Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

CHAPTER 2

GENERIC METHODOLOGIESAPPLICABLE TO MULTIPLE LANDUSE CATEGORIES2006 IPCC Guidelines for National Greenhouse Gas Inventories

2.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors
Harald Aalde (Norway), Patrick Gonzalez (USA), Michael Gytarsky (Russian Federation), Thelma Krug (Brazil),
Werner A. Kurz (Canada), Rodel D. Lasco (Philippines), Daniel L. Martino (Uruguay), Brian G. McConkey(Canada), Stephen Ogle (USA), Keith Paustian (USA), John Raison (Australia), N.H. Ravindranath (India),
Dieter Schoene (FAO), Pete Smith (UK), Zoltan Somogyi (European Commission/Hungary), Andre van Amstel(Netherlands), and Louis Verchot (ICRAF/USA)

2.2

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Contents
2

Generic Methodologies Applicable to Multiple Land-use Categories
2.1

Introduction ........................................................................................................................................... 2.6

2.2

Inventory framework ............................................................................................................................. 2.6

2.2.1

Overview of carbon stock change estimation ................................................................................ 2.6

2.2.2

Overview of non-CO2 emission estimation ................................................................................. 2.10

2.2.3

Conversion of C stock changes to CO2 emissions ....................................................................... 2.11

2.3

Generic methods for CO2 emissions and removals ............................................................................. 2.11

2.3.1

Change in biomass carbon stocks (above-ground biomass and below-ground biomass) ............ 2.11

2.3.1.1

Land remaining in a land-use category .................................................................................. 2.12

2.3.1.2

Land converted to a new land-use category ........................................................................... 2.19

2.3.2

Change in carbon stocks in dead organic matter ......................................................................... 2.21

2.3.2.1

Land remaining in a land-use category .................................................................................. 2.21

2.3.2.2

Land conversion to a new land-use category ......................................................................... 2.25

2.3.3

Change in carbon stocks in soils ................................................................................................. 2.28

2.3.3.1

Soil C estimation methods (land remaining in a land-use category andland conversion to a new land use) ........................................................................................ 2.29

2.4

Non-CO2 emissions ............................................................................................................................. 2.40

2.5Additional generic guidance for Tier 3 methods ................................................................................. 2.50

2.5.1

Measurement-based Tier 3 inventories........................................................................................ 2.50

2.5.2

Model-based Tier 3 inventories ................................................................................................... 2.52

References..................................................................................................................................................... 2.54

Equations
Equation 2.1

Annual carbon stock changes for the entire AFOLU Sector estimated as thesum of changes in all land-use categories.............................................................................. 2.6

Equation 2.2

Annual carbon stock changes for a land-use category as a sum of changes
in each stratum within the category ....................................................................................... 2.7

Equation 2.3

Annual carbon stock changes for a stratum of a land-use category as a sumof changes in all pools ........................................................................................................... 2.7

Equation 2.4

Annual carbon stock change in a given pool as a function of gains andlosses (Gain-Loss Method) .................................................................................................... 2.9

Equation 2.5

Carbon stock change in a given pool as an annual average difference
between estimates at two points in time (Stock-Difference Method) .................................. 2.10

Equation 2.6

Non-CO2 emissions to the atmosphere ................................................................................ 2.10

Equation 2.7

Annual change in carbon stocks in biomass in land remaining in aparticular land-use category (Gain-Loss Method) ............................................................... 2.12

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Equation 2.8

Annual change in carbon stocks in biomass in land remaining in thesame land-use category (Stock-Difference Method) ........................................................... 2.12

Equation 2.9

Annual increase in biomass carbon stocks due to biomass increment
in land remaining in same category ..................................................................................... 2.15

Equation 2.10 Average annual increment in biomass ................................................................................. 2.15
Equation 2.11 Annual decrease in carbon stocks due to biomass losses in land remaining
in same category .................................................................................................................. 2.16
Equation 2.12 Annual carbon loss in biomass of wood removals............................................................... 2.17
Equation 2.13 Annual carbon loss in biomass of fuelwood removal .......................................................... 2.17
Equation 2.14 Annual carbon losses in biomass due to disturbances ......................................................... 2.18
Equation 2.15 Annual change in biomass carbon stocks on land converted to otherland-use category (Tier 2).................................................................................................... 2.20
Equation 2.16 Initial change in biomass carbon stocks on land converted to another land category.......... 2.20
Equation 2.17 Annual change in carbon stocks in dead organic matter...................................................... 2.21
Equation 2.18 Annual change in carbon stocks in dead wood or litter (Gain-Loss Method)...................... 2.23
Equation 2.19 Annual change in carbon stocks in dead wood or litter (Stock-Difference Method) ........... 2.23
Equation 2.20 Annual carbon in biomass transferred to dead organic matter............................................. 2.24
Equation 2.21 Annual biomass carbon loss due to mortality ...................................................................... 2.24
Equation 2.22 Annual carbon transfer to slash ........................................................................................... 2.25
Equation 2.23 Annual change in carbon stocks in dead wood and litter due to land conversion................ 2.26
Equation 2.24 Annual change in carbon stocks in soils .............................................................................. 2.29
Equation 2.25 Annual change in organic carbon stocks in mineral soils .................................................... 2.30
Equation 2.26 Annual Carbon loss from drained organic soils (CO2) ........................................................ 2.35
Equation 2.27 Estimation of greenhouse gas emissions from fire .............................................................. 2.42

Figures

2.4

Figure 2.1

Generalized carbon cycle of terrestrial AFOLU ecosystems showing
the flows of carbon into and out of the system as well as between
the five C pools within the system. ........................................................................................ 2.8

Figure 2.2

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in biomass in a land-use category. .............................................. 2.14

Figure 2.3

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in dead organic matter for a land-use category ........................... 2.22

Figure 2.4

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in mineral soils by land-use category .......................................... 2.32

Figure 2.5

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in organic soils by land-use category .......................................... 2.33

Figure 2.6

Generic decision tree for identification of appropriate tier to estimate greenhousegas emissions from fire in a land-use category .................................................................... 2.44

Figure 2.7

Steps to develop a Tier 3 model-based inventory estimation system. ................................. 2.52

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

TablesTable 2.1

Example of a simple matrix (Tier 2) for the impacts of disturbances on carbon pools ....... 2.19

Table 2.2

Tier 1 default values for litter and dead wood carbon stocks .............................................. 2.27

Table 2.3

Default reference (under native vegetation) soil organic C stocks (SOCREF) for
Mineral Soils ....................................................................................................................... 2.31

Table 2.4

Fuel (dead organic matter plus live biomass) biomass consumption values
for fires in a range of vegetation types ................................................................................ 2.45

Table 2.5

Emission factors for various types of burning ..................................................................... 2.47

Table 2.6

Combustion factor values (proportion of prefire fuel biomass consumed) for fires
in a range of vegetation types .............................................................................................. 2.48

Boxes
Box 2.1

Alternative formulations of Equation 2.25 for Approach 1 activity data
versus Approach 2 or 3 activity data with transition matrices ............................................. 2.34

Box 2.2

Comparison between use of Approach 1 aggregate statistics and Approach 2
or 3 activity data with transition matrices ........................................................................... 2.36

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

2 GENERIC METHODOLOGIES APPLICABLETO MULTIPLE LAND-USE CATEGORIES
2.1

INTRODUCTION

Methods to estimate greenhouse gas emissions and removals in the Agriculture, Forestry and Other Land Use(AFOLU) Sector can be divided into two broad categories: 1) methods that can be applied in a similar way forany of the types of land use (i.e., generic methods for Forest Land, Cropland, Grassland, Wetlands, Settlements
and Other Land); and 2) methods that only apply to a single land use or that are applied to aggregate data on anational-level, without specifying land use. Chapter 2 provides mainly descriptions of generic methodologies
under category (1) for estimating ecosystem carbon stock changes as well as for estimating non-CO2 fluxes fromfire. These methods can be applied for any of the six land-use categories. Generic information on methods
includes:•general framework for applying the methods within specific land-use categories;•

choice of methods, including equations and default values for Tier 1 methods for estimating C stock changes
and non-CO2 emissions;•general guidance on use of higher Tier methods;•

use of the IPCC Emission Factor Data Base (EFDB); and•uncertainty estimation.

Specific details and guidance on implementing the methods for each of the land-use and land-use conversion
categories, including choosing emission factors, compiling activity data and assessing uncertainty, are given in
the chapters on specific land-use categories (see Chapters 4 to 9). Guidance on inventory calculations for eachspecific land use refers back to this chapter for description of methods where they are generic.

2.2

INVENTORY FRAMEWORKThis section outlines a systematic approach for estimating carbon stock changes (and associated emissions andremovals of CO2) from biomass, dead organic matter, and soils, as well as for estimating non-CO2 greenhousegas emissions from fire. General equations representing the level of land-use categories and strata are followed
by a short description of processes with more detailed equations for carbon stock changes in specific pools byland-use category. Principles for estimating non-CO2 emissions and common equations are then given. Specific,
operational equations to estimate emissions and removals by processes within a pool and by category, which
directly correspond to worksheet calculations, are provided in Sections 2.3 and 2.4.

2.2.1

Overview of carbon stock change estimation

The emissions and removals of CO2 for the AFOLU Sector, based on changes in ecosystem C stocks, areestimated for each land-use category (including both land remaining in a land-use category as well as land
converted to another land use). Carbon stock changes are summarized by Equation 2.1.

EQUATION 2.1
ANNUAL CARBON STOCK CHANGES FOR THE ENTIRE AFOLU SECTOR ESTIMATED AS THE SUM
OF CHANGES IN ALL LAND-USE CATEGORIES∆C AFOLU = ∆C FL + ∆CCL + ∆CGL + ∆CWL + ∆C SL + ∆COL
Where:
ΔC = carbon stock change
Indices denote the following land-use categories:
AFOLU
FL

2.6

= Agriculture, Forestry and Other Land Use= Forest Land2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

CL= CroplandGL= Grassland

WL= Wetlands

SL= Settlements

OL= Other LandFor each land-use category, carbon stock changes are estimated for all strata or subdivisions of land area (e.g.,
climate zone, ecotype, soil type, management regime etc., see Chapter 3) chosen for a land-use category(Equation 2.2). Carbon stock changes within a stratum are estimated by considering carbon cycle processes
between the five carbon pools, as defined in Table 1.1 in Chapter 1. The generalized flowchart of the carbon
cycle (Figure 2.1) shows all five pools and associated fluxes including inputs to and outputs from the system, as
well as all possible transfers between the pools. Overall, carbon stock changes within a stratum are estimated byadding up changes in all pools as in Equation 2.3. Further, carbon stock changes in soil may be disaggregated as
to changes in C stocks in mineral soils and emissions from organic soils. Harvested wood products (HWP) arealso included as an additional pool.

EQUATION 2.2
ANNUAL CARBON STOCK CHANGES FOR A LAND-USE CATEGORY AS A SUM OF CHANGES IN EACH
STRATUM WITHIN THE CATEGORY∆C LU = ∑ ∆C LU I
i

Where:ΔCLU = carbon stock changes for a land-use (LU) category as defined in Equation 2.1.
i= denotes a specific stratum or subdivision within the land-use category (by any combination of
species, climatic zone, ecotype, management regime etc., see Chapter 3), i = 1 to n.

EQUATION 2.3
ANNUAL CARBON STOCK CHANGES FOR A STRATUM OF A LAND-USE CATEGORY AS A SUM OF
CHANGES IN ALL POOLS∆C LU i = ∆C AB + ∆C BB + ∆C DW + ∆C LI + ∆C SO + ∆C HWP

Where:ΔCLUi = carbon stock changes for a stratum of a land-use category
Subscripts denote the following carbon pools:AB= above-ground biomassBB= below-ground biomass

DW = deadwoodLI= litterSO= soils

HWP = harvested wood products
Estimating changes in carbon pools and fluxes depends on data and model availability, as well as resources andcapacity to collect and analyze additional information (See Chapter 1, Section 1.3.3 on key category analysis).
Table 1.1 in Chapter 1 outlines which pools are relevant for each land-use category for Tier 1 methods, including
cross references to reporting tables. Depending on country circumstances and which tiers are chosen, stockchanges may not be estimated for all pools shown in Equation 2.3. Because of limitations to deriving default
data sets to support estimation of some stock changes, Tier 1 methods include several simplifying assumptions:2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 2.1

Generalized carbon cycle of terrestrial AFOLU ecosystems showing the flows
of carbon into and out of the system as well as between the five C pools
within the system.

Harvestedwood products

2.8

Above-groundbiomass

LitterBelow-groundbiomass

Dead wood

Increase of carbon
stocks due to growth

Transfer of carbon
between pools

Carbon fluxes due todiscrete events, i.e.,from harvest residues
and natural disturbance

Carbon fluxes due
to continuous
processes, i.e.
decomposition

Soil organic
matter2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories•

change in below-ground biomass C stocks are assumed to be zero under Tier 1 (under Tier 2, countryspecific data on ratios of below-ground to above-ground biomass can be used to estimate below-groundstock changes);•

under Tier 1, dead wood and litter pools are often lumped together as ‘dead organic matter’ (see discussion
below); and•dead organic matter stocks are assumed to be zero for non-forest land-use categories under Tier 1. For
Forest Land converted to another land use, default values for estimating dead organic matter carbon stocks
are provided in Tier 1.

The carbon cycle includes changes in carbon stocks due to both continuous processes (i.e., growth, decay) anddiscrete events (i.e., disturbances like harvest, fire, insect outbreaks, land-use change and other events).
Continuous processes can affect carbon stocks in all areas in each year, while discrete events (i.e., disturbances)cause emissions and redistribute ecosystem carbon in specific areas (i.e., where the disturbance occurs) and in
the year of the event.
Disturbances may also have long-lasting effects, such as decay of wind-blown or burnt trees. For practicality,
Tier 1 methods assume that all post-disturbance emissions (less removal of harvested wood products) are
estimated as part of the disturbance event, i.e., in the year of the disturbance. For example, rather than estimating
the decay of dead organic matter left after a disturbance over a period of several years, all post-disturbanceemissions are estimated in the year of the event.
Under Tier 1, it is assumed that the average transfer rate into dead organic matter (dead wood and litter) is equal
to the average transfer rate out of dead organic matter, so that the net stock change is zero. This assumptionmeans that dead organic matter (dead wood and litter) carbon stocks need not be quantified under Tier 1 for land
areas that remain in a land-use category 1. The rationale for this approach is that dead organic matter stocks,
particularly dead wood, are highly variable and site-specific, depending on forest type and age, disturbancehistory and management. In addition, data on coarse woody debris decomposition rates are scarce and thus it
was deemed that globally applicable default factors and uncertainty estimates can not be developed. Countries
experiencing significant changes in forest types or disturbance or management regimes in their forests areencouraged to develop domestic data to estimate the impact from these changes using Tier 2 or 3 methodologies
and to report the resulting carbon stock changes and non-CO2 emissions and removals.
All estimates of changes in carbon stocks, i.e., growth, internal transfers and emissions, are in units of carbon to
make all calculations consistent. Data on biomass stocks, increments, harvests, etc. can initially be in units of dry
matter that need to be converted to tonnes of carbon for all subsequent calculations. There are two fundamentallydifferent and equally valid approaches to estimating stock changes: 1) the process-based approach, which
estimates the net balance of additions to and removals from a carbon stock; and 2) the stock-based approach,which estimates the difference in carbon stocks at two points in time.
Annual carbon stock changes in any pool can be estimated using the process-based approach in Equation 2.4
which sets out the Gain-Loss Method that can be applied to all carbon gains or losses. Gains can be attributed to
growth (increase of biomass) and to transfer of carbon from another pool (e.g., transfer of carbon from the live
biomass carbon pool to the dead organic matter pool due to harvest or natural disturbances). Gains are always
marked with a positive (+) sign. Losses can be attributed to transfers of carbon from one pool to another (e.g.,
the carbon in the slash during a harvesting operation is a loss from the above-ground biomass pool), or emissions
due to decay, harvest, burning, etc. Losses are always marked with a negative (-) sign.

EQUATION 2.4
ANNUAL CARBON STOCK CHANGE IN A GIVEN POOL AS A FUNCTION OF GAINS AND LOSSES(GAIN-LOSS METHOD)∆C = ∆CG − ∆C L
Where:
ΔC = annual carbon stock change in the pool, tonnes C yr-1
ΔCG = annual gain of carbon, tonnes C yr-1

1

Emissions from litter C stocks are accounted for under Tier 1 for forest conversion to other land-use.

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Volume 4: Agriculture, Forestry and Other Land Use

ΔCL = annual loss of carbon, tonnes C yr-1
Note that CO2 removals are transfers from the atmosphere to a pool, whereas CO2 emissions are transfers from apool to the atmosphere. Not all transfers involve emissions or removals, since any transfer from one pool toanother is a loss from the donor pool, but is a gain of equal amount to the receiving pool. For example, a transferfrom the above-ground biomass pool to the dead wood pool is a loss from the above-ground biomass pool and again of equal size for the dead wood pool, which does not necessarily result in immediate CO2 emission to theatmosphere (depending on the Tier used).
The method used in Equation 2.4 is called the Gain-Loss Method, because it includes all processes that bringabout changes in a pool. An alternative stock-based approach is termed the Stock-Difference Method, which canbe used where carbon stocks in relevant pools are measured at two points in time to assess carbon stock changes,
as represented in Equation 2.5.

EQUATION 2.5
CARBON STOCK CHANGE IN A GIVEN POOL AS AN ANNUAL AVERAGE DIFFERENCE BETWEENESTIMATES AT TWO POINTS IN TIME (STOCK-DIFFERENCE METHOD)(Ct 2 − Ct1 )∆C =(t 2 − t1 )Where:
ΔC = annual carbon stock change in the pool, tonnes C yr-1
Ct1 = carbon stock in the pool at time t1, tonnes C
Ct2 = carbon stock in the pool at time t2, tonnes C
If the C stock changes are estimated on a per hectare basis, then the value is multiplied by the total area withineach stratum to obtain the total stock change estimate for the pool. In some cases, the activity data may be in theform of country totals (e.g., harvested wood) in which case the stock change estimates for that pool are estimateddirectly from the activity data after applying appropriate factors to convert to units of C mass. When using the
Stock-Difference Method for a specific land-use category, it is important to ensure that the area of land in thatcategory at times t1 and t2 is identical, to avoid confounding stock change estimates with area changes.
The process method lends itself to modelling approaches using coefficients derived from empirical research data.
These will smooth out inter-annual variability to a greater extent than the stock change method which relies on
the difference of stock estimates at two points in time. Both methods are valid so long as they are capable ofrepresenting actual disturbances as well as continuously varying trends, and can be verified by comparison withactual measurements.

2.2.2

Overview of non-CO 2 emission estimation

Non-CO2 emissions are derived from a variety of sources, including emissions from soils, livestock and manure,and from combustion of biomass, dead wood and litter. In contrast to the way CO2 emissions are estimated frombiomass stock changes, the estimate of non-CO2 greenhouse gases usually involves an emission rate from asource directly to the atmosphere. The rate (Equation 2.6) is generally determined by an emission factor for a
specific gas (e.g., CH4, N2O) and source category and an area (e.g., for soil or area burnt), population (e.g., forlivestock) or mass (e.g., for biomass or manure) that defines the emission source.

EQUATION 2.6
NON-CO2 EMISSIONS TO THE ATMOSPHERE
Emission = A • EF
Where:
Emission = non-CO2 emissions, tonnes of the non-CO2 gas
A= activity data relating to the emission source (can be area, animal numbers or mass unit, dependingon the source type)

EF = emission factor for a specific gas and source category, tonnes per unit of A

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Many of the emissions of non-CO2 greenhouse gases are either associated with a specific land use (e.g., CH4emissions from rice) or are typically estimated from national-level aggregate data (e.g., CH4 emissions fromlivestock and N2O emissions from managed soils). Where an emission source is associated with a single landuse, the methodology for that emission is described in the chapter for that specific land-use category (e.g.,methane from rice in Chapter 5 on Cropland). Emissions that are generally based on aggregated data are dealt
with in separate chapters (e.g., Chapter 10 on livestock-related emissions, and Chapter 11 on N2O emissions
from managed soils and CO2 emissions from liming and urea applications). This chapter describes only methods
to estimate non-CO2 (and CO2) emissions from biomass combustion, which can occur in several different landuse categories.

2.2.3

Conversion of C stock changes to CO2 emissions

For reporting purposes, changes in C stock categories (that involve transfers to the atmosphere) can be converted
to units of CO2 emissions by multiplying the C stock change by -44/12. In cases where a significant amount of
the carbon stock change is through emissions of CO and CH4, then these non-CO2 carbon emissions should be
subtracted from the estimated CO2 emissions or removals using methods provided for the estimation of thesegases. In making these estimates, inventory compilers should assess each category to ensure that this carbon is
not already covered by the assumptions and approximations made in estimating CO2 emissions.
It should also be noted that not every stock change corresponds to an emission. The conversion to CO2 from C, isbased on the ratio of molecular weights (44/12). The change of sign (-) is due to the convention that increases in
C stocks, i.e. positive (+) stock changes, represent a removal (or ‘negative’ emission) from the atmosphere,
while decreases in C stocks, i.e. negative (-) stock changes, represent a positive emission to the atmosphere.

2.3

GENERIC METHODS FOR CO 2 EMISSIONSAND REMOVALS

As outlined in Section 2.2, emissions and removals of CO2 within the AFOLU Sector are generally estimated on
the basis of changes in ecosystem carbon stocks. These consist of above-ground and below-ground biomass,
dead organic matter (i.e., dead wood and litter), and soil organic matter. Net losses in total ecosystem carbon
stocks are used to estimate CO2 emissions to the atmosphere, and net gains in total ecosystem carbon stocks areused to estimate removal of CO2 from the atmosphere. Inter-pool transfers may be taken into account whereappropriate. Changes in carbon stocks may be estimated by direct inventory methods or by process models.
Each of the C stocks or pools can occur in any of land-use categories, hence general attributes of the methods
that apply to any land-use category are described here. In particular cases, losses in carbon stocks or pools may
imply emissions of non-CO2 gases such as methane, carbon monoxide, non-methane volatile organic carbon andothers. The methods for estimating emissions of these gases are provided in Section 2.4. It is good practice tocheck for complete coverage of CO2 and non-CO2 emissions due to losses in carbon stocks or pools to avoid
omissions or double counting. Specific details regarding the application of these methods within a particular
land-use category are provided under the relevant land uses in Chapters 4 to 9.

2.3.1

Change in biomass carbon stocks (above-groundbiomass and below-ground biomass)

Plant biomass constitutes a significant carbon stock in many ecosystems. Biomass is present in both aboveground and below-ground parts of annual and perennial plants. Biomass associated with annual and perennial
herbaceous (i.e., non-woody) plants is relatively ephemeral, i.e., it decays and regenerates annually or every few
years. So emissions from decay are balanced by removals due to re-growth making overall net C stocks inbiomass rather stable in the long term. Thus, the methods focus on stock changes in biomass associated withwoody plants and trees, which can accumulate large amounts of carbon (up to hundreds of tonnes per ha) overtheir lifespan. Carbon stock change in biomass on Forest Land is likely to be an important sub-category becauseof substantial fluxes owing to management and harvest, natural disturbances, natural mortality and forest regrowth. In addition, land-use conversions from Forest Land to other land uses often result in substantial loss ofcarbon from the biomass pool. Trees and woody plants can occur in any of the six land-use categories althoughbiomass stocks are generally largest on Forest Land. For inventory purposes, changes in C stock in biomass are
estimated for (i) land remaining in the same land-use category and (ii) land converted to a new land-use category.
The reporting convention is that all emissions and removals associated with a land-use change are reported in the
new land-use category.

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Volume 4: Agriculture, Forestry and Other Land Use

2.3.1.1

L AND REMAINING IN A LAND - USE CATEGORY

Equation 2.3 includes the five carbon pools for which stock change estimates are required. This section presents
methods for estimating biomass carbon gains, losses and net changes. Gains include biomass growth in aboveground and below-ground components. Losses are categorized into wood fellings or harvest, fuelwood gathering,and losses from natural disturbances on managed land such as fire, insect outbreaks and extreme weather events(e.g., hurricanes, flooding). Two methods are provided for estimating carbon stock changes in biomass.
The Gain-Loss Method requires the biomass carbon loss to be subtracted from the biomass carbon gain
(Equation 2.7). This underpins the Tier 1 method, for which default values for calculation of increment andlosses are provided in this Volume to estimate stock changes in biomass. Higher tier methods use countryspecific data to estimate gain and loss rates. For all tiers, these estimates require country-specific activity data,
although for Tier 1, these data can be obtained from globally-compiled databases (e.g., FAO statistics).

EQUATION 2.7
ANNUAL CHANGE IN CARBON STOCKS IN BIOMASSIN LAND REMAINING IN A PARTICULAR LAND-USE CATEGORY (GAIN-LOSS METHOD)∆C B = ∆CG − ∆C L
Where:∆CB = annual change in carbon stocks in biomass (the sum of above-ground and below-ground biomass
terms in Equation 2.3) for each land sub-category, considering the total area, tonnes C yr-1
∆CG = annual increase in carbon stocks due to biomass growth for each land sub-category, considering
the total area, tonnes C yr-1
∆CL = annual decrease in carbon stocks due to biomass loss for each land sub-category, considering thetotal area, tonnes C yr-1
The changes in C stock in biomass for land remaining in the same land-use category (e.g., Forest Land
Remaining Forest Land) are based on estimates of annual gain and loss in biomass stocks. Countries using anyof the three tiers can adopt this method. This method can be used by countries that do not have national
inventory systems designed for estimating woody biomass stocks. Default data are provided in land-use category
chapters for inventory compilers who do not have access to country-specific data. Worksheets have also beendeveloped using the methods and equations (Annex 1).
The Stock-Difference Method requires biomass carbon stock inventories for a given land area, at two points intime. Annual biomass change is the difference between the biomass stock at time t2 and time t1, divided by the
number of years between the inventories (Equation 2.8). In some cases, primary data on biomass may be in theform of wood volume data, for example, from forest surveys, in which case factors are provided to convert wood
volume to carbon mass units, as shown in Equation 2.8.b.

EQUATION 2.8
ANNUAL CHANGE IN CARBON STOCKS IN BIOMASSIN LAND REMAINING IN THE SAME LAND-USE CATEGORY (STOCK-DIFFERENCE METHOD)(Ct2 − Ct1 )(a)∆C B =(t 2 − t1 )where
C = ∑ { Ai , j • Vi , j • BCEFS i , j • (1 + Ri , j ) • CFi , j }
i, j

(b)

Where:∆CB = annual change in carbon stocks in biomass (the sum of above-ground and below-ground biomass
terms in Equation 2.3 ) in land remaining in the same category (e.g., Forest Land Remaining ForestLand), tonnes C yr-1
C t2 = total carbon in biomass for each land sub-category at time t2, tonnes C

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

C t1 = total carbon in biomass for each land sub-category at time t1, tonnes CC= total carbon in biomass for time t1 to t2A= area of land remaining in the same land-use category, ha (see note below)V= merchantable growing stock volume, m3 ha-1i

= ecological zone i (i = 1 to n)j= climate domain j (j = 1 to m)R= ratio of below-ground biomass to above-ground biomass, tonne d.m. below-ground biomass (tonned.m. above-ground biomass)-1

CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
BCEFS = biomass conversion and expansion factor for expansion of merchantable growing stock volume
to above-ground biomass, tonnes above-ground biomass (m3 growing stock volume)-1, (see Table 4.5
for Forest Land). BCEFS transforms merchantable volume of growing stock directly into its aboveground biomass. BCEFS values are more convenient because they can be applied directly to volumebased forest inventory data and operational records, without the need of having to resort to basic
wood densities (D). They provide best results, when they have been derived locally and based
directly on merchantable volume. However, if BCEFS values are not available and if the biomass
expansion factor (BEFS) and D values are separately estimated, the following conversion can be used:
BCEFS = BEFS ● D
In applying the Gain-Loss or Stock-Difference Methods, the relevant area is clearly the area of land remaining in
the relevant category at the end of the year for which the inventory is being estimated. Any other land will be ina conversion category (see Section 2.3.1.2). The length of time that land remains in a conversion category after achange in land use is by default 20 years (the time period assumed for carbon stocks to come to equilibrium forthe purposes of calculating default coefficients in the 1996 IPCC Guidelines and retained for GPG-LULUCF andused here also, though other periods may be used at higher Tiers according to national circumstances). Under
default assumptions therefore land will be transferred from a conversion category to a remaining category after it
has been in a given land use for 20 years. Some carbon stock changes will take place in the year of conversion,
but nevertheless it is important to be consistent about the period for which land stays in the conversion categoryor the approaches to land area estimation described in the next Chapter will not work. Stock changes that arecompleted within 1 year after conversion will be related to the area converted annually and the relevant land
areas may need to be treated as a sub-category within the conversion category but nevertheless should remain in
the conversion category until the 20 year default or other conversion time period is completed.
The Stock-Difference Method will be applicable in countries that have national inventory systems for forests andother land-use categories, where the stocks of different biomass pools are measured at periodic intervals. The
stock-difference method requires greater resources and many countries may not have national inventory systems
for forests and other land-use categories. This method is suitable to countries adopting a Tier 3 and in some cases
a Tier 2 approach, but may not be suitable for countries using a Tier 1 approach due to limitations of data. It is
important to make sure that inventory system generates data on gains and losses of biomass carbon pools.
Either of the above two methods can be used for estimating biomass carbon stock changes for all land categories(e.g., Forest Land Remaining Forest Land, Grassland Remaining Grassland, and Cropland Remaining
Cropland) where perennial woody biomass may be present. Figure 2.2 can be used to assist inventory agencies
in identifying the appropriate tier to estimate changes in biomass carbon stocks.
Note that some biomass losses can lead to emissions of C other than as CO2, such as biomass consumption andemission as methane (CH4) by termites and wild mammals. 2 Default Tier 1 methods for these sources have notbeen developed, and countries wishing to estimate and report these emissions should develop and employ a Tier
3 approach.

2

CO2 and non-CO2 losses of carbon associated with biomass burning are estimated such that carbon emissions are notdouble-counted.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 2.2

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in biomass in a land-use category.

Start

Are detailed
data on biomass
available to estimate changes in C
stocks using dynamic models or
allometricequations?

Use the detailed biomass
data for Tier 3 method.

Yes

Box 3: Tier 3

No

Are countryspecific biomass dataand emission/removal factors
available?

Use country-specificbiomass data andemission/removal factors
for the Tier 2 method.

Yes

Box 2: Tier 2
No

Are changes
in C stocks in biomass inthis land classification a key
category1?

Yes
Collect data for the Tier
3 or Tier 2 method.

No

Are
aggregate data on
biomass growth andloss available?

Yes

No

Gather data on
biomass growthand biomass loss.

Use aggregate data anddefault emission/removal
factors for Tier 1 method.
Box 1: Tier 1

Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

A.

METHODS FOR ESTIMATING CHANGE IN CARBON STOCKS IN
BIOMASS (∆C B )A.1

Estimating annual increase in biomass carbon stocks (Gain-Loss Method),∆C GThis is the Tier 1 method that, when combined with default biomass growth rates, allows for any country to
calculate the annual increase in biomass, using estimates of area and mean annual biomass increment, for eachland-use type and stratum (e.g., climatic zone, ecological zone, vegetation type) (Equation 2.9).

EQUATION 2.9
ANNUAL INCREASE IN BIOMASS CARBON STOCKS DUE TO BIOMASS INCREMENTIN LAND REMAINING IN THE SAME LAND-USE CATEGORY∆CG = ∑ ( Ai , j • GTOTALi , j • CFi , j )i, jWhere:∆CG = annual increase in biomass carbon stocks due to biomass growth in land remaining in the sameland-use category by vegetation type and climatic zone, tonnes C yr-1
A

= area of land remaining in the same land-use category, ha

GTOTAL= mean annual biomass growth, tonnes d. m. ha-1 yr-1
i

= ecological zone (i = 1 to n)j= climate domain (j = 1 to m)

CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
GTOTAL is the total biomass growth expanded from the above-ground biomass growth (Gw) to include belowground biomass growth. Following a Tier 1 method, this may be achieved directly by using default values of GW
for naturally regenerated trees or broad categories of plantations together with R, the ratio of below-groundbiomass to above-ground biomass differentiated by woody vegetation type. In Tiers 2 and 3, the net annual
increment (IV) can be used with either basic wood density (D) and biomass expansion factor (BEFI) or directlywith biomass conversion and expansion factor (BCEFI) for conversion of annual net increment to above-groundbiomass increment for each vegetation type. Equation 2.10 shows the relationships.

EQUATION 2.10
AVERAGE ANNUAL INCREMENT IN BIOMASS
Tier 1GTOTAL = ∑ {GW • (1 + R)} Biomass increment data (dry matter) are used directly
GTOTAL = ∑ {IV • BCEFI • (1 + R)}

Tiers 2 and 3
Net annual increment data are used to estimate GW byapplying a biomass conversion and expansion factor

Where:GTOTAL = average annual biomass growth above and below-ground, tonnes d. m. ha-1 yr-1
GW = average annual above-ground biomass growth for a specific woody vegetation type, tonnes d. m.
ha-1 yr-1
R= ratio of below-ground biomass to above-ground biomass for a specific vegetation type, in tonned.m. below-ground biomass (tonne d.m. above-ground biomass)-1. R must be set to zero if assuming
no changes of below-ground biomass allocation patterns (Tier 1).

IV = average net annual increment for specific vegetation type, m3 ha-1 yr-1
BCEFI = biomass conversion and expansion factor for conversion of net annual increment in volume(including bark) to above-ground biomass growth for specific vegetation type, tonnes above-groundbiomass growth (m3 net annual increment)-1, (see Table 4.5 for Forest Land). If BCEFI values are not2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Useavailable and if the biomass expansion factor (BEF) and basic wood density (D) values are separately
estimated, then the following conversion can be used:
BCEFI = BEFI ● D
Biomass Expansion Factors (BEFI) expand merchantable volume to total above-ground biomass volume toaccount for non-merchantable components of increment. BEFI is dimensionless.
3

Estimates for BCEFI for woody (perennial) biomass on non-forest lands such as Grassland (savanna), Cropland(agro-forestry), orchards, coffee, tea, and rubber may not be readily available. In this case, default values of
BCEFI from one of the forest types closest to the non-forest vegetation can be used to convert merchantablebiomass to total biomass. BCEFI is relevant only to perennial woody tree biomass for which merchantablebiomass data are available. For perennial shrubs, grasses and crops, biomass increment data in terms of tonnes ofdry matter per hectare may be directly available and in this case use of Equation 2.10 will not be required.

A.2

Estimating annual decrease in biomass carbon stocks due to losses (GainLoss Method), ∆C L

Loss estimates are needed for calculating biomass carbon stock change using the Gain-Loss Method. Note thatthe loss estimate is also needed when using the Stock–Difference Method to estimate the transfers of biomass todead organic matter when higher Tier estimation methods are used (see below). Annual biomass loss is the sumof losses from wood removal (harvest), fuelwood removal (not counting fuelwood gathered from woody debris),and other losses resulting from disturbances, such as fire, storms, and insect and diseases. The relationship isshown in Equation 2.11.
EQUATION 2.11
ANNUAL DECREASE IN CARBON STOCKS DUE TO BIOMASS LOSSESIN LAND REMAINING IN THE SAME LAND-USE CATEGORY∆C L = Lwood − removals + L fuelwood + Ldisturbance
Where:∆CL = annual decrease in carbon stocks due to biomass loss in land remaining in the same land-use
category, tonnes C yr-1
Lwood-removals = annual carbon loss due to wood removals, tonnes C yr-1 (See Equation 2.12)
Lfuelwood= annual biomass carbon loss due to fuelwood removals, tonnes C yr-1 (See Equation 2.13)

Ldisturbance= annual biomass carbon losses due to disturbances, tonnes C yr-1 (See Equation 2.14)

Equation 2.11 and the following Equations 2.12 to 2.14 are directly applicable to Forest Land. These Equations(2.11 to 2.14) can also be used for estimating losses from Cropland and Grassland, if quantities of wood removal(harvesting), fuelwood removal, and loss due to disturbance are available for perennial woody biomass. In
intensively managed as well as highly degraded croplands and grasslands, the perennial woody biomass loss islikely to be small. Default biomass carbon loss values for woody crop species are provided for the Tier 1
cropland methodology (see Table 5.1). It is important to note that wood-removal used in Equation 2.11 should
be compared with the input to HWP in Chapter 12 for consistency.
The three terms on the right hand side of Equation 2.11 are obtained as follows:

Loss of biomass and carbon from wood removal (harvesting), L w o o d - r e m o v a l s

The method for estimating the annual biomass carbon loss due to wood-removals is provided in Equation 2.12.

3

In some applications, BEFs are used to expand dry-weight of merchantable components or stem biomass to total biomass,
excluding or including roots, or convert and expand merchantable or stem volume to above-ground or total biomass(Somogyi et al., 2006). As used in this document, biomass expansion factors always transform dry-weight of merchantablecomponents including bark to aboveground biomass, excluding roots.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

EQUATION 2.12
ANNUAL CARBON LOSS IN BIOMASS OF WOOD REMOVALS
Lwood − removals = {H • BCEFR • (1 + R) • CF }Where:
Lwood-removals = annual carbon loss due to biomass removals, tonnes C yr-1
H

= annual wood removals, roundwood, m3 yr-1

R

= ratio of below-ground biomass to above-ground biomass, in tonne d.m. below-ground biomass(tonne d.m. above-ground biomass)-1. R must be set to zero if assuming no changes of below-groundbiomass allocation patterns (Tier 1).

CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
BCEFR = biomass conversion and expansion factor for conversion of removals in merchantable volume tototal biomass removals (including bark), tonnes biomass removal (m3 of removals)-1, (see Table 4.5
for Forest Land). However, if BCEFR values are not available and if the biomass expansion factor forwood removals (BEFR) and basic wood density (D) values are separately estimated, then thefollowing conversion can be used:
BCEFR = BEFR ● D
If country-specific data on roundwood removals are not available, the inventory experts should use FAO
statistics on wood harvest. FAO statistical data on wood harvest exclude bark. To convert FAO statistical wood
harvest data without bark into merchantable wood removals including bark, multiply by default expansion factorof 1.15.

Loss of biomass and carbon from fuelwood removal, L f u e l w o o dFuelwood removal will often be comprised of two components. First, removal for fuelwood of living trees andparts of trees such as tops and branches, where the tree itself remains in the forest, will reduce the carbon in thebiomass of growing stock and should be treated as biomass carbon loss. The second component is gathering ofdead wood and logging slash. This will reduce the dead organic matter carbon pool. If it is possible it is goodpractice to estimate the two components separately. The biomass carbon loss due to fuelwood removal of live
trees is estimated using Equation 2.13.
EQUATION 2.13
ANNUAL CARBON LOSS IN BIOMASS OF FUELWOOD REMOVAL
L fuelwood = [{FGtrees • BCEFR • (1 + R)} + FG part • D] • CF

Where:
Lfuelwood= annual carbon loss due to fuelwood removals, tonnes C yr-1

FGtrees= annual volume of fuelwood removal of whole trees, m3 yr-1

FGpart= annual volume of fuelwood removal as tree parts, m3 yr-1

R

= ratio of below-ground biomass to above-ground biomass, in tonne d.m. below-ground biomass(tonne d.m. above-ground biomass)-1; R must be set to zero if assuming no changes of below-groundbiomass allocation patterns. (Tier 1)

CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
D= basic wood density, tonnes d.m. m-3

BCEFR = biomass conversion and expansion factor for conversion of removals in merchantable volume tobiomass removals (including bark), tonnes biomass removal (m3 of removals)-1, (see Table 4.5 forForest Land). If BCEFR values are not available and if the biomass expansion factor for wood
removals (BEFR) and basic wood density (D) values are separately estimated, then the following
conversion can be used:
BCEFR = BEFR ● D
Biomass Expansion Factors (BEFR) expand merchantable wood removals to total aboveground biomass volume
to account for non-merchantable components of the tree, stand and forest. BEFR is dimensionless.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

If country-specific data on roundwood removals are not available, the inventory experts should use FAO
statistics on wood harvest. It should be noted that FAO statistical data on wood harvest exclude bark. To convert
FAO statistical wood harvest data without bark into merchantable wood removals including bark, multiply by
default expansion factor of 1.15.
Wood harvest can comprise both wood and fuelwood removals (i.e., wood removals in Equation 2.12 caninclude both wood and fuelwood removal), or fuelwood removals can be reported separately using, both
Equations 2.12 and 2.13. To avoid double counting, it is good practice to check how fuelwood data arerepresented in the country and to use the equation that is most appropriate for national conditions. Furthermore,
the wood harvest from forests becomes an input to HWP (Chapter 12). Therefore, it is good practice to check forconsistent representation of wood-harvest data in Equations 2.12 and 2.13 and those in Chapter 12.

Loss of biomass and carbon from disturbance, L d i s t u r b a n c e

A generic approach for estimating the amount of carbon lost from disturbances is provided in Equation 2.14. In
the specific case of losses from fire on managed land, including wildfires and controlled fires, this methodshould be used to provide input to the methodology to estimate CO2 and non-CO2 emissions from fires.
EQUATION 2.14
ANNUAL CARBON LOSSES IN BIOMASS DUE TO DISTURBANCES
Ldisturbance = { Adisturbance • BW • (1 + R) • CF • fd }Where:
Ldisturbances = annual other losses of carbon, tonnes C yr-1 (Note that this is the amount of biomass that islost from the total biomass. The partitioning of biomass that is transferred to dead organic matter andbiomass that is oxidized and released to the atmosphere is explained in Equations 2.15 and 2.16).
Adisturbance = area affected by disturbances, ha yr-1
BW = average above-ground biomass of land areas affected by disturbances, tonnes d.m. ha-1R

= ratio of below-ground biomass to above-ground biomass, in tonne d.m. below-ground biomass(tonne d.m. above-ground biomass)-1. R must be set to zero if no changes of below-ground biomass
are assumed (Tier 1)

CF = carbon fraction of dry matter, tonne C (tonnes d.m.)-1
fd = fraction of biomass lost in disturbance (see note below)
Note: The parameter fd defines the proportion of biomass that is lost from the biomass pool: a standreplacing disturbance will kill all (fd = 1) biomass while an insect disturbance may only remove a
portion (e.g. fd = 0.3) of the average biomass C density. Equation 2.14 does not specify the fate of
the carbon removed from the biomass carbon stock. The Tier 1 assumption is that all of Ldisturbances isemitted in the year of disturbance. Higher Tier methods assume that some of this carbon is emitted
immediately and some is added to the dead organic matter pools (dead wood, litter) or HWP.
The amounts of biomass carbon transferred to different fates can be defined using a disturbance matrix that canbe parameterized to define the impacts of different disturbance types (Kurz et al., 1992). It is good practice, if
possible, to develop and use a disturbance matrix (Table 2.1) for each biomass, dead organic matter and soil
carbon pool, the proportion of the carbon remaining in that pool, and the proportions transferred to other pools,
to harvested wood products and to the atmosphere, during the disturbance event. The proportions in each row
always sum to 1 to ensure conservation of carbon. The value entered in cell A is the proportion of above-groundbiomass remaining after a disturbance (or 1 – fd, where fd is defined in Equation 2.14). The Tier 1 assumption isthat all of fd is emitted in the year of disturbance: therefore the value entered in cell F is fd. For higher Tiers,
only the proportion emitted in the year is entered in cell F and the remainder is added to cells B and C in the caseof fire, and B, C, and E in the case of harvest. It is good practice to develop disturbance matrix even under Tier 1
to ensure that all carbon pool transfers are considered, though all biomass carbon is assumed to be emitted in theyear of land conversion. It is important to note that some of the transfers could be small or insignificant.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

TABLE 2.1
EXAMPLE OF A SIMPLE MATRIX (TIER 2) FOR THE IMPACTS OF DISTURBANCES ON CARBON POOLSTo:

Above-groundbiomass

From:
Abovegroundbiomass
BelowgroundbiomassA

Belowgroundbiomass

Deadwood

LitterSoilorganic
matterHarvested
wood
products

Atmosphere

Sum ofrow(must
equal 1)

B

C

D

E

F

1
1
1

Dead wood

1

Litter

1

Soil organic
matterEnter the proportion of each pool on the left side of the matrix that is transferred to the pool at the top of each column.
All of the pools on the left side of the matrix must be fully populated and the values in each row must sum to 1.
Impossible transitions are blacked out.
Note: Letters A to F are cell labels that are referenced in the text.

2.3.1.2

L AND CONVERTED TO A NEW LAND - USE CATEGORY

The methods for estimation of emissions and removals of carbon resulting from land-use conversion from one
land-use category to another are presented in this section. Possible conversions include conversion from nonforest to Forest Land, Cropland and Forest Land to Grassland, and Grassland and Forest Land to Cropland.
The CO2 emissions and removals on land converted to a new land-use category include annual changes in carbon
stocks in above-ground and below-ground biomass. Annual carbon stock changes for each of these pools can be
estimated by using Equation 2.4 (ΔCB = ∆CG - ∆CL), where ∆CG is the annual gain in carbon, and ∆CL is theannual loss of carbon. ΔCB can be estimated separately for each land use (e.g., Forest Land, Cropland,
Grassland) and management category (e.g., natural forest, plantation), by specific strata (e.g., climate or foresttype).

METHODS FOR ESTIMATING CHANGE IN CARBON STOCKS IN BIOMASS(∆C B )i) Annual increase in carbon stocks in biomass, ∆C G
Tier 1: Annual increase in carbon stocks in biomass due to land converted to another land-use category can be
estimated using Equation 2.9 described above for lands remaining in a category. Tier 1 employs a default
assumption that there is no change in initial biomass carbon stocks due to conversion. This assumption can beapplied if the data on previous land uses are not available, which may be the case when land area totals are
estimated using Approach 1 or 2 described in Chapter 3 (non-spatially explicit land area data). This approach
implies the use of default parameters in Section 4.5 (Chapter 4). The area of land converted can be categorizedbased on management practices e.g., intensively managed plantations and grasslands or extensively managed(low input) plantations, grasslands or abandoned croplands that revert back to forest and should be kept inconversion category for 20 years or another time interval. If the previous land use on a converted area is known,
then the Tier 2 method described below can be used.

ii) Annual decrease in carbon stocks in biomass due to losses, ∆C L
Tier 1: The annual decrease in C stocks in biomass due to losses on converted land (wood removals or fellings,
fuelwood collection, and disturbances) can be estimated using Equations 2.11 to 2.14. As with increases incarbon stocks, Tier 1 follows the default assumption that there is no change in initial carbon stocks in biomass,
and it can be applied for the areas that are estimated with the use of Approach 1 or 2 in Chapter 3, and default
parameters in Section 4.5.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Useiii) Higher tiers for estimating change in carbon stocks in biomass, (∆C B )Tiers 2 and 3: Tier 2 (and 3) methods use nationally-derived data and more disaggregated approaches and(or) process models, which allow for more precise estimates of changes in carbon stocks in biomass. In Tier 2,
Equation 2.4 is replaced by Equation 2.15, where the changes in carbon stock are calculated as a sum of increasein carbon stock due to biomass growth, changes due to actual conversion (difference between biomass stocks
before and after conversion), and decrease in carbon stocks due to losses.

EQUATION 2.15
ANNUAL CHANGE IN BIOMASS CARBON STOCKS ON LAND CONVERTED TO OTHER LAND-USE
CATEGORY (TIER 2)∆C B = ∆CG + ∆CCONVERSION − ∆C L
Where:∆CB = annual change in carbon stocks in biomass on land converted to other land-use category, in tonnes
C yr-1∆CG = annual increase in carbon stocks in biomass due to growth on land converted to another land-use
category, in tonnes C yr-1
∆CCONVERSION = initial change in carbon stocks in biomass on land converted to other land-use category,in tonnes C yr-1
∆CL = annual decrease in biomass carbon stocks due to losses from harvesting, fuel wood gathering anddisturbances on land converted to other land-use category, in tonnes C yr-1
Conversion to another land category may be associated with a change in biomass stocks, e.g., part of the biomass
may be withdrawn through land clearing, restocking or other human-induced activities. These initial changes incarbon stocks in biomass (∆CCONVERSION) are calculated with the use of Equation 2.16 as follows:

EQUATION 2.16
INITIAL CHANGE IN BIOMASS CARBON STOCKS ON LAND CONVERTED TO ANOTHER LANDCATEGORY∆CCONVERSION = ∑ {( B AFTERi − BBEFOREi ) • ∆ATO _ OTHERS i } • CF
i

Where:∆CCONVERSION = initial change in biomass carbon stocks on land converted to another land category,
tonnes C yr-1
BAFTERi= biomass stocks on land type i immediately after the conversion, tonnes d.m. ha-1

BBEFOREi= biomass stocks on land type i before the conversion, tonnes d.m. ha-1

∆ATO_OTHERSi = area of land use i converted to another land-use category in a certain year, ha yr-1
CF = carbon fraction of dry matter, tonne C (tonnes d.m.)-1i= type of land use converted to another land-use category

The calculation of ∆CCONVERSION may be applied separately to estimate carbon stocks occurring on specific typesof land (ecosystems, site types, etc.) before the conversion. The ∆ATO_OTHERSi refers to a particular inventory
year for which the calculations are made, but the land affected by conversion should remain in the conversion
category for 20 years or other period used in the inventory. Inventories using higher Tier methods can define a
disturbance matrix (Table 2.1) for land-use conversion to quantify the proportion of each carbon pool before
conversion that is transferred to other pools, emitted to the atmosphere (e.g., slash burning), or otherwise
removed during harvest or land clearing.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Owing to the use of country specific data and more disaggregated approaches, the Equations 2.15 and 2.16
provide for more accurate estimates than Tier 1 methods, where default data are used. Additional improvement
or accuracy would be achieved by using national data on areas of land-use transitions and country-specificcarbon stock values. Therefore, Tier 2 and 3 approaches should be inclusive of estimates that use detailed area
data and country specific carbon stock values.

2.3.2

Change in carbon stocks in dead organic matter

Dead organic matter (DOM) comprises dead wood and litter (See Table 1.1). Estimating the carbon dynamics ofdead organic matter pools allows for increased accuracy in the reporting of where and when carbon emissions
and removals occur. For example, only some of the carbon contained in biomass killed during a biomassburning is emitted into the atmosphere in the year of the fire. Most of the biomass is added to dead wood, litterand soil pools (dead fine roots are included in the soil) from where the C will be emitted over years to decades,as the dead organic matter decomposes. Decay rates differ greatly between regions, ranging from high in warm
and moist environments to low in cold and dry environments. Although the carbon dynamics of dead organic
matter pools are well understood qualitatively, countries may find it difficult to obtain actual data with national
coverage on dead organic matter stocks and their dynamics.
In forest ecosystems, DOM pools tend to be largest following stand-replacing disturbances due to the addition ofresidual above-ground and below-ground (roots) biomass. In the years after the disturbance, DOM pools decline
as carbon loss through decay exceeds the rate of carbon addition through litterfall, mortality and biomass
turnover. Later in stand development, DOM pools increase again. Representing these dynamics requires separate
estimation of age-dependent inputs and outputs associated with stand dynamics and disturbance-related inputs
and losses. These more complex estimation procedures require higher Tier methods.

2.3.2.1

L AND REMAINING IN A LAND - USE CATEGORYThe Tier 1 assumption for both dead wood and litter pools for all land-use categories is that their stocks are notchanging over time if the land remains within the same land-use category. Thus, the carbon in biomass killedduring a disturbance or management event (less removal of harvested wood products) is assumed to be released
entirely to the atmosphere in the year of the event. This is equivalent to the assumption that the carbon in nonmerchantable and non-commercial components that are transferred to dead organic matter is equal to the amountof carbon released from dead organic matter to the atmosphere through decomposition and oxidation. Countries
can use higher tier methods to estimate the carbon dynamics of dead organic matter. This section describes
estimation methods if Tier 2 (or 3) methods are used.
Countries that use Tier 1 methods to estimate DOM pools in land remaining in the same land-use category,report zero changes in carbon stocks or carbon emissions from those pools. Following this rule, CO2 emissions
resulting from the combustion of dead organic matter during fire are not reported, nor are the increases in deadorganic matter carbon stocks in the years following fire. However, emissions of non-CO2 gases from burning of
DOM pools are reported. Tier 2 methods for estimation of carbon stock changes in DOM pools calculate thechanges in dead wood and litter carbon pools (Equation 2.17). Two methods can be used: either track inputs andoutputs (the Gain-Loss Method, Equation 2.18) or estimate the difference in DOM pools at two points in time
(Stock-Difference Method, Equation 2.19). These estimates require either detailed inventories that include
repeated measurements of dead wood and litter pools, or models that simulate dead wood and litter dynamics. It
is good practice to ensure that such models are tested against field measurements and are documented. Figure
2.3 provides the decision tree for identification of the appropriate tier to estimate changes in carbon stocks indead organic matter.
Equation 2.17 summarizes the calculation to estimate the annual changes in carbon stock in DOM pools:
EQUATION 2.17
ANNUAL CHANGE IN CARBON STOCKS IN DEAD ORGANIC MATTER∆C DOM = ∆C DW + ∆C LT
Where:∆CDOM= annual change in carbon stocks in dead organic matter (includes dead wood and litter),tonnes C yr-1
∆CDW

= change in carbon stocks in dead wood, tonnes C yr-1

∆CLT

= change in carbon stocks in litter, tonnes C yr-1

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 2.3

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in dead organic matter for a land-use categoryStart

Are data on
managed area and DOM stocks
at two periods of time available toestimate changes
in C stocks?

Yes

Use the data for Tier 2
method (StockDifference Method) orTier 3 Method.
Box 3: Tier 2

No

Are data on
managed area and annualtransfer into and out of DOM
stocks available?

Yes

Use the data for Tier 2
method (Gain-Loss
Method) or Tier 3
Method.
Box 2: Tier 2

No

Are changes in
C stocks in DOM
a key category1?

No

Assume that the deadorganic matter stock isin equilibrium.
Box 1: Tier 1

Yes
Collect data for Tier 2
method (Gain-Loss
Method or StockDifference Method2).
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: The two methods are defined in Equations 2.18 and 2.19, respectively.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

The changes in carbon stocks in the dead wood and litter pools for an area remaining in a land-use category
between inventories can be estimated using two methods, described in Equation 2.18 and Equation 2.19. The
same equation is used for dead wood and litter pools, but their values are calculated separately.
EQUATION 2.18
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD OR LITTER (GAIN-LOSS METHOD)∆C DOM = A • {( DOM in − DOM out ) • CF }Where:∆C DOMA= annual change in carbon stocks in the dead wood/litter pool, tonnes C yr-1

= area of managed land, ha

DOMin= average annual transfer of biomass into the dead wood/litter pool due to annual processes
and disturbances, tonnes d.m. ha-1 yr-1 (see next Section for further details).
DOMout = average annual decay and disturbance carbon loss out of dead wood or litter pool, tonnes d.m.
ha-1 yr-1
CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
The net balance of DOM pools specified in Equation 2.18, requires the estimation of both the inputs and outputs
from annual processes (litterfall and decomposition) and the inputs and losses associated with disturbances. In
practice, therefore, Tier 2 and Tier 3 approaches require estimates of the transfer and decay rates as well asactivity data on harvesting and disturbances and their impacts on DOM pool dynamics. Note that the biomass
inputs into DOM pools used in Equation 2.18 are a subset of the biomass losses estimated in Equation 2.7. The
biomass losses in Equation 2.7 contain additional biomass that is removed from the site through harvest or lost to
the atmosphere, in the case of fire.
The method chosen depends on available data and will likely be coordinated with the method chosen for biomass
carbon stocks. Transfers into and out of a dead wood or litter pool for Equation 2.18 may be difficult to estimate.
The stock difference method described in Equation 2.19 can be used by countries with forest inventory data that
include DOM pool information, other survey data sampled according to the principles set out in Annex 3A.3
(Sampling) in Chapter 3, and/or models that simulate dead wood and litter dynamics.

EQUATION 2.19
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD OR LITTER (STOCK-DIFFERENCEMETHOD)( DOM t2 − DOM t1 ) ∆C DOM =  A • • CFT

Where:∆CDOM
A= annual change in carbon stocks in dead wood or litter, tonnes C yr-1

= area of managed land, ha

DOMt1= dead wood/litter stock at time t1 for managed land, tonnes d.m. ha-1

DOMt2

= dead wood/litter stock at time t2 for managed land, tonnes d.m. ha-1

T = (t2 – t1) = time period between time of the second stock estimate and the first stock estimate, yr
CF = carbon fraction of dry matter (default = 0.37 for litter), tonne C (tonne d.m.)-1
Note that whenever the stock change method is used (e.g., in Equation 2.19), the area used in the carbon stock
calculations at times t1 and t2 must be identical. If the area is not identical then changes in area will confound theestimates of carbon stocks and stock changes. It is good practice to use the area at the end of the inventory
period (t2) to define the area of land remaining in the land-use category. The stock changes on all areas thatchange land-use category between t1 and t2 are estimated in the new land-use category, as described in thesections on land converted to a new land category.

INPUT OF BIOMASS TO DEAD ORGANIC MATTER
Whenever a tree is felled, non-merchantable and non-commercial components (such as tops, branches, leaves,roots, and noncommercial trees) are left on the ground and transferred to dead organic matter pools. In addition,

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Volume 4: Agriculture, Forestry and Other Land Useannual mortality can add substantial amounts of dead wood to that pool. For Tier 1 methods, the assumption isthat the carbon contained in all biomass components that are transferred to dead organic matter pools will be
released in the year of the transfer, whether from annual processes (litterfall and tree mortality), land
management activities, fuelwood gathering, or disturbances. For estimation procedures based on higher Tiers, itis necessary to estimate the amount of biomass carbon that is transferred to dead organic matter. The quantity ofbiomass transferred to DOM is estimated using Equation 2.20.

EQUATION 2.20
ANNUAL CARBON IN BIOMASS TRANSFERRED TO DEAD ORGANIC MATTER
DOM in = {Lmortality + Lslash + ( Ldisturbance • f BLol )}Where:DOMin= total carbon in biomass transferred to dead organic matter, tonnes C yr-1

Lmortality= annual biomass carbon transfer to DOM due to mortality, tonnes C yr-1 (See Equation 2.21)

Lslash= annual biomass carbon transfer to DOM as slash, tonnes C yr-1 (See Equations 2.22)

Ldisturbances = annual biomass carbon loss resulting from disturbances, tonnes C yr-1 (See Equation 2.14)
fBLol= fraction of biomass left to decay on the ground (transferred to dead organic matter) from loss
due to disturbance. As shown in Table 2.1, the disturbance losses from the biomass pool arepartitioned into the fractions that are added to dead wood (cell B in Table 2.1) and to litter (cell C),
are released to the atmosphere in the case of fire (cell F) and, if salvage follows the disturbance,transferred to HWP (cell E).

Note: If root biomass increments are counted in Equation 2.10, then root biomass losses must also be
counted in Equations 2.20, and 2.22.
Examples of the terms on the right hand side of Equation 2.20 are obtained as follows:

Transfers to dead organic matter from mortality, L m o r t a l i t yMortality is caused by competition during stand development, age, diseases, and other processes that are notincluded as disturbances. Mortality cannot be neglected when using higher Tier estimation methods. In
extensively managed stands without periodic partial cuts, mortality from competition during the stem exclusion
phase, may represent 30-50% of total productivity of a stand during its lifetime. In regularly tended stands,
additions to the dead organic matter pool from mortality may be negligible because partial cuts extract forest
biomass that would otherwise be lost to mortality and transferred to dead organic matter pools. Available datafor increment will normally report net annual increment, which is defined as net of losses from mortality. Since
in this text, net annual growth is used as a basis to estimate biomass gains, mortality must not be subtracted again
as a loss from biomass pools. Mortality must, however, be counted as an addition to the dead wood pool for Tier
2 and Tier 3 methods.
The equation for estimating mortality is provided in Equation 2.21:
EQUATION 2.21
ANNUAL BIOMASS CARBON LOSS DUE TO MORTALITY
Lmortality = ∑ ( A • GW • CF • m)
Where:
LmortalityA= annual biomass carbon loss due to mortality, tonnes C yr-1

= area of land remaining in the same land use, ha

Gw = above-ground biomass growth, tonnes d.m. ha-1 yr-1 (see Equation 2.10)
CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
m

2.24= mortality rate expressed as a fraction of above-ground biomass growth

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

When data on mortality rates are expressed as proportion of growing stock volume, then the term Gw in
Equation 2.21 should be replaced with growing stock volume to estimate annual transfer to DOM pools frommortality.
Mortality rates differ between stages of stand development and are highest during the stem exclusion phase ofstand development. They also differ with stocking level, forest type, management intensity and disturbancehistory. Thus, providing default values for an entire climatic zone is not justified because the variation within azone will be much larger than the variation between zones.

Annual carbon transfer to slash, L s l a s hThis involves estimating the quantity of slash left after wood removal or fuelwood removal and transfer ofbiomass from total annual carbon loss due to wood harvest (Equation 2.12). The estimate for logging slash is
given in Equation 2.22 and which is derived from Equation 2.12 as explained below:

EQUATION 2.22
ANNUAL CARBON TRANSFER TO SLASH
Lslash = [{H • BCEFR • (1 + R)} − {H • D}] • CF
Where:
Lslash = annual carbon transfer from above-ground biomass to slash, including dead roots, tonnes C yr-1
H

= annual wood harvest (wood or fuelwood removal), m3 yr-1

BCEFR = biomass conversion and expansion factors applicable to wood removals, which transform
merchantable volume of wood removal into above-ground biomass removals, tonnes biomass
removal (m3 of removals)-1. If BCEFR values are not available and if BEF and Density values are
separately estimated then the following conversion can be used:
BCEFR = BEFR ● D

Ro

D is basic wood density, tonnes d.m. m-3oBiomass Expansion Factors (BEFR) expand merchantable wood removals to total
aboveground biomass volume to account for non-merchantable components ofthe tree, stand and forest. BEFR is dimensionless.

= ratio of below-ground biomass to above-ground biomass, in tonne d.m. below-ground biomass(tonne d.m. above-ground biomass)-1. R must be set to zero if root biomass increment is not includedin Equation 2.10 (Tier 1)

CF = carbon fraction of dry matter, tonne C (tonne d.m.)-1
Fuelwood gathering that involves the removal of live tree parts does not generate any additional input of biomass
to dead organic matter pools and is not further addressed here.
Inventories using higher Tier methods can also estimate the amount of logging slash remaining after harvest bydefining the proportion of above-ground biomass that is left after harvest (enter these proportions in cells B and
C of Table 2.1 for harvest disturbance) and by using the approach defined in Equation 2.14. In this approach,activity data for the area harvested would also be required.

2.3.2.2

L AND CONVERSION TO A NEW LAND - USE CATEGORY

The reporting convention is that all carbon stock changes and non-CO2 greenhouse gas emissions associated witha land-use change be reported in the new land-use category. For example, in the case of conversion of Forest
Land to Cropland, both the carbon stock changes associated with the clearing of the forest as well as any
subsequent carbon stock changes that result from the conversion are reported under the Cropland category.
The Tier 1 assumption is that DOM pools in non-forest land categories after the conversion are zero, i.e., they
contain no carbon. The Tier 1 assumption for land converted from forest to another land-use category is that all
DOM carbon losses occur in the year of land-use conversion. Conversely, conversion to Forest Land results inbuildup of litter and dead wood carbon pools starting from zero carbon in those pools. DOM carbon gains onland converted to forest occur linearly, starting from zero, over a transition period (default assumption is 20
years). This default period may be appropriate for litter carbon stocks, but in temperate and boreal regions it isprobably too short for dead wood carbon stocks. Countries that use higher Tier methods can accommodate

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Volume 4: Agriculture, Forestry and Other Land Use

longer transition periods by subdividing the remaining category to accommodate strata that are in the later stagesof transition.
The estimation of carbon stock changes during transition periods following land-use conversion requires thatannual cohorts of the area subject to land-use change be tracked for the duration of the transition period. For
example, DOM stocks are assumed to increase for 20 years after conversion to Forest Land. After 20 years, thearea converted enters the category Forest Land Remaining Forest Land, and no further DOM changes are
assumed, if a Tier 1 approach is applied. Under Tier 2 and 3, the period of conversion can be varied dependingon vegetation and other factors that determine the time required for litter and dead wood pools to reach steadystate.
Higher Tier estimation methods can use non-zero estimates of litter and dead wood pools in the appropriate landuse categories or subcategories. For example, settlements and agro-forestry systems can contain some litter anddead wood pools, but because management, site conditions, and many other factors influence the pool sizes, noglobal default values can be provided here. Higher Tier methods may also estimate the details of dead organic
matter inputs and outputs associated with the land-use change.
The conceptual approach to estimating changes in carbon stocks in dead wood and litter pools is to estimate thedifference in C stocks in the old and new land-use categories and to apply this change in the year of theconversion (carbon losses), or to distribute it uniformly over the length of the transition period (carbon gains)
Equation 2.23:

EQUATION 2.23
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD AND LITTER DUE TO LAND CONVERSION(C − Co ) • Aon∆C DOM = n
Ton
Where:∆CDOM = annual change in carbon stocks in dead wood or litter, tonnes C yr-1
Co = dead wood/litter stock, under the old land-use category, tonnes C ha-1
Cn = dead wood/litter stock, under the new land-use category, tonnes C ha-1
Aon = area undergoing conversion from old to new land-use category, ha
Ton = time period of the transition from old to new land-use category, yr. The Tier 1 default is 20 years
for carbon stock increases and 1 year for carbon losses.
Inventories using a Tier 1 method assume that all carbon contained in biomass killed during a land-use
conversion event (less harvested products that are removed) is emitted directly to the atmosphere and none is
added to dead wood and litter pools. Tier 1 methods also assume that dead wood and litter pool carbon losses
occur entirely in the year of the transition.
Countries using higher Tier methods can modify Co in Equation 2.23 by first accounting for the immediate
effects of the land-use conversion in the year of the event. In this case, they would add to Co the carbon frombiomass killed and transferred to the dead wood and litter pools and remove from Co any carbon released fromdead wood and litter pools, e.g., during slash burning. In that case Co in Equation 2.23 would represent the deadwood or litter carbon stocks immediately after the land-use conversion. Co will transit to Cn over the transition
period, using linear or more complex dynamics. A disturbance matrix (Table 2.1) can be defined to account forthe pool transitions and releases during the land-use conversion, including the additions and removals to Co.
Countries using a Tier 1 approach can apply the Tier 1 default carbon stock estimates for litter, and if available
dead wood pools, provided in Table 2.2, but should recognize that these are broad-scale estimates withconsiderable uncertainty when applied at the country level. Table 2.2 is incomplete because of the paucity of
published data. A review of the literature has identified several problems. The IPCC definitions of dead organic
matter carbon stocks include litter and dead wood. The litter pool contains all litter plus fine woody debris up toa diameter limit of 10 cm (see Chapter 1, Table 1.1). Published litter data generally do not include the finewoody debris component, so the litter values in Table 2.2 are incomplete.
There are numerous published studies of coarse woody debris (Harmon and Hua, 1991; Karjalainen andKuuluvainen, 2002) and a few review papers (e.g., Harmon et al., 1986), and but to date only two studies arefound to provide regional dead wood carbon pool estimates that are based on sample plot data. Krankina et al.
(2002) included several regions in Russia and reported coarse woody debris (> 10 cm diameter) estimates of 2 to

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

7 Mg C ha-1. Cooms et al. (2002) reported regional carbon pools based on a statistical sample design for a small
region in New Zealand. Regional compilations for Canada (Shaw et al., 2005) provide estimates of litter carbon
pools based on a compilation of statistically non-representative sample plots, but do not include estimates ofdead wood pools. Review papers such as Harmon et al. (1986) compile a number of estimates from the literature.
For example, their Table 5 lists a range of coarse woody debris values for temperate deciduous forests of 11 – 38
Mg dry matter ha-1 and for temperate coniferous forests of 10 – 511 Mg dry matter ha-1. It is, however,statistically invalid to calculate a mean from these compilations as they are not representative samples of thedead wood pools in a region.
While it is the intent of these IPCC Guidelines to provide default values for all variables used in Tier 1
methodologies, it is currently not feasible to provide estimates of regional defaults values for litter (including
fine woody debris < 10 cm diameter) and dead wood (> 10 cm diameter) carbon stocks. Litter pool estimates(excluding fine woody debris) are provided in Table 2.2. Tier 1 methodology only requires the estimates in Table
2.2 for lands converted from Forest Land to any other land-use category (carbon losses) and for lands converted
to Forest Land (carbon gains). Tier 1 methods assume that litter and dead wood pools are zero in all non-forest
categories and therefore transitions between non-forest categories involve no carbon stock changes in these twopools.
TABLE 2.2
TIER 1 DEFAULT VALUES FOR LITTER AND DEAD WOOD CARBON STOCKS
Forest type
Broadleaf
deciduous

ClimateNeedleleaf
evergreen

Broadleaf
deciduous

Needleleaf
evergreen

Litter carbon stocks
of mature forests

Dead wood carbon stocks
of mature forests(tonnes C ha-1)(tonnes C ha-1)

Boreal, dry

25(10 - 58)31
(6 - 86)

n.a.b

n.a

Boreal, moist

39(11 - 117)55
(7 - 123)

n.a

n.a

Cold Temperate, dry

28(23 - 33)a27
(17 - 42) a

n.a

n.a

Cold temperate, moist

16(5 - 31) a

26(10 - 48) a

n.a

n.a

Warm Temperate, dry

28.2(23.4 - 33.0)a20.3(17.3 - 21.1)a

n.a

n.a

Warm temperate,
moist

13
(2 - 31) a

22(6 - 42)a

n.a

n.a

Subtropical

2.8(2 - 3)

4.1

n.a

n.a

Tropical

2.1(1 - 3)

5.2

n.a

n.a

Source:Litter: Note that these values do not include fine woody debris. Siltanen et al., 1997; and Smith and Heath, 2001;
Tremblay et al., 2002; and Vogt et al.,1996, converted from mass to carbon by multiplying by conversion factor of
0.37 (Smith and Heath, 2001).
Dead Wood: No regional estimates of dead wood pools are currently available – see text for further commentsaValues in parentheses marked by superscript “a” are the 5th and 95th percentiles from simulations of inventory
plots, while those without superscript “a” indicate the entire range.

bn.a. denotes ‘not available’

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Volume 4: Agriculture, Forestry and Other Land Use

2.3.3

Change in carbon stocks in soils

Although both organic and inorganic forms of C are found in soils, land use and management typically has alarger impact on organic C stocks. Consequently, the methods provided in these guidelines focus mostly on soilorganic C. Overall, the influence of land use and management on soil organic C is dramatically different in amineral versus an organic soil type. Organic (e.g., peat and muck) soils have a minimum of 12 to 20 percent
organic matter by mass (see Chapter 3 Annex 3A.5, for the specific criteria on organic soil classification), anddevelop under poorly drained conditions of wetlands (Brady and Weil, 1999). All other soils are classified as
mineral soil types, and typically have relatively low amounts of organic matter, occurring under moderate to welldrained conditions, and predominate in most ecosystems except wetlands. Discussion about land-use andmanagement influences on these contrasting soil types is provided in the next two sections.

MINERAL SOILS
Mineral soils are a carbon pool that is influenced by land-use and management activities. Land use can have alarge effect on the size of this pool through activities such as conversion of native Grassland and Forest Land to
Cropland, where 20-40% of the original soil C stocks can be lost (Mann, 1986; Davidson and Ackerman, 1993;
Ogle et al., 2005). Within a land-use type, a variety of management practices can also have a significant impact
on soil organic C storage, particularly in Cropland and Grassland (e.g., Paustian et al., 1997; Conant et al., 2001;
Ogle et al., 2004 and 2005). In principle, soil organic C stocks can change with management or disturbance if
the net balance between C inputs and C losses from soil is altered. Management activities influence organic C
inputs through changes in plant production (such as fertilization or irrigation to enhance crop growth), direct
additions of C in organic amendments, and the amount of carbon left after biomass removal activities, such as
crop harvest, timber harvest, fire, or grazing. Decomposition largely controls C outputs and can be influenced bychanges in moisture and temperature regimes as well as the level of soil disturbance resulting from the
management activity. Other factors also influence decomposition, such as climate and edaphic characteristics.
Specific effects of different land-use conversions and management regimes are discussed in the land-use specificchapters (Chapters 4 to 9).
Land-use change and management activity can also influence soil organic C storage by changing erosion rates
and subsequent loss of C from a site; some eroded C decomposes in transport and CO2 is returned to theatmosphere, while the remainder is deposited in another location. The net effect of changing soil erosion
through land management is highly uncertain, however, because an unknown portion of eroded C is stored inburied sediments of wetlands, lakes, river deltas and coastal zones (Smith et al., 2001).

ORGANIC SOILS
Inputs of organic matter can exceed decomposition losses under anaerobic conditions, which are common in
undrained organic soils, and considerable amounts of organic matter can accumulate over time. The carbon
dynamics of these soils are closely linked to the hydrological conditions, including available moisture, depth of
the water table, and reduction-oxidation conditions (Clymo, 1984; Thormann et al., 1999). Species compositionand litter chemistry can also influence those dynamics (Yavitt et al., 1997).
Carbon stored in organic soils will readily decompose when conditions become aerobic following soil drainage(Armentano and Menges, 1986; Kasimir-Klemedtsson et al., 1997). Drainage is a practice used in agriculture
and forestry to improve site conditions for plant growth. Loss rates vary by climate, with drainage under warmer
conditions leading to faster decomposition rates. Losses of CO2 are also influenced by drainage depth; liming;
the fertility and consistency of the organic substrate; and temperature (Martikainen et al., 1995). Greenhouse gas
inventories capture this effect of management.
While drainage of organic soils typically releases CO2 to the atmosphere (Armentano and Menges, 1986), there
can also be a decrease in emissions of CH4 that occur in un-drained organic soils (Nykänen et al., 1995).
However, CH4 emissions from un-drained organic soils are not addressed in the inventory guidelines with the
exception of a few cases in which the wetlands are managed (See Chapter 7, Wetlands). Similarly, national
inventories typically do not estimate the accumulation of C in the soil pool resulting from the accumulation ofplant detritus in un-drained organic soils. Overall, the rates of C gain are relatively slow in wetland environmentswith organic soils (Gorham, 1991), and any attempt to estimate C gains, even those created through wetland
restoration, would also need to address the increase in CH4 emissions. See additional guidance in Chapter 7
Wetlands.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

2.3.3.1

S OIL C ESTIMATION METHODS ( LAND REMAINING IN A
LAND - USE CATEGORY AND LAND CONVERSION TO A NEW
LAND USE )Soil C inventories include estimates of soil organic C stock changes for mineral soils and CO2 emissions fromorganic soils due to enhanced microbial decomposition caused by drainage and associated management activity.
In addition, inventories can address C stock changes for soil inorganic C pools (e.g., calcareous grasslands that
become acidified over time) if sufficient information is available to use a Tier 3 approach. The equation for
estimating the total change in soil C stocks is given in Equation 2.24:

EQUATION 2.24
ANNUAL CHANGE IN CARBON STOCKS IN SOILS∆C Soils = ∆C Mineral − LOrganic + ∆C Inorganic
Where:∆CSoils= annual change in carbon stocks in soils, tonnes C yr-1

∆CMineral= annual change in organic carbon stocks in mineral soils, tonnes C yr-1

LOrganic= annual loss of carbon from drained organic soils, tonnes C yr-1

∆CInorganic = annual change in inorganic carbon stocks from soils, tonnes C yr-1 (assumed to be 0 unlessusing a Tier 3 approach)
For Tier 1 and 2 methods, soil organic C stocks for mineral soils are computed to a default depth of 30 cm.
Greater depth can be selected and used at Tier 2 if data are available, but Tier 1 factors are based on 30 cm depth.
Residue/litter C stocks are not included because they are addressed by estimating dead organic matter stocks.
Stock changes in organic soils are based on emission factors that represent the annual loss of organic C
throughout the profile due to drainage. No Tier 1 or 2 methods are provided for estimating the change in soil
inorganic C stocks due to limited scientific data for derivation of stock change factors; thus the net flux forinorganic C stocks is assumed to be zero. Tier 3 methods can be used to refined estimates of the C stock changes
in mineral and organic soils and for soil inorganic C pools.
It is possible that countries will use different tiers to prepare estimates for mineral soils, organic soils, and soil
inorganic C, given availability of resources. Thus, stock changes for mineral and organic soils and for inorganic
C pools (Tier 3 only) are discussed separately. A generalized decision tree in Figures 2.4 and 2.5 can be used toassist inventory compilers in determining the appropriate tier for estimating stock changes for mineral andorganic soil C, respectively.

Tier 1 Approach: Default Method
Mineral soils

For mineral soils, the default method is based on changes in soil C stocks over a finite period of time. Thechange is computed based on C stock after the management change relative to the carbon stock in a reference
condition (i.e., native vegetation that is not degraded or improved). The following assumptions are made:(i)

Over time, soil organic C reaches a spatially-averaged, stable value specific to the soil, climate,land-use and management practices; and(ii)

Soil organic C stock changes during the transition to a new equilibrium SOC occurs in a linear
fashion.

Assumption (i), that under a given set of climate and management conditions soils tend towards an equilibrium
carbon content, is widely accepted. Although, soil carbon changes in response to management changes may
often be best described by a curvilinear function, assumption (ii) greatly simplifies the Tier 1 methodology andprovides a good approximation over a multi-year inventory period, where changes in management and land-use
conversions are occurring throughout the inventory period.
Using the default method, changes in soil C stocks are computed over an inventory time period. Inventory time
periods will likely be established based on the years in which activity data are collected, such as 1990, 1995,
2000, 2005 and 2010, which would correspond to inventory time periods of 1990-1995, 1995-2000, 2000-2005,
2005-2010. For each inventory time period, the soil organic C stocks are estimated for the first (SOC0-T) and last

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Volume 4: Agriculture, Forestry and Other Land Useyear (SOC0) based on multiplying the reference C stocks by stock change factors. Annual rates of carbon stockchange are estimated as the difference in stocks at two points in time divided by the time dependence of thestock change factors.

EQUATION 2.25
ANNUAL CHANGE IN ORGANIC CARBON STOCKS IN MINERAL SOILS( SOC0 − SOC(0 −T ) )∆C Mineral =D
SOC = ∑ SOC REFc ,s ,i • FLU c ,s ,i • FMGc ,s ,i • FI c ,s ,i • Ac, s , ic, s,i()(Note: T is used in place of D in this equation if T is ≥ 20 years, see note below)
Where:∆CMineral= annual change in carbon stocks in mineral soils, tonnes C yr-1

SOC0

= soil organic carbon stock in the last year of an inventory time period, tonnes C

SOC(0-T)= soil organic carbon stock at the beginning of the inventory time period, tonnes C

SOC0 and SOC(0-T) are calculated using the SOC equation in the box where the reference carbon stocks
and stock change factors are assigned according to the land-use and management activities andcorresponding areas at each of the points in time (time = 0 and time = 0-T)T

= number of years over a single inventory time period, yr

D= Time dependence of stock change factors which is the default time period for transition between
equilibrium SOC values, yr. Commonly 20 years, but depends on assumptions made in computing
the factors FLU, FMG and FI. If T exceeds D, use the value for T to obtain an annual rate of changeover the inventory time period (0-T years).

c= represents the climate zones, s the soil types, and i the set of management systems that are present
in a country.

SOCREF= the reference carbon stock, tonnes C ha-1 (Table 2.3)

FLU = stock change factor for land-use systems or sub-system for a particular land-use, dimensionless[Note: FND is substituted for FLU in forest soil C calculation to estimate the influence of natural
disturbance regimes.
FMG = stock change factor for management regime, dimensionlessFI= stock change factor for input of organic matter, dimensionlessA= land area of the stratum being estimated, ha. All land in the stratum should have common
biophysical conditions (i.e., climate and soil type) and management history over the inventory time
period to be treated together for analytical purposes.

Inventory calculations are based on land areas that are stratified by climate regions (see Chapter 3 Annex 3A.5,
for default classification of climate), and default soils types as shown in Table 2.3 (see Chapter 3, Annex 3A.5,
for default classification of soils). The stock change factors are very broadly defined and include: 1) a land-use
factor (FLU) that reflects C stock changes associated with type of land use, 2) a management factor (FMG)
representing the principal management practice specific to the land-use sector (e.g., different tillage practices incroplands), and 3) an input factor (FI) representing different levels of C input to soil. As mentioned above, FND is
substituted for FLU in Forest Land to account for the influence of natural disturbance regimes (see Chapter 4,
Section 4.2.3 for more discussion). The stock change factors are provided in the soil C sections of the land-use
chapters. Each of these factors represents the change over a specified number of years (D), which can varyacross sectors, but is typically invariant within sectors (e.g., 20 years for the cropland systems). In someinventories, the time period for inventory (T years) may exceed D, and under those cases, an annual rate ofchange in C stock may be obtained by dividing the product of [(SOC0 – SOC(0 –T)) ● A] by T, instead of D. See
the soil C sections in the land-use chapters for detailed step-by-step guidance on the application of this method.

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TABLE 2.3
DEFAULT REFERENCE (UNDER NATIVE VEGETATION) SOIL ORGANIC C STOCKS (SOCREF) FOR MINERAL SOILS(TONNES C HA-1 IN 0-30 CM DEPTH)
HAC soils1

LAC soils2

Sandy soils3

Spodicsoils4

Volcanic
soils5

Wetlandsoils6

Boreal

68

NA

10#

117

20#

146

Cold temperate, dry

50

33

34

NA

20#

Cold temperate, moist

95

85

71

115

130

Warm temperate, dry

38

24

19

NA

70#

Warm temperate, moist

88

63

34

NA

80

Tropical, dry

38

35

31

NA

50#

Tropical, moist

65

47

39

NA

70#

Tropical, wet

44

60

66

NA

130#

Tropical montane

88*

63*

34*

NA

80*

Climate region

87

88

86

Note: Data are derived from soil databases described by Jobbagy and Jackson (2000) and Bernoux et al. (2002). Mean stocks are shown.
A nominal error estimate of ±90% (expressed as 2x standard deviations as percent of the mean) are assumed for soil-climate types. NA
denotes ‘not applicable’ because these soils do not normally occur in some climate zones.
#

Indicates where no data were available and default values from 1996 IPCC Guidelines were retained.

* Data were not available to directly estimate reference C stocks for these soil types in the tropical montane climate so the stocks werebased on estimates derived for the warm temperate, moist region, which has similar mean annual temperatures and precipitation.
1

Soils with high activity clay (HAC) minerals are lightly to moderately weathered soils, which are dominated by 2:1 silicate clay
minerals (in the World Reference Base for Soil Resources (WRB) classification these include Leptosols, Vertisols, Kastanozems,
Chernozems, Phaeozems, Luvisols, Alisols, Albeluvisols, Solonetz, Calcisols, Gypsisols, Umbrisols, Cambisols, Regosols; in USDA
classification includes Mollisols, Vertisols, high-base status Alfisols, Aridisols, Inceptisols).

2

Soils with low activity clay (LAC) minerals are highly weathered soils, dominated by 1:1 clay minerals and amorphous iron andaluminium oxides (in WRB classification includes Acrisols, Lixisols, Nitisols, Ferralsols, Durisols; in USDA classification includes
Ultisols, Oxisols, acidic Alfisols).

3

Includes all soils (regardless of taxonomic classification) having > 70% sand and < 8% clay, based on standard textural analyses (inWRB classification includes Arenosols; in USDA classification includes Psamments).

4

Soils exhibiting strong podzolization (in WRB classification includes Podzols; in USDA classification Spodosols)

5

Soils derived from volcanic ash with allophanic mineralogy (in WRB classification Andosols; in USDA classification Andisols)

6

Soils with restricted drainage leading to periodic flooding and anaerobic conditions (in WRB classification Gleysols; in USDA
classification Aquic suborders).

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 2.4

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in mineral soils by land-use categoryStart

Do you havethe data and resources todevelop a Tier 3?

Use the data for Tier 3
method (e.g., use of models
and/or measurement-based
approach).

Yes

Box 3: Tier 3No

Do youhave country-specificdata on soil C stock changes due
to land use and management for mineralsoils or data to generate countryspecific reference C
stocks?

Use the data forTier 2 method.

Yes

Box 2: Tier 2

No

Are changes
n C stocks in mineral soils
a key category1?

No

Are
aggregate landuse and management data
available (e.g., FAO
statistics)?

Yes

Yes

Collect data for Tier 3
or Tier 2 method.

Use aggregate data anddefault emission/
removal factors for Tier
1 method.

No

Gather data onland use andmanagement.

Box 1: Tier 1
Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Figure 2.5

Generic decision tree for identification of appropriate tier to estimatechanges in carbon stocks in organic soils by land-use category

Start

Do
you havedata on activities likely
to alter the hydrological regime,
surface temperature, andvegetation compositionof organic soils?

Use the data for Tier 3
method to conduct a fullcarbon balance oforganic soils (model ormeasurement-based).

Yes

Box 3: Tier 3
NoDo
you have data that canbe used to derive country-specificemission factors for climate type andor classification scheme relevant
to organic soils?

Use the data forTier 2 method

Yes

Box 2: Tier 2

No

Are changes in
C stocks in organic soils
a key category1?

No

Are
aggregate
data available onorganic soils drained for
management
purposes?

Yes

Yes

Collect data for Tier 3
or Tier 2 method.

Use aggregate data anddefault emission factors
for Tier 1 method.

No

Gather data on
drained organicsoils.

Box 1: Tier 1
Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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Volume 4: Agriculture, Forestry and Other Land Use

When applying the Tier 1 or even Tier 2 method using Equation 2.25, the type of land-use and management
activity data has a direct influence on the formulation of the equation (See Box 2.1). Activity data collected with
Approach 1 fit with Formulation A, while activity data collected with Approach 2 or 3 will fit with Formulation
B (See Chapter 3 for additional discussion on the Approaches for activity data collection).

BOX 2.1
ALTERNATIVE FORMULATIONS OF EQUATION 2.25 FOR APPROACH 1 ACTIVITY DATA VERSUS APPROACH 2
OR 3 ACTIVITY DATA WITH TRANSITION MATRICES

Two alternative formulations are possible for Equation depending on the Approach used tocollected activity data, including
Formulation A (Approach 1 for Activity Data Collection)

(

)

(

)



 ∑ SOC REFc ,s ,i • FLU c ,s ,i • FMGc ,s ,i • FI c ,s ,i • Ac, s , i  −c , s , i0 ∑ SOC REFc ,s ,i • FLU c ,s ,i • FMGc ,s ,i • FI c ,s ,i • Ac, s , i c , s , i ( 0 −T )∆C Mineral =D

Formulation B (Approaches 2 and 3 for Activity Data Collection)

(
(

)
)

 SOC REFc ,s , p • FLU c ,s , p • FMGc ,s , p • FI c ,s , p − 0 • Ac, s , p 
REFc , s , p • FLU c , s , p • FMGc , s , p • FI c , s , pc, s, p 
( 0 −T ) ∆C Mineral =D∑  SOC

Where:
p = parcel of land
See the description of other terms under the Equation 2.25.
Activity data may only be available using Approach 1 for data collection (Chapter 3). These data
provide the total area at two points in time for climate, soil and land-use/management systems,
without quantification of the specific transitions in land use and management over the inventory
time period (i.e., only the aggregate or net change is known, not the gross changes in activity).
With Approach 1 activity data, mineral C stock changes are computed using formulation A of
Equation 2.25. In contrast, activity data may be collected based on surveys, remote sensing
imagery or other data providing not only the total areas for each land management system, but also
the specific transitions in land use and management over time on individual parcels of land. These
are considered Approach 2 and 3 activity data in Chapter 3, and soil C stock changes are computed
using formulation B of Equation 2.25. Formulation B contains a summation by land parcel (i.e.,"p" represents land parcels in formulation B rather than the set of management systems “i”) thatallows the inventory compiler to compute the changes in C stocks on a land parcel by land parcel
basis.

Special consideration is needed if using Approach 1 activity data (see Chapter 3) as the basis for estimating landuse and management effects on soil C stocks, using Equation 2.25. Approach 1 data do not track individual landtransitions, and so SOC stock changes are computed for inventory time periods equivalent to D years, or as close
as possible to D, which is 20 years in the Tier 1 method. For example, Cropland may be converted from fulltillage to no-till management between 1990 and 1995, and Formulation A (see Box 2.1) would estimate a gain in
soil C for that inventory time period. However, assuming that the same parcel of land remains in no-till between
1995 and 2000, no additional gain in C would be computed (i.e., the stock for 1995 would be based on no-till
management and it would not differ from the stock in 2000 (SOC0), which is also based on no-till management).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

If using the default approach, there would be an error in this estimation because the change in soil C stocks
occurs over 20 years (i.e., D = 20 years). Therefore, SOC(0 –T) is estimated for the most distant time that is usedin the inventory calculations up to D years before the last year in the inventory time periods (SOC0). For
example, assuming D is 20 and the inventory is based on activity data from 1990, 1995, 2000, 2005 and 2010,
SOC(0 –T) will be computed for 1990 to estimate the change in soil organic C for each of the other years, (i.e.,
1995, 2000, 2005 and 2010). The year for estimating SOC(0 –T) in this example will not change until activity data
are gathered at 2011 or later (e.g., computing the C stock change for 2011 would be based on the most distantyear up to, but not exceeding D, which in this example would be 1995).
If transition matrices are available (i.e., Approach 2 or 3 activity data), the changes can be estimated between
each successive year. From the example above, some no-till land may be returned to full tillage management
between 1995 and 2000. In this case, the gain in C storage between 1990 and 1995 for the land base returned tofull tillage would need to be discounted between 1995 and 2000. Further, no additional change in the C stocks
would be necessary for land returned to full tillage after 2000 (assuming tillage management remained the same).
Only land remaining in no-till would continue to gain C up to 2010 (i.e., assuming D is 20 years). Hence,
inventories using transition matrices from Approach 2 and 3 activity data will need to be more careful in dealing
with the time periods over which gains or losses of SOC are computed. See Box 2.2 for additional details. The
application of the soil C estimation approach is much simpler if only using aggregated statistics with Approach 1
activity data. However, it is good practice for countries to use transition matrices from Approach 2 and 3
activity data if that information is available because the more detailed statistics will provide an improved
estimate of annual changes in soil organic C stocks.
There may be some cases in which activity data are collected over time spans longer than the time dependence ofthe stock change factors (D), such as every 30 years with a D of 20. For those cases, the annual stock changes
can be estimated directly between each successive year of activity data collection (e.g., 1990, 2020 and 2050)
without over- or under-estimating the annual change rate, as long as T is substituted for D in Equation 2.25.

Organic soils

The basic methodology for estimating C emissions from organic (e.g., peat-derived) soils is to assign an annual
emission factor that estimates the losses of C following drainage. Drainage stimulates oxidation of organic
matter previously built up under a largely anoxic environment. Specifically, the area of drained and managed
organic soils under each climate type is multiplied by the associated emission factor to derive an estimate ofannual CO2 emissions (source), as presented in Equation 2.26:

EQUATION 2.26
ANNUAL CARBON LOSS FROM DRAINED ORGANIC SOILS (CO2)
LOrganic = ∑ ( A • EF ) cc

Where:
LOrganic = annual carbon loss from drained organic soils, tonnes C yr-1
A

= land area of drained organic soils in climate type c, ha
Note: A is the same area (Fos) used to estimate N2O emissions in Chapter 11, Equations 11.1 and 11.2

EF = emission factor for climate type c, tonnes C ha-1 yr-1
See the soil C sections in the land-use chapters for a detailed step-by-step guidance on the application of thismethod.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

BOX 2.2
COMPARISON BETWEEN USE OF APPROACH 1 AGGREGATE STATISTICS AND APPROACH 2 OR 3 ACTIVITYDATA WITH TRANSITION MATRICESAssume a country where a fraction of the land is subjected to land-use changes, as shown in the following table, where
each line represents one land unit with an area of 1 Mha (F = Forest Land; C = Cropland; G = Grassland):Land Unit ID

1990

1995

2000

2005

2010

2015

2020

1

F

C

C

C

C

C

C

2

F

C

C

C

G

G

G

3

G

C

C

C

C

G

G

4

G

G

F

F

F

F

F

5

C

C

C

C

G

G

G

6

C

C

G

G

G

C

C

For simplicity, it is assumed that the country has a single soil type, with a SOCRef (0-30 cm) value of 77 tonnes C ha-1,
corresponding to forest vegetation. Values for FLU are 1.00, 1.05 and 0.92 for F, G and C, respectively. FMG and FI are
assumed to be equal to 1. Time dependence of stock change factors (D) is 20 years. Finally, land-use is assumed to be in
equilibrium in 1990 (i.e., no changes in land-use occurred during the 20 years prior to 1990). When using Approach 1
activity data (i.e., aggregate statistical data), annual changes in carbon stocks are computed for every inventory year
following Equation 2.25 above. The following table shows the results of calculations:
1990

1995

2000

2005

2010

2015

2020

F (Mha)

2

0

1

1

1

1

1

G (Mha)

2

1

1

1

3

3

3

C (Mha)

2

5

4

4

2

2

2

SOC0 (Mt C)

457.4

435.1

441.2

441.2

461.2

461.2

461.2

SOC(0-T) (Mt C)

457.4

457.4

457.4

457.4

457.4

435.1

441.2

0.0

-1.1

-0.8

-0.8

0.2

1.3

1.0

∆CMineral(Mt C yr-1)

If Approach 2 or 3 data are used in which land-use changes are explicitly known, carbon stocks can be computed taking
into account historical changes for every individual land unit. The total carbon stocks for the sum of all units iscompared with the most immediate previous inventory year, rather than with the inventory of 20 years before- to
estimate annual changes in carbon stocks:
1990

1995

2000

2005

2010

2015

2020

SOC0 (Mt C) for unit 1

77.0

75.5

73.9

72.4

70.8.

70.8

70.8

SOC0 (Mt C) for unit 2

77.0

75.5

73.9

72.4

74.5

76.6

78.7

SOC0 (Mt C) for unit 3

80.9

78.3

75.8

73.3

70.8

73.3

75.8

SOC0 (Mt C) for unit 4

80.9

80.9

79.9

78.9

78.0

77.0

77.0

SOC0 (Mt C) for unit 5

70.8

70.8

70.8

70.8

73.3

75.8

78.3

SOC0 (Mt C) for unit 6

70.8

70.8

73.3

75.8

78.3

76.5

74.6

SOC0 (Mt C)

457.4

451.8

447.8

443.7

445.8

450.1

455.4

SOC(0-T) (Mt C)

457.4

457.4

451.8

447.8

443.7

445.8

450.1

∆CCCMineral (Mt C yr-1)

0.0

-1.1

-0.8

-0.8

0.4

0.9

1.0

Both methods yield different estimates of carbon stocks, and use of Approach 2 or 3 data with transition matrices would
be more accurate than use of Approach 1 aggregate statistics. However, estimates of annual changes of carbon stocks
would generally not be very different, as shown in this example. The effect of underlying data approaches on theestimates differ more when there are multiple changes in land-use on the same piece of land (as in land units 2, 3 and 6
in the example above). It is noteworthy that Approach 1, 2 and 3 activity data produce the same changes in C stocks if
the systems reach a new equilibrium, which occurs with no change in land-use and management for a 20-year time
period using the Tier 1 method. Consequently, no carbon stock increases or losses are inadvertently lost when applying
the methods for Approach 1, 2 or 3 activity data, but the temporal dynamics do vary somewhat as demonstrated above.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Soil inorganic C

The effects of land-use and management activities on soil inorganic C stocks and fluxes are linked to sitehydrology and depend on specific mineralogy of the soil. Further, accurate estimation of the effects requires
following the fate of discharged dissolved inorganic C and base cations from the managed land, at least until
they are fully captured in the oceanic inorganic C cycle. Thus, a comprehensive hydrogeochemical analysis thattracks the fate of dissolved CO2, carbonate and bicarbonate species and base cations (e.g., Ca and Mg) applied to,within, and discharged from, managed land over the long term is needed to accurately estimate net stock changes.
Such an analysis requires a Tier 3 approach.

Tier 2 Approach: Incorporating country-specific data
A Tier 2 approach is a natural extension of the Tier 1 method that allows an inventory to incorporate countryspecific data, while using the default equations given for mineral and organic soils. It is good practice forcountries to use a Tier 2 approach, if possible, even if they are only able to better specify certain components of
the Tier 1 default approach. For example, a country may only have data to derive country-specific reference C
stocks, which would then be used with default stock change factors to estimate changes in soil organic C stocks
for mineral soils.

Mineral soils

Country-specific data can be used to improve four components of the Tier 1 inventory approach for estimatingstock changes in mineral soils, including derivation of region or country-specific stock change factors and/orreference C stocks, in addition to improving the specification of management systems, climate, or soil categories(e.g., Ogle et al., 2003; Vanden Bygaart et al., 2004; Tate et al., 2005). Inventory compilers can choose toderive specific values for all of these components, or any subset, which would be combined with default values
provided in the Tier 1 method to complete the inventory calculations using Equation 2.25. Also, Tier 2 uses thesame procedural steps for calculations as provided for Tier 1.
1) Defining management systems. Although the same management systems may be used in a Tier 2 inventory
as found in the Tier 1 method, the default systems can be disaggregated into a finer categorization that better
represents management impacts on soil organic C stocks in a particular country based on empirical data (i.e.,
stock change factors vary significantly for the proposed management systems). Such an undertaking, however,is only possible if there is sufficient detail in the underlying data to classify the land area into the finer, moredetailed set of management systems.
2) Climate regions and soil types. Countries that have detailed soil classifications and climatic data have theoption of developing country-specific classifications. Moreover, it is considered good practice to specify betterclimate regions and soil types during the development of a Tier 2 inventory if the new classification improves
the specification of reference C stocks and/or stock change factors. In practice, reference C stocks and/or stockchange factors should differ significantly among the proposed climate regions and soil types based on an
empirical analysis. Note that specifying new climate regions and/or soil types requires the derivation of countryspecific reference C stocks and stock change factors. The default reference C stocks and stock change factors
are only appropriate for inventories using the default climate and soil types.
3) Reference C stocks. Deriving country-specific reference C stocks (SOCRef) is another possibility for
improving an inventory using a Tier 2 approach (Bernoux et al., 2002). Using country-specific data for
estimating reference stocks will likely produce more accurate and representative values. The derivation ofcountry-specific reference soil C stocks can be done from measurements of soils, for example, as part of acountry’s soil survey. It is important that reliable taxonomic descriptions be used to group soils into categories.
There are three additional considerations in deriving the country-specific values, including possible specificationof country-specific soil categories and climate regions (i.e., instead of using the IPCC default classification),choice of reference condition, and depth increment over which the stocks are estimated. Stocks are computed bymultiplying the proportion of organic carbon (i.e., %C divided by 100) by the depth increment (default is 30 cm),
bulk density, and the proportion of coarse-fragment free soil (i.e., < 2mm fragments) in the depth increment(Ogle et al., 2003). The coarse fragment-free proportion is on a mass basis (i.e., mass of coarse fragment-freesoil/total mass of the soil).
The reference condition is the land-use/cover category that is used for evaluating the relative effect of land-use
change on the amount of soil C storage (e.g., relative difference in C storage between a reference condition, suchas native lands, and another land use, such as croplands, forming the basis for FLU in Equation 2.25). In the Tier
1 method, the reference condition is native lands (i.e., non-degraded, unimproved lands under native vegetation),and it is likely that many countries will use this same reference in a Tier 2 approach. However, another land use
can be selected for the reference, and this would be considered good practice if it allows for a more robust
assessment of country-specific reference stock values. Reference stocks should be consistent across the land
uses (i.e., Forest Land, Cropland, Grassland, Settlements, and Other Land), requiring coordination among thevarious teams conducting soil C inventories for the AFOLU Sector.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Another consideration in deriving country-specific reference C stocks is the possibility of estimating C storage toa greater depth in the soil (i.e., lower in the profile). Default stocks given in Table 2.3 account for soil organic C
in the top 30 cm of a soil profile. It is good practice to derive reference C stocks to a greater depth if there issufficient data, and if it is clear that land-use change and management have a significant impact over the
proposed depth increment. Any change in the depth for reference C stocks will require derivation of new stockchange factors, given that the defaults are also based on impacts to a 30 cm depth.
4) Stock change factors. An important advancement for a Tier 2 approach is the estimation of country-specificstock change factors (FLU, FMG and FI). The derivation of country-specific factors can be accomplished using
experimental/measurement data and computer model simulation. In practice, deriving stock change factors
involves estimating a response ratio for each study or observation (i.e., the C stocks in different input ormanagement classes are divided by the value for the nominal practice, respectively).
Optimally, stock change factors are based on experimental/measurement data in the country or surrounding
region, by estimating the response ratios from each study and then analyzing those values using an appropriate
statistical technique (e.g., Ogle et al., 2003 and 2004; VandenBygaart et al., 2004). Studies may be found in
published literature, reports and other sources, or inventory compilers may choose to conduct new experiments.
Regardless of the data source, it is good practice that the plots being compared have similar histories andmanagement as well as similar topographic position, soil physical properties and be located in close proximity.
Studies should provide C stocks (i.e., mass per unit area to a specified depth) or the information needed toestimate SOC stocks (i.e., percent organic matter together with bulk density; proportion of rock in soil, which isoften measured as the greater than 2mm fraction and by definition contains no soil organic C). If percent organic
matter is available instead of percent organic carbon, a conversion factor of 0.58 can be used to estimate the C
content. Moreover, it is good practice that the measurements of soil C stocks are taken on an equivalent mass
basis (e.g., Ellert et al., 2001; Gifford and Roderick, 2003). In order to use this method, the inventory compiler
will need to determine a depth to measure the C stock for the nominal land use or practice, such as native lands
or conventional tillage. This depth will need to be consistent with the depth for the reference C stocks. The soil
C stock for the land-use or management change is then measured to a depth with the equivalent mass of soil.
Another option for deriving country-specific values is to simulate stock change factors from advanced models(Bhatti et al., 2001). To demonstrate the use of advanced models, simulated stock change factors can becompared to with measured changes in C stocks from experiments. It is good practice to provide the results ofmodel evaluation, citing published papers in the literature and/or placing the results in the inventory report. Thismethod is considered a Tier 2 approach because it relies on the stock change factor concept and the C estimation
method elaborated in the Tier 1 approach.
Derivation of country-specific management factors (FMG) and input factors (FI), either with empirical data oradvanced models, will need to be consistent with the management system classification. If more systems are
specified for the inventory, unique factors will need to be derived representing the finer categories for aparticular land use.
Another consideration in deriving country-specific stock change factors is their associated time dependence (Din Equation 2.25), which determines the number of years over which the majority of a soil organic C stockchange occurs, following a management change. It is possible to use the default time dependence (D) for theland-use sector (e.g., 20 years for cropland), but the dependence can be changed if sufficient data are available tojustify a different time period. In addition, the method is designed to use the same time dependence (D) for all
stock change factors as presented in Equation 2.25. If different periods are selected for FLU, FMG and FI, it will
be necessary to compute the influence of land use, management and inputs separately and divide the associatedstock change dependence. This can be accomplished by modifying Equation 2.25 so that SOC at time T and 0-T
is computed individually for each of the stock change factors (i.e., SOC is computed with FLU only, thencomputed with FMG, and finally computed with FI). The differences are computed for the stocks associated withland use, management, and input, dividing by their respective D values, and then the changes are summed.
Changes in C stocks normally occur in a non-linear fashion, and it is possible to further develop the timedependence of stock change factors to reflect this pattern. For changes in land use or management that cause adecrease in soil C content, the rate of change is highest during the first few years, and progressively declines
with time. In contrast, when soil C is increasing due to land-use or management change, the rate of accumulation
tends to follow a sigmoidal curve, with rates of change being slow at the beginning, then increasing and finallydecreasing with time. If historical changes in land-use or management practices are explicitly tracked by resurveying the same locations (i.e., Approach 2 or 3 activity data, see Chapter 3), it may be possible to implementa Tier 2 method that incorporates the non-linearity of changes in soil C stock.
Similar to time dependence, the depth over which impacts are measured may vary from the default approach.
However, it is important that the reference C stocks (SOCRef) and stock change factors (FLU, FMG, FI) be
determined to a common depth, and that they are consistent across each land-use sector in order to deal with

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

conversions among uses without artificially inflating or deflating the soil C stock change estimates. It is goodpractice to document the source of information and underlying basis for the new factors in the reporting process.

Organic soils

A Tier 2 approach for CO2 emissions associated with drainage of organic soils incorporates country-specificinformation into the inventory to estimate the emissions using Equation 2.26 (see the previous Tier 1 section for
additional discussion on the general equations and application of this method). Also, Tier 2 uses the sameprocedural steps for calculations as provided for Tier 1. Potential improvements to the Tier 1 approach may
include: 1) a derivation of country-specific emission factors, 2) specification of climate regions considered moresuitable for the country, or 3) a finer, more detailed classification of management systems attributed to a land-use
category.
Derivation of country-specific emission factors is good practice if experimental data are available. Moreover, it
is good practice to use a finer classification for climate and management systems if there are significant
differences in measured C loss rates among the proposed classes. Note that any derivation must be accompaniedwith sufficient land-use/management activity and environmental data to represent the proposed climate regions
and management systems at the national scale. Developing the Tier 2 inventory for organic soils has similar
considerations as mineral soils discussed in previous section.
Country-specific emission factors for organic soils can be based on measurements of annual declines in C stocks
for the whole soil profile. Another alternative is to use land subsidence as a surrogate measure for C loss
following drainage (e.g., Armentano and Menges, 1986). C loss is computed as a the fraction of the annual
subsidence attributed to oxidation of organic matter, C content of the mineralized organic matter, and bulk
density of the soil (Ogle et al., 2003).

Soil inorganic C

See discussion for this sub-category under Tier 1.

Tier 3: Advanced estimation systems
Tier 3 approaches for soil C involve the development of an advanced estimation system that will typically better
capture annual variability in fluxes, unlike Tier 1 and 2 approaches that mostly assume a constant annual change
in C stocks over an inventory time period based on a stock change factor. Essentially, Tiers 1 and 2 represent
land-use and management impacts on soil C stocks as a linear shift from one equilibrium state to another. To
understand the implications better, it is important to note that soil C stocks typically do not exist in an absoluteequilibrium state or change in a linear manner through a transition period, given that many of the driving
variables affecting the stocks are dynamic, periodically changing at shorter time scales before a new “near”
equilibrium is reached. Tier 3 approaches can address this non-linearity using more advanced models than Tiers
1 and 2 methods, and/or by developing a measurement-based inventory with a monitoring network. In addition,
Tier 3 inventories are capable of capturing longer-term legacy effects of land use and management. In contrast,
Tiers 1 and 2 approaches typically only address the most recent influence of land use and management, such as
the last 20 years for mineral C stocks. See Section 2.5 (Generic Guidance for Tier 3 methods) for additional
discussion on Tier 3 methods beyond the text given below.

Mineral soils

Model-based approaches can use mechanistic simulation models that capture the underlying processes drivingcarbon gains and losses from soils in a quantitative framework, such as the influence of land use andmanagement on processes controlling carbon input resulting from plant production and litter fall as well as
microbial decomposition (e.g., McGill, 1996; Smith et al., 1997b; Smith et al., 2000; Falloon and Smith, 2002;
and Tate et al., 2005). Note that Tier 3 methods provide the only current opportunity to explicitly estimate theimpact of soil erosion on C fluxes. In addition, Tier 3 model-based approaches may represent C transfers
between biomass, dead biomass and soils, which are advantageous for ensuring conservation of mass in
predictions of C stock changes in these pools relative to CO2 removals and emissions to the atmosphere.
Tier 3 modelling approaches are capable of addressing the influence of land use and management with adynamic representation of environmental conditions that affect the processes controlling soil C stocks, such as
weather, edaphic characteristics, and other variables. The impact of land use and management on soil C stocks
can vary as environmental conditions change, and such changes are not captured in lower Tiers, which may
create biases in those results. Consequently, Tier 3 approaches are capable of providing a more accurate
estimation of C stock changes associated with land-use and management activity.
For Tier 3 approaches, a set of benchmark sites will be needed to evaluate model results. Ideally, a series ofpermanent, benchmark monitoring sites would be established with statistically replicated design, capturing the
major climatic regions, soil types, and management systems as well as system changes, and would allow for
repeated measurements of soil organic C stocks over time (Smith, 2004a). Monitoring is based on re-sampling
plots every 3 to 5 years or each decade; shorter sampling frequencies are not likely to produce significant

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differences due to small annual changes in C stocks relative to the large total amount of C in a soil (IPCC, 2000;
Smith, 2004b).
In addition to model-based approaches, Tier 3 methods afford the opportunity to develop a measurement-based
inventory using a similar monitoring network as needed for model evaluation. However, measurement networks,
which serve as the basis for a complete inventory, will have a considerably larger sampling density to minimize
uncertainty, and to represent all management systems and associated land-use changes, across all climatic
regions and major soil types (Sleutel et al., 2003; Lettens et al., 2004). Measurement networks can be based on
soil sampling at benchmark sites or flux tower networks. Flux towers, such as those using eddy covariancesystems (Baldocchi et al., 2001), constitute a unique case in that they measure the net exchange of CO2 between
the atmosphere and land surface. Thus, with respect to changes in C stocks for the soil pool, flux tower
measurement networks are subject to the following caveats: 1) towers need to occur at a sufficient density torepresent fluxes for the entire country; 2) flux estimates need to be attributed to individual land-use sectors andspecific land-use and management activities; and 3) CO2 fluxes need to be further attributed to individual pools
including stock changes in soils (also biomass and dead organic matter). Additional considerations about soil
measurements are given in the previous section on Tier 2 methods for mineral soils (See stock change factor
discussion).
It is important to note that measurement based inventories represent full C estimation approaches, addressing all
influences on soil C stocks. Partial estimation of only land-use and management effects may be difficult.

Organic soils

Similar to mineral soils, CO2 emissions attributed to land use and management of organic soils can be estimatedwith a model or measurement based approach. Dynamic, mechanistic-based models will typically be used tosimulate underlying processes, while capturing the influence of land use and management, particularly the effectof variable levels of drainage on decomposition. The same considerations that were mentioned for mineral soils
are also important for model- and measurement-based approaches addressing soil C stock changes attributed to
management of organic soils.

Soil inorganic C

A Tier 3 approach may be further developed to estimate fluxes associated with management impacts on soil
inorganic C pools. For example, irrigation can have an impact on soil inorganic C stocks and fluxes, but thedirection and magnitude depends on the source and nature of irrigation water and the source, amount, and fate ofdischarged dissolved inorganic C. In arid and semi-arid regions, gypsum (CaSO4 . 2H2O) amendments can leadto an increase in soil inorganic C stocks depending on the amount of Ca2+ that replaces Na+ on soil colloids,
relative to reaction with bicarbonate and precipitation of calcite (CaCO3). Other land-use and management
activities, such as deforestation/afforestation and soil acidifying management practices can also affect soil
inorganic C stocks. However, these changes can cause gains or losses of C in this pool depending on sitespecific conditions and the amount attributable to the activity can be small.
Few models currently exist for estimating changes in soil inorganic C due to land use and management, and so a
Tier 3 approach may require considerable time and resources to implement. Where data and knowledge aresufficient and activities that significantly change soil inorganic C stocks are prevalent, it is good practice forcountries to do a comprehensive hydro-geochemical analysis that includes all important land-use andmanagement activities to estimate their effect on soil inorganic C stocks. A modelling approach would need toisolate the land-use and management activities from non-anthropogenic effects. Alternatively, a measurementbased approach can be used by periodically sampling benchmark sites in managed lands for determining
inorganic C stocks in situ, or possibly CO2 fluxes, in combination with a monitoring network for soil organic C
as discussed above for mineral soils. However, the amount and fate of dissolved inorganic C would require
further measurements, modelling, or simplifying assumptions, such as all leaching losses of inorganic C are
assumed to be emitted as CO2 to the atmosphere.

2.4

NON-CO 2 EMISSIONS

There are significant emissions of non-greenhouse gases from biomass burning, livestock and manure
management, or soils. N2O emissions from soils are covered in Chapter 11, where guidance is given on methods
that can be applied nationally (i.e., irrespective of land-use types) if a country chooses to use national scale
activity data. The guidance on CH4 and N2O emissions from livestock and manure are addressed only in Chapter
10 because emissions do not depend on land characteristics. A generic approach to estimating greenhouse gas
emissions from fire (both CO2 and non-CO2 gases) is described below, with land-use specific enhancements
given in the Forest Land, Grassland and Cropland chapters. It is good practice to check for complete coverage of
CO2 and non-CO2 emissions due to losses in carbon stocks and pools to avoid omissions or double counting.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Emissions from fire include not only CO2, but also other greenhouse gases, or precursors of greenhouse gases,that originate from incomplete combustion of the fuel. These include carbon monoxide (CO), methane (CH4),
non-methane volatile organic compounds (NMVOC) and nitrogen (e.g., N2O, NOx) species (Levine, 1994). In
the 1996 IPCC Guidelines and GPG2000, non-CO2 greenhouse gas emissions from fire in savannas and burningof crop residues were addressed along with emissions from Forest Land and Grassland conversion. The
methodology differed somewhat by vegetation type, and fires in Forest Land were not included. In the GPGLULUCF, emissions (CO2 and non-CO2) from fires were addressed, particularly in the chapter covering Forest
Land (losses of carbon resulting from disturbances). In the Cropland and Grassland chapters, only non-CO2
emissions were considered, with the assumption that the CO2 emissions would be counterbalanced by CO2
removals from the subsequent re-growth of the vegetation within one year. This assumption implies maintenanceof soil fertility – an assumption which countries may ignore if they have evidence of fertility decline due to fire.
In Forest Land, there is generally a lack of synchrony (non-equivalence of CO2 emissions and removals in theyear of reporting).
These Guidelines provide a more generic approach for estimating emissions from fire. Fire is treated as a
disturbance that affects not only the biomass (in particular, above-ground), but also the dead organic matter(litter and dead wood). The term `biomass burning` is widely used and is retained in these Guidelines, but
acknowledging that fuel components other than live biomass are often very significant, especially in forest
systems. For Cropland and Grassland having little woody vegetation, reference is usually made to biomassburning, since biomass is the main pool affected by the fire.
Countries should apply the following principles when estimating greenhouse gas emissions resulting from firesin Forest Land, Cropland and Grassland:•

Coverage of reporting: Emissions (CO2 and non- CO2) need to be reported for all fires (prescribed fires andwildfires) on managed lands (the exception is CO2 from Grassland, as discussed below). Where there is aland-use change, any greenhouse gas emission from fire should be reported under the new land-use category(transitional category). Emissions from wildfires (and escaped prescribed fires) that occur on unmanaged
lands do not need to be reported, unless those lands are followed by a land-use change (i.e., become
managed land).

•Fire as a management tool (prescribed burning): greenhouse gas emissions from the area burnt are reported,and if the fire affects unmanaged land, greenhouse gas emissions should also be reported if the fire isfollowed by a land-use change.

•

Equivalence (synchrony) of CO2 emissions and removals: CO2 net emissions should be reported where theCO2 emissions and removals for the biomass pool are not equivalent in the inventory year. For grassland
biomass burning and burning of agriculture residues, the assumption of equivalence is generally reasonable.
However, woody vegetation may also burn in these land categories, and greenhouse gas emissions fromthose sources should be reported using a higher Tier method. Further, in many parts of the world, grazing is
the predominant land use in Forest Land that are regularly burnt (e.g., grazed woodlands and savannas), andcare must be taken before assuming synchrony in such systems. For Forest Land, synchrony is unlikely if
significant woody biomass is killed (i.e., losses represent several years of growth and C accumulation), andthe net emissions should be reported. Examples include: clearing of native forest and conversion toagriculture and/or plantations and wildfires in Forest Land.

•

Fuels available for combustion: Factors that reduce the amount of fuels available for combustion (e.g., fromgrazing, decay, removal of biofuels, livestock feed, etc.) should be accounted for. A mass balance approachshould be adopted to account for residues, to avoid underestimation or double counting (refer to Section
2.3.2).

•

Annual reporting: despite the large inherent spatial and temporal variability of fire (in particular that fromwildfires), countries should estimate and report greenhouse gas emissions from fire on an annual basis.

These Guidelines provide a comprehensive approach for estimating carbon stock changes and non-CO2
emissions resulting from fire in the Forest Land (including those resulting from forest conversion), and non-CO2
emissions in the Cropland and Grassland. Non-CO2 emissions are addressed for the following five types ofburning: (1) grassland burning (which includes perennial woody shrubland and savanna burning); (2) agriculturalresidues burning; (3) burning of litter, understory and harvest residues in Forest Land, (4) burning following
forest clearing and conversion to agriculture; and (5) other types of burning (including those resulting fromwildfires). Direct emissions of CO2 are also addressed for items (3) and (4) and (5). Since estimating emissions
in these different categories have many elements in common, this section provides a generic approach toestimate CO2 and non-CO2 emissions from fire, to avoid repetition in specific land-use sections that address
emissions from fire in these Guidelines.

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Prescribed burning of savannas is included under the grassland biomass burning section (Chapter 6, Grassland,
Section 6.3.4). It is important to avoid double counting when estimating greenhouse gas emissions fromsavannas that have a vegetation physiognomy characteristic of Forest Land. An example of this is the cerradão
(dense woodland) formation in Brazil which, although being a type of savanna, is included under Forest Land,due to its biophysical characteristics.
In addition to the greenhouse gas emissions from combustion, fires may lead to the creation of an inert carbon
stock (charcoal or char). Post-fire residues comprise unburned and partially burnt components, as well as a small
amount of char that due to its chemical nature is highly resistant to decomposition. The knowledge of the rates ofchar formation under contrasting burning conditions and subsequent turnover rates is currently too limited(Forbes et al., 2006; Preston and Schmidt, 2006) to allow development of a reliable methodology for inventory
purposes, and hence is not included in these Guidelines. A technical basis for further methodologicaldevelopment is included in Appendix 1.
Additionally, although emissions of NMVOC also occur as a result of fire, they are not addressed in the present
Guidelines due to the paucity of the data and size of uncertainties in many of the key parameters needed for theestimation, which prevent the development of reliable emission estimates.

METHOD DESCRIPTION
Each relevant section in these Guidelines includes a three-tiered approach to address CO2 (where applicable) andnon-CO2 greenhouse gas emissions from fire. The choice of Tier can be made following the steps in the decision
tree presented in Figure 2.6. Under the Tier 1 approach, the formulation presented in Equation 2.27 can beapplied to estimate CO2 and non-CO2 emissions from fire, using the default data provided in this chapter and in
the relevant land-use sections of these Guidelines. Higher Tiers involve a more refined application of Equation
2.27.
Since Tier 1 methodology adopts a simplified approach to estimating the dead organic matter pool (see Section
2.3.2), certain assumptions must be made when estimating net greenhouse gas emissions from fire in thosesystems (e.g. Forest Land, and Forest Land converted to another land use), where dead organic matter can be amajor component of the fuel burnt. Emissions of CO2 from dead organic matter are assumed to be zero in foreststhat are burnt, but not killed by fire. If the fire is of sufficient intensity to kill a portion of the forest stand, under
Tier 1 methodology, the C contained in the killed biomass is assumed to be immediately released to theatmosphere. This Tier 1 simplification may result in an overestimation of actual emissions in the year of the fire,
if the amount of biomass carbon killed by the fire is greater than the amount of dead wood and litter carbon
consumed by the fire.
Non-CO2 greenhouse gas emissions are estimated for all fire situations. Under Tier 1, non-CO2 emissions are
best estimated using the actual fuel consumption provided in Table 2.4, and appropriate emission factors (Table
2.5) (i.e., not including newly killed biomass as a component of the fuel consumed). Clearly, if fire in forests
contributes significantly to net greenhouse gas emissions, countries are encouraged to develop a more complete
methodology (higher tiers) which includes the dynamics of dead organic matter and improves the estimates of
direct and post-fire emissions.
For Forest Land converted to another land uses, organic matter burnt is derived from both newly felled
vegetation and existing dead organic matter, and CO2 emissions should be reported. In this situation, estimatesof total fuel consumed (Table 2.4) can be used to estimate emissions of CO2 and non- greenhouse gases using
Equation 2.27. Care must be taken, however, to ensure that dead organic matter carbon losses during the landuse conversion are not double counted in Equations 2.27 (as losses from burning) and Equation 2.23 (as losses
from decay).
A generic methodology to estimate the emissions of individual greenhouse gases for any type of fire issummarized in Equation 2.27.
EQUATION 2.27
ESTIMATION OF GREENHOUSE GAS EMISSIONS FROM FIRE
L fire = A • M B • C f • Gef • 10 −3

Where:
Lfire = amount of greenhouse gas emissions from fire, tonnes of each GHG e.g., CH4, N2O, etc.
A

= area burnt, ha

MB = mass of fuel available for combustion, tonnes ha-1. This includes biomass, ground litter and deadwood. When Tier 1 methods are used then litter and dead wood pools are assumed zero, except
where there is a land-use change (see Section 2.3.2.2).

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Cf = combustion factor, dimensionless (default values in Table 2.6)
Gef = emission factor, g kg-1 dry matter burnt (default values in Table 2.5)
Note: Where data for MB and Cf are not available, a default value for the amount of fuel actually burnt(the product of MB and Cf ) can be used (Table 2.4) under Tier 1 methodology.
For CO2 emissions, Equation 2.27 relates to Equation 2.14, which estimates the annual amount of live biomass
loss from any type of disturbance.
The amount of fuel that can be burnt is given by the area burnt and the density of fuel present on that area. The
fuel density can include biomass, dead wood and litter, which vary as a function of the type, age and condition of
the vegetation. The type of fire also affects the amount of fuel available for combustion. For example, fuelavailable for low-intensity ground fires in forests will be largely restricted to litter and dead organic matter on
the surface, while a higher-intensity ‘crown fire’ can also consume substantial amounts of tree biomass.
The combustion factor is a measure of the proportion of the fuel that is actually combusted, which varies as afunction of the size and architecture of the fuel load (i.e., a smaller proportion of large, coarse fuel such as treestems will be burnt compared to fine fuels, such as grass leaves), the moisture content of the fuel and the type offire (i.e., intensity and rate of spread which is markedly affected by climatic variability and regional differences
as reflected in Table 2.6). Finally, the emission factor gives the amount of a particular greenhouse gas emitted
per unit of dry matter combusted, which can vary as a function of the carbon content of the biomass and thecompleteness of combustion. For species with high N concentrations, NOx and N2O emissions from fire can varyas a function of the N content of the fuel. A comprehensive review of emission factors was conducted byAndreae and Merlet (2001) and is summarized in Table 2.5.
Tier 2 methods employ the same general approach as Tier 1 but make use of more refined country-derived
emission factors and/or more refined estimates of fuel densities and combustion factors than those provided in
the default tables. Tier 3 methods are more comprehensive and include considerations of the dynamics of fuels(biomass and dead organic matter).

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 2.6

Generic decision tree for identification of appropriate tier to estimate
greenhouse gas emissions from fire in a land-use categoryStart

Are detailed
data on biomass burningavailable to estimate GHG emissions
using advanced models
or methods?

Use the detailed
biomass burning data
for Tier 3 method.

Yes

Box 3: Tier 3NoAre
country-specific activity
data emission factors
available?

Use country-specificactivity data andemission factors for the
Tier 2 method.

Yes

Box 2: Tier 2NoIsprescribed burningor wildfire a key
category1?

No

Are aggregate
data on biomass burningavailable?

Yes

Yes

Collect data for Tier 3
or Tier 2 method.

Use aggregate data anddefault emission factors
for Tier 1 method.

No

Gather dataon burning.

Box 1: Tier 1
Note:
1: See Volume 1 Chapter 4, "Methodological Choice and Identification of Key Categories" (noting Section 4.1.2 on limited resources), for
discussion of key categories and use of decision trees.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

TABLE 2.4
FUEL (DEAD ORGANIC MATTER PLUS LIVE BIOMASS) BIOMASS CONSUMPTION VALUES (TONNES DRY MATTER
HA-1) FOR FIRES IN A RANGE OF VEGETATION TYPES( To be used in Equation 2.27 , to estimate the product of quantities ‘ MB • Cf ’ , i.e., an absolute amount)
Vegetation type

Subcategory

Mean

SE

References

Primary tropical forest

83.9

25.8

7, 15, 66, 3, 16, 17, 45

Primary open tropical forest

163.6

52.1

21,

Primary tropical moist
forest

160.4

11.8

37, 73--

All primary tropical forests
Young secondary tropicalforest (3-5 yrs)
Secondary tropicalIntermediate secondary
forest (slash andtropical forest (6-10 yrs)burn)
Advanced secondary
tropical forest (14-17 yrs)
All secondary tropical forests

119.6

50.7

8.1

-

41.1

27.4

61, 3546.4

8.0

61, 73

42.2

23.6

66, 30All Tertiary tropical forest

54.1

-

66, 30Wildfire (general)

52.8

48.4

2, 33, 66

Crown fire

25.1

7.9

11, 43, 66, 41, 63, 64

Surface fire

21.6

25.1

43, 69, 66, 63, 64, 1

Post logging slash burn

69.6

44.8

49, 40, 66, 18

Land clearing fire

87.5

35.0

10, 6741.0

36.5

43, 45, 69, 47

Wildfire

53.0

53.6

66, 32, 9

Prescribed fire – (surface)

16.0

13.7

66, 72, 54, 60, 9

Post logging slash burn

168.4

168.8

25, 58, 46

Felled, wood removed, andburned (land-clearing fire)

132.6

-

69.4

100.8

Wildfire

19.8

6.3

32, 66

Post logging slash burn

77.5

65.0

55, 19, 14, 27, 66

Felled and burned (landclearing fire)

48.4

62.7

53, 24, 71

50.4

53.7

43, 56

Primary tropicalforest (slash andburn)Primary tropical dry forest

Boreal forest

All boreal forest

Eucalypt forests

All Eucalypt forests

Other temperate
forests

All “other” temperate forests

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61

62, 9

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TABLE 2.4 (CONTINUED)FUEL (DEAD ORGANIC MATTER PLUS LIVE BIOMASS) BIOMASS CONSUMPTION VALUES (TONNES DRY MATTER
HA-1) FOR FIRES IN A RANGE OF VEGETATION TYPES( To be used in Equation 2.27 , to estimate the product of quantities ‘ MB • Cf ’ , i.e., an absolute amount)
Vegetation type

Mean

SE

Shrubland (general)

26.7

4.2

43

Calluna heath

11.5

4.3

26, 39Sagebrush

5.7

3.8

66

Fynbos12.9

0.1

70, 6614.3

9.0

Savanna woodland

2.5

-

28

Savanna parkland

2.7

-

57

2.6

0.1

Savanna woodland

3.3

-

57

Savanna parkland

4.0

1.1

57, 6, 51

Tropical savanna

6

1.8

52, 73

Other savanna woodlands

5.3

1.7

59, 57, 31

All savanna woodlands (mid/late dry season burns)*
Tropical/sub-tropical
Savanna Grasslands/
grassland
Pastures (early dry
season burns)*
Grassland

4.6

1.5

2.1

-

28

-

-

48

All savanna grasslands (early dry season burns)*
Tropical/sub-tropicalgrassland

2.1

-

5.2

1.7

9, 73, 12, 57

Grassland

4.1

3.1

43, 9

Tropical pasture~

23.7

11.8

4, 23, 38, 66

Savanna

7.0

2.7

42, 50, 6, 45, 13, 65

10.0

10.1

Peatland41

1.4

68, 33Tundra10-33

Wheat residues

4.0

see Note b

Maize residues

10.0

see Note b

Rice residues

5.5

see Note b

Sugarcane a

6.5

see Note b

Shrublands

Subcategory

All Shrublands
Savanna woodlands(early dry season
burns)*All savanna woodlands (early dry season burns)

Savanna woodlands(mid/late dry season
burns)*

Savanna Grasslands/
Pastures (mid/late dry
season burns)*

All savanna grasslands (mid/late dry season burns)*
Other vegetation types

Agricultural residues(post harvest fieldburning)

References* Surface layer combustion only
~ Derived from slashed tropical forest (includes unburned woody material)

2.46

a

For sugarcane, data refer to burning before harvest of the crop.

b

Expert assessment by authors.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

TABLE 2.5
EMISSION FACTORS (g kg-1 DRY MATTER BURNT) FOR VARIOUS TYPES OF BURNING. VALUES ARE MEANS ± SD AND AREBASED ON THE COMPREHENSIVE REVIEW BY ANDREAE AND MERLET (2001)(To be used as quantity ‘Gef‘ in Equation 2.27)
Category

CO2

CO

CH4

N2O

NOX

Savanna and grassland

1613
± 95

65
± 20

2.3
± 0.9

0.21
± 0.103.9± 2.4

Agricultural residues

1515
± 177

92
± 84

2.7

0.07

2.5
± 1.0

Tropical forest

1580
± 90

104
± 20

6.8
± 2.0

0.20

1.6
± 0.7

Extra tropical forest

1569
± 131

107
± 37

4.7
± 1.9

0.26
±0.07

3.0
± 1.4

Biofuel burning

1550
± 95

78
± 31

6.1
± 2.2

0.06

1.1
± 0.6

Note: The “extra tropical forest’ category includes all other forest types.
Note: For combustion of non-woody biomass in Grassland and Cropland, CO2 emissions do not need to be estimated and reported,because it is assumed that annual CO2 removals (through growth) and emissions (whether by decay or fire) by biomass are in balance(see earlier discussion on synchrony in Section 2.4.

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TABLE 2.6
COMBUSTION FACTOR VALUES (PROPORTION OF PREFIRE FUEL BIOMASS CONSUMED) FOR FIRES IN A RANGE OFVEGETATION TYPES(Values in column ‘mean’ are to be used for quantity Cf in Equation 2.27 )Vegetation type

Subcategory

Mean

SD

References

Primary tropical forest

0.32

0.12

7, 8, 15, 56, 66, 3,
16, 53, 17, 45,

Primary open tropical forest

0.45

0.09

21

Primary tropical moist forest

0.50

0.03

37, 73

Primary tropical dry forest

-

-

0.36

0.13

Young secondary tropical forest(3-5 yrs)

0.46-Intermediate secondary tropicalforest (6-10 yrs)

0.67

0.21

61, 35

Advanced secondary tropicalforest (14-17 yrs)

0.50

0.10

61, 73All secondary tropical forests

0.55

0.06

56, 66, 34, 30

All tertiary tropical forest

0.59

-

Wildfire (general)

0.40

0.06

33

Crown fire

0.43

0..21

66, 41, 64, 63

surface fire

0.15

0.08

64, 63

Post logging slash burn

0.33

0.13

49, 40, 18

Land clearing fire

0.59

-

0.34

0.17

-

-

Prescribed fire – (surface)

0.61

0.11

72, 54, 60, 9

Post logging slash burn

0.68

0.14

25, 58, 46

Felled and burned (land-clearing
fire)

0.49

-

0.63

0.13

Post logging slash burn

0.62

0.12

Felled and burned (land-clearing
fire)

0.51

-

0.45

0.16

Primary tropical forest(slash and burn)

All primary tropical forests

Secondary tropicalforest (slash and burn)

Boreal forest

All boreal forest
Wildfire
Eucalyptus forests

All Eucalyptus forests
Other temperate forests

All “other” temperate forests

2.48

66

61

66, 3067
45, 4762

55, 19, 27, 14
53, 24, 71
53, 56

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

TABLE 2.6 (CONTINUED)COMBUSTION FACTOR VALUES (PROPORTION OF PREFIRE FUEL BIOMASS CONSUMED) FOR FIRES IN A RANGEOF VEGETATION TYPES(Values in column ‘mean’ are to be used for quantity Cf in Equation 2.27 )Vegetation type

Mean

SD

Shrubland (general)

0.95

-

Calluna heath

0.71

0.30

26, 56, 39

Fynbos0.61

0.16

70, 440.72

0.25

Savanna woodland

0.22

-

28

Savanna parkland

0.73

-

57

Other savanna woodlands

0.37

0.19

0.40

0.22

Savanna woodland

0.72

-

66, 57

Savanna parkland

0.82

0.07

57, 6, 51

Tropical savanna

0.73

0.04

52, 73, 66, 12

Other savanna woodlands

0.68

0.19

22, 29, 44, 31, 57

All savanna woodlands (mid/late dry season burns)*

0.74

0.14

Savanna Grasslands/
Pastures (early dry
season burns)*

0.74

-

28

-

-

48

0.74

-

Tropical/sub-tropical grassland

0.92

0.11

44, 73, 66, 12, 57

Tropical pasture~

0.35

0.21

4, 23, 38, 66

Savanna

0.86

0.12

53, 5, 56, 42, 50, 6,
45, 13, 44, 65, 66

0.77

0.26

Peatland

0.50-

20, 44Tropical Wetlands

0.70

-

44

Wheat residues

0.90

-

see Note b

Maize residues

0.80

-

see Note b

Rice residues

0.80

-

see Note b

Sugarcane a

0.80

-

see Note b

Shrublands

Subcategory

All shrublands
Savanna woodlands(early dry season
burns)*All savanna woodlands (early dry season burns)

Savanna woodlands(mid/late dry season
burns)*

Tropical/sub-tropical grassland
Grassland

All savanna grasslands (early dry season burns)*
Savanna Grasslands/
Pastures (mid/late dry
season burns)*

All savanna grasslands (mid/late dry season burns)*
Other vegetation types

Agricultural residues(Post harvest fieldburning)*

Surface layer combustion only~

Derived from slashed tropical forest (includes unburned woody material)a

For sugarcane, data refer to burning before harvest of the crop.

b

Expert assessment by authors.

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References
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2.5

ADDITIONAL GENERIC GUIDANCE FOR TIER
3 METHODS

The guidelines in this volume focus mainly on Tier 1 methods, along with general guidance to assist with thedevelopment of a Tier 2 inventory. Less attention is given to Tier 3 methods, but some general guidance isprovided in this section. Tier 3 inventories are advanced systems using measurements and/or modelling, withthe goal of improving the estimation of greenhouse gas (GHG) emissions and removals, beyond what is possible
with Tier 1 or 2 approaches. In this section, guidelines are elaborated that provide a sound scientific basis for thedevelopment of Tier 3 Inventories. These guidelines do not limit the selection of Tier 3 sampling schemes ormodelling approaches, but provide general guidance to assist the inventory developer in the implementation.
Specific issues surrounding Tier 3 approaches for individual source categories may be provided later in thevolume, and supplement the general guidance found in this section.

2.5.1

Measurement-based Tier 3 inventories

Inventories can be based on direct measurements of C stock changes from which emissions and removals ofcarbon are estimated. Measurement of some non-CO2 greenhouse gas emissions is possible, but because of thehigh spatial and temporal variability of non-CO2 emissions, Tier 3 methods will likely combine process models
with measurements to estimate non-CO2 emissions. Purely measurement-based inventories, e.g., based on
repeated measurements using a national forest inventory can derive carbon stock change estimates without
relying on process models, but they do require appropriate statistical models for the spatial and temporal scalingof plot measurements to a national inventory. Approaches based on dynamic models (e.g., process-basedmodels) to estimate national emissions will be discussed in Section 2.5.2. In general, six steps are involved with
implementation of a Tier 3 measurement-based inventory.
Step 1. Develop sampling scheme. Sampling schemes can be developed using a variety of approaches, but
typically involve some level of randomization of sampling sites within strata. (Even inventories based on aregular grid typically select the starting point of the grid at random). Inventory compilers will determine anappropriate approach given the size of their country, key environmental variables (e.g., climate) andmanagement systems in their region. The latter two may serve as stratification variables, assuming the sampling
scheme is not completely random. In addition, it is good practice for sampling to provide wide spatial coverageof emissions and/or removals for a particular key source category.
The inventory compiler should establish an appropriate time period over which sites will be re-sampled if using a
repeated measures design. The timing of re-measurement will depend on the rate of stock changes or non-CO2
greenhouse gas emissions. For example, re-measurement periods in boreal and some temperate regions, wheretrees grow slowly and DOM pools change little in single years, can be longer than in environments where carbon
dynamics are more rapid. Where fluxes are measured directly, greater temporal and spatial variability will
require more frequent or more intensive sampling to capture fluxes which might otherwise be missing from themeasurement record.
Some approaches do not include re-sampling of the same sites. Such designs are acceptable, but may limit thestatistical power of the analysis, and therefore lead to greater uncertainty. It is likely that a repeated measuresdesign will provide a better basis for estimating carbon stock changes or emissions in most countries.
It is good practice to develop a methodology handbook explaining the sampling scheme as part of Step 1. This
handbook can be useful for those involved with the measurements, laboratory analyses and other aspects of theprocess, as well as possibly providing supporting material for documentation purposes.
Step 2. Select sampling sites. Specific sampling sites will be located based on sampling design. It is goodpractice to have alternative sites for sampling in case it is not possible to sample some original locations. In a
repeated measures design, the sites will become a monitoring network that is periodically re-sampled.
Determining sampling locations will likely involve the use of a geographic information system. A geographic
database may include a variety of environmental and management data, such as climate, soils, land use, andlivestock operations, depending on the source category and stratification. If key data are not available at thenational scale, the inventory developer should re-evaluate the design and stratification (if used) in Step 1 andpossibly modify the sampling design.
Sampling may require coordination among different national ministries, provincial or state governments,
corporate and private land owners. Establishing relationships among these stakeholders can be undertaken before
collecting initial samples. Informing stakeholders about ongoing monitoring may also be helpful and lead togreater success in implementing monitoring programs.

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Step 3. Collect initial samples. Once the final set of sites are determined, a sampling team can visit thoselocations, establish plots and collect initial samples. The initial samples will provide initial carbon stocks, orserve as the first measure of emissions. It is good practice to establish field measurement and laboratory
protocols before the samples are collected. In addition, it may be helpful to take geographic coordinates of plot
locations or sample points with a global positioning system, and, if repeated measures are planned, topermanently mark the location for ease of finding and re-sampling the site in the future.
It is good practice to take relevant measurements and notes of the environmental conditions and management at
the site. This will confirm that the conditions were consistent with the design of the sampling scheme, and also
may be used in data analysis (Step 5). If a stratified sampling approach is used, and it becomes apparent thatmany or most sites are not consistent with the expected environmental conditions and management systems, it isgood practice to repeat Step 1, re-evaluating and possibly modifying the sampling scheme based on the new
information.
Step 4. Re-sample the monitoring network on a periodic basis. For repeated measures designs, sampling sites
will be periodically re-sampled in order to evaluate trends in carbon stocks or non-CO2 emissions over aninventory time period. The time between re-measurement will depend on the rate of stock changes or thevariability in emissions, the resources available for the monitoring program, and the design of the sampling
scheme.
If destructive sampling is involved, such as removing a soil core or biomass sample, it is good practice to resample at the same site but not at the exact location in which the sample was removed during the past.
Destructive sampling the exact location is likely to create bias in the measurements. Such biases would
compromise the monitoring and produce results that are not representative of national trends.
Step 5. Analyze data and determine carbon stock changes/non-CO2 emissions, and infer national emissions andremoval estimates and measures of uncertainty. It is good practice to select an appropriate statistical method fordata analysis based on the sampling design. The overall result of the statistical analysis will be estimates ofcarbon stock changes or measurements of emissions from which the national emission and removal estimates canbe derived. It is good practice to also include estimates of uncertainty, which will include measurement errors inthe sample collection and laboratory processing (i.e., the latter may be addressed using standards and throughcross-checking results with independent labs), sampling variance associated with monitoring design and otherrelevant sources of uncertainty (see discussion for each source category later in this volume in addition to theuncertainty chapter in Volume 1). The analysis may include scaling of measurements to a larger spatial or
temporal domain, which again will depend on the design of the sampling scheme. Scaling may range from
simple averaging or weighted averaging to more detailed interpolation/extrapolation techniques.
To obtain national estimates of stock changes or emission of non-CO2 greenhouse gases, it is often necessary to
extrapolate measurements using models that take into consideration environmental conditions, management andother activity data. While the net changes of carbon-based greenhouse gasses can (at least in theory) be estimatedpurely by repeated measurements of carbon stocks, statistical and other models are often employed to assist in
the scaling of plot measures to national estimates. National emission estimates of non-CO2 greenhouse gases areunlikely to be derived from measurements alone because of the expense and difficulty in obtaining themeasurement. For example, N2O emissions from forest fires cannot be measured empirically but are typicallyinferred from samples, activity data on the area burnt, and fuel consumption estimates. In contrast, soil N2O
emissions can be readily estimated using chambers, but it would be very expensive to establish a network withthe sampling intensity needed to provide national emission estimates based solely on measurements without useof models for extrapolation.
It is good practice to analyze emissions relative to environmental conditions in addition to the contribution ofvarious management practices to those trends. Interpretation of the patterns will be useful in evaluating
possibilities for future mitigation.
Step 6. Reporting and Documentation. It is good practice to assemble inventory results in a systematic andtransparent manner for reporting purposes. Documentation may include a description of the sampling schemeand statistical methods, sampling schedule (including re-sampling), stock change and emissions estimates andthe interpretation of emission trends (e.g., contributions of management activities). In addition, QA/QC should
be completed and documented in the report, including quality assurance procedures in which peer-reviewers not
involved with the analysis evaluate the methodology. For details on QA/QC, reporting and documentation, see
the section dealing with the specific source category later in this volume, as well as information provided inVolume 1, Chapter 6.

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2.5.2

Model-based Tier 3 inventories

Model-based inventories are developed using empirical, process-based or other types of advanced models. It isgood practice to have independent measurements to confirm that the model is capable of estimating emissions
and removals in the source categories of interest (Prisley and Mortimer, 2004). In general, seven steps are used
to implement a Tier 3 model-based inventory (Figure 2.7).
Step 1. Select/develop a model for calculating the stock changes and/or greenhouse gas emissions. A model
should be selected or developed that more accurately represents stock changes or non-CO2 greenhouse gas
emissions than is possible with Tiers 1 and 2 approaches. As part of this decision, it is good practice to consider
the availability of input data (Steps 3) and the computing resources needed to implement the model (Step 5).
Figure 2.7

Steps to develop a Tier 3 model-based inventory estimation system

Model
Selection/Development.

Evaluation using
calibration data.

Unableto locate necessary
input data?

Does notcapture general trends
from experiments?

Identify Model Inputs.

Assess Uncertainties.

Implement Model.

Evaluation withindependent data.
(Good Practice)

Model results
unacceptable?

Reporting/Documentation.

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories

Step 2. Evaluation with calibration data. This is a critical step for inventory development in which model
results are compared directly with measurements that were used for model calibration/parameterization (e.g.,
Falloon and Smith, 2002). Comparisons can be made using statistical tests and/or graphically, with the goal ofdemonstrating that the model effectively simulates measured trends for a variety of conditions in the source
category of interest. It is good practice to ensure that the model responds appropriately to variations in activity
data and that the model is able to report results by land-use category as per the conventions laid out in Chapter 3.
Re-calibration of the model or modifications to the structure (i.e., algorithms) may be necessary if the model
does not capture general trends or there are large systematic biases. In some cases, a new model may be selected
or developed based on this evaluation. Evaluation results are an important component of the reporting
documentation, justifying the use of a particular model for quantifying emissions in a source category.
Step 3. Gather spatio-temporal data on activities and relevant environmental conditions that are needed as
inputs to a model. Models, even those used in Tiers 1 and 2 approaches, require specific input information inorder to estimate greenhouse gas emissions and removals associated with a source category. These inputs may
range from weather and soils data to livestock number, forest types, natural disturbances or cropping
management practices. It is good practice for the input data to be consistent with spatio-temporal scale of themodel (i.e., algorithms). For example, if a model operates on a daily time step then the input data should provide
information about daily variation in the environmental characteristic or activity data. In some cases, input data
may be a limiting factor in model selection, requiring some models to be discarded as inappropriate given theavailable activity and/or environmental data.
Step 4. Quantify uncertainties. Uncertainties are due to imperfect knowledge about the activities or processes
leading to greenhouse gas fluxes, and are typically manifested in the model structure and inputs. Consequently,
uncertainty analyses are intended to provide a rigorous measure of the confidence attributed to a model estimatebased on uncertainties in the model structure and inputs, generating a measure of variability in the carbon stockchanges or non-CO2 greenhouse gas fluxes. Volume 1, Chapter 3 provides specific guidance on appropriate
methods for conducting these analyses. Additional information may also be provided for specific source
categories later in this volume.
Step 5. Implement the model. The major consideration for this step is that there are enough computing resources
and personnel time to prepare the input data, conduct the model simulations, and analyze the results. This will
depend on the efficiency of the programming script, complexity of the model, as well as the spatial and temporalextent and resolution of the simulations. In some cases, limitations in computing resources may constrain thecomplexity and range of spatial or temporal resolution that can be used in implementing at the national scale (i.e.,
simulating at finer spatial and temporal scales will require greater computing resources).
Step 6. Evaluation with independent data. It is important to realise the difference between Steps 2 and 6. Step 2
involves testing model output with field data that were used as a basis for calibration (i.e., parameterization). In
contrast, evaluation with independent data is done with a completely independent set of data from model
calibration, providing a more rigorous assessment of model components and results. Optimally, independent
evaluation should be based on measurements from a monitoring network or from research sites that were notused to calibrate model parameters. The network would be similar in principle to a series of sites that are used
for a measurement-based inventory. However, the sampling does not need to be as dense because the network is
not forming the basis for estimating carbon stock changes or non-CO2 greenhouse gas fluxes, as in a purely
measurement-based inventory, but is used to check model results.
In some cases, independent evaluation may demonstrate that the model-based estimation system is inappropriate
due to large and unpredictable differences between model results and the measured trends from the monitoring
network. Problems may stem from one of three possibilities: errors in the implementation step, poor input data,or an inappropriate model. Implementation problems typically arise from computer programming errors, whilemodel inputs may generate erroneous results if these data are not representative of management activity or
environmental conditions. In these two cases, it is good practice for the inventory developer to return to either
Steps 3 or 6 depending on the issue. It seems less likely that the model would be inappropriate if Step 2 wasdeemed reasonable. However, if this is the case, it is good practice to return to the model selection/development
phase (Step 1).
During Step 2 that follows the selection/development step, it is good practice to avoid using the independent
evaluation data to re-calibrate or refine algorithms. If this occurs, these data would no longer be suitable forindependent evaluation, and therefore not serve the purpose for Step 6 in this inventory approach.
Step 7. Reporting and Documentation. It is good practice to assemble inventory results in a systematic andtransparent manner for reporting purposes. Documentation may include a description of the model, summary ofmodel input data sources, model evaluation results including sources of experiments and/or measurements datafrom monitoring network, stock change and emissions estimates and the interpretation of emission trends (i.e.,
contributions of management activities). QA/QC should be completed and documented in the report. For details

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on QA/QC, reporting and documentation, see the section dealing with the specific source category later in thisvolume, as well as information provided in Volume 1, Chapter 6.

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2.

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3.

Araújo, T., Carvalho, J., Higuchi, N., Brasil, A. and Mesquita, A. (1999). A tropical rainforest clearing experiment by biomass burningin the state of Pará, Brazil. Atmospheric Environment. 33: p. 1991-1998.

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