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

CHAPTER 2

GENERIC METHODOLOGIESAPPLICABLE TO MULTIPLE LANDUSE CATEGORIES2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Authors 1
Stephen Michael Ogle (USA), Werner Alexander Kurz (Canada), Carly Green (Australia), Andrea Brandon (New
Zealand), Jeffrey Baldock (Australia), Grant Domke (USA), Martin Herold (Netherlands), Martial Bernoux (FAO),
Ngonidzashe Chirinda (CGIAR, Zimbabwe), Robert de Ligt (Australia), Sandro Federici (San Marino), Emilio
Garcia-Apaza (Bolivia), Giacomo Grassi, (EU), Thomas Gschwantner (Austria), Yasumasa Hirata (Japan),
Richard Houghton (USA), Joanna Isobel House (UK), Shigehiro Ishizuka (Japan), Inge Jonckheere (Belgium),
Haruni Krisnawati (Indonesia), Aleksi Lehtonen (Finland), Mwangi James Kinyanjui (Kenya), Brian McConkey(Canada), Erik Næsset (Norway), Sini M. Niinistö (Finland), Jean Pierre Ometto (Brazil), Luis Panichelli(Argentina), Thomas Paul (New Zealand), Hans Petersson (Sweden), Shanti Reddy (Australia), Kristiina Regina(Finland), Marcelo Theoto Rocha (Brazil), Joachim Rock (Germany), Maria José Sanz Sanchez (Spain), Carlos
Sanquetta (Brazil), Atsushi Sato (Japan), Zoltan Somogyi (Hungary), Aleksandr Trunov (The Russian Federation),
Gabriel Vazquez-Amabile (Argentina), Marina Vitullo (Italy), Changke Wang (China), Robert Michael
Waterworth (Australia)

Contributing Authors
Max Collett (Australia), Mark Harmon (USA), Johannes Lehmann (USA), Cindy H. Shaw (Canada), Yasuhito
Shirato (Japan), Dominic Woolf (USA), Ronald E. McRoberts (USA)

1 Biomass and Dead Organic Matter Sections were prepared by lead authors, A. Brandon, G. Domke, M. Herold, Y. Hirata,

E. Garcia-Apaza, T. Gschwantner, R. Houghton, S. Ishizuka, H. Krisnawati, M.J. Kinyanjui, E. Næsset, J.P. Ometto, J. Rock,C. Sanquetta, M.J. Sanz Sanchez, A. Sato, R.E. Malimbwi, S.J. Wakelin and M. Vitullo; and contributing authors, M. Harmon
and C.H. Shaw.
Soil Carbon Section was prepared by lead authors, S.M. Ogle, J. Baldock, M. Bernoux, N. Chirinda, S. Ishizuka, A. Lehtonen,
B. McConkey, S. Niinistö, K. Regina, A. Trunov, G. Vazquez-Amabile, and C. Wang; and contributing authors, J. Lehmann,
Y. Shirato, and D. Woolf.
Additional Guidance on Tier 3 Methods Section was prepared by lead authors C. Green, I. Jonckheere, W. Kurz, R. de Ligt,
E. Næsset, S.M. Ogle, L. Panichelli, H. Petersson, M. T. Rocha, and R.M. Waterworth.
Interannual Variability Section was prepared by lead authors W.A. Kurz, S. Federici, G. Grassi, R. Houghton, J.I. House, R.
de Ligt, J.P. Ometto, T. Paul, H. Petersson, S. Reddy, M.T. Rocha, Z. Somogyi, and R.M. Waterworth.

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

Contents
2

Generic methodologies applicable to multiple land use categories

2.7

2.1

Introduction

2.7

2.2

Inventory Framework

2.7

2.3

2.2.1

Overview of carbon stock change estimation

2.7

2.2.2

Overview of non-CO2 emission estimation

2.10

2.2.3

Conversion of C stock changes to CO2 emissions

2.13

Generic methods for CO2 Emissions and Removals
2.3.1

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

2.13
2.13

2.3.1.1

Land remaining in a land-use category

2.13

2.3.1.2

Land converted to a new land-use category

2.13

2.3.1.3

Additional generic guidance for Tier 2 methods

2.13

2.3.2

Change in carbon stocks in dead organic matter

2.20

2.3.2.1

Land remaining in a land-use category

2.20

2.3.2.2

Land conversion to a new land-use category

2.25

2.3.3
2.3.3.1

Change in carbon stocks in soils

2.29

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

2.4

Non-CO2 emissions

2.48

2.5

Additional generic guidance for Tier 3 methods

2.57

2.6

2.5.1

Measurement-based Tier 3 inventories

2.57

2.5.2

Model-based Tier 3 inventories

2.60

Inter-annual variability
2.6.1

2.67

Definitional issues

2.68

2.6.1.1

Direct and indirect human effects, and natural effects

2.68

2.6.1.2

Natural disturbances

2.68

2.6.2

Relationship between different methodological approaches and the representation of emissions
and removals from interannual variability
2.69

2.6.3

Optional approach for reporting of emissions and removals from Natural Disturbances

2.6.4

Reporting the contribution of natural disturbances and anthropogenic effects to the emissions
and removals for managed lands
2.78

2.71

Annex 2A.1

Default Mineral Soil Reference C Stocks

2.81

Annex 2A.2

Additional Information for the Estimation of Soil Carbon Stock Change from Biochar
Amendments to Mineral Soils Using Tier 2 and 3 Methods
2.82

References

2.83

Equations
Equation 2.1

Annual carbon stock changes for the AFOLU Sector estimated as the sum of changes
in all land-use categories
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Equation 2.2

Annual carbon stock changes for a land-use category as a sum of changes in eachstratum within the category
2.8

Equation 2.3

Annual carbon stock changes for a stratum of a land-use category as a sum of changes
in all pools
2.8

Equation 2.4

Annual carbon stock change in a given pool as a function of gains and losses (GainLoss Method)
2.10

Equation 2.5

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

Equation 2.6

Non-CO2 emissions to the atmosphere

2.11

Equation 2.17

Annual change in carbon stocks in dead organic matter

2.21

Equation 2.18

Annual change in carbon stocks in dead wood or litter (Gain-Loss Method)

2.22

Equation 2.19

Annual change in carbon stocks in dead wood or litter (Stock-Difference Method)
2.23

Equation 2.20

Annual carbon in biomass transferred to dead organic matter

2.23

Equation 2.21

Annual biomass carbon loss due to mortality

2.24

Equation 2.22

Annual carbon transfer to slash

2.24

Equation 2.23

Annual change in carbon stocks in dead wood and litter due to land conversion 2.25

Equation 2.24 (Updated)

Annual change in carbon stocks in soils

2.29

Equation 2.25

Annual change in organic carbon stocks in mineral soils

2.33

Equation 2.25a

Annual change in biochar carbon stock in mineral soils receiving biochar additions
2.41

Equation 2.27

Estimation of greenhouse gas emissions from fire

2.50

Equation 2A.1.1

Estimation of soil organic carbon stocks

2.81

FiguresFigure 2.1

Generalized carbon cycle of terrestrial AFOLU ecosystems showing the flows ofcarbon into and out of the system as well as between the five C pools within the system................................................................................................................................. 2.9

Figure 2.2a

Generic decision tree for identification of appropriate allometric models to estimate
biomass or carbon stocks in organic matter
2.15

Figure 2.3

Generic decision tree for identification of appropriate tier to estimate changes incarbon stocks in dead organic matter for a land-use category
2.21

Figure 2.4

Generic decision tree for identification of appropriate tier to estimate changes incarbon stocks in mineral soils by land-use category
2.30

Figure 2.5

Generic decision tree for identification of appropriate tier to estimate changes incarbon stocks in organic soils by land-use category
2.31

Figure 2.6

Generic decision tree for identification of appropriate tier to estimate greenhouse gas
emissions from fire in a land-use category
2.50

Figure 2.6aConceptual illustration of how various anthropogenic (direct and indirect) and natural
factors simultaneously affect land-related GHG emissions and removals
2.67

Figure 2.6b

Example of the disaggregation of fire emissions in Australia into natural ‘background’emissions and fires due to human activity
2.72

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

Figure 2.6c

Example of the separation of Canada’s FL-FL emissions and removals into those due
to predominantly anthropogenic causes and those due to predominantly natural
disturbances
2.74

Figure 2.6dTime series of managed forest land total GHG net emission (anthropogenic + natural
disturbance (ND) and area burned
2.75

Figure 2.6eTime series of managed forest land GHG net emissions and removals

2.77

TablesTable 2.2 (Updated)

Tier 1 default values for litter and dead wood carbon stocks

2.27

Table 2.3 (Updated)

Default reference condition soil organic carbon stocks (SOC REF) for mineral soils(Tonnes C ha-1 in 0-30 cm depth)
2.35

Table 2.4 (Updated)

Fuel (dead organic matter plus live biomass) biomass consumption values (tonnes dry
matter ha-1) for fires in a range of vegetation types
2.52

Table 2.5

Emission factors (g kg-1 dry matter burnt) for various types of burning

Table 2.6 (Updated)

Combustion factor values (proportion of prefire fuel biomass consumed) for fires in arange of vegetation types
2.55

Table 2.6a (New)

Examples of documentation to assemble in support of transparent reporting of Tier 3
measurement based inventories
2.60

Table 2.6b (New)

Examples of documentation to assemble in support of transparent reporting of Tier 3
model-based inventories
2.66

Table 2.6c (New)General Guidance on Whether or Not the estimation method is able to distinguishbetween the impact of the individual drivers below on the interannual variability ofreported annual emission and removal estimates
2.70

2.54

Boxes
Box 2.0a (New)Consistency Between AFOLU Projects or Activities and IPCC Inventory Guidelines
2.11

Box 2.0b (New)Allometric Definitions

2.14

Box 2.0c (New)New Technology: Terrestrial Laser Scanning

2.17

Box 2.0d (New)Remote Sensing Technologies

2.18

Box 2.0e (New)Using a biomass map for GHG estimation: an example from the Brazilian Amazon
2.19

Box 2.1 (Updated)

Alternative formulations of Equation 2.25 for Approach 1 activity data versus
Approach 2 or 3 activity data with transition matrices
2.36

Box 2.2a (New)Using equivalent mass methods to derive mineral soil organic carbon stock change
factors2.40

Box 2.2b (New)GHG Emission Sources with biochar production

Box 2.2c (New)

Representing the impact of soil erosion and deposition on soil carbon stock changes
2.43

Box 2.2d (New)

Examples of Tier 3 Mineral Soil C Stock Change methods

Box 2.2e (New)Consideration of equivalent mass methods within Tier 3 modelling approaches 2.47

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2.42

2.44

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Box 2.2f (New)An example of model calibration, evaluation and improvement through data
assimilation
2.62

Box 2.2g (New)

Example of Model Evaluation and Improvement

2.63

Box 2.2h (New)

Examples of Quantification of Model Uncertainty

2.64

Box 2.2i (New)

Australian approach to estimating interannual variability due to natural disturbances
2.73

Box 2.2j (New)

Canada’s approach to estimating interannual variability from natural disturbances
2.74

Box 2.2k (New)Approach to estimating interannual variability from natural disturbances based on the
EU legislation
2.76

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

2

GENERIC METHODOLOGIES APPLICABLETO MULTIPLE LAND USE CATEGORIES

2.1

INTRODUCTION

No refinement

2.2

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

2.2.1

Overview of carbon stock change estimation

The emissions and removals of CO2 for the AFOLU Sector, based on changes in ecosystem C stocks, are estimatedfor each land-use category (including both land remaining in a land-use category as well as land converted toanother land use). Carbon stock changes are summarized by Equation 2.1.
EQUATION 2.1
ANNUAL CARBON STOCK CHANGES FOR THE AFOLU SECTOR ESTIMATED AS THE SUM OF
CHANGES IN ALL LAND-USE CATEGORIESC AFOLU  CFL  CCL  CGL  CWL  CSL  COL
Where:

C AFOLU= Total annual carbon stock change in the AFOLU sector; tonnes C yr-1

Indices denote the following land-use categories:
AFOLU = Agriculture, Forestry and Other Land Use
FL= Forest Land

CL

= CroplandGL= Grassland

WL= Wetlands

SL= Settlements

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

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

Where:

CLU= carbon stock changes for a land-use (LU) category as defined in Equation 2.1.

i= denotes a specific stratum or subdivision within the land-use category (by any combinationof species, climatic zone, ecotype, management regime etc., see Chapter 3), i = 1 to n.
EQUATION 2.3
ANNUAL CARBON STOCK CHANGES FOR A STRATUM OF A LAND-USE CATEGORY AS A SUM OF
CHANGES IN ALL POOLSCLUi  CAB  CBB  CDW  CLI  CSO  CHWP
Where:

CLU i= carbon stock changes for a stratum of a land-use category

Subscripts denote the following carbon pools:AB= above-ground biomassBB= below-ground biomass

DW= deadwoodLI= litterSO= soils

HWP= harvested wood products

Estimating changes in carbon pools and fluxes depends on data and model availability, as well as resources andcapacity to collect and analyse additional information (See Chapter 1, Section 1.3.3 on key category analysis).
Table 1.1 in Chapter 1 outlines which pools are relevant for each land-use category for Tier 1 methods, including
cross references to reporting tables. Depending on country circumstances and which tiers are chosen, stockchanges may not be estimated for all pools shown in Equation 2.3. Because of limitations to deriving default data
sets to support estimation of some stock changes, Tier 1 methods include several simplifying assumptions:
 change in below-ground biomass C stocks are assumed to be zero under Tier 1 (under Tier 2, country-specificdata on ratios of below-ground to above-ground biomass can be used to estimate below-ground stock changes); under Tier 1, dead wood and litter pools are often lumped together as ‘dead organic matter’ (see discussion
below); and dead organic matter stocks are assumed to be zero for non-forest land-use categories under Tier 1. For Forest
Land converted to another land use, default values for estimating dead organic matter carbon stocks areprovided in Tier 1.
The carbon cycle includes changes in carbon stocks due to both continuous processes (i.e., growth, decay) anddiscrete events (i.e., disturbances like harvest, fire, insect outbreaks, land-use change and other events). Continuous
processes can affect carbon stocks in all areas in each year, while discrete events (i.e., disturbances) cause
emissions and redistribute ecosystem carbon in specific areas (i.e., where the disturbance occurs) and in the yearof the event.
Disturbances may also have long-lasting effects, such as decay of wind-blown or burnt trees. For practicality, Tier
1 methods assume that all post-disturbance emissions (less removal of harvested wood products) are estimated aspart of the disturbance event, i.e., in the year of the disturbance. For example, rather than estimating the decay ofdead organic matter left after a disturbance over a period of several years, all post-disturbance emissions are
estimated in the year of the event.

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

Figure 2.1(unchanged) Generalized carbon cycle of terrestrial AFOLU ecosystems showing theflows of carbon into and out of the system as well as between the five Cpools within the system.

Harvestedwood products

Above-groundbiomass

LitterBelow-groundbiomass

Dead wood

Increase of carbon
stocks due to growth

Transfer of carbon
between pools

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

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

Soil organic
matterUnder Tier 1, it is assumed that the average transfer rate into dead organic matter (dead wood and litter) is equal
to the average transfer rate out of dead organic matter, so that the net stock change is zero. This assumption meansthat dead organic matter (dead wood and litter) carbon stocks need not be quantified under Tier 1 for land areasthat remain in a land-use category2. The rationale for this approach is that dead organic matter stocks, particularly
dead wood, are highly variable and site-specific, depending on forest type and age, disturbance history andmanagement. In addition, data on coarse woody debris decomposition rates are scarce and thus it was deemed that
globally applicable default factors and uncertainty estimates cannot be developed. Countries experiencing
significant changes in forest types or disturbance or management regimes in their forests are encouraged to develop
domestic data to estimate the impact from these changes using Tier 2 or 3 methodologies and to report the resulting
carbon stock changes and non-CO2 emissions and removals.
All estimates of changes in carbon stocks, i.e., growth, internal transfers and emissions, are in units of carbon to
make all calculations consistent. Data on biomass stocks, increments, harvests, etc. can initially be in units of dry
matter that need to be converted to tonnes of carbon for all subsequent calculations. There are two fundamentallydifferent and equally valid approaches to estimating stock changes: 1) the process-based approach, which estimates
the net balance of additions to and removals from a carbon stock; and 2) the stock-based approach, which estimates
the difference in carbon stocks at two points in time.
Annual carbon stock changes in any pool can be estimated using the process-based approach in Equation 2.4 whichsets out the Gain-Loss Method that can be applied to all carbon gains or losses. Gains can be attributed to growth(increase of biomass) and to transfer of carbon from another pool (e.g., transfer of carbon from the live biomass
carbon pool to the dead organic matter pool due to harvest or natural disturbances). Gains are always marked witha positive (+) sign. Losses can be attributed to transfers of carbon from one pool to another (e.g., the carbon in the

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

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slash during a harvesting operation is a loss from the above-ground biomass pool), or emissions due to decay,
harvest, burning, etc. Losses are always marked with a negative (-) sign.
EQUATION 2.4
ANNUAL CARBON STOCK CHANGE IN A GIVEN POOL AS A FUNCTION OF GAINS AND LOSSES(GAIN-LOSS METHOD)C  CG  C L
Where:
C= annual carbon stock change in the pool, tonnes C yr -1CG= annual gain of carbon, tonnes C yr-1

C L

= annual loss of carbon, tonnes C yr-1

Note that CO2 removals are transfers from the atmosphere to a pool, whereas CO2 emissions are transfers from apool to the atmosphere. Not all transfers involve emissions or removals, since any transfer from one pool to anotheris a loss from the donor pool but is a gain of equal amount to the receiving pool. For example, a transfer from theabove-ground biomass pool to the dead wood pool is a loss from the above-ground biomass pool and a gain ofequal size for the dead wood pool, which does not necessarily result in immediate CO2 emission to the atmosphere(depending on the Tier used).
The method used in Equation 2.4 is called the Gain-Loss Method, because it includes all processes that bring aboutchanges in a pool. An alternative stock-based approach is termed the Stock-Difference Method, which can be used
where carbon stocks in relevant pools are measured at two points in time to assess carbon stock changes, as
represented in Equation 2.5.
EQUATION 2.5
CARBON STOCK CHANGE IN A GIVEN POOL AS AN ANNUAL AVERAGE DIFFERENCE BETWEENESTIMATES AT TWO POINTS IN TIME (STOCK-DIFFERENCE METHOD)C (Ct2  Ct1 )(t2  t1 )Where:
C= annual carbon stock change in the pool, tonnes C yr -1

Ct1= carbon stock in the pool at time t1 , tonnes C

Ct 2= carbon stock in the pool at time t2 , tonnes C

If the C stock changes are estimated on a per hectare basis, then the value is multiplied by the total area withineach stratum to obtain the total stock change estimate for the pool. In some cases, the activity data may be in theform of country totals (e.g., harvested wood) in which case the stock change estimates for that pool are estimateddirectly from the activity data after applying appropriate factors to convert to units of C mass. When using the
Stock-Difference Method for a specific land-use category, it is important to ensure that the area of land in thatcategory at times t1 and t2 is identical, to avoid confounding stock change estimates with area changes.
The process method lends itself to modelling approaches using coefficients derived from empirical research data.
These will smooth out inter-annual variability to a greater extent than the stock change method which relies on the
difference of stock estimates at two points in time. Both methods are valid so long as they are capable ofrepresenting actual disturbances as well as continuously varying trends and can be verified by comparison withactual measurements.

2.2.2

Overview of non-CO2 emission estimation

Non-CO2 emissions are derived from a variety of sources, including emissions from soils, livestock and manure,and from combustion of biomass, dead wood and litter. In contrast to the way CO 2 emissions are estimated frombiomass stock changes, the estimate of non-CO2 greenhouse gases usually involves an emission rate from a source

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

directly to the atmosphere. The rate (Equation 2.6) is generally determined by an emission factor for a specificgas (e.g., CH4, N2O) and source category and an area (e.g., for soil or area burnt), population (e.g., for livestock)or mass (e.g., for biomass or manure) that defines the emission source.
EQUATION 2.6
NON-CO2 EMISSIONS TO THE ATMOSPHERE

Emission  A  EF
Where:
Emission= non-CO2 emissions, tonnes of the non-CO2 gas

A= activity data relating to the emission source (can be area, animal numbers or massunit, depending on the source type)EF= emission factor for a specific gas and source category, tonnes per unit of A

Many of the emissions of non-CO2 greenhouse gases are either associated with a specific land use (e.g., CH 4
emissions from rice) or are typically estimated from national-level aggregate data (e.g., CH4 emissions fromlivestock and N2O emissions from managed soils). Where an emission source is associated with a single land use,
the methodology for that emission is described in the chapter for that specific land-use category (e.g., methanefrom rice in Chapter 5 on Cropland). Emissions that are generally based on aggregated data are dealt with inseparate chapters (e.g., Chapter 10 on livestock-related emissions, and Chapter 11 on N2O emissions from managed
soils and CO2 emissions from liming and urea applications). This chapter describes only methods to estimate nonCO2 (and CO2) emissions from biomass combustion, which can occur in several different land-use categories.
BOX 2.0A (NEW)CONSISTENCY BETWEEN AFOLU PROJECTS OR ACTIVITIES AND IPCC INVENTORY GUIDELINES

The information presented in this Box is for information purposes only
IPCC guidelines have been designed for national GHG inventories (NGHGI). They are, however,often applied, in conjunction with other guidance, to estimate GHG emissions and removals fordifferent situations than those in a NGHGI. These different situations include scales (i.e. to any subaggregation of land), time resolution (i.e., on a non-annual basis), length of time series (i.e., for alimited period) and/or for selected carbon pools. Using IPCC guidelines for estimating emissions
and removals from sub-aggregations - i.e. projects and activities – can help countries maintainconsistency with the NGHGI. However, projects and activities can introduce additional complexities
including, but not limited to, system boundaries, double-counting, leakage, and attribution.
Moreover, projects and activities may use different definitions, sources of data, data and methods
compared to those used for the NGHGI, including different Approaches for land representation andmethodological Tiers, impacting the consistency between the two. These need to be considered when
applying the IPCC Guidelines outside of a NGHGI (IPCC, 2015), particularly when there is a needfor consistency and comparability.
Thus, when using IPCC guidelines for projects and activities the following steps should beconsidered:3

i)Define the spatial boundaries of the territory impacted by the activity;

ii)

Identify the land-use categories and subcategories of the NGHGI impacted by the activity.

iii)

Identify pools and gases impacted by the activity;

iv)

Identify the time frame (temporal boundaries) of the activity and ensure full reporting of
any legacy emissions and removals associated with it 3;v)

Develop estimates by applying methods consistent with IPCC guidance, so ensuringconsistency among the results of activities and the trends of times series of relevant NGHGI
categories.

To deal with the limited time frame of reducing deforestation and forest degradation mitigation activities, reporting methods
provided by the GFOI apply the stock difference approach to estimate the net difference between two long-term average C

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BOX 2.0A (NEW) (CONTINUED)CONSISTENCY BETWEEN AFOLU PROJECTS OR ACTIVITIES AND IPCC INVENTORY GUIDELINES

For example, 1) Reducing Emissions from deforestation and forest degradation and the role ofconservation, sustainable management of forests and enhancement of forest carbon stocks indeveloping countries (REDD-plus) activities could be identified in the NGHGI as IPCC categories,
subcategories, or sums of categories or sub-categories (GFOI, 2016), and relevant IPCC methods
applied consistently; 2) The Australian Government has developed a framework as part of the
Emissions Reduction Fund 4 for ensuring consistency in emissions estimation between AFOLU
project-level mitigation activities and Australia's NGHGI. This framework includes integrity
standards 5 to ensure emissions estimation methods are consistent with IPCC guidelines, andconsequently estimated GHG reductions are consistent with trends of times series of relevant
NGHGI categories.
Emissions and removals estimates for activities are likely to apply Approach 2 or 3 and Tier 2 or 3
methods because of the need to prepare GHG estimates that are more disaggregated per activity, e.g.
organic farming vs traditional farming or coppice vs high-stand, and per population, e.g. by livestock
sub-populations, crop types and forest types. Moreover, stratification of NGHGI
categories/subcategories into subdivisions helps avoid double counting of emissions and removals
from a single category that is impacted by more than an activity.
Stratification also supports transparency among activity report and NGHGI estimates when theactivity does not correspond to an entire NGHGI category. In many cases, activities and projects
require tracking of land where they occur through time, e.g. no tillage. In such cases, Approach 3
for land representation is required since it is the only approach that provides the spatially explicit
information (either wall-to-wall or from sampling) across time needed to track activities and drivers,
and to support estimation of GHG emissions or removals with higher accuracy. Where activities are
known to lead to permanent changes or the activity includes management practices that determine
temporary changes in the land cover, Approach 2 methods may provide sufficient information toprepare accurate estimates.
Where activity and project data have been collected and analysed consistently with good practice,they can be used in the NGHGI either for deriving activity data and/or emission factors, or any otherancillary data used for preparing GHG estimates for the land subject to the activity, or for calibrating
the model used in the NGHGI for the same land and/or verifying the outputs of such model. Where
data have inconsistencies with those collected for the NGHGI, iterations and cross-checks between
NGHGI experts and experts involved in the monitoring of the activity should be done until
improvements applied to the activity and/or the NGHGI estimates enable consistency. When using
data collected from activities and projects for improving or evaluating information and estimatesreported in the NGHGI, it is important to:i)Define and report the reference conditions (e.g. climate, soil, management system) for
which the data from the activity or project are valid and how it could be used in the NGHGI
compilation;ii)

Determine if the activity or emissions factor data in the project are representative of thenational average and, if not, apply methods that ensure the NGHGI is not biased by them,e.g. limiting the use of the data to the land subject to the activity or project only andmodifying the data used in the NGHGI to prevent bias

iii)

Define and report the level of variability (heterogeneity) of the data;iv)

Ensure the data is available and consistently applied for the entire time series.

stocks at a single point in time (i.e. by assuming instantaneous oxidation). This is to allow a complete reporting of total net
C stock changes associated with the activities, including lagged emissions and removals.
4

http://www.environment.gov.au/climate-change/government/emissions-reduction-fund/publications

5

http://www.environment.gov.au/climate-change/emissions-reduction-fund/publications/erf-methods-development

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

2.2.3

Conversion of C stock changes to CO 2 emissions

For reporting purposes, changes in C stock categories (that involve transfers to the atmosphere) can be converted
to units of CO2 emissions by multiplying the C stock change by -44/12. In cases where a significant amount of the
carbon stock change is through emissions of CO and CH 4, then these non-CO2 carbon emissions should be
subtracted from the estimated CO2 emissions or removals using methods provided for the estimation of these gases.
In making these estimates, inventory compilers should assess each category to ensure that this carbon is not alreadycovered by the assumptions and approximations made in estimating CO2 emissions.
It should also be noted that not every stock change corresponds to an emission. The conversion to CO 2 from C, isbased on the ratio of molecular weights (44/12). The change of sign (-) is due to the convention that increases in
C stocks, i.e. positive (+) stock changes, represent a removal (or ‘negative’ emission) from the atmosphere, while
decreases in C stocks, i.e. negative (-) stock changes, represent a positive emission to the atmosphere

2.3

GENERIC METHODS FOR CO 2 EMISSIONSAND REMOVALSNo refinement.

2.3.1

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

No refinement.

2.3.1.1 L AND REMAINING IN A L AND - USE CATEGORYNo refinement.

2.3.1.2 L AND CONVERTED TO A N EW LAND - USE CATEGORYNo refinement.

2.3.1.3 A DDITIONAL GENERIC GU IDANCE FOR T IER 2 METHODS
A. USING ALLOMETRIC MOD ELS FOR BIOMASS ESTI MATIO N
This section provides new guidance to inventory compilers on the use of allometric models (see Box 2.0b for
definitions) for quantifying volume, biomass and carbon stocks in land uses containing vegetation. Allometricmodels can be used with country specific data to estimate carbon stocks at the Tier 2 level. Allometric models may
also form part of more sophisticated Tier 3 approaches including measurement-based inventories and model-based
inventories.
Allometric models quantify the relationships between certain size variables of organisms. Allometric models 6 canbe used to estimate volume, biomass or carbon stocks of individuals, vegetation or forest stands. Allometric models
have been developed for a wide range of species, habitats, regions and environmental conditions (e.g. documented
in the GlobAllomeTree database (http://www.globallometree.org/; Schepaschenko et al, 2017). Allometric models
used for forest tree species are commonly estimated from individual trees through destructive sampling from a
population using a sampling design that provides accurate and representative data. As destructive sampling is
usually costly and labour intensive or ecologically sensitive, it makes sense to utilize existing allometric models
when valid under the respective conditions as outlined below (in the section on the use of allometric models).

6

The term “allometric equation” is also used when referencing to the mathematical descriptions of allometric models andrelationships. When the parameters are estimated from sample data and/or uncertainty is involved, “model” is the correct
term. Although allometric models are used to predict the values of a variable, for practical reasons in the context of theseguidelines the term estimates is also used.

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BOX 2.0B (NEW)ALLOMETRIC DEFINITIONSAllometry: The term allometry refers to the proportional relationship between the relative
dimensional relationships or growth rates of two size variables and therefore allometric relations
allow that one variable can be used to predict the corresponding value of another variable. For
example, tree diameter at breast height (DBH) can be used to estimate tree volume or total tree
biomass. Allometry can also describe the change of one part of an organism in relation to the changeof its body size, either in the same organism (while growing over time), in populations (e.g., tree
stands), or between species (e.g. different tree species). These changes follow rules, so the change
in proportion between two variables of an organism can be described mathematically.
Allometric model: An allometric model is a formula that quantitatively describes an allometric
relationship. The basic form is an equation: y=f(x) where y and x are the dependent and independent
variables. Often the equation is in the form of y = a*x^b + c, where a, b and c are parameters (please
note: “c” is not identical to the statistical error term “ε”). If “x” is equal to zero (e.g., if height isbelow breast height when using DBH to estimate tree biomass), then “y” is equal to the parameter“c”, noting that biologically “y” is always a positive number. Parameter “a” is the value of y if x is 1
and describes the initial ratio between x and y . The parameter “b” is also called an “allometric parameter”
or “allometric constant” and gives the proportionality between the relative increases of “x” and “y”(Fabrika und Pretzsch 2013; Picard et al. 2012). The general form of an allometric model, without
intercept (i.e. when “c” = 0), is also often represented in its logarithmic transformation as a linear
relationship, log(y) = log(a) + b*log(x) or ln(y) = ln(a) + b*ln(x). Other mathematical functions havealso been adopted to describe allometric relationships.
This basic model can be augmented by additional terms that include e.g. tree height as a second
predictor variable (e.g. Ketterings et al. 2001). Models are usually provided with a residual error
term (e.g., y = f(x) + ε), set in the model fitting against the sample data; to consider the residual
error, calculated for each model, can be used to assess the uncertainty related to use of the selectedmodel in the estimation process.
T he u se o f a llo met ri c mo d el s
The choice of appropriate allometric models should be based on several criteria including the availability ofcountry-specific data, the meta-data about the allometric models, the coincidence of data with the models’ domainof validity according to the meta-data, and the appropriateness of the allometric model by comparing the estimatesto ones obtained with the Tier 1 method (Figure 2.2a). The accuracy of the models may be lower than e.g. available
default factors or Biomass Emission Factors (BEFs), so it is good practice to choose the method with the higher
accuracy. When applying an allometric model for predicting the biomass of a given species or at a given site, dataon required variables must be available as e.g. from national forest inventories (Tomppo et al. 2010, Vidal et al.
2016). For woody plant species, these variables commonly include DBH and height, and to lesser extent crownvariables such as crown length or crown width. For shrubs or smaller trees and understorey vegetation, diametersnearer to the ground or shoot length may be used, among other variables. Carbon fractions and basic wood density
may also be required for some models. Individual tree estimates can then be aggregated up to provide volume,
biomass or carbon stock estimates at higher spatial scales (e.g. by plot, region or nation-wide). Tree-level estimates
may refer to the whole tree, or individual components like above-ground and below-ground parts, stem, branches
and/or foliage. Allometric models may be used within a specified forest stratum, to estimate above-ground andbelow-ground biomass estimation from direct measurements e.g. forest inventory plots. Allometric models may
also be used for non-woody plant biomass estimates. Data collection programmes are often designed to collect thedata specifically for this purpose.
Allometries are influenced by an individual’s growing conditions and size classes, so in each case the allometricmodels developed will have a limited domain of validity. When selecting an appropriate allometric model, checkthe associated metadata supplied. Conditions such as: Ecoregion, geographic range, environmental factors (e.g., ecosystem, climatic or soil types), Representativeness of the model in consideration of individual size range and sampled population,
 Plant components estimated (e.g., above-ground, below-ground, stem, branches, foliage), Species functional traits (e.g., wood density and tree architecture), Land or crop management practices, current and historic,

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

should be assessed for their suitability (Henry et al. 2011; Rock 2007; Vieilledent et al. 2012) as well as sample
size and accuracy assessment. The use of existing allometric models beyond the range they were developed formay result in a lack of accuracy (e.g. Mugasha et al 2016; Nam et al, 2016), depending on the degree to which
external variables control the partitioning of biomass among components and the geometric relationships of thespecies. The applicability of a model can also be tested using a representative data set (e.g. Paul et al, 2016; PerezCruzado et al, 2015; Youkhana et al 2017). The accuracy of the allometric model should be assessed by evaluating
the related statistical indicators.
Figure 2.2a

Generic decision tree for the identification of appropriate allometricmodels to estimate volume, biomass or carbon stocks
Start

Are the data for theapplication of allometricmodels available?

No

Yes

Are metadata about
the domain of validity of
the allometric models
available?

No

Are representative
data for assessing the domainof validity of allometricmodels available?

Yes

Yes

Does the domainof validity of the allometricmodels match theavailable data?

Can the data beacquired to resolve themismatch with the domainof validity?

Yes

Are the allometricmodels providing moreaccurate estimates compared to
the Tier 1 method ?

No

No

No

Yes

Use Tier 1 method and/or continue
evaluating other allometric models

Yes

Apply the allometric models

Generalized and site or species-specific allometric models have been developed for use in different circumstances.
While species-specific models will give more accurate estimates for the respective tree species (all other aspects
being the same as the ones for which the model was developed) (Henry et al. 2011), generalized models may be2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

better suited in regions with a very large diversity of tree species, where models are lacking for a large proportionof species. The use of species-specific models however is encouraged for the species for which specific models
and appropriate input data are available. For natural forests, which may contain many different species, applicationof species-specific allometric models may be impractical; in this case, a model specific for the ecosystem type canbe used (Krisnawati et al, 2012). When species-specific or ecosystem-specific models are not available, regionally
relevant allometric models can be applied (Chave et al., 2004). Generic models developed based on a large numberof sample trees across landscapes tend to be more reliable than locally developed models if these are based on onlya small number of individuals (Chave et al 2005; Chave et al 2014; Paul et al, 2016).
Sta n d lev e l mo del s a n d t he ir eq ua t io n s
When individual or species specific allometric models for biomass or carbon stocks are not appropriate, stand level
allometric models, which may include canopy height, basal area and community age as predictor variables, may
be applicable to estimate biomass parameters. Stand-level allometric models using canopy height estimate carbon
stocks per unit area based on the assumption that canopy height is directly proportional to biomass (Mascaro et al,
2011; Saatchi et al, 2011). Information on canopy height can be predicted from ground-based inventory or byremote sensing such as airborne Light Detection and Ranging (LiDAR), polarimetric interferometry SAR or
airborne imagery. Auxiliary information such as digital elevation models are necessary to predict canopy heightfrom airborne and satellite-borne imagery because only canopy surface elevation can be predicted from them. The
accuracy of carbon stock estimation from canopy height depends on the number of field measurement plots used
to estimate the relationship between canopy height and carbon stocks. Basal area is an important parameter tounderstand stand characteristics and it is used in the model to estimate stand volume or stand biomass. Basal area
is estimated easily in the field using simple equipment. When basal area is used in the stand-level model to estimate
biomass or carbon stocks, mean tree height is also needed in the model (Lang et al, 2016; Mensah et al, 2016). The
stand-level allometric model estimated from community age estimates carbon stocks per unit area by assumingthat community biomass increases monotonically as the forest ages, and then drawing a saturation curve forcommunity age (Inoue et al, 2010). It is applicable where land use is rotated at fixed intervals, so that a mosaic ofcommunities of different ages exists.
Ti er 3 me tho d s
The hierarchical tier structure implies that use of higher tiers (Tier 2 or Tier 3) usually results in an increased
accuracy of the method and/or emissions factor and other parameters used in the estimation of the emissions andremovals. Tier 3 approaches for biomass carbon stock change estimation allow for a variety of methods, including
measurement-based forest inventories. Measurement-based Tier 3 inventories require detailed national forest
inventories containing data on growing stock, and, ideally, repeated measurements from which periodic increments
can be estimated. In some circumstances these data are used directly in empirical models while in other cases they
are supplemented with allometric models (for example, Chambers et al. (2001) and Baker et al. (2004) for the
Amazon; Seiler et al. (2014) for tropical forest of Bolivia, Jenkins et al. (2004) and Kurz and Apps (2006) forNorth America; and Zianis et al. (2005) for Europe, Paul et al. (2016) for Australia, Luo et al. (2014) for China,
Youkhana et al 2017 for tropical grasses), calibrated to national circumstances that allow for direct estimation of
biomass increment or growth. Model-based Tier 3 inventories build on model-specific input data and may containallometric models as empirical model components. Additional information related to the use of higher Tiermethods can be found in Section 2.5.
Un certa in ty
Sources of uncertainty when using allometric models include:
1.

Model-related uncertainty, i.e. the uncertainty related to the model used, stemming from the estimation of theparameters of this model and residual variability around model;

2.

Sampling variability and measurement errors in input data (see volume 1, chapter 3, section 3.1.6 foradditional information);3.

The uncertainty of transferring the model to trees not used for estimation of the parameters (lack ofrepresentativeness) (see volume 1, chapter 3, section 3.1.6 for additional information).

Magnitudes of the effects of the first and second sources should be reported with the model, the latter can be
reduced by careful selection of models.
Reca lc ula t io n s
Recalculations of C stocks may be necessary, if new and/or better data or methodology becomes available. When
BEF’s are replaced with parameters that are estimated using allometries, recalculations across the time series will
be required. The replacement of generalised models with species-specific models also may require recalculations.
It should be noted that allometry can change over time (Lopez-Serrano et al. 2005), for example, if the thinningregime in a plantation forest is changed. This may influence the ratio of crown biomass / DBH and, over time, thetrees in this plantation may show different allometric relationships at two distant points in time. An updated

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

allometric model would therefore be required in order to reflect the impact of the changes. In this case, to ensure
time series consistency, apply the guidance provided in Volume 1 Chapter 5 and in Volume 4, Chapter 4 in relation
to the Forest Land category
New tec h no lo g ie sRemotely sensed data from airborne or terrestrial platforms can be useful sources of information for deriving
variables relevant for constructing and validating allometric models. They can improve measurements of height,
volume and crown dimensions of individual trees that are difficult to collect with traditional ground-based
approaches, particularly in dense and complex canopies. They can underpin a new generation of allometric models
which have tree height and crown size as explanatory variables (Jucker et al, 2017). Of particular potential isterrestrial laser scanning, offering a means to collect data on tree volume in a non-destructive manner (see Box2.0c).
BOX 2.0C (NEW)NEW TECHNOLOGY: TERRESTRIAL LASER SCANNING

Terrestrial laser scanning is a ground-based active remote sensing technique which can be used toderive 3D vegetation structure, and compute key variables such as tree height, stem diameter, crowndimensions and tree volume for above-ground biomass predictions and to develop and validate
allometric models (Calders et al., 2015). These under-canopy terrestrial laser systems emit millionsof laser pulses that reflect off solid objects such as trunks, branches and leaves and form 3D point
clouds. Individual trees can be segmented from plot-scale point cloud data and individual tree point
clouds can then be used to reconstruct the woody elements of a tree.
Terrestrial laser scanning provides non-destructive and highly detailed measurements independentof the size and shape of a tree that are otherwise only available from destructive methods (Disney etal., 2018). Aboveground biomass calculated from the point cloud data is independent of allometry
and with quantifiable accuracy. Many trees can be sampled and measured in an efficient manner andcan provide most of the fundamental data needed to develop new or test the usefulness of existing
allometric models for NGHGIs. Terrestrial laser scanning has proven useful for large and complex
tropical trees (Gonzalez de Tanago et al., 2018). Terrestrial laser scanners cannot measure
belowground or look inside trees, i.e. they do not provide information on wood density or whether
a tree is hollow.

B. USING ABOVEGRO UND BI OMASS DENSITY MAP CO NSTRUCTED FROMREMOTELY SENSED DATA FOR BIOMASS ESTIMAT ION
Biomass density maps are wall-to-wall, polygon- or pixel-based predictions of above-ground biomass for woody
plants and trees.
Co n si de ra tio n w h en d e v elo pi ng bio ma ss de n si ty ma ps
Biomass density maps are constructed by combining remotely sensed data (see Box 2.0d) and field observations.
They have been developed at national scales (e.g., Avitabile et al., 2012) as well as for continental to global scales(e.g., Baccini et al., 2012; Saatchi et al., 2011, Avitabile et al., 2016). The characteristics and usefulness of biomass
density maps for NGHGIs depend on multiple factors:
1.

The definitions for forest and aboveground woody biomass used to produce the map and how this definition
relates to the one used in the NGHGI.

2.

The type of remotely sensed data sources in terms of spatial resolution, temporal coverage and the degree towhich the signal responds to aboveground biomass (sensitivity). The response depends on the type andbiomass ranges of the woody plants. Different remote sensing technologies have varying abilities forpredicting biomass for different types of woody plants (i.e. boreal versus tropics) and combining remotelysensed data from multiple sources can increase sensitivity and the resulting accuracy of biomass density
predictions.

3.

The method used to construct the map. Such methods can range from simple interpolation of field estimatesof biomass density using spatial covariates to more complex modelling of above-ground woody biomass using
field estimates and observed remotely sensed signals.

4.

The availability and reliability of biomass estimates obtained from field data needed to produce and validate
the biomass density map. Combining co-located remotely sensed data and field observations can bechallenging because of the size and shape of the primary elements (i.e. field plot size and shape versus
geometric resolution of remotely sensed data), the timing of their acquisition, accuracy of geolocations, and2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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differences in the variables and parameters that are measured and estimated in the field and predicted fromthe remotely sensed data.
5.

The degree to which map uncertainty is characterized and the manner in which it is used to assess bias andprecision for large area estimates in support of NGHGIs (see Volume IV, Chapter 3).
BOX 2.0D (NEW)REMOTE SENSING TECHNOLOGIES

Optical, Synthetic Aperture Radar (SAR) and Light Detection and Ranging (Lidar) sensors areavailable currently as remote sensing data sources for producing biomass density maps. Data fromoptical satellite sensors are classified into three types on the basis of their spatial resolution; coarse
resolution data with a pixel size greater than about 250 m (e.g., MODIS), medium resolution datawith a pixel size of 10-80 m (e.g., Landsat and Sentinel 1 and 2), and fine resolution data with apixel size smaller than 10 m (e.g., Rapideye or SPOT and ALOS-2).
SAR and LiDAR are active sensors available as air borne and space borne instruments whose derived
metrics are used to predict height, volume or biomass of woody plants and trees. SAR emits
microwave pulses obliquely and measures attributes of the pulses that are reflected back from the
Earth’s surface towards the sensor. In forest land, emitted pulses reflect from the ground, or canopy
or trunk of woody plants and trees. Using the strength of the signal of the reflected pulses, volume
or biomass of woody plants and trees can be predicted as demonstrated for satellite data from ALOSPALSAR and Sentinel 1 (Santoro and Cartus, 2018). LiDAR emits laser pulses and measures thetraveling time from the sensor to the target which can be converted to distance. When the LiDAR
emitter is aimed at woody plants and trees, these laser pulses can be reflected by the woody
components, the leaves within the canopy, or the ground surface. Using the difference of a laser
pulse reflected from canopy and ground surface, the height, volume or biomass of woody plants andtrees can be predicted (Næsset 1997a,b, Lim et al 2003). Starting in 2019, a series of targeted spacebased missions will improve the capabilities for forest biomass predictions from LiDAR (e.g. GEDI,ICESAT-2) and SAR (e.g. BIOMASS, NISAR), that might be found useful for national purposes(Herold et al. 2019).
Besides mapping biomass density, there are evolving approaches that monitor changes in biomass density throughtime directly from remotely sensed data (Baccini et al., 2017). Such approaches require consistent measurements
and estimates, and such consistency can be challenging when different satellite data sources and different ways ofprocessing and analysing the data are used. In principle, the direct prediction of wall-to-wall biomass change has
the advantage of including all detectable change events, including those occurring in forest remaining forest (i.e.,
forest degradation and regrowth) which are not considered when a single biomass map is combined with activity
data characterizing land use change. However, the sensitivity of the remotely sensed data to subtle biomass changes
needs to be carefully evaluated. The mapped biomass change might also not distinguish between anthropogenic or
natural causes and not fully characterize all components of the carbon emissions. For example, some carbon loss
may have accumulated as dead organic matter (e.g., dead wood or litter), and additional data are usually required
to estimate the fate of that initial biomass (e.g., burned, left on site, and removed from the site).
Because above-ground woody biomass is the variable predicted from remotely sensed data, additional informationsuch as country-specific data for root-to-shoot ratios are needed to estimate carbon stocks in other pools.
G ui da nce o n t he u se o f bio ma ss d en s ity ma p s f o r na t io na l GH G i nv e n to rie s
Biomass density maps can be used to enhance the stratification of ground carbon inventories, to improve theestimation of carbon emissions by increasing data density in under-sampled or inaccessible areas, and as anindependent data source for verification purposes (provided that the field data were not used to predict the biomass
density maps used for stratification).
Use of biomass maps for the estimation of carbon emissions at Tier 2 and Tier 3 levels can be achieved in several
ways:
1.

Combination with activity data where a biomass density map provides the base to estimate emission factors.
Such analyses require consistency among the activity data and biomass maps concerning definitions,geolocation, and spatial and temporal data characteristics. The use of regionally aggregated emission factor
analysis (i.e., using average estimates for different forest types, or change trajectories) helps to reduce inherent
pixel-level uncertainties in biomass map data for national-scale estimations. Countries have used such an
approach to increase data density in areas under-sampled by ground inventories (see Box 2.0e).

2.

Estimate biomass change directly from multi-temporal biomass density maps. Such an approach would
provide an assessment of carbon stock changes in above-ground biomass from land use change and, in

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

particular, it would also include changes within forests remaining forests such as degradation and regrowth,management and harvest, and natural disturbances. Such analysis requires consistent and well-calibrated
biomass density maps using ground and remotely sensed data to accurately estimate biomass changes; aquality requirement that has so far not been achieved for the NGHGIs at this stage. Improvements in both thefield estimates of biomass change and remote sensing technologies and analysis in the coming years can leadto such approaches becoming more efficient and accurate for NGHGI purposes.
3.

Biomass density maps can be integrated with remote sensing-assisted, time-series of land change and/or with
Tier 3 models to localize emissions estimates. This way the biomass map data can be linked to land and carbon
evolution over time that better reflect the complexity of forest-related carbon fluxes. Critical for this type of
application is the consistency among the various data sources and models concerning definitions (forest,
biomass pools), and, spatial and temporal data characteristics. Map unit uncertainties in biomass maps
propagate to larger area estimates and can lead to substantial uncertainties in national emissions estimation if
not properly considered.

The application of such approaches requires maps well-calibrated for national circumstances. Many available
large-area biomass maps, such as global biomass maps, might not be consistent with national definitions of forest
and/or biomass pools, and often exhibit large systematic errors in the estimation of carbon stock and changes fornational and local assessments (Avitabile et al., 2016). Since countries may have national products, including
biomass maps, large-area biomass maps can be useful for the purpose of independent comparison and verification.
Depending on how a map is produced and how it is used to enhance NGHGIs, additional metadata on the applied
models and procedures used to produce the map, such as for example the covariance matrix of model parametersof a model that was used to generate the map (see Volume 1, Chapter 6, section 6.1.4.2), may be required forcharacterization and reporting of uncertainty in a fully compliant way, particularly for application to countryspecific circumstances.
BOX 2.0E (NEW)USING A BIOMASS MAP FOR GHG ESTIMATION: AN EXAMPLE FROM THE BRAZILIAN AMAZON

Brazil is applying a methodology for estimating forest biomass combining data from airborne
LiDAR, satellite remote sensing and forest inventories. The aim for using the biomass map for the
NGHGI is to provide coverage over the whole Amazon where the availability and quality of grounddata varies. Deforestation and associated land use change in the Amazon are heterogeneous andpatchy. Related estimates of carbon emissions carry some level of uncertainty unless this spatial
variability in both types of change and biomass variability is captured.
The methodology to estimate the biomass was based on 1,000 LiDAR transects randomly distributed
across 3.5 million km2 of the Amazon forests. Aboveground biomass is estimated at three different
levels. At field plot level (first level), the data are used to validate the biomass estimated by LiDAR(second level) by adopting and using the models and data provided by Chave et al 2014 and Longoet al 2016. A total of 407 field plots were used for this validation. At the third level the biomass wasestimated by extrapolating the biomass to the Brazilian Amazon Biome by the use of MODIS
vegetation index, Shuttle Radar Topography Mission data, precipitation data from the TropicalRainfall Measuring Mission and Synthetic Aperture Radar data of the Phased Array type L-band
Synthetic Aperture Radar, soil and vegetation maps. A nonparametric regression method (Random
Forest) is used for correlating the above ground biomass within the LiDAR transects to a list ofvariables, and then used for the extrapolation of the biomass to the region. The coefficient ofdetermination and the root mean squared error between the third level extrapolated biomass data andthe LiDAR data were R2=0.7485 and RMSE=27.18 MgCha-1, respectively. In this process, theSRTM elevation data were the most important variable for the biomass extrapolation process,followed by the TRMM precipitation data and Enhanced Vegetation Index data. The estimatedbiomass map uncertainty is calculated by propagating the uncertainties through the different levelsof biomass estimation, i.e., field plots, LiDAR and satellite (Longo et al 2016). This process allows
us to obtain total uncertainty estimates for each pixel in the final biomass map.

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BOX 2.0E (NEW) (CONTINUED)
USING A BIOMASS MAP FOR GHG ESTIMATION: AN EXAMPLE FROM THE BRAZILIAN AMAZON

Biomass map of the Amazon biome in Brazil (Ometto et al. 2018)

2.3.2

Change in carbon stocks in dead organic matterNo refinement in Introduction.

2.3.2.1 L AND REMAINING IN A L AND - USE CATEGORYThe Tier 1 assumption for both dead wood and litter pools (see table 1.1 for definitions) for all land-use categories
is that their stocks are not changing over time if the land remains within the same land-use category. Thus, thecarbon in biomass killed during a disturbance or management event (less removal of harvested wood products) is
assumed to be released entirely to the atmosphere in the year of the event. This is equivalent to the assumptionthat the carbon in non-merchantable and non-commercial components that are transferred to dead organic matter
is equal to the amount of carbon released from dead organic matter to the atmosphere through decomposition andoxidation. Countries can use higher tier methods to estimate the carbon dynamics of dead organic matter. Thissection describes estimation methods if Tier 2 (or 3) methods are used.
Countries that use Tier 1 methods to estimate dead organic matter (DOM) pools in land remaining in the sameland-use category, report zero changes in carbon stocks or carbon emissions from those pools. Following this rule,
CO2 emissions resulting from the combustion of dead organic matter during fire are not reported, nor are theincreases in dead organic matter carbon stocks in the years following fire. However, emissions of non-CO2 gases
from burning of DOM pools are reported. Tier 2 methods for estimation of carbon stock changes in DOM pools
calculate the changes in dead wood and litter carbon pools (Equation 2.17). Two methods can be used: either track
inputs and outputs (the Gain-Loss Method, Equation 2.18) or estimate the difference in DOM pools at two points
in time (Stock-Difference Method, Equation 2.19). These estimates require either detailed inventories that include
repeated measurements of dead wood and litter pools, or models that simulate dead wood and litter dynamics. It
is good practice to ensure that such models are tested against field measurements and are documented. Figure 2.3
provides the decision tree for identification of the appropriate tier to estimate changes in carbon stocks in deadorganic matter.

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

Figure 2.3

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

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

Yes

Use the data for Tier 2 method (StockDifference Method) or Tier 3 Method
Box 3:Tier 2 and 3

No

Are data on manged
area and annual transfer into and out of
DOM stocks available?

Yes

Use the data for Tier 2 method (GainLoss Method) or Tier 3
Box 2:Tier 2 and 3

No

Are changes in C stocks in DOM a key
category¹?

No

Assume that the dead organic
matter stock is in equilibrium
Box 1:Tier 1

Yes

Collect data for Tier 2 method (GainLoss Method or Stock Difference
Method²)

Note:
1: See Volume 1 Chapter 4 "Methodological Choice and Identification of key Categories” (noting Section 4.1.2 on limited resources),for discussion of key categories and use of decision trees
2: The two methods are defined in Equations 2.18 and 2.19, respectively.

Equation 2.17 summarizes the calculation to estimate the annual changes in carbon stock in DOM pools:
EQUATION 2.17
ANNUAL CHANGE IN CARBON STOCKS IN DEAD ORGANIC MATTERCDOM  CDW  CLT
Where:∆CDOM = annual change in carbon stocks in dead organic matter (includes dead wood and litter), tonnes C
yr-1

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Volume 4: Agriculture, Forestry and Other Land Use∆CDW = change in carbon stocks in dead wood, tonnes C yr -1∆CLT= change in carbon stocks in litter, tonnes C yr-1

The changes in carbon stocks in the dead wood and litter pools for an area remaining in a land-use category
between inventories can be estimated using two methods, described in Equation 2.18 and Equation 2.19. The same
equation is used for dead wood and litter pools, but their values are calculated separately.
EQUATION 2.18
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD OR LITTER (GAIN-LOSS METHOD)

CDOM  A  {( DOM in  DOM out )  CF }Where:

CDOM= annual change in carbon stocks in the dead wood/litter pool, tonnes C yr -1

A= area of managed land, ha

DOM in= average annual transfer of biomass into the dead wood/litter pool due to annual processes
and disturbances, tonnes d.m. ha-1 yr-1 (see next Section for further details).

DOM out= average annual decay and disturbance carbon loss out of dead wood or litter pool, tonnes
d.m. ha-1 yr-1

CF

= carbon fraction of dry matter, tonne C (tonne d.m.) -1

The net balance of DOM pools specified in Equation 2.18, requires the estimation of both the inputs and outputs
from annual processes (litterfall and decomposition) and the inputs and losses associated with disturbances. In
practice, therefore, Tier 2 and Tier 3 approaches require estimates of the transfer and decay rates as well as activity
data on harvesting and disturbances and their impacts on DOM pool dynamics. Note that the biomass inputs into
DOM pools used in Equation 2.18 are a subset of the biomass losses estimated in Equation 2.7. The biomass losses
in Equation 2.7 contain additional biomass that is removed from the site through harvest or lost to the atmosphere,
in the case of fire.
The method chosen depends on available data and will likely be coordinated with the method chosen for biomass
carbon stocks. Transfers into and out of a dead wood or litter pool for Equation 2.18 may be difficult to estimate.
The stock difference method described in Equation 2.19 can be used by countries with forest inventory data that
include DOM pool information, other survey data sampled according to the principles set out in Annex 3A.3
(Sampling) in Chapter 3, and/or models that simulate dead wood and litter dynamics.
When the gain – loss method is chosen, inventory measurements may provide estimates for DOM stocks.
Alternatively, relevant information on transfers out of the litter and dead wood pools through decomposition canbe found in the literature. Care must be taken not to confound decomposition flow “rates” and decomposition“rate-constants” (e.g., k’s) when DOMout is estimated. DOMout using the second approach is the product of therate-constant describing the proportion lost per year and the stock of DOM (e.g., DOMout = k *DOM). One should
be aware that decomposition rate-constants describe total losses and not just those via respiration. The fate ofleached and fragmented carbon is not well understood; much of the material is likely respired but whether this isslower or faster than the source material is highly variable. Negative exponential decay models are commonly used
to determine the decomposition rate-constants that characterize the volume, mass, or density loss in dead woodand litter over time (Cook et al. 2016, Harmon et al. 2000, Russell et al. 2014). While models to predict volume,
biomass, or density loss are relatively simple, the decomposition rate-constants may vary substantially. The
decomposition of dead wood and litter mass is driven by many factors including: woodiness (i.e., wood and barkversus foliage); position (i.e., standing versus downed dead wood); species of the material decomposing; state ofdecomposition (i.e., fresh versus highly decomposed) and decomposers present (e.g., the presence of termites
and/or soil biota); climate under the canopy (for example condition by openness of the canopy) (Lavelle et al.,
1993; Hattenschwiler et al., 2005, Harmon et al. 2011, García‐Palacios et al., 2013, Russell et al., 2014, Filser et
al. 2016, Chertov et al. 2017, Hu et al., 2017, Kornarnov et al. 2017), among others. Having specific informationon these attributes will help to assign a specific decomposition constant to a particular DOM stock (Rock et al.
2008).

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

EQUATION 2.19
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD OR LITTER (STOCK-DIFFERENCEMETHOD) ( DOM t2  DOM t1 ) 
C DOM   A   CFT
Where:

CDOM= annual change in carbon stocks in dead wood or litter, tonnes C yr -1

A= area of managed land, ha

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

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

T = (t2 – t1)= time period between time of the second stock estimate and the first stock estimate, yr

CF= carbon fraction of dry matter (default for litter = 0.37 (Smith & Heath 2002), default fordead wood (temperate species) = 0.5 tonne C (tonne d.m.)-1

Note that whenever the stock change method is used (e.g., in Equation 2.19), the area used in the carbon stock
calculations at times t1 and t2 must be identical. If the area is not identical then changes in area will confound theestimates of carbon stocks and stock changes. It is good practice to use the area at the end of the inventory period(t2) to define the area of land remaining in the land-use category. The stock changes on all areas that change landuse category between t1 and t2 are estimated in the new land-use category, as described in the sections on land
converted to a new land category.

INPUT OF BIOMASS TO DEAD ORGANIC MATTER
Whenever a tree is felled, non-merchantable and non-commercial components (such as tops, branches, leaves,roots, and non-commercial trees) are left on the ground and transferred to dead organic matter pools. In addition,
annual mortality can add substantial amounts of dead wood to that pool. For Tier 1 methods, the assumption isthat the carbon contained in all biomass components that are transferred to dead organic matter pools will be
released in the year of the transfer, whether from annual processes (litterfall and tree mortality), land management
activities, fuelwood gathering, or disturbances. For estimation procedures based on higher Tiers, it is necessary toestimate the amount of biomass carbon that is transferred to dead organic matter. The quantity of biomasstransferred to DOM is estimated using Equation 2.20.
EQUATION 2.20
ANNUAL CARBON IN BIOMASS TRANSFERRED TO DEAD ORGANIC MATTER

DOM in  {Lmortality  Lslash  ( Ldisturbance  f BLol )}Where:

DOM in= total carbon in biomass transferred to dead organic matter, tonnes C yr-1

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

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

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

Note: If root biomass increments are counted in Equation 2.10, then root biomass losses must also be counted in
Equations 2.20, and 2.22.

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Volume 4: Agriculture, Forestry and Other Land Use

Examples of the terms on the right-hand side of Equation 2.20 are obtained as follows:

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

Lmortality   ( A  GW  CF  m)
Where:

Lmortality = annual biomass carbon transfer to DOM due to mortality, tonnes C yr-1

A

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

GW= above-ground biomass growth, tonnes d.m. ha-1 yr-1 (see Equation 2.10)

CF= carbon fraction of dry matter, tonne C (tonne d.m.) -1m= mortality rate expressed as a fraction of above-ground biomass growth

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

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

Lslash  H  BCEFR  (1  R)  H  D  CF
Where:

Lslash= annual biomass carbon transfer to DOM as slash, tonnes C yr-1, including dead roots, tonnes
C yr-1H= annual wood harvest (wood or fuelwood removal), m3 yr-1

BCEFR

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

BCEFR  BEFR  D

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use CategoriesoD is basic wood density, tonnes d.m. m-3

o Biomass Expansion Factors ( BEFR ) expand merchantable wood removals tototal aboveground biomass volume to account for non-merchantable componentsof the tree, stand and forest. BEFR is dimensionless.

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

CF= carbon fraction of dry matter, tonne C (tonne d.m.) -1

Fuelwood gathering that involves the removal of live tree parts does not generate any additional input of biomass
to dead organic matter pools and is not further addressed here.
Inventories using higher Tier methods can also estimate the amount of logging slash remaining after harvest bydefining the proportion of above-ground biomass that is left after harvest (enter these proportions in cells B and
C of Table 2.2 for harvest disturbance) and by using the approach defined in Equation 2.14. In this approach,activity data for the area harvested would also be required.

2.3.2.2 L AND CONVERSION TO A NEW LAND - USE CATEGORY
The reporting convention is that all carbon stock changes and non-CO2 greenhouse gas emissions associated witha land-use change be reported in the new land-use category. For example, in the case of conversion of Forest Land
to Cropland, both the carbon stock changes associated with the clearing of the forest as well as any subsequentcarbon stock changes that result from the conversion are reported under the Cropland category.
The Tier 1 assumption is that DOM pools in non-forest land categories after the conversion are zero, i.e., they
contain no carbon. The Tier 1 assumption for land converted from forest to another land-use category is that all
DOM carbon losses occur in the year of land-use conversion. Conversely, conversion to Forest Land results inbuild-up of litter and dead wood carbon pools starting from zero carbon in those pools. DOM carbon gains on land
converted to forest occur linearly, starting from zero, over a transition period (default assumption is 20 years). Thisdefault period may be appropriate for litter carbon stocks, but in temperate and boreal regions it is probably too
short for dead wood carbon stocks. Countries that use higher Tier methods can accommodate longer transition
periods by subdividing the remaining category to accommodate strata that are in the later stages of transition.
The estimation of carbon stock changes during transition periods following land-use conversion requires thatannual cohorts of the area subject to land-use change be tracked for the duration of the transition period. For
example, DOM stocks are assumed to increase for 20 years after conversion to Forest Land. After 20 years, thearea converted enters the category Forest Land Remaining Forest Land, and no further DOM changes are assumed,
if a Tier 1 approach is applied. Under Tier 2 and 3, the period of conversion can be varied depending on vegetationand other factors that determine the time required for litter and dead wood pools to reach steady state.
Higher Tier estimation methods can use non-zero estimates of litter and dead wood pools in the appropriate landuse categories or subcategories. For example, settlements and agro-forestry systems can contain some litter anddead wood pools, but because management, site conditions, and many other factors influence the pool sizes, noglobal default values can be provided here. Higher Tier methods may also estimate the details of dead organic
matter inputs and outputs associated with the land-use change.
The conceptual approach to estimating changes in carbon stocks in dead wood and litter pools is to estimate thedifference in C stocks in the old and new land-use categories and to apply this change in the year of the conversion(carbon losses), or to distribute it uniformly over the length of the transition period (carbon gains) Equation 2.23:
EQUATION 2.23
ANNUAL CHANGE IN CARBON STOCKS IN DEAD WOOD AND LITTER DUE TO LAND CONVERSIONC DOM (Cn  Co )  Aon
Ton

Where:

CDOM = annual change in carbon stocks in dead wood or litter, tonnes C yr-1
Co

= dead wood/litter stock, under the old land-use category, tonnes C ha-1

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Volume 4: Agriculture, Forestry and Other Land Use

Cn= dead wood/litter stock, under the new land-use category, tonnes C ha-1

Aon

= area undergoing conversion from old to new land-use category, ha

Ton= time period of the transition from old to new land-use category, yr. The Tier 1 default is 20 years
for carbon stock increases and 1 year for carbon losses.

Inventories using a Tier 1 method assume that all carbon contained in biomass killed during a land-use conversion
event (less harvested products that are removed) is emitted directly to the atmosphere and none is added to deadwood and litter pools. Tier 1 methods also assume that dead wood and litter pool carbon losses occur entirely inthe year of the transition.
Countries using higher Tier methods can modify Co in Equation 2.23 by first accounting for the immediate effectsof the land-use conversion in the year of the event. In this case, they would add to Co the carbon from biomass
killed and transferred to the dead wood and litter pools and remove from C o any carbon released from dead woodand litter pools, e.g., during slash burning. In that case Co in Equation 2.23 would represent the dead wood or litter
carbon stocks immediately after the land-use conversion. Co will transit to Cn over the transition period, using
linear or more complex dynamics. A disturbance matrix (Table 2.1) can be defined to account for the pooltransitions and releases during the land-use conversion, including the additions and removals to Co.
Countries using a Tier 1 approach can apply the Tier 1 default carbon stock estimates for litter, and if available
dead wood pools, provided in Table 2.2, but should recognize that these are broad-scale estimates withconsiderable uncertainty when applied at the country level. Table 2.2 is incomplete because of the paucity of
published data. A review of the literature has identified several problems. The IPCC definitions of dead organic
matter carbon stocks include litter and dead wood. The litter pool contains all litter plus fine woody debris up to adiameter limit of 10 cm (see Chapter 1, Table 1.1). Published litter data generally do not include the fine woody
debris component, so the litter values in Table 2.2 are incomplete.
There are numerous published studies of coarse woody debris (Harmon and Hua, 1991; Karjalainen andKuuluvainen, 2002) and a few review papers (e.g., Harmon et al., 1986), and but to date only two studies are foundto provide regional dead wood carbon pool estimates that are based on sample plot data. Krankina et al. (2002)
included several regions in Russia and reported coarse woody debris (> 10 cm diameter) estimates of 2 to 7 Mg C
ha-1. Cooms et al. (2002) reported regional carbon pools based on a statistical sample design for a small region in
New Zealand. Regional compilations for Canada (Shaw et al., 2005) provide estimates of litter carbon pools basedon a compilation of statistically non-representative sample plots, but do not include estimates of dead wood pools.
Review papers such as Harmon et al. (1986) compile a number of estimates from the literature. For example, their
Table 5 lists a range of coarse woody debris values for temperate deciduous forests of 11 – 38 Mg dry matter ha-1
and for temperate coniferous forests of 10 – 511 Mg dry matter ha-1. It is, however, statistically invalid to calculatea mean from these compilations as they are not representative samples of the dead wood pools in a region.
While it is the intent of these IPCC Guidelines to provide default values for all variables used in Tier 1
methodologies, it is currently not feasible to provide estimates of regional defaults values for litter (including finewoody debris < 10 cm diameter) and dead wood (> 10 cm diameter) carbon stocks. Litter pool estimates (excluding
fine woody debris) are provided in Table 2.2. Tier 1 methodology only requires the estimates in Table 2.2 for lands
converted from Forest Land to any other land-use category (carbon losses) and for lands converted to Forest Land
(carbon gains). Tier 1 methods assume that litter and dead wood pools are zero in all non-forest categories andtherefore transitions between non-forest categories involve no carbon stock changes in these two pools.

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

TABLE 2.2 (UPDATED)TIRE 1 DEFAULT VALUES FOR LITTER AND DEAD WOOD CARBON STOCKS
Forest type
Climate 1

Broadleaf deciduous

Needleleaf evergreen

All vegetation types

References2

Litter carbon stocks (tonnes C ha-1)
Mean

Min/Max

Mean

Min/Max

Mean

Min/Max

Boreal coniferous
forest19.1

4.0-38.7

40.3

4.0-117.4

31.4

4.0-117.4

93, 98, 99,
100, 101

Boreal tundra
woodland

29.3

23.7-33.7

67.4

23.7-85.1

49.5

23.7-85.1

100, 101

Polar

n.a

n.a

n.a

n.a

n.a

n.a

n.a.

Subtropical desert

n.a

n.a

n.a

n.a

n.a

n.a

n.a.

Subtropical humid
forest5.6

4.4-8.1

6.8

4.7-11.6

8.7

1.2-24.0

6, 7, 44, 93,
98, 99, 103

Subtropicalmountain system

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

Subtropical steppe

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

Temperatecontinental forest

23.9

4.6-64.4

66.3

6.0-279.1

47.8

4.6-279.1

93, 98, 99,
100, 101

Temperate desert

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

Temperate
mountain system

3.4

n.a.

3.9

n.a.

3.7

3.4-3.998

Temperate oceanic
forest

n.a.

n.a.

n.a.

n.a.

2.9

n.a.

15

Temperate steppe

36.9

7.6-98.8

26.4

7.1-43.0

28.7

3.8-98.8

97, 98, 100,
101

Tropical dry forest

n.a.

n.a.

n.a.

n.a.

2.4

2.1-2.7

11

Tropical moist
forest4.3

2.0-9.0

14.8

n.a.

5.9

1.9-14.8

21, 93, 98

Tropical mountainsystem

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

n.a.

Tropical rainforest

2.5

n.a.

4.7

n.a.

4.8

2.1-16.4

11, 26, 35, 89,
93, 99Dead wood carbon stocks (tonnes C ha-1)
ClimateMin/Max

Min/Max

Min/Max

Min/Max

Min/Max

Min/Max

Min/Max

Boreal coniferous
forest16.4

2.3-50.7

22.2

4.1-76.5

19.7

2.3-76.5

46, 54, 55, 56,
59, 62, 63, 70,
81, 87, 88, 93

Boreal tundra
woodland

5.7

n.a.

1.3

0.5-2.4

3.1

0.5-6.1

5, 70

Polar

n.a

n.a

26.2

n.a.

26.2

n.a.

70

Subtropical desert

n.a

n.a

64.4

14.4134.5

64.4

14.4134.5

40

Subtropical humid
forest4.1

2.5-7.5

10.9

3.5-32.8

13.2

0.2-43.8

6,7,44, 46, 68,93

Subtropicalmountain system

n.a.

n.a.

11.8

7.2-16.3

11.8

7.2-16.3

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 2.2 (UPDATED) (CONTINUED)TIRE 1 DEFAULT VALUES FOR LITTER AND DEAD WOOD CARBON STOCKS
Forest type
Climate 1

Subtropical steppe

Needleleaf evergreen

All vegetation types

References2

Dead wood carbon stocks (tonnes C ha-1)
Mean

Min/Max

Mean

Min/Max

Mean

Min/Max

n.a.

n.a.

6.8

6.0-7.7

6.8

6.0-7.7

27

Temperatecontinental forest

23.6

1.6-150.0

22.1

2.1-59.5

23.0

1.6-150.0

1, 2, 23, 28, 36,
37, 46, 54, 55,
64, 70, 80, 83,
87, 92, 93,
95,110Temperate desert

n.a.

n.a.

10.5

n.a.

10.5

n.a.

22

Temperate
mountain system

21.2

2.8-80.6

48.1

1.7-181.8

37.6

1.7-181.8

3, 9, 10, 12, 13,
17, 25, 29, 30,
31, 33, 34, 39,
41, 50, 57, 58,
60, 67, 68, 69,
71, 75, 76, 78,
82, 84, 90, 91,
105, 109

Temperate oceanic
forest40.5

2.8-95.0

24.3

n.a.

36.8

2.8-95.0

15, 16, 24, 32,
52, 61, 85, 86

Temperate steppe

26.2

9.7-50.0

8.0

n.a

21.7

8.0 -50.0

4, 70, 83, 98

Tropical dry forest

16.0

14.7-17.3

n.a.

n.a.

9.0

1.3-17.3

11, 20Tropical moist
forest8.4

1.2-21.2

3.4

n.a.

8.0

1.2-21.2

19, 20, 21, 38,4893, 96, 107

Tropical mountainsystem

3.3

n.a.

n.a.

n.a.

3.3

n.a.

20

0.6-218.9

11, 14, 18, 26,
35, 42, 43, 45,
46, 47, 49, 51,
53, 65, 66, 72,
73, 74, 79, 89,
93, 94, 104,
105, 107, 108

Tropical rainforest

1

Broadleaf deciduous

17.7

0.9-218.9

1.9

n.a.

14.8

FAO. 2012. Forest Resources Assessment Working Paper 179.

2

References: 1Canada NFI, 2006; 2Alban and Perala, 1992; 3Arthur and Fahey, 1992; 4Barney and Fahey, 1992; 5Barney and Van Cleve,
1973; 6Beets et al. 2011; 7Beets et al. 2014; 8Beets, 1980; 9Bingham and Sawyer Jr., 1988; 10Blackwell et al., 1992; 11FRA2015, Brazil;
12Brown and See, 1981; 13Busing, 1998; 14Chambers et al., 2000; 15FRA2015, Chile; 16Christensen, 1977; 17Clark et al., 1998;
18Cochrane et al., 1999; 19Collins, 1981; 20Delaney et al., 1998; 21FRA2015, Ecuador; 22Fahey, 1983; 23Falinski, 1978; 24Frangi et al.,
1997; 25Franklin et al., 1984; 26FRA2015, French Guyana; 27Fule and Covington, 1994; 28Goodburn and Lorimer, 1998; 29Gore andPatterson, III, 1986; 30Gosz, 1980; 31Grahom and Cromack, 1982; 32Green and Peterken, 1998; 33Grier, 1978; 34Grier et al., 1981;
35FRA2015, Guyana; 36Hale et al., 1999; 37Harmon and Chen, 1991; 38Harmon et al., 1995; 39Harmon et al., 1986; 40Harmon et al.,
1987; 41Harmon, 1980; 42Higucki and Biot, 1995; 43Hofer et al., 1996; 44Holdaway et al., 2017; 45Hughes et al., 2000; 46Japanese NFI,
2018; 47John, 1973; 48Jordan, 1989; 49Kauffman and Uhl, 1990; 50Keenan et al., 1993; 51Kira, 1978; 52Kirby et al., 1998; 53Klinge,
1973; 54Krankina et al., 1999; 55Krankina, Unpublished; 56Lamas and Fries, 1994; 57Lambert et al., 1980; 58Lang, 1985; 59Lee et al.,
1997; 60Lesica et al., 1990; 61Levett et al., 1985; 62Linder and Ostlund, 1992; 63Linder et al. 1997; 64MacMillan, 1981; 65Martinelli etal., 1988; 66Martius, 1997; 67McCarthy and Bailey, 1994; 68McMinn and Hardt, 1996; 69Muller and Liu, 1991; 70Canada NFI, 2018b;
71Nicholas and White, 1984; 72Proctor et al. 1983; 73Revilla, 1987; 74Robertson and Daniel, 1989; 75Robertson and Bowser, 1999;
76Roskoski, 1980; 77Sackett, 1980; 78Sackett, 1979; 79Saldarriaga et al., 1988; 80Shifley et al., 1997; 81Sippola, 1998; 82Sollins, 1982;
83Spetich et al., 1999; 84Spies et al., 1988; 85Stewart and Burrows, 1994; 86Stokland, ; 87Storozhenko, 1997; 88Sturtevant et al., 1997;
89FRA2015, Suriname; 90Taylor and Fonda, 1990; 91Tritton 1980; 92Tyrrell and Crow, 1994; 93Ugawa et al., 2012; 94Uhl et al., 1988;
95van Hees and Clerkx, 1999; 96Zhou et al.,; 97FRA2015, Argentina; 98Domke et al., 2016; 99Japan NFI, 2018; 100Canada NFI, 2018;
101Canada NFI, 2018a; 102Shaw et al. 2005; 103Beets et al., 2012; 104Klinge et al., 1975; 105Kaufman et al., 1988; 106Nicholas andWhite, 1985; 107Revilla, 1986; 108Revilla, 1988; 109Sollins et al., 1980; 110Lang and Forman, 1978
n.a. denotes ‘not available’

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

2.3.3

Change in carbon stocks in soils

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

MINERAL SOILS
Mineral soils contain an organic carbon pool that is influenced by land-use and management activities. Land use
can have a large effect on the size of this pool through activities such as conversion of native Grassland and Forest
Land to Cropland, where 20-40 percent of the original soil C stocks can be lost (Mann 1986; Davidson &Ackerman 1993; Ogle et al. 2005). Within a land-use type, a variety of management practices can also have asignificant impact on soil organic C storage, particularly in Cropland and Grassland (e.g., Paustian et al. 1997;
Conant et al. 2001; Ogle et al. 2004 and 2005). In principle, soil organic C stocks can change with management
or disturbance if the net balance between C inputs and C losses from soil is altered. Management activities
influence organic C inputs through changes in plant production (such as fertilisation or irrigation to enhance crop
growth), direct additions of C in organic amendments, and the amount of carbon left after biomass removal
activities, such as crop harvest, timber harvest, fire, or grazing. Decomposition largely controls C outputs and canbe influenced by changes in moisture and temperature regimes as well as the level of soil disturbance resulting
from the management activity. Other factors also influence decomposition, such as climate and edaphic
characteristics. Specific effects of different land-use conversions and management regimes are discussed in theland-use specific chapters (Chapters 4 to 9).
Land-use change and management activity can also influence soil organic C storage by changing erosion rates andsubsequent loss of C from a site; some eroded C decomposes in transport and CO 2 is returned to the atmosphere,
while the remainder is deposited in another location. The net effect of changing soil erosion through land
management is highly uncertain, however, because an unknown portion of eroded C is stored in buried sedimentsof wetlands, lakes, river deltas and coastal zones (Smith et al. 2001).

ORGANIC SOILSNo refinement. See Chapter 2, Sections 2.2 and 2.3 of the 2013 Wetlands Supplement.

2.3.3.1 S OIL O RGANIC C ESTIMATION METHODS ( LAND REMAINING
IN A LAND - USE CATEGORY AND LAN D CO NVERSIO N TO A NE W
LAND USE )Soil C inventories include estimates of soil organic C stock changes for mineral soils and CO 2 emissions fromorganic soils due to enhanced microbial decomposition caused by drainage and associated management activity.
In addition, inventories can address C stock changes for soil inorganic C pools (e.g., calcareous grassland that
become acidified over time) if sufficient information is available to use a Tier 3 approach. The equation for
estimating the total change in soil C stocks is given in Equation 2.24:
EQUATION 2.24 (UPDATED)ANNUAL CHANGE IN CARBON STOCKS IN SOILS

CSoils  CMineral  LOrganic  CInorganic
Where:

CSoils= annual change in carbon stocks in soils, tonnes C yr-1

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

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

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CInorganic = annual change in inorganic carbon stocks from soils, tonnes C yr -1 (assumed to be 0 unlessusing a Tier 3 approach)
For Tier 1 methods, soil organic C stocks for mineral soils are computed to a default depth of 30 cm because
default reference soil organic C stocks (SOCREF – see Equation 2.25 and Table 2.3) and stock change factors (e.g.
FLU, FMG and FI see Equation 2.25) are based on a 30 cm depth. In addition, the reference condition is defined asthat present in native lands (i.e. non-degraded, unimproved lands under native vegetation) for the default reference
soil organic C stocks (SOCREF). For Tier 2, a different reference condition and depth can be used as described in
the section on Tier 2 methods. Residue/litter C stocks are not included in Tier 1 because they are addressed byestimating dead organic matter stocks (see section 2.3.2). Inventories can also estimate the change in mineral soilorganic C stock due to biochar amendments to soils (Tier 2 and Tier 3 only). Stock changes in organic soils are
based on emission factors that represent the annual loss of organic C throughout the profile due to drainage andassociated management activity.
No Tier 1 or 2 methods are provided for estimating the change in soil inorganic C stocks (∆𝐶𝐼𝑛𝑜𝑟𝑔𝑎𝑛𝑖𝑐 ) due tolimited scientific data for derivation of stock change factors; thus, the net flux for inorganic C stocks is assumed
to be zero. Tier 3 methods could be developed to estimate changes in the stock of inorganic carbon in mineral ororganic soils.
It is possible that compilers will use different tiers to prepare estimates for mineral soils, organic soils, biochar
amendments and soil inorganic C, depending on the availability of resources. Thus, stock changes are discussed
separately for organic carbon in mineral and organic soils and for inorganic C pools (Tier 3 only). Generalised
decision trees in Figures 2.4 and 2.5 can be used to assist inventory compilers in determining the appropriate tierfor estimating stock changes for mineral and organic soil C, respectively.

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

Figure 2.4

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

Do you have thedata and resources
to develop a Tier 3
Method?

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

Box 3:Tier 3No

Do you havecountry-specific data on soil C stockchanges due to land use and management
for mineral soils or data to generate
country-specific reference
C-stocks?

Yes

Use the data for Tier 2 stock change
methodBox 2:Tier 2

No

Are there changes
in C stocks in mineral soils a
Key category?

Yes

Collect data for Tier 3 or Tier 2
methodNo

Are aggregate land-use andmanagement data available (e.g.,
FAO statistics)?

No

Gather data on land
use and management

Yes

Use aggregate data and default
emission/removals factors for Tier 1
stock method
Box 1:Tier 1

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Figure 2.5

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

Do you havedata on activities likely to alter
the hydrological regime, surface temperature,and vegetation
Composition oforganic soils?

Use the data for Tier 3 method to
conduct a full carbon balance oforganic soils (model oreasurement-based).

Yes

Box 3:Tier 3No

Do you havedata that can be used to derivecountry-specific emission factors
for climate type and or classification
scheme relevant toorganic soils?

Yes

Use the data for Tier 2 method

Box 2:Tier 2

No

Are changes in C stocks inorganic soils a key category1?

No

Are Aggregate data
available on organic soils
drained for Management
purposes?

Yes

Yes

Collect data for Tier 3 or Tier 2 method

Use aggregate data and default emission
factors for Tier 1 method

No

Gather data on drainedorganic soils.

Box 1:Tier 1

Tier 1 – Def ault Method
Mineral soils
For mineral soils, the stock change factor method is based on changes in soil C stocks (∆𝐶𝑀𝑖𝑛𝑒𝑟𝑎𝑙 ) over a finite
period of time of 20 years (Equation 2.25). The change in organic C stock in mineral soil (𝑆𝑂𝐶𝑀𝑖𝑛𝑒𝑟𝑎𝑙 ) is computedby calculating the organic C stock remaining after a management change relative to the organic C stock in areference condition and summing this change over all climate zones, soil types and management practices includedin the inventory. The soil organic C stock present under the reference condition for the Tier 1 method is definedas that in non-degraded, unimproved lands under native vegetation (Table 2.3). The following assumptions are
made:
(i)

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

Soil organic C stock change during the transition to a new equilibrium SOC occurs in a linear fashion
over a period of 20 years.

Assumption (i), that under a given set of climate and management conditions soils tend towards an equilibriumorganic C stock, is widely accepted. Although, soil organic C stock changes in response to management changes
may often be best described by a curvilinear function, assumption (ii) greatly simplifies the Tier 1 methodology
and provides a good approximation over a multi-year inventory period, where changes in management and landuse conversions are occurring throughout the inventory period.

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

Using the default method, changes in mineral soil organic C stocks are computed over an inventory time period.
Inventory time periods will likely be established based on the years in which activity data are collected, such as
1990, 1995, 2000, 2005 and 2010, which would correspond to inventory time periods of 1990-1995, 1995-2000,
2000-2005, 2005-2010. For each inventory time period, the soil organic C stocks are estimated for the first (SOC 0-T)
and last year (SOC0) based on multiplying the reference C stocks by stock change factors. Annual rates of carbon
stock change are estimated as the difference in stocks at two points in time divided by the time dependence of thestock change factors.
EQUATION 2.25
ANNUAL CHANGE IN ORGANIC CARBON STOCKS IN MINERAL SOILSCMineral ( SOC0  SOC(0T ) )D

SOCMineral   SOCREFc ,s ,i  FLUc ,s ,i  FMGc ,s ,i  FIc ,s ,i  Ac , s ,ic , s ,i

(Note: T is used in place of D in the ∆𝐶𝑀𝑖𝑛𝑒𝑟𝑎𝑙 equation if T is ≥ 20 years, see note below
associated with the parameter 𝐷)
Where:

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

SOC0

= mineral soil organic C stock (SOCMineral ) in the last year of an inventory time period, tonnes
C

SOC(0 T )= mineral soil organic C stock (SOCMineral ) at the beginning of the inventory time period,
tonnes C

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

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

c= represents the climate zones included in the inventorys= represents the soil types included in the inventoryi= represents the set of management systems included in the inventory.

SOCMineral= total mineral soil organic C stock at a defined time, tonnes C

SOCREFc ,s ,i = the soil organic C stock for mineral soils in the reference condition, tonnes C ha -1 (Table
2.3)

FLU c ,s ,i= stock change factor for mineral soil organic C land-use systems or sub-systems for aparticular land-use, dimensionless[Note: FND is substituted for FLU in forest soil organic C stock calculations to estimate theinfluence of natural disturbance regimes (see Chapter 4, Section 4.2.3 for more discussion)].

FMGc ,s ,i= stock change factor for mineral soil organic C for management regime, dimensionless

FIc ,s ,i= stock change factor for mineral soil organic C for the input of organic amendments,
dimensionless

Ac , s ,i= land area of the stratum being estimated, ha[Note: All land in the stratum should have common biophysical conditions (i.e., climate andsoil type) and management history over the inventory time period to be treated together for
analytical purposes.]2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Inventory calculations are based on land areas that are stratified by climate regions (see Chapter 3 Annex 3A.5,
for default classification of climate), and default soils types as shown in Table 2.3 (see Chapter 3, Annex 3A.5, fordefault classification of soils). The stock change factors are very broadly defined and include: 1) a land-use factor(FLU) that reflects C stock changes associated with type of land use, 2) a management factor (F MG) representing
the principal management practice specific to the land-use sector (e.g., different tillage practices in cropland), and
3) an input factor (FI) representing different levels of C input to soil. As mentioned above, F ND is substituted for
FLU in Forest Land to account for the influence of natural disturbance regimes (see Chapter 4, Section 4.2.3 formore discussion). The stock change factors are provided in the soil C sections of the land-use chapters. Each ofthese factors represents the change over a specified number of years (D), which can vary across sectors, but istypically invariant within sectors (e.g., 20 years for the cropland systems). In some inventories, the time period forinventory (T years) may exceed D, and under those cases, an annual rate of change in C stock may be obtained by
dividing the product of [(SOC0 – SOC(0 –T)) ● A] by T, instead of D. See the soil C sections in the land-use chaptersfor detailed step-by-step guidance on the application of this method.
When applying the stock change factor method using Equation 2.25, the type of land-use and management activity
data has a direct influence on the formulation of the equation (See Box 2.1). Formulation A is based on activity
data collected with Approach 1, while Formulation B is based on activity data collected with Approaches 2 or 3
(Box 2.1). See Chapter 3 for additional discussion on the approaches for activity data collection.
Special consideration is needed if using Approach 1 activity data (see Chapter 3) as the basis for estimating landuse and management effects on soil C stocks, using Equation 2.25. Approach 1 data do not track individual landtransitions, and so SOC stock changes are computed for inventory time periods equivalent to D years, or as close
as possible to D, which is 20 years in the Tier 1 method. For example, Cropland may be converted from full tillage
to no-till management between 1990 and 1995, and Formulation A (see Box 2.1) would estimate a gain in soil C
for that inventory time period. However, assuming that the same parcel of land remains in no-till between 1995
and 2000, no additional gain in C would be computed (i.e., the stock for 1995 would be based on no-till
management and it would not differ from the stock in 2000 (SOC 0), which is also based on no-till management).
If using the default approach, there would be an error in this estimation because the change in soil C stocks occurs
over 20 years (i.e., D = 20 years). Therefore, SOC(0 –T) is estimated for the most distant time that is used in theinventory calculations up to D years before the last year in the inventory time periods (SOC0). For example,
assuming D is 20 years and the inventory is based on activity data from 1990, 1995, 2000, 2005 and 2010, SOC(0–T) will be computed for 1990 to estimate the change in soil organic C for each of the other years, (i.e., 1995, 2000,
2005 and 2010). The year for estimating SOC(0 –T) in this example will not change until activity data are gatheredat 2011 or later (e.g., computing the C stock change for 2011 would be based on the most distant year up to, but
not exceeding D, which in this example would be 1995).
If transition matrices are available (i.e., Approach 2 or 3 activity data), the changes can be estimated between eachsuccessive year. From the example above, some no-till land may be returned to full tillage management between
1995 and 2000. In this case, the gain in C storage between 1990 and 1995 for the land base returned to full tillage
would need to be discounted between 1995 and 2000. Further, no additional change in the C stocks would benecessary for land returned to full tillage after 2000 (assuming tillage management remained the same). Only landremaining in no-till would continue to gain C up to 2010 (i.e., assuming D is 20 years). Hence, inventories usingtransition matrices from Approach 2 and 3 activity data will need to be more careful in dealing with the time
periods over which gains or losses of SOC are computed. See Box 2.2 for additional details. The application of
the soil C estimation approach is much simpler if only using aggregated statistics with Approach 1 activity data.
However, it is good practice for countries to use transition matrices from Approach 2 and 3 activity data if that
information is available because the more detailed statistics will provide an improved estimate of annual changes
in soil organic C stocks.
There may be some cases in which activity data are collected over time spans longer than the time dependence ofthe stock change factors (D), such as every 30 years with a D of 20. For those cases, the annual stock changes canbe estimated directly between each successive year of activity data collection (e.g., 1990, 2020 and 2050) without
over- or under-estimating the annual change rate, as long as T is substituted for D in Equation 2.25.

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

TABLE 2.3 (UPDATED)DEFAULT REFERENCE CONDITION SOIL ORGANIC CARBON STOCKS (SOCREF) FOR MINERAL SOILS (TONNES C HA-1 IN 0-30
CM DEPTH) 1, 2
IPCC soil class 6
IPCC Climate Zone 5High activity clay
soils (HAC) 7

Low activity clay
soils (LAC) 8

Sandy soils(SAN) 9Polar Moist/Dry (Px - undiff)13

59 ± 41% (24)NA

27 ± 67% (18)

Boreal Moist/Dry (Bx - undiff)13

63 ± 18% (35)NA

10 ± 90% 4

Cool temperate dry (C2)43 ± 8% (177)33 ± 90% 3

13 ± 33% (10)Cool temperate moist (C1)81 ± 5% (334)76 ± 51% (6)51 ± 13% (126)Warm temperate dry (W2)24 ± 5% (781)19 ± 16% (41)10 ± 5% (338)Warm temperate moist (W1)64 ± 5% (489)55 ± 8% (183)36 ± 23% (39)

Tropical dry (T4)21 ± 5% (554)19 ± 10% (135)9 ± 9% (164)

Tropical moist (T3)

40 ± 7% (226)38 ± 5% (326)27 ± 12% (76)Tropical wet (T2)

60 ± 8% (137)52 ± 6% (271)46 ± 20% (43)Tropical montane (T1)

51 ± 10% (114)

44 ± 11% (84)52 ± 34% (11)

Spodic soils(POD) 10

Volcanic soils(VOL) 11

Wetland soils(WET) 12

Polar Moist/Dry (Px - undiff)13

NO

NA

NA

Boreal Moist/Dry (Bx - undiff)13

117 ± 90% 3

20 ± 90% 4

116 ± 65% (6)Cool temperate dry (C2)NO

20 ± 90% 4

87 ± 90% 3

Cool temperate moist (C1)128 ± 14% (45)136 ± 14% (28)128 ± 13% (42)Warm temperate dry (W2)NO

84 ± 65% (10)74 ± 17% (49)

Warm temperate moist (W1)

143 ± 30% (9)138 ± 12% (42)135 ± 28% (28)NA

50 ± 90% 4

22 ± 17% (32)

Tropical moist (T3)NA

70 ± 90% 4

68 ± 17% (55)

Tropical wet (T2)NA

77 ± 27% (14)49 ± 19% (33)

Tropical montane (T1)NA

96 ± 31% (10)82 ± 50% (12)Tropical dry (T4)

Note: Data are derived from Batjes (2010) and Batjes (2011) unless otherwise noted through the use of superscripts.
1

NA denotes that soil categories the soil category may occur in a climate zone, but no data was available. NO denotes that the soil typedoes not normally occur within a climate zone. 2 All values are presented in the format of the mean for the soil by climate combination ±the 95% confidence limit expressed as a percentage of the mean (that is ± 1.96 * standard error /mean *100). Values in parentheses are thenumber of soils included in the derivation of mean and standard error values for each combination of soil and climate types. 3 Indicateswhere no data were available from Batjes (2011) but values were derived for the 2006 IPCC Guidelines for National Greenhouse Gas
Inventories and have been used in the table. No values of n were available. A nominal error estimate of ±90% of the mean was assigned
as per the 2006 IPCC Guidelines. 4 Indicates where no data were available either from Batjes (2011) or in the 2006 IPCC Guidelines for
National Greenhouse Gas Inventories. Mean values present the default values used in the 1996 IPCC Guidelines. No values of n wereavailable. A nominal error estimate of ±90% of the mean was assigned as per the 2006 IPCC Guidelines. 5 Climate classes are definedaccording to (IPCC 2006, p. 3.39) using elevation, mean annual temperature, mean annual precipitation, mean annual precipitation topotential evapotranspiration ratio and frost occurrence. 6 Soil classes are inferred from the FAO-1990/WRB-2006 classification inaccordance with IPCC (2006, p. 3.40 - 3.41). 7 Soils with high activity clay (HAC) minerals are lightly to moderately weathered soils
dominated by 2:1 silicate clay minerals (in the World Reference Base for Soil Resources (WRB) classification: Leptosols, Vertisols,
Kastanozems, Chernozems, Phaeozems, Luvisols, Alisols, Albeluvisols, Solonetz, Calcisols, Gypsisols, Umbrisols, Cambisols, Regosols;in USDA classification: Mollisols, Vertisols, high-base status Alfisols, Aridisols, Inceptisols). 8 Soils with low activity clay (LAC) minerals
are highly weathered soils, dominated by 1:1 clay minerals and amorphous iron and aluminium oxides (in WRB classification: Acrisols,
Lixisols, Nitisols, Ferralsols, Durisols; in USDA classification: Ultisols, Oxisols, acidic Alfisols). 9 Soils (regardless of taxonomic
classification) having > 70% sand and < 8% clay (in WRB classification: Arenosols; in USDA classification: Psamments). 10 Soils
exhibiting strong podzolization (in WRB classification includes Podzols; in USDA classification Spodosols). 11 Soils derived from volcanic
ash with allophanic mineralogy (in WRB classification Andosols; in USDA classification Andisols). 12 Soils with restricted drainage
leading to periodic flooding and anaerobic conditions (in WRB classification Gleysols; in USDA classification Aquic suborders). 13 The
Boreal dry and Boreal moist zones and the Polar dry and Polar moist zones were not differentiated. Results presented represent the SOC 30
stocks for the undifferentiated (undiff.) Boreal (Bx) and Polar (Px) classes.

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

Two alternative formulations are possible for Equation 2.25 depending on the Approach used tocollect activity data, including
Formulation A (Approach 1 for Activity Data Collection)  SOCREFc ,s ,i  FLUc ,s ,i  FMGc ,s ,i  FIc ,s ,i  Ac , s ,i   c , s ,i
0







  SOCREFc ,s ,i  FLUc ,s ,i  FMGc ,s ,i  FIc ,s ,i  Ac , s ,i  c , s ,i
 (0T )CMineral DFormulation B (Approaches 2 and 3 for Activity Data Collection)







  SOC 
REFc ,s , p  FLU c ,s , p  FMGc ,s , p  FI c ,s , p0  Ac , s , p  c , s , p  SOC

F

F

F

REFc ,s , p
LU c ,s , p
MGc ,s , p
I c ,s , p(0 T ) CMineral D
Where:
p = a parcel of land representing an individual unit of area over which the inventory calculations are
performed.
See the description of other terms under the Equation 2.25.
Activity data may only be available using Approach 1 for data collection (Chapter 3). These data
provide the total area at two points in time for climate, soil and land-use/management systems,
without quantification of the specific transitions in land use and management over the inventory time
period (i.e., only the aggregate or net change is known, not the gross changes in activity). With
Approach 1 activity data, mineral C stock changes are computed using formulation A of Equation
2.25. In contrast, activity data may be collected based on surveys, remote sensing imagery or otherdata providing not only the total areas for each land management system, but also the specifictransitions in land use and management over time on individual parcels of land. These are considered
Approach 2 and 3 activity data in Chapter 3, and soil C stock changes are computed using
formulation B of Equation 2.25. Formulation B contains a summation by land parcel (i.e., "p"represents land parcels in formulation B rather than the set of management systems “i”) that allows
the inventory compiler to compute the changes in C stocks on a land parcel by land parcel basis.

BOX 2.2 (UPDATED)COMPARISON BETWEEN USE OF APPROACH 1 AGGREGATE STATISTICS AND APPROACH 2 OR 3 ACTIVITY DATAWITH TRANSITION MATRICESThis box examines the application of Equation 2.25 to calculate ∆𝐶𝑚𝑖𝑛𝑒𝑟𝑎𝑙 . Assume a country wherea fraction of the land is subjected to land-use changes, as shown in the following table, where eachline represents one land unit with an area of 1 Mha (F = Forest Land; C = Cropland; G = Grassland).
Where a land-use change occurs, it is assumed to occur in the year following the previous inventory
year (e.g. for land unit 1, the conversion from F to C occurred at the start of 1991 such that for thefive years from the start of 1991 to the end of the 1995 inventory year the land was under land-use
C)

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

BOX 2.2 (UPDATED) (CONTINUED)COMPARISON BETWEEN USE OF APPROACH 1 AGGREGATE STATISTICS AND APPROACH 2 OR 3 ACTIVITYDATA WITH TRANSITION MATRICESLand Unit ID
1

1990

1995

2000

2005

2010

2015

2020

F

C

C

C

C

C

C

2

F

C

C

C

G

G

G

3

G

C

C

C

C

G

G

4

G

G

F

F

F

F

F

5

C

C

C

C

G

G

G

6

C

C

G

G

G

C

C

For simplicity, it is assumed that the country has a single soil type, with a SOCREF (0-30 cm soil
C stock under native forest vegetation) value of 77 tonnes C ha-1. Values for FLU are 1.00, 1.05
and 0.92 for F, G and C, respectively. FMG and FI are assumed to be equal to 1. The timedependence of the stock change factors (D) is 20 years. Finally, the soil C stock is assumed to
be at equilibrium in 1990 (i.e., no changes in land-use occurred during the 20 years prior to1990). When using Approach 1 activity data (i.e., aggregate statistical data), annual changes in
C stocks are computed for every inventory year following Equation 2.25 above. The following
table shows the results of calculations1:
1990

1995

2000

2005

2010

2015

2020

F (Mha)

2

0

1

1

1

1

1

G (Mha)

2

1

1

1

3

3

3

C (Mha)

2

5

4

4

2

2

2

SOC0 (Mt C)

457.4

435.1

441.2

441.2

461.2

461.2

461.2

SOC(0-T) (Mt C)

457.4

457.4

457.4

457.4

457.4

435.1

441.2

∆CMineral (Mt C yr-1)

0.0

-1.1

-0.8

-0.8

0.2

1.3

1.0

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

1995

2000

2005

2010

2015

2020

SOC0 (Mt C) for unit 1

77.0

75.5

73.9

72.4

70.8

70.8

70.8

SOC0 (Mt C) for unit 2

77.0

75.5

73.9

72.4

74.5

76.6

78.7

SOC0 (Mt C) for unit 3

80.9

78.3

75.8

73.3

70.8

73.3

75.8

SOC0 (Mt C) for unit 4

80.9

80.9

79.9

78.9

78.0

77.0

77.0

SOC0 (Mt C) for unit 5

70.8

70.8

70.8

70.8

73.3

75.8

78.3

SOC0 (Mt C) for unit 6

70.8

70.8

73.3

75.8

78.3

76.5

74.6

SOC0 (Mt C)

457.4

451.8

447.8

443.7

445.8

450.1

455.4

SOC(0-T) (Mt C)

457.4

457.4

451.8

447.8

443.7

445.8

450.1

∆CMineral

0.0

-1.1

-0.8

-0.8

0.4

0.9

1.0

(Mt C yr-1)

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BOX 2.2 (UPDATED) (CONTINUED)COMPARISON BETWEEN USE OF APPROACH 1 AGGREGATE STATISTICS AND APPROACH 2 OR 3 ACTIVITY DATAWITH TRANSITION MATRICESBoth methods yield different estimates of C stocks, and use of Approach 2 or 3 data with landtransition matrices would be more accurate than use of Approach 1 aggregate statistics. However,estimates of annual changes of C stocks would not differ greatly, as shown in this example. The
effect of underlying data approaches on the estimates differ more when there are multiple changes
in land-use on the same piece of land (as in land units 2, 3 and 6 in the example). It is noteworthy
that Approach 1, 2 and 3 activity data produce the same changes in C stocks if the systems reach a
new equilibrium, which occurs with no change in land-use and management for a 20-year time
period using the Tier 1 method. Consequently, no C stock increases or losses are inadvertently lostwhen applying the methods for Approach 1, 2 or 3 activity data, but the temporal dynamics do vary
somewhat as demonstrated above. A spreadsheet is available with the full set of calculations:
Vol4_Ch2_Spreadsheet_Box_2.2_Calculations.xlsx.

Organic soils
No refinement. See Chapter 2, Section 2.2 of the 2013 Wetlands Supplement.

Soil inorganic C
No refinement.

Tier 2 Methods
Mineral soils
A Tier 2 method is an extension of the Tier 1 method that allows an inventory to incorporate country-specific data.
It is good practice for countries to use a Tier 2 method, if possible, even if they are only able to better specify
certain components of the Tier 1 method. For example, a compiler may only have data to derive country-specificreference C stocks, which would then be used with default stock change factors to estimate changes in soil organic
C stocks for mineral soils.
Country-specific data can be used to improve four components when applying the Tier 1 equations for estimatingstock changes in mineral soils. The components include a) derivation of region or country-specific stock change
factors, b) reference condition C stocks, c) specification of management systems, and/or d) classification of climate
and soil categories (e.g., Ogle et al., 2003; VandenBygaart et al., 2004; Tate et al., 2005). Inventory compilers canchoose to derive specific values for all of these components, or any subset, which would be combined with default
values provided in the Tier 1 method to complete the inventory calculations using Equation 2.25. Also, the Tier 2
method uses the same procedural steps for calculations as provided for Tier 1.
1) Defining management systems. Although the same management systems may be used in a Tier 2 inventory as
found in the Tier 1 method, the default systems can be disaggregated into a finer categorisation that better
represents management impacts on soil organic C stocks in a particular country based on empirical data (i.e., stockchange factors vary significantly for the proposed management systems). Such an undertaking, however, is only
possible if there is sufficient detail in the underlying data to classify the land area into the finer, more detailed setof management systems.
2) Climate regions and soil types. Countries that have detailed soil classifications and climatic data have the optionof developing country-specific classifications. Moreover, it is considered good practice to specify better climate
regions and soil types during the development of a Tier 2 inventory if the new classification improves the
specification of reference C stocks and/or stock change factors. In practice, reference C stocks and/or stock change
factors should differ significantly among the proposed climate regions and soil types based on an empirical analysis.
Note that specifying new climate regions and/or soil types requires the derivation of country-specific reference C
stocks and stock change factors. The default reference soil C stocks and stock change factors are only appropriate
for inventories using the default climate and soil types.
3) Reference C stocks. Deriving country-specific reference condition soil C stocks (SOCREF) is another possibility
for improving an inventory using a Tier 2 method (Bernoux et al. 2002), which will likely produce more accurate
and representative values. Country-specific stocks can be estimated from soil measurements, for example, as partof a country’s soil survey. It is important that reliable taxonomic descriptions be used to group soils into categories.
Three additional points require consideration when deriving the country-specific values, including possible
specification of country-specific soil categories and climate regions (i.e., instead of using the IPCC default
classification), choice of reference condition, and choice of depth increment over which the stocks are estimated.

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

Stocks are computed by multiplying the proportion of organic C (i.e., %C divided by 100) by the depth increment(default is 30 cm), bulk density, and the proportion of coarse-fragment free soil (i.e., < 2mm fragments) in thedepth increment (Ogle et al. 2003). The coarse fragment-free proportion is on a mass basis (i.e., mass of coarse
fragment-free soil/total mass of the soil). If the soil C reference condition differs from that used in Table 2.3 or thesoil depth used differs from 30 cm, then appropriate country specific soil C stocks for the reference condition andstock change factors must be derived. For developing a Tier 2 method, it would also be possible to define reference
SOC stocks and SOC stock change factors using an equivalent mass approach (see Box 2.2b) rather than an
approach based on a fixed depth.
The soil reference condition is the land-use/cover category (or condition within a land-use/cover category) that isused for evaluating the relative effect of land-use change on the amount of soil C storage (e.g., relative differencein soil C storage between a reference condition, such as native lands, and another land use, such as cropland,
forming the basis for FLU in Equation 2.25). It is likely that many countries will use the Tier 1 default soil reference
condition in a Tier 2 method. However, another land use or condition can be selected to define the reference
condition, which is good practice if it allows for a more accurate assessment of soil C stock changes. The samereference condition should be used for each climate zone and soil type, regardless of the land use. The soil C stock
associated with the reference condition is then multiplied by land use, input and management factors to estimate
the stocks at the beginning and last year in an inventory time period (See Equation 2.25).
Another consideration in deriving country-specific reference soil C stocks is the possibility of estimating C stocks
to a different depth in the soil. Default soil C stocks given in Table 2.3 are based on the amount of soil organic C
in the top 30 cm of a soil profile. A different depth can be selected and used for Tier 2 methods if all appropriate
data are available. Consideration should be given to the introduction of bias (positive or negative) that may arise
in response to the depth selected. For example, where depth is set to 20 cm and cultivation mixes soils to adepth >20 cm, an apparent difference in SOC stock between cultivated and uncultivated soils may be observed forthe 20cm depth that is not representative of the change in SOC stocks to the depth over which mixing occurs in
the cultivated soil. It is good practice to derive reference condition soil C stocks to the depth at which land use andmanagement impact soil C stocks, but this will require that the data are available or could be acquired to theselected depth. Any change in the depth for reference condition soil C stocks will require derivation of new stockchange factors (e.g. FLU, FMG and FI see Equation 2.25) consistent with the depth selected because the defaults are
based on impacts to a 30 cm depth.
It is possible to use a soil C model to derive steady state soil C stocks indicative of the soil reference condition for
the various combinations of soil type and climate that exist within a country. However, this would require sufficienttesting of the model used to provide evidence that the model is adequate for this purpose (See Section 2.5.2 formore information). Further information related to soil sampling strategies and how to derive soil reference C stocks
can be found in Batjes (2011), as well as in a range of soil sampling and analysis texts (e.g. Carter & Gregorich
2008; de Gruijter et al. 2006)
4) Stock change factors. An important advancement for a Tier 2 method is the estimation of country-specific stockchange factors (FLU, FMG and FI). The derivation of country-specific factors can be accomplished using
experimental/measurement data and computer model simulation. In practice, deriving stock change factors
involves estimating a response ratio for each study or observation (i.e., the C stocks in different input ormanagement classes are divided by the value for the nominal practice, respectively).
Optimally, stock change factors are based on experimental/measurement data in the country or surrounding region,by estimating the response ratios from each study and then analysing those values using an appropriate statisticaltechnique (e.g., Ogle et al. 2003 and 2004; VandenBygaart et al. 2004). Studies may be found in publishedliterature, reports and other sources, or inventory compilers may choose to conduct new experiments. Regardlessof the data source, it is good practice that the plots being compared have similar histories and management as wellas similar topographic position, soil physical properties and be located in close proximity. Studies should provide
soil C stocks (i.e., mass per unit area to a specified depth) or the information needed to calculate soil C stocks (i.e.,
percent organic carbon together with bulk density; proportion of rock in soil, which is often measured as the greaterthan 2mm fraction and by definition contains negligible soil organic C). If percent organic matter is available
instead of percent organic carbon, a conversion factor of 0.58 can be used to estimate the C content. Moreover, it
is good practice that the measurements of soil C stocks are taken on an equivalent mass basis (e.g., Ellert et al.
2001; Gifford & Roderick, 2003). In order to use this method, the inventory compiler will need to determine a
depth to measure the C stock for the nominal land use or practice, such as native lands or conventional tillage. This
depth will need to be consistent with the depth for the reference C stocks. The soil C stock for the land-use or
management change is then measured to a depth with the equivalent mass of soil. Box 2.2b provides further
information on issues associated with conducting an inventory on an equivalent mass basis.
Another option for deriving country-specific values is to simulate stock change factors from advanced models(Bhatti et al., 2001). To demonstrate the use of advanced models, simulated stock change factors can be comparedto with measured changes in C stocks from experiments. It is good practice to provide the results of model2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

evaluation, citing published papers in the literature and/or placing the results in the inventory report. This approachis considered a Tier 2 method because it relies on the stock change factor concept and the C estimation methodelaborated in the Tier 1 method.
Derivation of country-specific management factors (FMG) and input factors (FI), either with empirical data oradvanced models, will need to be consistent with the management system classification. If more systems are
specified for the inventory, unique factors will need to be derived representing the finer categories for a particular
land use.
Another consideration in deriving country-specific stock change factors is their associated time dependence (D in
Equation 2.25), which determines the number of years over which the majority of a soil C stock change occurs,
following a management change. It is possible to use the default time dependence (D) for the land-use sector (e.g.,
20 years for cropland), but the dependence can be changed if sufficient data are available to justify a different time
period. In addition, the method is designed to use the same time dependence (D) for all stock change factors aspresented in Equation 2.25. If different periods are selected for F LU, FMG and FI, it will be necessary to computethe influence of land use, management and inputs separately and divide the associated stock change dependence.
This can be accomplished by modifying Equation 2.25 so that SOC at time T and 0-T is computed individually foreach of the stock change factors (i.e., SOC is computed with F LU only, then computed with FMG, and finallycomputed with FI). The differences are computed for the stocks associated with land use, management, and input,
dividing by their respective D values, and then the changes are summed.
Changes in soil C stocks normally occur in a non-linear fashion, and it is possible to further develop the timedependence of stock change factors to reflect this pattern. For changes in land use or management that cause adecrease in soil C content, the rate of change is highest during the first few years, and progressively declines withtime. In contrast, when soil C is increasing due to land-use or management change, the rate of accumulation tends
to follow a sigmoidal curve, with rates of change being slow at the beginning, then increasing and finallydecreasing with time. If historical changes in land-use or management practices are explicitly tracked by resurveying the same locations (i.e., Approach 2 or 3 activity data, see Chapter 3), it may be possible to implementa Tier 2 method that incorporates the non-linearity of changes in soil C stock.
BOX 2.2A (NEW)USING EQUIVALENT MASS METHODS TO DERIVE MINERAL SOIL ORGANIC CARBON STOCK CHANGE FACTORS

Soil 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 to a certain soil depth changes in response to altered management practices
associated with land use change (e.g. uprooting forest vegetation, land levelling, and rain compaction due tothe disappearance of the cover of tree canopy). In addition, soil bulk density may be affected differently byparticular management practices within a given land use (e.g. tillage and machinery traffic within cropping
systems or the extent of compaction induced by different animal at stocking rates within pasture systems).
Where the soil bulk density changes due to land use and/or management, the comparison of the soil carbon
stocks between the cropland, settlements, grassland, wetlands, or forest land to the same depth introduceschanges to soil carbon stocks as a direct consequence of changes in soil bulk density (Ellert & Bettany 1995).
With a management induced change in soil bulk density, it is possible to calculate a change in soil carbon stock
to a fixed depth in the absence of any change in soil carbon content. Therefore, it is more robust to calculate
soil carbon stock change on an equivalent mass basis rather than on a fixed-depth basis (Toriyama et al. 2011;
Bruun et al. 2013; Halvorson et al. 2016; Hu et al., 2016). The equivalent mass approach has more rigorouscomparability when the bulk density between cropland, grassland, wetland, settlements and forest land ismarkedly different even if the site is within close proximity. It is important to realise that comprehensive dataof soil carbon concentration and soil bulk density would be required to derive stock change factors across allland uses. The changing mass of organic carbon itself will affect the equivalent soil mass and therefore
equivalent mass basis is not appropriate for organic soils. There are proposals for methods based on only
equivalent mass of the mineral soil portion (McBratney & Minasny 2010) that would reduce the effect ofchanging soil organic mass distorting the equivalent soil mass. Adopting an equivalent-mass based carbon
stock inventory requires thorough consideration of the challenges.
The impact of biochar C amendments on mineral soils can also be estimated with a Tier 2 method for mineral soils
using Equation 2.25A and adding this estimate to the result in Equation 2.25.7

7 Biochar is a solid carbonised product from thermochemical conversion through pyrolysis (heating with limited air). The term

biochar is used herein only to refer to materials that have been produced under process conditions in which relatively easily
mineralisable organic materials are converted to more persistent forms by heating to above 350 °C with limited air through a

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

EQUATION 2.25A
ANNUAL CHANGE IN BIOCHAR CARBON STOCK IN MINERAL SOILS RECEIVING BIOCHARADDITIONSn

BCMineral   BCTOTp • FC p • Fperm pp 1Where:

BCMineral = the total change in carbon stocks of mineral soils associated with biochar amendment, tonnes
sequestered C yr-1

BCTOTp= the mass of biochar incorporated into mineral soil during the inventory year for eachbiochar production type p , tonnes biochar dry matter yr-1

FC p

= the organic carbon content of biochar for each production type p , tonnes C tonne-1 biochar
dry matter

Fperm p= fraction of biochar carbon for each production type p remaining (unmineralised) after 100
years, tonnes sequestered C tonne-1 biochar Cn= the number of different production types of biocharCountry-specific values the C content of the forms of biochar included in the inventory ( FC p in units of tonnes C
tonne-1 biochar on a dry mass basis) can be measured directly from representative samples of biochar. Countryspecific values may also be based on published data on carbon content of biochar produced using the same
feedstock and process conditions as the biochar that is applied to soils in the country.
The fraction of biochar C remaining after 100 years is defined by the parameter Fperm p . It is not possible to measure
this value directly due to the time scales involved. So, this parameter is estimated from other data. The elemental
composition of biochar, specifically the ratio of hydrogen to organic carbon (H/Corg) or ratio of oxygen to organiccarbon (O/Corg), has been shown to correlate non-linearly with biochar residence time (Spokas 2010; Lehmann etal. 2015). Therefore, country-specific Tier 2 estimates of Fperm p can be based on H/Corg or O/Corg measured directlyfrom representative samples of biochar, or from published data for biochar produced using similar process
conditions as the biochar that is applied to soils in the country. This parameter can also be derived from the biochar
elemental composition using published equations relating this composition to mean residence time or half-life (forexample H/Corg, Lehmann et al. 2015; or O/Corg, Spokas 2010), and extrapolated to the permanence time frame
assuming one-, two-, or three-pool exponential decay (Zimmerman 2010; Herath et al. 2015; Lehmann et al. 2015).
A justification should be provided if a permanence time frame other than 100 years is used.
Since the impact of biochar amendments is a separate calculation and summed with the result from Equation 2.25
in the Tier 2 method, it is essential that biochar C is not included as an organic amendment in the estimates of
SOCMineral in Equation 2.25.

gasification or pyrolysis process. This guidance does not deal with pyrolytic organic materials that result from wild fires oropen fires, and is only applicable for biochar added to mineral soils.

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BOX 2.2B (NEW)GHG EMISSION SOURCES WITH BIOCHAR PRODUCTION

Biochar production involves emissions from several different sectors and source categories. All
GHG emissions and removals are reported in a greenhouse gas inventory, but estimation andreporting is done based on sources in which the activity occurs. The guidance in this section isaddressing C stock changes associated with the end-product use of biochar amendments to mineralsoils. However, other emissions do occur along the biochar feedstock supply chains that are
estimated in other source categories. For example, the harvesting and use of forest wood biomass
for biochar production would be part of reported C stock changes in Forest Land Remaining Forest
Land (Volume 4). Moreover, biomass may be grown specifically as a feedstock and the C stockchanges are estimated and reported under the appropriate source categories for land use associatedwith feedstock production (Volume 4). For plant residues and manures, their utilisation as feedstock
reduces input of organic amendments to soil and thereby affects soil C stocks in cropland andgrassland, and possibly other land uses receiving manure amendments (Volume 4). For wastematerials, their utilisation as feedstock reduces input to waste streams and is addressed in thecalculation of emissions from waste management (Volume 5). There may also be use of fossil fuelsin the harvesting, transport and pyrolysis of the feedstock and a potential release of other non-CO2
greenhouse gases during the heating process that would be included in the energy sector (Volume
2).

Organic soils
No refinement. See Chapter 2, Section 2.2 of the 2013 Wetlands Supplement.

Soil inorganic C
No refinement.

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

Mineral soils
Model-based approaches can use mechanistic simulation models that capture the underlying processes drivingcarbon gains and losses from soils in a quantitative framework, such as the influence of land use and managementon processes controlling carbon input resulting from plant production and litter fall as well as microbial
decomposition (e.g., McGill, 1996; Smith et al., 1997b; Smith et al., 2000; Falloon and Smith, 2002; Tate et al.,2005; Campbell&Paustian, 2015). Note that Tier 3 methods provide the only current opportunity to explicitly
estimate the impact of soil erosion on C fluxes (Box 2.2d). In addition, Tier 3 model-based approaches may
represent C transfers between biomass, dead biomass and soils, which are advantageous for ensuring conservationof mass in predictions of C stock changes in these pools relative to CO2 removals and emissions to the atmosphere.
Tier 3 modelling approaches are capable of addressing the influence of land use and management with a dynamic
representation of environmental conditions that affect the processes controlling soil C stocks, such as weather,edaphic characteristics, and other variables. The impact of land use and management on soil C stocks can vary as
environmental conditions change, and such changes are not captured in lower Tiers, which may create biases inthose results. Tier 3 methods can also include lateral flows of C associated with erosion and deposition (See Box2.2c). Consequently, Tier 3 approaches are capable of providing a more accurate estimation of C stock changes
associated with land-use and management activity if the modelling approach has been calibrated to the range ofenvironmental conditions, soil properties and management practices to which the model will subsequently beapplied (See Section 2.5 for more information).

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

For Tier 3 approaches, a set of benchmark sites will be needed to evaluate model results. Ideally, a series ofpermanent, benchmark monitoring sites would be established with statistically replicated design, capturing the
major climatic regions, soil types, and management systems as well as system changes, and would allow for
repeated measurements of soil organic C stocks over time (Smith, 2004a). Monitoring is based on re-sampling
plots every 3 to 5 years or each decade; shorter sampling frequencies are not likely to produce significant
differences due to small annual changes in C stocks relative to the large total amount of C in a soil (IPCC, 2000;
Smith, 2004b).
BOX 2.2C (NEW)
REPRESENTING THE IMPACT OF SOIL EROSION AND DEPOSITION ON SOIL CARBON STOCK CHANGES

Soil erosion and/or deposition can have marked effect on measured carbon stocks (Chappell et al.
2016). Soil carbon stock changes due to soil erosion/deposition are not considered to be embedded
in factors for land-use change or land management. In practice, it is difficult to determine whether
soil erosion/deposition effects are or are not included in stock change factors derived from empirical
data. Different land use changes and subsequent management practices could result in different
extents of soil movement. For example, land-use change from forest or grassland to cropland, orland management change from no-till to full tillage are typically associated with increased soil
movement. The amounts of soil erosion or deposition are rarely measured or documented in datasetsthat have quantified soil carbon stock changes.
One option to include the effects of soil erosion and deposition is using well-tested models that
capture these dynamics with required input data to make estimates of the effect of pasterosion/deposition on soil carbon stocks (Van Oost et al. 2005; Causarano et al. 2007). However,use of such models also requires having empirical data on erosion/deposition effects on carbon
stocks for evaluation of the model predictions. Another option is to consistently apply a rationalethat identifies measured data of soil carbon stock changes that are affected by erosion/deposition forthe development of Tier 2 or 3 methods, developing factors related to erosion/deposition impacts,
and then applying these factors in areas affected by erosion/deposition.
In addition to model-based approaches, Tier 3 methods afford the opportunity to develop a measurement-based
inventory using a similar monitoring network as needed for model evaluation. However, measurement networks,
which serve as the basis for a complete inventory, will have a considerably larger sampling density to minimise
uncertainty, and to represent all management systems and associated land-use changes, across all climatic regions
and major soil types (Sleutel et al., 2003; Lettens et al., 2004). Measurement networks can be based on soil
sampling at benchmark sites or flux tower networks. Flux towers, such as those using eddy covariance systems(Baldocchi et al., 2001), constitute a unique case in that they measure the net exchange of CO2 between theatmosphere and land surface. Thus, with respect to changes in C stocks for the soil pool, flux tower measurement
networks are subject to the following caveats: 1) towers need to occur at a sufficient density to represent fluxes
for the entire country; 2) flux estimates need to be attributed to individual land-use sectors and specific land-use
and management activities; and 3) CO2 fluxes need to be further attributed to individual pools including stockchanges in soils (also biomass and dead organic matter). Additional considerations about soil measurements are
given in the previous section on Tier 2 methods for mineral soils (See stock change factor discussion).
It is important to note that measurement-based inventories represent full C estimation approaches, addressing all
influences on soil C stocks. Partial estimation of only land-use and management effects may be difficult, however.
Examples in Box 2.2d provide illustrations of Tier 3 methods for estimating change in mineral soil C stocks,
including information such as type of data required, brief description of the models and methods that are used toapply the models. For Tier 3 methods, it is important to calibrate and test models against field measurements thatreflect the variability in climate, soil type and land use over which the model will be applied (See Section 2.5.2
for more information). Application of the equivalent mass approach may be possible for calculating soil C stocks
with Tier 3 models, and is discussed in Box 2.2e.

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BOX 2.2D (NEW)EXAMPLES OF TIER 3 MINERAL SOIL C STOCK CHANGE METHODS

Four examples of Tier 3 model applications for soil organic C stock changes are elaborated in thissection based on government reporting to the UNFCCC by the Australia, Finland, Japan and UnitedStates.
Australia
Australia has implemented a Tier 3 inventory approach based on the use of the FullCAM model(Richards 2001; Richards & Evans 2004) to estimate management induced changes in the stock oforganic carbon held in the 0-30 cm soil depth layer over time. Australian lands included in theinventory were allocated to forest land, cropland, grassland, deforested land, forest land converted
to cropland and grassland, grassland converted to forest land, and land with sparse woody vegetation
based on national land use mapping (ABARES 2016) and remote sensing protocols (Caccetta et al.
2012) Detailed presentations of the soil carbon accounting processes under all land uses can befound in the National Inventory Reports (NIR) (http://www.environment.gov.au/climatechange/greenhouse-gas-measurement/publications/national-inventory-report-2015).
Here asummary is provided of the Tier 3 approach as applied to soil organic carbon stocks for croplandand grassland.
The FullCAM model simulates soil carbon stock change in 25m x 25m areas across Australia. Thissize was selected as it represented the finest scale to which the remote sensing process (Caccetta etal. 2012; Tupek et al. 2016) can detect land use change and quantify movement of lands between
the various classes included in the inventory. The data requirements and processes used to quantify
the impact of management on Australia’s 0-30 cm stock of soil organic carbon can be summarised
as follows:
1) Spatially explicit daily and monthly climatic data (average temperature, total rainfall and total
pan evaporation) are extracted from the Australian Bureau of Meteorology database and theninterpolated using thin plate smoothing splines according to (Kesteven & Lansberg 2004).
Additionally, spatially explicit estimates of soil clay content and water holding capacity are
extracted
from
the
Soil
and
LandscapeGridofAustralia(www.clw.csiro.au/aclep/soilandlandscapegrid/). These data represent required inputs themodelling described in steps 4 and 5.
2) The initial 0-30 cm total soil organic carbon stock is defined using a national map derived by
Viscarra Rossel et al. (2014). This total stock is then allocated to three measurable organiccarbon fractions (particulate, humus and resistant forms) that provide estimates for therespective stocks of resistant plant material, humus and inert carbon required to initialize the
FullCAM model (Baldock et al. 2013; Skjemstad et al. 2004; Viscarra Rossel & Hicks 2015).
3) The types of crops and pastures grown, the applied management practices (e.g. tillage andresidue management) and their relative allocations within defined land areas are calculatedusing national agricultural statistics derived from censuses conducted every five years(http://www.abs.gov.au/Agriculture).
4) For the bulk of Australian crops and pastures, total growth is defined by the availability of water
received as rainfall. Thus, a plant growth model applying species specific transpiration
efficiency terms to the amount of water made available to growing plants is used to estimate
above ground dry matter production. This production is then used along with plant species
specific harvest indices (Unkovich et al. 2010) and root:shoot ratios to define the mass of carbon
entering the soil and/or deposited on the soil surface for each monthly time step within the
FullCAM simulation model. Within irrigated systems, plant growth attains defined plant
specific maximum values each year.
5) The FullCAM model is then initialized and run on a monthly time step. During each step,
decomposition of decomposable and resistant plant materials and humus pools of C occurs
according to first order decay equations. The values of the decomposition rate constants
associated with the resistant plant material and humus pools of carbon within the model were
calibrated to Australian conditions to the corresponding measured stocks of soil carbon fractions

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

BOX 2.2D (NEW) (CONTINUED)
EXAMPLES OF TIER 3 MINERAL SOIL C STOCK CHANGE METHODS

5) (continued) of soil temperature and water content on decomposition is modelled through theapplication of decomposition rate constant modifiers as done in the Rothamsted Carbon Model(RothC) soil carbon model (Jenkinson 1990).
The impact of management on soil carbon stocks is quantified by running the described modelling
process forward from 1970 under two scenarios. In both scenarios, the same relative spatial
allocation of regimes (combinations of crop or pasture species and management practice) is usedfrom 1970 to 1990. From 1990 onwards, the relative spatial allocation of regimes is held constantat 1990 values in the first scenario. For the second scenario, the regimes are varied from 1991
onwards to reflect the temporal variations in regimes defined within the available data. The firstscenario thus estimates the soil carbon stock that would have been attained with no change inmanagement from that present in 1990; while the second scenario estimates the soil carbon stock
attained when management changes over time are accounted for. The net impact of management
since 1990 is then calculated as the difference in the soil organic carbon stock between the two
scenarios.
FinlandFinland uses Yasso07 soil carbon model as a Tier 3 method to report carbon stock changes on forest
and agricultural lands as well as in the cases of land use change (Statistics Finland 2017). Yasso07
is based on a few explicit assumptions on soil carbon cycling and these assumptions form aconceptual model further formulated into mathematical equations (Tuomi et al. 2011b; US EPA
2017). The model has four state variables based on the solubility of the organic material (acid-,water-, ethanol- and non-soluble and in addition, there is a humus pool that has the lowest decay
rate.
The model is used in the NGHGI to generate annual C stock change rates per hectare based onregional estimates of organic matter input (forest and crop statistics) and annual climate parameters.
Litter input is given in the four solubility fractions based on laboratory measurements. Organic
matter decays in the five model fractions driven by temperature and precipitation. The resulting C
stock change rates are applied on the respective land areas to produce regional estimates of C stockchange. The model is used consistently across different land use categories so that e.g. the initial C
allocation to different model compartments in forest land converted to cropland is based on the
results of the simulation of forest soil remaining forest soil.
Model parameters rely on a large global database of measurements of litter decay, wood decay andsoil carbon and all parameter values have been estimated using Markov chain Monte Carlo method.
Alternative details in the model structure have been evaluated using Bayesian criteria (Tuomi et al.
2011a). The results of Yasso07 model are characterized by statistical probability distributions that
represent uncertainty about the parameter values. The Yasso07 approach makes it possible and easy
to add new data to the database and develop the model continuously (model-data-fusion). The model
has been extensively tested against independent data on forest land (Dalsgaard et al. 2016; Lehtonen
et al. 2016; Rantakari et al. 2012; Tupek et al. 2016) and also on cropland (Karhu et al. 2012).
Yasso07 is a standard component of Max Planck Institute Earth System Model (Goll et al. 2017)
and the model is used for UNFCCC reporting in several countries (e.g. Austria, Benin, Czech
Republic, Estonia, Ireland, Finland, Latvia, Norway, Romania and Switzerland), see Hernandez etal. (2017). The model is widely used because it is simple, transparent, verifiable, freely available
and easy to apply. For more information, consult http://en.ilmatieteenlaitos.fi/yasso.

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Volume 4: Agriculture, Forestry and Other Land Use

BOX 2.2D (NEW) (CONTINUED)
EXAMPLES OF TIER 3 MINERAL SOIL C STOCK CHANGE METHODS

Japan
Japan uses a Tier 3 method to estimate soil organic C stock changes in agriculture land (cropland
and managed grassland) based on RothC. RothC model is a soil carbon dynamic model validated byusing long-term field experiments (Coleman & Jenkinson 1996). In order to apply the model toJapanese agricultural condition, the model was tested against long-term experimental data sets inJapanese agricultural lands. It was found that the original model could apply for non-volcanic uplandsoils without any modification or calibration (Shirato & Taniyama 2003), however, the model
required modification for Andosols and paddy soils by taking unique mechanisms of soil C dynamics
in these soils into account. For Andosols, the decomposition rate constant of the HUM (humified
organic matter) pool of RothC was reduced because the presence of Al-humus complexes enhances
its stability and resistance to decomposition (Shirato et al. 2004). For paddy soils, the decomposition
rate constants of all four active C pools was reduced on the basis of differences in organic matterdecomposition rates between upland and paddy (submerged in the rice growing season) soil
conditions (Shirato & Yokozawa 2005). Model performance was verified by comparing the model
output with measured soil C stock data under various climate condition, soil types and land uses.
The model is applied at the country scale (Yagasaki & Shirato 2014) using weather data (monthly
average temperature, precipitation, and open-pan evaporation), soil property data (soil clay content,
depth of surface soil, carbon content at the starting year, and bulk density), land use data and otheractivity data (carbon input from crop residue and organic manure) and calculated at each standard
mesh (100 x 100m). The weather, soil property and land use data are available as spatially explicit
data set, while carbon input from crop residue and organic manure are calculated by statistical dataand survey data available based on public administration boundary basis. The all obtained data areallocated to each standard mesh and then run the model.
In the NGHGI, the model is used to generate average C stock change rates per hectare in eachprefecture and in each sub-category (rice field, upland crop fields, orchards and managed grassland).
This is because the land use data used for the model estimation (grid-based data set) and used forthe official land classification in the NGHGI (statistical data) are not consistent very much and soJapan put its priority using a consistent land area data among every estimate relating to agriculture
land in AFOLU sector. This is one of the key challenges of the model application to the NGHGI andthe development of a standard spatially explicit land use data set is needed for the further
improvement of estimations.
United States of America
The United States uses a Tier 3 method based on the DayCent Ecosystem Model to estimate soil C
stock changes in cropland and grassland (Ogle et al. 2010, US EPA 2017). DayCent is a processbased model that simulated soil organic matter dynamics using a three-pool structure originallydeveloped for the Century Model (Parton et al. 1998; Parton et al. 1987). Model testing andparameterisation of DayCent has been conducted across a wide range of cropland and grassland sites
globally. For the inventory, the model is applied using land use data that are compiled through anational survey, National Resources Inventory (NRI) (Nusser et al. 1998; Nusser & Goebel 1997).
The NRI has a two-stage sample with recorded history, starting in 1979, for approximately 400,000
survey locations that are cropland or grassland throughout the conterminous United States. Eachsurvey location that is identified as cropland also has the specific crop rotation histories that weregrown by the farmer. Daily weather and soils data are needed to drive the model, and thisinformation is based on national datasets. Remote sensing data is used to inform production
estimates based on MODIS Enhanced Vegetation Index products. Other data are also incorporated
into the analysis, such as N fertilization rate data compiled through surveys.

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BOX 2.2D (NEW) (CONTINUED)
EXAMPLES OF TIER 3 MINERAL SOIL C STOCK CHANGE METHODSOne of the key challenges in developing a Tier 3 method is to robustly address uncertainties.
Compilers in the United States have addressed uncertainties in model inputs (e.g., fertilization rates,
tillage practices and organic amendments), model structure and parameterization, and propagate
uncertainty through the model application using an Approach 2 method (i.e., Monte Carlo Analysis)(Ogle et al. 2010). Model structure and parameterization is addressed using an empirically-based
method in which observed experimental data are compared to simulation results, and predictive
ability of the model is quantified using statistical methods (Ogle et al. 2007). These experimental
observations are independent from the data that are used to parameterise the model. The resulting
statistical equation is applied to adjust for biases in model results, if needed, and address theprecision of the model C stock changes. The major advantage of the Tier 3 method is that the results
are much more precise than Tier 1 and 2 methods, with uncertainty ranging from ±60% in the Tier
1 method to about ±20% for the Tier 3 method (US-EPA 2017). The improved precision is due tothe process-based framework in the DayCent model that incorporates more drivers of soil C stockchanges than lower Tier methods. However, without adequate activity data or a model with
sufficient prediction capability, a Tier 3 method could produce less precise results than lower-tiermethods.

BOX 2.2E (NEW)CONSIDERATION OF EQUIVALENT MASS METHODS WITHIN TIER 3 MODELLING APPROACHES

Process models that are used to estimate carbon stock changes over time, such as Century (Parton etal. 1987) and RothC (Coleman & Jenkinson 1996) can also be affected by changing soil bulk density
by the nature of the carbon stock data used for model parameterisation. These types of models
simulate the mass balance of organic carbon over time to a defined soil depth (e.g., 30 cm or analternative). The models require initialisation at which point an initial carbon stock is determinedalong with an initial soil mass in some cases (although the soil mass is rarely determined explicitly,
it is implicit in the model application). The models therefore use an equivalent soil mass approach
to simulate changes in carbon stocks since the estimated carbon stocks are unaffected by concurrent
soil bulk density changes. If the models are parameterised to carbon stocks on an equivalent mass
basis, then the carbon stock changes estimated by the parametrised model, and for a factor derived
from those modelled estimates, will be for soil carbon change on an equivalent mass basis. However,the carbon stock change calculated from carbon stock measurements for a fixed depth is the net
effect of the effect of soil bulk density changes on carbon stocks and the effect of biochemical
processes on carbon stocks. Therefore, when parameterised using fixed-depth carbon stock data, themodel will be estimating the net effect of these processes, so the modelled carbon stock estimates
only will be appropriate for the fixed depth and cannot address changes in mass of the soil over time.
Careful consideration of the effects of model assumptions and choice of data used for model
parametrisation and testing is required to understand and properly report the basis of the carbon
stock changes that are estimated directly or indirectly by a model based on parameterisation withdata from fixed depths.
Tier 3 methods can be used to model the loss of biochar C over time after its application to mineral soils and toaccount for GHG sources and sinks not captured in Tier 2, to address changes to N 2O or CH4 fluxes from soils8,
to estimate changes to net primary production (and associated C inputs to soil organic C pool), the mechanisms
and effects underlying interactions with soil, climate and other environmental variables. Although positive primingof labile soil organic matter is not expected to have a significant impact in the long term (Annex 2A.2), negative
priming leading to an increase in soil organic carbon stocks could have a substantial impact in soils amended withbiochar (Woolf et al. 2012). Similarly, to the extent that there are reductions in net emissions of N 2O and CH4
from soil and increases in plant growth, there could be a larger impact of biochar additions on reducing greenhousegas emissions (Gaunt & Lehmann 2008; Woolf et al. 2010; Hammond et al. 2011). It is also important to recognise

8 Impacts of biochar amendments on N O are estimated in the methods for soil N O emissions (Chapter 11), and impacts on2
2

CH4 emissions are estimated from specific land uses in the inventory, such as Rice Cultivation (Chapter 5) and Wetlands(Chapter 7).

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Volume 4: Agriculture, Forestry and Other Land Usethat the dynamic nature of biochar decomposition is important because its net impact on soil C stocks and GHG
emissions varies with time, which can be better addressed with a Tier 3 model.
Examples of advanced modelling approaches include representing the dynamic impact of biochar decomposition
over long time scales (Lenton & Vaughan 2009), and process-based modelling using biochar-specific LCA models(e.g. Roberts et al. 2010; Hammond et al. 2011; Shackley et al. 2012; Sparrevik et al. 2013). There are also
applications that have focused on soil greenhouse gas emission balances, together with modelling ofdecomposition rates (H/Corg ratio; Lehmann et al. 2015) and priming (Woolf & Lehmann, 2012; Wang et al. 2016).
In addition, models have been used to simulate nitrous oxide reductions (Cayuela et al. 2013, 2014) as a functionof H/Corg ratio (Cayuela et al. 2015) and feedbacks to primary plant productivity (Jeffery et al. 2011, 2015) andassociated impacts on SOC stocks (Whitman et al. 2010, 2011).

Organic soils
No Refinement. See Chapter 2 of the 2013 Wetlands Supplement.

Soil inorganic C
No Refinement.

2.4

NON-CO 2 EMISSIONS

There are significant emissions of non-greenhouse gases from biomass burning, livestock and manure management,or soils. N2O emissions from soils are covered in Chapter 11, where guidance is given on methods that can beapplied nationally (i.e., irrespective of land-use types) if a country chooses to use national scale activity data. The
guidance on CH4 and N2O emissions from livestock and manure are addressed only in Chapter 10 because
emissions do not depend on land characteristics. A generic approach to estimating greenhouse gas emissions fromfire (both CO2 and non-CO2 gases) is described below, with land-use specific enhancements given in the Forest
Land, Grassland and Cropland chapters. It is good practice to check for complete coverage of CO2 and non-CO2
emissions due to losses in carbon stocks and pools to avoid omissions or double counting.
Emissions from fire include not only CO2, but also other greenhouse gases, or precursors of greenhouse gases, thatoriginate from incomplete combustion of the fuel. These include carbon monoxide (CO), methane (CH 4), nonmethane volatile organic compounds (NMVOC) and nitrogen (e.g., N2O, NOx) species (Levine, 1994). In the 1996
IPCC Guidelines and GPG2000, non-CO2 greenhouse gas emissions from fire in savannas and burning of crop
residues were addressed along with emissions from Forest Land and Grassland conversion. The methodology
differed somewhat by vegetation type, and fires in Forest Land were not included. In the GPG-LULUCF, emissions(CO2 and non-CO2) from fires were addressed, particularly in the chapter covering Forest Land (losses of carbon
resulting from disturbances). In the Cropland and Grassland chapters, only non-CO2 emissions were considered,with the assumption that the CO2 emissions would be counterbalanced by CO2 removals from the subsequent regrowth of the vegetation within one year. This assumption implies maintenance of soil fertility – an assumption
which countries may ignore if they have evidence of fertility decline due to fire. In Forest Land, there is generally
a lack of synchrony (non-equivalence of CO2 emissions and removals in the year of reporting).
These Guidelines provide a more generic approach for estimating emissions from fire. Fire is treated as a
disturbance that affects not only the biomass (in particular, above-ground), but also the dead organic matter (litterand dead wood). The term `biomass burning` is widely used and is retained in these Guidelines but acknowledging
that fuel components other than live biomass are often very significant, especially in forest systems. For Croplandand Grassland having little woody vegetation, reference is usually made to biomass burning, since biomass is themain pool affected by the fire.
Countries should apply the following principles when estimating greenhouse gas emissions resulting from fires inForest Land, Cropland and Grassland:

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

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

Equivalence (synchrony) of CO2 emissions and removals: CO2 net emissions should be reported where theCO2 emissions and removals for the biomass pool are not equivalent in the inventory year. For grassland

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

biomass burning and burning of agriculture residues, the assumption of equivalence is generally reasonable.
However, woody vegetation may also burn in these land categories, and greenhouse gas emissions from thosesources should be reported using a higher Tier method. Further, in many parts of the world, grazing is the
predominant land use in Forest Land that are regularly burnt (e.g., grazed woodlands and savannas), and caremust be taken before assuming synchrony in such systems. For Forest Land, synchrony is unlikely if
significant woody biomass is killed (i.e., losses represent several years of growth and C accumulation), andthe net emissions should be reported. Examples include: clearing of native forest and conversion to agriculture
and/or plantations and wildfires in Forest Land.


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

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

These Guidelines provide a comprehensive approach for estimating carbon stock changes and non-CO2 emissions
resulting from fire in the Forest Land (including those resulting from forest conversion), and non-CO2 emissions
in the Cropland and Grassland. Non-CO2 emissions are addressed for the following five types of burning: (1)grassland burning (which includes perennial woody shrubland and savanna burning); (2) agricultural residues
burning; (3) burning of litter, understory and harvest residues in Forest Land, (4) burning following forest clearing
and conversion to agriculture; and (5) other types of burning (including those resulting from wildfires). Directemissions of CO2 are also addressed for items (3) and (4) and (5). Since estimating emissions in these different
categories have many elements in common, this section provides a generic approach to estimate CO2 and non-CO2
emissions from fire, to avoid repetition in specific land-use sections that address emissions from fire in these
Guidelines.
Prescribed burning of savannas is included under the grassland biomass burning section (Chapter 6, Grassland,
Section 6.3.4). It is important to avoid double counting when estimating greenhouse gas emissions from savannasthat have a vegetation physiognomy characteristic of Forest Land. An example of this is the cerradão (dense
woodland) formation in Brazil which, although being a type of savanna, is included under Forest Land, due to its
biophysical characteristics.
In addition to the greenhouse gas emissions from combustion, fires may lead to the creation of an inert carbon
stock (charcoal or char). Post-fire residues comprise unburned and partially burnt components, as well as a small
amount of char that due to its chemical nature is highly resistant to decomposition. The knowledge of the rates ofchar formation under contrasting burning conditions and subsequent turnover rates is currently too limited (Forbes
et al., 2006; Preston and Schmidt, 2006) to allow development of a reliable methodology for inventory purposes,
and hence is not included in these Guidelines. A technical basis for further methodological development is includedin Appendix 1.
Additionally, although emissions of NMVOC also occur as a result of fire, they are not addressed in the present
Guidelines due to the paucity of the data and size of uncertainties in many of the key parameters needed for theestimation, which prevent the development of reliable emission estimates.

METHOD DESCRIP TION
Each relevant section in these Guidelines includes a three-tiered approach to address CO2 (where applicable) andnon-CO2 greenhouse gas emissions from fire. The choice of Tier can be made following the steps in the decision
tree presented in Figure 2.6. Under the Tier 1 approach, the formulation presented in Equation 2.27 can be applied
to estimate CO2 and non-CO2 emissions from fire, using the default data provided in this chapter and in the relevant
land-use sections of these Guidelines. Higher Tiers involve a more refined application of Equation 2.27.
Since Tier 1 methodology adopts a simplified approach to estimating the dead organic matter pool (see Section
2.3.2), certain assumptions must be made when estimating net greenhouse gas emissions from fire in those systems(e.g. Forest Land, and Forest Land converted to another land use), where dead organic matter can be a major
component of the fuel burnt. Emissions of CO2 from dead organic matter are assumed to be zero in forests that are
burnt, but not killed by fire. If the fire is of sufficient intensity to kill a portion of the forest stand, under Tier 1
methodology, the C contained in the killed biomass is assumed to be immediately released to the atmosphere. This
Tier 1 simplification may result in an overestimation of actual emissions in the year of the fire, if the amount ofbiomass carbon killed by the fire is greater than the amount of dead wood and litter carbon consumed by the fire.
Non-CO2 greenhouse gas emissions are estimated for all fire situations. Under Tier 1, non-CO2 emissions are best
estimated using the actual fuel consumption provided in Table 2.7, and appropriate emission factors (Table 2.8)
(i.e., not including newly killed biomass as a component of the fuel consumed). Clearly, if fire in forests contributes
significantly to net greenhouse gas emissions, countries are encouraged to develop a more complete methodology

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Volume 4: Agriculture, Forestry and Other Land Use(higher tiers) which includes the dynamics of dead organic matter and improves the estimates of direct and postfire emissions.
For Forest Land converted to other land uses, organic matter burnt is derived from both newly felled vegetationand existing dead organic matter, and CO2 emissions should be reported. In this situation, estimates of total fuelconsumed (Table 2.6) can be used to estimate emissions of CO2 and non- greenhouse gases using Equation 2.27.
Care must be taken, however, to ensure that dead organic matter carbon losses during the land-use conversion are
not double counted in Equations 2.27 (as losses from burning) and Equation 2.23 (as losses from decay).
A generic methodology to estimate the emissions of individual greenhouse gases for any type of fire is summarisedin Equation 2.27.
EQUATION 2.27
ESTIMATION OF GREENHOUSE GAS EMISSIONS FROM FIRE

L fire  A  M B  C f  Gef  103
Where:

L fire= amount of greenhouse gas emissions from fire, tonnes of each GHG e.g., CH 4, N2O, etc.

A

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

Cf= combustion factor, dimensionless (default values in Table 2.6)

Gef

= emission factor, g kg-1 dry matter burnt (default values in Table 2.5)

Note. Where data for MB and Cf are not available, a default value for the amount of fuel actually burnt (the productof MB and Cf) can be used (Table 2.4) under Tier 1 methodology.
For CO2 emissions, Equation 2.27 relates to Equation 2.14, which estimates the annual amount of live biomass
loss from any type of disturbance.
The amount of fuel that can be burnt is given by the area burnt and the density of fuel present on that area. The
fuel density can include biomass, dead wood and litter, which vary as a function of the type, age and condition of
the vegetation. The type of fire also affects the amount of fuel available for combustion. For example, fuel available
for low-intensity ground fires in forests will be largely restricted to litter and dead organic matter on the surface,
while a higher-intensity ‘crown fire’ can also consume substantial amounts of tree biomass.
The combustion factor is a measure of the proportion of the fuel that is actually combusted, which varies as afunction of the size and architecture of the fuel load (i.e., a smaller proportion of large, coarse fuel such as treestems will be burnt compared to fine fuels, such as grass leaves), the moisture content of the fuel and the type offire (i.e., intensity and rate of spread which is markedly affected by climatic variability and regional differences asreflected in Table 2.4). Finally, the emission factor gives the amount of a particular greenhouse gas emitted perunit of dry matter combusted, which can vary as a function of the carbon content of the biomass and thecompleteness of combustion. For species with high N concentrations, NO x and N2O emissions from fire can varyas a function of the N content of the fuel. A comprehensive review of emission factors was conducted by Andreae
and Merlet (2001) and is summarised in Table 2.5.
Tier 2 methods employ the same general approach as Tier 1 but make use of more refined country-derived emission
factors and/or more refined estimates of fuel densities and combustion factors than those provided in the default
tables. Tier 3 methods are more comprehensive and include considerations of the dynamics of fuels (biomass anddead organic matter).

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

Figure 2.6

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

Are detailed dataon biomass burning available toestimate GHG emissions using
advanced models
or methods?

Use the detailed biomass burning data
for Tier 3 method .

Yes

Box 3: Tier 3
No

Are countory-specificactivity data emission factors
available?

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

Yes

Box 2: Tier 2No

Is prescribed burning or
wildfire a key
category1?

No

Are aggregate
data on biomass burningavailable?

No
Gather data on
burning.

Yes

Collect data for Tier 3 orTier 2 method.

Yes

Use aggregate data and default
emission factors for aTier 1 method.

Box 1: Tier 1

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

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Volume 4: Agriculture, Forestry and Other Land Use

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

Subcategory

Mean

SE

References

Primary tropical forest

83.9

25.8

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

Primary open tropical forest

163.6

52.1

21,

Primary tropical moist forest

160.4

11.8

37, 73

Primary tropical dry forest

-

-

119.6

50.7

8.1

-

41.1

27.4

61, 3546.4

8.0

61, 73All secondary tropical forests

42.2

23.6

66, 30All Tertiary tropical forest

54.1

-

66, 30Wildfire (general)

52.8

48.4

2, 33, 66

Crown fire

25.1

7.9

11, 43, 66, 41, 63, 64

Surface fire

21.6

25.1

43, 69, 66, 63, 64, 1

Post logging slash burn

69.6

44.8

49, 40, 66, 18

Land clearing fire

87.5

35.0

10, 6741.0

36.5

43, 45, 69, 47

Wildfire

53.0

53.6

66, 32, 9

Prescribed fire – (surface)

16.0

13.7

66, 72, 54, 60, 9

Post logging slash burn

168.4

168.
8

25, 58, 46

Felled, wood removed, and burned (landclearing fire)

132.6

-

62, 9

69.4

32, 66

Primary tropical forest (slashand burn)

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

Boreal forest

All boreal forest

Eucalypt forests

61

Wildfire

19.8

100.
8
6.3

Post logging slash burn

77.5

65.0

55, 19, 14, 27, 66

Felled and burned (land-clearing fire)

48.4

62.7

53, 24, 71

50.4

53.7

43, 56

Shrubland (general)

26.7

4.2

43

Calluna heath

11.5

4.3

26, 39Sagebrush

5.7

3.8

66

Fynbos12.9

0.1

70, 6614.3

9.0

All Eucalypt forests

Other temperate forests

66

All “other” temperate forests

Shrublands

All Shrublands

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

Savanna woodland

Mea
n
2.5

-

28

Savanna parkland

2.7

-

57

2.6

0.1

Savanna woodland

3.3

-

57

Savanna parkland

4.0

1.1

57, 6, 51

Tropical savanna

6

1.8

52, 73

Other savanna woodlands

5.3

1.7

59, 57, 31

4.6

1.5

Tropical/sub-tropical grassland

2.1

-

28

Grassland

-

-

48

2.1

-

Tropical/sub-tropical grassland

5.2

1.7

9, 73, 12, 57

Grassland

4.1

3.1

43, 9

Tropical pasture~

23.7

11.8

4, 23, 38, 66

Savanna

7.0

2.7

42, 50, 6, 45, 13, 65

10.0

10.1

Peatland41

1.4

68, 33Tundra10-33

Vegetation type
Savanna woodlands (early dry
season burns)*

Subcategory

All savanna woodlands (early dry season burns)

Savanna woodlands (mid/latedry season burns)*

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

All savanna grasslands (early dry season burns)*

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

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

References

Other vegetation types

Agricultural residues (postharvest field burning)

MB = AGR(T) x FracBrunt(T)See Equation 11.6 in
Chapter 11, Volume 4
for AGR(T) calculation* Surface layer combustion only
~ Derived from slashed tropical forest (includes unburned woody material)a

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

b

Expert assessment by authors.

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

CO2

CO

CH4

N2O

NOX

Savanna and grassland

1613
± 95

65
± 20

2.3
± 0.9

0.21
± 0.103.9± 2.4

Agricultural residues

1515
± 177

92
± 84

2.7

0.07

2.5
± 1.0

Tropical forest

1580
± 90

104
± 20

6.8
± 2.0

0.20

1.6
± 0.7

Extra tropical forest

1569
± 131

107
± 37

4.7
± 1.9

0.26
±0.07

3.0
± 1.4

Biofuel burning

1550
± 95

78
± 31

6.1
± 2.2

0.06

1.1
± 0.6

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

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

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

Mean

SD

References

Primary tropical forest

0.32

0.12

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

Primary open tropical forest

0.45

0.09

21

Primary tropical moist forest

0.50

0.03

37, 73

Primary tropical dry forest

-

-

0.36

0.13

Young secondary tropical forest (35 yrs)

0.46

-

Intermediate secondary tropicalforest (6-10 yrs)

0.67

0.21

61, 35

Advanced secondary tropical forest(14-17 yrs)

0.50

0.10

61, 73All secondary tropical forests

0.55

0.06

56, 66, 34, 30

All tertiary tropical forest

0.59

-

Wildfire (general)

0.40

0.06

33

Crown fire

0.43

0.21

66, 41, 64, 63

surface fire

0.15

0.08

64, 63

Post logging slash burn

0.33

0.13

49, 40, 18

Land clearing fire

0.59

-

0.34

0.17

-

-

Prescribed fire – (surface)

0.61

0.11

72, 54, 60, 9

Post logging slash burn

0.68

0.14

25, 58, 46

Felled and burned (land-clearing
fire)

0.49

-

0.63

0.13

Post logging slash burn

0.62

0.12

Felled and burned (land-clearing
fire)

0.51

-

0.45

0.16

Primary tropical forest(slash and burn)

Subcategory

All primary tropical forests

Secondary tropical forest(slash and burn)

Boreal forest

All boreal forest
Wildfire

Eucalyptus forests

All Eucalyptus forests

Other temperate forests

All “other” temperate forests

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61

66, 3067
45, 4762

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

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

Subcategory

Mean

SD

Shrubland (general)

0.95

-

Calluna heath

0.71

0.30

26, 56, 39

Fynbos0.61

0.16

70, 440.72

0.25

Savanna woodland

0.22

-

28

Savanna parkland

0.73

-

57

Other savanna woodlands

0.37

0.19

0.40

0.22

Savanna woodland

0.72

-

66, 57

Savanna parkland

0.82

0.07

57, 6, 51

Tropical savanna

0.73

0.04

52, 73, 66, 12

Other savanna woodlands

0.68

0.19

22, 29, 44, 31, 57

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

0.74

0.14

Savanna Grasslands/
Pastures (early dry season
burns)*

0.74

-

28

-

-

48

0.74

-

Tropical/sub-tropical grassland

0.92

0.11

44, 73, 66, 12, 57

Tropical pasture~

0.35

0.21

4, 23, 38, 66

Savanna

0.86

0.12

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

0.77

0.26

Peatland

0.50-

20, 44Tropical Wetlands

0.70

-

44

Wheat residues

0.90

-

see Note b

Maize residues

0.80

-

see Note b

Rice residues

0.80

-

see Note b

Sugarcane a

0.80

-

see Note b

Other Crops

0.85

-

see Note b

Shrublands

All shrublands

Savanna woodlands (early
dry season burns)*

All savanna woodlands (early dry season burns)

Savanna woodlands(mid/late dry season
burns)*

Tropical/sub-tropical grassland
Grassland

All savanna grasslands (early dry season burns)*

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

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

References
44

22, 29Other vegetation types

Agricultural residues(Post-harvest fieldburning)*

Surface layer combustion only; ~ Derived from slashed tropical forest (includes unburned woody material);refer to burning before harvest of the crop; b Expert assessment by authors.

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a

For sugarcane, data

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

2.5

ADDITIONAL GENERIC GUIDANCE FOR TIER
3 METHODS

Tier 3 inventories are advanced systems using measurements and/or modelling, with the goal of improving theestimation of greenhouse gas (GHG) emissions and removals, beyond what is possible with Tier 1 or 2 methods.
In this section, guidelines are elaborated that provide a sound scientific basis for the development of Tier 3
Inventories in the AFOLU sector. These guidelines do not limit the selection of Tier 3 sampling schemes ormodelling methods but provide general guidance to assist the inventory developer in their implementation.
AFOLU inventory compilers are advised to read this section in conjunction with general guidance for Tier 3
methods relevant to all sectors found in Volume 1, Chapter 6.

2.5.1

Measurement-based Tier 3 inventories

Inventories can be based on direct measurements from which emissions and removals of carbon are estimated.
Purely measurement-based inventories, e.g., based on repeated measurements using a national forest inventory orsimilar estimation methods can produce carbon stock change estimates but still rely on appropriate statisticalmodels, such as allometric models or volume and wood density functions. Inventories using measurement-based
methods also need to select appropriate statistical sampling estimators to produce a national inventory from theplot estimates. Moreover, inventory plot remeasurements will typically require additional data or methods to arrive
at estimates of GHG emissions from disturbance events, in particular for non-CO2 GHG. Measurement of nonCO2 greenhouse gas emissions is possible, but because of the high spatial and temporal variability, Tier 3 methods
for estimating non-CO2 emissions typically use a combination of models (see Section 2.5.2) and measurements.
Many countries using a measurement-based Tier 3 method will already have well established national inventories.
Typically, these inventories have been established for purposes other than collecting data for estimating carbon
stock changes and non-CO2 emissions (e.g., National Forest Inventories for timber resource assessments or soil
resource mapping for agricultural planning). In general, the following six steps should be considered when
implementing a measurement-based Tier 3 inventory.
Ste p 1 . Dev elo p a sa mpl ing s ch e me , in cl ud i n g sa m p le u nit (p lo t) de sig n a ndme a s ure me nt s t o be co l lect ed.
Sampling schemes can be developed using a variety of methods such as simple random, stratified random,
systematic or model-based sampling. When designing a sampling scheme, countries often also consider factors
such as spatial variability and temporal dynamics of carbon stocks, key environmental variables (e.g., climate) andmanagement systems (e.g., harvested forest land, grazed grassland).
When using a repeated measurement design, the timing of re-measurement may be influenced by the rate of change
experienced. For example, re-measurement periods in boreal and some temperate regions, where trees grow slowly
and DOM pools change little in single years, can be longer than in environments where carbon dynamics are more
rapid. When implementing a measurement-based Tier 3 inventory, the inventory compiler should take into
consideration that it will not be possible to estimate emissions and removals using the stock-difference method
until a minimum of two measurement cycles have been conducted (often 10 years or longer in total).
Some sampling schemes do not include re-sampling of the same sites (e.g., temporary inventory plot designs).
Such designs may limit the statistical power of the analysis when estimating change, and therefore lead to greater
uncertainty in estimates of carbon stock change. Repeated measurement designs with permanent plot locations
typically provide a better basis for estimating carbon stock changes or emissions. The utility of permanent plots isoften greater if they are accurately georeferenced to facilitate the use of spatial auxiliary variables, such as fromremote sensing (GFOI, 2016).
For some carbon pools, such as soil carbon, litter and woody debris, it is not necessarily possible to remeasure thesame material through time (i.e., if taking a soil core, that soil has been removed from the site and cannot beremeasured, unlike measuring the same trees through time). However, multiple samples can be taken at each time
step to capture local site scale heterogeneity in the carbon stock and detect changes over time with each re-samplingof a site (Ellert et al., 2002, Conant et al., 2003). Where countries use direct measurement methods for soil C, thesampling design needs to ensure that a sufficient number of samples are taken at each measurement time for
estimating stock change (Spencer et al., 2011).
Inventory and plot designs should consider the practicality of implementation given country circumstances (e.g.,terrain, access, safety, vegetation type). The types and number of measurements will depend on the plot design,
the underlying population of carbon pools to be reported and the data requirements of methods adopted to estimate
carbon stocks and stock changes from the plot data.

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It is good practice to develop a methodology handbook (e.g., Canadian Forest Service, 2008; US Forest Service,2006) explaining the entire sampling scheme as part of Step 1. This handbook can be useful for those involved
with the measurements, laboratory analyses and other aspects of the process, as well as possibly providing
supporting material for documentation purposes. The handbook should document the plot design, in particular
how plots are to be located and, in the case of repeated measurement designs, re-located for future measurements(Vidal et al., 2016).
Ste p 2 . Se lec t sa mp l e si t es.
Specific sampling sites will be located based on sampling design. It is good practice to have an appropriate process
in place for selecting alternative sites in case it is not possible to sample some original locations. In a repeatedmeasurement design, the sites will become a monitoring network that is periodically re-sampled.
Determining sampling locations will likely involve the use of a geographic information system. A geographic
database may include information on land use and land-use changes (i.e., activity data) as well as a variety ofenvironmental and management data, such as climate, soils, land use, and livestock operations, depending on thesource category and stratification. If key geographic data are not available at the national scale, or are spatiallyinconsistent, the inventory developer may either 1) re-evaluate the design and stratification (if used) in Step 1 andpossibly modify the sampling design or 2) re-develop the geographic data to meet the inventory requirements.
Normally the sampling intensity should be the same within a stratum but not necessarily between strata. However,where the stratification is based on land use and is updated for each inventory, changes in land use between
measurement periods can complicate the estimation of changes in carbon stocks over time. As such, it is goodpractice to use stratification methods that do not lead to bias or time-series inconsistencies due to changes in landuse.
Sampling may require coordination among different national ministries, provincial or state governments, corporate
and private land owners. Establishing relationships among these stakeholders can be undertaken before collecting
initial samples. Informing stakeholders about ongoing monitoring may also be helpful and lead to greater successin implementing monitoring programs.
Ste p 3 . Co l lec t i nit ia l s a mp le s .
Once the plot locations have been determined, a measurement team can visit those locations, establish plots andcollect initial measurements and samples. It is helpful to take geographic coordinates of plot locations or sample
points with a global positioning system (GPS) to help relocate them later, noting that GPS readings are often notaccurate enough to relocate the exact plot location, especially under dense forest canopies. As such, if repeatedmeasurements are planned, it is good practice to permanently mark the location for ease of finding and re-sampling
the site in the future. Where possible these markers should not be visible to the land owner (e.g., utility ballmarkers that can be buried in the soil and re-located precisely over time).
It is good practice to take relevant measurements and notes of the environmental conditions and management at
the site. This will confirm that the conditions were consistent with the design of the sampling scheme, and also
may be used in data analysis (Step 5). If a stratified sampling approach is used, and it becomes apparent that manyor most sites are not consistent with the expected environmental conditions and management systems, it is goodpractice to repeat Step 1, re-evaluating and possibly modifying the sampling scheme based on the new information.
Ste p 4 . Re- sa mp le t h e mo n it o r i ng n etw o r k o n a pe rio di c ba s i s.
For repeated measurement designs, sampling sites will be periodically re-sampled with the time between remeasurement dependant on the rate of stock changes or the variability in emissions, the resources available for themonitoring program, and the design of the sampling scheme. It is good practice to avoid any impact ofmeasurement techniques on C stocks and their dynamics (i.e. no destructive sampling) where permanent sample
plots are used.
If destructive sampling is involved, such as removing a soil core or dead organic matter sample, it is good practice
to re-sample at the same site but not at the exact location in which the sample was removed during the past.
Destructive sampling the exact location is likely to create bias in the measurements. Such biases would
compromise the monitoring and produce results that are not representative of national trends. When destructive
sampling of trees is undertaken, for example to develop or validate allometric equations, the samples are usuallytaken from locations or species that are considered representative of the trees in the plots.
Ste p 5 . Ana ly se da t a a n d det e r mi n e ca rbo n sto c k cha ng e s/ no n - C O 2 e mi s s io n s, a nd
inf er na tio na l e mi s sio n s a nd re mo v a l e sti ma te s a nd th eir u nc erta i nty .
A well-designed sampling scheme will provide an unbiased estimate and variance for the measured quantities (SeeVolume I, Chapter 3 for more information). The overall result of the statistical analysis will be estimates of carbon
stock changes or measurements of emissions from which the national emission and removal estimates can bederived.

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

To derive estimates of carbon stock changes or emissions from measurements collected on the plots typicallyrequires the use of models that relate these measurements to carbon stocks. The types of models and the uncertainty
associated with them vary depending on measurements taken and the carbon pools being estimated. Examples ofthese models include allometric equations for estimating tree and deadwood biomass, root:shoot ratios for
estimating belowground biomass (Mokany et al., 2006) and the use of spectral signatures to estimate soil carbon(Baldock et al., 2013).
When estimating uncertainty for carbon stock changes and/or emissions it is good practice to include all relevant
sources of uncertainty, including the sampling scheme, plot measurements and model parameters and structure
and laboratory processing methods (see discussion for each source category later in this volume in addition to theuncertainty chapter in Volume 1). Overall uncertainty can be reduced by increasing the sampling intensity, using
additional strata or covariates to explain more of the variance or improving the models. Model uncertainty may
be relatively small, at least in situations with well-developed models calibrated for national situations, or relatively
large where global models are applied.
To obtain national estimates of carbon stock changes or emission of non-CO2 greenhouse gases, it may benecessary to interpolate or extrapolate measurements using spatial statistical analyses and models that take into
consideration environmental conditions, management and other activity data. Such models are necessary becauseof the expense and difficulty in obtaining a sufficient sampling intensity to infer C stock changes or emissions
directly from the survey sample. For example, CH4 and N2O emissions from forest fires are typically inferred fromdata on the area burnt, and fuel consumption estimates derived from specific case studies. In a similar fashion, soil
N2O emissions could be readily estimated using chambers, but this can be very expensive to establish a networkwith the sampling intensity needed to provide national emission estimates based solely on measurements without
use of models for extrapolation. Alternatively, compilers may use a model-based approach in these cases, whichis informed by the limited sample of C stock or emission measurements (See Section 2.1.2).
It is good practice to analyse emissions relative to environmental conditions in addition to the contribution ofvarious management practices to those trends. Interpretation of the patterns will be useful in evaluating possibilities
for future mitigation.
Ste p 6 . Re po rt ing a n d Do c u me nt a t io n.
It is good practice to assemble inventory results in a systematic and transparent manner for reporting purposes.
Documentation typically includes a description of the sampling scheme and statistical methods, sampling schedule(including re-sampling), stock change and emissions estimates and the interpretation of emission trends (e.g.,contributions of management activities). In addition, QA/QC should be completed and documented in the report.
For details on QA/QC, reporting and documentation, see the section dealing with the specific source category laterin this volume, as well as information provided in Volume 1, Chapter 6.
When developing/collating documentation for reporting Tier 3 measurement-based methods it is good practice to:describe the sampling design and/or measurements;



describe any changes in the design or measurements through time and how these changes are addressed toensure time series consistency in carbon stocks or emissions;

describe the models used to calculate carbon stock changes and non-CO2 emissions from the measurements,
including the uncertainty;

describe how area estimates are derived from the survey, such as a national forest inventory, and harmonizedwith land representation data for other land-uses;

discuss the influence of time periods between measurement cycles on estimated C stock changes or emission
estimates, and how this impact is incorporated into the uncertainty analysis; and

document, if applicable, how Tier 3 measurement methods are applied consistently with Tier 2 or Tier 3
model-based methods to prevent errors of omission or commission in reported carbon stock changes or
emissions for the entire spatial and temporal domain of the country.

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TABLE 2.6A (NEW)EXAMPLES OF DOCUMENTATION TO ASSEMBLE IN SUPPORT OF TRANSPARENT REPORTING OF TIER 3 MEASUREMENTBASED INVENTORIES

Step 1. Develop sampling scheme, including sample sizeand design and measurements to be collected.

A description of the sampling scheme including size anddesign and measurements to be collected
Reason for adopting the selected sampling scheme

Step 2. Select sample sites.

Description of the process for selecting sample sites andprocesses for dealing with exclusions/replacements

Step 3. Collect initial samples.

Sample collection and quality assurance / quality control
protocols.

Step 4. Re-sample the monitoring network on a periodic
basis.

Description of re-sampling strategy and reasoning for
adopted resampling period

Step 5. Analyse data and determine carbon stock changes
and other sources of emissions, and infer nationalemissions and removals estimates and measures of
uncertainty.

Data processing and quality assurance / quality control
protocols including how adopted re-sampling period ishandled when making carbon stock change estimates andtheir associated uncertainty.

Step 6. Reporting and Documentation

All of the above material summarised into a report for
third party review.

2.5.2

Model-based Tier 3 inventories

Model-based Tier 3 inventories are developed using empirical (e.g. forest growth curves that represent carbon
stock increase with tree age.), process-based (e.g. model representation of underlying physiological, biophysical,
and management processes that drive carbon dynamics in ecosystems), hybrid (e.g. the development of forest
growth curves from empirical data combined with a process model calibrated from research data on dead organic
matter dynamics) and/or other types of models. Just as Tier 3 measurement-based methods typically also require
models to estimate carbon stock changes (see Section 2.1.1), Tier 3 model-based inventories require measurements
to calibrate and validate the models used to estimate carbon stock changes.
It is unlikely that one single model will be suitable for estimating emissions and removals for all carbon pools andnon-CO2 gases across all land uses, land-use changes and management actions. Therefore, inventory compilers
will need to select a suite of different models to develop estimates of interest. In many cases existing models need
to be adapted, coupled and/or integrated to provide a complete estimate of emissions and removals in the source
categories of interest.
When selecting a model, it is important to consider how it will be used and interact with other models. This isparticularly important when using Tier 3 mass-balance models in combination with Tier 1 or 2 emissions factors(e.g. if different soil carbon models or methods are used for different land-uses, how will the carbon pools betransferred between them in the case of land-use change). If changes in modelling methods within the reportingtime series occur adequate steps should be taken to ensure time series consistency.
Models may be run individually for different land uses and carbon pools and the results combined or brought
together in a single framework using coupling and integration techniques. Individual model simulations are
typically used where multiple agencies are responsible for developing different parts of the inventory (e.g., theforest agency responsible for forest lands, the agriculture agency responsible for cropland and grassland).
Coupling different models is a convenient strategy for addressing effects with different time and space scales. In
contrast, model integration links different modelling approaches to elucidate the complex dimension of time andspace dynamics (Panichelli & Gnansounou, 2015), helping ensure consistency in land representation, carbon pools
and input variables (Brack et al., 2006). Integration frameworks can also help organize data and estimation methods
at any level of methodological complexity and facilitate the systematic progression from simpler to more complex
methods (GFOI, 2016).
In all cases, models used in Tier 3 methods ensure higher accuracy only when they are correctly implemented andcapable of representing the population of interest. In general, the following seven steps are used to correctly
implement a Tier 3 model-based inventory (see also Figure 1, Volume 1, Chapter 6, Section 2.4).
Ste p 1 . M o de l se le ct io n o r d ev elo p me nt
Inventory compilers can choose from a wide range of model types depending on reporting needs, data availability
and capacity. As part of model selection or development, it is good practice to consider if the model/s:adequately represent the range of land uses, ecosystems and management practices in the region or country;

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Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categoriesallow for the quantification of uncertainty;



reduce uncertainty relative to other available methods (e.g., Tier 1 methods) or estimates are improved in otherways (e.g., more complete coverage of carbon pools or lands);

can be run and maintained in an operational context with available time and resources (e.g., input data isreadily available, staff have sufficient experience and knowledge, suitable compute infrastructure is available);

produce outputs that can be used for reporting emissions and removals by relevant land-use categories;

produce time-series consistent results;are compatible with other existing models used in the inventory; and

are well documented and tested.

Multiple models will likely be selected as potentially suitable as part of Step 1. These models can then be tested
prior to implementation using steps 2 and 3 below. Therefore, before moving to Step 2, at least a sub-set of the
input data required to run the model should be collected or collated, including input variables (such as forest
species or type, climate, soil characteristics), and any existing parameters and data required for further model
calibration and evaluation. In some cases, input data may be a limiting factor in model selection or development,
requiring some models to be discarded or modified to accommodate the available activity and/or environmental
data.
Ste p 2 . M o de l Ca li bra t io n
Model calibration (i.e. parameterisation) is the process of selecting or adjusting model parameters to obtain resultsthat best represent the processes of interest in the region (and time period) for which the model will be applied.
The model calibration procedure readies a model for its further use in analyses. For example, replacing default
growth curves with those specific to the tree species or site conditions to which the model will be applied or
replacing climate averages with regional climate data are examples of model parameterisation.
Calibration data should represent the population. In practice, this does not mean that all environmental conditions
are covered, but that the calibration data includes a range of the conditions existing the country that isrepresentative of national circumstances.
Model sensitivity analyses may be used to determine the most important parameters for calibration. In a sensitivity
analysis, parameter values are varied through a series of simulations to determine the associated change in model
output. The parameters are ranked from most to least sensitive based on the level of change in model output. Sometechniques also incorporate measurements into the sensitivity analysis (Sobol, 2001). The most sensitive
parameters are typically calibrated to improve the agreement between modelled and measured carbon stocks, stockchanges or non-CO2 greenhouse gas emissions.
There are multiple methods for calibrating models. Simpler empirical models (e.g., empirical forest growth models
based on forest age or site indices) are commonly developed by fitting functions to data on carbon stocks or stockchanges using standard statistical methods and software. More advanced models (e.g., hybrid or process-basedmodels) typically have numerous, interrelated parameters. For these models calibration is often completed usingparameter optimisation methods that vary the model parameters within known ranges to best match known results(e.g., carbon stocks). There are several methods for doing this, including generic algorithms, machine learning andBayesian. The methods may also be used to propagate error through the inventory analysis (e.g., Hararuk et al.,2017).
In all cases it is good practice to document the calibration procedure and results.
Re-calibration of the model or modifications to the structure may be necessary if the model does not capture
general trends or there are large systematic biases. Full evaluation of the model is described in Step 3. See Box
2.2f for examples of model calibration.

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BOX 2.2F (NEW)AN EXAMPLE OF MODEL CALIBRATION, EVALUATION AND IMPROVEMENT THROUGH DATA ASSIMILATION

The development of Canada’s Carbon Budget Model for the Canadian Forest Sector started in 1989
and is continually being improved through new data collection, analysis and model enhancements.
As part of this process, Shaw et al., (2014) assessed CBM-CFS3’s ability to predict ecosystemcarbon stocks in independent plots established as part of Canada’s national forest inventory (NFI).
The study demonstrated close agreement in the predictions of total ecosystem carbon stocks (within
1percent) but found some compensating errors (bias) in specific pools, ecozones, and plots withdifferent tree species.
To further improve the CBM-CFS3 performance in Canadian forest ecosystems, a Bayesian Markov
Chain Monte Carlo (MCMC) technique was used to calibrate 45 model parameters by assimilating
carbon stocks of six deadwood and soil carbon pools estimated from 635 plots from Canada’s
National Forest Inventory (Hararuk et al., 2017). These plots were randomly split into two groups;
calibration (n = 326), used to calibrate the parameters, and validation (n = 309), used to evaluate theperformance of the model with calibrated parameters.
Calibration led to most improvement in the simulation of carbon stocks in small and fine woody
debris, reducing RMSE by 54.3 percent, followed by the snag stems (RMSE reduced by 23.2
percent), and coarse woody debris (13 percent). Twenty of the 45 parameters were well constrainedby the available data. The calibrated parameters resulted in increased rates of carbon cycling in fineand coarse woody debris and the soil organic layer, distinct carbon dynamics in hardwood andsoftwood dominated stands, and increased temperature sensitivity of the carbon contained in themineral soil.
While parameter calibration considerably improved the simulation of the small and fine woody
debris and snags stem pools, model representation of the branch snag, coarse woody debris, soilorganic layer, and mineral soil pools were not substantially improved. This indicated the need forthe inclusion of additional processes in carbon dynamics simulation or a change in the modellingparadigm. Model improvements may be achieved by including a lignin effect on deadwood decay
and by including the effects of tree species, soil types, and mosses (see Box 2.2g) in the CBM-CFS3.
Further data assimilation analyses are ongoing.
Ste p 3 . Ev a lua t io n o f M o del B e ha v io ur
Once the model has been calibrated, it should be evaluated to demonstrate that the model effectively simulates
measured trends for the source category of interest. Evaluation can also support the justification for selecting,developing or possibly improving a particular model for the inventory analysis.
It is good practice to use measurements independent of those used for model calibration when evaluating model
behaviour and to confirm that the model is capable of estimating emissions and removals in the source categories
of interest (Falloon and Smith, 2002; Prisley and Mortimer, 2004). In practice, this is typically achieved by setting
aside a subset of data collected for model calibration to be used exclusively for model evaluation. Comparisons
between model output and independent measurements can be made using statistical tests and/or graphically. In
addition to evaluation with independent data, other evaluation checks may be useful, including:range checks to show that estimates of carbon stocks and changes in all pools do not exceed pre-defined
expected limits;in models that track both stocks and flows between carbon pools and the atmosphere, that mass-balance isbeen maintained through all simulations;

use of other statistical methods for assessing model behaviour, such as resampling methods (e.g.,bootstrapping); and

assessment of the sensitivity of various parameters in the model (sensitivity analysis).

It is good practice to ensure that the model responds appropriately to variations in activity data and environmental
conditions occurring in the spatial and temporal domain where the model will be applied. Re-calibration of themodel or modifications to the structure (i.e., algorithms) may be necessary if the model does not capture generaltrends or there are large systematic biases. In some cases, a new model may be selected or developed based on thisevaluation. Evaluation results are an important component of the reporting documentation. See Box 2.2g forexamples of model evaluation and improvement.

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BOX 2.2G (NEW)EXAMPLE OF MODEL EVALUATION AND IMPROVEMENT

Finland
The sample sizes in soil carbon inventories are usually not adequate for national level soil carbon
stock change assessment with few exceptions (e.g., Sweden, and Germany, see Gamfeldt et al., 2014
and Grüneberg et al., 2014). As such, most countries use soil carbon models to estimate carbon stockchanges then evaluate the results using repeated soil inventories. In general, it has been shown thatmodels can estimate soil carbon stock change in the same magnitude as that measured, but
uncertainties of both measurements and model estimates are often higher than actual measurements(Ortiz et al., 2009; Rantakari et al., 2012). This makes the evaluation of model outputs challenging.
Two soils carbon models are commonly used in Finland: Yasso07 and ROMULv. An evaluation of
the performance of these models against forest soil carbon stock measurements was undertaken by
Lehtonen et al. (2016). Both models require estimates of carbon input from vegetation. Litter input
from trees was estimated using litter production rates from research sites and stem volume maps
from the National Forest Inventory. Inputs from understorey vegetation were estimated using new
biomass models.
To evaluate the models, both were applied across Finland and run until steady state was achieved;
thereafter, measured soil carbon stocks were compared with model estimates. The evaluation showedthat the role of understorey litter input was underestimated by Yasso07, especially in northern
Finland, and the inclusion of soil water holding capacity in the ROMULv model improved
predictions, especially in southern Finland. Simulations and measurements indicated that models
using only litter quality and quantity and weather data underestimate soil carbon stock in southern
Finland, and this underestimation is due to omission of the impact of droughts on the decompositionof organic layers. The model evaluation results imply improving estimates of understorey litter
production in the northern latitudes would be an area for improvement in greenhouse gas inventories(Lehtonen et al., 2016).
CanadaAn evaluation of CBM-CFS3 ability to predict ecosystem carbon stock estimates derived from anentirely independent data set from the initial establishment of Canada’s new National Forest
Inventory (Gillis et al., 2005) was undertaken (Stinson et al., 2016). Estimates of abovegroundbiomass, dead organic matter and soil carbon stocks from up to 696 ground plots were compared tomodel-derived estimates (Shaw et al., 2014). Model simulations for each ground plot used only thetype of input data available to the NFCMARS for the NIR in 2010.None of the model’s default
parameters were altered. Ecosystem total C stocks estimated by CBM-CFS3 were unbiased (meandifference = 1.9 Mg ha−1, p = 0.397), and significantly correlated (r = 0.54, p > 0.001) with groundplot-based estimates. Although the overall C stock estimates were within 1 percent of the observed
values, detailed analyses also revealed compensating biases specific to pools, ecozones or leading
species. Contribution to ecosystem total C stocks error from soil was large, and from deadwood andaboveground biomass small. Results for percent error in the aboveground biomass (7.5 percent) anddeadwood (30.8 percent) pools compared favourably to the GPG-LULUCF standards of 8 percent
and 30 percent, respectively. Further details are provided in Shaw et al. (2014).
Subsequent analyses assessed the reasons for the consistent under prediction of organic carbon
stocks in low productivity boreal sites, in which mosses can contribute 30 percent or more of total
ecosystem Net Primary Production (Bona et al., 2013). Although mosses are not a carbon stock thatis included in the IPCC pools, it is increasingly evident that omitting them will result in significant
under prediction of both carbon stocks and fluxes in forest ecosystems with high moss cover. Bonaet al. (2016) estimated that in poorly drained upland black spruce forests of boreal Canada as muchas 31–49 percent of the total carbon stocks are potentially contributed by mosses alone. A new moss
module was developed and added to the CBM-CFS3 and off-line comparisons indicate that
representing moss carbon stocks and inputs will reduce bias in organic carbon stock estimates (Bonaet al., 2016).
Ste p 4 . Co l lec t a n d co l l a t e re q uir e mo d el da ta in p uts
Models require specific input data to estimate greenhouse gas emissions and removals associated with a source
category. These inputs may range from weather and soils data to livestock numbers, forest types, natural
disturbances or cropping management practices. While much of this data may have been collected as part of themodel selection process (Step 1), additional data may need to be collected prior to full implementation. For

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example, the climate data used in model selection may have only been for specific points, while for implementation
the model will require the data spatially over large areas. In these cases, the new spatial input data may need to be
developed to implement the model at the desired spatio-temporal scale.
Ste p 5 . M o de l I mp l e me nt a t io n
The major consideration when implementing the model is to obtain enough computing resources and personnel
time to prepare the input data, conduct the model simulations, and analyse the results. In some cases, limitations
in computing resources may constrain the complexity and range of spatial or temporal resolution that can be usedin implementing the model at the national scale (i.e. simulating at finer spatial and temporal scales will require
greater computing resources). An initial analysis of computing needs should be explored during model selection
and development (Step 1). It may be possible to increase the efficiency of this process using programming scripts,
re-coding parts of the model and adjusting the spatial and temporal extent and resolution of the simulations. It may
also be possible to implement the model on computing resources that are outside the agency (e.g. cloud-based
computing).
Ste p 6 . A ss es s u nce rt a i nt y
Uncertainty analysis should not be confused with sensitivity analysis. Uncertainty analysis determines theprobabilities of a range of estimates that can be used to derive confidence intervals for the estimates, and to develop
plans to further reduce uncertainties. Sensitivity analysis is conducted to determine the relative change in model
output given changes in model input values, which can be informative for model calibration (See Step 3).
In many Tier 3 models, Monte Carlo analyses can be used to simulate the uncertainty arising from the large numberof possible parameters in the systems. Empirical analyses may also be an option to quantify uncertainty in model
structure and parameterization based on an evaluation of model prediction error for sites with known inputs (SeeBox 2.2h). In general, uncertainties are quantified at national scales on annual time steps for reporting but may
also be estimated at finer spatial and temporal scales. However, it may not be feasible or sensible to apply fullMonte Carlo simulations to, for example, every spatial unit in a country. Given the computing resource and timerequirements, it may also not be necessary to repeat a full Monte Carlo analysis every year. For example, in thecase where only the activity data time series has been updated, but no other material changes to the inventory havebeen made, uncertainty estimates can be extrapolated to the additional years in the time series. A smaller test may
also be run to demonstrate there has been no material change in uncertainty.
BOX 2.2H (NEW)EXAMPLES OF QUANTIFICATION OF MODEL UNCERTAINTYThis box is provided for information purposes and for the presentation of examples of quantificationof uncertainties in Tier 3 modelling approaches.
CanadaBoth uncertainty and sensitivity analyses were conducted on Canada’s CBM-CFS3 integration
framework (Metsaranta et al., 2017) and uncertainty analysis results are summarized below.
A wide range of factors that contribute to the uncertainty in the model estimates were varied using
Monte-Carlo simulations using the entire national system. These factors include the processes used
to initialize dead organic matter and soil carbon pools, biomass increment data (a multiplier with arange of ±50 percent was applied to net biomass increment), activity data (wildfire (±10 percent),
insects (±25 percent), and harvest (range varies by jurisdiction)), selection of stands during theallocation of natural disturbances to affected stands, and parameters defining litter input and deadorganic matter pool dynamics. Parameter ranges for 32 biomass turnover and dead organic mattercarbon modelling parameters were obtained from the literature and used as minimum and maximum
values of triangular distributions (with mode set to the CBM-CFS3 default value). All parametervalues and input data were varied independently, because the correlation structure among parameters
could not be estimated.
Input data for Canada’s 230 million ha of managed forest are contained in 20 CBM-CFS3 databases,
each representing a specific region in Canada. Monte Carlo simulations for each of

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BOX 2.2H (NEW) (CONTINUED)
EXAMPLES OF QUANTIFICATION OF MODEL UNCERTAINTY

these 20 databases were conducted independently and the sample size for national totals wasincreased by summing random combinations of the 100 Monte Carlo runs from the 20 projects togenerate 1000 randomly recombined estimates of national totals. The approximated 95 percent
confidence interval (CI) was defined from the 2.5th and 97.5th percentiles of these national
estimates.
Under the assumptions of this analysis, the 95 percent confidence interval width averaged 32.2 Tg
C·year−1 (+16.6 and –15.6 Tg C·year−1) for net biome production (total stock changes) relative to an
overall simulation median of –0.8 Tg C·year−1 from 1990 to 2014. The largest sources of uncertainty
were related to factors determining biomass increment and the parameters used to model soil anddead organic matter carbon dynamics. Some of these processes also vary in their intrinsic degree ofpredictability (Luo et al., 2015), and some factors causing large contributions to uncertainty may
prove difficult to reduce (e.g., fine root turnover and its spatial and temporal variations).
United States of America
Uncertainty analysis for agricultural soil carbon and N2O emissions have been conducted for the US
greenhouse gas inventory (Ogle et al. 2010; Del Grosso et al. 2010; US EPA, 2017). A Tier 3 method
is applied to generate emissions estimates with application of the DayCent ecosystem model. This
process-based model simulates plant production, soil organic matter formation, nutrient cycling,water flows, and temperature regimes (Parton et al. 1998). Uncertainty is quantified through a
combination of Monte Carlo simulations, an empirical analysis of model prediction error, andpropagation of variance associated with the land representation survey data.
The inventory is compiled by simulating plant production and soil processes based on land use
histories at about 400,000 locations that are part of a national survey, the National Resources
Inventory (NRI) (Nusser et al. 1998, Nusser and Goebel 1997). The major input uncertainties in the
Tier 3 model application are associated with fertilization and tillage management and are quantified
in probability distribution functions (PDFs), representing the likelihood of different fertilization
rates, tillage practices and manure amendments. The model is applied using a Monte Carlo Analysis
in a series of 100 simulations for each NRI survey locations based on random draws from the PDFs.
In turn, the analysis produces 100 estimates of soil C stock changes and N 2O emissions for eachsurvey location.
Model prediction error, including bias and precision, is quantified in statistical models with anempirical analysis based on a comparison of model output to measured observations of soil C stocks
and N2O emissions from experimental sites (Ogle et al. 2007). The model inputs are mostly known
for the DayCent model simulations of the experimental sites and so the primary sources of
uncertainty that are quantified in this analysis are associated with model structure andparameterisation, in addition to the variance in measured observations. Moreover, the experimentalsites are independent from model calibration allowing for an independent evaluation of model
prediction error. The resulting empirical model is applied to the DayCent model output to adjust forbiases, to the extent needed, and to quantify precision in model results.
In a final step, variance associated with the NRI is derived based on the standard variance estimator
for a stratified two-stage sample design (Särndal et al. 1992) and propagated through calculations toestimate national totals for the inventory (Ogle et al. 2010). The largest source of uncertainty in theanalysis is associated with model structure and parameterization, as quantified in the empiricalanalysis. This source accounts for more than 80 percent of the total uncertainty in soil carbon stockchange and N2O emission estimates at the national scale, highlighting the importance of further
improving the model to reduce uncertainty.
Ste p 7 . Ver if ica t io n o f inv ent o ry e sti ma t es w it h i n de pe nd en t da ta
NGHGI estimates from Tier 3 models can be difficult to verify because alternative measurements often do notexist at the national scale. This is not unique to the AFOLU sector. There may however, be opportunities to verify
component estimates against independent data. For example, model derived estimates of crop yield, or timber
harvest can be compared against independent data such as crop or timber production statistics. Such comparisonsrequire a good understanding of the methods used for both the Tier 3 and the comparative estimates, to avoid
interpreting possible discrepancies as an indicator of problems in the Tier 3 model, when the discrepancy is in fact
due to methodological differences.

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Another useful step in verification of inventory estimates is to compare current estimates against those in theinventory submissions of prior years. Changes in time series estimates that are not consistent with changes in
activity or other input data should be examined and understood as these could be indicative of a variety of problems,
including errors in data processing. Developing quality assurance/quality control (QA/QC) procedures that
document the changes in estimates attributed to each change in input data, model parameters, or othermethodological changes can assist inventory compilers in the verification of inventory estimates.
Verification of inventory estimates can also be based on measurements from a monitoring network or fromresearch sites that were not used to calibrate model parameters or evaluate model behaviour. The network would
be similar in principle to a series of sites that are used for a measurement-based inventory. However, theuncertainty of the estimates (output) from a model-based approach does not depend directly on the sample size
and therefore the sampling need not be as dense. In some cases, verification may demonstrate that the model-based
estimation system is inappropriate due to large and unexplainable differences between model results and themeasured trends from the monitoring network. Problems may stem from one of three possibilities: errors in theimplementation step, poor input data, or an inappropriate model. Implementation problems typically arise fromcomputer programming or data input errors, while model inputs may generate erroneous results if these data are
not representative of management activity or environmental conditions. In these cases, it is good practice for theinventory compiler to return to either Steps 2 or 5 depending on the issue. It seems less likely that the model would
be inappropriate if Step 2 was deemed reasonable. However, if this is the case, it is good practice to return to themodel selection/development phase (Step 1) or to further refine the existing model.
In addition to verifying inventory estimates, independent data may also be used to check areas estimates for landuse and land use change includingthat land area is conserved over time;changes between land-use types are logical in terms of the type, frequency and time periods between changes,
defined by the country;consistency between input data (e.g. area to be disturbed by disturbance type X) and model simulation results(e.g., area actually disturbed in the model by disturbance type X).

Ste p 8 . Re po rt ing a n d Do c u me nt a t io n
It is good practice to assemble inventory results in a systematic and transparent manner for reporting purposes.
Documentation of model-based Tier 3 inventory systems should include those items listed in Table 2.6b. For
further details on QA/QC, reporting and documentation, see the sections dealing with the specific source categories
later in this volume, as well as information provided in Volume 1, Chapter 6.
TABLE 2.6B (NEW)EXAMPLES OF DOCUMENTATION TO ASSEMBLE IN SUPPORT OF TRANSPARENT REPORTING OF TIER 3 MODEL-BASEDINVENTORIES

Step 1 – Model selection or development

A description of the model
Reason for choosing or designing the model demonstrating
applicability
Discussion of any likely consequences if the model is used outside
the domain that the model is parameterised to simulate.

Step 2 - Model calibrationDescription of the process undertaken to calibrate the model anddocumentation of the data sources informing the manual or
automated calibration.

Step 3 – Evaluate model behaviour

Results of the analysis verifying model behaviour using independentmeasurements to confirm that the model is capable of estimating
carbon stocks, stock changes and/or emissions and removals in thesource/sink categories of interest. The sources of independent data
should also be documented.

Step 5 - Implement the modelOverview of procedures that are used to apply the model.

Step 6 - Quantify uncertainties



Description of the approach taken to estimate uncertainty in themodel outputs.

Step 7 - Verification of inventory estimatesSummary of the verification results for the inventory.

Step 8 – Reporting and Documentation



Information on QA/QC steps

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2.6

INTER-ANNUAL VARIABILITY

In the AFOLU sector, the management of land is used as the best approximation of human influence and thus,
estimates of emissions and removals on managed land are used as a proxy for anthropogenic emissions andremovals on the basis that the preponderance of anthropogenic effects occurs on managed lands (see Vol. 4 Chapter1). This allows for consistency, comparability, and transparency in estimation. Referred to as the Managed Land
Proxy (MLP), this approach is currently recognised by the IPCC as the only universally applicable approach toestimating anthropogenic emissions and removals in the AFOLU sector (IPCC 2006, IPCC 2010). However, it isalso recognised that the estimated emissions and removals on managed lands can represent a combination of bothanthropogenic (direct and indirect) and natural effects (Vol. 4 Chapter 1 p1.5; IPCC 2010; see Fig. 2.6A).
Some of the emissions and removals from managed land are characterised by high interannual variability.
Interannual variability (IAV) refers to the variability in the annual emissions and removals (E/R) estimates between
years within a time series. In the AFOLU sector, the application of the MLP means that IAV can be caused byboth anthropogenic and natural causes. The three main causes of IAV in GHG emissions and removals in the
AFOLU sector are (1) natural disturbances (such as wildfires, insects, windthrow, and ice storms), which can cause
large immediate and delayed emissions and kill trees; (2) climate variability (e.g. temperature, precipitation, anddrought), which affects photosynthesis and respiration (Ciais et al. 2005; Aragão et al. 2018); and, (3) variation in
the rate of human activities, including land use (such as forest harvesting), and land-use change (Stinson et al.
2011; Pilli et al. 2016; Kurz et al. 2018).
In some countries IAV in emissions from natural disturbances can be larger than the IAV of emissions caused byhuman activities such as forest management. For example, IAV in Canada’s 1990 to 2016 time series of annual
emission and removals due to natural disturbances is much larger than the IAV in the emissions and removals on
the remaining managed forest land (Figure 2.6C). The NGHGIs for Portugal (Figure 6-32 of Portugal’s NIR 2018
(Portuguese Environmental Agency 2018)) and Australia (Table 6.21 of Australia’s NIR 2016 Volume 2(Commonwealth of Australia 2018)) are two other examples of time series with high IAV. In some countries, theareas burned by wildfires can vary by two orders of magnitude between years (Stinson et al. 2011; Miller et al.
2012; Genet et al. 2018). In other countries, the majority of IAV may be due to human activities.
When the MLP is used and the IAV in emissions and removals due to natural disturbances is large, it is difficult
to gain a quantitative understanding of the role of human activities compared to the impacts of natural effects. In
such situations, disaggregating9 MLP emissions and removals into those that are considered to result from humanactivities and those understood to result from natural effects may provide increased understanding of the emissions
and removals that are due to human activities such as, land use (including harvesting) and land-use change. In thisway, disaggregation can contribute to improved understanding of the trends in emission and removals due tohuman activities and mitigation actions that are taken to reduce anthropogenic emissions and preserve and enhancecarbon stocks.
Disaggregating emissions and removals according to anthropogenic and natural effects has been recognised as ascientific challenge (Canadell et al. 2007; Vetter et al. 2008; IPCC 2010; Kurz 2010; Smith 2010; Brando et al.
2014; Henttonen et al. 2017). It is not yet possible to fully and accurately separate emissions and removals
associated with human activity from those associated with natural effects. The last IPCC Expert Meeting Reporton this topic encouraged further development of scientific methods (IPCC 2010).
Recognizing that some but not all countries may choose to address emissions and removals from natural
disturbances on managed land outside the inventory process, this guidance is provided as an option that may be
used by countries that choose to disaggregate their reported MLP emissions and removals (i.e. all emissions andremovals on managed land) into those that are considered to result from human activities and those that areconsidered to result from natural disturbances. These supplementary approaches may be of interest to countries
with AFOLU sector emissions where IAV due to natural effects is large. The section first addresses definitional
issues, followed by a description of whether or not different methodological approaches used to estimate C stockchanges quantify the IAV of emissions and removals. A generic approach to report on disaggregation of thecontribution of natural disturbances in reporting on total emissions and removals on managed lands is thenprovided, along with country-specific examples of methodological approaches to disaggregating anthropogenic
effects and natural disturbances on managed lands.

9

Disaggregating means that an estimate is separated into its component parts.

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2.6.1

Definitional issues

2.6.1.1 D IRECT AND INDIRECT H UMAN EFFECTS , AND NATURAL
EFFECTS
Anthropogenic (i.e., direct and indirect human) effects and natural effects are described in Vol. 4 Chapter 1. Figure
2.6a summarizes the main factors that cause these effects and their occurrences in managed and unmanaged lands.
The specific effects included in estimates reported in NGHGIs depend on the estimation method and data used,which differ in approach and complexity among countries (see Table 2.6c). Describing how the various effects arereflected in the estimates of emissions and removals, based on the estimation method and data used, increases thetransparency of the NGHGI and its understanding by the scientific and policy communities (Grassi et al. 2018,section 2.6.2). Useful information may include definition and spatial maps of managed land, information on areasof forest being harvested and those subject to other management, and information on the main determinants of the
GHG fluxes (e.g., forest age structure, harvested volumes, harvest cycle).
Figure 2.6a:Conceptual illustration of how various anthropogenic (direct andindirect) and natural factors affect land-related GHG emissions andremovals in managed and unmanaged lands (Source: Grassi et al.
(2018)).
Managed land

Unmanaged land

Direct-human induced effects• Land use change• Harvest and other management

Indirect-human induced effects• Climate change induced change in temperature,
precipitation, length of growing season• Atmospheric CO2 fertilisation and N deposition,
impact of air pollution• Changes in natural disturbances regime

Natural effects• Natural interannual variability• Natural disturbances

Direct human-induced effects of any management activity on emissions or removals, by definition, only occur onmanaged lands. Indirect human-induced effects (i.e., the second order impacts of human activities on emissions or
removals mediated through environmental change) and natural effects can occur on both unmanaged and managed
lands. The “anthropogenic GHG emissions and removals by sinks are defined as all those occurring on ‘managed
land’” (Vol. 4, Ch. 1). The natural effects “tend to average out over time and space” (Vol. 4, Ch. 1), provided
that there are no trends in disturbance rates, such as increased annual area burned as a result of climate change.
Nonetheless, their IAV in emissions and removals can have an important impact on annual NGHGIs. Dependingon the estimation method and data used, GHG estimates for managed land may capture all or only some of thisIAV (see Section 2.6.2).
The IPCC describes the MLP as a method to approximate estimates of anthropogenic emissions and removals, but
this proxy estimate also contains emissions and removals resulting from natural disturbances (IPCC 2006; IPCC
2010). This section introduces an approach that countries can apply on a voluntary basis within the MLP in order
to indicate those emissions and removals considered to result from human activity, and those that are understoodto result from natural disturbances. This is achieved by disaggregating the estimated emissions and removals due
to natural disturbances (ND E/R) within the estimated total MLP emissions and removals. This remaining
aggregate of emissions and removals associated with human activity might still include some effects of IAV ofnatural disturbances and other natural effects on anthropogenic emissions and removals.

2.6.1.2 N ATURAL DISTURBANCESDisturbances, in particular wildfires, can contribute to large IAV in emissions. The number, frequency andintensity of fire events are strongly controlled by climate and weather, fuels, ignition sources, and human activities.
High temperatures, past levels of fire suppression, and persistent drought events are key drivers of forest fires, for2.68

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instance in the Western US (Westerling 2016), in the Amazon region (Morton et al. 2013) or in Indonesia (Schimel
et al. 2015). However, land use and land-use change such as deforestation and peatland drainage can influence thelikelihood and impacts of fire (Page & Hooijer 2016). In the Brazilian Cerrado, severe drought events explain theloss of almost 30 percent of aboveground woody biomass (de Miranda et al. 2014). Other natural disturbanceswith large IAV include storm damage (Yamashita et al. 2002; Lindner et al. 2010). Insects tend to follow outbreakcycles, thus causing more long-term trends that contribute to interdecadal rather than interannual variations (Kurzet al. 2008; Hicke et al. 2012). However, like IAV, the inter-decadal variability can also make it difficult to identify
trends in emissions and removals that result from human activities.
Defi n itio n o f na t ura l d i st ur ba n ce s
Natural disturbances in the context of the AFOLU sector are non-anthropogenic events or non-anthropogenic
circumstances that cause significant emissions and are beyond the control of, and not materially influenced by acountry. These include wildfires, insect and disease infestations, extreme weather events and/or geologicaldisturbances, beyond the control of, and not materially influenced by a country. Natural disturbances exclude
human activities such as harvesting, prescribed burning and fires associated with activities such as slash and burn. 10
Non-anthropogenic events refer to non-human induced events (e.g. fire initiated by lightening, damage by windstorms), non-anthropogenic circumstances refer to non-human induced conditions that exacerbate these
disturbances (e.g., fire occurring during particularly harsh conditions like strong winds, high temperature, drought,etc.). For information on how to document that disturbances are beyond the control of and not materially influenced
by the country, see Section 2.6.4 below.
The methodological guidance provided in this section is aimed at disaggregating emissions and removals inecosystems where natural disturbances cause large IAV in emissions within the MLP and where subsequent
removals occur over a multi-year period of time. Therefore, this methodological guidance is applicable to natural
disturbances in forests, and in woody grassland, undrained wetlands or undrained peatlands, but not in other land
categories where human actions materially determine and/or deeply influence the conditions and circumstances
associated with significant emissions by disturbances (such in drained peatlands and in cropland).
B a la nc e o f e mi s s io n s a nd s u bs eq ue nt re mo v a l s
A fundamental assumption under the MLP is that carbon emissions and removals associated with natural effects
will average out over space and time (see also Volume 4, Chapter 1). Therefore, consistent with this assumption,
the CO2 emissions (from above and below ground biomass, dead organic matter and soil carbon) from areas
affected by natural disturbances are expected to be balanced by subsequent removals across the landscape at somefuture point in time. This expectation has no established time limit because the time to balance depends on thetypes of ecosystems affected by disturbances and their rates of regrowth.
At stand level, changes in growing conditions could affect this expectation, in particular if environmental
conditions contribute to regeneration failure of stands that were affected by natural disturbances, e.g. landslides
and erosion after wildfire, making it more difficult to achieve the balance. Conversely, if environmental changes
contribute to increased growth rates or reduced mortality rates, then the balance will be achieved faster. In thecase of repeated disturbances on the same area, the time to reach balance for that area may increase.

2.6.2

Relationship between different
methodologicalapproaches and the representation of emissions andremovals from interannual variability

The choice of estimation method and data affects the extent to which the IAV of different drivers is reflected inreported estimates (see Table 2.6c). Countries can apply different estimation methods to report their emissions andremovals capturing the anthropogenic components with different temporal resolution and disaggregation ofvariables (annual to periodic, averaged or disaggregated by drivers). Table 2.6c provides information on how the
choice of estimation method affects whether or not factors contributing to IAV of reported emissions and removals
are captured in NGHGIs. This table may help countries in understanding and describing how the various effects
are reflected in the estimates of emissions and removals, therefore increasing the understanding of NGHGIs by thescientific and policy communities.

10 Information on natural disturbance definitions and approaches applied in the Kyoto Protocol accounting can be found in

IPCC. (2014) In: 2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol, eds.
T. Hiraishi, T. Krug, K. Tanabe, N. Srivastava, J. Baasansuren, M. Fukuda & T. G. Troxler, IPCC, Switzerland.

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TABLE 2.6C (NEW)GENERAL GUIDANCE ON WHETHER OR NOT THE ESTIMATION METHOD IS ABLE TO DISTINGUISH BETWEEN THE IMPACT
OF THE INDIVIDUAL DRIVERS BELOW ON THE INTERANNUAL VARIABILITY OF REPORTED ANNUAL EMISSION ANDREMOVAL ESTIMATES - NOTE THAT SOME EXCEPTIONS MAY OCCUR, DEPENDING ON THE DATA USED

Drivers
Direct Human

IndirectHuman

Natural climate
variability

NaturalDisturbances

Stock Difference11
Periodic measurements (multi-year)

No

No

No

No

Stock Difference12
Annual measurements

Yes

Yes

Yes

Yes

Gai
nLoss

Biomass growth
based on Emission
Factors or empiricalyield tables

Yes

No

No

Yes

Growth based onprocess (or hybrid)
model

Yes

Yes

Yes

Yes

Dead and soilorganic matter
dynamics based on
Emission Factors

Yes

No

No

No

Dead and soilorganic matter
dynamics withconstant climate

Yes

No

No

Yes

Dead and soilorganic matter
dynamics withvariable climate

Yes

Yes

Yes

Yes

Dead and soil organic matter pools

13

Live biomass pools

MethodThe Stock Difference method calculates net emissions/removals (E/R) as the difference in estimated C stocks forrelevant pools measured at two points in time. Average annual net E/R can be calculated by dividing the C stock
difference of a period by the number of years between the two observations. Periodic stock assessments without
auxiliary data therefore do not allow the quantification of the IAV of emissions and removals and its relation to
the various drivers.
With annual measurements of ecosystem carbon stocks, e.g. via subsets of annual plot measurements in acontinuous forest inventory, the quantification of IAV of emissions and removals becomes possible. Periodic orannual subsets of inventories can by themselves not detect IAV unless auxiliary data – such as area annually burned,
harvest rates or other specific plot-level measurements on the timing of tree mortality – are used to inform about
IAV (Röhling et al. 2016). For non-CO2 emissions (e.g., CH4 and N2O from fires), auxiliary data on the type of
disturbance that caused carbon losses would be required when the stock difference method is used.
The Gain-Loss method requires annual data on forest management, land-use change and natural disturbances andwhen these are available it can provide estimates of the IAV of net emissions. Depending on the estimation
methodology and the data sets used, it may capture some or all of the impacts of drivers of the IAV of annual
emissions and removals. A Gain-Loss approach utilising yield tables or constant emission factors (EF) will beinsensitive to natural climate variability and, therefore, will only be able to distinguish between the direct humanimpact and natural disturbance impacts on IAV of emissions and removals. Gain-Loss methods that utilise climatesensitive growth and mortality models (Richards & Evans 2004; Waterworth et al. 2007; Hember et al. 2018), or
11 Forest inventories with multi-year period remeasurement and no auxiliary data cannot detect IAV. In some cases, periodic

measurements on permanent sample plots are augmented with additional annual data thus increasing the ability to estimate
IAV.
12 Forest inventories with annual remeasurements for the same plots can detect IAV but are rarely implemented.
13 The assumption for the Gain-Loss method is that activity data such as harvest, land-use change, and natural disturbances areavailable annually.

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

climate sensitive models of dead and soil organic matter dynamics (see Figure 6 in Liski et al. (2006)) can, in
addition, estimate the indirect human and natural climate variability impacts on the IAV of emissions and removals.

2.6.3

Optional approach for reporting of emissions andremovals from Natural Disturbances

It is good practice for countries to apply the MLP and to estimate and report all emissions and removals that occur
on managed lands. This section describes a generic approach for use by countries that choose to report on thefurther disaggregation of emissions and subsequent removals from natural disturbances from the total emissions
and removals estimated using the MLP. As discussed above, disturbances may have a natural and an anthropogenic
component. This reporting guidance aims to assist countries choosing to report on the disaggregation of emissions
and subsequent removals associated with human activity and those associated with natural disturbances within the
total emissions and subsequent removals estimates of the MLP.
The elements of a generic approach are provided below, followed by examples of how the approach has been
implemented to date:
1. Quantification of the total emissions and removals from Managed Lands (consistent with MLP)
Estimate total E/R consistent with the MLP. Guidance provided by the IPCC for each relevant land category
applies for the estimation of associated emissions and subsequent removals due to regrowth within the MLP.
This is the total MLP flux, i.e. the first order approximation of the anthropogenic emissions and removals,
which also includes emissions and subsequent removals from areas that are identified as subject to natural
disturbances.
2. Reporting on the country-specific approach to applying the definition of natural disturbances
Consistent with the generic definition of natural disturbances provided in section 2.6.1.2, countries describe
their approach when applying the definition of natural disturbances consistently over time. The country
description includes the types of disturbances for which the disaggregation of emissions and subsequent
removals is implemented. The description also explains how the country excludes from natural disturbances
the impacts of human activities, e.g., salvage logging, prescribed burning, slash and burn and deforestation.
3. Identification of emissions and removals due to natural disturbances
The emissions and subsequent removals associated with natural disturbances are identified by applying the ND
definition to either the individual (stand-level) disturbed areas or the total (landscape-level) emissions from all
disturbances in the year14. In identifying those emissions and removals, it is good practice to avoid the inclusionof emissions and removals that are materially affected by human actions 15. Both approaches provide for the:(i) Identification of the lands and area of land affected by each disturbance, as well as a description of themethods and criteria applied.
(ii) For those lands, estimation of the emissions and subsequent removals associated with natural
disturbances only (e.g. salvage logging emissions and associated subsequent removals are not included),as well as a description of the methods and criteria applied.
If a country chooses to disaggregate ND emissions and removals, then it is good practice to disaggregate as
anthropogenic the emissions and subsequent removals associated with management activities occurring on land
affected by natural disturbances, including salvage logging and deforestation. Consequently, subsequent
removals are disaggregated between human activities and natural disturbances, proportionally to the C stock
losses these activities have caused, until the CO2 emissions from natural disturbances are balanced by removals.
For example, if salvage logging follows wildfire, and the wildfire caused instant emissions of 20 t CO2 perhectare and subsequent salvage logging caused an additional 40 t CO 2, then 20 t CO2 of subsequent removals
are disaggregated as natural disturbances, and all remaining removals are disaggregated as anthropogenic
effects. This could be implemented sequentially (i.e. the first 20 t CO 2 removals are disaggregated as due tonatural causes, and all subsequent removals to anthropogenic causes) or in parallel (i.e. in this example, forevery tonne of CO2 removal, one third is disaggregated as due to natural causes, and the remaining two thirds
to anthropogenic causes). In both cases, once natural emissions are balanced by removals disaggregated as
natural causes, the remaining removals are considered anthropogenic.
14 Methodological guidance on quantification of associated emissions and removals are given in the chapters with generalguidance (Chapter 2 and 3) as well in the category-specific chapters (Chapter 4 and 6)".
15 Noting that a portion of the emissions and removals considered to be associated with natural disturbances may be affectedby human actions.

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Disaggregation of CO2 removals following natural disturbances can be implemented at the landscape level byapportioning these based on, for instance, the proportion of area disturbed of total forest area and the proportionof C stock lost of total C stock. For example, if in a year X in a country Y, Z ha of forest land is subject towildfires, representing 0.1percent of the total forest area and 25percent of the total carbon stock present in theburned area is lost; the percentage of total CO2 removals in the entire forest land apportioned to natural
disturbances in this example is 0.025percent (i.e., 0.1percent*25percent) for year X. If the emissions fromnatural disturbances in year X were 25 Mt CO2, then the removals in subsequent years are considered natural
until the sum of the removals equals that amount.
Although the different approaches above (i.e., sequential or parallel disaggregation of removals subsequent tonatural disturbances, stand vs. landscape level) affect the annual disaggregation, as long as the expectation of
the balance between emissions from natural disturbances and the subsequent removals is fulfilled (see Section
6.2.1.2), and as long as emissions and subsequent removals are treated consistently, in the long term the totals
are the same. Furthermore, in all cases it is good practice to report information on assumptions and methods
implemented to disaggregate subsequent CO2 removals.
When land-use change (e.g., forest land converted to cropland) follows a natural disturbance (e.g., wildfire),
then emissions associated with land-use changes after natural disturbances as well as the emissions from theprior natural disturbance, are considered to be anthropogenic emissions. If regrowth occurs on that land, thenany subsequent removals are also considered anthropogenic.
4. Disaggregation of the MLP
The natural disturbance component is subtracted from the total estimate of MLP emissions and removals,
yielding an estimate of the emissions and removals associated with human activity on managed land. Bothcomponents are estimated and reported as part of the total MLP emissions and removals. In countries where
natural disturbance contributes large IAV to E/R, the component of the MLP emissions and removals identified
as associated with human activity is expected to have a lower IAV than the MLP emissions and removals
because the variability resulting from natural disturbances has been disaggregated.
Given the expectation of the balance described above (Section 2.6.1.2), when emissions from natural
disturbances are disaggregated, it is good practice that subsequent removals are also disaggregated until thebalance has been reached. In this case, it is also good practice to disaggregate to the natural disturbancecomponent those removals in each inventory year that are contributed by lands that were affected by natural
disturbances prior to the start of the time series. In many ecosystems it may take decades for removals following
natural disturbances to balance emissions from the disturbances. If it is not possible to estimate directly theamount of emissions that need to be balanced, for example if natural disturbances occurred before the reporting
period, the time when the balance is expected can be approximated based on the estimated length (years) of
the recovery period (see example in Box 2.2j). This ensures a consistent application of the balance principle
throughout the time series.
In addition to CO2 emissions, natural disturbances may cause non-CO2 emissions, e.g. wildfires cause N2O andCH4 emissions. While CO2 emissions are assumed to average out across time because of vegetation regrowth
after disturbance, non-CO2 emissions are not taken up by vegetation and therefore there is no expectation thatthese emissions will be balanced by removals because the biological, chemical and physical processes that
result in the complete decay of CH4 and N2O in the atmosphere are not captured in existing IPCC inventory
methods.
Examples of methodological approaches that have been developed are presented for Australia (Box 2.2i), Canada(Box 2.2j) and for an EU country (Box 2.2k).

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BOX 2.2I (NEW)
AUSTRALIAN APPROACH TO ESTIMATING INTERANNUAL VARIABILITY DUE TO NATURAL DISTURBANCESThis box is for information only and neither adds guidance nor overrules guidance provided.
In Australia, all lands are considered managed lands. All areas and carbon stock changes on managed
land from anthropogenic and ‘natural disturbances’16 are reported, consistent with the MLP. ‘Natural
disturbance’ emissions and removals are considered to be caused by non-anthropogenic events andcircumstances beyond the control of, and not materially influenced by, human activity despiteextensive efforts by emergency management organizations to prevent, manage and control such
events.
Both initial carbon losses and subsequent recoveries in carbon stocks are modelled as part of the
disturbance event, and carbon stocks are spatially tracked until pre-disturbance levels are reached toensure completeness and balance in reporting. Most Australian wildfires are not stand-replacing andcarbon stocks typically recover after 11 years (Roxburgh et al. 2015). Estimates are prepared using
a process (hybrid) model with DOM/SOM dynamics with variable climate (FullCAM).
‘Natural disturbances’ are defined as occurring in a year which is an outlier (exceeding the 95percent
probability level) in the series of annual carbon stock losses due to wildfire at the national level and,spatially, as fires in those regions (States) experiencing abnormal fire activity in that year. (A fulldescription of the method to identify outliers can be found in Volume 2 of Australia’s NIR 2016 Section 6.4.1.3)‘Natural disturbance’ emissions and removals are modelled on a spatial basis and, consistent with
the MLP, included in reporting after averaging out initial carbon stock losses and subsequent
recovery17. This leaves the trend in carbon stock changes as the dominant result of human activity(e.g. from prescribed burning, normal seasonal wildfires – see “B” in Figure 2.6B).

120,000
100,000
80,00060,00040,00020,0000-20,000-40,000-60,000

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016

kt CO2-e

The approach ensures that Australia’s modelled implementation of the MLP is comparable withestimates generated using other methods, such as Tier 3 stock-difference approaches, that tend to
average out IAV due to natural causes over space (scaling from plots to region) and time (averaging
between periodic re-measurements). All carbon stock changes on managed land from anthropogenic
and natural disturbances are transparently reported in Australia’s NIR.

A: Interannual variability due to wildfire, including natural ‘background’ emissions and removals
B: Trend in fires due to human activity

Figure 2.6b: Example of the disaggregation of wildfire emissions in Australia into ‘natural
disturbance’ emissions and removals and the emissions and removals from fires due to humanactivity.

16 References to ‘natural disturbances’ in this box refer to the natural ‘background’ of greenhouse gas emissions and removals

by sinks described in 2006 IPCC Guidelines Vol 4, page 1.5: (Managed land proxy) “Finally, while local and short-term
variability in emissions and removals due to natural causes can be substantial (e.g. emissions from fire – footnote 1), thenatural ‘background’ of greenhouse gas emissions and removals by sinks tends to average out over time and space.”
17 2006 IPCC Guidelines Vol 4, page 1.5: (Managed land proxy) “Finally, while local and short-term variability in emissions

and removals due to natural causes can be substantial (e.g. emissions from fire – footnote 1), the natural ‘background’ of2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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BOX 2.2J (NEW)CANADA’S APPROACH TO ESTIMATING INTERANNUAL VARIABILITY FROM NATURAL DISTURBANCESThis box is for information only and neither adds guidance nor overrules guidance provided.
In the 2017 National GHG Inventory Report18 Canada revised its reporting approach to increase thetransparency of the reporting of anthropogenic emissions and removals on Forest Land remaining
Forest Land (FL-FL). The new approach disaggregated the emissions and subsequent removals on
managed lands affected by natural disturbances from those on the remaining lands subject to forest
management. The concept of the MLP was maintained: the sum of these two emission and removal
components are identical to the total emissions and removals for FL-FL under the MLP. Canada’s
2018 National GHG Inventory Report19 further refined the approach. The methods are described indetail by (Kurz et al. 2018) and are summarized here.
Canada defined natural disturbances as all stand-replacing wildfires and all disturbances of othernatural causes (insects, windthrow etc.) that result in more than 20 percent tree mortality (biomass)in affected stands. The threshold of 20 percent was selected because large areas of forests are affectedby insects that cause low levels of mortality and/or growth reductions. Disturbances with impacts
below this threshold are considered part of the natural, small-scale forest mortality that affect standdynamics such as self-thinning.
For all areas affected by stand-replacing fire disturbances, annual CO2 and non-CO2 GHG emissions
and subsequent CO2 removals are summarized in the natural disturbance land category for several
decades following the fire event. The time at which stands affected by natural disturbances transition
back to the category of lands affected by forest management varies across Canada and is determinedby the age at which stands are eligible for harvest, typically 60 to 90 years. For other natural
disturbances that cause more than 20 percent biomass mortality, E/R are summarised in the natural
disturbance category until the pre-disturbance biomass values are reached. For the 1990 to 2016 timeseries, stands regenerating following wildfire that are younger than the age at which stands are
eligible for harvest is summarised in the natural disturbance category: removals that occur after 1989
in stands that have been affected by stand-replacing wildfires prior to 1990 are therefore contributing
to balancing emissions from wildfires that occurred since 1990. The 56 Mha of managed forest
affected by wildfire disturbances prior to 1990 contribute in 1990 estimated removals of 64 Mt CO2eyr-1. From 1990 to 1994 these cumulative annual removals are larger than the emissions fromwildfires since 1990, making the lands subject to natural disturbances net sinks (Kurz et al. 2018).
This approach contributes to balanced reporting as otherwise only removals from stands affected bynatural disturbances after 1990 would appear in the natural disturbance component.
The disaggregation of fluxes improves the estimate of human impacts: reported emissions andremovals without natural disturbances showed clear temporal trends that are correlated with changes
in the rates of human activities such as rates of clear-cut harvesting (Figure 2.6C). In areas strongly
affected by the Mountain Pine Beetle outbreak (Kurz et al. 2008) the trend in emissions reported forlands affected by forest management is still somewhat influenced by the impacts of the beetle
because that area is decreasing (Kurz et al. 2018). The high IAV resulting primarily from fires isreported separately (Table 6.5 in Canada’s NIR 2018). Further methodological details are provided
in Canada’s NIR 2018, Sections 6.3.1 and in Annex 3.5.2.3 and in (Kurz et al. 2018).

greenhouse gas emissions and removals by sinks tends to average out over time and space. This leaves the greenhouse gas
emission and removals from managed lands as the dominant result of human activity.”18

http://unfccc.int/files/national_reports/annex_i_ghg_inventories/national_inventories_submissions/application/zip/can2017-nir-13apr17.zip
19 https://unfccc.int/documents/65715

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BOX 2.2J (NEW) (CONTINUED)
CANADA’S APPROACH TO ESTIMATING INTERANNUAL VARIABILITY FROM NATURAL DISTURBANCESA300

Net Flux (Mt CO2 eq)

200

100

0

-100

-200

-300

Anthropogenic Component
300

14000
12000

200

Net Flux (Mt CO 2 eq)

Total

10000
100
8000
0
6000

Area (kha)B

Natural Disturbance Component

-100

4000
-200

2000

-300

0

Wildfire Areas

Insect Disturbance Areas

Natural Disturbance Component
0

1400

1200

Net Flux (Mt CO2 eq)

-50

1000
-100
800

-150
600

Area (kha)

C

-200

400
-250

200
0

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016

-300

Area of forestry activities

Anthropogenic Component

Anthropogenic Component + HWP

Figure 2.6c: Example of the disaggregation of Canada’s FL-FL emissions and removals into thoseoccurring on lands dominated by natural disturbance impacts and those occurring in the remaining
managed forest (A). Note the high IAV in the natural disturbance fluxes (up to 250 Mt CO2e/yr) (B)on the area affected by natural disturbances (primarily wildfires) and the low IAV of fluxes on theremaining managed forest area (C) which are correlated with forest management activities (e.g.
primarily area of forest harvest). Fluxes in panel C are shown without (solid line) and with (dashedline) the emissions from harvested wood products. Data from Canada’s 2018 NIR and figure from
(Kurz et al. 2018)).

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BOX 2.2K (NEW)APPROACH TO ESTIMATING INTERANNUAL VARIABILITY FROM NATURAL DISTURBANCES BASED ON THE EU
LEGISLATION20

This box is for information only and neither adds guidance nor overrules guidance provided. This
example demonstrates a methodological approach that has not yet been implemented.
Forests of example country Z21 are prone to wildfires that in years with extreme weather conditions(e.g. drought, especially if combined with strong winds) may cause large emissions from biomassburning and cause high IAV in the net CO2 balance. Although, the country recognizes that most of
its wildfires are human-induced either intentionally, e.g. pyromaniacs, or unintentionally, e.g.
campfires, fireworks, cigarettes or other causes, some have natural causes. Consequently, emissions
from wildfires have both an anthropogenic and a natural component.

Figure 2.6d: Time series of managed forest land total GHG net emission (anthropogenic + natural
disturbance (ND) and area burned. Blue bars (left Y-axis) represent annual total net GHG emission(Gg CO2e) from managed forest land net sink. The dashed red line (right Y-axis) represents theannual area burned (kha).
To disaggregate the natural component of emissions and removals from wildfires, the country uses
its national definition of natural disturbances: Natural Disturbances are those wildfires that are nonanthropogenic events or non-anthropogenic circumstances that cause significant emissions inforests and are beyond the control of, and not materially influenced by, the Country’s land use andmanagement practices. These practices exclude salvage logging and prescribed burning.
All wildfires are considered not materially influenced by the country’s land use and management
practices since the use of fire is forbidden in any forest land and the country has an advanced nationalfire management system for fire prevention, fire monitoring and fire suppression in all land uses,
including forest land.
To identify wildfires that cause significant emissions and are beyond the control of the country’s
fire management system and are therefore considered natural disturbances, the country looks forstatistical outliers that fall outside the 95 percent confidence interval of the variability of thehistorical time series of the annual GHG emissions from wildfires 22. To do so, the distribution ofemissions from wildfires is established, and it is assumed that all values within the normal

20 The presented methodology is based on the EU Regulation 2018/841
21 Data for this example are derived from the Italian GHG inventory
22 Such time series do not include emissions from salvage logging nor emissions from wildfires that are followed by adeforestation event. The time series can start before the base year of the country and may include all years for which data are
available. For this example, the time series starts in 1971.

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BOX 2.2K (NEW) (CONTINUED)
APPROACH TO ESTIMATING INTERANNUAL VARIABILITY FROM NATURAL DISTURBANCES BASED ON THE EU
LEGISLATION

distribution are exclusively associated with the anthropogenic component 23, any outlier value, in theupper tail, is considered as the signal of a disturbance event that is unlikely to have been generated
by anthropogenic causes alone and therefore includes a natural component.
In practice, first a historical time series of annual emissions 24 from wildfires is constructed startingfrom 1971, i.e., the base year (1990) of the NGHGI of the country minus 20 years. Then, using an
iterative process, outliers (if any) that are larger than the mean plus two times 25 the standard
deviation are removed from the time series in successive iterations, until an outlier-free normal
distribution is obtained.
The resulting time series, as well as its mean (referred to below as the background level ofanthropogenic emissions from wildfires) and two times its standard deviation (referred to below as
the margin) excludes all outliers. Based on these statistics, natural disturbances are those that occur
in years when the total immediate emissions from wildfires are larger than the background level plus
the margin and emissions from these natural disturbances are quantified as the amount exceeding
the background level. This amount is disaggregated from the anthropogenic component.
To establish the balance between immediate CO2 emissions (F) and total subsequent CO2 removals26(R) due to natural disturbances, and to avoid introducing artificial trends to the time series, thecountry also estimates and reports removals occurring from land disturbed in the X years prior to
the inventory year, where X27 is the length of the period that is needed for forest vegetation (byrelevant forest types and site types) to recover the pre-disturbance C stock. The CO2 removals arequantified under the assumption that forest vegetation fully recovers within X years after wildfires.
This assumption is based on the current legislation that forbids conversion of burnt forests to otherland uses and that prescribes post-fire management activities aimed at rehabilitating the pre-fireforest vegetation. Consequently, the average amount of subsequent annual removals (Rannual) to be𝐹disaggregated for X years of a past ND event28 is equivalent to and ∑𝑋0 𝑅𝑎𝑛𝑛𝑢𝑎𝑙 = 𝑅 = 𝐹 (where 0
𝑋
is the year in which the natural disturbances occur and X the time needed for C stocks to recover totheir pre-disturbance level).

23 The average value of this distribution is the so -called background level of emissions associated with disturbances and it isconsidered anthropogenic.
24 The country includes the emissions of fire events only, delayed emissions associated with the decay of biomass that was

killed during the fire are not considered
25 This is an approximation of Student’s t value for data series with number of data >= 30.
26 Calculated directly from the biomass net increment (ΔC

G of IPCC equation 2.7)

27 For this example, X has been estimated to be 20 years for the entire country’s territory.
28 This means that in any year Y of the NGHGI the amount of CO𝐹∑𝑌𝑌−𝑋 ( )𝑋 (𝑌−𝑋)𝐹(where ( )𝑋 (𝑌−𝑋)

2 removals to be disaggregated is equivalent to theare the annual CO= removals occurring on all lands disturbed in the period Y-X that have

not yet achieved their pre-disturbance level of C stocks.)

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BOX 2.2K (NEW) (CONTINUED)
APPROACH TO ESTIMATING INTERANNUAL VARIABILITY FROM NATURAL DISTURBANCES BASED ON THE EU
LEGISLATION

Figure 2.6e: Time series of managed forest land GHG net emissions and removals (Gg CO2e). Bluebars (net sink) represent annual anthropogenic GHG net emissions (Gg CO2e) from managed forest
land; red bars (source and green line (sink)) disaggregated GHG emissions and subsequent CO2
removals from natural disturbances in managed forest land, respectively. The coefficient of variationof the time series is 0.184.

2.6.4

Reporting the contribution of natural disturbances
and anthropogenic effects to the emissions andremovals for managed lands

Voluntary disaggregation of the total of emissions and removals in the MLP into those that are associated withhuman effects and those due to natural disturbances may provide a clearer picture of the impact of management
activities. It is understood that a complete separation of the direct human impacts from natural impacts is, at thistime, not possible due to limitations of scientific methods (IPCC 2010) but disaggregating the emissions andsubsequent removals that are associated with natural disturbances on managed lands may be a helpful first step.
The MLP total is the sum of all emissions and removals on managed land. Box 2.2l describes a possible approachto reporting the total E/R from MLP plus the two components from:
1. Natural disturbances;
2. Anthropogenic activities (direct and indirect human effects).
The first component includes emissions from natural disturbances and subsequent net removals from regrowth.
Emissions may include delayed emissions from dead organic matter that was added by the disturbance to thealready existing dead organic matter pools.
The second component includes emissions and removals directly and indirectly associated with human activity
calculated as the difference between MLP total emissions and removals minus those associated with natural
disturbances.
In those cases where natural disturbance fluxes are large compared to the anthropogenic component of the MLP,
the optional disaggregation of estimates of the emissions and removals associated with natural disturbances canidentify the estimated trends of the emissions and removals on managed land associated with human activity, as
demonstrated in recent NGHGI reports (e.g., Boxes 2.2I, 2.2J).

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Transparency:
For those countries that choose to identify, quantify and report disaggregated natural disturbance emissions andsubsequent removals, it is good practice to document disaggregated emissions and removals in the MLP, and theapproaches, assumptions and methods used.
It is good practice to document the following: Consistency of the country approach with the generic definition of natural disturbances provided in Section
2.6.1.2, if any.
 The types of natural disturbances for which emissions and subsequent removals are identified, quantified anddisaggregated within MLP reporting.
 How the requirements associated with the above definition of natural disturbances are met, including that theidentified ND events are “non-anthropogenic events or non-anthropogenic circumstances”, which can bedemonstrated by providing information to show that the disturbances were “not materially influenced by, andbeyond the control of, a country”.
 How the emissions and removals that are materially influenced by human actions are excluded from the natural
disturbances component.
The demonstration that natural disturbances were “not materially influenced by, and beyond the control of, acountry” is based on scientific reasoning or evidence and documentation on practicable efforts to prevent, manage
or control the occurrences that led to the natural disturbances. Such evidence and practicable efforts may include
but are not limited to: Studies showing the prevalent direct cause of fires in a given region, forest type and climate zone; information
on weather conditions related to the disturbance events or to the cumulative affected areas; Application of preventative measures or modifying factors related to the occurrence or propagation of the
disturbances that may reduce the likelihood and/or magnitude of the disturbances occurring; Efforts to manage or control the disturbances when they occur, to the extent possible.
It is good practice to document the methods used to identify, quantify and disaggregate the impact of ND on GHG
emissions and removals, including information on:
 How the method is consistent with the expectation that the CO2 emissions from areas affected by natural
disturbance will be balanced by subsequent removals.
 The methods by which GHG fluxes are disaggregated from total MLP fluxes.
 For lands subject to ND, documentation on how subsequent land use and land-use change, if any, is identified
and how GHG fluxes previously disaggregated as associated with natural disturbances are re-assigned to theanthropogenic component following land-use change.
Documentation on the manner in which emissions associated with human activities that occur after the natural
disturbance event (such as salvage logging and site rehabilitation or other activities that do not cause a land-use tochange), and subsequent removals, are estimated and disaggregated.

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BOX 2.2L (NEW)EXAMPLE OF THE TABLE FORMAT THAT COULD BE USED FOR VOLUNTARY DISAGGREGATION OF TOTALESTIMATED FLUXES ON MANAGED LANDS INTO ANTHROPOGENIC AND NATURAL DISTURBANCE COMPONENTS

Land-use category

e.g. Forest land remaining forest land
Start
year†
… … … Inventory year

Years
Total Area under the MLP (kha)
Gains
Carbon stock change

LossesNet

non-CO2 emissions

Emissions

Net E/R plus non-CO2

Total*

Annual area of natural disturbances (kha)29
Area subject to natural disturbances (kha) 30
Gains
Carbon stock change

LossesNet

non-CO2 emissions

Emissions

Net E/R plus non-CO2

Total

Remaining area of managed land (kha)
Gains
Carbon stock change

LossesNet

non-CO2 emissions

Emissions

Net E/R plus non-CO2

Total #† This is the first year in the inventory time series, e.g. 1990.
* This is the total MLP estimate of net emissions and removals, i.e. the first order approximation of the anthropogenic
emissions and removals# This is the optional disaggregated estimate of the anthropogenic emissions and removals

29 The area of natural disturbance in the year it first occurs.
30 The cumulative area which has been subject to natural disturbances up to and including the current inventory year, minus thearea of natural disturbances on which past CO2 emissions are considered to be balanced by subsequent removals since theoccurrence of the natural disturbance. In the cumulative area totals, areas affected multiple times are included only once.

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

Annex 2A.1

Default Mineral Soil Reference C Stocks

Data presented in Table 2.3 were derived from Batjes (2011) and Batjes (2010) unless no values were available
for particular combinations of IPCC Climate Zones and IPCC soil types. Where no values were available, values
were taken from the 2006 IPCC Guidelines for National Greenhouse Gas or the 1996 IPCC Guidelines.
Reference C Stocks for the mineral soils C method were derived for IPCC climate zones (IPCC 2006 p. 3.39) and
IPCC soil classes (IPCC 2006 pp. 3.40-3.41). Soil data are from the ISRIC-WISE database (10250 profiles)
complimented with 1900 additional geo-referenced profiles from under represented temperate and boreal sites.
Data from all soils were screened and where organic carbon contents were determined using the Walkley Black
analysis, values were adjusted based on a conversion factor of 1.3 to estimate corresponding values that would
have been obtained by dry combustion analysis. Profiles were collected between 1925 and 2010 with two-thirdsof the pedons sampled between 1955 and 1995. Profiles were classified as “cultivated or disturbed” vs“(semi)natural”. Only profiles flagged as being under native vegetation (classified as “(semi)natural”) were
included (a total of 5560 profiles equating to approximately 1.6 times that used in the 2006 IPCC Guidelines). The
profiles also had a better geographical distribution across the globe compared to those use to derive reference
carbon stock values within the 2006 IPCC Guidelines.
The following equation was used to compute SOC stocks:
EQUATION 2A.1.1
ESTIMATION OF SOIL ORGANIC CARBON STOCKSk

Td    i  Pi  Di   1  Si i 1

Where:

Td= total amount of organic carbon over depth, d, (in kg m−2)i= bulk density of layer i (Mg m−3)Pi

= the proportion of organic carbon in layer i (g C Kg −1)

Di= thickness of the layer (m)Si= volume of the fraction of fragments >2 mm

Gaps in bulk density and coarse fragment >2mm content data were filled using pedo(taxo)-transfer functions
presented by Batjes et al. (2007) on the basis of soil type, soil textural class and soil depth. IPCC Tier 1 methods
consider changes in 0-30 cm soil depth layer; however, best-estimates were also derived for 0-50 and 0-100 cm
soil depth layers.

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Annex 2A.2

Additional Information for the Estimation of Soil
Carbon Stock Change from Biochar Amendments
to Mineral Soils Using Tier 2 and 3 Methods

Therm ochemical Conversion Technologies
For the purpose of this methodology, biochar is defined as a solid material generated by heating biomass to atemperature in excess of 350 °C under conditions of controlled and limited oxidant concentrations to prevent
combustion. These processes can be classified as either pyrolysis (in which oxidants are excluded), or gasification(in which oxidant concentrations are low enough to generate syngas).
Torrefaction and hydrothermal carbonisation (also called liquefaction) are not included because they do not
generate solid products that are significantly more persistent in soil than the original organic feedstock material(Libra et al. 2011; Kammann et al. 2012). Both of these processes typically utilise temperatures below 350°C,
with torrefaction operating under dry feedstock conditions in ambient pressure, while hydrothermal carbonisation
uses pressurised wet aqueous slurries. In contrast, pyrolysis operates at temperatures at 350°C and above (typicallybut not always below 700°C) under variable times, and gasification utilises temperatures between 500 and 1500°C
and typically short times (Boateng et al. 2015), both in dry conditions. Dry conditions are defined here in termsof the feedstock moisture, whereby feedstocks can have moisture up to 20percent after pre-drying; in comparison,
wet slurries typically have liquid water contents above 80percent.

Prim ing of native soil organic carbon by bi ochar am endm ents
Mineralisation of native soil organic carbon is on average reduced by 4 percent (95 percent CI = -8.1–0.8percent)after biochar additions to soil (Wang et al. 2015). Similar to laboratory trials (Kuzyakov et al. 2014), field trials
also show reductions in mineralisation of native soil organic carbon close to a decade after biochar additions (Wenget al. 2017) as well as in biochar-rich soils after several millennia (Liang et al. 2010). Known mechanisms thatwould cause an increase in mineralisation involve co-metabolism (Whitman et al. 2015) that operates over the
short term by supplying easily mineralisable organic matter as a source of energy to metabolise native organic
matter (Zimmerman et al. 2011). Conservatively, we assume no effect of biochar on existing soil organic matterin the long term.

Nitrous oxide em issions from soil after biochar am endm ents
Meta-analyses have found that nitrous oxide emissions are on average reduced between 54 percent (Cayuela et al.
2014), 38 percent (Borchard et al. 2018), 32 percent (Liu et al. 2018) to 0 percent (Verhoeven et al. 2017) after
addition of biochar to soil. Any reductions in nitrous oxide emissions due to biochar additions typically decline
over several years after application (Fungo et al. 2017). Furthermore, assessments of nitrous oxide emissions
several years after biochar additions are indicative of long-term emission reductions although at lower rates, sincechanges in biochar properties occur slowly over long periods of time (decades and centuries) compared to changes
observed during the initial days to years (Nguyen et al. 2008).
High-N feedstocks generate biochar with some microbially available N (Wang et al. 2012) and can lead to shortterm (days to weeks) increases in total nitrous oxide emissions if produced at lower temperatures (< 600 °C)(Cayuela et al. 2013). However, charring consistently reduces nitrous oxide emissions originating from thenitrogen in nitrogen-rich organic materials (Rose et al. 2016), as easily mineralisable amino-groups are converted
to polyaromatic nitrogen-carbon structures (Knicker 2007).
Due to limiting evidence demonstrating the long-term persistence of soil nitrous oxide emission reductions, it isconservatively assumed that biochar does not reduce nitrous oxide emissions from soil in the Tier 1 method.
However, any bioavailable N additions associated with biochar amendments should be included in the calculationsof direct and indirect soil nitrous oxide emissions (Volume 4, Chapter 11) as part of organic N inputs. Thisapproach will be conservative in terms of the influence of biochar on greenhouse gas emissions for the Tier 1
method.

Biochar Am endm ents to Organic Soils
No methods are provided in this guidance for estimating the impact of amending organic soils with biochar.
Compilers may be able to develop a Tier 3 method for estimating the impact of biochar C amendments to organicsoils, but it is important to recognise that the dynamics may be different, particularly with respect to priming. Few
studies have investigated the impact of priming by biochar on organic soils. However, one study that has
investigated priming of organic horizons in a forest soil found substantial losses of soil C over a ten-year periodwith charcoal additions (Wardle et al. 2008). Wardle et al. (2008) did not use isotopes and were therefore unable
to attribute these losses unequivocally to the organic soil C or to the charcoal. Nor was their study able to determine
the extent to which enhanced mass loss of organic soil carbon was due to mineralisation, or was due to verticaltransport of the C into the soil column as dissolved or colloidal organic carbon (Lehmann & Sohi 2008).
Nonetheless, the Wardle et al. (2008) study did indicate the possibility that priming of soil organic matterdecomposition by biochar may lead to a net loss of soil C in organic soils.

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

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Harmon, M.E., Krankina, O.N. & Sexton, J. (2000). Decomposition vectors: a new approach to estimating woody
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