Europe

no datano datano data47

no data34

no datano data

19

11

7

11

no datano data67

no datano data4

East Asia and South-East Asia

no data4

no datano data1

94

1

no datano data

Oceaniano datano datano datano data

23

77

no datano datano dataSouth Asia (Indian subcontinent)no datano data100

no datano datano datano datano datano data

Latin America and the Caribbean

no data58

42

no datano datano datano datano datano data

Sub-Saharan Africa

no datano datano datano datano data90

no datano data

10

North America

no datano datano datano datano datano datano datano data

100

Russian Federation

no datano datano datano datano datano datano datano data100

Western Europe

no datano datano datano datano datano datano datano data100

Eastern Europe

no datano datano datano datano datano datano datano data

100

Near East (Middle East) and North Africa

no datano datano datano datano datano datano datano data100

East Asia and South-East Asia

no datano datano datano datano datano datano datano data

100

Oceaniano datano datano datano datano datano datano datano data100

South Asia (Indian subcontinent)no datano datano datano datano datano datano datano data100

Latin America and the Caribbean

no datano datano datano datano datano datano datano data

100

Sub-Saharan Africa

no datano datano datano datano datano datano datano data100

Region

North America

ChickenLayer

ChickenBroiler

Near East (Middle East) and North Africa

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10.125Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.9 (NEW) (CONTINUED)
ANIMAL WASTE MANAGEMENT SYSTEM (AWMS) REGIONAL AVERAGES FOR POULTRY AND OTHER1 ANIMALSAWMS (%)
AnimalCategory

Low
productivity

LagoonLiquid/SlurrySolidstorage

DrylotPasture/Range/
Paddock

Pit >1month

Daily
spreadDigester

Other(Poultry manurewith litter)

North America

no datano datano datano data50

no data50

no datano data

Russian Federation

no datano datano datano data50

no data50

no datano dataWestern Europe

no datano datano datano data50

no data50

no datano data

Eastern Europe

no datano datano datano data50

no data50

no datano data

Near East (Middle East) and North Africa

no datano datano datano data50

no data50

no datano data

East Asia and South-East Asia

no datano datano datano data50

no data50

no datano data

Oceaniano datano datano datano data50

no data50

no datano dataSouth Asia (Indian subcontinent)no datano datano datano data50

no data50

no datano data

Latin America and the Caribbean

no datano datano datano data50

no data50

no datano data

Sub-Saharan Africa

no datano datano datano data50

no data50

no datano data

Region1

For Other animal, the IPCC expert group reviewed the national inventory submissions as well as guidance in the 2006 IPCC Guidelines under the UNFCCC and concluded that common distribution of systems used to
manage manure as follows:1

Horses, camelelids, mules and asses, and other grazing animals; should use the data supplied for goats as a proxy;
American bison should use the same manure management fractions as beef cattle in North America;
Deer and reindeer manure deposited at 100% in PRP;
Ostrich (Emu) manure is 80% in PRP and 20 percent managed in solid based systems;
Manure of rabbits and fur-bearing animals is 100% managed in a solid based system.
Hence, countries may apply the same allocation of MMS in the calculation of N2O emissions from manure stored in manure management systems. However, countries are encouraged to develop a country-specific dataset
on MMS used to manage manure generated by these categories of animals.

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Chapter 10: Emissions from Livestock and Manure Management

Information required to determine climate zones according to Chapter 3 ofVolume 4 Current Guideline
Outlined below are the conditions required to determine the climate zone required for the selection of a country’s
MCF, according to the IPCC climate zone determination as defined in Volume 4, Chapter 3, Annex 3A.5, Figure
3A.5.2. Where possible, if countries span multiple climate zones, efforts should be made to disaggregate animal
populations into climate zones. If this is not possible, partys are advised to select the climate zone covering thegreatest surface area of their country or regions of their country for which they have distinct animal populations.
Briefly, all data is drawn from “The Climate Reseach Unit (CRU) or the CGIAR-Consortium for Spatial
Information (CSI) 1985-2015.” Climate zones are differentiated based on the factors of mean annual temperature,elevation, mean annual precipitation and the ratio of mean annual precipitation to precipitation for this 30 year
average annual weather data compilation.
Therefore, as identified in Volume 4, Chapter 3, of these guidelines climate zones are defined:
Tropical Montane: has > 18oC mean annual temperature and at an elevation greater than 1000m;
Tropical Wet: has > 18oC mean annual temperature and mean annual precipitation >2000mm;
Tropical Moist: has > 18oC mean annual temperature and mean annual precipitation >1000mm;
Tropical Dry: has > 18oC mean annual temperature and mean annual precipitation < 1000mm;
Tropical Moist: has > 18oC mean annual temperature and mean annual precipitation >1000mm;
Warm temperate moist: has > 10oC mean annual temperature and a ratio of potential evapotranspiration toprecipitation > 1;
Warm temperate dry: has > 10oC mean annual temperature and a ratio of potential evapotranspiration toprecipitation < 1;
Cool temperate moist: has > 0oC mean annual temperature and a ratio of potential evapotranspiration toprecipitation > 1;
Cool temperate dry: has > 0oC mean annual temperature and a ratio of potential evapotranspiration to precipitation< 1;
Boreal moist: has < 0oC mean annual temperature but some monthly temperatures > 10oC and a ratio of potential
evapotranspiration > 1;
Boreal dry: has < 0oC mean annual temperature but some monthly temperatures > 10oC and a ratio of potential
evapotranspiration to precipitation < 1;
Polar moist: has < 0oC mean annual temperature but all monthly temperatures < 10oC and a ratio of potential
evapotranspiration > 1;
Polar dry: has < 0oC mean annual temperature but all monthly temperatures < 10oC and a ratio of potential
evapotranspiration to precipitation < 1.

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10.127Volume 4: Agriculture, Forestry and Other Land Use

Figure 10A.1 (New)10.128Mapping of IPCC climate zones (taken from Volume 4, Chapter 3, Annex 3A.5)

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

TABLE 10A.10 (NEW)COMPARISON OF MANURE STORAGE TYPE DEFINITIONS USED BY THE IPCC AND BY THE EMEP/EEA AIR POLLUTANTEMISSION INVENTORY GUIDEBOOK 2016 (HTTPS://WWW.EEA.EUROPA.EU/PUBLICATIONS/EMEP-EEA-GUIDEBOOK-2016)
System IPCC

System EMEP / EEA

Definition

Pasture/Range/Paddock (PRP)

Grazing

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

No definition given

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

Heaps

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

No definition given

A paved or unpaved open confinement
area without any significant vegetative
cover. Dry lots do not require the additionof bedding to control moisture. Manure
may be removed periodically and spreadon fields.

Liquid/Slurry

Tanks

Manure is stored as excreted or with someminimal addition of water in either tanks
or earthen ponds outside the livestockbuilding, usually for periods of less than 1
year;
Storage with a low surface area to depth
ratio; normally steel or concrete cylinders

Liquid/Slurry, With natural crust
cover

Crust

Natural or artificial layer on the surface ofslurry which reduces the diffusion ofgasses to the atmosphere

Liquid/Slurry, cover

Cover

Rigid or flexible structure that covers themanure and is impermeable to water andgassesUncovered anaerobic lagoon

Lagoons

Storage with a large surface area to depth
ratio; normally shallow excavations in thesoil

Pit storage below animal
confinements

In-house slurry pit

Mixture of excreta and washing water,
stored within the livestock building,usually below the confined animals

Anaerobic digester

Biogas treatment

Anaerobic fermentation of slurry and/or
solid

Burned for fuelNo definition given

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

Deep bedding

In-house deep litter

Mixture of excreta and bedding,
accumulated on the floor of the livestockbuilding

Daily spread

Solid storage

Dry lot2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.129Volume 4: Agriculture, Forestry and Other Land Use

TABLE 10A.10 (NEW) (CONTINUED)COMPARISON OF MANURE STORAGE TYPE DEFINITIONS USED BY THE IPCC AND BY THE EMEP/EEA AIR POLLUTANTEMISSION INVENTORY GUIDEBOOK 2016 (HTTPS://WWW.EEA.EUROPA.EU/PUBLICATIONS/EMEP-EEA-GUIDEBOOK-2016)
System IPCC

Composting

System EMEP / EEA

Definition

In-vessel

Forced-aeration composting

Aerobic decomposition of manure withforced ventilation

Static pile

Composting, passive windrow

Aerobic decomposition of manure without
forced ventilation

Intensive
windrow

No EMEP equivalent

Composting Passive
windrow

No EMEP equivalent

Laying hens – solid
Broilers – litter
Other poultry - litter

Similar to cattle and swine deep bedding
except usually not combined with a dry lot
or pasture. Typically used for all poultry
breeder flocks, for alternative systems forlayers and for the production of meat typechickens (broilers) and other fowl. Litterand manure are left in place with added
bedding during the poultry production
cycle and cleaned between poultry cycles,
typically 5 to 9 weeks in productive
systems.

Laying hens – slurry

May be similar to open pits in enclosed
animal confinement facilities or may bedesigned and operated to dry the manure
as it accumulates. The latter is known as ahigh-rise manure management system andis a form of passive windrow composting
when designed and operated properly.
Some intensive poultry farms installed themanure belt under the cage, where themanure is dried inside housing.

Aerobic treatment

No EMEP equivalent

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

No definition given

Slurry separation

The separation of the solid and liquidcomponents of slurry

No definition given

Acidification

The addition of strong acid to reduce
manure pH

Poultry manure with litter

Poultry manure without litter10.1302019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Annex 10A.3

Spreadsheet example for the calculation of acountry or regions specific MCF

MCF CALCULATIONS AND EXAMPLE SPREADSHEETThis Annex was developed to explain how MCFs in the guidelines have been derived and to provide a detailed
step by step protocol for inventory compilers to calculate country or region specific MCFs. Application of themodel at the national scale requires national or regional monthly air temperature profiles as well as the average
number and timing of the emptying of manure storages. Temperature data can be downloaded from various
agencies such as the National Oceanic and Atmospheric Administration (NOAA) or the European Environmental
Agency. Manure removal statistics may be taken from farm practice surveys or from expert consultation.
Compilers should develop an estimate of the average number of manure removals per year and the months of thehighest frequency of removals. If regional practices vary within a country, compilers should develop MCFs thatare representative of regional practice by entering consistent manure removal statistics with regional temperature
profiles. If regional MCFs are calculated, the national MCF should be weighted based on the number of animals
feeding into the regional manure management systems represented by the manure removal profile and the regionaltemperature profile.
Further, to support the 2019 Refinement, a spreadsheet was created to enable users to calculate a site-specific
Methane Conversion Factor (MCF). The spreadsheet uses the same calculations that were used to calculate the
MCF Table in the guidance document, but has been designed for use by inventory compilers to do calculations fortheir country-specific circumstances. The spreadsheed is available in the supplemental data supplied with this
Chapter, identified as Supplemental Information Chapter 10, Volume IV, 2019 Refinement, and available on the
IPCC TFI website.
The calculation procedure outlined in the spreadsheet contains three main sections:
Inputs to the model;
Model calculations;
Results from the model.
As an explanation of procedures, within each section, cells are colour coded. Compilers are required to develop
input data for anything that is indicated by yellow highlighted cells, and have the option of editing the orange
highlighted cells if needed, but only if country-specific information is available for those parameters. Other cells
are not meant to be edited by the user.
Figure 10A.2 (New)Colour code for cells in the example spreadsheet

MOD EL INPU T
Te mp e ratur e
The Input required to run the model at a national scale and recreate the spreadsheet is shown below (Figures
10A.3(New) and 10A.4(New) ). In this section, the compiler should input 12 months of temperature data (degreesC) in cells D9:D20, based on average monthly temperatures for the region for which they wish to develop the
MCF.
If the compiler has estimates of national or regional manure temperature, “Manure” should be selected in cell D6.
As a result, the spreadsheet will copy the user-input temperature into cells E9:E20, for further use in the analysis.
If the compiler is using national or regional air temperature (not manure temperature), “Air” should be selected incell D6. As a result, the spreadsheet will generate an estimate for manure temperature in cells E9:E20. The
estimates are based on the following logic:• Manure temperature lags 1-month behind air temperature.
o e.g., Tmanure in June = Tair in May.
• The minimum manure temperature will be used (1 degree C by default; user adjustable).
o e.g., for Tair = -9 C, Tmanure = 1 C.

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10.131Volume 4: Agriculture, Forestry and Other Land Use

If and only if the storage is emptied once per year, manure temperature will be reduced by a dampening factor (3degrees C by default; user adjustable).
i.e. Tman = Tair – damping factor; e.g., 12 = 15 – 3The logic equation is implemented in Excel as follows, for example,=IF($D$6="Manure",D9,IF($F$21>1,MAX(D20,f_Tmin),MAX(D20-f_T2damping,f_Tmin)))

in

cell

E9:

Broken into steps:
If $D$6="Manure" then the result in E9 will equal D9
If $D$6 is not "Manure" (i.e. it is “Air”) then the second IF statement is operated
IF $F$21>1 (i.e. multiple removals per year), then no damping is applied
Manure temperature is selected as air temperature from the previous month, and it is always
greater or equal to the minimum temperature,i.e. E9 will equal MAX(D20,f_Tmin). In this case, D20 (-6.7) is less than the minimum, so theresult in E9 is the minimum (1.0).
IF $F$21=1 then damping is applied
Damping is applied by subtracting the damping factor: D20-f_T2damping
The temperature is always greater or equal to the minimum temperature, using the MAX() function.
The compiler should then identify the months when manure is removed from the storage in column F (F9:F20).
This can be indicated by a “Y” indicating months when manure was removed, and an “N” for months when manure
is not removed. The number of months when manure was removed is counted and displayed in cell F21.

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10A.3 (New)Temperature and manure removal inputs to the modelNote. Top panel: alphanumeric values in each cell. Middle panel: dropdown menu to select “Air” or “Manure”.
Bottom panel: all formulae are visible.
Con stan ts an d O the r In put Pa rame te r s
The inventory compiler is required to provide several other inputs in the section shown below (Figure
10A.4(New)). The name of each parameter is provided in column H, the numeric value of the parameter is incolumn I, the units are in column J, the source of each value is given in column K, additional notes are in columns
L and M, default values are in column N. To make equations more easily understood, the Microsoft Excel featureof “Named Cells” has been used to name the cells in column I, and the name of each cell is shown in column O
for convenience. For example, cell I7 is given the name “VS_PROD_YR”. See Figure 10A.4(New) for a full listof named cells.
Additional information about the input parameters:• VS Excretion – based on IPCC guidance.
• VS% liquid storage – this indicates what percentage of excreted VS is handled as a liquid. For example. 100
percent indicates that all excreted VS enters the liquid storage;• A lower number (say, 75 percent) could indicate that a portion of the solids is separated by a screwpress andhandled as a solid (25 percent) while the remaining 75 percent is handled as liquid;• The compiler must provide a B0 value for the manure. Refer to IPCC guidance;

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10.133Volume 4: Agriculture, Forestry and Other Land Use• The compiler may, optionally, adjust the minimum temperature (and temperature damping factors);• The compiler also has the option to adjust the emptying efficiency, which indicates the percentage of manure
removed from storage at each removal. By default this is set to 95 percent, indicating that 5 percent of the VSremain in storage after emptying. Set this value to 100 percent for complete removal.
Figure 10A.4 (New)Constants and other input parameters for the modelNote. Constants and other input parameters for the model are shown in the top panel. Named Cells in column I are
shown in column O, and in the Name Manager dialog box (bottom panel). No formulae exist in this part of the
spreadsheet.

MODEL CALCULATIONS
The model calculations are run for three years, in order to ensure VS available has stabilized on an annual basis.
For example, in Figure 10A.5(New), we see that VS Available (column J) increases substantially from the firstyear to the second year (J64 vs J65), and then stabilizes in the third year (J66). This is because the first year begins
from a perfectly empty storage, whereas the second year is emptied according to the Emptying Efficiency
parameter (95 percent removed / 5 percent remaining; Figure 10A.4(New)).
The model approach is as follows:
Column B: Month of year, over 3 years. These month numbers are used to extract input data shown in Figure
10A.3(New).
Column C: Average manure temperature in each month. This is extracted from cells E9:E20 (Figure 10A.3(New))
using a VLOOKUP function (Figure 10A.6(New)).
Column D: temperature is converted from Celsius to Kelvin, using Excel’s CONVERT function 10A.6(New) .
Column E: the temperature-dependent f parameter is calculated using the van’t Hoff-Arrhenius equation (Mangino
et al. 2001; IPCC 2006), with updated input parameters shown in Figure 10A.4(New).
Column F: monthly VS excreted is calculated by dividing the annual VS input parameter by 12.

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Column G: monthly VS loaded is calculated by multiplying VS excreted by the percentage stored as liquid. In thisexample, the two are equal because VS_PCT_LIQUID is 100 percent (Figure 10A.4(New)).
Column H: monthly manure emptying is extracted from cells F9:F20 (Figure 10A.3(New)) using a VLOOKUP
function (Figure 10A.6(New)).
Column I: the quantity of VS emptied is calculated. The logic is as follows: if emptying occurred, then calculate
the mass of VS available to be removed using the mass of VS available in the previous month minus the mass of
VS consumed in the previous month. Then, multiply the result by the EMPTY_EFFICIENCY parameter (Figures
10A.4(New) and 10A.6(New)).
Column J: the mass of VS available for producing methane is calculated. In the first month of the first year this isequal to the mass of VS loaded. In all other months, this is calculated as the VS loaded in the current month + VS
available in the previous month – VS consumed in the previous month – VS emptied in the current month.
Column K: the mass of VS consumed is calculated by multiplying VS available by f.
Column L: the volume of CH4 produced is calculated by multiplying VS consumed by B0.
Using these values and equations, the compiler should be able to reproduce graphics such as the profile of manuretemperature, volatile solids and methane production shown in Figure 10A.7(New).
Figure 10A.5 (New)Monthly model inputs and outputs over a three year period

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Figure 10A.6 (New)Formulae used in the modelNote. To conserve space, only 12 months are shown. Top panel: Temperature (Column C and D), coefficient(Column E), VS excreted, (Column F) and VS loaded (Column G). Middle panel: month of emptying (Columns
H), VS emptied (Column I), VS available (Column J), VS consumed (Column K) and CH4 Produced (Column L).
Bottom panel: sums in rows 64:66 for selected columns.

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10A.7 (New)

Example of monthly patterns in Year 3: manure temperature, VSavailable (kg), VS emptied (kg), and methane production.

MODEL RESULTS
The MCF is calculated in the Results section. This is done using the third year outputs. In this particular example,
the input air temperature is from the Cool Temperate Moist region and the retention time is 6-months. The resulting
MCF (21 percent) is identical with the guidance document (21 percent).
Figure 10A.8 (New)

Summary of Year 3 VS and methane production, and calculation of
MCF.

Note. Top panel shows results, bottom panel shows equations.

NOTE ABOUT TERMINOLOGY:
The terms “VS Available” and “VS Consumed” are used here to be consistent with the 2006 IPCC Guidelines andMangino et al. 2001 approach. However, these terms require some clarification to avoid misinterpretation: (1)2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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The term “VS Consumed” does not represent the real VS degraded but a conceptual quantity of VS removed fromthe liquid/slurry storage against the total biomethane potential at 35°C (i.e. the conceptual proportion of the B0
consumed at that point). Therefore, just as B0 reports the quantity of CH4 produced per kg of VS (i.e. all fractions,degradable and non-degradable), the concept of “VS Consumed” removes all fractions of VS from storage. Thisapproach is convenient because it uses the B0 as the integrator of all fractions of VS degradability, and reports thetotal methane produced from all fractions as if they were incubated for infinite time, while the f parameter
introduces a temperature dependence. Though this is convenient for modelling, and is consistent with the B0, thisdoes not represent the physical reality of the liquid/slurry storage. (2) Since “VS Consumed” does not equate withthe amount of VS degraded in the storage, the “VS available” does equate with the amount of VS that would
actually be measured in a storage. Therefore, researchers should not attempt to compare measured VS with “VSavailable”. (3) The strength of this approach is its simplicity and the fact that the maximum amount of methanethat can be produced is equal to the total VS produced multiplied by the B0. In other words, the model cannot
produce more methane than the B0. (4) The MCF is the ratio of predicted “VS Consumed” to the total VS thatentered the storage over one year. The method does not address VS destruction. If the “VS Consumed” were
multiplied by B’ (m3 CH4/kg VS destroyed), the result be would be erroneous because “VS Consumed” is not VS
Destroyed. This is not to say that B’ cannot be used to model methane production, but simply that it is notcompatible with the “VS Consumed” concept. (5) Although B0 does not need to enter the MCF calculation, therole of B0 is to be multiplied by the MCF, as stated in equation 10.23 of the IPCC 2006 Guidelines and this 2019
Refinement.

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Annex 10A.4

Calculations of Methane Conversion Factors(MCFs) for biogas systems

MCF s fo r th e b iogas dig est e r
Biogas digesters are an important manure management systems. There are different types of biogas digesters,
including centralised biogas digester plants, animal farm based biogas digesters, and digesters that co-digest animal
manures and organic residues. Some biogas digesters such as farm based biogas digesters may include prestorage,
like pit storage below animal confinement, or outdoor storage. Co-digestion may include energy crops, and/or
different types of organic waste in varying combinations.
Default MCFs of biogas digesters are provided in Table 10.17 and Table 10A.11.
MCFs depend on the amount of B0 that is realised under on farm conditions and on the amount of produced biogasthat leakes from the biogas plant either during storage or energy production. Calculations to identify default factors
were carried out by varying the level of leakage from the biogas installations. Calculations are based on amodification of the default values in Table 10.17 and Table 10A.11using the equation defined in Haenel et al.
(2018).
TABLE 10A.11 (NEW)METHANE CONVERSION FACTOR (MCFDG) INCLUDING BIOGAS DIGESTER AND DIGESTATE STORAGE1
Biogas digester
qualityhigh quality
biogasdigester，Ldig=0.01
low quality
biogasdigester，Ldig=0.1
1

Climate Zone

Storage gastight level

cold

temperate

warm

High quality gastight storage, Lsto,gt =0.01

1.00%

1.00%

1.00%

Low quality gastight storage, Lsto,gt =0.1

1.41%

1.41%

1.41%

Open storage, Lsto,gt=1

3.55%

4.38%

4.59%

Average

1.99%

2.27%

2.33%

High quality gastight storage, Lsto,gt=0.01

9.59%

9.59%

9.59%

Low quality gastight storage, Lsto,gt =0.1

10.00%

10.00%

10.00%

Open storage, Lsto,gt =1

12.14%

12.97%

13.17%

Average

10.58%

10.85%

10.92%

the value of MCF for digestate storage (MCFngts) is based on the MCF value of anaerobic lagoon.

Methane emissions from biogas digesters include the unused biogas (can be defined as leakage) and emissions
from storage of the digestate. The MCF calculation from biogas digesters should be based on the following
equation (Haenel et al. 2018):
EQUATION 10A.1 (NEW)CALCULATION OF MCF FOR THE COMBINATION “DIGESTER + DIGESTATE STORAGE”

MCFdg =(

vCH 4 , prod − vCH 4 ,used − vCH 4 ,   flared + MCFresidues • B0 − vCH 4 , prod)

B0

Where:
MCFdg= effective methane conversion factor for the combination “digester + digestate storage”,
percentvCH 4 , prod

= specific volume of methane produced in the digester (related to VS input), m3 CH4 kg-1 VSvCH 4 ,used= specific volume of methane used for energy production (related to VS input), m3 CH4 kg-1

vCH 4 ,   flared= specific volume of methane flared (related to VS input), m3 CH4 kg-1VS

VS

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MCFresidues = methane conversion factor for the storage of digested manure, percent= maximum methane producing capacity per kg of VS input T, m3 CH4 kg-1 of VS excreted

B0

In practice, the residence time necessary to fully exploit the maximum methane producing capacity B0 is not fully
reached in the gas collection system. In the following, the difference, i.e. the potentially still purgeable amount ofgas ( B0 – vCH 4 , prod ), is denoted as “potential of residual gas” that is assumed to be known, and the ratio of whichto B0 is described by the entity μrg:

EQUATION 10A.2 (NEW)CALCULATION OF RELATIVE AMOUNT OF POTENTIAL OFF GAS RELATED TO B0µrg =B0 − vCH 4 , prod
B0

Where:µrg= relative amount of residual gas related to B0 (with 0 ≤ μrg ≤ 1 m3 m-3)

B0

= maximum methane producing capacity per kg of VS, m3 CH4 kg-1 VSvCH 4 , prod

= specific volume of methane produced in the digester (related to VS input), m3 CH4 kg-1 VS

In practice, the amount of residual gas, µrg is not given as a share of the maximum methane producing capacity
B0, but as a share of the amount of gas usable for energy production. Hence, a new entity νrg can be defined which
is closely related to µrg .
The µrg can be calculated as follows:
EQUATION 10A.3 (NEW)CALCULATION OF RELATIVE AMOUNT OF RESIDUAL GAS RELATED TO B0

µrg =

vrg

1 + vrg

Where:µrg= relative amount of residual gas related to B0 (with 0 ≤ µrg ≤ 1 m3 m-3)

vrg

= relative amount of residual gas related to νCH4,prod (with 0 ≤ vrg ≤ 1 m3 m-3)
EQUATION 10A.4 (NEW)CALCULATION OF RELATIVE AMOUNT OF RESIDUAL GAS RELATED TO CH4 PRODUCTIONvrg =B0 − vCH 4 , prodvCH 4 , prod

Where:

10.140vrg= relative amount of residual gas related to νCH4,prod (with 0 ≤ νrg ≤ 1 m3 m-3)

B0

= maximum methane producing capacity per kg of VS, m3 CH4 kg-1VSvCH 4 , prod

= specific volume of methane produced in the digester (related to VS input), m3 CH4 kg-1VS

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Chapter 10: Emissions from Livestock and Manure Management

The term vCH 4 , prod – vCH 4 ,used – vCH 4 ,   flared in equation 10A.1 is part of the digester’s methane balance (related to
VS input) which can be completed by the methane loss vCH 4 ,leak due to leakage.
EQUATION 10A.5 (NEW)DIGESTER’S METHANE BALANCEvCH 4 , prod − vCH 4 ,used − vCH 4 , flared − vCH 4 ,leak =0

Where:
vCH 4 , prod

= specific volume of methane produced in the digester (related to VS input), m3 CH4 kg-1 VSvCH 4 ,used= specific volume of methane used for energy production (related to VS input), m3 CH4 kg-1
VSvCH 4 ,   flared= specific volume of methane flared (related to VS input), m3 CH4 kg-1VSvCH 4 ,leak= specific volume of methane due to leakage and maintenance works (related to VS input),m3 CH4 kg-1 VS

The loss of methane vCH 4 ,leak due to leakage is calculated as part of the total amount of CH4 produced in thedigester. The ratio of these two quantities is defined as the leakage rate Ldig of the digester. Ldig is assumed tobe known.
EQUATION 10A.6 (NEW)CALCULATION OF METHANE LEAKAGE RATE OF DIGESTERvCH 4, leak = Ldig • vCH 4, prod

Where:
vCH 4 ,leakLdig
vCH 4 , prod

= specific volume of methane due to leakage and maintenance works (related to VS input),m3 CH4 kg-1 VS= leakage rate of the digester, related to νCH4,prod (with 0 ≤𝐿𝐿dig ≤ 1 m3 m-3).

= specific volume of methane produced in the digester (related to VS input), m3 CH4 kg-1 VS

In order to give the effective methane conversion factor of the combination, “digester + digestate storage” as afunction of the three parameters, “relative amount of residual gas”, “leakage rate” and “MCF of the digestatestorage”, the methane conversion factor of the combination, “digester + digestate storage” can be calculated asfollows (Equation 10A.7):
EQUATION 10A.7 (NEW)CALCULATION OF METHANE CONVERSION FACTOR

MCFdg =
(1 − µrg ) • Ldig + µrg • MCFresidues
Where:
MCFdg= effective methane conversion factor for the combination “digester + digestate storage”,
percentµrg= relative amount of residual gas related to B0 (with 0 ≤ µrg ≤ 1 m3 m-3)

Ldig

= leakage rate of the digester, related to νCH4,prod (with 0 ≤ Ldig ≤ 1 m3 m-3)

MCFresidues = methane conversion factor for the storage of digested manure, percent

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For the factors of Ldig and µrg it is recommended to use country-specific data; if country specific data areunavailable. the following procedure is recommended: For high quality biogas digesters, the default Ldig is
recommended to be 0.01 (Rösemann et al. 2017); for low quality biogas digesters, Ldig = 0.1 is recommended(Table 10A-4 to Table 10A-9 2006 IPCC Guidelines). The value of 0.046 is used for µrg based on Haenel et al.
(2018) The values presented in Table 10A.11 are derived using Equation 10A.7 and the values cited here. Prestorage loss estimates are not included as no default values can be identified from the literature. Also, it is assumed
that these losses will be low as they represent economic losses due to lower biogas production. However equations
are included below if compilers wish to adapt their emission estimates to country-specific circumstances.
It is assumed that even a gastight storage of digestate has a certain leakage. This leakage rate is described by thestorage-specific leakage rate Lsto, gt . Taking into account the leakage rate and the relative share of gastight storageof digestate xgts , the MCFresidues can be calculated following Equation 10A.8:
EQUATION 10A.8 (NEW)CALCULATION OF METHANE CONVERSION FACTOR OF RESIDUES xgts =
MCF
xgts • Lsto , gt + 1 −
 • MCFngts
residues 100 Where:MCFresidues= methane conversion factor for the storage of digestate, percent

xgts= share of gastight storage of the digestate, percent

Lsto, gt

= leakage rate of the gastight storage (with 0 ≤ Lsto, gt ≤ 1 m3 m-3). For high quality gastightstorage of the digestate Lsto, gt is assumed to be 0.01 m3 m-3. For low quality gastight storageof the digestate, Lsto, gt is assumed to be 0.1 m3 m-3. For open storage of the digestate, Lsto, gt
is assumed to be 1.0 m3 m-3

MCFngts= methane conversion factor for the non-gastight storage of digestate, percent. It is assumed
that MCFngts is same to the storage of raw manure.

Biogas plants that are fed with animal manures have, as a rule, a pre-storage for the feedstock before it enters thedigester. The CH4 losses from the pre-storage reduce the CH4 production potential in the digester and the storageof the digestate. This could be taken into account by modifying equations 10A.7. As a consequence Equation(10A.7) is transformed to the MCFdg+ps equation as Equation 10A.9.
EQUATION 10A.9 (NEW)CALCULATION OF METHANE CONVERSION FACTOR FOR THE COMBINATION “PRESTORAGE +DIGESTER + DIGESTATE STORAGE”= MCFps + (1 − MCFps ) MCFdg
MCFdg+ps= MCFps + (1 − MCFps )[(1 − µrg ) Ldig + µrg  MCFresidues ]Where:
MCFdg+ps= effective methane conversion factor for the combination “prestorage + digester +digestatestorage”, percent

MCFps= methane conversion factor for prestorage, percent; Table 10.17 provides the default values
for different prestorage systems

MCFdg= methane conversion factor for combination “digester+digestate storage”, percent, see
above

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Chapter 10: Emissions from Livestock and Manure Managementµrg= relative amount of residual gas related to B0 (with 0 ≤ µrg ≤ 1 m3 m-3)

Ldig

= leakage rate of the digester, related to νCH4,prod (with 0 ≤ Ldig ≤ 1 m3 m-3)

MCFresidues= methane conversion factor for the storage of digestate (in m3 m-3).

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Annex 10A.5

Equations relating all direct and indirect N2Oemissions from manure along all stages inagricultural production for livestock

As explained in section 10.5.6, nitrogen excreted by animals contributes to several direct and indirect N2O emission
as it cascades through livestock and crop cultivation systems. It is therefore crucial to accurately estimate nitrogenexcretion rates. The total direct and indirect N2O emissions associated with the excretion of nitrogen of an animal
type is an important quantity to assess the benefit from improving the estimation of the N-excretion coefficient forthat animal type. However, the total direct and indirect N2O emissions from animal excretion cannot be easily
estimated using the equations given in Chapter 10 and 11 of the 2006 IPCC Guidelines and this 2019 Refinement.
This annex provides a set of equations, based on the equations given in Chapter 10 and 11, that allows thequantification of total direct and indirect N2O emissions from nitrogen excretion of each animal type T. They arereported in Equations 10A.10 through 10A27.
The definition of the symbols used in the set of equations is given below Equation 10A.27, grouped by symbols.
Note that for internal consistency, the symbol N is used for all nitrogen flows in kg N animal-1 yr-1; the symbol F
is used for all animal-independent nitrogen flows or nitrogen flows for the total animal population in kg N yr-1; thesymbol Frac is used for all fractions in kg N (kg N)-1 or percent, the symbol EF is used for all N2O emission factors
in kg N2O-N (kg N)-1, and the symbol N2O is used for all N2O emissions in kg N2O-N yr-1 . Not in all cases
therefore, the symbols are identical to those used in the Equations given in Chapters 10 and 11.
EQUATION 10A.10 (NEW)TOTAL N2O EMISSIONS FOR ANIMAL TYPE T

N 2O(T ) = N 2Omm (T ) + N 2OAM (T ) + N 2OPRP (T )EQUATIONS 10A.11 AND 10A.12 (NEW)TOTAL N2O EMISSIONS FROM MANURE MANAGEMENT FOR ANIMAL TYPE T

N 2O( mm ,T ) = N 2OD ( mm ,T ) + N 2OG ( mm ,T ) + N 2OL ( mm ,T )


 44
N 2O
=
mm (T )
 ∑ Fmm (T , S ) •  EF3( S ) + ( FracGasMS )(T , S ) • EF4 + ( FracLeachMS )(T , S ) • EF5   • 28
 S
EQUATIONS 10A.13 THROUGH 10A.14 (NEW)TOTAL, DIRECT AND INDIRECT N2O EMISSIONS FROM THE APPLICATION OF MANURE TO
MANAGED SOILS FOR ANIMAL TYPE T=
N 2O AM (T ) N 2OD , AM (T ) + N 2O I , AM (T )N 2O D , AM (=FAM (T ) • (1 − Frac AM , Rice ) • EF1 + Frac AM , Rice • EF1FR  •T)

N 2OI , AM ( T )= FAM ( T ) •  FracGASM • EF4 + FracLEACH −( H ) • EF5  •10.144

44
28

44
28

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Chapter 10: Emissions from Livestock and Manure Management

EQUATION 10A.15 (NEW)TOTAL AMOUNT OF ANIMAL MANURE N APPLIED TO SOILS OTHER THAN BY GRAZING ANIMALSFOR ANIMAL TYPE T

(

)



=
FAM (T ) ∑  Fmm(T , S ) • (1- FracLossMS )(T , S ) + Fbedding (T , S )  + Fcodigestate  • Frac APPL(T )



 S 


EQUATION 10A.16 (NEW)FRACTION OF TOTAL ANIMAL MANURE N LOST IN MANURE MANAGEMENT SYSTEMS FORANIMAL TYPE T

FracLossMS (T , S ) = FracGASMS (T , S ) + FracLEACHMS (T , S ) + FracN2 MS ( S ) + EF3( S )EQUATION 10A.17 (NEW)FRACTION OF ANIMAL MANURE N AVAILABLE FOR APPLICATION TO MANAGED SOILS, APPLIEDTO MANAGED SOILS FOR ANIMAL TYPE T

Frac APPL (T ) =1 − ( FracFEED (T ) + FracFUEL (T ) + FracCNST (T ) )EQUATION 10A.18 THROUGH 10A.19 (NEW)TOTAL, DIRECT AND INDIRECT N2O EMISSIONS FROM N IN URINE AND DUNG DEPOSITED BYGRAZING ANIMALS ON PASTURE, RANGE AND PADDOCK (TIER 1) FOR ANIMAL TYPE T=
N 2O PRP (T ) N 2O D , PRP (T ) + N 2O I , PRP (T )44
N 2O D ,PRP ( T =) ( FPRP ,CPP ( T ) • EF3 PRP ,CPP ) + ( FPRP ,SO ( T ) • EF3 PRP ,SO ) •28
N 2O I ,RPR ( T=) FRPR ( T ) •  FracGASM • EF4 + FracLEACH −( H ) • EF5  •44
28

EQUATION 10A.20 (NEW)RELATIONSHIP BETWEEN AVERAGE ANNUAL NITROGEN FLOWS ASSOCIATED WITH AN
INDIVIDUAL ANIMAL [KG N ANIMAL-1 YR-1] AND THE ANNUAL NITROGEN FLOW FOR THE ANIMALPOPULATION OF LIVESTOCK CATEGORY/SPECIES T IN A COUNTRY [KG N YR-1]=
F POP(T ) • N

EQUATION 10A.21 (NEW)TOTAL MANURE-N EXCRETED=
N (T ) N MMS (T ) + N PRP (T )2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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EQUATION 10A.22 AND 10A.23 (NEW)NITROGEN EXCRETION CALCULATED EITHER USING A DEFAULT FRACTION OF RETENTION (TIER
1) OR DIRECTLY FROM RETENTION DATA

Nex(T ) = N intake (T ) • (1- FracRET (T ) )=Nex(T ) N int ake (T ) − N RET (T )EQUATION 10A.24 (NEW)TOTAL MANURE-N IN MANURE MANAGEMENT SYSTEMSN MMS=(T )∑ ( POP • Nex • Frac
S

(T )

(T )

S (T , S ))EQUATION 10A.25 (NEW)MANURE-N MANAGED IN SYSTEM S

N mm (T , S ) = POP(T ) • Nex(T ) • FracS (T , S )EQUATION 10A.26 (NEW)MANURE-N DEPOSITED BY GRAZING ANIMALS, WITH X=CPP,SO

N PRP ( X ) = POP( X ) • Nex( X ) • FracS ( X ,G )EQUATION 10A.27 (NEW)N IN BEDDING MATERIAL ADDED TO MANAGED MANURE

N bedding=
POP(T ) • N beddingMS (T , S )(T , S )Where,= number of head of livestock species/category T in the country, heads

POP(T )

Annual total nitrogen flows, F, and annual average nitrogen flows per head, N:
F(T ) and N (T )= animal manure nitrogen excreted for livestock species/category T in the
country, kg N yr-1 and kg N animal-1 yr-1

Fcodigestate= amount of nitrogen from co-digestates added to biogas plants, kg N yr-1

FMMS (T ) and N MMS (T )= animal manure nitrogen excreted for livestock species/category T in manure
management systems in the country, kg N yr-1 and kg N animal-1 yr-1

FPRP (T ) and N PRP (T )= animal manure nitrogen excreted for livestock species/category T on pasture,
range and paddock in the country, kg N yr-1 and kg N animal-1 yr-1

FPRP ,CPP (T ) and N PRP ,CPP (T )= animal manure nitrogen excreted for cattle, pig and poultry species/category
T on pasture, range and paddock in the country, kg N yr-1 and kg N animal-1
yr-1

FPRP , SO (T ) and N PRP , SO (T )= total animal manure nitrogen excreted for sheep and other livestock
species/category T on pasture, range and paddock in the country, kg N yr-1 and
kg N animal-1 yr-1

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Fmm (T , S ) and N mm (T , S )= animal manure nitrogen excreted for livestock species/category T in manure
management system S in the country, kg N yr-1 and kg N animal-1 yr-1

Fbedding (T , S ) and N bedding (T , S )= nitrogen in bedding material added for livestock species/category T in
manure management system S in the country, kg N yr-1 and kg N in bedding
animal-1 yr-1

FAM (T , S ) and N AM (T , S )= annual amount of animal manure N applied to soils for each livestock
species/category T, kg N yr-1 and kg N animal-1 yr-1

Fintake (T ) and N intake (T )= annual intake of N in feed for each livestock species/category T, kg N yr-1
and kg N animal-1 yr-1

Fretention (T ) and N retention (T )= annual retention of N each livestock species/category T, kg N yr-1 and kg N
animal-1 yr-1

Fex (T ) and N ex (T )= annual average N excretion of species/category T in the country, kg N yr-1
and kg N animal-1 yr-1

Annual N2O emissions for the total population of each livestock species/category T

N 2 O(T )= total annual N2O emissions

N 2 Omm (T )= total annual N2O emissions from Manure Management for each livestock
species/category T in the country, kg N2O yr-1

N 2 OD , mm (T )= direct annual N2O emissions from Manure Management for each livestock
species/category T in the country, kg N2O yr-1

N 2 OG , mm (T )= indirect annual N2O emissions from volatilization of NH3+NOx from Manure
Management for each livestock species/category T in the country, kg N2O yr-1

N 2 OL , mm (T )= indirect annual N2O emissions from leaching and run-off from Manure
Management for each livestock species/category T in the country, kg N2O yr-1

N 2 OAM (T )= total annual N2O emissions from manure nitrogen applied to cultivated soils
for each livestock species/category T, kg N2O yr-1

N 2 OPRP (T )= total annual N2O emissions from manure nitrogen deposited on pasture,
range and paddock for each livestock species/category T, kg N2O yr-1

N 2 OD , AM (T )= direct annual N2O emissions from manure nitrogen applied to cultivatedsoils for each livestock species/category T in the country, kg N2O yr-1

N 2 OI , AM (T )= indirect annual N2O emissions from manure nitrogen applied to cultivatedsoils for each livestock species/category T in the country, kg N2O yr-1

N 2 OD , PRP (T )= direct annual N2O emissions from pasture, range and paddock for each
livestock species/category T in the country, kg N2O yr-1

N 2 OI , PRP (T )= indirect annual N2O emissions from pasture, range and paddock for each
livestock species/category T in the country, kg N2O yr-1

N2O emission factors
EF1= emission factor for direct N2O emissions from N inputs to cultivated soils,
kg N2O –N (kg N input)-1

EF1FR= emission factor for direct N2O emissions from N inputs to flooded rice, kg
N2O –N (kg N input)-1

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EF3 PRP , X= emission factor for direct N2O emissions from urine and dung N depositedon pasture, range and paddock by grazing animals, kg N2O –N (kg N input)-1;
X=CPP: Cattle, Poultry and Pigs; X=SO: Sheep and Other animals

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)-1

EF4

= emission factor for N2O emissions from atmospheric deposition of nitrogenon soils and water surfaces, kg N2O -N (kg NH3-N + NOx-N volatilised)-1

EF5= emission factor for N2O emissions from nitrogen leaching and runoff, kg
N2O -N (kg N leached and runoff)-1

Fractions

FracS (T , S )= fraction of manure N excreted that is managed in manure management
system S for each livestock species/category T, dimensionless

FracS ( X ,G )= fraction of manure N excreted that is deposited by grazing cattle, poultry or
pigs (X=CPP) or sheep or other animals (X=SO), dimensionless

FracGasMS (T , S )= fraction of managed manure nitrogen for livestock species/category T thatvolatilises as NH3 and NOx in the manure management system S

FracLeachMS (T , S )= fraction of managed manure nitrogen losses for livestock species/category T
due to runoff and leaching during solid and liquid storage of manure in manure
management system S

FracN2 MS= fraction of managed manure nitrogen for each livestock species/category T
that is lost in the manure management system S, percent as N2

FracLossMS (T , S )= total fraction of managed manure nitrogen for livestock category T that islost in the manure management system S

FracGASM (T , S )= fraction of applied organic N fertiliser materials (FON) and of urine and dungN deposited by grazing animals (FPRP) that volatilises as NH3 and NOx, kg N
volatilised (kg of N applied or deposited)-1

FracLEACH − ( H )= fraction of all N added to/mineralised in managed soils in regions whereleaching/runoff occurs that is lost through leaching and runoff, kg N (kg of N
additions)-1

Frac APPL (T )= fraction of animal manure N available for application to managed soils which
is applied to managed soils for each livestock species/category T,
dimensionless

FracFEED (T )= fraction of managed manure used for feed for each livestock species/category
T, dimensionless

FracFUEL (T )= fraction of animal manure N available for application to managed soils used
for fuel for each livestock species/category T, dimensionless

FracCNST (T )= fraction of animal manure N available for application to managed soils used
for construction for each livestock species/category T, dimensionless

Frac AM , Rice= fraction of animal manure N applied to managed soils which is applied toflooded rice, dimensionless

FracRET10.148= fraction of feed intake N that is retained by the animal in body mass or
livestock products for each livestock species/category T, dimensionless2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 10: Emissions from Livestock and Manure Management

Annex 10.B
10B.1

Data and Explanatory Text for Development of
New Parameters in the 2019 Refinement

Raw data used to compile Annex 10A.1 entericfermentation Tier 1 emission factors, volatile solids andnitrogen excretion for cattle and buffalo

A database was compiled from peer-reviewed articles, scientific and statistical reports found via a comprehensive
literature search in google scholar (https://scholar.google.com) and Elsevier (www.sciencedirect.com) web searchengines.
All raw data collected from literature sources and used as a basis by the IPCC authors to refine the final values
presented in Tables 10A.1–10A.4 are available in the supplemental data supplied with this Chapter, maintained on
the IPCC document website, there identified as Supplemental Information Chapter 10, Volume IV, 2019
Refinement.
Since agricultural production systems of certain countries may be transitioning from low productivity local
subsistence systems to higher productivity systems, the IPCC expert team of the 2019 Refinement aimed to collectfrom a variety of literature sources published to date and report the final data differentiated by production systemand performance parameters on cattle (dairy and non-dairy) and buffaloes for each world region.
It should be noted that the IPCC expert team assumed that such regions as North America, Western and Eastern
Europe, and Oceania may be defined as regions where only high-production systems are in practice. However,Latin America, Asia, Africa, Middle East and Indian subcontinent experience transition period from low
productivity local subsistence systems to higher productivity systems, hence, data on performance parameters andfeeding situations of cattle and buffaloes reared in the two production systems were recorded by the IPCC expert
team.
Moreover, the IPCC expert team of the 2019 Refinement updated regional representation in Tables 10A.1–10A.4.
The 2006 IPCC Guidelines contain data aggregated performance parameters and enteric fermentation emission
factors (EFs) for Africa and Middle East, however, values of nitrogen excretion rate (Nex) were presented
separately for these regions, i.e. for Africa and Middle East. Hence, the IPCC expert team improved consistency
in the reporting, and the team collected raw data and conducted the estimation of enteric fermentation EFs, Volatilesolids (VS) and Nex rates for Africa and Middle East, separately, for the both productivity systems.
In addition, the IPCC expert team updated the representation of non-dairy cattle and buffalo sub-categories.
Namely, the 2006 IPCC Guidelines contain data on performance parameters, diets and feeding situations, andcorresponding EFs, VS and Nex for three main sub-categories of non-dairy cattle (i.e., mature females, mature
males and young), the 2019 Refinement contains data and corresponding EFs, VS and Nex for four main subcategories: mature females, mature males, replacement and growing animals, and calves.
The majority of values determining performance parameters, diets and feeding situations in different regions of
the world were updated by the IPCC expert team in the 2019 Refinement. Namely:
To develop region-average performance parameters, diets and feeding situations of dairy and non-dairy cattle forNorth America, 12 peer-reviewed publications were examined by the IPCC expert team (Appuhamy et al. 2016;
Basarab et al. 2005; Capper 2011; Dong et al. 2014; Jayasundara et al. 2016; Legesse et al. 2016; Mulliniks et al.
2017; Niu et al. 2018; Ominski et al. 2007; Sheppard et al. 2015; Stackhouse-Lawson et al. 2012; Waldrip et al.
2013). Final values relied on the expert judgement and consensus of the authoring team.
To update performance parameters, diets and feeding situation of dairy and non-dairy cattle reared in Western
Europe, 7 peer-reviewed publications were examined by the IPCC expert team (Bannink et al. 2011; Bannink et
al. 2016; FAO 2017; Gerrits et al. 2014; Hammond et al. 2016; Huuskonen 2017; Spek et al. 2013). Data
determining diets and feeding situations for dairy and non-dairy cattle of Western Europe were updated; allperformance parameters, with exception of milk yield per head of dairy cow, were carried over from the 2006
IPCC Guidelines.
To deliver the regional-average final value on performance parameters, diets and feeding situation of dairy andnon-dairy cattle of Oceania, 10 data sources were examined by the IPCC expert team (studies and datasets ofstatistical offices) were examined by the IPCC expert team (Australian Government Department of Climate
Change 2006; Dairy Technical Working Group 2015; Fick 2016; Pickering & Wear 2013; Statistics NZ 2018a;
Australian Bureau of Agricultural and Resource Economics and Sciences 2018; Dairy Australia 2018; Dairy NZ& LIC 2018; Statistics NZ 2018b). The final values (reported in Table 10A.1 and Table 10A.2) were refined based

2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.149Volume 4: Agriculture, Forestry and Other Land Useon data collected for Australia and New Zealand. Hence, the IPCC expert team encourages small Pacific Island
nations to use enteric fermentation EFs, VS and Nex rates developed for Asian region, as productivity systems are
more similar.
Overall, 35 data sources (scientific publications and statistical datasets) were examined by the IPCC expert team
to obtain raw data on performance parameters, diets and feeding situations required to develop region-average
final values for dairy and non-dairy cattle reared in Eastern Europe (Amerkhanov et al. 2016; Azaubaeva 2008;
Bakharev 2012; Dunin et al. 2011; FAOSTAT 2017; Furaeva 2013; Gayirbegov & Mandjiev 2013; Golubkov
2015; Golubkov et al. 2015; Goncharova & Kibkalo 2011; Goncharova et al. 2009; Gren 2013; Gubaidullin et al.
2011; Haysanov 2011; Ilichev et al. 2011; IPCC 2006; Kalnickij & Haritonov 2008; Kostenko & Pyrozhenko
2012; Leontev et al. 2013; Levakhin et al. 2011; Litovchenko 2012; Mamaev et al. 2017; Nekrasov et al. 2013;
Nosyreva Yu & Tokareva 2014; Pracht 2013; RUSSTAT 2016; Samorukov et al. 2013a; Samorukov et al. 2013b;
Sharkaev & Kochetkov 2012; Sharkaeva 2012; Sharkaeva 2013; Sheveleva & Bakharev 2013; Shevkhuzhev et al.
2015; Tekeev & Chomaev 2011; Zadnepryanskiy & Zakirko 2012). Since Russian Federation, Ukraine and Belarus
have the largest herd among other Eastern European countries (FAOSTAT 2017), the IPCC expert team focused
mainly on the analysis of cattle management practice of these countries. Initial data for other Eastern European
countries were taken from GLEAM model (FAO 2017). To make adjustment regarding the region-average final
values reported in Table 10A.1 and Table 10A.2, the IPCC expert team considered the contribution of each country
to the total dairy and non-dairy cattle population of Eastern Europe and corresponding parameters.
Raw data on cattle performance parameters, diets and feeding situations were distinguished between two
production systems (i.e. low and high) of Latin America and collected by the IPCC expert team respectively. In
total, 52 publications were examined by the IPCC expert team (Albarrán-Portillo et al. 2015; Albertini et al. 2012;
Amaral et al. 2005; Ancco 2015; Ministerio de Ganadería 2017; Barajas Merchan et al. 2017; Barrantes 2000;
Bartl et al. 2009; IPCC 2006; IBGE 2017; Becoña 2012; Ítavo et al. 2014; Cândido et al. 2015; Cardoso et al.
2017b; Castro et al. 2012; Chavez 2010; Ciudad de México Financiera Rural 2009; Cunha et al. 2016; Euclides &Medeiros 2003; FAO 2017; Huhn et al. 1982; Ítavo et al. 2014; Kolling et al. 2018; Lima et al. 2018; Machado
Filho et al. 2014; Mariani et al. 2009; Mata e Silva et al. 2017; McManus et al. 2011; Medeiros et al. 2010;
Modernel et al. 2013; Oliveira et al. 2014; Pajuelo Montalvo 2003; Pajuelo Montalvo 2008; Peres et al. 2012;
Primavesi et al. 2004; Queiroz et al. 2011; Quispe et al. 2016; Reis 1998; Restle et al. 2003; Ribeiro et al. 2016;Rodriguez 2018; Rojas & Gómez 2005; Rosa et al. 2001; Ruiz & Sandoval 2014; Santos et al. 2011; Sgroi 2017;
Silva et al. 2017; Simões et al. 2009; Teixeira et al. 2013; Teodoro & Madalena 2002; Teodoro & Madalena 2005;
Verruma & Salgado 1994). Moreover, to clarify some parameters, the IPCC expert team conducted interviews
with lead researchers of Latin America, namely: Sebastián Galbusera (Argentina); Dr. Pablo Soca, Faculty ofAgronomy (Uruguay); Santiago Fariña, Dirceto of the Dairy Redearch Progmaram. National Institute ofAgricultural Research (Uruguay); Dr. Laura Astigarraga, Faculty of Agronomy (Uruguay); Dr. Luiz Gustavo
Ribeiro Pereira (Embrapa Dairy Cattle, Brazil); Dr. Pablo Soca, Faculty of Agronomy (Brazil). It should be notedthat these above-listed publications were sources for initial data mostly for non-dairy cattle reared in Brazil,
Uruguay, Peru and Argentina, and for dairy cattle husbandry practice applied in Brazil. However, initial data on
performance parameters, diets and feeding situations applied for dairy cattle of other countries of Latin America
were obtained from GLEAM model (FAO 2017). To adjust the final values recorded in Table 10A.1 and Table
10A.3, the dairy and non-dairy cattle population kept in each country of Latin America was taken into
consideration by the IPCC expert team.
Performance parameters, diet and feeding situation of dairy and non-dairy cattle of the whole Asian region forlow- and high-productivity systems were refined based on detailed data obtained from 50 publications (Alejandrino
et al. 1999; Zi et al. 2003; Sutarno 2015; Hieu Vu et al. 2016; Widiawati et al. 2016; Gunawan & Jakaria 2011;
Lapitan et al. 2008; Ramírez-Restrepo et al. 2017; Martojo 2012; Philippines Statistics Authority 2017; Moran
2012; Thanh 2014; Ichinohe et al. 2014; Dinh 2007; Department of Veterinary Services 2013; Panandam &Raymond 2005; Lam 2011; Gioi et al. 2012; Ariff et al. 2015; Waldron et al. 2015; Putra et al. 2015; Garcia et al.
2006; Zhai et al. 2006; FAO et al. 2014; Gerber et al. 2011; Xie et al. 2016; Cui et al. 2014; Yang et al. 2013;
Xue et al. 2014; Dong et al. 2015; Zi et al. 2003; Huai et al. 1993; Ma et al. 2007; Ma et al. 2012; Wattiaux et al.
2002; Zhou 1998; Beldman et al. 2014; Xie et al. 2012b; Qiao et al. 2013; Dong et al. 2017; Han et al. 2016;
Wang et al. 2014; Taneja 1999; FAO 2003; Wang et al. 2017; MAAR 2013; Hu & Zhang 2003; Gao et al. 2011;
Cheng 1984; Gao et al. 2013). Raw data were obtained from the literature sources to determine parameters
corresponding to low- and high-producing systems. Performance parameters of dairy and non-dairy cattle and theirfeeding systems were investigated for the following countries: China, Indonesia, Malaysia, Philippines andVietnam. Moreover, the data of GLEAM model (FAO 2017) were used in a greater degree. A contribution of eachcountry to the total cattle population of Asian region was considered by the IPCC expert team to adjust final values
reported in Table 10A.1 and Table 10A.3.
Data on performance parameters, diets and feeding situations of dairy cattle reared in low- and high-productivity
systems of African region were directly obtained from GLEAM model (FAO 2017). The model contains a
compherensive dataset for the both types of productivity system applied in Kenya, Ethiopia, Uganda, Tanzania.

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Chapter 10: Emissions from Livestock and Manure Management

Moreover, performance parameters, diet and feeding situation of non-dairy cattle reared in the both productivity
systems of African region were obtained as results of the analysis of 101 scientific articles and statistical reports(Abdel Rahman 2007; Abdelhadi & Babiker 2009; Abera 2016; Abraha et al. 2009; Addisu et al. 2010; Adebambo
2001; Adesina 2012; Ageeb & Hillers 1991; Ahamefule et al. 2007; Ahmed & Zubeir 2013; Ahmed Hassan 2010;
Alemayehu et al. 2013; Ali et al. 2015; Alsiddig et al. 2010; Asimwe et al. 2015; Bashir & El Zubeir 2013; Bayemi
et al. 2005; Behnke & Osman 2012; Blench 1999; Central Statistical Agency 2017; Chabo et al. 2003; Corbet etal. 2006; Dekeba et al. 2006; Du Toit et al. 2013; Edea et al. 2013; Elrshied & Ishag 2015; Engida et al. 2015;
Essien 2003; Ethiopia F.D.R. 2011; FAO 2017; FAO & IAEA 2011; FAOSTAT 2017; Farmer & Mbwika 2012;
Gebre Mariam et al. 2013; Goopy et al. 2018; Groeneveld et al. 1998; Gwaza & Momoh 2016; Haile et al. 2011;
Halala 2015; Haren & Idris 2015; Ilatsia et al. 2011; International Livestock Centre for Africa 1977; Ismail et al.
2014; Kahi et al. 2006; Kanai & Zagi 2013; Kashoma et al. 2011; Kouazounde et al. 2015; Kubkomawa 2017;
Kurwijila & Bennett 2011; Lukuyu et al. 2012; Lukuyu et al. 2016; Mai et al. 2012; Mandefro et al. 2017; Mapiye
et al. 2011; Masama et al. 2003; Mekonnen et al. 2012; Ministry of Livestock and Fisheries Development of
Tanzania 2014; Ministry of Livestock and Fisheries Development of Tanzania 2015; Mlote 2013; Mpofu 1996;
Msanga et al. 2012; Muhuyi et al. 1999; Muriuki 2011; Musa et al. 2011; Mwambene et al. 2012; Mwambene etal. 2014; Mwanyumba et al. 2015; Myburgh et al. 2012; Nell 2006; Nell et al. 2014; Nouala et al. 2003; Nweze
et al. 2012; Olorunnisomo 2013; Onono et al. 2013; Osman 1985; Pico 2004; Rakwadi et al. 2016; Raphaka 2008;
Rege 1999; Rewe et al. 2006; Said et al. 2003; Salako 2014; Scholtz & Theunissen 2010; Shirima et al. 2016;
Shittu et al. 2008; Siegmund-Schultze et al. 2012; Statistics Botswana 2016; Stein et al. 2009; Strous 2010;
Strydom 2008; Strydom et al. 2000; Strydom et al. 2008; Tefera 2013; Tegegne et al. 2013; Temoso et al. 2016;
Tesfa et al. 2016; Theunissen et al. 2013; Wurzinger et al. 2006; Young et al. 2005; Yousif & El- Moula 2006;
Zerabruk & Vangen 2005). In addition to a rich dataset developed for low- and high-productivity systems, total
population of non-dairy cattle of African region and contribution of the non-dairy population reared in each Africancountry to the total (FAOSTAT (2017) was taken into consideration by the IPCC expert team to determine thefinal values on performance parameters, diet and feeding situation representative for the whole African region.
Overall, 24 publications were examined by the IPCC expert team to obtain raw data on performance parameters,
diets and feeding situations of dairy and non-dairy cattle reared in low- and high-productivity systems of Middle
East (Akbaş et al. 2006; Turkish Statistical Institute 2017; Karakok 2007; CBAT 2017; Chashnidel et al. 2007; daCunha et al. 2010; Sadeghi-Sefidmazgi et al. 2012; FAO et al. 2014; Fatahnia et al. 2010; Gerber et al. 2011;
IPCC 2006; Kamalzadeh et al. 2008; Kara et al. 2014; Karakok 2007; Koçyiğit et al. 2014; MFAL 2011; SadeghiSefidmazgi et al. 2012; Statistical Centre of Iran 2011; Tasdemir et al. 2011; Ula 2016; USDA 2015; Ustuner et
al. 2016; Özlütürk et al. 2006; Yalcin et al. 2017; Yilmaz et al. 2012). Due to an extend of contribution to the total
dairy and non-dairy cattle population of Middle East, the focus of the IPCC expert team was made to investigate
dairy and non-dairy cattle management practice applied in Turkey and Iran. The raw data obtained from literaturesources for these two countries were adjusted to be a basis to evaluate the final values on performance parameters,
diets and feeding situations representative for low- and high-productivity systems of the whole Middle East region.
Raw data on performance parameters, diets and feeding situations of low- and high-productivity systems applied
for dairy and non-dairy cattle in Indian subcontinent were derived as a result of the analysis of 47 publications(Ahmad et al. 2004; Ahmad et al. 2013; Birthal & Parthasarathy Rao 2002; Boro et al. 2016; Bradfield & Ismail
2012; Chowdhry 2007; da Cunha et al. 2010; Department of Animal Husbandry 2013; Deshetti et al. 2016;
Dhingra et al. 2017; FAO 2017; FAO et al. 2014; Garg et al. 2013; Gerber et al. 2011; IPCC 2006; Jabbar et al.
2009; Kayastha et al. 2008; Kenyanjui et al. 2009; Khan 2011; Khan et al. 2008; Khan et al. 2009; Khan et al.
2016; Landes et al. 2017; Mahakur et al. 2017a; Mahakur et al. 2017b; Manoj 2009; Moaeen-ud-Din & Bilal 2017;
Nahar et al. 2016; National Bureau of Animal Genetic Resources 2017; Pathak et al. 2013; Patra 2012; Rahman
et al. 2012; Rahman et al. 2015; Roy et al. 2016; Saha et al. 2004; Saha et al. 2012; Sambhaji 2013; Sarkar et al.
2006; Sharma et al. 2014; Singhal et al. 2005; Sirohi et al. 2012; Sodhi et al. 2007; Sontakke et al. 2014; Thombre
et al. 2015; Tomar & Sharma 2002; Yadava 2009; Yasothai 2014). Taking into consideration, the largest
contribution of cattle population of India, Pakistan and Bangladesh to the total dairy and non-dairy cattle
population of the region, the main focus of the IPCC expert team was to collect and investigate low-productivity
and high-productivity cattle farming of these countries. The final values reported in Table 10A.1 and Table 10A.3
were refined by the IPCC expert team based on consensus and were used to conduct estimations of entericfermentation EFs, VS and Nex rates for dairy and non-dairy cattle in low- and high-productivity systems of Indian
subcontinent.
Data to complete calculations of enteric fermentation emission factors, VS and nitrogen excretion rates forbuffaloes were significantly updated in the 2019 Refinement in comparison with the information reported in the2006 IPCC Guidelines. Namely, the 2006 IPCC Guidelines report the initial data employed and corresponding
EFs for two main regions: Indian subcontinent and Other regions. The IPCC expert team of the 2019 Refinement
extended regional representation and collected data for the same regions, which were applied to present EFs ofcattle. Hence, data on buffalo performance parameters, diets and feeding situation for Western Europe, Eastern
Europe, Latin America, Asia, Africa and Middle East were developed and reported in addition to presented dataset
developed for Indian subcontinent. North America and Oceania were omitted from the reporting as according to2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

10.151Volume 4: Agriculture, Forestry and Other Land Use

the data presented in (FAOSTAT 2017), the regions do not have any buffalo husbandry practice. Moreover, regionaverage final values on performance parameters, diets and feeding situations were reported for four buffalo
subcategories for all regions: mature males, mature females, growing and replacing animals and calves. The only
exception was made for Indian subcontinent, where the final values were presented for five subcategories: breeding
mature males, working mature males, mature females, growing and replacing animals and calves.
The IPCC expert team has decided not to distinguish performance parameter values, diets and feeding situation
between low productivity and high-productivity systems implemented in buffalo husbandry across the world
regions as it was conducted for dairy and non-dairy cattle, but to collect and report region-average values.
Overall, eight peer-reviewed publications were examined by the IPCC expert team to deliver performanceparameters, diets and feeding situation of buffaloes kept in Western Europe (Borghese 2013; Condor et al. 2008;
FAO 2005; FAO 2017; Gonzalez Gonzalez 2011; IPCC 2006; Neglia et al. 2014; Sabia et al. 2014; Zicarelli et al.
2007). Since Italy is a main contributor to the total buffalo population of Western Europe (FAOSTAT 2017), thebuffalo husbandry practice of Italy was considered to be a representative for the whole Western Europe. However,data on buffalo performance parameters of Germany and Greece were also taken into consideration to adjust final
values reported in Table 10A.4. The calculation of enteric fermentation EFs, VS and Nex rates were conductedbased on Tier 2 method of the 2019 Refinement.
To deliver data on performance parameters, diet characterisation and feeding situation of buffaloes of Eastern
Europe, the focus in the research completed by the IPCC expert team was mainly made on investigation of buffalo
husbandry implemented in Bulgaria and Romania. In total, 11 publications (i.e. case studies and statistical reports)
were examined to complete a dataset needed to compute enteric fermentation EFs, VS and Nex rates (Atanasov etal. 2012; Borghese 2013; Dimov & Tzankova 2003; FAO 2005; FAOSTAT 2017; IPCC 2006; MZH 2016; MZH
2017; Nikolov 2011; Peeva et al. 2011; Peeva et al. 2013; Tzankova & Dimov 2003).
To develop a dataset on performance parameters, diets and feeding situation representing buffalo husbandry
practice of Latin America, overall 25 studies were examined by the IPCC expert team (Andrade & Garcia 2005;
Andrighetto et al. 2003; Andrighetto et al. 2003; Bailone et al. 2017; Cardoso et al. 1997; Cardoso et al. 2017a;
Coelho et al. 2004; Damé et al. 2010; dos Santos et al. 2016; Gonçalves 2008; Jorge 2005; Jorge et al. 2002; Limaet al. 2014; Macedo et al. 2001; Maeda et al. 2007; Oliveira et al. 2009; Rassi et al. 2009; Rezende et al. 2017;Rodrigues et al. 2001; Sales et al. 2018; Santos et al. 2014; Zeoula et al. 2014; Tonhati et al. 2000; Tonhati et al.
2009; Verruma & Salgado 1994). Moreover, the IPCC expert team organized interviews with top researchers inthis area to specify the findings and to cover lacking information from the scientific publications (Cristiana
Andrighetto (UNESP-Dracena); Dr. José Ribamar Felipe Marques (Embrapa Amazônia Oriental)). In general, thefinal values recorded in Table 10A.4 for Latin America represent mainly characteristics of buffalo herd of Brazil,
as the investigation of buffalo performance parameters reared in other countries of Latin America was notconducted by the IPCC expert team.
Overall, 42 publications (case studies and statistical reports) were examined by the IPCC expert team to derive
raw data on buffalo performance parameters, diets and feeding situation of Asian region (Abd El-Salam & ElShibiny 2011; Batosarnma 2006; Berthouly 2008; Carabao situation report 2017; Chang & Huang 2003; Cruz
2007; Cruz 2010; Cruz 2012; Das et al. 2004; Deb et al. 2016; Djaja 2011; Djajanegara & Diwyanto 2002; FAO
2003; FAO 2017; Flores et al. 2007; Han et al. 2007; Huai & Jun 1995; Kusnadi & Praharani 2009; Lambertz etal. 2014; Li et al. 2018a; Loculan 2002; Meyer et al. 2000; Mingala et al. 2017; Nanda & Nakao 2003; Nha et al.
2008; Phomsouvanh 2002; Prabowo 2012; Premasundera 2002; Qin et al. 2013; Qingkun et al. 2002; Riedel et al.
2012; Sanh 2007; Sivarajasingam 1987; Skunmun et al. 2002; Somapala 2002; Suryanto et al. 2002; Taneja 1999;
Tuyen 2009; Van Sanh 2007; Wanapat & Rowlinson 2007; Yang et al. 2007; Yang et al. 2013a). The detailed
analysis of buffalo husbandry practice applied in China, Indonesia, Laos, Philippines, Sri Lanka, Thailand andVietnam was conducted to underlay for adjustment, based on consensus of the authoring team, of the final valuesof Table 10A.4.
According to the data reported (FAOSTAT 2017), only Egypt has population of domesticated buffaloes among
other African counties. Hence, to deliver data on performance parameters, diets and feeding situation employed tocompute enteric fermentation EFs, VS and Nex rates for buffaloes reared in African region, 22 publications
determining Egyptian buffalo husbandry practice was examined by the IPCC expert team (Abd-Allah et al. 2015;
Ali et al. 2009; Asheeri & Amal 2012; Ashour et al. 2007; Habeeb et al. 2016; FAO 2005; FAO et al. 2014;
FAOSTAT 2017; Gerber et al. 2011; Habeeb et al. 2016; Hassan & Abdel-Raheem 2013; Ibrahim 2012; Ibrahim
2012; IPCC 2006; Khattab et al. 2011; Marai et al. 2001; Marai et al. 2009; Morsy et al. 2016; Presicce 2011;
Radwan 2016; Shahin et al. 2010; Soliman 2009; WAAP 2007). Data on buffalo for other African countries wereomitted from the analysis, the final values reported in Table 10A.4 and used in the calculations are relied on theexpert judgement and consensus of the authoring team.
The analysis of 27 publications (case studies and statistical reports) resulted in evaluation of the region-average
final values on performance parameters, diets and feeding situation of buffaloes husbandry for Middle East (Azary

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Chapter 10: Emissions from Livestock and Manure Management

et al. 2007; Turkish Statistical Institute 2017; Çelikeloğlu et al. 2015; Chashnidel et al. 2007; DAD-IS 2017;
Dezfuli 2010; Dezfuli et al. 2011; FAO 2017; FAO et al. 2014; FAOSTAT 2017; Gerber et al. 2011; GLEAM
FAO 2017; Hossein-zadeh et al. 2012; IPCC 2006; Işik & Gül 2016; Jaayid et al. 2011; Mahmoudzadeh & Fazaeli
2009; Mahmoudzadeh et al. 2007; Manafiazar et al. 2007; Naserian & Saremi 2007; Porter et al. 2016; Şekerden
2013; Soysal 2013; Soysal et al. 2005; Tariq et al. 2013; Turkish Statistical Institute 2017; Yavuz & Zulauf 2004).
Due to the availability and representation of publications, the focus of the IPCC expert team was mostly made on
buffaloes reared in Tukey and Iran, the data collected for these countries were considered as a basis to madeadjustment regarding region-average final values for Middle East reported in Table 10A.4.
To deliver initial data required to calculate enteric fermentation EFs, VS and Nex rates for buffaloes reared inIndian subcontintent, 37 publications (case studies and statistical reports) were examined by the IPCC expert team(Afzal et al. 2009; AGRI-IS 2017; Anitha et al. 2011; Anjum et al. 2012a; Anjum et al. 2012b; Basra & Nisa
2003; Breeding survey book 2013; Dahiya & Singh 2013; Dhingra et al. 2017; FAO 2017; FAO et al. 2014;
FAOSTAT 2017; FICCI 2014; Gami et al. 2017; Garg et al. 2018; Gerber et al. 2011; Gupta et al. 2016; IPCC
2006; Jabbar et al. 2009; Jha et al. 2011; Khan et al. 2008; Khan et al. 2010; Khare & Baghel 2010; Kumar &Dass 2006; Kumar et al. 2011; Pathak 2005; Patra 2012; Prusty et al. 2016; Ranjhan 2007; Shahzad et al. 2011;
Shekhar et al. 2010; Singal 2001; Singh 2002; Singh et al. 2012; Singh et al. 2015; Singh et al. 2017; Tariq et al.
2013; Tauqir et al. 2011). Data on performance parameters, diets and feeding situation of different breeds ofbuffaloes in India and Pakistan were adjusted to make the expert decision on the final values reported in Table
10A.4 and to conduct the calculations.

10B.2

Estimation of Cattle/Buffalo CH4 conversion factors (Ym)

Dairy Yms were developed considering summary statistics from the database consisting of results from 3,353 cows
used in Niu et al. (2018) (Table 10B.1(New)) as well as data synthesis presented in the articles of Appuhamy etal. (2016), Hellwing et al. (2016) and Jayasundera et al. (2016). It was noted by the IPCC panel that these studies
were not representative of global dairy systems and for that reason simple means developed through statisticalanalyses were deemed not to be reliable. Final values relied on the expert judgement and consensus of the authoring
team. The summary statistics from Niu et al. (2018) are presented below (Table 10B.1), dividing that large data
set into high, medium and low levels of milk productivity.
In the case of all productivity systems, clear differences were identified between the North American and theEuropean feeding and production systems. The strongest contrasting factor was the proportion of neutral detergent
fibre (NDF) in the diets of the two regions. Based on these summary statistics the Ym is clearly driven by the
relationship with NDF within the two regional production categories (Figure 10B.1).
To provide additional guidance for the selection of the methane conversion rates, NDF thresholds were established(Table 10B.1). For the highest production categories based on the North American and European statistics a low
NDF <35 percent DMI and a high NDF >35 percent DMI categories were developed with values equivalent to 5.7
and 6.0 percent GEI respectively.
In the case of the values for the mid-range productivity, the value of 6.3 was determined assuming that NDF values
were greater than 37 percent DMI as the values for medium and low producing animals from North America could
not be considered to be representative of low production and lower quality diets. For countries that can clearly
demonstrate that the NDF of their feed has been greater than 37 percent DMI, it is recommended to use the highproduction value that corresponds to the NDF content of the feed.
For low-productivity, the unweighted mean value of 6.5 from the European and North American data, consistent
with the 2006 IPCC Guidelines was selected. The panel did not consider that there was reliable data to modify thevalue from the 2006 IPCC Guidelines value considering the very wide variety of diets that could be occurring
globally for low productivity dairy cattle. However, it is proposed in the text that if dairy cattle are fed mainly on
low quality forages countries are recommended to use the non dairy forage diet Ym of 7.0.

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10.153Volume 4: Agriculture, Forestry and Other Land Use

Figure 10B.1 (New)

Relationships between mean and median neutral detergent fibre (NDF)
and methane conversion rate (Y m ) from summary statistics of Niu et al.
(2018).

8.0
7.0

y = 0.2198x - 1.9538
R² = 0.9596

6.0
5.0

y = 0.2495x - 3.0522
R² = 0.9751

4.0
3.0
2.0
1.0
0.0

20.0

25.0

30.0

35.0

40.0

Mean values

Median values

Linear (Mean values)

Linear (Median values)

45.0

TABLE 10B.1 (NEW)SUMMARY STATISTICS FROM NIU ET AL. (2018) DATABASE
All data

EuropeNorth America

Annual milk productiongrouping<5000

50008500

>8500

<5000

50008500

>8500

<5000

50008500>8500

Ym median (%GEI)

6.3

6.0

5.7

7.3

6.4

6.0

5.9

5.5

5.2

Ym mean (%GEI)

6.2

5.9

5.7

7.1

6.4

6.1

5.8

5.3

5.2

Ym SD (%GEI)

1.3

1.2

1.1

1.0

1.1

0.9

1.2

1.1

1.04

Ym unweighted mean(%GEI)

6.5

5.9

5.7

Annual milk production
median (kg)

3,809

6,784

10,511

4,192

6,884

10,184

3,716

6,667

11,018Annual milk production
mean (kg)

3,619

6,783

10,840

4,036

6,849

10,538

3,483

6,709

11,245

Annual milk production
SD (kg)

988

980

1706

740

965

1603

1,034

995

1,757

NDF median (%DM)

38

37

35

41

39

37

35

34

32

NDF mean (%DM)

37

37

35

41

38

37

34

35

33

NDF SD (%DM)

8.3

7.7

5.5

6.5

8.0

4.9

8.0

7.0

5.2

EE median (%DM)

2.8

3.0

3.9

3.5

3.4

3.9

2.5

2.7

3.8

EE mean (%DM)

2.9

3.2

3.8

3.4

3.4

3.9

2.6

2.9

3.8

EE SD (%DM)

0.8

1.0

1.1

0.7

0.9

1.0

0.8

1.2

1.2

Number of cows

551

1,392

1,410

165

814

805

326

556

604

NA

Note: EE refers to ether extract an analytical method to estimate dietary fats and fatty acids

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10B.2 (NEW)THRESHOLD CALCULATION BASED ON NDF CORRECTION
NDF

Mean

Median

32

5.08

4.93

33

5.30

5.18

34

5.52

5.43

35

5.74

5.68

36

5.96

5.93

37

6.18

6.18

38

6.40

6.43

39

6.62

6.68

40

6.84

6.93

In the case of beef cattle, a total of 113 measurements were compiled from 35 studies. Studies were divided bytheir dominant diet type into three categories, high forage diets, mixed diets (mixed forage and concentrate) andfeedlot diets. Summary statistics were compiled and group averages are reported. Due to the variability in the data,
values were rounded based on expert judgement. An overall average was developed for the feedlot and non-feedlot
diets. Non feedlot diets were differentiated between dominantly forage based diets and mixed concentrate diets.
There is important variability in the results of studies that attempt to develop relationships between feed quality
and methane yield. Nonetheless, numerous empirical and biochemical modelling studies demonstrate both thestatistical significance and the biochemical processes that relate reductions in methane production with theintroduction of concentrates to ruminant diets (Mills et al. 2001; Mills et al. 2003; Ellis et al. 2006; Ellis et al.
2007; Ellis et al. 2009; Ellis et al. 2010; Alemu et al. 2011; Bannink et al. 2011; Ellis et al. 2014; EscobarBahamondes et al. 2016; Kebreab et al. 2016). For this reason, methane conversion rates are produced from asummary of low, medium and high dietary forage proportions. Raw data used in the development of these values
are published in the IPCC TFI website.
TABLE 10B.3 (NEW)SUMMARY OF DATA COMPILED FOR THE COMPILATION OF YM VALUES FOR CATTLE AND BUFFALO
Category

Measurement method

Average
Body
Weight (kg)Methane yield(g/kg DMI)

SD
(±)

Ym
(% GEI)SD (±)nHigh forage

Chambers (24), SF6 (30),
Micro-meteorological (2)

451

23.0

4.6

7.21

1.5

56

Intermediate
forageChambers (17), SF6 (7)

401

21.0

3.8

6.3

1.2

24

Feedlot(low forage)

Chambers (11), SF6 (5),Head boxes (17)

450

12.99

3.3

3.842

1.0

33

Boadi and Wittenberg (2002); Pinares-Patiño et al. (2003); Boadi et al. (2004); Beauchemin and McGinn (2005); Beauchemin and
McGinn (2006a); Beauchemin and McGinn (2006b); Chaves et al. (2006); Doreau et al. (2011); Jordan et al. (2006a); Jordan et al.
(2006b); Lovett et al. (2003); Beauchemin et al. (2007); Hegarty et al. (2007); Hart et al. (2009); McGinn et al. (2009); Mc Geough etal. (2010a); Mc Geough et al. (2010b); Doreau et al. (2011); Hales et al. (2012); Kennedy and Charmley (2012); Staerfl et al. (2012);
Chung et al. (2013); Hünerberg et al. (2013a); Hünerberg et al. (2013b); Fiorentini et al. (2014); Hales et al. (2014); Hales et al. (2015);
Romero-Perez et al. (2014); Troy et al. (2015); Romero-Perez et al. (2015); Nascimento et al. (2016); Vyas et al. (2016a); Vyas et al.
(2016b); Baron et al. (2017); Hales et al. (2017).
1

Rounded to 7.0 for Table 10.12.

2

Rounded to 4.0 for Table 10.12.

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10.155Volume 4: Agriculture, Forestry and Other Land Use

10B.3

Estimation of Default Emission Factor(s) based on Goat
Tier 2 parameters

A database was compiled from peer-reviewed articles that studied in-vivo methane (CH4) production from goatenteric fermentation and N excretion. These studies were identified through a comprehensive literature search
performed in Google scholar and ResearchGate and from sources that carried out review work such as a recent
study attempting to derive statistical models for prediction of enteric CH4 from goats (Patra & Lalhriatpuii 2016)
and a New Zealand technical report for CH4 and N excretion rates for goats (Lassey 2012). Data were directly
extracted from the individual studies identified. Authors were contacted in order to fill in gaps of information fromthe studies.
Overall, 63 publications were obtained from a varied sample of countries and 18 different goat breeds (Aguilera
et al. 1990; Prieto et al. 1990; Shibata et al. 1992; Haque et al. 1997; AFRC 1998; Haque et al. 1998; Islam et al.
2000; Islam et al. 2001; Rapetti et al. 2002; Puchala et al. 2005; Rapetti et al. 2005; Tovar-Luna et al. 2007b;
Tovar-Luna et al. 2007c; Tovar-Luna et al. 2007a; Animut et al. 2008; Bhatta et al. 2008; Haque et al. 2008;
Vermorel et al. 2008; Li et al. 2010; López et al. 2010a; López et al. 2010b; Tovar-Luna et al. 2010b; Tovar-Lunaet al. 2010a; Gerber et al. 2011; López et al. 2011; Tovar-Luna et al. 2011; Abecia et al. 2012; Jeong et al. 2012;
Lassey 2012; López et al. 2012; Mitsumori et al. 2012; Puchala et al. 2012a; Puchala et al. 2012b; Romero-Huelva
et al. 2012; Yang et al. 2012; Bhatta et al. 2013; Chethan et al. 2013; López & Fernández 2013; MartínezFernández et al. 2013; Miri et al. 2013; López et al. 2014; Martínez-Fernández et al. 2014; Nielsen et al. 2014;
Romero-Huelva & Molina-Alcaide 2014; Ibáñez et al. 2015a; Ibáñez et al. 2015b; Lu et al. 2015; Wang & Xue
2015; Arif et al. 2016; Castro-Lima et al. 2016; Criscioni & Fernández 2016; Lu et al. 2016; Patra & Lalhriatpuii
2016; Wang et al. 2016a; Wang et al. 2016b; Arco-Pérez et al. 2017; Barbosa et al. 2017; Keli et al. 2017; Kumar
et al. 2017; Na et al. 2017; Romero-Huelva et al. 2017; Tovar-Luna et al. 2017; Azlan et al. 2018; Fernández etal. 2018; Li et al. 2018b; Na et al. 2018a; Na et al. 2018b; Puchala et al. 2018).
Although there was a total of 290 treatment means, treatments that were using substances with antimethanogenic
properties were excluded before analysis. The minimum prerequisite for a study to be included in the data set wasthat Ym values (or gross energy and CH4 output energy) were reported.
Information on feed and diet characteristics, feed intake, breed, animal type, digestibility, and rumen werecollected in the final data set. Table 10B.4 shows the mean and the range of some of the diet and animal variables
for the different studies. Values were quite heterogeneous. For example, dry matter intake ranged between 0.14
and 2.51 kg DM intake/day animal (0.93 on average).
The concentrations of crude protein (CP), neutral detergent fibre (NDF) and starch were within the range of 6-26
percent (mean value of 15 percent), 18-74 percent (mean value of 42 percent) and 1-42 percent (mean value of 19
percent), respectively.
Methane production was expressed as grams per day, liters per day, megajoules per day, or as a proportion of GE
or DE; therefore, the following factors were used in converting units: 1 g = 1.40 L =55.5 kJ; 1 L = 0.716 g = 39.54
kJ.
TABLE 10B.4 (NEW)MEAN, MEDIAN, MAXIMUM, MINIMUM AND QUARTILE 1 AND 3 (Q1 AND Q3) VALUES FOR A SELECTION FEED DIETCOMPOSITION, FEED INTAKE, BODY WEIGHT AND MILK PRODUCTIVITY
Digestibility (%)

Feed intake

Body
weight

Milk
yield

DM

OM

N

NDF

GE

DM
(kg/day)GE(MJ/day)DE(MJ/day)

kg/animal(kg/day
animal)

Mean

68%

69%

72%

54%

71%

0.94

18.77

12.18

39.82

1.90

Median

69%

71%

73%

53%

72%

0.78

15.20

9.44

40.05

1.59

Max

83%

91%

84%

82%

83%

2.59

46.68

29.90

64.00

3.69

Min

49%

40%

44%

18%

52%

0.14

4.64

6.02

14.53

0.81

Q1

64%

65%

67%

46%

67%

0.62

11.80

8.44

33.45

1.31

Q3

74%

76%

78%

60%

76%

1.14

26.12

11.09

47.55

2.28

The CH4 emissions also varied greatly in the dataset. Table 10B.5 shows the methane emissions expressed indifferent units and metrics.

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Chapter 10: Emissions from Livestock and Manure Management

TABLE 10B.5 (NEW)MEAN, MEDIAN, MAXIMUM, MINIMUM AND QUARTILE 1 AND 3 (Q1 AND Q3) VALUES FOR CH4
PRODUCTION RESULTS REFERRED AS A PROPORTION OF GROSS ENERGY INTAKE (CH4CONVERSION FACTOR: YM), DAY-1, KG DM INTAKE-1, KG OF MILK PRODUCED-1 AND KG OF BODYWEIGHT-1

CH4
Ym

MJ/dayMJ/kg DM

MJ/kg milk

J/kg BW

Mean value

5.3%

0.9

1.0

0.8

23.1

Median

5.3%

0.8

1.0

0.8

20.5

Max

10.3%

3.8

4.7

1.7

73.6

Min

1.2%

0.2

0.3

0.2

5.3

Q1

4.3%

0.6

0.8

0.6

15.8

Q3

6.3%

1.0

1.2

1.1

27.4

The average methane emission was 16.2 g CH4/animal day, 18.3 g CH4/kg DM intake, 0.42 g CH4/ kg BW (datanot shown). Average/median methane conversion factor (Ym) was 5.3 percent, which is in the range of the recent
value obtained by the study by Patra & Lalhriatpuii (2016), which included 42 studies.
We analyzed the relationship between methane output and diet type (e.g. diet digestibility, percent forage use) but
there were no any clear statistical relationships between diet type and enteric methane output (data not shown). In
general increased body weight and milk yield resulted in greater CH4 output but body weight and milk yield did
not show any statistical relationship with Ym (data not shown).
Methane output per animal was positively correlated with dry matter (Fig 10B.2) and gross energy (Fig 10B.3)
intake (R2=0.60; P<0.00001).
Figure 10B.2 (New)Annual enteric methane output per animal expressed in mass inrelation to daily dry matter (DM) intake.

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Figure 10B.3 (New)Daily enteric methane output per animal expressed in energy inrelation to daily gross energy (GE) intake.

In order to develop Tier 1 EF for enteric CH4 from goats for both low and high production systems the following
steps were followed:• Average goat weight (LW) for high and low production systems were estimated using global world informationfrom Gerber et al. (2013). For high and low production systems it was estimated average weight values of 50
kg and 28 kg, respectively.
• Daily dry matter intake per animal was estimated as a function of animal weight using the equation from AFRC
(1998).
• Using the equation from Fig 10B.2 we obtained kg CH4/animal yr as a function of the previously estimatedvalue of daily dry matter intake.
EF for Tier 1 resulted in 8.7 and 4.9 kg CH4/head yr for high and low production systems, respectively. These
values are both lower than those estimated in Vermorel et al. (2008) from French systems (11.9 kg CH4/head yr1) and that for high production systems is similar to that proposed by Lassey ( 2012) for New Zealand goat herd.
Considering the data analysed, a Ym of 5.5 percent has been chosen. No clear evidence was found to develop Ym
factors separately as a function of diet quality or production system.
In order to develop default values for N excretion rates (for Table 10.19) the following steps were followed:
Average goat weight (LW) for each global region were estimated using global world information from GLEAM
FAO.
Daily N intake per animal and day was estimated as a function of average goat weight using the relationshipdeveloped from this database relating goat weight and N intake (Fig 10B.4) (R2=0.48; P<0.00001).
Daily N excretion rate was subsequently calculated using the relationship also found using this database relating
daily N intake and daily N excretion (Fig 10B.5) (R2=0.89; P<0.00001) and transforming values to excretion rates
espressed as kg N (1000 kg animal mass)-1) day-1.
Figure 10B.4 (New)10.158

Daily N intake per animal expressed in relation to animal weight.

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Chapter 10: Emissions from Livestock and Manure Management

Figure 10B.5 (New)Daily N excretion output per animal expressed in relation to daily N
intake.

In order to develop default values for volatile solids (VS) excretion rates (for Table 10.14A) we calculated daily
VS excretion from goats for each world region according to equation 10.24. Gross energy intake was derived basedon DM intake using the conversion factor 18.45 MJ/kg DM. We assumed that digestibility of the feed was 50 and
60 percent for developing and developed countries, respectively. The ash content of the feed was assumed to be 8
percent. We assumed the animal weights for each region based on information from FAO work: GLEAM FAO.

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10B.4

Feed intake estimates using a simplified Tier 2 method

Prediction of DMI for cattle based on body weight and estimated dietary net energy
concentration (NE m a ) or digestible energy values (DE percent):
Several studies have shown that dry matter intake (DMI) is highly and positively related to methane emissions. In
some cases it has been reported that up to 92 percent of the variability in enteric methane emissions could be
explained by DMI alone (Charmley et al. 2016). Most models developed to predict enteric methane emissions
usually include either DMI or some form of feed intake. There are a number of models already developed with theobjective of predicting DMI and these could be used in conjunction with emission factors to estimate entericmethane emissions in a Tier 2 approach. Appuhamy et al. (2016) evaluated 40 prediction equations using data that
included measured DMI and feed quality attributes from North America, Europe and Australia/New Zealand. The
best performing models in each region were then re-evaluated using calculated DMI and compared with estimatesthat used measured DMI. They evaluated several DMI prediction equations including the Cornell Net
Carbohydrate and Protein System (CNCPS, Fox et al. 1992) as modified by Arnerdal (2005), National ResearchCouncil (NRC 2001) (developed based on North America cows), Lindgren et al. (2001) and Arnerdal (2005)(developed using data from cows in Europe), and Vazquez and Smith (2000) model (developed from
Australia/New Zealand data). Appuhamy et al. (2016) reported that models using estimated DMI predicted entericmethane emissions just as good as the measured data and concluded that enteric methane emissions from dairy
cows can be predicted successfully with estimated DMI, particularly using the modified CNCPS model.
Appuhamy et al. (2018) further evaluated the comprehensive (IPCC-CMP) and simplified (IPCC-SMP) IPCC
models (IPCC 2006) to predict DMI as well as the modified CNCPS and NRC (2001) models to predict DMI using
an independent data. The modified CNCPS relying on BW and fat corrected milk yield (Eq. 1) more accurately
predicted DMI (RMSPE = 14.1 percent) than NRC (RMSPE = 19.4 percent), IPCC-SMP (RMSPE = 16.9 percent),and IPCC-CMP (RMSPE = 23.4 percent). Overall, the results demonstrated that DMI of dairy cows can bepredicted successfully using information such as milk yield, milk fat content, and body weight (BW) that areroutinely available in dairy farms.
DMI (kg/d) = 0.0185 * BW (kg) + 0.305 * fat corrected milk (kg/d)

Eq [1]

A simplified approach can also be used to estimate DMI of beef cattle, updated based on the most recent
methodologies as described by NASEM (2017). For growing and finishing cattle, equations are:CalvesDMI (kg/d) = (BW0.75 * (0.2435 * NEmf – 0.0466 * NEmf2 – 0.1128)) / NEmf

Eq. [2]Yearlings
DMI (kg/d) = (BW0.75 * (0.2435 * NEmf – 0.0466 * NEmf2 – 0.0869)) / NEmf

Eq. [3]Feedlot cattle (high grain diets)
Steers:DMI (kg/d) = 3.830 + 0.0143 * BW * 0.96

Eq. [4]Heifers:

DMI (kg/d) = 3.184 + 0.01536 * BW * 0.96

Eq. [5]

Where:

BW = body weight (kg), NEmf = Mcal/kg feed DM

Eq. [6]

Mature Cows
Forage type

Digestibility

Low quality

Forage DMI Capacity (kg/day), % of BW (kg)Non-lactating

Lactating

<52

1.8

2.2

Average quality

52-59

2.2

2.5

High quality

>59

2.5

2.7

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Chapter 10: Emissions from Livestock and Manure Management

10B.5

Basis for Changes to MCF Calculations for Liquid/Slurry

The following briefly summarizes the 2006 IPCC Guidelines approach and improvements included in the current
approach.

2006 IPCC Guidelines Model for Liquid/Slurry:
The 2006 IPCC Guidelines MCF for liquid slurry was based on the following relationship:
MCF = fwhere f was calculated with the following temperature-dependent Arrhenius function, derived from Mangino etal. (2001), which is based on Safley & Westerman (1990):
f = EXP[(Ea×(T2-T1))/(R×T2×T1)]
where,
f is a dimensionless fraction (0 to 1). Originally, Safley & Westerman (1990) used f to design an anaerobic digestion
system at a lower temperature (T2) based on known performance of a digester at a warmer temperature (T1).
Ea is the activation energy. Originally, Safley and Westerman used Ea = 15175 cal/mol, based on an earlier study.
Mangino et al. (2001) continued to use 15175 cal/mol.
T2 is the variable temperature (K). Defined by Safley & Westerman (1990) as the unknown anaerobic digester
temperature. Mangino et al. (2001) defined T2 as the monthly temperature of the anaerobic lagoon (assuming
equality with monthly average air temperature). The 2006 IPCC Guidelines defined T2 as the annual averagetemperature of a region.
T1 is the reference temperature (K). Defined by Safley & Westerman (1990) as 30 °C (303.16 K). Mangino et al.
(2001) and the 2006 IPCC Guidelines use the same value.
R is the gas constant 1.987 cal k-1 mol-1.

The reasons for modification of MCF, though the Methane conversion factor(MCF) remains an uncertain parameter.
First and foremost, in the 2006 IPCC Guidelines, the MCF parameter violates a first-principle of inventory
development: comparability. The use of an annual average temperature to calculate MCF systematicallyunderestimates the annual MCF due to the mathematical principle known as Jensen’s Inequality which applies tonon-linear functions such as the Arrhenius equation (VanderZaag et al. 2018). Using this mathematical principle
it can be shown that for a 1-month retention time, the annual average MCF calculated based on monthly
temperature will always exceed the MCF calculated from the annual average temperature. Therefore, the 2006
IPCC Guidelines MCF values are underestimates, and the level of underestimation is greatest for countries withlarge seasonal temperature extremes.
The model in the 2006 IPCC Guidelines also used a management and design practices (MDP) factor which reduced
the mass of VS entering the manure storage or lagoon. Since VS cannot simply vanish, there needs to bejustification for altering the VS loading rate. In the modified method, the MCF calculation used an MDP = 1.0,
which means we are assuming the VS Excretion rates are correct, and that VS Excreted enters the liquid manurestorage. MDP factors may be used in specific cases such as when solid-liquid separation systems are used,
whereby VS is removed from the liquid system and transferred to a solid system. However in most cases the useof MDP factor is indicative of an inaccurate B0 or VS input into the manure storage system.
For the sake of completeness, it is worth pointing out that the quantity of VS entering liquid storage could begreater than VS excreted (implied MDP >1.0). For instance, the use of straw bedding results in additional VS
entering the liquid storage. Another example is waste milk (from treated cows, or from cleaning milking systems)
on dairy farms which adds VS to the storage. Secondly, it is well known that the retention time of liquid manure
in storage is a critical parameter in determining MCF, and the 2006 IPCC Guidelines state “both temperature andretention time play an important role in the calculation of MCF”. However, the 2006 IPCC Guidelines calculationsof MCF (Table 1), give very little focus to retention time. Previous Good Practice Guidance and Uncertainty
Management in National Greenhouse Gas Inventories (GPG2000) recommended that future MCFs be modelled
accounting for the storage period (Zeeman & Gerbens 2000). Furthermore, the work of Safley & Westerman(1990) showed that the same amount of VS destruction can be achieved by longer retention time at lower
temperature compared with shorter retention time at higher temperature. Furthermore the suggestion to use
formula 1 for batch-fed storage/digesters that is currently in 2006 IPCC Guidelines would not result in a value thatis comparable to the default annual temperature values, because this equation would inherently require inclusionof retention time.

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Recent year-round field studies in climates where the annual average air temperature was <10°C have reported
MCFs in the range of 0.61 (Wightman & Woodbury 2016) , ≥0.57 (Balde et al. 2016a) at liquid/slurry dairy manurestorages, and greater for anaerobic lagoons (Leytem et al. 2017). Controlled studies at or around 20°C without
added inoculum reported MCF of 55 percent over 165 d (VanderZaag et al. 2010) and 32 percent over 150-d(Masse et al. 2008). Another study showed the MCF increased non-linearly with the duration of storage (Le Richeet al. 2016). Previous IPCC Guidance reported an MCF of 39 percent, 45 percent, and 72 percent for liquid/slurryfor Cool, Temperate, and Warm climates, respectively (Zeeman & Gerbens 2000). They also stated thatliquid/slurry storage tanks were considered to have ≥6 month retention time. Therefore, the interaction between
retention time and temperature has long been recognized, but the calculation of MCFs has not been fullytransparent about how this important interaction has been handled (or how it should be handled by practitioners)
and therefore has made comparability with measurements challenging.
Thirdly, the single temperature time step given in the IPCC guidelines suggests a level of certainty that is simply
not supported by the experimental results, considering the approach being used.
TABLE 10B.6 2006 IPCC GUIDELINES TABLE OF MCF VALUES FOR LIQUID/SLURRY (TABLE 10.17)

Proposed Changes:
The proposed change is to use a spreadsheet model to calculate MCF using monthly temperature in each IPCC
climate region, and for a specific liquid manure retention time (e.g. the table below). Therefore, this approachproduces MCF values that account for both temperature and retention time, while leaving the users to decide which
retention time is appropriate for their manure management systems. The spreadsheet model <Vol4_Ch10_
MCF_Calculation-Spreadsheet> for calculation of country-specific MCFs is available at https://www.ipccnggip.iges.or.jp/public/2019rf/pdf/4_Volume4/Vol4_Ch10_%20MCF_Calculation-Spreadsheet.xlsx.
TABLE 10B.7 (NEW)MCFS CALCULATED FOR EACH RETENTION TIME AND CLIMATE (SELECTED IPCC CLIMATE REGIONS SHOWN)
RETENTION Tropical
TIME
MontaneTropical1
Wet

Tropical1
Moist

Warm
Warm
Cool
Tropical1
Temperate Temperate Temperate
Dry
Moist
Dry
Moist

Cool
Temperate
Dry

1 Month

0.25

0.38

0.36

0.42

0.13

0.15

0.06

0.08

3 Month

0.43

0.61

0.57

0.62

0.24

0.28

0.12

0.16

4 Month

0.50

0.67

0.64

0.68

0.29

0.32

0.15

0.19

6 Month

0.59

0.76

0.73

0.74

0.37

0.41

0.21

0.26

12 Month

0.73

0.80

0.80

0.80

0.55

0.64

0.31

0.42

21.5

25.9

25.2

25.6

13.9

14.0

4.6

5.8

Tavg C

Note that an upper limit MCF of 80% has been imposed for consistency with the anaerobic lagoon MCFs at high temperatures and longretention times
1

Changes in liquid/slurry MCF, compared to the 2006 IPCC Guidelines are
summarized below:

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Chapter 10: Emissions from Livestock and Manure Management#1 – Tim este p:
Monthly temperature (proposed) instead of annual average temperature (IPCC 2006)
Methane emissions are non-linearly related to temperature, therefore Jensen’s inequality states that the use of theaverage temperature will lead to systematic underestimation. As a result, monthly average air temperature is
proposed for the calculation of MCF, rather than annual average temperature. Therefore, it is proposed that MCF
for liquid/slurry be calculated using the Mangino et al. (2001) spreadsheet model, with the regional climate datafrom the IPCC defined climate regions. Additional details below.
#2 – Ret en tion Tim e:
S ev era l r ete nt ion t im es (p ropo sed ) in stea d of 1 -mon th imp lied r et ent io n t ime ( IPCC
2006)
Retention time is a crucial parameter determining the extent of methane emissions and the quantity of VS in storageat any given time, therefore affecting the MCF. The 2006 IPCC Guidelines used a 1-month retention time for all
liquid/slurry systems by using MCF = f, based on an annual average temperature. Using a 1-month retention timeis unrealistic, since the majority of liquid/slurry storages are meant for storage over several months or more.
Therefore, it is proposed to calculate MCF based on five retention times: 1 month, 3 months, 4 months, 6 months,
and 12 months.
Proposed “good practice” in the case of countries that do not have information on retention times is to use the six
month retention time.
#3 – Act ivat ion En erg y ( Ea) :Up dat ed Ea va lue (19347 ca l/mo l p ropo s ed) in st ead o f 15175 ca l/mo l ( IPCC 2006)
Recent research from Petersen et al. (2016) and Elsgaard et al. (2016) propose a new Ea value of 81 kJ/mol =19347 cal/mol. It is proposed to use this updated value.
#4 – Ref e ren c e Te mp e rat ur e ( T 1 ):Up dat ed T1 va lue (308.16 pro pos ed ) in st ead of 303 .16 K ( IP CC 2006)
The value of T1 used by 2006 IPCC Guidelines and Mangino et al. (2001) is directly taken from Safley &Westerman (1990). The original intent of Safley and Westerman was comparing performance of a known andunknown anaerobic digester performance. In Mangino et al. (2001) and the 2006 IPCC Guidelines the value of T1
defines the temperature at which f = 1.0, therefore T1 defines the temperature at which the B0 will be reached in
one month. There is considerable literature on laboratory methods for incubating manure to measure methane
potential (e.g. BMP, B0) and it is customary for the temperature of these incubations to be ca. 35°C, rather than
30°C. With a temperature of 35°C it would be reasonable to expect the B0 to nearly be reached in 30 days (i.e. one
month) (e.g. Owen et al. 1979; Pham et al. 2013). Therefore, it is proposed to change T1 to 308.16 K (=35 +273.16).
#5 – Manu re Te mp e ratu r e ( T 2 ):Man ur e t em pe rat ur e lagg ing b ehin d T a i r (p ropo s ed) inst ead o f equa l T a i r (I PCC 2006 )Most of the time, manure temperature does not equal air temperature. The temperature of liquid manure tends tolag behind air temperature. While models for manure temperature do exist (Rennie et al. 2017) this is too complex
for the general guidelines. As a pragmatic alternative, a 1-month lag is proposed, i.e., set T2 = Tair from the previous
month. It has also been shown (Rennie et al. 2018) that manure storages which are emptied once per year at theend of the growing season before winter stay cooler than air temperature during the summer. Therefore, only inthe case of once per year emptying (i.e. 12 month retention time), a downward temperature shift of 3°C has also
been applied.
#6 – VS ca rr yove r af ter emp t ying:
Aft e r ma nur e is re move d, 5 pe r cen t r ema ins ( pro pos ed) , in st ead of c omp let e em pt ying
( IPCC 2006)
It has been shown in several studies that farms do not completely empty liquid/slurry storages due to the practicalchallenge of doing so at the farm-scale (Balde et al. 2016b). Therefore, it is proposed that 5 percent of VS isretained in storage after emptying, rather than 0 percent (i.e. completely clean) assumption implied in the 2006
IPCC Guidelines calculations. It is noteworthy that the IPCC 2000 Good Practice Guide (Zeeman & Gerbens
2000) mention approximately 15 percent of the manure storage cannot be emptied.

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10B.6

Revision of methane from dung deposited onto pasture
range and paddocks (Table 10.17)

Data se t
Cai et al. (2017) included 26 data, however some of these were omitted due to incomplete information to allow an
estimation of methane conversion factor (MCF) and/or emission factors on the basis of volatile solids (VS) content.
Therefore, the number of values retained was 20. Our review of the literature identified a further 25 suitable values(Carran et al. 2003; Saggar et al. 2003; Sherlock et al. 2003b; Sherlock et al. 2003a; Kelly et al. 2016), resulting
in a total of 45 data values spanning six countries (Table 10B.8). Data were available for dairy cattle (25), beef
cattle (9), sheep (8) and yaks (3). Data was assessed for suitability, in terms of length of study, sufficient replicationand inclusion of key manure characteristics to allow estimation of the emission factors (g CH4/kg VS; Table 10B.9)
and MCF (percent; Table 10.17). Some studies also presented emissions on the basis of mass of CH4 emitted perunit of faecal dry matter (FDM). Therefore, we have also supplied emission factors using these units (g CH4/kg
FDM) for countries with access to total FDM (Table 10B.9).
TABLE 10B.8 (NEW)REGIONAL DISTRIBUTION OF DATA USED TO DERIVE MCF AND METHANE EF FROM EXCRETION VOLITILESOLIDS DATA ON PRP
Sheep

Total

Country

CattleAustralia

13

13

Brazil

4

4

China

3

Japan

5

New Zealand

6

UK

6

Total

37

2

5
5

6

12

8

45

6

Emission factors
Methane conversion factors (MCF) and emission factors were estimated for both cattle and sheep, where yaks
were grouped with cattle (Table 10B.9). For estimating MCFs and emission factors based on VS content, ash
content of dung is required. We estimated dung ash content to be 17.9 percent for pasture-fed sheep, beef cattle
and dairy cattle (Fries et al. 1982; Karn 1991; Waghorn et al. 1999; Andueza et al. 2017). Data from a UK study(Defra, 2014) suggested that the IPCC B0 values were appropriate for cattle, we therefore assumed the IPCC values
for sheep were also reasonable estimates. For yaks, we used the IPCC default B0 value for buffalo (0.100).
There was no significant difference in values for cattle and sheep regardless of the method of representing methane
emissions (P > 0.05). For the refinement of the 2006 IPCC Guidelines we therefore suggest an aggregated value
is used. We also explored the possibility of disaggregating EF values by climatic zones, however the limited sizeof the dataset did not support this. Therefore, an aggregated value regardless of temperature is suggested for therefinement.
When adopting a Tier 2 approach, the MCF must be used in conjunction with a single B0 value of 0.19 m3 CH4 kgof VS excreted to ensure consistency with the Tier 1 emission factor provided in Table 10.14.

1

TABLE 10B.9 (NEW)METHANE CONVERSION FACTOR (MCF) AND METHANE EMISSION FACTORS (PER KG FAECAL DRY MATTER (FDM)) AND
VOLATILE SOLIDS (VS) FOR CATTLE AND SHEEP
Livestock
speciesMCF (%)
Average, (Std Dev)

EF (g CH4/kg FDM)
Average, (Std Dev)

EF (g CH4/kg VS)
Average, (Std Dev)

Cattle0.46 (0.38)

0.49 (0.42)

0.59 (0.51)

Sheep

0.52 (0.40)

0.53 (0.42)

0.65 (0.51)

Average

0.47 (0.38)

0.50 (0.42)

0.60 (0.51)

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Chapter 10: Emissions from Livestock and Manure Management

10B.7

Estimation of default values for MCFs, EFs for direct N2Oemissions, NH3 and N2 volatilized as well as NO3 leachedfrom solid storage and composting systems

M ethodo logie s
The estimation of updated MCF values, EF for direct N2O emissions and NO3 leaching and N2 from both (i) solid
storage and (i) two composting systems (static pile and passive windrow) are based on an extensive meta-analysisof 50 peer-reviewed research articles involving 304 observations and published in open access by Pardo et al.
(2015). In this study it was quantified the response of GHG emissions, NH3 emissions, and total N losses todifferent solid waste management strategies (conventional solid storage, turned composting, forced aerated
composting, covering, compacting, addition/substitution of bulking agents and the use of additives).
For solid storage, new treatments have been proposed to be incorporated in the 2019 Refinement:
covering/compacted (both treatments had similar effects on GHG emissions), addition/substitution of bulking
agents and the use of additives. In the 2006 IPCC Guidelines for National GHG inventories default emission
factors for solid storage were based on expert IPCC judgement and a single study (Amon et al. 2001). In Pardo etal. (2015) the estimation of MCF values and EF for direct N2O emissions from solid storage (without treatment)is based on data from 30 studies at the farm level.
For the new treatments, MCF values and EFs for direct N2O emissions have been based on:• 9 studies for compacting and covering;• 11 studies for addition/substitution of bulking agents;• 6 studies for use of additives.
For the rest of the management systems, MCF values and EF for direct N2O emissions have been based on:• 22 studies for solid storage;• 6 studies for composting-static piles (Forced aeration);• 11 studies for composting-Passive windrow (infrequent turning).
Based on the 2006 IPCC Guidelines climate zone classification two factors were defined: Temperature, which
involved two categories (i) Warm temperate and (ii) Cool temperate; and annual rainfall rate, including (i) Dry,(ii) Moist and (iii) Wet conditions.
CH 4 MC F
For the absolute CH4-C emission values, Pardo et al. (2015) used untreated solid storage as a reference system.
They compared estimated percent C lost as CH4 using the 2006 IPCC Guidelines method (IPCC 2006 MCF) withthe values obtained at the different studies (Figure 10B.6).

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Figure 10B.6 (New)Comparison between ranges of CH 4 -C emissions observed in collectedstudies in Pardo et al. (2015) (new) with estimations for the same studies according to the 2006
IPCC Guidelines methodology. Figure adapted from Pardo et al. (2015).

For untreated solid storage systems Pardo et al. (2015) showed that overall values were within the 2006 IPCC
Guidelines range for CH4 emissions (Figure 10B.6) and confirmed that the differences between cold and temperateconditions were in agreement with those indicated by the 2006 IPCC Guidelines not shown here, Figure S3b in
Pardo et al. (2015). There were not enough studies under warm conditions and therefore, the assumption is to keep
the same values indicated by the 2006 IPCC Guidelines.
Values for new solid storage treatments and composting (static pile and passive windrow) are estimated using thereference value from the untreated solid storage system and the relative differences observed in Pardo et al. (2015).
For the new treatments, covering or compacted solid storage resulted in emissions in the same range as in solid
storage not shown here, Figure 2b in Pardo et al. (2015) and estimated reduction of 75 percent and 50 percent wasobserved due to bulking agent addition and additives, respectively not shown here, Figure 2b in Pardo et al. (2015).
The differences amongst climatic zones were assumed to be in the same proportion as that found for untreatedsolid storage systems.
Both composted static piles and static windrows were estimated to produce 50 percent of the CH4 emitted fromsolid storage not shown here, Figure 2b in Pardo et al. (2015), which results in consistently greater values thanthose indicated by the 2006 IPCC Guidelines. As a difference to the 2006 IPCC Guidelines, CH4 emissions were
found to be temperature dependent for both composting systems (2006 IPCC Guidelines did not indicate
temperature differences for static piles).
N 2 O EF3 ( Tab le 10 .21)
According to the data examined in Pardo et al. (2015), there was no evidence to assume a lower EF for solid
storage systems (0.005 kg N2O–N kg−1 N excreted) than for passive windrow composting (0.01 kg N2O–N kg−1N
excreted). In fact, an EF of 0.5 percent (0.005kg N2O-N kg initial N-1) and 1 percent (0.01kg N2O-N kg initial N1) were found for composting-passive windrow and solid storage, respectively.
Composting static pile, in contrast to the 2006 IPCC Guidelines, was found to emit greater N2O emissions thanpassive windrows (not shown here, Fig. 3a in Pardo et al. 2015).
For the different treatments of solid storage, whereas Pardo et al. (2015) found no different effect on N2O after
compaction or covering, for both the addition of bulking agents or additives, a reduction of about 50 percent
compared with conventional solid storage was observed not shown here, Figure 2a in Pardo et al. (2015).
NH 3 lo ss e s
For solid storage and composting relative values compared to solid storage reflect results obtained from metaanalysis by Pardo et al. (2015) (Fig 10B.7). Ammonia and NOx default values in Table 10.22 for conventional
solid manure storage and other systems defined both in IPCC Guidelines and EMEP/EEA (2016) have been takenfrom current EMEP/EEA air pollutant emission inventory guidebook 2016. EFs from EMEP/EEA (2016) values,
which are expressed per TAN excreted and for each of the phases of the manure management, have been re-

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Chapter 10: Emissions from Livestock and Manure Management

calculated to be expressed as a funtion of total N excreted considering the mass balance flow between the different
manure management phases prior to manure application (housing, yards and storage). For other categories notpresent in EMEP/CORINAIR (2016), first for stages prior to storage, we have estimated percent N lost using
default EMEP/CORINAIR EFs for conventional manure storage and assuming no N2O or N2 losses, andsubsequently, during storage, we used the relative effect between solid storage and the alternative solid storage
categories (e.g. composting) (Fig 10B.7) as the basis to estimate NH3+NOx EF during storage.
Figure 10B.7 (New)
Effect on cumulative NH 3 -N emissions of different solid storage andcomposting methods compared with conventional solid storage. Figure adapted from Pardo etal. (2015)

NO 3 lea ch ing and N 2 lo ss es
Nitrate leaching/run-off has been estimated from the database from Pardo et al. (2015). For solid storage andcomposting some of the studies included measurements of N leaching (15), some of which estimated N2 from thetotal N balance, but only one included measurements of N2 (Moral et al. 2012). As a median value about 3 percent
is estimated to be lost as NO3 leaching/run-off (range: 0-38 percent). This value is subject to large uncertainty. Infact these trials may not represent common practices where the efficiency of collection of excreta N is much lower
and can lead to as great as 50 percent losses (e.g. Lekasi et al. 2001; Rufino et al. 2007). Nitrogen (N2) losses,
have only been, to our knowledge, measured by Moral et al. (2012) (12 percent) and even though they could be
estimated as a result of an N balance from trials where all N flows except N2 have been measured, the results arevery uncertain (0-55 percent). For N2, an estimated median value of 12 percent was found; coinciding with themeasured value by Moral et al. (2012). Systems that do not percolate but are subject to large water input will havegreater N2 losses and lower NO3 leaching-runoff. The opposite effect will be expected with rainy areas with nocontainment and large possibilities for run-off/leaching. Values must be considered with large caution.
A further summary review was carried out to identify run-off/leaching values from dry lots and manure pack. As
observed in the 2006 IPCC Guidelines runoff and leaching values varied greatly citing ranges of 3 to 6 percent of
N excreted (Eghball & Power 1994) or 5 to 19 percent (Bierman et al. 1999). In humid environments losses canbe significant reaching 22-25 percent (Uusi-Kämppä 2002). However, uncovered holding and feeding pens without
runoff containment tend to be in drier climates simply due to challenges in moisture control in more humid
environments. Furthermore, considerable numbers of cattle are raised in drier climates and as a result considerably
more studies exist looking at runoff from feedlots and drylots. Likewise recent attempts have been made to attempt
to model these losses to the environment (Kizil et al. 2006; Williams et al. 2006). These studies tend to place therange of runoff loss between 1 percent and roughly 7 (Kizil et al. 2006; Erickson & Klopfenstein 2010; Vadas &Powell 2013). It is proposed the value of 3.5 percent with an uncertain range of 0 to 7 percent be considered as adefault leaching factor for open, uncovered, uncontained drylots and bedded pack to provide a Tier 1 estimate of
the fraction of N excreted lost to the environment.
Inventory compilers must be careful to consider that this refers to N lost to the environment surrounding the pensor leached into the soil. If runoff is captured and returned to agricultural fields these losses must not be considered.
In humid environments, in cases where manure is left exposed to rainfall, inventory compilers should consider theuse of the upper bounds of the leaching fraction and furthermore to consider the development of a country specificleaching fraction.
R ev iew of th e e ffe ct of s lur r y sto re so lid cover s an d natur a l c ru st on em iss io ns of CH 4
an d N 2 O

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The review found 18 papers dealing with the impact of solid covers or natural crusts on CH4 and/or N2O emissions
from slurry stores. 11 of them were suitable to be included here to deduce emission factors (Amon et al. 2006;
Clemens et al. 2006; Guarino et al. 2006; Amon et al. 2007; VanderZaag et al. 2008; VanderZaag et al. 2009;
Aguerre et al. 2012; Nielsen et al. 2013; Hou et al. 2015; Matulaitis et al. 2015; Misselbrook et al. 2016).
For CH4 emissions from Liquid/Slurry, the 2006 IPCC Guidelines state that by judgement of the IPCC Expert
Group, a reduction of 40 percent due to crust cover may be applied when a thick, dry, crust is present. The newreview carried our within the 2019 Refinement confirms this judgement (VanderZaag et al. 2008; Aguerre et al.
2012; Nielsen et al. 2013). A solid cover reduces CH4 emissions by 25 to 50 percent (range: 0 to 90 percent)(Amon et al. 2006; Clemens et al. 2006; Guarino et al. 2006; Amon et al. 2007; VanderZaag et al. 2008;
VanderZaag et al. 2009; Hou et al. 2015; Matulaitis et al. 2015; Misselbrook et al. 2016).
For N2O emissions from Liquid/Slurry with natural crust cover a detailed literature review carried out during the2019 Refinement revealed only very few new datasets on the measurement of N2O emissions from manure storesand the influence of crusting. These datasets agree that N2O emissions increase when a crust is formed, but do notgive concrete numbers on the level of increase (VanderZaag et al. 2008; Aguerre et al. 2012).
For N2O emissions from Liquid/Slurry with a cover a detailed literature review carried out during the 2019
Refinement revealed only few new datasets on the measurement of N2O emissions from manure stores. These
datasets encompass a large range from a 50 percent reduction to a 100 percent increase in N2O emissions whenslurry stores are covered. The 2019 Refinement therefore suggest to use the emission factor of crust cover (Amonet al. 2006; Clemens et al. 2006; Guarino et al. 2006; Amon et al. 2007; VanderZaag et al. 2009; Hou et al. 2015;
Misselbrook et al. 2016).

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Chapter 10: Emissions from Livestock and Manure Management

References
Section 10.2 Livestock
Characterisation

Population

and

Feed

REFERENCES NEWLY CITED IN THE 2019 REFINEMENTAFRC (1995) Energy and protein requirements of ruminants. An advisory manual prepared by the AFRC
Technical Committee on Response to Nutrients. Wallingford, UK: CAB International. pp 159.
AFRC (1998) The Nutrition of Goats. Wallingford: CAB International. pp 118.
Animut G., Puchala R., Goetsch A.L., Patra A.K., Sahlu T., Varel V.H., Wells J. (2008) Methane emission bygoats consuming different sources of condensed tannins. Animal feed science and technology 144: 228-241.
Arnerdal S. (2005) Predictions for voluntary dry matter intake in dairy cows. Thesis. Department of Animal
Nutrition and Management, Swedish University of Agricultural Sciences.
Fox D.G., Sniffen C.J., O'connor J.D., Russell J.B., Van Soest P.J. (1992) A Net Carbohydrate and Protein System
for Evaluating Cattle Diets: III. Cattle Requirements and Diet Adequacy. Journal of Animal Science 70: 35783596.
IPCC, FAO, IFAD (2015) Emerging activities to combat climate change – use of FAO data and IPCC GHG
Inventory Guidelines for Agriculture and Land Use. In: Report of the joint FAO-IPCC-IFAD Expert Meeting,Eds. Tubiello, F.N., Neeff, T., Tanabe, K., Baasansuren, J., Fukuda, M.. M.. Report of the joint FAO-IPCCIFAD Expert Meeting, Pub. IGES, Japan.
MacLeod M., Vellinga T., Opio C., Falcucci A., Tempio G., Henderson B., Makkar H., Mottet, A., Robinson, T.,
Steinfeld, H., Gerber, P. (2017) Invited review: A position on the Global Livestock Environmental Assessment
Model (GLEAM). animal 12(2): 1-15.
National Academies of Sciences, Engineering and Medicine (2016) Nutrient Requirements of Beef Cattle: Eighth
Revised Edition. Washington, DC: The National Academies Press. pp 494.
Nousiainen J., Rinne M., Huhtanen P. (2009) A meta-analysis of feed digestion in dairy cows. 1. The effects offorage and concentrate factors on total diet digestibility. Journal of Dairy Science 92: 5019-5030.

REFERENCES COPIED FROM THE 2006 IPCC GUIDELINESAAC (1990) Feed Standards for Australian Livestock Ruminants. Commonwealth Scientific and Industrial
Research Organization (CSIRO) Publications. East Melbourne, Victoria, Australia: Australian Agricultural
Council.
AFRC (1990) Nutritive Requirements of Ruminant Animals: Energy. Rep. 5. Wallingford, UK: CAB International
Bamualim A., Kartiarso (1985) Nutrition of draught animals with special reference to Indonesia. In: Draught
Animal Power for Production. Australian Centre for International agricultural Research (ACIAR), Proceedings
Series No. 10, ed. JW Copland. Canberra, A.C.T., Australia: ACIAR.
FAO, IDF, IFCN (2014) World mapping of animal feeding systems in the dairy sector. Rome, Italy: Food andAgriculture Organization of the United Nations, International Dairy Federation, IFCN Dairy Research Network.
pp 160.
Johnson, D. E. 1986. Climatic stress and production efficiency. Limiting the Effects of Stress on Cattle. Western
Regional Res. Pub. #009 and Utah Agric. Exp. Sta. Res. Bull. 512:17-26.
Gibbs M.J., Conneely D., Johnson D., Lassey K.R., Ulyatt M.J. (2002) CH4 emissions from enteric fermentation.
in: Background Papers: IPCC Expert Meetings on Good Practice Guidance and Uncertainty Management in
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Gibbs M.J., Johnson D.E. (1993) Livestock Emissions. In: International Methane Emissions. Washington, D.C.,
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JW Copland. Canberra, A.C.T., Australia: ACIAR.

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Jurgens M.H. (1988) Animal Feeding and Nutrition, Sixth Edition. Dubuque, Iowa, U.S.A.: Kendall/HuntPublishing Company.
Lawrence P.R. (1985) A review of nutrient requirements of draught oxen. In: Draught Animal Power for
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JW Copland. Canberra, A.C.T., Australia: ACIAR.
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NRC (2001) Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001. Washington, DC: The
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Section 10.3

Methane Emissions from Enteric Fermentation

REFERENCES NEWLY CITED IN THE 2019 REFINEMENT
Aguerre M.J., Wattiaux M.A., Powell J.M., Broderick G.A., Arndt C. (2011) Effect of forage-to-concentrate ratio
in dairy cow diets on emission of methane, carbon dioxide, and ammonia, lactation performance, and manure
excretion. Journal of Dairy Science 94: 3081-3093.
Ahirwar R.R., Singh, A., Qureshi, M.I. (2010) A study of managemental practices in Water buffalo (Bubalus
Bubalis) in India. Buffalo Bulletin 29: 43-51.
Alemu A.W., Dijkstra J., Bannink A., France J., Kebreab E. (2011) Rumen stoichiometric models and theircontribution and challenges in predicting enteric methane production. Animal feed science and technology 16667: 761-778.
Ali A., Abdel-Razek A.K., Derar R., Abdel-Rheem H., Shehata S. (2009) Forms of Reproductive Disorders in
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Fiorentini G., Carvalho I.P.C., Messana J.D., Castagnino P.S., Berndt A., Canesin R.C., Frighetto R.T.S.,
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Neglia G., Balestrieri A., Gasparrini B., Cutrignelli M.I., Bifulco G., Salzano A., Cimmino R., D’occhio M.,
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Yilmaz O., Wilson R.T. (2012) The domestic livestock resources of Turkey: Economic and social role, species
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Section 10.4

Methane Emissions from Manure Management

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Hou Y., Velthof G.L., Oenema O. (2015) Mitigation of ammonia, nitrous oxide and methane emissions frommanure management chains: a meta-analysis and integrated assessment. Glob Chang Biol 21: 1293-1312.
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covered liquid dairy manure during summer storage and autumn agitation. Transactions of the Asabe 52: 599.

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International Symposium. Bloomington MN. May 29-31 Bio-Engineering Division, Silsoe Research Institute,Wrest Park, Silsoe, Bedford, MK45 4HS.

Section 10.5

N 2 O Emissions from Manure Management

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References Annexes
REFERENCES NEWLY CITED IN THE 2019 REFINEMENT

Annex 10A.1 Data underlying methane default emission
factors for Enteric Fermentation, Volatile solids and nitrogenexcretion and retention fractions for Cattle and Buffalo(see References in Annex 10B.1)

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10.177Volume 4: Agriculture, Forestry and Other Land Use

Annex 10A.2 Additional data and information for the
calculation of methane and nitrous oxide from Manure
Management
FAO (2017) Global Livestock Environmental Assessment Model v2.0. Data reference year 2010. Revision 4,
June 2017. Available at:
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Model (GLEAM). animal 12(2): 1-15.

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Chapter 10: Emissions from Livestock and Manure Management

Annex 10A.3 MCF Spreadsheet example for the calculationof a country or regions specific MCF
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Inventories. Prepared by the National Greenhouse Gas Inventories Programme, eds. HS Eggleston, L
Buendia, K Miwa, T Ngara, K Tanabe. Japan: IGES.

Annex 10A.4 Calculations of Methane Conversion Factors(MCFs) for biogas systems
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Osterburg B. (2017) Calculation of gaseous and particulate emissions from German agriculture 1990 – 2015.
Report on methods and data (RMD). Submission 2017. Thünen Rep 46.

Annex 10A.5 Equations relating all direct and indirect N2 O
emissions from manure along all stages in agricultural
production for livestock
No references.

Annex 10.B
Data and Explanatory Text for Development of
New Parameters in the 2019 Refinement
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10B.2 Estimation of Cattle/Buffalo CH 4 conversion factors(Y m )(References in Section 10.3 Methane Emissions from Enteric Fermentation)

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10B.3 Estimation of Default Emission Factor(s) for Goat Tier
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Castro-Lima A.R., Fernandes M.H.M.d.R., Teixeira I.A.M.d.A., Frighetto R.T.S., Bompadre T.F.V., Biagioli B.,
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Chethan K.P., Verma A.K., Singh P. (2013) Effect of fumaric acid supplementation on methanogenesis and rumen
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Ibáñez C., López M.C., Criscioni P., Fernández C. (2015) Effect of replacing dietary corn with beet pulp on energy
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55: 56-63.

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Ibáñez C., Moya V.J., Arriaga H., López D.M., Merino P., Fernández C. (2015) Replacement of cereal with low
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Keli A., Ribeiro L.P.S., Gipson T.A., Puchala R., Tesfai K., Tsukahara Y., Sahlu T., et al. (2017) Effects of pastureaccess regime on performance, grazing behavior, and energy utilization by Alpine goats in early and midlactation. Small Ruminant Research 154: 58-69.
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López M.C., Ródenas L., Piquer O., Cerisuelo A., Cervera C., Fernández C. (2011) Determinación de producciónde metano en caprinos alimentados con dietas con distintos cereales. Archivos de Zootecnia 60: 943-951.
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López M.C., Ródenas L., Piquer O., Martínez E., Cerisuelo A., Pascual J.J., Fernández C. (2010) Effect ofDifferent Physical form Alfalfa on Methane Production in Murciano-Granadina Dairy Goats. Journal ofApplied Animal Research 38: 93-96.
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Lu Q., Wu J., Wang M., Zhou C., Han X., Odongo E.N., Tan Z., et al. (2016) Effects of dietary addition of cellulase
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Martínez-Fernández G., Abecia L., Ramos-Morales E., Martin-García A.I., Molina-Alcaide E., Yáñez-Ruiz D.R.
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Miri V.H., Tyagi A.K., Ebrahimi S.H., Mohini M. (2013) Effect of cumin (Cuminum cyminum) seed extract on
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Na Y., Li D.H., Choi Y., Kim K.H., Lee S.R. (2018) Effects of feeding level on nutrient digestibility and entericmethane production in growing goats (Capra hircus hircus) and Sika deer (Cervus nippon hortulorum). AsianAustralasian Journal of Animal Sciences 31:1238-1243.
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Shibata M., Terada F., Iwasaki K., Kurihara M., Nishida T. (1992) Methane Production in Heifers, Sheep andGoats Consuming Diets of Various Hay-Concentrate Ratios. Nihon Chikusan Gakkaiho 63: 1221-1227.
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10B.4 Feed intake estimates using a simplified Tier 2 methodAppuhamy J.A., France J., Kebreab E. (2016) Models for predicting enteric methane emissions from dairy cows
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Nötfor. Almqvist & Wiksell.

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NRC (2001) Nutrient Requirements of Dairy Cattle: Seventh Revised Edition, 2001, ed. BoAaNR National
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The National Academies Press. pp. 408.

10B.5 Basis for
Liquid/Slurry

Changesto

MCF

CalculationsforBalde H., VanderZaag A.C., Burtt S., Evans L., Wagner-Riddle C., Desjardins R.L., MacDonald J.D. (2016)
Measured versus modelled methane emissions from separated liquid dairy manure show large model
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Balde H., VanderZaag A.C., Burtt S.D., Gordon R.J., Desjardins R.L. (2016) Does Fall Removal of the Dairy
Manure Sludge in a Storage Tank Reduce Subsequent Methane Emissions? Journal of Environmental Quality
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Elsgaard L., Olsen A.B., Petersen S.O. (2016) Temperature response of methane production in liquid manures andco-digestates. Science of the Total Environment 539: 78-84.
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10B.6 Revision of MCFs from dung deposited onto pasture,
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10B.7 Estimation of default emission factors for MCF CH 4
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49: 113-122.

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Erickson G., Klopfenstein T. (2010) Nutritional and management methods to decrease nitrogen losses from beef
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2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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