Forest Land

3.2

FOREST LANDThis section of the Guidance provides methods for estimating carbon stock changes and greenhouse gas
emissions and removals associated with changes in biomass and soil organic carbon on forest lands and lands
converted to forest land. It is consistent with the approach in the Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC Guidelines) whereby the annual change in biomass is calculated from thedifference between biomass growth and loss terms. The Guidance:
Addresses the five carbon pools identified in Section 3.1;•

Links biomass and soil carbon pools for the same land areas at the higher tiers;•

Includes emissions of carbon on managed lands due to natural losses caused by fire, windstorms, pest anddisease outbreaks;•

Provides methods to estimate non-CO2 greenhouse gas emissions; and•Should be used together with the approaches for obtaining consistent area data described in Chapter 2.

Section 3.2 is organised into two parts. Section 3.2.1, the first section, covers the methodology to estimatechanges in carbon stocks in five pools on forest areas which have been forest for at least the past 20 years1. The
second section, Section 3.2.2, addresses changes in carbon stocks on lands converted more recently to forest.
Section 3.2.1 describes how the decision tree in Figure 3.1.1, given in Section 3.1.6, should be used to facilitatechoices on tier level for carbon pools and non-CO2 gases.
As stated in the IPCC Guidelines, natural, undisturbed forests should not be considered either an anthropogenic
source or sink and are excluded from national inventory estimation. This chapter therefore provides guidance on
estimating and reporting of anthropogenic sources and sinks of greenhouse gases for managed forests only. The
definition of managed forest is discussed in Section 3.1.2.1. Definitions at the national level should be appliedconsistently over time and cover all forests subject to periodic or ongoing human intervention, including the fullrange of management practices from commercial timber production to non-commercial purposes.
The IPCC Guidelines contain the default assumption that all carbon in harvested biomass is oxidised in theremoval year, but gives flexibility to include carbon storage in harvested wood products (HWP) if existing stocks
can be shown to be increasing. Accounting for HWP is also under consideration by the SBSTA. Pending theoutcome of negotiations, estimation methods for HWP are discussed in a separate section (Appendix 3a.1). This
indicates the state of methodological development and does not affect the advice in the IPCC Guidelines, or
prejudge the outcome of the negotiations referred to.

3.2.1

Forest Land Remaining Forest Land

Greenhouse gas inventory for the land-use category ‘Forest land Remaining Forest land (FF)’ involves
estimation of changes in carbon stock from five carbon pools (i.e. aboveground biomass, belowground biomass,
dead wood, litter, and soil organic matter), as well as emissions of non-CO2 gases from such pools. The
summary equation, which estimates the annual emissions or removals from FF with respect to changes in carbon
pools is given in Equation 3.2.1.
EQUATION 3.2.1
ANNUAL EMISSIONS OR REMOVALS FROM FOREST LAND REMAINING FOREST LAND
∆CFF = (∆CFFLB + ∆CFFDOM + ∆CFFSoils)
Where:∆CFF = annual change in carbon stocks from forest land remaining forest land, tonnes C yr-1
∆CFFLB = annual change in carbon stocks in living biomass (includes above- and belowground biomass)in forest land remaining forest land; tonnes C yr-1

1

Lands that have been converted to another land use should be tracked under the appropriate sections for as long as carbon
dynamics are influenced by the conversion and follow up dynamics. 20 years is consistent with IPCC Guidelines, but Tier
3 methods may use longer periods where appropriate to national circumstances.

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Chapter 3: LUCF Sector Good Practice Guidance∆CFFDOM = annual change in carbon stocks in dead organic matter (includes dead wood and litter) inforest land remaining forest land; tonnes C yr-1
∆CFFSoils = annual change in carbon stocks in soils in forest land remaining forest land; tonnes C yr-1
To convert tonnes C to Gg CO2, multiply the value by 44/12 and 10-3. For the convention (signs), refer to Section
3.1.7 or Annex 3A.2 (Reporting Tables and Worksheets).

3.2.1.1

C HANGE IN CARB ON STOCKS IN LIVING BIOMASS

Carbon stock change is calculated by multiplying the difference in oven dry weight of biomass increments andlosses with the appropriate carbon fraction. This section presents methods for estimating biomass increments andthe losses. Increments include biomass growth. Losses include fellings, fuelwood gathering, and natural losses.

3.2.1.1.1

METHODOLOGI CAL ISSUES

3.2.1.1.1.1

Choice of Method

Two methods are feasible for estimating carbon stock changes in biomass:
Method 1 (also called the default method) requires the biomass carbon loss to be subtracted from the biomass
carbon increment for the reporting year (Equation 3.2.2).
EQUATION 3.2.2
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN FOREST LAND REMAINING FOREST LAND (DEFAULT METHOD)∆CFFLB = (∆CFFG – ∆CFFL)
Where:∆CFFLB = annual change in carbon stocks in living biomass (includes above- and belowground biomass)in forest land remaining forest land, tonnes C yr-1
∆CFFG = annual increase in carbon stocks due to biomass growth, tonnes C yr-1
∆CFFL = annual decrease in carbon stocks due to biomass loss, tonnes C yr-1
Method 2 (also called the stock change method) requires biomass carbon stock inventories for a given forest
area at two points in time. Biomass change is the difference between the biomass at time t2 and time t1, divided
by the number of years between the inventories (Equation 3.2.3).
EQUATION 3.2.3
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN FOREST LAND REMAINING FOREST LAND (STOCK CHANGE METHOD)∆CFFLB = (C t2 – C t1) / (t2 – t1)
and
C = [V ● D • BEF2] ● (1 + R) ● CF
Where:∆C FFLB = annual change in carbon stocks in living biomass (includes above- and belowground biomass)in forest land remaining forest land, tonnes C yr-1
C t2 = total carbon in biomass calculated at time t2, tonnes C
C t1 = total carbon in biomass calculated at time t1, tonnes C
V = merchantable volume, m3 ha-1
D = basic wood density, tonnes d.m. m-3 merchantable volume
BEF2 = biomass expansion factor for conversion of merchantable volume to aboveground tree biomass,dimensionless.

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Forest LandR = root-to-shoot ratio, dimensionless
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
The default method is applicable for all tiers, while the data requirements for the stock change method exclude
this option for the Tier 1 approach. In general the stock change method will provide good results for relatively
large increases or decreases of biomass, or where very accurate forest inventories are carried out. However forforest areas of mixed stands, and/or where biomass change is very low compared to the total amount of biomass,
there is a risk with the stock change method of the inventory error being larger than the expected change. In suchconditions incremental data may give better results. The choice of using default or stock change method at theappropriate tier level will therefore be a matter for expert judgment, taking the national inventory systems andforest properties into account.
The default method for estimating the changes in aboveground and belowground biomass uses a series ofequations. These require activity data on area of different land-use categories, according to different forest types
or management systems, corresponding emission and removal factors, and factors to estimate biomass loss. The
accuracy of the estimate depends on the tier chosen for biomass estimation, and the data available.
It is good practice to choose tier by following the decision tree as shown in Figure 3.1.1. This promotes efficientuse of available resources, taking into account whether the biomass of this category is a key category as
described in Chapter 5, Section 5.4. In general:
Tier 1: Tier 1 applies to countries in which either the subcategory (forest land remaining forest land or biomass
carbon pool) is not a key category or little or no country-specific activity data and emission/removal factors exist
nor can be obtained.
Tier 2: Tier 2 applies where forest land remaining forest land or biomass carbon is a key category. Tier 2 should
be used in countries where country-specific estimates of activity data and emission/removal factors are available
or can be gathered at expenses that weigh favourably against expenses required for other land-use categories.
Tier 3: Tier 3 applies where the forest land remaining forest land or biomass carbon is a key category. Thisrequires use of detailed national forest inventory data supplemented by dynamic models or allometric equations
calibrated to national circumstances that allow for direct calculation of biomass increment. Tier 3 approach forcarbon stock change allows for a variety of methods, and implementation may differ from one country toanother, due to differences in inventory methods and forest conditions. Proper documentation of the validity andcompleteness of the data, assumptions, equations and models used is therefore a critical issue at Tier 3.
E Q UA T IO N S F O R E S T IM AT IN G CH A N G E I N C ARB O N S TO CK S I N LI V IN G
B IO M AS S ( ∆ C F F L B ) U S IN G TH E D EF A UL T M E TH O D
An n ual I ncr eas e i n Car bon Sto c ks d ue to B io mas s I nc re me nt in Fore s t lan dRe ma i ni ng F o re st la n d (∆C F F G )Estimation of annual increase in carbon stocks due to biomass increment in forest land remaining forest land
requires estimates of area and annual increment of total biomass, for each forest type and climatic zone in the
country (Equation 3.2.4). The carbon fraction of biomass has a default value of 0.5, although higher tier methods
may allow for variation with different species, different components of a tree or a stand (stem, roots and leaves)and age of the stand.
EQUATION 3.2.4
ANNUAL INCREASE IN CARBON STOCKS DUE TO BIOMASS INCREMENT
IN FOREST LAND REMAINING FOREST LAND
∆CFFG = ∑ij (Aij ● GTOTALij) ● CF
Where:∆CFFG = annual increase in carbon stocks due to biomass increment in forest land remaining forest landby forest type and climatic zone, tonnes C yr-1
Aij = area of forest land remaining forest land, by forest type (i = 1 to n) and climatic zone (j = 1 to m), ha
GTOTALij = average annual increment rate in total biomass in units of dry matter, by forest type (i = 1 to n)
and climatic zone (j = 1 to m), tonnes d.m. ha-1 yr-1
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1

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Chapter 3: LUCF Sector Good Practice Guidance

Averag e A n nua l In cre me n t in B io mas s ( G T O T A L )GTOTAL is the expansion of annual increment rate of aboveground biomass (GW) to include its belowground part,
involving multiplication by the ratio of belowground biomass to aboveground biomass (often called the root-toshoot ratio (R)) that applies to increments. This may be achieved directly where GW data are available as in thecase of naturally regenerated forests or broad categories of plantation. In case GW data are not available, theincrement in volume (IV) can be used with biomass expansion factor for conversion of annual net increment toaboveground biomass increment. Equation 3.2.5 shows the relationship:
EQUATION 3.2.5
AVERAGE ANNUAL INCREMENT IN BIOMASSGTOTAL = GW ● (1 + R)

(A) In case aboveground biomass increment (dry matter) data areused directly. Otherwise GW is estimated using equation B or its
equivalent

GW = IV ● D ● BEF1

(B) In case net volume increment data are used to estimate GW.

Where:GTOTAL = average annual biomass increment above and belowground, tonnes d.m. ha-1 yr-1
GW = average annual aboveground biomass increment, tonnes d.m. ha-1 yr-1; Tables 3A.1.5 and 3A.1.6
R= root-to-shoot ratio appropriate to increments, dimensionless; Table 3A.1.8

IV=average annual net increment in volume suitable for industrial processing, m3 ha-1 yr-1; Table
3A.1.7D= basic wood density, tonnes d.m. m-3; Table 3A.1.9

BEF1 = biomass expansion factor for conversion of annual net increment (including bark) to abovegroundtree biomass increment, dimensionless; Table 3A.1.10
Basic wood density (D) and biomass expansion factors (BEF) vary by forest type, age, growing conditions, standdensity and climate (Kramer, 1982; Brown, 1997; Lowe et al., 2000; Koehl, 2000). Table 3A.1.10 provides
default values of BEF by forest type and climatic zone for use with the minimum diameter ranges indicated. The
BEFs serve as substitute for the expansion ratios in the IPCC Guidelines which are used to calculate nonmerchantable biomass (limbs, small trees etc.) that are cut during felling and left to decay.
For countries using Tier 2 methods, it is good practice to use country-specific as well as species-specific basic
wood density and BEF values, if available nationally.
D as well as BEF values should be estimated at the species level in countries adopting Tier 3. BEFs for biomass
increment, growing stock and harvest differ for a given species or a stand. For Tiers 2 and 3, inventory experts
are encouraged to develop country-specific D and BEF values for growing stock, biomass increment andharvests separately. If country-specific factors and approaches are used, they should be appropriately verified
and documented in accordance with the general requirements set out in Chapter 5.
Due to country-specific conditions (e.g. Lehtonen et al., 2003; Smith et al., 2003) BEF and D may be combinedin one value. In such cases, the guidance given on BEF and D should be applied to the combined values asappropriate.
An n ual Dec rea se in Ca rbo n Stoc ks D ue to B io ma s s Lo s s in Fore st la n d R e ma i ni ng
Fore st la n d ( ∆C F F L )Annual biomass loss is a sum of losses from commercial roundwood fellings, fuelwood gathering, and otherlosses (Equation 3.2.6):
EQUATION 3.2.6
ANNUAL DECREASE IN CARBON STOCKS DUE TO BIOMASS LOSS
IN FOREST LAND REMAINING FOREST LAND

∆CFFL = Lfellings + Lfuelwood + Lother losses
Where:∆CFFL = annual decrease in carbon stocks due to biomass loss in forest land remaining forest land,
tonnes C yr-1
Lfellings = annual carbon loss due to commercial fellings, tonnes C yr-1 (See Equation 3.2.7)

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Forest Land

Lfuelwood = annual carbon loss due to fuelwood gathering, tonnes C yr-1 (See Equation 3.2.8)
Lother losses = annual other losses of carbon, tonnes C yr-1 (See Equation 3.2.9)
The equation for estimating the annual carbon loss due to commercial fellings is provided in Equation 3.2.7:
EQUATION 3.2.7
ANNUAL CARBON LOSS DUE TO COMMERCIAL FELLINGS
Lfellings = H ● D ● BEF2 ● (1– fBL) ● CF
Where:
Lfellings = annual carbon loss due to commercial fellings, tonnes C yr-1
H

= annually extracted volume, roundwood, m3 yr-1

D= basic wood density, tonnes d.m. m-3; Table 3A.1.9

BEF2 = biomass expansion factor for converting volumes of extracted roundwood to total abovegroundbiomass (including bark), dimensionless; Table 3A.1.10
fBL = fraction of biomass left to decay in forest (transferred to dead organic matter)
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
In applying this equation there are two choices:(i)

Total biomass associated with the volume of the extracted roundwood is considered as an
immediate emission. This is the default assumption and implies that fBL should be set to 0. This
assumption should be made unless changes in dead organic matter are being explicitly accounted
for, which implies use of higher tiers under Section 3.2.1.2 below.

(ii)

A proportion of the biomass is transferred to the dead wood stock. In this case, fBL should beobtained by expert judgment or based on empirical data (Tier 2 or 3). Annex 3.A.11 provides
default data on fBL for use at Tier 2.

The carbon loss due to fuelwood gathering is estimated using Equation 3.2.8:
EQUATION 3.2.8
ANNUAL CARBON LOSS DUE TO FUELWOOD GATHERING
Lfuelwood = FG ● D ● BEF2 ● CF
Where:
Lfuelwood = annual carbon loss due to fuelwood gathering, tonnes C. yr-1
FG = annual volume of fuelwood gathering, m3 yr-1
D = basic wood density, tonnes d.m. m-3; Table 3A.1.9
BEF2 = biomass expansion factor for converting volumes of extracted roundwood to total abovegroundbiomass (including bark), dimensionless; Table 3A.1.10
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
Other carbon losses in managed forest land include losses from disturbances such as windstorms, pest outbreaks,
or fires. A generic approach for estimating the amount of carbon lost from such disturbances is provided below.
In the specific case of losses from fire on managed forest land, including wildfires and controlled fires, thismethod should be used to provide input to the methodology in Section 3.2.1.4 (Non-CO2 Greenhouse Gas
Emissions) to estimate CO2 and non-CO2 emissions from fires.
It is good practice to report all areas affected by disturbances such as fires, pest outbreaks and windstorms that
occur in managed forest lands irrespective of whether these were the result of human activity. Natural
disturbances occurring on unmanaged forest, and not resulting in land-use change, should not be included.
Losses in biomass accounted as commercial harvest or fuelwood should not be included under the losses due toother disturbances.

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Chapter 3: LUCF Sector Good Practice Guidance

The impact of disturbances on forest ecosystem varies with the type and severity of disturbance, the conditions
under which they occur (e.g. weather) and the ecosystem characteristics. The proposed generic method
illustrated in Equation 3.2.9 assumes complete destruction of forest biomass in the event of a disturbance – hence
the default methodology addresses “stand-replacing” disturbances only. Countries reporting under Tier 3 should
consider both stand-replacing and non-stand replacing disturbances.
EQUATION 3.2.9
ANNUAL OTHER LOSSES OF CARBON
Lother losses = Adisturbance ● BW ● (1 – fBL) ● CF
Where:
Lother losses = annual other losses of carbon, tonnes C yr-1
Adisturbance = forest areas affected by disturbances, ha yr-1
BW = average biomass stock of forest areas, tonnes d.m. ha-1; Tables 3A.1.2, 3A.1.3, and 3A.1.4
fBL = fraction of biomass left to decay in forest (transferred to dead organic matter); Table 3A.1.11
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
Tier 1: Under Tier 1, disturbances are assumed to affect the aboveground biomass only; it is also assumed that
all aboveground biomass carbon is lost upon disturbance. Hence, fBL is equal to zero.
Tier 2: Countries reporting at higher tiers, which account for emissions/removals from all forest pools, have to
distinguish between the proportion of the pre-disturbance biomass that is destroyed and causes emissions ofgreenhouse gas, and that which is transferred into the dead organic matter pools and later decay.
Tier 3: Countries reporting under Tier 3 should consider all significant disturbances, both stand-replacing andnon-stand replacing. When accounting for the impact of non-stand-replacing disturbances, countries may add aterm to Equation 3.2.9 to adjust for the proportion of pre-disturbance biomass which is not affected by the
disturbance.
SUM M ARY OF S T EP S FOR E S TIM A T IN G C H AN GE I N C ARB O N S TO CKS I N
L IV IN G B I O M AS S ( ∆ C F F L B ) US I N G TH E DE F AU L T M E TH O DStep 1: Using guidance from Chapter 2 (approaches in representing land areas), categorise the area (A) of forest
land remaining forest land into forest types of different climatic zones, as adopted by the country. As apoint of reference, Table 3A.1.1 provides national level data of forest area and annual change in forest
area by region and by country as a means of verification;
Step 2: Estimate the average annual increment in biomass (GTOTAL) using Equation 3.2.5. If data of the averageannual aboveground biomass increment (GW) are available, use Equation 3.2.5A. If not available,
estimate GW using Equation 3.2.5B;
Step 3: Estimate the annual increase in carbon stocks due to biomass increment (∆CFFG) using Equation 3.2.4;
Step 4: Estimate the annual carbon loss due to commercial fellings (LW fellings) using Equation 3.2.7;
Step 5: Estimate annual carbon loss due to fuelwood gathering (LW fuelwood) using Equation 3.2.8;
Step 6: Estimate annual carbon loss due to other losses (Lother losses) using Equation 3.2.9;
Step 7: From the estimated losses in Steps 4 to 6, estimate the annual decrease in carbon stocks due to biomass
loss (∆CFFL) using Equation 3.2.6;
Step 8: Estimate the annual change in carbon stocks in living biomass (∆CFFLB) using Equation 3.2.2.

3.2.1.1.1.2

Choice of Emission/Removal Factors

Method 1 requires the annual biomass increment, according to each forest type and climatic zone in the country,
plus emission factors related to biomass loss including losses due to fellings, fuelwood gathering and natural losses.
AN NU A L I NC R EA S E I N B I O M ASS
An n ual Abov egro un d B io ma s s In cre me nt, G W
Tier 1: Tier 1 uses default values of the average annual increment in aboveground biomass (GW) which areprovided in Tables 3A.1.5 and 3A.1.6.

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Forest Land

Tier 2: Tier 2 method uses country-specific data to calculate the gross mean annual biomass increment GW. The
country-specific data is often linked to merchantable volumes (IV). Data on biomass expansion factor (BEF1) andbasic wood density (D) are needed to convert the available data to GW. Table 3A.1.7 provides the default values
for IV and Tables 3A.1.10 and 3A.1.9 provide default values for BEF1 and D, respectively.
Tier 3: Under Tier 3, a detailed forest inventory or monitoring system will be available which contains at least
data on growing stock, and, ideally, also on annual increment. If appropriate allometric biomass functions areavailable it is good practice to use those equations directly. Carbon fraction and basic wood density could also
be incorporated in such functions.
The detailed forest inventory should be used to provide initial conditions of forest carbon stocks in the forest
inventory year. When the year of inventory does not correspond with the commitment period, mean annual
increment or increment estimated by models (i.e. model capable of simulating forest dynamics), should be used.
Periodic forest inventories may be combined with annual planting and felling data to provide non-linear
interpolations of increment between inventory years.
B elo w g ro u n d B io ma s s I ncr e me n t
Tier 1: Belowground biomass increment, as a default assumption consistent with the IPCC Guidelines can be
zero. Alternatively, default values for root-to-shoot ratios (R), which could be used to estimate belowgroundbiomass, are provided Table 3A.1.8.
Tier 2: Country-specific root-to-shoot ratios should be used to estimate belowground biomass.
Tier 3: Nationally or regionally determined root-to-shoot ratios or increment models should be used. Preferably,
belowground biomass should be incorporated in models for calculating total biomass increment.
AN NU A L B I OM ASS L O S S
The IPCC Guidelines refer to biomass extraction (i.e. commercial fellings, removals for fuelwood and otherwood use, and natural losses) as total biomass consumption from stocks leading to carbon release. Equation 3.2.6
sets out the three components more precisely.
In addition to commercial fellings of industrial wood and saw logs, fuelwood are mentioned more specifically,
there may also be other types of non-commercial fellings, as wood cut for own consumption. This quantity may
not be included in official statistics and may need to be estimated by survey.
F el li ng s
When computing carbon loss due to commercial fellings, the following emission/removal factors are needed: extracted
volume of roundwood (H), basic wood density (D), and the fraction of biomass left to decay in forest (fBL).
Where it is separable, fellings data should not be counted from forest land being converted to another land use
since this would lead to double counting. The statistics on fellings are not likely to provide such separation onwhat lands the fellings are coming from, hence an amount of biomass similar to the biomass loss from lands
converted from forest should be subtracted from the total fellings.
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 anestimate based on the best information available. Usually, the yearbook appears with a two-year time lag.
Tier 1: FAO data can be used as a Tier 1 default for H in Equation 3.2.7. The roundwood data includes all wood
removed from forests which are reported in cubic meters underbark. The underbark data needs converting tooverbark for use with BEF2. For most tree species bark makes up about 10% to 20% of the overbark stem
volume. Unless country-specific data are available, 15% should be used as a default value and the FAO overbark
volume can be estimated by dividing the underbark estimate by 0.85 before using the values in Equation 3.2.7. It
is good practice to verify, supplement, update and check the quality of data based on any additional data fromnational or regional surveys.
Tier 2: Country-specific data should be used.
Tier 3: Country-specific removals data from different forest categories should be used at the resolution
corresponding to the Tier 3 forest model. If known, country-specific information on the dynamics of dead wood
decay should be used to describe the time evolution of non-harvested biomass.
F ue lw o o d g a t he r i ng
Estimation of carbon losses due to fuelwood gathering requires data on annual volume of fuelwood gathered(FG), basic wood density (D), and biomass expansion (BEF2) for converting volumes of collected roundwood tototal aboveground biomass.

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The way fuelwood extraction takes place in different countries varies from ordinary fellings to the gathering ofdead wood (the latter often as a fraction of ‘fBL’ of Equation 3.2.7.). This calls for different approaches whencalculating FG, as felling of trees for fuelwood use should be treated as carbon loss due to fellings .The equation
for fuelwood gathering, in comparison with the equation for commercial fellings, does not have a variable for‘fraction left to decay’, as it is assumed that a larger proportion of the trees is likely to be removed from forest.
On the other hand, fuelwood gathering from the forest floor should not be expanded, as it represents a reductionof the dead wood stock equal to the amount extracted. At the lower tiers it is assumed that this does not affect thestock in dead wood (see Sec. 3.2.1.2).
This section deals only with fuelwood gathering in forest land remaining forest land. In the sections ‘land
converted to cropland, grassland, etc’, explanation is given on how fuelwood used off-site, from the land use
conversion, should be treated and compensated for in the fuelwood statistics.
Tier 1: FAO provides statistics on fuelwood and charcoal consumption data for all countries. Thus, under Tier
1, FAO statistics can be used directly but should be checked for completeness because in some cases FAO data
may refer to specific activities taking place in particular forests rather than total fuelwood. If more complete
information is available nationally, it should be used. It is good practice to locate the national source of data for
the FAO such as the Ministry of Forests or Agriculture or any statistical organization. It is also good practice toseparate fuelwood gathering from forest land remaining forest land and that coming from forest land conversion
to other uses.
Tier 2: Country-specific data should be used, if available. It is good practice to verify and supplement the FAO
data from many national surveys and studies. Further, it is good practice to conduct a few regional surveys offuelwood consumption to validate the national or FAO data source. The national level, aggregate fuelwood
consumption could be estimated by conducting regional level surveys of rural and urban households at different
income levels, industries and establishments.
Tier 3: Fuelwood fellings data from national level studies should be used at the resolution required for the Tier
3 model, including the non-commercial fellings.
Traditional fuelwood gathering as well as commercial fuelwood felling from forest land remaining forest landsources should be generated at regional or disaggregated level through surveys. Fuelwood consumption depends
on household incomes. Thus, it may be possible to develop models to estimate fuelwood consumption. The
source of fuelwood should be clearly investigated to ensure no double counting occurs, between fuelwood fromforest land remaining forest land and forest land converted to other uses.
A country adopting Tier 3 should undertake a systematic approach to estimate fuelwood consumption along withsources, through survey of households, industries and establishments. The survey could be conducted in different
homogeneous climatic and socio-economic zones by adopting a statistical procedure (see Chapter 5, Section 5.3
on Sampling). Fuelwood consumption is likely to be different in rural and urban areas and during different
seasons of a year. Thus, the study should be conducted separately in rural and urban areas and in different
seasons. Fuelwood consumption models could be developed using income, level of urbanization, etc.
If fuelwood consumption data is in the form of commercial wood, reflecting only the merchantable wood, itneeds to be converted to whole stand biomass.
O t he r lo s se s
The estimate of other losses of carbon requires data on areas affected by disturbances (Adisturbance), the averagebiomass stocks of forest areas (BW), and the fraction of biomass left to decay in forest (fBL).
It is good practice to report all areas affected by disturbances such as fires, pest and disease outbreaks andwindstorms that occur in managed forest lands irrespective of whether these were the result of human activity.
However, natural disturbances occurring on unmanaged forest, and not resulting in land-use change, should not
be included. Depending on their intensity, fires, windstorms and pests outbreaks affect a variable proportion oftrees in a stand. It is good practice to categorise the affected area, as far as possible, according to the nature andintensity of disturbances. Losses in biomass accounted as commercial harvest or fuelwood should not be
included under the losses due to other disturbances.
Tier 1: Tier 1 approach is to obtain area of disturbance for the actual year. There are some international data
available on disturbances (see below) but in general default information is limited, and national assessment,
making use of data available at the local level following the disturbance, will be necessary to establish the area
affected. It may also be possible to use aerial survey data.
In the case of fire, both CO2 and non-CO2 emissions occur from combusted fuels (standing biomass including
understorey, slash, dead wood and litter). Fire may consume a high proportion of under storey vegetation. See
Section 3.2.1.4 for methodology to estimate non-CO2 emissions from fire and Equation 3.2.9 for calculating CO2
emissions from fire.

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Forest Land

Annex 3A.1 provides several tables to be used in connection with Equation 3.2.9.
•

Table 3A.1.12 provides 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 share lost is used;•

Table 3A.1.13 provides default values of biomass consumption to be used as [BW • (1– fBL)] in case thegrowing stock biomass data are not so good; and•Table 3A.1.14 provides default values of combustion efficiency in cases where fire is used as a means forland-use change.

Tier 2: Under Tier 2, biomass growing stock changes due to major disturbances will be taken into account by
forest category, type of disturbance and intensity. Average values for biomass stocks are obtained from national
data.
Tier 3: Estimation of growth rate using two inventories and the loss of biomass from disturbances that have
happened between the inventories are included. If the year of the disturbance is unknown, the result will be a
reduction of the average growth rate for the period. If disturbances occur after the last inventory, losses will haveto be calculated similar to Tier 2 approach.
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/projects/dfde
A UNEP database on global burnt area can be found at:
http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba/
However, one should note that the UNEP database is only valid for year 2000. In many countries interannual
variability in burned area is large, so these figures will not provide a representative average.

3.2.1.1.1.3

Choice of Activity DataAR EA O F M AN A G ED F O R E ST LA ND
All tiers require information on areas of managed forest land.
Tier 1: Tier 1 uses data of forest area which can be obtained through national statistics, from forest services(which may have information on areas of different management practices), conservation agencies (especially forareas managed for natural regeneration), municipalities, survey and mapping agencies. Cross-checks should bemade to ensure complete and consistent representation for avoiding omissions or double counting as specified in
Chapter 2. If no country data are available, aggregate information can be obtained from international data
sources (FAO, 1995; FAO 2001, TBFRA, 2000). It is good practice to verify, validate, and update the FAO datausing national sources.
Tier 2: Tier 2 uses country-defined national data sets with a resolution sufficient to ensure appropriate
representation of land areas in line with provisions of Chapter 2 of this report.
Tier 3: Tier 3 uses national data on managed forest lands 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.

3.2.1.1.1.4

Uncertainty Assessm ent

This section considers source-specific uncertainties relevant to inventory estimates made for forest landremaining forest. Estimating country-specific and/or disaggregated values entails getting more accurate
information on uncertainties than given below. Section 5.3 on Sampling, in Chapter 5, provides information on
uncertainties associated with sample-based studies.
EM IS S ION A ND R EM O VA L FA CT OR S
The uncertainty of basic wood density of pine, spruce and birch trees (predominantly stems) is under 20% in
studies of Hakkila (1968, 1979) in Finland. The variability between forest stands should be lower or at most thesame as for trees. It is concluded that overall uncertainty of country-specific basic wood density values should be
about 30%.
Lehtonen et al. (2003) analyzed stand level biomass expansion factors for pine, spruce and birch dominatedforests in Finland. The uncertainty of estimates was about 10%. The study was made for predominantly managed
forests, thus, it underestimates about 2 times the variation between forests in the boreal zone. Based on theabove, as estimated by expert judgment, overall uncertainty of BEFs should be 30%. The uncertainty of root-toshoot ratio is likely to have similar value of an order of 30%.

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The major source of uncertainty of estimates, in using default wood density and BEFs, is related to applicabilityof these parameters for diverse age and composition structure of specific stands. To reduce the uncertainty
associated with this issue, the countries are encouraged to develop country-specific BEFs or share regional
experience on values derived for forest stands that fit most in their conditions. In case the country-specific orregional-specific values are unavailable, the sources of default emission and removal factors should be checked
and their correspondence with specific conditions of a country should be examined. The efforts should be madeto apply the default values that have the highest correspondence with stand structure, climate and growth
conditions of a particular country.
Vuokila and Väliaho (1980) report values of increment for artificially regenerated pine and spruce stands in
Finland that vary by 50% around the average. The causes of variation include climate, site growth conditions,and soil fertility. Because artificially regenerated and managed stands are less variable than natural boreal
forests, the overall variability of default values for increment for this climatic zone is expected to be a factor of
two. Based on higher biological diversity of temperate and tropical forests, one can expect that their default
increment values may vary by a factor of three. The major ways to improve accuracy of estimates are associatedwith application of country-specific or regional increment stratified by forest type. If the default values ofincrement are used, the uncertainty of estimates should be clearly indicated and documented.
The data on commercial fellings are relatively accurate. Therefore, their uncertainty is less than 30%. However,the data on total fellings may be incomplete, due to illegal fellings and (or) underreporting due to tax regulations.
Wood that are used directly, without being sold or processed by others than the person taking the wood fromforest are not likely to be included in any statistics. However, it must be noted that illegal fellings andunderreporting in most cases constitute minor part of carbon stock withdrawals from forests and hence, theyshould not affect overall estimates and associated uncertainties so much. The amount of wood removed fromforests after storm breaks and pest outbreaks varies a lot both in time and volumes. No default data can beprovided on this type of losses. The uncertainties associated with these losses could be estimated by expert
judgment based on amount of damaged wood directly withdrawn from forest (if available) or based on the dataon the damaged wood subsequently used for commercial and other purposes.
If fuelwood gathering is treated separately from fellings, the relevant uncertainties might be high. International
data sources provide uncertainty estimates that could be used together with appropriate data on fuelwood. The
uncertainties for national data on fuelwood gathering could be obtained from local forestry service or statisticalagency or can be estimated with the use of expert judgment.
AC T IV I TY D A TA
Area data should be obtained using the methods in Chapter 2. Uncertainties vary between 1-15% in 16 European
countries (Laitat et al., 2000). The uncertainty of remote sensing methods is ±10-15%. Sub-units will havegreater uncertainty unless the number of samples is increased – other things being equal for uniform sampling an
area one tenth of the national total will have one tenth the number of sample points and hence the uncertainty
will be larger by about the square root of 10, or roughly 3.16. In case the national data on areas of forest lands
are not available, the inventory preparers should refer to international data sources and use uncertainty provided
by them.

3.2.1.2

C HANGE IN CARB ON STOCKS IN DEAD ORGANIC MATTERThis section elaborates good practices for estimating carbon stock changes associated with dead organic matter
pools. The IPCC Guidelines assume as a default that changes in carbon stocks in these pools are not significant
and can be assumed zero, i.e. that inputs balance losses so that net dead organic matter carbon stock changes are
zero. However, the IPCC Guidelines say that dead organic matter should be considered in future work on
inventory methods because the quantity of carbon in dead organic matter is a significant reservoir in many of theworld’s forests. Note that the dead organic matter pools only need to be estimated if Tier 2 or Tier 3 is chosen.
Separate guidance is provided here for two types of dead organic matter pools: 1) dead wood and 2) litter. Table
3.1.2 in Section 3.1.3 of this report provides detailed definitions of these pools. Equation 3.2.10 summarises thecalculation for change in dead organic matter carbon pools.
EQUATION 3.2.10
ANNUAL CHANGE IN CARBON STOCKS IN DEAD ORGANIC MATTERIN FOREST LAND REMAINING FOREST LAND

∆CFFDOM = ∆CFFDW + ∆CFF LT
Where:

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Forest Land

∆CFFDOM = annual change in carbon stocks in dead organic matter (includes dead wood and litter) inforest land remaining forest land, tonnes C yr-1
∆CFFDW = change in carbon stocks in dead wood in forest land remaining forest land, tonnes C yr-1
∆CFFLT = change in carbon stocks in litter in forest land remaining forest land, tonnes C yr-1

3.2.1.2.1

METHODOLOGI CAL ISSUESDE AD WO OD
Dead wood is a diverse pool with many practical problems for measuring in the field and associated uncertainties
about rates of transfer to litter, soil, or emissions to the atmosphere. Carbon in dead wood is highly variable
between stands across the landscape, both in managed stands (Duvall and Grigal, 1999; Chojnacky and Heath,2002) and even in unmanaged stands (Spies et al., 1988). Amounts of dead wood depend on the time of last
disturbance, the amount of input (mortality) at the time of the disturbance (Spies et al. 1988), natural mortality
rates, decay rate, and management. The proposed approach recognizes the regional importance of forest type,disturbance regime, and management regime on the carbon stocks in dead wood, and allows for theincorporation of available scientific knowledge and data.
L I T TE R
The accumulation of litter is a function of the annual amount of litterfall, which includes all leaves, twigs andsmall branches, fruits, flowers, and bark, minus the annual rate of decomposition. The litter mass is also
influenced by the time of last disturbance, and the type of disturbance. During the early stages of standdevelopment, litter increases rapidly. Management such as timber harvesting, slash burning, and site preparation
dramatically alter litter properties (Fisher and Binkley, 2000), but there are few studies clearly documenting theeffects of management on litter carbon (Smith and Heath, 2002).
The proposed approach recognizes the important impact of forest type, and disturbance regimes or management
activities on the carbon in litter, and allows for the incorporation of the available scientific knowledge and data.
The methodology assumes:•

Carbon in the litter pool eventually attains a spatially-averaged, stable value specific to the forest type,disturbance regime, and management practice;•

Changes leading to a new stable litter carbon value occur over a transition time. A column in Table 3.2.1
features updated default factors for the transition period. The value of carbon in litter generally stabilizes
sooner than aboveground biomass stocks; and•Carbon sequestration during the transition to a new equilibrium is linear.

3.2.1.2.1.1

Choice of Method

Depending on available data, the country may arrive at a different tier for the dead wood and litter pools.
Ca lc u la t io n pro c e d ur e f o r c ha ng e i n c a r bo n st o c ks i n dea d w o o d
The IPCC Guidelines do not require estimation or reporting on dead wood or litter, on the assumption that thetime average value of these pools will remain constant with inputs to dead matter pools balanced by outputs. The
GPG retains this default assumption but provides advice for reporting at higher tiers for Convention purposes
and to meet the requirements set out in Chapter 4.
The change in carbon stocks in dead wood for an area of forest land can be calculated using two options, given
in Equation 3.2.11 and Equation 3.2.12. The forest land areas should be categorised by forest type, disturbance
regime, management regime, or other factors significantly affecting dead wood carbon pools. Gross CO2
emissions from dead wood should be calculated as part of Equation 3.2.11 at Tier 2 or Tier 3.
EQUATION 3.2.11
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD IN FOREST LAND REMAINING FOREST LAND(OPTION 1)∆CFFDW = [A ● (Binto – Bout)] ● CF
Where:∆CFFDW = annual change in carbon stocks in dead wood in forest land remaining forest land, tonnes C yr-1
A

= area of managed forest land remaining forest land, ha

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Chapter 3: LUCF Sector Good Practice Guidance

Binto = average annual transfer into dead wood, tonnes d.m. ha-1 yr-1
Bout = average annual transfer out of dead wood, tonnes d.m. ha-1 yr-1
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
Binto, the annual transfer into the dead wood pool, includes biomass cut for harvest but left on the site, natural
mortality, and biomass from trees killed by fire or other disturbances, but not emitted at the time of disturbance.
Bout, average annual transfer out of dead wood pool, is the carbon emissions from the dead wood pool. These arecalculated by multiplying the dead wood carbon stock by a decay rate. The IPCC Guidelines, assume that Binto
and Bout balance so that ∆CFFDW equals zero.
The equation chosen depends on available data. Transfers into and out of a dead wood pool for Equation 3.2.11
may be difficult to measure. The stock change method described in Equation 3.2.12 is used with survey data
sampled according to the principles set out in Section 5.3.
EQUATION 3.2.12
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD IN FOREST LAND REMAINING FOREST LAND(OPTION 2)∆CFFDW = [A ● (Bt2 – Bt1) / T] ● CF
Where:∆CFFDW = annual change in carbon stocks in dead wood in forest land remaining forest land, tonnes C yr-1
A = area of managed forest land remaining forest land, ha
Bt1 = dead wood stock at time t1 for managed forest land remaining forest land, tonnes d.m. ha-1
Bt2 = dead wood stock at time t2 (the previous time) for managed forest land remaining forest land, tonnes
d.m. ha-1
T (= t2 – t1) = time period between time of the second stock estimate and the first stock estimate, yr
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
The decision tree in Figure 3.1.1 (Section 3.1.6) provides assistance in the selection of the appropriate tier level
for the implementation of estimation procedures. Theoretically, Equations 3.2.11 and 3.2.12 should give thesame carbon estimates. In practice, data availability and desired accuracy determine choice of equation.
Tier 1 (Default): The IPCC Guidelines, consistent with reporting under Tier 1, assume that the average transfer
rate into the dead wood pool is equal to the transfer rate out of the dead wood pool so the net change is zero. This
assumption means that magnitude of the dead wood carbon pool need not be quantified. Countries experiencing
significant changes in forest types, or disturbance or management regimes in their forests are encouraged todevelop domestic data to quantify this impact and report it under Tier 2 or 3 methodologies.
Tier 2: Equation 3.2.11 or Equation 3.2.12 is used, depending on the type of data available nationally. Activity
data are defined by the country by significant forest types, disturbance and management regimes, or otherimportant variables affecting dead wood pool. Where Equation 3.2.11 is used, transfer rates are determined forthe country or taken from matching regional sources such as data from nearby countries. Country-specific decay
rates are used to estimate carbon emissions from dead wood stocks. When country-specific dead wood carbon
stocks defaults are known, Equation 3.2.12 is used.
Tier 3: Tier 3 methods are used where countries have country-specific emission factors, and substantial national
methodology. Country-defined methodology may be based on detailed inventories of permanent sample plots fortheir managed forests, and/or models. The statistical design of the inventory, consistent with the principles set
out in Chapter 5, will provide information on the uncertainties associated with the inventory. Models used will
follow the principles set out in Chapter 5. Equation 3.2.11 or Equation 3.2.12 is used, depending on the available
data and methodology.
L I T TE R
Calc u latio n proc ed ur e for c hang e i n ca rbo n st oc ks i n litt er
The conceptual approach to estimating changes in carbon stocks in litter is to calculate the net annual changes inlitter stocks for an area of forest land undergoing a transition from state i to state j as in Equation 3.2.13:

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EQUATION 3.2.13
ANNUAL CHANGE IN CARBON STOCKS IN LITTER IN FOREST LAND REMAINING FOREST LAND
∆CFFLT = ∑i,j [(Cj – Ci) ● Aij] / Tij
where,
Ci = LTref(i) ● fman intensity(i) ● fdist regime(i)Where:∆CFFLT = annual change in carbon stocks in litter, tonnes C yr-1
Ci = stable litter stock, under previous state i, tonnes C ha-1
Cj = stable litter stock, under current state j, tonnes C ha-1
Aij = forest area undergoing a transition from state i to j, ha
Tij = time period of the transition from state i to state j, yr. The default is 20 years
LTref(i) = the reference stock of litter under native, unmanaged forest, corresponding to state i, tonnes C
ha-1
fman intensity(i) = adjustment factor reflecting the effect of management intensity or practices on LTref in statei, dimensionlessfdist regime(i)= adjustment factor reflecting a change in the disturbance regime with respect to LTref in state i,dimensionless
The values of the default adjustment factors reflecting the effect of management intensity or disturbance regime
are 1.0. Sometimes data on litter pools are collected in terms of dry matter, not carbon. To convert to dry mattermass of litter to carbon, multiply the mass by a default value of 0.370 (Smith and Heath, 2002), not the carbon
fraction used for biomass.
The transition from Ci to Cj is assumed to take place over a transition period of T years (default = 20 years). The
total litter carbon pool changes in any year equals the sum of the annual emissions/removals for all forest lands
having undergone changes in forest types, management practices or disturbance regimes for a period of timeshorter than T years. Updated default values are presented in Table 3.2.1 for litter carbon stocks for mature forest
land remaining forest, net accumulation rates for the 20 year default, updated default transition period lengths,
and net accumulation rates for the updated default transition period lengths.
The decision tree in Figure 3.1.1 (Section 3.1.6) provides assistance in the selection of the appropriate tier level
for the implementation of estimation procedures.
Tier 1 (Default): The IPCC Guidelines, consistent with reporting under Tier 1, assume that the average transfer
rate into the litter pool is equal to the transfer rate out of the litter pool so the net change is zero. This assumptionmeans that magnitude of the litter pool need not be quantified. Countries experiencing significant changes in forest
types or disturbance or management regimes in their forests are encouraged to develop domestic data to quantify
this impact and report it under Tier 2 or 3 methodologies.
Tier 2: Equation 3.2.13 or a formulation of Equation 3.2.11 for litter carbon is used, depending on the type ofdata available nationally. Activity data are defined by the country by significant forest types, disturbance andmanagement regimes, or other important variables affecting dead wood pool. Where transfer rates are
determined for the country or taken from matching regional sources such as data from nearby countries,
Equation 3.2.11 formulated for litter is used. Country-specific decay rates are used to estimate carbon emissions
from dead wood stocks. Where litter carbon pools are measured consistently over time, Equation 3.2.12 is used.
Tier 3: Methodology for estimating litter carbon changes involves the development, validation, andimplementation of a domestic inventory scheme or inventory systems combined with the use of models. This tierfeatures pools that are more closely linked, perhaps by taking measurements or samples of all forest pools at thesame location. Given the spatial and temporal variability and uncertainty in litter carbon, countries in which litterC changes from managed forests are a key category, are encouraged to quantify changes using statisticallydesigned inventories or advanced models proven to be capable of accurately predicting site-specific changes.
The statistical design of the inventory, consistent with the principles set out in Chapter 5, will provide
information on the uncertainties associated with the inventory. Models used will follow the principles set out in
Chapter 5. Depending on the available data and methodology, Equation 3.2.13 or a litter variant of Equation
3.2.11 is used.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3.2.1
UPDATED DEFAULTS FOR LITTER CARBON STOCKS (TONNES C HA-1) AND TRANSITION PERIOD (YEARS)(Net annual accumulation of litter carbon is based mostly on data for managed forest anddefault period of 20 years)
Forest Type
Broadleaf
Deciduous

Needleleaf
Evergreen

Broadleaf
Deciduous

Needleleaf
Evergreen

ClimateLitter carbon stock ofmature forests

Length oftransition period(years)Boreal, dry(tonnes C ha-1)25
31(10-58)(6-86)

Broadleaf
Deciduous

Needleleaf
Evergreen

Broadleaf
Deciduous

Needleleaf
Evergreen

Net annual
Net annual accumulation
accumulation of litterof litter C over length ofC, based on 20 year
transition period bc
default(tonnes C ha-1 yr-1)(tonnes C ha-1 yr-1)

50

80

0.5

0.4

1.2

1.6

Boreal, moist

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

50

80

0.8

0.7

2.0

2.8

Cold Temperate,dry

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

50

80

0.6

0.4

1.4

1.4

Cold temperate,
moist

16
(5-31) a26(10-48) a

50

50

0.3

0.5

0.8

1.3

20.3
28.2
Warm Temperate,(23.4-33.0)a (17.3-21.1)a
dry

75

75

0.4

0.3

1.4

1.0

Warm temperate,
moist

13
(2-31) a22(6-42)a

50

30

0.3

0.7

0.6

1.1

Subtropical

2.8
(2-3)

4.1

20

20

0.1

0.2

0.1

0.2

Tropical

2.1
(1-3)

5.2

20

20

0.1

0.3

0.1

0.3

Source: Siltanen et al., 1997; and Smith and Heath, 2002; Tremblay et al., 2002; and Vogt et al.,1996, converted from mass to carbon bymultiplying by conversion factor of 0.37 (Smith and Heath, 2002).
Note: Ages follow Smith and Heath (2002).
aValues in parentheses marked by superscript “a” are the 5th and 95th percentiles from simulations of inventory plots, while those without
superscript “a” indicate the entire range.

b

These columns indicates the annual increase in litter carbon when starting from bare ground in land converted forest land.

cNote that the accumulation rates are for carbon being absorbed from the atmosphere. However, depending on the methodology, these may
be transfers from other pools.

3.2.1.2.1.2

Choice of Emission/Removal Factors

DE AD WO OD
Tier 1: By default, it is assumed that the dead wood carbon stocks in all managed forests remaining forests arestable.
Tier 2: Country-specific values for transfer of carbon in live trees that are harvested to harvest residues can bederived from domestic 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. Country-specific values for disturbance regimes could be derived from scientific studies. If countryspecific input factors are derived, corresponding loss factors for harvest and disturbance regimes should also bederived from country-specific data.
Tier 3: For Tier 3, countries should develop their own methodologies and parameters for estimating changes indead wood. Such approaches should be undertaken as part of the national forest inventory, with periodic
sampling according to the principles set our in Section 5.3, which can be coupled with modeling studies tocapture the dynamics of all forest-related pools. Tier 3 methods provide estimates of greater certainty than lower
tiers and feature a greater link between individual forest pools. Some countries have developed disturbancematrices that provide, for each type of disturbance, a carbon reallocation pattern among different pools (Kurzand Apps, 1992). Other important parameters in a modeled dead wood carbon budget are decay rates, which mayvary with the type of wood and microclimatic conditions, and site preparation procedures (e.g. controlled
broadcast burning, or burning of piles). Equation 3.2.12 can be used with sample data obtained consistent withthe principles set out in Section 5.3. Table 3.2.2 provides data which may be useful for model intercomparison,but are not suitable as defaults.

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TABLE 3.2.2
UPDATED DEFAULTS OF NATURAL MORTALITY RATES, DEAD WOOD STOCKS, AND LIVE:DEAD RATIOS(Note that these are mostly based on semi natural and near natural forests)

Tropical forest

Average mortality rate
(fraction of standing biomass per year )0.0177

Evergreen forest

0.0116

1.059/49

Deciduous forest

0.0117
Average (median) dead wood stock(tonnes d.m. ha-1)
18.2

0.682/29

BiomeaTropical forest

Coefficient of Variation/Number of stands
0.616/61

Coefficient of Variation/Number of stands
2.12/37

Evergreen forest

43.4

1.12/64

Deciduous forest

34.7

1.00/62

Average (median) dead:live ratio

Coefficient of Variation/Number of stands

Tropical forest

0.11

0.75/10Evergreen forest

0.20

1.33/18

Deciduous forest

0.14

0.77/19

Sources: Harmon, M. E., O. N. Krankina, M. Yatskov, and E. Matthews. 2001. Predicting broad-scale carbon stores of woody detritus
from plot-level data. Pp. 533-552 In: Lal, R., J. Kimble, B. A. Stewart, Assessment Methods for Soil Carbon, CRC Press, New Yorka

For delineation of biomes, see Figure 3.1.3.

L I T TE R
Tier 1 (Default): In the IPCC Guidelines, consistent with reporting under Tier 1, litter inputs and outputs are
assumed to balance and the pools are therefore taken to be stable. Countries experiencing significant changes inforest types or disturbance or management regimes in their forests are encouraged to develop domestic data toquantify this impact and report it under Tier 2 or 3 methodologies. Default values are presented in Table 3.2.1.
These values may be used as an approximate calculation to determine if litter carbon is a key category, or as acheck for country-specific values.
Tier 2: It is good practice to use country level data on litter for different forest categories, in combination withdefault values if country or regional values are not available for some forest categories. Table 3.2.1 provides
updated default data on litter stocks, but these are not a substitute for national data, where available.
Tier 3: National level disaggregated litter carbon estimates are available for different forest types, disturbanceand management regimes, based on measurements from National Forest Inventories or from a dedicatedgreenhouse gas (GHG) Inventory Programme.

3.2.1.2.1.3

Choice of Activity Data

Activity data consist of areas of forest remaining forest summarised by major forest types, management
practices, and disturbance regimes. Total forest area should be consistent with those reported under othersections of this chapter, notably Section 3.2.1.1. The assessment of changes in dead organic matter is greatly
facilitated if this information can be used in conjunction with national soil and climate data, vegetation
inventories, and other geophysical data. The area summaries for the litter pool may be different than those for thedead wood pool when it is known that emission factors do not vary for some of the activity data, such as bymanagement practice.
Data sources will vary according to a country’s forest management system, from individual contractors orcompanies, to regulation bodies and government agencies responsible for forest inventory and management, andresearch 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 remotely sensed
imagery.

3.2.1.2.1.4

Uncertainty Assessm ent

The uncertainty associated with Tier 1 methods is so high that the dead organic matter pools were simply
assumed to be stable at a time that managed forests are growing. Logging residue created by harvest was
assumed to decay instantly at time of harvest, emitting its entire mass as carbon dioxide. Emissions from deadorganic matter due to disturbances like wildfires, or insect or disease infestation were ignored. The dynamics ofthe litter carbon pool were also ignored. When emissions are assumed equal to zero, describing uncertainty interms of percentage of the emissions is indeterminate. Any percentage multiplied by zero is zero.

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DE AD WO OD
An estimate for a maximum bound for carbon in dead wood is 25% of the amount of C in live biomass pools.
The maximum value in absolute terms in C in dead wood is 25% of the amount of C in live biomass pools
divided by five. Dividing by 5 simulates dead wood decaying in five years. The use of regional and countryspecific inventory data and models under Tiers 2 and 3 enables for significant reduction of uncertainties. A
survey of dead wood may be designed for any designated precision. Nationally determined values of within±30% may be reasonable for dead wood.
L I T TE R
Ranges in Table 3.2.1 may be analyzed for uncertainty defaults for litter. For litter pools, the uncertainty isapproximately a factor of one. For emissions or sequestration rates, the uncertainty is also approximately a factor
of one. The use of regional and country-specific inventory data and models under Tiers 2 and 3 enables for
significant reduction of uncertainties.

3.2.1.3

C HANGE IN CARB ON STOCKS IN SOILSThis section elaborates on estimation procedures and good practices for estimating change in carbon stocks fromand to forest soils. Separate guidance is provided for two types of forest soil carbon pools: 1) the organic fractionof mineral forest soils, and 2) organic soils. The change in carbon stocks in soils in forest land remaining forest
land (∆CFFSoils) is equal to the sum of changes in carbon stocks in the mineral soil (∆CFFMineral) and the organic
soil (∆CFFOrganic).
This report does not address the inorganic soil carbon pool, but notes the need for soil analytical procedures todistinguish between the organic and inorganic fractions where the latter is significant.
SO I L O R G A N I C M AT T ER
Soil organic matter refers to a complex of large and amorphous organic molecules and particles derived from thehumification of aboveground and belowground litter, and incorporated into the soil, either as free particles orbound to mineral soil particles. It also includes organic acids, dead and living microorganisms, and thesubstances synthesized from their breakdown products (Johnson et al., 1995).
It is good practice to separate mineral from organic forest soils, as default estimation procedures are different.
SO I L OR GA NI C M AT T ER I N M IN E RA L FO RE S T SO I LS
Globally, the organic carbon content of mineral forest soils (to 1 m depth) varies between less than 10 andalmost 20 kg C m-2, with large standard deviations (Jobbagy and Jackson, 2000). Mineral forest soils to that
depth contain approximately 700 Pg C (Dixon et al., 1994). Because the input of organic matter is largely fromaboveground litter, forest soil organic matter tends to concentrate in the upper soil horizons, with roughly half ofthe soil organic carbon of the top 100 cm of mineral soil being held in the upper 30 cm layer. The carbon held in
the upper profile is often the most chemically decomposable, and the most directly exposed to natural andanthropogenic disturbances.
Due to inconsistent classifications, there is no global estimate of the carbon content of forested organic soils.
Zoltai and Martikainen (1997) estimated that forested peatlands extend between 70 and 88 Mha (using a 30 cm
minimum depth), with a global carbon content in the order of 500 Pg.
BOX 3.2.1
ORGANIC SOILS, PEATLANDS AND WETLANDS

The expressions organic soils and peatlands are sometimes used interchangeably in the literature,
although the term “peat”, more commonly used in the ecological literature, really refers to theorigin of the organic material – principally moss fragments formed under anaerobic conditions.
The mere presence of peat is not sufficient to define the soil as organic. Note that organic soils
may be covered by LFH (litter, fermentation and humus) layers, however these organic layers
would not be found in an anaerobic environment.
Wetlands are identified and classified based on their hydrological properties, i.e. by the dominanceof anaerobic conditions. Bogs are wetlands with an organic substrate.
For the purpose of this document, all organic soils within the managed forest should be included in
the assessment, regardless of the origin of the organic matter, or the soil’s hydrological regime.

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Forest Land3.2.1.3.1

METHODOLOGI CAL ISSUES

Soil organic matter is in a state of dynamic balance between inputs and outputs of organic carbon. Inputs arelargely determined by the forest productivity, the decomposition of litter and its incorporation into the mineralsoil; rates of organic matter decay and the return of carbon to the atmosphere through respiration control outputs(Pregitzer, 2003). Other losses of soil organic carbon occur through erosion or the dissolution of organic carbon,
but these processes may not result in immediate carbon emissions.
In general, human activities and other disturbances alter the carbon dynamics of forest soils. Changes in forest
type, productivity, decay rates and disturbances can effectively modify the carbon contents of forest soils.
Different forest management activities, such as rotation length; harvest practices (whole tree or sawlog;
regeneration, partial cut or thinning); site preparation activities (prescribed fires, soil scarification); andfertilisation, interfere more or less strongly with soil organic carbon (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 carbon pool
(Li and Apps, 2002; de Groot et al., 2002).
M INE RA L SO I L S
In spite of a growing body of literature on the effect of forest types, management practices and otherdisturbances on soil organic carbon, the available evidence remains largely site- and study-specific, for the mostpart influenced by climatic conditions, soil properties, the time scale of interest, the soil depth considered and thesampling intensity (Johnson and Curtis, 2001; Hoover, 2003; Page-Dumroese et al., 2003). The current
knowledge remains inconclusive on both the magnitude and direction of carbon stock changes in mineral forest
soils associated with forest type, management and other disturbances, and cannot support broad generalisations.
The proposed approach acknowledges the regionally important impact of forest type, management activities or
disturbance regimes on the carbon budget of mineral forest soils, and allows for the incorporation of theavailable scientific knowledge and data. However, due to the incomplete scientific basis and resulting
uncertainty, the assumption in the IPCC Guidelines that forest soil carbon stocks remain constant is retained andaccordingly no default data will be provided at the Tier 1 level.
Conceptually, the default approach assumes a stable, spatially-averaged carbon content of mineral soils under
given forest types, management practices and disturbance regimes. This equilibrium value is altered when thesestates or conditions change. The following assumptions are made:(i)

Forest soil organic carbon (SOC) reaches over time a spatially-averaged, stable value specific to thesoil, forest type and management practices (e.g. tropical conifer plantation on a low-activity soil).
This value is a temporally averaged SOC best estimated over several rotations or disturbance cycles(Figure 3.2.1).

(ii)

Changes in forest type or management leading to a new stable SOC value occur over a transition
time equal to the length of a rotation or the return interval of natural disturbances, in years.

(iii)

SOC sequestration/release during the transition to a new equilibrium SOC occurs in a linear
fashion.

Soil organic carbon(g C pe r ha)

Figure 3.2.1 Two temporally averaged values of soil organic carbon corresponding to different
combinations of forest soils, management practices and disturbance regimes.

S OC jS OC i

T ime

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Chapter 3: LUCF Sector Good Practice GuidanceO R GA N IC SO I L S
As in mineral soils, the accumulation or loss of carbon in organic soils results from a balance between inputs andoutputs. When wet or moist conditions more or less hamper the decomposition of organic matter, input oforganic matter may exceed decomposition losses, and organic matter accumulates. The carbon released fromsaturated organic soils to the atmosphere is predominantly under the form of CH4, while under aerobic
conditions the C flux to the atmosphere is dominated by CO2. The C dynamics of organic soils are closely linked
to the site hydrological regimes: available moisture, depth of the water table, reduction-oxidation conditions(Clymo, 1984; Thormann et al., 1999); but also species composition and litter chemistry (Yavitt et al., 1997).
This C pool will readily respond to activities or events that affect aeration and decomposition conditions.
The drainage of organic soils releases CO2 by oxidation of the organic matter in the aerobic layer, although thisloss of carbon can be partially or entirely offset by: 1) greater inputs of organic matter from above; or 2)
decrease in natural fluxes of CH4. The magnitude of the CO2 emissions is related to drainage depth, the fertility
and consistence of the peat, and temperature (Martikainen et al., 1995). Abandonment of drainage in organicsoils reduces these CO2 emissions and may even re-establish the net carbon sequestration potential in forested
organic soils (see also Section 3a.3.2 (Organic soils managed for peat extraction) in Appendix 3a.3, and Section
3.2.1.4 (Non-CO2 Greenhouse Gas Emissions)). The CO2 released from organic matter oxidation after drainage
is considered anthropogenic. Emissions from undrained, and unmanaged forested peatlands are considered as
natural and are therefore not accounted for.
Other forest management activities are likely to 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 (Dubéet al., 1995).
While there is some evidence of the effects of anthropogenic activities on forested organic soils, the data andknowledge remain largely site-specific and can hardly be generalized. The net carbon flux of organic soils is
usually directly estimated from chamber or flux tower measurements (Lafleur, 2002).

3.2.1.3.1.1

Choice of Method

Calc u latio n proc ed ur e for c hang e i n ca rbo n st oc ks i n so il s
M INE RA L SO I L S
Conceptually, emissions or removals of carbon from the mineral forest soil pool can be calculated as annualchanges in soil organic carbon stocks for an area of forest land undergoing a transition from state i to state j,where each state corresponds to a given combination of forest type, management intensity and disturbance
regime. This is illustrated by Equation 3.2.14:
EQUATION 3.2.14
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILSIN FOREST LAND REMAINING FOREST LAND

∆CFFMINERAL = ∑ij [(SOCj – SOCi) ● Aij] / Tij
Where,
SOCi = SOCref ● fforest type (i) ● fman intensity (i) ● fdist regime (i)Where:∆CFFMineral = annual change in carbon stocks in mineral soils in forest land remaining forest land,
tonnes C yr-1
SOCi = stable soil organic carbon stock, under previous state i, tonnes C ha-1
SOCj = stable soil organic carbon stock, under current state j, tonnes C ha-1
Aij = forest area undergoing a transition from state i to j, ha
Tij = time period of the transition from SOCi to SOCj, yr. The default is 20 years.
SOCref = the reference carbon stock, under native, unmanaged forest on a given soil, tonnes C ha-1
fforest type (i)= adjustment factor reflecting the effect of a change from the native forest to forest type instate i, dimensionlessfman intensity (i) = adjustment factor reflecting the effect of management intensity or practices on forest in
state i, dimensionless

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IPCC Good Practice Guidance for LULUCF

Forest Landfdist regime (i) = adjustment factor reflecting the effect of a change in the disturbance regime to state i withrespect to the native forest, dimensionless
The transition from SOCi to SOCj is assumed to take place over a transition period of T years (default = 20
years). In other words ∆C > 0 as long as fewer than T years have elapsed since the onset of changes in forest
type, management practices, or disturbance regime. The total SOC changes in any year equals the sum of theannual emissions/removals for all forest lands having undergone changes in forest types, management practices
or disturbance regimes for a period of time shorter than T years.
The decision tree in Figure 3.1.1 (Section 3.1) provides assistance in the selection of the appropriate tier level forthe implementation of estimation procedures.
Tier 1: This tier is used for countries using the default procedure in the IPCC Guidelines, or for which thissubcategory is not significant, and little or no country-specific data exist on the SOC of mineral forest soils under
dominant forest types, management practices and disturbance regimes. Under Tier 1, it is assumed that when
forest remains forest the carbon stock in soil organic matter does not change, regardless of changes in forest
management, types, and disturbance regimes (i.e. SOCj = SOCi = … = SOCn) in other words that the carbon
stock in mineral soil remains constant so long as the land remains forest.
Tier 2: Countries where this subcategory is significant should develop or select representative adjustment factors
fforest type, fman intensity, and fdist regime reflecting the impact on mineral SOC of different forest types, management
practices or disturbance regimes, and SOCref for their own native, unmanaged forest ecosystems. Domestic
values for the transition period T should be developed, and the assumption of linear rates of SOC change can be
modified to better reflect the actual temporal dynamics of soil carbon sequestration or release.
Tier 3: Tier 3 is appropriate for countries where emissions/removals in the mineral soils of managed forests areimportant, while current knowledge and available data allow the development of an accurate and comprehensive
domestic estimation methodology. This involves the development, validation and implementation of a domestic
monitoring scheme and/or modelling tool and its associated parameters. The basic elements of any countryspecific 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 sections of the inventory, especially the other carbon pools under this Section 3.2.1;•

Determination of dominant soil types in each stratum;•

Characterisation of corresponding soil carbon pools, identification of determinant processes in SOC input
and output rates and the conditions under which these processes occur; and•Determination and implementation of suitable methods to estimate carbon emissions/removals from forest
soils for each stratum on an operational basis, including validation procedures; methodologicalconsiderations should 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), and Section 5.3 provides generic guidance on sampling techniques. Models developed or
adapted for this purpose should be peer-reviewed, and validated with observations representative of theecosystems under study and independent from the calibration data.

The methodology should be comprehensive, and include all managed forest lands and all anthropogenic
influence on SOC dynamics. Some assumptions underlying Tier 3 estimation procedures may depart from thoseinherent to the default methodology, provided sound scientific basis underlies new assumptions. Tier 3 may also
include factors that influence emissions and removals of C from forest soils that are not included in the default
approach. Finally, Tier 3 calculations are expected to be more refined temporally and spatially. It is goodpractice, at Tier-3 accounting level, to include SOC in an integrated ecosystem assessment of all forest carbon
pools, with explicit linkages between the soil, biomass and dead organic matter pools.
The national methodology should include a strong verification component, in which independent data arecollected for the verification of the applicability of defaults values and national parameters. Verification
activities should take place at a number of spatial and temporal scales, and may incorporate data from basic
inventory methods, remote sensing and modelling. Chapter 5 elaborates on general approaches to the verificationof inventory estimates.
O R GA N IC SO I L S
Current knowledge and data limitations constrain the development of a default methodology for estimating CO2
emissions to and from drained, organic forest soils. Guidance will be limited to the estimation of carbon
emissions associated with the drainage of organic soils in managed forests (Equation 3.2.15).

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Chapter 3: LUCF Sector Good Practice Guidance

EQUATION 3.2.15
CO2 EMISSIONS FROM DRAINED ORGANIC FOREST SOILS∆CFFOrganic = ADrained ● EFDrainage
Where:∆CFFOrganic = CO2 emissions from drained organic forest soils, tonnes C yr-1
ADrained = area of drained organic forest soils, ha
EFDrainage = emission factor for CO2 from drained organic forest soils, tonnes C ha-1 yr-1 (see Table 3.2.3)

TABLE 3.2.3
DEFAULT VALUES FOR CO2-C EMISSION FACTOR FOR DRAINED ORGANIC SOILS IN MANAGED FORESTS
BiomesEmissions factors (tonnes C ha-1 yr-1)
ValuesRangesTropical forests

1.36

0.82 – 3.82

Temperate forests

0.68

0.41 – 1.91

Boreal forests

0.16

0.08 – 1.09

Emissions are assumed to continue for as long as the aerobic organic layer remains and the soil is considered to
be an organic soil.
Tier 1: Tier 1 calculation procedures involve producing country-specific data on the area of drained, organic
forest soils and applying the appropriate default emissions factor. This tier is appropriate for countries in which
this subcategory is not significant, and in case where representative EFDrainage values are not available.
Tier 2: Tier 2 is suitable for countries where this subcategory is significant; these countries should develop or
select representative EFDrainage values.
Tier 3: Tier 3 methodology involves the estimation of CO2-C emissions and removals associated with the entire
area of forested organic soils, including all anthropogenic activities likely to alter the hydrological regime,
surface temperature and vegetation composition of forested organic soils; and major disturbances such as fires. It
is good practice, in Tier 3 estimation procedures, to conduct a full carbon balance of forested organic soils,
including fluxes of both CO2 and CH4. Tier 3 methodologies should also be consistent with the estimation
procedures for non-CO2 GHG in Section 3.2.1.4. Tier 3 estimation procedures are appropriate if a country’s
managed forest includes extensive areas of organic soils.
Figure 3.1.1 (Section 3.1) provides guidance in the selection of tiers for the estimation of CO2 emissions fromdrained, organic forest soils.

3.2.1.3.1.2

Choice of Emission/Removal Factors

M INE RA L SO I L S
The parameters to be estimated are SOCi,j, Tij, SOCref , fforest type, fman intensity, and fdist regime.
Tier 1: The current state of knowledge on managed forest soils does not allow the derivation of default soil
carbon stock parameters (SOCi,j). Default values for SOCref, the organic carbon content of mineral forest soils
under native vegetation, for 0-30 cm depth, are provided in Table 3.2.4.
Tier 2: Countries provide their own values of SOCref, compiled from published studies or surveys representativeof major native forest and soil types. Such values are typically obtained through the development and/or
compilation of large soil profile databases (Scott et al., 2002; NSSC, 1997; Siltanen et al., 1997).
The carbon content per unit area (or carbon stocks) should be reported in tonnes C ha-1 for a given soil depth orlayer (e.g. to 100 cm, or for the 0-30 cm layer). As shown in Equation 3.2.16, total SOC contents is obtained bysumming the SOC contents of the constituent soil horizons or layers; the SOC content of each horizon or layer iscalculated by multiplying the concentration of soil organic carbon in a sample (g C (kg soil)-1), with thecorresponding depth and bulk density (Mg m-3) and adjusting for the soil volume occupied by coarse fragments:

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Forest Land

TABLE 3.2.4
DEFAULT REFERENCE (UNDER NATIVE VEGETATION) SOIL ORGANIC C STOCKS (SOCREF)(tonnes C per ha for 0-30 cm depth)
RegionHAC soils1

LAC soils2

Sandy soils3

Spodic soils4

Volcanic soils5

Wetlands
soils6

Boreal

68

NA

10#

117

20#

146

#

Cold temperate, dry

50

33

34

NA

20

Cold temperate, moist

95

85

71

115

130

Warm temperate, dry
Warm temperate,
moist
Tropical, dry

38

24

19

NA

70#

88

63

34

NA

80

38

35

31

NA

50#

Tropical, moist

65

47

39

NA

70#

NA

#

Tropical, wet

44

60

66

130

87

88

86

Note: Data are derived from soil databases described by Jobbagy and Jackson (2000) and Bernoux et al. (2002). Mean stocks are shown.
A default error estimate of 95% (expressed as 2X standard deviations as percent of the mean are assumed for soil-climate types. NA
denotes ‘not applicable’ because these soils do not normally occur in some climate zones.
# indicates where no data were available and default values from IPCC Guidelines were retained.
1

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

2

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

3

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

4

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

5

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

6

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

EQUATION 3.2.16
SOIL ORGANIC CARBON CONTENT

SOC =horizon = n∑horizon =1

SOC horizon =horizon = n∑ ([SOC ] • BulkDensit y • Depth • (1 − frag ) • 10 )horizon

horizon = 1

Where:
SOC = representative soil organic carbon content for the forest type and soil of interest, tonnes C ha-1
SOChorizon = soil organic carbon content for a constituent soil horizon, tonnes C ha-1
[SOC] = concentration of soil organic carbon in a given soil mass obtained from lab analyses, g C (kgsoil)-1
Bulk Density = soil mass per sample volume, tonnes soil m-3 (equivalent to Mg m-3)
Depth = horizon depth or thickness of soil layer, mfrag = % volume of coarse fragments/100, dimensionless 2
Country- or region-specific values should be elaborated for the stable SOCi, SOCj, for the major combinations offorest types, management practices and disturbance regimes. Priority should be given to the factors that have the2[SOC] is usually determined on the fine earth fraction (commonly < 2 mm). The bulk density should be corrected for the
proportion of the soil volume occupied by coarse fragments (e.g. particles with a diameter > 2 mm).

IPCC Good Practice Guidance for LULUCF

3.43

Chapter 3: LUCF Sector Good Practice Guidancelargest overall effect, taking into account the impact on forest SOC and the extent of affected forests.
Management practices can be coarsely labelled as intensive (e.g. plantation forestry with intensive site
preparation and fertilisation) or extensive (natural forests with minimum intervention); these categories can also
be redefined according to national circumstances. The development of adjustment 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 & Curtis, 2001 and Hoover, 2003.) In practice,
it may not be always possible to separate the effects of a different forest types, intensive management practices
and altered disturbance regimes, in which case some adjustment 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 bepossible to derive SOCi directly without using reference carbon stocks and adjustment factors. Estimating theeffect of changing disturbance regimes over vast areas through sampling studies may create intractable logisticalproblems. Modelling studies provide an alternative approach for the derivation of these adjustment factors(Bhatti et al., 2001).
The duration of transition periods T between stable SOCi can be estimated from long-term monitoring ofchanges in forest SOC. The assumption of a linear rate of carbon stock changes during the transition from one
forest type/management regime to another can also be reassessed.
Tier 3: Country-specific methodologies and parameters are expected to be based on rigorous monitoring
programs, coupled with empirical and/or process modelling studies. The national system must represent all
significant forest types, management practices and disturbance regimes. Models must be validated withindependent observations from country or region-specific studies that cover the range of climatic conditions, soil
types and management practices. The same quality criteria as described under Tier 2 apply to SOC data.
Documentation on the structure, update frequency and procedures, and QA/QC procedures of SOC databases
should also be available.
O R GA N IC SO I L S
The parameters to be estimated are emission factor(s) for CO2 from drained organic forest soils: EFDrainage.
Tier 1: Table 3.2.3 provides default values for EFDrainage, derived from corresponding values for the conversion
to pastures/forests in the IPCC Guidelines, (Reference Manual, Section 5.3.9). These values apply for as long as
a drained organic soil remains.
Tier 2: Countries which develop their own emission factors or adopt ones that are different from the default
values should provide scientifically-based evidence of their reliability and representativeness, document theexperimental procedures used to derive them, and provide uncertainty estimates.

3.2.1.3.1.3

Choice of Activity Data

It is good practice to distinguish managed forests on mineral soils from those on organic soils. The defining
criteria of organic soils are provided in the Glossary. For the purpose of this assessment, the depth of the organic
layer itself is not as important as its presence; countries are therefore encouraged to use their own national depth
criterion for the distinction between organic and mineral soils. Mineral soils comprised all soils which do not
fulfill the definition of organic soils.
Forest inventories, where they include soil descriptions, are preferred data sources. Statistical, stratified sampling
programmes can provide an estimate of the proportion of the managed forest on organic soils, but will notindicate their location. However, it is an acceptable first step in the determination of the importance of forested
organic soils. Alternatively, an area estimate of forest on organic soils could be derived from overlaying soil
maps, and land cover or land use maps. However the relative uncertainty associated with this type of GIS
exercise is high, since it combines the omission and commission errors of all the maps used. Standard GIS
textbooks provide guidance on the treatment of error for overlay exercises.
M INE RA L SO I L S
Tier 2: Activity data consist of the major forest types, management practices, disturbance regimes and the areas
to which they apply, consistently with the guidance provided in Chapter 2 of the present report. The data should
preferably be linked to the national forest inventory, where one exists, or with national soil and climate
databases.
Typical changes are: conversion of unmanaged to managed forest; conversion of native forest into a new forest
type; intensification of forest management activities, such as site preparation, tree planting and shorter rotations;changes in harvesting 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,

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Forest Land

research institutions and agencies responsible for forest inventories. Data formats vary widely, and include,
among others, activity reports, forest management inventories and remotely sensed imagery.
Records should extend sufficiently far back as to include all significant changes having occurred over the T years
selected as the transition period, or else back-casting will be necessary.
Tier 3: It is good practice to adopt the same forest types, management practices and disturbance regimes as
those used for estimating emissions/removals in other forest pools.
O R GA N IC SO I L S
The activity data consist of ADrainage, the area of drained organic soil (including peatland) covered by forest.
Probable data sources are forest management records of industry or statutory forest authorities. Alternatively,
expert knowledge from within such organisations may be solicited.

3.2.1.3.1.4

Uncertainty Assessm ent

M INE RA L SO I L S
The greatest uncertainty arises from the determination of SOC values (in tonnes C ha-1) over large areas(Equation 3.2.14). Default values have a high inherent uncertainty when applied to specific countries. Standard
deviations of default reference soil carbon stocks under native vegetation are provided in Table 3.2.4.
For countries developing their own SOC values, the two major sources of uncertainty are soil bulk density andsoil volume occupied by coarse fragments. When computing forest SOC values, assume 40% uncertainty in bulk
density values, and a factor of 2 uncertainty for the soil volume occupied by coarse fragments. Assume that the
top 30 cm of mineral forest soils contain 50% of total SOC. Uncertainty associated with shallow sampling can be
reduced by providing scientific evidence on (1) the proportion of total SOC contained in the soil depth sampled;and (2) the depth at which SOC responds to changes in forest types, management practices and disturbance
regimes. Chapter 5, Box 5.2.4, provides generic guidance on the treatment of uncertainty when estimates arederived from model outputs.
O R GA N IC SO I L S
The largest uncertainties stem from CO2 emission factors for drained organic soils. Assume that EFDrainage varies
by a factor of 2. The measurement of carbon stocks on organic soils present a significant challenge because of
the great variability in bulk density (from 0.05 to 0.2 g cm3, a four-fold difference), and in the total depth of theorganic layer (an even large source of variability). Further uncertainty arises due to the failure of carbon stockchanges to distinguish between off-site transfer of carbon as dissolved organic matter versus emissions to theatmosphere.

3.2.1.4

N ON -CO 2 GREENH OUSE GAS EMISSIO NSThis section considers N2O emissions from forest soils and non-CO2 greenhouse gas emissions from biomassburning. N2O and NOx are mainly produced in soils as a byproduct of nitrification and denitrification. Emissions
are stimulated directly by N fertilisation of forests and drainage of wet forest soils (Appendix 3a.2), andindirectly through deposition of N from the atmosphere and leaching and runoff. The indirect N2O emissions are
addressed in the Agriculture Chapter of the IPCC Guidelines and therefore not considered here in order to avoid
double counting. Liming of forest soil may reduce N2O emissions in some environments, but increase emissions
in others (Klemedtsson et al., 1997, Mosier et al., 1998, Papen and Butterbach-Bahl, 1999). Forest management
such as clear cutting and thinning may increase N2O emissions. However, available data are insufficient andsomewhat contradictory, therefore in the present section the impact of these practices is not considered.
Afforestation with N-fixing tree species may increase N2O emissions for much of the lifetime of the forest, butthere is too limited data to provide a default methodology.
The CH4 sink in aerated and undisturbed forest soils is a natural process and is estimated to average at 2.4 kg
CH4/ha/yr (Smith et al., 2000). Forest management, particularly N fertilisation, may significantly alter this CH4
sink. Methods and data to estimate changes in methane oxidation are not provided at this time. As additional
information becomes available, a fuller consideration of various activities and their impacts on methane
oxidation from fertilised lands may be possible.

NITROUS OXIDEThe IPCC Guidelines in Chapter 4 Agriculture include N2O emissions from nitrogen fertilisation and also
account for N2O emissions from nitrogen deposition as “indirect N2O emissions”. Specific guidance is given
below applying the methods from Chapter 4 of the IPCC Guidelines to estimate fertiliser-based N2O emissions

IPCC Good Practice Guidance for LULUCF

3.45

Chapter 3: LUCF Sector Good Practice Guidancefrom forests. The methodology for estimating N2O emissions from drainage of wet forest soils is presented inAppendix 3a.2. Forests receive atmospheric nitrogen depositions and nitrogen in runoff and leaching fromadjacent agricultural fields. The Agriculture Chapter of the IPCC Guidelines already addresses these N2O
emissions from N deposition, runoff and leaching as “indirect emissions”. These emissions are not accounted
here, avoiding double-counting. It is assumed that the leaching and run-off from forests where nitrogen fertiliser
is applied into surrounding non-forest or unfertilised forest areas is negligible. This is justified because leaching
and runoff are smaller in forest than in agricultural land, and the emission factor used in the IPCC Guidelines
appears to be high.

3.2.1.4.1

METHODOLOGI CAL ISSUES

The method used to estimate N2O emissions from forest soils is identical to that provided in the IPCC Guidelines
for Agriculture and described in GPG2000. The basic equation, taken from GPG2000, is shown in Equation
3.2.17.
EQUATION 3.2.17
DIRECT N2O EMISSIONS FROM MANAGED FORESTS
N2O direct-NFF = (N2O direct-Nfertiliser + N2O direct-Ndrainage)
Where:
N2O direct-NFF = direct emissions of N2O from managed forests in units of Nitrogen, Gg N
N2O direct-Nfertiliser = direct emissions of N2O from forest fertilisation in units of Nitrogen, Gg N
N2O direct-Ndrainage = direct emissions of N2O from drainage of wet forest soils in units of Nitrogen, Gg N
The method for estimating N2O emissions from fertiliser application to forest is described in Equation 3.2.18 in
the sections below. The method for estimating N2O emissions from drainage of wet forest soils is described inAppendix 3a.2 and may be applied optionally where data are available.

3.2.1.4.1.1

Choice of Method

Figure 3.1.1 provides the decision tree to select the respective tier for N2O emissions from forest land. As shownin Equation 3.2.17, N2O emissions include two sources: forest fertilisation and drainage of wet forest soils.
Tier 1: Emission rates are the same for N2O fertilisation in forest and agricultural areas. Thus, good practicefrom GPG2000 should be used to estimate N2O emissions from nitrogen inputs as mineral or organic fertiliser toforests. N2O emissions from manure deposited by animals grazing in forest areas are reported in Agricultural
Soils part of the IPCC Guidelines Agriculture Chapter under Pasture/Range/Paddock emissions and should not
be estimated separately in the forest section.
Direct N2O emissions from forest fertilisation are calculated as in Equation 3.2.18:
EQUATION 3.2.18
DIRECT N2O EMISSIONS FROM FOREST FERTILISATIONN2O direct-Nfertiliser = (FSN + FON) ● EF1)
Where:
N2O direct-Nfertiliser = direct emissions of N2O from forest fertilisation in units of Nitrogen, Gg N
FSN = annual amount of synthetic fertiliser nitrogen applied to forest soils adjusted for volatilisation as
NH3 and NOx, Gg N
FON = annual amount of organic fertiliser nitrogen applied to forest soils adjusted for volatilisation as NH3
and NOx, Gg N
EF1 = emission factor for N2O emissions from N inputs, kg N2O-N / kg N input
In order to calculate N2O emissions using this equation, the amounts of N inputs, FSN and FON must be estimated.
It is good practice to adjust for the amount that volatilises as NH3 and NOx, using the same volatilisation factors
as in the agriculture chapter of the IPCC Guidelines. Indirect N2O emissions from the N volatilised are
calculated as in the agriculture chapter of the IPCC Guidelines.
Tier 2: Under Tier 2, country-specific information and additional management activities can be included inestimating nitrous oxide emissions:

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Forest LandCountries can use Equation 3.2.18 with an emission factor EF1 developed to meet the specific conditions of thecountry. Specific good practice guidance on how to derive country-specific EFs is given in Box 4.1, Good
Practice in Derivation of Country-Specific Emission Factors, page 4.62 of GPG2000. In addition, countries can
extend the estimation to take into account the impact of forest liming and management (thinning, harvest) on
N2O emission. Liming can reduce N2O emissions from forest in some environments and increase them in others.
Tier 3: Some models exist for estimating N2O emissions (Renault, 1999, Conen et al., 2000, Stange andButterbach-Bahl, 2002). Apply advanced models capable of representing the impacts of management practices
and other relevant driving variables. It is good practice to validate the models against measurements and to
document thoroughly the model parameterization and calibration.
Most models calculate the total N2O emissions which include more than the human-induced emissions. The
direct human-induced emissions could be estimated by running the model with and without fertilisation anddrainage, and using the difference as the direct human-induced component of the emissions.

3.2.1.4.1.2

Choice of Emission/Removal Factors

Tier 1: As noted in GPG2000, the default emission factor (EF1) is 1.25 % of applied N, and this value should be
used under Tier 1.
Tier 2: Countries may develop specific emission factors that are more appropriate for their countries. Specificgood practice guidance on how to derive country-specific emission factors is given in Box 4.1, Good Practice in
Derivation of Country-Specific Emission Factors, page 4.62 of GPG2000. The availability of country-specificfactors is essential if the effects of liming and management are to be considered.
Tier 3: In case N2O emission are estimated with models, it is necessary to make sure that the models distinguishbetween "indirect N2O" from N deposition (covered in Agriculture Chapter of the IPCC Guidelines) andfertilisation. The PnET-N-DNDC model, for instance, is a process oriented model which is already applicable toestimate N2O-emissions from forest soils (Butterbach-Bahl et al., 2001; Li et al., 2000).

3.2.1.4.1.3

Choice of Activity Data

N2O emissions from managed forest are calculated on the basis of mineral and organic nitrogen inputs in forest
soils. Some countries have data on fertilisation of forests separately from agriculture and will be able to makeestimations. However, many countries may only have national fertiliser sales statistics. If such data are notavailable, countries may follow the guidance below to separate the amount applied to agricultural soils and forest
soils, or they may report all emissions under Tier 1 in the agriculture sector. This should, however, be explicitly
noted in the inventory.
FSN: This is the same term used in the Agriculture Chapter of the IPCC Guidelines to refer to synthetic N
applied to agricultural soils adjusted for the amount that volatilises as NH3 and NOx, using the samevolatilisation factors as in the Agriculture Chapter of the IPCC Guidelines. Many countries have national
fertiliser sales statistics. Countries can determine the amount of synthetic nitrogen fertiliser applied in forest by
subtracting the amount of fertiliser used for agriculture from the national total nitrogen fertiliser applied.
Alternatively, estimate fertiliser application in forests as the product of an estimated area of fertilised forest andan average fertilisation rate.
Countries being able to distinguish between fertiliser applied to newly planted forests versus old forests can use a
Tier 2 level for estimating FSN. For fertiliser applied to those forest plantations which have not yet reachedcanopy closure, the adjustment for volatilisation losses should follow the agriculture chapter of the IPCC
Guidelines, i.e. taking account of the fraction of the N applied that is lost by volatilisation. For fertiliser applied
to closed-canopy forests, it can be assumed that the adjustment is zero, i.e. all volatilised N is assumed to remain
within the forest.
FON: Estimate organic nitrogen applied in forests from the tonnage of organic wastes spread in forest and theirnitrogen content. Adjustment for volatilisation losses follows the guidance given for FSN.

3.2.1.4.1.4

Uncertainty Assessm ent

Estimates of N2O emissions from fertilisation of forests can be highly uncertain because of a) high spatial andtemporal variability of the emissions, b) scarcity of long-term measurements and limited representativity of data
for larger regions, and c) uncertainty in spatial aggregation and uncertainty inherent to the emission factors andactivity data.
Tier 1: For EF1, FSN and FON , it is good practice to apply the uncertainty range applied in the agriculture source
category unless more detailed analyses are available.

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Chapter 3: LUCF Sector Good Practice Guidance

Emission factors: There are few measured data, mainly for boreal and temperate regions in Europe, on theeffects of fertilisation, liming and forest management. Measured emission factors of N2O have a skewed
distribution, which is likely to be log-normal.
EF1: Based on recent data (Smith et al., 1999; Mosier and Kroeze, 1999), GPG2000 suggests the best estimateof uncertainties of EF1 = 1.25% to range from 0.25% to 6%. The same uncertainty range is assumed for forest
emissions.
Activity data: If a country has separate statistics for fertiliser applied to forest and to agriculture, it can beassumed that the uncertainty in fertiliser statistics applied in forest is similar to the uncertainty in fertiliser
statistics applied in agriculture. In this case, the same uncertainty is applied in both source categories, e.g. 10%
or smaller for the amount of mineral fertiliser and 20% or smaller for the amount of organic waste (Chapter 4,Agriculture, of the IPCC Guidelines, and GPG2000). If a country derives the amount of fertiliser applied toforest and agriculture from a national total, an additional separate assessment of the uncertainty in the division isrequired. The total uncertainty will be country-specific and will probably be higher than in the separate statistics.
Tier 2: Good practice in derivation of country-specific emission factors is described in Box 4.1, Good Practicein Derivation of Country-Specific Emission Factors, page 4.62 of GPG2000.
Tier 3: Process-based models will probably provide a more realistic estimate but need to be calibrated andvalidated against measurements. Sufficient representative measurements are needed for validation purposes.
Generic guidance on uncertainty assessment for advanced methods is given in Section 5.2, Identifying andQuantifying Uncertainties. Stange et al. (2000) have performed uncertainty assessment for the PnET-N-DNDC
model. This can be taken as an example for how to proceed.

GREENHOUSE GAS EMISSIONS FROM BIOMASS BURNING
Biomass burning occurs in many types of land uses causing emissions of CO2, CH4, N2O, CO, and NOx. There
are two general types of biomass burning covered in this section: burning within managed forests and burning in
the course of land use conversion. The basic approach for estimating greenhouse gas emissions from biomassburning are the same regardless of the specific land use type. The basic approach is presented here andreferenced in other relevant sections of this chapter (e.g., lands converted to croplands). This section provides
good practice guidance for estimating emissions from biomass burning in:• Forest land remaining Forest land;• Land converted to Forest land;• Land converted to Cropland; and• Land converted to Grassland.
The IPCC Guidelines address both types of biomass burning in the LUCF sector (Chapter 5). Emissions fromburning for land use conversion are covered under Forest and Grassland Conversions and emissions fromburning for land management are covered under On-site burning of Forest Biomass. While presented separately
in the IPCC Guidelines, the same method and default factors are used for estimating emissions. In this GPG, themethodology for emissions from burning for land conversion remains essentially unchanged from the IPCC
Guidelines, but the scope of coverage of emissions from burning for land management is broadened in the caseof managed forest land to include the effect of both prescribed and wildfires on CO2 and non-CO2 emissions in
all managed forest lands.3
The GPG2000 covers burning for land management in agriculture. Guidance is provided to estimate emissions
from prescribed burning of savannas and field burning of agricultural residues covered under the Agricultural
sector. The CO2 released is assumed to be removed by photosynthesis of annual vegetation regrowing during thesubsequent year and therefore only non-CO2 gases are considered.

3.2.1.4.2

METHODOLOGI CAL ISSUES

Generally fires can be grouped into prescribed (or controlled) fires and wildfires. Fires associated with land
clearing and ecosystem management activities are usually controlled. Significant types of prescribed fires
include: (i) land clearing fires in the course of forest conversion, (ii) slash-and-burn agriculture, (iii) post-logging
burning of harvest residues (slash); and (iv) low-intensity prescribed fire for fuel load management. The purposeof these fires is usually to get rid of unwanted biomass. The average fire temperature is controlled, the burning
conditions more uniform, and emission factors less variable. In contrast, the characteristics of wildfires are highvariable: fire temperature, quantities of biomass available, thoroughness of the combustion and impact on forest
3

The elaboration is for forest land only because burning for land management in croplands and grasslands is covered by the
Agriculture sector of the GPG2000.

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Forest Land

stands all vary. Among wildfires, ground-level ones are less intensive and their impact on trees less severe thancrown fires. When managed land is burned, emissions resulting from both prescribed fires and wildfires should
be reported so that carbon losses on managed lands are taken into consideration.4
Estimating the impact of fire is more difficult for wildfires, especially high-temperature wildfires, than forcontrolled burns. As a consequence there is better knowledge on the effect of the latter than the former.
In managed forest, CO2 emissions from combustion need to be estimated because the uptake of carbon byregrowing vegetation is taken into account (Kirschbaum, 2000) – see Equations 3.2.2 and 3.2.6. It is therefore
good practice to estimate CO2 and non-CO2 emissions from biomass burning on managed forest lands. The
method for doing this is set out in the parts of Section 3.2.1.1 dealing with Equation 3.2.9. The release of CO2 infire is not synchronous with the rate of uptake by regrowing forest and may take many years to sequester thequantity of carbon released in a wildfire or prescribed burn. If methods are applied that do not capture removalsby regrowth after natural disturbances, then it is not necessary to report the CO2 emissions associated withnatural disturbance events. It is good practice to document this in a transparent manner.
The methodology described below can be used to estimate CH4, N2O, CO, and NOx emissions from biomassburning on managed forest land and emissions of these gases from fires associated with land use conversions.

3.2.1.4.2.1

Choice of Method

The existing methodology described in the IPCC Guidelines estimates carbon release during fires as 50%(assuming this to be the C content of biomass) of the mass of fuel actually combusted and uses this as a basis forthe calculation of non-CO2 emissions (see Equation 3.2.6). Some of the partially burnt fuel remains as charcoal,
which is relatively stable over time (Houghton, 1999).
Carbon release from burnt biomass as part of forest/grassland conversion is calculated using a simple
methodology described in the IPCC Guidelines (Section 5.3). This methodology is extended below, for all
vegetation types.
The emissions of non-CO2 gases can be estimated based on the total carbon released using Equation 3.2.19(Crutzen and Andreae, 1990; Andreae and Merlet, 2002):
EQUATION 3.2.19
ESTIMATION OF NON-CO2 EMISSIONS FROM C RELEASEDCH4 Emissions= (carbon released) ● (emission ratio) ● 16/12

CO Emissions= (carbon released) ● (emission ratio) ● 28/12

N2O Emissions= (carbon released) ● (N/C ratio) ● (emission ratio) ● 44/28

NOx Emissions= (carbon released) ● (N/C ratio) ● (emission ratio) ● 46/14

The extended methodology to estimate GHGs (CO2 and non-CO2) directly released in fires is summarised by thefollowing equation:
EQUATION 3.2.20
ESTIMATION OF GHGS DIRECTLY RELEASED IN FIRES
Lfire = A ● B ● C ● D ● 10-6
Where:
Lfire = quantity of GHG released due to fire, tonnes of GHG
A= area burnt, ha

B= mass of ‘available’ fuel, kg d.m. ha-1

C

= combustion efficiency (or fraction of the biomass combusted), dimensionless. (See Table 3A.1.12)

D= emission factor, g (kg d.m.)-1

Calculations are made separately for each greenhouse gas, using the appropriate emission factor.
4

Fire impact in unmanaged forest lands should not be reported.

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Chapter 3: LUCF Sector Good Practice Guidance

The accuracy of the estimates depends on the data available. Application of the decision tree in Figure 3.1.1 will
determine which of the Tier 1 to 3 methods to use. Under Tier 1, the above two approaches can be used toestimates emissions for each GHG using default data. Under Tier 2, country-specific activity data or emission
factors are used, while under Tier 3, both country-specific data and methods are used.

3.2.1.4.2.2

Choice of Removals/ Emission Factors

Tier 1: Firstly, the quantity of fuel burnt must be estimated. If no local data are available, this can be estimatedfrom Table 3.A.1.13 which tabulates the product of B (the available fuel, or biomass density on the land before
combustion) and C (the combustion efficiency). If ‘available fuel densities’ are available the combustion
efficiencies in Table 3.A.1.14 may be used. If combustion efficiency is needed, and more specific advice is notavailable, the IPCC default of 0.5 should be used. When the Equation 3.2.19 is used for the estimation of nonCO2, an emission ratio and a N/C ratio is required. The N/C ratio for the fuel burnt is approximated to be about
0.01 (Crutzen and Andreae, 1990). This is a general default value that applies to leaf litter, but lower values
would be appropriate for fuels with greater woody content, if data are available. Emission factors for use with
Equations 3.2.19 and 3.2.20 are provided in Tables 3.A.1.15 and 3.A.1.16 respectively.
Tiers 2 and 3: Use country-specific data and methods developed through field experiments.

3.2.1.4.2.3

Choice of Activity Data

The selection of activity data should follow the guidance in Section 3.2.1.1 “Other Carbon Losses” for fires inmanaged forests.
Tier 1: Area of wild fire varies markedly between countries and over time. In extreme drought years, wild fires
increase significantly. Thus, data on wild fires are highly country- and year- specific and cannot be generalized
by region. A global data base exists on annual area of vegetation fires at: http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba.
Tiers 2 and 3: Country level estimates of area burnt are used. These would generally be based on remotelysensed methods.

3.2.1.4.2.4

Uncertainty Assessm ent

Tier 1: Estimates of non-CO2 emissions from fires of forests can be highly uncertain because of: a) high spatial
and temporal variability of the emissions, b) scarcity of measurements and limited representativeness of data forlarger regions, and c) uncertainty in spatial aggregation and uncertainty inherent to the emission factors andactivity data.
Emission factors: There are few measured data; it is suggested to apply a 70% uncertainty range in emission
factors.
Activity data: Because of increased accuracy and global coverage of area burned by fire, uncertainty isrelatively small, in the range of 20-30%.
Tier 2: Applying country-specific data to emission factors will greatly reduce uncertainty.
Tier 3: Process-based models will probably provide a more realistic estimate but need to be calibrated andvalidated against measurements. Sufficient representative measurements are needed for validation purposes.

3.2.2

Land Converted to Forest Land

Managed land is converted to forest land by afforestation and reforestation, either by natural or artificial
regeneration (including plantations). These activities are covered under categories 5A, 5C, and 5D of IPCC
Guidelines. The conversion involves a change in land use. This section does not provide any guidance onregeneration in unmanaged forests. Converted areas are considered forest if they correspond to definition offorest adopted by the country. Lands converted to forest land are followed in conversion status for 20 years5.
After 20 years the areas are accounted for under Section 3.2.1 Forest land Remaining Forest land, although
longer term dynamics of recovery may need tracking for up to about 100 years after establishment of forest.
The estimation of emissions and removals of carbon from land use conversions to forest land is divided into four
sub-sections: Change in Carbon Stocks in Living Biomass (Section 3.2.2.1), Change in Carbon Stocks in Dead
Organic Matter (Section 3.2.2.2), Change in Carbon Stocks in Soils (Section 3.2.2.3) and Non-CO2 Greenhouse
Gas Emissions (Section 3.2.2.4). Each sub-section provides pool-specific good practice approach for emission
5

The IPCC Guidelines specify default value of 20 years but allow for 100 years if necessary to take account of long termcarbon dynamics in biomass, soil and litter pools.

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Forest Landand removal estimates. The CO2 emissions or removals for land converted to forest are summarised by Equation
3.2.21:EQUATION 3.2.21
ANNUAL CHANGE IN CARBON STOCKS IN LAND CONVERTED TO FOREST LAND 6∆C LF = ∆CLFLB + ∆CLFDOM + ∆CLFSoils
Where:∆C LF = annual change in carbon stocks in land converted to forest land, tonnes C yr-1
∆CLFLB = annual change in carbon stocks in living biomass (includes above- and belowground biomass)in land converted to forest land; tonnes C yr-1
∆CLFDOM = annual change in carbon stocks in dead organic matter (includes dead wood and litter) in land
converted to forest land; tonnes C yr-1
∆CLFSoils = annual change in carbon stocks in soils in land converted to forest land; tonnes C yr-1
To convert tonnes C to Gg CO2, multiply the value by 44/12 and 10-3. For the convention (signs), refer to Section
3.1.7 or Annex 3A.2 (Reporting Tables and Worksheets).

3.2.2.1

C HANGE IN CARB ON STOCKS IN LIVING BIOMASS

3.2.2.1.1

METHODOLOGI CAL ISSUESThis section presents good practice approach for calculation emissions and removals of CO2 by changes inbiomass on managed lands converted to forest land. This section covers the reporting categories of the IPCC
Guidelines “Changes in Forest and Other Woody Biomass Stocks” and “Abandonment of Managed Lands” asapplied to new forest land.

3.2.2.1.1.1

Choice of Method

Based on activity data and resources available, there are three tier methods that can be used by greenhouse gas
inventory preparers to estimate changes in biomass stocks. The decision tree in Figure 3.1.2 illustrates goodpractice in choosing a method to calculate CO2 removals and emissions in biomass on lands converted to forests.
Tier 1: Annual changes in carbon stocks in living biomass are estimated following default approach in the
IPCC Guidelines. Changes in carbon stocks in living biomass on land converted to forest through artificial andnatural regeneration are estimated with the use of Equation 3.2.22:
EQUATION 3.2.22
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASS IN LAND CONVERTED TO FOREST LAND(TIER 1)
∆CLFLB = ∆CLFGROWTH – ∆CLFLOSS
Where:∆CLFLB = annual change in carbon stocks in living biomass in land converted to forest land, tonnes C yr-1
∆CLFGROWTH @ annual increase in carbon stocks in living biomass due to growth in land converted toforest land, tonnes C yr-1
∆CLFLOSS = annual decrease in carbon stocks in living biomass due to losses from harvesting, fuel woodgathering and disturbances in land converted to forest, tonnes C yr-1

6

The default assumption in the IPCC Guidelines is that carbon does not accumulate in harvested wood products (HWP)
pools, though countries may report on HWP pools if they can document that existing stocks of long term forest products arein fact increasing (Box 5 of the IPCC Guidelines). Future treatment of HWP is under discussion by the UNFCCC (i.e. theConference of the Parties (COP) and COP7 decided that any changes to the treatment of HWP shall be in accordance with
future decisions of the COP [Decision 11/CP.7 para 4]). With this background, discussions on methodological issues for
HWP are placed in Appendix 3a.1

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Tier 1 can be applied even when previous land uses are not known, which may be the case if areas are estimatedusing Approach 1 or 2 from Chapter 2. It uses default parameters that are provided in Annex 3A.1 (Biomass
Default Tables).
Step 1: Annual Increase in Carbon Stocks in Living Biomass, ∆CLFGROWTH. The method follows Equation
3.2.4, Section 3.2.1 Forest land Remaining Forest land, which refers to Category 5A “Changes in Forest andOther Woody Biomass Stocks” of the IPCC Guidelines. As growth rate of a forest strongly depends on
management regime, a distinction is made between forests that are managed intensively (e.g. plantation forestry
with intensive site preparation and fertilisation) and extensively (e.g. naturally regenerated forests with minimum
human intervention). The calculations are made according to Equation 3.2.23:
EQUATION 3.2.23
ANNUAL INCREASE IN CARBON STOCKS IN LIVING BIOMASSIN LAND CONVERTED TO FOREST LAND

∆CLFGROWTH = [∑kAINT_MANk ● GTotal INT_MANk + ∑mAEXT_MANm ● GTotal EXT_MANm] ● CF
Where:∆CLFGROWTH @/ annual increase in carbon stocks in living biomass due to growth in land converted toforest land, tonnes C yr-1
AINT_MANk = area of land converted to intensively managed forest in condition k (including plantations), ha
GTotal INT_MANk @/ annual growth rate of biomass in intensively managed forest in condition k (including
plantations), tonnes d.m. ha-1 yr-1
AEXT_MANm = area of land converted to extensively managed forest in condition m, ha
GTotal EXT_MANm/@/annual growth rate of biomass in extensively managed forest in condition m, tonnes dm
ha-1 yr-1 (includes natural regeneration)k, m = represent the different conditions in which intensively and extensively managed forests are
growing
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
The annual increment in biomass of both intensively (GTotal INT_MAN) and extensively GTotal EXT_MAN) managed
forests is calculated in accordance with Equation 3.2.5, Section 3.2.1 Forest land Remaining Forest land and withthe use of default values provided in Tables 3A.1.5, 3A.1.6, 3A.1.7, 3A.1.8, 3A.1.9, and 3A.1.10 in Annex 3A.1.
The values from tables should be chosen with regard to tree species composition and climatic region. Data forextensively managed forests should be taken from Table 3.A.1.5 and for intensively managed forests from Table
3A.1.6 or 3A.1.7.
Step 2: Annual Decrease in Carbon Stocks in Living Biomass Due to Losses, ∆CLFLOSS. In case harvesting,fuel wood gathering and disturbances can be attributed to land converted to forest, annual losses in biomass
should be estimated with the use of Equation 3.2.24 that repeats the good practice approach given in Equation
3.2.6, Section 3.2.1, Forest land Remaining Forest land:
EQUATION 3.2.24
ANNUAL DECREASE IN CARBON STOCKS IN LIVING BIOMASS DUE TO LOSSESIN LAND CONVERTED TO FOREST LAND

∆CLFLOSS = Lfellings + Lfuelwood + Lother losses
Where:∆CLFLOSS = annual decrease in carbon stocks in living biomass due to losses in land converted to forest
land, tonnes C yr-1
Lfellings = biomass loss due to harvest of industrial wood and saw logs in land converted to forest land,
tonnes C yr-1
Lfuelwood = biomass loss due to fuelwood gathering in land converted to forest land, tonnes C yr-1
Lother losses = biomass loss due to fires and other disturbances in land converted to forest land, tonnes C yr-1

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Forest Land

The biomass loss due to harvest (Lfellings) is estimated with the use of Equation 3.2.7, Section 3.2.1, Forest land
Remaining Forest land, and default basic wood density and biomass expansion factor values provided in Tables
3A.1.9 and 3A.1.10 of Annex 3A.1. The good practice approaches for estimating biomass losses due to fuelwood gathering (Lfuelwood), fires and other disturbances (Ldisturbance) are also described in Section 3.2.1, Forest land
Remaining Forest land. If no data on losses on this land category are available, all loss terms should be set tovalue 0, thus also ∆CLFLOSS then equals 0. It is good practice to ensure consistent reporting on losses of biomass
between this category and Section 3.2.1 Forest land Remaining Forest land to prevent double accounting oromission of biomass loss.
Tier 2: The Tier 2 method is similar to Tier 1, but it uses more disaggregated approach and allows for more
precise estimates of changes in carbon stocks in biomass. The net annual CO2 removals in biomass are calculated
as a sum of removals due to growth of biomass on the areas converted to forest, changes in biomass due to actual
conversion (estimates the difference between initial biomass stocks on non-forest land before and afterconversion to forest e.g. by artificial regeneration), and losses on areas converted to forest (Equation 3.2.25):
EQUATION 3.2.25
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASS IN LAND CONVERTED TO FOREST LAND(TIER 2)
∆CLFLB = ∆CLFGROWTH + ∆CLFCONVERSION – ∆CLFLOSS
Where:∆CLFLB = annual change in carbon stocks in living biomass in land converted to forest land, tonnes C yr-1
∆CLFGROWTH @/ annual increase in carbon stocks in living biomass due to growth in land converted toforest land, tonnes C yr-1
∆CLFCONVERSION = annual change in carbon stocks in living biomass due to actual conversion to forest
land, tonnes C yr-1/∆CLFLOSS = annual decrease in carbon stocks in living biomass due to losses from harvesting, fuel woodgathering and disturbances in land converted to forest land, tonnes C yr-1
In addition to default values, the Tier-2 approach requires national data on: i) area converted to forest; ii) averageannual increase per ha in merchantable volume on land converted to forests, obtained e.g. from forest inventories(no default values can be provided); iii) change of carbon in biomass when non-forest land becomes forest (e.g.
by artificial regeneration) and iv) emissions due to loss of biomass on converted land. The approach may imply
the knowledge of the land-use change matrix, and hence the distribution of previous land uses.
Step 1: Annual Increase in Carbon Stocks in Living Biomass, ∆CLFGROWTH. The method follows the Tier 1
approach using Equation 3.2.23 above. The average annual increment in biomass of both intensively (GTotal
INT_MAN) and extensively (GTotal EXT_MAN) managed forests is calculated in accordance with Tier 2 good practice
approach, Section 3.2.1 Forest land Remaining Forest land and with the use of country-specific data on averageannual increase per ha in merchantable volume on land converted to forests (obtained e.g. from forest
inventories) and default basic wood density, biomass expansion factors and the ratio of belowground biomass toaboveground biomass provided in Tables 3A.1.7, 3A.1.8, 3A.1.9, 3A.1.10 in Annex 3A.1.
Step 2: Change in Carbon Stocks in Living Biomass Due to Conversion, ∆CLFCONVERSION. The change of
non-forest land to forest land (e.g. by artificial regeneration that includes clearing the vegetation on non-forestland) may cause change in the biomass stock in the conversion. The changes in carbon stocks in living biomass
due to land-use change are calculated with the use of Equation 3.2.26:
EQUATION 3.2.26
CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN LAND ANNUALLY CONVERTED TO FOREST LAND

∆CLFCONVERSION = ∑i [BAFTERi – BBEFOREi] ● ∆ATO_FORESTi ● CF
Where:∆CLFCONVERSION = change in carbon stocks in living biomass in land annually converted to forest land,
tonnes C yr-1/

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BBEFOREi = biomass stocks on land type i immediately before conversion, tonnes d.m. ha-1
BAFTERi = biomass stocks that are on land immediately after conversion of land type i, tonnes d.m. ha-1 (inother words, the initial biomass stock after artificial or natural regeneration)∆ATO_FORESTi/@/area of land-use i annually converted to forest land, ha yr-1/
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonnes d.m.)-1i= represent different types of land converted to forest

Note: The types of land should be stratified along biomass stocks before conversion
The ∆CLFCONVERSION can be expanded to take account of different carbon contents before transition. Tier 2 may
apply calculations on subdivisions of land area (regions, ecosystems, site types etc.).
Step 3: Change in Carbon Stocks in Living Biomass Due to Losses, ∆CLFLOSS. The annual losses in biomass
are estimated using Equation 3.2.24. This equation repeats good practice approach given in Equation 3.2.6,
Section 3.2.1, Forest land Remaining Forest land.
The biomass loss due to harvest (Lfellings) is estimated with the use of Equation 3.2.7, Section 3.2.1, Forest land
Remaining Forest land. Tables 3A.1.9 and 3A.1.10 in Annex 3A.1 provide default data on basic wood densityand biomass expansion factors. For Tier 2 and higher tiers, inventory experts are encouraged to develop countryspecific wood density and BEF values for growing stock increment and harvests. The good practice approaches
for estimating biomass losses due to fuel wood gathering (Lfuelwood), fires and other disturbances (Ldisturbance) arealso described in Section 3.2.1, Forest land Remaining Forest land. If no data on losses on this land category areavailable, all loss terms should be set to value 0, thus also ∆CLFLOSS then equals 0. It is good practice to ensure
consistent reporting on losses of biomass between this category and Section 3.2.1 Forest land Remaining Forest
land to avoid over- and underestimates due to double accounting or omissions.
Tier 3: The Tier 3 follows the same equations and steps as Tier 2, but should use substantial national
methodology and solely country-specific data. Tier 3 should be used, when land conversion to forest represents a
key category. In the inventory, Equations 3.2.25 and 3.2.26 are expanded on fine geographical scale andstratifications according to ecosystems, vegetation types, subdivision of biomass pools, and types of land before
the conversions are made. Country-defined methodologies may be based on systematic forest inventory or use
geo-referenced data, and/or models for accounting for changes in biomass. National activity data should havehigh resolution and be available on regular basis for all categories of converted lands and forest types establishedon them. The methodology should be described and documented as specified in Section 5.5.6 Documentation,
Archiving and Reporting.

3.2.2.1.1.2

Choice of Emission/Removal Factors

IN CR E AS E I N CA RB O N S TO CK S IN L I VI N G B IOM AS S, ∆C L F G
The calculations distinguish between two broad management practices: intensive (e.g. plantation forestry withintensive site preparation and fertilisation) and extensive (e.g. naturally regenerated forests with minimum
human intervention) ones. These categories can also be refined according to national circumstances, for example
based on stand origin e.g. natural or artificial regeneration.
Tier 1: The IPCC Guidelines provide default methodology only for aboveground biomass calculations. The
present GPG report provides good practice approach to estimate for living biomass obtained as a sum of aboveand belowground biomass pools (for pool description refer to Section 3.1 Introduction). The Tables 3A.1.5 and3A.1.6 in Annex 3A.1 represent default average annual increment values in aboveground biomass of intensively
and extensively managed forests (referred as plantations and naturally regenerated forests). The ratios of
belowground to aboveground biomass (root-to-shoot ratio) in Table 3A.1.8 should be used to account for
belowground biomass in living biomass estimations. Basic wood density (Table 3A.1.9) and biomass expansion
factors (Table 3A.1.10) allow for calculation of biomass as stipulated in Section 3.2.1 Forest land Remaining
Forest land.
Tier 2: It is good practice to determine wherever possible annual increment values, root-to-shoot ratios, basic
wood density, and biomass expansion factors in accordance with national conditions and use them incalculations under Tier 2 approach. The possible stratifications go along tree species composition, management
regime, stand age or volume, climatic region and soil type. Countries are encouraged to obtain specific biomass
sequestration and expansion factors through research efforts. Further guidance is given in Section 3.2.1 Forest
land Remaining Forest land.

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Forest Land

Tier 3: The accounting for carbon removals in biomass should be implemented based on country-specificannual growth rates and carbon fraction in biomass from dedicated forest inventories and/or models. The
inventory experts should ensure that the models and forest inventory data have been described in line with thesampling and other procedures outlined in Chapter 5, Cross-cutting Issues, of this report.
CHANGE IN BIOMASS STOCKS ON LAND BEFORE AND AFTER CONVERSION, ∆CLFCONVERSION
It is good practice to use values of biomass stocks for pre-conversion land uses that are consistent with values
used in calculations for other land categories. For example, if default carbon stock values were used to estimatechanges in carbon stocks in grassland remaining grassland, then the same default values should be used to assess
carbon stocks in grassland prior to their conversion to forest land.
Tier 1: The IPCC Guidelines do not include estimation of biomass changes in conversion process.
∆CLFCONVERSION is not included in Tier 1 calculations.
Tier 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 the calculations of carbon
stock changes in grassland, cropland, wetlands, settlements and forest categories, and obtained from national
agencies or sampling. A Tier 2 approach may use some combination of country-specific and default biomass
stocks (given in Tables 3A.1.2 and 3A.1.3). For default values of biomass stocks for pre-conversion land uses
refer to other land categories described in the present report.
Tier 3: Estimates and calculations should be performed based on country-specific survey and model data.
Surveys should be based on the principles outlined in Section 5.3, and models and data documented in line withprocedures outlined in Chapter 5, Cross-cutting Issues, of this report.
CH A N G E IN CA RB O N S TO C K S I N L IV IN G B IO M AS S D U E TO LO SS E S, ∆C L F L
Harvesting and natural disturbances such as windfall, fires and insect outbreaks can result in losses of carbon on
lands converted to forests. It is good practice to report on them. Section 3.2.1 Forest land Remaining Forest land,of this report provides a good practice approach for estimating losses of carbon due to harvest and natural
disturbance that is fully applicable and should be used for appropriate calculations under Section 3.2.2.1.1.1
above. If changes in C stocks are derived from repeated inventories, the losses from harvesting and disturbances
will be covered without a need to report on them separately. It is good practice to ensure consistent reporting onlosses of biomass between this category and Section 3.2.1 Forest land Remaining Forest land to prevent double
accounting or omission part of biomass loss.

3.2.2.1.1.3

Choice of Activity DataAR EA O F L AN D C O N VE R T ED, A I N T _ M A N , A E X T _ M A N , ∆ A T O _ F O R E S T
All tiers require information on areas converted to forest land for a period of 20 years. After 20 years the areas
are accounted for under Section 3.2.1 Forest land Remaining Forest land. Lands that undergo a conversion inprevailing use are covered here. Thus regeneration on existing forest land that was recently cleared as a result,for example, of harvesting or natural disturbance, should be accounted for in Section 3.2.1 Forest land
Remaining Forest land because no change in land use is involved. The same data on areas should be used for
Section 3.2.2.2 Change in Carbon Stocks in Dead Organic Matter, Section 3.2.2.3 Change in Carbon Stocks inSoils, and Section 3.2.2.4 Non-CO2 Greenhouse Gas Emissions. The stratification in area estimation should take
into consideration, if possible, the major soil types and biomass densities on land before and after conversion.
In order to be consistent with the reporting categories of the IPCC Guidelines, the areas of forests re-growing
naturally on abandoned lands should be distinguished from other land conversion to forest. The inventory
experts are encouraged to search for information on prior land use to make this distinction. When Approach 1 ofChapter 2 is used, additional data may be needed to distinguish between areas of natural and artificial
regeneration.
Tier 1: Activity data can be obtained through national statistics, from forest services (which may haveinformation on areas of different management practices), conservation agencies (especially for areas managed
for natural regeneration), municipalities, survey and mapping agencies. Cross-checks should be made to ensurecomplete and consistent representation for avoiding omissions or double counting as specified in Chapter 2. If no
country data are available, aggregate information can be obtained from international data sources (FAO, 1995;
FAO, 2001; TBFRA, 2000).
Expert judgment can be used about whether the new forests are predominantly intensively or extensively
managed. In that case AINT_MAN and AEXT_MAN, data can be obtained through multiplication of annual areachanges in kha or by the period of conversion (the default period is 20 years). If the proportions of areas of

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Chapter 3: LUCF Sector Good Practice Guidance

intensively and extensively managed forests can be estimated, this information can be used for further
partitioning the areas to obtain more accurate estimates.
Tier 2: The areas under different land categories subjected to conversion during a given year or over a period ofyears should be available. They come from national data sources and a land-use change matrix or its equivalentthat covers all possible transitions to forest land. Country-defined national data sets should have a resolution
sufficient to ensure appropriate representation of land areas in line with provisions of Chapter 2 of this report.
Tier 3: National activity data on conversion of land uses to forest through natural and artificial regeneration areavailable, possibly from different sources, notably national forest inventories, registers of land-use and land-use
changes, and remote sensing, as described in Chapter 2 of this report. These data should give a full accounting of
all land use transitions to forest land and disaggregate along climate, soil and vegetation types.

3.2.2.1.1.4

Uncertainty Assessm ent

Emission and removal factors: Non-zero default values of wood density and expansion factors may have afactor of two uncertainty associated with them. The major sources of uncertainty of default and country-specificdata are associated with averaging highly variable primary numbers and further extrapolation of average values
over broad areas. The use of regional and country-specific inventory data and models under Tiers 2 and 3
enables for significant reduction of uncertainties. Thus, the uncertainty of nationally determined values may be
within ±30% (Zagreev et al., 1992; Filipchuk et al., 2000). The measures to reduce uncertainties include:
increase of the number of representative sample plots and measurements over them; further stratification of
estimates on the basis of similarity in growth, microclimate and other environmental characteristics; anddevelopment of local and regional parameters on the basis of comprehensive surveys and information exchange.
If complex models are applied, the inventory experts should ensure their appropriate verification anddocumentation in accordance with Chapter 5 of this report.
Activity data: Uncertainties associated with activity data will depend on sources of information used nationallyand the approaches used for land area identification described in Chapter 2 of this report. The combination of
remote sensing data with ground-based surveys is the most cost-efficient method of measurements of areas ofland-use change. It provides for uncertainties as low as ±10-15% and should be applied under higher tiermethods. The major way to reduce uncertainty of area change estimates attributes to broad application ofadvanced land survey techniques on regional and local scale. However, its application may be limited bycapacities of particular countries. To reduce both uncertainties of area estimates and costs of use of precise
methods, regional remote sensing data centers could be established by several countries for sharing and common
use of the information obtained for the purposes of sustainable land management.

3.2.2.2

C HANGE IN CARB ON STOCKS IN DEAD ORGANIC MATTER

3.2.2.2.1

METHODOLOGI CAL ISSUES

Methods to quantify 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 conversion, and theestimates of the areas of lands converted during the period. Most other land uses will not have a dead wood or alitter pool, so that corresponding carbon pools prior to conversion can be taken as zero as a default assumption.
Unmanaged forest, where converted to managed forest, could have significant carbon in these pools, as well as
rangelands and wetlands, and also forest areas around settlements that may have been defined as settlements
based on nearby use rather than land cover. The zero default should therefore be checked at Tiers 2 and 3.
Conversion of non-forest to forest may occur so slowly that it may be difficult to distinguish when theconversion truly occurs; however, in these areas, if they were managed, the areas would probably be counted as
managed forest depending on crown cover and other thresholds.

3.2.2.2.1.1

Choice of Method

Calc u latio n proc ed ur e for c hang e i n ca rbo n st oc k in d ead w o o dConceptually once the carbon stock has been initiated to the value just prior to the conversion to forest (often
zero by default, as discussed in the previous paragraph), annual changes for areas converting by plantations andon sites managed for natural regeneration, categorized by previous land use and forest type, can be estimatedusing Equation 3.2.27:

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Forest Land

EQUATION 3.2.27
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD IN LAND CONVERTED TO FOREST LAND
∆CLFDW = {[ANatR ● (BintoNatR – BoutNatR)] + [AArtR ● (BintoArtR – BoutArtR)]} ● CF
where
BintoNatR = BstandingNatR ● MNatR and BintoArtR = BstandingArtR ● MArtR
Where:∆CLFDW = annual change in carbon stocks in dead wood in land converted to forest land, tonnes C yr-1
ANatR = area of land converted to forest land through natural regeneration, ha
AArtR = area of land converted to forest land through establishment of plantations, ha
Binto = average annual transfer of biomass into dead wood for forest area NatR or ArtR, tonnes d.m. ha-1 yr-1
Bout = average annual transfer of biomass out of dead wood for forest area NatR or ArtR, tonnes d.m. ha-1 yr-1
Bstanding = standing biomass stocks, tonnes d.m. ha-1
M = mortality rate, i.e. proportion of Bstanding transferred annually into dead wood pool, dimensionless
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
Transfers into and out of a dead wood pool are difficult to measure and the stock change method described in
Equation 3.2.28 may be easier to use than the previous equation if appropriate survey data are available,
collected, for example, in conjunction with the National Forest Inventory:
EQUATION 3.2.28
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD IN LAND CONVERTED TO FOREST LAND
∆CLFDW = [(Bt2 – Bt1) / T] ● CF
Where:∆CLFDW = annual change in carbon stocks in dead wood in land converted to forest land, tonnes C ha-1 yr-1
Bt2 = dead wood stock at time t2, tonnes d.m. ha-1
Bt1 = dead wood stock at time t1 (the previous time), tonnes d.m. ha-1
T = ( t2 – t1) = time period between time of the second stock estimate and the first stock estimate, yr
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonne d.m.)-1
The decision tree in Figure 3.1.2 (Section 3.1.6) provides assistance in the selection of the appropriate tier level
for the implementation of estimation procedures. Dead wood carbon estimates often differ significantly by
previous land use, forest type, and regeneration type. Theoretically, Equations 3.2.27 and 3.2.28 should give thesame carbon estimates. In practical terms, data availability and desired accuracy determines choice of equation.
Tier 1 (Default): The IPCC Guidelines, consistent with reporting under Tier 1, assume no change in dead woodcarbon in land converting to forest. This is consistent with Equation 3.2.27 on the assumption that annualtransfers into the dead wood pool are the same as transfer out, and with Equation 3.2.28 if inventory of carbon
stocks have been performed at different times.
Tier 2: Tier 2 uses Equation 3.2.27 when transfer rates into and out of the dead wood pool have been estimatedusing data from research plots sited nationally or in countries with similar conditions, and Equation 3.2.28 whencarbon stocks are measured. For comparative purposes, new plots, where established, should be sited on thebasis of the sampling principles set out in Section 5.3 with stratification by forest type and conversion regime.
Tier 3: Tier 3 methods can be used where countries have detailed inventories based on sample plots in their
managed forests, or detailed models validated against representative litter accumulation data. The statisticaldesign of the inventory (or for sample collection for model validation) should follow the principles set out in
Section 5.3, which will facilitate unbiased results and provide information on associated uncertainties.

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Chapter 3: LUCF Sector Good Practice GuidanceCalc u latio n proc ed ur e for c hang e i n ca rbo n st oc k in li tter
The approach to estimating change of carbon in litter reflects expected differences in patterns and duration ofchanges in litter carbon for intensively managed plantations and naturally regenerating forests on lands
converting to forest.
Conceptually once the carbon stock has been initialized to the value just prior to the conversion to forest (often
zero by default, as just discussed), annual changes for areas converting by plantations and on sites managed fornatural regeneration, categorized by previous land use and forest type, can be estimated using Equation 3.2.29:
EQUATION 3.2.29
ANNUAL CHANGE IN CARBON STOCKS IN LITTER IN LAND CONVERTED TO FOREST LAND
∆CLFLT = [ANatR ● ∆CNatR] + [AArtR ● ∆CArtR]
Where:∆CLF LT = annual change in carbon stocks in litter in land converted to forest land, tonnes C yr-1
ANatR = area of land converted into forest land through natural regeneration, ha
AArtR = area of land converted into forest land through establishment of plantations, ha∆CNatR = average annual change in carbon stocks in litter for forest area NatR, tonnes C ha-1 yr-1
∆CArtR = average annual change in carbon stocks in litter for forest area ArtR, tonnes C ha-1 yr-1
Alternatively the stock change methods described in Equation 3.2.30 may be used if appropriate survey data are
available:
EQUATION 3.2.30
ANNUAL CHANGE IN CARBON STOCKS IN LITTER IN LAND CONVERTED TO FOREST LAND
∆CLFLT = A ● (Ct2 – Ct1) / T
Where:∆CLF LT = annual change in carbon stocks in litter in land converted to forest land, tonnes C yr-1
A = area of land converted to forest land, ha
Ct2 = litter carbon stock at time t2, tonnes C ha-1
Ct1 = litter carbon stock at time t1 (the previous time), tonnes C ha-1
T (= t2 – t1) = time period between time of the second stock estimate and the first stock estimate, yr
Methodological choice for estimating this pool is made using the general decision tree for land converted toforest land in Figure 3.1.2. Litter carbon estimates often differ significantly by previous land use, forest type, andregeneration type. Theoretically, Equations 3.2.29 and 3.2.30 should give the same carbon estimates. In practicalterms, data availability and desired accuracy determines choice of equation.
Tier 1 (Default): The IPCC Guidelines, consistent with reporting under Tier 1, assume no change in carbon in
the litter pools in lands converting to forest. This is consistent with Equation 3.2.29 on the assumption thatannual transfers into the litter pool are the same as transfers out, and with Equation 3.2.30 when litter carbon
stocks are assumed stable.
Tier 2: Tier 2 uses Equation 3.2.29 when transfer rates into and out of the litter pool have been estimated using
data from research plots sited nationally or in countries with similar conditions, and Equation 3.2.30 whencarbon stocks are measured. For comparative purposes, new plots, where established, should be sited on thebasis of the sampling principles set out in Section 5.3 with stratification by forest type and conversion regime.
Tier 3: Tier 3 methods can be used where countries have detailed inventories based on sample plots in managed
forests, or detailed models validated against representative litter accumulation data. The statistical design of theinventory (or for sample collection for model validation) should follow the principles set out in Section 5.3,
which will facilitate unbiased results and provide information on associated uncertainties.

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Forest Land3.2.2.2.1.2

Choice of Emission/Removal Factors

DE AD WO OD
Tier 1: By default, consistent with reporting under Tier 1 in the IPCC Guidelines, it is assumed that the deadwood carbon stocks in non-forest lands converting to forests are stable. The net effect of emission and removal
factors is therefore equal to zero.
Tier 2: Country-specific values for mortality rates related to standing biomass stocks are derived from scientificstudies, or taken from nearby regions with similar forests and climate. If country-specific input factors arederived, corresponding loss factors for harvest and disturbance regimes could also be derived from countryspecific data. If only one of the pair of country-specific input and output factors are available, then theassumption should be made that the other one of the pair is equal to the known factor. Default factors in Table
3.2.2 can be used for some forest categories if country or regional values are not available.
Tier 3: Countries develop their own methodologies and parameters for estimating changes in dead wood. Suchapproaches are likely to involve permanent inventory measurement programs, related to fine-resolution activity
data, perhaps coupled modeling studies to capture the dynamics of all forest-related pools. Some countries havedeveloped disturbance matrices which for each type of disturbance provide a carbon reallocation pattern among
different pools (Kurz and Apps, 1992). Decay rates of dead wood may vary with the species of wood andmicroclimatic conditions, and site preparation procedures (e.g. controlled broadcast burning, or burning of piles).
Default factors in Table 3.2.2 can be used as a check on country-specific factors.
L I T TE R
Tier 1 (Default): By default, it is assumed that the litter carbon stocks in non-forest lands converting to forests
are stable. The net effect of emission and removal factors is therefore equal to zero. Countries experiencing
significant changes in forest types, or disturbance or management regimes in their forests are encouraged todevelop domestic data to quantify this impact and report it under Tier 2 or 3 methodologies.
Tier 2: Where these are available, it is good practice to use country level data for net litter accumulation rates
for lands converting to forest by different forest types, in combination with default values in the final column ofTable 3.2.1 if country or regional values are not available for some forest categories.
Tier 3: Countries develop their own methodologies and parameters for estimating changes in litter, using
national level disaggregated litter carbon estimates for different forest types, disturbance or management regimes
or both. These would be based on measurements from National forest inventories or other country-specificinformation, perhaps coupled with modeling studies to capture the dynamics of all forest-related pools. Updated
default factors in Table 3.2.1 can be used as a check for country-specific factors.

3.2.2.2.1.3

Choice of Activity Data

Activity data should be consistent with the activity data used for estimating changes in living biomass on land
areas undergoing conversion to forest. This can be obtained, consistent with the general principles set out in
Chapter 2 and as described in Section 3.2.2.1.1.3, through national statistics, from forest services, conservation
agencies, municipalities, survey and mapping agencies. Cross-checks should be made to ensure complete andconsistent representation of annually converted lands in order to avoid possible omissions or double counting.
Data should be disaggregated according to the general climatic categories and forest types in Table 3.2.1. Tier 3
inventories will require more comprehensive information on the establishment of new forests, with refined soil
classes, climates, and spatial and temporal resolution. All changes having occurred over the T years selected as
the transition period should be included with transitions longer ago than the past 20 years reported as a
subdivision of forest remaining forest.

3.2.2.2.1.4

Uncertainty Assessm ent

Uncertainties for dead organic matter on land converted to forest land may be quite small in absolute terms in thefirst few years after conversion. Non-forest lands would have none to little dead organic matter. DOM can only
occur once live vegetation is established, grows, and dies.
DE AD WO OD
The estimates for uncertainties of dead wood on land converted to forest land in the first few years afterconversion may be close to zero percent. It is almost certain that there is zero dead wood on non-forest land prior
to conversion to forest land. The longer the transition period chosen, the larger the uncertainties of dead wood onland converted to forest land. Uncertainties for dead wood on forest land remaining forest land are described in
Section 3.2.1.2.1.4.

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L I T TE R
The estimates for uncertainties of litter on land converted to forest land is very similar to estimates of
uncertainties of litter on forest land remaining forest land, described in Section 3.2.1.2.1.4. Litter builds up
relatively quickly. The shorter the transition period over which land stays in the category land converted to forest
land, the less the litter uncertainty.
Table 3.2.5 provides the sources of uncertainty in estimating CO2 emissions and removals from forest soils anddead organic matter pools, and indicates ways to reduce them.
Activity data: Uncertainties associated with activity data for dead organic matter should be consistent with theuncertainties for the activity data for estimating changes in living biomass on land area undergoing conversion toforest land, as described in Section 3.2.2.1.1.4.

3.2.2.3

C HANGE IN CARB ON STOCKS IN SOILSThis section describes estimation procedures for carbon emissions and removals from the soils in land converted
to forest land. Separate guidance is provided for two types of forest soil carbon pools: 1) the organic fraction ofmineral forest soils, and 2) organic soils. The change in carbon stocks in soils in land converted to forest land(∆CLFSoils) is equal to the sum of changes in carbon stocks in the mineral soils (∆CLFMineral) and organic soils(∆CLFOrganic).

3.2.2.3.1

METHODOLOGI CAL ISSUESM INE RA L SO I L S
Studies of soil carbon dynamics upon changes from non-forest to forest indicate a wide range of trends, rates andtiming. This variability is commonly explained by diffferences in experimental design and sampling procedures,
varying land-use histories, climates and forest types (Paul et al., 2002; Post & Kwon, 2000). Afforestation of
improved grasslands has resulted in small decreases in mineral soil C in the upper soil horizon, which may or
may not persist or be reversed over subsequent rotations (Paul et al., 2002). Site characteristics were also foundto be a strong determinant of C dynamics following afforestation on former pastures (Jackson et al., 2002).
Hence, there is no consistent pattern on the magnitude and direction of long-term soil C stock changes uponland-use changes from non-forest to managed forests (Post & Kwon 2000; Polglase et al., 2000).
Generally, soil C is found to accumulate following afforestation on croplands (Polglase et al., 2000). However,
the rate of soil carbon accumulation can depend strongly on initial conditions, which relate to the intensity of theprevious land-use and the remaining labile soil organic carbon prior to forest reestablishment (Post & Kwon,
2000). In spite of higher carbon inputs from litter, soil characteristics may also limit the contribution of SOC
accumulation to total carbon sequestration in the ecosystem upon forest regrowth (Richter et al., 1999).
Depending upon soil sampling depths, the redistribution of organic carbon along the soil may lead to incorrectconclusions on the net changes in soil carbon stocks.
The proposed approach acknowledges the potential for sequestration or losses of SOC on lands converted toforest lands; it allows for the incorporation of the available scientific knowledge and data on the direction andrate of SOC changes in newly established forests.
Conceptually, the methodology is consistent with the one developed in Section 3.2.1.3.1.1 (Choice of Methods),
in that it assumes a stable, spatially-averaged carbon content of mineral soils under given forest types,
management practices and disturbance regimes. It is based on the following assumptions:•

Change from non-forest to forest land is potentially associated with changes in SOC, eventually reaching astable end-point; and•SOC sequestration/release during the transition to a new equilibrium SOC occurs in a linear fashion.

O R GA N IC SO I L S
Afforestation activities or forest regrowth on organic soils may alter the moisture regime through changes ininterception of rainfall and evapotranspiration, and through increased organic matter inputs. These changes canmodify the carbon dynamics and balance between the release of CO2 and CH4 to the atmosphere, leading to the
expectation that land conversion to forest on drained organic soils – whether drained for this purpose, or
previously drained – will be an anthropogenic source of CO2. This is assumed not to be the case whereconversion to forest occurs without drainage.

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TABLE 3.2.5
SOURCES OF UNCERTAINTY IN ESTIMATING CO2 EMISSION/REMOVAL FROM FOREST SOIL AND DOM POOLS
Sources of uncertainty

Characteristics

Treatment

Activity data
Omission of managed forest
areas

Not all managed forest areas is characterizedby type, management practices and disturbance
regimes; changes in forest types, practices or
events are not documented

Document and monitor forest types, management
practices and disturbances.

Omission of relevant changes
in events or practices.

Omission of some LU changes, practices or
disturbances believed to cause GHG emissions
or removals

State and document; discuss likely effect on estimate
validity

Mapping of spatial activity
data (e.g. organic soils).

Areas or locations are not accurately mapped

Follow recommendations under Chapter 2 andstandard GIS texts for the treatment of uncertainty
associated with the manipulation of spatial data

Lack of proper stratification

Activity data are not stratified according to thevariables which most contribute to the overallvariability

Enhance the power of the sampling design throughimproved stratification

Use of default classification

National land-use classification incompatible
with IPCC default

Design cross-walkParameters, emission/removal factors
Use of default parameters or
emission/removal factors

Default values do not represent national
circumstances

Use default uncertainties. Prioritize improvements to
reduce highest uncertainty first.

Sampling design

Stratification, sampling intensity, incompletely
capture spatial variability

Quantify random uncertainty (see Chapter 5 or
GPG2000)

Inconsistent sampling
protocolHorizon sampling, depth, replication,
composite samples, handling of coarse
fragments, bulk density measurements are notconsistent

Improve and/or standardize sampling protocol;
develop cross-walk between different protocols

Layer thickness

Only superficial (0-30 cm) soil samples werecollected

Assume that 0-30 cm layer contains only 50% of
forest soil C; estimate uncertainty accordingly

Humus layer underneath boulders are notsamples – overestimation of litter C stocks

Evaluate and adjust the sampling design at the plotlevel according to microspatial variability

Inconsistent identification of soil horizons orreference depths

Vertical structure of soil profile should be assumed
constant during repetitive sampling in forest sites
without mechanical site preparation.

Bulk density (BD)bulk density not measured at all samplingsites; inaccurate bulk density values, especiallyin compact or dense subsoils;

Use additional data from literature or databases to
identify systematic error in BD and supplement
missing data; request that representative
measurements of BD be carried out

Coarse fragments

No assessment of the volume or mass of coarse
fragments

Use additional data from literature or databases to
identify systematic error in coarse fragment;
calibrate and standardize the assessment of thecoarse fragment content during sampling campaigns

Carbon concentration

Analytical methods for C analyses havechanged

Avoid changing analytical methods if possible;
develop correction factors from comparative lab
studies, or used published ones

Scaling up of EF
experimental values to large
areas(e.g EFDrainage)

Experimental values derived from site-specificstudies are applied to large areas.

Follow guidance in Chapter 5 for scaling-up

3.2.2.3.1.1

Choice of Method

M INE RA L SO I L S
Equation 3.2.31 indicates that the soil carbon stock change for any inventory year is equal to the sum of carbon
stock changes in new, intensively and extensively managed forests established for less than T years. The
equation reflects expected differences in patterns and duration of changes in SOC for intensively managed forestand extensively managed forest.

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EQUATION 3.2.31
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILS IN LAND CONVERTED TO FOREST LAND1∆CLFMineral = ∆CLFExt Forest + ∆CLFInt Forest
Where,∆CLFExt Forest = [(SOCExt Forest – SOCNon Forest Land) ● AExt Forest] / TExt Forest∆CLFInt Forest = [(SOCInt Forest – SOCNon Forest Land) ● AInt Forest] / TInt Forest
andSOCInt, Ext Forest = SOCref ● fforest type ● fman intensity ● fdist regime
Where:∆CLFMineral = annual change in carbon stocks in mineral soils for inventory year, tonnes C yr-1
∆CLFExt Forest= annual change in carbon stocks in mineral soils in land converted to extensively managed
forest land, tonnes C yr-1
∆CLFInt Forest = annual change in carbon stocks in mineral soils in land converted to intensively managed
forest land , tonnes C yr-1
SOCExt Forest = stable soil organic carbon stocks of the new, extensively managed forest, tonnes C ha-1
SOCInt Forest = stable soil organic carbon stocks of the new, intensively managed forest, tonnes C ha-1
SOCNon Forest Land = soil organic carbon stocks of the non-forest land prior to its conversion, tonnes C ha-1
AExt Forest = area of land converted to extensively managed forest, haAInt Forest = area of land converted to intensively managed forest, ha
TExt Forest = duration of the transition from SOCNon Forest Land to SOCExt Forest, yr
TInt Forest = duration of the transition from SOCNon Forest Land to SOCInt Forest, yr
SOCref = reference carbon stock, under native, unmanaged forest on a given soil, tonnes C ha-1
fforest type = adjustment factor for a forest type different from the native forest vegetation, dimensionlessfman intensity = adjustment factor for the effect of management intensity, dimensionlessfdist regime = adjustment factor reflecting the effect on SOC of a disturbance regime different from thenatural one, dimensionless
Note 1: These changes in carbon stocks should be reported annually for TExt Forest, and TInt Forest years,
respectively. For example, if a land is converted to intensively managed forest land and TInt Forest = 20
years, then the annual changes in carbon stocks in mineral soils on the area AInt Forest as calculated with
Equation 3.2.31 should be reported in the national inventory for 20 years following the conversion. The
total change in carbon stocks in mineral soils is the sum of all types of conversions to forest land.
Where non-forest land is reverting to unmanaged, native forest vegetation:
fforest type = fman intensity = fdist regime = 1, and
SOCInt, Ext Forest = SOCref
Annual changes in SOC occur as long as fewer than T years have elapsed since the non-forest to forest
conversion.
The decision tree in Figure 3.1.2 (Section 3.1.6) provides basic guidance for tier selection in the estimation
methodology.
Tier 1: Conversion of cropland and grassland to forest lands may optionally be considered at Tier 1, although
the effects on soil carbon stock of conversions to forest land are not considered as part of the default
methodology in the IPCC Guidelines7. There is no distinction between intensive and extensive management of
new forests, hence SOCExt Forest = SOCInt Forest = SOCref and TExt Forest = TInt Forest = TAff . The default equation istherefore simplified to:7

Although losses of soil carbon from conversions from forest and grassland to other categories are considered.

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Forest Land

EQUATION 3.2.32
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILS UPON AFFORESTATION1∆CLFMineral = [(SOCref – SOCNon Forest Land) ● AAff] / TAff
Where:∆CLFMineral = annual change in carbon stocks in mineral soils for inventory year, tonnes C yr-1
SOCref = reference carbon stock, under native, unmanaged forest on a given soil, tonnes C ha-1
SOCNon-forest Land = stable soil organic carbon on previous land use, either cropland or grassland ,tonnes C ha-1
AAff = the total afforested land derived from former cropland or grassland, haTAff = duration of the transition from SOCNon-forest Land to SOCref, yr
Note 1: These changes in carbon stocks should be reported annually for TAff years. For example, if a land
is afforested and TAff = 20 years, then the annual changes in carbon stocks in mineral soils on the area
AAff as calculated with Equation 3.2.32 should be reported in the national inventory for 20 years
following the conversion.
Tier 1 calculations are very uncertain; countries for which land conversion to forests is a key category shouldreport at Tier 2 or 3.
Tier 2: For Tier 2 calculations, the new forest types can initially be distinguished using two broad management
categories: intensive management practices (e.g. plantation forestry with intensive site preparation and fertilisation)
or extensive ones (natural forests with minimum intervention); these categories can also be refined according tonational circumstances, for example based on stand origin such as natural or artificial regeneration. New forests
established on lands whose former land-use was not cropland or grassland can be reported under this tier.
Tier 3: Tier 3 calculation procedures involve the development of a country-specific estimation methodology
supported by disaggregated activity data and parameters, stratified by the ecological and anthropogenic factors
which are nationally relevant. The methodology should be comprehensive, including all new managed forests,
and all anthropogenic factors influencing the SOC balance of these lands. Section 3.2.1.3.1.1, Choice ofMethods, provides a schematic outline of generic steps in the development of a domestic methodology.
O R GA N IC SO I L S
Where conversion to forest takes place on drained organic soils, countries should at Tiers 1 and 2 apply theestimation methodology described under the heading “Organic Soils” of Section 3.2.1.3.1.1 (Choice of Methods),
using Equation 3.2.33 below, which is a modified version of Equation 3.2.15. Tier 3 methods should be used where
extensive areas of drained organic soils have been converted to new forest lands. Emissions are assumed tocontinue for as long as the aerobic organic layer remains and the soil is considered to be an organic soil.
EQUATION 3.2.33
CO2 EMISSIONS FROM DRAINED ORGANIC SOILS IN LAND CONVERTED TO FOREST LAND
∆CLFOrganic= ADrained Aff ● EFDrainage
Where:∆CLFOrganic= CO2 emissions from drained organic forest soils in land converted to forest
land, tonnes C yr-1
ADrained Aff = area of drained organic soils in land converted to forest land, ha
EFDrainage = emission factor for CO2 from drained organic forest soils, tonnes C ha-1 yr-1

3.2.2.3.1.2

Choice of Emission/Removal Factors

M INE RA L SO I L S
The parameters to be estimated are SOCref, SOCExt Forest, SOCInt Forest, TInt Forest,TExt Forest, SOCNon Forest Land, fforest type,fman intensity, and fdist regime.
Tier 1: In Tier 1 calculations fforest type = fman intensity = fdist regime = 1, hence the new forest SOC = SOCRef. Default
SOCRef values under native vegetation for broad soil and climate categories are provided in Table 3.2.4.

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Since only the conversion of cropland and grasslands are considered, values SOCNon Forest Land should beconsistent with reported SOC values in croplands (see guidance in Section 3.3.1.2) or grasslands (see guidance inSection 3.4.1.2).
TNat Aff = TInt Aff = TAff the years for abandoned agricultural lands to recover to the native forest biomass under thenative vegetation type and climate, which may be in the range 20 to 100 years, or longer for temperate andboreal ecosystems. These long term dynamics would need following in the forest remaining forest category once
the land had been transferred from the conversion category.
Tier 2: In Tier 2 calculation procedures, countries provide their own values for SOCRef, SOCExt Forest, SOCInt
Forest, TInt forest, TExt Forest, SOCNon Forest Land, fforest type, fman intensity, and fdist regime.
The default values for SOCRef should be replaced by data that better reflect national circumstances, based on
relevant forest types, and natural disturbance regimes. Particular attention should be paid to SOCRef for which
defaults should only be used as the stable, end-point SOC upon afforestation where there is documented
evidence that the new forests are ecologically similar to native vegetation and not managed. Where forests havebeen established on areas with no historical forest, SOCRef may be derived from the most representative data
available in the literature or from soil surveys of comparable forests and soil types.
National values for SOCExt Forest, SOCInt Forest and fforest type, fman intensity, and fdist regime should be consistent with theforest types, management practices and disturbance regimes used in estimation procedures of the SOC in forests
remaining forests (Section 3.2.1.3.1.2, Choice of Emission/Removal Factors). Derivation of these parameters
should be carried out according to the guidance provided in the corresponding text of Section 3.2.1.3.1.2.
Values of SOCNon Forest Land should be consistent with those reported in the other land categories.
The time period required to reach stable forest SOC values should be estimated, taking into account that rates ofsoil C sequestration are slower than those in aboveground biomass, that superficial changes in SOC may only
present a partial picture of the vertical redistribution of carbon along the soil profile, that the transition may be
shorter for new forests that are intensively managed than for extensively managed ones, and that, everything else
being equal, in the long-term SOCInt Forest is likely to be lower than SOCExt Forest .
Linear C sequestration may be replaced by sigmoidal or equivalent representations, where data are available.
Tier 3: Countries develop their own methodologies and parameters for estimating changes in SOC associatedwith the creation of new forests. Such approaches will likely integrate rigorous, long-term monitoring programs,
coupled with numerical and/or dynamic modelling studies, and will be consistent with the methods used toestimate emissions/removals for the SOC pools of forest land remaining forest land. Models should be selectedbased on their capacity to adequately represent the range of conditions and practices that occur over the area ofinterest, and their compatibility with available national data. Because of the complexity of these models, it may
be difficult to quantify the uncertainty associated with the model outputs. The use of models should be supported
by an independent validation of model assumptions, parameters, rules and outputs over the entire range ofconditions and practices modelled.
O R GA N IC SO I L S
The emission factor to be estimated is EFDrainage, for the emissions of CO2 from drained organic soils converted toforest land [tonnes C ha-1 yr-1], as discussed under emission factors for organic soils in Section 3.2.1.3.1.2.
Default values are provided in Table 3.2.3.

3.2.2.3.1.3

Choice of Activity DataM INE RA L SO I L S
Activity data under Tier 1 consist of all croplands and grasslands converted to forests, either deliberately or as aresult of abandonment, estimated consistent with the guidance in Chapter 2. Typical conversion patterns show
plantation establishment on marginal agricultural lands, on abandoned degraded agricultural lands in marginallyproductive areas, or on agricultural land and abandoned lands for other reasons.
Activity data under Tiers 2 and 3 consist of all lands converted to forest land, located according to the generalclimatic categories , and distinguished based on management intensity (extensive or intensive) and stand origin(natural or artificial forest establishment).
Under all tiers, new forests should remain in the conversion category for the duration of the transition period(default = 20 years), and subsequently included in forest land remaining forest land. Assessment of changes inforest SOC is greatly facilitated if the land-use change information can be used in conjunction with national soil
and climate data, vegetation inventories, and other geophysical data, and long term soil carbon dynamics mayneed tracking in the forest land remaining forest land category after transferred at the end of the transition period.

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Forest Land

Data sources will vary according to a country’s land management systems, from individual contractors or companies,
to regulation bodies and government agencies responsible for land use planning, inventory and management, andresearch institutions. Data formats include, among others, activity reports submitted regularly within incentive
programs or as required by regulations, forest management inventories and remotely sensed imagery.
O R GA N IC SO I L S
The activity data consists of ADrained Aff , the area of drained organic soils converted to new forests. When organicsoils are drained for the purpose of afforesting the land, records will probably document the extent and locationof drainage activities in preparation for forest establishment. This may not be the case for the conversion ofpreviously drained soil, for which only the land conversion areas may be available. Additional surveys may beneeded, using the advice in Chapter 2 taking into account any need to adjust areas ascribed to previous land uses
to maintain consistent land area representation.

3.2.2.3.1.4

Uncertainty Assessm ent

Uncertainties in soil organic carbon data are basically the same in lands converted to forest land and in forest
land remaining forest land (Section 3.2.1.3.1.4). An additional source of uncertainty is associated with thevarying evidence on the effect of land conversion to forest land on the soil organic carbon (SOC): the direction
and rate of changes in SOC depend on the initial soil conditions at the time of conversion, and the soil's potential
for accumulating organic carbon. Unless there is contrary evidence, countries should assume a 30% uncertainty
on soil initial conditions.

3.2.2.4

N ON -CO 2 GREENH OUSE GAS EMISSIO NS

Non-CO2 gases from biomass burning are addressed in Section 3.2.1.4 (Greenhouse gas emissions from biomassburning).
In general, land conversion from cropland, grassland, settlements and other land to forest land tends not to altersources and removals of non-CO2 gases from soil as compared to the sources and removals occurring under thepreceding (cropland, grassland, settlements, other land) or new land use (forest land). This assumption may notalways hold true, for instance, if a grassland is ploughed for afforestation. However, insufficient data exist to
provide a default methodology. N2O emissions from management including fertilisation and drainage are
addressed in Section 3.2.1.4 and Appendix 3a.2.
NI T ROU S O X ID E
Figure 3.1.2 provides the decision tree to select the respective tier for N2O emissions from land converted toforest land. If data are available, the key category analysis should be performed separately for each land
conversion type (cropland to forest land, grassland to forest land, wetlands to forest land, settlements to forest
land, other land to forest land).
For all Tiers it is good practice to estimate N2O emissions from direct application of nitrogen to lands in theconversion to forest land category using the same methods described in Section 3.2.1.4.1 for forest landremaining forest land, remembering to avoid double counting with forest land remaining forest land, or
agriculture. If applications data cannot realistically be disaggregated below the forest land remaining forest landor even the agriculture level emissions should be lumped into the parent category, to avoid double counting. In
addition the following points apply:
Tier 1: It is assumed that the conversion to forest land does not lead to soil carbon losses. Based on theargument set out in Section 3.3.2.3 (Non-CO2 emissions from conversion to cropland), N2O emissions from soil
carbon mineralisation are also assumed to be zero. Lagged N2O emissions from nitrogen application during thepreceding land use and new land use (managed forest) are implicitly calculated in the inventory and do not need
to be reported separately, avoiding double counting.
Tier 2: Countries with repeated soil carbon inventories are encouraged to check the assumption that theconversion to forest land does not lead to soil carbon losses. If soil carbon losses can be documented, e.g. fromthe afforestation of grassland, then N2O emissions are reported using the same tiers and methodologies as for theconversion to cropland (Section 3.3.2.3, Non-CO2 emissions from conversion to cropland). Lagged N2Oemissions from nitrogen application during the preceding land use are implicitly calculated in the inventory anddo not need to be reported separately, avoiding double counting. At present, there is no adequate information toestimate the effect of carbon accumulation in soil on N2O emissions.
Tier 3: For countries reporting N2O emissions on a spatially explicit basis it is good practice to apply the samedetailed models as for lands remaining forest land, taking account of the interactions identified for Tier 1 andTier 2 above.

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The conversion of organic soils to forest land releases N2O in cases where the wetlands, especially organic soils,
are drained. It is good practice to report N2O emissions from drainage of organic soils for conversion to forest
land with the same tiers and methodology as N2O emissions from drained organic soils under forest land(Appendix 3a.2), assuring consistency.

3.2.3

CompletenessCompleteness is a requirement for inventory Quality Assurance and (QA) and Quality Control (QC), as outlined
in Chapter 5.5, and is defined, in the way set out in Chapter 1, by the coverage of the IPCC Guidelines.
This Guidance includes specific advice for all losses on managed forest areas (needed for the proper operation of
the methodology), which, at higher tiers, extends to all pools, rather than just aboveground biomass. CO2 andnon-CO2 emissions from fires and direct fertiliser application are included at all tiers and Appendix 3a.2
provides advice on nitrous oxide from drained organic soils. Good practice guidance on liming of forest soils is
identical with the guidance in the IPCC Guidelines and has not been elaborated further, although more detailed
methods are described in Chapter 4.

3.2.4

Developing a Consistent Time Series

It is good practice to develop a consistent time series of inventories of anthropogenic emissions and removals of
GHGs in all LULUCF categories, using the guidance in Section 5.6 (Time series consistency and recalculations).
Because activity data may only be available every few years, achieving time series consistency may requireinterpolation or extrapolation from longer time series or trends, possibly using information on changes in forest
policies and incentive schemes where drivers are needed.
To estimate emissions and removals of GHGs, whether by Tier 1, 2 or 3, ideally the same protocol (samplingstrategy, method, etc.) should be applied consistently to 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
methods 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 sinks categories are included, or moving to higher tier, the methods and data used to calculate estimates canbe updated and refined. In these circumstances, consistent recalculation of historical emissions and removals is agood practice (see Section 5.6.3, Recalculation of periodic data). In some cases, if some historical data are
missing, then they may need to be estimated from other data sources.
Consistent accounting over time of land areas included in the soil C emissions/sinks 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 be lost or double-counted due to accounting errors resulting from inconsistentdefinitions for climate and soil strata within other land-use categories. Consistent definition of each of the
management systems included in the inventory is required.
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, so that activity data are
stratified by common definitions of new forest types, management practices and disturbance regimes.
Often, changes in forest soils cannot be detected at time scale finer than a decade; it will be necessary tointerpolate between measurements in order to obtain annual estimates of emissions and removals.
Changes in forest types, practices and disturbances need 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. Difficulties may arise from lack of historical data on these activities or events. Historical data(including for non-CO2 emissions drained and rewetted areas) will inevitably be of coarser resolution than recent
data; some may have to be reconstructed, based on expert knowledge, which should be documented as set out in
Chapter 5.

3.2.5

Reporting and Documentation

The categories described in Section 3.2 can be reported using the reporting tables in Annex 3A.2. The general
requirements for reporting and documentation are set out in Chapter 5 of this report and in general it is a 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,

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Forest Landmeasurements arising from field site studies, associated protocols, and other basis of basic data) applied to
produce the national emissions/removals inventory. Elaborations on pool definition 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.
Documentation is also needed for demonstrating completeness, consistency of time series data and methods forinterpolating between samples and methods for interpolating between samples and years, and for recalculating,and avoidance of double counting as well as for performing QA/QC.
As Parties decide to progress through higher tier levels, whose calculation methods and data are not described in
the IPCC Guidelines or characterised by more disaggregated approaches, additional documentation is required tosupport 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 the estimates by
independent third parties, but it may prove impractical to include all documentation necessary in the national
inventory report. The inventory should therefore include summaries of approaches and methods used, andreferences to source of data such that the reported emissions estimates are transparent and steps adopted in theircalculation may be retraced.
Documentation is particularly important where the approach, calculation methods and data are not described in
the IPCC Guidelines, as in higher tier or more disaggregated approaches. In addition, it is a good practice toprovide documentation on:
Emission factors: Sources of the emission factors that were used (specific IPCC default values or otherwise)
have to be quoted. If country- or region-specific emission factors were used, and if new methods (other than thedefault IPCC methods) were used, the scientific basis of these emission factors and methods should becompletely described and documented. This includes defining the input parameters and describing the process bywhich 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 of the basis
for the selection of a different factor, describe how it was derived, compare it to other published emission
factors, explain any significant differences, and attempt to place bounds on the 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 the 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 were 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 modelling approach, key model assumptions, input and output data, parametervalues and parameterisation procedures, confidence intervals of model outputs, and the outcome of any
sensitivity analysis conducted on the output.
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.
Non-CO2 greenhouse gases: the requirements on reporting follow three same principles as for CO2, butparticular attention needs to be given to methods for avoidance of omission or double counting with respect toagriculture and between forest land remaining forest land, and transitions to forest land. Clarity is also needed on
coverage, between emissions estimated using the guidance in this chapter and any use made of the guidance in
the Annex 3A.2 (Reporting Tables and Worksheets). In view of the uncertainties clarity in methods andreporting may help advance scientific knowledge as well as serve the purposes of inventory review.

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3.2.6

Inventory Quality Assurance/Quality Control(QA/QC)The characteristics of the LULUCF sector mean that estimates of emissions and removals of GHGs to bereported by national inventories can have different level of precision, accuracy and levels of bias. Moreover, theestimates are influenced by the quality and consistency of data and information available in a country, as well as
gaps in knowledge; in addition, depending on the tier level used by a Party, figures can be affected by different
sources of errors, such as sampling errors, assessment errors, classification errors in remote sensing imagery,model 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 estimates. Additional quality control checks as outlined in Tier 2
procedures in Chapter 8, QA/QC, of GPG2000 and in Chapter 5.5 of this report, and quality assurance
procedures may also be applicable, particularly if higher Tier methods are used to estimate emissions. It is goodpractice to supplement the general QA/QC related to data processing, handling, and reporting and documenting,with source-specific category procedures discussed below.
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. The
basis for the estimates, whether 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 mostimportant for highly uncertain source categories and to give reasons for divergences between country-specificfactors and default or factors used by other countries. Countries with similar (ecological) conditions areencouraged to collaborate in the refinements of methods, emissions factors and uncertainty assessment.
AC T IV I TY D A TA CH E CK
The inventory agency should, where possible, check data comprising of all managed land areas, using
independent sources and compare them. Any differences in area records should be documented for the purposesof review. Activity data area totals should be summed across all land-use categories to insure that total area
involved in the inventory and its stratification across climate and soil types, remains constant over time. This
ensures that land areas are neither ‘created’ nor ‘lost’ over time, which would result in major errors in theinventory. When using country-specific data (such as data on standing biomass and biomass growth rates, carbon
fraction in aboveground biomass and biomass expansion factors, synthetic fertiliser consumption and synthetic
fertiliser consumption estimates) the inventory agency should compare them to the IPCC default values orinternationally well-established values such as those provided by the FAO and the International Fertilizer
Industry Association (IFA), 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 have been peer-reviewed. Assessments on test areas canbe used to validate the reliability of figures reported.
The inventory agency should make sure that QA/QC in the Agriculture source category has been implemented
and that nitrogen excretion, volatile losses and application rates to forest are consistent with the Agriculturesource category and overall consumption of fertilisers and organic wastes, avoiding double counting.
The inventory agency should make sure that the entire area of drained forest peatlands is considered, not only the
recent drainage in the reporting year, and that repeated drainage of a given area is not counted as new area.
IN T E RN A L A ND E X T ER NA L R E VI E W
The review processes as set out in Chapter 5 should be undertaken by experts preferably not directly involved in
the inventory development. The inventory agency should utilize experts in GHG removals and emissions in
LULUCF to conduct expert peer-review of the methods and data used. Given the complexity and uniqueness ofthe parameters used in calculating country-specific factors for some categories, selected specialists in the fieldshould be involved in such reviews. If soil factors are based on direct measurements, the inventory agency
should review the measurements to ensure that they are representative of the actual range of environmental andsoil management conditions, and inter-annual climatic variability, and were developed according to recognised
standards. The QA/QC protocol in effect at the sites should also be reviewed and the resulting estimatescompared between sites and with default-based estimates.

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