Chapter 4: Forest Land

CHAPTER 4

FOREST LAND

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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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), Stephen Ogle (USA), John Raison (Australia), Dieter Schoene (FAO), and N.H.
Ravindranath (India)
Nagmeldin G. Elhassan (Sudan), Linda S. Heath (USA), Niro Higuchi (Brazil), Samuel Kainja (Malawi), Mitsuo
Matsumoto (Japan), María José Sanz Sánchez (Spain), and Zoltan Somogyi (European Commission/Hungary)Contributing Authors
Jim B. Carle (FAO) and Indu K. Murthy (India)

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Chapter 4: Forest Land

Contents
4

Forest Land
4.1

Introduction ........................................................................................................................................... 4.7

4.2

Forest Land Remaining Forest Land ................................................................................................... 4.11

4.2.1
4.2.1.1

Choice of method ................................................................................................................... 4.11

4.2.1.2

Choice of emission factors ..................................................................................................... 4.14

4.2.1.3

Choice of activity data ........................................................................................................... 4.15

4.2.1.4

Calculation steps for Tier 1 .................................................................................................... 4.17

4.2.1.5

Uncertainty assessment .......................................................................................................... 4.19

4.2.2

Dead organic matter .................................................................................................................... 4.20

4.2.2.1

Choice of method ................................................................................................................... 4.20

4.2.2.2

Choice of emission/removal factors....................................................................................... 4.21

4.2.2.3

Choice of activity data ........................................................................................................... 4.22

4.2.2.4

Calculation steps for Tier 1 .................................................................................................... 4.22

4.2.2.5

Uncertainty assessment .......................................................................................................... 4.22

4.2.3

Soil carbon................................................................................................................................... 4.23

4.2.3.1

Choice of method ................................................................................................................... 4.23

4.2.3.2

Choice of stock change and emission factors ........................................................................ 4.25

4.2.3.3

Choice of activity data ........................................................................................................... 4.25

4.2.3.4

Calculation steps for Tier 1 .................................................................................................... 4.26

4.2.3.5

Uncertainty assessment .......................................................................................................... 4.27

4.2.4

4.3

Biomass ....................................................................................................................................... 4.11

Non-CO2 greenhouse gas emissions from biomass burning ........................................................ 4.27

4.2.4.1

Choice of method ................................................................................................................... 4.28

4.2.4.2

Choice of emissions factors ................................................................................................... 4.28

4.2.4.3

Choice of activity data ........................................................................................................... 4.28

4.2.4.4

Uncertainty assessment .......................................................................................................... 4.29

Land Converted to Forest Land ........................................................................................................... 4.29

4.3.1

Biomass ....................................................................................................................................... 4.30

4.3.1.1

Choice of method ................................................................................................................... 4.30

4.3.1.2

Choice of emission factors ..................................................................................................... 4.32

4.3.1.3

Choice of activity data ........................................................................................................... 4.33

4.3.1.4

Calculation steps for Tier 1 .................................................................................................... 4.34

4.3.1.5

Uncertainty assessment .......................................................................................................... 4.36

4.3.2

Dead organic matter .................................................................................................................... 4.36

4.3.2.1

Choice of method ................................................................................................................... 4.37

4.3.2.2

Choice of emission/removal factors....................................................................................... 4.37

4.3.2.3

Choice of activity data ........................................................................................................... 4.38

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Volume 4: Agriculture, Forestry and Other Land Use

4.3.2.4

Calculation steps for Tier 1 .................................................................................................... 4.38

4.3.2.5

Uncertainty assessment .......................................................................................................... 4.38

4.3.3
4.3.3.1

Choice of method ................................................................................................................... 4.39

4.3.3.2

Choice of stock change and emission factors ........................................................................ 4.40

4.3.3.3

Choice of activity data ........................................................................................................... 4.41

4.3.3.4

Calculation steps for Tier 1 .................................................................................................... 4.41

4.3.3.5

Uncertainty assessment .......................................................................................................... 4.42

4.3.4
4.4

Soil carbon................................................................................................................................... 4.39

Non-CO2 greenhouse gas emissions from biomass burning ........................................................ 4.42

Completeness, Time series, QA/QC, and Reporting and Documentation ........................................... 4.43

4.4.1

Completeness .............................................................................................................................. 4.43

4.4.2

Developing a consistent times series ........................................................................................... 4.43

4.4.3

Quality Assurance and Quality Control....................................................................................... 4.44

4.4.4

Reporting and Documentation ..................................................................................................... 4.45

4.5

Tables .................................................................................................................................................. 4.46

Annex 4A.1

Glossary for Forest Land ............................................................................................................. 4.72

Reference..................................................................................................................................................... 4.79

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Chapter 4: Forest Land

FiguresFigure 4.1

Global ecological zones, based on observed climate and vegetation
patterns (FAO, 2001) ............................................................................................................. 4.9

Figure 4.2

Global forest and land cover 1995 ....................................................................................... 4.10

TablesTable 4.1

Climate domains (FAO, 2001), climate regions (Chapter 3), and ecologicalzones (FAO, 2001) .............................................................................................................. 4.46

Table 4.2

Forest and land cover classes............................................................................................... 4.47

Table 4.3

Carbon fraction of aboveground forest biomass .................................................................. 4.48

Table 4.4

Ratio of below-ground biomass to above-ground biomass (R) ........................................... 4.49

Table 4.5

Default biomass conversion and expansion factors (BCEF) ............................................... 4.50

Table 4.6

Emission factors for drained organic soils in managed forests ............................................ 4.53

Table 4.7

Above-ground biomass in forests ........................................................................................ 4.53

Table 4.8

Above-ground biomass in forest plantations ....................................................................... 4.54

Table 4.9

Above-ground net biomass growth in natural forests .......................................................... 4.57

Table 4.10

Above-ground net biomass growth in tropical and sub-tropical forest plantations ............. 4.59

Table 4.11a

Above-ground net volume growth of selected forest plantation species ............................. 4.61

Table 4.11b

Mean annual increment (growth of merchantable volume) for some forest
plantation species................................................................................................................. 4.62

Table 4.12

Tier 1 estimated biomass values from Tables 4.7–4.11 (Except Table 4.11B).................... 4.63

Table 4.13

Basic wood density (D) of tropical tree species................................................................... 4.64

Table 4.14

Basic wood density (D) of selected temperate and boreal tree taxa .................................... 4.71

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Volume 4: Agriculture, Forestry and Other Land Use

Boxes

4.6

Box 4.1

Levels of detail ...................................................................................................................... 4.8

Box 4.2

Biomass conversion and expansion factors for assessing biomass andcarbon in forests .................................................................................................................. 4.13

Box 4.3

Examples of good practice approach in identification of lands converted
to Forest Land ...................................................................................................................... 4.34

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

4 FOREST LAND
4.1

INTRODUCTION

This chapter provides methods for estimating greenhouse gas emissions and removals due to changes in biomass,
dead organic matter and soil organic carbon on Forest Land and Land Converted to Forest Land. It builds on the
Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (1996 IPCC Guidelines) and the Good
Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF). The chapter:•

addresses all five carbon pools identified in Chapter 1 and transfers of carbon between different pools within
the same land areas;•

includes carbon stock changes on managed forests due to human activities such as establishing andharvesting plantations, commercial felling, fuelwood gathering and other management practices, in addition
to natural losses caused by fire, windstorms, insects, diseases, and other disturbances;•provides simple (Tier 1) methods and default values and outline approaches for higher tier methods for theestimation of carbon stock changes;•

provides methods to estimate non-CO2 greenhouse gas emissions from biomass burning (other non-CO2
emissions such as N2O emissions from soils are covered in Chapter 11);•should be used together with generic description of methods and equations from Chapter 2, and theapproaches for obtaining consistent area data described in Chapter 3.

The Guidelines provide methods for estimating and reporting sources and sinks of greenhouse gases only for
managed forests, as defined in Chapter 1. Countries should consistently apply national definitions of managed
forests over time. National definitions should cover all forests subject to human intervention, including the fullrange of management practices from protecting forests, raising plantations, promoting natural regeneration,commercial timber production, non-commercial fuelwood extraction, and abandonment of managed land.
This chapter does not include harvested wood products (HWP) which are covered by Chapter 12 of this Volume.
Managed Forest Land is partitioned into two sub categories and the guidance and methodologies are given
separately in two sections:•

Section 4.2 Forest Land Remaining Forest Land•

Section 4.3 Land Converted to Forest Land

Section 4.2 covers the methodology that applies to lands that have been Forest Land for more than the transition
period required to reach new soil carbon levels (default is 20 years). Section 4.3 applies to lands converted toForest Land within that transition period. The 20-year interval is taken as a default length of transition period forcarbon stock changes following land-use change. It is good practice to differentiate national forest lands by theabove two categories. The actual length of transition period depends on natural and ecological circumstances of aparticular country or region and may differ from 20 years.
Unmanaged forests, which are brought under management, enter the inventory and should be included in theLand Converted to Forest Land. Unmanaged forests which are converted to other land uses enter the inventory
under their post conversion land-use categories with the appropriate transition period for the new land-use
category.
If there are no data on land conversion and the period involved are available, the default assumption is that all
managed forest land belongs to the category Forest Land Remaining Forest Land and greenhouse gas (GHG)
emissions and removals are estimated using guidance given in Section 4.2.

Relevant carbon pools and non-CO 2 gases
The relevant carbon pools and non-CO2 gases for which methods are provided are given below:•

Biomass (above-ground and below-ground biomass)•

Dead organic matter (dead wood and litter)•

Soil organic matter•

Non-CO2 gases (CH4, CO, N2O, NOX)

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Volume 4: Agriculture, Forestry and Other Land Use

The selection of carbon pools or non-CO2 gases for estimation will depend on the significance of the pool andtier selected for each land-use category.

Forest land-use classification
Greenhouse gas emissions and removals per hectare vary according to site factors, forest or plantation types,
stages of stand development and management practices. It is good practice to stratify Forest Land into various
sub categories to reduce the variation in growth rate and other forest parameters and to reduce uncertainty (Box4.1). As a default, the Guidelines use the most recent ecological zone (see Table 4.1 in Section 4.5 and Figure
4.1 in this chapter) and forest cover (see Table 4.2 in Section 4.5 and Figure 4.2 in this chapter) classifications,developed by the Food and Agriculture Organization (FAO, 2001). National experts should use more detailed
classifications for their countries, if available and suitable, given the other data requirements.

BOX 4.1
LEVELS OF DETAIL

Stratification of forest types into homogeneous sub-categories, and if possible at regional or subregional level within a country, reduces the uncertainty of estimates of greenhouse gas emissions
and removals. For simplicity and clarity, this chapter discusses estimation of emissions andremovals at national level and for a relatively small number of subcategories of Forest Land. Thislevel of detail is designed to match the available sources of default input data, carbon contents andother assumptions. It is important, however, for users of these Guidelines to understand that they
are encouraged to carry out the greenhouse gas emissions inventory calculations at a finer level ofdetail, if possible. Many countries have more detailed information available about forests andland-use change than were used in constructing default values in this Chapter. These data should
be used, if suitable, for the following reasons:1. Geographic detail at regional rather than national level
Experts may find that greenhouse gas estimation for various regions within a country are necessary
to capture important geographic variations in ecosystem types, biomass densities, fractions ofcleared biomass which are burnt, etc.
2. Finer detail by subcategory
Experts may subdivide the recommended land-use categories and subcategories to reflect
important differences in climate, ecology or species, forest types, land-use or forestry practices,
fuelwood gathering patterns, etc.
In all cases, working at finer levels of disaggregation does not change the basic nature of themethod of estimations, although additional data and assumptions will generally be required beyond
the defaults provided in this Chapter. Once greenhouse gas emissions are estimated, using themost appropriate level of detail determined by the national experts, results should also be
aggregated up to the national level and the standard categories requested in these Guidelines. This
will allow for comparability of results among all participating countries. Generally, the data andassumptions used for finer levels of detail should also be reported to ensure transparency andrepeatability of methods.

Terminology
The terminology used in the methods for estimating biomass stocks and changes need to be consistent with theterminologies and definitions used by the Food and Agriculture Organization (FAO). FAO is the main source ofactivity data and emission factors for forest and other land-use categories in Tier 1 level calculations. Examplesof terminology from FAO are: biomass growth, mean annual increment, biomass loss, and wood-removal. The
Glossary in Annex 4A.1 includes definitions of these terminologies.

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Chapter 4: Forest Land
Figure 4.1

Global ecological zones, based on observed climate and vegetation patterns (FAO, 2001). Data for geographic information systems
available at http://www.fao.org.

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 4.2

4.10

Global forest and land cover 1995. Original spatial resolution of the forest
data is 1 km 2 (analysis U.S. Geological Survey (Loveland et al., 2000) andFAO (2001)). Data for geographic information systems available at
http://edc.usgs.gov.

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Chapter 4: Forest Land

4.2

FOREST LAND REMAINING FOREST LANDThis section deals with managed forests that have been under Forest Land for over 20 years (default), or for overa country-specific transition period. Greenhouse gas inventory for Forest Land Remaining Forest Land (FF)involves estimation of changes in carbon stock from five carbon pools (i.e., above-ground biomass, belowground biomass, dead wood, litter, and soil organic matter), as well as emissions of non-CO2 gases. Methods for
estimating greenhouse gas emissions and removals for lands converted to Forest Land in the past 20 years (e.g.,
from Cropland and Grassland) are presented in Section 4.3. The set of general equations to estimate the annual
carbon stock changes on Forest Land are given in Chapter 2.

4.2.1

Biomass

This section presents methods for estimating biomass gains and losses. Gains include total (above-ground andbelow-ground) biomass growth. Losses are roundwood removal/harvest, fuelwood removal/harvest/gathering,and losses from disturbances by fire, insects, diseases, and other disturbances. When such losses occur, belowground biomass is also reduced and transformed to dead organic matter (DOM).

4.2.1.1

C HOICE OF METHOD

Chapter 2 describes two methods, namely, Gain-Loss Method based on estimates of annual change in biomass
from estimates of biomass gain and loss (Equation 2.7) and a Stock-Difference Method which estimates thedifference in total biomass carbon stock at time t2 and time t1 (Equation 2.8).
The biomass gain-loss method is applicable for all tiers although the stock-difference method is more suited toTiers 2 and 3. This is because, in general, the stock-difference method will provide more reliable estimates for
relatively large increases or decreases of biomass or where very accurate forest inventories are carried out. For
areas with a mix of stands of different forest types, and/or where biomass change is very small compared to thetotal amount of biomass, the inventory error under the stock-difference method may be larger than the expectedchange. Unless periodic inventories give estimates on stocks of dead organic matter, in addition to growing stock,
one should be aware that other data on mortality and losses will still be required for estimating the transfer todead organic matter, harvested wood products and emissions caused by disturbances. Subsequent inventories
must also allow identical area coverage in order to get reliable results when using the stock-difference method.
The choice of using gain-loss or stock-difference method at the appropriate tier level will therefore be a matter ofexpert judgment, taking into account the national inventory systems, availability of data and information from
ecological surveys, forest ownership patterns, activity data, conversion and expansion factors as well as costbenefit analysis.
The decision tree as shown in Figure 1.2 in Chapter 1 should be used to guide choice between the Tiers. This
promotes efficient use of available resources, taking into account whether the biomass of this category is asignificant carbon pool or a key category as described in Volume 1, Chapter 4.
Tie r-1 Metho d ( Biomas s Ga in- Lo ss Me thod)
Tier 1 is feasible even when country-specific estimates of activity data and emission/removal factors are notavailable, and works when changes of the carbon pool in biomass on Forest Land Remaining Forest Land are
relatively small. The method requires the biomass carbon loss to be subtracted from the biomass carbon gain
(Equation 2.7). The annual change in carbon stocks in biomass can be estimated using the gain-loss method,where the annual increase in carbon stocks due to biomass growth and annual decrease in carbon stocks due tobiomass losses are estimated:•

The annual increase in biomass carbon stock is estimated using Equation 2.9, where area under each forest
sub-category is multiplied by mean annual increment in tonnes of dry matter per hectare per year.

•

Since the biomass growth is usually in terms of merchantable volume or above-ground biomass, the belowground biomass is estimated with a below-ground biomass to above-ground biomass ratio (Equation 2.10).
Alternatively, merchantable volume (m3) can be converted directly to total biomass using biomass
conversion and expansion factors (BCEFI ), (Equation 2.10).

•

If BCEFI values are not available 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

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Volume 4: Agriculture, Forestry and Other Land Use

Biomass Expansion Factors (BEFI) expand merchantable volume to total aboveground biomass
volume to account for non-merchantable components of the tree, stand and forest. BEFI is
dimensionless.
•

The average above-ground biomass of forest areas affected by disturbances are given in Tables 4.7 and 4.8;
net average annual above-ground biomass growth values are provided in Tables 4.9, 4.10, and 4.12; net
volume annual increment values are provided in Tables 4.11A and 4.11B; wood density is given in Tables
4.13 and 4.14; and below-ground biomass to above-ground biomass ratios (R) are given in Table 4.4. Refer
to Box 4.2 for detailed explanation on how to convert and expand volumes of growing stock, increment andwood removals to biomass.

•

In some ecosystems, basic wood density (D) can influence spatial patterns of forest biomass (Baker et al.,2004b). Tier 1 users who do not have measurements of wood density at the desired sub-strata level canestimate wood density by estimating the proportion of total forest biomass contributed by the 2-3 dominant
species and using species-specific wood density values (Tables 4.13 and 4.14) to calculate a weighted
average wood density value.

•

Annual biomass loss or decrease in biomass carbon stocks is estimated using Equation 2.11, which requires
estimates of annual carbon loss due to wood removals (Equation 2.12), fuelwood removal (Equation 2.13)
and disturbances (Equation 2.14). Transfer of biomass to dead organic matter is estimated using Equation
2.20, based on estimates of annual biomass carbon lost due to mortality (Equation 2.21), annual carbon
transfer to slash (Equation 2.22).

•

Biomass estimates are converted to carbon values using carbon fraction of dry matter (Table 4.3).

When either the biomass stock or its change in a category (or sub-category) is significant or a key category, it isgood practice to select a higher tier methodology for estimation. The choice of Tier 2 or 3 method depends on
the types and accuracy of data and models available, level of spatial disaggregation of activity data and national
circumstances.
If using activity data collected via Approach 1 (see Chapter 3), and it is not possible to use supplementary data toidentify the amount of land converted from and to Forest Land, the inventory compiler should estimate C stocks
in biomass on all Forest Land using the Tier 1 method described above for Forest Land Remaining Forest Land.
Tie r 2Tier 2 can be used in countries where country-specific estimates of activity data and emission/removal factors
are available or can be gathered at reasonable cost. Tier 2, same as Tier 1, uses Equations 2.7 to 2.14 (excluding
Equation 2.8). Species-specific wood density values (Tables 4.13 and 4.14) permit the calculation of biomass
from species-specific forest inventory data. It is possible to use the stock-difference method (Equation 2.8) at
Tier 2 where the necessary country-specific data are available
Tie r 3Tier 3 approach for biomass carbon stock change estimation allows for a variety of methods, including processbased models. Implementation may differ from one country to another, due to differences in inventory methods,
forest conditions and activity data. Transparent documentation of the validity and completeness of the data,assumptions, equations and models used is therefore a critical issue at Tier 3. Tier 3 requires use of detailed
national forest inventories when the stock-difference method is used (Equation 2.8). They may be supplemented
by allometric equations and models (for example, Chambers et al. (2001) and Baker et al. (2004a) for the
Amazon; Jenkins et al. (2004) and Kurz and Apps (2006) for North America; and Zianis et al. (2005) for
Europe), calibrated to national circumstances that allow for direct estimation of biomass growth.

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Chapter 4: Forest Land

BOX 4.2
BIOMASS CONVERSION AND EXPANSION FACTORS FOR ASSESSING BIOMASS AND CARBON IN FORESTS 1

Forest inventories and operational records usually document growing stock, net annual increment
or wood removals in m3 of merchantable volume. This excludes non-merchantable above-groundcomponents such as tree tops, branches, twigs, foliage, sometimes stumps, and below-groundcomponents (roots).
Assessments of biomass and carbon stocks and changes, on the other hand, focus on total biomass,
biomass growth and biomass removals (harvest), including non-merchantable components,
expressed in tons of dry-weight. Several methods may be used to derive forest biomass and itschanges. Above-ground biomass and changes can be derived in two ways, namely:(i) directly by measuring sample tree attributes in the field, such as diameters and heights, andapplying, species-specific allometric equations or biomass tables based on these equations once or
periodically.
(ii) indirectly by transforming available volume data from forest inventories, e.g., merchantable
volume of growing stock, net annual increment or wood removals (Somogyi et al., 2006).
The latter approach may achieve the transformation by applying biomass regression functions,which usually express biomass of species or species groups (t/ha) or its rate of change, directly as afunction of growing stock density (m3/ha), and age, eco-regions or other variables (Pan et al.,2004).
More commonly than these biomass regression functions, a single, discrete transformation factors 2
is applied to merchantable volume to derive above-ground biomass and its changes:(i) Biomass Expansion Factors (BEF) expand the dry weight 3 of the merchantable volume of
growing stock, net annual increment, or wood removals, to account for non-merchantablecomponents of the tree, stand, and forest. Before applying such BEFs, merchantable volume (m3)must be converted to dry-weight (tonne) by multiplying with a conversion factor known as basic
wood density (D) in (t/m3). BEFs are dimensionless since they convert between units of weight.
This method gives best results, when the BEFs have actually been determined based on dry
weights, and when locally applicable basic wood densities are well known.
(ii) Biomass Conversion and Expansion Factors (BCEF) combine conversion and expansion.
They have the dimension (t/m3) and transform in one single multiplication growing stock, net
annual increment, or wood removals (m3) directly into above-ground biomass, above-groundbiomass growth, or biomass removals (t).
BCEFs are more convenient. They can be applied directly to volume-based forest inventory dataand operational records without the need of having to resort to basic wood densities. They provide
best results, when they have been derived locally, based directly on merchantable volume.
Mathematically, BCEF and BEF are related by:
BCEF = BEF ● D
Application of this equation requires caution because basic wood density and biomass expansion
factors tend to be correlated. If the same sample of trees was used to determine D, BEF or BCEF,
conversion will not introduce error. If, however, basic wood density is not known with certainty,
transforming one into the other might introduce error, as BCEF implies a specific but unknown
basic wood density. Ideally, all conversion and expansion factors would be derived or theirapplicability checked locally.

1

Please see glossary (Annex 4A.1) for definitions of terms.

2

While these transformation factors are usually applied in discrete form, they can also be expressed and depicted ascontinuous functions of growing stock density, age, or other variables.

3

In some applications, biomass expansion factors expand dry-weight of merchantable components to total biomass,
including roots, or expand merchantable volume to above-ground or total biomass volume (Somogyi et al., 2006). As usedin this document, biomass expansion factors always transform dry-weight of merchantable volume including bark toabove-ground biomass, excluding roots.

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Both BEF and BCEF tend to decrease as a function of stand age, as growing stock density (volumeof growing stock per ha) increases. This is because of the increasing ratio of merchantable volume
to total volume. The decrease is rapid at low growing stock densities or for young stands and levels
out for older stands and higher stand densities.
The GPG-LULUCF provided only average default BEF values, together with wide ranges, andgeneral guidance on how to select applicable values for specific countries from these ranges. To
facilitate selection of more reliable default values, this document provides default factors as afunction of growing stock density in Table 4.5. Since more comprehensive and more recent data
were found in the literature, Table 4.5 contains BCEF defaults only. Countries that possesscountry-specific basic wood densities and BEF on a consistent basis may apply them to calculate
country-specific BCEF using the formula given above.
BCEF or BEF that apply to growing stock and net annual increment are different. In thisdocument, the following symbols are used:
BCEFS: biomass conversion and expansion factor applicable to growing stock; transforms
merchantable volume of growing stock into above-ground biomass.
BCEFI: biomass conversion and expansion factor applicable to net annual increment; transforms
merchantable volume of net annual increment into above-ground biomass growth.
BCEFR: biomass conversion and expansion factors applicable to wood removals; transforms
merchantable biomass to total biomass (including bark). BCEFR and BEFR for wood and fuelwood
removal will be larger than that for growing stock due to harvest loss (see Annex 4A.1 Glossary).
If a country specific value for harvest loss is not known, defaults are 10% for hardwoods and 8%
for conifers (Kramer and Akca, 1982). Default conversion and expansion factors for wood
removals can be derived by dividing BCEFS by (1– 0.08) for conifers and (1-0.1) for broadleaves.
It is good practice to estimate growing stock biomass, above-ground biomass growth and aboveground biomass removals by strata; to document these strata; and to aggregate results ex post.
Methods described above will yield above-ground biomass and its changes. Results must be
expanded to total biomass via applicable below-ground biomass to above-ground biomass ratios.

4.2.1.2

C HOICE OF EMISSION FACTORSThe Gain-Loss Method requires the above-ground biomass growth, biomass conversion and expansion factor(BCEF), BEF, and/or basic wood densities according to each forest type and climatic zone in the country, plus
emission factors related to biomass loss, including losses due to wood removals, fuelwood removals anddisturbances.

Annual biomass carbon gain, ΔC G
Mea n abo ve-g round b ioma ss gro wth ( in cr em ent), G W
Tie r 1
Default values of the above-ground biomass growth (GW) which are provided in Tables 4.9, 4.10 and 4.12 can beused at Tier 1. If available, it is good practice to use other regional default values for different forest types more
relevant to the country.
Tie r 2Tier 2 method uses more country-specific data to calculate the above-ground biomass growth, GW from countryspecific net annual increment of growing stock (IV). Tables 4.11a and 4.11b provide default values for IV.
Combined default biomass conversion and expansion factor (BCEFI) of Iv are provided in Table 4.5. Separate
data on biomass expansion factor for increment (BEFI) and basic wood density (D) can also be used to convert
the available data to GW. Tables 4.13 and 4.14 provide default values for basic wood density.
Tie r 3
Under Tier 3, process-based estimation will have access to detailed forest inventory or monitoring system withdata on growing stock and past and projected net annual increment and functions relating to growing stock or net
annual increment directly to biomass and biomass growth. It is also possible to derive net annual increment byprocess simulation. Specific carbon fraction and basic wood density should also be incorporated.
Forest inventories usually provide conditions of forest growing stock and net annual increment in the year of theinventory. When the year of inventory does not coincide with the year of reporting, interpolated or extrapolated

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Chapter 4: Forest Landnet annual increment or increment estimated by models (i.e., model capable of simulating forest dynamics),
should be used along with data on harvesting and disturbances to update inventory data to the year of interest.
Be lo w-g round b iomas s g rowth ( in cr em en t)
Tier 1
Below-ground carbon stock changes, as a default assumption consistent with the 1996 IPCC Guidelines, can be
zero. Alternatively, default values for below-ground biomass to above-ground biomass ratios (R) are to be used
to estimate below-ground biomass growth. Default values are provided in Table 4.4. Strictly, these ratios of
below-ground biomass to above-ground biomass are only valid for stocks, but no appreciable error is likely toobtain if they are applied to above-ground biomass growth over short periods.
Tie r 2
Country-specific below-ground biomass to above-ground biomass ratios should be used to estimate belowground biomass for different forest types.
Tie r 3For preference, below-ground biomass should be directly incorporated in models for calculating total biomass
increment and losses. Alternatively, nationally or regionally determined below-ground biomass to above-groundbiomass ratios or regression models (e.g., Li et al., 2003) may be used.

Annual carbon loss in biomass, ΔC L
Bioma ss lo ss du e to wood re mova ls, L w o o d - r e m o v a l s and L f u e l w o o dWhen computing carbon loss through biomass removals, the following factors are needed: Wood removal (H),
fuelwood removal as trees or parts of trees (FG), basic wood density (D), below-ground biomass to aboveground biomass ratio (R), carbon fraction (CF), BCEF for wood removals. While all wood removals represent aloss for the forest biomass pool, Chapter 12 provides guidance for estimating annual change in carbon stocks inharvested wood products.
D isturba nce s, L d i s t u r b a n c e
The estimate of other losses of carbon requires data on areas affected by disturbances (Adisturbance) and thebiomass of these forest areas (BW). Above ground biomass estimates of forest types affected by disturbance are
required, along with below-ground biomass to above-ground biomass ratio and fraction of biomass lost in
disturbance.
Chapter 2, Tables 2.4, 2.5 and 2.6 provide fuel biomass consumption values, emission factors, and combustion
factors needed for estimating proportion of biomass lost in fires and proportion to be transferred to dead organic
matter under higher tiers.
Tie r 1
The average biomass varies with the forest types and management practices. The default values are given in
Tables 4.7 and 4.8. In the case of fire, both CO2 and non-CO2 emissions occur from combusted fuels of aboveground biomass including understory. Fire may consume a high proportion of understory vegetation. In the caseof other disturbances, a fraction of above ground biomass is transferred to dead organic matter and under Tier 1,
all biomass in area subjected to disturbance is assumed to be emitted in the year of disturbance.
Tie r 2
Under Tier 2, biomass changes due to disturbances will be taken into account by forest category, type of
disturbance and intensity. Average values for biomass are obtained from country-specific data.
Tie r 3
In addition to calculating losses similar to Tier 2, Tier 3 can also adopt models, which typically employ spatiallyreferenced or spatially explicit information on the year and type of disturbance.

4.2.1.3

C HOICE OF ACTIVITY DATA

Ar ea of manag ed Fo res t La nd
All tiers require information on areas of managed Forest Land according to different forest types, climate,management systems, and regions.
Tie r 1
Tier 1 uses data of forest area which can be obtained through national statistics, from forest agencies (which may
have information on areas of different management practices), conservation agencies (especially for areas
managed for natural regeneration), municipalities, survey and mapping agencies. Cross-checks should be madeto ensure complete and consistent representation for avoiding omissions or double counting as specified in
Chapter 3. If no country data are available, aggregate information can be obtained from international data

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Volume 4: Agriculture, Forestry and Other Land Usesources (FAO, 1995; FAO, 2001; TBFRA, 2000). It is good practice to verify, validate, and update the FAO datausing national sources.
Tie r 2Tier 2 uses country-defined national data sets, according to different forest types, climate, management systems,
and regions, with a resolution sufficient to ensure appropriate representation of land areas in line with provisionsof Chapter 3 of this volume. Approach 2 of Chapter 3 is relevant for Tier 2.
Tie r 3Tier 3 uses country-specific data on managed Forest Land from different sources, notably national forest
inventories, registers of land use and land-use changes, or remote sensing. These data should give a fullaccounting of all land-use transitions to Forest Land and disaggregate along climate, soil, and vegetation types.
Geo-referenced area under different forest types may be used to track changes in area under different land-use
types, using Approach 3 of Chapter 3.
Wood re mov a ls
The inventory requires data on wood removals, including fuelwood removals and biomass losses due todisturbances, in order to calculate biomass stock changes and carbon pool transfers. In addition to wood
removals for industrial purposes, there may also be wood removals for small scale processing or direct sales to
consumers from land owners. This quantity may not be included in official statistics and may need to be
estimated by survey. Fuelwood from branches and tops of felled trees must be subtracted from transfers to thedead wood pool. Salvage of wood from areas affected by disturbances must also be subtracted from biomass, toensure that no double counting occurs in Tier 1 inventories in which the biomass in areas affected by
disturbances is already assumed released to the atmosphere.
In using production statistics, users must pay careful attention to the units involved. It is important to check
whether the information in the original data is reported in biomass, volumes underbark or overbark to ensure that
expansion factors are used only where appropriate and in a consistent way.
Unless restricted to Approach 1 land representation without supplementary data, so that all forest land is counted
under Forest Land Remaining Forest Land, wood removals from Forest Land being converted to another land
use should not be included in losses reported for Forest Land Remaining Forest Land since these losses arereported in the new land-use category. If the statistics on wood removals do not provide stratification on lands,
then an amount of biomass approximating the biomass loss from lands converted from Forest Land should be
subtracted from the total wood removals.
Extraction of roundwood is published in the UNECE/FAO Timber Bulletin and by FAO Yearbook of Forest
Products. The latter is based primarily on data provided by the countries. In the absence of official data, FAO
provides an estimate based on the best information available. Usually, the FAO yearbook appears with a twoyear time lag.
Tie r 1
FAO data can be used as a Tier 1 default for H in Equation 2.12 in Chapter 2. The roundwood data include allwood removed from forests which are reported in cubic meters underbark. The underbark data need conversion
to overbark before using BCEFR. Conversion from underbark to overbark volumes is done by using bark
percentages.
Tie r 2
Country-specific data should be used.
Tie r 3
Country-specific wood removals data from different forest categories should be used at the spatial resolution
chosen for reporting
Fu e lwood re mo va l
Estimation of carbon losses due to fuelwood removal requires annual volume of fuelwood removed (FG) andbasic wood density (D). Fuelwood is produced in different ways in countries and varies from ordinary timber
harvesting, to using parts of trees, to gathering of dead wood. Fuelwood constitutes the largest component ofbiomass loss for many countries, thus reliable estimates are needed for such countries. . If possible, fuelwood
removal from Forest Land Remaining Forest Land and that coming from Forest Land conversion to other uses
should be separated.
Tie r 1
FAO provides statistics on fuelwood and charcoal removals for all countries. FAO statistics are based on what isprovided by the concerned ministries/ departments in the countries and in some cases may not account fully forthe entire fuelwood and charcoal removal due to the limitations of national data collection and reporting systems.
Thus, under Tier 1, FAO statistics can be used directly but should be checked for completeness by the national

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Landsource of data for the FAO such as the Ministry of Forests or Agriculture or any statistical organization. FAO or
any national estimates should be supplemented from regional surveys or local studies on fuelwood consumption,
since fuelwood is collected from multiple sources; forests, timber processing residues, farms, homesteads,
village commons, etc. If more complete information is available nationally, it should be used.
Tie r 2
Country-specific data should be used, if available. Regional surveys of fuelwood removals can be used to verify
and supplement the national or FAO data source. At the national level, aggregate fuelwood removals can be
estimated by conducting regional level surveys of rural and urban households at different income levels,
industries and establishments.
Tie r 3
Fuelwood removals data from national level studies should be used at the resolution required for the Tier 3
model, including the non-commercial fuelwood removals. Fuelwood removal should be linked to forest types
and regions.
Different methods of fuelwood removal from Forest Land Remaining Forest Land should be accounted atregional or disaggregated level through surveys. The source of fuelwood should be identified to ensure that no
double counting occurs.
D isturba nce s
A database on rate and impact of natural disturbances by type, for all European countries (Schelhaas et al., 2001),
can be found at: http://www.efi.fi/
A UNEP database on global burnt area can be found at: http://www.grid.unep.ch/
However, one should note that the UNEP database is only valid for year 2000. In many countries inter-annual
variability in burnt area is large, so these figures will not provide a representative average. Many countries
maintain their own disturbance statistics e.g., Stocks et al. (2002) which can be employed in Tier 2 or Tier 3
approaches (Kurz and Apps, 2006).
The FRA2005 (FAO, 2005) should also be examined for data on disturbances.

4.2.1.4

C ALCULATION STEPS FOR T IER 1

T he fo llo wing s umma r iz e s s tep s for e s tima ting cha nge in c arbo n s to ck s in bio mas s (∆ C B )u s ing the de fau lt me thod s:
Step 1: Using guidance from Chapter 3 (approaches in representing land areas), categorise the area (A) of
Forest Land Remaining Forest Land into forest types of different climatic or ecological zones, as adopted by thecountry. As a point of reference, Annex 3A.1 of GPG-LULUCF (IPCC, 2003) provides national-level data offorest area and annual change in forest area by region and by country as a means of comparison. Alternatively
FAO also periodically provides area data;
Step 2: Estimate the annual biomass gain in Forest Land Remaining Forest Land (∆CG) using estimates of areaand biomass growth, for each forest type and climatic zone in the country available using Equations 2.9 and 2.10
in Chapter 2;
Step 3: Estimate the annual carbon loss due to wood removals (Lwood-removals) using Equation 2.12 in Chapter 2;
Step 4: Estimate annual carbon loss due to fuelwood removal (Lfuelwood) using Equation 2.13 in Chapter 2;
Step 5: Estimate annual carbon loss due to disturbance (Ldisturbance) using Equation 2.14 in Chapter 2, avoid
double counting of losses already covered in wood removals and fuelwood removals;
Step 6: From the estimated losses in Steps 3 to 5, estimate the annual decrease in carbon stocks due to biomass
losses (∆CL) using Equation 2.11 in Chapter 2;
Step 7: Estimate the annual change in carbon stocks in biomass (∆CB) using Equation 2.7 in Chapter 2.

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Volume 4: Agriculture, Forestry and Other Land Use

Example. The following example shows Gain-Loss Method (Tier 1) calculations of annual change
in carbon stocks in biomass (∆CB), using Chapter 2, Equation 2.7 (∆CB = (∆CG – ∆CL)), for ahypothetical country in temperate continental forest zone of Europe (Table 4.1, Section 4.5):-

the area of Forest Land Remaining Forest Land (A) within the country is 100,000 ha (seeChapter 3 for area categorization);-

it is a 25-year-old pine forest, average above-ground growing stock volume is 40 m3 ha-1;-

the merchantable round wood harvest over bark (H) is 1,000 m3 yr-1;-

whole trees fuel wood removal (FGtrees) is 500 m3 yr-1;-

area of insect disturbance is 2,000 ha yr-1 with above-ground biomass affected 4.0 tonne d.m.
ha-1.

Annual gain in biomass (∆CG) is a product of mean annual biomass increment (GTOTAL), area ofland (A) and carbon fraction of dry matter (CF); Equation 2.9 in Chapter 2 (ΔCG = ∑ij (A ● GTOTAL● CF). GTOTAL is calculated using Chapter 2, Equation 2.10 for given values of annual aboveground biomass growth (GW), below-ground biomass to above-ground biomass ratio (R), anddefault data tables in Section 4.5.
For the hypothetical country,
GW= 4.0 tonnes d.m. ha-1 yr-1 (Table 4.9);

R= 0.29 tonne d.m. (tonne d.m.)-1 for above-ground biomass of 50 to 150 t ha-1 (Table 4.4
with reference to Table 4.7 for above ground biomass);

GTOTAL= 4.0 tonnes d.m. ha-1 yr-1 ● (1 + 0.29) = 5.16 tonnes d.m. ha-1 yr-1 (Equation 2.10); andCF= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Consequently, (Equation 2.9): ∆CG = 100,000 ha ● 5.16 tonnes d.m. ha-1 yr-1 ● 0.47 tonne C (tonnedm)-1 = 242,520 tonnes C yr-1.
Biomass loss (∆CL) is a sum of annual loss due to wood removals (Lwood-removals), fuel woodgathering (Lfuelwood) and disturbances (Ldisturbance), Equation 2.11 in Chapter 2.
Wood removal (Lwood-removals) is calculated with Equation 2.12, Chapter 2, merchantable roundwood over bark (H), biomass conversion expansion factor (BCEFR), bark fraction in harvested
wood (BF), below-ground biomass to above-ground biomass ratio (R), carbon fraction of dry
matter (CF) and default tables, Section 4.5.
For the hypothetical country,
BCEFR= 1.11 tonnes d.m. m-3 (Table 4.5 with reference to growing stock volume 40 m3 ha-1);

BF= 0.1 tonne d.m. (tonne d.m.)-1. R = 0.29 tonne d.m. (tonne d.m.)-1 for above-groundbiomass 50 to 150 t ha-1 (Table 4.4, for above-ground biomass refer to Table 4.7); andCF= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Lwood-removals = 1,000 m3 yr-1 ● 1.11 tonnes d.m. m-3 (1 + 0.29 + 0.1) ● 0.47 tonne C (tonne d.m.)-1= 725.16 tonnes C yr-1 (Equation 2.12).
Fuelwood removal (Lfuelwood) is calculated using Equation 2.13, Chapter 2, wood removals as wholetrees (FGtrees), biomass conversion expansion factor (BCEFR), below-ground biomass to aboveground biomass ratio (R), carbon fraction of dry matter (CF) and default tables in Section 4.5. For
the hypothetical country,
BCEFR= 1.11 tonnes d.m. m-3 (Table 4.5 with reference to growing stock volume 40 m3 ha-1);R= 0.29 tonne d.m. (tonne dm)-1 for above-ground biomass 50 to 150 t ha-1 (Table 4.4, forabove-ground biomass refer to Table 4.7); andCF= 0.47 tonne C (tonne dm)-1 (Table 4.3).

Lfuelwood = 500 m3 yr-1 ● 0.75 tonne d.m. m-3 (1 + 0.29) ● 0.47 tonne C (tonne d.m.)-1= 336.50 tonne C yr-1 (Equation 2.13).

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Chapter 4: Forest Land

Annual carbon loss in biomass due to disturbances (Ldisturbance) is calculated using Equation 2.14,
Chapter 2, area of disturbances (Adisturbance), average above-ground biomass affected (BW), belowground biomass to above-ground biomass ratio (R), carbon fraction of dry matter (CF), fraction of
biomass lost in disturbance (fd) and default tables in Section 4.5. For the hypothetical country,R= 0.29 tonne d.m. (tonne dm)-1 for above-ground biomass 50 to 150 t ha-1 (Table 4.4, forabove-ground biomass refer to Table 4.7);

CF= 0.47 tonne C (tonne dm)-1 (Table 4.3); and fd = 0.3

Ldisturbance = 2,000 ha yr-1 ● 4.0 tonnes d.m. ha-1 (1 + 0.29) ● 0.47 tonne C (tonne dm)-1 ● 0.3= 1,455.12 tonnes C yr-1 (Equation 2.14)
Annual decrease in carbon stocks due to biomass losses (∆CL),∆CL= 725.16 tonnes C yr-1 + 336.50 tonnes C yr-1 + 1,455.12 tonnes C yr-1
= 2,516.78 tonnes C yr-1 (Equation 2.11)
Annual change in carbon stocks in biomass (∆CB)
Using Chapter 2, Equation 2.7 (∆CB = (∆CG – ∆CL)),∆CB = 242,520 tonnes C yr-1 – 2,516.78 tonnes C yr-1 = 240,003.22 tonnes C yr-1

4.2.1.5

U NCERTAINTY ASSESSMENT

This section considers source-specific uncertainties relevant to inventory estimates made for Forest Land
Remaining Forest Land. Estimating country-specific and/or disaggregated values requires more accurate
information on uncertainties than given below. Volume 1, Chapter 3 provides information on uncertainties
associated with sample-based studies. The literature available on uncertainty estimates on emission factors andactivity data is limited.
Em iss ion and r emo va l fa c tor s
FAO (2006) provides uncertainty estimates for forest carbon factors; basic wood density (10 to 40%); annual
increment in managed forests of industrialized countries (6 %); growing stock (industrialized countries 8%, nonindustrialized countries 30%); combined natural losses for industrialized countries (15%); wood and fuelwood
removals (industrialized countries 20%).
In Finland, the uncertainty of basic wood density of pine, spruce and birch trees is under 20% in studies of
Hakkila (1968, 1979). The variability between forest stands of the same species should be lower or at most thesame as for individual trees of the same species. In Finland, the uncertainty of biomass expansion factors forpine, spruce, and birch was approximately 10% (Lehtonen et al., 2003).
In eight Amazon tropical forest inventory plots, combined measurement errors led to errors of 10-30% in
estimates of basal area change over periods of less than 10 years (Phillips et al., 2002).
The major sources of uncertainty of wood density and biomass expansion factors are stand age, species
composition, and structure. To reduce uncertainty, countries are encouraged to develop country- or regionspecific biomass expansion factors and BCEFs that fit their conditions. In case country- or regional-specificvalues are unavailable, the sources of default parameters should be checked and their correspondence withspecific conditions of a country should be examined.
The causes of variation of annual increment include climate, site growth conditions, and soil fertility. Artificiallyregenerated and managed stands are less variable than natural forests. The major ways to improve accuracy of
estimates are associated with application of country-specific or regional increment stratified by forest type. If thedefault values of increment are used, the uncertainty of estimates should be clearly indicated and documented.
Tier 3 approaches can use growth curves stratified by species, ecological zones, site productivity andmanagement intensity. Similar approaches are routinely used in timber supply planning models and thisinformation can be incorporated into carbon accounting models (e.g., Kurz et al., 2002).
Data on commercial fellings are relatively accurate, although they may be incomplete or biased due to illegal
fellings and underreporting due to tax regulations. Traditional wood that is gathered and used directly, without
being sold, is not likely to be included in any statistics. Countries must carefully consider these issues. The
amount of wood removed from forests after storm breaks and pest outbreaks varies both in time and volume. No
default data can be provided on these types of losses. The uncertainties associated with these losses can be
estimated from the amount of damaged wood directly withdrawn from the forest or using data on damaged wood

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Volume 4: Agriculture, Forestry and Other Land Use

subsequently used for commercial and other purposes. If fuelwood gathering is treated separately from fellings,
the relevant uncertainties might be high, due to high uncertainty associated with traditional gathering.
Ac tiv ity da ta
Area data should be obtained using the guidance in Chapter 3 or from FAO (2000). Industrialized countries
estimated an uncertainty in forest area estimates of approximately 3% (FAO, 2000).

4.2.2

Dead organic matter

The general description of methods for estimating changes in carbon stocks in dead organic matter (DOM) pools(litter and dead wood) has been provided in Chapter 2.
This section focuses on methods for estimating carbon stock changes in dead organic matter pools for Forest
Land Remaining Forest Land. Tier 1 methods assume that the net carbon stock changes in DOM pools are zero
because the simple input and output equations used in Tier 1 methods are not suitable to capture the DOM pool
dynamics. Countries that want to quantify DOM dynamics need to develop Tier 2 or 3 methodologies. The
countries where DOM is a key category should adopt higher tiers and estimate DOM changes.
The dead wood (DW) pool contains carbon in coarse woody debris, dead coarse roots, standing dead trees, andother dead material not included in the litter or soil carbon pools. Estimating the size and dynamics of the deadwood pool poses many practical limitations, particularly related to field measurements. The uncertainties
associated with estimates of the rate of transfer from the DW pool to the litter and soil pools, and emissions to
the atmosphere are generally high. The amount of dead wood is highly variable between stands, both in managed(Duvall and Grigal, 1999; Chojnacky and Heath, 2002) and unmanaged lands (Spies et al., 1988). Amounts ofdead wood depend on the time since last disturbance, the type of the last disturbance, losses during disturbances,
the amount of biomass input (mortality) at the time of the disturbance (Spies et al., 1988), natural mortality rates,
decay rates, and management (Harmon et al., 1986).
Net litter accumulation rates can be estimated using the stock-difference method or the gain-loss method. The
latter requires an estimate of the balance of the annual amount of litterfall (which includes all leaves, twigs andsmall branches, fruits, flowers, roots, and bark) minus the annual rate of litter decomposition. In addition,
disturbances can add and remove carbon from the litter pool, influencing the size and composition of the litter
pool. The litter dynamics during the early stages of stand development depend on the type and intensity of thelast disturbance. Where disturbance has transferred biomass to DOM pools (e.g., wind-throw or insect kill), litterpools can be decreasing until losses are compensated by litter inputs. Where disturbance has removed litter (e.g.,
wildfire), litter pools can be increasing in the early stages of stand development if litter input exceeds decay.
Management such as timber harvesting, slash burning, and site preparation alter litter properties (Fisher andBinkley, 2000), but there are few studies clearly documenting the effects of management on litter carbon (Smithand Heath, 2002).

4.2.2.1

C HOICE OF METHOD

The decision tree in Figure 2.3 in Chapter 2 provides guidance in the selection of the appropriate tier level forthe implementation of estimation procedures. The choice of method is described jointly for dead wood and Litter
since the equations are identical for both, but the estimates are calculated separately for each of the two pools.
The estimation of changes in carbon stocks in DOM pool requires estimates of changes in carbon stocks of deadwood and litter pools (refer to Equation 2.17 of Chapter 2).
Tie r 1The Tier 1 method assumes that the dead wood and litter carbon stocks are in equilibrium so that the changes incarbon stock in the DOM pools are assumed to be zero. Countries experiencing significant changes in forest
types, disturbance or management regimes in their forests are encouraged to develop domestic data to quantify
the impacts from these changes using Tier 2 or 3 methodologies and to report the resulting stock changes andnon-CO2 emissions.
Tie r s 2 a nd 3
Two general methods are available for estimating the carbon stock changes in dead wood and litter. Similar
methods exist for the estimation of biomass carbon stock changes, and the choice of method for estimating DOM
changes may be affected by the choice of method for biomass carbon stock change estimation.
Gain-Loss Method: The Gain-Loss method uses a mass balance of inputs to and losses from the dead wood andlitter pools to estimate stock changes over a specified period. This involves estimating the area of managed
Forest Land Remaining Forest Land and the average annual transfer of carbon stock into and out of dead woodand litter pools (Equation 2.18 in Chapter 2). To reduce uncertainty, the area under Forest Land Remaining

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Chapter 4: Forest Land

Forest Land can be further stratified by climate or ecological zones, and classified by forest type, productivity,
disturbance regime, management practice, or other factors that affect dead wood and litter carbon pool dynamics.
Estimation of the net balance requires calculation on a per hectare basis of the annual transfers into the deadwood and litter pools from stem mortality, litterfall and turnover, and the losses from decomposition. In addition,in areas subject to management activities or natural disturbances, dead wood and litter will be added in the formof biomass residues, and transferred through harvest (salvage of standing dead trees), burning or othermechanisms.
It is good practice that the stratification of Forest Land adopted for DOM be identical to that used for theestimation of changes in biomass carbon stocks (Section 4.2.1).
Stock-Difference Method: This involves estimating the area of managed Forest Land Remaining Forest Land,
determining the dead wood and litter carbon stocks at two points of time and the calculation of the difference
between the two carbon stock estimates (Equation 2.19 in Chapter 2). The annual carbon stock change for theinventory year is obtained by dividing the change in carbon stock by the period (years) between the two
measurements. Method 2 is only feasible for countries which have forest inventories based on sample plots.
Calculating carbon stock changes as the difference of carbon stocks at two points in time requires that the area attime t1 and t2 is identical to ensure that reported carbon stocks are not the result of changes in area.
For Tiers 2 and 3 methods, both options, are data intensive and require field measurements and models for theirimplementation. Such models can build on the knowledge and information compiled for the simulation of forest
dynamics as used in the timber supply planning process (e.g. Kurz et al., 2002, and Kurz and Apps, 2006).

4.2.2.2

C HOICE OF EMISSION / REMOVAL FACTORS

Tie r 1
By default, it is assumed that the carbon stocks in the DOM pools in Forest Land Remaining Forest Land arestable. Carbon-dioxide emissions originating from dead wood and litter pools during wildfire are assumed to bezero, and accumulation of carbon in dead wood and litter pools during regrowth is also not counted. Non- CO2
emissions from wildfire, including CH4 and CO are estimated in Tier 1.
Tie r s 2 a nd 3
The parameter fBLol is the fraction of total biomass left to decay on the ground, see Chapter 2, Equation 2.20.
Resolution and accuracy of the transferred carbon will correspond to the expansion factors applied in calculating
losses.
Tier 2 estimation of fBlol requires national data on average proportions of carbon left after disturbances. Whennational data are incomplete, Chapter 2 provides two tables:•

Default values of combustion factor to be used as (1– fBL) in case the country has good growing stock
biomass data; in this case the proportion lost is used; see Table 2.6•

Default values of biomass removals to be used as [MB • (1– fBL)] in case the growing stock biomass data are
not reliable. MB is the mass of fuel available for combustion (see Table 2.4 and Equation 2.27 in Chapter 2).

Country-specific values for transfer of carbon in live trees that are harvested to harvest residues can be derived
from national expansion factors, taking into account the forest type (coniferous/broadleaved/
mixed), the rate of biomass utilization, harvesting practices and the amount of damaged trees during harvesting
operations. Both harvest and natural disturbances add biomass to dead wood and litter pools. Other management
practices (such as burning of harvest residues) and wildfire remove carbon from dead wood and litter pools. If
the area under each management practice and type of forest affected by disturbance are known, then disturbancematrices (see Chapter 2, Table 2.1; Kurz et al., 1992) can be used to define for each disturbance type theproportion of each biomass, dead organic matter, and soil carbon pool that is transferred to other pools, to theatmosphere, or removed from the forest during harvest.
Tier 3 estimation of fBlol, will require more detailed knowledge of the proportion of rapid emissions fromdisturbances such as fires and windstorms. Data should be obtained by on-site measurements or from studies ofsimilar disturbances. Disturbance matrices (see Chapter 2, Table 2.1) have been developed to define, for each
disturbance type, the proportion of biomass (and all other carbon pools) that is transferred to other carbon pools,
released to the atmosphere, or transferred to harvested wood products (Kurz et al., 1992). Disturbance matrices
ensure conservation of carbon when calculating the immediate impacts of harvest or disturbances on ecosystemcarbon.
Tier 3 methods rely on more complex forest carbon accounting models that track the rates of input and losses
from dead organic matter pools for each forest type, productivity, and age-class. Where comprehensive forest
inventories exist, that include re-measurement of dead organic matter pools, estimates of carbon stock changes

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can also be derived using the stock-difference approach described in Equation 2.19 in Chapter 2. It is goodpractice that inventory-based approaches with periodic sampling follow the principles set out in Chapter 3,
Annex 3A.3. Inventory-based approaches can be coupled with models to capture the dynamics of all forest
carbon pools. Tier 3 methods provide estimates of greater certainty than lower tiers and feature a greater link
between the dynamics of biomass and dead organic matter carbon pools. Other important parameters inmodelling dead wood and litter carbon budgets are decay rates, which may vary with the forest type and climaticconditions, and forest management practices (e.g., controlled broadcast burning or thinning and other forms ofpartial harvest).

4.2.2.3

C HOICE OF ACTIVITY DATACountries using a Tier 1 method require no activity data for estimation of changes in carbon stock in DOM in
Forest Land Remaining Forest Land.
Countries using higher tiers require activity data on the areas of Forest Land Remaining Forest Land classified
by major forest types, management practices, and disturbance regimes. Total forest area and all other activity
data should be consistent with that reported under other sections of this chapter, notably under biomass sectionof Forest Land Remaining Forest Land (Section 4.2.1). Country-specific activity data on the area annuallyaffected by harvest and disturbances can be derived from national monitoring programs. The assessment ofchanges in carbon stock in DOM is greatly facilitated if this information can be used in conjunction with nationalsoil and climate data, vegetation inventories, and other geophysical data.
Data sources will vary according to a country’s forest management system. Data can be compiled fromindividual contractors or companies, regulation bodies and governmental agencies responsible for forest
inventory and management, and from research institutions. Data formats vary widely, and include, among others,
activity reports submitted regularly within incentive programs or as required by regulations, forest management
inventories and from monitoring programs using remotely sensed imagery (Wulder et al., 2004).

4.2.2.4

C ALCULATION STEPS FOR T IER 1

Since Tier 1 assumes no change in DOM for Forest Land Remaining Forest Land, guidance on calculations
steps is not relevant.

4.2.2.5

U NCERTAINTY ASSESSMENT

Tier 1 by definition assumes stable carbon stocks so formal uncertainty analysis is not appropriate. In fact theassumption is almost never true at the stand level and unlikely to be true in general, although the resulting error
could be small for a forested landscape because increases in some stands could be off-set by decreases in others,
but for the entire landscape or country, dead organic matter pools can be either increasing or decreasing. An
understanding of the types of changes that are occurring in the forests of a country can provide some qualitative
insight into the direction of change in dead organic matter pools. For example, in some countries biomass
growing stocks are increasing because harvest and disturbance losses are smaller than growth increments. It is
likely that dead organic matter pools are also increasing, even if the rate of increase cannot be known unless a
Tier 2 or 3 estimation method is used.
Countries that use methods that assume all carbon losses occur in the year of disturbance are likely tooverestimate disturbance losses in the years of above-average disturbances, and underestimate true emissions inyears of below-average disturbances. Countries with fairly constant harvest or disturbance rates that rely on suchmethods are likely to be closer to the actual net carbon stock changes.
The uncertainty of estimates using higher Tier methods must be evaluated for each country using expert
judgment. It is fair to assume that the uncertainty in the estimates of changes of carbon stock in dead organic
matter is generally larger than that of the estimates of changes in carbon stock in biomass since, in mostcountries, considerably more data are available on biomass stocks than on dead organic matter stocks. Moreover,models that describe biomass dynamics are generally more advanced than models of dead organic matter
dynamics.
Given the increased importance of understanding the non-timber components of forest ecosystems, many
countries have revised their inventory procedures. More data on dead organic matter carbon stocks and theirdynamics are becoming available, which will allow inventory agencies to better identify, quantify and reduce
uncertainties in dead organic matter estimates in the years to come.

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Chapter 4: Forest Land

4.2.3

Soil carbon

This section elaborates on estimation procedures and good practices for estimating change in forest soil C stocks.
It does not include forest litter, which is a dead organic matter pool. Separate guidance is provided for two typesof forest soils: 1) mineral forest soils, and 2) organic forest soils.
The organic C content of mineral forest soils (to 1 m depth) typically varies between 20 to over 300 tonnes C
ha-1 depending on the forest type and climatic conditions (Jobbagy and Jackson, 2000). Globally, mineral forest
soils contain approximately 700 Pg C (Dixon et al., 1994), but soil organic C pools are not static due todifferences between C inputs and outputs over time. Inputs are largely determined by the forest productivity, thedecomposition of litter and its incorporation into the mineral soil and subsequent loss throughmineralization/respiration (Pregitzer, 2003). Other losses of soil organic C occur through erosion or the
dissolution of organic C that is leached to groundwater or loss through overland flow. A large proportion of
input is from above-ground litter in forest soils so soil organic matter tends to concentrate in the upper soil
horizons, with roughly half of the soil organic C in the upper 30 cm layer. The C held in the upper profile isoften the most chemically decomposable, and the most directly exposed to natural and anthropogenic
disturbances. This section only deals with soil C and does not address decomposing litter (i.e., dead organic
matter, see Section 4.2.2).
Human activities and other disturbances such as changes in forest type, productivity, decay rates anddisturbances can alter the C dynamics of forest soils. Different forest management activities, such as rotation
length; choice of tree species; drainage; harvest practices (whole tree or sawlog, regeneration, partial cut or
thinning); site preparation activities (prescribed fires, soil scarification); and fertilization, affect soil organic C
stocks (Harmon and Marks, 2002; Liski et al., 2001; Johnson and Curtis, 2001). Changes in disturbance regimes,
notably in the occurrence of severe forest fires, pest outbreaks, and other stand-replacing disturbances are also
expected to alter the forest soil C pool (Li and Apps, 2002; de Groot et al., 2002). In addition, drainage of forest
stands on organic soils reduces soil C stocks.
General information and guidelines on estimating changes soil C stocks are found in Chapter 2, Section 2.3.3,
and needs to be read before proceeding with the specific guidelines dealing with forest soil C stocks. Changes insoil C stocks associated with forests are computed using Equation 2.24 in Chapter 2, which combines the change
in soil organic C stocks for mineral soils and organic soils; and stock change for soil inorganic C pools (Tier 3only). This section elaborates on estimation procedures and good practices for estimating change in forest soil C
organic stocks (Note: It does not include forest litter, i.e., dead organic matter). Separate guidance is provided for
two types of forest soils: 1) mineral forest soils, and 2) organic forest soils. See Section 2.3.3.1 for general
discussion on soil inorganic C (no additional information is provided in the Forest Land discussion below).
To account for changes in soil C stocks associated with Forest Land Remaining Forest Land, countries need tohave, at a minimum, estimates of the total Forest Land area at the beginning and end of the inventory time period,
stratified by climate region and soil type. If land-use and management activity data are limited, Approach 1
activity data (see Chapter 3) can be used as the basis for a Tier 1 approach, but higher Tiers are likely to needmore detailed records or knowledge of country experts about the approximate distribution of forest management
systems. Forest Land classes must be stratified according to climate regions and major soil types, which can beaccomplished with overlays of suitable climate and soil maps.

4.2.3.1

C HOICE OF METHOD

Inventories can be developed using Tier 1, 2 or 3 approaches, and countries may choose to use different tiers formineral and organic soils. Decision trees are provided for mineral soils (Figure 2.4) and organic soils (Figure2.5) in Chapter 2 to assist inventory compilers with selection of the appropriate tier for their soil C inventory.
Min era l so ils
In spite of a growing body of literature on the effect of forest types, management practices and otherdisturbances on soil organic C, the available evidence remains largely site- and study-specific, but eventually
may be generalized based on the influence of climatic conditions, soil properties, the time scale of interest,
taking into consideration sampling intensity and effects across different soil depth increments (Johnson andCurtis, 2001; Hoover, 2003; Page-Dumroese et al., 2003). However, the current knowledge remains
inconclusive on both the magnitude and direction of C stock changes in mineral forest soils associated with
forest type, management and other disturbances, and cannot support broad generalizations.
Tie r 1
Due to incomplete scientific basis and resulting uncertainty, it is assumed in the Tier 1 method that forest soil C
stocks do not change with management. Furthermore, if using Approach 2 or 3 activity data (see Chapter 3), it is
not necessary to compute C stock changes for mineral soils (i.e., change in SOC stocks is 0).

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Volume 4: Agriculture, Forestry and Other Land Use

If using activity data collected via Approach 1 (see Chapter 3), and it is not possible to identify the amount ofland converted from and to Forest Land, then the inventory compiler should estimate soil C stocks for Forest
Land using the areas at and the end of the year for which the inventory is being estimated, and the difference
estimates the uptake or less of forest soil. The changes in soil C stocks for Forest Land are summed with thechanges in stocks for other land uses to estimate the influence of land-use change. If the compiler does notcompute a stock for Forest Land, it is likely to create systematic errors in the inventory. For example, land
converted from Forest Land to Cropland or Grassland will have a soil C stock estimated in the final year of theinventory, but will have no stock in the first year of the inventory (when it was forest). Consequently,
conversion to Cropland or Grassland is estimated as a gain in soil C because the soil C stocks are assumed to be
0 in the Forest Land, but not in Cropland and Grassland. This would introduce a bias into the inventory
estimates. SOC0 and SOC0-T are estimated for the top 30 cm of the soil profile using Equation 2.25 (Chapter 2).
Note that areas of exposed bedrock in Forest Land are not included in the soil C stock calculation (assume astock of 0).
Tie r 2Using Equation 2.25 (Chapter 2) soil organic C stocks are computed based on reference soil C stocks andcountry-specific stock change factors for forest type (FI), management (FMG) and natural disturbance regime (FD).
Note that the stock change factor for natural disturbance regime (FD) is substituted for the land-use factor (FLU)
in Equation 2.25. In addition, country-specific information can be incorporated to better specify reference C
stocks, climate regions, soil types, and/or the land management classification system.
Tie r 3Tier 3 approaches will require considerable knowledge and data allowing for the development of an accurate andcomprehensive domestic estimation methodology, including evaluation of model results and implementation of adomestic monitoring scheme and/or modelling tool. The basic elements of a country-specific approach are(adapted from Webbnet Land Resource Services Pty ltd, 1999):•

Stratification by climatic zones, major forest types and management regimes coherent with those used forother C pools in the inventory, especially biomass;•

Determination of dominant soil types in each stratum;•

Characterization of corresponding soil C pools, identification of determinant processes in SOC input andoutput rates and the conditions under which these processes occur; and•Determination and implementation of suitable methods to estimate carbon stock changes from forest soils
for each stratum on an operational basis, including model evaluation procedures; methodologicalconsiderations are expected to include the combination of monitoring activities – such as repeated forest soil
inventories - and modelling studies, and the establishment of benchmark sites. Further guidance on good soil
monitoring practices is available in the scientific literature (Kimble et al., 2003, Lal et al., 2001, McKenzie
et al., 2000). It is good practice for models developed or adapted for this purpose to be peer-reviewed, andvalidated with observations representative of the ecosystems under study and independent from thecalibration data.

O rgan ic so ils
Tie r 1
Currently, only C emissions due to drainage of forest organic soils are addressed in the Tier 1 method due to data
limitations and lack of sufficient knowledge that constrain the development of a more refined default
methodology. Using Equation 2.26 (Chapter 2), drained forest organic soils are stratified by climate type, andthen multiplied by a climate-specific emission factor to derive an estimate of annual C emissions. Areas
converted to Forest Land can be included in the total area estimate, in using Approach 1 land representation,
without supplementary data, to be able to identify land-use changes.
Tie r 2
For Tier 2, the same basic equation is used as in Tier 1 (Equation 2.26), but country-specific information isincorporated to better specify emission factors, climate regions, and/or develop a forest classification scheme,
relevant for organic soils.
Tie r 3Tier 3 methodology involves the estimation of CO2 emissions associated with management of forested organicsoils, including all anthropogenic activities likely to alter the hydrological regime, surface temperature, andvegetation composition of forested organic soils; and major disturbances such as fires.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land4.2.3.2

C HOICE OF STOCK CHANGE AND EMISSION FACTORSMin era l so ils
Tie r 1
It is not necessary to compute the stock estimates for Forest Land Remaining Forest Land with Approach 2 or 3
activity data (see Chapter 3). If using Approach 1 activity data, stock change factors, including input,
management and disturbance regime, are equal to 1 using the Tier 1 approach. Consequently, only reference C
stocks are needed to apply the method, and those are provided in Table 2.3 of Chapter 2.
Tie r 2
In a Tier 2 approach, stock change factors are derived based on a country-specific classification scheme formanagement, forest types, and natural disturbance regimes. A Tier 2 approach should also include the derivationof country-specific reference C stocks, and a more detailed classification of climate and soils than the default
categories provided with the Tier 1 method.
It is good practice to focus on the factors that have the largest overall effect, taking into account the impact onforest SOC and the extent of affected forests. Management practices can be coarsely labeled as intensive (e.g.,plantation forestry) or extensive (e.g., natural forest); these categories can also be redefined according to national
circumstances. The development of stock change factors is likely to be based on intensive studies at
experimental sites and sampling plots involving replicated, paired site comparisons (Johnson et al., 2002; Olsson
et al., 1996; see also the reviews by Johnson and Curtis, 2001; and Hoover, 2003). In practice, it may not be
possible to separate the effects of a different forest types, management practices and disturbance regimes, inwhich case some stock change factors can be combined into a single modifier. If a country has well-documented
data for different forest types under different management regimes, it might be possible to derive soil organic C
estimates directly without using reference C stocks and adjustment factors. However, a relationship to thereference C stocks must be established so that the impact of land-use change can be computed without artificial
increases or decreases in the C stocks due to a lack of consistency in the methods across the various land-use
categories (i.e., Forest Land, Cropland, Grassland, Settlements, and Other Land).
Inventories can also be improved by deriving country-specific reference C stocks (SOCref), compiled frompublished studies or surveys. Such values are typically obtained through the development and/or compilation oflarge soil profile databases (Scott et al., 2002; Siltanen et al., 1997). Additional guidance for deriving stockchange factors and reference C stocks is provided in Section 2.3.3.1 (Chapter 2).
Tie r 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion.
O rgan ic so ils
Tie r 1
Default emission factors are provided in Table 4.6 of Section 4.5, to estimate the loss of C associated withdrainage of organic soils.
Tie r 2Tier 2 approaches involve the derivation of emission factors from country-specific data. The main consideration
is whether forests types or management in addition to climate regions will be subdivided into finer classes.
These decisions will depend on experimental data that demonstrate significant differences in C loss rates. For
example, drainage classes can be developed for various forest management systems. In addition, management
activities may disrupt the C dynamics of the underlying organic soils. Harvest, for example, may cause a rise in
the water table due to reduced interception, evaporation and transpiration (Dubé et al., 1995).
Tie r 3
Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately
capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion.

4.2.3.3

C HOICE OF ACTIVITY DATAMin era l so ils
Tie r 1For the Tier 1 approach, it is assumed that forest soil C stocks do not change with management, and therefore it
is not necessary to classify forest into various types, management classes or natural disturbance regimes.
However, if using Approach 1 activity data (see Chapter 3), environmental data will be needed to classify the2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

country into climate regions and soil types in order to apply the appropriate reference C stocks to Forest Land.
A detailed description of the default climate classification scheme is given in Chapter 3, Annex 3A.5. If theinformation needed to classify climate types is not available from national databases, there are internationalsources of climate data such as United Nations Environmental Program. Data will also be needed to classify
soils into the default categories provided in Chapter 3, and if national data are not available to map the soil types,
international soils data provide a reasonable alternative, such as the FAO Soils Map of the World.
Tie r 2
Activity data for the Tier 2 approach consist of the major forest types, management practices, disturbance
regimes and the areas to which they apply. It is preferable for the data to be linked with the national forest
inventory, where one exists, and/or with national soil and climate databases. Typical changes include:
conversion of unmanaged to managed forest; conversion of native forest into a new forest type; intensification offorest management activities, such as site preparation, tree planting and rotation length changes; changes inharvesting practices (bole vs. whole-tree harvesting; amount of residues left on-site); frequency of disturbances(pest and disease outbreaks, flooding, fires, etc). Data sources will vary according to a country’s forest
management system, but could include individual contractors or companies, statutory forest authorities, research
institutions and agencies responsible for forest inventories. Data formats vary widely, and include, among others,
activity reports, forest management inventories and remote sensing imagery.
In addition, Tier 2 should involve a finer stratification of environmental data than the Tier 1 approach, including
climate regions and soil types, which would likely be based on national climate and soils data. If a finerclassification scheme is utilized in a Tier 2 inventory, reference C stocks will also need to be derived for the
more detailed set of climate regions and soil types, and the land management data will need to be stratified basedon the country-specific classification.
Tie r 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or moredetailed data on the combinations of climate, soil, topographic and management data are needed, relative to theTiers 1 and 2 methods, but the exact requirements will be dependent on the model or measurement design.
O rgan ic so ils
Tie r 1
Forests are not stratified into various systems using Tier 1 methods. However, land areas do need to be stratified
by climate region and soil type (see Chapter 3 for guidance on soil and climate classification) so that organicsoils may be identified and the appropriate default emission factor applied.
Tie r 2Tier 2 approaches may involve a finer stratification of management, forest type or disturbance regime, in amanner consistent with the country-specific emission factors for organic soils. For example, forest systems willneed to be stratified by drainage if management factors are derived by drainage class. However it is goodpractice for the classification to be based on empirical data that demonstrates significant differences in rates of Cchange for the proposed categories. In addition, Tier 2 approaches should involve a finer stratification of climate
regions.
Tie r 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or moredetailed data on the combinations of climate, soil, topographic and management data are needed, relative to theTiers 1 and 2 methods, but the exact requirements will be dependent on the model or measurement design.

4.2.3.4

C ALCULATION STEPS FOR T IER 1

Min era l so ils
Since Tier 1 assumes no change in mineral soil C stocks for Forest Land Remaining Forest Land, guidance on
calculations steps are not provided.
O rgan ic so ils
Step 1: Estimate the area of drained organic soils under managed forest in each climatic region of the country
for each year or for the last year in each time period of the inventory (e.g., emissions over an inventory time
period between 1990 and 2000 would be based on the land-use in 2000, assuming land-use and management are
only known for these two years during the inventory time period).
Step 2: Select the appropriate emission factor (EF) for annual losses of CO2 (from Table 4.6).
Step 3: Estimate total emissions by summing the product of area (A) multiplied by the emission factor (EF) for
all climate zones.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land4.2.3.5

U NCERTAINTY ASSESSMENT

Three broad sources of uncertainty exists in soil C inventories: 1) uncertainties in land-use and management
activity and environmental data; 2) uncertainties in reference soil C stocks if using Tier 1 or 2 approaches(mineral soils only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model
structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability
associated with Tier 3 measurement-based inventories. In general, precision of an inventory is increased (i.e.,
smaller confidence ranges) with more sampling to estimate values for the three broad categories. In addition,
reducing bias (i.e., improve accuracy) is more likely through the development of a higher Tier inventory thatincorporates country-specific information.
For Tier 1, uncertainties are provided with the reference C stocks in the first footnote of Table 2.3 (Chapter 2),
and emission factor uncertainties for organic soils are provided in Table 4.6, Section 4.5. Uncertainties in landuse and management data will need to be addressed by the inventory compiler, and then combined withuncertainties for the default factors and reference C stocks (mineral soils only) using an appropriate method,such as simple error propagation equations. Refer to Section 4.2.1.5 for uncertainty estimate for land area
estimates. However, it is good practice for the inventory compiler to derive uncertainties from country-specificactivity data instead of using a default level.
Default reference C stocks for mineral soils and emission factors for organic soils can have inherently highuncertainties, particularly bias, when applied to specific countries. Defaults represent globally averaged values ofland-use and management impacts or reference C stocks that may vary from region-specific values (Powers et al.,
2004; Ogle et al., 2006). Bias can be reduced by deriving country-specific factors using Tier 2 method or bydeveloping a Tier 3 country-specific estimation system. The underlying basis for higher Tier approaches will beresearch in the country or neighbouring regions that address the effect of land use and management on soil C. In
addition, it is good practice to further minimize bias by accounting for significant within-country differences inland-use and management impacts, such as variation among climate regions and/or soil types, even at theexpense of reduced precision in the factor estimates (Ogle et al., 2006). Bias is considered more problematic forreporting stock changes because it is not necessarily captured in the uncertainty range (i.e., the true stock change
may be outside of the reported uncertainty range if there is significant bias in the factors).
Uncertainties in land-use activity statistics may be improved through a better national system, such as
developing or extending a ground-based survey with additional sample locations and/or incorporating remotesensing to provide additional coverage. It is good practice to design a classification that captures the majority ofland-use and management activity with a sufficient sample size to minimize uncertainty at the national scale.
For Tier 2 methods, country-specific information is incorporated into the inventory analysis for purposes of
reducing bias. For example, Ogle et al. (2003) utilized country-specific data to construct probability distributionfunctions for US specific factors, activity data and reference C stocks for agricultural soils. It is good practice toevaluate dependencies among the factors, reference C stocks or land-use and management activity data. In
particular, strong dependencies are common in land-use and management activity data because management
practices tend to be correlated in time and space. Combining uncertainties in stock change/emission factors,
reference C stocks and activity data can be done using methods such as simple error propagation equations orMonte-Carlo procedures.
Tier 3 models are more complex and simple error propagation equations may not be effective at quantifying theassociated uncertainty in resulting estimates. Monte Carlo analyses are possible (Smith and Heath, 2001), but
can be difficult to implement if the model has many parameters (some models can have several hundredparameters) because joint probability distribution functions must be constructed quantifying the variance as wellas covariance among the parameters. Other methods are also available such as empirically-based approaches(Monte et al., 1996), which use measurements from a monitoring network to statistically evaluate therelationship between measured and modelled results (Falloon and Smith, 2003). In contrast to modelling,
uncertainties in measurement-based Tier 3 inventories can be determined from the sample variance,measurement error and other relevant sources of uncertainty.

4.2.4

Non-CO 2 greenhouse gas emissions from biomassburning

Both uncontrolled (wildfires) and managed (prescribed) fires can have a major impact on the non-CO2
greenhouse gas emissions from forests. In Forest Land Remaining Forest Land, emissions of CO2 from biomassburning also need to be accounted for because they are generally not synchronous with rates of CO2 uptake. Thisis especially important after stand replacing wildfire, and during cycles of shifting cultivation in tropical regions.

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Volume 4: Agriculture, Forestry and Other Land Use

Where the type of forest changes (e.g., conversion of natural forests to plantation forests), there may be net
emissions of CO2 from biomass burning during the initial years, in particular if significant woody biomass isburnt during the conversion. Over time, however, the impacts are not as great as those that result from Forest
Land Converted to Cropland or Grassland. Fire emissions during land-use conversion are reported in the newland-use category unless restricted Approach 1 land area representation is being used without supplementary
data to enable land use conversions to be identified explicitly, in which case fire emissions from Forest Landshould all be included in the Forest Land Remaining Forest Land category.
The general method for estimating greenhouse gas emissions in Forest Land Remaining Forest Land, and inLand Converted to Forest Land is described in Equation 2.27 in Chapter 2. Default tables for Tier 1 approach orcomponents of a Tier 2 approach are provided in that Section 2.4 of Chapter 2.

4.2.4.1

C HOICE OF METHOD

It is good practice that countries choose the appropriate Tier for reporting greenhouse gas emissions from fire,based on the decision tree in Figure 2.6 in Chapter 2. Where fire is a key category, emphasis should be on usinga Tier 2 or Tier 3 approach. For prescribed fires, country-specific data are required to generate reliable estimatesof emissions, since activity data, in general, are poorly reflected in global data sets. In Forest Land, both the CO2
emissions due to biomass burning and the CO2 removals resulting from vegetation regrowth need to beaccounted for when estimating the net carbon flux.

4.2.4.2

C HOICE OF EMISSIONS FACTORSThe mass of fuel available for combustion (MB of Equation 2.27) is critical for estimating the non-CO2 emissions.
Default data to support estimation of emissions under a Tier 1 approach are given in Tables 2.4 to 2.6 in Chapter
2. Countries need to judge how their vegetation types correspond with the broad vegetation categories describedin the default tables. Guidance for this is provided in Chapter 3 (Consistent Representation of Lands). Countries
using Tier 2 are likely to have national data at disaggregated level on MB, according to forest types andmanagement systems. Tier 3 estimation requires spatial estimates of MB according to different forest types,
regions and management systems. Tier 3 estimation methods can also distinguish fires burning at different
intensities, resulting in different amounts of fuel consumption.

4.2.4.3

C HOICE OF ACTIVITY DATA

Estimates of area burnt in Forest Land Remaining Forest Land are needed. A global database exists that covers
the area burnt annually by fires but this will not provide reliable data for the area burnt annually by prescribedfires in individual countries. It is good practice to develop national estimates of the area burnt and the nature of
the fires especially how they affect forest carbon dynamics (e.g., effects on tree mortality) to improve thereliability of national inventories. Countries using Tier 2 are likely to have access to national estimates. Tier 3
estimation requires regional and forest type specific estimates of area subjected to fire and fire intensity.
Summary of steps for calculating greenhouse gas emissions from biomass burning using Equation 2.27 in
Chapter 2:
Step 1: Using guidance from Chapter 3 (approaches in representing land-use areas), categorise the area ofForest Land Remaining Forest Land into forest types of different climatic or ecological zones, as adopted by thecountry for Equation 2.27. Obtain estimates of A (area burnt) from global database or from national sources.
Step 2: Estimate the mass of fuel (MB) available for combustion, in tonnes/ha, which includes biomass, litterand dead wood.
Step 3: Select combustion factor Cf (default values are in Table 2.6, Chapter 2).
Step 4: Multiply MB and Cf to provide an estimate of the amount of fuel combusted. If MB or Cf is unknown,
defaults for the product of MB and Cf are given in Table 2.4.
Step 5: Select emission factors Gef (default factors are in Table 2.5, Chapter 2).
Step 6: Multiply parameters A, MB, Cf, (or MB and Cf, Table 2.4) and Gef to obtain the quantity of greenhousegas emission from biomass burning. Repeat the steps for each greenhouse gas.

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Chapter 4: Forest Land4.2.4.4

U NCERTAINTY ASSESSMENTCountry-specific uncertainty estimates are to be estimated for Forest Land Remaining Forest Land. These result
from the product of the uncertainties associated with activity data (area burnt) and the emission factors. It isgood practice to provide error estimates (e.g., ranges, standard errors) and not to use country-specific data (forexample, if it is of a limited nature) or approaches, unless this leads to a reduction in uncertainties comparedwith a Tier 1 approach.

4.3

LAND CONVERTED TO FOREST LANDThis section provides methodological guidance on annual estimation of emissions and removals of greenhousegases, which occur on lands converted to Forest Land from different land-uses, including Cropland, Grassland,
Wetlands, Settlements, and Other land, through afforestation and reforestation, either by natural or artificial
regeneration (including plantations). The emissions and removals on abandoned lands, which are regenerating toforest due to human activities, should be also estimated under this section. It substitutes the method describedunder categories 5A, 5C, and 5D of the IPCC Guidelines. Land is converted to Forest Land by afforestation andreforestation, either by natural or artificial regeneration (including plantations). The anthropogenic conversion
includes promotion of natural re-growth (e.g., by improving the water balance of soil by drainage), establishmentof plantations on non-forest lands or previously unmanaged Forest land, lands of settlements and industrial sites,
abandonment of croplands, pastures or other managed lands, which re-grow to forest. Unmanaged forests are notconsidered as anthropogenic greenhouse gas sources or sinks, and are excluded from inventory calculations.
Where these unmanaged forests are affected by human activities such as planting, thinning, promotion of naturalregeneration or others, they change status and become managed forests, reported under the category Land
Converted to Forest Land, whose greenhouse gas emissions and removals should be included in the inventory
and estimated with the use of the guidance in this section. Land conversion may result in an initial loss of carbon
due to changes in biomass, dead organic matter, and soil carbon. But natural regeneration or plantation practices
lead to carbon accumulation and that is related to changes in the area of plantations and their biomass stocks.
Converted areas are considered Forest Land, if, following conversion, they correspond to definition of forest
adopted by the country. Land Converted to Forest Land is covered in this section of the national greenhouse gas
inventory until the time the soil carbon in new forests reach a stable level. A default period of 20 years 4 issuggested. Forest ecosystems may require a certain time to return to the level of biomass, stable soil and litterpools of undisturbed state. With this in mind and as a practical matter, the default 20-year time interval issuggested. Countries also have an option to extend the length of transition period. After 20 years or other timeinterval chosen, the converted lands become forest, i.e., the land areas are transferred from the Land Converted
to Forest Land category to Forest Land Remaining Forest Land (Section 4.2), where areas still becoming
established can be treated as a separate stratum if necessary. Logging followed by regeneration or re-growth
should be considered under Forest Land Remaining Forest Land category, since no land-use change is involved.
Some abandoned lands may be too infertile, saline, or eroded for forest re-growth to occur. In this case, either
the land remains in its current state or it may further degrade and lose organic matter. Those lands that remain
constant with respect to carbon flux can be ignored. However, in some countries, the degradation of abandoned
lands may be a significant problem and could be an important source of CO2. Where lands continue to degrade,both above-ground biomass and soil carbon may decline rapidly, e.g., due to erosion. The carbon in eroded soil
could be re-deposited in rivers, lakes or other lands downstream. For countries with significant areas of suchlands, this issue should be considered in a more refined calculation.
Classification of land: Land Converted to Forest Land can be classified based on climate domain and ecologicalzones and forest crown cover classes. The carbon stock varies with climate, biome or forest type, species mix,
management practices, etc. It is good practice to stratify lands into homogenous sub-categories (see Chapter 3)
to reduce uncertainty in estimates of greenhouse gas emissions.
The estimation of emissions and removals of carbon from land-use conversion to Forest Land is divided into
three sub-sections: Change in Carbon Stocks in Biomass (Section 4.3.1), Change in Carbon Stocks in Dead
Organic Matter (Section 4.3.2) and Change in Carbon Stocks in Soils (Section 4.3.3). The annual changes incarbon stocks on Land Converted to Forest Land are calculated using Equations 2.2 and 2.3 of Chapter 2 on the4

It is clear that most forest ecosystems will take longer than 100 years to return to the level of biomass, soil and litter pools
in undisturbed state; however human-induced activities can enhance the rate of return to stable state of carbon stocks. With
this in mind and as a practical matter, the default 20-year time interval is suggested to capture the establishment of theforest ecosystems. Countries also have the option to extend the length of the transition period, though a consistenttransition period will be required for the land use matrix system of land area representation to work properly.

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basis of annual changes in carbon stocks in biomass, dead organic matter (including dead wood and litter) andsoil. Changes in carbon stock in Land Converted to Forest Land are estimated using:•annual change in carbon stocks in above- and below-ground biomass•

annual change in carbon stocks in dead organic matter that includes dead wood and litter•

annual change in carbon stocks in soils

The approach for calculation of non-CO2 emissions is described in Section 4.3.4 based on methods given in
Chapter 2.
Application of these methods will only be possible if using Approach 2 or 3 land area representation as set out in
Chapter 3, or Approach 1 data with supplementary data to enable land-use conversions to be identified. The
actions to be taken in this case have already been identified in Section 4.2 above (Forest Land Remaining ForestLand).

4.3.1

Biomass

This section presents methodological guidance for calculation of emissions and removals of CO2 by changes inbiomass on Land Converted to Forest Land. It substitutes the methodology provided for reporting on “Changes
in Forest and Other Woody Biomass Stocks” and “Abandonment of Managed Lands” categories of the IPCC
Guidelines as applied to newly established forests.

4.3.1.1

C HOICE OF METHODThis section presents methodological guidance for calculation of emissions and removals of CO2 by changes inabove-ground and below-ground biomass on Land Converted to Forest Land. Based on key category analysis,
activity data and resources available, three tier methods are suggested to estimate changes in biomass stocks. The
decision tree in Figure 1.3 in Chapter 1 illustrates good practice approach for choosing the method to calculate
CO2 emissions and removals in biomass on Land Converted to Forest Land.
Tie r 1
Annual change in carbon stocks in biomass is estimated with the use of Equation 2.7 in Chapter 2. Tier 1 follows
the default approach. It implies the use of default parameters provided in Section 4.5. This approach can be also
applied, if the data on previous land uses are not available, which may be the case, when areas are estimatedusing Approach 1 from Chapter 3. It implies the use of default parameters in Tables 4.1 through 4.14.
Annual increase in carbon stocks in biomass, ∆CG. The calculations of ∆CG should be made according toEquation 2.9 in Chapter 2. As the growth rate of trees strongly depends on management regime, a distinction
should be made between intensively (e.g., plantation forestry) and extensively (naturally re-growing stands with
reduced or minimum human intervention) managed forests. The intensively and extensively managed forests canbe further stratified based on climate, species, management practices, etc. Hence, the annual increase in carbon
stocks can be estimated separately for intensively and extensively managed forests, using Equation 2.9 twice.
First, for intensively managed forests using relevant area (AI) and the relevant mean annual biomass growth(GTotal) for intensively managed forests and second, for extensively managed forests by using appropriate area(AE) and mean annual biomass growth (GTotal) data for extensively managed forests. GTOTAL is calculated using
Equation 2.10, Chapter 2, and default data tables in Section 4.5. The intensively managed and extensively
managed forests can be further stratified based on climate, species, forest management practices, etc. The default
data for extensively and intensively managed forests from the tables should be chosen with regard to tree species
composition and climatic region. The default data for extensively and intensively managed forests should betaken from Section 4.5, correspondingly.
Annual decrease in carbon stocks in biomass due to losses, ∆CL. Biomass loss due to wood removal(Lwood-removals), fuelwood removal (Lfuelwood) and disturbances (Ldisturbance) attributed to Land Converted to Forest
Land, is estimated using Equation 2.11 in Chapter 2.
The loss of biomass due to wood removal (Lwood-removals) is estimated with the use of Equation 2.12, of Chapter 2,
and default values of basic wood density and the data on round wood logging, biomass conversion expansion
factor, below-ground biomass to above-ground biomass ratio (R) and carbon fraction of dry matter (CF),provided in Section 4.5 tables. The biomass loss due to fuelwood removal (Lfuelwood) is estimated using Equation
2.13, fuelwood collecting data and relevant BCEFR for growing stock, R and CF from default tables in Section
4.5. The (Ldisturbance) could be estimated using Equation 2.14, in Chapter 2, area of disturbance, average growing
stock biomass of land areas affected by disturbances and appropriate R and CF from default tables in Section 4.5.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest LandThe ∆CL should be assumed 0, if no data on losses are available (for Equation 2.11). To prevent double
accounting or omission, consistent reporting of biomass loss should be maintained in Sections 4.2.1 and 4.3.1.
Tie r 2The Tier 2 method is similar to Tier 1, but it uses nationally derived data and more disaggregated activity data
and allows for more precise estimates of changes in carbon stocks in biomass. The net annual CO2 removals arecalculated as a sum of increase in biomass due to biomass growth on converted lands, changes due to actual
conversion (difference between biomass stocks before and after conversion) and losses on converted lands(Equations 15 and 16, Chapter 2).
In addition to default values, the application of Tier 2 (Equation 2.15) requires national data on: i) area annuallyconverted to forest; ii) average annual growth in carbon stocks in biomass per ha on converted lands, obtained
e.g., from forest inventories (no default data can be provided); iii) change in biomass carbon when non-forest
land becomes Forest Land; and iv) emissions due to loss of biomass on converted land. The approach may
require data on previous land uses as well as knowledge of land-use change matrix (see Table 3.4 in Chapter 3)
and carbon stocks on those lands.
∆CG should be estimated using Equation 2.9, where the area (A) of Land Converted to Forest Land should beconsidered separately along with respective mean annual increments for intensively and extensively managed
forests (further categorized based on species, climate, etc.) and summed up. Average annual increment inbiomass for managed forests is calculated in accordance with Tier 2 method as in Section 4.2.1, Forest Land
Remaining Forest Land and Equation 2.10, Chapter 2, based on country-specific data on average annual biomass
growth in merchantable volume per ha on land converted to forests (obtained e.g., from forest inventories) andon basic wood density, biomass conversion and expansion factors and below-ground to above-ground biomass
ratio.
∆CCONVERSION accounts for the initial change in biomass stocks resulting from the land-use conversion, e.g., partof biomass may be withdrawn through land clearing, restocking or other human-induced activities applied onland prior to artificial or natural regeneration. These changes in carbon stocks in biomass are calculated with theuse of Equation 2.16 in Chapter 2. This requires estimates of biomass stocks on land type i before (BBEFOREi) andafter (BAFTERi ) the conversion in tonnes d.m. ha-1, area of land-use i converted to Forest Land (∆ATO_FORESTi) in acertain year, and the carbon fraction of dry matter (CF).
The calculation of ∆CCONVERSION may be applied separately to account for different carbon stocks occurring on
specific types of land (ecosystems, site types, etc.) before the transition. The ∆ATO_FORESTi refers to the particularinventory year for which the calculations are made.
∆CL is estimated using Equation 2.11 in Chapter 2. Biomass loss due to wood removal (Lwood-removals), fuelwood
removal (Lfuelwood), and disturbances (Ldisturbance) should be estimated with the use of Equations 2.12 to 2.14, in
Chapter 2. Inventory compilers are encouraged to develop country-specific wood density and BEF or BCEF
values for growing stock increment and harvests to apply them in Equation 2.12 (for Tier 2 calculations).
Chapter 2 describes the method for calculation of biomass losses from fuelwood gathering (Lfuelwood) anddisturbances (Ldisturbance). The ∆CL should be assumed 0, if no data on losses are available. It is good practice toensure consistent reporting on biomass losses between Sections 4.2.2 and 4.2.3 to avoid over- andunderestimates due to double counting or omissions.
Tie r 3Tier 3 should be used when land conversion to Forest Land is a key category and leading to a significant changeof carbon stocks. It can follow the same equations and steps as Tier 2 or can use more complex methods andmodels, but in either case, it can make use of substantial national methods and country-specific data. The
Equations 2.15 and 2.16 can be expanded on the basis of finer geographical scale and sub-division to forest type,species, and land type before conversion. Country-defined methodologies may be based on regular forest
inventory or geo-referenced data and (or) models for accounting for changes in biomass. National activity data
can have high resolution and be available for all categories of converted lands and forest types established onthem. It is good practice to describe and document the methodology in accordance with Volume 1, Chapter 8(Reporting Guidance and Tables).
T ran sf er of b io mas s to d ead orga n ic ma tter
During the process of conversion of land to Forest Land as well as during the process of extraction of biomass
through felling, the non-commercial component of the biomass is left on the forest floor or transferred to deadorganic matter. Refer to Section 4.3.2 for description of the method and the assumptions about the fate of deadorganic matter.

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Volume 4: Agriculture, Forestry and Other Land Use

4.3.1.2

C HOICE OF EMISSION FACTORSAn nua l inc re as e in ca rbon s to cks in b iomas s, ∆ C G
The calculations distinguish between two broad management practices: intensive (e.g., plantation forestry withsite preparation, planting of selected species and fertilization) and extensive (natural regeneration with minimum
human intervention). These categories can also be refined according to national circumstances, for example
based on stand origin (e.g., natural or artificial regeneration, restocking, promotion of natural re-growth, etc.),
climate, species, management practice, etc.
Tie r 1
The methods for calculation of total biomass require above-ground and below-ground biomass pools (for pooldescriptions, refer to Chapter 1). The tables in Section 4.5 represent default values of average annual growth inabove-ground biomass for intensively (plantations) and extensively (naturally regenerated) managed forests,
biomass conversion and expansion factors, below-ground biomass to above-ground biomass ratio and carbon
fraction of dry matter (CF). The below-ground biomass to above-ground biomass ratio should be used to accountfor below-ground biomass in total biomass estimations. Basic wood density and biomass expansion factors,
which allow for calculation of ∆CG as described in Section 4.2.1 Forest Land Remaining Forest Land. It is goodpractice to explore any regional or otherwise relevant default values to the country.
Tie r 2
It is good practice to determine, wherever possible, annual increment values, below-ground biomass to aboveground biomass, basic wood density, and biomass conversion and expansion factors appropriate for national
conditions and use them in calculations under Tier 2. These categories can also be refined according to national
circumstances, for example based on stand origin (natural or artificial regeneration, restocking, promotion ofnatural re-growth, etc.), climate, species composition, and management regime. The further stratifications may
refer to tree species composition, management regime, stand age, climatic region and soil type, etc. Countries areencouraged to obtain specific biomass increment and expansion factors through research efforts. Additional
guidance is provided in Section 4.2.1.
Tie r 3The increment in biomass carbon stocks can be estimated based on country-specific annual biomass growth andcarbon fraction in biomass data that come from forest inventories, sample plots, research and (or) models. The
inventory compilers should ensure that the models and forest inventory data have been appropriately
documented and described in line with the requirements highlighted in Volume 1, Chapter 8.
Cha nge in b ioma ss s toc ks o n land b efo re and af ter co nv er s ion , ∆C C O N V E R S I O N
The calculations of biomass stocks before and after conversion should be made with the use of values consistent
with other land uses. For example, comparable values of carbon stock should be used to estimate initial carbon
stock for Grassland converted to Forest Land and for changes in biomass for Grassland Remaining Grassland.
Tie r 1
No estimate of ∆CCONVERSION is required for Tier 1 calculations.
Tie r 2
It is good practice to obtain and use, wherever possible, country-specific data on biomass stocks on land before
and after conversion. The estimates should be consistent with those used in calculations of carbon stock changes
in Cropland, Grassland, Wetlands, Settlements and Other Land, and should be obtained from national agencies
or surveys. Tier 2 may imply the use of a combination of country-specific and default data. For default biomass
stock values on land before the conversion, refer to other sections of this Volume.
Tie r 3
Estimates and calculations should be performed based on forest inventory and or model data. Forest inventory,models and data should be documented in line with procedures outlined in Volume 1, Chapter 8.
Cha nge in ca rbon s tocks in bio mass du e to lo ss e s, ∆ C L
Wood removal, fuelwood removal, and natural disturbances such as windfall, fires, and insect outbreaks result inloss of carbon on Land Converted to Forest Land that should be reported in accordance with good practice
approach provided in Section 4.2.1. The good practice approach provided in Section 4.2.1 for estimating lossesof carbon is fully applicable and should be used for appropriate calculations under Section 4.2.2. If changes incarbon stocks are derived from regular forest inventories, the losses from wood removal and disturbances will becovered without a need to report on them separately. It is good practice to ensure consistent reporting on lossesof biomass between Sections 4.2.1 and 4.2.2 to avoid double counting or omissions.
The data on logging of round wood should be taken from national sources or FAO. It should be noted that FAO
data on logging is in merchantable round wood over bark. Bark fraction in harvested wood (BF) should be

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Landapplied to account for bark in wood removals with harvest. If logging is significant in the country, the inventory
compilers are encouraged to use national harvest data or derive country-specific BF values.
In most countries, information on area disturbed is not likely to be available by the two sub-categories, Forest
Land Remaining Forest Land and Land Converted to Forest Land sub-categories. Given that the latter is, in mostcases, much smaller than the former, all disturbances can be applied to Forest Land Remaining Forest Land, or
the disturbed area can be pro-rated in proportion to the two land sub-categories.
Fuelwood consumption data are not normally reported separately for Forest Land Remaining Forest Land andLand Converted to Forest Land. Then it is likely that the default fuelwood data is likely to be reported in Forest
Land Remaining Forest Land. The reporting of fuelwood should be cross-checked between the two land subcategories to avoid double counting by checking with reporting of fuelwood in Forest Land Remaining Forest
Land.

4.3.1.3

C HOICE OF ACTIVITY DATA

Ar ea of land con v er ted to for es t, ∆A T O _ F O R E S T
All tiers require information on areas converted to Forest Land over the 20 years prior to the inventory year.
After 20 years or other time interval chosen, the lands converted to Forest Land, as defined in the country,
should be transferred to and accounted for under Section 4.2 (Forest Land Remaining Forest Land). The same
area data should be used for Sections 4.3.2 (Change in Carbon Stocks in Dead Organic Matter), Section 4.3.3(Change in Carbon Stocks in Soils), and Section 4.3.4 (Non-CO2 Greenhouse Gas Emissions). If possible, theseareas should be further disaggregated to take into consideration major soil types and biomass densities on land
before and after conversion. Box 4.3 gives examples of a good practice approach in identification of lands
converted to Forest Land. Subject to national data availability, the inventory compilers can also choose goodpractice approach on the basis of approaches provided in Chapter 3.
Different biomass growth rates should be used for calculations of biomass stocks for forests naturally re-growing
on abandoned lands and for forest plantations. To undertake calculations under Tiers 2 and 3, inventory
compilers are encouraged to obtain information on types of previous land uses for lands converted to ForestLand.
Tie r 1
Activity data can be obtained through national statistics, from forestry agencies (information on areas ofdifferent management practices), conservation agencies (naturally regenerated areas), municipalities, survey andmapping agencies. Expert judgment may be used to assess whether new forests are predominantly intensively or
extensively managed, if no recorded data are available. If the data on intensively and extensively managed areasof forests become available, these should be used for further partitioning areas to obtain more accurate estimates.
Cross-checks should be applied to ensure complete and consistent representation of data to avoid omissions or
double counting. If no country data are available, aggregate information can be obtained from international data
sources (FAO, 2001; TBFRA, 2000).
Tie r 2
Areas under different land uses subjected to conversion during a given year or over a period of years should beavailable. They can come from national data sources and a land-use change matrix or its equivalent that covers
all possible transitions to Forest Land. Country-defined national data sets should have a resolution sufficient toensure appropriate representation of land areas in line with provisions of Chapter 3 of this Volume. It is
important to estimate area converted to forest through natural regeneration and plantation approach.
Tie r 3National activity data on land conversion to Forest Land through natural and artificial regeneration should beavailable from different sources, notably national forest inventories, registers of land use and land-use changes
and remote sensing, as described in Chapter 3 of this Volume. These data should give a full accounting of allland-use transitions to Forest Land and can be further disaggregated along climate, soil, and vegetation types.
Area under plantations is usually available according to species and age of the stand.

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Volume 4: Agriculture, Forestry and Other Land Use

BOX 4.3
EXAMPLES OF GOOD PRACTICE APPROACH IN IDENTIFICATION OF LANDS CONVERTED TO FOREST LAND

National land management systems can allow for identification of land-use changes, and the land
census systems implemented in many countries also enables consistent representation and timely
tracking changes in land use. The national inventory compilers should take the data from land
management systems or censuses and use them as the basis for identification of converted lands.
The land conversion data may be obtained directly from companies, private owners, ministries andagencies, which undertake particular activities over converted lands.
In some countries, special accounting systems have been designed to estimate emissions andremovals over converted lands. The Australia National Carbon Accounting System (NCAS)<http://www.greenhouse.gov.au/> is an example of a good practice approach in identification ofland conversion. The NCAS is a sophisticated model-based tool that comprises data from resource
census, field studies, and remote sensing. It operates at high spatial and temporal scales. The
NCAS addresses all sectors of activity in land systems, including carbon pools and all greenhousegases as affected by human-induced activities. It allows for tracking afforestation and reforestation
activities within the territory of the country along with estimating emissions and removals relevantto them. As soon as the new data enter the NCAS, the inventory data are updated continuously.
Design and implementation of the NCAS and its components has been subjected to extensive peer
review and Quality Assurance/Quality Control regime (AGO, 2002).
Similar systems are being developed in New Zealand (Stephens et al., 2005; Trotter et al., 2005),
Canada (Kurz and Apps, 2006), and other countries. The use of such land management systems
contributes to development of high quality inventories and reduces the levels of uncertainty within
the sector.

4.3.1.4

C ALCULATION STEPS FOR T IER 1

T he fo llo wing s umma r iz e s s tep s for e s tima ting cha nge in c arbo n s to ck s in bio mas s (∆ C B )u s ing the de fau lt me thod s
Step 1: Estimate area converted to Forest Land (during the period 20 years before the year of the inventory)
from other land-use categories such as Cropland, Grassland, and Settlements. Refer to Chapter 3 for detailed
approaches for estimating Land Converted to Forest Land.
Step 2: Disaggregate the area converted to Forest Land according to intensively managed forest (throughplantation forestry) and extensively managed forest (through natural regeneration) based on the approach used
for conversion.
Step 3: Calculate the initial biomass loss associated with the land conversion, ∆CCONVERSION (Equation 2.16).
This can be stratified by land conversion methods.
Step 4: Estimate the annual increase in carbon stocks in biomass due to growth on Land Converted to Forest
Land (∆CG), for intensively managed forests at species and other sub-category level, using Equations 2.9 and2.10 in Chapter 2. Estimate annual increment of biomass at species and other sub-category level.
Step 5: Estimate the annual increase in carbon stocks in biomass growing on Land Converted to Forest Land(∆CG), for extensively managed forests at species and other sub-category level, using Equations 2.9 and 2.10 in
Chapter 2.
Step 6: Estimate annual loss or decrease in biomass (Lwood-removals) due to commercial fellings (industrial woodand sawn logs) using Equation 2.12 in Chapter 2.
Step 7: Estimate biomass loss due to fuelwood removal (Lfuelwood) on Land Converted to Forest Land using
Equation 2.13 in Chapter 2.
Step 8: Estimate annual carbon loss due to disturbance or other losses (Ldisturbance) using Equation 2.14 in
Chapter 2.
Step 9: Estimate the total loss of biomass carbon due to wood removal, fuelwood removal, and disturbance(∆CL) using Equation 2.11 in Chapter 2.
Step 10: Estimate the annual change in carbon stock in biomass (∆CB) on Land Converted to Forest Land using
Equation 2.15 in Chapter 2.

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Chapter 4: Forest Land

Example. The following example shows Gain-Loss method (Tier 1) calculations of annual change
in carbon stocks in biomass (∆CB in Equation 2.7, Chapter 2) for a hypothetical country intemperate continental forest zone of Europe (Table 4.1, Section 4.5). The area of non-forest land
converted to Forest Land (A) within the country is 1,000 ha (see Chapter 3 for area categorization).
The new forest is intensively managed 9-year-old pine plantation, average above-ground growing
stock volume is 10 m3 ha-1. Thinning removed 100 m3 yr-1 of merchantable round wood over bark(H); 50 m3 yr-1 of whole trees (FGtrees) were removed as fuel wood. The area of insect disturbance(Adisturbance) is 50 ha yr-1 with 1.0 tonne d.m. ha-1 of above-ground biomass affected (BW).
Annual gain in biomass (∆CG) is a product of mean annual biomass increment (GTOTAL), area ofland converted to Forest Land (A) and carbon fraction of dry matter (CF), Equation 2.9, Chapter 2.
GTOTAL is calculated using annual above-ground biomass increment (GW), below-ground biomass
to above-ground biomass ratio (R), (Equation 2.10, Chapter 2) and default data tables, Section 4.5.
For the hypothetical country,
GW= 4.0 tonnes d.m. ha-1 yr-1 (Table 4.12); andR= 0.40 tonne d.m. (tonne d.m.)-1 for above-ground biomass <50 t ha-1 (Table 4.4, withreference to Table 4.8 for above-ground biomass).

GTOTAL= 4.0 tonnes d.m. ha-1 yr-1 ● (1 + 0.40) = 5.6 tonnes d.m. ha-1 yr-1 (Equation 2.10)

CF= 0.47 tonne C (tonne d.m.)-1 (Table 4.3)

∆CG (Equation 2.9): = 1,000 ha ● 5.6 tonnes d.m. ha-1 yr-1 ● 0.47 tonne C (tonne d.m.)-1= 2,632 tonnes C yr-1
Biomass loss (∆CL) is a sum of annual loss due to wood removals (Lwood-removals), fuelwood
removal (Lfuelwood) and disturbances (Ldisturbance), Equation 2.11, Chapter 2.
Wood removals (Lwood-removals) is calculated using Equation 2.12 in Chapter 2 with merchantableround wood over bark (H), biomass conversion expansion factor (BCEFR), bark fraction in
harvested wood (BF), below-ground biomass to above-ground biomass ratio (R), carbon fractionof dry matter (CF), and default tables in Section 4.5. For the hypothetical country,
BCEFR= 2.0 tonnes d.m. m-3 (Table 4.5, with reference to volume of growing stock 10
m3 ha-1);

Default BEF= 0.1 tonne d.m. (tonne d.m.)-1;

R= 0.40 tonne d.m. (tonne d.m.)-1 for above-ground biomass <50 t ha-1 (Table
4.4, for above-ground biomass refer to Table 4.8); andCF= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Lwood-removals = 100 m3 yr-1 ● 2 tonnes d.m. m-3 (1 + 0.40 + 0.1) ● 0.47 tonne C (tonne d.m.)-1= 141 tonnes C yr-1 (Equation 2.12)
Fuelwood removal (Lfuelwood) is calculated using Equation 2.13, Chapter 2 with wood removals as
whole trees (FGtrees), biomass conversion expansion factor (BCEFR), below-ground biomass toabove-ground biomass ratio (R), carbon fraction of dry matter (CF), and default tables in Section
4.5. For the hypothetical country,
BCEFR= 2.0 tonnes d.m. m-3 (Table 4.5, with reference to growing stock volume 10 m3 ha-1);R= 0.40 tonne d.m. (tonne d.m.)-1 for above-ground biomass <50 t ha-1 (Table 4.4, withreference to Table 4.8 for above-ground biomass); andCF= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Lfuelwood= 50 m3 yr-1 ● 2.0 tonnes d.m. m-3 (1 + 0.40) ● 0.47 tonne C (tonne d.m.)-1= 65.80 tonnes C yr-1 (Equation 2.13)

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Volume 4: Agriculture, Forestry and Other Land Use

Annual carbon loss in biomass due to disturbances (Ldisturbance) is calculated using Equation 2.14,
Chapter 2 with area of disturbances (Adisturbance), average above-ground biomass affected (BW),below-ground biomass to above-ground biomass ratio (R), carbon fraction of dry matter (CF),fraction of biomass lost in disturbance (fd), and default tables in Section 4.5. For the hypotheticalcountry,fd= 0.3;

R= 0.40 tonne d.m. (tonne d.m.)-1 for above-ground biomass <50 t ha-1 (Table 4.4, withreference to Table 4.8 for above-ground biomass); andCF= 0.47 tonne C (tonne d.m.)-1 (Table 4.3).

Ldisturbance = 50 ha yr-1 ● 1.0 tonne d.m. ha-1 (1 + 0.40) ● 0.47 tonne C (tonne d.m.)-1 ● 0.3= 9.87 tonnes C yr-1 (Equation 2.14)
Annual decrease in carbon stocks due to biomass losses (∆CL),∆CL= 141.00 tonnes C yr-1 + 65.80 tonnes C yr-1 + 9.87 tonnes C yr-1
= 216.67 tonnes C yr-1 (Equation 2.11)
Annual change in carbon stocks in biomass (∆CB)
Using Chapter 2, Equation 2.7 (∆CB = (∆CG – ∆CL)),∆CB = 2,632 tonnes C yr-1 – 216.67 tonnes C yr-1 = 2,415.33 tonnes C yr-1 (Equation 2.7)

4.3.1.5

U NCERTAINTY ASSESSMENT

The emission factors required for estimating carbon stock changes for Land Converted to Forest Land are nearly
identical to those required for Forest Land Remaining Forest Land, but refer to lands converted to forests within
20 years of the inventory year (default period of conversion). The discussion on uncertainty for Forest Land
Remaining Forest Land also applies here. The uncertainty involved in the estimation of biomass stocks on land
before and after conversion is likely to be high. This uncertainty can be reduced by conducting sample fieldstudies in dominant land-use categories subjected to conversion to Forest Land. The uncertainty is likely to be
low for the wood removal (industrial round wood), since national statistics are likely to be maintained oncommercial harvests, although sometimes it may be difficult to separate commercial harvests due todeforestation from those that come from Forest Land Remaining Forest Land. However, the uncertainty is likely
to be high for fuelwood removal and gathering and biomass loss due to disturbance. The uncertainty involved forcommercial and traditional methods should be reduced by conducting sample surveys in different socioeconomic and climatic regions.
The critical activity data required for estimating carbon stock changes include the area of land converted and loss
rates of biomass during the initial conversion and thereafter. The level of uncertainty for area under intensive andextensive plantations is likely to be low since most countries maintain records of the area afforested andreforested. The uncertainty should be reduced by developing a land-use change matrix of Forest Land
Remaining Forest Land and for different categories of Land Converted to Forest Land, based on remote sensing
or other monitoring techniques. A combination of remote sensing and ground surveys could have an uncertainty
as low as 10-15%.

4.3.2

Dead organic matterIn this section, changes in carbon stock in dead organic matter pools are discussed for the land-use category
Land Converted to Forest Land. Cropland, Grassland, Settlements, and other land-use categories can bepotentially converted to Forest Land through planting or natural regeneration. It is likely that most non-forest
land will not have significant dead wood or litter carbon pools. Accordingly, the Tier 1 assumption is that carbon
stocks in dead wood and litter pools in non-forest land are zero, and that carbon in dead organic matter pools
increases linearly to the value of mature forests over a specified time period (default = 20 years). The Tier 1
assumption for the conversion of unmanaged to managed Forest Land is that the dead organic matter carbon
stocks in unmanaged forests are similar to those of managed forests and that no carbon stock changes need to bereported. In reality, other things being equal, dead organic matter carbon stocks in unmanaged forests are higher
than those in managed forests because harvest removes woody biomass that would otherwise contribute to long-

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Chapter 4: Forest Land

term dynamics of DOM pools (Kurz et al., 1998) and it is good practice that countries with high rates of
conversion of unmanaged to managed forests use higher Tier methods to estimate the resulting changes in DOM
carbon stocks.
Methods to estimate emissions and removals of carbon in dead organic matter pools following conversion ofland to Forest Land require estimates of the carbon stocks just prior to and just following the conversion, andestimates of the areas of lands converted during the inventory period. Some of the non-forest land-use categories,
such as Wetlands, Settlements, Cropland and Grassland can have significant carbon stock in the DOM pools. It
is good practice to assess whether the assumption of zero DOM pool sizes is justified for lands converted toForest Land. Higher Tier methods can specify the initial DOM pool sizes (e.g., in some land-use categories deadwood and litter pools are non-zero) and quantify the length of the transition period (default = 20 years) during
which DOM pools are changing as a result of a transition to Forest Land.

4.3.2.1

C HOICE OF METHOD

The general methods for estimating changes in carbon stock in dead organic matter pools have been described in
Chapter 2, Section 2.3.2. The decision tree in Figure 1.3 in Chapter 1 provides guidance in the selection of theappropriate tier level for the implementation of estimation procedures. Dead wood and litter carbon stock
estimates often differ significantly depending on previous land use, forest type, and regeneration type.
Tie r 1
For Land Converted to Forest Land, the Tier 1 assumption is that dead wood and litter pools increase linearly
from zero (in the non-forest land-use category) to the default values for the climate region over a period of T
years (the current default is 20 years for both litter and dead wood carbon pools). Human activities such asfuelwood collection and some silvicultural practices such as frequent thinnings can greatly affect the rate ofcarbon accumulation in dead wood and litter pools. It is good practice to assess whether the default pool sizes
and the assumed transitions periods are reasonable given a country’s climatic and management regimes. The 20year default period is appropriate for litter pools but likely too short for dead wood pools, particularly in colder
regions with slow growing vegetation. If the time required to accumulate DOM pools is longer than the default
period, then the Tier 1 assumptions may overestimate the rates of carbon accumulation. Where the area involvedin land-use conversion to forests is large, it is good practice to develop national estimates of the rates of litterand dead wood carbon accumulation in lands converted to Forest Land.
Tie r s 2 a nd 3
Changes in carbon stock in dead wood and litter pools under a Tier 2 or Tier 3 can be estimated using the two
methods outlined in Chapter 2 (Equations 2.18 and 2.19 in Chapter 2). It is good practice to stratify areas
converted to Forest Land according to the prior land use, the methods used during the conversion (e.g., site
preparation, treatment of residual biomass), and the productivity and characteristics of the forest that is
regrowing. All of these factors influence the magnitude and rate of change of carbon stock in the DOM pools on
Land Converted to Forest Land.
Countries using higher Tier methods are also encouraged to select more appropriate transition periods for litterand dead wood carbon stocks. Litter pools can stabilize relatively quickly as inputs balance outputs. Dead woodpools generally require much longer transition periods from non-forest to forest conditions. Moreover, both litterand dead wood carbon stock sizes are affected by many factors and countries using higher Tiers are encouraged
to select DOM stock values at maturity that adequately reflect national circumstances. Countries using Tier 3
modeling approaches will obtain estimates of dead organic matter stocks based on the simulated balance of input
and losses.

4.3.2.2

C HOICE OF EMISSION / REMOVAL FACTORS

Tie r 1Countries using a Tier 1 method require data on the default dead wood and litter carbon stocks in the six landuse categories in different climatic regions, as defined in Table 3.1, Chapter 3. The Tier 1 assumption is thatcarbon stocks in litter and dead wood pools in all non-forest land-use categories are zero. For lands converted toForest Land, the carbon stocks in dead wood and litter pools are assumed to increase linearly over the transition
period T (default is 20 years for both litter and dead wood C stocks). Thus, the annual rate of increase isestimated as the ratio between the difference in carbon stocks in the DOM pools in the non-forest and forest
categories, and the number of years in the transition period T.
Tie r s 2 a nd 3
The higher Tier methods described in Chapter 4, Section 4.2 Forest Land Remaining Forest Land are equallyapplicable to Land Converted to Forest Land. Additional emission and removal factors are required where the2006 IPCC Guidelines for National Greenhouse Gas Inventories

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impacts of the land-use conversion practices (e.g., site preparation and slash burning) are to be estimated.
Additional requirements may arise if the assumption that carbon stocks in dead wood and litter pools of nonforest land-use categories are zero cannot be justified, such as in some agro-forestry systems, in settlements with
substantial forest cover, and in other circumstances. This may pose special challenges because forest inventories
typically do not include such areas and other data sources need to be identified or measurement programs
implemented.

4.3.2.3

C HOICE OF ACTIVITY DATAThe Tier 1 method requires activity data on the annual rate of conversion to Forest Land. Activity data should beconsistent with those used for estimating changes in carbon stock in biomass on Land Converted to Forest Land,according to the general principles set out in Chapter 3. Activity data can be obtained from national statistics,
from forest management agencies, conservation agencies, municipalities, survey and mapping agencies. Where
reporting programs are used, it is good practice to implement verification procedures and cross-checks to ensurecomplete and consistent representation of Land Converted to Forest Land, to avoid omissions or double counting.
Data should be disaggregated according to the general climatic categories and forest types.
Inventories using higher Tiers will require more comprehensive information on the establishment of new forests,
with refined soil classes, climate, and spatial and temporal resolution.
All changes in dead organic matter pools occurring over the number of years (T) selected as the transition period
should be included. Lands where the transitions occurred more than T years ago are transferred to and reported
under the category Forest Land Remaining Forest Land.

4.3.2.4

C ALCULATION STEPS FOR T IER 1

T he fo llo wing s umma r iz e s s tep s for e s tima ting cha nge in c arbo n s to ck s in dead or gan ic
ma tter u s ing the defa u lt me thods
Step 1: Estimate area converted to Forest Land (during the period 20 years prior to the year of inventory) fromother land-use categories such as Cropland, Grassland and Settlements. Refer to Chapter 3 for detailed
approaches for estimating Land Converted to Forest Land.
Step 2: The Tier 1 assumption is that dead organic matter (dead wood and litter) carbon stocks on non-forest
land are zero. If national data on dead wood and litter carbon stocks in non-forest land are available,
disaggregate the area converted to Forest Land according to the land-use category of origin, e.g., Grassland,
Cropland, etc., using the same categories for which dead organic matter estimates are available. Default values
for litter carbon stocks in Forest Land are provided in Table 2.2. Statistically valid, regional default estimates fordead wood carbon stocks in forests are not available.
Step 3: Estimate the average annual increment of dead organic matter stocks, separately for dead wood andlitter, by dividing the difference in pre- and post-conversion carbon stocks by the time period of transition(Equation 2.23 in Chapter 2). The default Tier 1 assumption is that non-forest dead organic matter carbon stocks
are zero and that the period of transition is 20 years.
Step 4: Estimate the annual change in carbon stock in dead organic matter on Land Converted to Forest Landby multiplying the average annual increment (Step 3) by the area of lands converted to Forest Land over the past
20 (default) years.

4.3.2.5

U NCERTAINTY ASSESSMENT

In general, the magnitude of uncertainty in dead organic matter pools is larger than the uncertainty in biomass
estimates because much less data are typically available for DOM pools compared to biomass pools.
Uncertainties in area estimates made using the approaches suggested in Chapter 3 are indicated in Table 3.7 anduncertainties in assessing dead organic matter carbon stock changes may be several times larger than theuncertainty of biomass stock change estimates using default coefficients.
Although relatively few estimates of uncertainty, in changes in carbon stock in DOM pools, are available in theliterature or elsewhere, several sources of uncertainty can be identified for the estimates of changes in carbon
stock in dead organic matter pools on Land Converted to Forest Land. First, the assumption that carbon stocks in
DOM are zero in non-forest land is not always justified. Underestimating the true initial DOM stock size willlead to overestimates of the true accumulation rates. Second, the default values for litter and dead wood carbon
stock sizes are likely to be biased by being based upon estimates from land that was Forest Land for a long
period of time. Thus the stock sizes at the end of the transition period may be overestimated, again, leading to

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Chapter 4: Forest Landoverestimates of the accumulation rates. Third, the default transition period may be too long for litter carbon
stocks, leading to underestimates of the true accumulation rates. For the dead wood pool, however, the current
default assumption of a 20-year transition period is likely to be too short. Thus, the rate of carbon accumulationin the dead wood pool may be overestimated.

4.3.3

Soil carbon

Land conversions on mineral soils generally either maintain similar levels of C storage or create conditions that
increase soil C stocks, particularly if the land was previously managed for annual crop production (Post andKwon, 2000). However, under certain circumstances, Grassland conversion to Forest Land has been shown tocause small C losses in mineral soils for several decades following conversion (Davis and Condron, 2002; Paulet al., 2002). Emissions of C from organic soils will vary depending on the previous use and level of drainage.
Specifically, conversion from Cropland will tend to decrease emissions; conversions from Grassland will likely
maintain similar emission rates; while conversion from Wetlands often increases C emissions.
General information and guidelines on estimating changes soil C stocks are found in Section 2.3.3 in Chapter 2(including equations), and need to be read before proceeding with guidelines dealing with forest soil C stocks.
The total change in soil C stocks for Land Converted to Forest Land is computed using Equation 2.24 (Chapter2), which combines the change in soil organic C stocks for mineral soils and organic soils; and carbon stockchanges for inorganic soil C pools (Tier 3 only). This section provides specific guidance for estimating soilorganic C stock changes; see Section 2.3.3.1 (Chapter 2) for general discussion on soil inorganic C (noadditional information is provided in the Forest Land discussion below).
To account for changes in soil C stocks associated with Land Converted to Forest Land, countries need to have,at a minimum, estimates of the areas of Land Converted to Forest Land during the inventory time period,
stratified by climate region and soil type. If land-use and management data are limited, Approach 1 activity data
can be used as a starting point, along with knowledge of country experts of the approximate distribution of landuse types being converted. If previous lands uses and conversions for Land Converted to Forest Land are
unknown, SOC stocks changes can still be computed using the methods provided in Forest Land Remaining
Forest Land, but the land base will likely be different for forests in the current year relative to the initial year in
the inventory. It is critical, however, that the total land area across all land-use sectors be equal over theinventory time period (e.g., if 5 Million ha is converted from Cropland and Grassland to Forest Land during theinventory time period, then Forest Land will have an additional 5 Million ha in the last year of the inventory,
while Cropland and Grassland will have a corresponding loss of 5 Million ha in the last year), and the totalchange will be estimated when summing SOC stocks across all land uses. Land Converted to Forest Land is
stratified according to climate regions and major soil types, which could either be based on default or countryspecific classifications. This can be accomplished with overlays of climate and soil maps, coupled withspatially-explicit data on the location of land conversions.
Inventories can be developed using Tier 1, 2 or 3 approaches, with each successive Tier requiring more detailand resources than the previous. It is possible that countries will use different tiers to prepare estimates for the
separate components in this source category (i.e., soil organic C stocks changes in mineral soils and organic soils;
and stock changes associated with soil inorganic C pools).

4.3.3.1

C HOICE OF METHOD

Inventories can be developed using Tier 1, 2 or 3 approaches and countries may choose different tiers for mineraland organic soils. Decision trees are provided for mineral (Figure 2.4) and organic soils (Figure 2.5) in Section
2.3.3.1 (Chapter 2) to assist inventory compilers with selection of the appropriate tier for their soil C inventory.
Min era l so ils
Tie r 1
Change in soil organic C stocks can be estimated for mineral soils with land-use conversion to Forest Land using
Equation 2.25 (Chapter 2). For Tier 1, the initial (pre-conversion) soil organic C stock (SOC(0-T)) and C stock in
the last year of the inventory time period (SOC0) are determined from the common set of reference soil organic
C stocks (SOCREF) and default stock change factors (FLU, FMG, FI) as appropriate for describing land use andmanagement both pre- and post-conversion. Note that area of exposed bedrock in Forest Land or the previous
land use are not included in the soil C stock calculation (assume a stock of 0). Annual rates of stock changes arecalculated as the difference in stocks (over time) divided by the time dependence (D) of the stock change factors(default is 20 years).

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Tie r 2The Tier 2 approach for mineral soils also uses Equation 2.25 (Chapter 2), but involves country or regionspecific reference C stocks and/or stock change factors and possibly more disaggregated land-use activity andenvironmental data.
Tie r 3Tier 3 approaches will involve more detailed and country-specific models and/or measurement-based approaches
along with highly disaggregated land-use and management data. It is good practice that Tier 3 approaches
estimating soil C change from land-use conversions to Forest Land, employ models, monitoring networks and/or
data sets that are capable of representing transitions over time from other land uses, including Grassland,
Cropland, and possibly Settlements or other land uses. It is important that models be evaluated with independent
observations from country or region-specific field locations that are representative of the interactions of climate,soil and forest type/management on post-conversion change in soil C stocks.
O rgan ic so ils
Tie r 1 and Tie r 2
Land Converted to Forest Land on organic soils within the inventory time period is treated the same as Forest
Land Remaining Forest Land on organic soils. C losses for the newly converted Forest Land are computed using
Equation 2.26 (Chapter 2) if the soils are drained. Additional guidance on the Tiers 1 and 2 approaches are
given in Section 4.3.3.1.
Tie r 3
Similar to mineral soils, a Tier 3 approach will involve country-specific models and/or measurement-based
approaches along with highly disaggregated land-use and management data (see mineral soils above for
additional discussion).

4.3.3.2

C HOICE OF STOCK CHANGE AND EMISSION FACTORSMin era l so ils
Tie r 1For native unmanaged land, as well as for managed Forest Land, Settlements and nominally managed Grasslandwith low disturbance regimes, soil C stocks are assumed equal to the reference values (i.e., land use, disturbance(forests only), management and input factors equal 1), but it will be necessary to apply the appropriate stockchange factors to represent other systems which may be converted to Forest Land, such as improved anddegraded Grassland, as well as all Cropland systems. See the appropriate land-use section for default stockchange factors (Forest Land in 4.2.3.2, Cropland in Section 5.2.3.2, Grassland in 6.2.3.2, Settlements in 8.2.3.2,
and Other Land in 9.3.3.2). Default reference C stocks are found in Table 2.3 (Chapter 2).
Tie r 2Estimation of country-specific stock change factors is probably the most important development associated with
the Tier 2 approach. Differences in soil organic C stocks among land uses are computed relative to a reference
condition. If default reference C stocks are used, the reference condition is native vegetation that is neither
degraded nor improved through land-use and management practices. Stock change factors for land-use
conversion to native forests will be equal to 1 if the forest represents the reference condition. However, stockchange factors will need to be derived for Land Converted to Forest Land that do not represent the reference
condition, accounting for the influence of disturbance (FD), input (FI) and management (FMG), which are thenused to further refine the C stocks of the new forest system. See the appropriate section for specific information
regarding the derivation of stock change factors for other land-use sectors (Cropland in 5.2.3.2, Grassland in
Section 6.2.3.2, Settlements in 8.2.3.2, and Other Land in 9.3.3.2).
Reference C stocks can also be derived from country-specific data in a Tier 2 approach. However, reference
values should be consistent across the land uses (i.e., Forest Land, Cropland, Grassland, Settlements, Other
Land), and thus must be coordinated among the various teams conducting soil C inventories for AFOLU.
Tie r 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion.
O rgan ic so ils
Tie r 1 and Tie r 2
Land Converted to Forest Land on organic soils within the inventory time period is treated the same as Forest
Land Remaining Forest Land on organic soils, i.e., they have a constant emission factor applied to them, based

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Chapter 4: Forest Landon climate regime. Tier 1 emission factors are given in Table 4.6 (Section 4.5), while Tier 2 emission factors arederived from country or region-specific data.
Tie r 3
Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately
capture land-use and management effects. See Section 2.3.3.1 in Chapter 2 for further discussion.

4.3.3.3

C HOICE OF ACTIVITY DATAMin era l so ils
Tie r 1 and Tie r 2
For purposes of estimating soil carbon stock change, area estimates of Land Converted to Forest Land should be
stratified according to major climate regions and soil types. This can be based on overlays with suitable climate
and soil maps and spatially-explicit data of the location of land conversions. Detailed descriptions of the default
climate and soil classification schemes are provided in Chapter 3. Specific information is provided in the each of
the land-use sections regarding treatment of land-use/management activity data (Forest Land in Section 4.2.3.3,
Cropland in 5.2.3.3, Grassland in 6.2.3.3, Settlements in 8.2.3.3, and Other Land in 9.3.3.3).
One critical issue in evaluating the impact of Land Converted to Forest Land on soil organic C stocks is the typeof land-use and management activity data. Activity data gathered using Approach 2 or 3 (see Chapter 3 fordiscussion about Approaches) provide the underlying basis for determining the previous land use for Land
Converted to Forest Land. In contrast, aggregate data (Approach 1, Chapter 3) only provide the total amount ofarea in each land use and do not form a basis for determining specific transitions. Therefore, the previous land
use before conversion to Forest Land will be unknown. This is not problematic using Tier 1 or 2 methods
because the calculation is not dynamic and assumes a step change from one equilibrium state to another. Withaggregate data (Approach 1), changes in soil organic C stocks may be computed separately for each land-use
sector and then combined to obtain the total stock change. Some of the stock changes will result from less ormore land area in a particular sector, but such changes in the land base will be counter-balanced by aconcomitant increase or decrease in land area for another sector. Using this approach, it will be necessary for
coordination among each sector to ensure the total land base is remaining constant over time, given that someland area will be lost and gained within individual sectors during each inventory year due to land-use change.
Tie r 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or moredetailed data on the combinations of climate, soil, topographic and management data are needed, relative to Tier
1 or 2 method, but the exact requirements will be dependent on the model or measurement design.
O rgan ic so ils
Tie r 1 and Tie r 2
Land Converted to Forest Land on organic soils within the inventory time period is treated the same as Forest
Land Remaining Forest Land on organic soils; see Section 4.2.3.3.
Tie r 3
Similar to mineral soils, Tier 3 approaches will likely require more detailed data on the combinations of climate,soil, topographic and management data, relative to Tier 1 or 2 methods, but the exact requirements will bedependent on the model or measurement design.

4.3.3.4

C ALCULATION STEPS FOR T IER 1

Min era l so ils
The steps for estimating SOC0 and SOC(0-T) and net soil C stock change per ha of Land Converted to Forest
Land are as follows:
Step 1: Determine the land-use and management by mineral soil types and climate regions for land at the
beginning of the inventory period, which can vary depending on the time step of the activity data (0-T; e.g., 5, 10
or 20 years ago).
Step 2: Select the native reference C stock value (SOCREF), based on climate and soil type from Table 2.3, foreach area of land being inventoried. The reference C stocks are the same for all land-use categories to ensurethat erroneous changes in the C stocks are not computed due to differences in reference stock values among
sectors.

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Step 3: Select the land-use factor (FLU), management factor (FMG) and C input levels (FI) representing the landuse and management system present before conversion to forest. Values for FLU, FMG and FI are given in therespective section for the land-use sector (Cropland in Chapter 5, and Grassland in Chapter 6).
Step 4: Multiply these values by the reference soil C stock to estimate of ‘initial’ soil organic C stock (SOC(0-T))
for the inventory time period.
Step 5: Estimate SOC0 by repeating step 1 to 4 using the same native reference C stock (SOCREF), but with landuse, management and input factors that represent conditions in the last (year 0) inventory year. For Tier 1, allstock change factors are assumed equal to 1 for Forest Land (although for Tier 2, different values for thesefactors under newly converted Forest Land should be used, based on country-specific data).
Step 6: Estimate the average annual change in soil C stock for the area over the inventory time period,∆CCCMineral, (see Equation 2.25 in Chapter 2).
Step 7: Repeat Steps 1 to 6 if there are additional inventory time periods (e.g., 1990 to 2000, 2001 to 2010, etc.).
A numerical example is given below for afforestation of cropland soil.
Example: An area of 100,000 ha of cropland was planted to forest. The soil type is an Ultisol in a
tropical moist climate, which has a native reference stock, SOCRef (0-30 cm), of 47 tonnes C ha-1
(Table 2.3). The previous land use was annual row crops, with conventional tillage, no fertilization
and where crop residues are removed, so that the soil carbon stock at the beginning of theinventory time period (in this example, 5 yrs earlier in 1995) was (SOCRef ● FLU ● FMG ● FI) = 47
tonnes C ha-1 ● 0.48 ● 1 ● 0.92 = 20.8 tonnes C ha-1 (see Table 5.5, Chapter 5, for stock change
factor for cropland). Under Tier 1, managed forest is assumed to have the same soil C stock as thereference condition (i.e. all stock change factors are equal to 1). Thus, the average annual change
in soil C stock for the area over the inventory time period is estimated as (47 tonnes C ha-1 – 20.8
tonnes C ha-1) / 20 yrs = 1.3 tonnes C ha-1 yr-1. For the area reforested there is an increase of
131,000 tonnes C yr-1. (Note: 20 years is the time dependence of the stock change factor, i.e.,
factor represents annual rate of change over 20 years)

O rgan ic so ils
Calculation steps are the same as described in Section 4.2.3.4 above.

4.3.3.5

U NCERTAINTY ASSESSMENT

Uncertainty analyses for Land Converted to Forest Land are fundamentally the same as Forest Land Remaining
Forest Land. Three broad sources of uncertainty exists: 1) uncertainties in land-use and management activity
and environmental data; 2) uncertainties in reference soil C stocks if using Tier 1 or 2 approaches (mineral soils
only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model
structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability
associated with a Tier 3 measurement-based inventories. See the uncertainty section in Forest Land Remaining
Forest Land for additional discussion (Section 4.2.3.5).

4.3.4

Non-CO 2 greenhouse gas emissions from biomassburning

The guidance to estimate non-CO2 greenhouse gas emissions from biomass burning or fire on Land Converted toForest Land is discussed in Section 4.2.4. General guidance is also provided in Chapter 2, Section 2.4.
Guidance for estimating N2O emissions from forest soils is provided in Chapter 11.

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Chapter 4: Forest Land

4.4

COMPLETENESS, TIME SERIES, QA/QC, AND
REPORTING AND DOCUMENTATION

4.4.1

CompletenessCompleteness is a requirement for greenhouse gas inventories, and it is good practice to address all forest carbon
gains and losses including harvested wood products. Greenhouse gas inventory for Forest Land should include
all land under Forest Land and all land-use categories converted to Forest Land. For completeness, it is goodpractice to include all the carbon pools and non-CO2 greenhouse gases. Chapter 11, Section 11.2 provides advice
on N2O emission from drained organic soils. The forest area used for calculation for different carbon pools
should be the same. Emissions from organic soils and emissions or removals attributed to land-use change on
mineral soils should be estimated. Higher tiers include additional impacts of management and natural
disturbance regimes on mineral soil C stocks or emissions from organic soils, by incorporating country-specificinformation. A complete accounting of emissions and removals of CO2 associated with Forest Land Remaining
Forest Land and Land Converted to Forest Land, or from the effects of biomass burning in managed (andunmanaged, when applicable) Forest Land is necessary. It is good practice that all losses from biomass carbon
pools that result in transfers to dead organic matter pools are first accounted as changes to biomass carbon stocks.
It is good practice that countries using Tier 1 estimation methods do not account for carbon emissions from
DOM pools during fire or other disturbances because all DOM pool additions are assumed to have been releasedin the year of addition. Consequently, Tier 1 methods also preclude the accounting of DOM pool increases
following natural disturbances.

4.4.2

Developing a consistent times series

It is good practice to develop a consistent time series of inventories of anthropogenic emissions and removals ofgreenhouse gases for all AFOLU categories using the guidance in Volume 1, Chapter 5. Because activity data
may only be available every few years, achieving time series consistency may require interpolation or
extrapolation from longer time series or trends, possibly using information on changes in forest policies andincentive schemes where drivers are needed.
Consistent accounting over time of land areas included in biomass and soil C emissions and removals inventory
requires that activity data for all land-use categories be stratified by a common definition of climate and soil
types. Thus, areas subject to land-use change will not be lost or double-counted due to accounting errors
resulting from inconsistent definitions for climate and forest types and soil strata within other land-use categories.
To estimate emissions and removals of greenhouse gases, whether by Tier 1, 2 or 3, ideally the same protocol(sampling strategy, method, etc.) should be applied consistently every year in the time series, at the same level ofdisaggregation, and, where country-specific data are used, it is good practice to use the same coefficients andmethods for equivalent calculations at all points in the time series.
However, as inventory capacity and information and data sources availability improve over time, new sources
and sink categories should be included, or moving to higher tier, the methods and data used to calculate
estimates can be updated and refined. In these circumstances, consistent recalculation of historical emissions andremovals is a good practice. In some cases, if some historical data are missing, then they may need to be
estimated from other data sources. For example, the 2006 IPCC Guidelines now require estimation of emissionsof CO2 and non-CO2 from forests, which were not included under the 1966 Guidelines (refer to Chapter 1). The
level of knowledge and detail of emission estimates for soils will also improve over time, necessitatingrecalculation of historic inventories to take account of new data and/or methods. Often, changes in forest soils
cannot be detected at time scale finer than a decade; it will be necessary to interpolate between measurements inorder to obtain annual estimates of emissions and removals. Changes in forest types, practices and disturbancesneed to be tracked for long time periods determined for example by soil carbon dynamics or forest rotation periods
where these are specifically tracked in detailed model calculations.
Where countries use Tier 1 methods, estimates of DOM stock changes are only provided in the case of land-use
change to or from Forest Land. It is good practice to recalculate the entire time series of data if either the default
values for litter and dead wood carbon pools or the lengths of the transition periods are changed. It is also goodpractice to recalculate the entire time series of estimates if revisions to activity data, such as the rate of land-use
change, have occurred. As more ground plot and other sample data on dead wood and litter carbon stocks become
available in the future, countries are likely to improve the models used in higher Tier estimation procedures. It isgood practice to use the same model parameter values (such as litterfall rates, decay rates, disturbance impacts) for2006 IPCC Guidelines for National Greenhouse Gas Inventories

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the entire time series and to recalculate the entire time series if one or more of the model parameters have changed.
Failure to do so may result in artificial sources or sinks, for example as a result of decay rate modifications.

4.4.3

Quality Assurance and Quality Control

The characteristics of the greenhouse gas inventory estimate of Forest Land can have different level of precision,accuracy and levels of bias. Moreover, the estimates are influenced by the quality and consistency of data andinformation available in a country, as well as gaps in knowledge. In addition, depending on the tier level used bya country, estimates can be affected by different sources of errors, such as sampling errors, assessment errors,classification errors in remote sensing imagery, and modeling errors that can propagate to the total estimation.
It is good practice to execute quality control checks through Quality Assurance (QA) and Quality Control (QC)
procedures, and expert review of the emission estimation procedures. Additional quality control checks asoutlined in Tier 2 procedures in Volume 1, Chapter 6, and quality assurance procedures may also be applicable,
particularly if higher Tier methods are used to estimate emissions. It is good practice to supplement the general
QA/QC related to data processing, handling, and reporting and documenting, with source-specific category
procedures. QA/QC procedures should be documented separately for Forest Land Remaining Forest Land andfor Land Converted to Forest Land.
Agencies which collect data are responsible for reviewing the data collection methods, checking the data toensure that they are collected and aggregated or disaggregated correctly, and cross-checking the data with otherdata sources and with previous years to ensure that the data are realistic, complete and consistent over time. FAO
data needs to cross checked with other national sources for accuracy and consistency. The basis for the estimates(e.g., statistical surveys or ´desk estimates´) must be reviewed and described as part of the QC process.
Documentation is a crucial component of the review process because it enables reviewers to identify inaccuracy,
gaps and suggest improvements. Documentation and transparency in reporting is most important for highly
uncertain source categories and to give reasons for divergences between country-specific factors and default or
factors used by other countries. Countries with similar (ecological) conditions are encouraged to collaborate in
the refinements of methods, emissions factors and uncertainty assessment.
Activity data check: The inventory agency should, where possible, check data comprising of all managed land
areas, using independent sources and compare them. For many countries, FAO database could be the main
source and in such a case the data must be cross-checked with other sources. Any differences in area records
should be documented for the purposes of review. Activity data area totals should be summed across all land-use
categories to ensure that total area involved in the inventory and its stratification across climate and soil types
remains constant over time. This ensures that Forest Land areas are neither ‘created’ nor ‘lost’ over time, which
would result in major errors in the inventory. When using country-specific data (such as data on standing
biomass and biomass growth rates, carbon fraction in above-ground biomass and biomass expansion factors, andsynthetic fertilizer consumption estimates), the inventory agency should compare them to the IPCC default
values or the Emission Factor Database (EFDB) and note the differences.
The country-specific parameters should be of high quality, preferably peer-reviewed experimental data,
adequately described, and documented. The agencies performing the inventory are encouraged to ensure that
good practice methods have been used and the results peer-reviewed. Assessments on test areas can be used tovalidate the reliability of figures reported.
Internal and external review: The review processes as set out in Volume 1, Chapter 8 should be undertaken byexperts preferably not directly involved in the inventory development. The inventory agency should utilize
experts in greenhouse gas removals and emissions in AFOLU to conduct expert peer-review of the methods anddata used. Given the complexity and uniqueness of the parameters used in calculating country-specific factors
for some categories, selected specialists in the field should be involved in such reviews. If soil factors are basedon direct measurements, the inventory agency should review the measurements to ensure that they are
representative of the actual range of environmental and soil management conditions, and inter-annual climatic
variability, and were developed according to recognized standards. The QA/QC protocol in effect at the sites
should also be reviewed and the resulting estimates compared between sites and with default-based estimates.
It is good practice that countries using Tier 1 methods review and, if necessary, revise the default assumptions
for carbon stocks in litter and dead wood pools which are required for estimation of carbon losses following
deforestation. Countries that use higher tier methods are encouraged to calculate intermediate indicators of themodels used to develop estimates of DOM stock changes. For example, QA/QC procedures could compare
estimates of stock sizes, litterfall inputs, decay losses, etc., against literature values and other peer-reviewed
publications. Where possible, it is also good practice to compare model estimates against field measurements
and other data sources. One QA/QC check that is easily implemented in modelling systems is to calculate aninternal mass balance to ensure that the model neither produces nor loses carbon that is not reported as a source

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Chapter 4: Forest Landor a sink. For example, conservation of mass requirements include that losses from biomass pools are either
accounted as input to the DOM pools, are transferred outside of the forest ecosystem or released to theatmosphere (in case of fire). Further, harvest data can be used to check transfer (stop loss) estimates produced bymodels. A second QA/QC procedure that can be implemented in countries that use higher Tier estimation
methods is to establish upper and lower bounds for DOM pools stratified by regions, forest type, and soil type(organic vs. mineral soils). Any values, reported in inventories or estimated by models that fall outside thesebounds can be investigated further.

4.4.4

Reporting and Documentation

General requirements for reporting and documentation are set out in Volume 1, Chapter 8. In general it is goodpractice to archive and document all data and information (such as figures, statistics, sources of assumptions,modeling approaches, uncertainty analyses, validation studies, inventory methods, research experiments,
measurements arising from field site studies, associated protocols, and other basic data) applied to produce thenational emissions/removals inventory. Elaborations on carbon pool definitions should be reported, anddefinitions relevant to determining the extent of the managed land included in the inventory, together withevidence that these definitions have been applied consistently over time provided.
Documentation is needed for demonstrating completeness, consistency of time series data and methods forinterpolating between samples, methods and years, and for recalculating and avoidance of double counting aswell as for performing QA/QC. As inventory compilers decide to progress through higher tier levels, whose
calculation methods and data are not described in the present volume or characterized by more disaggregatedapproaches, additional documentation is required to support the use of more advanced and accurate
methodologies, country-defined parameters, and high resolution maps and data sets. However, at all tier levels,
explanation is needed for decisions regarding choice of methodology, coefficients, and activity data. The aim is
to facilitate reconstruction of estimates by independent third parties, but it may prove impractical to include all
documentation necessary in national inventory report. The inventory should therefore include summaries ofapproaches and methods used, and references to source of data such that the reported emissions estimates aretransparent and steps adopted in their calculation may be retraced.
Emission factors: Sources of the emission or removal factors that were used (specific IPCC default values orotherwise) have to be quoted. If country- or region- or forest type-specific emission factors were used, and if
new methods (other than the default IPCC methods) were used, the scientific basis of these emission factors andmethods should be completely described and documented. This includes defining the input parameters anddescribing the process by which these emission factors and methods are derived, as well as describing sources
and magnitudes of uncertainties. Inventory agencies using country-specific emission factors should provide
information on the basis of selection of a different factor, describe how it was derived, compare it to otherpublished emission factors, explain any significant differences, and attempt to place bounds on uncertainty.
Activity data: Sources of all activity data, such as areas, soil types and characteristics and vegetation covers,
used in the calculations should be provided (i.e., complete citations for statistical databases from which data
were drawn). Reference to the metadata for the databases are useful, including information on dates andfrequency of data collection, sampling procedures, analytical procedures used to obtain soil characteristics andminimum detectable change in organic carbon, and estimates of accuracy and precision. When activity data were
not obtained directly from databases, the information and assumptions that were used to derive the activity data
should be provided, as well as estimates of the uncertainty associated to the derived activity data. This applies inparticular when scaling up procedures are used to derive large-scale estimates; in these cases the statisticalprocedures should be described along with the associated uncertainty.
Results of model simulations: If inventory agencies used data output from models in their estimation
procedures, the rationale for model selection and use should be provided. It is a good practice to provide
complete citations of peer-reviewed publications in which the model is described, and modelling results areinterpreted and validated. Detailed information should be provided to enable reviewers to assess the model’svalidity, including the general modeling approach, key model assumptions, input and output data, parametervalues and parameterization procedures, confidence intervals of model outputs, and the outcome of any
sensitivity analysis conducted on the output. In addition, computer source code for models should bepermanently archived for future reference, along with all the input and output files.
Analysis of emissions: Significant fluctuations in emissions between years should be explained. A distinction
should be made between changes in activity levels and changes in emission coefficients from year to year, andthe reasons for these changes documented. If different emission factors are used for different years, the reasonsfor this should be explained and documented.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.45

Volume 4. Agriculture, Forestry and Other Land Use

4.5

TABLESTABLE 4.1
CLIMATE DOMAINS (FAO, 2001), CLIMATE REGIONS (CHAPTER 3), AND ECOLOGICAL ZONES (FAO, 2001)
Climate domain

DomainTropical

Domaincriteria

all months
without
frost; inmarineareas,
temperature>18°C

ClimateregionZone

Code

Tropical wet

Tropical rain forest

TAr

Tropical moist

Tropical moist deciduous
forestTAwa

Tropical dry forest

TAWb

Tropical shrubland

TBSh

Tropical desert

TBWh

arid: all months dry

Tropical mountainsystemsTM

altitudes approximately >1000 m,
with local variations

Subtropical humid forestSCfhumid: no dry season

Subtropical dry forest

SCs

Subtropical steppe

SBSh

Subtropical desert

SBWharid: all months dry

Subtropical mountainsystems

SM

altitudes approximately 800 m1000 m

Cooltemperate
moist

Temperate oceanic forest

TeDo

Temperate continental
forestTeDc

CooltemperatedryTemperate steppe

TeBSk

Temperate desert

TeBWkarid: all months dry

Cooltemperate
moist or dry

Temperate mountainsystems

TeM

altitudes approximately >800 m

Boreal moist

Boreal coniferous forest

Ba

coniferous dense forest dominantBoreal dry

Boreal tundra woodlandBbwoodland and sparse forest
dominant

Boreal mountain systems

BM

altitudes approximately >600 m

Polar

P

all months <10°C

Tropical dry

TropicalmontaneWarmtemperate
moist

Subtropical≥ 8 months
at atemperature
>10°C

Warm
temperatedryWarmtemperate
moist or dry

Temperate

BorealPolar

4-8 months
at atemperature>10°C≤ 3 months
at atemperature>10°C
all months<10°C

Ecological zone

Boreal moist
or dryPolar moist or
dry

Zone criteria
wet: ≤ 3 months dry, during
wintermainly wet: 3-5 months dry,during wintermainly dry: 5-8 months dry,during wintersemi-arid: evaporation >precipitationseasonally dry: winter rains, drysummersemi-arid:
evaporation >precipitation

oceanic climate: coldest
month >0°C
continental climate:coldest month <0°C
semi-arid: evaporation >precipitation

Climate domain: Area of relatively homogenous temperature regime, equivalent to the Köppen-Trewartha climate groups (Köppen,
1931).
Climate region: Areas of similar climate defined in Chapter 3 for reporting across different carbon pools.
Ecological zone: Area with broad, yet relatively homogeneous natural vegetation formations that are similar, but not necessarily
identical, in physiognomy.
Dry month: A month in which Total Precipitation (mm) ≤ 2 x Mean Temperature (ºC).

4.46

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.2
FOREST AND LAND COVER CLASSES
Forest or land cover class

Definition

ForestLand spanning more than 0.5 hectare with trees higher than 5 meters and a
canopy cover of more than 10 percent, or trees able to reach these thresholds insitu. It does not include land that is predominantly under agricultural or urban
land use.
Forest is determined both by the presence of trees and the absence of otherpredominant land uses. The trees should be able to reach a minimum height of
5 meters in situ. Areas under reforestation that have not yet reached but are
expected to reach a canopy cover of 10 percent and tree height of 5 meters areincluded, as are temporarily unstocked areas, resulting from humanintervention or natural causes, which are expected to regenerate.
Includes: areas with bamboo and palms provided that height and canopy covercriteria are met; forest roads, firebreaks and other small open areas; forest innational parks, nature reserves and other protected areas such as those of
specific scientific, historical, cultural or spiritual interest; windbreaks,
shelterbelts and corridors of trees with an area of more than 0.5 hectare andwidth of more than 20 meters; plantations primarily used for forestry orprotective purposes, such as rubber-wood plantations and cork oak stands.
Excludes: tree stands in agricultural production systems, for example in fruit
plantations and agroforestry systems. The term also excludes trees in urban
parks and gardens.

Other wooded land

Land not classified as “Forest”, spanning more than 0.5 hectare; with trees
higher than 5 meters and a canopy cover of 5-10 percent, or trees able to reachthese thresholds in situ; or with a combined cover of shrubs, bushes and trees
above 10 percent. It does not include land that is predominantly under
agricultural or urban land use.

Other land

All land that is not classified as Forest or Other Wooded Land.
Includes: agricultural land, meadows and pastures, built-up areas, barren land,etc; areas classified under the subcategory ‘Other Land with tree cover’.

Other land with tree coverLand classified as Other Land, spanning more than 0.5 hectare with a canopy
cover of more than 10 percent of trees able to reach a height of 5 meters atmaturity.
Includes: groups of trees and scattered trees in agricultural landscapes, parks,
gardens, and around buildings (provided that the area, height and canopy covercriteria are met); tree plantations established mainly for other purposes thanwood, such as fruit orchards and palm plantations.

Source: FAO, 2006. Global Forest Resources Assessment 2005 – progress towards sustainable forest management. FAO Forestry Paper
No. 147. Rome.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.47

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.3
CARBON FRACTION OF ABOVEGROUND FOREST BIOMASS
DomainDefault value

Tropical and
Subtropical

Temperate and
Boreal

4.48

Part of tree

Carbon fraction, (CF)[tonne C (tonne d.m.)-1]

All

0.47

References
McGroddy et al., 2004
Andreae and Merlet, 2001;
Chambers et al., 2001;
McGroddy et al., 2004; Lascoand Pulhin, 2003

All

0.47 (0.44 - 0.49)

wood

0.49

Feldpausch et al., 2004

wood, tree d < 10 cm

0.46

Hughes et al., 2000

wood, tree d ≥ 10 cm

0.49

Hughes et al., 2000

foliage

0.47

Feldpausch et al., 2004

foliage, tree d < 10
cm

0.43

Hughes et al., 2000

foliage, tree d ≥ 10
cm

0.46

Hughes et al., 2000

All

0.47 (0.47 - 0.49)

Andreae and Merlet, 2001;
Gayoso et al., 2002;
Matthews, 1993; McGroddy etal., 2004

broad-leaved

0.48 (0.46 - 0.50)

Lamlom and Savidge, 2003

conifers

0.51 (0.47 - 0.55)

Lamlom and Savidge, 2003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.4
RATIO OF BELOW-GROUND BIOMASS TO ABOVE-GROUND BIOMASS (R)DomainEcological zone

Above-groundbiomass

Tropical rainforest

Tropical moist deciduous forestR[tonne rootd.m. (tonne
shoot d.m.)-1]
0.37

above-ground biomass
0.20 (0.09 - 0.25)<125 tonnes ha-1
above-groundbiomass >125 tonnes ha- 0.24 (0.22 - 0.33)

References

Fittkau and Klinge, 1973
Mokany et al., 2006
Mokany et al., 2006

1

TropicalTropical dry forest

above-ground biomass
0.56 (0.28 - 0.68)<20 tonnes ha-1
above-ground0.28 (0.27 - 0.28)
biomass >20 tonnes ha-1

Tropical shrubland

0.40

Tropical mountain systems

Subtropical humid forest

0.27 (0.27 - 0.28)
above-ground biomass
0.20 (0.09 - 0.25)<125 tonnes ha-1
above-groundbiomass >125 tonnes ha- 0.24 (0.22 - 0.33)

Mokany et al., 2006
Mokany et al., 2006
Poupon, 1980
Singh et al., 1994
Mokany et al., 2006
Mokany et al., 2006

1

Subtropical

Subtropical dry forest

above-ground biomass
0.56 (0.28 - 0.68)<20 tonnes ha-1
above-ground0.28 (0.27 - 0.28)
biomass >20 tonnes ha-1

Subtropical steppe

0.32 (0.26 - 0.71)

Temperate

Temperate continental forest,
Temperate mountain systems

BorealBoreal coniferous forest, Boreal
tundra woodland, Boreal
mountain systems

Mokany et al., 2006
Mokany et al., 2006

no estimate
available

Subtropical mountain systems

Temperate oceanic forest,

Mokany et al., 2006

conifers above-groundbiomass< 50 tonnes ha-1
conifers above-groundbiomass 50-150 tonnes
ha-1
conifers above-groundbiomass > 150 tonnes
ha-1
Quercus spp. aboveground biomass >70
tonnes ha-1
Eucalyptus spp. aboveground biomass < 50
tonnes ha-1
Eucalyptus spp. aboveground biomass 50-150
tonnes ha-1
Eucalyptus spp. aboveground biomass > 150
tonnes ha-1
other broadleaf aboveground biomass < 75
tonnes ha-1
other broadleaf aboveground biomass 75-150
tonnes ha-1
other broadleaf aboveground biomass >150
tonnes ha-1
above-ground biomass<75 tonnes ha-1
above-groundbiomass >75 tonnes ha-1

2006 IPCC Guidelines for National Greenhouse Gas Inventories

0.40 (0.21 - 1.06)

Mokany et al., 2006

0.29 (0.24 - 0.50)

Mokany et al., 2006

0.20 (0.12 - 0.49)

Mokany et al., 2006

0.30 (0.20 - 1.16)

Mokany et al., 2006

0.44 (0.29 - 0.81)

Mokany et al., 2006

0.28 (0.15 - 0.81)

Mokany et al., 2006

0.20 (0.10 - 0.33)

Mokany et al., 2006

0.46 (0.12 - 0.93)

Mokany et al., 2006

0.23 (0.13 - 0.37)

Mokany et al., 2006

0.24 (0.17 - 0.44)

Mokany et al., 2006

0.39 (0.23 - 0.96)

Li et al., 2003; Mokany et al., 2006

0.24 (0.15 - 0.37)

Li et al., 2003; Mokany et al., 2006

4.49

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.5
DEFAULT BIOMASS CONVERSION AND EXPANSION FACTORS (BCEF), TONNES BIOMASS (M3 OF WOOD VOLUME)-1
BCEF for expansion of merchantable growing stock volume to above-ground biomass (BCEFS), for conversion of net annual increment (BCEFI) and for conversion of wood and fuelwood removal
volume to above-ground biomass removal (BCEFR)
Climatic zone

Forest type

pines

larch
Borealfirs and spruces

hardwoods

Growing stock level (m3)

BCEF
<20

21-50

51-100

>100

BCEFS

1.2 (0.85-1.3)

0.68 (0.5-0.72)

0.57 (0.52-0.65)

0.5 (0.45-0.58)

BCEFI

0.47

0.46

0.46

0.463

BCEFR

1.33

0.75

0.63

0.55

BCEFS

1.22 (0.9-1.5)

0.78 (0.7-0.8)

0.77 (0.7-0.85)

0.77 (0.7-0.85)

BCEFI

0.9

0.75

0.77

0.77

BCEFR

1.35

0.87

0.85

0.85

BCEFS

1.16 (0.8-1.5)

0.66 (0.55-0.75)

0.58 (0.5-0.65)

0.53 (0.45-0.605)

BCEFI

0.55

0.47

0.47

0.464

BCEFR

1.29

0.73

0.64

0.59

BCEFS

0.9 (0.7-1.2)

0.7 (0.6-0.75)

0.62 (0.53-0.7)

0.55 (0.5-0.65)

BCEFI

0.65

0.54

0.52

0.505

BCEFR

1.0

0.77

0.69

0.61

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.50

Chapter 4: Forest Land
TABLE 4.5 (CONTINUED)DEFAULT BIOMASS CONVERSION AND EXPANSION FACTORS (BCEF), TONNES BIOMASS (M3 OF WOOD VOLUME)-1
BCEF for expansion of merchantable growing stock volume to above-ground biomass (BCEFS), for conversion of net annual increment (BCEFI) and for conversion of wood and fuelwood removal
volume to above-ground biomass removal (BCEFR)
Climatic zone

Forest typehardwoods

Growing stock level (m3)

BCEF
<20

21-40

41-100100 -200

>200

BCEFS

3.0 (0.8-4.5)

1.7 (0.8-2.6)

1.4 (0.7-1.9)

1.05 (0.6-1.4)

BCEFI

1.5

1.3

0.9

0.6

0.8 (0.551.1)

BCEFR

3.33

1.89

1.55

1.17

0.48
0.89

Temperatepines

other conifers

BCEFS

1.8 (0.6 -2.4)

1.0 (0.65 -1.5)

0.75 (0.6-1.0)

0.7 (0.4-1.0)

0.7 (0.4-1.0)

BCEFI

1.5

0.75

0.6

0.67

0.69

BCEFR

2.0

1.11

0.83

0.77

0.77

BCEFS

3.0 (0.7-4.0)

1.4 (0.5-2.5)

1.0 (0.5-1.4)

0.75 (0.4-1.2)

BCEFI

1.0

0.83

0.57

0.53

0.7 (0.350.9)

BCEFR

3.33

1.55

1.11

0.83

0.60
0.77

Mediterranean,
dry tropical,
subtropical

hardwoods

conifers

<20

21-40

41-80

>80

BCEFS

5.0 (2.0-8.0)

1.9 (1.0-2.6)

0.8 (0.6-1.4)

0.66 (0.4-0.9)

BCEFI

1.5

0.5

0.55

0.66

BCEFR

5.55

2.11

0.89

0.73

BCEFS

6.0 (3.0-8.0)

1.2 (0.5-2.0)

0.6 (0.4-0.9)

0.55 (0.4-0.7)

BCEFI

1.5

0.4

0.45

0.54

BCEFR

6.67

1.33

0.67

0.61

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.51

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.5 (CONTINUED)DEFAULT BIOMASS CONVERSION AND EXPANSION FACTORS (BCEF), TONNES BIOMASS (M3 OF WOOD VOLUME)-1
BCEF for expansion of merchantable growing stock volume to above-ground biomass (BCEFS), for conversion of net annual increment (BCEFI) and for conversion of wood and fuelwood removal
volume to above-ground biomass removal (BCEFR)
Climatic zone

Forest typeconifers
Humid tropicalnatural forests

Growing stock level (m3)

BCEF
<10

11-20

21-40

41-60

61-80

80-120120-200

>200

BCEFS

4.0 (3.0-6.0)

1.75 (1.4-2.4)

1.25 (1.0-1.5)

1.0 (0.8-1.2)

0.8 (0.7-1.2)

0.76 (0.6-1.0)

0.7 (0.6-0.9)

0.7 (0.6-0.9)

BCEFI

2.5

0.95

0.65

0.55

0.53

0.58

0.66

0.70

BCEFR

4.44

1.94

1.39

1.11

0.89

0.84

0.77

0.77

BCEFS

9.0 (4.0-12.0)

4.0 (2.5-4.5)

2.8 (1.4-3.4)

2.05 (1.2-2.5)

1.7 (1.2-2.2)

1.5 (1.0-1.8)

1.3(0.9-1.6)

BCEFI

4.5

1.6

1.1

0.93

0.9

0.87

0.86

0.95 (0.71.1)

BCEFR

10.0

4.44

3.11

2.28

1.89

1.67

1.44

0.85
1.05

Note: Lower values of the ranges for BCEFS apply if growing stock definition includes branches, stem tops and cull trees; upper values apply if branches and tops are not part of growing stock, minimum top diameters in
the definition of growing stock are large, inventoried volume falls near the lower category limit or basic wood densities are relatively high. Continuous graphs, functional forms and updates with new studies can be foundat the forest- and climate- change website at: http://www.fao.org/forestry/
Average BCEF for inhomogeneous forests should be derived as far as possible as weighted averages. It is good practice to justify the factors chosen. To apply BCEFI, an estimate of the current average growing stock isnecessary. It can be derived from FRA 2005 at http://www.fao.org/forestry/
BCEFR values are derived by dividing BCEFS by 0.9
Sources: Boreal forests: Alexeyev V.A. and R.A. Birdseye, 1998; Fang J. and Z.M. Wang, 2001; temperate forests: Fang J. et al., 2001; Fukuda M. et al., 2003; Schroeder P. et al., 1997; Snowdon P. et.al., 2000; Smith
J. et. al., 2002; Brown S., 1999; Schoene D. and A. Schulte, 1999; Smith J. et al., 2004; Mediterranean forests: Vayreda et al., 2002; Gracia et al., 2002; tropical forests: Brown S. et al., 1989; Brown S. and A. Lugo,
1992; Brown S., 2002; Fang J.Y., 2001.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.52

Chapter 4: Forest Land

TABLE 4.6
EMISSION FACTORS FOR DRAINED ORGANIC SOILS IN MANAGED FORESTS
Emission factors (tonnes C ha-1 yr-1)
ClimateValuesRangesTropical

1.36

0.82 – 3.82

Temperate

0.68

0.41 – 1.91

Boreal0.16

0.08 – 1.09

Source: GPG-LULUCF, Table 3.2.3

TABLE 4.7
ABOVE-GROUND BIOMASS IN FORESTS
DomainEcological zone

Tropical rain forest

Tropical moist
deciduous forest

TropicalTropical dry forest

Tropical shrubland

Tropical mountainsystems
Subtropical humid
forestSubtropical dry
forestSubtropical
Subtropical steppe

Subtropicalmountain systems

Continent

Above-groundbiomass(tonnes d.m. ha-1)

Africa310 (130-510)

North and South America

300 (120-400)

Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)
North and South America
Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)
AfricaNorth and South America
Asia (continental)
Asia (insular)

280 (120-680)
350 (280-520)
260 (160-430)
220 (210-280)
180 (10-560)
290
120 (120-130)
210 (200-410)
130 (100-160)
160
70 (20-200)
80 (40-90)607040-19060-23050-22050-360220 (210-280)
180 (10-560)
290
140
210 (200-410)
130 (100-160)
160
70 (20-200)
80 (40-90)60705060-23050-22050-3602006 IPCC Guidelines for National Greenhouse Gas Inventories

References
IPCC, 2003
Baker et al., 2004a;
Hughes et al., 1999
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Sebei et al., 2001
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Montès et al., 2002
IPCC, 2003
IPCC, 2003
IPCC, 2003

4.53

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.7 (CONTINUED)ABOVE-GROUND BIOMASS IN FORESTS
DomainEcological zone

Continent
EuropeTemperate oceanic
forestTemperate

Temperatecontinental forest

Temperate mountainsystems

Boreal coniferous
forestBorealBoreal tundra
woodlandBoreal mountainsystems

Above-groundbiomass(tonnes d.m. ha-1)
120

References

North America

660 (80-1200)

New Zealand

360 (210-430)

South America

180 (90-310)

Asia, Europe (≤20 y)Asia, Europe (>20 y)
North and South America(≤20 y)North and South America(>20 y)Asia, Europe (≤20 y)Asia, Europe (>20 y)
North and South America(≤20 y)North and South America(>20 y)20
120 (20-320)

Hessl et al., 2004;
Smithwick et al., 2002
Hall et al., 2001
Gayoso and Schlegel, 2003;
Battles et al., 2002
IPCC, 2003
IPCC, 2003

60 (10-130)

IPCC, 2003

130 (50-200)

IPCC, 2003

100 (20-180)
130 (20-600)

IPCC, 2003
IPCC, 2003

50 (20-110)

IPCC, 2003

130 (40-280)

IPCC, 2003

Asia, Europe, North America
Asia, Europe, North America(≤20 y)Asia, Europe, North America(>20 y)Asia, Europe, North America(≤20 y)Asia, Europe, North America(>20 y)10-90

Gower et al., 2001

3-4

IPCC, 2003

15-20

IPCC, 2003

12-15

IPCC, 2003

40-50

IPCC, 2003

TABLE 4.8
ABOVE-GROUND BIOMASS IN FOREST PLANTATIONS
DomainEcological zone

Tropical rain forest

TropicalTropical moist
deciduous forest

4.54

Continent
Africa broadleaf > 20 y
Africa broadleaf ≤ 20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other
Africa broadleaf > 20 y
Africa broadleaf ≤ 20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other

Above-groundbiomass(tonnes d.m. ha-1)
300
100
200
60
200
300
240
150
220
130
150
80
120
40
90
270
120
100
180
100

References
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Kraenzel et al., 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Stape et al., 2004
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.8 (CONTINUED)ABOVE-GROUND BIOMASS IN FOREST PLANTATIONS
DomainEcological zone

Tropical dry forest

Tropical shrubland

Tropical mountainsystems

Subtropical humid
forestSubtropical
Subtropical dry
forestContinent
Africa broadleaf > 20 y
Africa broadleaf ≤ 20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other
Africa broadleaf
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other
Africa broadleaf > 20 y
Africa broadleaf ≤ 20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other
Africa broadleaf > 20 y
Africa broadleaf ≤ 20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia other2006 IPCC Guidelines for National Greenhouse Gas Inventories

Above-groundbiomass(tonnes d.m. ha-1)
70
30
60
20
90
110
90
60
90
60
20
20
15
60
60
50
30
40
30
60-15040-10030-10010-40
30-12060-17030-13030-8040-150
25-80
140
270
120
100
180
100
70
30
60
20
110
110
90
60
90
60

References
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Stape et al., 2004
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

4.55

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.8 (CONTINUED)ABOVE-GROUND BIOMASS IN FOREST PLANTATIONS
DomainEcological zone

Subtropical steppe

Subtropicalmountain systems

Temperate oceanic
forestContinent

Above-groundbiomass(tonnes d.m. ha-1)

Africa broadleaf
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf > 20 y
Asia broadleaf ≤ 20 y
Asia coniferous > 20 y
Asia coniferous ≤ 20 y
Africa broadleaf > 20 y
Africa broadleaf ≤ 20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia broadleaf
Asia otherAsia, Europe,
broadleaf > 20 yAsia, Europe,
broadleaf ≤ 20 yAsia, Europe,
coniferous > 20 yAsia, Europe,
coniferous ≤ 20 y
North America

20
20
15
60
60
50
30
80
10
20
100-12060-15040-10030-10010-40
30-12060-17030-13030-8040-15025-80

New Zealand150-350South America
Asia, Europe,
broadleaf > 20 yAsia, Europe,
broadleaf ≤ 20 yAsia, Europe,
coniferous > 20 yAsia, Europe,
coniferous ≤ 20 y
North America
South America
Asia, Europe > 20 yAsia, Europe ≤ 20 y
North America
Asia, Europe > 20 yAsia, Europe ≤ 20 y
North America

90-12020030150-25040
50-300Temperate

Temperatecontinental forestand mountainsystems

BorealBoreal coniferous
forest and mountainsystems
Boreal tundra
woodland

4.56

200
15
150-200
25-30
50-30090-120
40
5
40-50
25
5
25

References
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Hinds and Reid, 1957; Halland Hollinger, 1997; Hall,
2001
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.9
ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS
DomainEcological zone

Tropical rain forest

Tropical moist
deciduous forest

TropicalTropical dry forest

Tropical shrubland

Tropical mountainsystems

Subtropical humid
forestSubtropical
Subtropical dry
forestContinent

Above-groundbiomass growth(tonnes d.m. ha-1
yr-1)

Africa (≤20 y)
Africa (>20 y)

10
3.1 (2.3-3.8)

North America

0.9-18South America (≤20 y)
South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)2006 IPCC Guidelines for National Greenhouse Gas Inventories

11
3.1 (1.5-5.5)
7.0 (3.0-11.0)
2.2 (1.3-3.0)
13
3.4
5
1.3
7.0
2.0
9.0
2.0
11
3.0
2.4 (2.3-2.5)
1.8 (0.6-3.0)
4.0
1.0
6.0
1.5
7.0
2.0
0.2-0.7
0.9 (0.2-1.6)
4.0
1.0
5.0
1.3 (1.0-2.2)
2.0
1.0
2.0-5.0
1.0-1.5
1.8-5.0
0.4-1.4
1.0-5.0
0.5-1.0
3.0-12
1.0-3.0
7.0
2.0
9.0
2.0
11
3.0
2.4 (2.3-2.5)
1.8 (0.6-3.0)
4.0

Reference
IPCC, 2003
IPCC, 2003
Clark et al., 2003 ;Hughes et al., 1999
Feldpausch et al., 2004
Malhi et al., 2004
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Harmand et al., 2004
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Nygård et al., 2004
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

4.57

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.9 (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS
DomainEcological zone

Subtropical steppe

Subtropicalmountain systems

Temperate oceanic
forestTemperate

BorealTemperatecontinental forest
Temperate mountainsystems
Boreal coniferous
forestBoreal tundra
woodlandBoreal mountainsystems

4.58

Continent

Above-groundbiomass growth(tonnes d.m. ha-1
yr-1)

Reference

North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
Africa (≤20 y)
Africa (>20 y)
North and South America (≤20 y)
North and South America (>20 y)
Asia (continental ≤20 y)
Asia (continental >20 y)
Asia (insular ≤20 y)
Asia (insular >20 y)
EuropeNorth America
New ZealandSouth America
Asia, Europe, North America(≤20 y)Asia, Europe, North America(>20 y)

1.0
6.0
1.5
7.0
2.0
1.2 (0.8-1.5)
0.9 (0.2-1.6)
4.0
1.0
5.0
1.3 (1.0-2.2)
2.0
1.0
2.0-5.0
1.0-1.5
1.8-5.0
0.4-1.4
1.0-5.0
0.5-1.0
3.0-12
1.0-3.0
2.3
15 (1.2-105)
3.5 (3.2-3.8)
2.4-8.9

IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

4.0 (0.5-8.0)

IPCC, 2003

4.0 (0.5-7.5)

IPCC, 2003

Asia, Europe, North America

3.0 (0.5-6.0)

IPCC, 2003

Asia, Europe, North America

0.1-2.1

Asia, Europe, North America

0.4 (0.2-0.5)

IPCC, 2003

1.0-1.1

IPCC, 2003

1.1-1.5

IPCC, 2003

Asia, Europe, North America(≤20 y)Asia, Europe, North America(>20 y)Hessl et al., 2004
Coomes et al., 2002
Echevarria and Lara, 2004

Gower et al., 2001

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.10
ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL FOREST PLANTATIONS
DomainEcological zone

Tropical rain forest

Tropical moist
deciduous forest

TropicalTropical dry forest

Tropical shrubland

Tropical mountainsystems

Subtropical humid
forestSubtropical
Subtropical dry
forestContinent
Africa Pinus sp. ≤ 20 y
Africa other ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia Eucalyptus sp.
Asia other
Africa Eucalyptus sp. >20 y
Africa Eucalyptus sp. ≤20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Africa other ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia
Africa Eucalyptus sp. ≤20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Africa other ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia Eucalyptus sp.
Asia other
Africa Eucalyptus sp. >20 y
Africa Eucalyptus sp. ≤20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Africa other > 20 y
Africa other ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Asia
AfricaAmericas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia Eucalyptus sp.
Asia other
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia
Africa Eucalyptus sp. ≤20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Africa other ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia Eucalyptus sp.
Asia other2006 IPCC Guidelines for National Greenhouse Gas Inventories

Above-groundbiomass growth(tonnes d.m. ha-1
yr-1)
20
6 (5-8)20 (6-40)
20
15
20 (5-35)
5 (4-8)5 (2-8)25
20
15
10
9 (3-15)16
7 (4-10)8 (4-12)
6-20
8
13
10
8
10 (4-20)
20 (6-30)
7 (4-10)8 (4-12)10 (3-12)
15 (5-25)
7 (2-13)8 (5-14)5 (3-7)2.5
3 (0.5-6)
10
15
20
5
6 (1-12)10
10 (8-18)
10
2
4
3
5 (1-10)20 (6-32)
7 (4-10)8 (4-12)10 (3-12)8
13
108
10 (4-20)
20 (6-30)
7 (4-10)8 (4-12)10 (3-12)
15 (5-25)
7 (2-13)

References
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
Stape et al., 2004
IPCC, 2003
IPCC, 2003
Lugo et al., 1990
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

4.59

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.10 (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL FOREST PLANTATIONS
DomainEcological zone

Subtropical steppe

Subtropicalmountain systems

Temperate oceanic
forestTemperate

Temperatecontinental forestand mountainsystems

BorealBoreal coniferous
forest and mountainsystems
Boreal tundra
woodland

4.60

Continent
Africa Eucalyptus sp. >20 y
Africa Eucalyptus sp. ≤20 y
Africa Pinus sp. > 20 y
Africa Pinus sp. ≤ 20 y
Africa other > 20 y
Africa other ≤ 20 y
Americas Eucalyptus sp.
Americas Pinus sp.
Asia
AfricaAmericas Eucalyptus sp.
Americas Pinus sp.
Americas Tectona grandis
Americas other broadleaf
Asia Eucalyptus sp.
Asia otherAsia, Europe,
broadleaf > 20 yAsia, Europe,
broadleaf ≤ 20 yAsia, Europe,
coniferous > 20 yAsia, Europe,
coniferous ≤ 20 y
North America
New ZealandSouth America
Asia, Europe,
broadleaf > 20 yAsia, Europe,
broadleaf ≤ 20 yAsia, Europe,
coniferous > 20 yAsia, Europe,
coniferous ≤ 20 y
North America
South America
Asia, Europe > 20 yAsia, Europe ≤ 20 y
North America
Asia, Europe > 20 yAsia, Europe ≤ 20 y
North America

Above-groundbiomass growth(tonnes d.m. ha-1
yr-1)
8 (5-14)5 (3-7)2.5
3 (0.5-6)
10
15
20
5
6 (1-12)10
10 (8-18)
10
2
4
3
5 (1-10)

References
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003
IPCC, 2003

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.11A
ABOVE-GROUND NET VOLUME GROWTH OF SELECTED FOREST PLANTATION SPECIES
Tree species

Above-ground net volume growth(m3 ha-1 y-1)

Acacia auriculiformis

6 - 20

Acacia mearnsii

14 - 25

Araucaria angustifolia

8 - 24

Araucaria cunninghamii

10 - 18

Casuarina equisetifolia

6 - 20Casuarina junghuhniana

7 - 11

Cordia alliadora

10 - 20

Cupressus lusitanica

8 - 40Dalbergia sissoo

5-8

Eucalyptus camaldulensis

15 - 30

Eucalyptus deglupta

14 - 50

Eucalyptus globulus

10 - 40

Eucalyptus grandis

15 - 50

Eucalyptus robusta

10 - 40

Eucalyptus saligna

10 - 55

Eucalyptus urophylla

20 - 60

Gmelina arborea

12 - 50

Leucaena leucocephala

30 - 55

Pinus caribaea v. caribaea

10 - 28

Pinus caribaea v. hondurensis

20 - 50

Pinus oocarpa

10 - 40

Pinus patula

8 - 40Pinus radiata

10 - 50

Swietenia macrophylla

7 - 30Tectona grandis

6 - 18Terminalia ivorensis

8 - 17

Terminalia superba

10 - 14

Source: Ugalde and Perez, 2001

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.61

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.11B
MEAN ANNUAL INCREMENT (GROWTH OF MERCHANTABLE VOLUME) FOR SOME FOREST PLANTATION SPECIES
Planted forest type/regionTree species

Mean annual increment (MAI)
over rotation (m3 ha-1 yr-1)
MAI min

MAI max

2.2
15.0
1.4
2.0
6.6
5.0
15.0
12.0
8.5
2.5
21.0
4.0
7.3
3.0

4.0
20.0
2.6
6.0
9.4
7.5
24.0
14.0
12.0
3.5
43.0
15.0
17.3
8.8

15.0
15.0
20.0
10.0
10.0
25.0
10.0
15.0

30.0
30.0
70.0
20.0
25.0
40.0
30.0
35.0

4.0
1.9
12.5
1.1
1.8
1.2
1.5
1.2
1.5
0.9

6.1
3.5
20.0
2.4
3.2
3.7
2.4
1.5
1.7
1.0

2.0
13.0
1.4
1.9
6.0
4.0
14.0
10.0
7.0
5.0

6.0
21.0
2.8
4.3
12.0
8.0
20.0
14.0
16.0
8.0

Productive plantations

AfricaAsia

Acacia mellifera
Acacia nilotica
Acacia senegal
Acacia seyal
Ailanthus excelsa
Bamboo bamboo
Cupressus spp.
Eucalyptus spp.
Khaya spp.
Tectona grandis
Eucalyptus camaldulensis
Pinus spp.
Tectona grandis
Xylia xylocapa

Acacia spp.
Araucaria angustifolia
Eucalyptus spp.
South America
Hevea brasiliensis
Mimosa scabrella
Pinus spp.
Populus spp.
Tectona grandis
Productive, semi-natural forests
Acacia albida
Acacia mellifera
Acacia nilotica
Acacia senegal
Acacia seyal
AfricaAcacia tortilis
Acacia tortilis var siprocarpa
Balanites aegyptiaca
Sclerocarya birrea
Ziziphus mauritiana
Protective plantations

Africa4.62

Acacia mellifera
Acacia nilotica
Acacia senegal
Acacia seyal
Ailanthus spp.
Bamboo bamboo
Cupressus spp.
Eucalyptus spp.
Khaya spp.
Tectona grandis

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.11B (CONTINUED)
MEAN ANNUAL INCREMENT (GROWTH OF MERCHANTABLE VOLUME) FOR SOME FOREST PLANTATION SPECIES
Planted forest type/regionMean annual increment (MAI)
over rotation (m3 ha-1 yr-1)

Tree species

Protective Semi-natural plantations
Acacia albida
Acacia mellifera
Acacia nilotica
Acacia senegal
Acacia seyal
AfricaAcacia tortilis
Acacia tortilis var siprocarpa
Balanites aegyptiaca
Sclerocarya birrea
Ziziphus mauritiana

MAI min

MAI max

4.0
1.7
12.0
1.1
1.8
1.3
1.6
1.2
1.5
0.9

6.2
3.2
15.0
2.4
3.3
3.5
2.4
1.5
1.7
1.0

Source: FAO at http://www.fao.org/forestry/

TABLE 4.12
TIER 1 ESTIMATED BIOMASS VALUES FROM TABLES 4.7–4.11 (EXCEPT TABLE 4.11B)(VALUES ARE APPROXIMATE; USE ONLY FOR TIER 1)

ClimatedomainTropical

Subtropical

Temperate

BorealEcological zone

Above-groundbiomass innatural forests(tonnes d.m.
ha-1)

Above-groundbiomass inforestplantations(tonnes d.m.
ha-1)

Above-ground
net biomass
growth innatural forests(tonnes d.m.
ha-1 yr-1)

Above-ground
net biomass
growth inforestplantations(tonnes d.m.
ha-1 yr-1)

Tropical rain forest

300

150

7.0

15.0

Tropical moist deciduous forest

180

120

5.0

10.0

Tropical dry forest

130

60

2.4

8.0

Tropical shrubland

70

30

1.0

5.0

Tropical mountain systems

140

90

1.0

5.0

Subtropical humid forest

220

140

5.0

10.0

Subtropical dry forest

130

60

2.4

8.0

Subtropical steppe

70

30

1.0

5.0

Subtropical mountain systems

140

90

1.0

5.0

Temperate oceanic forest

180

160

4.4

4.4

Temperate continental forest

120

100

4.0

4.0

Temperate mountain systems

100

100

3.0

3.0

Boreal coniferous forest

50

40

1.0

1.0

Boreal tundra woodland

15

15

0.4

0.4

Boreal mountain systems

30

30

1.0

1.0

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.63

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Adina cordifolia
0.58-0.59
Asia
5
Aegle marmelo
0.75
Asia
5
Afzelia bipidensis
0.67-0.79
Africa3Agathis sp.
0.44
Asia
5
Aglaia llanosiana
0.89
Asia
5
Agonandra brasiliensis
0.74
Americas
4
Aidia ochroleuca
0.78
Africa5
Alangium longiflorum
0.65
Asia
5
Albizia sp.
0.52
Americas
5
Albizzia amara
0.70
Asia
5
Albizzia falcataria
0.25
Asia
5
Alcornea sp.
0.34
Americas5
Aldina heterophylla
0.73
Americas
4
Aleurites trisperma
0.43
Asia
5
Alexa grandiflora
0.59
Americas
4Alexa imperatricis
0.52
Americas
4
Allophyllus africanus
0.45
Africa5
Alnus ferruginea
0.38
Americas
5
Alnus japonica
0.43
Asia
5
Alphitonia zizyphoides
0.50
Asia
5
Alphonsea arborea
0.69
Asia
5
Alseodaphne longipes
0.49
Asia
5
Alstonia congensis
0.33
Africa5
Amburana cearensis
0.43
Americas1
Amoora sp.
0.60
Asia
5
Amphimas
0.63
Africa5pterocarpoides
Anacardium excelsum
0.41
Americas4
Anacardium giganteum
0.44
Americas4
Anadenanthera
0.86
Americas4
macrocarpa
Andira inermis
0.64
Americas4Andira parviflora
0.69
Americas4Andira retusa
0.67
Americas5
Aniba amazonica
0.52-0.56 Americas
1
Aniba canelilla
0.92
Americas4
Aningeria robusta
0.44-0.53
Africa3
Anisophyllea
0.63
Africa5
obtusifolia
Anisophyllea zeylanica
0.46
Asia
5
Anisoptera sp.
0.54
Asia
5
Annonidium mannii
0.29
Africa5Anogeissus latifolia
0.78-0.79
Asia
5
Anopyxis klaineana
0.74
Africa5
Anthocephalus
0.33-0.36Asia5chinensis
Anthocleista keniensis
0.50
Africa5
Anthonotha
0.78
Africa5
macrophylla
Anthostemma
0.32
Africa5
aubryanum
Antiaris africana
0.38
Americas5
Antiaris sp.
0.38
Africa5
Antidesma pleuricum
0.59
Asia
5
Antrocaryon
0.50
Africa5klaineanum
Apeiba aspera
0.28
Americas1
Apeiba echinata
0.36
Americas5
Apeiba peiouma
0.20
Americas
4
Aphanamiris
0.52
Asia
5
perrottetiana
Apuleia leiocarpa
0.70
Americas
1
Apuleia molaris
0.76
Americas4
Araucaria bidwillii
0.43
Asia
5
Ardisia cubana
0.62
Americas1
Artocarpus comunis
0.70
Americas5
Artocarpus sp.
0.58
Asia
5
Aspidosperma album
0.76
Americas4

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Aspidosperma
0.67
Americas1
macrocarpon
Aspidosperma
0.86
Americas4
obscurinervium
Astronium gracile
0.73
Americas4Astronium graveolens
0.75
Americas4Astronium lecointei
0.73
Americas5
Astronium ulei
0.71
Americas4Astronium urundeuva
1.21
Americas
4
Aucoumea klaineana
0.31-0.48
Africa3
Autranella congolensis
0.78
Africa5
Azadirachta sp.
0.52
Asia
5
Bagassa guianensis
0.69
Americas
4
Baillonella toxisperma
0.70
Africa3
Balanites aegyptiaca
0.63
Africa5
Balanocarpus sp.
0.76
Asia
5
Banara guianensis
0.61
Americas5
Baphia kirkii
0.93
Africa5
Barringtonia edulis
0.48
Asia
5
Basiloxylon exelsum
0.58
Americas5
Bauhinia sp.
0.67
Asia
5
Beilschmiedia louisii
0.70
Africa5
Beilschmiedia nitida
0.50
Africa5
Beilschmiedia sp.
0.61
Americas5
Beilschmiedia tawa
0.58
Asia
5
Berlinia sp.
0.58
Africa5
Berrya cordifolia
0.78
Asia
5
Bertholletia excelsa
0.62
Americas4Bischofia javanica
0.54-0.62Asia
5Bixa arborea
0.32
Americas
4
Bleasdalea vitiensis
0.43
Asia
5
Blighia welwitschii
0.74
Africa5
Bocoa sp.
0.42
Americas1
Bombacopsis quinata
0.39
Americas1
Bombacopsis sepium
0.39
Americas5
Bombax costatum
0.35
Africa3
Bombax paraense
0.39
Americas1
Borojoa patinoi
0.52
Americas5
Boswellia serrata
0.50
Asia
5
Bowdichia
0.39
Americas2
coccolobifolia
Bowdichia crassifolia
0.39
Americas2
Bowdichia nitida
0.79
Americas4
Bowdichia virgilioides
0.52
Americas2
Brachystegia sp.
0.52
Africa5
Bridelia micrantha
0.47
Africa5
Bridelia squamosa
0.50
Asia
5
Brosimum acutifolium
0.55
Americas4
Brosimum alicastrum
0.69
Americas4
Brosimum guianense
0.96
Americas4
Brosimum lactescens
0.70
Americas1
Brosimum
0.58
Americas4
parinarioides
Brosimum potabile
0.53
Americas4
Brosimum rubescens
0.87
Americas4
Brosimum utile
0.40-0.49 Americas
1
Brysenia adenophylla
0.54Americas5
Buchenavia capitata
0.63
Americas4
Buchenavia huberi
0.79
Americas4
Buchenavia latifolia
0.45
Asia
5
Buchenavia oxycarpa
0.72
Americas4
Buchenavia viridiflora
0.88
Americas1
Bucida buceras
0.93
Americas
5
Bursera serrata
0.59
Asia
5
Bursera simaruba
0.29-0.34 Americas
5
Butea monosperma
0.48
Asia
5
Byrsonima coriacea
0.64
Americas5
Byrsonima spicata
0.61
Americas44.642006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Byrsonima
0.33
Americas2
verbascifolia
Cabralea canjerana
0.55
Americas4
Caesalpinia sp.
1.05
Americas5
Calophyllum
0.53
Americas4
brasiliense
Calophyllum sp.
0.46
Americas1
Calophyllum sp.
0.53
Asia
5
Calpocalyx klainei
0.63
Africa5
Calycarpa arborea
0.53
Asia
5
Calycophyllum
0.74
Americas1
spruceanumCampnosperma
0.37
Americas1
panamensis
Cananga odorata
0.29
Asia
5
Canarium sp.
0.44
Asia
5
Canthium monstrosum
0.42
Asia
5
Canthium
0.63
Africa5
rubrocostratum
Carallia calycina
0.66
Asia
5
Carapa guianensis
0.55
Americas4
Carapa procera
0.59
Africa5
Cariniana integrifolia
0.49
Americas4
Cariniana micrantha
0.64Americas4
Caryocar glabrum
0.65
Americas1
Caryocar villosum
0.72
Americas
4
Casearia battiscombei
0.50
Africa5Casearia sp.
0.62
Americas5
Cassia javanica
0.69
Asia
5
Cassia moschata
0.71
Americas5
Cassia scleroxylon
1.01
Americas4
Cassipourea euryoides
0.70
Africa5
Cassipourea malosana
0.59
Africa5
Castanopsis
0.51
Asia
5
philippensis
Casuarina equisetifolia
0.81
Americas5
Casuarina equisetifolia
0.83
Asia
5
Casuarina nodiflora
0.85
Asia
5
Catostemma commune
0.50
Americas1
Cecropia sp.
0.36
Americas
5
Cedrela odorata
0.42
Americas1
Cedrela odorata
0.38
Asia
5
Cedrela sp.
0.40-0.46 Americas
5
Cedrela toona
0.43
Asia
5
Cedrelinga
0.45
Americas1
catenaeformis
Ceiba pentandra
0.18-0.39
Africa3
Ceiba pentandra
0.28
Americas4
Ceiba pentandra
0.23
Asia
5
Ceiba samauma
0.57
Americas1
Celtis luzonica
0.49
Asia
5
Celtis schippii
0.59
Americas1
Celtis sp.
0.59
Africa5
Centrolobium sp.
0.65
Americas
5
Cespedesia
0.63
Americas5
macrophylla
Cespedesia spathulata
0.54
Americas1
Chaetocarpus
0.80
Americas5
schomburgkianus
Chisocheton pentandrus
0.52
Asia
5
Chlorophora excelsa
0.48-0.66
Africa3
Chlorophora tinctoria
0.73
Americas4Chloroxylon swietenia
0.76-0.80
Asia
5
Chorisia integrifolia
0.28
Americas1
Chrysophyllum
0.56
Africa5albidum
Chukrassia tabularis
0.57
Asia
5
Citrus grandis
0.59
Asia
5
Clarisia racemosa
0.59
Americas4

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Cleidion speciflorum
0.50
Asia
5
Cleistanthus eollinus
0.88
Asia
5
Cleistanthus
0.87
Africa5
mildbraedii
Cleistocalyx sp.
0.76
Asia
5
Cleistopholis patens
0.36
Africa5
Clusia rosea
0.67
Americas5
Cochlospermum
0.27
Asia
5
gossypium
Cochlospermum
0.26
Americas5orinocensis
Cocos nucifera
0.50
Asia
5
Coda edulis
0.78
Africa5
Coelocaryon preussii
0.56
Africa5Cola sp.
0.70
Africa5
Colona serratifolia
0.33
Asia
5
Combretodendron
0.57
Asia
5
quadrialatum
Conopharyngia holstii
0.50
Africa5
Copaifera officinalis
0.61
Americas1
Copaifera pubifora
0.56
Americas1
Copaifera religiosa
0.50
Africa5
Copaifera reticulata
0.63
Americas4Cordia alliodora
0.48
Americas5
Cordia bicolor
0.49
Americas4Cordia gerascanthus
0.74
Americas5
Cordia goeldiana
0.48
Americas4Cordia millenii
0.34
Africa5
Cordia platythyrsa
0.36
Africa5
Cordia sagotii
0.50
Americas4Cordia sp.
0.53
Asia
5
Corynanthe pachyceras
0.63
Africa5
Corythophora rimosa
0.84
Americas4
Cotylelobium sp.
0.69
Asia
5
Couepia sp.
0.70
Americas
5
Couma macrocarpa
0.50
Americas4
Couratari guianensis
0.54
Americas4
Couratari multiflora
0.47
Americas4
Couratari oblongifolia
0.49
Americas4
Couratari stellata
0.63
Americas4
Crataeva religiosa
0.53
Asia
5
Cratoxylon arborescens
0.40
Asia
5
Croton megalocarpus
0.57
Africa5
Croton xanthochloros
0.48
Americas
5Cryptocarya sp.
0.59
Asia
5
Cryptosepalum staudtii
0.70
Africa5
Ctenolophon
0.78
Africa5englerianus
Cubilia cubili
0.49
Asia
5
Cullenia excelsa
0.53
Asia
5
Cupressus lusitanica
0.43-0.44 Americas
5
Curatella americana
0.41
Americas2
Cylicodiscus
0.80
Africa5gabonensis
Cynometra alexandri
0.74
Africa5
Cynometra sp.
0.80
Asia
5
Cyrilla racemiflora
0.53
Americas5
Dacrycarpus imbricatus 0.45-0.47
Asia5
Dacrydium sp.
0.46
Asia
5
Dacryodes buttneri
0.44-0.57
Africa3
Dacryodes excelsa
0.52-0.53 Americas
5
Dacryodes sp.
0.61
Asia
5
Dactyodes colombiana
0.51
Americas5Dalbergia paniculata
0.64
Asia
5
Dalbergia retusa.
0.89
Americas5Dalbergia stevensonii
0.82
Americas
5
Daniellia oliveri
0.53
Africa3
Declinanona calycina
0.47
Americas5
Decussocarpus vitiensis
0.37
Asia
5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.65

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Degeneria vitiensis
0.35
Asia
5
Dehaasia triandra
0.64
Asia
5
Dendropanax arboreum
0.40
Americas
4
Desbordesia pierreana
0.87
Africa5
Detarium senegalensis
0.63
Africa5
Dialium excelsum
0.78
Africa5
Dialium guianense
0.88
Americas4
Dialium sp.
0.80
Asia
5
Dialyanthera sp.
0.36-0.48 Americas
5
Diclinanona calycina
0.47
Americas4
Dicorynia ghuianensis
0.65
Americas4
Dicorynia paraensis
0.60
Americas5
Didelotia africana
0.78
Africa5
Didelotia letouzeyi
0.50
Africa5
Didymopanax sp.
0.74
Americas5Dillenia sp.
0.59
Asia
5
Dimorphandra mora
0.99
Americas
5
Dinizia excelsa
0.86
Americas4
Diospyros sp.
0.82
Africa5
Diospyros sp.
0.47
Americas1
Diospyros sp.
0.70
Asia
5
Diplodiscus paniculatus
0.63
Asia
5
Diploon cuspidatum
0.85
Americas4
Diplotropis martiusii
0.74
Americas1
Diplotropis purpurea
0.78
Americas4Dipterocarpus caudatus
0.61
Asia
5
Dipterocarpus
0.56
Asia
5
eurynchus
Dipterocarpus gracilis
0.61
Asia
5
Dipterocarpus
0.62
Asia
5
grandiflorus
Dipterocarpus kerrii
0.56
Asia
5
Dipterocarpus
0.57
Asia
5
kunstlerii
Dipterocarpus sp.
0.61
Asia
5
Dipterocarpus
0.52
Asia
5
warburgii
Dipteryx odorata
0.93
Americas4Dipteryx polyphylla
0.87Americas4Discoglypremna
0.32
Africa5caloneura
Distemonanthus
0.58
Africa5benthamianus
Dracontomelon sp.
0.50
Asia
5
Dryobalanops sp.
0.61
Asia
5
Drypetes sp.
0.63
Africa5
Drypetes variabilis
0.71
Americas4
Dtypetes bordenii
0.75
Asia
5
Durio sp.
0.53
Asia
5
Dussia lehmannii
0.59
Americas5
Dyera costulata
0.36
Asia
5
Dysoxylum
0.49
Asia
5
quercifolium
Ecclinusa bacuri
0.59
Americas4
Ecclinusa guianensis
0.63
Americas
5Ehretia acuminata
0.51
Africa5
Elaeocarpus serratus
0.40
Asia
5
Emblica officinalis
0.80
Asia
5
Enantia chlorantha
0.42Africa5
Endiandra laxiflora
0.54
Asia
5
Endlicheria sp.
0.50
Americas1
Endodesmia
0.66
Africa5calophylloides
Endopleura uchi
0.78
Americas4
Endospermum sp.
0.38
Asia
5
Entandrophragma utile
0.53-0.62
Africa3Enterolobium
0.34
Americas4cyclocarpum

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent ReferenceEnterolobium
0.35
Asia
5
cyclocarpum
Enterolobium
0.40
Americas4maximumEnterolobium
0.78
Americas4
schomburgkii
Eperua falcata
0.78
Americas4
Epicharis cumingiana
0.73
Asia
5
Eribroma oblongum
0.60
Africa5
Eriocoelum
0.50
Africa5
microspermum
Eriotheca
0.45
Americas4longipedicellata
Erisma uncinatum
0.47
Americas1
Erismadelphus ensul
0.56
Africa5
Erythrina sp.
0.23
Americas5Erythrina subumbrans
0.24
Asia
5
Erythrina vogelii
0.25
Africa5
Erythrophleum
0.70-0.88
Africa3
ivorense
Erythrophloeum
0.65
Asia
5
densiflorum
Eschweilera amazonica
0.90
Americas
4
Eschweilera coriacea
0.78
Americas
4
Eschweilera ovata
0.81
Americas
4
Eschweilera sagotiana
0.79
Americas4
Eucalyptus citriodora
0.64
Asia
5
Eucalyptus deglupta
0.34
Asia
5
Eucalyptus robusta
0.51
Americas
5
Eugenia sp.
0.65
Asia
5
Eugenia stahlii
0.73
Americas5Euxylophora paraensis
0.70
Americas4
Fagara macrophylla
0.69
Africa5
Fagara sp.
0.69
Americas5
Fagraea sp.
0.73
Asia
5
Ficus benjamina
0.65
Asia
5
Ficus insipida
0.50
Americas1
Ficus iteophylla
0.40
Africa5
Fumtumia latifolia
0.45
Africa5
Gallesia integrifolia
0.51
Americas1
Gambeya sp.
0.56
Africa5
Ganua obovatifolia
0.59
Asia
5
Garcinia myrtifolia
0.65
Asia
5
Garcinia punctata
0.78
Africa5
Garcinia sp.
0.75
Asia
5
Gardenia turgida
0.64
Asia
5
Garuga pinnata
0.51
Asia
5
Genipa americana
0.51
Americas4Gilletiodendron
0.87
Africa5
mildbraedii
Gluta sp.
0.63
Asia
5
Glycydendron
0.66
Americas4
amazonicum
Gmelina arborea
0.41-0.45
Asia
5
Gmelina vitiensis
0.54
Asia
5
Gonocaryum
0.64
Asia
5
calleryanum
Gonystylus punctatus
0.57
Asia
5
Gossweilerodendron
0.40
Africa5balsamiferum
Goupia glabra
0.68
Americas1
Grewia tiliaefolia
0.68
Asia
5
Guarea cedrata
0.48-0.57
Africa3
Guarea chalde
0.52
Americas5
Guarea guidonia
0.68
Americas4
Guarea kunthiana
0.60
Americas1
Guatteria decurrens
0.52
Americas1
Guatteria olivacea
0.51
Americas4
Guatteria procera
0.65
Americas
44.66

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Guazuma ulmifolia
0.50-0.52 Americas
5
Guibourtia demeusii
0.70-0.84
Africa3
Guillielma gasipae
0.95-1.25 Americas
5
Gustavia speciosa
0.34
Americas1
Hannoa klaineana
0.28
Africa5
Hardwickia binata
0.73
Asia
5
Harpullia arborea
0.62
Asia
5
Harungana
0.45
Africa5
madagascariensis
Helicostylis tomentosa
0.72
Americas4
Heritiera sp.
0.56
Asia
5
Hernandia Sonora
0.29
Americas5
Hevea brasiliensis
0.49
Americas4
Hevea brasiliensis
0.53
Asia
5
Hexalobus crispiflorus
0.48
Africa5
Hibiscus tiliaceus
0.57
Asia
5
Hieronyma chocoensis
0.59-0.62 Americas
1
Hieronyma laxiflora
0.55
Americas1
Himatanthus articulatus
0.38
Americas2
Hirtella davisii
0.74
Americas
5
Holoptelea grandis
0.59
Africa5
Homalanthus
0.38
Asia
5
populneus
Homalium sp.
0.70
Africa5
Homalium sp.
0.76
Asia
5
Hopea acuminata
0.62
Asia
5
Hopea sp.
0.64
Asia
5
Huberodendron patinoi
0.50
Americas1
Humiria balsamifera
0.66
Americas4
Humiriastrum excelsum
0.75
Americas4
Humiriastrum procera
0.70
Americas5
Hura crepitans
0.36
Americas4
Hyeronima
0.64
Americas4alchorneoides
Hyeronima laxiflora
0.59
Americas5
Hylodendron
0.78
Africa5gabonense
Hymenaea courbaril
0.77
Americas1
Hymenaea davisii
0.67
Americas5
Hymenaea oblongifolia
0.62
Americas1
Hymenaea parvifolia
0.95
Americas4
Hymenolobium
0.64
Americas4
excelsum
Hymenolobium
0.65
Americas
4modestum
Hymenolobium
0.67
Americas4pulcherrimum
Hymenostegia
0.78
Africa5pellegrini
Inga alba
0.62
Americas4
Inga edulis
0.51
Americas1
Inga paraensis
0.82
Americas4
Intsia palembanica
0.68
Asia
5
Irvingia grandifolia
0.78
Africa5
Iryanthera grandis
0.55
Americas4
Iryanthera sagotiana
0.57
Americas4
Iryanthera trocornis
0.72
Americas
4
Jacaranda copaia
0.33
Americas
4
Joannesia heveoides
0.39
Americas
4
Julbernardia globiflora
0.78
Africa5
Kayea garciae
0.53
Asia
5
Khaya ivorensis
0.40-0.48
Africa3
Kingiodendron
0.48
Asia
5
alternifolium
Klainedoxa gabonensis
0.87
Africa5
Kleinhovia hospita
0.36
Asia
5
Knema sp.
0.53
Asia
5
Koompassia excelsa
0.63
Asia
5

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Koordersiodendron
0.65-0.69Asia
5pinnatum
Kydia calycina
0.72
Asia
5
Lachmellea speciosa
0.73
Americas5
Laetia procera
0.63
Americas1
Lagerstroemia sp.
0.55
Asia
5
Lannea grandis
0.50
Asia
5
Lecomtedoxa klainenna
0.78
Africa5
Lecythis idatimon
0.77
Americas4
Lecythis lurida
0.83
Americas4
Lecythis pisonis
0.84
Americas4
Lecythis poltequi
0.81
Americas4
Lecythis zabucaja
0.86
Americas
4
Letestua durissima
0.87
Africa5
Leucaena leucocephala
0.64
Asia
5
Licania macrophylla
0.76
Americas4
Licania oblongifolia
0.88
Americas4
Licania octandra
0.77
Americas4
Licania unguiculata
0.88
Americas1
Licaria aritu
0.80
Americas4
Licaria cannella
1.04
Americas4
Licaria rigida0.73
Americas4
Lindackeria sp.
0.41
Americas
5
Linociera domingensis
0.81
Americas5
Lithocarpus soleriana
0.63
Asia
5
Litsea sp.
0.40
Asia
5
Lonchocarpus sp.
0.69
Americas5
Lophira alata
0.84-0.97
Africa3
Lophopetalum sp.
0.46
Asia
5
Lovoa trichilioides
0.45
Africa5Loxopterygium sagotii
0.56
Americas5
Lucuma sp.
0.79
Americas
5
Luehea sp.
0.50
Americas
5
Lueheopsis duckeana
0.62
Americas4Mabea piriri
0.59
Americas5
Macaranga denticulata
0.53
Asia
5
Machaerium sp.
0.70
Americas5
Maclura tinctoria
0.71
Americas1
Macoubea guianensis
0.40
Americas5
Madhuca oblongifolia
0.53
Asia
5
Maesopsis eminii
0.41
Africa5
Magnolia sp.
0.52
Americas5
Maguira sclerophylla
0.57
Americas5
Malacantha sp.
0.45
Africa5
Mallotus philippinensis
0.64
Asia
5
Malouetia duckei
0.57
Americas4
Mammea africana
0.62
Africa5
Mammea americana
0.62
Americas5
Mangifera indica
0.55
Americas5
Mangifera sp.
0.52
Asia
5
Manilkara amazonica
0.85
Americas4
Manilkara bidentata
0.87
Americas1
Manilkara huberi
0.93
Americas4
Manilkara lacera
0.78
Africa5
Maniltoa minor
0.76
Asia
5
Maquira sclerophylla
0.57
Americas4
Marila sp.
0.63
Americas5Markhamia platycalyx
0.45
Africa5
Marmaroxylon
0.81
Americas4
racemosum
Mastixia philippinensis
0.47
Asia
5
Matayba domingensis
0.70
Americas5
Matisia hirta
0.61
Americas5
Mauria sp.
0.31
Americas1
Maytenus sp.
0.71
Americas5
Melanorrhea sp.
0.63
Asia
5
Melia dubia
0.40
Asia
5
Melicope triphylla
0.37
Asia
5
Meliosma macrophylla
0.27
Asia
5

2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.67

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Melochia umbellata
0.25
Asia
5
Memecylon
0.77
Africa5
capitellatum
Metrosideros collina
0.70-0.76
Asia
5
Mezilaurus itauba
0.70
Americas
4
Mezilaurus lindaviana
0.68
Americas4Michelia sp.
0.43
Asia
5
Michropholis sp.
0.61
Americas5
Microberlinia
0.70
Africa5
brazzavillensis
Microcos coriaceus
0.42
Africa5
Microcos stylocarpa
0.40
Asia
5
Micromelum
0.64
Asia
5
compressum
Micropholi guyanensis
0.65
Americas4
Micropholi venulosa
0.67
Americas
4Milletia sp.
0.72
Africa5Milliusa velutina
0.63
Asia
5
Mimusops elengi
0.72
Asia
5
Minquartia guianensis
0.76
Americas1
Mitragyna parviflora
0.56
Asia
5
Mitragyna stipulosa
0.47
Africa5
Monopetalanthus
0.44-0.53
Africa3
heitzii
Mora excelsa
0.80
Americas
4Mora gonggrijpii
0.78
Americas1Mora megistosperma
0.63
Americas1
Mouriri barinensis
0.78
Americas1
Mouriria sideroxylon
0.88
Americas5
Musanga cecropioides
0.23
Africa5
Myrciaria floribunda
0.73
Americas5
Myristica platysperma
0.55
Americas4
Myristica sp.
0.53
Asia
5
Myroxylon balsamum
0.78
Americas1
Myroxylon peruiferum
0.78
Americas1
Nauclea diderrichii
0.63
Africa5
Nealchornea yapurensis
0.61
Americas1
Nectandra rubra
0.57
Americas5
Neesia sp.
0.53
Asia
5
Neonauclea bernardoi
0.62
Asia
5
Neopoutonia
0.32
Africa5
macrocalyx
Neotrewia cumingii
0.55
Asia
5
Nesogordonia
0.65
Africa5papaverifera
Ochna foxworthyi
0.86
Asia
5
Ochroma pyramidale
0.30
Asia
5
Ochtocosmus africanus
0.78
Africa5
Ocotea guianensis
0.63
Americas4
Ocotea neesiana
0.63
Americas4
Octomeles sumatrana
0.27-0.32
Asia
5
Odyendea sp.
0.32
Africa5
Oldfieldia africana
0.78
Africa5
Ongokea gore
0.72
Africa5
Onychopetalum
0.61
Americas4
amazonicum
Ormosia coccinea
0.61
Americas1
Ormosia paraensis
0.67
Americas4
Ormosia schunkei
0.57
Americas1
Oroxylon indicum
0.32
Asia
5
Otoba gracilipes
0.32
Americas1
Ougenia dalbergiodes
0.70
Asia
5
Ouratea sp.
0.66
Americas5
Oxystigma oxyphyllum
0.53
Africa5
Pachira acuatica
0.43
Americas
5
Pachyelasma
0.70
Africa5
tessmannii
Pachypodanthium
0.58
Africa5
staudtii

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Palaquium sp.
0.55
Asia
5
Pangium edule
0.50
Asia
5
Paraberlinia bifoliolata
0.56
Africa5
Parashorea stellata
0.59
Asia
5
Paratecoma peroba
0.60
Americas
5
Paratrophis glabra
0.77
Asia
5
Parinari excelsa
0.68
Americas
4Parinari glabra
0.87
Africa5
Parinari montana
0.71
Americas
4Parinari rodolphii
0.71
Americas
4Parinari sp.
0.68
Asia
5
Parkia multijuga
0.38
Americas4
Parkia nitada
0.40
Americas4
Parkia paraensis
0.44
Americas4
Parkia pendula
0.55
Americas4
Parkia roxburghii
0.34
Asia
5
Parkia ulei
0.40
Americas4
Pausandra trianae
0.59
Americas1
Pausinystalia
0.56
Africa5brachythyrsa
Pausinystalia sp.
0.56
Africa5
Payena sp.
0.55
Asia
5
Peltogyne paniculata
0.89
Americas4
Peltogyne paradoxa
0.91
Americas4
Peltogyne
0.89
Americas1
porphyrocardia
Peltophorum
0.62
Asia
5
pterocarpum
Pentace sp.
0.56
Asia
5
Pentaclethra macroloba
0.43
Americas1
Pentaclethra
0.78
Africa5
macrophylla
Pentadesma butyracea
0.78
Africa5
Persea sp.
0.40-0.52 Americas
5
Peru glabrata
0.65
Americas5
Peru schomburgkiana
0.59
Americas
5
Petitia domingensis
0.66
Americas5
Phaeanthus
0.56
Asia
5
ebracteolatus
Phyllanthus discoideus
0.76
Africa5Phyllocladus
0.53
Asia
5
hypophyllus
Phyllostylon
0.77
Americas4
brasiliensis
Pierreodendron
0.70
Africa5
africanum
Pinus caribaea
0.51
Americas
5
Pinus caribaea
0.48
Asia
5
Pinus insularis
0.47-0.48
Asia
5
Pinus merkusii
0.54
Asia
5
Pinus oocarpa
0.55
Americas
5
Pinus patula
0.45
Americas
5
Piptadenia communis
0.68
Americas
4
Piptadenia grata
0.86
Americas1
Piptadenia suaveolens
0.75
Americas
4
Piptadeniastrum
0.56
Africa5
africanum
Piratinera guianensis
0.96
Americas5
Pisonia umbellifera
0.21
Asia
5
Pithecellobium
0.56
Americas5
guachapele
Pithecellobium
0.36
Americas1
latifolium
Pithecellobium saman
0.49
Americas1
Pittosporum
0.51
Asia
5
pentandrum
Plagiostyles africana
0.70
Africa5
Planchonia sp.
0.59
Asia
5
Platonia insignis
0.70
Americas54.682006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Platymiscium sp.
0.71-0.84 Americas
5
Podocarpus oleifolius
0.44
Americas1
Podocarpus rospigliosii
0.57
Americas1
Podocarpus sp.
0.43
Asia
5
Poga oleosa
0.36
Africa5
Polyalthia flava
0.51
Asia
5
Polyalthia suaveolens
0.66
Africa5
Polyscias nodosa
0.38
Asia
5
Pometia sp.
0.54
Asia
5
Poulsenia armata
0.37-0.44 Americas
1
Pourouma sp.
0.32
Americas
5
Pouteria anibifolia
0.66
Americas1
Pouteria anomala
0.81
Americas
4
Pouteria caimito
0.87
Americas
4
Pouteria guianensis
0.90
Americas
4
Pouteria manaosensis
0.64
Americas
4
Pouteria oppositifolia
0.65
Americas
4
Pouteria villamilii
0.47
Asia
5
Premna angolensis
0.63
Africa5Premna tomentosa
0.96
Asia
5
Prioria copaifera
0.40-0.41 Americas
5
Protium heptaphyllum
0.54
Americas4
Protium tenuifolium
0.65
Americas4
Pseudolmedia laevigata 0.62-0.63 Americas
1
Pseudolmedia laevis
0.71
Americas1
Pteleopsis hylodendron
0.63
Africa5
Pterocarpus marsupium
0.67
Asia
5
Pterocarpus soyauxii
0.62-0.79
Africa3
Pterocarpus vernalis
0.57
Americas1
Pterogyne nitens
0.66
Americas
4
Pterygota sp.
0.52
Africa5
Pterygota sp.
0.62
Americas1
Pycnanthus angolensis
0.40-0.53
Africa3
Qualea albiflora
0.50
Americas5
Qualea brevipedicellata
0.69
Americas4
Qualea dinizii
0.58
Americas5
Qualea lancifolia
0.58
Americas4
Qualea paraensis
0.67
Americas4
Quararibea asterolepis
0.45
Americas1
Quararibea bicolor
0.52-0.53 Americas
1
Quararibea cordata
0.43
Americas1
Quassia simarouba
0.37
Americas4
Quercus alata
0.71
Americas5
Quercus costaricensis
0.61
Americas5
Quercus eugeniaefolia
0.67
Americas5
Quercus sp.
0.70
Asia
5
Radermachera pinnata
0.51
Asia
5
Randia cladantha
0.78
Africa5
Raputia sp.
0.55
Americas5
Rauwolfia macrophylla
0.47
Africa5
Rheedia sp.
0.60
Americas1
Rhizophora mangle
0.89
Americas4
Ricinodendron
0.20
Africa5
heudelotii
Rollinia exsucca
0.52
Americas4
Roupala moniana
0.77
Americas4
Ruizierania albiflora
0.57
Americas4
Saccoglottis gabonensis
0.74
Africa5
Saccoglottis guianensis
0.77
Americas4
Salmalia malabarica
0.32-0.33
Asia
5Samanea saman
0.45-0.46
Asia
5
Sandoricum vidalii
0.43
Asia
5
Santiria trimera
0.53
Africa5
Sapindus saponaria
0.58
Asia
5
Sapium ellipticum
0.50
Africa5
Sapium luzontcum
0.40
Asia
5
Sapium marmieri
0.40
Americas1
Schefflera morototoni
0.36
Americas1
Schizolobium parahyba
0.40
Americas1

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Schleichera oleosa
0.96
Asia
5
Schrebera arborea
0.63
Africa5
Schrebera swietenoides
0.82
Asia
5
Sclerolobium
0.62
Americas4chrysopyllum
Sclerolobium paraense
0.64
Americas4
Sclerolobium
0.65
Americas4peoppigianum
Scleronema
0.61
Americas4
micranthum
Sclorodophloeus
0.68
Africa5
zenkeri
Scottellia coriacea
0.56
Africa5
Scyphocephalium
0.48
Africa5ochocoa
Scytopetalum tieghemii
0.56
Africa5
Semicarpus anacardium
0.64
Asia
5
Serialbizia acle
0.57
Asia
5
Serianthes melanesica
0.48
Asia
5
Sesbania grandiflora
0.40
Asia
5
Shorea assamica forma
0.41
Asia
5
philippinensis
Shorea astylosa
0.73
Asia
5
Shorea ciliata
0.75
Asia
5
Shorea contorta
0.44
Asia
5
Shorea palosapis
0.39
Asia
5
Shorea plagata
0.70
Asia
5
Shorea polita
0.47
Asia
5
Shorea robusta
0.72
Asia
5
Shorea sp. (balau)
0.70
Asia
5
Shorea sp. (dark red
0.55
Asia
5
meranti)
Shorea sp. (light red
0.40
Asia
5
meranti)
Sickingia sp.
0.52
Americas5Simaba multiflora
0.51
Americas5
Simarouba amara
0.36
Americas1
Simira sp.
0.65
Americas1
Sindoropsis letestui
0.56
Africa5
Sloanea guianensis
0.79
Americas5
Sloanea javanica
0.53
Asia
5
Sloanea nitida
1.01
Americas
4
Soymida febrifuga
0.97
Asia
5
Spathodea campanulata
0.25
Asia
5
Spondias lutea
0.38
Americas4
Spondias mombin
0.31-0.35 Americas
1
Spondias purpurea
0.40
Americas4
Staudtia stipitata
0.75
Africa5
Stemonurus luzoniensis
0.37
Asia
5
Sterculia apetala
0.33
Americas4
Sterculia pruriens
0.46
Americas4
Sterculia rhinopetala
0.64
Africa5
Sterculia speciosa
0.51
Americas4
Sterculia vitiensis
0.31
Asia
5
Stereospermum
0.62
Asia
5
suaveolens
Strephonema
0.56
Africa5
pseudocola
Strombosia
0.71
Asia
5
philippinensis
Strombosiopsis
0.63
Africa5tetrandra
Strychnos potatorum
0.88
Asia
5
Stylogyne sp.
0.69
Americas5
Swartzia fistuloides
0.82
Africa5
Swartzia laevicarpa
0.61
Americas1
Swartzia panacoco
0.97
Americas4
Swietenia macrophylla
0.43Americas1
Swietenia macrophylla
0.49-0.53
Asia
52006 IPCC Guidelines for National Greenhouse Gas Inventories

4.69

Volume 4. Agriculture, Forestry and Other Land Use

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

TABLE 4.13 BASIC WOOD DENSITY (D) OF TROPICAL TREESPECIES (OVEN-DRY TONNES (MOIST M-3))

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Swintonia foxworthyi
0.62
Asia
5
Swintonia sp.
0.61
Asia
5
Sycopsis dunni
0.63
Asia
5
Symphonia globulifera
0.58
Africa5Symphonia globulifera
0.58
Americas1
Syzygium cordatum
0.59
Africa5
Syzygium sp.
0.69-0.76
Asia
5
Tabebuia rosea
0.54
Americas1
Tabebuia serratifolia
0.92
Americas1
Tabebuia stenocalyx
0.55-0.57 Americas
5
Tachigalia
0.53
Americas4
myrmecophylla
Talisia sp.
0.84
Americas5
Tamarindus indica
0.75
Asia
5
Tapirira guianensis
0.50
Americas4Taralea oppositifolia
0.80
Americas1
Tectona grandis
0.50-0.55
Asia
5
Terminalia amazonica
0.65
Americas1
Terminalia citrina
0.71
Asia
5
Terminalia copelandii
0.46
Asia
5
Terminalia ivorensis
0.40-0.59
Africa3
Terminalia microcarpa
0.53
Asia
5
Terminalia nitens
0.58
Asia
5
Terminalia oblonga
0.73
Americas1
Terminalia pterocarpa
0.48
Asia
5
Terminalia superba
0.40-0.66
Africa3
Terminalia tomentosa
0.73-0.77
Asia
5
Ternstroemia
0.53
Asia
5
megacarpa
Tessmania africana
0.85
Africa5
Testulea gabonensis
0.60
Africa5
Tetragastris altissima
0.74
Americas4Tetragastris panamensis
0.76
Americas4Tetrameles nudiflora
0.30
Asia
5
Tetramerista glabra
0.61
Asia
5
Tetrapleura tetraptera
0.50
Africa5
Thespesia populnea
0.52
Asia
5
Thyrsodium guianensis
0.63
Americas4
Tieghemella africana
0.53-0.66
Africa3
Toluifera balsamum
0.74
Americas5
Torrubia sp.
0.52
Americas5
Toulicia pulvinata
0.63
Americas5
Tovomita guianensis
0.60
Americas5
Trattinickia sp.
0.38
Americas5
Trema orientalis
0.31
Asia
5
Trema sp.
0.40
Africa5
Trichilia lecointei
0.90
Americas4
Trichilia prieureana
0.63
Africa5
Trichilia propingua
0.58
Americas5
Trichoscypha arborea
0.59
Africa5
Trichosperma
0.41
Americas5
mexicanum
Trichospermum richii
0.32
Asia
5
Triplaris cumingiana
0.53
Americas5Triplochiton
0.28-0.44
Africa3
scleroxylon.
Tristania sp.
0.80
Asia
5
Trophis sp.
0.44
Americas1
Turpinia ovalifolia
0.36
Asia
5
Vantanea parviflora
0.86
Americas4
Vatairea guianensis
0.70
Americas4
Vatairea paraensis
0.78
Americas4
Vatairea sericea
0.64
Americas4
Vateria indica
0.47
Asia
5
Vatica sp.
0.69
Asia
5
Vepris undulata
0.70
Africa5
Virola michelii
0.50
Americas4
Virola reidii
0.35
Americas1
Virola sebifera
0.37
Americas1

1 = Baker et al., 2004b; 2 = Barbosa and Fearnside, 2004;
3 = CTFT, 1989; 4 = Fearnside, 1997; 5 = Reyes et al., 1992
SpeciesDensity Continent Reference
Vismia sp.
0.41
Americas5Vitex doniana
0.40
Africa5
Vitex sp.
0.52-0.57 Americas
5
Vitex sp.
0.65
Asia
5
Vitex stahelii
0.60
Americas
5
Vochysia densiflora
0.29
Americas1
Vochysia ferruginea
0.37
Americas1
Vochysia guianensis
0.53
Americas
4
Vochysia lanceolata
0.49
Americas1
Vochysia macrophylla
0.36Americas1
Vochysia maxima
0.47
Americas
4
Vochysia melinonii
0.51
Americas
4
Vochysia obidensis
0.50
Americas
4
Vochysia surinamensis
0.66
Americas4
Vouacapoua americana
0.79
Americas
4Warszewicsia coccinea
0.56
Americas
5
Wrightia tinctorea
0.75
Asia
5
Xanthophyllum
0.63
Asia
5
excelsum
Xanthoxylum
0.46
Americas5
martinicensis
Xanthoxylum sp.
0.44
Americas5
Xylia xylocarpa
0.73-0.81
Asia
5
Xylopia frutescens
0.64
Americas5
Xylopia nitida
0.57
Americas4
Xylopia staudtii
0.36
Africa5
Zanthoxylum rhetsa
0.33
Asia
5
Zizyphus sp.
0.76
Asia
5

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TABLE 4.14
BASIC WOOD DENSITY (D) OF SELECTED TEMPERATE AND BOREAL TREE TAXA
D[oven-dry tonnes (moist
m-3)]

SourceAbies spp.

0.40

2

Acer spp.

0.52

2

Alnus spp.

0.45

2

Betula spp.

0.51

2

Fagus sylvatica

0.58

2

Fraxinus spp.

0.57

2

Larix decidua

0.46

2

Picea abies

0.40

2

Picea sitchensis

0.40

3

Pinus pinaster

0.44

4

Pinus radiata

0.38 (0.33 - 0.45)

1Pinus strobus

0.32

2

Pinus sylvestris

0.42

2

Populus spp.

0.35

2

Prunus spp.

0.49

2

Pseudotsuga menziesii

0.45

2

Quercus spp.

0.58

2

Taxon

Salix spp.

0.45

2

Tilia spp.

0.43

2

1 = Beets et al., 2001
2 = Dietz, 1975
3 = Knigge and Shulz, 1966
4 = Rijsdijk and Laming, 1994

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Annex 4A.1

Glossary for Forest Land

Terminology for stocks and changes in forests as defined in this volume
ComponentState

Increase

Decrease from harvest

growing stock

net annual increment

removals

growing stock biomass

increment biomass

removals biomass

above-ground biomass
growthbelow-ground biomass
growthabove-ground biomass
removals
below-ground biomass1
removals

total biomass growthbiomass removals

Merchantable volume
Biomass in themerchantable volume
Total above-groundbiomass
Total below-groundbiomass
Total above-ground andbelow-ground biomass
Carbonabove-ground biomass
below-ground biomass
total biomass

carbon in …
(in any of the compartments above, e.g., carbon in growing stock or biomass removals), orin litter, dead wood and soil organic matter

ABOVE-GROUND BIOMASSAll biomass of living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches,bark, seeds, and foliage.
Note: In cases where forest understory is a relatively small component of the above-ground biomass carbon pool,
it is acceptable for the methodologies and associated data used in some tiers to exclude it, provided the tiers areused in a consistent manner throughout the inventory time series.

ABOVE-GROUND BIOMASS GROWTH
Oven-dry weight of net annual increment (s.b.) of a tree, stand or forest plus oven-dry weight of annual growthof branches, twigs, foliage, top and stump. The term “growth” is used here instead of “increment”, since thelatter term tends to be understood in terms of merchantable volume.

AFFORESTATION2
The direct human-induced conversion of land that has not been forested for a period of at least 50 years toforested land through planting, seeding and/or the human-induced promotion of natural seed sources.

AGROFORESTRY
A land-use system that involves deliberate retention, introduction, or mixture of trees or other woody perennials
in crop and animal production systems to take advantage of economic or ecological interactions among thecomponents (Dictionary of Forestry, helms, 1998, Society of American Foresters).

BASIC WOOD DENSITY
Ratio between oven dry mass and fresh stem-wood volume without bark.

BELOW-GROUND BIOMASSAll biomass of live roots. Fine roots of less than (suggested) 2mm diameter are often excluded because theseoften cannot be distinguished empirically from soil organic matter or litter.

B I O M A S S C O N V E R S I O N A N D E X P A N S I O N F A C T O R (BCEF)
A multiplication factor that coverts merchantable volume of growing stock, merchantable volume of net annual
increment, or merchantable volume of wood-removal and fuelwood-removals to above-ground biomass, aboveground biomass growth, or biomass removals, repectively. Biomass conversion and expansion factors for
growing stock (BCEFS), for net annual increment (BCEFI), and for wood-removal and fuelwood-removals
1

Occurs in some cases, e.g., where root stocks (walnut) or entire root systems are removed (biomass harvesting).

2

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

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(BCEFR) usually differ. As used in these guidelines, they account for above-ground components only. For moredetail see Box 4.2.

B I O M A S S E X P A N S I O N F A C T O R (BEF)
A multiplication factor that expands the dry-weight of growing stock biomass, increment biomass, and biomassof wood-removal or fuelwood-removals to account for non-merchantable or non-commercial biomass
components, such as stump, branches, twigs, foliage, and, sometimes, non-commercial trees. Biomass expansion
factors usually differ for growing stock (BEFS), net annual increment (BEFI), and wood-removal and fuelwoodremovals (BEFR). As used in these guidelines, biomass expansion factors account for above-ground components
only. For more detail see Box 4.2.

BIOMASS REMOVALS
Biomass of wood-removal and firewood-removals (s.b.) plus oven-dry weight of branches, twigs, foliage of thetrees or stands removed.

CANOPY COVER
See crown cover

CARBON CONTENT
Absolute amount of carbon in a pool or parts of it.

CARBON FRACTION
Tonnes of carbon per tonne of biomass dry matter.

CARBON IN…
See table above; absolute amount in tonnes, obtained by multiplying amount of biomass in respective component
by the applicable carbon fraction, usually 50%.

CARBON STOCK
The quantity of carbon in a pool.

CARBON STOCK CHANGE
The carbon stock in a pool changes due to gains and losses. When losses exceed gains, the stock decreases, andthe pool acts as a source; when gains exceed losses, the pools accumulate carbon, and the pools act as a sink.

CLOSED FOREST
Formations where trees, in the various stories and the undergrowth, cover a high proportion of the ground(>40%).

CONVERSION
Change of one land use to another.

CONVERSION FACTOR
Multiplier that transforms the measurement units of an item without affecting its size or amount. For example,
basic wood density is a conversion factor that transforms green volume of wood into dry weight.

CROWN COVER
The percentage of the ground covered by a vertical projection of the outermost perimeter of the natural spread of
the foliage (cannot exceed 100%).

DEAD WOOD
Includes all non-living woody biomass not contained in the litter, either standing, lying on the ground, or in thesoil. Dead wood includes wood lying on the surface, dead roots, and stumps, larger than or equal to 10cm indiameter (or the diameter specified by the country).

DEAD WOOD BIOMASSAll non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil.
Dead wood includes wood lying on the surface, dead roots down to a diameter of 2mm, and stumps larger thanor equal to 10cm in diameter or any other diameter used by the country.

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DEFORESTATION3
The direct human-induced conversion of forested land to non-forested land.

DISTURBANCE
A disturbance is defined as an environmental fluctuation and destructive event that disturb forest health,structure, and/or change resources or physical environment at any given spatial or temporal scale. Disturbancesthat affect health and vitality which include biotic agents such as insects and diseases, and abiotic agents such as
fire, pollution, and extreme weather conditions (see also below, mortality and other disturbance).

DISTURBANCE BY DISEASESDisturbances caused by diseases attributable to pathogens such as bacteria, fungi, phytoplasma, or virus.

DISTURBANCE BY FIRE
Disturbance caused by wildfire regardless of whether it broke out inside or outside the Forest. A wildfire is any
unplanned and uncontrolled wildland fire which, regardless of ignition source, may require suppression response.

DISTURBANCE BY INSECTS
Disturbance caused by insect pests that are detrimental to tree health.

DRY (FOREST)
Moisture regimes for boreal and temperate zones are defined by the ratio of mean annual precipitation (MAP)and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by
precipitation alone: Dry (MAP < 1,000mm), Moist (MAP: 1,000-2,000mm) and Wet (MAP > 2,000mm).

D R Y M A T T E R ( D.M. )Dry matter refers to biomass that has been dried to an oven-dry state, often at 70ºC.

FELLINGS
Volume (over bark) of all trees, living or dead, above a 10cm diameter at breast height, felled annually in forests
or other wooded land. It includes volume of all felled trees whether or not they are removed. It includes
silvicultural and pre-commercial thinning and cleanings of trees of more than 10cm diameter, left in the forest,and natural losses that are recovered.
Note: In these guidelines, only the terms “wood-removal” and “fuelwood-removals” are used, consistent withGFRA 2005. Removals are generally a subset of fellings.

FOREST4
Forest is a minimum area of land of 0.05 – 1.0 hectares with tree crown cover (or equivalent stocking level) of
more than 10 – 30 per cent with trees with the potential to reach a minimum height of 2 – 5 metres at maturity insitu. A forest may consist either of closed forest formations where trees of various storeys and undergrowthcover a high portion of the ground or open forest. Young natural stands and all plantations which have yet toreach a crown density of 10 – 30 per cent or tree height of 2 – 5 metres are included under forest, as are areas
normally forming part of the forest area which are temporarily unstocked as a result of human intervention suchas harvesting or natural causes but which are expected to revert to forest.

FOREST INVENTORY
System for measuring the extent, quantity, and condition of a forest, usually by sampling:1.

A set of objective sampling methods designed to quantify the spatial distribution, composition, and rates ofchange of forest parameters within specified levels of precision for the purpose of management;

2.

The listing of data from such a survey. May be made of all forest resources including trees and othervegetation, fish, insects, and wildlife, as well as street trees and urban forest trees.

3

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

4

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

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FOREST LANDThis category includes all land with woody vegetation consistent with thresholds used to define Forest Land in
the national greenhouse gas inventory. It also includes systems with a vegetation structure that currently fall
below, but in situ could potentially reach the threshold values used by a country to define the Forest Landcategory.

FOREST MANAGEMENT5
A system of practices for stewardship and use of forest land aimed at fulfilling relevant ecological (including
biological diversity), economic and social functions of the forest in a sustainable manner.

FOREST PLANTATION
Forest stands established by planting or/and seeding in the process of afforestation or reforestation. They are
either of introduced species (all planted stands), or intensively managed stands of indigenous species, whichmeet all the following criteria: one or two species at planting, even age class, and regular spacing.

FUELWOOD-REMOVAL
The wood removed for energy production purposes, regardless of whether for industrial, commercial, or
domestic use. Fuel wood includes wood collected or removed directly from forest or other wooded land forenergy purposes only. It excludes fuelwood which is produced as a by-product or residual matter from theindustrial processing of round wood. It includes removal from fellings in an earlier period and from trees killed
or damaged by natural causes. It also includes removal by local people or owners for their own use.

GROWING STOCK
Volume over bark of all living trees more than X cm in diameter at breast height. It includes the stem fromground level or stump height up to a top diameter of Y cm, and may also include branches to a minimum
diameter of W cm. Countries indicate the three thresholds (X, Y, W in cm) and the parts of the tree that are notincluded in the volume. Countries also indicate whether the reported figures refer to volume above ground or
above stump. The diameter is measured at 30cm above the end of the buttresses if these are higher than 1 meter.
It includes windfallen living trees and excludes smaller branches, twigs, foliage, flowers, seeds, and roots.

GROWING STOCK BIOMASS
Oven-dry weight of the growing stock (s.a.).

HARVEST LOSS
Difference between the assessed merchantable volume of growing stock and the actual volume of the harvested
timber. Due to different measurement rules for standing and felled timber, losses are from bucking, breakage,
defect.

INCREMENT BIOMASS
Oven-dry weight of (merchantable) net annual increment of a tree, stand, or forest.

INTENSIVE FOREST MANAGEMENT
A regime of forest management, where silvicultural practices define the structure and composition of forest
stands. A formal or informal forest management plan exists.
A forest is not under intensive management, if mainly natural ecological processes define the structure andcomposition of stands.

INTRODUCED SPECIESA species introduced outside of its normal past and current distribution.

LITTER
Includes all non-living biomass with a size greater than the limit for soil organic matter (suggested 2mm) andless than the minimum diameter chosen for dead wood (e.g., 10cm), lying dead, in various states ofdecomposition above or within the mineral or organic soil. This includes the litter layer as usually defined in soil

5

Forest management has particular meaning under the Marrakesh Accords, which may require subdivision of the managed
forest as described in Chapter 4.

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Volume 4: Agriculture, Forestry and Other Land Usetypologies. Live fine roots above the mineral or organic soil (of less than the minimum diameter limit chosen for
below-ground biomass) are included in litter where they cannot be distinguished from it empirically.

L O W A C T I V I T Y C L A Y (LAC) S O I L S
Soils with low activity clay (LAC) minerals are highly weathered soils dominated by 1:1 clay mineral andamorphous iron and aluminium oxides (in FAO classification included: Acrisols, Nitosols, Ferrasols).

MANAGED FOREST
A managed forest is forest land subjected to conditions defined for managed land.

MANAGED LANDManaged land is land where human interventions and practices have been applied to perform production,
ecological or social functions.

MERCHANTABLE VOLUME
Mechantable volume is the volume overbark of all trees defined using the conditions described for growing
stocks. Further, this can be applied to growing stocks as well as net annual increment and wood removals.

MOIST (FOREST)
Moisture regimes for boreal and temperate zones are defined by the ratio of mean annual precipitation (MAP)and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by
precipitation alone: Dry (MAP < 1,000mm), Moist (MAP: 1,000-2,000mm) and Wet (MAP > 2,000mm).

MORTALITY
Trees dying naturally from competition in the stem-exclusion stage of a stand or forest. As used here, mortality
does not include losses due to disturbances (s.a.).

NATURAL FOREST
A forest composed of indigenous trees and not classified as a forest plantation.

NATURAL REGENERATION
Re-establishment of a forest stand by natural means i.e., by natural seeding or vegetative regeneration. It may beassisted by human intervention e.g., by scarification of the soil or fencing to protect against wildlife or domestic
animal grazing.

NET ANNUAL INCREMENT
Average annual volume of gross increment over the given reference period minus mortality (s.a.), of all trees to a
specified minimum diameter at breast height. As used here, it is not net of losses due to disturbances (s.a.).

ORGANIC SOILS
Soils are organic if they satisfy the requirements 1 and 2, or 1 and 3 below (FAO, 1998):
1) Thickness of organic horizon greater than or equal to 10cm. A horizon of less than 20cm must have 12
percent or more organic carbon when mixed to a depth of 20cm.
2) Soils that are never saturated with water for more than a few days must contain more than 20 percent
organic carbon by weight (i.e., about 35 percent organic matter).
3) Soils are subject to water saturation episodes and has either:a.

At least 12 percent organic carbon by weight (i.e., about 20 percent organic matter) if the soil
has no clay; orb.

At least 18 percent organic carbon by weight (i.e., about 30 percent organic matter) if the soil
has 60% or more clay; orc.

An intermediate, proportional amount of organic carbon for intermediate amounts of clay.

OTHER DISTURBANCE
Disturbance caused by factors other than fire, insects, or diseases. May include areas affected by drought,
flooding, windfalls, acid rain, etc.

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PEAT SOIL (ALSO HISTOSOL)
A typical wetland soil with a high water table and an organic layer of at least 40cm thickness (poorly drainedorganic soil).

POOL/CARBON POOL
A reservoir. A system which has the capacity to accumulate or release carbon. Examples of carbon pools areforest biomass, wood products, soils, and the atmosphere. The units are in mass.

REFORESTATION6
Direct human-induced conversion of non-forested land to forested land through planting, seeding and/or thehuman-induced promotion of natural seed sources, on land that was forested but that has been converted to nonforested land. For the first commitment period, reforestation activities will be limited to reforestation occurringon those lands that did not contain forest on 31 December 1989.

REMOVAL BIOMASS
Oven dry weight of wood removals.

REVEGETATION7
A direct human-induced activity to increase carbon stocks on sites through the establishment of vegetation thatcovers a minimum area of 0.05 hectares and does not meet the definitions of afforestation and reforestation
contained here.

ROOT-SHOOT RATIO
Ratio of below-ground biomass to above-ground biomass; applies to above-ground biomass, above-groundbiomass growth, biomass removals and may differ for these components.

ROUNDWOOD
All round wood felled or otherwise harvested and removed; it comprises all wood obtained from removals e.g.,quatities removed from forests and from trees outside forests, including wood recovered from natural felling andlogging losses during a period. In the production statistics, it represents the sum of fuelwood, including wood forcharcoal, saw-and veneer logs, pulpwood and other industrial roundwood. In the trade statistics, it represents thesum of industrial roundwood, and fuelwood, including wood for charcoal. It is reported in cubic meters
excluding bark.

SANDY SOILS
Includes all soils (regardless of taxonomic classification) having > 70% sand and < 8% clay (based on standard
textural measurements (in FAO classification include: Arenosols, sandy Regosols)).

SAVANNA
Savannas are tropical and subtropical formations with continuous grass cover, occasionally interrupted by trees
and shrubs. Savannas are found in Africa, Latin America, Asia and Australia.

SEASONAL (FOREST)
Semi-deciduous forests with a distinct wet and dry season and rainfall between 1,200 and 2,000 mm per year.

STAND–REPLACING DISTURBANCES
Major disturbances which kill or remove all the existing trees above the forest floor vegetation. Minor
disturbances leave some of the pre-disturbance trees alive.

SHRUB
Woody perennial plants, generally more than 0.5 meters and less than 5 meters in height at maturity and without
definite crown. Height limits for trees and shrubs should be interpreted with flexibility, particularly the minimum
tree and maximum shrub height, which may vary between 5 and 7 meters.

6

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

7

In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land Use, Land-use Change and Forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

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SOIL CARBON
Organic carbon in mineral and organic soils (including peat) to a specified depth chosen by the country andapplied consistently through the time series. Live fine roots of less than 2mm (or other value chosen by thecountry as diameter limit for below-ground biomass) are included with soil organic matter where they cannot be
distinguished from it empirically.

SOIL ORGANIC MATTER
Includes organic carbon in mineral soils to a specified depth chosen by the country and applied consistently
through the time series. Live and dead fine roots and DOM within the soil, that are less than the minimum
diameter limit (suggested 2mm) for roots and DOM, are included with soil organic matter where they cannot be
distinguished from it empirically. The default for soil depth is 30cm and guidance on determining countryspecific depths is given in Chapter 2.3.3.1.

SPODIC SOILS
Soils exhibiting strong podzolization (in FAO classification includes many Podzolic groups).

TOTAL BIOMASS
Growing stock biomass of trees, stands or forests plus biomass of branches, twigs, foliage, seeds, stumps, andsometimes, non-commercial trees. Differentiated into above-ground biomass and below-ground biomass (s.a.). If
there is no misunderstanding, possible also just to use “biomass” with the same meaning.

TOTAL BIOMASS GROWTH
Biomass of the net annual increment (s.a.) of trees, stands, or forests, plus the biomass of the growth ofbranches, twigs, foliage, seeds, stumps, and sometimes, non-commercial trees. Differentiated into above-groundbiomass growth and below-ground biomass growth (s.a.). If there is no misunderstanding, possible also just to
use “biomass growth” with the same meaning. The term “growth” is used here instead of “increment”, since thelatter term tends to be understood in terms of merchantable volume.

TREE
A woody perennial with a single main stem, or in the case of coppice with several stems, having a more or lessdefinitive crown. Includes bamboos, palms, and other woody plants meeting the above criteria.

VOLUME OVERBARK
Growing stock or merchantable wood measured outside, that is including the bark. Bark adds 5-25% of total
volume, depending on tree diameter and bark thickness of species. The weighted average bark percentage
calculated from the data of TBFRA 2000 is 11% of the volume outside bark.

VOLUME UNDERBARK
Growing stock or merchantable wood without the bark. See above.

WET (FOREST)
Moisture regimes for boreal and temperate zones are defined by the ratio of mean annual precipitation (MAP)and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by
precipitation alone: Dry (MAP < 1,000mm), Moist (MAP: 1,000-2,000mm) and Wet (MAP > 2,000mm).

WOODY BIOMASS
Biomass from trees, bushes and shrubs, for palms, bamboos not strictly correct in the botanical sense.

WOOD FUEL
Also wood-based fuels, wood-derived biofuels. All types of biofuels originating directly or indirectly fromwoody biomass.

WOOD-REMOVAL
The wood removed (volume of round wood over bark) for production of goods and services other than energy
production (fuelwood). The term removal differs from fellings as it excludes felled trees left in the forest. It
includes removal from fellings of an earlier period and from trees killed or damaged by natural causes. It also
includes removal by local people or owners for their own use. As the term “removal” is used in the context ofclimate change to indicate sequestration of greenhouse gases from the atmosphere, removal in the context offorest harvesting should always be used as “wood-removal or fuelwood-removal” to avoid misunderstandings.

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References
Australian Greenhouse Gas Office (AGO) (2002). Greenhouse Gas Emissions from Land Use Change inAustralia: An Integrated Application of the National Carbon Accounting System (2002).
Andreae, M.O. and Merlet, P. (2001). Emission of trace gases and aerosols from biomass burning. Global
Biogeochemical Cycles 15: 955-966.
Armentano, T.V. and Menges, E.S. (1986). Patterns of change in the carbon balance of organic soil-wetlands ofthe temperate zone. Journal of Ecology 74: 755-774.
Baker, T.R., Phillips, O.L., Malhi, Y., Almeida, S., Arroyo, L., Di Fiore, A., Erwin, T., Higuchi, N., Killeen, T.J.,
Laurance, S.G., Laurance, W.F., Lewis, S.L., Monteagudo, A., Neill, D.A., Vargas, P.N., Pitman, N.C.A.,
Silva, J.N.M. and Martínez, R.V. (2004a). Increasing biomass in Amazonian forest plots. PhilosophicalTransactions of the Royal Society of London B 359: 353-365.
Baker, T.R., Phillips, O.L., Malhi, Y., Almeida, S., Arroyo, L., Di Fiore, A., Erwin, T., Killeen, T.J., Laurance,
S.G., Laurance, W.F., Lewis, S.L., Lloyd, J., Monteagudo, A., Neill, D.A., Patiño, S., Pitman, N.C.A.,
Silva, J.N.M. and Martínez, R.V. (2004b). Variation in wood density determines spatial patterns inAmazonian forest biomass. Global Change Biology 10: 545-562.
Barbosa, R.I. and Fearnside, P.M. (2004). Wood density of trees in open savannas of the Brazilian Amazon.
Forest Ecology and Management 199: 115-123.
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