Chapter 4: Forest Land

CHAPTER 4

FOREST LAND

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Volume 4: Agriculture, Forestry and Other Land Use

Authors 1
Grant Domke (USA), Andrea Brandon (New Zealand), Rodel Diaz-Lasco (Philippines), Sandro Federici (SanMarino), Emilio Garcia-Apaza (Bolivia), Giacomo Grassi (EU), Thomas Gschwantner (Austria), Martin Herold(Netherlands), Yasumasa Hirata (Japan), Åsa Kasimir (Sweden), Mwangi James Kinyanjui (Kenya), Haruni
Krisnawati (Indonesia), Aleksi Lehtonen (Finland), Rogers E. Malimbwi (Tanzania), Sini Niinistö (Finland),Stephen Michael Ogle (USA), Thomas Paul (New Zealand), Nijavalli H. Ravindranath (India), Joachim Rock(Germany), Carlos Roberto Sanquetta (Brazil), Maria José Sanz Sanchez (Spain), Marina Vitullo (Italy), Stephen
John Wakelin (New Zealand), Jianhua Zhu (China).

Contributing authors
Raul Abad Vinas (Spain), Valerio Avitabile (Italy), Luca Birigazzi (Italy), Simone Rossi (Italy), Danaë M.A.
Rozendaal (Netherlands), Louis Verchot (Colombia).

1 Biomass Carbon Sections were prepared by lead authors, G. Domke, A. Brandon, R. Diaz Lasco, S. Federici, E. Garcia-

Apaza, G. Grassi, T. Gschwantner, M. Herold, Y. Hirata, M.J. Kinyanjui, H. Krisnawati, R.E. Malimbwi, S. Niinistö, S.M.
Ogle, T. Paul, N.H. Ravindranath, J. Rock, C.R. Sanquetta, M.J. Sanz Sanchez, Y. Son, M. Vitullo, S.J. Wakelin, and J. Zhu;
and contributing authors, R. Abad-Vinas, V. Avitabile, L. Birigazzi, S. Rossi, D.M.A. Rozendaal, and L. Verchot.
Soil Carbon Sections were prepared by lead authors, S. Niinistö, A. Lehtonen, Å. Kasimir, and S.M. Ogle.

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Chapter 4: Forest Land

Contents
4

Forest land5
4.1

introduction ......................................................................................................................................... 4.5

4.2

forest land remaining forest land ......................................................................................................... 4.5
4.2.1 Biomass ......................................................................................................................................... 4.5
4.2.2 Dead organic matter ...................................................................................................................... 4.5
4.2.3 Soil carbon ..................................................................................................................................... 4.5
4.2.3.1 Choice of method ..................................................................................................................... 4.6
4.2.3.2 Choice of stock change and emission factors .......................................................................... 4.7
4.2.3.3 Choice of activity data ............................................................................................................. 4.9
4.2.3.4 Calculation steps for Tier 1 ...................................................................................................... 4.9
4.2.3.5 Uncertainty assessment ............................................................................................................ 4.9
4.2.4 Non-CO2 greenhouse gas emissions from biomass burning ........................................................ 4.10

4.3

Land converted to forest land ............................................................................................................ 4.10
4.3.1 Biomass ....................................................................................................................................... 4.10
4.3.2 Dead organic matter .................................................................................................................... 4.11
4.3.3 Soil carbon ................................................................................................................................... 4.11
4.3.3.1 Choice of method ................................................................................................................... 4.11
4.3.3.2 Choice of stock change and emission factors ........................................................................ 4.12
4.3.3.3 Choice of activity data ........................................................................................................... 4.13
4.3.3.4 Calculation steps for Tier 1 .................................................................................................... 4.13
4.3.3.5 Uncertainty assessment .......................................................................................................... 4.14

4.4

Completeness, time series, QA/QC, and reporting and documentation ............................................ 4.14
4.4.1 Completeness............................................................................................................................... 4.14
4.4.2 Developing a consistent time series ............................................................................................. 4.14
4.4.3 Quantity Assurance and Quality Control ..................................................................................... 4.16
4.4.4 Reporting and Documentation ..................................................................................................... 4.17

4.5

Tables ................................................................................................................................................ 4.17

References .......................................................................................................................................................... 4.53

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Volume 4: Agriculture, Forestry and Other Land Use

TablesTable 4.4 (Updated)

Ratio of below-ground biomass to above-ground biomass (R) .................................. 4.18

Table 4.7 (Updated)

Above-ground biomass in natural forests ................................................................... 4.22

Table 4.8 (Updated)

Aboveground biomass (AGB) in forest plantations .................................................... 4.26

Table 4.9 (Updated)

Above-ground net biomass growth in natural forests ................................................. 4.34

Table 4.10 (Updated)

Above-ground net biomass growth in tropical and sub-tropical plantation forests .... 4.39

Table 4.11 (Updated)

Reported Mean Annual Increment (growth rate of merchantable volume) values for someplantation forest species .............................................................................................. 4.42

Table 4.12 (Updated)

Biomass values from tables 4.7–4.10 ......................................................................... 4.47

Boxes
Box 4.3a (New)

Developing Tier 2 stock change factors for forest land ................................................ 4.8

Box 4.3b (New)

Example of resolving forest data gaps through extrapolation based on functional
relationships ................................................................................................................ 4.16

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Chapter 4: Forest Land

4

FOREST LAND

4.1

INTRODUCTION

No refinement.

4.2

FOREST LAND REMAINING FOREST LAND

4.2.1

Biomass

No refinement.

4.2.2

Dead organic matterNo refinement.

4.2.3

Soil carbon

This section elaborates on estimation procedures and good practices for estimating change in forest soil C stocks.
It does not include forest litter, which is a dead organic matter pool. Separate guidance is provided for two typesof forest soils: 1) mineral forest soils, and 2) organic forest soils.
The organic C content of mineral forest soils (to 1 m depth) typically varies between 20 to over 300 tonnes C ha-1
depending on the forest type and climatic conditions (Jobbagy and Jackson 2000). Globally, mineral forest soils
contain approximately 700 Pg C (Dixon et al. 1994), but soil organic C pools are not static due to differences
between C inputs and outputs over time. Inputs are largely determined by the forest productivity, thedecomposition of litter and its incorporation into the mineral soil and subsequent loss throughmineralization/respiration (Pregitzer 2003). Other losses of soil organic C occur through erosion or the dissolutionof organic C that is leached to groundwater or loss through overland flow. A large proportion of input is fromabove-ground litter in forest soils, so soil organic matter tends to concentrate in the upper soil horizons, withroughly half of the soil organic C in the upper 30 cm layer. In some forest ecosystems, rooting zones of trees
extend considerable deeper than 30 cm, which can increase the share of soil organic carbon in deeper layers(Nepstad et al. 1994). Changes in soil carbon stocks in response to management actions such as thinning and clearcutting have been detected below 20–30 cm, but not in all studies or all depths (Achat et al. 2015a; James andHarrison 2016; Gross et al. 2018). Moreover, the scarcity of measurements increases uncertainty related to soil
carbon stock changes deeper in soil. The C held in the upper profile is often the most chemically decomposable,
and the most directly exposed to natural and anthropogenic disturbances. This section only deals with soil C anddoes not address decomposing litter (i.e., dead organic matter, see Section 4.2.2).
Human activities and other disturbances such as changes in forest type, productivity, decay rates and disturbances
can alter the C dynamics of forest soils. Different forest management activities, such as rotation length; choice oftree species; drainage; harvest practices (whole tree or sawlog, regeneration, partial cut or thinning); site
preparation activities (prescribed fires, soil scarification); and fertilization, affect soil organic C stocks (Harmonand Marks, 2002; Liski et al. 2001; Johnson and Curtis 2001). Changes in disturbance regimes, notably in theoccurrence of severe forest fires, pest outbreaks, and other stand-replacing disturbances are also expected to alter
the forest soil C pool (Li and Apps 2002; de Groot et al. 2002). In addition, drainage of forest stands on organicsoils reduces soil C stocks.
General information and guidelines on estimating changes soil C stocks are found in Chapter 2, Section 2.3.3, andneeds to be read before proceeding with the specific guidelines dealing with forest soil C stocks. Changes in soil
C stocks associated with forests are computed using Equation 2.24 in Chapter 2, which combines the change insoil organic C stocks for mineral soils and organic soils; and stock change for soil inorganic C pools (Tier 3 only).
This section elaborates on estimation procedures and good practices for estimating change in forest soil C organic
stocks (Note: It does not include forest litter, i.e., dead organic matter). Separate guidance is provided for two
types of forest soils: 1) mineral forest soils, and 2) organic forest soils. See Section 2.3.3.1 for general discussion
on soil inorganic C (no additional information is provided in the Forest Land discussion below).
To account for changes in soil C stocks associated with Forest Land Remaining Forest Land, countries need tohave, at a minimum, estimates of the total Forest Land area at the beginning and end of the inventory time period,
stratified by climate region and soil type. If land-use and management activity data are limited, Approach 1 activity

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Volume 4: Agriculture, Forestry and Other Land Usedata (see Chapter 3) can be used as the basis for a Tier 1 approach, but higher Tiers are likely to need more detailed
records or knowledge of country experts about the approximate distribution of forest management systems. Forest
Land classes must be stratified according to climate regions and major soil types for Tier 1, which can beaccomplished with overlays of suitable climate and soil maps. Further stratification may be useful for developmentof Tier 2 or 3 methodology for a country.

4.2.3.1 C HOICE OF METHOD
Inventories can be developed using Tier 1, 2 or 3 approaches, and countries may choose to use different tiers formineral and organic soils. Decision trees are provided for mineral soils (Figure 2.4) and organic soils (Figure 2.5)
in Chapter 2 to assist inventory compilers with selection of the appropriate tier for their soil C inventory.

Mineral soils
In spite of a growing body of literature on the effect of forest types, management practices and other disturbanceson soil organic C, the available evidence remains largely site- and study-specific, but eventually may begeneralized based on the influence of climatic conditions, soil properties, the time scale of interest, taking into
consideration sampling intensity and effects across different soil depth increments (Johnson and Curtis 2001;
Hoover 2003; Page-Dumroese et al. 2003). However, the current knowledge remains inconclusive on both themagnitude and direction of C stock changes in mineral forest soils associated with forest type, management andother disturbances, and cannot support broad generalizations.
Tie r 1
Current scientific basis is not sufficient to develop Tier 1 default emission factors for quantification of effects offorest management by IPCC climate zones. Thus, it is assumed in the Tier 1 method that forest soil C stocks do
not change with management. Recent studies indicate, that effects of forest management actions on soil C stocks
can be difficult to quantify and reported effects have been variable and even contradictory (see Box 4.3a).
Furthermore, if using Approach 2 or 3 activity data (see Chapter 3), it is not necessary to compute C stock changes
for mineral soils (i.e., change in SOC stocks is 0). If using activity data collected via Approach 1 (see Chapter 3),
and it is not possible to identify the amount of land converted from and to Forest Land, then the inventory compiler
should estimate soil C stocks for Forest Land using the areas at the beginning and the end of the inventory period
in order to estimate the change in soil carbon stock. The changes in soil C stocks for Forest Land are summed withthe changes in stocks for other land uses to estimate the influence of land-use change. If the compiler does notcompute a stock for Forest Land, it is likely to create systematic errors in the inventory. For example, land
converted from Forest Land to Cropland or Grassland will have a soil C stock estimated in the final year of theinventory, but will have no stock in the first year of the inventory (when it was forest). Consequently, conversion
to Cropland or Grassland is estimated as a gain in soil C because the soil C stocks are assumed to be 0 in the Forest
Land, but not in Cropland and Grassland. This would introduce a bias into the inventory estimates. SOC0 and
SOC0-T are estimated for the top 30 cm of the soil profile using Equation 2.25 (Chapter 2). Note that areas of
exposed bedrock in Forest Land are not included in the soil C stock calculation (assume a stock of 0). Further
clarification on soil organic carbon estimation is presented in Section 2.3.3.1.
Tie r 2Using Equation 2.25 (Chapter 2) soil organic C stocks are computed based on reference soil C stocks and countryspecific stock change factors for forest type (FI), management (FMG) and natural disturbance regime (FND). Notethat the stock change factor for natural disturbance regime (FND) is substituted for the land-use factor (FLU) in
Equation 2.25. In addition, country-specific information can be incorporated to better specify reference C stocks,
climate regions, soil types, and/or the land management classification system.
Tie r 3Tier 3 approaches will require considerable knowledge and data allowing for the development of an accurate andcomprehensive domestic estimation methodology, including evaluation of model results and implementation of adomestic monitoring scheme and/or modelling tool. The basic elements of a country-specific approach are(adapted from Webbnet Land Resource Services Pty ltd, 1999):• Stratification by climatic zones, major forest types and management regimes coherent with those used for other
C pools in the inventory, especially biomass;• Determination of dominant soil types in each stratum;• Characterization of corresponding soil C pools, identification of determinant processes in SOC input and output
rates and the conditions under which these processes occur; and• Determination and implementation of suitable methods to estimate carbon stock changes from forest soils foreach stratum on an operational basis, including model evaluation procedures; methodological considerations

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Chapter 4: Forest Landare expected to include the combination of monitoring activities – such as repeated forest soil inventories - andmodelling studies, and the establishment of benchmark sites. Further guidance on good soil monitoring
practices is available in the scientific literature (Kimble et al. 2003, Lal et al. 2001, McKenzie et al. 2000). It
is good practice for models developed or adapted for this purpose to be peer-reviewed and validated withobservations representative of the ecosystems under study and independent from the calibration data.
More guidance on Tier 3 methods is given in Chapter 2.3.3.1, such as examples of Tier 3 modelling methods inBox 2.2d. The examples provide information about types of data required, brief descriptions of models, methods
that are used to apply the models, and how using a Tier 3 model has changed results. General guidance on
measurement-based and model-based Tier 3 inventories for the AFOLU sector can be found in Section 2.5.

Organic soils
No refinement.
See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.2.

4.2.3.2 C HOICE OF STOCK CHANGE AND EMISSION FACTORS
Mineral soils
Tie r 1
It is not necessary to compute the stock estimates for Forest Land Remaining Forest Land with Approach 2 or 3
activity data (see Chapter 3). If using Approach 1 activity data, stock change factors, including input, managementand disturbance regime, are equal to 1 using the Tier 1 approach. Consequently, only reference C stocks areneeded to apply the method, and those are provided in Table 2.3 of Chapter 2.
Tie r 2
In a Tier 2 approach, stock change factors are derived based on a country-specific classification scheme formanagement, forest types, and natural disturbance regimes. A Tier 2 approach should include the derivation ofcountry-specific reference C stocks, and a more detailed classification of climate and soils than the default
categories provided with the Tier 1 method. The depth for evaluating soil C stock changes can differ from 30 cm
with the Tier 2 method. However, this will require consistency with the depth of the reference C stocks (SOCREF)
and stock change factors (i.e., FLU, FI, and FMG) to ensure consistent application of methods for determining theimpact of land use change on soil C stocks. Box 4.3a provides information and references that can be used as astarting point for developing Tier 2 factors for forest management as well as observations on related challenges.
It is good practice to focus on the factors that have the largest overall effect, taking into account the impact onforest SOC and the extent of affected forests. Management practices can be coarsely labeled as intensive (e.g.,plantation forestry) or extensive (e.g., natural forest); these categories can also be redefined according to national
circumstances. The development of stock change factors is likely to be based on intensive studies at experimentalsites and sampling plots involving replicated, paired site comparisons (Johnson et al. 2002; Olsson et al. 1996; seealso the reviews by Johnson and Curtis 2001; and Hoover 2003). In practice, it may not be possible to separate
the effects of different forest types, management practices and disturbance regimes, in which case stock change
factors should be combined into a single modifier. If a country has well-documented data for different forest types
under different management regimes, it might be possible to derive soil organic C estimates directly without usingreference C stocks and adjustment factors. However, a relationship to the reference C stocks must be establishedso that the impact of land-use change can be computed without artificial increases or decreases in the C stocks due
to a lack of consistency in the methods across the various land-use categories (i.e., Forest Land, Cropland,
Grassland, Settlements, and Other Land).
Inventories can also be improved by deriving country-specific reference C stocks (SOCREF), compiled frompublished studies or surveys. Such values are typically obtained through the development and/or compilation oflarge soil profile databases (Siltanen et al. 1997; Scott et al. 2002; Batjes 2011; De Vos et al. 2015). Additional
guidance for deriving stock change factors and reference C stocks is provided in Section 2.3.3.1 (Chapter 2).

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Volume 4: Agriculture, Forestry and Other Land Use

BOX 4.3A (NEW)DEVELOPING TIER 2 STOCK CHANGE FACTORS FOR FOREST LAND

Although the scientific basis is not sufficient for deriving default stock change factors for forest land,country specific Tier 2 factors can be developed if there is adequate data available to represent
national circumstances. Several meta-analyses and reviews provide analyses and references tosupport incorporation of country-specific data into a Tier 2 method with estimation of management
effects and corresponding stock change factors (FMG) for Forest Land Remaining Forest Land.
Quantification of management effects becomes increasingly important in cases in which forests
represent a significant sink or source or in which changes in management intensity or practices resultin gains or losses compared to earlier practices. Increased removal of harvest residues or stumps forbioenergy is one example of changes in management intensity and practices. Most analyses havefocused on the effects following harvests of different intensities (e.g., Johnson and Curtis 2001;
Achat et al. 2015a; James and Harrison 2016; Zhou et al. 2013). Response ratios or effect sizes basedon measurements of soil carbon stocks reflect all changes associated with a management action;
thus, separate carbon stock factors for input of organic matter (FI) cannot be derived from the existingdata.
Most field experiments have been carried out in cool temperate regions, and meta-analyses orreviews on harvest effects can be found to support adaptation of Tier 2 methods for these regions
Nave et al. 2010; Thiffault et al., 2011; Clarke et al. 2015; Hume et al. 2017). When selecting
harvesting experiments on which to base the calculation of stock change factors, several factors need
to be considered: intensity of harvest, treatment of harvest residues and other site preparation
practices, such as burning, time since the management action, and soil layers and sampling depths(Liao et al. 2010; Strömgren et al. 2013; Achat et al. 2015b; James and Harrison 2016; Dean et al.
2017; Hume et al. 2017). Tree species composition, i.e., conifers versus broad-leaved or mixed
species, could also influence the management effect although the influence can be confounded byother factors (e.g. Hume et al. 2017). The question of control conditions for evaluating themanagement action is of great importance because the control is often not a native reference
condition, but rather another managed forest (Dean et al. 2017). This should be taken into accountwhen estimating a stock change factor based on several field studies as well as the relationship tocountry-specific reference soil C stock.
Conclusions on the harvesting effects differ between meta-analyses, which could be partly due todifferences in field experiment set-ups and the different data selection and weighting procedures. As
an example, whole-tree harvests resulted in average 7.5percent smaller carbon stocks in mineral soil
than the stocks measured 10–30 years after stem-only harvests (Achat et al. 2015a). However, noeffect of whole-tree harvest was found in some other meta-analyses (Clarke et al. 2015; Hume et al.
2017) or a positive effect was reported (James and Harrison 2016). However, there was a tendency
for smaller carbon stocks in forest floor after whole-tree harvesting compared to stem-only
harvesting or pre-treatment conditions (Johnson and Curtis 2001; Thiffault et al. 2011; Clarke et al.
2015).
Considerable spatial variability increases the challenge to detect relatively small management effects
in soil C stocks (Jandl et al. 2007). However, most studies include only the first one or two decades
after the harvest, which may too short to reveal impact of forest management actions on soil carbon
stock changes, especially in cool climate regions with long rotation periods (Clarke et al. 2015; Deanet al. 2017). Non-linearity in the responses has also been observed. For example, an increase in soil
C stocks after an initial decrease has been observed for a group of studies on Spodosols from a cooland humid climate with longer monitoring periods, up to eight decades of typical rotation lengths(James and Harrison 2016).
In addition to guidance in this Chapter 4.2.3.2 above, detailed guidance on estimation of countryspecific stock change factors and reference C stocks in general is given in Chapter 2, in Section
2.3.3.1., including guidance on using models to derive carbon stock change factors.
Tie r 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion.

Organic soils
No refinement.

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Chapter 4: Forest Land

See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.2.

4.2.3.3 C HOICE OF ACTIVITY DATA
Mineral soils
Tie r 1For the Tier 1 approach, it is assumed that forest soil C stocks do not change with management, and therefore it is
not necessary to classify forest into various types, management classes or natural disturbance regimes. However,
if using Approach 1 activity data (see Chapter 3), environmental data will be needed to classify the country into
climate regions and soil types in order to apply the appropriate reference C stocks to Forest Land. A detailed
description of the default climate classification scheme is given in Chapter 3, Annex 3A.5. If the informationneeded to classify climate types is not available from national databases, there are international sources of climate
data such as United Nations Environmental Program. Data will also be needed to classify soils into the default
categories provided in Chapter 3, and if national data are not available to map the soil types, international soils
data provide a reasonable alternative, such as the FAO Soils Map of the World.
Tie r 2
Activity data for the Tier 2 approach consist of the major forest types, management practices, disturbance regimes
and the areas to which they apply. It is preferable for the data to be linked with the national forest inventory, where
one exists, and/or with national soil and climate databases. Typical changes include conversion of unmanaged to
managed forest; conversion of forest type (native forest into a new forest type, such as plantation of exotic species
and vice versa); intensification of forest management activities, such as site preparation, tree planting, interval andintensity of thinning and rotation length changes; changes in harvesting practices (bole vs. whole-tree harvesting;amount of residues left on-site); and the frequency of disturbances (e.g., pest and disease outbreaks, flooding, fires,
typhoon/cyclone/hurricane, snow damage). Data sources will vary according to a country’s forest management
system, but could include individual contractors or companies, statutory forest authorities, research institutions
and agencies responsible for forest inventories. Data formats vary widely, and include, among others, activity
reports, forest management inventories and remote sensing imagery.
In addition, Tier 2 methods should involve a finer stratification of environmental data than the Tier 1 approach,including climate regions and soil types, which would likely be based on national climate and soils data. If a finerclassification scheme is utilized in a Tier 2 inventory, reference C stocks will also need to be derived for the moredetailed set of climate regions and soil types, and the land management data will need to be stratified based on the
country-specific classification.
Tie r 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed
data on the combinations of climate, soil, topographic and management data are needed, relative to the Tiers 1 and2 methods, but the exact requirements will be dependent on the model or measurement design.

Organic soils
No refinement.
See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.2.

4.2.3.4 C ALCULATION STEPS FOR T IER 1
No refinement.

4.2.3.5

U NCERTAINTY ASSESSMENT

Three broad sources of uncertainty exists in soil C inventories: 1) uncertainties in land-use and management
activity and environmental data; 2) uncertainties in reference soil C stocks if using Tier 1 or 2 approaches (mineralsoils only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model
structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability associatedwith Tier 3 measurement-based inventories. In general, precision of an inventory is increased (i.e., smaller
confidence ranges) with more sampling to estimate values for the three broad categories. In addition, reducing

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Volume 4: Agriculture, Forestry and Other Land Usebias (i.e., improve accuracy) is more likely through the development of a higher Tier inventory that incorporates
country-specific information.
For Tier 1, uncertainties are provided with the reference C stocks in the first footnote of Table 2.3 (Chapter 2), andemission factor uncertainties for organic soils are provided in Table 4.6, Section 4.5. For organic soils, seeguidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands,
Chapter 2, Section 2.2. Uncertainties in land-use and management data will need to be addressed by the inventory
compiler, and then combined with uncertainties for the default factors and reference C stocks (mineral soils only)
using an appropriate method, such as simple error propagation equations. Refer to Section 4.2.1.5 for uncertainty
estimate for land area estimates. However, it is good practice for the inventory compiler to derive uncertainties
from country-specific activity data instead of using a default level.
Default reference C stocks for mineral soils and emission factors for organic soils can have inherently highuncertainties, particularly bias, when applied to specific countries. Defaults represent globally averaged values ofland-use and management impacts or reference C stocks that may vary from region-specific values (Powers et al.
2004; Ogle et al. 2006). Bias can be reduced by deriving country-specific factors using Tier 2 method or bydeveloping a Tier 3 country-specific estimation system. The underlying basis for higher Tier approaches will beresearch in the country or neighbouring regions that address the effect of land use and management on soil C. In
addition, it is good practice to further minimize bias by accounting for significant within-country differences inland-use and management impacts, such as variation among climate regions and/or soil types, even at the expenseof reduced precision in the factor estimates (Ogle et al. 2006). Bias is considered more problematic for reporting
stock changes because it is not necessarily captured in the uncertainty range (i.e., the true stock change may beoutside of the reported uncertainty range if there is significant bias in the factors).
Uncertainties in land-use activity statistics may be improved through a better national system, such as developing
or extending a ground-based survey with additional sample locations and/or incorporating remote sensing toprovide additional coverage. It is good practice to design a classification that captures the majority of land-use
and management activity with a sufficient sample size to minimize uncertainty at the national scale.
For Tier 2 methods, country-specific information is incorporated into the inventory analysis for purposes of
reducing bias. For example, Ogle et al. 2003 utilized country-specific data to construct probability distributionfunctions for US specific factors, activity data and reference C stocks for agricultural soils. It is good practice toevaluate dependencies among the factors, reference C stocks or land-use and management activity data. In
particular, strong dependencies are common in land-use and management activity data because management
practices tend to be correlated in time and space. Combining uncertainties in stock change/emission factors,
reference C stocks and activity data can be done using methods such as simple error propagation equations orMonte-Carlo procedures.
Tier 3 models are more complex and simple error propagation equations may not be effective at quantifying theassociated uncertainty in resulting estimates. Monte Carlo analyses are possible (Smith and Heath 2001), but canbe difficult to implement if the model has many parameters (some models can have several hundred parameters)
because joint probability distribution functions must be constructed quantifying the variance as well as covarianceamong the parameters (see e.g. Peltoniemi et al. 2006; Metsaranta et al. 2017). However, if soil model parameters
have been estimated with a Bayesian approach, the resultant joint probability distribution for the parameters canbe sampled in a Monte Carlo Analysis to capture parameter uncertainty, along with sampling of probability
distribution functions for model inputs and other associated data, see Lehtonen and Heikkinen (2016). Other
methods are also available such as empirically-based approaches (Monte et al. 1996), which use measurementsfrom a monitoring network to statistically evaluate the relationship between measured and modelled results(Falloon and Smith 2003; Ogle et al. 2007). In contrast to modelling, uncertainties in measurement-based Tier 3
inventories can be determined from the sample variance, measurement error and other relevant sources of
uncertainty.

4.2.4

Non-CO2 greenhouse gas emissions from biomassburning

No refinement.

4.3

LAND CONVERTED TO FOREST LAND

4.3.1

Biomass

No refinement.

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Chapter 4: Forest Land

4.3.2

Dead organic matterNo refinement.

4.3.3

Soil carbon

Land conversions on mineral soils generally either maintain similar levels of C storage or create conditions that
increase soil C stocks, particularly if the land was previously managed for annual crop production (Post and Kwon,
2000). However, under certain circumstances, Grassland conversion to Forest Land has been shown to cause small
C losses in mineral soils for several decades following conversion (Davis and Condron 2002; Paul et al. 2002).
Emissions of C from organic soils will vary depending on the previous use and level of drainage. Specifically,
conversion from Cropland will tend to decrease emissions; conversions from Grassland will likely maintain similar
emission rates; while conversion from Wetlands often increases C emissions.
General information and guidelines on estimating changes soil C stocks are found in Section 2.3.3 in Chapter 2(including equations) and need to be read before proceeding with guidelines dealing with forest soil C stocks. The
total change in soil C stocks for Land Converted to Forest Land is computed using Equation 2.24 (Chapter 2),
which combines the change in soil organic C stocks for mineral soils and organic soils; and carbon stock changes
for inorganic soil C pools (Tier 3 only). This section provides specific guidance for estimating soil organic C stockchanges; see Section 2.3.3.1 (Chapter 2) for general discussion on soil inorganic C (no additional information isprovided in the Forest Land discussion below).
To account for changes in soil C stocks associated with Land Converted to Forest Land, countries need to have,at a minimum, estimates of the areas of Land Converted to Forest Land during the inventory time period, stratified
by climate region and soil type. If land-use and management data are limited, Approach 1 activity data can be used
as a starting point, along with knowledge of country experts of the approximate distribution of land-use types being
converted. If previous lands uses and conversions for Land Converted to Forest Land are unknown, SOC stocks
changes can still be computed using the methods provided in Forest Land Remaining Forest Land, but the landbase will likely be different for forests in the current year relative to the initial year in the inventory. It is critical,
however, that the total land area across all land-use sectors be equal over the inventory time period (e.g., if 5
Million ha is converted from Cropland and Grassland to Forest Land during the inventory time period, then Forest
Land will have an additional 5 Million ha in the last year of the inventory, while Cropland and Grassland will havea corresponding loss of 5 Million ha in the last year), and the total change will be estimated when summing SOC
stocks across all land uses. Land Converted to Forest Land is stratified according to climate regions and major
soil types, which could either be based on default or country-specific classifications. This can be accomplishedwith overlays of climate and soil maps, coupled with spatially-explicit data on the location of land conversions.
Inventories can be developed using Tier 1, 2 or 3 approaches, with each successive Tier requiring more detail andresources than the previous. It is possible that countries will use different tiers to prepare estimates for the separate
components in this source category (i.e., soil organic C stocks changes in mineral soils and organic soils; and stockchanges associated with soil inorganic C pools).

4.3.3.1 C HOICE OF METHOD
Inventories can be developed using Tier 1, 2 or 3 approaches and countries may choose different tiers for mineraland organic soils. Decision trees are provided for mineral (Figure 2.4) and organic soils (Figure 2.5) in Section
2.3.3.1 (Chapter 2) to assist inventory compilers with selection of the appropriate tier for their soil C inventory.

Mineral soils
Tie r 1
Change in soil organic C stocks can be estimated for mineral soils with land-use conversion to Forest Land using
Equation 2.25 (Chapter 2). For Tier 1, the initial (pre-conversion) soil organic C stock (SOC(0-T)) and C stock in
the last year of the inventory time period (SOC0) are determined from the common set of reference soil organic C
stocks (SOCREF) and default stock change factors (FLU, FMG, FI) as appropriate for describing land use andmanagement both pre- and post-conversion. Note that area of exposed bedrock in Forest Land or the previous land
use are not included in the soil C stock calculation (assume a stock of 0). Annual rates of stock changes arecalculated as the difference in stocks (over time) divided by the time dependence (D) of the stock change factors(default is 20 years).

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Volume 4: Agriculture, Forestry and Other Land Use

Tie r 2The Tier 2 approach for mineral soils also uses Equation 2.25 (Chapter 2), but involves country or region-specificreference C stocks and/or stock change factors and possibly more disaggregated land-use activity andenvironmental data.
Tie r 3Tier 3 approaches will involve more detailed and country-specific models and/or measurement-based approaches
along with highly disaggregated land-use and management data. It is good practice that Tier 3 approaches
estimating soil C change from land-use conversions to Forest Land, employ models, monitoring networks and/or
data sets that are capable of representing transitions over time from other land uses, including Grassland, Cropland
and possibly Settlements or other land uses. It is important that models be evaluated with independent observations
from country or region-specific field locations that are representative of the interactions of climate, soil and forest
type/management on post-conversion change in soil C stocks.

Organic soils
No refinement.
See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.3.

4.3.3.2 C HOICE OF STOCK CHANGE AND EMISSION FACTORS
Mineral soils
Tie r 1For native unmanaged land, as well as for managed Forest Land, Settlements and nominally managed Grasslandwith low disturbance regimes, soil C stocks are assumed equal to the reference values (i.e., land use, disturbance(forests only), management and input factors equal 1), but it will be necessary to apply the appropriate stock change
factors to represent other systems which may be converted to Forest Land, such as improved and degraded
Grassland, as well as all Cropland systems. See the appropriate land-use section for default stock change factors(Forest Land in 4.2.3.2, Cropland in Section 5.2.3.2, Grassland in 6.2.3.2, Settlements in 8.2.3.2, and Other Landin 9.3.3.2). Default reference C stocks are found in Table 2.3 (Chapter 2).
Tie r 2Estimation of country-specific stock change factors is probably the most important development associated with
the Tier 2 approach. Differences in soil organic C stocks among land uses are computed relative to a reference
condition. If default reference C stocks are used, the reference condition is native vegetation that is neither
degraded nor improved through land-use and management practices. Stock change factors for land-use conversion
to native forests will be equal to 1 if the forest represents the reference condition. However, stock change factors
will need to be derived for Land Converted to Forest Land that do not represent the reference condition, accounting
for the influence of disturbance (FD), input (FI) and management (FMG), which are then used to further refine the
C stocks of the new forest system. See the appropriate section for specific information regarding the derivation ofstock change factors for other land-use sectors (Cropland in 5.2.3.2, Grassland in Section 6.2.3.2, Settlements in
8.2.3.2, and Other Land in 9.3.3.2).
Reference C stocks can also be derived from country-specific data in a Tier 2 approach. Reference values in Tier
1 correspond to non-degraded, unimproved lands under native vegetation, but other reference conditions can also
be chosen for Tier 2. In general, reference C stocks should be consistent across the land uses (i.e., Forest Land,
Cropland, Grassland, Wetlands, Settlements, Other Land) (see section 2.3.3.1). Therefore, the same reference stock
should be used for each climate zone and soil type, regardless of the land use. The reference stock is then multipliedby land use, input and management factors to estimate the stock for each land use based on the set of management
systems that are present in a country. In addition, the depth for evaluating soil C stock changes can be different
with the Tier 2 method. However, this will require consistency with the depth of the reference C stocks (SOCREF)
and stock change factors for all land uses (i.e., FLU, FI, and FMG) to ensure consistency in the application of methods
for estimating the impact of land use change on soil carbon stocks. Additional guidance is provided in Chapter 2,
Section 2.3.3.1.
The carbon stock estimates may be improved when deriving country-specific factors for FLU and FMG, byexpressing carbon stocks on a soil-mass equivalent basis rather than a soil-volume equivalent (i.e., fixed depth)basis. This is because the soil mass in a certain soil depth changes with the various operations associated with land
use that affect the density of the soil, such as uprooting, land levelling, tillage, and rain compaction due to thedisappearance of the cover of tree canopy. However, it is important to realize that all data used to derive stockchange factors across all land uses must be on an equivalent mass basis if this method is applied. This will be

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

Chapter 4: Forest Land

challenging to do comprehensively for all land uses. See Box 2.2c in Chapter 2, Section 2.3.3.1 for more
information.
Tie r 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion.

Organic soils
No refinement.
See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.3.

4.3.3.3

C HOICE OF ACTIVITY DATA

Mineral soils
Tie r 1 and Tie r 2
For purposes of estimating soil carbon stock change, area estimates of Land Converted to Forest Land should be
stratified according to major climate regions and soil types. This can be based on overlays with suitable climate
and soil maps and spatially-explicit data of the location of land conversions. Detailed descriptions of the default
climate and soil classification schemes are provided in Chapter 3. Specific information is provided in the each of
the land-use sections regarding treatment of land-use/management activity data (Forest Land in Section 4.2.3.3,
Cropland in 5.2.3.3, Grassland in 6.2.3.3, Wetlands in 7.2.3.2, Settlements in 8.2.3.3, and Other Land in 9.3.3.3).
One critical issue in evaluating the impact of Land Converted to Forest Land on soil organic C stocks is theprevious land-use and management activity. Activity data gathered using Approach 2 or 3 (see Chapter 3 fordiscussion about Approaches) provide the underlying basis for determining the previous land use and managementfor Land Converted to Forest Land. In contrast, aggregate data (Approach 1, Chapter 3) only provide the total
amount of area in each land use and do not form a basis for determining specific transitions. Moreover, aggregate
data only represent the net changes in land use and management rather than the gross changes, which could beconsiderably larger and may have an impact on the total soil C stock changes. Regardless, with aggregate data
(Approach 1), changes in soil organic C stocks may be computed separately for each land-use category and then
combined to obtain the total stock change even if the total changes do not capture the full dynamics occurring withland use change. Using this approach, it will be necessary for coordination among each land-use category to ensure
the total land base is remaining constant over time, given that some land area will be lost and gained within
individual land-use category during each inventory year due to land-use change. Further clarification on soilorganic C estimation methods in case of land-use change is presented in Section 2.3.3.1.
Tie r 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed
data on the combinations of climate, soil, topographic and management data are needed, relative to Tier 1 or 2
method, but the exact requirements will be dependent on the model or measurement design.

Organic soils
No refinement.
See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.3.

4.3.3.4 C ALCULATION STEPS FOR T IER 1Mineral soils
The steps for estimating SOC0 and SOC(0-T) and net soil C stock change per ha of Land Converted to Forest Landare as follows:
Step 1: Determine the land-use and management by mineral soil types and climate regions for land at the
beginning of the inventory period, which can vary depending on the time step of the activity data (0-T; e.g., 5, 10
or 20 years ago).

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

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Volume 4: Agriculture, Forestry and Other Land Use

Step 2: Select the native reference C stock value (SOCREF), based on climate and soil type from Table 2.3, foreach area of land being inventoried. The reference C stocks are the same for all land-use categories to ensure thaterroneous changes in the C stocks are not computed due to differences in reference stock values among sectors.
Step 3: Select the land-use factor (FLU), management factor (FMG) and C input levels (FI) representing the landuse and management system present before conversion to forest. Values for FLU, FMG and FI are given in therespective section for the land-use sector (Cropland in Chapter 5, and Grassland in Chapter 6).
Step 4: Multiply these values by the reference soil C stock to estimate of ‘initial’ soil organic C stock (SOC(0-T))
for the inventory time period.
Step 5: Estimate SOC0 by repeating step 1 to 4 using the same native reference C stock (SOCREF), but with landuse, management and input factors that represent conditions in the last (year 0) inventory year. For Tier 1, all stockchange factors are assumed equal to 1 for Forest Land (although for Tier 2, different values for these factors under
newly converted Forest Land should be used, based on country-specific data).
Step 6: Estimate the average annual change in soil C stock for the area over the inventory time period, ∆CCCMineral,(see Equation 2.25 in Chapter 2).
Step 7: Repeat Steps 1 to 6 if there are additional inventory time periods (e.g., 1990 to 2000, 2001 to 2010, etc.).
A numerical example is given below for afforestation of cropland soil.
Example: An area of 100,000 ha of cropland was planted to forest. The soil type is an Ultisol in a
tropical moist climate, which has a native reference stock, SOCRef (0-30 cm), of 47 tonnes C ha-1
(Table 2.3). The previous land use was annual row crops, with conventional tillage, no fertilization
and where crop residues are removed, so that the soil carbon stock at the beginning of the inventory
time period (in this example, 5 yrs earlier in 1995) was (SOCRef ● FLU ● FMG ● FI) = 47 tonnes C ha1● 0.48 ● 1 ● 0.92 = 20.8 tonnes C ha-1 (see Table 5.5, Chapter 5, for stock change factor for
cropland). Under Tier 1, managed forest is assumed to have the same soil C stock as the reference
condition (i.e. all stock change factors are equal to 1). Thus, the average annual change in soil C
stock for the area over the inventory time period is estimated as (47 tonnes C ha-1 – 20.8 tonnes Cha-1) / 20 yrs = 1.3 tonnes C ha-1 yr-1. For the area reforested there is an increase of 131,000 tonnes
C yr-1. (Note: 20 years is the time dependence of the stock change factor, i.e., factor represents annual
rate of change over 20 years)

Organic soils
No refinement.
See guidance in 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
Wetlands, Chapter 2, Section 2.3.

4.3.3.5

U NCERTAINTY ASSESSMENT

No refinement.

4.4

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

4.4.1

CompletenessNo refinement.

4.4.2

Developing a consistent time series

It is good practice to develop a consistent time series of inventories of anthropogenic emissions and removals ofgreenhouse gases for all AFOLU categories using the guidance in Volume 1, Chapter 5. Because forest-related
activity data and emission factors may only be available every few years, achieving time series consistency may
require interpolation or extrapolation from longer timeseries or trend.

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

Chapter 4: Forest LandIn addition to the general guidance on gap filling (e.g. on linear interpolation or extrapolation) in Volume 1,
Chapter 5, further guidance is provided here on how to ensure methodological consistency in the case of the Forest
Land category. When extrapolation may allow reflecting the evolution of the main drivers of emissions andremovals during the period to be gap filled, including forest increment and harvest, with a greater level of accuracy
than a linear interpolation or extrapolation.
Generally, these functional relationships are expressed in models which are applied to simulate the dynamics ofcarbon stocks in different pools, taking into account a number of interrelated variables.These variables include:
forest characteristics (i.e. forest types, soil types, tree species composition, growing stock, age-class structure) andmanagement practices (i.e. regeneration modality, rotation lengths, thinning frequency, etc.); the carbon pools andgases; the estimation parameters for HWP; the treatment of natural disturbances; the possible inclusion of impactof “indirect human-induced effects” (see Section 2.5), such as human-induced climate and environmental changes(e.g., temperature, precipitation, CO2 and nitrogen deposition feedbacks) that affect growth, mortality,
decomposition rates and natural disturbances regimes.
Among these, harvest volume is a key driver of emissions and removals. To this regard, if the actual harvest
volume for the period to be extrapolated is known with confidence, then the model may directly apply this harvest
volume, in combination with the other variables above. However, sometimes no reliable statistics on harvest
volume (or other suitable proxies) are available for the period to be gap-filled. In this case, it is good practice toassume that the historical management practices continue during the period to be gap-filled. These practices shouldbe those applied (and documented) in the existing time series, e.g. for the “calibration period” (see below). The
functional relationships between available timber stocks, age structure dynamics, the increment and the harvest
volume under the continuation of management practices (which is the basis of yield tables for forest management)
can be used to calculate a consistent time series of annual C stock gains (forest net increment) and annual C stock
losses (e.g. harvest, etc.). For example, if a given tree species is typically harvested at 80 years, the extrapolation
based on functional relationships will apply this harvesting age (i.e. the historical forest management practice) also
in the period to be gap-filled, taking into account the age structure dynamics (e.g. if the forest is getting older,
more area reaching 80 years may be available); the carbon gains will be calculated using the forest net increment
associated with the age structure and harvest volume simulated for the period to be gap-filled. An example ofresolving data gaps in Forest Land through an extrapolation based on functional relationships is provided in Box
4.3b.
It is good practice that the model used for extrapolation utilizes information on the methodological elements abovethat is consistent with those used in the rest of the time series.
A change in any of the variables above used in the existing (non-extrapolated) time series (e.g., adding a newcarbon pool) triggers a methodological inconsistency, to be addressed through a re-run, for the entire time series,of the model used for the extrapolation. Such re-run should ensure consistency in the variables described above.
As a general check for the consistency, it is good practice to demonstrate that the model used for the extrapolation
reproduces the existing time series, for a selected “calibration period”. The length of this calibration period may
depend on various factors, but it is preferable to have at least 5 or 10 years of comparison between the model’s
results and the existing time series. If the model results for the calibration period fall within the estimated range of
uncertainty of the existing time series (as documented in the GHG inventory), any remaining discontinuity between
the existing time series and the portion extrapolated may be addressed through the application of the “overlap”
technique (Volume 1, Chapter 5.3.3.1) to extrapolated data. This procedure will affect the level of modelled GHG
estimates, but not their trend. If, for the calibration period, the model’s results do not fall within the reported rangeof uncertainty of the existing time series, it is not good practice to use these results for extrapolating the time series.
An example of resolving forest data gaps through extrapolation based on functional relationships is provided inBox 4.3b

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Volume 4: Agriculture, Forestry and Other Land Use

BOX 4.3B (NEW)EXAMPLE OF RESOLVING FOREST DATA GAPS THROUGH EXTRAPOLATION BASED ON FUNCTIONALRELATIONSHIPS

Consider a case in which the stock difference method (see Volume 4, Chapter 2.3) is applied toconstruct a consistent time series between 1990 and 2015. Suppose that the next complete forest
inventory will be reported in 2025, and that no reliable harvest data after 2015 is available. Until thisinventory becomes available, the GHG emissions after 2015 may need to be extrapolated.
One option is to apply a linear extrapolation to the historical time series. Another option, to beconsidered especially when age structure dynamics exert a relevant impact on the trend of forest
CO2 fluxes, is to extrapolate the historical GHG emissions through functional relationships. To thisaim, a model may be used to calculate, for the period to be gap-filled, the net increment and theharvest volumes associated with the continuation of historical management practices.
A theoretical example of the impact of different extrapolation approaches is provided in thefollowing table, for selected years and for the living biomass of forests that are assumed to approach
maturity.
For the purpose of extrapolating based on functional relationships, a model calculates the harvest
volumes in the period to be gap-filled through the intersection between the continuation of historicalforest management practices and the available timber stocks as affected by the age-related forest
dynamics.
Historical period

Linearextrapolation

Extrapolation basedon functional
relationships(ktC yr-1)

2000

2015

2020

2020

Net increment

20.0

26.0

28.0

26.0

Harvest

14.0

17.0

18.0

22.0

Net change

6.0

9.0

10.0

4.0

In this example, the net forest increment has increased in the historical period (2000-2015) morethan the increase in harvest volumes. As a result, the sink (net change in C) has also increased. A
linear extrapolation of this trend would lead to a further increase on the sink in 2020. However, in
this example, the forests are aging, i.e. more forest area reaches maturity. As a consequence,
assuming the continuation of the historical forest management practices, in 2020 the net increment
is expected to saturate (i.e. in the table it remains at the 2015 levels) and the total harvest volume isexpected to increase (because more area will reach maturity, and thus more biomass will be readyto be harvested). The resulting sink would also decline, in contrast with what obtained by the linear
extrapolation. In this theoretical case, the extrapolation based on functional relationships may beconsidered to provide a more realistic estimate of GHG emissions in the period to be gap-filled.
Where countries use Tier 1 methods, estimates of dead organic matter (DOM) stock changes are only provided in
the case of land-use change to or from Forest Land. It is good practice to recalculate the entire time series of data
if either the default values for litter and dead wood carbon pools or the lengths of the transition periods are changed.
It is also good practice to recalculate the entire time series of estimates if revisions to activity data, such as the rateof land-use change, have occurred. As more ground plot and other sample data on dead wood and litter carbon
stocks become available in the future, countries are likely to improve the models used in higher Tier estimation
procedures. It is good practice to use the same model parameter values (such as litterfall rates, decay rates,
disturbance impacts) for the entire time series and to recalculate the entire time series if one or more of the model
parameters have changed. Failure to do so may result in artificial sources or sinks, for example as a result of decayrate modifications.

4.4.3

Quantity Assurance and Quality Control

No refinement.

4.16

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

Chapter 4: Forest Land

4.4.4

Reporting and Documentation

No refinement.

4.5

TABLES

Table 4.1
No refinement.
Table 4.2
No refinement.
Table 4.3
No refinement.

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

4.17

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.4 (UPDATED)RATIO OF BELOW-GROUND BIOMASS TO ABOVE-GROUND BIOMASS (R) [TONNE ROOT D.M. (TONNE SHOOT D.M.)-1]

DomainEcologicalzone1

Continent

AfricaTropicalRainforest

North andSouth
America

Asia

AfricaTropical
Moist
TropicalNorth andSouth
America
Asia

AfricaTropicalDry

North andSouth
America
Asia

TropicalMountainNorth andSouth
America
Asia

4.18

Origin(Natural/Plantation)Abovegroundbiomass(tonnesha-1)

R [tonneroot d.m.
(tonneshootd.m.)-1]

Uncerta
inty

Uncerta
inty
References
type

Natural

≤ 125

0.825

±90%

default

1, 2Natural

> 125

0.532

±90%

default

2, 3Natural

≤ 125

0.221

0.036

SD

4

Planted

≤ 125

0.170

0.11

SD

5

Natural

> 125

0.221

0.036

SD

4

Planted

> 125

0.170

0.11

SD

5

Natural

≤ 125

0.207

0.072

SD

6, 7, 8Planted

≤ 125

0.325

0.025

SD

8

Natural

> 125

0.212

0.077

SD

7, 8, 9, 10, 11

Natural

≤ 125

0.232

±90%

default

12

Natural

> 125

0.232

±90%

default

12

Natural≤ 125

0.2845

0.061

SD

12

Natural

> 125

0.284

0.061

SD

12

Natural

≤ 125

0.323

0.073

SD

1, 13, 14, 5

Natural

> 125

0.246

0.036

SD

12, 16Natural

≤ 125

0.332

0.247

SD

1, 12, 17, 18,
19

Natural

> 125

0.379

0.040

SD

12

Natural

≤ 125

0.334

0.040

SD

4, 12, 20

Natural

> 125

0.379

0.040

SD

12

Natural

≤ 125

0.440

±90%

default

12

Natural

> 125

0.379

0.040

SD

12

Natural

≤ 125

0.348

±90%

default

4

Planted

≤ 125

0.205

±90%

default

12

Natural

> 125

0.283

0.16

SD

21

Natural

≤ 125

0.322

0.084

SD

22, 23Natural

> 125

0.345

0.280

SD

22, 232019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.4 (UPDATED) (CONTINUED)RATIO OF BELOW-GROUND BIOMASS TO ABOVE-GROUND BIOMASS (R) [TONNE ROOT D.M. (TONNE SHOOT D.M.)-1]

DomainEcologicalzone1

Continent

AfricaSubtropical
Humid

North andSouth
America
Asia

Sub-tropical

SubtropicalDry

Subtropical
SteppeNorth andSouth
America
Asia
North andSouth
America
Asia

EuropeTemperate

Oceanic

North andSouth
America

Origin(Natural/
Plantation)

Abovegroundbiomass(tonnesha-1)

R [tonneroot d.m.
(tonneshootd.m.)-1]

Uncer
taintyUncerta
References
inty
type

Natural

≤ 125

0.232

±90%

default

12

Natural

> 125

0.232

±90%

default

12

Natural

≤ 125

0.175

±90%

default

12

Natural

> 125

0.284

±90%

default

12

Natural

≤ 125

0.230

±90%

default

12

Natural

> 125

0.246

±90%

default

12

Natural

≤ 125

0.336

±90%

default

12

Natural

> 125

0.352

0.047

SD

12

Natural

≤ 125

0.440

0.184

SD

12

Natural

> 125

0.440

0.184

SD

12

Natural

≤ 125

1.338

±90%

default

12

Natural

> 125

1.338

±90%

default

12

Planted

≤ 125

2.158

±90%

default

12

Natural/Pl
anted(OtherBroadleaf)

all sizeclasses

0.192

±90%

default

24

Natural(Conifer)

≤ 125

0.359

±90%

default

12

Natural(OtherBroadleaf)

>125

0.172

±90%

default

12

Planted(Conifer)

>125

0.206

±90%

default

12, 25, 26,
27

Planted(Conifer)

all sizeclasses

0.359

0.145

SD

28

Planted(Quercus)

≤ 125

1.400

±90%

default

29

Natural(Conifer)

≤ 125

0.337

±90%

default

12

Natural(Conifer)

>125

0.338

±90%

default

12

Natural(OtherBroadleaf)

≤ 125

0.466

±90%

default

12, 30Natural(OtherBroadleaf)

>125

0.190

±90%

default

12, 31

Planted(Conifer)

>125

0.203

±90%

default

12, 322019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

4.19

Volume 4: Agriculture, Forestry and Other Land Use

TABLE. 4.4 (UPDATED) (CONTINUED)RATIO OF BELOW-GROUND BIOMASS TO ABOVE-GROUND BIOMASS (R) [TONNE ROOT D.M. (TONNE SHOOT D.M.)-1]

DomainEcologicalzone1

Oceanic

Continent

Oceania

Temperate

EuropeContinental

4.20

North
and
South
America

Origin(Natural/Pla
ntation)Abovegroundbiomass(tonnesha-1)

R [tonneroot d.m.
(tonneshootd.m.)-1]

Uncertainty

Uncerta
inty
type

References

Natural(Eucalyptus)

≤ 125

0.464

±90%

default

12

Natural(Eucalyptus)

>125

0.257

±90%

default

12

Natural(OtherBroadleaf)

≤ 125

0.213

±90%

default

34-36

Natural(OtherBroadleaf)

>125

0.313

±90%

default

37, 38Planted(Conifer)

all sizeclasses

0.190

±90%

default

39

Planted(Conifer)

≤ 125

0.634

±90%

default

12

Planted(Conifer)

>125

0.294

±90%

default

12

Planted(Eucalyptus)

≤ 125

0.391

±90%

default

12

Planted(Eucalyptus)

>125

0.188

±90%

default

12, 40Natural(Quercus)

>125

0.477

±90%

default

12

Planted(Conifer)

≤ 125

0.340

±90%

default

12

Natural(OtherBroadleaf)

≤ 125

0.481

±90%

default

12

Natural(OtherBroadleaf)

>125

0.277

±90%

default

12

Planted(Conifer)

≤ 125

0.237

±90%

default

122019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE. 4.4 (UPDATED) (CONTINUED)RATIO OF BELOW-GROUND BIOMASS TO ABOVE-GROUND BIOMASS (R) [TONNE ROOT D.M. (TONNE SHOOT D.M.)-1]

DomainTemperate

BorealEcologicalzone1

Oceanic
Continental
Mountain

Coniferous,
tundrawoodland,mountainsystems

Continent

Asia-

Origin(Natural/Plantation)Abovegroundbiomass(tonnesha-1)

R [tonneroot d.m.
(tonneshootd.m.)-1]

Uncer
taintyUncerta
inty
type

References

Natural(Conifer)

≤ 125

0.243

±90%

default

33

Natural(Conifer)

>125

0.262

±90%

default

33

Natural(OtherBroadleaf)

≤ 125

0.225

±90%

default

33

Natural(OtherBroadleaf)

>125

0.229

±90%

default

33

Planted(Conifer)

≤ 125

0.224

±90%

default

33

Planted(Conifer)

>125

0.232

±90%

default

33

Planted(otherBroadleaf)

≤ 125

0.307

±90%

default

33

Planted(otherBroadleaf)

>125

0.248

±90%

default

33

≤ 75

0.390

0.23 0.96

Range

12, 46>75

0.240

0.15 0.37

Range

12, 46-

1
Forest Resources Assessment (FRA). (2015). Global Eological Zones for FAO Forest Reporting 2010 Update. Forest Resources
Assessment Working Paper 179.

References:
1 Masota, A.M., et al., 2016; 2 Njana, M.A., et al., 2015; 3 Masota, A.M., et al., 2015; 4 FAO, 2015; 5 Sanquetta, et al., 2011; 6Saner, P., et al., 2012; 7 Murdiyarso, M., et al., 2015; 8 Kotowska, M.M., et al., 2015; 9 Lu, X.T., et al., 2010; 10 Niiyama K, etal., 2010; 11 Krisnawati, H., et al., 2014; 12 Mokany, K., et al., 2006; 13 Wang, X.P., et al., 2008; 14 Li, X., et al., 2010; 15 Monda,
Y., et al., 2016; 16 Gautum, T.P., Mandal, T.N., 2016; 17 Mugasha, W.A., et al., 2013; 18 Malimbwi, R.E., et al., 2016; 19 Makero,et al., 2016; 20 Sato, T., et al., 2015; 21 Moser, G., 2011; 22 Iqbal, K., et al., 2014; 23 Sharma, D.P., 2009; 24 Skovsgaard, J.P., NordLarsen, T., 2012; 25 Green C., et al., 2007; 26 Urban, J., et al., 2015; 27 Xiao, C.W., et al., 2003; 28 Levy, P.E., et al., 2004; 29
Cotillas, M., et al., 2016; 30 Gargaglione, et al., 2010; 31 Frangi, J.L., et al., 2005; 32 Miller, A.T., et al., 2006; 33 Luo, Y., et al.,2014; 34 Schwendenmann, L., Mitchell, N., 2014; 35 Watson, A,. O'Loughlin, C., 1985; 36 Watson, A., 1995; 37 Beets, P.N., 1980; 38
Miller, R. B. 1963; 39 Beets PN, et al., 2007; 40 Oliver GR, et al., 2009; 41 Battles, J. J., et al., 2002; 42 Laclau P. 2003; 43 Grimm,U., Fassbender, H., 1981; 44 Edwards, P., Grubb, P., 1977; 45 Scott, N.A., et al., 2005; 46 Li, et al., 2003.

Table 4.5
No refinement.
Table 4.6
No refinement.

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

4.21

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.7 (UPDATED)ABOVE-GROUND BIOMASS IN NATURAL FORESTS (TONNES D.M. HA-1)

DomainEcologicalzone 1

Continent

AfricaTropicalrainforest

North
and
South
America

Asia

AfricaAbovegroundbiomass[tonnesd.m. ha-1]

Uncer
taintyUncerta
inty
type

References

Primary

404.2

120.4

SD

1-12

Secondary >20 years

212.9

143.1

SD

5-7, 11, 13-16

Secondary ≤20 years

52.8

35.6

SD

9-11, 14, 15, 17

Primary

307.1

104.9

SD

3, 4, 9, 10, 18-21

Secondary >20 years

206.4

80.4

SD

9, 10, 22-28

Status/condition2

Secondary ≤20 years

75.7

34.5

SD

9, 10, 14, 22, 23,
28-32

Primary

413.1

128.5

SD

3, 4, 9, 10, 33-35

Secondary >20 years

131.6

20.7

SD

9, 10, 36, 37

Secondary ≤20 years

45.6

20.6

SD

9, 10, 37-39

Primary

236.6

104.7

SD

1, 2, 16

72.8

36.4

SD

9, 10, 16, 40-47

Primary

187.3

94.0

SD

3, 4, 9, 10, 18-21

Secondary >20 years

131.0

54.2

SD

9, 10, 22-26

Secondary >20 years
Secondary ≤20 years

Tropicalmoist
deciduous
forest

North
and
South
America

55.7

28.7

SD

9, 10, 22, 23, 25,
26

67.7

93.4

SD

9, 10, 35, 48-50

69.6

47.5

SD

1, 2, 43, 44, 5153

Primary

127.5

72.6

SD

18-21

Secondary >20 years

118.9

81.3

SD

9, 10, 22, 23, 54

Secondary ≤20 years
Primary

Asia

Secondary >20 years
Secondary ≤20 years

TropicalPrimary
AfricaSecondary >20 years
Secondary ≤20 years

Tropicaldryforest

North
and
South
AmericaSecondary ≤20 years

32.2

24.2

SD

9, 10, 22, 23, 54,
55

184.6

144.5

SD

9, 10, 35, 48, 56

48.4

45.8

SD

44, 57, 58

71.5

46.4

SD

59

38.3

33.0

SD

59

Primary
Asia

Secondary >20 years
Secondary ≤20 years
Primary

AfricaSecondary >20 years
Secondary ≤20 years

Tropicalshrublands

North
and
South
AmericaPrimary
Secondary >20 years
Secondary ≤20 years
Primary

Asia

Secondary >20 years
Secondary ≤20 years

4.22

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

Chapter 4: Forest Land

TABLE 4.7 (UPDATED) (CONTINUED)ABOVE-GROUND BIOMASS IN NATURAL FORESTS (TONNES D.M. HA-1)

DomainEcologicalzone 1

Continent

Abovegroundbiomass[tonnesd.m. ha-1]

Unce
rtain
ty

Uncertainty
type

References

190.0

131.2

SD

1-4, 9, 10, 42-44,
47, 53, 60-68

Primary

195.0

95.6

SD

3, 4, 9, 10, 18-21

Secondary >20 years

184.4

111.0

SD

Secondary ≤20 years

75.9

51.1

SD

Primary

433.5

147.5

SD

3, 4, 9, 10, 34, 35

66.4

61.0

SD

9, 10, 50, 71-73

54.1

20.6

SD

59

84.5

42.9

SD

59

323.0

157.7

SD

9, 10

258.4

128.1

SD

9, 10

65.2

27.1

SD

59

115.9

46.2

SD

59

70.9

26.2

SD

59

50.5

23.9

SD

59

44.0

26.0

SD

59

41.6

24.7

SD

59

Status/condition2

Primary
AfricaSecondary >20 years
Secondary ≤20 years

TropicalTropicalmountainsystems

North andSouth
America

Asia

Secondary >20 years
Secondary ≤20 years

9, 10, 22, 23, 26,
69
9, 10, 22, 23, 26,
69, 70Primary
AfricaSecondary >20 years
Secondary ≤20 years

Subtropicalhumid
forests

North andSouth
AmericaPrimary
Secondary >20 years
Secondary ≤20 years
Primary

Asia

Secondary >20 years
Secondary ≤20 years
Primary

AfricaSecondary >20 years
Secondary ≤20 years

Subtropical

Subtropicaldryforests

North andSouth
AmericaPrimary
Secondary >20 years
Secondary ≤20 years
Primary

Asia

Secondary >20 years
Secondary ≤20 years
Primary

AfricaSecondary >20 years
Secondary ≤20 years

Subtropical
steppeNorth andSouth
AmericaPrimary
Secondary >20 years
Secondary ≤20 years
Primary

Asia

Secondary >20 years
Secondary ≤20 years

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

4.23

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.7 (UPDATED) (CONTINUED)ABOVE-GROUND BIOMASS IN NATURAL FORESTS (TONNES D.M. HA-1)

DomainEcologicalzone 1

Continent

Abovegroundbiomass[tonnesd.m. ha-1]

Uncertainty

Uncertain
ty type

References

35.1

22.2

SD

59

74.6

40.1

SD

59

250.2

59.4

SD

9, 10

155.2

41.7

SD

9, 10

Primary

n.a

n.a

n.a

Secondary >20 years

170.4±57.85

95% CI

Secondary ≤20 years

n.a

n.a

n.a

Primary

301.1

±90%

default

76-79

Secondary >20 years

214.7

±90%

default

77

Secondary ≤20 years

27.8

±90%

default

77

Primary

n.a

n.a

n.a

Secondary >20 years

185.9

153.8

SD

80

Secondary ≤20 years

57.9

78.6

SD

80

Primary

n.a

n.a

n.a

Secondary >20 years

116.0±18.37

95% CI75

Secondary ≤20 years

90.9±40.43

95% CI75

Primary

332.4

±90%

default

77-79

Secondary >20 years

162.0

±90%

default

77, 81-83

Secondary ≤20 years

51.6

±90%

default

77, 81-83

Primary

n.a

n.a

n.a

Secondary >20 years

128.9

240.3

SD

80

Secondary ≤20 years

46.0

99.5

SD

80

Primary

289.8

±90%

default

84

n.a

n.a

n.a

Primary

126.1

±90%

default

77

Secondary >20 years

153.9

±90%

default

77,85-90

Secondary ≤20 years

22.3

±90%

default

77

Primary

352.7

±17

95%CI

91

Secondary >20 years

120.5

±22.3

95%CI

91

Secondary ≤20 years

57.5±14.2895%CI

92

Status/condition2

Primary
AfricaSecondary >20 years
Secondary ≤20 years

SubtropicalSub-tropicalmountainsystems

North andSouth
AmericaPrimary
Secondary >20 years
Secondary ≤20 years
Primary

Asia

Secondary >20 years
Secondary ≤20 years

Asia

Mountain

EuropeNorth andSouth
America

Asia

Temperate

Continental

EuropeNorth andSouth
America

Asia

Secondary >20 years
Secondary ≤20 years

Oceanic

EuropeOceania

4.24

75

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

Chapter 4: Forest Land

TABLE 4.7 (UPDATED) (CONTINUED)ABOVE-GROUND BIOMASS IN NATURAL FORESTS (TONNES D.M. HA-1)
DomainEcologicalzone 1

Oceanic

Temperate Desert

SteppeConiferous

BorealContinent

North andSouth
America
North andSouth
America
North andSouth
America
North andSouth
America

TundrawoodlandNorth andSouth
AmericaMountainNorth andSouth
America

Abovegroun
d biomass[tonnes d.m.
ha-1]

Uncertainty

Uncertainty type

Primary

n.a

n.a

n.a

Secondary >20 years

354.1

455.7

SD

80

Secondary ≤20 years

213.9

227.1

SD

80

Primary

n.a

n.a

n.a

Secondary >20 years

44.0

39.7

SD

80

Secondary ≤20 years

25.6

35.1

SD

80

Primary

n.a

n.a

n.a

Secondary >20 years

118.5

459.9

SD

80

Secondary ≤20 years

42.9

76.5

SD

80

Primary

62.9

28.1

SD

93

Secondary >20 years

n.a

n.a

n.a

Secondary ≤20 years

n.a

n.a

n.a

Primary

63.7

30.1

SD

93

Secondary >20 years

104.2

±90%

default

94

Secondary ≤20 years

n.a

n.a

n.a

Primary

n.a

n.a

n.a

Secondary >20 years

n.a

n.a

n.a

Secondary ≤20 years

1.9

±90%

default

Status/condition2

References

94

Forest Resources Assessment (FRA). (2015). Global Eological Zones for FAO Forest Reporting 2010 Update. Forest Resources Assessment
Working Paper 179.
1

Some categories include sub-strata for primary forests, which are defined as old-growth forests that are intact or with no active humanintervention, and secondary forests which include all other forests. The table considers a forest definition of at least 10% tree canopy cover (74).
2References
1 Lewis, S. L. et al., 2013; 2 Lewis, S. L. et al., 2013; 3 Sullivan, M. J. P. et al., 2017; 4 Sullivan, M. J. P. et al., 2016; 5 Gatti, R. C. et al., 2015;
6 Gatti, R. C., Laurin, G. V., Valentini, R., 2017; 7 Vaglio Laurin, G. et al., 2013; 8 Adou Yao, C. Y. et al., 2005; 9 Anderson-Teixeira, K. J. etal., 2018; 10 Anderson-Teixeira, K. J. et al., 2018; 11 N'Guessan, A. E. et al., 2019; 12 Xu, L. et al., 2017; 13 Pirotti, F., et al., 2014; 14 Palm,
C. A. et al., 1999; 15 Omeja, P. A., et al., 2011; 16 Mitchard, E. T. A. et al., 2009; 17 Thenkabail, P. S., et al., 2004; 18 Brienen, R. J. W. et al.,2015; 19 Brienen, R. J. W. et al., 2014; 20 Mitchard, E. T. A. et al., 2014; 21 Alvarez-Davila, E. et al., 2017; 22 Poorter, L. et al., 2016; 23
Poorter, L. et al., 2016; 24 Rutishauser, E. et al., 2015; 25 Martinez-Sanchez, J. L., et al, 2015; 26 Pena, M. A., Duque, A., 2013; 27 Robinson,
S. J. B., et al., 2015; 28 Salimon, C. I., Brown, I. F., 2000; 29 Silva, C. et al., 2016; 30 Vasconcelos, S. S. et al., 2008; 31 Jacobi, J. et al., 2014;
32 Schroth, G., et al., 2002; 33 Qie, L. et al., 2017; 34 Slik, J. W. F. et al., 2015; 35 Slik, J. W. F. et al., 2013; 36 Morel, A. C. et al., 2011; 37
Mukul, S. A., Herbohn, J., Firn, J., 2016; 38 Ewel, J., Chai, P., Lim, M., 1983; 39 Hiratsuka, M., et al., 2006; 40 Manlay, R. J. et al., 2002; 41
Kalaba, F. K., et al., 2013; 42 DVRF, 2016; 43 MITADER, 2018; 44 NAFORMA, 2015; 45 FAO, SEP-REDD+, 2017; 46 Carreiras, J. M. B.,
Vasconcelos, M. J., Lucas, R. M., 2012; 47 Dees, M., 2018; 48 WWF, Obf, 2013; 49 Altrell, D., et al., 2005; 50 FAO, 2005; 51 Carreiras, J. M.
B., Melo, J. B., Vasconcelos, M. J., 2013; 52 Ryan, C. M. et al., 2012; 53 Mukosha, J., Siampale, A., 2009; 54 Atkinson, E. E., Marin-Spiotta,
E., 2015; 55 Salinas-Melgoza, M. A., et al., 2017; 56 McNicol, I. M. et al., 2015; 57 Raharimalala, O., et al., 2012; 58 Johansson, S. G.,
Kaarakka, V. J., 1992; 59 Santoro, M. et al., 2018; 60 Mekuria, W., et al., 2011; 61 Otuoma, J. et al., 2016; 62 Giday, K., et al., 2013; 63
DeVries, B., et al., 2012; 64 Drichi, P., 2003; 65 Avitabile, V., et al., 2012; 66 Katumbi, N. M., et al., 2017; 67 Kinyanjui, M. J. et al., 2014; 68
Nyirambangutse, B. et al., 2017; 69 Monreal, C. M. et al., 2005; 70 Myster, R. W., 2017; 71 Fujiki, S., et al., 2017; 72 Chan, N., Takeda, S.,
2016; 73 Avitabile, V. et al., 2016; 74 Hansen, M. C. et al., 2013; 75 Luo, Y., et al., 2014; 76 Trotsiuk, V., et al., 2016; 77 Avitabile, V., Camia,A., 2018; 78 Gazda, A., et al., 2015; 79 Gazda, A., et al., 2015; 80 June 18, 2018. Forest Inventory and Analysis Database, St. Paul, MN: U.S.
Department
ofAgriculture,
ForestService,
Northern
Research
Station.
Available
only
oninternet:
https://apps.fs.usda.gov/fia/datamart/datamart.html]; 81 Uri, V., et al. 2012; 82 Lang, M., et al., 2016; 83 Varnagiryte-Kabašinskiene, I., et al.,2014; 84 Sato, T. J For Res 2010; 85 Nunes L, et al., 2013; 86 Granier, A., et al., 2000; 87 Latifi, H., et al., 2015; 88 Kattenborn, T., et al., 2015;89 Ningthoujam, R. K., et al., 2016; 90 Husmann, K., et al., 2018; 91Holdaway, R.J., et al. 2017; 92 Beets PN, et al., 2014; 93 Keith, H., et al.,2009; 94 September 25, 2017. Forest Inventory and Analysis Database, St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern
Research Station. Available only on internet: http://apps.fs.fed.us/fiadb-downloads/datamart.html

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

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.8 (UPDATED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainEcological
Zone1

Tropicalrain forest

TropicalTropicalmoist
deciduous

4.26

Continent

Species

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

References

AfricaBroadleaf

≤20

100

±90%

10

AfricaBroadleaf

>20

300

±90%

10

AfricaPinus sp.

≤20

60

±90%

10

AfricaPinus sp.

>20

200

±90%

10

Americas

Eucalyptus sp.

200

±90%

10

Americas

Other Broadleaf

150

±90%

10

Americas

Pinus sp.

300

±90%

10

Americas

Tectona grandis

>20

240

±90%

13

Asia

Acaciaauriculiformis≤20

99-119

±90%

20

Asia

Acacia mangium

<20

93.6

64.20

28

Asia

Broadleaf

220

±90%

10

Asia

Dipterocarp sp.

>20

452.2

149.9014

Asia

Eucalyptus sp.

≤20

46-161

43.70

20

Asia

Gmelina arborea

<20

97.6

23.60

14

Asia

Hevea brasiliensis<20113-132

±90%

18

Asia

Mangifera indica

<20

13.5

4.90

7

Asia

Rhizophora sp.

>20

152.2

±90%

1

Asia

Mixed

>20

69

±90%

3

Asia

Oil Palm<2018.4-35.4

±90%

33

Asia

Oil Palm

>20

48.5

9.20

33

Asia

Paraserianthes
falcataria

<20

64.4

38.80

14

Asia

Sweitenia
macrophylla

>20

512.8

170.4014

AfricaBroadleaf

>20

150

±90%

10

AfricaBroadleaf

≤20

80

±90%

10

AfricaRhizophora sp.

111-483±90%

34

AfricaPinus sp.

≤20

40-166±90%

10,1

AfricaTectona grandis

<20

195.5

±90%

16

AfricaTectona grandis

>20

428.9

±90%

16

AfricaPinus sp.

>20

120-193.3

±90%

10,16

AmericasAnthocephaluschinensis

<20

144

±90%

2

AmericasCoffea sp.

46.9-57.5

±90%

15

Americas

Eucalyptus sp.

90

±90%

31

Americas

Other Broadleaf

100

±90%

10

Americas

Pinus sp.

270

±90%

10

>20
>20

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

Chapter 4: Forest Land

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainEcological
Zone1

Tropicalmoist
deciduous

TropicalTropicaldry forest

Continent

Species

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

References

Americas

Swietenia
macrophylla

<20

94

±90%

2

Americas

Swietenia
macrophylla

>20

121

±90%

2

Americas

Tectona grandis

<20

84

±90%

24

Americas

Tectona grandis

>20

284

±90%

24

Asia

Acaciaauriculiformis

>20

177

7.60

6

Asia

Acaica mangium

>20

211

3.30

6

Asia

Broadleaf

≤20

21.90

5

Asia

Broadleaf

>20

55.60

5

Asia

Cassia montana

<20

5.71

±90%

4

Asia

Cedeus libani

≤20

15.1

±90%

8

Asia

Eucalyptus sp.

<20

41.78

±90%

4

Asia

Eucalyptus sp.

>20

260

97.40

6

Asia

Oil Palm<20

124-202

±90%

29

Asia

Other

100

±90%

10

Asia

Swietenia
macrophylla

>20

193

17.00

6

Asia

Tectona grandis<20

121.88

±90%

9

Asia

Tectona grandis

>20

93.72

64.70

6

AfricaBroadleaf

≤20

30

±90%

10

AfricaBroadleaf

>20

70

±90%

10

AfricaPinus sp.

≤20

20-75.6

±90%

10,16

AfricaPinus sp.

>20

60-193.9

±90%

10,16

AfricaTectona grandis

<20

38.33

0.40

22

Americas

Eucalyptus sp.

90

±90%

31

Americas

Oil Palm

<20

40-62

±90%

26

Americas

Oil Palm>2050-100±90%

12Americas

Other Broadleaf

60

±90%

10

Americas

Pinus sp.

110

±90%

10

Americas

Tectona grandis

90

±90%

10

Asia

Acacia sp.

7.54-58.21

±90%

4

Asia

Adina cordifolia

14.8

±90%

11

Asia

Adansonia digitata

28.6

±90%

11

Asia

Albizia procera

<20

4.9

±90%

11

Asia

Azadirachta indica<20

30.6-55.64

±90%

11,19

Asia

Bombax ceiba

64.7

±90%

11

Asia

Broadleaf

90

±90%

10

<20

93.33147.76
107.05224.48

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

4.27

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainEcological
Zone1

Tropicaldry forest

TropicalTropicalshrubland

4.28

AGB
(Tonnesd.m. ha-1)

SD

Refer
ences

5.5

±90%

11

≤20

11.07

6.79

35

Dendrocalamus
strictus

<20

48.2

±90%

19

Asia

Eucalyptus sp.

≤20

21.67

±90%

37

Asia

Ficus sp.

25.4

±90%

11

Asia

Gmelina arborea

≤20

6.65

1.37

35

Asia

Leucaena
leucocephala

<20

53.35

±90%

19

Asia

Madhuca indica

35.2

±90%

11

Asia

Mangifera indica

24.2

±90%

11

Asia

Rhizophora sp.

<20

125.5

2.60

25

Asia

Manilkara elengi

<20

7.4

±90%

11

Asia

Miliusa tomentosa

<20

4.8

±90%

11

Asia

Mitragyna
parviflora

18.1

±90%

11

Asia

Other

60

±90%

10

Asia

Pongamia pinnata

≤20

8.57

2.00

35

Asia

Populus deltoides

<20

37.5

34.40

21

Asia

Prosopis juliflora

<20

3.56

±90%

4

Asia

Salvadora oleoides

12.2

±90%

11

Asia

Samanea saman

30.9

±90%

11

Asia

Sterculia urens

8.2

±90%

11

Asia

Swietenia mahogani

28.7

±90%

11

Asia

Tamarindus indica

88.8

±90%

11

Asia

Tectona grandis

<20

21.8

±90%

19

Asia

Terminalia sp.

>20

45.5-71.1

±90%

11

Asia

Terminalia sp.

<20

8.2

±90%

11

Asia

Ziziphus mauritiana

<20

8

±90%

11

AfricaBroadleaf

20

±90%

10

AfricaPinus sp.

≤20

15

±90%

10

AfricaPinus sp.

>20

20

±90%

10

Americas

Eucalyptus sp.

60

±90%

10

Americas

Other Broadleaf

30

±90%

10

Americas

Pinus sp.

60

±90%

10

Americas

Tectona grandis

50

±90%

10

Asia

Acacia sp.

≤20

11.78-47.99

±90%

27,32

Asia

Azadirachta indica

≤20

53.32

±90%

32

Continent

Species

Asia

Courapita
guianensis

Asia

Dalbergia sissoo

Asia

Age
(yr)

<20

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

Chapter 4: Forest Land

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainEcological
Zone1

Tropicalshrubland

TropicalTropicalmountainsystems

Subtropicalhumid forest

Subtropical

Subtropicaldry forest

Continent

Species

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

Refere
nces

40

±90%

10

Asia

Broadleaf

Asia

Broadleaf

>20

263.3

±90%

17

Asia

Casuarina
equisetifolia

≤20

9.12

±90%

32

Asia

Other

30

±90%

10

Asia

Pongamia pinnata

≤20

9.03

±90%

32

Asia

Tectona grandis

≤20

31.66

±90%

32

AfricaBroadleaf≤2040-100±90%

10AfricaBroadleaf>2060-150±90%

10AfricaPinus sp.

≤20

30-40

±90%

10

AfricaPinus sp.

>2030-100±90%

10Americas

Eucalyptus sp.

30-120±90%

10Americas

Other Broadleaf

30-80

±90%

10

Americas

Pinus sp.

60-170±90%

10Americas

Tectona grandis

30-130

±90%

10

Asia

Broadleaf

40-150

±90%

10

Asia

Other

25-80

±90%

10

Americas

Eucalyptus sp.

140

±90%

10

Americas

Other Broadleaf

100

±90%

10

Americas

Pinus sp.

270

±90%

10

Americas

Tectona grandis

120

±90%

10

Asia

Broadleaf

180

±90%

10

Asia

Other

100

±90%

10

North America

Populus sp.

<20

23.07

20.40

36

North America

Eucalyptus sp.

<20

2.45

2.99

36

North America

Oaks and otherhardwoods

<20

7.88

12.05

36

North America

Oaks and otherhardwoods

≥20

11.09

20.56

36

North America

Pinus sp.

<20

19.65

17.01

36

North America

Pinus sp.

≥20

45.53

24.66

36

AfricaBroadleaf

≤20

30

±90%

10

AfricaBroadleaf

>20

70

±90%

10

AfricaPinus sp.

≤20

20

±90%

10

AfricaPinus sp.

>20

60

±90%

10

Americas

Eucalyptus sp.

110

±90%

10

Americas

Other Broadleaf

60

±90%

10

Americas

Pinus sp.

110

±90%

10

Americas

Tectona grandis

90

±90%

10

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

4.29

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainEcological
Zone1

Subtropicaldry forest

Subtropical

Subtropical
steppeSubtropicalmountainsystems

4.30

Continent

Species

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

References

Asia

Broadleaf

<20

69.45

48.89

39

Asia

Broadleaf>20

137.64

77.29

39

Asia

Coniferous

<20

63.18

38.07

39

Asia

Coniferous>20

127.61

63.31

39

Asia

Cunninghamia sp.

<20

62.96

37.38

39

Asia

Cunninghamia sp.

>20

148.6

72.32

39

Asia

Eucalyptus sp.

<20

68.72

55.05

39

Asia

Other

60

±90%

39

Asia

Picea abies>20

138.23

47.42

39

Asia

Pinus massoniana

<20

54.75

40.55

39

Asia

Pinus massoniana>20

163.4566.07

39

AfricaBroadleaf

20

±90%

10

AfricaPinus sp.

≤20

15

±90%

10

AfricaPinus sp.

>20

20

±90%

10

Americas

Eucalyptus sp.

60

±90%

10

Americas

Other Broadleaf

30

±90%

10

Americas

Pinus sp.

60

±90%

10

Americas

Tectona grandis

50

±90%

10

Asia

Broadleaf

≤20

10

±90%

10

Asia

Broadleaf

>20

80

±90%

10

Asia

Coniferous≤20100-120

±90%

10

Asia

Coniferous

>20

20

±90%

10

North America

Oaks and otherhardwoods<20

3.59-8.75

±90%

36

North America

Pinus sp.

<20

22.8

19.91

36

North America

Pinus sp.

≥20

46.69

16.55

36

Asia

Acer velutinum

<20

90.03

±90%

23

Asia

Alnus subcordata<20

103.53

±90%

23

Asia

Arizone cypress

<20

25.72

0.11

30

Asia

Robinia
pseudoacacia

<20

8.85

0.54

30

Asia

Pinus brutia

<20

50.62

0.52

30

Asia

Fraxinus excelsior

<20

56.07

±90%

23

Asia

Morus sp.

<20

9.87

0.33

30

Asia

Pinus nigra≤20

20.05-38.46

±90%

23,8

Asia

Prunus avium

<20

37.92

±90%

23

Asia

Quercuscastanifolia

<20

72.82

±90%

23

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

Chapter 4: Forest Land

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainSubtropical

Ecological
Zone1

Subtropicalmountainsystems

Temperate
oceanic
forestTemperate

Temperatecontinental
forest andmountainsystems

Temperatecontinental
forestSpecies

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

References

Asia

Tilia begonifolia

<20

71.88

±90%

23

North America

Pseudotsuga
menziesii

<20

53.93

±90%

36

North America

Oaks and otherhardwoods

<20

3.68

4.53

36

North America

Pinus sp.

<20

14.51

14.54

36

North America

Pinus sp.

≥20

24.87

25.85

36

AfricaBroadleaf≤2040-100±90%

10AfricaBroadleaf>2060-150±90%

10AfricaPinus sp.

≤20

10-40

±90%

10

AfricaPinus sp.

>2030-100±90%

10Americas

Eucalyptus sp.

30-120±90%

10Americas

Other Broadleaf

30-80

±90%

10

Americas

Pinus sp.

60-170±90%

10Americas

Tectona grandis

30-130

±90%

10

Asia

Broadleaf

40-150

±90%

10

Asia

Other

25-80

±90%

10

Asia, Europe

Broadleaf

≤20

30

±90%

10

Asia, Europe

Broadleaf

>20

200

±90%

10

Asia, Europe

Coniferous

≤20

40

±90%

10

Asia, Europe

Coniferous>20150-250±90%

10North America

Populus sp.

≥20

76.19

51.72

36

North America

Pseudotsuga
menziesii

<20

15.35

18.86

36

North America

Pseudotsuga
menziesii

≥20

95.8

73.39

36

North America

Pinus sp.

<20

3.87

±90%

36

North America

Pinus sp.

≥20

131.27143.7536

South America

Coniferous

90-120±90%

10Asia, Europe

Broadleaf

≤20

15

±90%

10

Asia, Europe

Broadleaf

>20

200

±90%

10

Asia, Europe

Coniferous

≤20

25-30

±90%

10

Asia, Europe

Coniferous>20150-200±90%

10

North America

Coniferous

50-300±90%

10North America

Coniferous

50-300±90%

10South America

Coniferous

90-120±90%

10North America

Populus sp.

<20

88.35

±90%

36

North America

Populus sp.

≥20

55.71

14.47

36

Continent

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

4.31

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainEcological
Zone1

Temperatecontinental
forestTemperate

Temperate
mountainsystem

4.32

Species

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

References

North America

Pseudotsuga
menziesii≥2042.62-96.65

±90%

36

North America

Abies sp.

<20

5.62

6.63

36

North America

Abies sp.

≥20

21.49

10.62

36

North America

Oaks and otherhardwoods

<20

6.7

12.63

36

North America

Oaks and otherhardwoods

≥20

23.72

46.23

36

North America

Pinus sp.

<20

31.45

28.87

36

North America

Pinus sp.

≥20

80.94

68.21

36

North America

Picea sp.

<20

9.89

8.14

36

North America

Picea sp.

≥20

77.34

131.8836

Asia

Larix sp.

<20

57.49

32.16

39

Asia

Larix sp.

>20

112.88

56.21

39

Asia

Pinus koraiensis

<20

58.23

18.89

39

Asia

Pinus koraiensis>20

132.13

72.18

39

Asia

Pinus sylvestris

<20

18

8.95

39

Asia

Pinus sylvestris

>20

58.6

18.57

39

Asia

Pinus tabuliformis

<20

34.02

14.15

39

Asia

Pinus tabuliformis

>20

59.39

35.26

39

Asia

Poplar sp.

<20

66.74

45.30

39

Asia

Robinia
pseudoacacia

<20

29.44

13.20

39

Asia

Robinia
pseudoacacia

>20

54.46

16.99

39

North America

Populus sp.

<20

55.98

±90%

36

North America

Douglas fir

<20

13.56

18.81

36

North America

Douglas fir

≥20

89.22

71.32

36

North America

Abies sp.

<20

3.02

3.11

36

North America

Abies sp.

≥20

40.48

71.99

36

North America

Oaks and otherhardwoods

<20

3.77

5.76

36

North America

Pinus sp.

<20

6.93

14.26

36

North America

Pinus sp.

≥20

29.07

35.39

36

North America

Picea sp.

<20

5.92

11.25

36

North America

Picea sp.

≥20

50.27

38.11

36

Asia

Acaciacrassicarpa

<20

31.5

±90%

38

Asia

Castanopsis
hystrix

<20

16.6

±90%

38

Continent

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

Chapter 4: Forest Land

TABLE 4.8 (UPDATED) (CONTINUED)ABOVEGROUND BIOMASS (AGB) IN FOREST PLANTATIONS (TONNES D.M. HA-1)
DomainSpecies

Age
(yr)

AGB
(Tonnesd.m. ha-1)

SD

References

Asia

Eucalyptus sp.

<20

34.6

±90%

38

Asia

Mixed Plantation

<20

19.2

±90%

38

North America

Populus sp.

≥20

51.8-60.05

±90%

36

North America

Quercus andother hardwoods

≥20

41.06

29.99

36

North America

Pinus sp.

<20

48.57

65.55

36

North America

Pinus sp.

<20

4.75

6.72

36

North America

Pinus sp.

≥20

84.88

24.75

36

North America

Pinus sp.

≥20

3.6

4.70

36

Borealconiferous
forest andmountainsystems

Asia, Europe

Coniferous

≤20

5

±90%

10

Asia, Europe

Coniferous

>20

40

±90%

10

North America

Coniferous

40-50

±90%

10

BorealtundrawoodlandAsia, Europe

Coniferous

≤20

5

±90%

10

Asia, Europe

Coniferous

>20

25

±90%

10

Ecological
Zone1
Temperate
mountainsystemTemperate
Temperate
steppeBorealContinent

1
Forest Resources Assessment (FRA). (2015). Global Eological Zones for FAO Forest Reporting 2010 Update. Forest Resources
Assessment Working Paper 179.

References
1 Arief, W. et al., 2013; 2 Lugo, A. E., et al. 2012; 3 Arora P., Chaudhry S., 2017; 4 Arul, P.L, Karthick, A., 2013; 5 Banerjee, S. K.,
Prakasam, U. K., 2013; 6 De Costa, W. A. J. M., Suranga, H.R., 2012; 7 Guiabao, E. G., 2016; 8 Fataei, E, Varamesh, S., 2016; 9 Giri, C.,et al., 2014; 10 IPCC, 2003; 11 Ishan, Y.P., et al., 2013; 12 Klaarenbeek, F.W., 2009; 13 Kraenzel, M.B., et al. 2003; 14 Lasco, R.D.,
Pulhin, F.B., 2003; 15 Soto-Pinto, L., Aguirre-Dávila, C., 2015; 16 Masota, A.M., et al., 2016; 17 Mohit, K., 2017; 18 Muhdi, et al., 2016;19 Nadagouda, V.R., et al., 1997; 20 Nambiar, E.K.S., Harwood, C.E., 2014; 21 Negi, M.S., Tandon, V. N., 1997; 22 Odiwe, A.I., et al.,2012; 23 Ostadhashemi, R., et al., 2014; 24 Pérez Cordero, L.D., Kanninen, M., 2003; 25 Sahu, S.C., et al., 2016; 26 Sanquetta, C.R., etal., 2015; 27 Singh, K.C., 2005; 28 Siregar, S.T.H., et al., 2008; 29 Sitompol, S.M., Hairiah, K., 2000; 30 Sohrabi, H., et al., 2016; 31
Stape, J.L, et al., 2004; 32 Swamy, K.R., et al., 2015; 33 Syahrinudin, 2005; 34 Trettin, C.C, et al., 2016; 35 Umrao, R., et al., 2010; 36
September 25, 2017. Forest Inventory and Analysis Database, St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern
Research Station. Available only on internet: http://apps.fs.fed.us/fiadb-downloads/datamart.html]; 37 Yadava, A. K., 2010; 38 Yuanqi, C.,et al., 2015; 39Yunjian, L., et al., 2014.

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

4.33

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.9 (UPDATED)ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS1,2,3,4 (TONNES D.M. HA-1 YR-1)

DomainEcological
Zone4

Continent

AfricaTropicalrainforest

North andSouth
America

Asia

AfricaTropicalTropicalmoist
deciduous
forestNorth andSouth
America

Asia

AfricaTropicaldry forest
North andSouth
America

4.34

Abovegroundbiomass
growth[tonnes d.m.
ha-1 yr-1]

Uncertainty

Uncertainty
type

References

Primary

1.3

3.5

SD

1, 2

Secondary>
20 years

3.5

3.3

SD

3-8

Secondary≤
20 years

7.6

5.9

SD

3-7, 9

Primary

1.0

2.0

SD

2, 10, 11Secondary>
20 years

2.3

1.1

SD

3, 4, 12-15

Secondary≤
20 years

5.9

2.5

SD

3, 4, 6, 12-14

Primary

0.7

2.2

SD

2, 16

Secondary>
20 years

2.7

3.1

SD

3, 4, 17

Secondary≤
20 years

3.4

3.9

SD

3, 4, 17-19

Primary6

0.4

±90%

default

Secondary>
20 years

0.9

0.7

SD

20, 21

Secondary≤
20 years

2.9

1.0

SD

20, 21Primary

0.4

2.1

SD

2, 10, 11Secondary>
20 years

2.7

1.7

SD

3, 4, 12, 13,
15, 22

Secondary≤
20 years

5.2

2.3

SD

3, 4, 12, 13, 22

Primary

0.4±90%

default

7

Secondary>
20 years

0.9

±90%

default

8

Secondary≤
20 years

2.4

0.3

SD

3, 4

Primary---

Secondary>
20 years

1.6

±90%

default

9

Secondary≤
20 years

3.9

±90%

default

10

Primary---

Secondary>
20 years

1.6

1.1

SD

12, 13

Secondary≤
20 years

3.9

2.4

SD

12, 13, 23

Status/Condition

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

Chapter 4: Forest Land

TABLE 4.9 (UPDATED) (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS1,2,3,4 (TONNES D.M. HA-1 YR-1)
DomainEcological
Zone4

Tropicaldry forest

Continent

Asia

AfricaNorth andSouth
America
Tropicalshrublands
Asia(Continental)
TropicalAsia(Insular)

AfricaTropicalmountainsystemNorth andSouth
America

Asia

Abovegroundbiomass
growth [tonnes
d.m. ha-1 yr-1]

UncertaintyUncertainty
type

Primary---

Secondary>
20 years

1.6

±90%

default

11

Secondary≤
20 years

3.9

±90%

default

12

Primary

0.9 (0.2-1.6)*±90%

default

24

Secondary>
20 years

0.9 (0.2-1.6)*±90%

default

24

Secondary≤
20 years

0.2-0.7±90%

default

24

Primary

1.0*

±90%

default

24

Secondary>
20 years

1.0*

±90%

default

24

Secondary≤
20 years

4.0

±90%

default

24

Primary

1.3 (1.0-2.2)*±90%

default

24

Secondary>
20 years

1.3 (1.0-2.2)*±90%

default

24

Secondary≤
20 years

5.0

±90%

default

24

Primary

1.0*

±90%

default

24

Secondary>
20 years

1.0*

±90%

default

24

Secondary≤
20 years

2.0

±90%

default

24

Primary

0.5±90%

default

13

Secondary>
20 years

1.8

±90%

default

14

Secondary≤
20 years

5.5

6.8

SD

25-27

Primary

0.5

1.9

SD

2, 10, 11Secondary>
20 years

1.8

0.8

SD

3, 4, 12, 13Secondary≤
20 years

4.4

1.6

SD

3, 4, 12, 13,
22

Primary

-0.7

3.1

SD

2, 16

Secondary>
20 years

1.1

0.4

SD

3, 4, 28, 29

Secondary≤
20 years

2.9

0.1

SD

3, 4, 28-30

Status/Condition

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

References

4.35

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.9 (UPDATED) (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS1,2,3,4 (TONNES D.M. HA-1 YR-1)
DomainEcological
Zone4

Continent

AfricaSubtropicalhumid
forestNorth andSouth
America

Asia

AfricaNorth andSouth
America

Subtropical
Subtropicaldry forest

Asia(continental)

Asia(insular)

AfricaSubtropical
steppeNorth andSouth
America

4.36

Abovegroundbiomass
growth [tonnes
d.m. ha-1 yr-1]

Uncertainty

Uncertainty
type

Primary---

Secondary>
20 years

1.0

±90%

default

15

Secondary≤
20 years

2.5

±90%

default

16

Primary---

Secondary>
20 years

1.0

±90%

default

17

Secondary≤
20 years

2.5

±90%

default

18

Primary---

Secondary>
20 years

1.0

0.9

SD

3, 4, 31

Secondary≤
20 years

2.5

0.8

SD

3, 4, 31

Primary

1.8 (0.6-3.0)*±90%

default

24

Secondary>
20 years

1.8 (0.6-3.0)*±90%

default

24

Secondary≤
20 years

2.4 (2.3-2.5)±90%

default

24

Primary

1.0*

±90%

default

24

Secondary>
20 years

1.0*

±90%

default

24

Secondary≤
20 years

4.0

±90%

default

24

Primary

1.5*

±90%

default

24

Secondary>
20 years

1.5*

±90%

default

24

Secondary≤
20 years

6.0

±90%

default

24

Primary

2.0*

±90%

default

24

Secondary>
20 years

2.0*

±90%

default

24

Secondary≤
20 years

7.0

±90%

default

24

Primary

0.9 (0.2-1.6)*±90%

default

24

Secondary>
20 years

0.9 (0.2-1.6)*±90%

default

24

Secondary≤
20 years

1.2 (0.8-1.5)±90%

default

24

Primary

1.0*

±90%

default

24

Secondary>
20 years

1.0*

±90%

default

24

Secondary≤
20 years

4.0

±90%

default

24

Status/Condition

References

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

Chapter 4: Forest Land

TABLE 4.9 (UPDATED) (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS1,2,3,4 (TONNES D.M. HA-1 YR-1)
DomainEcological
Zone4

Continent

Asia(continental)
Subtropical
steppeAsia(insular)

Subtropical

AfricaSubtropicalmountainsystemNorth andSouth
America

Asia

New
Zealand
Oceanic

EuropeNorth andSouth
AmericaTemperate

Continental

MountainNorth andSouth
America
North andSouth
America

Status/Condition

Abovegroundbiomass
growth [tonnes
d.m. ha-1 yr-1]

Uncertainty

Uncertainty
type

References

Primary

1.3 (1.0-2.2)*±90%

default

24

Secondary>20 years

1.3 (1.0-2.2)*±90%

default

24

Secondary≤20 years

5.0

±90%

default

24

Primary

1.0*

±90%

default

24

Secondary>20 years

1.0*

±90%

default

24

Secondary≤20 years

2.0

±90%

default

24

Primary---

Secondary>20 years

0.5

±90%

default

19

Secondary≤20 years

2.5

±90%

default

20

Primary---

Secondary>20 years

0.5

±90%

default

21

Secondary≤20 years

2.5

±90%

default

22

Primary---

Secondary>20 years

0.5

0.3

SD

3, 4, 32

Secondary≤20 years

2.5

0.03

SD

3, 4, 32

Primary

0.37

±0.85

95%CI

33

Secondary>20 years

2.12

±0.82

95%CI

33

Secondary≤20 years

3.12

0.83

SE

34

All

2.3

-

-

35

Secondary>20 years

4.94

0.25

SD

36

Secondary≤20 years

3.5

0.87

SD

36

Secondary>20 years

1.97

0.01

SD

36

Secondary≤20 years

1.96

0.04

SD

36

Secondary>20 years

2.09

0.02

SD

36

Secondary≤20 years

1.38

0.07

SD

36

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

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.9 (UPDATED) (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN NATURAL FORESTS1,2,3,4 (TONNES D.M. HA-1 YR-1)
DomainAbovegroundbiomass
growth [tonnes
d.m. ha-1 yr-1]

Uncertainty

Uncer
taintytype

References

Secondary>20
years

0.2

0.02

SD

36

Secondary≤20
years

-

-

SD

36

Secondary>20
years

1.43

0.05

SD

36

Secondary≤20
years

0.64

0.1

SD

36

Coniferous

Asia,
Europe,North
America

All0.1-2.1--

35

Tundrawoodland

Asia,
Europe,North
AmericaAll

0.4(0.2-0.5)

Range

24

Primary or
secondary>20
years

1.1-1.5--

24

Mountain

Asia,
Europe,North
AmericaSecondary≤20
years

1.0-1.1--

24

Ecological
Zone4

Continent

DesertNorth andSouth
AmericaTemperate
North andSouth
AmericaSteppeBorealStatus/Condition

1
Aboveground net biomass growth is defined as net change in total aboveground biomass over time. In this respect, both forest
productivity and mortality are accounted for.
2
Some categories include sub-strata for primary forests defined as old growth forests that are intact or with no active human intervention,and secondary forests which include all other forests. The table considers a forest definition of at least 10% tree canopy cover.
3

For above-ground biomass growth rates with no standard deviation, IPCC Tier 1 default uncertainties apply.

Forest Resources Assessment (FRA). (2015). Global Eological Zones for FAO Forest Reporting 2010 Update. Forest Resources
Assessment Working Paper 179.
4

Observations on ecological zone and continent columns
Above-ground biomass growth rate was taken from: Tropical moist deciduous forest - North and South America (Primary); Tropicalmoist deciduous forest - North and South America (Primary); Tropical moist deciduous forest - Africa (Secondary>20 years); Tropicaldry forest – North and South America (Secondary>20 years); Tropical dry forest – North and South America (Secondary≤20 years);
Tropical dry forest – North and South America (Secondary>20 years); Tropical dry forest – North and South America (Secondary≤20
years); Tropical mountain system – North and South America (Primary); Tropical mountain system – North and South America(Secondary>20 years); Subtropical humid forest – Asia (Secondary>20 years); Subtropical humid forest – Asia (Secondary≤20 years)
Subtropical humid forest – Asia (Secondary>20 years); Subtropical humid forest – Asia (Secondary≤20 years); Subtropical mountainsystem – Asia (Secondary>20 years); Subtropical mountain system – Asia (Secondary≤20 years); Subtropical mountain system – Asia
(Secondary>20 years); Subtropical mountain system – Asia (Secondary≤20 years).
Note: SD = standard deviation, CI = confidence interval, SE = standard error.
*Recommendation based on IPCC 2006 estimates for Forests > 20 years.
References
1 Lewis, S. L., et al., 2009; 2 Lopez-Gonzalez, G. et al., 2011; 3 Anderson-Teixeira, K. J., et al., 2018a; 4 Anderson-Teixeira, K. J., etal., 2018b; 5 Omeja, P. A. et al., 2011; 6 Palm, C.A., et al., 1999; 7 N'Guessan, A. E., et al., 2019; 8 Gourlet-Fleury, S., et al., 2013; 9Thenkabail, P. S., et al., 2004; 10 Brienen, R. J. W., et al., 2014: 11 Brienen, R. J. W., et al., 2015; 12 Poorter, L. et al., 2016a; 13 Poorter
L. et al., 2016b; 14 Salimon, C. I., Brown, I. F., 2000; 15 Rutishauser, E., et al., 2015; 16 Qie, L., et al., 2017; 17 Mukul, S. A., Herbohn,J., Firn, F., 2016; 18 Hiratsuka, M., et al., 2006; 19 Ewel, J. J., Chai, P., Tsai, L. M., 1983; 20 Kalaba, F. K., et al., 2013; 21 Manlay, R.,
et al., 2002; 22 Peña, M. A., Duque, A., 2013; 23 Salinas-Mendoza, M. A. et al., 2017; 24 IPCC, 2003; 25 Otuoma, J., et al., 2016; 26
Giday, K., et al., 2013; 27 Mekurja, W., Veldkamp, E., Corre, M. D., 2010; 28 Tang, J. W., et al., 1998; 29 Fujiki, S., 2017; 30 Chan, N.,
Takeda, S., 2016; 31 Schomakers, J., et al., 2017; 32 Dang, C. L., Wu, Z. L., 1991; 33 Holdaway, R.J., et al. 2017; 34 Beets P.N., et al.
2014; 35 IPCC 2006; 36 June 18, 2018. Forest Inventory and Analysis Database, St. Paul, MN: U.S. Department of Agriculture, Forest
Service, Northern Research Station. (Available only on internet: https://apps.fs.usda.gov/fia/datamart/datamart.html).

4.38

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

Chapter 4: Forest Land

TABLE 4.10 (UPDATED)ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL PLANTATION FORESTS (TONNES D.M. HA-1 YR-1)

DomainEcologicalzone1

Continent

AfricaTropicalrainforest

North andSouth
America

Asia

AfricaTropicalmoist
deciduous
forestNorth andSouth
AmericaSpecies

AfricaTropicaldry forest

North andSouth
America

Asia

AfricaTropicalshrubland
North andSouth
AmericaAsia

Range[tonnes d.m.
ha-1 yr-1]2

References

Pinus sp. ≤ 20 y

20Other ≤ 20 y

6

5-8

1

Eucalyptus sp.

20

6-40

1

Pinus sp.

20

1

Tectona grandis

15

1

Other broadleaf

20

5-35

1

Eucalyptus sp.

5

4-8

1

Other

5

2-8

1

Eucalyptus sp. >20 y

25

1

Eucalyptus sp. ≤20 y

20

1

Other ≤ 20 y

9

Eucalyptus sp.

16

Tectona grandis

8

4-12

1

Other broadleaf

6-20

6-20

3

Asia

TropicalAbove-groundbiomass[tonnes d.m.
ha-1 yr-1]

1

3-15

1
2

8

1

Eucalyptus sp. ≤20 y13

1

Pinus sp. > 20 y

9

7-10

4

Pinus sp. ≤ 20 y

6

5-8

4

Other ≤ 20 y

10

4-20

1

Eucalyptus sp.

20

6-30

1

Pinus sp.

7

4-10

1

Tectona grandis

8

4-12

1

Other broadleaf

10

3-12

1

Eucalyptus sp.

15

5-25

1

Other

7

2-13

1

Eucalyptus sp. >20 y

8

5-14

1

Eucalyptus sp. ≤20 y

5

3-7

1

Pinus sp. > 20 y

2.5

Pinus sp. ≤ 20 y

3

Other > 20 y

101

Other ≤ 20 y

151

Eucalyptus sp.

20

1

Pinus sp.

51

62019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

1
0.5-6

1-12

1

1

4.39

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.10 (UPDATED) (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL PLANTATION FORESTS (TONNES D.M. HA-1 YR-1)

DomainEcologicalzone1

Continent

Species

AfricaTropicalNorth andSouth America

Tropicalmountainsystems

Asia

Subtropicalhumid forest

North andSouth America

Subtropicaldry forest

North andSouth America

Asia

Subtropical

AfricaSubtropical
steppeNorth andSouth America

Subtropicalmountainsystems

4.40

References
1

Eucalyptus sp.

10

Pinus sp.

101

Tectona grandis

2

1

Other broadleaf

41

Eucalyptus sp.

3

1

Other

5

1-10

1

Eucalyptus sp.

20

6-32

1

Pinus sp.

7

4-10

1

Tectona grandis

8

4-12

1

Other broadleaf

10

3-12

1

8-18

1

8

1

Eucalyptus sp. ≤20 y13

1

Pinus sp. > 20 y

101

Pinus sp. ≤ 20 y

81

Other ≤ 20 y

10

4-20

1

Eucalyptus sp.

20

6-30

1

Pinus sp.

7

4-10

1

Tectona grandis

8

4-12

1

Other broadleaf

10

3-12

1

Eucalyptus sp.

15

5-25

1

Other

7

2-13

1

Eucalyptus sp. >20 y

8

5-14

1

Eucalyptus sp. ≤20 y

5

3-7

1

Pinus sp. > 20 y

2.5

Pinus sp. ≤ 20 y

3

Other > 20 y

101

Other ≤ 20 y

151

Eucalyptus sp.

20

1

Pinus sp.

5

1

Asia

6

Africa10

North andSouth America

Range[tonnesd.m. ha-1yr-1]210

Asia
AfricaAbove-groundbiomass[tonnes d.m.
ha-1 yr-1]

1
0.5-6

1-12

1

1
1

Eucalyptus sp.

10

8-18

1

Pinus sp.

101

Tectona grandis

2

1

Other broadleaf

4

12019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 4: Forest Land

TABLE 4.10 (UPDATED) (CONTINUED)ABOVE-GROUND NET BIOMASS GROWTH IN TROPICAL AND SUB-TROPICAL PLANTATION FORESTS (TONNES D.M. HA-1 YR-1)

DomainEcologicalzone1

Continent

Subtropical

Subtropicalmountainsystems

Asia

Continental

Range[tonnesd.m. ha-1yr-1]2

References
1

Eucalyptus sp.

3

Other

5

1-10

1

North andSouth
AmericaSecondary >20 years

2.9

0.1

5

Secondary ≤20 years

4.1

0.2

5

MountainNorth andSouth
AmericaSecondary >20 years

7.7

0.1

5

Secondary ≤20 years

5.5

0.3

5

Oceanic

North andSouth
AmericaSecondary >20 years

8.3

0.5

5

Secondary ≤20 years

4.2

0.6

5

SteppeNorth andSouth
AmericaSecondary >20 years

1.7

0.3

5

Secondary ≤20 years

3

0.8

5

Coniferous

Asia,
Europe,North
AmericaSecondary >20 years

1.0

1Secondary ≤20 years

1.0

1Secondary >20 years

0.4

1Secondary ≤20 years

0.4

1Secondary >20 years

1.0

1Secondary ≤20 years

1.0

1Temperate

BorealSpecies

Above-groundbiomass[tonnes d.m.
ha-1 yr-1]

Tundrawoodland

Asia,
Europe,North
AmericaMountain

Asia,
Europe,North
America1
Forest Resources Assessment (FRA) (2015). Global Eological Zones for FAO Forest Reporting 2010 Update. Forest Resources
Assessment Working Paper 179.
2

If a single estimate is included in this column it refers to the standard deviation of the mean estimate.

References
1 IPCC 2003; 2 Stape et al. 2004; 3 Lugo et al. 1990; 4 Masota et al. 2016; 5 June 18, 2018. Forest Inventory and Analysis Database, St.
Paul, MN: U.S. Department of Agriculture, Forest Service, Northern Research Station (Available only on internet:
http://apps.fs.fed.us/fiadb-downloads/datamart.html).

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

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.11 (UPDATED)REPORTED MEAN ANNUAL INCREMENT (GROWTH RATE OF MERCHANTABLE VOLUME) VALUES FOR SOME PLANTATIONFOREST SPECIES (M3 HA-1 YR-1)
Continent

World

Africa4.42

Region/Country

GeneralGeneralTree species

Plantation
Purpose

MAI
min

MAI
max

S.D.2

Reference

Acacia auriculiformis

Productive

6

20

3.5

5, 8

Acacia mearnsii

Productive

14

25

2.8

5, 8

Araucaria angustifolia

Productive

8

24

4.0

5, 8

Araucaria cunninghamii

Productive

10

18

2.0

5, 8

Casuarina equisetifolia

Productive

6

20

3.5

5, 8

Casuarina junghuhniana

Productive

7

11

1.0

5, 8

Cordia alliodora

Productive

10

20

2.5

5, 8

Cupressus lusitanica

Productive

8

40

8.0

5, 8

Dalbergia sissoo

Productive

5

8

0.8

5, 8

Eucalyptus camaldulensis

Productive

15

30

3.8

5, 8

Eucalyptus deglupta

Productive

14

50

9.0

5, 8

Eucalyptus globulus

Productive

10

40

7.5

5, 8

Eucalyptus grandis

Productive

15

50

8.8

5, 8

Eucalyptus robusta

Productive

10

40

7.5

5, 8

Eucalyptus saligna

Productive

10

55

11.3

5, 8

Eucalyptus urophylla

Productive

20

60

10.0

5, 8

Gmelina arborea

Productive

12

50

9.5

5, 8

Leucaena leucocephala

Productive

30

55

6.3

5, 8

Pinus caribaea var. caribaea

Productive

10

28

4.5

5, 8

Pinus caribaea var. hondurensis

Productive

20

50

7.5

5, 8

Pinus oocarpa

Productive

10

40

7.5

5, 8

Pinus patula

Productive

8

40

8.0

5, 8

Pinus radiata

Productive

10

50

10.0

5, 8

Swietenia macrophylla

Productive

7

30

5.8

5, 8

Tectona grandis

Productive

6

18

3.0

5, 8

Terminalia ivorensis

Productive

8

17

2.3

5, 8

Terminalia superba

Productive

10

14

1.0

5, 8

Acacia mellifera

Productive

2.2

4.0

0.5

6, 8

Acacia nilotica

Productive

15.0

20.0

1.3

6, 8

Acacia senegal

Productive

1.4

2.6

0.3

6, 8

Acacia seyal

Productive

2.0

6.0

1.0

6, 8

Ailanthus excelsa

Productive

6.6

9.4

0.7

6, 8

Bamboos

Productive

5.0

7.5

0.6

6, 8

Cupressus spp.

Productive

15.0

24.0

2.3

6, 8

Eucalyptus spp.

Productive

12.0

14.0

0.5

6, 8

Khaya spp.

Productive

8.5

12.0

0.9

6, 8

Tectona grandis

Productive

2.5

3.5

0.3

6, 8

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

Chapter 4: Forest Land

TABLE 4.11 (UPDATED) (CONTINUED)REPORTED MEAN ANNUAL INCREMENT (GROWTH RATE OF MERCHANTABLE VOLUME) VALUES FOR SOME PLANTATIONFOREST SPECIES (M3 HA-1 YR-1)
Continent

Region/Country

Plantation
Purpose

MAI
min

MAI
max

S.D.2

Reference

Acacia albida

Productive
semi-natural

4.0

6.1

0.5

6, 8

Acacia mellifera

Productive
semi-natural

1.9

3.5

0.4

6, 8

Acacia nilotica

Productive
semi-natural

12.5

20.0

1.9

6, 8

Acacia senegal

Productive
semi-natural

1.1

2.4

0.3

6, 8

Acacia seyal

Productive
semi-natural

1.8

3.2

0.4

6, 8

Acacia tortilis

Productive
semi-natural

1.2

3.7

0.6

6, 8

Acacia tortilis var. siprocarpa

Productive
semi-natural

1.5

2.4

0.2

6, 8

Balanites aegyptiaca

Productive
semi-natural

1.2

1.5

0.1

6, 8

Sclerocarya birrea

Productive
semi-natural

1.5

1.7

0.1

6, 8

Ziziphus mauritiana

Productive
semi-natural

0.9

1.0

0.0

6, 8

Acacia mellifera

Protective

2.0

6.0

1.0

6, 8

Acacia nilotica

Protective

13.0

21.0

2.0

6, 8

Acacia senegal

Protective

1.4

2.8

0.4

6, 8

Acacia seyal

Protective

1.9

4.3

0.6

6, 8

Ailanthus spp.

Protective

6.0

12.0

1.5

6, 8

Bamboos

Protective

4.0

8.0

1.0

6, 8

Cupressus spp.

Protective

14.0

20.0

1.5

6, 8

Eucalyptus spp.

Protective

10.0

14.0

1.0

6, 8

Khaya spp.

Protective

7.0

16.0

2.3

6, 8

Tectona grandis

Protective

5.0

8.0

0.8

6, 8

E and S

Acacia mearnsii / melanoxylon

Productive

10

12

0.5

6, 8

N

Acacia nilotica

Productive

15

20

1.3

6, 8

N

Acacia nilotica

Productive
semi-natural

12.5

20

1.9

6, 8

N

Acacia senegal

Productive

1.4

2.6

0.3

6, 8

N

Acacia senegal

Productive
semi-natural

1.1

2.4

0.3

6, 8

N

Acacia seyal

Productive

2

6

1.0

6, 8

N

Acacia seyal

Productive
semi-natural

1.8

3.2

0.4

6, 8

E and S

Eucalyptus grandis

Productive

18

24

1.5

6, 8

E and S

Eucalyptus nitens

Productive

22

28

1.5

6, 8

GeneralAfricaTree species

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

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.11 (UPDATED) (CONTINUED)REPORTED MEAN ANNUAL INCREMENT (GROWTH RATE OF MERCHANTABLE VOLUME) VALUES FOR SOME PLANTATIONFOREST SPECIES (M3 HA-1 YR-1)
Continent

AfricaAsia

4.44

Region/Country

Tree species

Plantation
Purpose

MAI
min

MAI
max

S.D.2

ReferenceN

Eucalyptus spp.

Productive

12

14

0.5

6, 8

E and S

Pinus elliottii

Productive

12

18

1.5

6, 8

N and C

Pinus elliottii

Productive

7

8

0.3

6, 8

N

Pinus halapensis

Productive
semi-natural

1

2

0.3

6, 8

AfricaPinus patula

Productive

12

18

1.5

6, 8

AfricaPinus pinaster

Productive
semi-natural

1

2

0.3

6, 8

AfricaPinus radiata

Productive

12

16

1.0

6, 8

Congo

Eucalyptus spp.

Experimental

13.8

25

2.8

10

Asia

Eucalyptus camaldulensis

Productive

21.0

43.0

5.5

6, 8

Asia

Pinus spp.

Productive

4.0

15.0

2.8

6, 8

S and SE

Acacia mangium

Productive

19

40

5.3

6, 8

E and S

Castanea molissima

Productive

1

6

1.3

6, 8

E and S

Cunninghamia lanceolata

Productive

2.5

13.5

2.8

6, 8

E and S

Cunninghamia lanceolata

Productive
semi-natural

2.5

13.5

2.8

6, 8

E

Eucalyptus spp.

Productive

1.6

8.7

1.8

6, 8

S and SE

Eucalyptus spp.

Productive

7

12

1.3

6, 8

S and SE

Eucalyptus spp.

Productive
semi-natural

8

12

1.0

6, 8

W and C

Eucalyptus spp.

Productive

4

10

1.5

6, 8

Asia

Pinus massoniana

Productive
semi-natural

2.8

16.3

3.4

6, 8

Asia

Populus spp. and cultivars

Productive

3.7

18.5

3.7

6, 8

Asia

Populus spp. and cultivars

Productive
semi-natural

3.7

17.7

3.5

6, 8

Asia

Populus spp. and cultivars

Productive

5

12

1.8

6, 8

Asia

Tectona grandis

Productive

4

17.3

3.3

6, 8

Asia

Tectona grandis

Productive
semi-natural

4

6

0.5

6, 8

China

Dalbergia sissoo

Productive

4

6

0.5

1

China

Eucalyptus spp.

Productive

8

12

1.0

1

China

Gmelina arborea

Productive

10

15

1.3

1

China

Acacia nilotica

Productive

3

4

0.3

1

China

Populus spp.

Productive

20

25

1.3

1

China

Tectona grandis

Productive

0.6

7

1.6

1

TurkeyPinus pinaster

Productive

9.8

22.4

3.2

4

TurkeyEucalyptus camaldulensis

Productive

18.3

24.1

1.5

4

TurkeyPopulus spp. and cultivars

Productive

23.5

55.1

7.9

4

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

Chapter 4: Forest Land

TABLE 4.11 (UPDATED) (CONTINUED)REPORTED MEAN ANNUAL INCREMENT (GROWTH RATE OF MERCHANTABLE VOLUME) VALUES FOR SOME PLANTATIONFOREST SPECIES (M3 HA-1 YR-1)
Continent

Asia

EuropeNorth
andCentralAmerica
Oceania

Region/Country

Tree species

Plantation Purpose

MAI
min

MAI
max

S.D.2

Reference

TurkeyPinus brutia

Productive

1

15.4

3.6

4

Vietnam

Acacia hybrid

Experimental

24.4

39.4

3.8

3

Vietnam

Acacia mangium

Productive

11

23

3.0

9

VietnamMelia azedarach

Productive

15

17

0.5

9

EuropeFagus sylvatica

Productive

4

14

2.5

6, 8

EuropeFagus sylvatica

Productive semi-natural

2

14

3.0

6, 8

EuropeLarix decidua

Productive

7

13

1.5

6, 8

EuropeLarix decidua

Productive semi-natural

2

11

2.3

6, 8

EuropePicea abies

Productive

3.5

6

0.6

6, 8

EuropePicea abies

Productive semi-natural

1.5

15

3.4

6, 8

EuropePinus pinaster

Productive

4.7

13.8

2.3

6, 8

EuropePinus sylvestris

Productive

2.5

14

2.9

6, 8

EuropePinus sylvestris

Productive semi-natural

1

10

2.3

6, 8

EuropeQuercus robur

Productive

3

9

1.5

6, 8

EuropeQuercus robur

Productive semi-natural

1.5

10

2.1

6, 8

SwedenPinus sylvestris

Productive semi-natural

3.3

5.3

0.5

7

SwedenPicea abies

Productive semi-natural

3.4

10

1.7

7

SwedenLarix sibirica

Productive semi-natural

4

5.9

0.5

7

SwedenPinus contorta

Productive semi-natural

4.6

6.9

0.6

7

SwedenBetula pendula

Productive semi-natural

3

8

1.3

7

SwedenPopulus spp. andcultivars

Productive semi-natural

12

16

1.0

SwedenQuercus robur

Productive semi-natural

3.9

5.2

0.3

7

FinlandPinus sylvestris

Productive semi-natural

2

5

0.8

7

Finland

Picea abies

Productive semi-natural

3

7

1.0

7

FinlandBetula pendula

Productive semi-natural

3

7

1.0

7

NorwayPinus sylvestris

Productive semi-natural

1.5

3.5

0.5

7

NorwayPicea abies

Productive semi-natural

4

8.5

1.1

7

NorwayPicea sitchensis

Productive semi-natural

12

18

1.5

7

North
and
CentralAmerica

Pinus taeda

Productive

9

10

0.3

6, 8

Oceania

Eucalyptus
globulus

Productive

15.6

25

2.4

6, 8

Oceania

Pinus radiata

Productive

15.7

21

1.3

6, 8

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

7

4.45

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.11 (UPDATED) (CONTINUED)REPORTED MEAN ANNUAL INCREMENT (GROWTH RATE OF MERCHANTABLE VOLUME) VALUES FOR SOME PLANTATIONFOREST SPECIES (M3 HA-1 YR-1)
ContinentSouth
America

Region/Country

Tree species

South America

Tectona grandis

South America

Plantation
Purpose

MAI
min

MAI
max

S.D.2

Reference

Productive

7.3

17.3

2.5

6, 8

Xylia xylocarpa

Productive

3.0

8.8

1.5

6, 8

South America

Acacia spp.

Productive

15.0

30.0

3.8

6, 8

South America

Araucaria
angustifolia

Productive

15.0

30.0

3.8

South America

Eucalyptus spp.

Productive

20.0

70.0

12.5

6, 8

South America

Hevea brasiliensis

Productive

10.0

20.0

2.5

6, 8

South America

Mimosa scabrella

Productive

10.0

25.0

3.8

6, 8

South America

Pinus spp.

Productive

25.0

40.0

3.8

6, 8

South America

Populus spp.

Productive

10.0

30.0

5.0

6, 8

South America

Tectona grandis

Productive

15.0

35.0

5.0

6, 8

South America

Eucalyptus spp.

Productive

15

70

13.8

6, 8

South America

Pinus radiata

Productive

14

34

5.0

6, 8

Brazil

Khaya ivorensis

Productive

18

25

1.8

11

BrazilSchizolobium
amazonicum

Productive

10

33

5.8

2

1

Updated and replaced former Table 4.11A and 4.11B from the 2006 IPCC Guidelines

2Standard deviation estimated from the min and max estimates.

6, 8

Note: E: East, S: South, N: North, SE: Southeast, W: West, C: Central
References
1 Chuande X. 2001; 2 Cordeiro et al. 2015; 3 Dell B., Daping X., Thu P.Q. 2012; 4 Erkan, N., 2003; 5 FAO, 2001; 6 FAO, 2006; 7
Haapanen, M., et al., 2015; 8 IPCC, 2006; 9 Kien, N.D., 2014; 10 Nzila, J.D., et al., 2004; 11 Silva, L.F., et al., 2016.

4.46

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

Chapter 4: Forest Land

TABLE 4.12 (UPDATED)BIOMASS VALUES FROM TABLES 4.7–4.10

DomainEcologicalzone1

Continent

AfricaTropicalrainforest

North andSouth
America

Asia

AfricaTropicalTropicalmoist
deciduous
forestNorth andSouth
America

Asia

AfricaTropicaldry forest
North andSouth
America

Abovegroundbiomass innaturalforests(tonnes d.m.
ha-1)2

Abovegroundbiomass inforestplantations(tonnesd.m. ha-1)3

Aboveground net
biomass
growth innaturalforests(tonnes d.m.
ha-1 yr-1)4

Aboveground net
biomass
growth inforestplantations(tonnes d.m.
ha-1 yr-1)5

Primary

404.2

n.a.

1.3

n.a.

Secondary>20 years

212.9

200-300

3.5

n.a.

Secondary≤ 20 years

52.8

60-1007.6

5-8

Primary

307.1

n.a.

1.0

n.a.

Secondary>20 years

206.4

150-300

2.3

5-40

Secondary≤20 years

75.7

150-300

5.9

5-40

Primary

413.1

n.a.

0.7

n.a.

Secondary>20 years

131.6

48.5-512.8

2.7

2-8

Secondary≤20 years

45.6

13.5-161

3.4

2-8

Primary

236.6

n.a.

0.4

n.a.

Secondary>20 years

72.8

120-483

0.9

n.a.

Secondary≤ 20 years

72.8

40-195

2.9

3-15

Primary

187.3

n.a.

0.4

n.a.

Secondary>20 years

131.0

46.9-284

2.7

4-20

Secondary≤20 years

55.7

46.9-195

5.2

4-20

Primary

67.7

n.a.

0.4

n.a.

Secondary>20 years

67.7

93.7-260

0.9

8Secondary≤20 years

67.7

5.7-202

2.4

8

Primary

69.6

n.a.

n.a.

n.a.

Secondary>20 years

69.6

60-193.9

1.6

6-13

Secondary≤ 20 years

69.6

20-75.6

3.9

4-20

Primary

127.5

n.a.

n.a.

n.a.

Secondary>20 years

118.9

50-110

1.6

4-30

Secondary≤20 years

32.2

40-62

3.9

4-30

Status/condition

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

4.47

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.12 (UPDATED) (CONTINUED)
BIOMASS VALUES FROM TABLES 4.7–4.10

DomainEcologicalzone1

Tropicaldry forest

Continent

Asia

AfricaTropicalshrublands

TropicalNorth andSouth
America

Asia

AfricaTropicalmountainsystems

North andSouth
America

Asia

4.48

Abovegroundbiomass innaturalforests(tonnesd.m. ha-1)2

Abovegroundbiomass inforestplantations(tonnesd.m. ha-1)3

Aboveground net
biomass
growth innaturalforests(tonnes d.m.
ha-1 yr-1)4

Aboveground net
biomass
growth inforestplantations(tonnes d.m.
ha-1 yr-1)5

Primary

184.6

n.a.

n.a.

n.a.

Secondary>20 years

184.6

45.5-88.8

1.6

2-25

Secondary≤20 years

184.6

3.56-125.5

3.9

2-25

Primary

48.4

n.a.

0.9

n.a.

Secondary>20 years

48.4

20

0.9

2.5-14

Secondary≤ 20 years

48.4

15-20

0.2-0.7

3-7

Primary

71.5

n.a.

1.0

n.a.

Secondary>20 years

71.5

30-60

1.0

5-20

Secondary≤20 years

71.5

30-60

4.0

5-20

Primary

38.3

n.a.

1.0-1.3

n.a.

Secondary>20 years

38.3

30-263.3

1.0-1.3

1-12

Secondary≤20 years

38.3

9.0-53.3

2.0-5.0

1-12

Primary

190.0

n.a.

0.5

n.a.

Secondary>20 years

190.0

30-1501.8

10Secondary≤ 20 years

190.0

30-1005.5

10Primary

195.0

n.a.

0.5

n.a.

Secondary>20 years

184.4

30-170

1.8

8-18

Secondary≤20 years

75.9

30-1704.4

8-18

Primary

433.5

n.a.

-0.7

n.a.

Secondary>20 years

66.4

25-1501.1

1-10

Secondary≤20 years

66.4

25-1502.9

1-10

Status/condition

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

Chapter 4: Forest Land

TABLE 4.12 (UPDATED) (CONTINUED)
BIOMASS VALUES FROM TABLES 4.7–4.10

DomainEcologicalzone1

Continent

AfricaSubtropicalhumid
forests

North
and
South
America

Asia

AfricaSubtropical
Subtropicaldry forests

North
and
South
America

Asia

AfricaSubtropical
steppe

North
and
South
America

Abovegroundbiomass innaturalforests(tonnesd.m. ha-1)2

Abovegroundbiomass inforestplantations(tonnesd.m. ha-1)3

Aboveground net
biomass
growth innaturalforests(tonnes d.m.
ha-1 yr-1)4

Aboveground net
biomass
growth inforestplantations(tonnes d.m.
ha-1 yr-1)5

Primary

54.1

n.a.

n.a.

n.a.

Secondary>20 years

54.1

n.a.

1.0

n.a.

Secondary≤ 20 years

54.1

n.a.

2.5

n.a.

Primary

84.5

n.a.

n.a.

n.a.

Secondary>20 years

84.5

11.1-270

1.0

3-32

Secondary≤20 years

84.5

2.45-270

2.5

3-32

Primary

323.0

n.a.

n.a.

n.a.

Secondary>20 years

258.4

100-180

1.0

8Secondary≤20 years

258.4

100-180

2.5

8Primary

65.2

n.a.

1.8

n.a.

Secondary>20 years

65.2

60-70

1.8

8

Secondary≤ 20 years

65.2

20-30

2.4

4-20

Primary

115.9

n.a.

1.0

n.a.

Secondary>20 years

115.9

60-110

1.0

3-30

Secondary≤20 years

115.9

60-1104.0

3-30

Primary

70.9

n.a.

1.5-2.0

n.a.

Secondary>20 years

70.9

60-163.5

1.5-2.0

2-25

Secondary≤20 years

70.9

54.8-69.5

6.0-7.02-25

Primary

50.5

n.a.

0.9

n.a.

Secondary>20 years

50.5

15-20

0.9

2.5-14

Secondary≤ 20 years

50.5

15-20

1.2

0.5-15

Primary

44.0

n.a.

1.0

n.a.

Secondary>20 years

44.0

30-60

1.0

5-20

Secondary≤20 years

44.0

3.6-604.0

5-20

Status/condition

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

4.49

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.12 (UPDATED) (CONTINUED)
BIOMASS VALUES FROM TABLES 4.7–4.10

DomainEcologicalzone1

Subtropical
steppeContinent

Asia

AfricaSubtropical
Subtropicalmountainsystems

North andSouth
America

Asia

Asia

Temperate

Mountain

EuropeNorth andSouth
America

4.50

Abovegroundbiomass innaturalforests(tonnesd.m. ha-1)2

Abovegroundbiomass inforestplantations(tonnesd.m. ha-1)3

Aboveground net
biomass
growth innaturalforests(tonnesd.m. ha-1yr-1)4

Aboveground net
biomass
growth inforestplantations(tonnesd.m. ha-1yr-1)5

Primary

41.6

n.a.

1.0-1.3

n.a.

Secondary>20 years

41.6

20-80

1.0-1.3

1-12

Secondary≤20 years

41.6

10-1202.0-5.0

1-12

Primary

35.1

n.a.

n.a.

n.a.

Secondary>20 years

35.1

30-1500.5

10Secondary≤ 20 years

35.1

10-1002.5

10Primary

74.6

n.a.

n.a.

n.a.

Secondary>20 years

74.6

24.9-170

0.5

2-18

Secondary≤20 years

74.6

3.7-170

2.5

2-18

Primary

250.2

n.a.

n.a.

n.a.

Secondary>20 years

155.2

n.a.

0.5

1-12

Secondary≤20 years

155.2

8.9-103.5

2.5

1-12

Primary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

170.4

n.a.

n.a.

3.0

Secondary≤ 20 years

n.a.

16.6-34.6

n.a.

3.0

Primary

301.1

n.a.

n.a.

n.a.

Secondary>20 years

214.7

n.a.

n.a.

3.0

Secondary≤20 years

27.8

n.a.

n.a.

3.0

Primary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

185.9

29.1-89.2

4.4

9

Secondary≤20 years

57.9

3.0-56.0

3.1

10

Status/condition

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

Chapter 4: Forest Land

TABLE 4.12 (UPDATED) (CONTINUED)
BIOMASS VALUES FROM TABLES 4.7–4.10

DomainEcologicalzone1

Abovegroundbiomass innaturalforests(tonnesd.m. ha-1)2

Abovegroundbiomass inforestplantations(tonnesd.m. ha-1)3

Aboveground net
biomass
growth innaturalforests(tonnesd.m. ha-1yr-1)4

Aboveground net
biomass
growth inforestplantations(tonnesd.m. ha-1yr-1)5

Primary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

11654.5-132.1

n.a.

4.0

Secondary≤ 20 years

90.9

18-66.7

n.a.

4.0

Primary

332.4

n.a.

n.a.

n.a.

Secondary>20 years

162

n.a.

n.a.

4.0

Secondary≤20 years

51.6

n.a.

n.a.

4.0

Primary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

128.9

21.5-96.7

3.6

4

Secondary≤20 years

46

5.688.35

3.3

5

Primary

289.8

n.a.

n.a.

n.a.

Secondary>20 yearsn.a.

150-200

n.a.

4.4

Secondary≤ 20 years

n.a.

30-40

n.a.

4.4

Primary

126.1

n.a.

2.3

n.a.

Secondary>20 years

153.9

150-200

2.3

4.4

Secondary≤20 years

22.3

30-40

2.3

4.4

Primary

352.7

n.a.

0.37

n.a.

Secondary>20 years

120.5

n.a.

2.12

4.4

Secondary≤20 years

57.5

n.a.

3.12

4.4

Primary

n.a.

n.a.

n.a.

n.a.

North andSouth
AmericaSecondary>20 years

354.1

76.2-131.3

9.1

10

Secondary≤20 years

213.9

3.9-120

6.3

6

Asia
EuropeNorth andSouth
AmericaPrimary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

44

n.a.

0.6

n.a.

Secondary≤20 years

25.6

n.a.

0.5

n.a.

Continent

Asia

Continental

EuropeNorth andSouth
America

Asia

Temperate

EuropeOceanic

Oceania

DesertStatus/condition

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

4.51

Volume 4: Agriculture, Forestry and Other Land Use

TABLE 4.12 (UPDATED) (CONTINUED)
BIOMASS VALUES FROM TABLES 4.7–4.10

Ecologicalzone1

Continent

SteppeAsia
Europe
North
and
South
America

DomainTemperate

Asia
EuropeNorth
America

BorealConiferous
TundrawoodlandMountain

Asia
EuropeNorth
America

Asia
EuropeNorth
America

Abovegroundbiomass innaturalforests(tonnesd.m. ha-1)2

Abovegroundbiomass inforestplantations(tonnesd.m. ha-1)3

Aboveground net
biomass
growth innaturalforests(tonnes d.m.
ha-1 yr-1)4

Aboveground net
biomass
growth inforestplantations(tonnes d.m.
ha-1 yr-1)5

Primary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

118.5

3.6-84.9

3.5

11

Secondary≤20 years

42.9

4.8-48.8

2.3

4

Primary

62.9

n.a.

0.1-2.1

n.a.

Secondary>20 years

n.a.

40-50

0.1-2.2

1.0

Secondary≤20 years

n.a.

5.0-500.1-2.3

1.0

Primary

n.a.

n.a.

0.4

n.a.

Secondary>20 years

63.7

25

0.4

0.4

Secondary≤20 years

104.2

5

0.4

0.4

Primary

n.a.

n.a.

n.a.

n.a.

Secondary>20 years

n.a.

40-50

1.1-1.5

1.0

Secondary≤20 years

1.9

5.0-501.0-1.1

1.0

Status/condition

1
Forest Resources Assessment (FRA) (2015). Global Ecological Zones for FAO Forest Reporting 2010 Update. Forest Resources Assessment
Working Paper 179.
2

For information related to uncertainties and references refer to table 4.7

3

For information related to uncertainties and references refer to table 4.8

4

For information related to uncertainties and references refer to table 4.9

5

For information related to uncertainties and references refer to table 4.10

Annex 4A-1

Glossary for Forest Land

No refinement.

4.52

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

Chapter 4: Forest Land

References
REFERENCES NEWLY CITED IN THE 2019 REFINEMENT
Soil C References
Achat, D. L., Deleuze, C., Landmann, G., Pousse, N., Ranger, J., and Augusto, L. (2015b). Quantifying
consequences of removing harvesting residues on forest soils and tree growth–A meta-analysis. Forest Ecology
and Management 348: 124–141.
Achat, D.L., Fortin, M., Landmann, G., Ringeval, B. and Augusto, L. (2015a). Forest soil carbon is threatened byintensive biomass harvesting. Scientific Reports 5, 15991.
Clarke, N., Gundersen, P., Jönsson-Belyazid, U., Kjønaas, O. J., Persson, T., Sigurdsson, B. D., ... and Vesterdal,
L. (2015). Influence of different tree-harvesting intensities on forest soil carbon stocks in boreal and northerntemperate forest ecosystems. Forest Ecology and Management 351: 9–19.
Dean, C., Kirkpatrick, J. B. and Friedland, A. J. (2017). Conventional intensive logging promotes loss of organiccarbon from the mineral soil. Global Change Biology 23: 1–11.
De Vos, B., Cools, N., Ilvesniemi, H., Vesterdal, L., Vanguelova, E. and Carnicelli. S. (2015). Benchmark values
for forest soil carbon stocks in Europe: Results from a large scale forest soil survey. Geoderma 251: 33–46.
Falloon, P. and Smith, P. (2003). Accounting for changes in soil carbon under the Kyoto Protocol: need for
improved long‐term data sets to reduce uncertainty in model projections. Soil Use and Management 19: 265–
269.
Gross, C. D., James, J.N.,Turnblom, E. C. and Harrison, R.B. (2018). Thinning treatments reduce deep soil carbon
and nitrogen stocks in a Coastal Pacific Northwest forest. Forests 9(5): 238.
Hume, A. M., Chen, H.Y.H. and Taylor, A.R. (2017). Intensive forest harvesting increases susceptibility ofnorthern forest soils to carbon, nitrogen and phosphorus loss. Journal of Applied Ecology.
James, J. and Harrison, R. (2016). The effect of harvest on forest soil carbon: A meta-analysis. Forests 7(12): 308.
Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D.W., Minkkinen, K. andByrne, K. A. (2007). How strongly can forest management influence soil carbon sequestration? Geoderma
137(3): 253–268.
Johnson, D.W. and Curtis, P.S. (2001). Effects of forest management on soil C and N storage: meta-analysis.
Forest Ecology and Management 140: 227–238.
Lehtonen, A., Heikkinen, J., (2015). Uncertainty of upland soil carbon sink estimate for Finland. Canadian Journalof Forest Research 46: 310–322.
Liao, C., Luo, Y., Fang, C., and Li, B. (2010). Ecosystem carbon stock influenced by plantation practice:implications for planting forests as a measure of climate change mitigation. PloS one 5(5): e10867.
Metsaranta, J.M., Shaw, C., Kurz, W.A., Boisvenue, C. and Morken, S., 2017. Uncertainty of inventory-based
estimates of the carbon dynamics of Canada’s managed forest (1990–2014). Canadian Journal of Forest
Research 47: 1082–1094.
Monte, L., Håkanson, L., Bergström, U., Brittain, J. and Heling, R. (1996). Uncertainty analysis and validation ofenvironmental models: the empirically based uncertainty analysis. Ecological Modelling 91: 139–152.
Nave, L. E., Vance, E. D., Swanston, C. W., and Curtis, P. S. (2010). Harvest impacts on soil carbon storage intemperate forests. Forest Ecology and Management 259(5): 857–866.
Nepstad, D.C., de Carvalho, C.R., Davidson, E.A., Jipp, P.H., Lefebvre, P.A., Negelros, G.H., da Silva, E.D.,
Stone, T.A., Trumbore, S.E. and Vieira, S. (1994). The role of deep roots in the hydrological and carbon cyclesof Amazonian forests and pastures. Nature 372: 666–669.
Ogle, S.M., Breidt, F.J., Easter, M., Williams, S. and Paustian, K. (2007). An empirically based approach forestimating uncertainty associated with modelling carbon sequestration in soils. Ecological Modelling 205:
453–463.
Peltoniemi, M., Palosuo, T., Monni, S. and Mäkipää, R. (2006). Factors affecting the uncertainty of sinks andstocks of carbon in Finnish forests soils and vegetation. Forest Ecology and Management 232: 75–85.
Smith, J.E. and Heath, L.S. (2001). Identifying influences on model uncertainty: an application using a forest
carbon budget model. Environmental Management 27: 253–267.

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